Relative contributions of the major human CYP450 to the metabolism of icotinib and its implication in prediction of drug–drug interaction between icotinib and CYP3A4 inhibitors/inducers using physiologically based pharmacokinetic modeling

The effect of icotinib or apatinib on the pharmacokinetic profile of oxycodone in rats and the underlying mechanism

This study aimed to investigate the interactions between icotinib/apatinib and oxycodone in rats and to unveil the underlying mechanism. An ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed and validated to determine oxycodone and its demethylated metabolite simultaneously. In vivo, Sprague-Dawley (SD) male rats were administered oxycodone with or without icotinib or apatinib. Blood samples were collected and subjected to UPLC-MS/MS analysis. An enzyme incubation assay was performed to investigate the mechanism of drug-drug interaction using both rat and human liver microsomes (RLM and HLM). The results showed that icotinib markedly increased the AUC(0-t) and AUC(0-∞) of oxycodone but decreased the CLz/F. The Cmax of oxycodone increased significantly upon co-administration of apatinib. In vitro, the Km value of oxycodone metabolism was 101.7 ± 5.40 μM and 529.6 ± 19.60 μM in RLMs and HLMs, respectively. Icotinib and apatinib inhibited the disposition of oxycodone, with a mixed mechanism in RLM (IC50 = 3.29 ± 0.090 μM and 0.95 ± 0.88 μM, respectively) and a competitive and mixed mechanism in HLM (IC50 = 22.34 ± 0.81 μM and 0.48 ± 0.05 μM, respectively). In conclusion, both icotinib and apatinib inhibit the metabolism of oxycodone in vitro and in vivo. Therefore, the dose of oxycodone should be reconsidered when co-administered with icotinib or apatinib.

Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs

INTRODUCTION

Physiologically based pharmacokinetics (PBPK) models are increasingly used in the drug research and development, especially in anti-cancer drugs. Between 2001 and 2020, a total of 89 small-molecule targeted antitumor drugs were approved in China and the United States, some of which already included PBPK modeling in their application or approval packages. This article intended to review the prevalence and application of PBPK model in these drugs.

METHOD

Article search was performed in the PubMed to collect English research articles on small-molecule targeted anti-cancer drugs using PBPK modeling. The selected articles were classified into nine categorizes according to the application areas and further analyzed.

RESULT

From 2001 to 2020, more than 60% of small-molecule targeted anti-cancer drugs (54/89) were studied using PBPK model with a wide range of application. Ninety research articles were included, of which 48 involved enzyme-mediated drug-drug interaction (DDI). Of these retrieved articles, Simcyp, GastroPlus, and PK-Sim were the most widely model building platforms, which account for 63.8%, 15.2%, and 8.6%, respectively.

CONCLUSION

PBPK modeling is commonly and widely used to research small-molecule targeted anti-cancer drugs.

Globally Approved EGFR Inhibitors: Insights into Their Syntheses, Target Kinases, Biological Activities, Receptor Interactions, and Metabolism

Targeting the EGFR with small-molecule inhibitors is a confirmed valid strategy in cancer therapy. Since the FDA approval of the first EGFR-TKI, erlotinib, great efforts have been devoted to the discovery of new potent inhibitors. Until now, fourteen EGFR small-molecule inhibitors have been globally approved for the treatment of different types of cancers. Although these drugs showed high efficacy in cancer therapy, EGFR mutations have emerged as a big challenge for these drugs. In this review, we focus on the EGFR small-molecule inhibitors that have been approved for clinical uses in cancer therapy. These drugs are classified based on their chemical structures, target kinases, and pharmacological uses. The synthetic routes of these drugs are also discussed. The crystal structures of these drugs with their target kinases are also summarized and their bonding modes and interactions are visualized. Based on their binding interactions with the EGFR, these drugs are also classified into reversible and irreversible inhibitors. The cytotoxicity of these drugs against different types of cancer cell lines is also summarized. In addition, the proposed metabolic pathways and metabolites of the fourteen drugs are discussed, with a primary focus on the active and reactive metabolites. Taken together, this review highlights the syntheses, target kinases, crystal structures, binding interactions, cytotoxicity, and metabolism of the fourteen globally approved EGFR inhibitors. These data should greatly help in the design of new EGFR inhibitors.

