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Cytochrome P450 3A4 (CYP3A4) is one of the major drug metabolizing enzymes in the human body and metabolizes ∼30-50% of clinically used drugs. Inhibition of CYP3A4 must always be considered in the development of new drugs. Time-dependent inhibition (TDI) is an important P450 inhibition type that could cause undesired drug-drug interactions. Therefore, identification of CYP3A4 TDI by a rapid convenient way is of great importance to any new drug discovery effort. Here, we report the development of in silico classification models for prediction of potential CYP3A4 time-dependent inhibitors. On the basis of the CYP3A4 TDI data set that we manually collected from literature and databases, both conventional machine learning and deep learning models were constructed. The comparisons of different sampling strategies, molecular representations, and machine-learning algorithms showed the benefits of a balanced data set and the deep-learning model featured by GraphConv. The generalization ability of the best model was tested by screening an external data set, and the prediction results were validated by biological experiments. In addition, several structural alerts that are relevant to CYP3A4 time-dependent inhibitors were identified via information gain and frequency analysis. We anticipate that our effort would be useful for identification of potential CYP3A4 time-dependent inhibitors in drug discovery and design.

The capsid assembly modulator JNJ-56136379 (bersacapavir) disrupts hepatitis B virus replication. It is metabolized via cytochrome P450 (CYP) 3A, but little is known about the drug-drug interactions of JNJ-56136379 when combined with drugs that inhibit or are metabolized by CYP3A. In a phase 1, open-label trial (NCT03945539), healthy adults received 1 dose of JNJ-56136379 with and without 21 days of prior exposure to itraconazole 200 mg (CYP3A inhibitor). In a second phase 1, open-label trial (NCT03111511), healthy women received 1 dose of drospirenone/ethinyl estradiol and midazolam before and after 15 days of JNJ-56136379. Itraconazole increased the area under the plasma concentration-time curve (AUC) of JNJ-56136379 by 38%. JNJ-56136379 reduced the maximum observed concentration and AUC of midazolam (CYP3A substrate) by 42%-54%, increased AUC of ethinyl estradiol by 1.6-fold, but had no effect on drospirenone pharmacokinetics. Overall, these results demonstrated that a strong CYP3A inhibitor (itraconazole) modestly increased JNJ-56136379 exposure. Furthermore, JNJ-56136379 was a weak inducer of CYP3A (midazolam) and increased ethinyl estradiol exposure; coadministration of high-dose estrogen-based contraceptives and JNJ-56136379 is not recommended.

International audience; Midazolam is a benzodiazepine frequently used for sedation in patients hospitalized in the intensive care unit (ICU) for coronavirus disease 2019 (COVID-19). This drug is primarily metabolized by cytochrome P450 3A (CYP3A) isoenzymes. Several studies have suggested that inflammation, frequently observed in these patients, could modulate CYP3A activity. The objective of this work was to study the impact of inflammation on midazolam pharmacokinetics in patients with COVID-19. Forty-eight patients hospitalized in the ICU for COVID-19 and treated with midazolam administered by continuous infusion were included in this study. Midazolam and α-hydroxymidazolam concentrations were measured and patient data, including the use of CYP3A inhibitors, were collected. Total and unbound concentrations of midazolam and α-hydroxymidazolam were measured in plasma using a validated liquid-chromatography coupled with mass spectrometry method. Inflammatory condition was evaluated by C-reactive protein (CRP) level measurement. Both drug concentrations and CRP measurements were performed on 354 plasma samples. CRP elevation was significantly associated with the α-hydroxymidazolam/midazolam plasma ratio decrease, whether for the unbound fraction or for the total fraction. Conversely, inflammation was not associated with protein binding modifications. Logically, α-hydroxymidazolam/midazolam plasma ratio was significantly reduced when patients were treated with CYP3A inhibitors. In this study, we showed that inflammation probably reduces the metabolism of midazolam by CYP3A. These results suggest that molecules with narrow therapeutic margins and metabolized by CYP3A should be administrated with care in case of massive inflammatory situations.

