New ICH Guidelines: ICH Q13 on Conti Manufacturing and ICH Q14 on AQbD

New ICH Guidelines:

*ICH Q13* on Continuous Manufacturing &
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*ICH Q14* on ATP – QbD (Analytical target profile and quality by design)
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New ICH Guidelines: ICH Q13 on Conti Manufacturing and ICH Q14 on AQbD

In a press release from 22 June the International Council for Harmonisation (ICH) has announced that they will prepare new topics for the future. The Assembly agreed to begin working on two new topics for ICH harmonisation:

Analytical Procedure Development and Revision of Q2(R1) Analytical Validation (Q2(R2)/Q14)
and
Continuous Manufacturing (Q13)

The long anticipated revision of ICH Q2(R1) “Guideline on Validation of Analytical Procedures: Text and Methodology” has been approved and the work plan is scheduled to commence in Q3 2018. It is intended that the new guidelines will be consistent with ICH Q8(R2), Q9, Q10, Q11 and Q12 .

The AQbD approach is very important to collect information in order to get an understanding and control of sources of variability of the analytical procedure by defining the control strategy.

Based on the Analytical Target Profile (ATP) the objective of the test and the quality parameters can be defined. By performing the validation (qualification) in the QbD concept, sufficient confidence can be achieved in order to consistently generate the analytical results that meet the ATP requirements.

So far there has been a lack of an Analytical Development Guideline, which the new ICH Development Guideline is supposed to compensate. Currently analytical procedures are mainly validated according to the classical validation parameters and these procedures mainly focus on HPLC Methods. Therefore this ICH topic has a top priority for the pharmaceutical industry. It is expected that the Revision of the Q2 (R1) Guideline will help to implement new and innovative analytical methods.

For more details please read the complete ICH Press Release (Kobe, Japan, June 2018).

http://www.ich.org/ichnews/press-releases/view/article/ich-assembly-kobe-japan-june-2018.html

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Report from the EMA-FDA QbD pilot program

Report from the EMA-FDA QbD pilot program

In March 2011, the European Medicines Agency (EMA) and the United States Food and Drug Administration (US FDA) launched, under US-EU Confidentiality Arrangements, a joint pilot program for the parallel assessment of applications containing Quality by Design (QbD) elements.

The aim of this program was to facilitate the consistent implementation of QbD concepts introduced through International Council for Harmonisation (ICH) Q8, Q9 and Q10 documents and harmonize regulatory decisions to the greatest extent possible across the two regions.

To facilitate this, assessors/reviewers from US and EU exchanged their views on the implementation of ICH concepts and relevant regulatory requirements using actual applications that requested participation into the program. The program was initially launched for three years. Following its first phase, both agencies agreed to extend it for two more years to facilitate further harmonization of pertinent QbD-related topics.

The program officially concluded in April 2016. During this period, the agencies received 16 requests to participate. One submission was rejected because the approach presented was not limited to QbD applications, and another application was not reviewed because it was never filed by the applicant.

In total, two Marketing Authorisation Applications (MAA)/New Drug Applications (NDA), three variation/supplements and nine scientific advice applications were evaluated under this program. One MAA/NDA was assessed under the parallel assessment pathway, with the rest following the consultative advice route. Based on the learnings during the pilot, FDA and EMA jointly developed and published three sets of Question and Answer (Q&A) documents.

These documents also addressed comments from the Japanese Pharmaceuticals and Medical Devices Agency (PMDA), which participated as an observer, offering input to further facilitate harmonization. The objective of these Q&A documents was to generate review guides for the assessors/reviewers and to communicate pilot outcomes to academia and industry.

Additionally, these documents captured any differences in regulatory expectations due to regional requirements, e.g. inclusion of process validation information in the dossier. The following topics were covered in each of the three Q&A documents: –

Q&A (1) published on Aug 20, 2013 included the following topics: (a) Quality target product profile (QTPP) and critical quality attributes (CQA), (b) Criticality, (c) Level of detail in manufacturing process descriptions, and (d) QbD for analytical methods1 –

Q&A (2) published on Nov 1, 2013 on Design Space Verification, that included definition, presentation, justification (including potential scale-up effects) and verification of design spaces both for active substances and finished products2 –

Q&A (3) published on Dec 19, 2014 included the following topics: (a) Level of detail in the dossier regarding Risk Assessment (RA), (b) Level of detail in the dossier regarding Design of Experiments (DOE) and Design Space3 R

 

Additionally, the FDA-EMA pilot provided the agencies an opportunity to harmonize regulatory expectations for the following precedent-setting applications that were reviewed under the consultative advice pathway: – The first continuous manufacturing (CM) based application submitted to both agencies.

Based on the learnings from this application, the following areas related to CM were harmonized: batch definition; control of excipients; material traceability; strategy for segregation of nonconforming material; real-time release testing (RTRT) methods and prediction models; and good manufacturing practice (GMP) considerations for RTRT, validation strategy, models, and control strategy. – A post approval supplement that included a broad based post-approval change management plan/comparability protocol.

Both agencies were harmonized on the expected level of detail in the protocol and considerations for implementation of a risk based approach to evaluate the changes proposed in the protocol. In line with the scope of the QbD pilot program, joint presentations of key findings were publically presented and discussed with stakeholders at different conferences.

These included the Joint EMAParenteral Drug Association QbD workshop4 organized in 2014 which also included participation from FDA and PMDA.

Overall, it is concluded that, on the basis of the applications submitted for the pilot, there is solid alignment between both Agencies regarding the implementation of multiple ICH Q8, Q9 and Q10 concepts. The FDA/EMA QbD pilot program opened up a platform for continuous dialogue which may lead to further communication on areas of mutual interest to continue the Agencies’ support for innovation and global development of medicines of high quality for the benefit of patients.

Both agencies are currently exploring potential joint activities with specific focus on continuous manufacturing, additional emerging technologies, and expedited/accelerated assessments (e.g. PRIME, Breakthrough). Additionally, EMA and FDA are hosting experts from each other’s organisations to facilitate dialog and explore further opportunities.

References: 1. EMA-FDA pilot program for parallel assessment of Quality-by-Design applications: lessons learnt and Q&A resulting from the first parallel assessment http://www.ema.europa.eu/docs/en_GB/document_library/Other/2013/08/WC500148215.pdf

2. FDA-EMA Questions and Answers on Design Space Verification http://www.ema.europa.eu/docs/en_GB/document_library/Other/2013/11/WC500153784.pdf

3. FDA-EMA Questions and answers on level of detail in the regulatory submissions http://www.ema.europa.eu/docs/en_GB/document_library/Other/2014/12/WC500179391.pdf

4. Joint European Medicines Agency/Parenteral Drug Association quality-by-design workshop http://www.ema.europa.eu/ema/index.jsp?curl=pages/news_and_events/events/2013/12/event_detai l_000808.jsp&mid=WC0b01ac058004d5c3

ENHANCED ANALYTICAL METHOD CONTROL STRATEGY CONCEPT

ENHANCED ANALYTICAL METHOD CONTROL STRATEGY CONCEPT

The benefits of quality by design (QbD) concepts related to both product (ICH Q8)1 and drug substance (ICH Q11)2 are well-established, particularly in regards to the potential to use knowledge to affect process changes without major regulatory hurdles, i.e., revalidation/regulatory filing, etc. Less wellestablished, but potentially of significant value, is the application of the same concepts to analytical methods.

Analytical methods play an obvious key role in establishing the quality of final product as they establish conformance with product acceptance criteria (i.e., specifications) and indicate the integrity of the product through indication of product stability. Analytical methods are validated, like manufacturing processes, but what if the operational ranges could be established during method validation when demonstrating fitness for purpose?

Would it be possible to drive method improvement, especially post validation in the same way that the concept of continuous improvement is a key driver for manufacturing processes? Despite this attractive “value proposition”, there is to date little evidence that as an industry this is being practically realized.

The result is that many methods used in a QC environment lag well behind technical developments in the analytical field, often leading to the use of suboptimal procedures that impact adversely on the efficiency within the laboratory. The challenge is to create an environment whereby such changes can be made efficiently and effectively.

One approach is to apply the principles of ICH Q8−10; delivering a science and risk based approach to the development and validation of analytical methods, establishing a method operable design region (MODR) within which changes can be made. Such a framework is illustrated in Figure 1.

 

This starts with a definition of the effective requirements of the method, an analytical target profile (ATP), this taking the specific form of acceptance criteria for method performance. Such a process can be used to not only establish effective analytical methods but is also supportive of continual improvement, specifically within the MODR. However, such a concept is potentially limited in that the expectation is that changes are restricted to within the MODR.

Such restrictions may inhibit continuous improvement. A prime example is change of stationary phase or a change from HPLC to UPLC; both fall outside of the original MODR. Historically such changes have been notoriously difficult and often therefore avoided unless imperative. A recent publication13 examined this, presenting a method enhancement concept that would allow minor changes outside of the MODR. This is based on the realization that performance of any analytical method is based on the conduct of a system suitability test (SST); such tests ensure the method’s fitness for purpose.

Karlsson et al. stated that changes outside of the initial MODR may be possible provided that the method principle is unchanged, failure modes are the same, and the SST is capable of detecting these, both for the original method and for any method changes that fall outside of the original MODR. Put simplychanges can be made provided the SST criteria are passed. A change from HPLC to UPLC was used to illustrate this. Revalidation of the method is still required, but critically such changes do not require regulatory interaction but can be managed through internal quality systems.

1 ICH Q8 Pharmaceutical Development. http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q8_
R1/Step4/Q8_R2_Guideline.pdf.
(2) ICH Q11 – Development and Manufacture of Drug Substances
(Chemical Entities and Biotechnological/Biological Entities) Q11.http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q11/Q11_Step_4.pdf (Aug 2009).

 

////////ENHANCED,  ANALYTICAL METHOD CONTROL , STRATEGY CONCEPT

 

What Your ICH Q8 Design Space Needs: A Multivariate Predictive Distribution

 

Multivariate predictive distribution quantifies the level of QA in a design space. “Parametric bootstrapping” can help simplify early analysis and complement Bayesian methods.

The ICH Q8 core definition of design space is by now somewhat familiar: “The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality” [1]. This definition is ripe for interpretation. The phrase “multidimensional combination and interaction” underscores the need to utilize multivariate analysis and factorial design of experiments (DoE), while the words “input variables (e.g., material attributes) and process parameters” remind us of the importance of measuring the right variables.

However, in presentations and articles discussing design space, not much focus has been given to the key phrase, “assurance of quality”. This does not seem justified, given that guidance documents such as ICH Q8, Q9, Q10, PAT, etc. are inundated with the words “risk” and “risk-based.” For any ICH Q8 design space constructed, surely the core definition of design space begs the question, “How much assurance?” [2]. How do we know if we have a “good” design space if we do not have a method for quantifying “How much assurance?” in a scientifically coherent manner?

 

The Flaws of Classical MVA

Classical multivariate analysis and DoE methodology fall short of providing convenient tools to allow one to answer the question “How much assurance?” There are two reasons for this. One is that multivariate analysis and DoE have historically focused on making inferences primarily about response means. But simply knowing that the response means of a process meet quality specifications is not sufficient to allow one to conclude that the next batch of drug product will meet specifications. One reason for this is that we always have batch-to-batch variation. Building a design space based upon overlapping mean responses will result in one that is too large, harboring operating conditions with a low probability of meeting all of the quality specifications.  Just because each of the process mean quality responses meet specification does not imply that the results of the next batch will do so; there will always be variation about these means.

However, if we can quantify the entire (multivariate) predictive distribution of the process quality responses as a function of “input variables (e.g., material attributes) and process parameters”, then we can compute the probability of a future batch meeting the quality specifications. The multivariate predictive distribution of the process quality responses incorporates all of the information about the means, variation, and correlation structure among the response types.

Another, more technically subtle reason, that classical multivariate analysis and DoE methodology do not provide straightforward tools to construct a proper design space is that, beyond inference about response means, they are oriented towards construction of prediction intervals or regions. For a process with multiple critical quality responses, it is awkward to try to use a classical 95% prediction region (which is not rectangular in shape) to compare against a (rectangular) set of specifications for multiple quality responses. On the other hand, using individual prediction intervals does not take into account the correlation structure among the quality responses. What is needed instead is a “multivariate predictive distribution” for the quality responses. The proportion of this predictive distribution that sits inside of the rectangular set of quality specifications is then simply a quantification of “how much assurance”.

 

The Stochastic Nature of Our Processes

 

Complex manufacturing processes are inherently “stochastic processes”. This means that while such process may have underlying mechanistic model relationships between the input factors and process responses, nonetheless such relationships are embedded with random variation. Conceptually, for many complex manufacturing processes, even if “infinitely” accurate measurement devices could be used, such processes would still produce quality responses that vary from batch to batch and possibly within batch. This is why classical multivariate analysis and DoE methodology, which focuses on inference for response means, present an insufficient tool set. This is not to say that such methods are not necessary; indeed they are. However, we need to better understand the multivariate stochastic nature of complex manufacturing processes in order to quantify “how much assurance” relative to an ICH Q8 design space.

The concept of a multivariate distribution is useful for understanding complex manufacturing processes, with regard to both input variables and response variables.  Figure 1 shows a hypothetical illustration of the relationships among various input variables and response variables. Notice that some of the input variables and the response variables are described by multivariate distributions. In Figure 1 we have a model which describes the relationships between the input variables, the common cause process variability and the quality responses. This relationship is captured by the multivariate mathematical function f=(f₁,…,f_r), which maps various multivariate input variables, x, z, e, θ  to the multivariate quality response variable, Y=(Y₁,…,Y_r). Here, x=(x₁,…,xk) lists the controllable process factors (e.g. pressure, temperature, etc.), while z=(z₁,…,z_h) lists process variables which are noisy, such as input raw materials, process set-point deviations, and possible ambient environmental conditions (e.g. humidity).

The variable e=(e1,…,er) represents the common-cause random variability that is inherent to the process. It is natural that z and e be represented by (multivariate) distributions that capture the mean, variation about the mean, and correlation structure of these random variables. The parameter θ = (θ₁,…θ_p) represents the list of unknown model parameters. While such a list can be thought of as composed of fixed unknown values, for purposes of constructing a predictive distribution, it may be easier to describe the uncertainty associated with these unknown model parameters by a multivariate distribution. The function f then transmits the uncertainty in the variables z, e, and θ to the response variables, Y. In other words, the “input distributions” for z, e, and θ combine to produce the predictive distribution for Y through the process model function f.

 
Figure 1. Multivariate distributions and the role they play in the design space reliability. Here, it is clear that the multivariate predictive distribution associated with the process control values (x = (x₁,…, x_k) ) results in an unreliable process.

