Drug-drug Interaction Studies Research Articles

Exploring inter-ethnic and inter-patient variability and optimal dosing of osimertinib: a physiologically based pharmacokinetic modeling approach.

Purpose: This study aimed to develop and validate a physiologically based pharmacokinetic (PBPK) model for osimertinib (OSI) to predict plasma trough concentration (Ctrough) and pulmonary EGFRm+ (T790M and L858R mutants) inhibition in Caucasian, Japanese, and Chinese populations. The PBPK model was also utilized to investigate inter-ethnic and inter-patient differences in OSI pharmacokinetics (PK) and determine optimal dosing regimens. Methods: Population PBPK models of OSI for healthy and disease populations were developed using physicochemical and biochemical properties of OSI and physiological parameters of different groups. And then the PBPK models were validated using the multiple clinical PK and drug-drug interaction (DDI) study data. Results: The model demonstrated good consistency with the observed data, with most of prediction-to-observation ratios of 0.8-1.25 for AUC, Cmax, and Ctrough. The PBPK model revealed that plasma exposure of OSI was approximately 2-fold higher in patients compared to healthy individuals, and higher exposure observed in Caucasians compared to other ethnic groups. This was primarily attributed to a lower CL/F of OSI in patients and Caucasian. The PBPK model displayed that key factors influencing PK and EGFRm+ inhibition differences included genetic polymorphism of CYP3A4, CYP1A2 expression, plasma free concentration (fup), albumin level, and auto-inhibition/induction on CYP3A4. Inter-patient PK variability was most influenced by CYP3A4 variants, fup, and albumin level. The PBPK simulations indicated that the optimal dosing regimen for patients across the three populations of European, Japanese, and Chinese ancestry was OSI 80mg once daily (OD) to achieve the desired range of plasma Ctrough (328-677nmol/L), as well as 80mg and 160mg OD for desirable pulmonary EGFRm+ inhibition (>80%). Conclusion: In conclusion, this study's PBPK simulations highlighted potential ethnic and inter-patient variability in OSI PK and EGFRm+ inhibition between Caucasian, Japanese, and Chinese populations, while also providing insights into optimal dosing regimens of OSI.

A prospective study of drug-drug interaction between antiepileptic drugs and meropenem in patients in a tertiary hospital in China from January 2020 to March 2023.

This study aimed to determine the minimum interaction between different antiepileptic drugs (AEDs) and meropenem (MEPM) for clinical treatment. The data of 91 patients enrolled in the neurology department from January 2020 to March 2023 for clinical trials were measured and observed. Self-controlled studies were conducted to monitor the trough concentrations of valproic acid (VPA), carbamazepine (CBZ) and levotiracetam (LEV) before and after MEPM usage. Relevant indicators of liver and kidney function were also monitored. The serum VPA trough concentrations were 36.25±8.22 μg/ml at 24±12 h and 34.99±11.17 μg/ml at 96±12 h after MEPM use; the difference was significant (p<0.05). Decreased CBZ trough concentrations were also identified after MEPM usage (96±12 h), whereas LEV trough concentrations were not affected. An increased liver injury rate (χ2 =8.744, p<0.05) and a decreased kidney injury rate (χ2 =5.393, p<0.05) were found in the VPA group only. The interaction between VPA and MEPM decreased serum VPA concentrations, increased liver injury rates, and decreased kidney injury rates. In addition, the co-administration of MEPM and CBZ reduced serum CBZ concentrations. Clinicians should be aware of this potential interaction and closely monitor the relevant biochemical indices and number of seizures.

A Study of Potential Drug-Drug Interactions in Peptic Ulcer Patients in Dr. M. Djamil Hospital Padang in 2019

A peptic ulcer is a gastrointestinal tract disorder caused by excessive secretion of acid and pepsin by the gastric mucosa. The combined use of drugs in peptic ulcer management may potentially cause drug-to-drug interactions. Drug interactions are caused when two or more drugs interact and may affect the effectiveness of the other drugs. This study aims to analyze the potential drug reactions in peptic ulcer patients in the internal medicine ward of Dr. M. Djamil General Hospital Padang in 2019. This study was a descriptive, non-experimental study with retrospective data, while data analysis used Drugs.com and Medscape applications. The study results revealed that 29 patients fit the inclusion criteria and 5 (16.6%) potential drug pharmacokinetic interactions. The severity of drug interactions shows 4 cases (80%) with moderate severity potential and 1 case (20%) with minor severity potential. The study concluded that there were potential drug interactions in peptic ulcer patients in the internal medicine ward of Dr. M. Djamil General Hospital Padang in 2019.

