Bioanalytical strategy used in development of pharmacokinetic (PK) methods that support biosimilar programs.

The development of biosimilar products is expected to grow rapidly over the next five years as a large number of approved biologics reach patent expiry. The pathway to regulatory approval requires that similarity of the biosimilar to the reference product be demonstrated through physiochemical and structural characterization, as well as within in vivo studies that compare the safety and efficacy profiles of the products. To support nonclinical and clinical studies pharmacokinetic (PK) assays are required to measure the biosimilar and reference products with comparable precision and accuracy. The most optimal approach is to develop a single PK assay, using a single analytical standard, for quantitative measurement of the biosimilar and reference products in serum matrix. Use of a single PK assay for quantification of multiple products requires a scientifically sound testing strategy to evaluate bioanalytical comparability of the test products within the method, and provide a solid data package to support the conclusions. To meet these objectives, a comprehensive approach with scientific rigor was applied to the development and characterization of PK assays that are used in support of biosimilar programs. Herein we describe the bioanalytical strategy and testing paradigm that has been used across several programs to determine bioanalytical comparability of the biosimilar and reference products. Data from one program is presented, with statistical results demonstrating the biosimilar and reference products were bioanalytically equivalent within the method. The cumulative work has established a framework for future biosimilar PK assay development.

Method transfer for ligand-binding assays: recommendations for best practice.

To support clinical trials, bioanalytical methods are often transferred from one laboratory to another. With the rising number of large-molecule therapeutic proteins submitted for US FDA approval, the demand for large-molecule bioanalytical support and, subsequently, method transfer increases. Ligand-binding assays are the methods most commonly used to quantify endogenous and therapeutic proteins for the assessment of biomarkers and pharmacokinetic parameters. The goal of this review is to provide an overview of ligand-binding assay method transfer, essential parameters for partial method validation and to lay out a strategy to increase the chance of success. The recommendations herein are based on a summary of current publications and the authors' specific experiences, to help increase workload efficiency, maintain positive collaborations with partners and meet program timelines.

A strategy for improving comparability across sites for ligand binding assays measuring therapeutic proteins.

Outsourcing and multi-site testing has increased for ligand binding assays supporting protein therapeutic measurement. It is common to combine and compare data across studies with data from multiple bioanalytical sites. We designed a prospective study to determine the benefits of increasing control over the transfer process to improve ruggedness. The experiment involved the testing of 30 incurred samples at 3 stages with incremental laboratory harmonization in standard/quality controls and assay components: Stage I represented a transfer of a detailed protocol and critical reagents. Stage II, a single source of standards and quality controls were provided to each site. Stage III, standards and quality controls plus a ready-to-use kit were provided. The results indicated that all testing facilities failed agreement testing using the stage I procedure. The introduction of standards from a single source improved the agreement. The modification reduced variation by 33% compared to the stage I approach. There was no additional benefit when a packaged kit was provided. In conclusion, introduction of a single source of standards and quality controls reduced the inter-site component of variation and should allow for combinability of data.