Rifampin- and Silymarin-Mediated Pharmacokinetic Interactions of Exogenous and Endogenous Substrates in a Transgenic OATP1B Mouse Model.

Organic anion-transporting polypeptides (OATP) 1B1 and OATP1B3, encoded by the SLCO gene family of the solute carrier superfamily, are involved in the disposition of many exogenous and endogenous compounds. Preclinical rodent models help assess risks of pharmacokinetic interactions, but interspecies differences in transporter orthologs and expression limit direct clinical translation. An OATP1B transgenic mouse model comprising a rodent Slco1a/1b gene cluster knockout and human SLCO1B1 and SLCO1B3 gene insertions provides a potential physiologically relevant preclinical tool to predict pharmacokinetic interactions. Pharmacokinetics of exogenous probe substrates, pitavastatin and pravastatin, and endogenous OATP1B biomarkers, coproporphyrin-I and coproporphyrin-III, were determined in the presence and absence of known OATP/Oatp inhibitors, rifampin or silymarin (an extract of milk thistle [Silybum marianum]), in wild-type FVB mice and humanized OATP1B mice. Rifampin increased exposure of pitavastatin (4.6- and 2.8-fold), pravastatin (3.6- and 2.2-fold), and coproporphyrin-III (1.6- and 2.1-fold) in FVB and OATP1B mice, respectively, but increased coproporphyrin-I AUC0-24h only (1.8-fold) in the OATP1B mice. Silymarin did not significantly affect substrate AUC, likely because the silymarin flavonolignan concentrations were at or below their reported IC50 values for the relevant OATPs/Oatps. Silymarin increased the Cmax of pitavastatin 2.7-fold and pravastatin 1.9-fold in the OATP1B mice. The data of the OATP1B mice were similar to those of the pitavastatin and pravastatin clinical data; however, the FVB mice data more closely recapitulated pitavastatin clinical data than the data of the OATP1B mice, suggesting that the OATP1B mice are a reasonable, though costly, preclinical strain for predicting pharmacokinetic interactions when doses are optimized to achieve clinically relevant plasma concentrations.

Evaluation of the drug-drug interaction potential of brigatinib using a physiologically-based pharmacokinetic modeling approach.

Brigatinib is an oral anaplastic lymphoma kinase (ALK) inhibitor approved for the treatment of ALK-positive metastatic non-small cell lung cancer. In vitro studies indicated that brigatinib is primarily metabolized by CYP2C8 and CYP3A4 and inhibits P-gp, BCRP, OCT1, MATE1, and MATE2K. Clinical drug-drug interaction (DDI) studies with the strong CYP3A inhibitor itraconazole or the strong CYP3A inducer rifampin demonstrated that CYP3A-mediated metabolism was the primary contributor to overall brigatinib clearance in humans. A physiologically-based pharmacokinetic (PBPK) model for brigatinib was developed to predict potential DDIs, including the effect of moderate CYP3A inhibitors or inducers on brigatinib pharmacokinetics (PK) and the effect of brigatinib on the PK of transporter substrates. The developed model was able to predict clinical DDIs with itraconazole (area under the plasma concentration-time curve from time 0 to infinity [AUC∞] ratio [with/without itraconazole]: predicted 1.86; observed 2.01) and rifampin (AUC∞ ratio [with/without rifampin]: predicted 0.16; observed 0.20). Simulations using the developed model predicted that moderate CYP3A inhibitors (e.g., verapamil and diltiazem) may increase brigatinib AUC∞ by ~40%, whereas moderate CYP3A inducers (e.g., efavirenz) may decrease brigatinib AUC∞ by ~50%. Simulations of potential transporter-mediated DDIs predicted that brigatinib may increase systemic exposures (AUC∞) of P-gp substrates (e.g., digoxin and dabigatran) by 15%-43% and MATE1 substrates (e.g., metformin) by up to 29%; however, negligible effects were predicted on BCRP-mediated efflux and OCT1-mediated uptake. The PBPK analysis results informed dosing recommendations for patients receiving moderate CYP3A inhibitors (40% brigatinib dose reduction) or inducers (up to 100% increase in brigatinib dose) during treatment, as reflected in the brigatinib prescribing information.

