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Anurag S. Rathore is a professor in the Department of Chemical Engineering at the Indian Institute of Technology Delhi and a member of BioPharm International's Editorial Advisory Board, Tel. +91.9650770650, firstname.lastname@example.org.
Key considerations for defining your overall control strategy.
Enhanced process and product understanding are the basic tenets of Quality by Design (QbD). A QbD approach for setting specifications would involve harnessing this understanding not only from clinical and nonclinical data available for the product but also from other similar products. Manufacturing using meaningful, science-based specifications will ensure that we attain the optimal balance between manufacturing flexibility and product safety.
The safety of biotech therapeutic products is paramount to their successful commercialization. Concerns that have been frequently cited include adulteration, changes to product quality over its lifecycle, changes to product quality during distribution, complexities of biotech processes, potency, stability, and environmental impact.1 Product specifications have long been regarded as a safeguard with respect to product safety. They have been defined as "a list of tests, references to analytical procedures, and appropriate acceptance criteria which are numerical limits, ranges, or other criteria for the tests described."2 In traditional manufacturing, they have been regarded as the final hurdle that a manufacturing lot must overcome before its release for commercial use. However, in the Quality by Design (QbD) paradigm, they are one part of the overall control strategy that has been designed to ensure product quality and consistency.3,4
This article is the 19th in the "Elements of Biopharmaceutical Production" series and will present an approach for setting specifications for biotech therapeutic products following the QbD principles.
Regulatory procedures that influence specification setting for biotechnology-derived protein products, including the International Conference of Harmonization (ICH) guidelines, have been reviewed in the literature.5,6 In traditional manufacturing, specifications were set based on the small number of large-scale batches that had been manufactured before filing for regulatory approval. Each batch was then tested against those specifications to ensure product safety. In the QbD paradigm, however, prior knowledge can play a major role in setting specifications. This may result in broad specifications for attributes whose relationship to product safety and efficacy is well understood to not be significant through product-specific or platform data and relatively narrow specifications for attributes for which the impact on safety and efficacy is not fully understood or is found to be significant.7,8 Unlike in traditional manufacturing, product specifications under QbD are solely for confirmation of product quality because the process control strategy ensures that the specifications are met.
The concept of a clinical design space can be used to quantify the clinical experience with a product.3 This would be in the form of a multidimensional design space with each critical quality attribute (CQA) serving as a dimension. The size of the clinical design space for a given product will depend on the number of lots put in the clinic, the availability of applicable data from other similar products, and the extent of product heterogeneity that has been introduced during the clinical trials. The clinical design space is expected to be limited in the early phases of clinical development when only a few lots have been introduced into the clinic, but then would grow as the product reaches an advanced stage of product development and more clinical data become available.
The design space concept also can be extended to product quality.3 Similar to the clinical design space, a design space would also be multidimensional, with each CQA serving as a dimension. The product design space will be documented in the regulatory filing in the form of in-process controls and drug substance and drug product specifications that define the acceptable variability in CQAs. The size of the product design space for a given product will depend on a multitude of factors, including:
Table 1 presents a subset of the typical release tests that have been reported in the literature for monoclonal antibody (MAb) products.15 The tests include indicators of quantity (protein concentration), purity (chromatography), identity (electrophoresis, peptide mapping), potency (antigen binding assay), impurities (host cell proteins [HCPs], nucleic acids, endotoxins), and other general properties (pH, volume, appearance). The table also presents proposed specifications for a mock MAb product along with release data from five lots that have been put into the clinic. Per the discussion above, the product design space will be defined through the specifications submitted in the regulatory filing.
Table 1. Typical release tests used for monoclonal antibody products.15 Also shown are mock specifications and data for five lots used in the clinical trials.
Developing and setting specifications in the QbD paradigm should be a data-based exercise that allows for incorporating data as it becomes available during the product lifecycle. Each specification has three key elements:7,8
The first step is to perform a risk assessment to determine which quality attributes are important to the clinical performance of the product (safety and efficacy).7 All product quality attributes are considered and assessed for risk; this approach is illustrated in Figure 1. Data available on the product or from other platform products are analyzed.
