QbD Sitagliptin


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

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


Abstract Image

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.



A biocatalytic manufaturing route for januvia – Society of Chemical …

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.



Example of QbD Application in Japan

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



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.

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

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

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

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



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

Counterfeit of medicines causes 37,000 job losses in EU Pharma Industry

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Counterfeit medicine is an increasing problem for public health and economy. This is no longer a problem of certain regions such as Asia and Africa. It has now also become an issue in the EU and US. The European Union Intellectual Property Office (EUIPO) published a press release on 29 September 2016 in which they state that fake medicines cost the EU pharmaceutical sector 10.2 billion Euro every year. Read more about the latest figures on counterfeit medicines


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Counterfeit medicine is an increasing problem for public health and economy. This is no longer a problem of certain regions such as Asia and Africa. It has now also become an issue in the EU and the US. In the past, counterfeit medicines could not enter the legal supply chain in the EU and US. But the problem has now also been arising in western countries. A number ofcases of counterfeit medicines were detected recently. In order to cope with this increasing problem, the EU has introduced a regulation which requires that as of 9th February 2019 certain medicinal products can only enter the EU market if a 2D barcode is used as a safety feature. This code must be applied on the packaging in readable form.

The European Union Intellectual Property Office (EUIPO) published a press release on 29 September 2016 in which they state that fake medicines cost the EU pharmaceutical sector 10.2 billion Euro every year. The counterfeit products cause a loss of 4.4% of the legitimate sales of pharmaceuticals. This means “37,700 jobs directly lost across the pharmaceutical sector in the EU” according to the report. Only for Germany, an annual loss of 1 billion Euro has been calculated which caused a direct job loss of 6,951. Regarding other countries, the figures are: Italy 1.59 billion, France 1 billion, Spain 1,17 billion and UK 605 million loss annually.

Source: Press Release EUIPO, September 29, 2016

//////////Counterfeit of medicines, 37,000 job losses,  EU Pharma Industry

The impact of the FDA Combination Products Guidance on Nasal and Oral Inhalation Drug Products

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The FDA draft guidance for combination products has a substantial impact on the development of Oral Inhalation and Nasal Drug Products (OINDPs) as it requires that the manufacturers have to be compliant not only with CGMPs for the drugs (21 CFR Parts 210 and 211) but also with the quality system (QS) regulations for devices (21 CFR Part 820). Find out more about the FDA Draft Guidance for Combination Products.


Based on the CGMP requirements for single-entity and co-packaged combination products (21 CFR Part 4) the manufacturers of Oral Inhalation and Nasal Drug Products (OINDPs) have to be compliant with CGMPs for the drug constituent part(s) (21 CFR Parts 210 and 211) and the quality system (QS) regulations for device constituent part(s) (21 CFR Part 820).

This can be achieved either by a drug CGMP-based streamlined approach (21 CFR 4.4(a)) or a QS regulation-based streamlined approach (21 CFR 4.4(b)).  Following the first approach the combination product manufacturers have to be compliant with the drug CGMP and device QS regulation requirements:

– 21 CFR 820.20 – Management responsibility
– 21 CFR 820.30 – Design controls
– 21 CFR 820.50 – Purchasing controls
– 21 CFR 820.100 – Corrective and preventive actions
– 21 CFR 820.170 – Installation
– 21 CFR 820.200 – Servicing

The OINDP manufacturers have to be clearly stated in their submission and at the initiation of a pre-approval inspection (PAI) whether they are operating under the drug CGMP or QS regulation-based approach.

Here you can see the complete FDA Draft Guidance on Combination Products including the requirements for Oral Inhalation and Nasal Drug Products.
////// FDA Combination Products Guidance, Nasal and Oral Inhalation,  Drug Products

FDA presentation at the ECA Conference Particles in Parenterals

Image result for visual inspection of medicinal products for parenteral use.

At the Particles in Parenterals Conference Dr Stephen Langille from the US FDA gave a talk on the FDA’s current thinking with regard to the visual inspection of medicinal products for parenteral use.


