A Rational Approach for Setting and Maintaining Specifications for Biological and Biotechnology–Derived Products—Part 3

ABSTRACT

This paper discusses an approach for the establishment and lifecycle management of biological and biotechnology-derived product specifications. The views presented are consistent with the concept of Quality by Design (QbD), in which critical quality attributes (CQAs) are distinguished from parameters used to monitor process consistency. Specifications and the corresponding limits as applied to CQAs serve to ensure that the product is fit for use, whereas control limits are a manufacturer’s tool to monitor shifts and trends in the manufacturing process. In the current paradigm, inappropriate use of specifications creates a disincentive for continuous process understanding; more suitable approaches to analyzing development and manufacturing data are discussed. Statistical methods are presented for deriving and interpreting data against specifications that better manage the risk to the customer of receiving product with diminished safety or efficacy, as well as the risk to the manufacturer of earmarking a satisfactory lot as unacceptable. The recommendations are presented as a rational approach to setting and maintaining specifications, while recognizing that their applicability may not be suitable in all cases, given the heterogeneity of types of regulated biological and biotechnology-derived products and their unique challenges.

The purpose of this paper, which has been developed by the Working Group on Specifications and Formulations of the Pharmaceutical Research and Manufacturers of America (PhRMA) Biologics and Biotechnology Leadership Committee, is to provide guidance on a lifecycle approach to setting global specifications for biological and biotechnology-derived products. In the pharmaceutical industry, specifications are legally binding criteria that a product must meet in order to be marketed. They ensure the consistency and quality of the product and help ensure that it is safe and efficacious over the shelf life of the product. Specifications evolve during product development and ideally should embrace future process capability. This is true for biological and biotechnology-derived products for which there may be limited experience at the time of regulatory filings (including the marketing application), and for which early commercial production often is necessary to gain a better understanding of product quality attributes, methods, and limits.

Wyeth

Parts 1 and 2 of this article, published in the June and July issues of BioPharm International, included four sections: Terminology; Stages of the Lifecycle of a Product; Components of a Biological and Biotechnology Product Specification; and Current Issues Related to the Development of Specifications. This final section describes a suggested approach for developing and maintaining a total quality system.

A SUGGESTED APPROACH FOR DEVELOPING AND MAINTAINING A TOTAL QUALITY SYSTEM

A rational approach to setting and maintaining specifications should incorporate the following fundamental principles:

• A total quality system should seek to minimize risks to the customer and manufacturer alike. When in conflict, however, practice should err on the side of minimizing customer risk.

• If possible, specifications should be established from sound scientific data and reflect fitness for use.

• Specifications should be established to guarantee that the product is fit for use throughout the shelf life of the product.

• Control limits are a manufacturer's tool to monitor shifts and trends in the manufacturing process, and to study the effect of process improvements.

• Specifications should be set to control the average lot attribute rather than individual dosage units or measurements. This approach reflects current industry practice of mapping average quality attributes to population clinical response during product development.

• A lifecycle approach should acknowledge that material and substantial information is necessary to establish specifications, and thus restrictive limits should not be set during development. In some cases, it may not be possible to set limits until post-licensure, after early product manufacture and test method experience have captured information adequate to set meaningful limits.

• Product limits provide a basis for assessing product quality attributes at significant points in time, such as at release and at the end of shelf life. Limits should not be overused in studies that aim to explore process or product stability.

On the basis of these principles, the following guidance is recommended for setting limits for biological and biotechnology-derived products.

1. Capture scientific data during development to set limits that reflect fitness for use throughout the shelf life of the product.

With the goal of setting specifications that reflect fitness for use, development studies should be performed to identify quality attributes that may be linked to clinical outcomes, and to set release limits that guarantee the quality of the product throughout its shelf life. Setting specifications in this way requires a coordinated effort among the preclinical, clinical, and nonclinical development teams. The goal of identifying quality attributes that may be linked to fitness for use may be accomplished by using sound scientific judgment and by evaluating the historical performance of products with similar quality attributes.

Preclinical studies in animals or cell culture can help assess how changes in quality attributes may affect clinical outcome. These studies form the beginning of our understanding about which quality attributes may be clinically meaningful, and also can provide a basis for setting preliminary or final limits on those attributes. Clinical studies in target populations may be designed to explore or support ranges for meaningful quality attributes, which together with preclinical experiments form the basis of specifications. Nonclinical studies on final product should be designed to explore and define the properties of the product and analytical methods under conditions that are likely to occur during manufacture, testing, handling, and product shelf life.

