Influence of age and co-medication on dolutegravir glucuronidation in paediatric patients.

Dolutegravir (DTG) is primarily metabolized by uridine diphosphate glucuronosyltransferases, forming the pharmacologically inactive DTG glucuronide (DTG-gluc). We described the dolutegravir metabolic ratio (DTG-MR; DTG-gluc AUC0-24h divided by DTG AUC0-24h) in 85 children with HIV aged 3 months to 18 years receiving DTG in the CHAPAS-4 (ISRCTN22964075) and ODYSSEY (NCT02259127) trials. Additionally, we assessed the influence of age, body weight, nucleoside/nucleotide reverse transcriptase inhibitor (NRTI) backbone, rifampicin use and kidney function on DTG-MR. The overall geometric mean (CV%) DTG-MR was 0.054 (52%). Rifampicin use was the only significant factor associated with DTG-MR (P < .001) in multiple linear regression. DTG-MR geometric mean ratio was 1.81 (95% CI: 1.57-2.08) for children while on vs. off rifampicin. This study showed that overall DTG-MR in children was similar to adults, unaffected by age or NRTI backbone, and increased with rifampicin co-administration. These findings support future paediatric pharmacokinetic modelling and extrapolation from adult data.

Drug Transporters in Drug Disposition - Highlights from the Year 2023.

Drug transporter field is rapidly evolving with significant progress in in vitro and in vivo tools and, computational models to assess transporter-mediated drug disposition and drug-drug interactions (DDIs) in humans. On behalf of all co-authors, I am pleased to share the fourth annual review highlighting articles published and deemed influential in the field of drug transporters in the year 2023. Each co-author independently selected peer-reviewed articles published or available online in the year 2023 and summarized them as shown previously (Chothe et al. 2021; Chothe et al. 2022, 2023) with unbiased perspectives. Based on selected articles, this review was categorized into four sections: 1) transporter structure and in vitro evaluation, 2) novel in vitro/ex vivo models, 3) endogenous biomarkers, and 4) PBPK modeling for evaluating transporter DDIs (Table 1). As the scope of this review is not to comprehensively review each article, readers are encouraged to consult original paper for specific details. Finally, I appreciate all the authors for their time and continued support in writing this review.

Assessment of drug-drug interactions among patients with hematologic malignancy: A clinical pharmacist-led study.

INTRODUCTION: Patients with hematologic malignancies often receive multiple medications, leading to potential drug-drug interactions (DDIs). Identifying and managing these DDIs is crucial for ensuring patient safety and effective care. This study aimed to identify and describe DDIs and associated factors in hematologic malignancy patients. METHODS: This prospective interventional study was conducted at a referral center and included hospitalized patients with hematologic malignancies who were receiving at least four concurrent medications. A pharmacist initially compiled a comprehensive list of all medications through patient interviews and medication reviews, and subsequently, identified and categorized potential DDIs using the Lexi-interact® and Micromedex® databases. The clinical pharmacist then evaluated the clinical impact of the identified DDIs in every individual patient and provided appropriate interventions to resolve them. RESULTS: A total of 200 patients met the inclusion criteria for the study, with 1281 DDIs identified across 337 distinct types. The majority of identified DDIs exhibited major severity (52.1%) and pharmacokinetic mechanisms (50.3%), with an unspecified onset (79.4%) and fair evidence (67%). Of the identified DDIs, 81.1% were considered clinically significant, prompting 1059 pharmacotherapy interventions by the clinical pharmacist. Additionally, a significant relationship was observed between the number of drugs used during hospitalization and the occurrence of DDIs (P < 0.001, r = 0.633). CONCLUSION: DDIs are highly prevalent among hospitalized patients with hematologic malignancies, with their occurrence increasing alongside the number of medications administrated. The intervention of a clinical pharmacist is crucial to evaluate the clinical impact of these DDIs and implement effective interventions for their management.

In Vitro Assessment of Inhibitory Effects of Kinase Inhibitors on CYP2C9, 3A and 1A2: Prediction of Drug-Drug Interaction Risk with Warfarin and Direct Oral Anticoagulants.