Dr Stephen Langille from the US FDA gave a talk on the FDA’s current thinking with regard to the visual inspection of medicinal products for parenteral use. In his presentation he showed the number of recalls caused by visible particulate matter over the last 11 years. For him, most of the recalls were justified when the types of particles found were taken into consideration. He also emphasized that something is possibly wrong in the visual inspection process if particles found in the market are bigger than 1000 µm.

The prevention of particles is very important to him. From his perspective the best particle is one which is not in the product. Also important to him are threshold studies, meaning to show the minimum particle size which can still be detected (dependent of product and type of container). These threshold studies are crucial for the setup of the test sets and the qualification of the inspectors of the manual inspection. He also mentioned the semi-automated inspection process. For him semi-automated inspection is good for detecting container-closure issues, like missing stoppers. But he also questioned whether an inspection time of about one second is suitable to detect particles with a size of 200µm for example. In the discussion he was asked about FDA’s opinion on the USP chapter <790>. In his opinion, USP chapter <790> can be an effective tool for ensuring that the manufacturing process and 100% inspection process are adequate to limit visible particle contamination. However, cGMPs must be followed during the manufacturing and visual inspection process. Meeting the requirements of USP <790> should not be used to excuse not meeting cGMPs.

You will find the complete presentation in the members area of the ECA webpage.

.///////////FDA presentation, ECA Conference , Particles in Parenterals

Critical Impurities in Pharmaceutical Water

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The quality of the source water used to produce pharmaceutical water plays an important role for both the design of the treatment and the validation of the water system. FDA Warning Letters over the past few years have shown that compliance with the specification of pharmaceutical water is not enough. A validation of the treatment process is expected. This includes documentation of the process capacity to produce pharmaceutical water according to specification. If we do not know the quality of the source water, however, the purification capacity is not known either. As a consequence, fluctuations of the quality of the source (feed) water quality may lead to water that does not comply with the specification after purification. Or it is not known up to which quality level of the source water pharmaceutical water that complies with the specification can be produced. Therefore, it is important to know the impurities respectively their concentration in the source (feed) water.
The production of pharmaceutical water is always based on drinking water. The specifications for drinking water however (for Germany, stipulated in the Trinkwasserverordnung; for the U.S., in the National Primary Drinking Water Regulation) are defined very broadly compared to Pharmacopoeial specifications.

The quality of the drinking water varies widely as well, as drinking water may come from different sources (ground water or surface water). Even the ground water quality varies locally, e. g., depending on the season. This is why water purification plants for the pharmaceutical industry are not ready-made goods, but individual solutions that have to be developed by the future user and the plant supplier together. The plant supplier will always ask about the quality of the drinking water so that he can offer the appropriate processing technologies.

In particular, he will need the following information. For this purpose, it is useful to provide the plant engineer with various drinking water analyses over a minimum period of twelve months.

For the design of a pharmaceutical water plant, the indicator parameters according to the Trinkwasserverordnung (conductivity, iron, manganese, sulphate and pH value) are important, as the amount of the ionic load determines the treatment process. For instance, a single-stage or double-stage reverse osmosis may be sufficient to obtain adequate quality at low conductivity levels. Iron and manganese are limited by the drinking water ordinance, but will lead to irreversible membrane damage at the reverse osmosis plant when their limits (according to the Trinkwasserverordnung) are exceeded.

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Furthermore, information on the total hardness is indispensable, as it has a major influence on the design of the softening plant – as well as on carbonate hardness or base capacity which are used to calculate the amount of dissolved carbon dioxide. This parameter restricts the use of EDI or may require further treatment, such as membrane degassing.

Depending on the origin of the drinking water, a responsible plant engineer should measure the colloid index (SDI 15) before designing the plant. Especially with surface water, higher amounts are to be expected. A colloid index of more than 5%/min can already have a negative impact on the operation of a reverse osmosis plant (membrane blocking and/or fouling) and may require additional treatment techniques, such as ultrafiltration before the main plant. While the colloid index is never determined via the water supplier, the silicate content is often indicated in the drinking water analysis. A silicate content of more than 25 ppm can become critical for a combination of reverse osmosis and EDI and should also be determined in case it is not indicated in the analysis.

