Search

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.

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 Global Bioanalysis Consortium (GBC) set up an international team to explore the impact of immunogenicity on pharmacokinetic (PK) assessments. The intent of this paper is to define the field and propose best practices when developing PK assays for biotherapeutics. We focus on the impact of anti-drug antibodies (ADA) on the performance of PK assay leading to the impact on the reported drug concentration and exposure. The manuscript describes strategies to assess whether the observed change in the drug concentration is due to the ADA impact on drug clearance rates or is a consequence of ADA interference in the bioanalytical method applied to measure drug concentration. This paper provides the bioanalytical scientist guidance for developing ADA-tolerant PK methods. It is essential that the data generated in the PK, ADA, pharmacodynamic and efficacy/toxicity evaluations are viewed together. Therefore, the extent for the investigation of the PK sensitivity to the presence of ADA should be driven by the project needs and risk based.

Over 400 professionals representing pharmaceutical companies, CROs, and multiple regulatory agencies participated in the 6th Workshop on Recent Issues in Bioanalysis (WRIB). Like the previous sessions, this event was in the format of a practical, focused, highly interactive and informative workshop aiming for high-quality, improved regulatory compliance and scientific excellence. Numerous ‘hot’ topics in bioanalysis of both small and large molecules were shared and discussed, leading to consensus and recommendations among panelists and attendees representing the bioanalytical community. The major outcome of this year’s workshop was the noticeable alignment of multiple bioanalytical guidance/guidelines from different regulatory agencies. This represents a concrete step forward in the global harmonization of bioanalytical activities. The present 2012 White Paper acts as a practical and useful reference document that provides key information and solutions on several topics and issues in the constantly evolving world of bioanalysis.

Application of research-grade diagnostic kits in clinical drug development has grown commensurate with the increased interest in utilization of biomarkers as drug development tools. Since novel biomarkers are frequently macromolecular, immunoassay methodology comprises the ‘technology-of-choice’ for biomarker quantification. In particular, commercial research-grade immunoassay kits are appealing for use in biomarker quantification during clinical phase drug development because of their ready availability, ease of operation and perceived convenience. However, bioanalytical validation issues arise often during the application of commercial kits, as GLP regulatory-compliant application places greater demands on kit design and performance. In this review, we have used the receptor activator of nuclear factor κB ligand (RANKL) as a model system to offer some insights into the challenges that can be encountered in the application of ‘research-grade’ diagnostic kits in support of clinical drug development. Currently only a few assays are available commercially for the determination of circulating concentrations of sRANKL. Of these, two immunoassay designs have been most often. The first design employs human osteoprotegerin to capture unbound sRANKL from serum and, thereby, provides a measure of circulating free concentrations. In contrast, the other common assay design first involves preincubation of serum samples with human osteoprotegerin to convert the free fraction of sRANKL to the osteoprotegerin-bound complex. The bound fraction is subsequently captured by an anti-osteoprotegerin antibody. In both immunoassay designs, detection is accomplished with an anti-sRANKL enzyme conjugation system. In this report we review these sRANKL immunoassay designs critically from the perspective of their potential suitability as drug development biomarker tools. In addition, analytical challenges relevant to the application of these ‘research-grade’ diagnostic kits for regulatory-compliant determination of sRANKL concentrations are discussed.

The Third AAPS/FDA Bioanalytical Workshop, entitled "Quantitative Bioanalytical Methods Validation and Implementation: Best Practices for Chromatographic and Ligand Binding Assays" was held on May 1-3, 2006 in Arlington, VA. The format of this workshop consisted of presentations on bioanalytical topics, followed by discussion sessions where these topics could be debated, with the goal of reaching consensus, or identifying subjects where addition input or clarification was required. The discussion also addressed bioanalytical validation requirements of regulatory agencies, with the purpose of clarifying expectations for regulatory submissions. The proceedings from each day were reviewed and summarized in the evening sessions among the speakers and moderators of the day. The consensus summary was presented back to the workshop on the last day and was further debated. This communication represents the distillate of the workshop proceedings and provides the summary of consensus reached and also contains the validation topics where no consensus was reached.

