Introduction
New medicines are approved and manufactured following basic research,
nonclinical tests, and clinical tests. The efficacy and toxicity of the
drug candidate compounds are evaluated in both nonclinical and clinical
tests. However, toxicities not observed in clinical trials may appear
after the medicines are sold. For example, serious side effects of the
combination of sorivudine and 5-fluorouracil were reported in the 1990s
after sorivudine was made available for medical consumption. These
unexpected toxicities are due to drug–drug interactions (DDI) caused by
the concomitant administration of multiple drugs for effective
healing.(Diasio, (1998)) All of the countless combinations of the new
drugs with other drugs are impossible to be evaluated during
pre-production testing. Therefore, a detailed understanding of
pharmacokinetics with DDI is necessary to reduce the risks associated
with drug combinations.(Beijnen and Schellens, (2004)) However, due to
the species difference between experimental animals and human,
experimental animals cannot adequately reproduce the reactions on
humans. Therefore, it becomes difficult to accurately predict and
understand the pharmacokinetics of drug–drug interactions in the human
body through animal testing.(Dehne et al., (2017))
To address this, pharmacokinetic (PK) models have been actively
utilized. The prediction of pharmacokinetics in vivo is made
possible by mathematical models of variability over time of drug
concentration through absorption, distribution, metabolism, excretion,
and accumulation in tissues and cohesion with drug proteins.
Furthermore, efficacy and toxicity can be predicted effectively from the
drug dose by combining PK and pharmacodynamic (PD) models (PK–PD),
thereby representing the relationship between drug concentration and
physiological effects.(Abaci and Shuler, (2015)) A PK–PD model is
established based on drug-specific parameters obtained from in
vitro tests using cultured cells and microsomes and then evaluated by
comparing with results of animal and clinical tests.(Prantil-Baun et
al., (2018)) However, data from clinical tests are limited to
concentrations of the compounds, metabolites in blood or excreta, drug
efficacy to disease, and side effects. Additionally, the drug
accumulation in organs and tissues and physiological efficacy such as
activation and deactivation could be evaluated by animal tests. However,
the detailed evaluation of pharmacokinetics remains challenging because
data from animal tests are discrete, and it is difficult to continuously
observe the efficacy and drug concentration. Regarding in vitrotests using cultured cells, in vivo environments are not
sufficiently reproduced, and the co-culture of multiple organ model
cells is challenging. Therefore, novel cell-based assay systems, which
supplement conventional in vitro tests and animal tests, are
essential for the accurate prediction and increased understanding of
pharmacokinetics of DDI.(Ishida, (2018))
Microfluidics-based in vitro culture models such as
organs-on-a-chip (OsoC) and microphysiological systems (MPS) have
recently been considered as novel cell-based assay systems for
pharmacokinetic research.(Bhatia and Ingber, (2014); Esch et al.,
(2015); Marx et al., (2017)) To evaluate organ interactions in
vitro , several organ model cells are cultured in different compartments
connected by microchannels on MPS. Parameters of MPS, such as flow ratio
between organ models, residence time of circulating medium in the organ
parts, and ratio of the cell number to medium volume, accommodate the PK
model by the design of microchannels.(Abaci and Shuler, (2015))
Therefore, MPS may be useful to evaluate the PK model and examine
drug-specific physiological effects.(Lee et al., (2017)) The usefulness
of combining the PK model and MPS has been demonstrated by the
consistency between the drug concentration calculations obtained from
the PK model and the experimental results obtained using MPS.(Sung et
al., (2010)) In addition, the calculated parameters using MPS
experimental results were more similar to those of in vivo than
to those of conventional in vitro tests.(Lee et al., (2019)) The
combination of the PK model and MPS has also been useful for research
involving not only drugs but also substances, such as glucose, in the
body.(Lee et al., (2019)) However, previous studies have not undertaken
the study of concomitant administration with multi drugs using MPS;
thus, the usefulness for DDI studies using PK models and MPS has yet to
be shown. Therefore, we estimated drug-specific parameters such as
extraction ratios and subsequently evaluated the changes in drug
efficacy due to DDI. We demonstrate the usability of the combination of
the PK model and MPS in this DDI study. We further propose a multi-organ
microphysiological system (MO–MPS), with a liver part as the metabolic
model and a cancer part as the drug target model, and a PK–PD model
describing the MO–MPS. A prodrug, CPT-11, was used to evaluate the drug
efficacy of the metabolite in the liver part of the MPS. Drug-specific
parameters were estimated by using the proposed PK–PD model from the
results of drug efficacy tests by varying the flow ratio to liver part
and lung cancer part in the MPS. The DDI were evaluated by comparing the
results of the concomitant administration experiment using the MPS and
the results of simulation using the proposed PK–PD model with the
estimated parameters. We found that it was possible to evaluate and
understand DDI by comparing the results of the PK-PD model and the
MO-MPS.
Materials and Methods