ICH Q12: Guideline on Technical and Regulatory Considerations for Pharmaceutical Product Lifecycle Management

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ICH Q12: Guideline on Technical and Regulatory Considerations for Pharmaceutical Product Lifecycle Management, 1-2

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Recent ICH quality guidelines (Q8–Q11)(3−6) have focused on providing guidance on the development and manufacture of drug substances (Q11)(6) and drug products (Q8),(3) showing “baseline” and “enhanced” scientific approaches, and utilizing quality risk management tools (Q9) within the pharmaceutical quality management system (Q10). To further support the implementation of these development and manufacturing approaches, ICH recognized the value in providing tools and approaches for the management of post-approval chemistry, manufacturing, and controls (CMC) changes based on product and process understanding that could be employed by all ICH participants. Several useful tools had been established in different regions, and it was recognized that pharmaceutical innovation and continuous improvement would be optimally supported if best practices could be employed in similar ways across the regions. Achieving this harmonization would result in more efficient manufacture and change and would also increase the value of the pharmaceutical quality system and support continued optimization of the utilization of valuable resources within regulatory agencies and inspectorates (e.g., toward oversight of critical rather than noncritical changes, incentivizing industry’s understanding and management of manufacturing). The ICH Concept Paper for the development of this guidance was endorsed in 2014.(7)
The drafted consensus document is now available for public comment (step 2 of the ICH process),(8) with comments being collected by the regions during 2018 (with various comment deadlines).
The draft guidance includes some potentially very important approaches for future CMC change management, and importantly, the tools and approaches being developed are seen as usable across the range of pharmaceutical product types (including drug–device combinations) and applicable to existing products as well as newly approved products.
An approach of particular importance that is included in the guideline is the “post-approval change management protocol” (PACMP), which allows for specific changes to be predescribed to regulators and agreement to be reached on the scientific approach and data expectations that will support the change. This ability to predefine how to successfully make a change will bring great clarity and predictability to the planning and prosecution of, particularly, complex change types (often viewed as major changes needing “prior approval” in current regulatory change systems). Furthermore, the predetermination of data necessary to support the change allows for the final communication of the change to be a simple matter of confirming the suitability of the change with the expected data and for the regulatory change class to be reduced on the basis of the prior agreement of the change management approach. Importantly, a PACMP can be either agreed for a single change for a single product or constructed and agreed in a more wide-ranging manner to support multiple similar changes to be conducted on more than one product. This is of immense potential value to industry and regulators alike. Annex II of the draft guideline provides illustrative examples of different types of PACMPs, giving an example of a PACMP for a single change (to a manufacturing site for a drug substance) and an example of the more general management of such a site change.
In a section of the guideline on supporting post-approval changes for marketed products, where considerable manufacturing experience has been accrued, important approaches are given for the management of changes in analytical procedures and discussing how data requirements for changes (for stability data) can be impacted by product and process understanding.
In addition, the guidance seeks to provide an approach to differentiate the levels of regulatory oversight of particular changes on the basis of known impact and criticality of the potential change to product quality. The ability to differentiate change expectations on the basis of actual product understanding is a natural extension of the approaches taken in ICH Q8 and Q11, where for example product and process understanding can establish a “Design Space” for manufacturing and control within which changes are not seen as requiring regulatory oversight. In the draft of Q12, this concept is further developed by the concept of “Established Conditions” (ECs), with discussion of how investment in understanding can impact submission expectations (with Appendix I of the draft guideline providing an illustration of CTD sections that contain ECs and Annex I suggesting illustrative examples of ECs for both chemical products and biological products) and post-approval change management expectations. Importantly, the guidance discusses how this approach could be used for existing products, where the manufacturing process may have been described without any differentiation of change management expectations, leading to inefficient use of both industry and regulatory resources.
The draft guideline also includes a suggested system for the collation of such “agreed” regulatory change mechanisms for a product via use of a product lifecycle management (PLCM) approach, wherein the agreed changes can be clearly collated alongside the manufacturing commitments and the agreed (lesser) change reporting category for the changes. Annex III of the draft documentation provides an example of a PLCM document.
The guideline also contains content describing the pharmaceutical quality system (PQS) change management expectations (with Appendix II of the guideline providing further illustration of principles of change management) and the relationship between industry and regulators and importantly between regulatory assessment and inspection needed to support strong implementation of the approaches within Q12.
The draft guideline clearly already provides tools and approaches for change management of immense potential value. Nevertheless, the opportunity to comment on the draft is always an important step in the development of an ICH guideline, and it is important to ensure that comments assist in providing the clearest possible final guidance that will be readily and consistently implemented to mutual industry and regulator benefit. It is noteworthy that the current draft of the guideline includes wording suggesting that some concepts may not be implementable at the current time across every region. It will be of greatest benefit if the tools and approaches as described and agreed in the finalized guidance will be available for use on as wide a global basis as possible, in line with the ongoing vision of ICH for science-based, harmonized, and efficient regulation of pharmaceuticals.
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3  Pharmaceutical Development Q8(R2), Current Step 4 version, dated August 2009.
4 Quality Risk Management Q9, Current Step 4 version, dated Nov 9, 2005.
5 Pharmaceutical Quality System Q10, Current Step 4 version, dated June 4, 2008.
6 Development and Manufacture of Drug Substances (Chemical Entities and Biotechnological/Biological Entities) Q11, Current Step 4 version, dated May 1 2012.
7 Final Concept Paper Q12: Technical and Regulatory Considerations for Pharmaceutical Product Lifecycle Management, dated July 28 2014, endorsed by the ICH Steering Committee on Sept 9, 2014.
8 Technical and Regulatory Considerations for Pharmaceutical Product Lifecycle Management Q12, draft version endorsed on Nov 16, 2017.