The controllable process factors x can then be used to move the distribution of response variables Y to be better situated within the region bounded by the quality specifications. In Figure 1, the rectangular gray region in the lower left is the region bounded by the quality specifications for “percent dissolved” and “friability” for a tablet production process. Here, one can see that the process operating at operating set point x it not reliable because only about 65% of the predictive distribution is within the region carved out by the quality specifications. (The concentric elliptical contours are labelled by the proportion of the predictive distribution inside of each contour.)

In Figure 2, however, one can see that the operating set point x is associated with a much more reliable process since about 90% of the predictive distribution is within the region carved out by the quality specifications. Situations like that in Figure 2 can then be used to create a design space in the following way: A design space (for a process with multiple critical quality responses) can be thought of as the collection of all controllable process factors (x-points) such that the an acceptably high percentage of the (multivariate) predictive distribution of critical quality responses falls within the region outlined by the (multiple) quality specifications. 

Figure 2. Multivariate distributions and the role they play in the design space reliability.  Here, it is clear that the multivariate predictive distribution associated with the process control values (x = (x₁,…, x_k) ) results in a process with a higher reliability.

Slight modifications of the above definition may be needed to accommodate situations involving feedback/feedforward control, etc. But a predictive distribution of the quality responses is required nonetheless in order to address the question of “How much assurance?”

 

From Bayesian to Bootstrapping

Viewing Figures 1 and 2 shows that one can get a predictive distribution (for the quality responses in Y) if one has access to the various input distributions, in particular the multivariate distribution representing the uncertainty due to the unknown model parameters. But how can one compute the distribution representing the uncertainty due to the unknown model parameters? One possible approach is to use Bayesian statistics.

The Bayesian statistical paradigm provides a conceptually straightforward way to construct a multivariate statistical distribution for the unknown model parameters associated with a manufacturing process. This statistical distribution for the unknown model parameters is known as the “posterior” distribution by Bayesian statisticians. The resulting distribution for Y (in Figures 1 and 2) is called the posterior predictive distribution. This distribution accounts for uncertainty due to common-cause process variation as well as the uncertainty due to unknown model parameters. It also takes into account the correlation structure among multiple quality response types. In addition, the Bayesian approach also allows for the inclusion of prior information (e.g., from previous pilot plant and laboratory experiments). In fact, use of the posterior predictive distribution is an effective way to do process optimization with multiple responses [3]. An excellent overview of these Bayesian methods is given in the recent book Process Optimization: A Statistical Approach [4]. An application of the Bayesian approach to Design space construction is given by this author in [5].

An alternative approach for obtaining a distribution to represent the uncertainty due to the unknown model parameters involves a concept called “parametric bootstrapping”. This concept is somewhat more transparent than the Bayesian approach. A simple form of the parametric bootstrap approach can be described as follows. First fit your process model to experimental data to estimate the unknown model parameters. Using computer simulation, simulate a new set of (artificial) responses so that you will have new responses similar to the ones obtained from your real experiment. Using the artificial responses, estimate a new set of model parameters. Do this many more times (10,000 say) to obtain many more sets of model parameter estimates. Each of these model parameters (obtained from each artificial data set) will be somewhat different, due to the variability in the data. The collection of all these sets of model parameter estimates forms a bootstrap distribution of the model parameters that expresses their uncertainty. An experiment with a large number of runs (i.e. a lot of information) will tend to have a tighter distribution, expressing the fact that we have only a little uncertainty about the model parameters. But an experiment with only a little information will tend to yield a bootstrap distribution that is more spread out. This is particularly the case, if the common cause variation in the process is also large.

The parametric bootstrap distribution can then be used in place of the Bayesian posterior distribution to enable one to obtain a predictive distribution for the quality responses. This simple parametric bootstrap approach will approximate the Bayesian posterior distribution of model parameters, but it will tend to produce a parameter distribution that is smaller (i.e., a bit too small) than the one obtained using the Bayesian approach. More sophisticated parametric bootstrap approaches are possible [6] which may result in more accurate approximations to the Bayesian posterior distribution of model parameters.

Software Selection

 

Unfortunately, the DoE software available in many point-and-click statistics packages does not produce multivariate predictive distributions. It therefore does not provide a way to quantify “How much assurance” one can assign to the Design space. So clearly, easy-to-use software is a current bottleneck to allowing experimenters to produce Design spaces based upon multivariate predictive distributions.

However, there are some software packages that consulting statisticians can use to produce approximate predictive distributions. The SAS statistical package has a procedure call PROC MODEL. (It is part of SAS’s econometric suite of procedures.)  This procedure will analyze multivariate response regression models. It has a Monte Carlo option which will produce simulations that allow one to sample from a predictive distribution. This is useful if the process you have can be modeled using PROC MODEL.

The R statistical programming language has functions which will allow the user to do a Bayesian analysis for certain multivariate response regression models. In addition R has a boot function which will allow the user (in conjunction with other R statistical functions) to perform a parametric bootstrap analysis, which can eventually be incorporated into an R program for creating an approximate multivariate predictive distribution. In any case, the SAS and R software does require some statistical sophistication in order to create the appropriate programming steps. Easier to use point-and-click software is still very much needed to lower the computational hurdles for generating multivariate predictive distributions needed for ICH Q8 design space development.

 

Putting It All Together

 

Viewing the multivariate world from our limited three-dimensional perspective is often a challenge. This is no different for a design space involving multiple controllable factors.  For the design space definition above, it is convenient to use a probability function, P(x), which assigns a probability (i.e., reliability measure) to each set of process operating conditions, denoted by x = (x₁,…, x_k). Here, P(x) is the probability that all of the critical quality responses simultaneously meet their specification limits. As stated previously, this probability can be thought of as the proportion of the predictive distribution contained within the region bounded by the quality specifications. The design space is then the set of all x such that P(x) is at least some acceptable reliability value. One can then plot P(x) vs. x to view the design space, provided that the dimension of x (i.e., k) is not too large. A simplified example discussed in [5] and [7] is briefly described below.

In an early phase synthetic chemistry study, a prototype design space using a predictive distribution was created for an active pharmaceutical ingredient (API). The (four) quality responses considered were: “% starting material isomer” (<0.15%), “% product isomer” (<2%), “% of impurity #1” (<3.5%), and “% API purity” (>95%). Four controllable experimental factors (x = (x₁,…, x₄)) were used in a DoE to model how they (simultaneously) influenced the four quality responses. These factors were x₁=temperature (oC), x₂=pressure (psi), x₃= catalyst loading (eq.), and x₄=reaction time (hrs).

A Bayesian statistical analysis was used to construct the multivariate predictive distribution for the four quality responses. For the above quality specifications (in parentheses), and for various x-points, the proportion of the multivariate predictive distribution inside the specification region was computed using many Monte Carlo simulations. In other words, for each x-point the corresponding P(x) value was computed. A multipanel contour plot of P(x) vs. the four experimental factors is shown in Figure 3 below. The white regions indicate the regions of larger reliability for simultaneously meeting all of the process specifications. Although these absolute reliability levels are less than ideal, this example is useful for illustration purposes.

Figure 3. Multipanel contour plots of the DS for the early phase synthetic chemistry example. (Used with permission from the Association for Quantitative Management.)

As it turned out, the “optimal” factor-level configurations were such that the associated proportions of the predictive distributions were only about 60% to 70% inside of the quality specification region. Part of the reason for this is that the “input” distributions corresponding to the “unknown model parameters” and the “common cause variation” were too large. A more detailed analysis of this data, conducted in [7], indicates that if a larger experimental design had been used and the process variation reduced by 30%, the more reliable factor conditions would be associated with reliability measures that were to the 95% level and beyond. This shows the importance of reducing the variation for as many of the process “input” distributions (of the kind shown in Figures 1 and 2) as is possible.

In the case where the dimension of x is not small, a (read-only) sortable spreadsheet could be used to make informed movements within such a design space [5]. While the spreadsheet approach does not provide a high-level (i.e., bird’s eye) view of the design space (as in Figure 3), it does show (locally) how P(x) changes as various controllable factors are changed. In theory, it may also be possible to utilize mathematical optimization routines applied to P(x) to move optimally within a design space whose dimension is not small.

Developing Design Spaces Based Upon Predictive Distributions: A Summary

 

Emphasize efficient experimental designs, particularly for multiple unit operations. For design space construction, clearly it is desirable to have good data that can be efficiently collected. As such, good experimental design and planning is critical. It is particularly useful to have good data that can be efficiently collected across various unit operations in a multi-step process. The performance of one unit operation may depend upon various multiple aspects of previous unit operations. When viewed as a whole, a process may have a variety of interacting variables that span its unit operations. This is an aspect of process optimization and design space construction that requires more research by statisticians and their clients (chemical engineers, chemometricians, pharmaceutical scientists, etc.). Some work has been done [8] but more is needed.  Poor experimental designs can be costly and possibly lead to biases and/or too much uncertainty about unknown model parameters. This is not helpful for developing a good multivariate predictive distribution with which to construct a design space.

Depend upon easy-to-use statistical software for multivariate predictive distributions. As mentioned previously in this article, the availability of easy-to-use statistical software for creating multivariate predictive distributions is a key bottleneck for the development of design spaces that quantify “how much assurance” can be associated with meeting quality specifications. More broadly, it has been shown recently that multivariate predictive distributions can be very useful for process optimization involving multiple responses [3], [4]. Monte Carlo simulation software applications such as @Risk and Crystal Ball provide point-and-click software for process simulation. However, a key issue that still remains involves producing a multivariate distribution that reflects the uncertainty of unknown model parameters associated with a process. Producing such a distribution is not always statistically simple.

The Bayesian approach provides a unifying paradigm, but the computational issues do require careful assessment of model and distributional assumptions, and in many cases, careful judgement involving convergence of algorithms. The parametric bootstrap approach is more transparent but may produce design spaces that are a bit too large, unless certain technical refinements are brought to bear. Nonetheless, the statistical methods exist to produce good multivariate distributions that represent the uncertainty of unknown model parameters. As always, modeling and inference need to be done with care, using data from appropriately designed experiments.

Use computer simulation to better understand reliability and risk assessments for complex processes. If one can state that they understand a process quantitatively, even a stochastic process, then one should expect that they can simulate such a process reasonably well. Given that many pharmaceutical processes may involve a series of complex unit operations, computer simulation may prove to be helpful for understanding how various process factors and conditions combine to influence multivariate quality responses. With proper care, computer simulation may help process engineers and scientists to better understand the multivariate nature of their multi-step manufacturing procedures. Of course, experimental validation of computer simulation findings with real data may still be needed. In addition, process risk assessments based largely upon judgement (e.g., through tools such as FMEA) can be enhanced through the use of better quantitative methods such as probability measures [9] (e.g., obtained through computer simulations). The issue of ICH Q8 design space provides a further incentive to utilize and enhance such computer simulation tools.

Have a clear understanding of the processes involved and lurking risks, not just the blind use of mathematical models. We must always keep in mind that our statistical predictions, such as multivariate predictive distributions, are based upon mathematical models that are approximations to the real world environment. How much assurance we can accurately attribute to a design space will be influenced by the predictive distribution, and all of the modeling and input information (e.g. input distributions) that went into developing it. Generally speaking, of course, all decisions and risk measurements based upon statistical methods are influenced to varying degrees by what mathematical modeling assumptions are used, quantifications of design space assurance are fundamentally no different.

However, seemingly good predictive modeling (along with accurate statistical computation) may not be sufficient to provide a design space with long term, high-level assurance of meeting quality specifications. The issue of process robustness is also important, not only for design space construction but in other areas of QbD as well.  This issue has many facets and I believe it needs to be given more emphasis in pharmaceutical product development and manufacturing. So as not to go too far on a tangent, I will only briefly summarize some of the issues involved.

In quality engineering, making a process robust to noisy input variables is known as “robust parameter design” (RPD). The Japanese industrial engineer Genichi Taguchi pioneered this approach to quality improvement. The basic idea is to configure the controllable (i.e., non-noisy) process factors in such as way to dampen the influence of the noise process variables (e.g., raw material attributes or temperature/pressure fluctuations in the process). A convenient aspect of the predictive distribution approach to design space, and process optimization in general, is that it provides a technically and computationally straightforward way to do RPD. We simply simulate the nose variable distribution (shown in Figures 1 and 2) and then configure the controllable factors (x-points) to increase the probability of meeting the quality specifications. If the controllable factors are able to noticeably dampen down the influence of the noise variables, typically the probability that the quality responses will meet specifications will increase (assuming that the mean responses meet their specifications). See [4], [5], and [10] for RPD examples involving multivariate predictive distributions.

Less well known, however, are two more subtle issues that can cause problems with predictive distributions. These are lurking variables and heavy-tailed distributions.  Process engineers and scientists need to brainstorm and test various possibilities for a change in the process or its inputs that could increase the risk that the predictive distribution is overly optimistic or is not stable over time.

Some predictive distributions may have what are called “heavy tails”. (The degree of “heavy tailedness” is called kurtosis by statisticians.) We need to be careful with such distributions as they are more likely to suddenly produce values far from the center of the distribution, than for a normal (i.e., Gaussian) distribution.

If the process can be simulated on a computer, sensitivity analyses can be done to assess the effect of various shocks to the system or changes to the input or predictive distributions, such as heavier tails. An interesting overview of these two issues and of how quality and risk combine can be found in [11].

In conclusion, the ability to understand randomness and think stochastically is important as multivariate random variation pervades all complex production processes. Given that we are forced to deal with randomness (in multivariate form, no less), Monte Carlo simulation has become a useful way to gain some insight into the combined effects of controllable and random effects present in a complex production process. (Interested readers may want to visit The American Society for Quality’s web site on Probabilistic Technology available at http://www.asq.org/communities/probabilistic-technology/index.html). Computer simulation can help our intuition for understanding stochastic processes. Such intuition in humans is not always on the mark.  We can all be fooled by randomness. See for example the book by Taleb [12].

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References
1. ICH (2005). “ICH Harmonized Tripartite Guideline: Pharmaceutical Development, Q8.”

2. Peterson, J. J. Snee, R. D., McAllister, P.R., Schofield, T. L., and Carella, A. J., (2009) “Statistics in the Pharmaceutical Development and Manufacturing” (with discussion),  Journal of Quality Technology, 41, 111-147.

3. Peterson, J. J. (2004), “A Posterior Predictive Approach to Multiple Response Surface Optimization”, Journal of Quality Technology, 36, 139-153.

4. Del Castillo, E. (2007), Process Optimization: A Statistical Approach, Springer, N.Y.

5. Peterson, J. J. (2008), “A Bayesian Approach to the ICH Q8 Definition of Design Space”, Journal of Biopharmaceutical Statistics, 18, 959-975.

6. Davison, C. and Hinkley, D.V. (1997), Bootstrap Methods and Their Application, Cambridge University Press, Cambridge, UK.

7. Stockdale, G. W. and Cheng, A. (2009), “Finding Design Space and a Reliable Operating Region using a Multivariate Bayesian Approach with Experimental Design”, Quality Technology and Quantitative Management, (to appear).