Effects of Strong Inhibition of Cytochrome P450 3A and UDP glucuronosyltransferase 1A9 and Strong Induction of Cytochrome P450 3A on the Pharmacokinetics, Safety, and Tolerability of Soticlestat: Two Drug-Drug Interaction Studies in Healthy Volunteers.

Two open-label, phase 1 studies (NCT05064449, NCT05098041) investigated the effects of cytochrome P450 (CYP) 3A inhibition (via itraconazole), UDP glucuronosyltransferase (UGT) 1A9 inhibition (via mefenamic acid), and CYP3A induction (via rifampin) on the pharmacokinetics of soticlestat and its metabolites M-I and M3. In period 1 of both studies, participants received a single dose of soticlestat 300 mg. In period 2, participants received itraconazole on days 1-11 and soticlestat 300 mg on day 5 (itraconazole/mefenamic acid study; part 1); mefenamic acid on days 1-7 and soticlestat 300 mg on day 2 (itraconazole/mefenamic acid study; part 2); or rifampin on days 1-13 and soticlestat 300 mg on day 11 (rifampin study). Twenty-eight healthy adults participated in the itraconazole/mefenamic acid study (14 per part) and 15 participated in the rifampin study (mean age, 38.1-40.7 years; male, 79-93%). For maximum observed concentration, the geometric mean ratios (GMRs) of soticlestat + itraconazole, mefenamic acid, or rifampin to soticlestat alone were 116.6%, 107.3%, and 13.2%, respectively, for soticlestat; 10.7%, 118.0%, and 266.1%, respectively, for M-I, and 104.6%, 88.2%, and 66.6%, respectively, for M3. For area under the curve from time 0 to infinity, the corresponding GMRs were 124.0%, 100.6%, and 16.4% for soticlestat; 13.3%, 117.0%, and 180.8% for M-I; and 120.3%, 92.6%, and 58.4% for M3. Soticlestat can be administered with strong CYP3A and UGT1A9 inhibitors, but not strong CYP3A inducers (except for antiseizure medications, which will be further evaluated in ongoing phase 3 studies). In both studies, all treatment-emergent adverse events were mild or moderate. SIGNIFICANCE STATEMENT: These drug-drug interaction studies improve our understanding of the potential changes that may arise in soticlestat exposure in patients being treated with CYP3A inhibitors, UGT1A9 inhibitors, or CYP3A inducers. The results build on findings from previously published soticlestat studies and provide important information to help guide clinical practice. Soticlestat has shown positive phase 2 results and is currently in phase 3 development for the treatment of seizures in patients with Dravet syndrome and Lennox-Gastaut syndrome.

Study of the ketohexokinase inhibitor PF-06835919 as a clinical cytochrome P450 3A inducer: Integrated use of oral midazolam and liquid biopsy.

PF-06835919, a ketohexokinase inhibitor, presented as an inducer of cytochrome P450 3A4 (CYP3A4) invitro (human primary hepatocytes), and static mechanistic modeling exercises predicted significant induction invivo (oral midazolam area under the plasma concentration-time curve [AUC] ratio [AUCR] = 0.23-0.79). Therefore, a drug-drug interaction study was conducted to evaluate the effect of multiple doses of PF-06835919 (300 mg once daily × 10 days; N = 10 healthy participants) on the pharmacokinetics of a single oral midazolam 7.5 mg dose. The adjusted geometric means for midazolam AUC and its maximal plasma concentration were similar following co-administration with PF-06835919 (vs. midazolam administration alone), with ratios of the adjusted geometric means (90% confidence interval [CI]) of 97.6% (90% CI: 79.9%-119%) and 98.9% (90% CI: 76.4%-128%), respectively, suggesting there was minimal effect of PF-06835919 on midazolam pharmacokinetics. Lack of CYP3A4 induction was confirmed after the preparation of subject plasma-derived small extracellular vesicles (sEVs) and conducting proteomic and activity (midazolam 1'-hydroxylase) analysis. Consistent with the midazolam AUCR observed, the CYP3A4 protein expression fold-induction (geometric mean, 90% CI) was low in liver (0.9, 90% CI: 0.7-1.2) and non-liver (0.9, 90% CI: 0.7-1.2) sEVs (predicted AUCR = 1.0, 90% CI: 0.9-1.2). Likewise, minimal induction of CYP3A4 activity (geometric mean, 90% CI) in both liver (1.1, 90% CI: 0.9-1.3) and non-liver (0.9, 90% CI: 0.5-1.5) sEVs was evident (predicted AUCR = 0.9, 90% CI: 0.6-1.4). The results showcase the integrated use of an oral CYP3A probe (midazolam) and plasma-derived sEVs to assess a drug candidate as inducer.