N-Nitrosamine (NA) impurities are considered genotoxic and have gained attention due to the recall of several marketed drug products associated with higher-than-permitted limits of these impurities. Rifampicin is an index inducer of multiple cytochrome P450s (CYPs) including CYP2B6, 2C8, 2C9, 2C19, and 3A4/5 and an inhibitor of OATP1B transporters (single dose). Hence, rifampicin is used extensively in clinical studies to assess drug-drug interactions (DDIs). Despite NA impurities being reported in rifampicin and rifapentine above the acceptable limits, these critical anti-infective drugs are available for therapeutic use considering their benefit-risk profile. Reports of NA impurities in rifampicin products have created uncertainty around using rifampicin in clinical DDI studies, especially in healthy volunteers. Hence, a systematic investigation through a literature search was performed to determine possible alternative index inducer(s) to rifampicin. The available strong CYP3A inducers were selected from the University of Washington DDI Database and their in vivo DDI potential assessed using the data from clinical DDI studies with sensitive CYP3A substrates. To propose potential alternative CYP3A inducers, factors including lack of genotoxic potential, adequate safety, feasibility of multiple dose administration to healthy volunteers, and robust in vivo evidence of induction of CYP3A were considered. Based on the qualifying criteria, carbamazepine, phenytoin, and lumacaftor were identified to be the most promising alternatives to rifampicin for conducting CYP3A induction DDI studies. Strengths and limitations of the proposed alternative CYP3A inducers, the magnitude of in vivo CYP3A induction, appropriate study designs for each alternative inducer, and future perspectives are presented in this paper.

Dietary polyphenols such as quercetin and curcumin have been extensively administered to patients with cancer in the form of herbal supplements. They may have a synergistic anticancer effect; however, a risk of pharmacokinetic interactions with selective CDK-4/6 inhibitors that are metabolized by the CYP3A4 enzyme exists. Considering these pharmacokinetic aspects, the current study examined the effects of curcumin and quercetin on human CYP3A4 to ascertain CYP3A4-mediated herb-drug interactions with CDK inhibitors. In this study, using in silico methods and CYP3A4 inhibition kinetics in human liver microsomes and recombinant CYP3A4 enzymes, the effects of concentration-dependent inhibition of CYP3A4 by quercetin and curcumin on CDK inhibitors metabolism were examined. Based on our in-silico docking findings, curcumin and quercetin were considerably bound to CYP3A4 protein and displace CDK inhibitors from the CYP3A4 substrate binding domain. The IC

Poor adherence to oral anticoagulants is a significant problem in atrial fibrillation (AF) patients with comorbidities as it increases the risk for cardiac and thromboembolic events.The primary objective was to evaluate adherence to direct oral anticoagulants (DOACs) or warfarin using group-based trajectory modeling (GBTM). The secondary objective was to identify the predictors of adherence to oral anticoagulants. Finally, to report the drug interactions with DOACs/warfarin.This retrospective study was conducted among continuously enrolled Medicare Advantage Plan members from January 2016-December 2019. AF patients with comorbid hypertension, diabetes and hyperlipidemia using warfarin/DOACs were included. Monthly adherence to DOAC/warfarin was measured using proportion of days covered (PDC) and then modeled in a logistic GBTM to identify the distinct patterns of adherence. Logistic regression model was conducted to identify the predictors of adherence to oral anticoagulants adjusting for all baseline characteristics. Concomitant use of DOACs/warfarin with CYP3A4,P-gp inhibitors were measured.Among 317 patients, 137 (43.2%) and 79 (24.9%) were DOAC, and warfarin users, respectively. The adherence trajectory model for DOACs included gradual decline (40.4%), adherent (38.8%), and rapid decline (20.8%). The adherence trajectories for warfarin adherence included gradual decline (8.9%), adherent (59.4%), and gaps in adherence (21.7%). Predictors of adherence included type of oral anticoagulant, stroke risk score, low-income subsidy, and baseline PDC. CYP3A4,P-gp drugs were co-administered with DOACs /warfarin resulting in adverse events.Adherence to oral anticoagulants is suboptimal. Interventions tailored according to past adherence trajectories may be effective in improving patient's adherence.

