INTRODUCTION
Combined drug therapies have become increasingly popular in recent years
due to their ability to simultaneously treat multiple conditions with a
personalized combination of drugs (Gertz & Dispenzieri, 2020; Lu et
al., 2017). However, the potential for drug-drug interactions (DDIs) to
cause adverse effects and serious safety issues is a concern. In
particular, when a perpetrator drug inhibits the metabolism of victim
drugs via the CYP3A enzyme, it can lead to an increase in exposure of
the victim drugs. To minimize the risk of negative effects from DDIs,
the potential for interaction between perpetrator and victim drugs can
be assessed during the drug discovery and development process and after
the drug has been approved (Yang, Pfuma Fletcher, Huang, Zineh &
Madabushi, 2021). A mechanistic static model can be used to evaluate the
ratio of the concentration-time area under the curve (AUC) for a test
article (i.e. a drug) after co-administration with a perpetrator of
CYP3A to the AUC for the test article after dosing alone (AUCR,
AUCi/AUC) (Gomez-Mantilla, Huang & Peters, 2023). If this ratio is
found to be greater than the traditional threshold of 1.25, it is
considered an indicator of a potential DDI, and further studies are
performed to evaluate the DDI risk. However, static models have
limitations in that they use a single concentration of a drug and can
over-predict the extent of DDI. Dynamic models that take into account
the time-varying concentrations of drugs and metabolites may provide a
more accurate assessment of DDIs.
Physiologically based pharmacokinetic (PBPK) modeling is widely adopted
by the pharmaceutical industry for evaluating DDIs due to its ability to
consider the time-course of drug concentrations, resulting in more
accurate predictions (Lin, Chen, Unadkat, Zhang, Wu & Heimbach, 2022).
If the PBPK model can predict the observed DDI accurately, and
sensitivity analysis indicates minimal impact, a clinical DDI study may
not be necessary (Shebley et al., 2018). However, the decision to
conduct such a study ultimately depends on various factors, such as
regulatory requirements, the potential consequences of the DDI, the
stage of drug development, and the predictive performance (accuracy and
reliability) of the PBPK-DDI model. To evaluate the predictive
performance of the PBPK model for DDIs, we used the PBPK model to
predict the DDIs of 35 substrates after co-administration with an
inhibitor, and then compare them with the observed results (Ren, Sai,
Chen, Zhang, Tang & Yang, 2021). The comparison revealed that 75% of
the predicted AUCR values by the model were within a 2-fold range of the
observed AUCR values, with the assumption of 100% of the fraction
metabolized (fm ). This preliminary analysis
suggests that incorporating reported ”fm ” into
the prediction model for certain drugs (crizotinib, macitentan,
panobinostate, and ruxolitinib) could result in improved accuracy.
However, further testing on fm and an expanded
study with a larger sample size are necessary to accurately evaluate the
improvement in predictive performance.
A commonly used method for quantifying fminvolves conducting an in vitro assay using human liver
microsomes (HLM). This assay requires incubating a compound with HLM in
the presence and absence of an inhibitor of interest and determining the
clearance (Clint) of the compound under both conditions
(Murayama et al., 2018). HLM are a prevalent in vitro model for
determining drug metabolism or fm , as they are
affordable and cost-effective. In addition to traditional methods such
as in vitro or in vivo testing, in silico methods can be
employed to predict the fate of molecules in the body through computer
simulations (Watanabe et al., 2023). These methods are generally more
economical. In this paper, we will describe the use of both in
vitro and in silico methods to determine fmvalues. We will then use these values to update previous PBPK models of
the substrates and further predict DDIs. Finally, we will evaluate the
accuracy of the different methods used.