The loss rates determined in stability studies can be used in conjunction with preclinical or clinically derived specifications, as well as release assay variability, to calculate release limits that provide assurance that product released to market is safe and effective and will remain so throughout its shelf life. Figure 4 shows an example of how to set release specifications for an attribute that shows loss over time. The lower specification limit corresponds to the mean of a lot that showed satisfactory performance in clinical trials. (Note that this represents the reportable value from release testing of the clinical lot, together with either additional information about the lot or a compendial or consensus limit for the quality attribute.) The product is earmarked for a 24-month shelf life to allow adequate time for product distribution and consumption. The total loss from release through expiry (solid line) is combined with uncertainties in the release assay and loss estimates to derive the lower release limit (LRL) for the lot. The total loss value may be determined from the sum of individual losses under various storage and handling conditions (e.g., room temperature handling during packaging, labeling, and inspection of a refrigerated product, long-term storage at the labeled temperature, loss after reconstitution and storage in a physician's office).

Figure 4. An example of how to set final product release specifications for an attribute that shows loss over time, taking into account all stability losses during handling and storage (solid line)and release assay variability

The fundamental concept behind the strategy of release limits is represented by the equations that follow:

URL = USL – 1.64 x σ_assay

in which:

URL = upper release limit

LRL = lower release limit

USL = upper specification limit

LSL = lower specification limit

1.64 = statistical factor associated with 95% confidence

σ_assay = release assay variability expressed in the form of SD

b_i = loss rate associated with i^th

storage and handling conditions

t_i = time at i^th storage and handling conditions

σ_bi = variability of loss rate associated with i^th storage and handling conditions

and

i indexes multiple storage and handling conditions.

Finally, development data can be used to forecast process capability for manufactured product. Quality by design (QbD) tools can be used to mimic the ranges of normal operating conditions for the product, leading to a preliminary estimate of process capability. Those forecasts should be verified through early manufacturing experience, and updated as necessary to formulate control or process capability limits (LCL, UCL) for the process. Standards of practice for establishing control limits vary throughout the industry. The application of standard tools for quality assessment can help mitigate risk and facilitate the implementation of a strategic quality system.¹⁶

The ideal quality system for final product is illustrated in Figure 5. The specification limits (LSL, USL) reflect restrictions within which product is fit for use, and must conform throughout shelf life. The release limits (LRL, URL) ensure these limits are met at release and throughout product shelf life, whereas the control or process capability limits (LCL, UCL) describe process and assay variability. Under this paradigm, final product would be out of specifications (OOS) at release if it falls outside the release limits. On the other hand, a lot would be out-of-trend (OOT) if it falls outside the control limits. An OOT lot signals a possible problem with the process, which is subject to investigation. An OOT lot would nevertheless be fit for use, and thereby could be safely released to the market.

Figure 5. The ideal quality system for final product, showing the relationship among specification, release and control limits. The specification limits (LSL,USL) reflect restrictions within which product is fit for use,and must conform though tout shelf life. The release limits (LRL,URL)ensure these limits are met at release and throughout product shelf life,whereas the control or process capability limits (LCL,UCL) describe process and assay variability. Under this paradigm, final product would be out of specifications (OOS)at release if it falls outside the release limits.

Process capability is reflected in the difference between control and release limits. Satisfactory process capability will allow the process to vary over long-term manufacturing experience while remaining within specifications. Unsatisfactory process capability, which will lead to frequent OOS results, will give the manufacturer an incentive to improve process consistency, and thus process capability. In keeping with the regulatory relief expected from the implementation of Quality by Design (QbD), the manufacturer is at liberty to update control limits without strict regulatory oversight, as long as these remain within the release limits, and to improve process capability without regulatory consequence in the form of more restrictive release limits.

2. When limits are to be supported by process consistency rather than fitness for use, set limits that appropriately reflect the long-term process distribution, and provide incentives for continuous process monitoring and improvement.

Limits on attributes that are not linked to clinical safety or efficacy may be set based on process consistency and should appropriately acknowledge the long-term process distribution.

Control limits that are based on process consistency are an integral part of a total quality system in which a robust process is developed and then maintained. In such a system, control limits are used to monitor the process. QbD principles should be acknowledged during development to ensure that manufacturing control limits are appropriate. Process understanding can help ensure that limits accurately reflect product consistency. Under this paradigm, control limits should be set with adequate full-scale manufacturing experience, where possible, changing the factors that impact consistency and define the design space for the process.

Release limits that are set at the boundaries of process capability create the misconception that product that is atypical or "statistically aberrant" is unfit for use. Lots manufactured using an adequately controlled process will appear unacceptable as more information is gathered and the process matures. Process improvements will become difficult to implement, because these are likely to result in a shift in the "quality" profile for the product. To avoid these problems, release limits should be set beyond the limits of process capability to provide opportunity to monitor for shifts and trends and to implement process improvements without affecting supply to the market.