Physiologically Based Pharmacokinetic Modeling for Maribavir to Inform Dosing in Drug-Drug Interaction Scenarios with CYP3A4 Inducers and Inhibitors.

Maribavir, an orally available antiviral agent, has been approved in multiple countries for the treatment of patients with refractory post-transplant cytomegalovirus (CMV) infection and/or disease. Maribavir is primarily metabolized by CYP3A4; coadministration with CYP3A4 inducers and inhibitors may significantly alter maribavir exposure, thereby affecting its efficacy and safety. The effect of CYP3A4 inducers and inhibitors on maribavir exposure was evaluated based on a drug-drug interaction (DDI) study and physiologically-based pharmacokinetic (PBPK) modeling. The effect of rifampin (a strong inducer of CYP3A4 and moderate inducer of CYP1A2), administered at a 600mg dose once daily, on maribavir pharmacokinetics was assessed in a clinical phase1 DDI study in healthy participants. A full PBPK model for maribavir was developed and verified using invitro and clinical pharmacokinetic data from phase1 studies. The verified PBPK model was then used to simulate maribavir DDI interactions with various CYP3A4 inducers and inhibitors. The DDI study results showed that coadministration with rifampin decreased the maribavir maximum plasma concentration (Cmax), area under the plasma concentration-time curve (AUC), and trough concentration (Ctrough) by 39%, 60%, and 82%, respectively. Based on the results from the clinical DDI study, the coadministration of maribavir with rifampin is not recommended. The PBPK model did not predict a clinically significant effect of CYP3A4 inhibitors on maribavir exposure; however, it predicted that strong or moderate CYP3A4 inducers, including carbamazepine, efavirenz, phenobarbital, and phenytoin, may reduce maribavir exposure to a clinically significant extent, and may prompt the consideration of a maribavir dosing increase, in accordance with local approved labels and/or regulations.

Ciprofol is primarily glucuronidated by UGT1A9 and predicted not to cause drug-drug interactions with typical substrates of CYP1A2, CYP2B6, and CYP2C19

Ciprofol is a novel intravenous anesthetic agent. Its major glucuronide metabolite, M4, is found in plasma and urine. However, the specific isoforms of UDP-glucuronosyltransferases (UGTs) that metabolize ciprofol to M4 remain unknown. This study systematically characterized UGTs that contribute to the formation of M4 using human liver microsomes (HLM), human intestinal microsomes (HIM), and human recombinant UGTs. The inhibitory potential of ciprofol and M4 against major human UGTs and cytochrome P450 enzymes (P450s) was also explored. In vitro-in vivo extrapolation (IVIVE) and physiologically-based pharmacokinetic (PBPK) simulations were performed to predict potential in vivo drug-drug interactions (DDIs) caused by ciprofol. Glucuronidation of ciprofol followed Michaelis-Menten kinetics in both HLM and HIM with apparent Km values of 345 and 412 μM, Vmax values of 2214 and 444 nmol min−1·mg protein−1, respectively. The in vitro intrinsic clearances (CLint = Vmax/Km) for ciprofol glucuronidation by HLM and HIM were 6.4 and 1.1 μL min−1·mg protein−1, respectively. Human recombinant UGT studies revealed that UGT1A9 is the predominant isoform mediating M4 formation, followed by UGT1A7, with UGT1A8 playing a minor role. Ciprofol competitively inhibited CYP1A2 (Ki = 12 μM) and CYP2B6 (Ki = 4.7 μM), and noncompetitively inhibited CYP2C19 (Ki = 29 μM). No time-dependent inhibition by ciprofol was noted for CYP1A2, CYP2B6, or CYP2C19. In contrast, M4 showed limited or no inhibitory effects against selected P450s. Neither ciprofol nor M4 inhibited UGTs significantly. Initial IVIVE suggested potential ciprofol-mediated inhibition of CYP1A2, CYP2B6, and CYP2C19 inhibition in vivo. However, PBPK simulations showed no significant effect on phenacetin, bupropion, and S-mephenytoin exposure or peak plasma concentration. Our findings are pertinent for future DDI studies of ciprofol as either a perpetrator or victim drug.