OBJECTIVE: This study aimed to evaluate the cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) potential of kinase inhibitors with warfarin and direct oral anticoagulants (DOACs). METHODS: An in vitro CYP probe substrate cocktail assay was used to study the inhibitory effects of fifteen kinase inhibitors on CYP2C9, 3A, and 1A2. Then, DDI predictions were performed using both mechanistic static and physiologically-based pharmacokinetic (PBPK) models. RESULTS: Linsitinib, masitinib, regorafenib, tozasertib, trametinib, and vatalanib were identified as competitive CYP2C9 inhibitors (Ki = 1.4, 1.0, 1.1, 3.8, 0.5, and 0.1 µM, respectively). Masitinib and vatalanib were competitive CYP3A inhibitors (Ki = 1.3 and 0.2 µM), and vatalanib noncompetitively inhibited CYP1A2 (Ki = 2.0 µM). Moreover, linsitinib and tozasertib were CYP3A time-dependent inhibitors (KI = 26.5 and 400.3 µM, kinact = 0.060 and 0.026 min-1, respectively). Only linsitinib showed time-dependent inhibition of CYP1A2 (KI = 13.9 µM, kinact = 0.018 min-1). Mechanistic static models identified possible DDI risks for linsitinib and vatalanib with (S)-/(R)-warfarin, and for masitinib with (S)-warfarin. PBPK simulations further confirmed that vatalanib may increase (S)- and (R)-warfarin exposure by 4.37- and 1.80-fold, respectively, and that linsitinib may increase (R)-warfarin exposure by 3.10-fold. Mechanistic static models predicted a smaller risk of DDIs between kinase inhibitors and apixaban or rivaroxaban. The greatest AUC increases (1.50-1.74) were predicted for erlotinib in combination with apixaban and rivaroxaban. Linsitinib, masitinib, and vatalanib were predicted to have a smaller effect on apixaban and rivaroxaban AUCs (AUCR 1.22-1.53). No kinase inhibitor was predicted to increase edoxaban exposure. CONCLUSIONS: Our results suggest that several kinase inhibitors, including vatalanib and linsitinib, can cause CYP-mediated drug-drug interactions with warfarin and, to a lesser extent, with apixaban and rivaroxaban. The work provides mechanistic insights into the risk of DDIs between kinase inhibitors and anticoagulants, which can be used to avoid preventable DDIs in the clinic.

Characteristics of CYP3A4-related potential drug-drug interactions in outpatients receiving prescriptions from multiple clinical departments.

BACKGROUND: Drug-drug interactions (DDIs) increase the incidence of adverse drug reactions (ADRs). In a previous report, we revealed that the incidence of potential DDIs due to the same CYP molecular species in one prescription exceeds 90% among patients taking six or more drugs and that CYP3A4 markedly influences the increase in the number of potential DDIs in clinical practice. However, the factors contributing to an increased number of potential DDIs in prescriptions from multiple clinical departments remain poorly clarified. METHODS: This observational study was performed at five pharmacies in Okayama Prefecture, Japan. Patients who visited these pharmacies from 11 April 2022 to 24 April 2022 were included, except those who had prescriptions only from a single clinical department. A stratified analysis was performed to determine the incidence of CYP3A4-related potential DDIs according to the number of drugs taken. Additionally, factors associated with an increase in the number of drugs involved in CYP3A4-related potential DDIs were identified using multiple linear regression analysis. In this study, potential DDIs for the prescription data subdivided by clinical department, containing two or more drugs, were used as control data. RESULTS: Overall, 372 outpatients who received prescriptions from multiple clinical departments were included in the current study. The number of drugs contributing to CYP3A4-related potential DDIs increased with an increase in the number of clinical departments. Notably, in cases taking fewer than six drugs, prescriptions from multiple clinical departments had a higher frequency of CYP3A4-related potential DDIs than those in prescriptions subdivided by clinical department. Multiple regression analysis identified "Cardiovascular agents", "Agents affecting central nervous system", and "Urogenital and anal organ agents" as the top three drug classes that increase CYP3A4-related potential DDIs. CONCLUSION: Collectively, these results highlight the importance of a unified management strategy for prescribed drugs and continuous monitoring of ADRs in outpatients receiving prescriptions from multiple clinical departments even if the number of drugs taken is less than six.

Effect of midostaurin on the pharmacokinetics of P-gp, BCRP, and CYP2D6 substrates: assessing potential drug-drug interactions in healthy participants : Brief title: Drug-drug interaction of midostaurin.

