title: Clinical dose-response for a broad set of biological products: A model-based meta-analysis.
abstract: Characterizing clinical dose-response is a critical step in drug development. Uncertainty in the dose-response model when planning a dose-ranging study can often undermine efficiency in both the design and analysis of the trial. Results of a previous meta-analysis on a portfolio of small molecule compounds from a large pharmaceutical company demonstrated a consistent dose-response relationship that was well described by the maximal effect model. Biologics are different from small molecules due to their large molecular sizes and their potential to induce immunogenicity. A model-based meta-analysis was conducted on the clinical efficacy of 71 distinct biologics evaluated in 91 placebo-controlled dose-response studies published between 1995 and 2014. The maximal effect model, arising from receptor occupancy theory, described the clinical dose-response data for the majority of the biologics (81.7%, n = 58). Five biologics (7%) with data showing non-monotonic trend assuming the maximal effect model were identified and discussed. A Bayesian model-based hierarchical approach using different joint specifications of prior densities for the maximal effect model parameters was used to meta-analyze the whole set of biologics excluding these five biologics ( n = 66). Posterior predictive distributions of the maximal effect model parameters were reported and they could be used to aid the design of future dose-ranging studies. Compared to the meta-analysis of small molecules, the combination of fewer doses, narrower dosing ranges, and small sample sizes further limited the information available to estimate clinical dose-response among biologics.
authors.affiliation: 1 Biometrics and Data Management, Global Product Development, Groton, CT, USA.
authors.forename: Joseph
authors.initials: J
authors.lastname: Wu
authors.affiliation: 2 Early Clinical Development, Worldwide Research & Development, Cambridge, MA, USA.
authors.forename: Anindita
authors.initials: A
authors.lastname: Banerjee
authors.affiliation: 2 Early Clinical Development, Worldwide Research & Development, Cambridge, MA, USA.
authors.forename: Bo
authors.initials: B
authors.lastname: Jin
authors.affiliation: 3 Statistical Research Consulting Center, Global Product Development, Cambridge, MA, USA.
authors.forename: Sandeep M
authors.initials: SM
authors.lastname: Menon
authors.affiliation: 4 Pharmacometrics, Global Product Development, Cambridge, MA, USA.
authors.forename: Steven W
authors.initials: SW
authors.lastname: Martin
authors.affiliation: 2 Early Clinical Development, Worldwide Research & Development, Cambridge, MA, USA.
authors.forename: Anne C
authors.initials: AC
authors.lastname: Heatherington
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001685:Biological Factors / Q000008:administration & dosage*; D004305:Dose-Response Relationship, Drug; D054796:Drug Dosage Calculations; D006801:Humans; D015233:Models, Statistical
chemical_list: D001685:Biological Factors
keywords: Bayesian hierarchical model; Emax model; Model-based meta-analysis; biologics; dose–response