Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery

Minimizing in vitro and in vivo testing
in early drug discovery
with the use of physiologically based pharmacokinetic (PBPK) modeling
and machine learning (ML) approaches has the potential to reduce discovery
cycle times and animal experimentation. However, the prediction success
of such an approach has not been shown for a larger and diverse set
of compounds representative of a lead optimization pipeline. In this
study, the prediction success of the oral (PO) and intravenous (IV)
pharmacokinetics (PK) parameters in rats was assessed using a “bottom-up”
approach, combining in vitro and ML inputs with a PBPK model. More
than 240 compounds for which all of the necessary inputs and PK data
were available were used for this assessment. Different clearance
scaling approaches were assessed, using hepatocyte intrinsic clearance
and protein binding as inputs. In addition, a novel high-throughput
PBPK (HT-PBPK) approach was evaluated to assess the scalability of
PBPK predictions for a larger number of compounds in drug discovery.
The results showed that bottom-up PBPK modeling was able to predict
the rat IV and PO PK parameters for the majority of compounds within
a 2- to 3-fold error range, using both direct scaling and dilution
methods for clearance predictions. The use of only ML-predicted inputs
from the structure did not perform well when using in vitro inputs,
likely due to clearance miss predictions. The HT-PBPK approach produced
comparable results to the full PBPK modeling approach but reduced
the simulation time from hours to seconds. In conclusion, a bottom-up
PBPK and HT-PBPK approach can successfully predict the PK parameters
and guide early discovery by informing compound prioritization, provided
that good in vitro assays are in place for key parameters such as
clearance.