PURPOSE: Midostaurin, approved for FLT3-mutated acute myeloid leukemia and advanced systemic mastocytosis, is mainly metabolized by cytochrome P450 (CYP) 3A4. Midostaurin exhibited potential inhibitory effects on P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic anion-transporting polyprotein 1B1, and CYP2D6 in in vitro studies. This study investigated the pharmacokinetic (PK) effects of midostaurin on P-gp (digoxin), BCRP (rosuvastatin) and CYP2D6 (dextromethorphan) substrates in healthy adults. METHODS: This was an open-label, single-sequence, phase I clinical study evaluating the effect of single-dose midostaurin (100 mg) on the PK of digoxin and rosuvastatin (Arm 1), and dextromethorphan (Arm 2). Participants were followed up for safety 30 days after last dose. In addition, the effect of midostaurin on the PK of dextromethorphan metabolite (dextrorphan) was assessed in participants with functional CYP2D6 genes in Arm 2. RESULTS: The effect of midostaurin on digoxin was minor and resulted in total exposure (AUC) and peak plasma concentration (Cmax) that were only 20% higher. The effect on rosuvastatin was mild and led to an increase in AUCs of approximately 37-48% and of 100% in Cmax. There was no increase in the primary PK parameters (AUCs and Cmax) of dextromethorphan in the presence of midostaurin. The study treatments were very well tolerated with no occurance of severe adverse events (AEs), AEs of grade ≥ 2, or deaths. CONCLUSION: Midostaurin showed only a minor inhibitory effect on P-gp, a mild inhibitory effect on BCRP, and no inhibitory effect on CYP2D6. Study treatments were well tolerated in healthy adults.

Clinical Significance and Patterns of Potential Drug-Drug Interactions in Cardiovascular Patients: Focus on Low-Dose Aspirin and Angiotensin-Converting Enzyme Inhibitors.

Objective: This study assessed the patterns and clinical significance of potential drug-drug interactions (pDDIs) in patients with diseases of the cardiovascular system. Methods: Electronic health records (EHRs), established in 2018-2023, were selected using the probability serial nested sampling method (n = 1030). Patients were aged 27 to 95 years (65.0% men). Primary diagnosis of COVID-19 was present in 17 EHRs (1.7%). Medscape Drug Interaction Checker was used to characterize pDDIs. The Mann-Whitney U test and chi-square test were used for statistical analysis. Results: Drug numbers per record ranged from 1 to 23 in T-List and from 1 to 20 in P-List. In T-List, 567 drug combinations resulted in 3781 pDDIs. In P-List, 584 drug combinations resulted in 5185 pDDIs. Polypharmacy was detected in 39.0% of records in T-List versus 65.9% in P-List (p-value < 0.05). The rates of serious and monitor-closely pDDIs due to 'aspirin + captopril' combinations were significantly higher in P-List than in T-List (p-value < 0.05). The rates of serious pDDIs due to 'aspirin + enalapril' and 'aspirin + lisinopril' combinations were significantly lower in P-List compared with the corresponding rates in T-List (p-value < 0.05). Serious pDDIs due to administration of aspirin with fosinopril, perindopril, and ramipril were detected less frequently in T-List (p-value < 0.05). Conclusions: Obtained data may suggest better patient adherence to 'aspirin + enalapril' and 'aspirin + lisinopril' combinations, which are potentially superior to the combinations of aspirin with fosinopril, perindopril, and ramipril. An abundance of high-order pDDIs in real-world clinical practice warrants the development of a decision support system aimed at reducing pharmacotherapy-associated risks while integrating patient pharmacokinetic, pharmacodynamic, and pharmacogenetic information.

[Renal failure and drug-drug interactions]. / Bei Niereninsuffizienz auf Arzneimittel-Interaktionen achten!

Renal failure is common and comes with a steep increasing prevalence in older patients. It is a frequent aspect in multimorbidity and associated with polypharmacia. Based on available literature an overview is given concerning important drug-drug interactions and how to avoid or manage them. Among a large variety of possible interactions anticoagulation and diuretic therapy still represent the highest clinical relevance.

Grace periods and exposure misclassification in self-controlled case-series studies of drug-drug interactions.

