ICH Q8 Research Articles

Transfer of analytical procedures: A panel of strategies selected for risk management, with emphasis on an integrated equivalence-based comparative testing approach

In 2001, a multidisciplinary team made of analytical scientists and statisticians at sanofi-aventis has published a methodology which has governed, from that time, the transfers from R&D sites to Manufacturing sites of the release monographs. This article provides an overview of the recent adaptations brought to this original methodology taking advantage of our experience and the new regulatory framework, and, in particular, the risk management perspective introduced by ICH Q9. Although some alternate strategies have been introduced in our practices, the comparative testing one, based equivalence testing as statistical approach, remains the standard for assays lying on very critical quality attributes. This is conducted with the concern to control the most important consumer's risk involved at two levels in analytical decisions in the frame of transfer studies: risk, for the receiving laboratory, to take poor release decisions with the analytical method and risk, for the sending laboratory, to accredit such a receiving laboratory on account of its insufficient performances with the method. Among the enhancements to the comparative studies, the manuscript presents the process settled within our company for a better integration of the transfer study into the method life-cycle, just as proposals of generic acceptance criteria and designs for assay and related substances methods. While maintaining rigor and selectivity of the original approach, these improvements tend towards an increased efficiency in the transfer operations.

Development of a Process Analytical Technology (PAT) for in-line monitoring of film thickness and mass of coating materials during a pan coating operation

The aim of this study was to perform in-line Near Infrared (NIR) measurements inside a pan coater to monitor a coating operation in real-time, by predicting the increases in mass of coating materials and coating thickness. A polymer combination of ethylcellulose/poly(vinyl-alcohol)–poly(ethylene-glycol) graft copolymer was used as functional aqueous coating. Coated tablets were sampled at regular intervals during the coating operation, then subjected to either simple and fast weighing ( n = 50) or accurate and non-destructive Terahertz Pulsed Imaging (TPI) measurements ( n = 3). Off-line NIR spectra analysis revealed that the coating operation could efficiently be controlled by focusing on two distinct NIR regions, related to absorption bands of ethylcellulose. Principal component analysis of in-line NIR spectra gave a clear classification of the collected coated tablets. Real-time quantitative monitoring of the coating operation was successfully performed from partial least square calibration models built using either TPI or weighing as reference method. Coating thicknesses as well as mass of coating materials used as primary values provided accurate NIR predictions. A comparison study demonstrated that both reference methods led to reliable and accurate real-time monitoring of the coating operation. This work demonstrated that in-line NIR measurements associated with multivariate analyses can be implemented to monitor in real-time a pan coating operation in order to fulfil the expectations of ICH Q8 guideline on pharmaceutical development, especially in terms of PAT control strategy and reduced end-product testing.

A QbD case study: Bayesian prediction of lyophilization cycle parameters.

As stipulated by ICH Q8 R2 (1), prediction of critical process parameters based on process modeling is a part of enhanced, quality by design approach to product development. In this work, we discuss a Bayesian model for the prediction of primary drying phase duration. The model is based on the premise that resistance to dry layer mass transfer is product specific, and is a function of nucleation temperature. The predicted duration of primary drying was experimentally verified on the lab scale lyophilizer. It is suggested that the model be used during scale-up activities in order to minimize trial and error and reduce costs associated with expensive large scale experiments. The proposed approach extends the work of Searles et al. (2) by adding a Bayesian treatment to primary drying modeling.

Application of new methodologies based on design of experiments, independent component analysis and design space for robust optimization in liquid chromatography

HPLC separations of an unknown sample mixture and a pharmaceutical formulation have been optimized using a recently developed chemometric methodology proposed by W. Dewé et al. in 2004 and improved by P. Lebrun et al. in 2008. This methodology is based on experimental designs which are used to model retention times of compounds of interest. Then, the prediction accuracy and the optimal separation robustness, including the uncertainty study, were evaluated. Finally, the design space (ICH Q8(R1) guideline) was computed as the probability for a criterion to lie in a selected range of acceptance. Furthermore, the chromatograms were automatically read. Peak detection and peak matching were carried out with a previously developed methodology using independent component analysis published by B. Debrus et al. in 2009. The present successful applications strengthen the high potential of these methodologies for the automated development of chromatographic methods.