All microbiological parameters have been regulated in the Trinkwasserverordnung. However, you should always remember that the supplier guarantees the quality only up to the point of transfer. With regards to the total bacteria count in particular, regular tests are necessary in order to identify seasonal fluctuations.


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Image result for Pharmaceutical Water

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Process Validation of Existing Processes and Systems Using Statistically Significant Retrospective Analysis

Process Validation of Existing Processes and Systems Using Statistically Significant Retrospective Analysis – Part One of Three

The FDA defined Process Validation in 1987 by the following: “Process Validation is establishing documented evidence which provides a high degree of assurance that a specific process will consistently produce a product meeting its predetermined specification and quality attributes.” 1The purpose of this article is to discuss how to validate a process by introducing some basic statistical concepts to use when analyzing historical data from Batch Records and Quality Control Release documents to establish specifications and quality attributes for an existing process. In an ideal world, the qualification of processing equipment, utilities, facilities, and controls would commence at the start up of a new plant or the implementation of a new system. This would be followed by the validation of the process based on developmental data and used to establish the product ranges for in process and final release testing. However, the ideal case may not exist and thus there are incidences where commissioning of facilities or new systems occurs concurrently with the qualification and process validation; or the facility and equipment are “existing” and there is no such documentation. Some facilities, equipment, or processes pre-date the above definition by many years and therefore have never been validated on qualified equipment, utilities, facilities, and controls. Additionally, in some cases, no developmental data exists to establish the product ranges for in process and release testing.

Basics of Process Validation

Before examining the existing processes, it is important to first understand the basic concepts of Process Validation. Figure 1 is a flow chart defining Process Validation from the developmental stage to the plant floor. As a simplistic example, a process begins with the raw materials being released, then the raw materials are mixed, pH is adjusted, purification occurs by gel chromatography, excipients are added for final formulation, and the product is filled and terminally sterilized. Each of these steps has defined functions and therefore would have a designed goal. For example, purification would not begin until the desired pH is reached in the previous step. Therefore, the desired pH is an in process attribute of the pH adjustment stage and the amount of buffer used to adjust the pH is a processing parameter. Each of these steps has attributes that one would want to monitor to determine that the product is being produced acceptably at that step such that the next process step can start. The ranges for these attributes are generally determined by process development data so that if the process attributes are met, then there is a high degree of confidence that the final container is filled with a product of acceptable attributes as determined by the developmental data.

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During Process Validation, there needs to be approved Standard Operating Procedures (SOPs) in place that Plant Operators have been trained on. Analytical testing should be performed by SOPs and the Quality Control (QC) analysts should be trained in these SOPs as well. The analytical tests should have been previously validated by normal analytical methods validation and documented as such. Also, the equipment used to prepare product must be documented to be qualified for its installation, operation, and performance, commonly referred to as IQ, OQ, and PQ. There are methods for performing such qualification retrospectively, but for the purpose of this discussion, it is only important to note that the equipment must be qualified.

Relating Process Validation to Existing Processes

Existing processes may lack developmental data for in process ranges and release testing. If a retrospective analysis of existing data is used to establish process ranges, including input, output, and in process testing parameters, then the process can be treated like a new process by following the basics of Prospective Process Validation. The difference between traditional Prospective Process Validation and Prospective Process Validation based on retrospective analysis is that in place of developmental data to establish ranges, the retrospective analysis reviews data from past Batch Production Records, QC test reports, product specs, etc. Thus the items that need to be in place are: approved SOPs, and Batch Records with personnel training, equipment qualification (IQ, OQ, PQ), QC methods validated, approved SOPs and training for QC personnel. With these in place, all that is missing is a Process Validation Protocol with defined ranges. The best sources of this information are approved completed batch records, process deviation reports, QC Release data, and small-scale studies. From these, the following items must be completed:

  • Critical parameters and input and output parameters must be defined.
  • A statistically valid time frame or number of batches must be determined.
  • The data used to establish the parameters must be extracted from controlled documents.
  • The data extracted from the controlled documents will be analyzed to establish ranges.

Each one of these steps will be examined in the following sections to describe them in further detail.