////////////////ICH Q12, Guideline, Technical and Regulatory Considerations, Pharmaceutical Product, Lifecycle Management

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

ICH

New ICH Guidelines:

*ICH Q13* on Continuous Manufacturing &
🎛🎚

*ICH Q14* on ATP – QbD (Analytical target profile and quality by design)

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

ICH Q11 Q and A Document

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ICH Q11   Q and A Document


The topic of starting materials has been a vexed topic for some period. Indeed concerns relating to lack of clarity and issues pertaining to practical implementation led the EMA in Sept 2014 to publish a reflection paper—Reflection on the requirements for selection and justification of starting materials for the manufacture of chemical active substances.(10) The paper sought to outline key issues as well as authority expectations; specific areas of interest identified included the following:

1.

Variance in interpretation between applicant and reviewer.

2.

The registration of short syntheses that employ complex custom-made starting materials.

3.

Lack of details preventing authorities being able to assess the suitability of a proposed registered starting material and its associated control strategy.

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While the consensus was that overall this provided a useful perspective of at least the EMA’s interpretation of ICH Q11(2) and requirements for starting material selection, further discussion was required to address this subject to the satisfaction of both industry and regulators.
In November 2016 ICH released a Q&A document pertaining to ICH Q11, the purpose of which is to clarify the expectations regarding the selection and justification of starting materials for drug substance manufacture. This has reached Step 2,(11) a consensus document released for public comment, the deadline being March 2017.
The document comprises a series of sections, beginning with an introduction. This makes clear that the focus of the Q&A document is on chemical entity drug substances, excluding Biologics at least in the sense of definition of starting materials.
Another important proposal made within the introduction is that API starting materials that have already been accepted by regulatory authorities (e.g., for use in authorized medicinal products) would not need to be rejustified against the ICH Q11 general principles or the recommendations included in this Q&A document, unless significant changes are made to the manufacturing processes and controls.
This is important as a concern may have been that criteria defined in the document would be retrospectively applied. It is though caviated, stating that a starting material accepted for one manufacturer’s process may not be considered acceptable for a different manufacturer’s process.
It also states that designation of starting materials should be based on process knowledge for the intended commercial process. It emphasizes that all of the general principles in ICH Q11 Section 5 should always be considered holistically, together with the clarifications in this Q&A document, rather than applying a single general principle or Q&A clarification in isolation. This was a thread central to the argumentation within the earlier EMA guideline.