The self-controlled case-series (SCCS) research design is increasingly used in pharmacoepidemiologic studies of drug-drug interactions (DDIs), with the target of inference being the incidence rate ratio (IRR) associated with concomitant exposure to the object plus precipitant drug versus the object drug alone. While day-level drug exposure can be inferred from dispensing claims, these inferences may be inaccurate, leading to biased IRRs. Grace periods (periods assuming continued treatment impact after days' supply exhaustion) are frequently used by researchers, but the impact of grace period decisions on bias from exposure misclassification remains unclear. Motivated by an SCCS study examining the potential DDI between clopidogrel (object) and warfarin (precipitant), we investigated bias due to precipitant or object exposure misclassification using simulations. We show that misclassified precipitant treatment always biases the estimated IRR toward the null, whereas misclassified object treatment may lead to bias in either direction or no bias, depending on the scenario. Further, including a grace period for each object dispensing may unintentionally increase the risk of misclassification bias. To minimize such bias, we recommend 1) avoiding the use of grace periods when specifying object drug exposure episodes; and 2) including a washout period following each precipitant exposed period.

Hyperkalemic effect of drug-drug interaction between esaxerenone and trimethoprim in patients with hypertension: a pilot study.

BACKGROUND: We examined whether the pharmacodynamic drug-drug interaction between esaxerenone and trimethoprim enhances the hyperkalemic effect. METHODS: A retrospective observational study was conducted to identify patients >18 years undertaking esaxerenone alone or esaxerenone plus trimethoprim at Mie University Hospital from May 2019 to December 2022. We performed propensity score-matching (1:1) to compare between-group differences in the maximum change in serum potassium levels (ΔK) using the Mann-Whitney U test. For esaxerenone plus trimethoprim, Spearman's correlation coefficients were used to examine correlations between ΔK and variables, including changes in blood urea nitrogen (ΔBUN), serum creatinine levels (ΔCr), and weekly trimethoprim cumulative dose. RESULTS: Out of propensity score-matched groups (n=8 each), serum potassium levels significantly increased after administration of esaxerenone alone (4.4 [4.2 to 4.7] meq/L to 5.2 [4.7 to 5.4] meq/L, p=0.008) and esaxerenone plus trimethoprim (4.2 [4.0 to 5.1] meq/L to 5.4 [4.7 to 5.5] meq/L, p=0.023). ΔK did not significantly differ between the groups (esaxerenone alone; 0.6 [0.3 to 0.9] meq/L vs. esaxerenone plus trimethoprim; 1.0 [0.4 to 1.3] meq/L, p=0.342). ΔK positively correlated with ΔBUN (r=0.988, p<0.001) or ΔCr (r=0.800, p=0.017). There was a trend of correlation of ΔK with a weekly cumulative trimethoprim dose (r=0.607, p=0.110). CONCLUSIONS: The hyperkalemic effect of the drug-drug interaction between esaxerenone and trimethoprim is not notable and related to renal function and trimethoprim dosage.

Effect of Itraconazole, a CYP3A4 Inhibitor, and Rifampin, a CYP3A4 Inducer, on the Pharmacokinetics of Vatiquinone.

Vatiquinone is a small molecule inhibitor of 15-lipoxygenase in development for patients with Friedreich's ataxia. The objective of this analysis was to determine the effect of a cytochrome P450 isoform 3A4 (CYP3A4) inhibitor and inducer on vatiquinone pharmacokinetics (PKs). The coadministration of 400 mg of vatiquinone with 200 mg of itraconazole (a CYP3A4 inhibitor) resulted in increased maximum observed concentration (Cmax) of vatiquinone and systemic exposure (AUC0-inf) by approximately 3.5- and 2.9-fold, respectively. The coadministration of 400 mg of vatiquinone with 600 mg of rifampin (a CYP3A4 inducer) resulted in decreased vatiquinone Cmax and AUC0-inf by approximately 0.64- and 0.54-fold, respectively. The terminal half-life of vatiquinone was not affected by itraconazole or rifampin. These clinical study results confirm the in vitro reaction phenotyping data that shows that CYP3A4 plays an important role in vatiquinone metabolism. The result of this analysis together with phase 3 efficacy and safety data, population PK analysis, and the exposure-response relationship will determine if the extent of vatiquinone changes in the presence of CYP3A4 inhibitors and inducers are considered clinically relevant.