Critical parameters and input and output parameters defined.

In The Guidelines on General Principles of Process Validation, 15 MAY 1987, it states that:

The validity of acceptance specifications should be verified through testing and challenge of the product on a sound scientific basis during the initial development and production phase.1

It is important to determine which parameters in your process are critical to the final product. When determining these parameters and attributes a variety of personnel with different expertise should be utilized. Assembling a team of professionals is a starting point and this committee should be a multi-disciplined team including Quality, Validation, Systems Engineering, Facility Engineering, Pharmaceutical Sciences (or R&D), and Manufacturing. When determining the parameters and attributes which are critical, it is important to consider those which if they were not controlled or achieved, then the result would have an adverse effect on the product. A risk assessment should be performed to analyze what the risk is and what the results are if a specific parameter or attribute is not controlled or achieved (e.g. the resulting product would be flawed). Risk assessment is defined by The Ontario Ministry of Agriculture, Food and Rural Affairs as:

  1. the probability of the negative event occurring because of the identified hazard,
  2. the magnitude of the impact of the negative advent, and
  3. consideration of the uncertainty of the data used to assess the probability and the impact of the components. 2

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Figure 2 is a list of general questions to consider when assessing risk while Figure 3 is an example of Fault Tree Analysis – a formal approach to evaluating risk, where a Top Level Event is observed and through questions and observations the cause of the event can be determined.

National Center for Drugs and Biologics and National Center for Devices and Radiological Health, “Guidelines on General Principles of Process Validation,” Rockville MD. 15 MAY 1987.National Center for Drugs and Biologics and National Center for Devices and Radiological Health, “Guidelines on General Principles of Process Validation,” Rockville MD. 15 MAY 1987.Ontario Ministry of Agriculture, Food and Rural Affairs (2000), Queen’s Printer for Ontario, Last Updated March 22, 2000; WEB:http://www.gov.on.ca/omafra/english/research/risk/assum1b.html.

Process Validation of Existing Processes and Systems Using Statistically Significant Retrospective Analysis – Part Two of Three

A Statistically Valid Time Frame or Number of Batches

How large of a sample set is needed of previously recorded data to determine ranges that are truly representative of the process, and will the ranges be useful in the Validation effort and not set one up for failure? This is a difficult question to answer, and it is important to note that the batches selected should have no changes between them, thus be produced with the same processing conditions. The draft FDA Guidance for Industry, Manufacturing, Processing, or Holding Active Pharmaceutical Ingredients from March of 1998 suggests that 10-30 consecutive batches be examined to assess process consistency. 1

This is a good target statistically because when selecting a sample size or population, the concept of Normality or Degree of Normality becomes important. As a general rule of thumb, the population should be large enough that the distribution of the samples within the population approaches a Normal or Gaussian distribution (one defined by a bell-shaped curve). Thus in theory if the samples are normally distributed then 99% of the samples will fall within the +/- 3 Standard Deviation (S.D.) range. 2 When considering this, one should think in the sense of Statistical Significance (the P-Value). The P-Value is the significance that a given sample will be indicative of the whole population. Thus at 99% (a P-Value of 0.01) then a given sample has 1% chance of falling outside the +/- 3 S.D. range or (assuming no relationship with other variables) the sample has a 99% statistical significance as representative of the population. Finally, when considering the central limit theorem, which has the underlying concept that as the sample size gets larger, the distribution of the population becomes more normal, then in general a sample size of 10 – 30 as the FDA suggests would have a high chance of being distributed normally.

The data used to establish the parameters must be extracted from controlled documents.
When the number of batches to review is selected, the next step is to determine from what documents the processing data will be extracted. Typically the range establishing data must be taken from approved and controlled documents (see the examples below).

Examples of Controlled Documents:

  • Batch Production Records (BPRs)
  • Quality Control (QC) Lab Reports
  • Limits establish by Licensure
  • Product License Agreement (PLA)
    – Biologic License Agreement (BLA)
    – New Drug Application (NDA) or Abbreviated (ANDA)
  • Product Release Specifications
  • Small scale bench studies simulating plant environment.

