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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In vitro cell‐based data can be used to support the extension of pharmaceutical approval to patient subsets with unique genetic variants. A set of conditions should be satisfied to support the extension of approval. The disease mechanism should be well described, and the impact of variants on protein function should be reasonably understood. The incidence data should show that clinical trials for the variants in question are not practical. The overall safety and efficacy of the drug should be clear in adequate and well‐controlled clinical trials. The clinical trial should include patients found to be responders and nonresponders so that both positive and negative predictive power of the in vitro assay may be measured. The mechanism of action of the drug should be clearly defined and should be consistent with the disease mechanism. The assay system should be qualified, including the following points: (i) each variant construct should be confirmed by bidirectional sequencing; (ii) the in vitro assay should directly measure the variant protein function in comparison with the reference protein; (iii) the assay should be formally validated to the extent possible, clearly demonstrating precision, reproducibility, and sensitivity used to support the efficacy claim; and (iv) the primary data should be available for inspection and analytical validation. The overall goal is a robust and validated cell‐based system that can be shown to predict the outcome of targeted therapy. [ABSTRACT FROM AUTHOR]

To ensure US patients realize the public health benefit of a robust, competitive market for biosimilar products, the US Food and Drug Administration (FDA) is focused on improving the efficiency of biosimilar development and approvals. As noted above and outlined in FDA guidance documents, biosimilars may be approved based on PK and PD biomarker data without a comparative clinical study with efficacy end point(s).[2] Reliance on PK and PD biomarker data allows for shorter and less costly clinical studies than can often be conducted in healthy participants. Under circumstances where an established and sensitive PD biomarker does not exist, there is an opportunity to explore and identify new PD biomarkers to facilitate the use of PD biomarker data in clinical pharmacology studies. [Extracted from the article]