Population pharmacokinetic model of rifampicin for personalized tuberculosis pharmacotherapy: Effects of SLCO1B1 polymorphisms on drug exposure.

BACKGROUND: Rifampicin (RIF) exhibits high pharmacokinetic (PK) variability among individuals; a low plasma concentration might result in unfavorable treatment outcomes and drug resistance. This study evaluated the contributions of non- and genetic factors to the interindividual variability of RIF exposure, then suggested initial doses for patients with different weight bands. METHODS: This multicenter prospective cohort study in Korea analyzed demographic and clinical data, the solute carrier organic anion transporter family member 1B1 (SLCO1B1) genotypes, and RIF concentrations. Population PK modeling and simulations were conducted using nonlinear mixed-effect modeling. RESULTS: In total, 879 tuberculosis (TB) patients were divided into a training dataset (510 patients) and a test dataset (359 patients). A one-compartment model with allometric scaling for effect of body size best described the RIF PKs. The apparent clearance (CL/F) was 16.6% higher among patients in the SLCO1B1 rs4149056 wild-type group than among patients in variant group, significantly decreasing RIF exposure in the wild-type group. The developed model showed better predictive performance compared with previously reported models. We also suggested that patients with body weights of <40 kg, 40-55 kg, 55-70 kg, and >70 kg patients receive RIF doses of 450, 600, 750, and 1050 mg/day, respectively. CONCLUSIONS: Total body weight and SLCO1B1 rs4149056 genotypes were the most significant covariates that affected RIF CL/F variability in Korean TB patients. We suggest initial doses of RIF based on World Health Organization weight-band classifications. The model may be implemented in treatment monitoring for TB patients.

Evaluation of the Cytochrome P450 3A and P-glycoprotein Drug-Drug Interaction Potential of Futibatinib.

Futibatinib, a selective, irreversible fibroblast growth factor receptor 1-4 inhibitor, is being investigated for tumors harboring FGFR aberrations and was recently approved for the treatment of FGFR2 fusion/rearrangement-positive intrahepatic cholangiocarcinoma. In vitro studies identified cytochrome P450 (CYP) 3A as the major CYP isoform in futibatinib metabolism and indicated that futibatinib is likely a P-glycoprotein (P-gp) substrate and inhibitor. Futibatinib also showed time-dependent inhibition of CYP3A in vitro. Phase I studies investigated the drug-drug interactions of futibatinib with itraconazole (a dual P-gp and strong CYP3A inhibitor), rifampin (a dual P-gp and strong CYP3A inducer), or midazolam (a sensitive CYP3A substrate) in healthy adult participants. Compared with futibatinib alone, coadministration of futibatinib with itraconazole increased futibatinib mean peak plasma concentration and area under the plasma concentration-time curve by 51% and 41%, respectively, and coadministration of futibatinib with rifampin lowered futibatinib mean peak plasma concentration and area under the plasma concentration-time curve by 53% and 64%, respectively. Coadministration of midazolam with futibatinib had no effect on midazolam pharmacokinetics compared with midazolam administered alone. These findings suggest that concomitant use of dual P-gp and strong CYP3A inhibitors/inducers with futibatinib should be avoided, but futibatinib can be concomitantly administered with other drugs metabolized by CYP3A. Drug-drug interaction studies with P-gp-specific substrates and inhibitors are planned.

[Development of Prediction Methods for Drug-Drug Interactions Based on the Construction of Physiologically Based Pharmacokinetic Models for Hepatic OATP Substrate Drugs and Endogenous Substrates].