AIMS: P-glycoprotein (P-gp) and CYP3A4-interacting drugs influence plasma levels of non-vitamin K antagonist oral anticoagulants (NOACs). However, the clinical relevance is questioned. Therefore, the impact of pharmacokinetically-interacting drugs on the effectiveness and safety of NOACs in patients with atrial fibrillation (AF) was investigated. METHODS: A meta-analysis was performed based on randomized controlled trials and observational studies retrieved from Pubmed and Embase that investigated the impact of concomitantly used P-gp/CYP3A4-interacting drugs on the risk-benefit profile of NOACs in AF patients. RESULTS: Fifteen studies were included, investigating 21 711 and 306 421 NOAC-treated AF patients with and without P-gp/CYP3A4 inhibitor use respectively, while only 1 study included P-gp/CYP3A4 inducers. In NOAC-treated AF patients, concomitant use of P-gp/CYP3A4 inhibitors was associated with significantly higher major bleeding (relative risk [RR] 1.10, 95% confidence interval [CI; 1.01-1.19]) and all-cause mortality risks (RR 1.14, 95%CI [1.05-1.23]) compared to not using P-gp/CYP3A4 inhibitors, while the risks of stroke/systemic embolism (RR 0.88, 95%CI [0.77-1.01]), intracranial bleeding (RR 0.89, 95%CI [0.68-1.15]) and gastrointestinal bleeding (RR 1.09, 95%CI [0.91-1.30]) were not significantly different. Concomitant use of amiodarone with NOACs was associated with lower thromboembolic (RR 0.75, 95%CI [0.61-0.92]), similar major bleeding (RR 0.92, 95%CI [0.80-1.07]) but higher mortality risks (RR 1.21, 95%CI [1.05-1.39]). Coadministration of verapamil or diltiazem was associated with higher major bleeding risks (RR 1.64, 95%CI [1.31-2.06]), but comparable thromboembolic (RR 1.10, 95%CI [0.75-1.61]) and mortality risks (RR 1.01, 95%CI [0.77-1.33]). CONCLUSION: Given the higher bleeding and mortality risks in NOAC-treated AF patients concomitantly using P-gp/CYP3A4 inhibitors, close monitoring is warranted.

Asciminib is a first-in-class inhibitor of BCR::ABL1, specifically targeting the ABL myristoyl pocket. Asciminib is a substrate of CYP3A4 and P-glycoprotein (P-gp) and possesses pH-dependent solubility in aqueous solution. This report summarizes the results of two phase I studies in healthy subjects aimed at assessing the impact of CYP3A and P-gp inhibitors, CYP3A inducers and acid-reducing agents (ARAs) on the pharmacokinetics (PK) of asciminib (single dose of 40 mg). Asciminib exposure (area under the curve [AUC]) unexpectedly decreased by ~40% when administered concomitantly with the strong CYP3A inhibitor itraconazole oral solution, whereas maximum plasma concentration (C

Ripretinib is a switch control KIT kinase inhibitor approved for treatment of adults with advanced gastrointestinal stromal tumors who received prior treatment with 3 or more kinase inhibitors, including imatinib. Ripretinib and its active metabolite (DP-5439) are cleared mainly via cytochrome P450 enzyme 3A4/5 (CYP3A4/5), and ripretinib solubility is pH-dependent, thus the drug-drug interaction potentials of ripretinib with itraconazole (strong CYP3A inhibitor), rifampin (strong CYP3A inducer), and pantoprazole (proton pump inhibitor) were each evaluated in open-label, fixed-sequence study designs. Overall, 20 participants received ripretinib 50 mg alone and with itraconazole 200 mg once daily, 24 participants received ripretinib 100 mg alone and with rifampin 600 mg once daily, and 25 participants received ripretinib 50 mg alone and with pantoprazole 40 mg once daily. Ripretinib exposure increased with concomitant itraconazole, with geometric least-squares (LS) mean ratios of ripretinib area under the concentration-time curve from 0 to ∞ (AUC

We characterized the impact of chronic kidney disease (CKD) on the cytochrome P450 (CYP) 3A4-mediated metabolism of saxagliptin to its metabolite, 5-hydroxysaxagliptin, using a physiologically based pharmacokinetic (PBPK) model. A PBPK model of saxagliptin and its CYP3A4 metabolite, 5-hydroxysaxagliptin, was constructed and validated for oral doses ranging from 5 to 100 mg. The observed ratios of area under the plasma concentration-time curve (AUC) and maximum plasma concentration (C

Clinical drug interaction studies with itraconazole and rifampicin have demonstrated that acalabrutinib is a sensitive substrate of CYP3A. A physiologically based pharmacokinetic (PBPK) model was developed based on the data of these studies. One of the active CYP3A metabolites, ACP-5862, was identified but never studied in a drug interaction scenario. This study aims to evaluate both parent and metabolite exposure change with coadministration of moderate CYP3A inhibitors and its impact on safety and efficacy.In an open label, randomized, 2-period study, we investigated the effect of coadministration of fluconazole or isavuconazole on the pharmacokinetics of acalabrutinib. Bruton tyrosine kinase receptor occupancy and safety were compared between different treatments. Experimental data were compared to PBPK simulation results.Least square means of acalabrutinib maximum plasma concentration and area under the curve increased 1.37 (1.14-1.64) and 1.60 (1.45-1.77)-fold in the presence of isavuconazole and 1.48 (1.10-1.98) and 2.16 (1.94-2.40)-fold in the presence of fluconazole, respectively. For ACP-5862, these values are 0.72 (0.63-0.82) and 0.91 (0.86-0.97) fold for isavuconazole and 0.65 (0.49-0.87) and 0.95 (0.91-0.99) fold for fluconazole coadministration. The PBPK model was able to recover acalabrutinib and ACP-5862 PK profiles in the study. Bruton tyrosine kinase receptor occupancy change was minimal in the presence of isavuconazole. There were no deaths, serious adverse events (AEs), or subject discontinuation due to AEs in this study. Only mild (Grade 1) AEs were reported during the study, by 17% of the study population.Our results demonstrate the impact of fluconazole and isavuconazole on the pharmacokinetics of acalabrutinib and ACP-5862, and suggest that no dose adjustment is needed for concomitant administration with moderate CYP3A inhibitors. the current PBPK model can be used to propose dose adjustment for drug interactions via CYP3A.