The questions and answers are aligned to specific sections within the guideline, although all questions are focused specifically on Section 5—Selection of starting material and source material. There are 16 questions in total covering the following aspects:

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1.

Significant Structural Fragment: how should this be interpreted

2.

Impact on Impurity profile of final product: this relates to the guidance within Q11 to include all stages that impact on impurity profile of the drug substance. This seeks to clarify what level would be defined as impactful.

3.

Clarification of persistence

4.

How an applicant should determine which steps impact the profile of mutagenic impurities in the drug substance.

5.

Do all steps that involve mutagenic reagents, impurities, or establish regio- or stereochemical configurations, need to be included in the process description?

6.

Clarification of the stated need to describe “enough” of the drug substance manufacturing

7.

Should all the ICH Q11 general principles be considered and met in selecting starting materials?

8.

Application of Q11 principles to telescoped processes

9.

Application of Q11 to linear and convergent syntheses

10.

Starting material specifications: key attributes

11.

Noncommercial starting materials

12.

Differences: Commercially available vs Custom Synthesis

13.

Requirements for justification of commercial availability

14.

Scope: postapproval change–preregistered starting materials

15.

Life cycle management

16.

Starting material Q11 vs Q7 definition: clarification that this is effectively the same

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It is beyond the scope of this review to examine the specific text associated with all 16 questions. In general though the position articulated throughout is pragmatic and also, in general, answers are clear and relatively concise.

Key points of note include

  • “Significant structural fragment”: the document highlights the frequent misconception that this means that a proposed starting material should be structurally similar to the drug substance. It makes clear that in fact the intent is simply to help distinguish between reagents, catalysts, solvents, or other raw materials (which do not contribute a “significant structural fragment” to the molecular structure of the drug substance) from materials that do.

  • Questions 5.2, 5.4, and 5.5 relate to mutagenic impurities. The answers provided should be useful in assisting an applicant in applying the risk based approach defined within ICH M7.(5)Prior to this there was a general misconception that a step that involved a mutagenic impurity needed to be part of the registered process. Such an assertion took no account of the highly reactive nature of such impurities and their propensity to be effectively purged.(12) The answer to question 5.2 makes clear the framework for defining the impact of an MI on the quality of the final drug substance, aligning this directly to M7 and the adoption of the widely applied 30% of the limit principle, i.e. prove levels in the final active drug substance are below 30% of the acceptable limit. The answer to question 5.4. provides a commentary of the actual practical steps involved in assessing the impact of an MI. Importantly within this it contextualizes the actual risk posed by low level MIs with the following important statement

    • “Such mutagenic impurities and by-products are usually present at much lower concentrations than reagents, solvents, and intermediates. Therefore, the risk that such impurities will carry over significantly into the drug substance from early reaction steps is lower than for reagents, solvents, or intermediates from the same steps.”

In essence, provided any MI associated with a starting material is demonstrably controlled it is not necessary to register stages that simply employ the use of mutagenic reagents.

  • Another important point addressed within the document is ‘persistency.’ It seeks to make clear that even where an impurity associated with a starting material does impact on the quality of the drug substance that control can be defined at the stage of the starting material. A classic example would be a stereoisomer. General downstream processing would have little impact on levels. In many cases this has led to a view that the step where such an impurity might arise must form part of the registered process, i.e. the stage of introduction of chirality. This now makes clear that, provided this is effectively controlled within the starting material, registration of earlier stages is not required.

