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Current cardiac safety testing focuses on detecting drug-induced QT C prolongation as a surrogate for risk of Torsade de Pointes. The nonclinical strategy, described in International Conference on Harmonization (ICH) S7B, includes in vitro assessment of hERG block or ventricular repolarization delay and in vivo QT prolongation. Several studies have reported predictive values of ICH S7B results for clinical QT C outcomes for small molecules; none has examined peptides and proteins other than monoclonal antibodies. To address this knowledge gap, information for peptides and proteins submitted to the US Food and Drug Administration (FDA) was collected. Results of hERG assays, ventricular repolarization assays, and in vivo QT assessment were compared with clinical QT C study outcomes. The results show that 14% clinical QT C studies for approved and investigational products failed to exclude 10-ms QT C prolongation. Clinical QT C prolongation for these molecules lacked concentration-dependence which is expected for hERG block-mediated mechanism or QT C prolongation could not be excluded due to characterization in the clinical study. The hERG and ventricular repolarization assays do not identify clinical QT C prolongation potential for peptides and proteins. Lack of alignment between hERG and ventricular repolarization assay results and clinical QT C outcomes suggests that the mechanisms of QT C prolongation by some peptides and proteins are unrelated to direct cardiac ion channel block. Similar to large targeted proteins and monoclonal antibodies, peptides and proteins regardless of size have a low likelihood of direct cardiac ion channel interactions. This characteristic supports waiving the requirement for thorough QT assessment for products comprised of naturally occurring amino acids unless proarrhythmia potential is suggested by nonclinical or clinical data., (Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

The US Food and Drug Administration (FDA) Biosimilars Guidance describes how biosimilars may be approved based on clinical pharmacokinetic and pharmacodynamic (PD) biomarker data, without comparative clinical studies with efficacy end points. This type of clinical development program, however, has only been implemented for a small number of FDA-approved biosimilar products over the last decade. To encourage the use of PD biomarkers in biosimilar development and approval, the Duke-Margolis Center for Health Policy collaborated with the FDA to host a two-day virtual public workshop entitled "Pharmacodynamic Biomarkers for Biosimilar Development and Approval" on September 20-21, 2021. The public workshop was a forum for global regulators, biopharmaceutical developers, and academic researchers to discuss the current and future role of PD biomarkers in improving the efficiency of biosimilar development and approval. The workshop objectives included: (i) discuss the current and potential future state of leveraging PD biomarkers for biosimilar development and approval; (ii) summarize the FDA's initiatives to advance biosimilar development; (iii) describe stakeholders' experience with PD biomarkers in biosimilar development; and (iv) explain research efforts to promote broader application of PD biomarkers in biosimilar development. This document summarizes presentations and panel discussions from each session of the two-day September 2021 public workshop covering the application of PD biomarkers for biosimilar development., (© 2022 The Authors. Clinical Pharmacology & Therapeutics © 2022 American Society for Clinical Pharmacology and Therapeutics. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

The US Food and Drug Administration (FDA) has taken steps to bring efficiency to the development of biosimilars, including establishing guidance for the use of pharmacokinetic and pharmacodynamic (PD) similarity study data without a comparative clinical study with efficacy end point(s). To better understand the potential role for PD biomarkers in biosimilar development and inform best practices for biomarker selection and analysis, we conducted a randomized, double-blinded, placebo-controlled, single-dose, parallel-arm clinical study in healthy participants. Eighty-four healthy participants (n = 12 per dose arm) received either placebo or one of three doses of either interferon β-1a (7.5-30 μg) or pegylated interferon β-1a (31.25-125 μg) to evaluate the maximum change from baseline and the baseline-adjusted area under the effect curve for the biomarkers neopterin in serum and myxovirus resistance protein 1 in blood. Both PD biomarkers increased following product administration with clear separation from baseline (neopterin: 3.4-fold and 3.9-fold increase for interferon β-1a and pegylated interferon β-1a, respectively; myxovirus resistance protein 1: 19.0-fold and 47.2-fold increase for interferon β-1a and pegylated interferon β-1a, respectively). The dose-response curves support that therapeutic doses were adequately sensitive to detect differences in both PD biomarkers for consideration in a PD similarity study design. Because baseline levels of both biomarkers are low compared with on-treatment values, there was little difference in using PD measures adjusted to baseline compared with the results without baseline adjustment. This study illustrates potential methodologies for evaluating PD biomarkers and an approach to address information gaps when limited information is publicly available for one or more PD biomarkers., (Published 2022. This article is a U.S. Government work and is in the public domain in the USA.)