3. Take action that is appropriate to the type of attribute and the purpose of different types of limits.

The action that should be taken when a quality attribute exceeds its specifications is not the same as the action that should be taken when an attribute exceeds its control limits. Exceeding a specification signals that the product may not be fit for use, while exceeding a control limit signals that the process may not be in adequate control and should be monitored for shifts and trends.

Figure 6 compares the actions that might be taken when a quality attribute exceeds a specification or control limit, depending on whether the attribute is linked to fitness for use or is a consistency attribute used to monitor the product or the process. Following down the left side of the flow chart, a quality attribute that is linked to fitness for use may have specifications that were defined during development as well as control limits that have been updated throughout the lifecycle of the product. Failure to meet specifications results in a formal OOS investigation, which may result in withholding the lot or withdrawing it from the market. A lot that meets specifications but fails to meet its control limits will result in an OOT investigation. This may result in corrective action when an assignable cause has been linked to the OOT event.

Figure 6. Flow chart that compares the actions that might be taken with a quality attribute that is linked to fitness for use with those that might be taken with a consistency attribute that is used to monitor the product or the process.

On the other hand (following down the right side of the flow chart), a quality attribute that is not linked to fitness for use but is used to monitor product or process consistency may have control limits that are updated throughout the lifecycle of the product. A failure to meet control limits will result in an OOT investigation. That investigation may result in corrective action when an assignable cause has been linked to the OOT event.

4. Treat post-marketing stability studies as a control strategy for the process, rather than an assessment of the lot's stability.

Annual post-licensure stability studies should be designed to monitor stability characteristics of the product, such as changes in a quality attribute, rather than the level of the quality attribute at points throughout shelf life.

The evaluation of a pharmaceutical products does not end with its approval for marketing. After marketing, sponsors monitor their products to ensure that they conform to their experience during development. The US Food and Drug Administration generally requires a commitment to monitor the stability of at least one lot of each approved product produced each year. This approach to ensuring post-licensure stability could potentially be enhanced to provide increased confidence that the manufacturer produces product that is safe and efficacious until its expiry. This must be balanced, however, against the risk of a program that would yield false signals of product instability, resulting in unnecessary effort on the part of industry and regulatory agencies to ascertain the cause.

The industry and regulators may wish to consider novel models that combine strategic design and analysis for monitoring post-licensure stability to detect meaningful changes in the stability profile of the product. These models require a shift from the historical post-licensure stability paradigm of requiring that individual stability time-point measurements meet expiry specifications to a new approach of stability monitoring in which common statistical techniques are used to evaluate the composite data representing product on the market. A standard regression analysis can be used either to obtain slopes that can be monitored for shifts or trends in the stability characteristics of the product, or to model ongoing lots for shelf-life characteristics such as predicted potency at expiry.¹⁷

Such a paradigm of stability monitoring promotes data collection. When the data are analyzed using regression analysis, the inclusion of additional time points or lots provides a more precise estimate of product stability, with less risk to the customer and manufacturer alike. In contrast, when time-point results are held to specifications, there is an increased risk of "failure" because of repeat testing, and thus a disincentive for data collection.

The following summarizes the potential opportunities that can result from the design, analysis, and interpretation of annual stability studies that promote data collection to achieve better control of product on the market:

• One or more commercial lots may be enrolled into the post-licensure stability program. A single lot may be used if product is stable, or if there is an adequate range between product release and predicted value at expiry. More lots might be enrolled when there is greater risk that commercial lots may fall below their specification limit at expiry.

• Standard stability time points, as described in regulatory guidelines, need not be used, if this approach is balanced against the total number of lots on stability. Studying more lots with fewer time points per lot provides a more representative profile of product on the market than studying more time points on a single lot.

• Data from a single lot, or from all lots currently on stability, might be analyzed using common statistical analysis techniques. The predicted value from such an analysis will yield a better estimate of the true value of a lot over time than an individual stability time point measurement will, and the combined analysis from lots on stability provide a reliable forecast of the quality of product on the market.

• The annual stability program might come to be recognized as "quality control on the product," rather than a study of the particular lots on stability. In this manner, the annual stability program becomes a part of the overall product quality system.

5. Use statistical approaches, where possible, to enhance the design of a study and to obtain reliable product understanding.

Quality by Design (QbD) means that product and process performance characteristics are scientifically designed to meet specific objectives, not merely empirically derived from performance of test batches. Statistical methods should be used to enhance the design of manufacturing experiments and to make optimal use of the experimental information.

