Outsourcing has become a staple of pharmaceutical research and development. Its growth over the last 25 years has been driven by cost reduction, productivity enhancement, and de-risking strategies with the increasing use of specialist providers that have a history of success against challenging targets. The outsource model, whether for synthesis, biological assays, or animal studies, presents challenges and opportunities to further increase productivity. Tim Cheeseright at Torx Software Limited discusses the Design-Make-Test-Analyse (DMTA) cycle, central to the discovery of new small molecule therapeutics and agrochemicals, which has periodically received attention as a metric for improving productivity in research and development.
‘Improving Productivity in Discovery Research: Workflow Management’
Design-Make-Test-Analyse cycle and productivity
The Design-Make-Test-Analyse (DMTA) cycle, central to the discovery of new small molecule therapeutics and agrochemicals, has periodically received attention as a metric for improving productivity in research and development. The rationale for improving DMTA is simple: the path to a drug candidate will inevitably involve multiple iterations of design. The faster a project team gets around the cycle, the more iterations can be completed in a set time, or the shorter the time that it will take to complete or close a project.
The focus on DMTA stems from the post-combinatorial chemistry analysis of large pharma in the early 2000s. At that time, it had become clear that making thousands of compounds in each cycle was less important than increasing the number of cycles in a project lifetime. Cycle times of 120 days were not uncommon in large companies – reflecting just 6 iterations of DMTA in a typical 2-year project lifetime. This focus on cycle time brought averages down drastically, but further improvements are still possible, and since the process will be repeated many times throughout a project, small gains are amplified to deliver significant enhancements in productivity.
Recent advances in computational methods and artificial intelligence (AI) approaches have highlighted other key areas for improvement. However, DMTA remains central to the process of discovering new small molecules. The inclusion of computational approaches has further highlighted the need to streamline communication, particularly through the Design-Make transition, as complex algorithms can often generate highly novel molecules that need expert input from synthetic chemists before the decision is made to embark on synthesis.
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