8. Perry, L. A., Montgomergy, D.C., and Fowler, J. W. (2002), “Partition Experimental Designs for Sequential Processes: Part II – Second Order Models”, Quality and Reliability Engineering International, 18, 373-382.

9. Claycamp, H. G. (2008). “Room for Probability in ICH Q9: Quality Risk Management”, Institute of Validation Technology conference: Pharmaceutical Statistics 2008 Confronting Controversy, March 18-19, Arlington, VA

10. Miro-Quesada, G., del Castillo, E., and Peterson, J.J., (2004) “A Bayesian Approach for Multiple Response Surface Optimization in the Presence of Noise Variables”, Journal of Applied Statistics, 31, 251-270

11. Kenett, Ron S  and Tapiero, Charles S. (2009),”Quality, Risk and the Taleb Quadrants” presented at the IBM Quality & Productivity Research Conference, June 3rd, 2009.  Available at SSRN: http://ssrn.com/abstract=1433490

12. Taleb, Nassim (2008) Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets, Random House, New York.

/////// ICH Q8 Design Space,  Multivariate Predictive Distribution,QA,  design space, Parametric bootstrapping, complement Bayesian methods.

QbD Sitagliptin

Application of On-Line NIR for Process Control during the Manufacture of Sitagliptin

George Zhou^*, Shane Grosser, Lei Sun, Gabriel Graffius, Ganeshwar Prasad, Aaron Moment, Angela Spartalis,Paul Fernandez, John Higgins, Busolo Wabuyele, and Cindy Starbuck

Global Science, Technology and Commercialization, Merck Sharp & Dohme Corporation P.O. Box 2000, Rahway, New Jersey 07065, United States

Org. Process Res. Dev., 2016, 20 (3), pp 653–660

DOI: 10.1021/acs.oprd.5b00409

Publication Date (Web): February 12, 2016

Copyright © 2016 American Chemical Society

*E-mail: george_zhou@merck.com.

Abstract

The transamination-chemistry-based process for sitagliptin is a through-process, which challenges the crystallization of the active pharmaceutical ingredient (API) in a batch stream composed of multiple components. Risk-assessment-based design of experiment (DoE) studies of particle size distribution (PSD) and crystallization showed that the final API PSD strongly depends on the seeding-point temperature, which in turn relies on the solution composition. To determine the solution composition, near-infrared (NIR) methods had been developed with partial least squares (PLS) regression on spectra of simulated process samples whose compositions were made by spiking each pure component, either sitagliptin free base (FB), water, isopropyl alcohol (IPA), dimethyl sulfoxide (DMSO), or isopropyl acetate (IPAc), into the process stream according to a DoE. An additional update to the PLS models was made by incorporating the matrix difference between simulated samples in lab and factory batches. Overall, at temperatures of 20–35 °C, the NIR models provided a standard error of prediction (SEP) of less than 0.23 wt % for FB in 10.56–32.91 wt %, 0.22 wt % for DMSO in 3.77–19.18 wt %, 0.32 wt % for IPAc in 0.00–5.70 wt %, and 0.23 wt % for water in 11.20–28.58 wt %. After passing the performance qualification, these on-line NIR methods were successfully established and applied for the on-line analysis of production batches for compositions prior to the seeding point of sitagliptin crystallization.

http://pubs.acs.org/doi/abs/10.1021/acs.oprd.5b00409?journalCode=oprdfk

Next…………..

A biocatalytic manufaturing route for januvia – Society of Chemical …

http://www.soci.org/~/media/Files/…/2011/…/Jake_Janey_Presentation.ashx

Nov 2, 2011 – 9 Steps, 52% overall yield, >100Kg of sitagliptin prepared ….. FDA filings requires “Quality by Design”: A way to allow process changes within.

A PRESENTATION

A PRESENTATION

Example of QbD Application in Japan

https://www.pmda.go.jp/files/000213677.pdf

Aug 11, 2016 – QbD assessment experience in Japan … Number of Approved Products with QbD … Active Ingredient : Sitagliptin Phosphate Hydrate.

WILL BE UPDATED WITH MORE, WATCH OUt

/////////

Name	Explanation
Active Pharmaceutical Ingredient (API)	An active pharmaceutical ingredient (API) is a substance used in a finished pharmaceutical product, intended to furnish pharmacological activity or to otherwise have direct effect in the diagnosis, cure, mitigation, treatment or prevention of disease, or to have direct effect in restoring, correcting or modifying physiological functions in human beings.
Annual Product Reviews (APR)	The Annual Product Reviews (APR) include all data necessary for evaluation of the quality standards of each drug product to determine the need for changes in drug product specifications or manufacturing or control procedures. The APR is required by the U.S. Code of Federal Regulations.
ANVISA	The Brazilian Health Surveillance Agency (in Portuguese, Agência Nacional de Vigilância Sanitária) is a governmental regulatory body in Brazil. Similar to the FDA in the United States, it oversees the approval of drugs and other health products and regulates cosmetics, food products, and other health-related industries.
Biologic License Application (BLA)	The Biologics License Application (BLA) is a request for permission to introduce, or deliver for introduction, a biologic product into commerce in the U.S.
CFDA	The China Food and Drug Administration is similar to the FDA in the United States and is responsible for regulating food and drug safety.
cGMP	Current Good Manufacturing Practices govern the design, monitoring, and control of manufacturing facilities and processes and are enforced by the US FDA. Compliance with these regulations helps safeguard a drug’s identity, strength, quality, and purity.
COFEPRIS	The Federal Commission for Protection against Sanitary Risks (in Spanish, Comisión Federal para la Protección contra Riesgos Sanitarios) is a government agency in Mexico. It regulates food safety, drugs, medical devices, organ transplants, and environmental protection.
Common Technical Document (CTD)	The Common Technical Document (CTD) is the mandatory common format for new drug applications in the EU and Japan, and the U.S. The CTD assembles all the Quality, Safety and Efficacy information necessary for a drug application.
European Medicines Agency (EMA)	The European Medicines Agency (EMA) is a decentralised agency of the European Union (EU), located in London. It began operating in 1995. The Agency is responsible for the scientific evaluation, supervision and safety monitoring of medicines developed by pharmaceutical companies for use in the EU.
Food and Drug Administration (FDA)	The Food and Drug Administration (FDA) is an agency within the U.S. Department of Health and Human Services. The FDA is responsible for the approval of new pharmaceutical products for sale in the U.S. and performs audits at the companies participating in the manufacture of pharmaceuticals to ensure that they comply with regulations.
Human growth hormone	A growth hormone (GH or HGH) is a peptide hormone produced by the pituitary gland that stimulates growth in children and adolescents. It is involved in several body processes, including cell reproduction and regeneration, regulation of body fluids, and metabolism. It can be produced by the body (ie, somatotropin) or genetically engineered (ie, somatropin).
In-Process Control (IPC)	In-Process Controls (IPC) are checks performed during production in order to monitor and if necessary to adjust the process to ensure that the product conforms its specification.
Interferons (INFs)	Interferons are proteins produced by the body as part of the immune response. They are classified as cytokines, proteins that signal other cells to trigger action. For example, a cell infected by a virus will release interferons to stimulate the defenses of nearby cells.
Interleukins	Interleukins are proteins produced by cells as an inflammatory response. Most interleukins help leukocytes communicate with and direct the division and differentiation of other cells.
Investigational Medicinal Product Dossier (IMPD)	The Investigational Medicinal Product Dossier (IMPD) is the basis for approval of clinical trials by the competent authorities in the EU. The IMPD includes summaries of information related to the quality, manufacture and control of the Investigational Medicinal Product, data from non-clinical studies and from its clinical use.
Investigational New Drug (IND)	An Investigational New Drug application is provided to the FDA to obtain permission to test a new drug in humans in Phase I – III clinical studies. The IND is reviewed by the FDA to ensure that study participants will not be placed at unreasonable risk.
Marketing Authorization Application (MAA)	The Marketing Authorization Application (MAA) is a common document used as the basis for a marketing application across all European markets, plus Australia, New Zealand, South Africa, and Israel. This application is based on a full review of all quality, safety, and efficacy data, including clinical study reports.
Master batch records	These general manufacturing instructions, which are required by cGMP, are the bases for a precise, detailed description of a pharmaceutical manufacturing process. They ensure that all proper ingredients are included, each process step is completed, and the process is controlled.
Medicines and Healthcare Products Regulatory Agency (MHRA)	The Medicines and Healthcare products Regulatory Agency (MHRA) regulates medicines, medical devices and blood components for transfusion in the UK. MHRA is an executive agency, sponsored by the Department of Health.
MFDS	The Ministry of Food and Drug Safety (formerly the Korean Food & Drug Administration) is a government agency that oversees the safety and efficacy of drugs and medical devices in South Korea.
Monoclonal antibodies	Monoclonal antibodies are antibodies made in a laboratory from identical immune cells that are clones of a single cell. They are distinct from polyclonal antibodies, which are made from different immune cells.
NDA	A New Drug Application (NDA) is the vehicle submitted to the FDA by drug companies in order to gain approval to market a new product. Safety and efficacy data, proposed package labeling, and the drug’s manufacturing methods are typically included in an NDA.
New Drug Application (NDA)	The New Drug Application (NDA) is the vehicle through which drug sponsors formally propose that the FDA approve a new chemical pharmaceutical for sale and marketing in the U.S.
Oligonucleotides	These short nucleic acid chains (made up of DNA or RNA molecules) are used in genetic testing, research, and forensics.
Parenteral	Parenteral medicine is taken or administered in a manner other than through the digestive tract. Intravenous and intramuscular injections are two examples.
Peptide hormones	Peptide hormones are proteins secreted by organs such as the pituitary gland, thyroid, and adrenal glands. Examples include follicle-stimulating hormone (FSH) and luteinizing hormone. Similar to other proteins, peptide hormones are synthesized in cells from amino acids.
PMDA	The Pharmaceuticals Medical Devices Agency is an independent administrative agency that works with the Ministry of Health, Labour and Welfare to oversee the safety and quality of drugs and medical devices in Japan.
Process Analytical Technology (PAT)	These analytical tools help monitor and control the manufacturing process, including accommodating for variability in material and equipment, in order to ensure consistent quality.
Product Quality Reviews (PQR)	The Product Quality Reviews (PQR) of all authorized medicinal products, is conducted with the objective of verifying the consistency of the existing process, the appropriateness of current specifications for both starting materials and finished product, to highlight any trends and to identify product and process improvements. The PQR is required by the EU GMP Guideline.
Quality by Design (QbD)	This concept involves a holistic, proactive, science- and risk-based approach to the development and manufacturing of drugs. At the heart of QbD is the idea that quality is achieved through in-depth understanding of the product and the process by which it is developed and manufactured.
Restricted Access Barrier System (RABS)	This advanced aseptic processing system provides an enclosed environment that reduces the risk of contamination to the product, containers, closures, and product contact surfaces. As a result, it can be used in many applications in a fill-finish area.
Scale-up	Scale-up involves taking a small-scale manufacturing system developed in the laboratory to a commercially viable, robust production process.
Six Sigma	Six Sigma is a set of quality management methods, techniques, and tools used to improve manufacturing, transactional, and other business processes. The goal is to enhance quality (as well as employee morale and profits) by identifying and eliminating the cause of errors and process variations.
Target Product Profile (TPP)	This key strategic document summarizes the features of an intended drug product. Characteristics may include the dosage form, route of administration, dosage strength, pharmacokinetics, and drug product quality criteria.
TFDA	The Taiwan Food & Drug Administration is a governmental body devoted to enhancing food safety and drug quality in that country.

QbD: Controlling CQA of an API

The importance of Quality by Design (QbD) is being realized gradually, as it is gaining popularity among the generic companies. However, the major hurdle faced by these industries is the lack of common guidelines or format for performing a risk-based assessment of the manufacturing process. This article tries to highlight a possible sequential pathway for performing QbD with the help of a case study. The main focus of this article is on the usage of failure mode and effect analysis (FMEA) as a tool for risk assessment, which helps in the identification of critical process parameters (CPPs) and critical material attributes (CMAs) and later on becomes the unbiased input for the design of experiments (DoE). In this case study, the DoE was helpful in establishing a risk-based relationship between critical quality attributes (CQAs) and CMAs/CPPs. Finally, a control strategy was established for all of the CPPs and CMAs, which in turn gave rise to a robust process during commercialization. It is noteworthy that FMEA was used twice during theQbD: initially to identify the CPPs and CMAs and subsequently after DoE completion to ascertain whether the risk due to CPPs and CMAs had decreased.

Quality by Design in Action 1: Controlling Critical Quality Attributes of an Active Pharmaceutical Ingredient

Abdul Qayum Mohammed^†‡, Phani Kiran Sunkari^†, P. Srinivas^*‡, and Amrendra Kumar Roy^*†

^† CTO-III, Dr. Reddy’s Laboratories Ltd, Plot 116, 126C and Survey number 157, S.V. Co-operative Industrial Estate, IDA Bollaram, Jinnaram Mandal, Medak District, Telangana 502325, India

^‡ Department of Chemistry, Osmania University, Hyderabad, Telangana 500007, India

Org. Process Res. Dev., 2015, 19 (11), pp 1634–1644

DOI: 10.1021/op500295a, http://pubs.acs.org/doi/abs/10.1021/op500295a

*Telephone: +919701346355. Fax: + 91 08458 279619. E-mail: amrendrakr@drreddys.com (A.K.R.)., *E-mail:sripabba85@yahoo.co.in (P.S.).

////// QbD, DoE, FMEA, ANOVA, Design space.

New aspects of developing a dry powder inhalation formulation applying the quality-by-design approach

The current work outlines the application of an up-to-date and regulatory-based pharmaceutical quality management method, applied as a new development concept in the process of formulating dry powder inhalation systems (DPIs). According to the Quality by Design (QbD) methodology and Risk Assessment (RA) thinking, a mannitol based co-spray dried formula was produced as a model dosage form with meloxicam as the model active agent.

The concept and the elements of the QbD approach (regarding its systemic, scientific, risk-based, holistic, and proactive nature with defined steps for pharmaceutical development), as well as the experimental drug formulation (including the technological parameters assessed and the methods and processes applied) are described in the current paper.

Findings of the QbD based theoretical prediction and the results of the experimental development are compared and presented. Characteristics of the developed end-product were in correlation with the predictions, and all data were confirmed by the relevant results of the in vitro investigations. These results support the importance of using the QbD approach in new drug formulation, and prove its good usability in the early development process of DPIs. This innovative formulation technology and product appear to have a great potential in pulmonary drug delivery.

Fig. 1.

Steps and elements of the QbD methodology completed by the authors and applied in the early stage of pharmaceutical development.

“By identifying the critical process parameters, the practical development was more effective, with reduced development time and efforts.”

Edina Pallagi, our QbD evangelist from Hungary shares her team’s experience applying QbD to Dry Powder Inhalation Formulation.