Quantitative prediction of the potential for drug-drug interaction (DDI) is essential to guarantee the safety and efficacy of drugs. DDI screening, modeling, and prediction is standard practice in the pharmaceutical industry. This review describes our work on (1) the establishment of a standard framework for determining physiologically based pharmacokinetic (PBPK) model structures and parameters useful for quantitatively analyzing DDIs via hepatic organic anion transporting polypeptides (OATPs). By analyzing clinically observed DDIs involving several statins as substrates, and cyclosporin A and rifampicin as inhibitors, similar in vivo inhibition constants for OATPs by each inhibitor were obtained, regardless of the substrate. (2) We took a PBPK modeling-based approach to define rate-determining processes in hepatic elimination of several OATPs and CYP3A dual substrates using our clinical DDI data with specific inhibitors for OATPs and CYP3A. Essential in vivo parameters (the passive diffusion/active transport ratio in the uptake, and the fraction of intrinsic clearance in the total drug elimination from the hepatocytes) dominating the rate-determining process in hepatic elimination were estimated quantitatively. (3) Finally, using our clinical DDI data with rifampicin, we established a PBPK model for coproporphyrin I (CP-I), which is expected to act as an endogenous substrate (biomarker) supporting the prediction of DDI involving hepatic OATPs. Our PBPK modeling-based approach with several in vitro experiments using CP-I and OATP probe substrates (statins) demonstrated the usefulness of the translation of the effect of an OATP inhibitor on CP-I pharmacokinetics into that on OATP probe substrates in drug discovery and development.

Evaluation of the Pharmacokinetic Drug Interaction of Capmatinib With Itraconazole and Rifampicin and Potential Impact on Renal Transporters in Healthy Subjects.

Capmatinib is a highly specific, potent, and selective mesenchymal-epithelial transition factor inhibitor predominantly eliminated by cytochrome P450 (CYP) 3A4 and aldehyde oxidase. Here, we investigated the effects of a strong CYP3A inhibitor (itraconazole) and a strong CYP3A inducer (rifampicin) on single-dose pharmacokinetics of capmatinib. In addition, serum creatinine and cystatin C were monitored to assess the potential inhibition of renal transporters by capmatinib. This was an open-label, 2-cohort (inhibition and induction), 2-period (capmatinib alone and inhibition/induction periods) study in healthy subjects. In the inhibition cohort, capmatinib (400 mg/day) was given alone, then with itraconazole (200 mg/day for 10 days, 5-day lead-in before coadministration). In the induction cohort, capmatinib (400 mg/day) was given alone, then with rifampicin (600 mg/day for 9 days, 5-day lead-in before coadministration). Fifty-three subjects (inhibition cohort, n = 27; induction cohort, n = 26) were enrolled. Coadministration of itraconazole resulted in an increase of capmatinib area under the plasma concentration-time curve from time 0 to infinity by 42% (geometric mean ratio [GMR], 1.42; 90%CI, 1.33-1.52) with no change in maximum plasma concentration (GMR, 1.03; 90%CI, 0.866-1.22). Coadministration of rifampicin resulted in a reduction of capmatinib area under the plasma concentration-time curve from time 0 to infinity by 66.5% (GMR, 0.335; 90%CI, 0.300-0.374) and a decrease in maximum plasma concentration by 55.9% (GMR, 0.441; 90%CI, 0.387-0.502). After a single dose of capmatinib, a transient increase in serum creatinine was observed with no change in serum cystatin C concentration during the 3-day monitoring period. In conclusion, coadministration of itraconazole or rifampicin resulted in clinically relevant changes in systemic exposure to capmatinib. The transient increase in serum creatinine without any increase in cystatin C suggests inhibition of renal transport by capmatinib.

Physiologically based pharmacokinetic modeling of ponatinib to describe drug-drug interactions in patients with cancer.

PURPOSE: This study aimed to investigate the drug-drug interactions of ponatinib with strong, moderate, or weak CYP3A4 inhibitors/inducers by developing physiologically based pharmacokinetic (PBPK) models. METHODS: Simcyp® Ver 20.1 (Certara Inc., Sheffield, UK) was used to construct a PBPK model for ponatinib and to predict its interaction with strong, moderate, or weak CYP3A4 inhibitors/inducers. The constructed model was validated by comparing predicted values with actual observed values. Inhibitors or inducers that increased or decreased the area under the plasma concentration curve of ponatinib by more than two-fold when used in combination were considered significant. RESULTS: The PBPK model of ponatinib accurately represented its oral pharmacokinetics. It also reasonably predicted its pharmacokinetics when combined with ketoconazole and rifampicin. No weak to strong CYP3A4 inhibitor combinations significantly increased the AUC of ponatinib. However, the strong CYP3A4 inducers rifampicin (oral, 600 mg QD) and phenytoin (oral, 100 mg TID) decreased AUC by 60-70% and 50%, respectively. CONCLUSIONS: The PBPK model predicted a significant drug interaction when ponatinib was combined with a strong CYP3A4 inducer. Conversely, the combination with weak-to-strong CYP3A4 inhibitors did not suggest a drug interaction with ponatinib.