Figure 1. Illustration of an approach for setting specifications for product quality attributes
After attributes have been identified for specifications, the next step is to come up with justifiable acceptable criteria.7,8,13,16,17 As discussed above, setting product specifications requires sifting through a variety of analyzing data from sources (clinical studies, animal models, pharmacokinetic studies, analysis of manufacturing lots, etc.) This necessitates the use of appropriate statistical tools. Multivariate techniques are more effective than univariate methods for the two-step process of first treating the raw data and then combining data sets to produce meaningful results.17
Here are some key considerations that should be kept in mind when setting specifications:2,5,6,16
Figure 2 presents an illustration of the data presented in Table 1 with the ratio of the product and clinical design spaces plotted against the clinical lot number. A ratio of 1 would mean that the specification is the same as the variability in product quality seen in the clinic. It can be seen that product-related impurities such as percent purity by high performance size exclusion chromatography (HP SEC) and percent purity by ion exchange chromatography (IEC) are at ratios <2. In contrast, process related impurities such as HCP and DNA are at ratios >10. This reflects our knowledge about how a particular attribute affects the safety, efficacy, and consistency of the product. The less knowledge we have, the more we must depend on the clinical experience of the product to justify a specification.3
Figure 2. Ratio of the product quality and clinical design spaces for a hypothetical monoclonal antibody product. The quality attributes shown have been chosen from Table 1.
Product-related quality attributes fall into two categories.2 The first is product-related variants, which include species such as deamidation that are related to the product and have potency, clearance, immunogenicity, and safety properties similar to the product. The second group covers product-related impurities such as aggregation, which differ in the above-mentioned properties from the product. Figure 3 illustrates the comparison between the clinical and product quality design spaces for two product-related impurities. It is seen that the product quality design space as defined by the specifications is only slightly broader than the clinical experience for percent purity by HP SEC (specification: ≥98%; clinical experience: 99.1–99.8%) and percent purity by IEC (specification: ≥95%; clinical experience: 97.5–100.0%). The broader product design space in these cases would still need to be justified by nonclinical studies evaluating the safety and efficacy of these impurities or by clinical and nonclinical studies related to these impurities with other platform molecules.
Figure 3. Illustration of clinical and product design spaces for a few chosen product related quality attributes from Table 1
Process-related impurities are impurities that are derived from the manufacturing process.2 These could be extractables and leachables from the media or the additives such as cell culture media components and chromatography column leachables. Specification setting for many of these process-related impurities is driven by regulatory expectations. Figure 4 illustrates the clinical and product design spaces for three host cell impurities, namely HCPs, residual DNA, and endotoxin. It should be noted that HCP and residual DNA data are plotted on the log scale (left axis), whereas the endotoxin data are plotted on a linear scale (right axis). In comparison to the product quality attributes presented in Figure 3, it is seen that the product design space is significantly larger than the clinical design space for the host cell impurities. This is primarily a result of a better understanding of these impurities and their impact on product safety and efficacy, as well as the demonstrated excess capability of the process to remove these impurities. In addition, prior knowledge from other products expressed in the same host cell is very applicable for these impurities.
Figure 4. Illustration of clinical and product design spaces for three host cell impurities chosen from Table 1. Host cell protein (HCP) and residual DNA data are plotted on the log scale (left axis) wheras the endotoxin data are plotted on a linear scale (right axis).
Setting specifications in the QbD paradigm will involve using product knowledge, process knowledge, prior knowledge, and appropriate statistical methods to define meaningful specifications. This approach also must include a continuous improvement element, so that the specifications are revisited and their appropriateness re-examined to reflect changes in process (process improvements, technology transfer, scale-up, equipment changes), analytical methods (novel techniques), and product knowledge (new clinical and nonclinical data).
Anurag S. Rathore, PhD, is a biotech CMC consultant and a faculty member at the Indian Institute of Delhi, India, 9650770650, email@example.com. Rathore is also a member of BioPharm International's editorial advisory board.
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