The paper covers:

• QbD methodology the researchers applied
• Formulation of dry powder inhalation – API and excipients
• QTPP, CQA and CPPs  identified for pulmonary use along with target, justification and explanation
• Characterization test methods
• Knowledge Space development
• QbD software used

New aspects of developing a dry powder inhalation formulation applying the quality-by-design approach

• Edina Pallagi^{a, , ,} ,
• Keyhaneh Karimi^b,
• Rita Ambrus^b,
• Piroska Szabó-Révész^b,
• Ildikó Csóka^a

• ^a Institute of Drug Regulatory Affairs, University of Szeged, Faculty of Pharmacy, Szeged, Hungary
• ^b Department of Pharmaceutical Technology, University of Szeged, Faculty of Pharmacy, Szeged, Hungary

 http://www.sciencedirect.com/science/article/pii/S0378517316306330

International Journal of Pharmaceutics

Volume 511, Issue 1, 10 September 2016, Pages 151–160

http://dx.doi.org/10.1016/j.ijpharm.2016.07.003

///////

GMP IN AN API PILOT PLANT

 

GMP……API PILOT PLANT

PRESENTATION

Pilot plant scaleup techniques used in pharmaceutical manufacturing from Sunil Boreddy Rx

 

Pilot plant and scale-up techniques are both integral and critical to drug discovery and development process for new medicinal products. A major decision focuses on that point where the idea or process is advanced from a research oriented program targeted towards commercialization.

The speed of drug discovery has been accelerating at an exponential rate. The past two decades particular have witnessed amazing inventions and innovations in pharmaceutical research, resulting in the ability to produce new drugs faster than even before.

The new drug applications (NDAs) and abbreviated new drug applications (ANDA) are all-time high. The preparation of several clinical batches in the pilot plant provides its personnel with the opportunity to perfect and validate the process. Also different types of laboratories have been motivated to adopt new processes and technologies in an effort to stay at the forefront scientific innovation.

MY PRESENTATION

Anthony crasto scaleup techniques & pilot plant from Anthony Melvin Crasto Ph.D

 

 

Pharmaceutical pilot plants that can quickly numerous short-run production lines of multiple batches are essential for ensuring success in the clinical testing and bougainvilleas study phases. Drug formulation research time targets are met by having a well-designed facility with the appropriate equipment mix, to quickly move from the laboratory to the pilot plant scale 1. In pilot plant, a formula is transformed into a viable, robust product by the development of a reliable and practical method of manufacture that effects the orderly transition from laboratory to routine processing in a full scale production facility where as the scale up involves the designing of prototype using the data obtained from the pilot plant model.

Pilot plant studies must includes a close examination of formula to determine its ability to withstand batch-scale and process modifications; it must includes a review of range of relevant processing equipment also availability of raw materials meeting the specification of product and during the scale up efforts in the pilot plant production and process control are evaluated, validated and finalized.

In addition, appropriate records and reports issued to support Good Manufacturing Practices and to provide historical development of the production formulation, process, equipment train, and specifications

A manufacturer’s decision to scale up / scale down a process is ultimately rooted in the economics of the production process, i.e., in the cost of material, personnel, and equipment associated with the process and its control.

When developing technologies, there are a number of steps required between the initial concept and completion of the final production plant. These steps include the development of the commercial process, optimization of the process, scale-up from the bench to a pilot plant, and from the pilot plant to the full scale process. While the ultimate goal is to go directly from process optimization to full scale plant, the pilot plant is generally a necessary step.

Reasons for this critical step include: understanding the potential waste streams, examination of macro-processes, process interactions, process variations, process controls, development of standard operating procedures, etc. The information developed at the pilot plant scale allows for a better understanding of the overall process including side processes. Therefore, this step helps to build the information base so that the technology can be permitted and safely implemented.

Should be versatile pilot plant that is entirely GMP and facilitates the development of API’s in scalable, safe and environmentally friendly ways.

The combination of facilities, experience and flexibility enable an integral Contract Manufacturing service ranging from laboratory to industrial scale; it should manufacture under regulation small amounts of high added value active substances or key intermediate products.

 

Product quality: Operations that depend on people for executing manual recipes are subject to human variability. How precisely are the operators following the recipe? Processes that are sensitive to variations in processing will result in quality variation. Full recipe automation that controls most of the critical processing operations provides very accurate, repeatable material processing. This leads to very highly consistent product quality.

 Improved production: Many biotech processes have extremely long cycle times (some up to 6 months), and are very sensitive to processing conditions. It is not uncommon for batches to be lost for unexplained reasons after completing a large portion of the batch cycle time. The longer the batch cycle time and the more sensitive production is to processing conditions, the more batch automation is justified. Imagine losing a batch of very valuable product because the recipe was not precisely followed!

 Process optimization: Increasing the product yield can be done by making small changes in processing conditions to improve the chemical conversions or biological growth conditions. Manual control offers a limited ability to finely implement small changes to processing conditions due to the inherent lack of precision in human control. Conversely, computers are very good at controlling conditions precisely. In addition, advanced control capabilities such as model predictive control can greatly improve process optimization. This results in higher product yield and lower production cost. This consideration is highly relevant to pilot plant facilities where part of the goal is to learn how to make the product.

 Recordkeeping: A multi-unit recipe control system is capable of collecting detailed records as to how a batch was made and relates all data to a single batch ID. Data of this nature can be very valuable for QA reporting, QA deviation investigations, and process analysis.

 Safety: Operators spend less time exposed to chemicals when the process is fully automated as compared to manual control. Less exposure to the process generally results in a safer process.

A good batch historian should be able to collect records for a production run to include the following information:  Product and recipe identification

 User defined report parameters

 Formulation data and relevant changes

 Procedural element state changes (Operations, unit procedures, procedures)

 Phase state changes

 Operator changes

 Operator prompts and responses

 Operator comments

 Equipment acquisitions and releases

 Equipment relationships

 Campaign creation data (recipe, formula values, equipment, etc.)

 Campaign modifications

 Campaign execution activity

 Controller I/O subsystem events from the Continuous Historian

 Process alarms

 Process events

 Device state changes.

 

Raw materials

Buildings and facilities. GMPs under the 21 Code of Federal Regulations (CFR) Part 211.42 state that buildings or areas used in the receiving, storage, and handling of raw materials should be of suitable size, construction and location to allow for the proper cleaning, maintenance, and operation (7). The common theme for this section of CFR Parts 210 and 211 is the prevention of errors and contamination. In principle, the requirements for buildings and facilities used in early phase manufacturing are not significantly different than those for later phases or even commercial production. However, there are some areas that are unique to early clinical trial manufacturing.

Control of materials. The CFR regulations under Part 211.80 provide good direction with respect to lot identification, inventory, receipt, storage, and destruction of materials (7). The clear intent is to ensure patient safety by establishing controls that prevent errors or cross-contamination and ensure traceability of components from receipt through clinical use. In general, the requirements for the control of materials are identical across all phases of development, so it is important to consider these requirements when designing a GMP facility within a laboratory setting.

For example, all materials must be assigned a unique lot number and have proper labeling. An inventory system must provide for tracking each lot of each component with a record for each use. Upon receipt, each lot should be visually examined for appropriate labeling and for evidence of tampering or contamination. Materials should be placed into quarantine or in the approved area or reject area with proper labeling to identify the material and prevent mix-ups with other materials in the storage area. Provision should be made for materials with special storage requirements (e.g., refrigeration, high security). The storage labeling should match the actual conditions that the material is being stored and should include expiry/retest dates for approved materials. Although such labeling is inconvenient for new materials where the expiration or retest date may change as more information is known, this enables personnel to be able to determine quickly whether a particular lot of a material is nearing or exceeding the expiration or retest date. General expiry/retest dates for common materials should be based on manufacturer’s recommendation or the literature.

Finally, there are clear regulatory and environmental requirements for the destruction of expired or rejected materials. It is important to observe regional and international requirements regarding the use of animal sourced materials (12). It is recommended to use materials that are not animal sourced and that there be available certification by the raw material manufacturers that they contain no animal sourced materials. If animal sourced raw materials must be used, then certifications by the raw material manufacturers that they either originate from certified and approved (by regulatory bodies) sources for use in human pharmaceuticals, or that the material has been tested to the level required for acceptance by regulatory agencies (following US, EU, or Japanese guidelines, as applicable) is required.

Direct advantages for customers

• Shorter implementation time for product by determination of the product suitability as well as the necessary process cycle
• Optimized adjustment of the processing times in the production lines (trains) by relatively precise estimation of the drying times
• Definition of effective cleaning processes (CIP/WIP and SIP)
• Definition of the selection criteria based on the weighting of the customer, e.g.: drying time, quality (form of crystal, activity, etc.), cleanout, ability of CIP, price

 

An overview of further trials and test functions, that can be realized in the new pilot plant facility:

• Product tests for determination of suitability
• Scale-up tests as basis for the extrapolation on production batches regarding drying time, filling degree, crystalline transformation and grain spectrum
• Optimization of the process cycle
• Optimization of the machine
• Data acquisition and analysis

SEE THIS SECTION IN ACTION…………..KEEP WATCHING

Case study 1

Designed and equipped for the manufacturing of solid oral dosage form

http://www.idifarma.com/us/index.php?option=com_content&view=article&id=106

Designed and equipped for the manufacturing of solid oral dosage forms, specialized in high-activity substances (cytostatic, cytotoxic, hormonal, hormone inhibitors). It has ancillary areas for the proper management of materials intended for clinical trials of new drugs.

Equipment:

• • Oystar Hüttlin Pilotmix 75T single-pot high shear mixer.
• • Container mixer, 60 and 200 liters containers.
• • Fette 1200iC rotary tablet press, fully equipped for formulation development and manufacturing.
• • Oystar Manesty XL Lab 01 tablet coater, with interchangeable drums.
• • IMA Clinipack blister machine.
• • Quadro Comill sieve
• • Capsunorm 2000 manual capsul filler
• • Sepha press-out deblister
• • Fette Gratex deduster
• • Process control laboratory: Dr. Schleuniger tablet hardness tester, Erweka tablet disintegration tester, Erweka friability tester, Mettler Toledo moisture content determination equipment.

…………………………….

CASE STUDY 2

OPERATION OF PILOT PLANT FOR CLINICAL LOTS OF BIOPHARMACEUTICALS

http://www.peq.coppe.ufrj.br/biotec/presentations/Papamichael_RioDeJaneiro2009_secure.pdf

 

 

 

 

 

CASE STUDY 3

 

Good Manufacturing Practices in Active Pharmaceutical Ingredients Development

http://apic.cefic.org/pub/5gmpdev9911.pdf

Example below

3. Introduction Principles basic to the formulation of this guideline are: ·

Development should ensure that all products meet the requirements for quality and purity which they purport or are represented to possess and that the safety of any subject in clinical trials will be guaranteed. ·

During Development all information directly leading to statements on quality of critical intermediates and APIs must be retrievable and/or reconstructable. ·

The system for managing quality should encompass the organisational structure, procedures, processes and resources, as well as activities necessary to ensure confidence that the API will meet its intended specifications for quality and purity. All quality related activities should be defined and documented. Any GMP decision during Development must be based on the principles above.

During the development of an API the required level of GMP control increases. Using these guidelines, the appropriate standard may be implemented according to the intended use of the API. Firms should apply proper judgement, to discern which aspects need to be addressed during different development stages (non-clinical, clinical, scale-up from laboratory to pilot plant to manufacturing site).

Suppliers of APIs and/or critical intermediates to pharmaceutical firms should be notified on the intended use of the materials, in order to apply appropriate GMPs. The matrix (section 8) should be used in conjunction with text in section 7, as is only intended as an initial guide. READ MORE AT…. http://apic.cefic.org/pub/5gmpdev9911.pdf

 

CASE STUDY 4

http://www.steroglass.it/doc_area_download/ita/process/20LT_PILOT_PLANT.pdf

 

 

CASE STUDY 5

 

Health Canada

http://www.hc-sc.gc.ca/dhp-mps/compli-conform/gmp-bpf/question/gmp-bpf-eng.php

The Good Manufacturing Practices questions and answers (GMP Q&A) presented below have been updated following the issuance of the “Good Manufacturing Practices Guidelines, 2009 Edition Version 2 (GUI-0001)“.

This Q&A list will be updated on a regular basis.

Premises – C.02.004

Equipment – C.02.005

Personnel – C.02.006

Sanitation – C.02.007 & C.02.008

Raw Material Testing – C.02.009 & C.02.010

Manufacturing Control – C.02.011 & C.02.012

Quality Control Department – C.02.013, C.02.014 & C.02.015

Packaging Material Testing – C.02.016 & C.02.017

Finished Product Testing – C.02.018 & C.02.019

Records – C.02.020, C.02.021, C.02.022, C.02.023 & C.02.024

Samples – C.02.025 & C.02.026

Stability – C.02.027 & C.02.028

Sterile Products – C.02.029

 

 

 

CASE STUDY 6

http://www.pharmtech.com/early-development-gmps-drug-product-manufacturing-small-molecules-industry-perspective-part-iii?rel=canonical

Also

• Part VII GMPs for Small Molecule Drugs in Early Development Workshop Summary
• Early Development GMPs for Small-Molecule Specifications: An Industry Perspective (Part V)
• Early Development GMPs for Stability (Part IV)
• GMPs for Method Validation in Early Development: An Industry Perspective (Part II)
• GMPs for Small-Molecule Drugs in Early Development (Part I)
• GMPs for Small-Molecule Drugs in Early Development: Workshop Summary-Part VI

CASE STUDY 7

 

http://www.niper.gov.in/tdc_2013.pdf

 

 

 

CASE STUDY 8

Multi-kilo scale-up under GMP conditions

Examples of flow processes being used to produce exceptionally large amounts of material are becoming increasingly common as industrial researchers become more knowledgeable about the benefits of continuous reactions. The above examples from academic groups serve to illustrate that reactions optimized in small reactors processing tens to hundreds of mg hour⁻¹ of material can be scaled up to several grams per hour. Projects in process chemistry are often time-sensitive, however, and production of multiple kg of material may be needed in a short amount of time. An example of how the efficient scaling of a flow reaction can save time and reduce waste is provided by a group of researchers at Eli Lilly in their kg synthesis of a key drug intermediate under GMP conditions . In batch, ketoamide 13 was condensed with NH₄OAc and cyclized to form imidazole 14 at 100 °C in butanol on a 1 gram scale. However, side product formation became a significant problem on multiple runs at a 250 g scale. It was proposed that this was due to slow heat up times of the reactor with increasing scale, as lower temperatures seemed to favour increased degradation over productivecyclization. Upon switching to a 4.51 mL flow reactor, another optimization was carried out which identified methanol as a superior solvent that had been neglected in batch screening due to its low boiling point at atmospheric pressure. Scale-up to a 7.14 L reactor proceeded smoothly without the need for reoptimization, and running on this scale with a residence time of 90 minutes for a six-day continuous run provided 29.2 kg of product after recrystallization (approximately 207 g hour⁻¹). The adoption of a flow protocol by a group of industrial researchers in a scale-up with time constraints demonstrates both the effectiveness and maturity of flow chemistry. While the given reaction was used to produce kilograms of material for a deadline, continuous operation without further optimization could produce over 1 metric tonne of product per year in a reactor that fits into a GC oven.