The data extracted from the controlled documents will be analyzed to establish ranges.
Having established where the data will be selected from, the data must then be analyzed for specific trends such to define ranges for the Process Validation Protocol’s acceptance criteria. This acceptance criteria will be what the “Actual” process data collected during the execution of the Protocol will be compared to, in order to verify its acceptance. This part is where much thought needs to be applied so that the acceptance criteria are not so tight that failure is eminent or so broad that the achievement of the criteria proves nothing. Listed below are general steps that can be incorporated to determine the analysis.

  1. Draw “X Charts” and analyze for outliersApply a model to determine Normality
  2. Determine the +/- 3 S.D. range and plot on Trend Chart
  3. Determine the Confidence Interval as compared to +/- 3 S.D.
  4. Process Capability Indices
  5. Recording the Maximum and Minimum
  6. Assign Acceptance Criteria Range and justify

Drawing Trend Charts

Trend Charts, also referred to as X-Charts, are a good way of plotting data points from a set of data where the target is the same metric (for example pH as measured at a specific point in the process). It is a matter of defining the X-axis by the number of samples and the Y-axis by the metric that is being used. As an example, the X-axis could be a list of the batches by batch number and the Y axis could be pH. Figure 4 is an example of a type of trend chart. This way the data is presented graphically and can be appreciated with respect to setting a range.

With the data plotted, one can quickly assess any visible trends in the data. Additionally one can no begin the task of applying statistics to the data. It is important to determine if there are outliers in the data. Outliers may exist and can usually be rationalized by adverse events in processing as long as they are reported appropriately. Outliers can also exist as samples that are “statistically insignificant.” As mentioned before, the P-Value is the significance that a given sample will be indicative of the whole population so that outliers would have a very low P-Value. One method for determining outliers is to use a box-plot where a box is drawn from a lower point (defined typically by the 25th percentile) to an upper point (typically the 75th percentile). The “H-spread” is defined as the distance between the upper and lower points. 3 Outliers are then determined to be any data that falls outside a predetermined multiplier of the H-spread. For example the lower outlier limit and upper outlier limit are defined as 4 times the H-Spread, anything above or below these limits is statistically insignificant and are outliers.

Apply a model to determine Normality

With the accumulated data plotted, the Degree of Normality should be investigated so that the data can be analyzed by the appropriate method. There are several models for determining the Degree of Normality; some common ones are the Kolmogorov-Smirnov test, Chi-Square goodness-of-fit test, and Shapiro-Wilks’ W test. 4 Once the Degree of Normality is determined a more appropriate statistical method can be applied for setting ranges. If the data is determined to be non-Normal than there are two approaches to evaluating the data. The first way is to apply a Nonparametric Statistical model (e.g. the Box-Cox Transformation 5 ), however, these tests are considered to be less powerful and less flexible in terms of the conclusions that they provide, so it is preferred to increase the sample size such that a normal distribution is approached. 5 If the data is determined to be Normal or the sample size is increased such that the data is distributed more normally, then the data can be better analyzed for it’s range characteristics.

Click on any image for larger view

Determine the +/– 3 SD range and plot on Trend Chart

The data having now been displayed graphically should be analyzed mathematically. This can be done by using simple statistics where the mean is determined as well as the standard deviation. The mean refers to the average of the samples in the population. The standard deviation is the measure of the variation in the population from the mean. If the distribution proves to be normal, as from our normality tests above or by selecting a large enough population such that the central limit theorem predicts the distribution to be normal, then it stands that 99% of the data will fall within the +/–3 SD range. Using our example from Figure 4, the data is analyzed for its mean and standard deviation using the displayed formulas in Figure 5. Once this is determined, the +/– 3 SD can be applied to the trend charts by drawing them as limits at their values. This graphically displays the data as it is applied per batch and how it fits within the statistical limit of +/– 3 SD (see Figure 6.)

