Genetic Engineering & Biotechnology News

NOV15 2017

Genetic Engineering & Biotechnology News (GEN) is the world's most widely read biotech publication. It provides the R&D community with critical information on the tools, technologies, and trends that drive the biotech industry.

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26 | NOVEMBER 15, 2017 | GENengnews.com | Genetic Engineering & Biotechnology News Andrew Anderson, Graham A. McGibbon, Ph.D., and Sanjivanjit K. Bhal, Ph.D. Over the past few years, global regulatory authorities have intensified their promulga- tion of quality-by-design (QbD) principles. QbD affords many important long-term benefits, not the least of which is "antifra- gility": the ability to become more resilient and robust in the face of adversity. 1 Attempts to implement QbD, however, can have a dramatic impact on product development groups and their supporting corporate infor- matics infrastructure. This article will discuss how QbD require- ments impact risk assessment, process assess- ment, material assessment, documentation, and traceability, and how these functions can be addressed using informatics software. One of the challenges in applying QbD prin- ciples in process development is establishing an acceptable Quality Target Product Profile (QTPP). This challenge may be met through: • Evaluation of input Material Quality Attributes (MQAs) • Evaluation of the quality impact of Critical Process Parameters (CPPs) • Consolidated evaluation of every MQA and CPP for all input materials and unit operations MQA assessment requires the careful consideration of input materials to ensure that their physical/(bio)chemical proper- ties or characteristics are within appropriate limits, ranges, or distributions. Furthermore, for CPP assessment, unit operation process parameter ranges must be evaluated to deter- mine the impact of parameter variability on product quality. The contribution of each unit operation in any pharmaceutical or biophar- maceutical manufacturing process—whether these operations are chemical (such as steps in a synthetic process) or physical (such as fil- tering, stirring, agitating, heating, chilling, or product formulation)—must be assessed. Impurity control strategy development is an example of this iterative evaluation pro- cess (Figure 1). For regulatory submission of a substance or product under development, information from many activities is necessary to complete the quality module of a Com- mon Technical Document (CTD or eCTD). Disparate Data Sources Initially, chemical structure information may be available from chemists' individual electronic laboratory notebooks (ELNs), but the affiliated unit operation details and the complete supporting molecular characteriza- tion data are not usually directly available. Some of this data and interpreted informa- tion may have been transcribed into spread- sheets within Microsoft Excel ® —the system most widely used for managing process and impurity data. In those Excel spreadsheets, synthetic process and supporting analytical and chro- matographic data are abstracted to numbers, text, and images, and the raw data is stored in archives. Separate reports are often need- ed to assemble subsets of analytical charac- terization information and interpretations. The analytical information is transposed for decision-making purposes, but review of the decision-supporting data is, at best, imprac- tical because it has been sequestered into dif- ferent systems. Batch-to-batch comparison data is also transcribed into Excel spreadsheets in an at- tempt to bring all the relevant information together into one system—unfortunately, one ill-suited to support rich chemical and scien- tific information. Project teams spend weeks on the assembly of this information for in- ternal reporting and external submissions. This abstracted and repeatedly transcribed information is then reviewed to establish and implement control strategies in compliance with a QbD approach. So, the challenge for product development project teams is to not only plan and conduct the process experiment's unit operations, but to acquire, analyze, and then, (most impor- tant) assemble and interpret the various data from analysis of input materials and process information. Since the development process is iterative, all the salient data must be cap- tured and dynamically consolidated as pro- cess operations are conducted to enable facile review of the information for ongoing risk assessment of impurities. Concurrently, test-method development must demonstrate robust detection capabili- ties. That is, a test must be up to the task of generating a complete impurity profile, one that encompasses all significant impurities, known or suspected, from each process. Cur- rently, control strategies rely on unrelated instruments and systems to acquire, analyze, and summarize impurity profile data and to record the interpretations made during pro- cess-route development and optimization. A Comprehensive Approach In order to establish effective process and analytical impurity control strategies, a com- house R&D and manufacturing for cell and gene therapies. While many of the major biopharmaceutical companies are likely to establish a presence in cell and/or gene thera- pies, (as with biosimilars, ADCs and other newer-product classes), even the giants may not have the technical competence that will be needed. Factors contributing to a lack of needed cell/gene therapies manufacturing capacity include: • CMOs are and will be a primary manufacturer of cell and gene therapies. • At present, according to industry ob- servers, most commercial cell/gene therapy developers today would prefer to outsource their bioprocessing, presuming cost-competi- tive and capable CMO services are available. • More than or equal to 80% of com- mercial product developers currently outsource product manufacturing (mostly academic/nonprofit research organizations' early-phase work). • With cell/gene therapies a new area, very few developer companies have the nec- essary dedicated facilities, hardware, experi- enced staff, in-house platform, and licensed technologies, etc. • Staff with needed expertise will be a major limiting factor. Overly qualified staff, including many PhDs, are currently used in both R&D and manufacturing, because see- ing and fixing problems, at present, are not resolvable by technicians. Development of cell/gene therapy manu- facturing capacity and these industry sectors will be largely dependent on CMOs. Further pushing use of CMOs for cell/gene therapies manufacturing at all stages of development is the fact that manufacturing-relevant tech- nologies, particularly platforms, do not yet exist—or they are proprietary—and it may be up to CMOs to do the needed licensing of multiple bioprocessing-related technologies. Conclusions Current bioprocessing technologies in use are primitive by mainstream biopharma- ceutical standards. This is particularly true with many cellular therapies involving large numbers of manual fluid transfers (pipetting, no aseptic connections, manual handling of cells, use of glove boxes, shake flasks vs. bio- reactors used for cell culture, etc.). Manufac- turing is often complicated by the inability to sterilize viral and cellular intermediates and final products by filtration or heat. The many autologous (patient's cells returned to same patient) cell therapies in development are often labor intensive, with a good por- tion requiring man-weeks of labor just to produce therapy for a single patient (a rea- son, perhaps, for very high prices for these products). With cell therapies, there is also wide variation in where or who will perform product manufacturing, which can include the developer or its CMO—and many cell therapies are expected to involve local or in- hospital or even patient-bedside therapeutics manufacture. With related bioprocessing automation largely lacking, bringing such products to market will be very difficult, if even feasible. However, the situation with gene therapies, most involving culture of vi- ral vectors, is much different—better, even— because the associated vectors are generally already being manufactured in conventional, single-use bioreactors. Achieve QbD by Managing Impurity Data Bioprocessing Figure 1. An iterative quality- by-design approach for controlling impurities in a biomanufacturing process. Tutorial Cheminformatics Enables QbD Strategies for Process and Analytical Impurity Control Cell and Gene Therapies Continued from page 25

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