Evaluation of drug-drug interaction potential for pemigatinib using physiologically based pharmacokinetic modeling.

Pemigatinib is a potent inhibitor of fibroblast growth factor receptor being developed for oncology indications. It is primarily metabolized by cytochrome P450 (CYP) 3A4, and the ratio of estimated concentration over concentration required for 50% inhibition ratio for pemigatinib as an inhibitor of P-glycoprotein (P-gp), organic cation transporter-2 (OCT2), and multidrug and toxin extrusion protein-1 (MATE1) exceeds the cutoff values established in regulatory guidance. A Simcyp minimal physiologically based pharmacokinetic (PBPK) with advanced dissolution, absorption, and metabolism absorption model for pemigatinib was developed and validated using observed clinical pharmacokinetic (PK) data and itraconazole/rifampin drug-drug interaction (DDI) data. The model accurately predicted itraconazole DDI (approximate 90% area under the plasma drug concentration-time curve [AUC] and approximate 20% maximum plasma drug concentration [Cmax ] increase). The model underpredicted rifampin induction by 100% (approximate 6.7-fold decrease in AUC and approximate 2.6-fold decrease in Cmax in the DDI study), presumably reflecting non-CYP3A4 mechanisms being impacted. The verified PBPK model was then used to predict the effect of other CYP3A4 inhibitors/inducers on pemigatinib PK and pemigatinib as an inhibitor of P-gp or OCT2/MATE1 substrates. The worst-case scenario DDI simulation for pemigatinib as an inhibitor of P-gp or OCT2/MATE1 substrates showed only a modest DDI effect. The recommendation based on this simulation and clinical data is to reduce pemigatinib dose for coadministration with strong and moderate CYP3A4 inhibitors. No dose adjustment is required for weak CYP3A4 inhibitors. The coadministration of strong and moderate CYP3A4 inducers with pemigatinib should be avoided. PBPK modeling suggested no dose adjustment with P-gp or OCT2/MATE1 substrates.

Moxifloxacin Concentrations in the Knee Joint, Tibial Bone, and Soft Tissue When Combined with Rifampicin: A Randomized Porcine Microdialysis Study.

BACKGROUND: Peri and postoperative antibiotics are key adjuvant treatment tools in the management of periprosthetic joint infection (PJI). The aim of this study was to evaluate the effect of rifampicin on the area under the moxifloxacin concentration-time curve from 0 to 24 hours (AUC0-24) in the synovial fluid of the knee joint, tibial bone, and adjacent subcutaneous tissue under steady-state conditions using microdialysis in a porcine model. METHODS: Twenty female pigs were randomized to receive oral treatment with moxifloxacin monotherapy (Group A, n = 10) of 400 mg once daily for 3 days or a combination therapy (Group B, n = 10) of 400 mg of moxifloxacin once daily for 3 days and 450 mg of rifampicin twice daily for 7 days. Microdialysis was used for sampling the synovial fluid of the knee joint, tibial cancellous and cortical bone, and adjacent subcutaneous tissues. Plasma samples were taken as a reference. Measurements were obtained for 24 hours. RESULTS: Coadministration of moxifloxacin and rifampicin resulted in reductions of the moxifloxacin AUC0-24 in all targeted tissue compartments by 67% to 85% (p < 0.05). The corresponding change in plasma was 20% (p = 0.49). For both groups, the tissue penetration (the ratio of tissue free fraction AUC0-24 to plasma free fraction AUC0-24 [fAUCtissue/fAUCplasma]) was incomplete in all investigated compartments. The highest moxifloxacin tissue penetration was in the knee joint synovial fluid: 0.59 (Group A) and 0.24 (Group B). The lowest tissue penetration was in the cortical bone: 0.17 (Group A) and 0.03 (Group B). CONCLUSIONS: We found a significant reduction of the moxifloxacin concentration, expressed as the AUC0-24, in tissues relevant to acute PJI treatment when coadministered with rifampicin. CLINICAL RELEVANCE: The concentrations within the targeted tissue compartments were reduced significantly more than the concentrations in plasma, which may be particularly important as plasma concentrations are used in clinical practice to assess moxifloxacin treatment sufficiency.