  1. U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research, Guidance for Industry, Manufacturing, Processing, or Holding Active Pharmaceutical Ingredients, March 1998, 36
  2. StatSoft, Inc. (1999). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  3. Rice Virtual Lab in Statistics (1993-2000), David M. Lane, HyperStat Online, Houston TX, WEB:http://www.ruf.rice.edu/~lane/rvls.html
  4. StatSoft, Inc. (1999). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.htm
  5. Box, G.E.P., and Cox, D.R. (1964), “An Analysis of Transformations,” J. Roy. Stat. Soc., Ser. B.

Process Validation of Existing Processes and Systems Using Statistically Significant Retrospective Analysis – Parts Three of Three

Determine the Confidence Interval as compared to +/- 3S.D. With the +/- 3 S.D. ranges determined, it can be considered important to evaluate what confidence there is that the next data point will fall within this range. The rationale for determining this level is to justify that the +/- 3 S.D. range provides a confidence that 99% of the data is within that range. Similar to the +/- 3 S.D. range, the confidence interval is a range between which the next measurement would fall. This level is typically 99% or greater. Thus a 99% confidence interval means, “there is 99% insurance that the next value would be in the range.” To calculate a 99% Confidence Interval, one needs to consider the area under the standard normal curve, the mean, the standard deviation, and the population size. Determining this level can be done using the formula in Figure 7. The confidence interval can be added to our previous example and is displayed in Figure 8.

Figure 8: Trend Chart with +/- 3 S.D. and Confidence Interval.

Figure 7: Definition of Confidence Interval Formulas.

Following our example:

Confidence Interval (99.9%) = 6.23 ± snc ( 0.151202/(30) 1/2)

Confidence Interval (99.9%) = 6.95 to 6.02

The confidence interval at 99.9% is slightly more narrow than the +/- 3 S.D. range which follows the trend if the +/- 3 S.D. range provides that 99% of the data will be within the range if the data is normally distributed.

As can be seen in the trend charts, the data fits well within the +/- 3 S.D. range, and therefore the confidence level is very high that the next data point that is collected will be within this range. Therefore this range may be appropriate to use as acceptance criteria based on the statistics. If the confidence level was wider than the +/- 3 S.D., then the data would have to be analyzed such to investigate if there were errors in calculating the degree of normality, the +/- 3 S.D., the confidence level, the outliers, or errors in the sampling technique to show that it was not computational error.

Process Capability Factors

Another method to setting ranges to be used as acceptance criteria are the Process Capability Indices defined by:

CPu = (USL – µ) / s (or in our example 3 S.D.)

CPl = (µ – LSL) / s (or in our example 3 S.D.)


USL = Upper Specification Limit

LSL = Lower Specification Limit

An industry accepted standard CP would be 1.33.1 This would mean that only 0.003% of the testing results would be out of the specification or 99.997% would be within the specification. This is a similar concept to confidence level.

Recording the Maximum and Minimum

Recording the maximum and minimum values in the data is important because it is a quick way to see if the data is all within the +/- 3 S.D. range. Additionally, if the maximum and minimum are within the +/- 3 S.D. range, than there is an additional level of confidence since all of the data would be within the range. Lastly, the data may be determined to be non-normally distributed and in such case, confidence may predict to high a possibility for failure at the +/- 3 S.D. range so in the interim, the maximum, and minimum values can be selected to be the range until further data can be collected to define the range (this refers back to increasing the sample size in order to approach a more normal distribution).

Assign Acceptance Criteria Range and Justify

Using all of the above analysis techniques, knowledge of the process and agreement on by a cross-discipline committee, acceptance criteria ranges can be assigned for the critical parameters and attributes. A general course of action would be to start by recording all the data at a given point in a spreadsheet, calculating the mean, S.D., population size, +/- 3 S.D., 95% and 99% confidence intervals, plotting the trend charts with appropriate ranges, and then deciding on which range makes the best sense. When selecting the acceptance criteria, a cross-functional committee should be utilized with backgrounds of QA, Manufacturing, Validation, R&D, and Engineering present. The

ranges should be selected and justified by scientifically sound data and conclusions. The ranges should be within the PAR for the product, which means that if +/- 3 S.D. is selected, the range should be checked at the upper and lower limits to verify that acceptable product is prepared. This should be done prior to a final agreement on the range and incorporation into the validation protocol. A report should be written to document the ranges with the rationale for selecting them and the justification for determining the limits as well as any determination that the ranges are within the PAR. Additionally, those ranges which are not to be included should be discussed within the report to justify why they are not to be recorded. A process validation protocol should be prepared with theses ranges for acceptance criteria and the process should be run at a target within the acceptance criteria ranges at least three consecutive times using identical procedures to verify that the process is valid.


