The current state of Bayesian methods in nonclinical pharmaceutical statistics: Survey results and recommendations from the DIA/ASA-BIOP Nonclinical Bayesian Working Group.

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.

Comparisons of outlier tests for potency bioassays.

Potency bioassays are used to measure biological activity. Consequently, potency is considered a critical quality attribute in manufacturing. Relative potency is measured by comparing the concentration-response curves of a manufactured test batch with that of a reference standard. If the curve shapes are deemed similar, the test batch is said to exhibit constant relative potency with the reference standard, a critical requirement for calibrating the potency of the final drug product. Outliers in bioassay potency data may result in the false acceptance/rejection of a bad/good sample and, if accepted, may yield a biased relative potency estimate. To avoid these issues, the USP<1032> recommends the screening of bioassay data for outliers prior to performing a relative potency analysis. In a recently published work, the effects of one or more outliers, outlier size, and outlier type on similarity testing and estimation of relative potency were thoroughly examined, confirming the USP<1032> outlier guidance. As a follow-up, several outlier detection methods, including those proposed by the USP<1010>, are evaluated and compared in this work through computer simulation. Two novel outlier detection methods are also proposed. The effects of outlier removal on similarity testing and estimation of relative potency were evaluated, resulting in recommendations for best practice.

Effect of a statistical outlier in potency bioassays.

The USP<1032> guidelines recommend the screening of bioassay data for outliers prior to performing a relative potency (RP) analysis. The guidelines, however, do not offer advice on the size or type of outlier that should be removed prior to model fitting and calculation of RP. Computer simulation was used to investigate the consequences of ignoring the USP<1032> guidance to remove outliers. For biotherapeutics and vaccines, outliers in potency data may result in the false acceptance/rejection of a bad/good lot of drug product. Biological activity, measured through a potency bioassay, is considered a critical quality attribute in manufacturing. If the concentration-response potency curve of a test sample is deemed to be similar in shape to that of the reference standard, the curves are said to exhibit constant RP, an essential criterion for the interpretation of a RP. One or more outliers in the concentration-response data, however, may result in a failure to declare similarity or may yield a biased RP estimate. Concentration-response curves for test and reference were computer generated with constant RP from four-parameter logistic curves. Single outlier, multiple outlier, and whole-curve outlier scenarios were explored for their effects on the similarity testing and on the RP estimation. Though the simulations point to situations for which outlier removal is unnecessary, the results generally support the USP<1032> recommendation and illustrate the impact on the RP calculation when application of outlier removal procedures are discounted.

Analytical procedure validation and the quality by design paradigm.

Since the adoption of the ICH Q8 document concerning the development of pharmaceutical processes following a quality by design (QbD) approach, there have been many discussions on the opportunity for analytical procedure developments to follow a similar approach. While development and optimization of analytical procedure following QbD principles have been largely discussed and described, the place of analytical procedure validation in this framework has not been clarified. This article aims at showing that analytical procedure validation is fully integrated into the QbD paradigm and is an essential step in developing analytical procedures that are effectively fit for purpose. Adequate statistical methodologies have also their role to play: such as design of experiments, statistical modeling, and probabilistic statements. The outcome of analytical procedure validation is also an analytical procedure design space, and from it, control strategy can be set.

Quality Controls in Ligand Binding Assays: Recommendations and Best Practices for Preparation, Qualification, Maintenance of Lot to Lot Consistency, and Prevention of Assay Drift.

Quality controls (QCs) are the primary indices of assay performance and an important tool in assay lifecycle management. Inclusion of QCs in the testing process allows for the detection of system errors and ongoing assessment of the reliability of the assay. Changes in the performance of QCs are indicative of changes in the assay behavior caused by unintended alterations to reagents or to the operating conditions. The focus of this publication is management of QC life cycle. A consensus view of the ligand binding assay (LBA) community on the best practices for factors that are critical to QC life cycle management including QC preparation, qualification, and trending is presented here.

A Novel Method for Qualification of a Potency Assay through Partial Computer Simulation.