Again, the key concerns raised by the EMA reflection paper(10) are reflected on, i.e. registration of short syntheses that employ complex custom-made starting materials and a lack of details preventing authorities being able to assess the suitability of a proposed registered starting material and its associated control strategy. Arguably in that context the answer provided to question 5.6 is the most critical in the Q&A document.
Question 5.6 ICH Q11 states that “enough of the drug substance manufacturing process should be described in the application···” What considerations should an applicant apply in the selection of the proposed starting materials to ensure that enough of the drugs substance manufacturing process will be described in the process description in Section 3.2.S.2.2 of the application?

The response highlights several key aspects

Of primary importance is that the applicant must first evaluate which chemical transformation steps in the manufacturing process impact the impurity profile of the drug substance. With the clarification now provided in respect to MIs and “persistency” this should be more straightforward than previously was the case.

Another key point made is the need for an applicant to examine steps immediately upstream of those identified as critical and within those upstream to consider if:

  • They include a unit operation that has been added to the manufacturing process to control specific impurities that would otherwise impact the impurity profile of the drug substance.

The key point made here is that you cannot simply add multiple purification steps prior to a proposed starting material.

  • Tight control (e.g., within narrow parameter ranges) is required to prevent generation of impurities that would otherwise impact the impurity profile of the drug substance.

If either are the case then these should be included within the registered synthesis.

Perhaps the most contentious aspect of the response though is the caveat that if having conducted the assessment described and if based on this the result is that only a small number of chemical transformation steps need to be registered, the Q&A document articulates a need to include one or more additional steps. The reasons stated for this needing to be considered are

  • Due to the risk of contamination arising from a late starting material and the impact this would have on drug substance quality and

  • The risk of changes made to the route/process for the starting material impacting again on drug substance quality.

Many organizations will I’m sure challenge this, as it seems to suggest little or no control around quality of starting materials; in reality that is far from the case.
Ultimately does the response to this question and the overall document adequately address regulatory concerns, in particular those outlined EMA? Only time will tell, but overall this is a welcome development.
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References


  1. 1.Guideline on setting health based exposure limits for use in risk identification in the manufacture of different medicinal products in shared facilities EMA/CHMP/ CVMP/ SWP/169430/ 2012,http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2014/11/WC500177735.pdf.

  2. 2.ICH Q11 – Development and Manufacture of Drug Substances (Chemical Entities and Biotechnological/Biological Entities)Q11http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q11/Q11_Step_4.pdf.

  3. 3.TeasdaleA.Regulatory Highlights Org. Process Res. Dev. 201519 ( 4494– 498DOI: 10.1021/acs.oprd.5b00085

  4. 5.Assessment and Control of Dna Reactive (Mutagenic)Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk M7(R1) March 2017,http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Multidisciplinary/M7/M7_R1_Addendum_Step_4_31Mar2017.pdf.

  5. 8.HarveyJ.TeasdaleA.FleetwoodA.Management of organic impurities in small molecule medicinal products: Deriving safe limits for use in early development Regul. Toxicol. Pharmacol. 201784116– 123,DOI: 10.1016/j.yrtph.2016.12.011

  6. 9.Questions and answers on implementation of risk based prevention of cross contamination in production and ‘Guideline on setting health based exposure limits for use in risk identification in the manufacture of different medicinal products in shared facilities’ (EMA/CHMP/CVMP/SWP/169430/ 2012) .http://www.ema.europa.eu/docs/en_GB/document_library/Other/2017/01/WC500219500.pdf.

  7. 10.Reflection paper on the requirements for selection and justification of starting materials for the manufacture of chemical active substanceshttp://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2014/10/WC500175228.pdf.

  8. 11.ICH guideline Q11 on development and manufacture of drug substances (chemical entities and biotechnological/biological entities) – questions and answershttp://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q11/Q11_Q_A_Step_2.pdf.