Rifampin and ritonavir increase oral availability and elacridar enhances overall exposure and brain accumulation of the NTRK inhibitor larotrectinib.

INTRODUCTION: Larotrectinib is an FDA-approved oral small-molecule inhibitor for neurotrophic tropomyosin receptor kinase (NTRK) fusion-positive cancer treatment. Here larotrectinib pharmacokinetic behavior upon co-administration with prototypical inhibitors of the efflux transporters ABCB1/ABCG2 (elacridar), the SLCO1A/1B (OATP1A/1B) uptake transporters (rifampin), and the drug-metabolizing enzyme CYP3A (ritonavir), respectively, was investigated. METHODS: Inhibitors were orally administered prior to oral larotrectinib (10 mg/kg) to relevant genetically modified mouse models. Larotrectinib plasma and tissue homogenate concentrations were measured by a liquid chromatography-tandem mass spectrometric assay. RESULTS: Elacridar increased oral availability (2.7-fold) and markedly improved brain-to-plasma ratios (5.0-fold) of larotrectinib in wild-type mice. Mouse (m)Oatp1a/1b but not hepatic transgenic human (h)OATP1B1 or -1B3 restricted larotrectinib oral availability and affected its tissue distribution. Rifampin enhanced larotrectinib oral availability not only in wild-type mice (1.9-fold), but surprisingly also in Slco1a/1b-/- mice (1.7-fold). Similarly, ritonavir increased the larotrectinib plasma exposure in both wild-type (1.5-fold) and Cyp3a-/- mice (1.7-fold). Intriguingly, both rifampin and ritonavir decreased liver and/or intestinal larotrectinib levels in all related experimental groups, suggesting additional inhibition of enterohepatic Abcb1a/1b activity. CONCLUSIONS: Elacridar enhances both larotrectinib plasma and tissue exposure and especially relative brain penetration, which might be therapeutically relevant. Hepatic mOatp1a/1b but not hOATP1B1 or -1B3 transported larotrectinib. Additionally, rifampin enhances larotrectinib systemic exposure, most likely by inhibiting mOatp1a/1b, but probably also hepatic and/or intestinal mAbcb1. Similar to rifampin, dual-inhibition functions of ritonavir affecting both CYP3A enzymes and enterohepatic Abcb1 transporters enhanced larotrectinib oral availability. The obtained insights may be used to further optimize the clinical-therapeutic application of larotrectinib.

Physiologically Based Pharmacokinetic Modeling of Transporter-Mediated Hepatic Disposition of Imaging Biomarker Gadoxetate in Rats.

Physiologically based pharmacokinetic (PBPK) models are increasingly used in drug development to simulate changes in both systemic and tissue exposures that arise as a result of changes in enzyme and/or transporter activity. Verification of these model-based simulations of tissue exposure is challenging in the case of transporter-mediated drug-drug interactions (tDDI), in particular as these may lead to differential effects on substrate exposure in plasma and tissues/organs of interest. Gadoxetate, a promising magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 (OATP1B1) and multidrug resistance-associated protein 2 (MRP2). In this study, we developed a gadoxetate PBPK model and explored the use of liver-imaging data to achieve and refine in vitro-in vivo extrapolation (IVIVE) of gadoxetate hepatic transporter kinetic data. In addition, PBPK modeling was used to investigate gadoxetate hepatic tDDI with rifampicin i.v. 10 mg/kg. In vivo dynamic contrast-enhanced (DCE) MRI data of gadoxetate in rat blood, spleen, and liver were used in this analysis. Gadoxetate in vitro uptake kinetic data were generated in plated rat hepatocytes. Mean (%CV) in vitro hepatocyte uptake unbound Michaelis-Menten constant (Km,u) of gadoxetate was 106 µM (17%) (n = 4 rats), and active saturable uptake accounted for 94% of total uptake into hepatocytes. PBPK-IVIVE of these data (bottom-up approach) captured reasonably systemic exposure, but underestimated the in vivo gadoxetate DCE-MRI profiles and elimination from the liver. Therefore, in vivo rat DCE-MRI liver data were subsequently used to refine gadoxetate transporter kinetic parameters in the PBPK model (top-down approach). Active uptake into the hepatocytes refined by the liver-imaging data was one order of magnitude higher than the one predicted by the IVIVE approach. Finally, the PBPK model was fitted to the gadoxetate DCE-MRI data (blood, spleen, and liver) obtained with and without coadministered rifampicin. Rifampicin was estimated to inhibit active uptake transport of gadoxetate into the liver by 96%. The current analysis highlighted the importance of gadoxetate liver data for PBPK model refinement, which was not feasible when using the blood data alone, as is common in PBPK modeling applications. The results of our study demonstrate the utility of organ-imaging data in evaluating and refining PBPK transporter IVIVE to support the subsequent model use for quantitative evaluation of hepatic tDDI.