Results from recently completed clinical studies suggest the dopamine D1 receptor positive allosteric modulator (PAM) mevidalen (

Palbociclib is an oral inhibitor of cyclin-dependent kinases 4 and 6 used in the treatment of locally advanced and metastatic breast cancer, and is extensively metabolized by cytochrome P450 enzyme 3A4 (CYP3A4). A pharmacokinetic/pharmacodynamic relationship between palbociclib exposure and neutropenia is well known. This study aimed to investigate the effects of the moderate CYP3A4 inhibitor erythromycin on the pharmacokinetics of palbociclib. We performed a randomized crossover trial comparing the pharmacokinetics of palbociclib monotherapy 125 mg once daily (q.d.) with palbociclib 125 mg q.d. plus oral erythromycin 500 mg three times daily for seven days. Pharmacokinetic sampling was performed at steady-state for both dosing schedules. Eleven evaluable patients have been enrolled. For palbociclib monotherapy, geometric mean area under the plasma concentration-time curve from zero to infinity (AUC0–24h), maximum plasma concentration (Cmax), and minimum plasma concentration (Cmin) were 1.46 × 103 ng•h/mL (coefficient of variation (CV) 45.0%), 80.5 ng/mL (CV 48.5%), and 48.4 ng/mL (CV 38.8%), respectively, compared with 2.09 × 103 ng•h/mL (CV 49.3%, P = 0.000977), 115 ng/mL (CV 53.7%, P = 0.00562), and 70.7 ng/mL (CV 47.5%, P = 0.000488) when palbociclib was administered concomitantly with erythromycin. Geometric mean ratios (90% confidence intervals) of AUC0–24h, Cmax, and Cmin for palbociclib plus erythromycin vs. palbociclib monotherapy were 1.43 (1.24–1.66), 1.43 (1.20–1.69), and 1.46 (1.30–1.63). Minor differences in adverse events were observed, and only one grade ≥ 3 toxicity was observed in this short period of time. To conclude, concomitant intake of palbociclib with the moderate CYP3A4 inhibitor erythromycin resulted in an increase in palbociclib AUC0–24h and Cmax of both 43%. Therefore, a dose reduction of palbociclib to 75 mg q.d. is rational, when palbociclib and moderate CYP3A4 inhibitors are used concomitantly.

Finerenone is a nonsteroidal, selective mineralocorticoid receptor antagonist (MRA) that recently demonstrated its efficacy to delay chronic kidney disease (CKD) progression and reduce cardiovascular events in patients with CKD and type 2 diabetes. Here, we report the development of a physiologically-based pharmacokinetic (PBPK) model for finerenone and its application as a victim drug of cytochrome P450 3A4 (CYP3A4)-mediated drug-drug interactions (DDI) using the open-source PBPK platform PK-Sim® which has recently been qualified for this application purpose. First, the PBPK model for finerenone was developed using physico-chemical, in vitro and clinical (including mass balance) data. Subsequently, the finerenone model was validated regarding the contribution of CYP3A4 metabolism to total clearance by comparing to observed data from dedicated clinical interaction studies with erythromycin (simulated geometric mean ratios of the area under the plasma concentration-time curve (AUCR) of 3.46 and geometric mean peak plasma concentration ratios (Cmax R) of 2.00 versus observed of 3.48 and 1.88, respectively) and verapamil (simulated AUCR of 2.91 and Cmax R of 1.86 versus observed of 2.70 and 2.22, respectively). Finally, the finerenone model was applied to predict clinically untested DDI studies with various CYP3A4 modulators. An AUCR of 6.31 and a Cmax R of 2.37 was predicted with itraconazole, of 5.28 and 2.25 with clarithromycin, 1.59 and 1.40 with cimetidine, 1.57 and 1.38 with fluvoxamine, 0.19 and 0.32 with efavirenz, and 0.07 and 0.14 with rifampicin. This PBPK analysis provides a quantitative basis to guide the label and clinical use of finerenone with concomitant CYP3A4 modulators.