Analysis and critical review of ICH Q8, Q9 and Q10 from a generic pharmaceutical industry view point

Generic industry aims to produce safe, efficient, built-in quality medicines that will satisfy patients’ requirements and will be competitive on the market. In this paper, assessment of the need for quality by design (QbD) and process analytical technology (PAT) implementation by generic industry was made, by analysis of the ICH Q8, Q9 and Q10 guidelines and their implementation in European regulation. The review of the guidelines indicates differences in the life cycle of a generic medicine, leading to a final conclusion in terms of generic industry. PAT provides statistical analysis and real time quality monitoring, as the basis for proactive quality management. Using QbD/PAT, quality is proved and improved throughout the entire life cycle. Better understanding of the product and processes within a defined design space leads to easier proof of built-in quality throughout the life cycle of the medicine, faster and easier regulatory evaluation, faster time to market, as well as post marketing savings regarding costs and time. Implementation of QbD/PAT as a systematic approach together with risk assessment as part of quality management system is a useful challenge to the generic industry and gives an opportunity for technological, temporal, financial and quality improvement. It was concluded that having in mind its’ own manufacturing capabilities the applicant should optimize the implementation of QbD in accordance with current good manufacturing practice guidelines. Implementation of QbD/PAT is an innovative challenge for the generic industry. Managing pharmaceutical quality system allows the top management to make right decisions at the right time.

Statistical Thinking and Knowledge Management for Quality-Driven Design and Manufacturing in Pharmaceuticals

The purpose of this article is to present the evolution of quality principles and how they have been implemented in the pharmaceutical industry. The article discusses the challenges that the FDA PAT Guidance and the ICH Q8, Q9 and Q10 Guidelines present to industry and provides a comprehensive overview of the basic tools that can be used to effectively build quality into products. The principles of the design of experiments, the main tools for statistical process analysis and control, and the requisite culture change necessary to facilitate statistical, knowledge-based management are also addressed.

Knowledge As The Currency Of Managing Risk: A Novel Framework To Unite Quality Risk Management And Knowledge...

This particular study represents novel thinking around the concepts of Knowledge Management (KM) and Quality Risk Management (QRM) practices, both as they relate to each other, as well as the regulatory governance on which life science organizations rely. Further, this paper is relevant to the ongoing revision of ICH Q9, Quality Risk Management, announced by ICH earlier this year which aims to improve the understanding and consistent application of QRM, specifically related to QRM outputs and risk-based decision making. These two areas have been identified in this paper as topics that may benefit from better linkages between QRM and KM, as a better use of knowledge can lead to risk assessments that have lower levels of subjectivity via an increased use of scientific data, and more reliable estimates of risk and risk reduction. Focusing risk-based decision making on knowledge and designing KM systems to support QRM activities has many potential benefits. First and foremost, there can be increased protection and value for patients – via the decisions reached that resulted in consistently high quality and available medicines for patients, in addition to operational benefits for companies.

Application of Quality by Design Principles for the Definition of a Robust Crystallization Process for Casopitant Mesylate

GlaxoSmithKline (GSK) has identified casopitant as a potent NK1 antagonist. It was selected for its potential activities on a number of therapeutic targets such as inflammatory bowel disease, overactive bladder, CNS disorders and others. The mesylate salt of casopitant was selected for full development using a quality by design (QbD) approach in which a control strategy was developed from a design space, underpinned by process understanding and risk analysis for an enhanced level of quality assurance. As the concepts of design space and control strategy in the QbD framework move on from their inception to implementation, the ambiguity of definition has led to considerable discussion, diversity of opinions and uncertainty around how best to define these ideas. This contribution provides an outline of the sequential experimentation and analyses that led to the construction of a Bayesian approach to the ICH Q8 definition of Design Space. This uses a predictive approach for multiple response surface optimisat...

A Science and Risk Based Proposal for Understanding Scale and Equipment Dependencies of Small Molecule Drug Substance Manufacturing Processes

ICH Q8(R2) established general guidelines around the Relationship of a Design Space to Scale and Equipment (Section 2.4.4); however, the guideline is not intended to provide guidance on scientific strategy for the application and implementation. It is widely recognized that drug product processes are typically scale and equipment sensitive; however, the same expectation should not be applied broadly to drug substance processes due to fundamental difference between the disciplines. This paper proposes a scientific perspective and strategy of how to develop design spaces that include scale and equipment knowledge for drug substance processes, and how to use that knowledge to mitigate the risk when changing from laboratory thorough commercial facilities.

The ICH Q8 Definition of Design Space: A Comparison of the Overlapping Means and the Bayesian Predictive Approaches

The ICH Q8 defines “design space” (DS) 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.” Unfortunately, some pharmaceutical scientists appear to misinterpret the definition of DS as a process monitoring strategy. A more subtle and possibly more misleading issue, however, is the application of standard response surface methodology software applications in an attempt to construct a DS. The methodology of “overlapping mean responses” (OMR), available in many point-and-click oriented statistical packages, provides a tempting opportunity to use this methodology to create a DS. Furthermore, a few recent (and two possibly very influential) papers have been published that appear to propose the use of OMR as a way to construct a DS. However, such a DS may harbor operating conditions with a low probability of meeting process specifications. In this article we compare the OMR approach with a Bayesian predictive approach to DS, and show that the OMR approach produces DS’s that are too large and may contain conditions with a low probability of meeting process specifications. In some cases, even the best operating conditions do not have a high probability of meeting all process specifications.