Pharmacokinetics of oral antimycobacterials and dosing guidance for Mycobacterium avium complex treatment in cystic fibrosis.

BACKGROUND: Treatment failure of Mycobacterium avium complex (MAC) pulmonary disease occurs in about 30% of people with cystic fibrosis (CF) and may be a result of abnormal drug concentrations. METHODS: Prospective, cross-over, single-dose PK study of 20 pancreatic insufficient individuals with CF and 10 healthy controls (HC). CF subjects received simultaneous doses of oral azithromycin, ethambutol, and rifampin in the fasting state and with food and pancreatic enzymes, separated by two weeks. HC received fasting doses only. A non-compartmental model was used to estimate PK parameters of drugs and metabolites. RESULTS: Azithromycin maximum concentration (Cmax ) was higher and rifampin Cmax was lower in fasting CF subjects compared to HC, while other PK measures, including those for ethambutol, were similar. Addition of food and enzymes did not improve the Cmax of the antimycobacterial drugs. Nineteen of 20 CF subjects had one or more abnormal Cmax z-scores in either the fasting or fed state (or both), when compared to HC. CONCLUSION: PK profiles of azithromycin and ethambutol were similar between CF and HC, except azithromycin Cmax was slightly higher in people with CF after a single dose. Rifampin PK parameters were altered in persons with CF. Addition of food and enzymes in CF subjects did not improve PK parameters. Standard dosing guidelines should be used as a starting point for people with CF initiating MAC therapy and therapeutic drug monitoring should be routinely performed to prevent the possibility of treatment failure due to abnormal drug concentrations. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02372383 Prior abstract publication: 1. Martiniano S, Wagner B, Brennan L, Wempe M, Anderson P, Nick J, Sagel S. Pharmacokinetics of oral MAC antibiotics in cystic fibrosis. Am J Resp Crit Care Med A4842-A4842, 2017. 2. Martiniano SL, Wagner BD, Brennan L, Wempe MF, Anderson PL, Nick JA, Sagel SD. Pharmacokinetics of oral MAC antibiotics in cystic fibrosis. J Cyst Fibros 16: S52-53, 2017.

A Microdose Cocktail to Evaluate Drug Interactions in Patients with Renal Impairment.

Renal impairment (RI) is known to influence the pharmacokinetics of nonrenally eliminated drugs, although the mechanism and clinical impact is poorly understood. We assessed the impact of RI and single dose oral rifampin (RIF) on the pharmacokinetics of CYP3A, OATP1B, P-gp, and BCRP substrates using a microdose cocktail and OATP1B endogenous biomarkers. RI alone had no impact on midazolam (MDZ), maximum plasma concentration (Cmax ), and area under the curve (AUC), but a progressive increase in AUC with RI severity for dabigatran (DABI), and up to ~2-fold higher AUC for pitavastatin (PTV), rosuvastatin (RSV), and atorvastatin (ATV) for all degrees of RI was observed. RIF did not impact MDZ, had a progressively smaller DABI drug-drug interaction (DDI) with increasing RI severity, a similar 3.1-fold to 4.4-fold increase in PTV and RSV AUC in healthy volunteers and patients with RI, and a diminishing DDI with RI severity from 6.1-fold to 4.7-fold for ATV. Endogenous biomarkers of OATP1B (bilirubin, coproporphyrin I/III, and sulfated bile salts) were generally not impacted by RI, and RIF effects on these biomarkers in RI were comparable or larger than those in healthy volunteers. The lack of a trend with RI severity of PTV and several OATP1B biomarkers, suggests that mechanisms beyond RI directly impacting OATP1B activity could also be considered. The DABI, RSV, and ATV data suggest an impact of RI on intestinal P-gp, and potentially BCRP activity. Therefore, DDI data from healthy volunteers may represent a worst-case scenario for clinically derisking P-gp and BCRP substrates in the setting of RI.

