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Recombinant adeno‐associated virus (AAV) has become a popular platform for many gene therapy applications. The strength of AAV‐based products is a critical quality attribute that affects the efficacy of the drug and is measured as the concentration of vector genomes, or physical titer. Because the dosing of patients is based on the titer measurement, it is critical for manufacturers to ensure that the measured titer of the drug product is close to the actual concentration of the batch. Historically, dosing calculations have been performed using the measured titer, which is reported on the drug product label. However, due to recent regulatory guidance, sponsors are now expected to label the drug product with nominal or "target" titer. This new expectation for gene therapy products can pose a challenge in the presence of process and analytical variability. In particular, the manufacturer must decide if a dilution of the drug substance is warranted at the drug product stage to bring the strength in line with the nominal value. In this paper, we present two straightforward statistical methods to aid the manufacturer in the dilution decision. These approaches use the understanding of process and analytical variability to compute probabilities of achieving the desired drug product titer. We also provide an approach for determining an optimal assay replication strategy for achieving the desired probability of meeting drug product release specifications. [ABSTRACT FROM AUTHOR]

Bioassays are regulated, analytical methods used to ensure proper activity (potency) of biological products at release and during long-term storage. Potency is commonly reported on a relative basis by comparing and calibrating a concentration–response curve from the test material to that of a reference standard material. The relative potency approach depends on an assumption that the two concentration–response curves exhibit similar (equivalent) shapes, except for a potency shift. In certain circumstances, however, biological factors preclude the similarity assumption, and the traditional approach becomes unworkable. The antibody-mediated cytotoxicity assay is one example where the similarity assumption does not always hold. Other examples also arise in the fields of toxicology and pharmacology. In this work, we present a non-constant mean relative potency approach which averages the relative potency across a common range of the concentration-response curves. The proposed method captures the changing nature of the relative potency into a summary statistic that can be reported for batch calibration and quality control purposes. We provide inferential methods for this statistic and summarize the results of a simulation comparing these methods across a number of non-constant relative potency scenarios and assay conditions. [ABSTRACT FROM AUTHOR]

A validated method is used to measure quality attributes of manufactured lots to support the production of active ingredient or drug product for a regulatory filing or product release. Various regulatory guidelines provide expectations on how to validate a method, including accuracy and precision. Validation studies are not typically designed to have the size or scope needed to fully characterize a method. We can and should, however, capitalize on the historical data that is generated from method development, transfer, and clinical trial lot release to facilitate the characterization. Using prior information from these sources affords a more confident characterization of the method's accuracy and precision. This article focuses on Bayesian methods for using the stream of available method data. Considerations are given for prior distributions, including methods to control the influence of prior data. Two case studies are presented to illustrate the continuous validation approach. This is a joint effort of the DIA Bayesian Scientific Working Group and the American Statistical Association Biopharmaceutical Nonclinical Working Group. [ABSTRACT FROM AUTHOR]

Summary: The use of Bayesian methods to support pharmaceutical product development has grown in recent years. In clinical statistics, the drive to provide faster access for patients to medical treatments has led to a heightened focus by industry and regulatory authorities on innovative clinical trial designs, including those that apply Bayesian methods. In nonclinical statistics, Bayesian applications have also made advances. However, they have been embraced far more slowly in the nonclinical area than in the clinical counterpart. In this article, we explore some of the reasons for this slower rate of adoption. We also present the results of a survey conducted for the purpose of understanding the current state of Bayesian application in nonclinical areas and for identifying areas of priority for the DIA/ASA‐BIOP Nonclinical Bayesian Working Group. The survey explored current usage, hurdles, perceptions, and training needs for Bayesian methods among nonclinical statisticians. Based on the survey results, a set of recommendations is provided to help guide the future advancement of Bayesian applications in nonclinical pharmaceutical statistics. [ABSTRACT FROM AUTHOR]

