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Drug-induced liver injury (DILI) is a risk during drug development. This study presents a machine learning model to predict bile salt export pump (BSEP) inhibitors, aiding in early identification of potential DILI risks.

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Area of Science:

  • Hepatology
  • Pharmacology
  • Computational Chemistry

Background:

  • The ATP-dependent bile salt export pump (BSEP) regulates bile salt transport from hepatocytes.
  • BSEP inhibition is linked to cholestatic drug-induced liver injury (DILI) due to toxic bile salt accumulation.
  • High attrition rates in drug development and postmarketing withdrawals are associated with DILI.

Purpose of the Study:

  • To investigate in silico methods for identifying BSEP inhibitors.
  • To develop and validate a predictive model for BSEP inhibition.
  • To assist in prioritizing compounds for in vitro testing to mitigate DILI risk.

Main Methods:

  • Exploration of various in silico approaches to identify potential BSEP inhibitors.
  • Development of a consensus machine learning classification model.
  • Derivation of an applicability domain for the predictive model.
  • Validation of the model using in vitro experimental measurements.

Main Results:

  • A consensus machine learning model was developed to accurately predict BSEP inhibitors.
  • The model demonstrated the ability to flag potential inhibitors within a defined applicability domain.
  • In vitro validation correctly identified eight out of eleven predicted BSEP inhibitors.

Conclusions:

  • In silico approaches, particularly machine learning, can effectively predict BSEP inhibitors.
  • The developed model aids in prioritizing compounds for in vitro testing, potentially reducing DILI risks.
  • This strategy supports safer drug development by identifying compounds with lower DILI potential early on.