This White Paper is focused on the technical aspects regarding quantifying pharmaceutically derived inorganic elements in biomatrices in support of GLP nonclinical and clinical studies using inductively coupled plasma (ICP) techniques. For decades ICP has been used in support of Environmental Protection Agency analyses and has more recently been applied for use in the pharmaceutical industry. Current bioanalytical method validation and sample analysis regulatory guidance applies to chromatographic platforms used for analysis of large- and small-molecule PK and TK assessments; however, it is not directly applicable to all aspects of various ICP techniques. Increasingly, quadrupole and high-resolution ICP–MS methods of analysis are being used to quantify inorganic elements contained in pharmaceutical compounds and biomatrices. Many elements occur endogenously in biomatrices, affecting quantification of blanks, standard curve samples, QC samples, and the selection of appropriate levels for the LLOQ.

This chapter explains how immunoassays and other ligand-binding assays are applied during the different stages of drug identification and development in the pharmaceutical industry. The importance of the need to change paradigms to address the requirements of biologic development is stressed and the trends reflecting that need are described. Methods used in high-throughput screening to identify target compounds are reviewed. Lead optimization, the next phase, is described in detail and examples of how specific biomarkers benefited from thorough characterization during this phase are provided. The use of safety biomarkers and the subject of assay validation are described. The different stages of clinical studies are explained including how immunoassays are used to define and understand pharmacokinetics and pharmacodynamics. The implications for laboratories that provide the tests are explained, particularly the differences from routine diagnostic testing.

“Good people are good because they’ve come to wisdom through failure” – William Saroyan. Since the Crystal City III conference considerable effort has been spent defining what constitutes appropriate incurred sample reanalysis analysis. While what to do, such as the number of samples and acceptance criteria, is becoming generally recognized, it is important to step back and examine as a community what we are learning by this exercise. Through discussions with colleagues throughout the industry who specialize in macromolecule analysis, it was clear most incurred sample reanalysis testing passes without issue, but in some cases there are lessons to be learned. We thank all who would share, in confidence, their failures as well as their investigations so as a community we can learn. We would suggest scientist can be substituted for people in the quote from William Saroyan.

I NTRODUCTION Bioanalysis, employed for the quantitative determination of drugs and their metabolites in biological fl uids, plays a signifi cant role in the evaluation and interpretation of bioequivalence, pharmacokinetic (PK), and toxicokinetic studies. The quality of these studies, which are often used to support regulatory fi lings, is directly related to the quality of the underlying bioanalytical data. It is therefore important that guiding principles for the validation of these analytical methods be established and disseminated to the pharmaceutical community. The fi rst American Association of Pharmaceutical Scientists (AAPS)/Food and Drug Administration (FDA) Bioanalytical Workshop in 1990 focused on key issues relevant to bioanalytical methodology and provided a platform for scientifi c discussions and deliberations. The workshop and the report 1 raised awareness of the need for validated bioanalytical methods for the regulatory acceptance of bioequivalence and pharmacokinetic data. Although the workshop addressed bioanalysis in general, it acknowledged the differences between chromatographic and ligand binding (nonchromatographic based) methods. The workshop identifi ed the essential parameters for bioanalytical method validation, ie, accuracy, precision, selectivity, sensitivity, reproducibility, limit of detection, and stability. The outcome of the fi rst workshop and its report resulted in improved quality of data submissions to regulatory agencies. Following the fi rst workshop report 1 and the experience

Sailstad & Associates, Inc., 6 Porchlight Court, Durham, NC 27707, USA Email: sailstad@aol.com Most bioanalysts state that they are interested in applying statistical tests to help identify the correct fitting algorithm for their standard curve data, but indicate that they do not have the appropriate tools to make such assessments. Initially, when asked to review such articles I was skeptical of the author’s intention. Was this merely an attempt to help with the marketing of a commercial product? Upon a closer review it becomes clear the author’s intention is to serve the bioanalytical community with a process that will be accessible for both evaluation and modification of its utility. John Burrows, who is retired, is providing a good starting point for a means to statistically assess and then assign linear standard curve fitting algorithms. The software uses Excel to tabulate the numbers used in the computations and should allow nonstatisticians the opportunity to test drive the proposed approach. As a bioanalyst, I have felt comfortable with the assignment of the chosen fitting algorithm after reviewing the residual plots for the standard curve data over a limited number of validation runs. Yet, I wondered, if I had more data would that change my opinion? As a community using the approach provided in these articles we can assess if such modeling improves our standard approaches. By using this tool and improving upon its capabilities to include nonlinear curve fitting algorithms, the bioanalytical community can assess if this adds value. Pending a favorable review by the bioanalytical community, such a process might be justified to be included in the various laboratory information systems that are now the cornerstones of data analysis in bioanalytical laboratories.