Multifactor design of experiments (DoE) can be used to identify parameters that affect the process or to define the design space for the process. Proper analysis of the data provides scientific understanding and empirical confirmation of the process. Mathematical modeling of the factor responses helps reveal process parameters that affect a critical quality attribute, and thereby offers a basis for maintaining product quality. Such modeling provides more value than evaluating conformance lots against specifications.

Likewise, stability studies should be designed to meet the goal of the evaluation. Typically, the goal of the stability study is to estimate the quality attribute at significant points in time (e.g., at expiry) or the degradation rate at meaningful temperatures. In these cases, the study should be designed and analyzed to obtain the most reliable (i.e., least variable) estimate of the parameter of interest. A study to predict the quality attribute at significant points in time will select intervals that adequately represent the targeted region and will use common statistical methods to forecast response. A study of the effect of temperature or physical exposure might ideally compare results only at the beginning and end of the exposure to obtain the most reliable estimate of the effect. These goals, as well as those of validation, are best met using simple statistical methods rather than comparing results to specifications. Holding stability and validation results to specifications engenders a disincentive for collecting valuable product and process information.

SUMMARY

Setting limits for final drug product is part of an overall quality strategy that includes the control of raw materials, excipients, in-process testing, and good manufacturing practices. Quality cannot be tested into the product.

Likewise, process understanding cannot be achieved by holding quality attribute measurements to specifications. Strategic studies such as process validation, as well as development and post-licensure stability studies, should be adequately designed and analyzed to obtain relevant and reliable information. Over-interpretation of measurements from these studies creates a disincentive for collecting valuable process and product information. A proper evaluation of the data will yield information that can be used to develop a total quality system to control the process and protect the customer.

A rational approach to setting specifications for biological and biotechnology-derived products involves a clear understanding of the purposes of the limits, as well as recognition of risks to both the customer and manufacturer. A primary goal of specifications is to ensure that only quality product is released to the market. When a quality attribute has been demonstrated to have clinical impact, the release limits should acknowledge release assay variability and product stability. The purpose of the release limit is to guarantee that a released lot will have a clinically acceptable level of quality through the end of shelf life. When a quality attribute is unlikely to have clinical impact, the information generated by the assay should be used to monitor the process. The control limit should acknowledge the process distribution, which is composed of product variability and release assay variability. An adequate number and variety of representative lots is necessary to characterize that distribution and establish control limits.

Control limits on a clinically meaningful quality attribute should be used in conjunction with specifications to monitor product for shifts or trends. Control limits should ideally fall within release limits, to maintain satisfactory process capability. When release limits are set to coincide with control limits, satisfactory lots will be OOS because of small process fluctuations or chance alone. In such a scenario, manufacturing improvements are difficult to implement because of either the increased risk of yielding OOS results or the risk of being required to set tighter limits. Practices such as setting release limits to coincide with control limits or narrowing release limits after a process improvement has been implemented may increase product cost without providing added quality, and thus fail to serve the best interest of the customer.

Post-licensure stability studies are an additional tool to study shifts or trends in a product profile. Post-licensure studies should not be viewed as a study of the lot on stability, but rather as a control of the stability characteristics of the product. Control limits on stability properties of the product such as the slope can be used to help control the overall product profile throughout the lifecycle of the product.

A common understanding and acceptance of these principles by industry and regulators will enhance the quality and availability of biological and biotechnology-derived products.

REFERENCES

16. Montgomery DC. Introduction to Statistical Quality Control. New York: John Wiley & Sons; 1991.

17. Fairweather WR, Mogg R, Bennett PS, Zhong J, Morrisey C, Schofield TL. Monitoring the stability of human vaccines, J Biopharm Statistics. 2003;13,395–413.

Timothy Schofield is senior director, nonclinical statistics, at Merck Research Laboratories, Merck & Co., West Point, PA, and the corresponding author, timothy_schofield@merck.com, 215.652.6801; Izydor Apostol, PhD, is scientific director, analytical and formulation sciences, at Amgen Inc., Thousand Oaks; Gerhard Koeller, PhD, is vice president, quality and compliance biopharmaceuticals, at Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany; Susan Powers, PhD, is biotech quality technology leader, Wyeth Pharmaceuticals, Collegeville ,PA; Mary Stawicki is associate director, regulatory affairs biopharm CMC, at GlaxoSmithKline, Collegeville, PA, and Richard A. Wolfe, PhD, is director and team leader, biopharma operations, at Pfizer Global Manufacturing, Chesterfield, MO.