Comparative review of drug-drug interactions with epidermal growth factor receptor tyrosine kinase inhibitors for the treatment of non-small-cell lung cancer

The development of small-molecule tyrosine kinase inhibitors (TKIs) that target the epidermal growth factor receptor (EGFR) has revolutionized the management of non-small-cell lung cancer (NSCLC). Because these drugs are commonly used in combination with other types of medication, the risk of clinically significant drug–drug interactions (DDIs) is an important consideration, especially for patients using multiple drugs for coexisting medical conditions. Clinicians need to be aware of the potential for clinically important DDIs when considering therapeutic options for individual patients. In this article, we describe the main mechanisms underlying DDIs with the EGFR-TKIs that are currently approved for the treatment of NSCLC, and, specifically, the potential for interactions mediated via effects on gastrointestinal pH, cytochrome P450-dependent metabolism, uridine diphosphate-glucuronosyltransferase, and transporter proteins. We review evidence of such DDIs with the currently approved EGFR-TKIs (gefitinib, erlotinib, afatinib, osimertinib, and icotinib) and discuss several information sources that are available online to aid clinical decision-making. We conclude by summarizing the most clinically relevant DDIs with these EFGR-TKIs and provide recommendations for managing, minimizing, or avoiding DDIs with the different agents.

In Vitro and In Situ Characterization of the Intestinal Absorption of Capilliposide B and Capilliposide C from Lysimachia capillipes Hemsl

The goal of this investigation was to determine the processes and mechanism of intestinal absorption for capilliposide B (CAPB) and capilliposide C (CAPC) from the Chinese herb, Lysimachia capillipes Hemsl. An analysis of basic parameters, such as drug concentrations, time, and behavior in different intestinal segments was analyzed by liquid chromatography-tandem mass spectrometry (LC-MS). The susceptibility of CAPB and CAPC to various inhibitors such as P-glycoprotein (P-gp) inhibitor (verapamil); multidrug resistance-associated protein 2 (MRP2) inhibitor (indomethacin); cytochrome P450 protein 3A4 (CYP3A4) inhibitor (ketoconazole); and the co-inhibitor of P-gp, MRP2 and CYP3A4 (cyclosporine A) were assessed using both caco-2 cell monolayer and single-pass intestinal perfusion (SPIP) models. As a result, CAPB and CAPC are both poorly absorbed in the intestines and exhibited segment-dependent permeability. The intestinal permeability of CAPB and CAPC were significantly increased by the co-treatment of verapamil, indomethacin. In addition, the intestinal permeability of CAPB was also enhanced by ketoconazole and cyclosporine A. It can be concluded that the intestinal absorption mechanisms of CAPB and CAPC involve processes such as facilitated passive diffusion, efflux transporters, and enzyme-mediated metabolism. Both CAPB and CAPC are suggested to be substrates of P-gp and MRP2. However, CAPB may interact with the CYP3A4 system.

Metabolic characteristics of Tanshinone I in human liver microsomes and S9 subcellular fractions

Tanshinone I (TSI) is a lipophilic diterpene in Salvia miltiorrhiza with versatile pharmacological activities. However, metabolic pathway of TSI in human is unknown. In this study, we determined major metabolites of TSI using a preparation of human liver microsomes (HLMs) by HPLC-UV and Q-Trap mass spectrometer. A total of 6 metabolites were detected, which indicated the presence of hydroxylation, reduction as well as glucuronidation. Selective chemical inhibition and purified cytochrome P450 (CYP450) isoform screening experiments revealed that CYP2A6 was primarily responsible for TSI Phase I metabolism. Part of generated hydroxylated TSI was glucuronidated via several glucuronosyltransferase (UGT) isoforms including UGT1A1, UGT1A3, UGT1A7, UGT1A9, as well as extrahepatic expressed isoforms UGT1A8 and UGT1A10. TSI could be reduced to a relatively unstable hydroquinone intermediate by NAD(P)H: quinone oxidoreductase 1 (NQO1), and then immediately conjugated with glucuronic acid by a panel of UGTs, especially UGT1A9, UGT1A1 and UGT1A8. Additionally, NQO1 could also reduce hydroxylated TSI to a hydroquinone intermediate, which was immediately glucuronidated by UGT1A1. The study demonstrated that hydroxylation, reduction as well as glucuronidation were the major pathways for TSI biotransformation, and six metabolites generated by CYPs, NQO1 and UGTs were found in HLMs and S9 subcellular fractions.

