Simulating the drug discovery pipeline: a Monte Carlo approach

Area of Science:

• Pharmaceutical Research and Development
• Computational Drug Discovery

Background:

• The early drug discovery phase is critical for pharmaceutical R&D, initiating a lengthy and expensive process.
• A robust clinical development pipeline relies heavily on efficient early-stage drug discovery.
• Currently, no published in silico models exist to simulate project progression through discovery milestones.

Purpose of the Study:

• To develop and present an in silico model for simulating drug discovery project progression.
• To analyze the impact of various factors on productivity within the drug discovery pipeline.

Main Methods:

• Development of a simulation model for virtual drug discovery projects.
• Analysis of multiple variables influencing productivity metrics.
• Examination of project progression through key discovery milestones.

Main Results:

• The model predicts an optimal number of scientists for a given portfolio to maximize preclinical candidate output.
• Simulations indicate that exceeding the optimal number of scientists does not increase yearly output.
• Project progression is characterized by irregular clustering of candidates entering preclinical development.

Conclusions:

• The in silico model can aid in analyzing historical performance and setting future expectations.
• It provides a basis for optimizing resource allocation and discussing best practices in drug discovery teams.
• The model helps in understanding and managing the inherent variability in drug discovery project timelines.