Innovation in Pharmaceutical Experimentation Part 1: Review of Experimental Designs Used in Industrial Pharmaceutics Research and Introduction to Bayesian D-Optimal Experimental Design

The concept of design space, as described in the ICH Harmonized Tripartite Guideline Q8 for Pharmaceutical Development (Q8 Pharmaceutical development, ICH harmonized tripartite guidelines, in International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use, 2005), was introduced to justify regulatory flexibility in pharmaceutical manufacturing operations. The basis for this concept is that advanced understanding of variables affecting product quality, obtained either through historical operation or demonstrated through process modeling, justifies replacement of traditional process targets with acceptable operational ranges. Process adjustments that allow operation within the region defined by the design space do not require regulatory oversight. Whereas there are many advantages to having the flexibility to operate within such ranges, the concept is only valid when the design space has been adequately described by appropriate experimentation. Given the complexity of pharmaceutical processes and the number of variables to consider in developing operational ranges, only a well-executed program of experiments, supported by appropriate statistical analyses, could provide the necessary information to truly capture a design space. Thus, the risk of employing a design space is that the model will be applied to a region outside of the approved design space, either through a false statistical inference or by omitting some important factor effects. This article presents a review of the experimental strategies typically employed to develop pharmaceutical processes with special emphasis on the assumptions and limitations of the approaches. An alternative strategy that provides an opportunity to build on previous information efficiently without requiring extraordinary skill in statistics, Bayesian optimal design, is introduced as an alternative to the classical approaches.     

Verification of Design Spaces Developed at Subscale

Recent concerns about the applicability of design space boundaries developed on small scale to commercial manufacturing processes have been raised by regulators worldwide. These concerns center around the scalability of unit operations and their corresponding process parameters, and the impact this has on the desired attributes of the drug substance or product. Requests have been made to verify design space boundaries with data generated at commercial scale. Because it is not always feasible to manufacture large-scale batches, alternative approaches to verification are necessary. The following article discusses various science-based strategies that could be used to verify design space boundaries. These approaches balance the requirements to address regulatory concerns and ensure that quality standards are maintained for both drug substances and products, within the operating constraints currently facing the pharmaceutical industry.     

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.     

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.     

Application of Quality by Design Principles to Legacy Drug Products

The concept of Quality by Design (QbD) is of paramount importance in designing and developing reproducible and robust drug products, processes and analytical methods, thus enabling regulatory compliance and ensuring manufacturability. Risk assessment, design space, and control strategy constitute the key elements of the QbD framework. In this paper, a data-based approach to developing robust pharmaceutical processes is presented and illustrated with an application to a drug product during a site transfer process. The key objective in applying QbD principles is to ensure that the product is designed and manufactured to consistently meet quality requirements. The approach presented simultaneously considers the variability in raw materials, quality critical process parameters and critical quality attributes. By nature, large historical databases of raw material (active ingredients and excipients) and process data exists for legacy products. Multivariate statistical models were employed to extract knowledge on critical variables. Furthermore, a number of design of experiments (DOE) were performed in the joint space of the raw materials and the manipulated process variables to develop the design space and control strategy with feedback control. The result was a joint space that combines the interaction of all the input variables such as raw materials and process parameters that have been proven to provide high quality. Throughout this paper, the use of multivariate statistical analysis and DOE and how they are applied to define meaningful raw materials specification and design space to achieve QbD are discussed.     

Development of a Design Space for a Unit Operation: Illustration Using Compression-Mix Blending Process for the Manufacture of a Tablet Dosage Form

This paper describes the development of an orthogonal design space for a compression-mix blending unit operation for the manufacture of tablet dosage form using an empirical approach. Potential critical process parameters identified through a risk assessment process were assessed through a full-factorial design of experiment for impact on material attributes and drug product critical quality attributes (DP CQA). The impact on each individual attribute measured as responses were subjected to statistical analysis by analysis of variance and regression models were built on the statistically significant effects (p < 0.05). Design space for relevant DP CQA was created using 95% predicted interval estimates. Orthogonal design space for the unit operation was proposed by overlaying design spaces generated for individual DP CQAs. The resulting orthogonal design space made implementation of manufacturing flexibility in to routine manufacturing process and into control strategy simpler and straightforward.     