  9. 12.TeasdaleA.Risk Assessment of Genotoxic Impurities in New Chemical Entities: Strategies to Demonstrate Control Org. Process Res. Dev. 201317221– 230DOI: 10.1021/op300268u

  10. 13.EU Guidelines for Good Manufacturing Practice for Medicinal Products for Human and Veterinary Usehttps://ec.europa.eu/health/sites/health/files/files/eudralex/vol-4/chapter_5.pdf.

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Selection and justification of starting materials: new Questions and Answers to ICH Q11 published

 

The ICH Q11 Guideline describing approaches to developing and understanding the manufacturing process of drug substances was finalised in May 2012. Since then the pharmaceutical industry and the drug substance manufacturers had time to get familiar with the principles outlined in this guideline. However, experience has shown that there is some need for clarification. Thus the Q11 Implementation Working Group recently issued a Questions and Answers Document.

http://www.gmp-compliance.org/enews_05688_Selection-and-justification-of-starting-materials-new-Questions-and-Answers-to-ICH-Q11-published_15619,15868,S-WKS_n.html

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The ICH Q11 Guideline describes approaches to developing and understanding the manufacturing process of drug substances. It was finalised in May 2012 and since then the pharmaceutical industry and the drug substance manufacturers had time to get familiar with the principles outlined in this guideline. However, experiences during implementation of these principles within this 4 years period have shown that there is need for clarification in particular with regard to the selection and justification of starting materials.

On 30 November 2016 the ICH published a Questions and Answers document “Development and Manufacture of Drug Substances (Chemical Entities and Biotechnological/Biological Entities)” which was developed by the Q11 Implementation Working Group. This document aims at addressing the most important ambiguities with respect to starting materials and at promoting a harmonised approach for their selection and justification as well as the information that should be provided in marketing authorisation applications and/or Drug Master Files.

In the following some examples of questions and answers from this document:

Question:
ICH Q11 states that “A starting material is incorporated as a significant structural fragment into the structure of the drug substance.” Why then are intermediates used late in the synthesis, which clearly contain significant structural fragments, often not acceptable as starting materials?

Answer:
The selection principle about “significant structural fragment” has frequently been misinterpreted as meaning that the proposed starting material should be structurally similar to the drug substance. However, as stated in ICH Q11, the principle is intended to help distinguish between reagents, catalysts, solvents, or other raw materials (which do not contribute a “significant structural fragment” to the molecular structure of the drug substance) from materials that do. … The presence of a “significant structural fragment” should not be the sole basis for of starting material selection. Starting materials justified solely on the basis that they are a “significant structural fragment” probably will not be accepted as starting materials by regulatory authorities, as the other principles for the appropriate selection of a proposed starting material also require consideration.

Question:
Do the ICH Q11 general principles for selection of starting materials apply to processes where multiple chemical transformations are run without isolation of intermediates?

Answer:
Yes. The ICH Q11 general principles apply to processes where multiple chemical transformations are run without isolation of intermediates. In the absence of such isolations (e.g., crystallization, precipitations), other unit operations (e.g., extraction, distillation, the use of scavenging agents) should be in place to adequately control impurities and be described in the application. The drug substance synthetic process should include appropriate unit operations that purge impurities.
The ICH Q11 general principles also apply for sequential chemical transformations run continuously. Non isolated intermediates are generally not considered appropriate starting materials.

Question:
Is a “starting material” as described in ICH Q11 the same as an “API starting material” as described in ICH Q7?

Answer:
Yes. ICH Q11 states that the Good Manufacturing Practice (GMP) provisions described in ICH Q7 apply to each branch of the drug substance manufacturing process beginning with the first use of a “starting material”. ICH Q7 states that appropriate GMP (as defined in that guidance) should be applied to the manufacturing steps immediately after “API starting materials” are entered into the process … . Because ICH Q11 sets the applicability of ICH Q7 as beginning with the “starting material”, and ICH Q7 sets the applicability of ICH Q7 as beginning with the “API starting material”, these two terms are intended to refer to the same material.
ICH Q7 states that an “API Starting Material” is a raw material, intermediate, or an API that is used in the production of an API. ICH Q7 provides guidance regarding good manufacturing practices for the drug substance; however, it does not provide specific guidance on the selection and justification of starting materials. When a chemical, including one that is also a drug substance, is proposed to be a starting material, all ICH Q11 general principles still need to be considered.

