Dynamic and Static Simulations of Fluvoxamine-Perpetrated Drug-Drug Interactions Using Multiple Cytochrome P450 Inhibition Modeling, and Determination of Perpetrator-Specific CYP Isoform Inhibition Constants and Fractional CYP Isoform Contributions to Victim Clearance

A Comprehensive CYP2D6 Drug-Drug-Gene Interaction Network for Application in Precision Dosing and Drug Development

Conducting clinical studies on drug-drug-gene interactions (DDGIs) and extrapolating the findings into clinical dose recommendations is challenging due to the high complexity of these interactions. Here, physiologically-based pharmacokinetic (PBPK) modeling networks present a new avenue for exploring such complex scenarios, potentially informing clinical guidelines and handling patient-specific DDGIs at the bedside. Moreover, they provide an established framework for drug-drug interaction (DDI) submissions to regulatory agencies. The cytochrome P450 (CYP) 2D6 enzyme is particularly prone to DDGIs due to the high prevalence of genetic variation and common use of CYP2D6 inhibiting drugs. In this study, we present a comprehensive PBPK network covering CYP2D6 drug-gene interactions (DGIs), DDIs, and DDGIs. The network covers sensitive and moderate sensitive substrates, and strong and weak inhibitors of CYP2D6 according to the United States Food and Drug Administration (FDA) guidance. For the analyzed CYP2D6 substrates and inhibitors, DD(G)Is mediated by CYP3A4 and P-glycoprotein were included. Overall, the network comprises 23 compounds and was developed based on 30 DGI, 45 DDI, and seven DDGI studies, covering 32 unique drug combinations. Good predictive performance was demonstrated for all interaction types, as reflected in mean geometric mean fold errors of 1.40, 1.38, and 1.56 for the DD(G)I area under the curve ratios as well as 1.29, 1.43, and 1.60 for DD(G)I maximum plasma concentration ratios. Finally, the presented network was utilized to calculate dose adaptations for CYP2D6 substrates atomoxetine (sensitive) and metoprolol (moderate sensitive) for clinically untested DDGI scenarios, showcasing a potential clinical application of DDGI model networks in the field of model-informed precision dosing.

Interplay of UDP-Glucuronosyltransferase and CYP2C8 for CYP2C8 Mediated Drug Oxidation and Its Impact on Drug-Drug Interaction Produced by Standardized CYP2C8 Inhibitors, Clopidogrel and Gemfibrozil

BACKGROUND AND OBJECTIVE

Early investigations into drug-drug interactions (DDIs) involving cytochrome P450 2C8 (CYP2C8) have highlighted the complexity of interactions between CYP2C8 substrate drugs, including montelukast, desloratadine, pioglitazone, repaglinide, and cerivastatin (the latter two being OATP1B1 substrates), and standardized CYP2C8 inhibitors such as clopidogrel (Clop) and gemfibrozil (Gem). These interactions have proven challenging to predict based solely on simple CYP inhibition. A hypothesis has emerged suggesting that these substrate drugs first distribute to UDP-glucuronosyltransferase (UGT) before undergoing oxidation by CYP2C8, resulting in bidirectional elimination. The process of drug distribution to UGT is believed to significantly impact these DDIs. This study aims to explore the intricate interplay between UGT and CYP2C8 in the context of DDIs involving CYP2C8 substrates affected by Clop and Gem.

METHODS

Plasma-level data for the unchanged drug and its metabolite, drawn from the respective literature, formed the basis of our analysis. We evaluated the enzymatic inhibitory activities of DDIs and utilized simulations to estimate plasma levels of the unchanged victim drug and its metabolite in each DDI. This was accomplished by employing a functional relationship that considered the fractional contributions of CYP2C8 and UGT to clearance, perpetrator-specific inhibitory activities against CYP2C8, and drug distribution to UGT.

RESULTS

Our findings emphasize the pivotal role of UGT-mediated distribution in the context of CYP2C8 substrate metabolism, particularly in the complex DDIs induced by Clop and Gem. In these DDIs, Gem exerts inhibitory effects on both UGT and CYP2C8, whereas Clop (specifically its metabolite, Clop-COOH) solely targets CYP2C8. Importantly, the inhibition of CYP2C8 by both Clop and Gem is achieved through a non-competitive mechanism, driven by the actions of their acyl-glucuronides. Clop and Gem exhibit inhibition activities accounting for 85% (pAi,CYP2C8 = 7) and 93% (pAi,CYP2C8 = 15), respectively. In contrast, Gem's inhibition of UGT is relatively modest (50%, pAi,UGT(d) = 2), and it operates through a non-specific, competitive process in drug distribution to UGT. Within this context, our UGT-CYP2C8 interplay model offers an accurate means of predicting the alterations resulting from DDIs, encompassing changes in plasma levels of the unchanged drug and its metabolites, as well as shifts in metabolite formation rates. Our analysis highlights the critical importance of considering the fractional contributions of CYP2C8 and UGT to the victim drug's clearance (fm,CYP2C8; fm,UGT) in DDI prediction. Furthermore, our examination of DDIs involving OATP1B1 substrate drugs underscores that accounting for the hepatic uptake transporters' role in the liver is superfluous in DDI prediction.