Development of UPLC-MS/MS method for studying the pharmacokinetic interactions of fuzuloparib with curcumin in rats.

Fuzuloparib is a novel orally bioactive poly-ADP-ribose polymerase inhibitor (PARPi), which was approved by the Chinese Regulatory Agency (CRA) in 2020 for the treatment of platinum-sensitive recurrent ovarian, fallopian tube, and primary peritoneal cancers. This study firstly presents a rapid and accurate ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method for analyzing the levels of fuzuloparib and its major metabolite (SHR165202), and to investigate drug-drug interaction between fuzuloparib and curcumin in vitro and in vivo studies. After protein precipitation with acetonitrile, mobile phase consisted of acetonitrile and 0.1 % formic acid with a gradient elution was used to successfully separate fuzuloparib, SHR165202 and talazoparib (internal standard, IS). The results indicated that fuzuloparib and SHR165202 had good linearity over the calibration range of 2-50 ng/mL and 1-20 ng/mL, respectively. The precision, accuracy, stability, matrix effect, and extraction recovery required for methodological validation all complied with the requirements of the Bioanalytical Method Validation Guidelines. In vitro microsome incubation experiments, curcumin exhibited inhibitory effect on fuzuloparib in both rat liver microsomes (RLM) and human liver microsomes (HLM) with half-maximal inhibitory concentration (IC50) value of 10.54 µM and 47.64 µM, respectively, and the corresponding mechanism was non-competitive. Furthermore, the inhibitory mechanism of curcumin on fuzuloparib was validated through molecular docking. In pharmacokinetic experiments in rats, curcumin significantly altered the plasma exposure of fuzuloparib, resulting in significant increases in AUC(0-t) and Cmax of fuzuloparib and a significant decrease in CLz/F. Moreover, the metabolite SHR165202 showed significant increases in AUC(0-t), AUC(0-∞), Tmax and Cmax and a significant decrease in CLz/F. This further supports the notion that curcumin could inhibit the metabolism of fuzuloparib. Therefore, when co-administering fuzuloparib and curcumin in clinic, it is recommended to monitor plasma levels of fuzuloparib and pay close attention to adverse effects. If necessary, the dose of fuzuloparib needs to be reduced.

The metabolic activation of and platelet response to vicagrel vary with P-glycoprotein deficiency, rather than P-glycoprotein inhibition, in mice.

This study aimed to determine changes in the hydrolysis of vicagrel, a substrate drug of arylacetamide deacetylase (Aadac) and carboxylesterase 2 (Ces2), in P-glycoprotein (P-gp)-deficient or P-gp-inhibited mice and to elucidate the mechanisms involved.Male wild-type (WT) and P-gp knock-out (KO) mice were used to investigate the systemic exposure of vicagrel thiol active metabolite H4 and platelet response to vicagrel, and the mRNA and protein expression levels of intestinal Aadac and Ces2. Moreover, WT mice were administered vicagrel alone or in combination with elacridar (a potent P-gp inhibitor) to determine drug-drug interactions.Compared with WT mice, P-gp KO mice exhibited significant increases in the systemic exposure of H4, the protein expression levels of intestinal Aadac and Ces2, and inhibition of ADP-induced platelet aggregation by vicagrel. Further, the H4 exposure was positively correlated with intestinal Aadac protein expression levels but did not vary with short-term inhibition of P-gp efflux activity by elacridar.P-gp-deficient mice, rather than elacridar-treated mice, exhibited significant upregulation of intestinal Aadac and Ces2 and thus, enhanced metabolic activation of and platelet response to vicagrel, suggesting that the metabolic activation of vicagrel may vary with P-gp deficiency, not P-gp inhibition, in mice.

Probe substrates assay estimates the effect of polyphyllin H on the activity of cytochrome P450 enzymes in human liver microsomes.

