Biomarker Signatures for Patient Subgroup Selection in Clinical Drug Development

• Activity Number: 72
• Type: Contributed
• Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Xin Huang*+ and Paul Trow and Yan Sun and Viswanath Devanarayan
• Companies: AbbVie and AbbVie and AbbVie and AbbVie
• Keywords: Predictive ; Prognostic ; Multivariate ; Machine learning ; Variable selection

Abstract

Mechanistic relationships between putative biomarkers, clinical baseline and related predictors versus clinical outcome (efficacy/safety) are usually unknown, and must be deduced empirically from experimental data. Such relationships enable the implementation of a personalized medicine strategy in clinical trials to help stratify patients in terms of clinical response, treatment differentiation, disease progression, etc. The relationship between some biomarkers & clinical baseline predictors versus clinical outcome are typically stepwise or nonlinear, often requiring complex models to develop the prognostic and predictive signatures. For the sake of easier interpretation and implementation in the clinic, defining a multivariate biomarker signature in terms of thresholds on the biomarker combinations would be preferable. In this talk, we outline some methods for developing such signatures in the context of binary and survival endpoints. Results from simulations and case-study illustration will also be provided.