CONCLUSION

These findings substantially enhance our comprehension of CYP2C8-mediated oxidation and DDIs, holding crucial implications for drug development and the planning of clinical trials involving these inhibitors.

Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions: Research Methods and Applications

Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug-drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a single perspective. Therefore, this review describes and compares the current DDI evaluation methods based on two aspects: pharmacokinetic interaction and pharmacodynamic interaction. The methods summarized in this paper mainly include probe drug cocktail methods, liver microsome and hepatocyte models, static models, physiologically based pharmacokinetic models, machine learning models, in vivo comparative efficacy studies, and in vitro static and dynamic tests. This review aims to serve as a useful guide for interested researchers to promote more scientific accuracy and clinical practical use of DDI studies.

Disease-drug and drug-drug interaction in COVID-19: Risk and assessment

COVID-19 is announced as a global pandemic in 2020. Its mortality and morbidity rate are rapidly increasing, with limited medications. The emergent outbreak of COVID-19 prompted by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) keeps spreading. In this infection, a patient's immune response plays pivotal role in the pathogenesis. This inflammatory factor was shown by its mediators that, in severe cases, reach the cytokine at peaks. Hyperinflammatory state may sparks significant imbalances in transporters and drug metabolic machinery, and subsequent alteration of drug pharmacokinetics may result in unexpected therapeutic response. The present scenario has accounted for the requirement for therapeutic opportunities to relive and overcome this pandemic. Despite the diminishing developments of COVID-19, there is no drug still approved to have significant effects with no side effect on the treatment for COVID-19 patients. Based on the evidence, many antiviral and anti-inflammatory drugs have been authorized by the Food and Drug Administration (FDA) to treat the COVID-19 patients even though not knowing the possible drug-drug interactions (DDI). Remdesivir, favipiravir, and molnupiravir are deemed the most hopeful antiviral agents by improving infected patient’s health. Dexamethasone is the first known steroid medicine that saved the lives of seriously ill patients. Some oligopeptides and proteins have also been using. The current review summarizes medication updates to treat COVID-19 patients in an inflammatory state and their interaction with drug transporters and drug-metabolizing enzymes. It gives an opinion on the potential DDI that may permit the individualization of these drugs, thereby enhancing the safety and efficacy.

A Physiologically-Based Pharmacokinetic (PBPK) Model Network for the Prediction of CYP1A2 and CYP2C19 Drug-Drug-Gene Interactions with Fluvoxamine, Omeprazole, S-mephenytoin, Moclobemide, Tizanidine, Mexiletine, Ethinylestradiol, and Caffeine

Physiologically-based pharmacokinetic (PBPK) modeling is a well-recognized method for quantitatively predicting the effect of intrinsic/extrinsic factors on drug exposure. However, there are only few verified, freely accessible, modifiable, and comprehensive drug–drug interaction (DDI) PBPK models. We developed a qualified whole-body PBPK DDI network for cytochrome P450 (CYP) CYP2C19 and CYP1A2 interactions. Template PBPK models were developed for interactions between fluvoxamine, S-mephenytoin, moclobemide, omeprazole, mexiletine, tizanidine, and ethinylestradiol as the perpetrators or victims. Predicted concentration–time profiles accurately described a validation dataset, including data from patients with genetic polymorphisms, demonstrating that the models characterized the CYP2C19 and CYP1A2 network over the whole range of DDI studies investigated. The models are provided on GitHub (GitHub Inc., San Francisco, CA, USA), expanding the library of publicly available qualified whole-body PBPK models for DDI predictions, and they are thereby available to support potential recommendations for dose adaptations, support labeling, inform the design of clinical DDI trials, and potentially waive those.

Inhibition and induction of CYP enzymes in humans: an update

The cytochrome P450 (CYP) enzyme family is the most important enzyme system catalyzing the phase 1 metabolism of pharmaceuticals and other xenobiotics such as herbal remedies and toxic compounds in the environment. The inhibition and induction of CYPs are major mechanisms causing pharmacokinetic drug–drug interactions. This review presents a comprehensive update on the inhibitors and inducers of the specific CYP enzymes in humans. The focus is on the more recent human in vitro and in vivo findings since the publication of our previous review on this topic in 2008. In addition to the general presentation of inhibitory drugs and inducers of human CYP enzymes by drugs, herbal remedies, and toxic compounds, an in-depth view on tyrosine-kinase inhibitors and antiretroviral HIV medications as victims and perpetrators of drug–drug interactions is provided as examples of the current trends in the field. Also, a concise overview of the mechanisms of CYP induction is presented to aid the understanding of the induction phenomena.