With the recent publication of this draft Q&A Document with the complete title “Development and Manufacture of Drug Substances (Chemical Entities and Biotechnological/Biological Entities) Questions and Answers (regarding the selection and justification of starting materials)” on the ICH website it reached Step 2b of the ICH Process and now enters the consultation period.  Comments may be provided by e-mailing to the ICH Secretariat at admin@ich.org.

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extra info…………
A PRESENTATION

Ever since the FDA issued its landmark guidance Pharmaceutical GMPs-A Risk Based Approach in 2004, the industry has been struggling with how to demonstrate process understanding as a basis for quality. Bolstered by guidance from ICH, specifically Q6-Q10, the pieces have long been in place to build a solution that is philosophically consistent with these best practice principles. Even so, the evolution to process understanding as a basis for quality has been slow. Pressure to accelerate this transformation spiked in 2011 when the FDA issued its new guidance on process validation that basically mandated the core components of ICH Q6-10 as part of Stages 1 and 2. To be fair, enforcement has been uneven and that fact has further impeded adoption, with the compliance inspectors themselves struggling to acquire the necessary skills to fully evaluate statistical arguments of process control and predictability.

One area debated since 2008 is the application of GMPs and demonstration of control for drug substances. Drug substance suppliers and drug product manufacturers have used the tenets of ICH Q7A as the foundation for deciding where GMPs can be reasonably implemented, to establish the final intermediate (FI) and the regulatory starting material (RSM). However, the ability to support the quality of the drug substance has a profound impact on the ability to defend the drug product quality. In the last few years it has become apparent that it was not reasonable to apply the same requirements for drug products to drug substances because the processes can be markedly different. In response to this need, the ICH issued a new guidance; Q11: Development and Manufacture of Drug Substances (Chemical Entities and Biotechnological/Biological Entities). The key ICH documents that impact Q11 are shown in Figure 1.


Figure 1. Guidances Impacting ICH Q11.

The FDA formally adopted ICHQ11 in November 2012 and its purpose is two-fold. First, it offers guidance on the information to provide in Module 3 of the Common Technical Document (CTD) Sections 3.2.S.2.2 – 3.2.S.2.6 (ICH M4Q). Second, and perhaps most importantly, it attempts to clarify the concepts defined in the ICH guidelines on Pharmaceutical Development (Q8), Quality Risk Management (Q9), and Pharmaceutical Quality System (Q10) as they pertain to the development and manufacture of drug substances.

What makes ICH Q11 so important is its emphasis on control strategy. This concept was introduced in ICH Q10 as “a planned set of controls, derived from current product and process understanding that assures process performance and product quality.”

Within the drug product world, the control strategy concept has been elusive as industry grapples with moving from a sample-and-test concept of quality to one of process understanding and behavior. This concept is even more removed for drug substance manufacturers and, in some cases, is more difficult to implement. But Q11 is much more than a mere framework for control strategy. The guidance is structured very similarly to the concepts discussed in the new 2011 Process Validation guidance. Looking closely, Q11 addresses:
• Product Design/Risk Assessment/CQA Determination
• Defining the Design Space and establishing a control strategy
• Process validation and analysis
• Information required for Sections 3.2.S.2.2 – 3.2.S.2.6 of the eCTD
• Lifecycle management

Product design/Risk assessment/CQA determination

Within the context of process development, the guidance defines similar considerations to those defined in the Stage 1 activity of Process Validation. Understanding the quality linkage between the drug substance’s physical, chemical, and microbiological characteristics, and the final drug products’ Quality Target Product Profile (QTPP), is the primary objective of the product and process design phase. The product’s QTPP is comprised of the final product Critical to Quality Attributes (CQAs). Identifying the raw material characteristics of the drug substance that can impact the drug product is a critical first step in developing a defensible control strategy. Employing risk analysis tools at the outset can help focus the process development activities upon the unit operations that have the potential to impact the final product’s CQAs. In the case of biological drug substances, any knowledge regarding mechanism of action and biological characterization, such as studies that evaluate structure-function relationships, can contribute to the assessment of risk for some product attributes.