	Scheme 20 Kilogram-scale synthesis of an imidazole API precursor.

 

 

 

…………………………….

Definitions

Plant: A plant is a place where an industrial or manufacturing process takes place. It may also be expressed as a place where the 5 M’s that are; man, materials, money, method and materials are brought together for the manufacture of products.

Pilot Plant: A part of a manufacturing industry where a laboratory scale formula is transformed into a viable product by development of reliable practical procedures of manufacturing.

Scale-Up: This is the art of designing a prototype based on the information or data obtained from a pilot plant model.

cGMP: current Good Manufacturing Processes refer to an established system of ensuring that products are consistently produced and controlled according to quality standards. It is designed to minimize risk involved in any industrial design. GMP covers all aspects of production from the starting materials, premises and equipment to the training and personal hygiene of staff within industries. Detailed, written procedures are essential for each process that could affect the quality of the finished product. There must be a system to provide documented proof that correct procedures are consistently followed at each step in the manufacturing process every time a product is made.

SCALING UP FROM PILOT PLANTS

When scaling up, it is of utmost importance to consider all aspects of risk and futuristic expansion. The pilot plant is usually a costly apparatus and therefore the decision of building it is always a hard one. The function of a pilot plant is not just to prove that the laboratory experiments work, but;

1. To test technologies that are about to be implemented on industrial plants before establishment
2. To evaluate performance specifications before the actual installation of industrial plant.
3. Evaluation of reliability of mathematical models within real environment.
4. Economic considerations for production involving process optimization and automated control systems.

GMP GENERAL PRACTISES

Facilities and Equipment Systems

• Ø Cleaning and maintenance
• Ø Facility layout and air handling systems for prevention of cross-contamination (e.g. Penicillin, beta-lactams, steroids, hormones, cytotoxic, etc.)
• Ø Specifically designed areas for the manufacturing operations performed by the firm to prevent contamination or mix-ups.

Facilities

• Ø General air handling systems
• Ø Control system for implementing changes in the building
• Ø Lighting, potable water, washing and toilet facilities, sewage and refuse disposal
• Ø Sanitation of the building, use of rodenticides, fungicides, insecticides, cleaning and sanitizing agents.

GMP FOR PLANT DESIGN

The application of GMP to plant design is primary to the establishment of such plants. Regulatory boards have precedence over these operations helping to establish a proper and functional system in plant design.

Design Review

l Conceptual drawings;

From plant design drawings which are inspected and approved by cGMP regulatory bodies (such as Department of Petroleum Resources in Nigeria), approvals are issued depending on adherence to specifications such as muster points, proper spacing of fuel sources from combustion units and other more elaborate considerations.

l Proposed plant layouts;

A choice of location for plant and layout play an important role on environmental impact. Hence, environmental impact assessment is a major part of GMP. Industries must be located at least 100M from closest residential quarter (depending of materials processed in plant).

l Flow diagrams for facility

For optimization and efficiency purposes, flow diagrams for complete refinery process are important for review with intent to ensure they conform to GMP

l Critical systems and areas

Some areas in a plant may require extra safety precautions in operations. The cGMP makes provision for such special considerations with the creation of customized set of operational guidelines that ensure safety and wellness of staff and environment alike.

cGMP EXAMPLE: FOOD PROCESSING PLANT

Outlined below are the cGMP considerations in the establishment and handling of a food processing plant.

Safety of Water

1. Process water is safe, if private supply should be tested at least annually.

2. Backflow prevention by an air gap or back flow prevention device. Sinks that are used to prepare food must have an air-gap.

Food Contact Surface

1. Designed, maintained, and installed so that it is easy to clean and to withstand the use, environment, and cleaning compounds.

2. If cleaning is necessary to protect against microorganisms, food-contact surfaces shall be cleaned in this sequence: wash with detergent, rinse with clear water, and then use an approved sanitizer. The sanitizer used shall be approved for use on food-contact surfaces. UA three-compartment ware washing sink or other equivalent methods shall be used for this purpose.

3. Gloves shall be clean/sanitary. Outer garments suitable.

Prevention of Cross-Contamination

1. Food handlers use good hygienic practices; hands shall be washed before starting work, after absence from work station, or when they become contamination (such as with eating or smoking).

2. Signs shall be posted in processing rooms and other appropriate areas directing employees that handle unprotected food, food-contact surfaces, food packaging materials to wash their hands prior to starting to work, after each absence from the work station, and whenever hands may become contaminated.

3. Plant design so that the potential for contamination of food, food-contact surfaces, or packaging materials is reduced to the extent possible.

4. Physical separation of raw and finished products.

Hand Washing Sinks and Toilet Facilities

1. Hand washing sinks, properly equipped, shall be conveniently located to exposed food processing areas. Ware washing sinks shall not be used for this purpose.

2. Adequate supply of hot and cold water under pressure.

3. Toilet facilities; adequate and accessible, self-closing doors.

4. Sewage disposal system shall be installed and maintained according to State law.

Protection from Adulteration (Food, Food Contact Surfaces, and Packaging Materials)

1. Food processing equipment designed to preclude contamination with lubricants, fuel, metal fragments, contaminated water, or other sources of contamination.

2. Food processed so that production methods to not contaminate the product.

3. Raw materials, works-in-process, filling, assembly, packaging, and storage and transportation conducted so that food is not contaminated.

4. Protection from drip and condensate overhead.

5. Ventilation adequate and air not blown on food or food-contact surfaces.

6. Lights adequately shielded.

7. Compressed air or gas mechanically introduced adequately filtered.

Scope of services

• Engineering support
• Representation of the construction owner (equipment, construction: supervision of general contractors, GMP concept draft)
• Basic and detailed design
• Support during the implementation phase
• Clean room planning (incl. lab areas)
• Construction management
• Qualification
• Validation support

Toxic Items: Labelling, Use, and Storage

1. Products used approved and used according to product’s label.

2. Sanitizer used on food-contact surfaces must be approved for that use.

3. Shall be securely stored, so unauthorized use is prevented.

Personnel Disease Control

1. Food handler, who has illness or open lesion, or other source of microbiological contamination that presents a reasonable possibility of contamination of food, food-contact surfaces, or packaging materials shall be excluded from such operations.

2. Adequate training in food protection, dangers of poor personal hygiene, and unsanitary practices shall be provided.

3. Management shall provide adequate supervision and competent training to ensure compliance with these provisions.

Pest Control

1. Management shall provide an adequate pest control program so that pests are excluded from the plant.

2. Program shall ensure that only approved pesticides are used and applied per the product’s label.

Plant Construction and Design

1. Walls, floors, and ceilings constructed so that they can be adequately cleaned and kept in good repair.

2. Adequate lighting provided.

3. Adequate ventilation or controls to minimize odours and vapours.

4. Adequate screening or protection of outer openings.

5. Grounds maintained free of litre, weeds, and pooling water.

6. Roads, yards, and parking lots maintained so that food is not contaminated.

Equipment

1. Equipment, utensils, and seams on equipment – adequately cleanable, properly maintained, designed, and made of safe materials.

2. Refrigerators and freezers equipped with adequate thermometer.

3. Instruments and control devices – accurate and maintained.

4. Compressed air or gas designed/treated so that food is not contaminated.

Equipment. Most equipment used to manufacture early GMP drug product is be managed under a qualification, preventive maintenance, and calibration program for the GMP facility. However, in early development, there may occasionally be a need to use equipment that is not part of such a program. Rather than performing a comprehensive qualification for a piece of equipment not expected to be frequently used, an organization may choose to qualify it for a single step or campaign. Documentation from an installation qualification/operational qualification (IQ/OQ) and or performance verification at the proposed operating condition is sufficient. For example, if solution preparation needs a mixer with a rotation speed of 75 rpm, then documentation in the batch record using a calibrated tachometer to verify that the mixer was operating at 75 rpm will suffice.

The use of dedicated or disposable equipment or product contact parts may be preferable to following standard cleaning procedures to ensure equipment is clean and acceptable for use. However, not all equipment or equipment parts are disposable or may have a substantial cost that makes disposal prohibitive. In that case, the product contact parts could be dedicated to a specific drug substance for use in drug product manufacture. Dedicating product contact parts to a compound may be costly and may be avoided in some cases by carefully considering product changeover and effective cleaning methods when purchasing equipment.

Another item to consider with respect to equipment, is that the more complicated the equipment is to run or maintain, the less desirable it might be for early GMP batches. In most cases, simple equipment is adequate and will uses less material and consume less total time for preparation, operation, and cleaning activities.

Weights and Measures

1. Scales used to measure net weight of contents shall be designed so they can be calibrated.

2. Products in interstate commerce – net weights/measurements also in metric.

 

CONCLUSION

Plant establishment is an activity that has kept rising from the inception of the industrial revolution until date. Giving rise to increase in raw material demand, increased pollution levels, higher energy demand, and overall greater economic output. As history and record keeping has served for an even longer period, it becomes necessary for adaptation to be made to avoid incidents and accidents that have occurred previously and also those that can be anticipated without actual devastating effect.

The development of the GMP is as a result of observed challenges in industry and environment over years of industrialization. It becomes necessary to upset these poor trends that have developed as a result of industrialization by so doing increasing the pros and reducing the cons.

GMP protects consumer, produce, equipment, and conserves the processes as a whole, leading to a more efficient sustainable process defining a new standard for yields and profit and eliminating the tendency of compromise made by industrialists to increase overall profits at the risk of staff and environment.

 

Batch documentation and execution

Batch record documentation preparation. Manufacturing documentation is a basic requirement for all phases of clinical development. 21 CFR Parts 211.186 and 211.188 describe master production and batch production records, respectively (7). The stated purpose of the master production record is to “assure uniformity from batch to batch.” Although the record assurance is important for a commercial validated manufacturing process, it does not necessarily apply to clinical-development batches. Material properties, manufacturing scale, and quality target product profile frequently change from batch to batch. Therefore, batch production records are the appropriate documentation for clinical trial supplies. Batch production records for Phase 1 materials should minimally include:

• Name, strength, and description of the dosage form
• A complete list of active and inactive ingredients, including weight or measure per dosage unit and total weight or measure per unit
• Theoretical batch size (number of units)
• Manufacturing and control instructions.

These minimum requirements are consistent with the FDA Guidance for Industry: cGMP for Early Phase Investigational Drugs, which requires a record of manufacturing that details the materials, equipment, procedures used and any problems encountered during manufacturing (2). The records should allow for the replication of the process. On this basis, there is flexibility in the manner for which documentation of batch activities can occur, provided that the documentation allows for the post execution review by the quality unit and for the retention of these records.

 

Batch documentation approvals. Review and approval of executed batch records by the Quality unit is required per 21 CFR Part 211.192 (7). This review and approval is required for all stages of clinical manufacturing. Pre-approvals of batch records should be governed by internal procedures as there is no requirement in CFR 21 that the Quality unit pre-approves the batch record (though this is highly recommended in order to minimize the chance of errors). Indeed, Table I shows that pre-approval of batch records by the Quality Unit is practiced by all 10 companies that participated in the IQ Consortium’s drug-product manufacturing survey related to early development. Batch records must be retained for at least 1 year after the expiration of the batch according to CFR Part 211.180, but many companies keep their GMP records archived for longer terms.

Room clearance. 21 CFR Part 211.130 requires inspection of packaging and labeling facilities immediately before use to ensure that all drug products from previous operations have been removed. This inspection should be documented and can be performed by any qualified individual.

Although line clearance for bulk manufacture is not specifically mentioned in the CFR, it is expected that a room clearance be performed. At a minimum, this clearance should be performed prior to the initiation of a new batch (i.e., prior to batch materials entering a processing room).

Hold time. During the early stages of development, final dosage form release testing confirms product quality and support establishment of hold times later in the clinical development. There is no requirement to establish hold times for work in process in early development. Specific formulation and stability experience, which is usually limited at this stage of development, should be leveraged to assess any substantial variations from expected batch processing times. The data gathered from these batches and subsequent development can be used to help establish hold times for future batches. (Exceptions to this approach may include solution or suspension preparations used in solid dosage form manufacturing, where procedures typically govern allowable hold times to ensure the absence of microbial contamination in the final product.)

Change control. Changes to raw materials, processes, and products during early development are inevitable. It is not required that these changes be controlled by a central system but rather may be appropriately documented in technical reports and manufacturing batch records. Any changes in manufacturing process from a previous batch should be captured as part of the batch record documentation and communicated to affected areas. The rationale for these changes should also be documented as this serves as a source for development history reports and for updating regulatory filings. The authors recommend that those changes that could affect a regulatory filing be captured in a formal system.

Process changes. Process parameters should be recorded but do not need to be predetermined because processes may not be fixed or established in early development. Given the limited API availability in early development, a clinical batch is often the first time a product is manufactured at a particular scale or using a particular process train. Therefore, process changes should be expected. Process trains and operating parameters must be documented in the batch record but changes should not trigger an exception report or CAPA. Changes should be documented as an operational note or modification to the batch record in real time. Such changes driven by technical observations should not require prior approval by the Quality unit, but should have the appropriate scientific justification (via formulator/scientist) or the appropriate flexibility built into the batch record to allow for the changes. This documentation should be available for Quality review prior to product disposition.

Calculation of yield. Actual yields should be calculated for major processing steps to further process understanding and enable optimization of processes. Expected yield tolerances are not always applicable to early development manufacture. At this stage of early development, when formulation and process knowledge is extremely limited, there may be no technical basis for setting yield tolerances and, therefore, this yield may not be an indicator of the quality of the final product.

In-process controls and R&D sampling. In-process tests and controls should follow basic requirements of GMPS to document consistency of the batch. For capsule products, these requirements may include capsule weights and physical inspection. For tablet products, compression force or tablet hardness and weights should be monitored together with appearance. R&D sampling, defined as samples taken for purposes of furthering process understanding but not utilized for batch disposition decisions, is a normal part of all phases of clinical manufacturing. In early development manufacturing, a sampling plan is required for in-process control tests, but not for R&D samples. However, for the purpose of material accountability, R&D sampling should be documented as part of batch execution. For these samples, testing results may be managed separately, and are not required to be included in regulatory documentation.

Facilities and equipment

Regardless of the scale of manufacturing, the facility used for manufacturing clinical trial supplies must meet the basic GMP requirements as described in the regulations and guidance documents. Below are three scenarios for early development and the advantages of each as pertaining to early development. The first involves a pilot plant facility designed and equipped for routine GMP operations. The second scenario aims to establish a GMP area within a laboratory environment. The third example focuses on conducting GMP manufacturing or leveraging the practice of pharmacy in close proximity to the clinical site.