For biotherapeutics and vaccines, potency is measured in a bioassay that compares the concentration-response curves of a new batch to that of a reference standard. Acceptable accuracy and precision of potency measurement is critical to the manufacturing of these products. These characteristics of a bioassay are typically assessed in a procedure that is carried out with samples spanning the acceptable range for the product. During early development, however, a full validation study such as that which is carried out in late development can be costly as it relates to the likelihood of eventual program success. For these reasons, the laboratory may look for alternative ways to ensure the validity of the bioassay across a range that will support product development. One such alternative combines information from a reduced procedure using only reference standard and 100% relative potency concentration-response data sets, together with computer simulation, to estimate missing relative potency values across the desired range. Fits to the reduced dataset provide estimates of bioassay model parameters such as those for an S-shaped potency assay that follows a four-parameter logistic relationship, along with estimates of their variance-covariance structure and independent experimental unit (e.g., well-to-well or animal-to-animal) errors. Using Bayesian Markov Chain Monte Carlo modeling, the predictive distribution of the concentration-response data for the desired levels of relative potency is generated. Results from use of the reduced procedure are compared to results calculated from a full dataset in Monte Carlo simulation and in a motivating example.LAY ABSTRACT: For biotherapeutics and vaccines, potency is measured in a bioassay that compares the concentration-response curves of a new batch to that of a reference standard. Acceptable accuracy and precision of potency measurement is critical to the manufacturing of these products. These characteristics of a bioassay are typically assessed in a procedure that is carried out with samples spanning the acceptable range for the product. During early development, however, a full validation study such as that which is carried out in late development can be costly as it relates to the likelihood of eventual program success. For these reasons, the laboratory may look for alternative ways to ensure the validity of the bioassay across a range that will support product development. One such alternative combines information from a reduced procedure using only reference standard and 100% relative potency concentration-response data sets, together with computer simulation, to estimate missing relative potency values across the desired range. Bayesian Markov Chain Monte Carlo modeling is used to generate the distributions of the missing potency levels. Results from use of the reduced procedure are compared to results calculated from a full dataset in Monte Carlo simulation and in a motivating example.

Evaluation of performance, acceptance, and compliance of an auto-injector in healthy and rheumatoid arthritic subjects measured by a motion capture system.

PURPOSE: The study aimed to develop a motion capture system that can track, visualize, and analyze the entire performance of self-injection with the auto-injector. METHODS: Each of nine healthy subjects and 29 rheumatoid arthritic (RA) patients with different degrees of hand disability performed two simulated injections into an injection pad while six degrees of freedom (DOF) motions of the auto-injector and the injection pad were captured. We quantitatively measured the performance of the injection by calculating needle displacement from the motion trajectories. The max, mean, and SD of needle displacement were analyzed. Assessments of device acceptance and usability were evaluated by a survey questionnaire and independent observations of compliance with the device instruction for use (IFU). RESULTS: A total of 80 simulated injections were performed. Our results showed a similar level of performance among all the subjects with slightly larger, but not statistically significant, needle displacement in the RA group. In particular, no significant effects regarding previous experience in self-injection, grip method, pain in hand, and Cochin score in the RA group were found to have an impact on the mean needle displacement. Moreover, the analysis of needle displacement for different durations of injections indicated that most of the subjects reached their personal maximum displacement in 15 seconds and remained steady or exhibited a small amount of increase from 15 to 60 seconds. Device acceptance was high for most of the questions (ie, >4; >80%) based on a 0-5-point scale or percentage of acceptance. The overall compliance with the device IFU was high for the first injection (96.05%) and reached 98.02% for the second injection. CONCLUSION: We demonstrated the feasibility of tracking the motions of injection to measure the performance of simulated self-injection. The comparisons of needle displacement showed that even RA patients with severe hand disability could properly perform self-injection with this auto-injector at a similar level with the healthy subjects. Finally, the observed high device acceptance and compliance with device IFU suggest that the system is convenient and easy to use.

Calibration Curves in Quantitative Ligand Binding Assays: Recommendations and Best Practices for Preparation, Design, and Editing of Calibration Curves.

The accuracy of reported sample results is contingent upon the quality of the assay calibration curve, and as such, calibration curves are critical components of ligand binding and other quantitative methods. Regulatory guidance and lead publications have defined many of the requirements for calibration curves which encompass design, acceptance criteria, and selection of a regression model. However, other important aspects such as preparation and editing guidelines have not been addressed by health authorities. The goal of this publication is to answer many of the commonly asked questions and to present a consensus and the shared views of members of the ligand binding assay (LBA) community on topics related to calibration curves with focus on providing recommendations for the preparation and editing of calibration curves.

Comment and Completion: Implementation of Parallelism Testing for Four-Parameter Logistic Model in Bioassays.

Parallelism testing between two four-parameter logistic curves has been widely discussed over the last decade. Current tests available in common statistical software used in laboratories have been shown to be highly flawed. In 2012, Yang et al. showed an easy way to implement an intersection union test based on confidence intervals on ratios of parameters of both curves. The method was automated using a fully good manufacturing practice-compliant software package. Although the rationale is correct and efficient, a small mistake appears in the computation of the confidence intervals in the paper and may lead to error when implementing the intersection union test in a software package. Because parallelism testing is both a prerequisite for the determination of relative potency of bioassays and a regulatory requirement, it is important to rectify this mistake. In this paper, we show the actual formulas to be used to compute confidence interval on ratios of parameters.