Misunderstanding <Emphasis Type="Italic">Design Space</Emphasis>: a Robust Drug Product Control Strategy Is the Key to Quality Assurance

ICH guidelines Q8/11, Q9, and Q10 introduced risk-based approaches and enhanced scientific understanding as an opportunity to encourage continuous process improvement for pharmaceutical manufacturing. Conceptually, Quality by Design (QbD) promised to improve confidence in quality through the lifecycle of pharmaceutical products. A primary incentive for industry is the prospect of global regulatory concordance for new applications and post approval changes. Unfortunately, during the last decade, the industry has experienced regulatory divergence regarding the interpretation of ICH guidelines across geographic regions. Rather than truly harmonized regulatory expectations, localized interpretations of ICH guidance have resulted in different technical requirements posing significant challenges for a global industry. As a result, the increased complexity of manufacturing supply chains and the regulatory burden associated with maintaining compliance with these diverse regulatory expectations serves as a barrier to continual improvement and innovation. The QbD paradigm has effectively demonstrated a risk-based link between a product’s control strategy and patient needs that has prompted meaningful improvement in the industry’s approach to product quality assurance. Divergent interpretations of the concepts and definitions used in the modern QbD approach to product development and manufacturing, however, has led to challenges in achieving a common implementation of design space, control strategy, prior knowledge, proven acceptable range, and normal operating range. While the concept of design space remains an appealing focal point for demonstrating process understanding, the authors suggest that Control Strategy is the most important QbD concept, and one that assures product quality for patients. A focus by both regulators and manufacturers on the significance of Control Strategy could facilitate management of post approval changes to improve manufacturing processes and enhance product quality while also engendering regulatory harmonization.     

Clinical Relevance of Dissolution Testing in Quality by Design

Quality by design (QbD) has recently been introduced in pharmaceutical product development in a regulatory context and the process of implementing such concepts in the drug approval process is presently on-going. This has the potential to allow for a more flexible regulatory approach based on understanding and optimisation of how design of a product and its manufacturing process may affect product quality. Thus, adding restrictions to manufacturing beyond what can be motivated by clinical quality brings no benefits but only additional costs. This leads to a challenge for biopharmaceutical scientists to link clinical product performance to critical manufacturing attributes. In vitro dissolution testing is clearly a key tool for this purpose and the present bioequivalence guidelines and biopharmaceutical classification system (BCS) provides a platform for regulatory applications of in vitro dissolution as a marker for consistency in clinical outcomes. However, the application of these concepts might need to be further developed in the context of QbD to take advantage of the higher level of understanding that is implied and displayed in regulatory documentation utilising QbD concepts. Aspects that should be considered include identification of rate limiting steps in the absorption process that can be linked to pharmacokinetic variables and used for prediction of bioavailability variables, in vivo relevance of in vitro dissolution test conditions and performance/interpretation of specific bioavailability studies on critical formulation/process variables. This article will give some examples and suggestions how clinical relevance of dissolution testing can be achieved in the context of QbD derived from a specific case study for a BCS II compound.     

Risk management for moisture related effects in dry manufacturing processes: a statistical approach

A risk- and science-based approach to control the quality in pharmaceutical manufacturing includes a full understanding of how product attributes and process parameters relate to product performance through a proactive approach in formulation and process development. For dry manufacturing, where moisture content is not directly manipulated within the process, the variability in moisture of the incoming raw materials can impact both the processability and drug product quality attributes. A statistical approach is developed using individual raw material historical lots as a basis for the calculation of tolerance intervals for drug product moisture content so that risks associated with excursions in moisture content can be mitigated. The proposed method is based on a model-independent approach that uses available data to estimate parameters of interest that describe the population of blend moisture content values and which do not require knowledge of the individual blend moisture content values. Another advantage of the proposed tolerance intervals is that, it does not require the use of tabulated values for tolerance factors. This facilitates the implementation on any spreadsheet program like Microsoft Excel. A computational example is used to demonstrate the proposed method.     

Quality by Design-Driven Process Development of Cell Culture in Bioreactor for the Production of Foot-And-Mouth Veterinary Vaccine

Quality by design (QbD) principle has been established as a guideline to emphasize the understanding of the relationship of product quality with process control. Vaccine product have characteristics of security and high efficiency, but it also has features such as complexity and rigorous regulatory for production. This case study describes an example of QbD-driven process development for manufacturing a veterinary vaccine produced with baby hamster kidney-21 cells. The study revealed that cell culture duration was the most significant factor affecting 50% tissue culture infectious doses (TCID₅₀) and antigenic titer, and the factors of culture temperature and pH at infection phase exhibited less effect. Culture temperature at infection phase was the only significant factor for total protein. Through the Monte Carlo simulation, the design spaces of process parameters were determined. Meanwhile, the excellent and robust performance in manufacturing scale (4000-L) validated the effectiveness of this strategy. A reliable and robust multivariate process parameter range, that is, design space, was identified by this systematic approach. Our investigation presents a successful case of QbD principle, which encourages other researchers to combine the methodology into other biopharmaceutical manufacturing process.     