GMP facility for drug-product manufacture. The traditional approach in GMP drug-product manufacture is to use a dedicated facility (often called a pilot plant) for early phase clinical trials. Advantages of this approach include that the quality systems for the facility (i.e., maintenance, calibration, cleaning, change management, CAPA, and documentation) are well defined, and that training and other activities required for maintaining GMP compliance are centralized. Other drivers to use a pilot plant in early development may be the need for specialized equipment, or larger batch sizes in special situations.

GMP area within a laboratory setting. In some cases, it may be advantageous to establish a GMP area within a “laboratory setting” (i.e., a drug-development facility not dedicated to the production of clinical supplies) for the manufacture of drug product in early development. The rationale for this approach might be to avoid the significant investment in setting up a dedicated facility and to create simpler, more flexible systems that meet GMP requirements but are tailored for the specific activity envisioned. Examples where this approach might be considered include the need for special containment not available in the pilot-plant; the need to work with radioactive or hazardous materials, use of controlled substances and the production of “one-off manufactured” product used for proof of concept. The business rationale should be documented and approved by the manufacturing and Quality groups. As long as the appropriate GMP controls are maintained, especially as related to operator safety, cleaning, and prevention of cross-contamination, there is no compliance barrier to using “lab-type” facilities for the manufacturing of early phase clinical batches. Before GMP manufacturing is initiated, however, a risk assessment should be conducted and documented. Inclusion of representatives from Quality, analytical, clinical manufacturing, product development, and environmental health and safety would be prudent. When selecting/designing an early development clinical manufacturing facility, consideration should be made for the receipt, storage, dispensing, and movement of materials. The manufacturing processes in the nondedicated area must protect the product, patient, and the manufacturing operators.

Additionally, companies should consider what items are appropriate for the manufacture. For example, the use of a certified laminar flow hood may be a better choice for manufacturing than a fume hood, because the former is designed to prevent contamination of the product, protect the operator, and the laboratory environment. In addition, with the appropriate cleaning, a laminar flow hood can more easily be used for multiple products. Small scale/manual equipment or procedures may be the best approach because the space is likely to be limited. With a small batch size, the use of small scale or manual equipment/procedures will minimize yield loss. Additional measures to be assessed include appropriate gowning and operator personal protection devices, area and operator monitoring for potent or radiolabeled drug exposure, and so forth.

Documentation of the facility preparation, product manufacture, and the return of the facility to the previous state, if needed, is recommended. This documentation should describe the rationale for the manufacture in the nondedicated area, risk assessment, preparation of the area, cleaning procedures, and list of responsible persons. This documentation can reference existing procedures or standard operating procedures (SOPs) along with documents associated with the meetings and preparation for the manufacture of the batch. Batch records and cleaning records should be part of the documentation and should follow the company’s data-retention policy.

Receipt and approval

Specifications. It is a GMP requirement that all raw materials for the manufacture of drug product have appropriate specifications to ensure quality. The compendial requirements should be used for setting specifications provided the material is listed in at least one pharmaceutical compendium (e.g., US, European, and Japanese Pharmacopeias). It is important that the use of materials meeting the requirements of a single compendium is acceptable for use in early phase clinical studies conducted in the US, Europe, and Japan. For example, a material that meets USP criteria and is used in the manufacture of a drug product should be acceptable for use in early clinical studies in the European Union. In the absence of a pharmaceutical compendium monograph, the vendor specification and/or alternative compendial specifications such as USP’s Food Chemical Codex should guide specification setting. In any case, the sponsor is responsible for the establishment of appropriate specifications. Therefore, it is the authors’ position that good practice is to have at least a basic understanding of the manufacture, chemistry, and toxicology of the materials to guide appropriate specification setting.

Material testing and evaluation. The minimum testing required for incoming materials is visual inspection and identification. However, as mentioned above, the appropriate tests should be determined for the material based on the knowledge of the manufacture, chemistry, and toxicology. If the vendor is qualified, then the certificate of analysis may be acceptable in conjunction with the visual inspection and identification testing (see “Vendor Qualification” section below).

Approval for use. Ideally, manufacture of a bulk drug product should begin with approved material specifications and with materials that are fully tested and released. However, there are circumstances where it may not be feasible to start manufacture with approved specifications and fully tested and released materials, including API. Manufacturing prior to final release (sometimes called manufacturing “at risk”) may be acceptable, however, because the quality system ensures that all specifications are approved, test results are within specifications, and all relevant documents are in place before the product is released for administration to humans. The “risk” must lie fully with the manufacturer and not with the patient.

Vendor qualification. Vendors supplying excipients, raw materials, or API must be qualified by the sponsor. Appropriate qualification should depend on the stage of development and an internal risk assessment. For, example if a vendor has a history of supplying the pharmaceutical industry and the material is to be used in early development, a paper assessment (e.g., a questionnaire) should be sufficient. If a supplier does not have a history of supplying the pharmaceutical industry, a risk assessment should be performed and depending on the outcome a site audit may be required prior to accepting material for use.

Ideally, vendors should be qualified prior to using raw materials for manufacture. However, it is acceptable for qualification to proceed in parallel as long as documentation/risk assessments are available prior to product release and as in the previous section all risk lies with the manufacturer and not the patient.

 

A production mixing unit is usually not geometrically similar to the mixer used for process development. Such differences can make scale-up from the laboratory or pilot plant challenging. A solution to these problems is to systematically calculate and evaluate mixing characteristics for each geometry change.

Geometric similarity is often used in mixing scale-up because it greatly simplifies design calculations. Geometric similarity means that a single ratio between small scale and large scale applies to every length dimension (see figure). With geometric similarity, all of the length dimensions in the large-scale equipment are set by the corresponding dimensions in the small-scale equipment. The only remaining variable for scale-up to large-scale mixing is the rotational speed — one or more mixing characteristics, such as tip speed, can be duplicated by the appropriate selection of a large-scale mixer speed.

The two most popular and effective geometric scale-up methods are equal tip speed and equal power per volume. Equal tip speed results when the small-scale mixer speed is multiplied by the inverse geometric ratio of the impeller diameters to get the large-scale mixer speed:

N2 = N1(D1/D2)

Equal power per volume involves a similar calculation, except the geometry ratio is raised to the two-thirds power:

N2 = N1(D1/D2)(2/3)

This expression for power per volume only applies strictly for turbulent conditions, where the power number is constant, but is approximately correct for transition-flow mixing.

Avoid mix-ups
As we have seen, taking successive steps allows the development of alternative solutions to scale-up. Similar methods can be used to scale-down process problems for investigation in a pilot-plant or laboratory simulation. Here, too, non-geometric similarity often is a problem. Such scale-down calculations should help pinpoint appropriate operating speeds to test in the small-scale mixer.
In any scale-up or scale-down evaluation, some variables can be held constant while others must change. For example, even with geometric similarity, scale-up will result in less surface per volume because surface area increases as the length squared and volume increases as length cubed. Similarly, keeping blend time constant rarely is practical with any significant scale change. Larger tanks take longer to blend than smaller ones. Also, Reynolds number is expected to increase as size increases. In addition, standard operating speeds or available impeller sizes may necessitate a final adjustment to the scale-up calculations.

Rules for scale-up always have exceptions but understanding the effects of scale-up, especially non-geometric scale-up, can provide valuable guidance. Indeed, appreciation of the tradeoffs involved in non-geometric scale-up may be crucial for success with large-scale mixing processes.

REFERENCES

1 https://docs.google.com/viewer?url=http%3A%2F%2Fwww.sunbio.com%2Fsub%2FSunbio%2520GMP%2520Capabilty.ppt

2 http://apic.cefic.org/pub/5gmpdev9911.pdf

3 http://www.pharmtech.com/early-development-gmps-drug-product-manufacturing-small-molecules-industry-perspective-part-iii?rel=canonical

“ICH Q7a. Good Manufacturing Practice for Active Pharmaceutical Ingredients” (Draft 6, October 19th, 1999, section 19).

“ICH Q6a. Specifications: test procedures and acceptance criteria for new drug substances and new drug products: chemical substances”.

“Good Manufacturing Practices for Active Pharmaceutical Ingredients” (EFPIA / CEFIC Guideline, August, 1996).

“Quality Management System for Active Pharmaceutical Ingredients Manufacturers” (APIC/CEFIC May 1998).

“Good Manufacturing Practices Guide for Bulk Pharmaceutical Excipients”, The International Pharmaceutical Excipients Council (October 1995).

“21 Code of Federal Regulations, parts 210 to 211”, U.S. Food & Drug Administration. “Guide to inspection of Bulk Pharmaceutical Chemicals”, U.S. Food & Drug Administration, (Revised Edition: May 1994).

“Guidance for Industry. ANDAs: Impurities in Drug Substances”, U.S. Food and Drug Administration, CDER (June 1998).

“Guideline on the Preparation of Investigational New Drug Products”, U.S. Food & Drug Administration, CDER (March 1991).

“EC Guides to GMP, Annex 13: Manufacture of Investigational Medicinal Products” (Revised Dec. 1996).

“GMP Compliance during Development”, David J. DeTora. Drug Information Journal, 33, 769-776, 1999.

FDA Guidance documents on internet address: http://www.fda.gov/cder/guidance /index.htm

EMEA Guidance documents on internet address: http://www.eudra.org.

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Raw Material Variation into QbD Risk Assessment

Areas of discussion included how expectations for raw material control are evolving within changing regulatory and business paradigms including quality by design (QbD), counterfeiting, complex supply chains, and sourcing changes. discussed risk assessment and mitigation strategies along with supplier risk management plans.

Regulatory Considerations

the lack of a consistent definition of raw materials in regulations pertaining to the pharmaceutical industry. In its Q7 guideline, the International Conference on Harmonisation of Technical Requirements for the Registration of Pharmaceuticals for Human Use (ICH) defines raw materials as “starting materials, reagents, and solvents intended for use in the production of intermediates or APIs.” However, the term as defined by different speakers could cover a wide range of materials including the following:

• starting or source materials (cell lines, viral or bacterial stocks, media components, chemicals, tissues, serum, water)

• in-process materials (resins, buffers, filters, column housings, tubing, reagents)

• excipients

• packaging components, both primary and secondary (syringes, vials, stoppers, plungers, crimps, boxes, trays, and labels)

• device/delivery components (pen/ injector components, IV bags, filters). Some regulations directly consider the control of raw materials, but they are not comprehensive and are scattered among the US Code of Federal Regulations (CFR), ICH, and other regulations/guidances. Although the regulations are not extensive, the need to control raw materials was clear from all presenters and is implicit in the sources cited below:

• 21 CFR 610.15 regarding constituents

• 21 CFR 211.80 regarding components and containers/closures

• 21 CFR 211.110 regarding control of in-process materials • ICH Q5A/D for cell substrates and viral safety

• ICH Q7 discussing the need to control materials with appropriate specifications

• ICH Q10 stating that a biomanufacturer is responsible for the quality of purchased materials

• the US bill “Country-of-Origin Labeling for Pharmaceutical Ingredients,” proposed in September 2008

• QbD principles requiring an understanding of the criticality of quality attributes for raw materials and their effect on processes and products.

Developing Control

Strategies Control of raw materials is essential to maintaining safety. Thorough knowledge of raw materials can mitigate the potential for contamination derived from such sources as microbes, chemicals, prions, and pyrogens. Raw material control for safety also includes identification — being able to verify that you have received the correct material — because the presence of an incorrectly identified material in a manufacturing process could compromise safety.

Control of raw materials is essential to ensure lot-to-lot consistency because variation in them can directly affect the variation of both product and process. So manufacturers must understand the critical material attributes (CMAs) of their raw materials and which of those affect variability — as well as how to control that variability.

You must show that you are using appropriate analytical methods to characterize raw materials. Raw materials such as polyethylene glycol (PEG) isomers, trace materials in media and water, container and closure materials, and chromatography resins all have the potential to affect lot-to-lot consistency. An effective raw material control program will also ensure consistent supplies.

A single source for a vital raw material can be a significant financial and quality-assurance risk. If a supplier goes out of business or experiences quality problems, can that raw material be obtained elsewhere? Has a second source been qualified in case the primary source is no longer available? Does the second source have the capacity to meet your needs? A QbD approach to raw material control requires that you understand the impact on your product’s critical quality attributes.

You will need to show that you understand the effect of raw material variability on your product as well as on your manufacturing process. Use of multiple lots during development can provide data on raw material lot-to-lot variability and its related effects on process and product. When that is not feasible, a manufacturer may consider including different lots of raw materials during bench-scale studies. In addition to the raw materials themselves, you should gain an understanding of whether and how raw material degradants might affect your process or product.

A QbD approach can use relevant knowledge to help you define how to go about setting specifications, in-process controls (IPCs), and handling conditions. Testing of Raw Materials The forum discussed what levels of testing are important for specific raw materials. A supplier’s certificate of analysis (CoA) is never sufficient for raw materials because good manufacturing practices (GMPs) require appropriate testing, and at a minimum, testing for identity. The material ordered may include additives, preservatives, degradation products, or contaminants. You must verify that the CoA is appropriate for control of the raw material, but you can’t assume that at the outset.

Similarly, CoA verification may be necessary only once a year once your experience with a given supplier has shown that quality is consistent. Vendor qualification is an important factor in defining your testing needs. To ensure the quality of raw materials against adulteration, identity testing is essential. Currently, tests with fingerprint techniques — e.g., nuclear magnetic resonance (NMR) imaging and Raman, nearinfrared (NIR), and Fourier-transform infrared (FTIR) spectroscopy — are used to assure the identity and quality of raw materials.

Whatever techniques you use, it is important to retain samples for future investigations. Photographic libraries of materials and their packaging have also proven useful for identifying and preventing use of counterfeit products. How often and in how much depth you need to verify a CoA through independent testing is an important consideration, especially for environments in which counterfeiting or contamination can occur.

Once you understand the CMAs of your raw materials, you need to identify which tests are relevant for testing specific quality attributes (QAs) of those raw materials. Sampling plans need careful consideration and should be risk based, dependent on the nature and use of the RM, and any regulatory requirements. Such plans should always be justified in a report available for inspection and/or filing.

It is important to consider RM stability and whether any special tests for degradants are needed for release of the material over time. A stability profile will dictate the purchasing program (storage of large quantities or buying as needed) as well as affect the associated testing strategy.

Supply Quality Management:

Ensuring Quality and Availability It is becoming increasingly evident in the current supply chain environment that management of suppliers and the “cold chain” is essential to assuring the quality of raw materials. How often and how thoroughly you perform vendor audits depends on your experience with a given vendor.