Since the ideal case of validating a process during its implementation does not always exist in the pharmaceutical, biopharmaceutical, biotechnology or medical device industries, it may be important to determine a way to validate these processes using historical data. The historical data can be found in a variety of places as long as it is approved (e.g. approved and completed BPRs or quality control release documents, etc). A cross-functional team should perform a risk assessment on the parameters and attributes to determine which ones would be included in the process validation. A range establishing study for the attributes and parameters should be performed to evaluate historical data and analyze the data set for the concepts of normality, variation (standard deviation), and confidence. With a high degree of confidence, acceptance criteria ranges should be set for each parameter and attribute and a process validation protocol should be written with the appropriate ranges. This protocol should be approved and executed at target settings within the acceptance criteria ranges, from the start of the manufacturing process to the finish using qualified equipment, approved SOPs, and trained operators. In a final report for the process validation, the degree to which the process is valid would be determined by the satisfaction of the approved acceptance criteria.


1. Box, G.E.P., and Cox, D.R. (1964), “An Analysis of Transformations,” J. Roy. Stat. Soc., Ser. B., 26, 211.

2. Kieffer, Robert and Torbeck, Lynn, (1998), Pharmaceutical Technology, (June), 66.

3. Lane, David M., Rice Virtual Lab in Statistics (1993-2000), HyperStat Online, Houston TX, WEB: http://www.ruf.rice.edu/~lane/rvls.html.

4. National Center for Drugs and Biologics and National Center for Devices and Radiological Health,(1987) “Guidelines on General Principles of Process Validation,” Rockville MD. 15 MAY.

5. Ontario Ministry of Agriculture, Food and Rural Affairs (2000), Queen’s Printer for Ontario, Last Updated March 22, 2000; Web: http://www.gov.on.ca/omafra/english/research/risk/assum1b.html.

6. StatSoft, Inc. (1999). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html.

7. U.S. Department of Helath and Human Service, Food and Drug Administration, Center for Drug Evaluation and Research, Center for Biologics Evaluation and Research, Center for Veterinary Medicine, “Guidance for Industry: Manufacturing, Processing, or Holding Active Pharmaceutical Ingredients,” Rickville MD. March 1998, 36.

//////////Process Validation,  Existing Processes and Systems, Retrospective Analysis

What are the GMP Responsibilities of the Marketing Authorisation Holders?


The European Medicines Agency (EMA) has published a concept paper to summarise the GMP responsibilities of the Marketing Authorisation Holders (MAH).


The GMP/GDP Inspectors Working Group of the European Medicines Agency (EMA) has published a concept paper to summarise the GMP responsibilities of the Marketing Authorisation Holders (MAH). It is not intended to introduce any new responsibilities on MAHs but to document existing requirements in a better way.

The current EU GMP-Guidelines define in several chapters and annexes GMP tasks and responsibilities of the MAH. However, there seems to be a lack of clarity and understanding as to what these responsibilities actually are in their totality, and what they mean for MAHs at a practical level. All these tasks and responsibilities have now been summarised in this concept paper:

  • Chapter 1: responsibility to evaluate the results of the PQR review
  • Chapter 7: responsibility to put contracts in place
  • Chapter 8: responsibilities concerning quality defects, risk-reducing actions and notification of possible disruption in supply
  • Annex 2: responsibility to put contracts in place
  • Annex 12: obligations to approve the design of irradiation cycles, and agreeing the location for retention of irradiation cycle records.
  • Annex 16: requirement to identify the site and QP responsible for certifying each batch (in the case of multiple sites authorised to manufacture / import / certify the same product) and the statement that the “ultimate responsibility for the performance of a medicinal product over its lifetime, its safety, quality and efficacy” lies with the MAH
  • Annex 19: responsibilities for ensuring that reference and retention samples are taken, and stored.

