Current Perspectives on Ligand-Binding Assay Practices in the Quantification of Circulating Therapeutic Proteins for Biosimilar Biological Product Development

Bioanalysis in biosimilar biological product development (BPD) plays a critical role in demonstrating pharmacokinetic (PK) similarity across products. The 2018 FDA Bioanalytical Method Validation guidance for industry provides general principles in the development, validation, and conduct of bioanalytical assays. Given that the PK similarity assessment in BPD programs involves two or more non-identical products, there are additional considerations for bioanalytical methods. Here in, we provide our perspectives on the definition of (1) a single bioanalytical method in the context of BPD in supporting a PK similarity study, (2) bioanalytical method comparability during accuracy and precision experiments to determine the potential bias difference prior to assessing other validation parameters, and (3) bioanalytical method validations that support PK similarity assessments.

Retrospective Analysis of Bioanalytical Method Validation Approaches in Biosimilar Biological Product Development

Development and validation of a bioanalytical method for biosimilar biological product development (BPD) can be challenging. It requires the development of a bioanalytical method that reliably and accurately measures both proposed biosimilar and reference products in a biological matrix. This survey summarizes the current state of bioanalysis in BPD. Bioanalytical data from 28 biosimilar biologic license applications submitted to U.S. Food and Drug Administration (FDA) up to December 2018 were analyzed. The aim of the analysis was to provide (i) a summary of the bioanalytical landscape for BPD, (ii) a cumulative review of bioanalytical method validation approaches to aid in understanding how a specific method was selected, and (iii) a summary of data regarding bioanalytical bias differences between products. Results show diversity of the bioanalytical approaches used, as well as the observed differences in bioanalytical bias. Our findings highlight the need for understanding the critical aspects of BPD bioanalysis and clarifying BPD bioanalytical best practices, which could help ensure consistent method validation approaches in the BPD community.

Systematic and quantitative assessment of the effect of chronic kidney disease on CYP2D6 and CYP3A4/5

Recent reviews suggest that chronic kidney disease (CKD) can affect the pharmacokinetics of nonrenally eliminated drugs, but the impact of CKD on individual elimination pathways has not been systematically evaluated. In this study we developed a comprehensive dataset of the effect of CKD on the pharmacokinetics of CYP2D6‐ and CYP3A4/5‐metabolized drugs. Drugs for evaluation were selected based on clinical drug–drug interaction (CYP3A4/5 and CYP2D6) and pharmacogenetic (CYP2D6) studies. Information from dedicated CKD studies was available for 13 and 18 of the CYP2D6 and CYP3A4/5 model drugs, respectively. Analysis of these data suggested that CYP2D6‐mediated clearance is generally decreased in parallel with the severity of CKD. There was no apparent relationship between the severity of CKD and CYP3A4/5‐mediated clearance. The observed elimination‐route dependency in CKD effects between CYP2D6 and CYP3A4/5 may inform the need to conduct clinical CKD studies with nonrenally eliminated drugs for optimal use of drugs in patients with CKD.

Biomarker Qualification: Toward a Multiple Stakeholder Framework for Biomarker Development, Regulatory Acceptance, and Utilization

The discovery, development, and use of biomarkers for a variety of drug development purposes are areas of tremendous interest and need. Biomarkers can become accepted for use through submission of biomarker data during the drug approval process. Another emerging pathway for acceptance of biomarkers is via the biomarker qualification program developed by the Center for Drug Evaluation and Research (CDER, US Food and Drug Administration). Evidentiary standards are needed to develop and evaluate various types of biomarkers for their intended use and multiple stakeholders, including academia, industry, government, and consortia must work together to help develop this evidence. The article describes various types of biomarkers that can be useful in drug development and evidentiary considerations that are important for qualification. A path forward for coordinating efforts to identify and explore needed biomarkers is proposed for consideration.

Physiologically based pharmacokinetic modeling: from regulatory science to regulatory policy

Assessment of controllable sources of intra- and interpatient variability in drug response is of critical importance in the regulatory evaluation of new drugs.(1) Although determinants of response variability would ideally be understood and accounted for before approval of a new pharmaceutical product, this is rarely the case for all; clinical trials in specific populations that definitively test optimal dosing in patient management strategies are not routinely performed prior to drug approval.

