![]() ![]() ![]() ![]() The process is repeated many times, with the expected value calculated at each iteration.īeginning with a pool of drug candidates, the optimal pipeline structure should approach equal sized pipeline widths as rapidly as possible. The cost of each phase and value for successful drugs are recorded for each simulation. Drugs that are above a cutoff stringency advance forward, with the process repeated for all phases. Hypothetical “drug quality scores” were drawn from normal distributions which reflect industry probabilities of success between phases. maximize vtot = vpreNpre + v1N1 + v2N2 + v3N3).Īdditionally, pipeline evolution was modeled to evaluate the implications of a “predictive” drug quality assay. ![]() The overall goal is to maximize the total value of drugs progressing through the pipeline (i.e. Npre≥ N1 ≥ N2 ≥ N3), and (2) the sum total of the cost times the number of drugs in each phase must be less than or equal to the total budget (i.e. The model is subject to two constraints: (1) the pipeline must reduce in size with each subsequent phase (i.e. Preclinical, Phase I, Phase II, and Phase III, with variables including: the total budget for the entire pipeline (B), and the number (Ni), cost (ci), and net present value (vi) of drugs in each phase (i). What is the optimal “shape” for a drug pipeline: A flute? A megaphone? A trumpet? In other words, what set of stringencies for passage of candidates to the next stage in development maximizes the value of the pipeline as a whole, and how do assays predicting drug quality influence this optimum?Ī linear programming formulation was developed for four phases of a drug pipeline. ![]()
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