Observational study of drug-drug interactions in oncological inpatients

ObjectiveTo determine the prevalence of potential clinically relevant drug-drug interactions in adult oncological inpatients, as well as to describe the most frequent interactions. A standard database was used. MethodAn observational, transversal, and descriptive study including patients admitted to the Oncology Service of a reference hospital. All prescriptions were collected twice a week during a month. They were analysed using Lexicomp® database, recording all interactions classified with a level of risk: C, D or X. ResultsA total of 1 850 drug-drug interactions were detected in 218 treatments. The prevalence of treatments with at least one clinically relevant interaction was 95%, being 94.5% for those at level C and 26.1% for levels D and X. The drugs most commonly involved in the interactions detected were opioid analgesics, antipsychotics (butyrophenones), benzodiazepines, pyrazolones, glucocorticoids and heparins, whereas interactions with antineoplastics were minimal, highlighting those related to paclitaxel and between metamizole and various antineoplastics. ConclusionsThe prevalence of clinically relevant drug-drug interactions rate was very high, highlighting the high risk percentage of them related to level of risk X. Due to the frequency of onset and potential severity, highlighted the concomitant use of central nervous system depressants drugs with risk of respiratory depression, the risk of onset of anticholinergic symptoms when combining morphine or haloperidol with butylscopolamine, ipratropium bromide or dexchlorpheniramine and the multiple interactions involving metamizole.

Effect of probenecid on blood levels and renal elimination of furosemide and endogenous compounds in rats: Discovery of putative organic anion transporter biomarkers

Transporter-mediated drug-drug interactions (DDIs) are assessed using probe drugs and in vitro and in vivo models during drug development. The utility of endogenous metabolites as transporter biomarkers is emerging for prediction of DDIs during early phases of clinical trials. Endogenous metabolites such as pyridoxic acid and kynurenic acid have shown potential to predict DDIs mediated by organic anion transporters (OAT1 and OAT3). However, these metabolites have not been assessed in rats as potential transporter biomarkers. We carried out a rat pharmacokinetic DDI study using probenecid and furosemide as OAT inhibitor and substrate, respectively. Probenecid administration led to a 3.8-fold increase in the blood concentrations and a 3-fold decrease in renal clearance of furosemide. High inter-individual and intra-day variability in pyridoxic acid and kynurenic acid, and no or moderate effect of probenecid administration on these metabolites suggest their limited utility for prediction of Oat-mediated DDI in rats. Therefore, rat blood and urine samples were further analysed using untargeted metabolomics. Twenty-one m/z features (out of >8000 detected features) were identified as putative biomarkers of rat Oat1 and Oat3 using a robust biomarker qualification approach. These m/z features belong to metabolic pathways such as fatty acid analogues, peptides, prostaglandin analogues, bile acid derivatives, flavonoids, phytoconstituents, and steroids, and can be used as a panel to decrease variability caused by processes other than Oats. When validated, these putative biomarkers will be useful in predicting DDIs caused by Oats in rats.

Physiologically Based Pharmacokinetic Modeling of the Drug-Drug Interaction Between CYP3A4 Substrate Glasdegib and Moderate CYP3A4 Inducers in Lieu of a Clinical Study.

Glasdegib (DAURISMO) is a hedgehog pathway inhibitor approved for the treatment of acute myeloid leukemia (AML). Cytochrome P450 3A4 (CYP3A4) has been identified as a major metabolism and clearance pathway for glasdegib. The role of CYP3A4 in the clearance of glasdegib has been confirmed with clinical drug-drug interaction (DDI) studies following the coadministration of glasdegib with the strong CYP3A4 inhibitor ketoconazole and the strong inducer rifampin. To evaluate potential drug interactions with CYP3A4 modulators, the coadministration of glasdegib with a moderate CYP3A4 inducer, efavirenz, was evaluated using physiologically based pharmacokinetic (PBPK) modeling using the Simcyp simulator. The glasdegib compound file was developed using measured physicochemical properties, data from human intravenous and oral pharmacokinetics, absorption, distribution, metabolism, and excretion studies, and invitro reaction phenotyping results. The modeling assumptions, model parameters, and assignments of fractional CYP3A4 metabolism were verified using results from clinical pharmacokinetics (PK) and DDI studies with ketoconazole and rifampin. The verified glasdegib and efavirenz compound files, the latter of which was available in the Simcyp simulator, were used to estimate the potential impact of efavirenz on the PK of glasdegib. PBPK modeling predicted a glasdegib area under the concentration-time curve ratio of 0.45 and maximum plasma concentration ratio of 0.75 following coadministration with efavirenz. The PBPK results, in lieu of a formal clinical study, informed the drug label, with the recommendation to double the clinical dose of glasdegib when administered in conjunction with a moderate CYP3A4 inducer, followed by a resumption of the original dose 7days post-discontinuation.