A Bayesian Design Space Approach to Robustness and System Suitability for Pharmaceutical Assays and Other Processes

The ICH Q2 (R1) Guidance on Validation of Analytical Procedures states that a robustness assessment for an analytical method should provide “an indication of its reliability during normal usage.” The concept of “design space” as specified in the ICH Q8 Guidance may be used to create a zone of reliable robustness for an analytical method or pharmaceutical process. A Bayesian approach to design space as outlined by Peterson (2004) accounts for model parameter uncertainty, correlation among the quality responses at each fixed operating condition, and method response multiplicity. Two examples are provided to illustrate the application of a Bayesian design space to assessing reliability/robustness. One example is about assessing the ability of an HPLC analytical method to meet system suitability criteria and the other deals with a crystallization process for an active pharmaceutical ingredient.

Process analytical technology (PAT) for biopharmaceutical products: Part II. Concepts and applications

Implementing real-time product quality control meets one or both of the key goals outlined in FDA's PAT guidance: "variability is managed by the process" and "product quality attributes can be accurately and reliably predicted over the design space established for materials used, process parameters, manufacturing, environmental, and other conditions." The first part of the paper presented an overview of PAT concepts and applications in the areas of upstream and downstream processing. In this second part, we present principles and case studies to illustrate implementation of PAT for drug product manufacturing, rapid microbiology, and chemometrics. We further present our thoughts on how PAT will be applied to biotech processes going forward. The role of PAT as an enabling component of the Quality by Design framework is highlighted. Integration of PAT with the principles stated in the ICH Q8, Q9, and Q10 guidance documents is also discussed.

Near-infrared chemical imaging (NIR-CI) for counterfeit drug identification—A four-stage concept with a novel approach of data processing (Linear Image Signature)

A new stage concept was developed to reliably identify counterfeit tablets which are very similar to the genuine drug product. This concept combines single-point near-infrared spectroscopy (NIRS) and near-infrared chemical imaging (NIR-CI) with statistical variance analysis. The advantage of NIR-CI over NIRS is the potential to determine not only the amount, but also the spatial distribution of ingredients within a single tablet. Previously published NIR-CI studies used homogeneity as a key indicator for the identification of counterfeits. The state of the art approach for estimating homogeneity is to record the average and % standard deviation of predicted classification scores (i.e. concentrations) for a given component within a specimen. A disadvantage of this approach is the partial loss of spatial information. In view of this, we developed a new method using much more of the spatial information for the estimation of homogeneity. The method is based on (1) summation and unfolding of multidimensional predicted classification scores, which results in a Linear Image Signature (LIS) and (2) multivariate LIS data analysis (LIS-MVA). It could be demonstrated that this kind of NIR-CI data analysis represents an innovative approach for the identification of counterfeit tablets. Moreover, this procedure is applicable to determine the product variability, i.e. process signature of a given product thus being a valuable tool within the Quality by Design (QbD) approach of the ICH Q8 guideline.

The use of the SeDeM Diagram expert system to determine the suitability of diluents–disintegrants for direct compression and their use in formulation of ODT

The new SeDeM Diagram expert system was used to analyze the suitability of 43 excipients for direct compression with disintegrant properties from eight chemical families. The SeDeM Diagram expert system is a new method for use in tablet preformulation and formulation studies. It provides the profile of a substance in powder form in terms of its suitability for direct compression. This study, which was based on the current concept of “Quality by Design ICH Q8”, evaluated the pharmacotechnical properties of disintegrants in powder form and selected the candidates that were most suitable for direct compression and their use in formulation of orally disintegrating tablets (ODT). To achieve this, each disintegrant and its chemical families were individually analyzed. It was concluded that nine disintegrants had an SeDeM value with the index of good compression (IGC) over 5. Most of these disintegrants were from the microcellulose family. Other disintegrants had indexes that were close to 5. It is assumed that these excipients can be used in direct compression, when they are added to other excipients.

PQLI Application of Science- and Risk-based Approaches (ICH Q8, Q9, and Q10) to Existing Products

This paper describes progress made by the Legacy Products Task Team within the ISPE Product Quality Lifecycle Implementation (PQLI) initiative. It discusses the opportunities and the required business and technical processes to justify and deliver a quality by design (QbD) project for an existing product. A process flow is included that summarizes business, technical, and regulatory considerations. A quality risk management-based approach is suggested. Relevant case studies also are presented. Comments are welcome.

