How to achieve confidence in drug discovery and development: managing risk (from a reductionist to a holistic approach)

ChemMedChem. 2009 Jun;4(6):923-33. doi: 10.1002/cmdc.200900056.

Abstract

Confidence in mechanism: Creating a more holistic understanding of disease pathophysiology and an early confidence in the mechanism under investigation could help facilitate the selection of not only the most appropriate targets but also the best mechanisms for disease intervention and how to select and optimise the best compounds. Drug target and candidate selection are two of the key decision points within the drug discovery process for which all companies use certain selection criteria to make decisions on which targets to accept into their discovery pipelines and which compounds will pass into development. These steps not only help define the overall productivity of every company but they are also decisions taken without full predictive knowledge of the risks that lie ahead or how best to manage them. In particular, the process of selecting new targets does not normally involve full evaluation of the risk(s) in the mechanism under investigation (the modulation of the target), which may result in an inability to fully connect in vitro and animal model results to the disease (clinical) setting. The resulting poor progression statistics of many compounds in the clinic is at least partially the result of a lack of understanding of disease pathophysiology. Notably, the lack of efficacy is still a major reason for failure in the clinic.1 Creating a more holistic understanding of disease pathophysiology and an early confidence in the mechanism under investigation could help facilitate the selection of not only the most appropriate targets but also the best mechanisms for disease intervention and how to select and optimise the best compounds.

MeSH terms

  • Central Nervous System Diseases / drug therapy
  • Chemistry, Pharmaceutical* / methods
  • Chemistry, Pharmaceutical* / trends
  • Drug Discovery*
  • Models, Animal
  • Models, Chemical
  • Research Design
  • Risk Assessment