Effect of Verapamil, a P-glycoprotein-1 and Cytochrome P450 3A4 Inhibitor, on Pharmacokinetics and Metabolic Stability of Ripretinib: A Drug-Drug Interaction Study in Rats.

Ripretinib was developed to target a whole range of KIT proto-oncogene mutations and platelet-derived growth factor receptor A (PDGFR-A) kinases found in certain cancers and myeloproliferative neoplasms, particularly gastrointestinal stromal tumours (GISTs). This study investigated the effect of verapamil, a potential inhibitor of P-glycoprotein-1 (P-gp1) and cytochrome P450 3A4 (CYP3A4), on the pharmacokinetics of ripretinib in rats when administered orally together. This study also assessed the metabolic stability and in vitro cellular absorption of ripretinib in the presence of verapamil. A novel sensitive time-saving liquid chromatography tandem mass spectometry (LC-MS/MS) technique for determining ripretinib in rat plasma was developed and validated. A Zorbax SB C18 column was used for the separation and analysis of ripretinib with a mobile phase consisting of 50:50 (%v/v) acetonitrile and 10mM ammonium formate buffer at a flow rate of 0.4 mL/min. Imatinib was used as an internal standard (IS) in the method. The pharmacokinetic characteristics of ripretinib were evaluated in Wistar rats by successfully administering an oral dosage of 5 mg/kg body weight of ripretinib in the presence of verapamil (10 mg/kg body weight). Subsequently, rat liver microsomes were used to assess the effect of verapamil on ripretinib metabolic stability, and absorption was tested using a Caco-2 cell transwell model. Ripretinib and IS were identified using multiple reaction monitoring (MRM) modes by mass spectrometry and showed ion transitions of 510.09→94.06m/z and 494.26→ 394.16m/z, respectively. The high-performance liquid chromatography (HPLC) method successfully eluted ripretinib and IS at retention times of 0.91 and 0.68 min, respectively, and the method was validated for all parameters and met the criteria for acceptance. Co-administration of verapamil increased the maximum concentration (Cmax) of ripretinib from 437±84ng/mL to 492±50ng/mL (12%), and the area under the concentration-time curve from 0 to the last sampling time t (AUC0-t) increased by approximately 40.6%. Verapamil significantly reduced the basolateral-to-apical transfer of ripretinib through Caco-2 cells. Findings also showed that verapamil increased the metabolic stability of ripretinib. The study results indicate that the co-administration of ripretinib with CYP3A4 and/or P-gp1 inhibitors is associated with significant drug-drug interactions that affect the pharmacokinetics of ripretinib. Further research in human subjects is suggested to confirm dosage adjustment and therapeutic drug monitoring of ripretinib when administered along with P-gp1/CYP3A4 inhibitors ensuring patient safety and optimizing the therapeutic benefits of ripretinib.

Applied physiologically-based pharmacokinetic modeling to assess uridine diphosphate-glucuronosyltransferase-mediated drug-drug interactions for Vericiguat.

Vericiguat (Verquvo; US: Merck, other countries: Bayer) is a novel drug for the treatment of chronic heart failure. Preclinical studies have demonstrated that the primary route of metabolism for vericiguat is glucuronidation, mainly catalyzed by uridine diphosphate-glucuronosyltransferase (UGT)1A9 and to a lesser extent UGT1A1. Whereas a drug-drug interaction (DDI) study of the UGT1A9 inhibitor mefenamic acid showed a 20% exposure increase, the effect of UGT1A1 inhibitors has not been assessed clinically. This modeling study describes a physiologically-based pharmacokinetic (PBPK) approach to complement the clinical DDI liability assessment and support prescription labeling. A PBPK model of vericiguat was developed based on invitro and clinical data, verified against data from the mefenamic acid DDI study, and applied to assess the UGT1A1 DDI liability by running an in silico DDI study with the UGT1A1 inhibitor atazanavir. A minor effect with an area under the plasma concentration-time curve (AUC) ratio of 1.12 and a peak plasma concentration ratio of 1.04 was predicted, which indicates that there is no clinically relevant DDI interaction anticipated. Additionally, the effect of potential genetic polymorphisms of UGT1A1 and UGT1A9 was evaluated, which showed that an average modest increase of up to 1.7-fold in AUC may be expected in the case of concomitantly reduced UGT1A1 and UGT1A9 activity for subpopulations expressing non-wild-type variants for both isoforms. This study is a first cornerstone to qualify the PK-Sim platform for use of UGT-mediated DDI predictions, including PBPK models of perpetrators, such as mefenamic acid and atazanavir, and sensitive UGT substrates, such as dapagliflozin and raltegravir.