A Bayesian Approach to the ICH Q8 Definition of Design Space

Manufacturers of pharmaceuticals and biopharmaceuticals are facing increased regulatory pressure to understand how their manufacturing processes work and to be able to quantify the reliability and robustness of their manufacturing processes. In particular, the ICH Q8 guidance has introduced the concept of design space. The ICH Q8 defines 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.” However, relatively little has been put forth to date on how to construct a design space from data composed of such variables. This study presents a Bayesian approach to design space based upon a type of credible region first appearing in Peterson's work.This study considers the issues of constructing a Bayesian design space, design space reliability, the inclusion of process noise variables, and utilization of prior information, as well as an outline for organizing information about a design space so that manufacturing engineers can make informed changes as may be needed within the design space.

PQLI Definition of Criticality

This paper describes progress made by the Criticality Task Team within the ISPE PQLI initiative. It aims to provide a concise, coherent, and universal approach for determining criticality for parameters, material attributes, conditions, and quality attributes. The work also clarifies the risk based distinctions governing the assignment of criticality to provide consistency and facilitate the adoption and implementation of Quality by Design (QbD) principles in the development of pharmaceutical manufacturing processes. The application of the concept of criticality presented in this paper aligns with the principles of ICH Q8, Q9 and Q10 guidelines.

PQLI Key Topics - Criticality, Design Space, and Control Strategy

This paper gives an overview of progress made by the ISPE PQLI initiative - a global industry-led initiative aimed at facilitating the implementation of ICH Q8, Q9, and ultimately Q10 guidance. Through this initiative ISPE is spearheading the effort to help industry begin to define areas where they will be able to provide the technical framework for the implementation of key elements of Quality by Design (QbD) - a systematic approach to development that begins with predefined objectives and emphasizes product and process understanding based on sound science and quality risk management. Three topic areas, Design Space, Criticality, and Control Strategy were selected for specific focus and discussion, and this paper gives an overview of progress in these three areas.

Defining Process Design Space for Biotech Products: Case Study of Pichia pastoris Fermentation

The concept of "design space" has been proposed in the ICH Q8 guideline and is gaining momentum in its application in the biotech industry. It has been defined 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." This paper presents a stepwise approach for defining process design space for a biologic product. A case study, involving P. pastoris fermentation, is presented to facilitate this. First, risk analysis via Failure Modes and Effects Analysis (FMEA) is performed to identify parameters for process characterization. Second, small-scale models are created and qualified prior to their use in these experimental studies. Third, studies are designed using Design of Experiments (DOE) in order for the data to be amenable for use in defining the process design space. Fourth, the studies are executed and the results analyzed for decisions on the criticality of the parameters as well as on establishing process design space. For the application under consideration, it is shown that the fermentation unit operation is very robust with a wide design space and no critical operating parameters. The approach presented here is not specific to the illustrated case study. It can be extended to other biotech unit operations and processes that can be scaled down and characterized at small scale.

API Quality by Design Example from the Torcetrapib Manufacturing Process

The concept and application of quality by design (QbD) principles has been and will undoubtedly continue to be an evolving topic in the pharmaceutical industry. However, there are few and limited examples that demonstrate the actual practice of incorporating QbD assessments, especially for active pharmaceutical ingredients (API) manufacturing processes described in regulatory submissions. We recognize there are some inherent and fundamental differences in developing QbD approaches for drug substance (or API) vs drug product manufacturing processes. In particular, the development of relevant process understanding for API manufacturing is somewhat challenging relative to criteria outlined in ICH Q8 ( http://www.ich.org/cache/compo/276–254–1.html ) guidelines, which are primarily oriented toward application of QbD for drug product manufacturing. This position paper provides a perspective of QbD application for API manufacture using an example from the torcetrapib API manufacturing process. The work includes a risk assessment, examples of multivariate design, and a proposed criticality assessment, all of which coalesce into an example of design space. Torcetrapib was a project in phase III development as a potent and selective inhibitor of cholesteryl ester transfer protein before being terminated in late 2006. The intent of Pfizer was to submit torcetrapib under the QbD paradigm (route selection, robustness, and reagent/solvent selection during phases I to III are significantly important in establishing a manufacturing process that would have the most flexibility in the final design space. For more information on this development phase for torcetrapib see Damon et al., Org Process Res Dev, 10(3):464–71, 2006, Org Process Res Dev, 10(3):472–80, 2006).