Pharmaceutical R & D pipeline management under trial duration uncertainty

Elvan Gokalp, Juergen Branke

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)

Abstract

We consider a pharmaceutical Research & Development (R & D) pipeline management problem under two significant uncertainties: the outcomes of clinical trials and their durations. We present an Approximate Dynamic Programming (ADP) approach to solve the problem efficiently. Given an initial list of potential drug candidates, ADP derives a policy that suggests the trials to be performed at each decision point and state. For the classical R&D pipeline planning problem with deterministic trial durations, we compare our ADP approach with other methods from the literature, and find that it can find better solutions more quickly in particular for larger problem instances. For the case with stochastic trial durations, we compare the ADP algorithm with a myopic approach and show that the expected net profit obtained by the derived ADP policy is higher (almost 20% for a 10-drug portfolio).
Original languageEnglish
Article number106782
JournalComputers and Chemical Engineering
Volume136
Early online date20 Feb 2020
DOIs
Publication statusPublished - 8 May 2020

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