Drug substance CQAs typically include those properties or characteristics that affect identity, purity, biological activity, and stability of the final drug product. In the case of biotechnological/biological products, most of the CQAs of the drug product are associated with the drug substance and thus are a direct result of the design of the drug substance or its manufacturing process. When considering CQAs for the drug substance, it is important to not overlook the impact of impurities because of their potential impact on drug product safety. For chemical entities, these include organic impurities (including potentially mutagenic impurities), inorganic impurities such as metal residues, and residual solvents.

For biotechnological/biological products, impurities may be process-related or product-related (see ICH Q6B). Process-related impurities include: cell substrate-derived impurities (e.g., Host Cell Proteins [HCP] and DNA); cell culture-derived impurities (e.g., media components); and downstream-derived impurities (e.g., column leachable). Determining CQAs for biotechnology/biological products should also include consideration of contaminants, as defined in Q6B, including all adventitiously introduced materials not intended to be part of the manufacturing process (e.g., viral, bacterial, or mycoplasma contamination).

Defining the design space and establishing a control strategy

ICH Q8 describes a tiered approach to establishing final processing conditions that consists of moving from the knowledge space to the process design space and finally the control space. ICH Q8 and Q11 define the Design Space as “the multidimensional combination and interaction of input variables (e.g., material attributes) and process parameters that have been demonstrated to provide assurance of quality.” In the drug product world the terminology typically applied to the design space is the Proven Acceptable Range (PAR) that used to equate to the validated range.

Here is why this is important: the ability to accurately assess the significance and effect of the variability of material attributes and process parameters on drug substance CQAs, and hence the limits of a design space, depends on the extent of process and product understanding. The challenge with drug substance processes is where to apply the characterization. ICH Q7A recognizes that upstream of the RSM does not require GMP control. The design space can be developed based on a combination of prior knowledge, first principles, and/or empirical understanding of the process. A design space might be determined per unit operation (e.g., reaction, crystallization, distillation, purification), or a combination of selected unit operations should generally be selected based on their impact on CQAs.

In developing a control strategy, both upstream and downstream factors should be considered. Starting material characteristics, in-process testing, and critical process parameters variation control are the key elements in a defensible control strategy. For in-process and release testing criteria the resolution of the measurement tool should be considered before making any conclusions.

Process validation

ICH Q11’s description of process validation mimics the same description in ICH Q7A but offers up an alternative for continuous verification that mirrors the concepts in ICH Q8 and the new process validation guidance. As mentioned, the enforcement of the new guidance by the FDA has been uneven, but positioning the process validation to satisfy the new guidance requires the drug substance manufacturer to formally implement characterization and validation standards, just as a drug product manufacturer would be required to do.

Life-cycle management

The quality system elements and management responsibilities described in ICH Q10 are intended to encourage the use of science-based and risk-based approaches at each lifecycle stage, thereby promoting continual improvement across the entire product lifecycle. There should be a systematic approach to managing knowledge related to both drug substance and its manufacturing process throughout the lifecycle. This knowledge management should include but not be limited to process development activities, technology transfer activities to internal sites and contract manufacturers, process validation studies over the lifecycle of the drug substance, and change management activities.

Conclusion

The new ICH Q11 guidance represents the most recent example of the FDA’s commitment to the principles of QbD to define an integrated framework for implementing the principles of ICH Q6-Q10. Although the guidance does not mandate adopting ICH Q8, the considerations required to create a defensible control strategy require a much higher level of process understanding than the conventional approach of sample and test, once the foundation of product development. Defining the requirements is another example of where the FDA is going in terms of expectations for drug substance and drug product understanding. If effectively enforced, this can be a significant step forward, pushing the industry toward a QbD philosophy for process and product development.