Utilization of OATP1B Biomarker Coproporphyrin-I to Guide Drug-Drug Interaction Risk Assessment: Evaluation by the Pharmaceutical Industry.

Drug-drug interactions (DDIs) involving hepatic organic anion transporting polypeptides 1B1/1B3 (OATP1B) can be substantial, however, challenges remain for predicting interaction risk. Emerging evidence suggests that endogenous biomarkers, particularly coproporphyrin-I (CP-I), can be used to assess in vivo OATP1B activity. The present work under the International Consortium for Innovation and Quality in Pharmaceutical Development was aimed primarily at assessing CP-I as a biomarker for informing OATP1B DDI risk. Literature and unpublished CP-I data along with pertinent in vitro and clinical DDI information were collected to identify DDIs primarily involving OATP1B inhibition and assess the relationship between OATP1B substrate drug and CP-I exposure changes. Static models to predict changes in exposure of CP-I, as a selective OATP1B substrate, were also evaluated. Significant correlations were observed between CP-I area under the curve ratio (AUCR) or maximum concentration ratio (Cmax R) and AUCR of substrate drugs. In general, the CP-I Cmax R was equal to or greater than the CP-I AUCR. CP-I Cmax R < 1.25 was associated with absence of OATP1B-mediated DDIs (AUCR < 1.25) with no false negative predictions. CP-I Cmax R < 2 was associated with weak OATP1B-mediated DDIs (AUCR < 2). A correlation was identified between CP-I exposure changes and OATP1B1 static DDI predictions. Recommendations for collecting and interpreting CP-I data are discussed, including a decision tree for guiding DDI risk assessment. In conclusion, measurement of CP-I is recommended to inform OATP1B inhibition potential. The current analysis identified changes in CP-I exposure that may be used to prioritize, delay, or replace clinical DDI studies.

Quantitative prediction of transporter-mediated drug-drug interactions using the mechanistic static pharmacokinetic (MSPK) model

Guidance/guidelines on drug-drug interactions (DDIs) have been issued in Japan, the United States, and Europe. These guidance/guidelines provide decision trees for conducting metabolizing enzyme-mediated clinical DDI studies; however, the decision trees for transporter-mediated DDIs lack quantitative prediction methods. In this study, the accuracy of a net-effect mechanistic static pharmacokinetics (MSPK) model containing the fraction transported (ft) of transporters was examined to predict transporter-mediated DDIs. This study collected information on 25 oral drugs with new active reagents that were used in clinical DDI studies as perpetrators (42 cases) from drugs approved in Japan between April 2016 and June 2020. The AUCRs (AUC ratios with and without perpetrators) of victim drugs were predicted using the net-effect MSPK model. As a result, 83 and 95% of the predicted AUCRs were within 1.5- and 2-fold error in the observed AUCRs, respectively. In cases where the victims were statins in which pharmacokinetics several transporters are involved, 70 and 91% of the predicted AUCRs were within 1.5- and 2-fold errors, respectively. Therefore, the net-effect MSPK model was applicable for predicting the AUCRs of victims, which are substrates for multiple transporters.

Coadministration of bedaquiline and pyrifazimine reduce exposure to toxic metabolite N-desmethyl bedaquiline.

Background: A new, effective anti-tuberculosis (TB) regimen containing bedaquiline (BDQ) and pyrifazimine (TBI-166) has been recommended for a phase IIb clinical trial. Preclinical drug-drug interaction (DDI) studies of the combination of BDQ and TBI-166 have been designed to support future clinical trials. In this study, we investigated whether a DDI between BDQ and TBI-166 affects the pharmacokinetics of BDQ. Methods: We performed in vitro quantification of the fractional contributions of the fraction of drug metabolism by individual CYP enzymes (f m) of BDQ and the inhibition potency of key metabolic pathways of TBI-166. Furthermore, we conducted an in vivo steady-state pharmacokinetics study in a murine TB model and healthy BALB/c mice. Results: The in vitro f m value indicated that the CYP3A4 pathway contributed more than 75% to BDQ metabolism to N-desmethyl-bedaquiline (M2), and TBI-166 was a moderate (IC50 2.65µM) potential CYP3A4 inhibitor. Coadministration of BDQ and TBI-166 greatly reduced exposure to metabolite M2 (AUC0-t 76310 vs 115704hng/mL, 66% of BDQ alone), whereas the exposure to BDQ and TBI-166 did not changed. The same trend was observed both in healthy and TB model mice. The plasma concentration of M2 decreased significantly after coadministration of BDQ and TBI-166 and decreased further during treatment in the TB model. Conclusions: In conclusion, our results showed that the combination of BDQ and TBI-166 significantly reduced exposure to the toxic metabolite M2 by inhibiting the activity of the CYP3A4 pathway. The potential safety and efficacy benefits demonstrated by the TB treatment highly suggest that coadministration of BDQ and TBI-166 should be studied further.