A biosimilar is a biological product that is highly similar to and has no clinically meaningful differences from a US Food and Drug Administration (FDA)-approved reference product. The development and approval of biosimilars is critical to enhancing the availability of safe, effective, and affordable treatment options for patients. Utilization of pharmacodynamic (PD) biomarkers can help streamline biosimilar development programs as the current process can be costly and time-consuming. Whereas PD biomarkers have not been prominently used across biosimilar approvals to date, moving forward, there is ample opportunity to increase the use of PD biomarkers in biosimilar development programs in place of comparative clinical studies with efficacy end point(s). This includes utilizing PD biomarkers that were not used as surrogate end points in approval of reference products. This mini-review summarizes how PD biomarkers have been used in biosimilar development programs to date and then discusses evidentiary considerations for PD biomarkers. In addition, study design considerations for clinical pharmacokinetic and PD assessment of proposed biosimilars are discussed. Finally, the FDA's applied regulatory science activities related to PD biomarkers for biosimilars conducted in support of the FDA's Biosimilars Action Plan are reviewed. This included conducting three clinical studies to address information gaps about PD biomarkers for biosimilars and inform general methodological best practices. In summary, enhancing our understanding of key evidentiary considerations and optimal study designs for incorporating PD biomarkers in the evaluation of proposed biosimilars can help bring more treatment options to patients faster., (Published 2022. This article is a U.S. Government work and is in the public domain in the USA. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Proteomics has the potential to identify pharmacodynamic (PD) biomarkers for similarity assessment of proposed biosimilars without relying on clinical efficacy end points. In this study, with 36 healthy participants randomized to therapeutic doses of interferon-beta 1a products (IFNβ-1a) or pegylated-IFNβ-1a (pegIFNβ-1a) approved to treat multiple sclerosis or placebo, we evaluated the utility of a proteomic assay that profiles > 7,000 plasma proteins. IFNβ-1a and pegIFNβ-1a resulted in 248 and 528 differentially expressed protein analytes, respectively, between treatment and placebo groups over the time course. Thirty-one proteins were prioritized based on a maximal fold change ≥ 2 from baseline, baseline adjusted area under the effect curve (AUEC) and overlap between the 2 products. Of these, the majority had a significant AUEC compared with placebo in response to either product; 8 proteins showed > 4-fold maximal change from baseline. We identified previously reported candidates, beta-2microglobulin and interferon-induced GTP-binding protein (Mx1) with ~ 50% coefficient of variation (CV) for AUEC, and many new candidates (including I-TAC, C1QC, and IP-10) with CVs ranging from 26%-129%. Upstream regulator analysis of differentially expressed proteins predicted activation of IFNβ1 signaling as well as other cytokine, enzyme, and transcription signaling networks by both products. Although independent replication is required to confirm present results, our study demonstrates the utility of proteomics for the identification of individual and composite candidate PD biomarkers that may be leveraged to support clinical pharmacology studies for biosimilar approvals, especially when biologics have complex mechanisms of action or do not have previously characterized PD biomarkers., (Published 2022. This article is a U.S. Government work and is in the public domain in the USA.)

