DISCUSSION
Accurate prediction of potential
DDIs is crucial in ensuring patient safety and efficacy of drugs during
the drug discovery and development process. PBPK-DDI models rely on the
accurate representation of various physiological parameters and
processes to predict the DDIs. We used two methods to determine the
metabolism and fm of test articles for
improvement of DDI prediction are as follows: i) In vitroapproach using HLM. This method directly measures the fraction
metabolized by simulating the metabolic process in the human liver. ii)
in silico approach using mathematical models. This method predicts the
metabolism and fm based on the molecular
structure and properties of the substance, without the need for
laboratory experimentation. By incorporating differentfm values by in vitro method or in silico
method, instead of assuming 100% values, the accuracy of PBPK models
can be improved, leading to more precise predictions. The use of actualin vitro determined values for predicting DDIs leads to improved
accuracy compared to using in silico methods. This can help to better
understand the pharmacokinetics of drugs in different populations and
inform drug development and regulatory decisions.
In addition to liver microsomes, other in vitro systems such as
hepatocytes and recombinant enzymes can be used to determine the
metabolic fate of drugs, fm , and evaluate their
potential for DDIs (Lindmark, Lundahl, Kanebratt, Andersson & Isin,
2018; Youdim et al., 2008). Each system has its own advantages and
limitations, and the choice of system depends on the specific research
question being addressed and the properties of the drug under
investigation. Hepatocytes contain a much broader range of metabolic
enzymes such as alcohol dehydrogenase (ADH) and aldehyde dehydrogenase
(ALDH), which are involved in the metabolism of alcohol, and
glucuronosyltransferases, sulfotransferases, and glutathione
S-transferases (Klammers et al., 2022; Lindmark, Lundahl, Kanebratt,
Andersson & Isin, 2018). Herein, the hepatocytes have been considered
better for in vitro evaluation on fm as
they provide a more accurate representation of the metabolic activities
of the liver (Lindmark, Lundahl, Kanebratt, Andersson & Isin, 2018).
However, microsomes are still widely used due to their ease of
preparation, cost-effectiveness, and stability, and they still provide
valuable information about drug metabolism and evaluation onfm . In this paper, the prediction of DDIs using
the PBPK-DDI model with incorporation of fmvalues from HLM is relatively accurate and does not significantly
overestimate the results. Since most of these drugs are primarily
metabolized by the P450 enzyme system in the liver, the in vitrostudies using HLM can provide a good prediction of the potential for
DDIs in vivo . If other enzymes, such as ADH, ALDH,
glucuronosyltransferases, sulfotransferases, and glutathione
S-transferases are involved into drug metabolism, hepatocytes are a
better choice for determining the fm as they
contain these various enzyme systems and can provide a comprehensive
view of the drug metabolism process.
The study found that Clliver and fraction metabolized by
CYP3A4 are significant factors in understanding DDI as showed in Table 1
and Figure 1. Drugs with high Clliver are more likely to
be affected by CYP3A inhibitors, which can result in changes in the
drug’s exposure and potential interactions. There is total 6 victim
drugs which exposure increased higher than 3 times among 33 compounds
when co-administered with ketoconazole and all of these six victim drugs
possess higher Clliver (>15 L
h-1). On the other hand, drugs with low
Clliver may not see significant changes in clearance
when exposed to CYP3A inhibitors as the body’s elimination processes for
the drug are already at their limit. There are 18 drugs with lower
Clliver (<15 L h-1) among
the 33 drugs and their exposure increase less than 3 times after
co-administered ketoconazole. When the fm is low,
it indicates that the CYP3A isoform is not a major contributor to the
metabolism of a drug, and therefore, inhibitors of CYP3A such as
ketoconazole may not have a significant effect on the metabolism of the
drug. Herein, the above 6 victim drugs with exposure increased higher
than 3 times not only possess higher clearance (>15 L
h-1) but also higher fm(>75%).
The tested fm values from the two methods are not
consistent with the actual, real-life conditions of the biological
system being studied, as showed in Figure S5. It seems to indicate a
problem with the methods used or a limitation in the ability of the
methods to accurately reflect the true situation in vivo .
However, the predictions of DDIs were improved much by integration ofin vitro fm , which are indeed more accurate than
predictions based on in silico fm or 100% offm . The underlying cause of this phenomenon is
that variation between in vitro and in vivo measurements
of fm is lower than that between in silicofm and in vivo fm for the
drugs with high clearance as showed in Figure 2. The difference betweenin vitro fm and in vivo fmis unlikely to have a significant impact on the assessment of potential
DDIs since the evaluation of low-clearance compounds for DDIs is not
sensitive to variations in fm . The discrepancy
between in vitro and in vivo measurements offm is always observed for compounds with low
clearance. The lower the clearance value, the slower the rate of drug
metabolism, leading to smaller changes in drug concentration in HLM
system. The limited accuracy of detection (±15%) can make it
challenging to determine the effect of inhibitors on the drug’s
metabolism.
We used the remaining amount of substrate and in vitro clearance
as an index to calculate the fraction metabolized by CYP3A4 with or
without an inhibitor respectively. The first method is simple and
straightforward and provides a rough estimate of the fraction
metabolized by CYP3A4 by using the remaining amount of substrate as an
index. On the other hand, the second method provides a more controlled
and direct way to evaluate the metabolic fate of the substrate by
measuring the rate of metabolism in a laboratory setting and provide
more accurate and precise results compared to the first method.
It is important to note that abiraterone and naloxegol were not enrolled
in this paper. Abiraterone is a pro-drug, which metabolism process may
involve multiple enzyme systems. Therefore, a more comprehensive
evaluation of multiple enzyme systems is necessary to accurately assess
the fm of abiraterone. The present PBPK-DDI model
considers the metabolic pathways and enzymes involved in drug
metabolism, but transporters also play a significant role in DDIs and
cannot be overlooked. Naloxegol is a substrate for both CYP3A and a
transporter, the developed PBPK-DDI model may not accurately predict the
potential for DDI, as the interplay of metabolic and transport
mechanisms is complex. The DDI risk of naloxegol is obviously
under-estimated using the PBPK-DDI model. Therefore, it may further
improve the prediction to consider both metabolic and transport
mechanisms when evaluating the potential for DDIs, to provide a more
comprehensive understanding of the interactions between drugs.