A manufacturer’s general experience with a vendor (prior knowledge) is an important criterion used to evaluate that vendor’s suitability to supply raw materials. Items to consider when selecting a vendor include its quality systems and its solvency, as well as its length of time in business, its geographic area, and whether it supplies multiple industries or just one or two drug manufacturers. Those form part of a risk assessment relating to suppliers to be described in more detail below.

Ensuring both the availability and qualification of secondary suppliers is important as well. Practices such as split purchasing may help ensure that you have good working relationships with multiple vendors. Strict change control sections should be included in supplier agreements and should include details requiring a vendor to notify you of changes in its product or suppliers. Such agreements should also provide for impact assessments from both supplier and manufacturer in the event that a supplier makes any changes. Supply chain traceability is not as straightforward as it might seem.

Although most manufacturers use country-of-origin (COO) questionnaires, those often prove less than ideal in revealing what you need to know. It is critical to craft questions that get the in-depth answers you need. For example, rather than asking “Do you purchase supplies from any high-risk countries?” you might ask “From what countries do you purchase supplies?” If the specified countries include any you consider to be high risk, you can follow up or choose another supplier.

It is critical to use risk-assessment techniques for determining traceability to avoid a false sense of security that can lead to costly or even deadly errors. It is sometimes unclear exactly what roles are played by whom in a supply chain.

Which companies are manufacturers, which are distributors, and which are intermediaries is not necessarily clear. A company that simply repackages a raw material from 55-gallon drums into smaller containers may consider itself a manufacturer. Due diligence will help ensure that you really know where your raw materials originated. As part of assessing supply chain complexity, forum participants were informed of a proposed program whereby industry creates a system of cooperative audits in which vendors would be audited by a selected team representing all industry rather than multiple auditors from each company continuously auditing suppliers.

The resulting audits would lead to certification that would assure all purchasers that each vendor meets certain defined criteria. Such a “360° Rx” program would enable increased depth of supplier audits and save manufacturers time and money (see box, right). The Role of Compendial Standards: Compendia provide some assurance of minimum quality standards for specified materials. However, compendial standards may differ among the pharmacopoeias.

Few of the complex raw materials (e.g., culture media, soy, yeastolates, most growth factors) used in biotechnology manufacturing are compendial, and those that are (e.g., insulin) may not have the appropriate compendial limits on specific quality attributes — or even test for quality attributes necessary to control pharmaceutical manufacturing. Even for standard chemical raw materials (e.g., trace metals), compendial standards may not focus on quality attributes relevant for biotechnology process and product quality assurance.

Those may be product- and/or process-specific. Furthermore, compendial standards do not necessarily help control for contamination, counterfeiting, or supply chain issues because a supplier can simply state it meets compendia — a statement that currently requires no certification

Risk Management

Risk assessments are an important tool for ensuring the safety, efficacy, consistency, and supply of pharmaceutical products. Many companies in both the United States and the European Union are using ICH Q9 as a basis for risk assessment, control, communication, and future review.

Risk assessments should begin by identifying all raw materials and assessing their criticality to product safety, efficacy, and supply. RM risk assessments require cross-functional input from all departments including supply, product development, manufacturing, quality control, quality assurance, clinical, and any other contributors. It was clear from this forum’s discussions that risk assessments are only as good as the people who carry them out. Having the right expertise over a spectrum of areas is vital if any risk assessment is to be meaningful. Multiple risk assessment tools exist, but in general, a good risk assessment must address concepts such as impact/ severity and likelihood/detectability.

One tool discussed at the forum (Figure 1) used nine blocks to score a supplier’s performance against material risk levels for audits, supplier qualification, supplier monitoring, change control, material specifications and testing, quality agreements, supplier certification, and sourcing, or other appropriate combinations of factors. Risk assessment should also be performed in relation to country of origin. It is critical to be able to trace your raw materials to their source. Just as a biopharmaceutical manufacturer audits its suppliers, those suppliers must also know, audit, and qualify their own distributors.

It is now well known that there are high-risk geographic areas where additional caution should be exercised to assure purity and identity of sourced materials. A potentially overlooked risk assessment issue is that manufacturers need to evaluate their raw materials and products in relation to opportunities for someone to make a profit through adulteration (e.g., by diluting a product to increase volume, and thus sales income). Any materials identified in such an evaluation should be managed with particular caution.

Risk assessments ensure that appropriate control strategies and raw materials (e.g., grade, origin) have been selected, which is relevant to a QbD approach. For regulatory filings, acceptable specifications, raw materials, and control strategies are tested with the necessary acceptance criteriia to ensure the performance of a process and the quality of its ultimate products. A periodic risk review should include more than a mere cursory review of individual risk assessments. It should reevaluate the risk program itself based on experience and lessons learned. Your risk assessment should be phase-appropriate, and as such it will change as data become available throughout development.

Early on, your raw materials risk assessment can be based on platform and previous knowledge, on the quality assurance of your suppliers, and adventitious agent introduction. As a manufacturing process develops, you will need to reevaluate that risk assessment including commercial considerations of scale and production frequency, highrisk raw materials control strategy, and handling and storage requirements.

During commercialization, design of experiments (DoEs) and collated knowledge will further define the CQAs of both product and RMs as well as potential and actual interactions among RMs, process, and product. At that point, you will be able to define and justify the raw materials for your commercial process and refine their specifications.

By the time your product is ready for market launch, you will have updated the failure modes and effects analysis (FMEA), completed your raw materials specifications, set your sourcing strategy, put in place your supplier qualification program, defined your raw material control strategy, and made your risk assessment ready for filing. The morning’s session resulted in a list of elements to be included in a raw materials risk assessment

 

Elements of Raw Material

Risk Assessments Is the raw material biological, chemical, or physical (such as tubing or stoppers, materials that are not actual components of the end product)? How likely is the raw material to introduce biological or chemical contamination?

Is the raw material or are its degradants able to directly affect the safety and/or efficacy of a drug substance (e.g., toxicity, chemical modifications)?

How complex is the raw material itself or its impurity profile? How much prior knowledge (e.g., historical or published knowledge, current experience) do you have regarding the raw material? What is the Intended use of the raw material (e.g., as a buffer, reagent, or excipient)?

Where in the manufacturing process will this raw material will be used (upstream/ downstream)?

What is the extent of supply chain traceability (considering country of origin, supply chain complexity, and supply chain security)?

What is the extent of supplier quality assurance (from audits, monitoring, historical experience)?

How extensive is the characterization of the raw material (how well can the raw material be characterized; standard existing methods or novel techniques; the RM’s impact on test methods)?

How stable is the raw material? Is the raw material new to the process or a result of a change to an existing raw material (if a change, what studies have been executed to assure comparability)?

What is the depth of knowledge of the RM’s own manufacturing process to assess the risk associated with its use (e.g., process contaminants)?

Does the use of the raw material in a manufacturing environment present safety and/or handling risks? Does your process have the ability to clear the raw material?

Are there associated business risks (e.g., a solesource or multiple-source material, unique or not to the pharmaceutical industry, custom-made or not, and the supplier’s ability to consistently meet specific requirements)?

What is your level of understanding of the raw material CMAs?

Benefits of Implementing QbD

Benefits for the FDAEnhances scientific foundation for review Provides for better coordination across review, compliance, and inspection Improves information in regulatory submissions Provides for better consistency Improves quality of review (establishing a quality management system for CMC) Provides for more flexibility in decision making Ensures decisions made on scientific and not on empirical information Involves various disciplines in decision making Uses resources to address higher risks

Benefits for Industry Ensures better design of products with fewer problems in manufacturing Reduces number of manufacturing supplements required for postmarket changes; relies on process and risk understanding and risk mitigation Allows for implementation of new technology to improve manufacturing without regulatory scrutiny Allows for possible reduction in overall costs of manufacturing; creates less waste Ensures less hassle during review, reduces deficiencies, speeds approvals Improves interaction with the FDA; operates on a scientific rather than on a process level Allows for continuous improvements in products and manufacturing processes Allows for better understanding of how APIs and excipients affect manufacturing Relates manufacturing to clinical during design Provides a better overall business model

Frequently Used QbD Terms 

 

Quality Attribute: A physical, chemical, or microbiological property or characteristic of a material that directly or indirectly alters quality	Critical Quality Attribute (CQA): A quality attribute that must be controlled within predefined limits to ensure that a product meets its intended safety, efficacy, stability, and performance
Real-Time Release (RTR): Ability to evaluate and ensure acceptable quality of an in-process and/or final product based on process data, including valid combination of assessment of material attributes by direct and/or indirect process measurements and assessment of critical process parameters and their effects on in-process material attributes	Process Parameter: An input variable or condition of a manufacturing process that can be directly controlled in the process. Typically, such parameters are physical or chemical (e.g., temperature, process time, column flow rate, column volume, reagent concentration, or buffer pH).
Critical Process Parameter (CPP): A process parameter whose variability has an influence on a CQA and therefore should be monitored or controlled to ensure a process produces a desired quality.	Process Performance Attribute: An output variable or outcome that cannot be directly controlled but is an indicator that a process performed as expected
Key Process Parameter (KPP): An input process parameter that should be carefully controlled within a narrow range and is essential for process performance; a key process parameter does not affect product quality attributes. If the acceptable range is exceeded, it may affect the process (e.g., yield, duration) but not product quality.	Non-Key Process Parameter: An input parameter that has been demonstrated to be easily controlled or has a wide acceptable limit. Such parameters may influence quality or process performance if acceptable limits are exceeded.
Design Space: The multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality; working within a design space is not considered to be a change requiring regulatory approval. Movement out of a design space is considered to be a change and would normally initiate a regulatory postapproval change process. Design space is proposed by an applicant and is subject to regulatory assessment and approval (ICH Q8).	Control Strategy: A planned set of controls, derived from current product and process understanding, that ensures process performance and product quality; such controls can include parameters and attributes related to drug substance and drug product materials and components, facility and equipment operating conditions, in-process controls, finished-product specifications, and associated methods, and frequency of monitoring and control (ICH Q10).
Quality Target Product Profile (QTPP): A prospective summary of the quality characteristics of a drug product that ideally will be achieved to ensure desired quality, taking into account safety and efficacy of a drug product

Inna Ben-Anat, Global QbD Director of Teva Pharmaceuticals

Meet Inna Ben-Anat, Global QbD Director of Teva Pharmaceuticals. Inna is a key thought leader in Quality by Design for generics. https://www.linkedin.com/pub/inna-ben-anat/6/47a/670 Ben-Anat, InnaASSOCIATE DIRECTOR, HEAD OF QDD STRATEGY | TEVA PHARMACEUTICALSAssociate Director, Head of QbD Strategy Chemical Engineer with a degree in Quality Assurance and Reliability (Technion-Israel Institute of Technology). QbD Strategy Leader at Teva (USA). Headed the implementation of a global QbD training programme. More than 12 years of pharmaceutical development experience. Inna Ben-Anat is a Quality by Design (QbD) Strategy Leader in Teva Pharmaceuticals USA. In this role, Inna has implemented global QbD training program, and is supporting R&D teams in developing Quality by Design strategies, optimizing formulations and processes and assisting develop product specifications. Additionally, Inna supports Process Engineering group with process optimization during scale-up and supports Operations in identification and resolution of any technical issues. Inna has extensive expertise in process development, design of experiments for process and product optimization, and statistical data analysis, and has more than 10 years of pharmaceutical development experience. Inna received her BSc degree in Chemical Engineering and ME degree in Quality Assurance and Reliability in Engineering from Technion-Israeli Institute of Technology. Teva Pharmaceutical Industries Ltd. 5 Basel Street PO Box 3190 Petah Tiqwa , 4951033 Israel

Education

Technion – Israel Institute of Technology

ME, QA&R in Engineering

2005 – 2007

Technion – Israel Institute of Technology

BSc, Chemical Engineering

1997 – 2001

Experience

Director, Global QbD Strategy and Product Robustness

Teva Pharmaceuticals

December 2013 – Present (1 year 7 months)

Assc. Director, Global QbD Strategy

Teva Pharmaceuticals

May 2013 – Present (2 years 2 months)

QbD Strategy Leader

Teva Pharmaceuticals

March 2011 – Present (4 years 4 months)

Senior Process Development Engineer

Teva Pharmaceuticals

2007 – March 2011 (4 years)

Analytical R&D Manager

TransPharma Medical

2005 – 2006 (1 year)

Analytical R&D

TransPharma Medical

2001 – 2005 (4 years)

Laboratory Scientist-Teva Jerusalem

Teva Pharmaceuticals

2000 – 2001 (1 year)

Employment History

• Director, Global QbD Strategy and Product Robustness

  Teva Pharmaceutical Industries Ltd.

• QbD Strategy Leader

  Teva Pharmaceutical Industries Ltd.

• Analytical Research and Development Manager

  TransPharma Medical Ltd

see

• QbD in Generics IFPAC 2015 Teva Inna Ben Anat QbDWorks

  http://www.slideshare.net/…/qbd-in-generics-ifpac-2015-teva–inna–ben–anat-qb…

  Mar 31, 2015 – Listen to the Full Interview: http://qbdworks.com QbD in Generics – Interview with Inna Ben–Anat by QbDWorks.com.