Understanding Pharmaceutical Quality by Design

This review further clarifies the concept of pharmaceutical quality by design (QbD) and describes its objectives. QbD elements include the following: (1) a quality target product profile (QTPP) that identifies the critical quality attributes (CQAs) of the drug product; (2) product design and understanding including identification of critical material attributes (CMAs); (3) process design and understanding including identification of critical process parameters (CPPs), linking CMAs and CPPs to CQAs; (4) a control strategy that includes specifications for the drug substance(s), excipient(s), and drug product as well as controls for each step of the manufacturing process; and (5) process capability and continual improvement. QbD tools and studies include prior knowledge, risk assessment, mechanistic models, design of experiments (DoE) and data analysis, and process analytical technology (PAT). As the pharmaceutical industry moves toward the implementation of pharmaceutical QbD, a common terminology, understanding of concepts and expectations are necessary. This understanding will facilitate better communication between those involved in risk-based drug development and drug application review.     

Quality by design approach for formulation development: a case study of dispersible tablets

The focus of the current investigations was to apply quality by design (QbD) approach to the development of dispersible tablets. Critical material and process parameters are linked to the critical quality attributes of the product. Variability is reduced by product and process understanding which translates into quality improvement, risk reduction and productivity enhancement. The risk management approach further leads to better understanding of the risks, ways to mitigate them and control strategy is proposed commensurate with the level of the risk. Design space in combination with pharmaceutical quality management system provide for flexible regulatory approaches with opportunity for continuous improvement that benefit patient and manufacturer alike. The development of dispersible tablet was proposed in the current study through a QbD paradigm for a better patient compliance and product quality. The quality target product profile of a model biopharmaceutical class II drug was identified. Initial risk analysis led to the identification of the critical quality attributes. Physicochemical characterization and compatibility studies of the drug with commonly used excipients were performed. Experiments were designed with focus on critical material and process attributes. Design space was identified and risk factors for all the possible failure modes were below critical levels after the implementation of control strategy. Compliance to the design space provides an opportunity to release batches in a real time. In conclusion, QbD tools together with risk and quality management tools provided an effective and efficient paradigm to build the quality into dispersible tablet.     

A Quality by Design Approach to Developing and Manufacturing Polymeric Nanoparticle Drug Products

The translation of nanomedicines from concepts to commercial products has not reached its full potential, in part because of the technical and regulatory challenges associated with chemistry, manufacturing, and controls (CMC) development of such complex products. It is critical to take a quality by design (QbD) approach to developing nanomedicines—using a risk-based approach to identifying and classifying product attributes and process parameters and ultimately developing a deep understanding of the products, processes, and platform. This article exemplifies a QbD approach used by BIND Therapeutics, Inc., to industrialize a polymeric targeted nanoparticle drug delivery platform. The focus of the approach is on CMC affairs but consideration is also given to preclinical, clinical, and regulatory aspects of pharmaceutical development. Processes are described for developing a quality target product profile and designing supporting preclinical studies, defining critical quality attributes and process parameters, building a process knowledge map, and employing QbD to support outsourced manufacturing.     

Pharmaceutical Quality by Design: Product and Process Development, Understanding, and Control

PURPOSE: The purpose of this paper is to discuss the pharmaceutical Quality by Design (QbD) and describe how it can be used to ensure pharmaceutical quality. MATERIALS AND METHODS: The QbD was described and some of its elements identified. Process parameters and quality attributes were identified for each unit operation during manufacture of solid oral dosage forms. The use of QbD was contrasted with the evaluation of product quality by testing alone. RESULTS: The QbD is a systemic approach to pharmaceutical development. It means designing and developing formulations and manufacturing processes to ensure predefined product quality. Some of the QbD elements include: Defining target product quality profile; Designing product and manufacturing processes; Identifying critical quality attributes, process parameters, and sources of variability; Controlling manufacturing processes to produce consistent quality over time. CONCLUSIONS: Using QbD, pharmaceutical quality is assured by understanding and controlling formulation and manufacturing variables. Product testing confirms the product quality. Implementation of QbD will enable transformation of the chemistry, manufacturing, and controls (CMC) review of abbreviated new drug applications (ANDAs) into a science-based pharmaceutical quality assessment.     

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).     