The GMP/GDP Inspectors Working Group thinks that the issues outlined above “are not without important consequences. The way in which MAHs are expected to interact with the manufacturing sites registered in a marketing authorisation is not sufficiently clear, given the diverse ways in which the various MAH responsibilities are set out in the EC Guide to GMP, and differing (often complex) supply chains.”

So what is next?

According to the Concept Paper, “It is recommended that the GMP/GDP Inspectors Working Group (GMP/GDP IWG) should produce a reflection paper intended for Part III of the EU GMP Guide or in another appropriate location (e.g. as proposed by the GMP/GDP IWG). This would capture all of the responsibilities that apply to MAH companies to enable manufacturers to comply with GMP. It would also result in a more complete picture of the regulatory environment with respect to GMP in which the MAH operates.”

The deadline for comments on the concept paper is end of November 2016. Comments should be sent to adm-gmdp@ema.europa.eu.

////////GMP Responsibilities,  Marketing Authorisation Holders

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.


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

GDUFA: FDA’s new Guidance on Self-Identification of Generic Drug Manufacturers

Image result for Generic Drug User Fee Amendments

GDUFA: FDA’s new Guidance on Self-Identification of Generic Drug Manufacturers

FDA’s new Guidance requesting generic drug manufacturers who want to export to the USA to self-identify has recently been published in a finalised form. Read more here about what types of generic drug manufacturers are affected and which company data are required by the FDA.


The GDUFA (Generic Drug User Fee Amendments) is a legislative package which came into force in 2012 and entitles the US-American FDA to collect fees from generic drug manufacturers, who strive for a marketing authorisation for the American market. An annual fee has to be paid after the successful registration.

The core of the document is the obligation to “Self-Identify” for those companies that have to submit essential site-related information to the FDA. The details of this self-identification are set in a Guidance for Industry entitled “Self-Identification of Generic Drug Facilities, Sites, and Organizations” published on 22 September 2016 by the FDA in the finalised form.

The Guidance describes the following elements:

1. Which types of generic facilities, sites, and organizations are required to self-identify?

2. What information is requested?

3. What technical standards are to be used for electronically submitting the requested information?

4. What is the penalty for failing to self-identify?

Hereinafter, you will find a short summary of these four topics:

1. Companies that manufacture finished generic medicinal products for human use or the APIs for them, or both are required to self-identify as well as companies that package the finished generic drug into the primary container and label it. Besides, sites that – pursuant to a contract with the applicant (generic drug manufacturer) – repack/redistribute the finished drug from a primary container  into a different primary container are also required to submit a self-identification as well as sites that perform bioequivalence/bioavailability studies. Last but not least, the obligation to self-identify also concerns sites that are listed in the application dossier as contract laboratories for the sampling and performing of analytical testing.

2. Essential data are: the D-U-N-S number (a unique nine-digit sequence specific for each site / each distinct physical location of an entity), the “Facility Establishment Identifier, FEI” (an identifier used by the FDA for the planning and tracking of inspections) and general information with regard to the facility (company owner, type of business operation, contact data, information about the manufacture of non generic drugs).

3. The HLS standard (Health Level Seven Structured Product Labeling) requested for generic applications (ANDAs)  has to be also used for the submission of self-identification information. A detailed description of this standard can be found in the Guidance “Providing Regulatory Submissions in Electronic Format – Drug Establishment Registration and Drug Listing“.

4. Companies that fail to self-identify do not have to expect an explicit penalty. However, such a failure leads to two drawbacks: first, the likelihood of a site inspection by the FDA prior to approval is higher. The second drawback which is much more serious is that all the APIs or finished drugs from a manufacturer who hasn’t self-identified are deemed misbranded. For the FDA, such products are not allowed for importation in the USA.

To the satisfaction of the FDA, the regulations set in the GDUFA and the provisions laid down in the new Guidance represent a major contribution to an enhanced transparency in particular of complex supply chains.

//////////GDUFA, FDA,  new Guidance,  Self-Identification, Generic Drug Manufacturers