Predicting Drug-Induced Liver Injury with Bayesian Machine Learning

Drug induced liver
injury (DILI) can require significant risk management
in drug development and on occasion can cause morbidity or mortality,
leading to drug attrition. Optimizing candidates preclinically can
minimize hepatotoxicity risk, but it is difficult to predict due to
multiple etiologies encompassing DILI, often with multifactorial and
overlapping mechanisms. In addition to epidemiological risk factors,
physicochemical properties, dose, disposition, lipophilicity, and
hepatic metabolic function are also relevant for DILI risk. Better
human-relevant, predictive models are required to improve hepatotoxicity
risk assessment in drug discovery. Our hypothesis is that integrating
mechanistically relevant hepatic safety assays with Bayesian machine
learning will improve hepatic safety risk prediction. We present a
quantitative and mechanistic risk assessment for candidate nomination
using data from in vitro assays (hepatic spheroids,
BSEP, mitochondrial toxicity, and bioactivation), together with physicochemical
(cLogP) and exposure (Cmaxtotal) variables from a chemically
diverse compound set (33 no/low-, 40 medium-, and 23 high-severity
DILI compounds). The Bayesian model predicts the continuous underlying
DILI severity and uses a data-driven prior distribution over the parameters
to prevent overfitting. The model quantifies the probability that
a compound falls into either no/low-, medium-, or high-severity categories,
with a balanced accuracy of 63% on held-out samples, and a continuous
prediction of DILI severity along with uncertainty in the prediction.
For a binary yes/no DILI prediction, the model has a balanced accuracy
of 86%, a sensitivity of 87%, a specificity of 85%, a positive predictive
value of 92%, and a negative predictive value of 78%. Combining physiologically
relevant assays, improved alignment with FDA recommendations, and
optimal statistical integration of assay data leads to improved DILI
risk prediction.