Cytochrome P450 enzymes (CYPs) play a crucial role in phase I metabolic reactions. The activity of CYPs would affect therapeutic efficacy and may even induce toxicity. Given the complex components of traditional Chinese medicine, it is important to understand the effect of active ingredients on CYPs activity to guide their prescription. This study aimed to evaluate the effect of polyphyllin H on the activity of CYPs major isoforms providing a reference for the clinical prescription of polyphyllin H and its source herbs. The effects of polyphyllin H were evaluated in pooled human liver microsomes using probe substrates of CYP1A2, 2A6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4 to determine their activities. The Lineweaver-Burk was used to model the inhibition, and a time-dependent inhibition experiment was performed to understand the characteristics of the inhibition. Polyphyllin H significantly suppressed the activity of CYP1A2, 2D6, and 3A4 with IC50 values of 6.44, 13.88, and 4.52 µM, respectively. The inhibition of CYP1A2 and 2D6 was best fitted with a competitive model, yielding the inhibition constant (Ki) values of 3.18 and 6.77 µM, respectively. The inhibition of CYP3A4 was fitted with the non-competitive model with the Ki value of 2.38 µM. Moreover, the inhibition of CYP3A4 was revealed to be time-dependent with the inhibition parameters inhibition constant (KI) and inactivation rate constant (Kinact) values of 2.26 µM-1 and 0.045 min-1. Polyphyllin H acted as a competitive inhibitor of CYP1A2 and 2D6 and a non-competitive and time-dependent inhibitor of CYP3A4.

Innovative Approaches to Optimize Clinical Transporter Drug-Drug Interaction Studies.

Of the 450 cell membrane transporters responsible for shuttling substrates, nutrients, hormones, neurotransmitters, antioxidants, and signaling molecules, approximately nine are associated with clinically relevant drug-drug interactions (DDIs) due to their role in drug and metabolite transport. Therefore, a clinical study evaluating potential transporter DDIs is recommended if an investigational product is intestinally absorbed, undergoes renal or hepatic elimination, or is suspected to either be a transporter substrate or perpetrator. However, many of the transporter substrates and inhibitors administered during a DDI study also affect cytochrome P450 (CYP) activity, which can complicate data interpretation. To overcome these challenges, the assessment of endogenous biomarkers can help elucidate the mechanism of complex DDIs when multiple transporters or CYPs may be involved. This perspective article will highlight how creative study designs are currently being utilized to address complex transporter DDIs and the role of physiology-based -pharmacokinetic (PBPK) models can play.

Ovicidal activity of diaryl dichalcogenides and ivermectin on Fasciola hepatica: A novel candidate for a blending-based therapeutic strategy.

Fasciolosis is a food and waterborne disease caused by Fasciola spp., representing a global health burden to various hosts, including humans and other animals. This study investigates the in vitro activity of tellurium- and selenium-containing diaryl dichalcogenides: diacetal ditelluride (LQ07), diacetal diselenide (LQ62), and diacetyl diselenide (LQ68) alone and in combination with ivermectin (IVM) against eggs of Fasciola hepatica. The eggs were exposed for 12 h with each organochalcogen (OC) (0.1 - 2 mmol l-1) and IVM (0.01 - 2 mmol l-1) following an incubation of 15 days, allowing embryonation. The inhibitory concentration of 50 % (IC50) of each OC or IVM was tested with the IC10, IC30, and IC50 of IVM or each OC, respectively. LQ07, LQ62, and LQ68, as well as IVM, demonstrated a concentration-dependent ovicidal activity. The peak ovicidal activity of 99.74 % was achieved when IVM was tested at 2.0 mmol l-1. LQ62 and LQ68 demonstrated greater ovicidal activity, having an IC50 < 0.32 mmol l-1 being 6.25-fold more toxic than IVM alone. The percentage of dead eggs was significantly higher in the IVM group (early mortality), as Se-containing OCs led to the (miracidia) embryonation of the eggs with no hatching (late mortality). Blending Se-containing OCs and IVM showed an additive effect of up to 27 % against F. hepatica eggs. The present data contribute to the potential use of blending-based therapeutic strategies to combat F. hepatica infections in eradication programs worldwide. The combinations may also act against multidrug-resistant strains, reinstating drug-based parasite control.

Physiologically based pharmacokinetic modeling of CYP2C8 substrate rosiglitazone and its metabolite to predict metabolic drug-drug interaction.