Validation of pharmaceutical manufacturing processes is a regulatory requirement and plays a key role in the assurance of drug quality, safety, and efficacy. The FDA guidance on process validation recommends a life-cycle approach which involves process design, qualification, and verification. The European Medicines Agency makes similar recommendations. The main purpose of process validation is to establish scientific evidence that a process is capable of consistently delivering a quality product. A major challenge faced by manufacturers is the determination of the number of batches to be used for the qualification stage. In this article, we present a Bayesian assurance and sample size determination approach where prior process knowledge and data are used to determine the number of batches. An example is presented in which potency uniformity data is evaluated using a process capability metric. By using the posterior predictive distribution, we simulate qualification data and make a decision on the number of batches required for a desired level of assurance. [ABSTRACT FROM PUBLISHER]

Background: Blood-based biomarkers, especially P-tau217, have been gaining interest as diagnostic tools to measure Alzheimer disease (AD) pathology., Methods: We developed a plasma P-tau217 chemiluminescent immunoassay using 4G10E2 and IBA493 as antibodies, a synthetic tau peptide as calibrator, and the Quanterix SP-X imager. Analytical validation performed in a College of American Pathologists-accredited CLIA laboratory involved multiple kit lots, operators, timepoints, and imagers. Florbetapir positron emission tomography was used to quantify amyloid for clinical validation., Results: Precision across 80 runs was ≤20% CV using 23 patient-derived samples ranging from 0.09 U/mL to 3.35 U/mL. No significant lot-to-lot differences were observed. There was no interference from purified tau (2N4R) or lipemia, but hemolysis greater than 2 + was not acceptable. Functional analytical sensitivity (lower limit of quantitation) was 0.08 U/mL. Linearity studies support the use of a standard 1:2 plasma dilution. Samples demonstrated stability at 7 freeze/thaw cycles, with room temperature and refrigerated stability established for up to 72 hours. The final analytical measurement range was 0.08 to 2.81 U/mL. The calibration curve maintained ≤20% CV for raw signal intensity and 80% to 120% relative error for back-fitted concentration using a log-log power regression. Initial clinical assessment using plasma samples from 1091 individuals screened in TRAILBLAZER-ALZ 2 demonstrated an area under the curve of 91.6% (95% CI 0.90-0.94) with brain amyloid as the comparator. Positive and negative predictive value was >90% and >85%, respectively., Conclusions: Through analytical validation, this assay demonstrated robust performance across multiple lots, operators, and instruments and could be used as a tool for diagnosing AD., (© Association for Diagnostics & Laboratory Medicine 2025.)

For manufacturers of sterile drug products, steam sterilization is a common method used to provide assurance of the sterility of manufacturing equipment and products. The validation of sterilization processes is a regulatory requirement and relies upon the estimation of key resistance parameters of microorganisms. Traditional methods have relied upon point estimates for the resistance parameters. In this paper, we propose a Bayesian method for estimation of the well-known D T , z , and F o values that are used in the development and validation of sterilization processes. A Bayesian approach allows the uncertainty about these values to be modeled using probability distributions, thereby providing a fully risk-based approach to measures of sterility assurance. An example is given using the survivor curve and fraction negative methods for estimation of resistance parameters, and we present a means by which a probabilistic conclusion can be made regarding the ability of a process to achieve a specified sterility criterion. LAY ABSTRACT: For manufacturers of sterile drug products, steam sterilization is a common method used to provide assurance of the sterility of manufacturing equipment and products. The validation of sterilization processes is a regulatory requirement and relies upon the estimation of key resistance parameters of microorganisms. Traditional methods have relied upon point estimates for the resistance parameters. In this paper, we propose a Bayesian method for estimation of the critical process parameters that are evaluated in the development and validation of sterilization processes. A Bayesian approach allows the uncertainty about these parameters to be modeled using probability distributions, thereby providing a fully risk-based approach to measures of sterility assurance. An example is given using the survivor curve and fraction negative methods for estimation of resistance parameters, and we present a means by which a probabilistic conclusion can be made regarding the ability of a process to achieve a specified sterility criterion., (© PDA, Inc. 2017.)