Metabolic Pathway of Icotinib In Vitro: The Differential Roles of CYP3A4, CYP3A5, and CYP1A2 on Potential Pharmacokinetic Drug-Drug Interaction

Icotinib is the first self-developed small molecule drug in China for targeted therapy of non-small cell lung cancer. To date, systematic studies on the pharmacokinetic drug-drug interaction of icotinib were limited. By identifying metabolite generated in human liver microsomes and revealing the contributions of major cytochromes P450 (CYPs) in the formation of major metabolites, the aim of the present work was to understand the mechanisms underlying pharmacokinetic and pharmacological variability in clinic. A liquid chromatography/UV/high-resolution mass spectrometer method was developed to characterize the icotinib metabolites. The formation of 6 major metabolites was studied in recombinant CYP isozymes and human liver microsomes with specific inhibitors to identify the CYPs responsible for icotinib metabolism. The metabolic pathways observed in vitro are consistent with those observed in human. Results demonstrated that the metabolites are predominantly catalyzed by CYP3A4 (77%∼87%), with a moderate contribution from CYP3A5 (5%∼15%) and CYP1A2 (3.7%∼7.5%). The contribution of CYP2C8, 2C9, 2C19, and 2D6 is insignificant. Based on our observations, to minimize drug-drug interaction risk in clinic, coprescription of icotinib with strong CYP3A inhibitors or inducers must be weighed. CYP1A2, a highly inducible enzyme in the smoking population, may also represent a determinant of pharmacokinetic and pharmacological variability of icotinib, especially in lung cancer patients with smoking history.

Comparison of the inhibition potentials of icotinib and erlotinib against human UDP-glucuronosyltransferase 1A1

UDP-glucuronosyltransferase 1A1 (UGT1A1) plays a key role in detoxification of many potentially harmful compounds and drugs. UGT1A1 inhibition may bring risks of drug–drug interactions (DDIs), hyperbilirubinemia and drug-induced liver injury. This study aimed to investigate and compare the inhibitory effects of icotinib and erlotinib against UGT1A1, as well as to evaluate their potential DDI risks via UGT1A1 inhibition. The results demonstrated that both icotinib and erlotinib are UGT1A1 inhibitors, but the inhibitory effect of icotinib on UGT1A1 is weaker than that of erlotinib. The IC₅₀ values of icotinib and erlotinib against UGT1A1-mediated NCHN-O-glucuronidation in human liver microsomes (HLMs) were 5.15 and 0.68 μmol/L, respectively. Inhibition kinetic analyses demonstrated that both icotinib and erlotinib were non-competitive inhibitors against UGT1A1-mediated glucuronidation of NCHN in HLMs, with the K_i values of 8.55 and 1.23 μmol/L, respectively. Furthermore, their potential DDI risks via UGT1A1 inhibition were quantitatively predicted by the ratio of the areas under the concentration–time curve (AUC) of NCHN. These findings are helpful for the medicinal chemists to design and develop next generation tyrosine kinase inhibitors with improved safety, as well as to guide reasonable applications of icotinib and erlotinib in clinic, especially for avoiding their potential DDI risks via UGT1A1 inhibition.

Evaluation of Cytochrome P450 3A4-Mediated Drug-Drug Interaction Potential for Cobimetinib Using Physiologically Based Pharmacokinetic Modeling and Simulation

BACKGROUND AND OBJECTIVES

Cobimetinib is eliminated mainly through cytochrome P450 (CYP) 3A4-mediated hepatic metabolism in humans. A clinical drug-drug interaction (DDI) study with the potent CYP3A4 inhibitor itraconazole resulted in an approximately sevenfold increase in cobimetinib exposure. The DDI risk for cobimetinib with other CYP3A4 inhibitors and inducers needs to be assessed in order to provide dosing instructions.

METHODS

A physiologically based pharmacokinetic (PBPK) model was developed for cobimetinib using in vitro data. It was then optimized and verified using clinical pharmacokinetic data and itraconazole-cobimetinib DDI data. The contribution of CYP3A4 to the clearance of cobimetinib in humans was confirmed using sensitivity analysis in a retrospective simulation of itraconazole-cobimetinib DDI data. The verified PBPK model was then used to predict the effect of other CYP3A4 inhibitors and inducers on cobimetinib pharmacokinetics.