Evaluation of iberdomide and cytochrome p450 drug-drug interaction potential in vitro and in a phase 1 study in healthy subjects.

PURPOSE: Iberdomide is a cereblon E3 ligase modulator capable of redirecting the protein degradation machinery of the cell towards the elimination of target proteins potentially driving therapeutic effects. In vitro studies demonstrated that iberdomide predominantly undergoes oxidative metabolism mediated by cytochrome P450 (CYP) 3A4/5 but had no notable inhibition or induction of CYP enzymes. Consequently, the potential of iberdomide as a victim of drug-drug interactions (DDI) was evaluated in a clinical study with healthy subjects. METHODS: A total of 33 males and 5 females with 19 subjects per part were enrolled. Part 1 evaluated the pharmacokinetics (PK) of iberdomide alone (0.6 mg) and when administered with the CYP3A and P-gp inhibitor itraconazole (200 mg twice daily on day 1 and 200 once daily on days 2 through 9). Part 2 evaluated the PK of iberdomide alone (0.6 mg) and with CYP3A4 inducer rifampin (600 mg QD days 1 through 13). Plasma concentrations of iberdomide and the active metabolite M12 were determined by validated liquid chromatography-tandem mass spectrometry assay. RESULTS: Coadministration of iberdomide with itraconazole increased iberdomide peak plasma concentration (Cmax) 17% and area under the concentration curve (AUC) approximately 2.4-fold relative to administration of iberdomide alone. The Cmax and AUC of iberdomide were reduced by approximately 70% and 82%, respectively, when iberdomide was administered with rifampin compared with iberdomide administered alone. Exploratory assessment of metabolite M12 concentrations demonstrated that CYP3A is responsible for M12 formation. CONCLUSIONS: Caution should be taken when coadministering iberdomide with strong CYP3A inhibitors. Coadministration of iberdomide with strong CYP3A inducers is not advised. CLINICAL TRIAL REGISTRATION: Clinical trial identification number is NCT02820935 and was registered in July 2016.

Physiologically Based Pharmacokinetic Models of Probenecid and Furosemide to Predict Transporter Mediated Drug-Drug Interactions.

PURPOSE: To provide whole-body physiologically based pharmacokinetic (PBPK) models of the potent clinical organic anion transporter (OAT) inhibitor probenecid and the clinical OAT victim drug furosemide for their application in transporter-based drug-drug interaction (DDI) modeling. METHODS: PBPK models of probenecid and furosemide were developed in PK-Sim®. Drug-dependent parameters and plasma concentration-time profiles following intravenous and oral probenecid and furosemide administration were gathered from literature and used for model development. For model evaluation, plasma concentration-time profiles, areas under the plasma concentration-time curve (AUC) and peak plasma concentrations (Cmax) were predicted and compared to observed data. In addition, the models were applied to predict the outcome of clinical DDI studies. RESULTS: The developed models accurately describe the reported plasma concentrations of 27 clinical probenecid studies and of 42 studies using furosemide. Furthermore, application of these models to predict the probenecid-furosemide and probenecid-rifampicin DDIs demonstrates their good performance, with 6/7 of the predicted DDI AUC ratios and 4/5 of the predicted DDI Cmax ratios within 1.25-fold of the observed values, and all predicted DDI AUC and Cmax ratios within 2.0-fold. CONCLUSIONS: Whole-body PBPK models of probenecid and furosemide were built and evaluated, providing useful tools to support the investigation of transporter mediated DDIs.