Idasanutlin is a potent inhibitor of the p53-MDM2 interaction that enables reactivation of the p53 pathway, which induces cell cycle arrest and/or apoptosis in tumor cells expressing functional p53. It was investigated for the treatment of solid tumors and several hematologic indications such as relapsed/refractory acute myeloid leukemia, polycythemia vera, or non-Hodgkin lymphoma. For safety reasons, it cannot be given in healthy volunteers for drug-drug interaction (DDI) explorations. This triggered the need for in silico explorations on top of the one available CYP3A clinical DDI study with posaconazole in solid tumor patients. Idasanutlin's clearance is dependent on CYP3A4/2C8 forming its major circulating metabolite M4, with contributions from UGT1A3 and biliary excretion. Idasanutlin and M4 have low permeability, very low clearance, and extremely low unbound fraction in plasma (0.001), which makes in vitro data showing inhibition on CYP3A4/2C8 enzymes challenging to translate to clinical relevance. Physiologically-based pharmacokinetic models of idasanutlin and M4 have been established to simulate perpetrator and victim DDI scenarios and to evaluate whether further DDI studies in oncology patients are necessary. Modeling indicated that idasanutlin and M4 would show no or weak clinical inhibition of selective CYP3A4/2C8 substrates. Co-administered strong CYP3A and CYP2C8 inhibitors might lead to weak or moderate idasanutlin exposure increases, and the strong inducer rifampicin might cause moderate exposure reduction. As the simulated idasanutlin systemic exposure changes would be within the range of observed intrinsic variability, the target population can take co-medications that are either CYP2C8/3A4 inhibitors or weak/moderate CYP2C8/3A4 inducers without dose adjustment. SIGNIFICANCE STATEMENT: Clinical trials for idasanutlin are restricted to cancer patients, which imposes practical, scientific, and ethical challenges on drug-drug interaction investigations. Furthermore, idasanutlin and its major circulating metabolite have very challenging profiles of absorption, distribution, metabolism and excretion including high protein binding, low permeability and a combination of different elimination pathways each with extremely low clearance. Nonetheless, physiologically-based pharmacokinetic models could be established and applied for drug-drug interaction risk assessment and were especially useful to provide guidance on concomitant medications in patients.

Gelsemine (GA), the principal alkaloid in Gelsemium elegans Benth, exhibits potent and specific antinociception in chronic pain without the induction of apparent tolerance. However, GA also exerts neurotoxicity and hepatotoxicity when overdosed, and potential detoxification pathways are urgently needed. Cytochrome P450 enzymes (CYPs) are important phase I enzymes involved in the detoxification of xenobiotic compounds. The study aimed to investigate the role of CYPs-mediated metabolism in GA-induced toxicity. Microsomes, chemical special inhibitors and human recombinant CYPs indicated that GA was mainly metabolized by CYP3A4/5. The major metabolite of GA was isolated and identified as 4-N-demethyl-GA by high-resolution mass spectrometry and nuclear magnetic resonance technology. The CYP3A4 inhibitor ketoconazole significantly inhibited the metabolism of GA. This drastically increased GA toxicity which is caused by increasing the level of malondialdehyde and decreasing the level of the superoxide dismutase in mice. In contrast, the CYP3A4 inducer dexamethasone significantly increased GA metabolism and markedly decreased GA toxicity in mice. Notably, in CYP3A4-humanized mice, the toxicity of GA was significantly reduced compared to normal mice. These findings demonstrated that CYP3A4-mediated metabolism is a robust detoxification pathway for GA-induced toxicity.

Xenobiotics can interact with cytochromes P450 (CYPs), resulting in drug–drug interactions, but CYPs can also contribute to drug–disease interactions, especially in the case of inflammation, which downregulates CYP activities through pretranscriptional and posttranscriptional mechanisms. Interleukin‐6 (IL‐6), a key proinflammatory cytokine, is mainly responsible for this effect. The aim of our study was to develop a physiologically based pharmacokinetic (PBPK) model to foresee the impact of elevated IL‐6 levels in combination with drug interactions with esomeprazole on CYP3A and CYP2C19. Data from a cohort of elective hip surgery patients whose CYP3A and CYP2C19 activities were measured before and after surgery were used to validate the accurate prediction of the developed models. Successive steps were to fit models for IL‐6, esomeprazole, and omeprazole and its metabolite from the literature and to validate them. The models for midazolam and its metabolite were obtained from the literature. When appropriate, a correction factor was applied to convert drug concentrations from whole blood to plasma. Mean ratios between simulated and observed areas under the curve for omeprazole/5‐hydroxy omeprazole, esomeprazole, and IL‐6 were 1.53, 1.06, and 0.69, respectively, indicating an accurate prediction of the developed models. The impact of IL‐6 and esomeprazole on the exposure to CYP3A and CYP2C19 probe substrates and respective metabolites were correctly predicted. Indeed, the ratio between predicted and observed mean concentrations were