Rosiglitazone is an activator of nuclear peroxisome proliferator-activated (PPAR) receptor gamma used in the treatment of type 2 diabetes mellitus. The elimination of rosiglitazone occurs mainly via metabolism, with major contribution by enzyme cytochrome P450 (CYP) 2C8. Primary routes of rosiglitazone metabolism are N-demethylation and hydroxylation. Modulation of CYP2C8 activity by co-administered drugs lead to prominent changes in the exposure of rosiglitazone and its metabolites. Here, we attempt to develop mechanistic parent-metabolite physiologically based pharmacokinetic (PBPK) model for rosiglitazone. Our goal is to predict potential drug-drug interaction (DDI) and consequent changes in metabolite N-desmethyl rosiglitazone exposure. The PBPK modeling was performed in the PKSim® software using clinical pharmacokinetics data from literature. The contribution to N-desmethyl rosiglitazone formation by CYP2C8 was delineated using vitro metabolite formation rates from recombinant enzyme system. Developed model was verified for prediction of rosiglitazone DDI potential and its metabolite exposure based on observed clinical DDI studies. Developed model exhibited good predictive performance both for rosiglitazone and N-desmethyl rosiglitazone respectively, evaluated based on commonly acceptable criteria. In conclusion, developed model helps with prediction of CYP2C8 DDI using rosiglitazone as a substrate, as well as changes in metabolite exposure. In vitro data for metabolite formation can be successfully utilized to translate to in vivo conditions.

Drug-induced enzyme activity inhibition and CYP3A4 genetic polymorphism significantly shape the metabolic characteristics of furmonertinib.

This study aimed to elucidate the impact of variations in liver enzyme activity, particularly CYP3A4, on the metabolism of furmonertinib. An in vitro enzyme incubation system was established for furmonertinib using liver microsomes and recombinant CYP3A4 baculosomes, with analytes detected by LC-MS/MS. The pharmacokinetic characteristics of furmonertinib were studied in vivo using Sprague-Dawley rats. It was found that telmisartan significantly inhibited the metabolism of furmonertinib, as demonstrated by a significant increase in the AUC of furmonertinib when co-administered with telmisartan, compared to the furmonertinib-alone group. Mechanistically, it was noncompetitive in rat liver microsomes, while it was mixed competitive and noncompetitive in human liver microsomes and CYP3A4. Considering the genetic polymorphism of CYP3A4, the study further investigated its effect on the kinetics of furmonertinib. The results showed that compared to CYP3A4.1, CYP3A4.29 had significantly increased activity in catalyzing furmonertinib, whereas CYP3A4.7, 9, 10, 12, 13, 14, 18, 23, 33, and 34 showed markedly decreased activity. The inhibitory activity of telmisartan varied in CYP3A4.1 and CYP3A4.18, with IC50 values of 8.56 ± 0.90 µM and 27.48 ± 3.52 µM, respectively. The key loci affecting the inhibitory effect were identified as ARG105, ILE301, ALA370, and LEU373. Collectively, these data would provide a reference for the quantitative application of furmonertinib.

DKPE-GraphSYN: a drug synergy prediction model based on joint dual kernel density estimation and positional encoding for graph representation.

Introduction: Synergistic medication, a crucial therapeutic strategy in cancer treatment, involves combining multiple drugs to enhance therapeutic effectiveness and mitigate side effects. Current research predominantly employs deep learning models for extracting features from cell line and cancer drug structure data. However, these methods often overlook the intricate nonlinear relationships within the data, neglecting the distribution characteristics and weighted probability densities of gene expression data in multi-dimensional space. It also fails to fully exploit the structural information of cancer drugs and the potential interactions between drug molecules. Methods: To overcome these challenges, we introduce an innovative end-to-end learning model specifically tailored for cancer drugs, named Dual Kernel Density and Positional Encoding (DKPE) for Graph Synergy Representation Network (DKPEGraphSYN). This model is engineered to refine the prediction of drug combination synergy effects in cancer. DKPE-GraphSYN utilizes Dual Kernel Density Estimation and Positional Encoding techniques to effectively capture the weighted probability density and spatial distribution information of gene expression, while exploring the interactions and potential relationships between cancer drug molecules via a graph neural network. Results: Experimental results show that our prediction model achieves significant performance enhancements in forecasting drug synergy effects on a comprehensive cancer drug and cell line synergy dataset, achieving an AUPR of 0.969 and an AUC of 0.976. Discussion: These results confirm our model's superior accuracy in predicting cancer drug combinations, providing a supportive method for clinical medication strategy in cancer.