A Drug-Drug Interaction Study to Evaluate the Impact of Simvastatin and Itraconazole on Erlotinib Pharmacokinetics in Rats by UPLC-MS/MS.

The goal of our study was to investigate the effects of single-dose simvastatin and itraconazole application on the pharmacokinetics of erlotinib in rats. Twenty-one male Sprague-Dawley rats were randomly divided into 3 groups, including erlotinib combined with simvastatin, erlotinib combined with itraconazole and erlotinib alone groups. The rats were given a single dose of 2 mg/kg simvastatin, 15 mg/kg itraconazole or 0.5% sodium carboxymethyl cellulose followed by 12 mg/kg erlotinib. The concentration of erlotinib in rat plasma was determined by UPLC-MS/MS. As internal standard, tinidazole was used for chromatographic analysis on the Kinetex C18 column (100×2.1 mm, 2.6 μm). Erlotinib was validated in the calibration range of 5-1000 ng/mL. The lower limit of quantification (LLOQ) was 5 ng/mL. The inter- and intra-day precisions for erlotinib were less than 10.56%, and the accuracies were in the range of 98.61-104.99%. The validated UPLC-MS/MS method was successfully applied to this study. Compared with the erlotinib alone group, the values of AUC0-t, AUC0-∞, Cmax, Vz/F and t1/2 in the simvastatin group showed no statistical differences among pharmacokinetic parameters (P>0.05). However, the values of AUC0-t, AUC0-∞ and Cmax, in the itraconazole group were approximately 1.32-fold, 1.32-fold and 1.34-fold higher, and the CL/F was lower than those in the erlotinib alone group; the difference was statistically significant (P<0.05). Simvastatin had no significant effect on the pharmacokinetics of erlotinib, whereas co-administration of itraconazole considerably increased the exposure of erlotinib. Therefore, we should pay more attention to the potential drug-drug interaction to ensure safety in cancer patient treatment.

Effects of Omeprazole and Verapamil on the Pharmacokinetics, Safety, and Tolerability of Mavacamten: Two Drug-Drug Interaction Studies in Healthy Participants.

Two open-label, Phase 1 studies assessed the effects of omeprazole (a weak to moderate cytochrome P450 [CYP] 2C19 inhibitor) and verapamil (a moderate CYP3A4 inhibitor) on the pharmacokinetics, safety, and tolerability of mavacamten. In the omeprazole study, healthy participants received mavacamten 15mg alone or with a 31-day course of omeprazole 20mg once daily. In the verapamil study, healthy participants received mavacamten 25mg alone or with a 28-day course of verapamil 240mg once daily. In the omeprazole study, 27 of 29 randomized participants completed the study. Nine participants receiving mavacamten alone were normal metabolizers (NMs) of CYP2C19 substrates, and 6 were rapid metabolizers; 8 NMs and 6 rapid metabolizers received mavacamten + omeprazole. In both studies, mavacamten showed no safety signals and was generally well tolerated. Overall mavacamten exposure (area under the plasma concentration-time curve) increased by approximately 50% with omeprazole coadministration; maximum observed concentration (Cmax ), time to Cmax , and elimination half-life were not affected appreciably. In the verapamil study, 25 of 26 randomized participants received the study drug(s) and were included in the pharmacokinetic analyses; 24 completed the study. In the pharmacokinetic population, 12 participants received mavacamten alone (11 NMs, 1 poor metabolizer) and 13 received mavacamten + verapamil (7 NMs, 4 intermediate metabolizers, 2 poor metabolizers). Following verapamil coadministration in NMs and intermediate metabolizers, mavacamten area under the plasma concentration-time curve was minimally increased (by less than 20%), and Cmax was modestly increased (by 52%). These results suggest that mavacamten can be coadministered with weak CYP2C19 and moderate CYP3A4 inhibitors.