Context The interaction between nobiletin and anemarsaponin BII could affect the pharmacological activity of these two drugs during their combination. Objective The co-administration of nobiletin and anemarsaponin BII was investigated to explore the interaction and the potential mechanism. Materials and methods Male Sprague-Dawley rats were only orally administrated with 50 mg/kg nobiletin as the control and another six rats were pre-treated with 100 mg/kg anemarsaponin BII for 7 d followed by the administration of nobiletin. The transport and metabolic stability of nobiletin were evaluated in vitro, and the effect of anemarsaponin BII on the activity of CYP3A4 was also assessed to explore the potential mechanism underlying the interaction. Results The increasing Cmax (2309.67 ± 68.06 μg/L vs. 1767.67 ± 68.86 μg/L), AUC (28.84 ± 1.34 mg/L × h vs. 19.57 ± 2.76 mg/L × h), prolonged t1/2 (9.80 ± 2.33 h vs. 6.24 ± 1.53 h), and decreased clearance rate (1.46 ± 0.26 vs. 2.42 ± 0.40) of nobilein was observed in rats. Anemarsaponin BII significantly enhanced the metabolic stability of nobiletin in rat liver microsomes (half-life increased from 31.56 min to 39.44 min) and suppressed the transport of nobiletin in Caco-2 cells (efflux rate decreased from 1.57 ± 0.04 to 1.30 ± 0.03). The inhibitory effect of anemarsaponin BII on CYP3A4 was also found with an IC50 value of 10.23 μM. Discussion and conclusions The interaction between anemarsaponin BII and nobiletin was induced by the inhibition of CYP3A4, which should draw special attention in their clinical co-administration.

In silico driven optimization of compound properties related to pharmacokinetics, pharmacodynamics, and safety is a key requirement in modern drug discovery. Nowadays, large and harmonized datasets allow to implement deep neural networks (DNNs) as a framework for leveraging predictive models. Nevertheless, various available model architectures differ in their global applicability and performance in lead optimization projects, such as stability over time and interpretability of the results. Here, we describe and compare the value of established DNN-based methods for the prediction of key ADME property trends and biological activity in an industrial drug discovery environment, represented by microsomal lability, CYP3A4 inhibition and factor Xa inhibition. Three architectures are exemplified, our earlier described multilayer perceptron approach (MLP), graph convolutional network-based models (GCN) and a vector representation approach, Mol2Vec. From a statistical perspective, MLP and GCN were found to perform superior over Mol2Vec, when applied to external validation sets. Interestingly, GCN-based predictions are most stable over a longer period in a time series validation study. Apart from those statistical observations, DNN prove of value to guide local SAR. To illustrate this important aspect in pharmaceutical research projects, we discuss challenging applications in medicinal chemistry towards a more realistic picture of artificial intelligence in drug discovery.

BACKGROUND High intrapatient variability in tacrolimus trough levels (Tac IPV) is associated with poor allograft outcomes. Tac IPV was previously calculated using trough levels 6-12 months after kidney transplantation (KT). Data on the accuracy of Tac IPV calculation over a longer period, the association between high Tac IPV and donor-specific antibody (DSA) development after KT in Asian patients, and the role of IPV in patients receiving concomitant cytochrome P450 (CYP)3A4/5 inhibitors (CYPinh) are limited. METHODS A retrospective review of patients who underwent KT at our center in 2005-2015, and who received Tac with mycophenolate during the first 2 years after KT was performed. IPV was calculated using Tac levels adjusted by dosage. DSA was monitored annually after KT using a Luminex microbead assay. RESULTS In total, 236 patients were enrolled. CYPinh were prescribed to 189 patients (80.1%): 145 (61.4%), 31 (13.1%), and 13 (5.5%) received diltiazem, fluconazole, and ketoconazole, respectively. Mean IPV calculated from adjusted Tac levels for 6-12 months (IPV6-12) and 6-24 months (IPV6-24) after KT were 20.64% ± 11.68% and 23.53% ± 10.39%, respectively. Twenty-six patients (11%) showed late rejection and/or DSA occurrence, and had significantly higher IPV6-24 (29.42% ± 13.78%) than others (22.77% ± 9.64%; P = 0.02). There was no difference in IPV6-12 (24.31% ± 14.98% versus 20.17% ± 10.90%; P = 0.18). IPV6-12 and IPV6-24 were comparable in patients who did and did not receive CYPinh. When using mean IPV6-24 as a cutoff, patients with higher IPV6-24 had a higher probability of developing DSA and/or late rejection (P = 0.048). CONCLUSIONS Tac IPV6-24 was higher and more significantly associated with DSA development and/or late rejection than Tac IPV6-12, independent of Tac trough level. This is the first study to demonstrate the impact of high IPV on DSA development in Asian patients, and that Tac IPV is comparable between patients with and without CYPinh.