• Inna Ben Anat Profiles | Facebook

  https://www.facebook.com/public/Inna–Ben–Anat

  View the profiles of people named Inna Ben Anat on Facebook. … Worked at TevaPharmaceuticals USAStudied at הטכניון – מכון טכנולוגי לישראל | Technion Lives  …

Petah Tikva, ISRAEL

1. Petah Tikva – Wikipedia, the free encyclopedia

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   Petah Tikva (Hebrew: פֶּתַח תִּקְוָה, IPA: [ˈpetaχ tikˈva], “Opening of Hope”) known as Em HaMoshavot (“Mother of the Moshavot”), is a city in the Central …

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Title	Year	Author(s)	Link
Understanding Pharmaceutical Quality by Design	2014	Lawrence X. Yu, Gregory Amidon, Mansoor A. Khan, Stephen W. Hoag, James Polli, G. K. Raju, Janet Woodcock	http://www.ncbi.nlm.nih.gov/pubmed/24854893
A quality by design study applied to an industrial pharmaceutical fluid bed granulation	2012	Vera Lourenço, Dirk Lochmann, Gabriele Reich, José C. Menezesa, Thorsten Herdling, Jens Schewitz	http://www.ncbi.nlm.nih.gov/pubmed/22446063
Application of the quality by design approach to the drug substance manufacturing process of an Fc fusion protein: towards a global multi-step design space.	2012	Eon-duval A, Valax P, Solacroup T, Broly H, Gleixner R, Strat CL, Sutter J.	http://www.ncbi.nlm.nih.gov/pubmed/22821774
Instrumented roll technology for the design space development of roller compaction process	2012	Vishwas V. Nesarikar, Nipa Vatsaraj, Chandrakant Patel, William Early, Preetanshu Pandey, Omar Sprockel, Zhihui Gao, Robert Jerzewski, Ronald Miller, Michael Levin	http://www.sciencedirect.com/science/article/pii/S037851731200066X
Rapid exploration of curing process design space for production of controlled-release pellets	2012	Katja Kristan andMatej Horvat	http://onlinelibrary.wiley.com/doi/10.1002/jps.23277/abstract
Automating push-through force testing of blister packs	2011	Jha, Salil	http://www.tabletscapsules.com/Content/getArticle.aspx?ItemID=0c90dc21-5939-4a8d-ab0b-10cee0940398
Quality by Design for ANDAs: An Example for Modified Release Dosage Forms	2011		http://qbdworks.com/wp-content/uploads/2014/06/QbD-Modified-Release-Dosage.pdf
QbD Status Update Generic Drugs	2011	Susan Rosencrance	http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/HowDrugsareDevelopedandApproved/ApprovalApplications/AbbreviatedNewDrugApplicationANDAGenerics/UCM292666.pdf
Design-for-Six-Sigma for Development of a Bioprocess Quality-by-Design Framework	2011	Junker B, Kosinski M, Geer D, Mahajan R, Chartrain M, Meyer B, Dephillips P, Wang Y, Henrickson R, Ezis K, Waskiewicz M.	http://www.ncbi.nlm.nih.gov/pubmed/22293236
Quality by design and process analytical technology for sterile products–where are we now?	2010	Riley BS, Li X.	http://www.ncbi.nlm.nih.gov/pubmed/21181513
Development of quality-by-design analytical methods	2010	Frederick G. Vogt andAlireza S. Kord	http://onlinelibrary.wiley.com/doi/10.1002/jps.22325/full
Roadmap for implementation of quality by design (QbD) for biotechnology products.	2009	Rathore AS	http://www.ncbi.nlm.nih.gov/pubmed/19647883
Quality by Design for Biopharmaceuticals: Principles and Case Studies	2009	Anurag S. Rathore (Editor), Rohin Mhatre (Editor)	http://astore.amazon.com/qbdworks-20/detail/0470282339
Quality by design for biopharmaceuticals	2009	Anurag S Rathore & Helen Winkle	http://www.nature.com/nbt/journal/v27/n1/abs/nbt0109-26.html
Formulation optimization of long-acting depot injection of aripiprazole by using D-optimal mixture design	2009	Nahata T, Saini TR.	http://www.ncbi.nlm.nih.gov/pubmed/19634350
Quality by Design: Get to the production scale quickly and successfully with Design of Experiments	2009	Norbert Pöllinger, Stefanie Feiler, Philippe Solot,	http://www.aicos.com/upload/downloads/Artikel/Stavex_QualityByDesign-e.pdf
Pharmaceutical quality by design: product and process development, understanding, and control.	2008	Yu LX	http://www.ncbi.nlm.nih.gov/pubmed/18185986
Quality by design for biopharmaceuticals	2009	Anurag S Rathore1 & Helen Winkle2	http://www.nature.com/nbt/journal/v27/n1/full/nbt0109-26.html
Why quality-by-design should be on the executive team’s agenda	2009	Ted Fuhr, Michele Holcomb, Paul Rutten	http://qbdworks.com/wp-content/uploads/2014/06/QbD_McKinsey.pdf
BioProcess Control: What the Next 15 Years Will Bring	2008	Michael Boudreau, Emerson Process Management, Inc. and Trish Benton, Broadley-James Corp.	http://www.pharmamanufacturing.com/articles/2008/062/?page=full
QbD for Better Method Validation and Transfer	2010	Phil Nethercote, Phil Borman, Tony Bennett, GSK; Gregory Martin, Complectors Consulting LLC, and Pauline McGregor, PMcG Consulting	http://www.pharmamanufacturing.com/articles/2010/060/
A Quality-by-Design Methodology for Rapid LC Method Development (Parts I, II and III)	2008	Ira Krull, Michael Swartz, Joseph Turpin, Patrick H. Lukulay, Richard Verseput	http://academy.chromatographyonline.com/lcgc/article/articleDetail.jsp?id=570537; http://academy.chromatographyonline.com/lcgc//article/articleDetail.jsp?id=579016;http://academy.chromatographyonline.com/lcgc//article/articleDetail.jsp?id=593247
What Your ICH Q8 Design Space Needs: A Multivariate Predictive Distribution	2010	John J. Peterson, GlaxoSmithKline Pharmaceuticals	http://www.pharmamanufacturing.com/articles/2010/097/?page=full
Quality-by-Design Using a Gaussian Mixture Density Approximation of Biological Uncertainties	2010	N. Rossner, Th. Heine, R. King	http://www.nt.ntnu.no/users/skoge/prost/proceedings/dycops-2010/Papers_CAB2010_common/WeMT1-02.pdf
An Example of Utilizing Mechanistic and Empirical Modeling in Quality by Design	2010	Daniel M. Hallow, Boguslaw M. Mudryk, Alan D. Braem, Jose E. Tabora, Olav K. Lyngberg, James S. Bergum, Lucius T. Rossano, Srinivas Tummala	http://rd.springer.com/article/10.1007%2Fs12247-010-9094-y
Process modeling and control in drug development and manufacturing	2010	Edited by Cynthia Oksanen and Salvador García-Muñoz	http://www.sciencedirect.com/science/article/pii/S0098135410001523
Bioprocesses: Modeling needs for process evaluation and sustainability assessment	2010	Concepción Jiménez-González, John M. Woodley	http://www.sciencedirect.com/science/article/pii/S0098135410001018
Computer-aided molecular design using the Signature molecular descriptor: Application to solvent selection	2009	Derick C. Weis , Donald P. Visco	http://www.sciencedirect.com/science/article/pii/S0098135409002683
Process modeling and optimization of batch fractional distillation to increase throughput and yield in manufacture of active pharmaceutical ingredient (API)	2010	Yubo Yang, , Rosalin Tjia	http://www.sciencedirect.com/science/article/pii/S0098135410001304
The use of modeling in spray drying of emulsions and suspensions accelerates formulation and process development	2009	James W. Ivey, Reinhard Vehring	http://www.sciencedirect.com/science/article/pii/S0098135410000748
Pharmaceutical process/equipment design methodology case study: Cyclone design to optimize spray-dried-particle collection efficiency	2010	Lisa J. Graham, Rebecca Taillon, Jim Mullina, Trevor Wigle	http://www.sciencedirect.com/science/article/pii/S0098135410001365
Practical application of roller compaction process modeling	2010	Gavin Reynolds, Rohit Ingale, Ron Roberts, Sanjeev Kothari, Bindhu Gururajan	http://www.sciencedirect.com/science/article/pii/S0098135410000955
Understanding variation in roller compaction through finite element-based process modeling	2010	John C. Cunninghama, Denita Winstead, Antonios Zavaliangos	http://www.sciencedirect.com/science/article/pii/S0098135410001407
Optimizing the design of eccentric feed hoppers for tablet presses using DEM	2010	William R. Ketterhagen, Bruno C. Hancock	http://www.sciencedirect.com/science/article/pii/S0098135410001560
Temperature and density evolution during compaction of a capsule shaped tablet	2010	Gerard R. Klinzing, Antonios Zavaliangos, John Cunningham, Tracey Mascaro, Denita Winstead	http://www.sciencedirect.com/science/article/pii/S0098135410001511
Drug product modeling predictions for scale-up of tablet film coating—A quality by design approach	2010	Andrew Prpich, Mary T. am Ende, Thomas Katzschner, Veronika Lubczyk, Holger Weyhers, Georg Bernhard	http://www.sciencedirect.com/science/article/pii/S0098135410000979
Handling uncertainty in the establishment of a design space for the manufacture of a pharmaceutical product	2010	Salvador García-Muñoz, , Stephanie Dolph, Howard W. Ward II	http://www.sciencedirect.com/science/article/pii/S0098135410000700
ICAS-PAT: A software for design, analysis and validation of PAT systems	2009	Ravendra Singh, Krist V. Gernaey, Rafiqul Gania,	http://www.sciencedirect.com/science/article/pii/S0098135409001732
An ontological knowledge-based system for the selection of process monitoring and analysis tools	2010	Ravendra Singh, Krist V. Gernaey, Rafiqul Gani	http://www.sciencedirect.com/science/article/pii/S009813541000150X
An ontological framework for automated regulatory compliance in pharmaceutical manufacturing	2009	M. Berkan Sesen, Pradeep Suresh, René Banares-Alcantara, Venkat Venkatasubramanian	http://www.sciencedirect.com/science/article/pii/S0098135409002336

Quality by Design in Action 1: Controlling Critical Quality Attributes of an Active Pharmaceutical Ingredient

The importance of Quality by Design (QbD) is being realized gradually, as it is gaining popularity among the generic companies. However, the major hurdle faced by these industries is the lack of common guidelines or format for performing a risk-based assessment of the manufacturing process. This article tries to highlight a possible sequential pathway for performing QbD with the help of a case study. The main focus of this article is on the usage of failure mode and effect analysis (FMEA) as a tool for risk assessment, which helps in the identification of critical process parameters (CPPs) and critical material attributes (CMAs) and later on becomes the unbiased input for the design of experiments (DoE). In this case study, the DoE was helpful in establishing a risk-based relationship between critical quality attributes (CQAs) and CMAs/CPPs. Finally, a control strategy was established for all of the CPPs and CMAs, which in turn gave rise to a robust process during commercialization. It is noteworthy that FMEA was used twice during the QbD: initially to identify the CPPs and CMAs and subsequently after DoE completion to ascertain whether the risk due to CPPs and CMAs had decreased……….http://pubs.acs.org/doi/abs/10.1021/op500295a

Quality by Design in Action 1: Controlling Critical Quality Attributes ofan Active Pharmaceutical Ingredient

Abdul Qayum Mohammed ^†‡, Phani Kiran Sunkari^†, P. Srinivas ^*‡, and Amrendra Kumar Roy ^*†

^† CTO-III, Dr. Reddy’s Laboratories Ltd, Plot 116, 126C and Survey number 157, S.V. Co-operative Industrial Estate, IDA Bollaram, Jinnaram Mandal, Medak District, Telangana 502325, India

^‡ Department of Chemistry, Osmania University, Hyderabad, Telangana 500007, India

Org. Process Res. Dev., Article ASAP

DOI: 10.1021/op500295a

Publication Date (Web): January 21, 2015

Copyright © 2015 American Chemical Society

*Telephone: +919701346355. Fax: + 91 08458 279619. E-mail: amrendrakr@drreddys.com (A.K.R.)., *E-mail:sripabba85@yahoo.co.in (P.S.).

http://pubs.acs.org/doi/abs/10.1021/op500295a

Key steps in implementation of QbD for a biotech product…….Quality by design for biopharmaceuticals

 

 

Identifying target product profile (TPP). TPP has been defined as a “prospective and dynamic summary of the quality characteristics of a drug product that ideally will be achieved to ensure that the desired quality, and thus the safety and efficacy, of a drug product is realized”. This includes dosage form and route of administration, dosage form strength(s), therapeutic moiety release or delivery and pharmacokinetic characteristics (e.g., dissolution and aerodynamic performance) appropriate to the drug product dosage form being developed and drug product-quality criteria (e.g., sterility and purity) appropriate for the intended marketed product. The concept of TPP in this form and its application is novel in the QbD paradigm.

Identifying CQAs. Once TPP has been identified, the next step is to identify the relevant CQAs. A CQA has been defined as “a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality”¹⁰. Identification of CQAs is done through risk assessment as per the ICH guidance Q9 . Prior product knowledge, such as the accumulated laboratory, nonclinical and clinical experience with a specific product-quality attribute, is key in making these risk assessments. Such knowledge may also include relevant data from similar molecules and data from literature references. Taken together, this information provides a rationale for relating the CQA to product safety and efficacy. The outcome of the risk assessment would be a list of CQAs ranked in order of importance. Use of robust risk assessment methods for identification of CQAs is novel to the QbD paradigm.

Defining product design space. After CQAs for a product have been identified, the next step is to define the product design space (that is, specifications for in-process, drug substance and drug product attributes). These specifications are established based on several sources of information that link the attributes to the safety and efficacy of the product, including, but not limited to, the following:

• Clinical design space
• Nonclinical studies with the product, such as binding assays, in vivo assays and in vitro cell-based assays
• Clinical and nonclinical studies with similar platform products
• Published literature on other similar products
• Process capability with respect to the variability observed in the manufactured lots

The difference between the actual experience in the clinic and the specifications set for the product would depend on our level of understanding of the impact that the CQA under consideration can have on the safety and efficacy of the product. For example, taking host cell proteins as a CQA, it is common to propose a specification that is considerably broader than the clinical experience. This is possible because of a greater ability to use data from other platform molecules to justify the broader specifications. On the other hand, in the case of an impurity that is unique to the product, the specifications would rely solely on clinical and nonclinical studies.

In QbD, an improved understanding of the linkages between the CQA and safety and efficacy of the product is required. QbD has brought a realization of the importance of the analytical, nonclinical and animal studies in establishing these linkages and has led to the creation of novel approaches.

Defining process design space. The overall approach toward process characterization involves three key steps. First, risk analysis is performed to identify parameters for process characterization. Second, studies are designed using design of experiments (DOE), such that the data are amenable for use in understanding and defining the design space. And third, the studies are executed and the results analyzed to determine the importance of the parameters as well as their role in establishing design space.

Failure mode and effects analysis (FMEA) is commonly used to assess the potential degree of risk for every operating parameter in a systematic manner and to prioritize the activities, such as experiments, necessary to understand the impact of these parameters on overall process performance. A team consisting of representatives from process development, manufacturing and other relevant disciplines performs an assessment to determine severity, occurrence and detection. The severity score measures the seriousness of a particular failure and is based on an estimate of the severity of the potential failure effect at a local or process level and the potential failure effect at end product use or patient level. Occurrence and detection scores are based on an excursion (manufacturing deviation) outside the operating range that results in the identified failure. Although the occurrence score measures how frequently the failure might occur, the detection score indicates the probability of timely detection and correction of the excursion or the probability of detection before end product use. All three scores are multiplied to provide a risk priority number (RPN) and the RPN scores are then ranked to identify the parameters with a high enough risk to merit process characterization.  FMEA outcome for a process chromatography step in a biotech process. RPN scores are calculated and operating parameters with an RPN score >50 are characterized using a qualified scaled-down model. For the case study presented here, these include gradient slope, temperature, flow rate, product loading, end of pool collection, buffer A pH, start of pool collection, volume of wash 1, buffer B pH, buffer C pH and bed height. Process characterization focused on parameters such as temperature, that have a high impact on the process (severity = 6), occur frequently in the manufacturing plant (occurrence = 6) and are difficult to quickly correct if detected (detection = 7). In contrast, parameters such as equilibration volume, with a low impact on the process (severity = 3), low occurrence (occurrence = 2) and a limited ability to detect and correct (detection = 5), were not examined in process characterization.

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