RESULTS

The PBPK model described cobimetinib pharmacokinetic profiles after both intravenous and oral administration of cobimetinib well and accurately simulated the itraconazole-cobimetinib DDI. Sensitivity analysis suggested that CYP3A4 contributes ~78 % of the total clearance of cobimetinib. The PBPK model predicted no change in cobimetinib exposure (area under the plasma concentration-time curve, AUC) with the weak CYP3A inhibitor fluvoxamine and a three to fourfold increase with the moderate CYP3A inhibitors, erythromycin and diltiazem. Similarly, cobimetinib exposure in the presence of strong (rifampicin) and moderate (efavirenz) CYP3A inducers was predicted to decrease by 83 and 72 %, respectively.

CONCLUSION

This study demonstrates the value of using PBPK simulation to assess the clinical DDI risk inorder to provide dosing instructions with other CYP3A4 perpetrators.

Application of physiologically based pharmacokinetic modeling in predicting drug-drug interactions for sarpogrelate hydrochloride in humans

Background

Evaluating the potential risk of metabolic drug–drug interactions (DDIs) is clinically important.

Objective

To develop a physiologically based pharmacokinetic (PBPK) model for sarpogrelate hydrochloride and its active metabolite, (R,S)-1-{2-[2-(3-methoxyphenyl)ethyl]-phenoxy}-3-(dimethylamino)-2-propanol (M-1), in order to predict DDIs between sarpogrelate and the clinically relevant cytochrome P450 (CYP) 2D6 substrates, metoprolol, desipramine, dextromethorphan, imipramine, and tolterodine.

Methods

The PBPK model was developed, incorporating the physicochemical and pharmacokinetic properties of sarpogrelate hydrochloride, and M-1 based on the findings from in vitro and in vivo studies. Subsequently, the model was verified by comparing the predicted concentration-time profiles and pharmacokinetic parameters of sarpogrelate and M-1 to the observed clinical data. Finally, the verified model was used to simulate clinical DDIs between sarpogrelate hydrochloride and sensitive CYP2D6 substrates. The predictive performance of the model was assessed by comparing predicted results to observed data after coadministering sarpogrelate hydrochloride and metoprolol.

Results

The developed PBPK model accurately predicted sarpogrelate and M-1 plasma concentration profiles after single or multiple doses of sarpogrelate hydrochloride. The simulated ratios of area under the curve and maximum plasma concentration of metoprolol in the presence of sarpogrelate hydrochloride to baseline were in good agreement with the observed ratios. The predicted fold-increases in the area under the curve ratios of metoprolol, desipramine, imipramine, dextromethorphan, and tolterodine following single and multiple sarpogrelate hydrochloride oral doses were within the range of ≥1.25, but <2-fold, indicating that sarpogrelate hydrochloride is a weak inhibitor of CYP2D6 in vivo. Collectively, the predicted low DDIs suggest that sarpogrelate hydrochloride has limited potential for causing significant DDIs associated with CYP2D6 inhibition.

Conclusion

This study demonstrated the feasibility of applying the PBPK approach to predicting the DDI potential between sarpogrelate hydrochloride and drugs metabolized by CYP2D6. Therefore, it would be beneficial in designing and optimizing clinical DDI studies using sarpogrelate as an in vivo CYP2D6 inhibitor.

Evaluating a physiologically based pharmacokinetic model for predicting the pharmacokinetics of midazolam in Chinese after oral administration

AIM

To evaluate the SimCYP simulator ethnicity-specific population model for predicting the pharmacokinetics of midazolam, a typical CYP3A4/5 substrate, in Chinese after oral administration.

METHODS

The physiologically based pharmacokinetic (PBPK) model for midazolam was developed using a SimCYP population-based simulator incorporating Chinese population demographic, physiological and enzyme data. A clinical trial was conducted in 40 Chinese subjects (the half was females) receiving a single oral dose of 15 mg midazolam. The subjects were separated into 4 groups based on age (20-50, 51-65, 66-75, and above 76 years), and the pharmacokinetics profiles of each age- and gender-group were determined, and the results were used to verify the PBPK model.

RESULTS

Following oral administration, the simulated profiles of midazolam plasma concentrations over time in virtual Chinese were in good agreement with the observed profiles, as were AUC and Cmax. Moreover, for subjects of varying ages (20-80 years), the ratios of predicted to observed clearances were between 0.86 and 1.12.

CONCLUSION

The SimCYP PBPK model accurately predicted the pharmacokinetics of midazolam in Chinese from youth to old age. This study may provide novel insight into the prediction of CYP3A4/5-mediated pharmacokinetics in the Chinese population relative to Caucasians and other ethnic groups, which can support the rational design of bridging clinical trials.