Crystal and Particle Engineering Strategies for Improving Powder Compression and Flow Properties to Enable Continuous Tablet Manufacturing by Direct Compression

Continuous manufacturing of tablets has many advantages, including batch size flexibility, demand-adaptive scale up or scale down, consistent product quality, small operational foot print, and increased manufacturing efficiency. Simplicity makes direct compression the most suitable process for continuous tablet manufacturing. However, deficiencies in powder flow and compression of active pharmaceutical ingredients (APIs) limit the range of drug loading that can routinely be considered for direct compression. For the widespread adoption of continuous direct compression, effective API engineering strategies to address power flow and compression problems are needed. Appropriate implementation of these strategies would facilitate the design of high-quality robust drug products, as stipulated by the Quality-by-Design framework. Here, several crystal and particle engineering strategies for improving powder flow and compression properties are summarized. The focus is on the underlying materials science, which is the foundation for effective API engineering to enable successful continuous manufacturing by the direct compression process.     

智能制造理念在血液制品产业的应用研究

目的：通过分析智能制造理念下,质量源于设计（quality by design,QbD）与血液制品生产的相关性、过程分析技术（process analytical technology,PAT）在血液制品QbD中的应用以及关键质量环节的QbD实施,以期推动我国血液制品行业升级,实现血液制品的智能生产。方法：采取前瞻性研究方法,查阅、检索以"智能制造" "血液制品" "质量源于设计"过程分析技术"为关键词的文献,对智能制造理念在血液制品的应用研究进行论述。结果与结论：基于我国制药工业的自动化与信息化的水平与现状,制药工业的"智能制造"已逐步发展起来,QbD已被引入我国新版药品GMP,强调了与药品注册、上市制度的有效衔接。在科学监管的要求下,QbD理念已成为血液制品行业界的共识,实施QbD,通过基于问题的审核（question-based review,QbR）,将有助于全面提高我国血液制品的质量,提升产品的竞争力。     

Applications of Clinically Relevant Dissolution Testing: Workshop Summary Report

This publication summarizes the proceedings of day 3 of a 3-day workshop on “Dissolution and Translational Modeling Strategies Enabling Patient-Centric Product Development.” Specifically, this publication discusses the current approaches in building clinical relevance into drug product development for solid oral dosage forms, along with challenges that both industry and regulatory agencies are facing in setting clinically relevant drug product specifications (CRDPS) as presented at the workshop. The concept of clinical relevance is a multidisciplinary effort which implies an understanding of the relationship between the critical quality attributes (CQAs) and their impact on predetermined clinical outcomes. Developing this level of understanding, in many cases, requires introducing deliberate but meaningful variations into the critical material attributes (CMAs) and critical process parameters (CPPs) to establish a relationship between the resulting in vitro dissolution/release profiles and in vivo PK performance, a surrogate for clinical outcomes. Alternatively, with the intention of improving the efficiency of the drug product development process by limiting the burden of conducting in vivo studies, this understanding can be either built, or at least enhanced, through in silico efforts, such as IVIVC and physiologically based pharmacokinetic (PBPK) absorption modeling and simulation (M&S). These approaches enable dissolution testing to establish safe boundaries and reject drug product batches falling outside of the established safe range (e.g., due to inadequate in vivo performance) enabling the method to become clinically relevant. Ultimately, these efforts contribute towards patient-centric drug product development and allow regulatory flexibility throughout the lifecycle of the drug product.     

Population Balance Model Validation and Predictionof CQAs for Continuous Milling Processes: toward QbDin Pharmaceutical Drug Product Manufacturing

Continuous tablet manufacturing has been investigated for its potential advantages (e.g., cost, efficiency, and controllability) over more conventional batch processes. One avenue for tablet manufacturing involves roller compaction followed by milling to form compactible granules. A better understanding of these powder processes is needed to implement Quality by Design in pharmaceutical manufacturing. In this study, ribbons of microcrystalline cellulose were produced by roller compaction and milled in a conical screen mill. A full factorial experiment was performed to evaluate the effects of ribbon density, screen size, and impeller speed on the product size distribution and steady-state mass holdup of the mill. A population balance model was developed to simulate the milling process, and a parameter estimation technique was used to calibrate the model with a subset of experimental data. The calibrated model was then simulated at other processing conditions and compared with additional unused experimental data. Statistical analyses of the results showed good agreement, demonstrating the model’s predictive capability in quantifying milled product critical quality attributes within the experimental design space. This approach can be used to optimize the design space of the process, enabling Quality by Design.