PBPK model describes the effects of comedication and genetic polymorphism on systemic exposure of drugs that undergo multiple clearance pathways

An important goal in drug development is to understand the effects of intrinsic and/or extrinsic factors (IEFs) on drug pharmacokinetics. Although clinical studies investigating a given IEF can accomplish this goal, they may not be feasible for all IEFs or for situations when multiple IEFs exist concurrently. Physiologically based pharmacokinetic (PBPK) models may serve as a complementary tool for forecasting the effects of IEFs. We developed PBPK models for four drugs that are eliminated by both cytochrome P450 (CYP)3A4 and CYP2D6, and evaluated model prediction of the effects of comedications and/or genetic polymorphism on drug exposure. PBPK models predicted 100 and ≥70% of the observed results when the conventional "twofold rule" and the more conservative 25% deviation cut point were applied, respectively. These findings suggest that PBPK models can be used to infer effects of individual or combined IEFs and should be considered to optimize studies that evaluate these factors, specifically drug interactions and genetic polymorphism of drug-metabolizing enzymes.

The pharmacogenetics research network: from SNP discovery to clinical drug response

The NIH Pharmacogenetics Research Network (PGRN) is a collaborative group of investigators with a wide range of research interests, but all attempting to correlate drug response with genetic variation. Several research groups concentrate on drugs used to treat specific medical disorders (asthma, depression, cardiovascular disease, addiction of nicotine, and cancer), whereas others are focused on specific groups of proteins that interact with drugs (membrane transporters and phase II drug-metabolizing enzymes). The diverse scientific information is stored and annotated in a publicly accessible knowledge base, the Pharmacogenetics and Pharmacogenomics Knowledge base (PharmGKB). This report highlights selected achievements and scientific approaches as well as hypotheses about future directions of each of the groups within the PGRN. Seven major topics are included: informatics (PharmGKB), cardiovascular, pulmonary, addiction, cancer, transport, and metabolism.

Cytochrome P4502D6 (CYP2D6) Gene Locus Heterogeneity: Characterization of Gene Duplication Events

Duplications and multiplications of active CYP2D6 genes can cause ultrarapid drug metabolism and lead to therapeutic failure. Multiple functional and non-functional duplication alleles have been further characterized. Duplications were detected by long-range polymerase chain reaction (PCR), PCR-restriction fragment length polymorphism, and sequence analysis. A PCR fragment encompassing the entire duplicated gene was utilized for detailed characterization. Duplications occurred at 1.3, 5.75, and 2.0% in Caucasian, African American, and racially mixed populations, respectively (n=887 total). Of those 28, 47, and 17% were non-functional CYP2D6*4 x N. Twelve unique duplication alleles were detected: *1 x N, *2 x N, *4 x N, *6 x N, *10 x N, *17 x N, *17 x N[spacer], *29 x N, *35 x N, *43 x N, *45 x N, and a novel non-functional tandem arrangement of a chimeric 2D7/2D6 and *1 gene. All novel duplications except *35 x N were found in African Americans. Accurate identification of gene duplication events is essential to avoid false-positive ultrarapid metabolism assignments and thus, overestimation of predicted activity and increased risk for unwanted adverse events.

Influence of phenotype and pharmacokinetics on beta-blocker drug target pharmacogenetics

Two common polymorphisms in the beta1-adrenergic receptor gene, Ser49Gly and Arg389Gly, are associated with variable antihypertensive response to metoprolol. We sought to determine whether similar pharmacogenetic associations were present with the negative chronotropic response phenotype to metoprolol. Metoprolol was titrated in 54 untreated hypertensive patients to achieve blood pressure control. We found no association between either resting or exercise heart rate at baseline (untreated) or in response to metoprolol by codon 389 genotype. In contrast, when compared by codon 49 genotype, Ser49 homozygotes had significantly higher resting heart rates at baseline (untreated) than Gly49 carriers (82+/-10 versus 74+/-11 bpm, respectively, P=0.016). When corrected for plasma concentration, we found no difference in reduction in exercise heart rate in response to metoprolol between Ser49 homozygotes and Gly49 carriers (0.75+/-0.11 versus 0.57+/-0.17%/ng/ml, respectively, P=0.37). However, if one fails to account for plasma concentration, trends toward a significant difference in heart rate reduction are seen between Ser49 homozygotes and Gly49 carriers (31% reduction versus 25% reduction, P=0.05). Our data suggest that neither the beta1-adrenergic receptor Arg389Gly, nor the Ser49Gly polymorphisms are associated with variable negative chronotropic response to metoprolol. In addition, our data highlight the importance of measuring metoprolol concentration in order to account for variable pharmacokinetics and avoid misinterpretation of the data.