Enabling Bioperformance Prediction Through Data Analytics

26 September 2022 13:45 - 14:15

  • The ability to capture and easily access structured experimental data is a prerequisite to robust data analytics. Measured compound attributes such as biorelevant solubility and formulation characteristics are key components of predictive models for oral absorption. Leveraging these models in discovery can guide molecule selection and formulation development in preclinical space prior to preclinical in vivo studies and help assess clinical bioperformance risk for compound prioritization purposes.
  • A platform was built within electronic laboratory notebooks for the structured data capture of properties relevant to preclinical and clinical bioperformance, and the generation of large, contextualized datasets.
  • The platform eases access to the data and merging with pharmacokinetics results, thereby enabling data analysis and visualization.
  • Importantly, bioperformance predictive tools were built and implemented. These include relatively simple predictors of fraction absorbed based on a small number of compound attributes, as well as more complex models - built using our experimental datasets - that related formulation selection and predicted PK.

Pierre Daublain, Principal Scientist, Merck