A Randomized, Double-Blind Study Comparing Pharmacokinetics and Pharmacodynamics of Proposed Biosimilar ABP 798 With Rituximab Reference Product in Subjects With Moderate to Severe Rheumatoid Arthritis.

ABP 798 is a proposed biosimilar to rituximab reference product (RP), an anti-CD20 monoclonal antibody. Pharmacokinetics (PK), pharmacodynamics (PD), and safety results from the comparative clinical study that evaluated the PK, PD, safety, efficacy, and immunogenicity of ABP 798 versus rituximab RP are presented here. Subjects with moderate to severe rheumatoid arthritis (RA) received 2 doses of ABP 798, United States-sourced RP (rituximab US) or European Union-sourced RP (rituximab EU), each consisting of two 1000-mg infusions 2 weeks apart. For the second dose (week 24), ABP 798- and rituximab EU-treated subjects received the same treatment; rituximab US-treated subjects transitioned to ABP 798. End points included area under the serum concentration-time curve from time 0 extrapolated to infinity and maximum observed serum concentration following the second infusion of the first dose (PK) and percentage of subjects with complete CD19+ cell depletion days 1-33 (PD). Primary analysis established PK similarity between ABP 798 and rituximab RP based on 90% confidence intervals of the adjusted geometric mean ratios being within a prespecified equivalence margin of 0.8 and 1.25. Complete CD19+ B-cell depletion on day 3 among groups confirmed PD similarity. These findings demonstrated PK/PD similarity between ABP 798 and rituximab RP in subjects with moderate to severe RA.

A randomized, double-blind, single-dose, three-arm, parallel group study to determine pharmacokinetic similarity of ABP 959 and eculizumab (Soliris® ) in healthy male subjects.

OBJECTIVES: ABP 959 is a proposed biosimilar to eculizumab, a monoclonal antibody targeting the human C5 complement protein. The objective of this randomized, double-blind, three-arm, study was to demonstrate pharmacokinetic (PK) and pharmacodynamic (PD) similarity of ABP 959 relative to the eculizumab reference product (RP) in healthy adult male subjects. METHODS: Eligible subjects aged 18-45 years were randomized to receive a 300-mg IV infusion of ABP 959, or FDA-licensed eculizumab (eculizumab US), or EU-authorized eculizumab (eculizumab EU). Primary PK endpoint was area under the total serum concentration-time curve from 0 to infinity (AUC0-∞ ); primary PD endpoint was area between the effect curve (ABEC) of CH50-time data. RESULTS: The geometric mean of PK and PD parameters were similar between ABP 959 versus eculizumab US and eculizumab EU; PK and PD similarity was established based on 90% confidence intervals of the geometric mean ratio being within prespecified equivalence margin of 0.8 and 1.25. The incidence of treatment-emergent adverse events was similar across groups. The incidence of binding anti-drug antibodies was similar across treatments; no subjects developed neutralizing antibodies. CONCLUSIONS: This study demonstrated PK and PD similarity of ABP 959 to eculizumab RP; safety and immunogenicity profiles were also similar.

Bayesian design of biosimilars clinical programs involving multiple therapeutic indications.

In this paper, we propose a Bayesian design framework for a biosimilars clinical program that entails conducting concurrent trials in multiple therapeutic indications to establish equivalent efficacy for a proposed biologic compared to a reference biologic in each indication to support approval of the proposed biologic as a biosimilar. Our method facilitates information borrowing across indications through the use of a multivariate normal correlated parameter prior (CPP), which is constructed from easily interpretable hyperparameters that represent direct statements about the equivalence hypotheses to be tested. The CPP accommodates different endpoints and data types across indications (eg, binary and continuous) and can, therefore, be used in a wide context of models without having to modify the data (eg, rescaling) to provide reasonable information-borrowing properties. We illustrate how one can evaluate the design using Bayesian versions of the type I error rate and power with the objective of determining the sample size required for each indication such that the design has high power to demonstrate equivalent efficacy in each indication, reasonably high power to demonstrate equivalent efficacy simultaneously in all indications (ie, globally), and reasonable type I error control from a Bayesian perspective. We illustrate the method with several examples, including designing biosimilars trials for follicular lymphoma and rheumatoid arthritis using binary and continuous endpoints, respectively.

Assessing temporal agreement between central and local progression-free survival times.

In oncology clinical trials, progression-free survival (PFS), generally defined as the time from randomization until disease progression or death, has been a key endpoint to support licensing approval. In the U.S. Food and Drug Administration guidance for industry, May 2007, concerning the PFS as the primary or co-primary clinical trial endpoint, it is recommended to have tumor assessments verified by an independent review committee blinded to study treatments, especially in open-label studies. It is considered reassuring about the lack of reader-evaluation bias if treatment effect estimates from the investigators' and independent review committees' evaluations agree. The agreement between these evaluations may vary for subjects with short or long PFS, while there exist no such statistical quantities that can completely account for this temporal pattern of agreements. Therefore, in this paper, we propose a new method to assess temporal agreement between two time-to-event endpoints, while the two event times are assumed to have a positive probability of being identical. This method measures agreement in terms of the two event times being identical at a given time or both being greater than a given time. Overall scores of agreement over a period of time are also proposed. We propose a maximum likelihood estimation to infer the proposed agreement measures using empirical data, accounting for different censoring mechanisms, including reader's censoring (event from one reader dependently censored by event from the other reader). The proposed method is demonstrated to perform well in small samples via extensive simulation studies and is illustrated through a head and neck cancer trial.