Abstract
BackgroundThe early drug discovery phase in pharmaceutical research and development marks the beginning of a long, complex and costly process of bringing a new molecular entity to market. As such, it plays a critical role in helping to maintain a robust downstream clinical development pipeline. Despite its importance, however, to our knowledge there are no published in silico models to simulate the progression of discrete virtual projects through a discovery milestone system.ResultsMultiple variables were tested and their impact on productivity metrics examined. Simulations predict that there is an optimum number of scientists for a given drug discovery portfolio, beyond which output in the form of preclinical candidates per year will remain flat. The model further predicts that the frequency of compounds to successfully pass the candidate selection milestone as a function of time will be irregular, with projects entering preclinical development in clusters marked by periods of low apparent productivity.ConclusionsThe model may be useful as a tool to facilitate analysis of historical growth and achievement over time, help gauge current working group progress against future performance expectations, and provide the basis for dialogue regarding working group best practices and resource deployment strategies.
Highlights
The early drug discovery phase in pharmaceutical research and development marks the beginning of a long, complex and costly process of bringing a new molecular entity to market
Despite the importance of the discovery phase in pharmaceutical research and development (R&D), studies are lacking in the literature to provide guidance regarding the optimal distribution of scientific resources to generate a sufficient quantity of preclinical candidate compounds at a rate that would lead to an new molecular entities (NME) entering the marketplace in a specific time-frame
Given the importance of the discovery phase to pharmaceutical R&D, we developed a Monte Carlo simulation algorithm for modeling discrete virtual projects as they move through the discovery milestone system ending with entry into preclinical development
Summary
Multiple variables were tested and their impact on productivity metrics examined. Simulations predict that there is an optimum number of scientists for a given drug discovery portfolio, beyond which output in the form of preclinical candidates per year will remain flat. The model further predicts that the frequency of compounds to successfully pass the candidate selection milestone as a function of time will be irregular, with projects entering preclinical development in clusters marked by periods of low apparent productivity
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