[Verification of Pharmacokinetic Approaches in Prior Drug Development]

In this review, 9 compounds with insufficient absorption characteristics, safety or efficacy were selected from among the compounds for which the author was in charge of development between 2000 and 2005, in order to evaluate the pharmacokinetic (PK) approaches used to develop these compounds. Optimization of the PK characteristics of a compound at the early stage of chemical design was found to be the most important factor for successful development. For example, (i) selecting class I or II drugs in the biopharmaceutical classification system, while avoiding efflux transporters, and introducing an appropriate dissociation moiety into a compound to make it soluble lead to sufficient drug absorption; (ii) designing compounds whose production of reactive metabolites, such as acyl glucuronide, does not largely affect total metabolism, yet helps to prevent abnormal PK caused by reactive metabolites. Other factors include (i) selection of a drug efficacy evaluation system based on the correct understanding of the relationship between PK and pharmacodynamics (PD) helps to solve species differences in PD; (ii) the establishment of a nonclinical study based on the identification of the involvement of specific cytochrome P450 molecules in the total metabolic clearance of a drug (f_m,CYPs) helps to solve species differences in PK; and (iii) PK analysis using the tube model for hepatic extraction kinetics, and knowledge of the f_m,CYPs of the victim drug, lead to successful drug-drug interaction (DDI) prediction. I hope that this review aids in future drug discovery or development.

Usefulness of Two-Compartment Model-Assisted and Static Overall Inhibitory-Activity Method for Prediction of Drug-Drug Interaction

Our study of drug-drug interaction (DDI) started with the clarification of unusually large DDI observed between ramelteon (RAM) and fluvoxamine (FLV). The main cause of this DDI was shown to be the extremely small hepatic availability of RAM (vF_h). Traditional DDI prediction assuming the well-stirred hepatic extraction kinetic ignores the relative increase of vF_h by DDI, while we could solve this problem by use of the tube model. Ultimately, we completed a simple and useful method for prediction of DDI. Currently, DDI prediction becomes more complex and difficult when examining issues such as dynamic changes in perpetrator level, inhibitory metabolites, etc. The regulatory agents recommend DDI prediction by use of some sophisticated methods. However, they seem problematic in requiring plural in vitro data that reduce the flexibility and accuracy of the simulation. In contrast, our method is based on the static and two-compartment models. The two-compartment model has advantages in that it uses common pharmacokinetics (PK) parameters determined from the actual clinical data, guaranteeing the simulation of the reference standard in DDI. Our studies confirmed that dynamic changes in perpetrator level do not make a difference between static and dynamic methods. DDIs perpetrated by FLV and itraconazole were successfully predicted by use of the present method where two DDI predictors [perpetrator-specific inhibitory activities toward CYP isoforms (pA_i, CYPs) and victim-specific fractional CYP-isoform contributions to the clearance (vf_m, CYPs)] are determined successively as shown in the graphical abstract. Accordingly, this approach will accelerate DDI prediction over the traditional methods.

Simulations of Cytochrome P450 3A4-Mediated Drug-Drug Interactions by Simple Two-Compartment Model-Assisted Static Method

In order to predict cytochrome P450 3A4 (CYP3A4)-mediated drug-drug interactions (DDIs), a simple 2-compartment model-assisted, overall inhibition activity (A_i,overall) method was derived based on 2 concepts. One concept was that the increase in blood victim level and fold increase in the area under the blood victim level curve produced by DDI are determined entirely by A_i,overall, the hepatic availability of the victim and fraction of urinary excreted unchanged victim, where A_i,overall is determined by the perpetrator-specific CYP isoform inhibition activities (A_i,CYPs, DDI predictor-1) and victim-specific fractional CYP isoform contributions (f_m,CYPs, predictor-2). The other concept was that a DDI can be bridged to other DDIs, so that any possible DDI produced by a given victim or a given perpetrator can be predicted by using these predictors. The A_i,CYP3A4s of 12 common CYP3A4 inhibitors were able to be determined and shown to be useful for the prediction of CYP3A4-mediated DDIs wherein victims were metabolized by multiple CYP isoforms. Additionally, it was demonstrated that f_m,CYP values with high confidence can be estimated by bridging DDIs produced by the same victim and different perpetrators. This bridging approach will accelerate prediction of DDIs produced by new chemical entities from the existing DDI database.