Utility of Coproporphyrin-I Determination in First-in-Human Study for Early Evaluation of OATP1B Inhibitory Potential Based on Investigation of Ensitrelvir, an Oral SARS-CoV-2 3C-Like Protease Inhibitor

Coproporphyrin-I (CP-I) has been investigated as an endogenous biomarker of organic anion transporting polypeptide (OATP) 1B. Here, we determined the CP-I concentrations in a cocktail drug-drug interaction (DDI) study of ensitrelvir to evaluate the OATP1B inhibitory potential because ensitrelvir had increased plasma concentrations of rosuvastatin in this study, raising concerns about breast cancer resistance protein and OATP1B inhibition. Furthermore, CP-I concentrations were compared between active and placebo groups in a first-in-human (FIH) study of ensitrelvir to verify whether the OATP1B inhibitory potential could be estimated at an early drug development stage. In the cocktail DDI study, CP-I did not differ between with/without administration of ensitrelvir, indicating that ensitrelvir has no OATP1B inhibitory effect. Although there were some individual variabilities in CP-I concentrations among the treatment groups in the FIH study, the normalization of CP-I concentrations with pre-dose values minimized these variabilities, suggesting that this normalized method would be helpful for comparing the CP-I from different participants. Finally, we concluded that CP-I concentrations were not affected by ensitrelvir in the FIH study. These results suggested that the CP-I determination in an FIH study and its normalized method can be useful for an early evaluation of the OATP1B-mediated DDI potential in humans.

A Simple UPLC-MS/MS Assay of Rifampin in a Small Volume of Human Plasma.

Rifampin (RIF) is a typical cytochrome P450 (CYP) 3A inducer and inhibitor of organic anion transporting polypeptide (OATP) 1B1 to assess drug-drug interaction (DDI) via CYP3A or OATP1B1 in clinical settings. To ensure sufficient exposure of RIF in DDI studies, it is important to determine plasma RIF concentrations. In this study, we developed a simple RIF assay in a small volume of human plasma by ultraperformance liquid chromatography with tandem mass spectrometry. RIF in 0.02 mL of plasma was extracted using protein precipitation and separated on a reverse phase column under gradient elution of three mobile phases, where the mobile phase C containing 1% formic acid was exclusively used to reduce the carryover of RIF. RIF and the internal standard were detected by multiple reaction monitoring in positive-ion electrospray ionization. RIF was quantifiable at 0.025-10 μg/mL without the carryover issue. The intra- and inter-run assays confirmed the reproducibility of the assay. Stability assessments ensured that RIF in human plasma was stable for 6 h at room temperature and for 409 days at -15 °C or below. The assay was successfully applied to a pharmacokinetic study with successful incurred sample reanalysis.

Potential and Challenges in Application of Physiologically Based Pharmacokinetic Modeling in Predicting Diarrheal Disease Impact on Oral Drug Pharmacokinetics.

Physiologically based pharmacokinetic (PBPK) modeling is a physiologically relevant approach that integrates drug-specific and system parameters to generate pharmacokinetic predictions for target populations. It has gained immense popularity for drug-drug interaction, organ impairment, and special population studies over the past two decades. However, an application of PBPK modeling with great potential remains rather overlooked - prediction of diarrheal disease impact on oral drug pharmacokinetics. Oral drug absorption is a complex process involving the interplay between physicochemical characteristics of the drug and physiological conditions in the gastrointestinal tract. Diarrhea, a condition common to numerous diseases impacting many worldwide, is associated with physiological changes in many processes critical to oral drug absorption. In this review, we outline key processes governing oral drug absorption, provide a high-level overview of key parameters for modeling oral drug absorption in PBPK models, examine how diarrheal diseases may impact these processes based on literature findings, illustrate the clinical relevance of diarrheal disease impact on oral drug absorption, and discuss the potential and challenges of applying PBPK modeling in predicting disease impacts. Significance Statement Statement Pathophysiological changes resulting from diarrheal diseases can alter important factors governing oral drug absorption, contributing to suboptimal drug exposure and treatment failure. Physiologically based pharmacokinetic (PBPK) modeling is an in silico approach that has been increasingly adopted for drug-drug interaction potential, organ impairment, and special population assessment. This minireview highlights the potential and challenges of using PBPK modeling as a tool to improve our understanding of how diarrheal diseases impact oral drug pharmacokinetics.