US Food and Drug Administration (FDA) guidance outlines how biosimilars can be developed based on pharmacokinetic (PK) and pharmacodynamic (PD) similarity study data in lieu of a comparative clinical efficacy study. There is a paucity of PD comparability studies in biosimilar development, leaving open questions about how best to plan these studies. To that end, we conducted a randomized, double-blinded, placebo-controlled, single-dose, parallel-arm clinical study in healthy participants to evaluate approaches to address information gaps, inform analysis best practices, and apply emerging technologies in biomarker characterization. Seventy-two healthy participants (n = 8 per arm) received either placebo or one of four doses of the proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors alirocumab (15-100 mg) or evolocumab (21-140 mg) to evaluate the maximum change from baseline (ΔPD max ) and the baseline-adjusted area under the effect curve (AUEC) for the biomarkers low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (apoB) in serum. We investigated approaches to minimize variability in PD measures. Coefficient of variation was lower for LDL-C than apoB at therapeutic doses. Modeling and simulation were used to establish the dose-response relationship and provided support that therapeutic doses for these products are adequately sensitive and are on the steep part of the dose-response curves. Similar dose-response relationships were observed for both biomarkers. ΔPD max plateaued at lower doses than AUEC. In summary, this study illustrates how pilot study data can be leveraged to inform appropriate dosing and data analyses for a PK and PD similarity study., (Published 2022. This article is a U.S. Government work and is in the public domain in the USA.)

The US Food and Drug Administration (FDA) guidance describes how pharmacodynamic (PD) biomarkers can be used to address residual uncertainty and demonstrate no clinically meaningful differences between a proposed biosimilar and its reference product without relying on clinical efficacy end point(s). Pilot studies and modeling can inform dosing for such PD studies. To that end, we conducted a randomized, double-blinded, placebo-controlled, single-dose, parallel-arm clinical study in healthy participants to evaluate approaches to address information gaps, inform best practices for analysis of biomarker samples and study results, and apply emerging technologies in biomarker characterization. Seventy-two healthy participants (n = 8 per arm) received either placebo or 1 of 4 doses of the interleukin-5 inhibitors mepolizumab (3-24 mg) or reslizumab (0.1-0.8 mg/kg). A clinical study using doses lower than approved therapeutic doses was combined with modeling and simulation to evaluate the dose-response relationship of the biomarker eosinophils. There was no dose-response relationship for eosinophil counts due to variability, although the mepolizumab 24 mg and reslizumab 0.8 mg/kg doses showed clear effects. Published indirect-response models were used to explore eosinophil data across doses from this study and the unstudied therapeutic doses. Simulations were used to calculate typical PD metrics, such as baseline-adjusted area under the effect curve and maximum change from baseline. The simulation results demonstrate sensitivity of eosinophils as a PD biomarker and indicate doses lower than the approved doses would have PD responses overlapping with variability in the placebo arm. The simulation results further highlight the utility of model-based approaches in supporting use of PD biomarkers in biosimilar development., (Published 2022. This article is a U.S. Government work and is in the public domain in the USA.)

We improved a previous pharmacological target adverse-event (TAE) profile model to predict adverse events (AEs) on US Food and Drug Administration (FDA) drug labels at the time of approval. The new model uses more drugs and features for learning as well as a new algorithm. Comparator drugs sharing similar target activities to a drug of interest were evaluated by aggregating AEs from the FDA Adverse Event Reporting System (FAERS), FDA drug labels, and medical literature. An ensemble machine learning model was used to evaluate FAERS case count, disproportionality scores, percent of comparator drug labels with a specific AE, and percent of comparator drugs with the reports of the event in the literature. Overall classifier performance was F1 of 0.71, area under the precision-recall curve of 0.78, and area under the receiver operating characteristic curve of 0.87. TAE analysis continues to show promise as a method to predict adverse events at the time of approval., (© 2020 Molecular Health GMBH. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/US Food and Drug Administration-sponsored Think Tank Meeting on May 22, 2018. The meeting was convened to evaluate the progress in the development of a new cardiac safety paradigm, the Comprehensive in Vitro Proarrhythmia Assay (CiPA). The opinions regarding these principles reflect the collective views of those who participated in the discussion of this topic both at and after the breakout session. Although primarily discussed in the context of in silico models, these principles describe the interface between experimental input and model-based interpretation and are intended to be general enough to be applied to other types of nonclinical models for proarrhythmia assessment. This document was developed with the intention of providing a foundation for more consistency and harmonization in developing and validating different models for proarrhythmia risk prediction using the example of the CiPA paradigm., (© 2019 The Authors Clinical Pharmacology & Therapeutics published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.)