Many women take drugs during their pregnancy to treat a variety of clinical conditions. To optimize drug efficacy and reduce fetal toxicity, it is important to determine or predict fetal drug exposure throughout pregnancy. Previously, we developed and verified a maternal-fetal physiologically based pharmacokinetic (m-f PBPK) model to predict fetal Kp,uu (unbound fetal plasma AUC/unbound maternal plasma AUC) of drugs that passively cross the placenta. Here, we used in vitro transport studies in Transwell, in combination with our m-f PBPK model, to predict fetal Kp,uu of drugs that are effluxed by placental P-glycoprotein (P-gp)-namely, dexamethasone, betamethasone, darunavir, and lopinavir. Using Transwell, we determined the efflux ratio of these drugs in hMDR1-MDCKcP-gpKO cells, in which human P-gp was overexpressed and the endogenous P-gp was knocked out. Then, using the proteomics-informed efflux ratio-relative expressive factor approach, we predicted the fetal Kp,uu of these drugs at term. Finally, to verify our predictions, we compared them with the observed in vivo fetal Kp,uu at term. The latter was estimated using our m-f PBPK model and published fetal [umbilical vein (UV)]/maternal plasma drug concentrations obtained at term (UV/maternal plasma). Fetal Kp,uu predictions for dexamethasone (0.63), betamethasone (0.59), darunavir (0.17), and lopinavir (0.08) were successful, as they fell within the 90% confidence interval of the corresponding in vivo fetal Kp,uu (0.30-0.66, 0.29-0.71, 0.11-0.22, 0.04-0.19, respectively). This is the first demonstration of successful prediction of fetal Kp,uu of P-gp drug substrates from in vitro studies. SIGNIFICANCE STATEMENT: For the first time, using in vitro studies in cells, this study successfully predicted human fetal Kp,uu of P-gp substrate drugs. This success confirms that the m-f PBPK model, combined with the ER-REF approach, can successfully predict fetal drug exposure to P-gp substrates. This success provides increased confidence in the use of the ER-REF approach, combined with the m-f PBPK model, to predict fetal Kp,uu of drugs (transported by P-gp or other transporters), both at term and at earlier gestational ages.

Background and Objective Increased glycine availability at the synaptic cleft may enhance N-methyl-D-aspartate receptor signalling and provide a promising therapeutic strategy for cognitive impairment associated with schizophrenia. These studies aimed to assess the pharmacokinetics of BI 425809, a potent glycine-transporter-1 inhibitor, when co-administered with a strong cytochrome P450 3A4 (CYP3A4) inhibitor (itraconazole) and inducer (rifampicin). Methods In vitro studies using recombinant CYPs, human liver microsomes, and human hepatocytes were conducted to determine the CYP isoforms responsible for BI 425809 metabolism. In addition, two open-label, fixed-treatment period, phase I studies in healthy male volunteers are described. Period 1: participants received oral BI 425809 25 mg (single dose) on day 1; period 2: participants received multiple doses, across 10 days, of oral itraconazole or rifampicin combined with a single dose of oral BI 425809 25 mg on day 4/7 of the itraconazole/rifampicin treatment, respectively. Pharmacokinetic and safety endpoints were assessed in the absence/presence of itraconazole/rifampicin and included area under the concentration-time curve (AUC) over the time interval 0–167 h (AUC0‒167; itraconazole), 0–168 h (AUC0‒168; rifampicin), or 0–infinity (AUC0-∞; rifampicin and itraconazole), maximum measured concentration (Cmax) of BI 425809, and adverse events. Results In vitro results suggested that CYP3A4 accounted for ≥ 90% of the metabolism of BI 425809. BI 425809 exposure (adjusted geometric mean ratio [%]) was higher in the presence of itraconazole (AUC0‒167: 265.3; AUC0−∞: 597.0; Cmax: 116.1) and lower in the presence of rifampicin (AUC0‒168: 10.3; AUC0−∞: 9.8; Cmax: 37.4) compared with BI 425809 alone. Investigational treatments were well tolerated. Conclusions Systemic exposure of BI 425809 was altered in the presence of strong CYP3A4 modulators, corroborating in vitro results that CYP3A4 mediates a major metabolic pathway for BI 425809. Trial Registration Number NCT02342717 (registered on 15 January 2015) and NCT03082183 (registered on 10 March 2017) Supplementary Information The online version contains supplementary material available at 10.1007/s13318-021-00723-y.

