Estimation of fm of victim drugs by human
microsomes
In this study, we measured changes in intrinsic clearance of 33
compounds in HLM, with and without ketoconazole, using the substrate
depletion method to evaluate the fraction metabolized by CYP3A4 for
these drugs. Figure S4 illustrates a moderate positive correlation
between in vitro Clint and
Clliver without inhibitors, indicating the reliability
of this method. In addition to the above in vitro method, we also
used in vivo and in silico prediction methods to determinefm . We calculated in vivofm using a PBPK-DDI model, by matching the
simulated DDI PK data of the test article to the observed DDI PK data.
We predicted in silico fm using computational
methods based on the drug’s molecular structure, using ADMET Predictor
module. The findings of our study are presented in Table 2, which
includes reported in vitro findings, phenotyping study
conclusions, and tested in vivo , in vitro , and in silicofm values, compared across three different
approaches. The in vitro fm results were
consistent with the reported in vitro fmor phenotyping study conclusions. However, weak correlations were
observed between the in vivo fm and in
silico fm values, as well as the in vitroresults (showed in the Figure S5). As previously mentioned, drugs with
higher Clliver are more likely to cause DDIs and are
more sensitive to changes in metabolic parameters such asfm . To investigate this further, we compared thein vitro fm minus in vivofm values between drugs with
Clliver above and below 15 L h-1, as
well as the predicted in silico fm minus in
vivo fm values. Figure 2 indicate that drugs
with a Clliver higher than 15 L h-1exhibit smaller variations of predicted in silicofm minus in viv o fmand in vitro fm minus in vivofm compared to drugs with a
Clliver lower than 15 L h-1.
Furthermore, when comparing drugs with a Clliver higher
than 15 L h-1, the difference between the in
vitro fm and in vivo fm was
smaller than that between in silico and in vivofm . These results suggest that in vitro
fm measurements may provide more accurate predictions
of DDI, particularly for drugs with higher Clliver.
Predictive performance of the PBPK-DDI
model
The PBPK-DDI model was used to predict DDI results by incorporating
different fm s, such as in silico, 100%, andin vitro fm . The results were presented in
Figures 4, 5, and 6, showing predicted AUCR, CmaxR, and
TmaxR values. Notably, the predicted
TmaxR was found to be accurate within 2 times the
observed TmaxR when using in silico or in vitro
fm methods, but not with 100%fm . The PBPK model utilizing in vitro
fm data outperforms the other two values offm in predicting CmaxR and AUCR,
as demonstrated by the close proximity of the predicted values to the
unit line (y=x) compared to the 100% fm and in
silico fm integrated PBPK-DDI model. As a
summary, in vitro fm was the most precise method,
as it accurately predicted the CmaxR of 31 drugs within
2 times of the measured results, and the AUCR of 28 drugs within 2 times
of the measured results. In contrast, the use of 100%fm and in silico fm led to
lower prediction accuracy. Only 24 drugs had their CmaxR
predicted within 2 times of the measured results when using 100%fm or in silico fm , and 26
drugs for CmaxR when using in silicofm . Similarly, only 24 and 25 drugs for AUCR were
predicted within 2 times of the measured CmaxR when
using 100% fm or in silicofm , respectively. The findings demonstrate that
incorporating in vitro fm data into PBPK models
significantly enhances the accuracy of predicting the extent of DDIs for
the parameters studied. While in silico fm shows
some promise, its impact on prediction is limited.