3.3 Evaluation of drug–drug interaction
Drug efficacy was predicted using the PK–PD model and the estimated
extraction ratios in the liver part. The extraction ratio of CPT-11 in
the liver part in concomitant administration of SV was set to 0.2%
because the CES2 expression would be half. On the other hand, the
extraction ratio of CPT-11 in the liver part in concomitant
administration of RTV was set to 0.4%, which is the same value as that
without concomitant administration, because CYP3A4 expression is
extremely low in HepG2, and CYP3A4 inhibition may be no effect on
pharmacokinetics. The extraction ratio of SN-38 in the liver part was
set to 8.4%. A549 cells density ratios exposed to CPT-11 for 72 h
without metabolic inhibitors, with RTV, and with SV were predicted to be
35.5%, 35.5% and 25.6%, respectively, of the control, i.e., no drugs
(Fig. 4A). Meanwhile, their experimental values using the MO–MPS were
29.5%, 20.3% and 38.8%, respectively. The cell density ratio with SV
is significantly higher than that without metabolic inhibitors. No
statistically significant difference was observed between without
metabolic inhibitor and with RTV although cell density with RTV was
lower than that without metabolic inhibitor (Fig. 4B).
Drug efficacy declined in the concomitant administration of SV because
SV suppresses CES2 expression, which metabolizes CPT-11 to SN-38. As
expected, drug efficacy did not change in the concomitant administration
of RTV, which was used as CYP3A4 inhibitor, because CYP3A4 expression on
HepG2 is quite low. However, drug efficacy in concomitant administration
of RTV was markedly increased in the experiment. SN-38 metabolism to
SN-38G is decreased by the concomitant administration of anti-HIV drugs,
suggesting that anti-HIV drugs impact the metabolism by UGT1A1.(Corona
et al., (2008)) HepG2 has low levels of CYP3A4 activity but still
express UGT1A1.(Westerink and Schoonen, (2007)) The increase in drug
efficacy may have been caused by the inhibition of UGT1A1 because the
AUC of SN-38 increased by the inhibition of not only CPT-11 metabolism
to APC and NPC but also SN-38 metabolism to SN-38G. Our findings
involving DDI of inhibitor chemicals were quite similar to the results
of previous studies, indicating that the DDI evaluation using PK-PD
model and MPS is useful.
Conclusion
Although MPS is useful for pharmacokinetic studies, it has not yet been
applied to DDI studies. Here, we proposed a series of DDI evaluation
methods using an MPS and a PK-PD model, and demonstrated their
usefulness. We established a microfluidics-based MO–MPS and its PK–PD
model. From the experimental results using CPT-11, we confirmed that the
MO–MPS could be used to evaluate the effect of metabolites on a drug
target model. Drug-specific parameters were estimated from the PK–PD
model and from the experimental results using the MO–MPS. DDI was
evaluated by comparing the calculated drug efficacy of the anti-cancer
drug by the PK–PD model with experimental results obtained using
metabolism inhibitor. The effect of concomitantly administered drugs on
the pharmacokinetic changes occurring in MPS can be more clearly
identified by evaluating DDI by the PK–PD model, using parameters
inferred from the experimental results. Our proposed method is useful in
evaluating not only liver metabolism but also the DDI effects for
different organ functions such as absorption and excretion. Furthermore,
this method can be applied to the evaluation of organ–organ interaction
using multi-organ MPS that reproduces enterohepatic circulation. In this
study, DDI was evaluated only in terms of drug efficacy. For a more
in-depth understanding of DDI, evaluating the changes in drug
concentration and metabolic capacity through mathematical models and
experiments is necessary. For this kind of evaluation, the contents of
the culture medium and cells in the MO–MPS should be measured using
liquid chromatography–mass spectrometry and PCR; we aim to perform this
in our future studies. Despite this limitation, our method still proved
to be valuable for the study and analysis of DDI.
Acknowledgements
This study was funded by the Program for Grant-in-Aid for Scientific
Research (B) 18H01849 and the Japan Agency for Medical Research and
Development (AMED).
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