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

 

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,…,fr), which maps various multivariate input variables, x, z, e, θ  to the multivariate quality response variable, Y=(Y1,…,Yr). Here, x=(x1,…,xk) lists the controllable process factors (e.g. pressure, temperature, etc.), while z=(z1,…,zh) 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,…θp) 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.

 Bayesian Method
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,…, xk) ) 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. 

Bayesian Method
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,…, xk) ) 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,…, xk). 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.

Pharma QbD
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].


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.

How does a company demonstrate the implementation of PQS in accordance with ICH?

Image result for Pharmaceutical Quality System

ICH Q10 was published in its final version already in 2008. However, today many companies still have problems to understand how to implement ICH Q10 “Pharmaceutical Quality System” into practice. Quality Assurance and GMP are basic requirements which have been implemented for many years in the pharmaceutical industry (including the API industry). So what is needed to demonstrate that a Pharmaceutical Quality System has been implemented? Please read more about the GMP Questions and Answers.

http://www.gmp-compliance.org/enews_05578_How-does-a-company-demonstrate-the-implementation-of-PQS-in-accordance-with-ICH_15515,S-QSB_n.html

ICH Q10 was published in its final version already in 2008. However, today many companies still have problems to understand how to implement ICH Q10 “Pharmaceutical Quality System” in practice. Quality Assurance and GMP are basic requirements which have been implemented for many years in the pharmaceutical industry (including the API industry). So what is needed to demonstrate that a Pharmaceutical Quality System has been implemented?

ICH offers a set of questions and answers which provide more details about the expectations. They were published in 2009 already but are not well-known by the industry. ICH writes: “When implemented, a company will demonstrate the use of an effective PQS through its documentation (e.g., policies, standards), its processes, its training/qualification, its management, its continual improvement efforts, and its performance against pre-defined key performance indicators (see ICH Q10 glossary on performance indicator). A mechanism should be established to demonstrate at a site how the PQS operates across the product lifecycle, in an easily understandable way for management, staff, and regulatory inspectors, e.g., a quality manual, documentation, flowcharts, procedures. Companies can implement a program in which the PQS is routinely audited in-house (i.e., internal audit program) to ensure that the system is functioning at a high level.”

The questions and answers document also states that there is no certification program in place for a Pharmaceutical Quality System. In addition, ICH provides information about how product-related inspections will differ in an ICH Q8, Q9 and Q10 environment. ICH writes: “In the case of product-related inspection (in particular, preauthorization) depending on the complexity of the product and/or process, greater collaboration between inspectors and assessors could be helpful (for example, for the assessment of development data). The inspection would normally occur at the proposed commercial manufacturing site, and there is likely to be greater focus on enhanced process understanding and understanding relationships, e.g., critical quality attributes (CQAs), critical process parameters (CPPs). The inspection might also focus on the application and implementation of quality risk management principles, as supported by the pharmaceutical quality system (PQS).”

In addition to ICH, regulatory authorities also provide further information. The British Authority MHRA, for example, answers the question: Should a company have a procedure to describe how it approaches QRM related to manufacture and GMP? The answer is: “Yes, the procedure should be integrated with the quality system and apply to planned and unplanned risk assessments. It is an expectation of Chapter 1 that companies embody quality risk management. The standard operating procedure (SOP) should define how the management system operates and its general approach to both planned and unplanned risk management. It should include scope, responsibilities, controls, approvals, management systems, applicability, and exclusions.”

The ECA Academy summarised the most relevant questions and answers from regulators like ICH, EMA, FDA etc in a GMP Questions & Answers Guide which allows readers of the document to search for certain GMP questions. A subject index at the beginning of the document lists the most frequent searched terms.

//////////PQS,  ICH, Pharmaceutical Quality System