Entrectinib is a selective inhibitor of ROS1/TRK/ALK kinases, recently approved for oncology indications. Entrectinib is predominantly cleared by cytochrome P450 (CYP) 3A4, and modulation of CYP3A enzyme activity profoundly alters the pharmacokinetics of both entrectinib and its active metabolite M5. We describe development of a combined physiologically based pharmacokinetic (PBPK) model for entrectinib and M5 to support dosing recommendations when entrectinib is co-administered with CYP3A4 inhibitors or inducers. A PBPK model was established in Simcyp® Simulator. The initial model based on in vitro–in vivo extrapolation was refined using sensitivity analysis and non-linear mixed effects modeling to optimize parameter estimates and to improve model fit to data from a clinical drug–drug interaction study with the strong CYP3A4 inhibitor, itraconazole. The model was subsequently qualified against clinical data, and the final qualified model used to simulate the effects of moderate to strong CYP3A4 inhibitors and inducers on entrectinib and M5 pharmacokinetics. The final model showed good predictive performance for entrectinib and M5, meeting commonly used predictive performance acceptance criteria in each case. The model predicted that co-administration of various moderate CYP3A4 inhibitors (verapamil, erythromycin, clarithromycin, fluconazole, and diltiazem) would result in an average increase in entrectinib exposure between 2.2- and 3.1-fold, with corresponding average increases for M5 of approximately 2-fold. Co-administration of moderate CYP3A4 inducers (efavirenz, carbamazepine, phenytoin) was predicted to result in an average decrease in entrectinib exposure between 45 and 79%, with corresponding average decreases for M5 of approximately 50%. The model simulations were used to derive dosing recommendations for co-administering entrectinib with CYP3A4 inhibitors or inducers. PBPK modeling has been used in lieu of clinical studies to enable regulatory decision-making.

CYP3A7 is a member of the cytochrome P450 (CYP) 3A enzyme sub-family that is expressed in the fetus and neonate. In addition to its role metabolizing retinoic acid and the endogenous steroid dehydroepiandrosterone sulfate (DHEA-S), it also has a critical function in drug metabolism and disposition during the first few weeks of life. Despite this, it is generally ignored in the preclinical testing of new drug candidates. This increases the risk for drug-drug interactions (DDI) and toxicities occurring in the neonate. Therefore, screening drug candidates for CYP3A7 inhibition is essential to identify chemical entities with potential toxicity risks for neonates. Currently, there is no efficient high-throughput screening (HTS) assay to assess CYP3A7 inhibition. Here, we report our testing of various fluorescent probes to assess CYP3A7 activity in a high-throughput manner. We determined that the fluorescent compound dibenzylfluorescein (DBF) is superior to other compounds in meeting the criteria considered for an efficient HTS assay. Furthermore, a preliminary screen of an HIV/HCV antiviral drug mini-library demonstrated the utility of DBF in a HTS assay system. We anticipate that this tool will be of great benefit in screening drugs that may be used in the neonatal population in the future.

Naldemedine (NAL), a peripherally acting μ-opioid receptor antagonist, is effective for opioid-induced constipation (OIC). However, diarrhea is the most common adverse event. We investigated the incidence of NAL-induced diarrhea in patients who started NAL at Nagasaki University Hospital between June 2017 and March 2019. Predictors of NAL-induced diarrhea were analyzed using a multivariate logistic regression model. Two hundred and forty-two patients were included in the present study, and NAL-induced diarrhea was observed in 17.8% (43 patients). The results of multiple logistic regression analyses identified the administration of opioid analgesics for 8 d or longer before the initiation of NAL (odds ratio (OR): 2.20, 95% confidence interval (95% CI): 1.04–4.64, p = 0.039), the combination of a laxative (OR: 2.22, 95%CI: 1.03–4.81, p = 0.042), and the combination of CYP3A4 inhibitors (strong/moderate) (OR: 2.80, 95%CI: 1.02–7.67, p = 0.045) as risk factors. Therefore, the development of diarrhea needs to be considered in patients with these risk factors. Furthermore, diarrhea may be controlled by the initiation of NAL within 7 d of opioid analgesics and, where possible, the discontinuation of or change in the combination of moderate or strong CYP3A4 inhibitors., Biological and Pharmaceutical Bulletin, 44(8), pp. 1081-1087; 2021