Abstract.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.
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.
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. Electronic supplementary material The online version of this article (10.1208/s12248-019-0354-6) contains supplementary material, which is available to authorized users.
PurposeThe study aimed to develop a motion capture system that can track, visualize, and analyze the entire performance of self-injection with the auto-injector.MethodsEach 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).ResultsA 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.ConclusionWe 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.
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 procedure that are effectively fit for purpose.Adequate statistical methodologies have also their role to play: such as design of experiments, statistical modelling and probabilistic statements. The outcome of analytical procedure validation is also an analytical procedure Design Space and from it, control strategy can be set.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.