Development of Rifapentine-Loaded PLGA-Based Nanoparticles: In vitro Characterisation and in vivo Study in Mice.

BACKGROUND: Tuberculosis (TB) is a leading cause of death amongst infectious diseases. The poor response to antitubercular agents necessitates the long-term use of high drug doses, resulting in low patient compliance, which is the main reason for chemotherapy failure and contributes to the development of multidrug-resistant TB. Patient non-compliance has been a major obstacle in the successful management of TB. The aim of this work was to develop and characterise rifapentine (RPT)-loaded PLGA-based nanoparticles (NPs) for reducing dosing frequency. METHODS: RPT-loaded PLGA and PLGA-PEG NPs were prepared using premix membrane homogenisation combined with solvent evaporation method. The resulting NPs were characterised in terms of physicochemical characteristics, toxicity, cellular uptake and antitubercular activity. NPs were further evaluated for pharmacokinetic and biodistribution studies in mice. RESULTS: The resulting NPs showed suitable and safe physicochemical characteristics and could be taken up by macrophages. RPT-loaded NPs were more effective against Mycobacterium tuberculosis than free RPT. In vivo studies revealed that NPs could improve pharmacokinetic parameters, particularly for RPT/PLGA-PEG NPs. Moreover, both formulations had no toxicity to the organs of mice and could reduce hepatotoxicity. CONCLUSION: The application of PLGA-based NPs as sustained-release delivery vehicles for RPT could prolong drug release, modify pharmacokinetics, increase antitubercular activity and diminish toxicity, thereby allowing low dosage and frequency.

Physiologically based pharmacokinetic modeling and simulation to predict drug-drug interactions of ivosidenib with CYP3A perpetrators in patients with acute myeloid leukemia.

PURPOSE: Develop a physiologically based pharmacokinetic (PBPK) model of ivosidenib using in vitro and clinical PK data from healthy participants (HPs), refine it with clinical data on ivosidenib co-administered with itraconazole, and develop a model for patients with acute myeloid leukemia (AML) and apply it to predict ivosidenib drug-drug interactions (DDI). METHODS: An HP PBPK model was developed in Simcyp Population-Based Simulator (version 15.1), with the CYP3A4 component refined based on a clinical DDI study. A separate model accounting for the reduced apparent oral clearance in patients with AML was used to assess the DDI potential of ivosidenib as the victim of CYP3A perpetrators. RESULTS: For a single 250 mg ivosidenib dose, the HP model predicted geometric mean ratios of 2.14 (plasma area under concentration-time curve, to infinity [AUC0-∞]) and 1.04 (maximum plasma concentration [Cmax]) with the strong CYP3A4 inhibitor, itraconazole, within 1.26-fold of the observed values (2.69 and 1.0, respectively). The AML model reasonably predicted the observed ivosidenib concentration-time profiles across all dose levels in patients. Predicted ivosidenib geometric mean steady-state AUC0-∞ and Cmax ratios were 3.23 and 2.26 with ketoconazole, and 1.90 and 1.52 with fluconazole, respectively. Co-administration of the strong CYP3A4 inducer, rifampin, predicted a greater DDI effect on a single dose of ivosidenib than on multiple doses (AUC ratios 0.35 and 0.67, Cmax ratios 0.91 and 0.81, respectively). CONCLUSION: Potentially clinically relevant DDI effects with CYP3A4 inducers and moderate and strong inhibitors co-administered with ivosidenib were predicted. Considering the challenges of conducting clinical DDI studies in patients, this PBPK approach is valuable in ivosidenib DDI risk assessment and management.

A population approach of rifampicin pharmacogenetics and pharmacokinetics in Mexican patients with tuberculosis.

The aim of this study was to develop a population pharmacokinetic model of rifampicin (RMP) in Mexican patients with tuberculosis (TB) to evaluate the influence of anthropometric and clinical covariates, as well as genotypic variants associated with MDR1 and OATP1B1 transporters. A prospective study approved by Research Ethics Committee was performed at Hospital Central in San Luis Potosí, Mexico. TB patients under DOTS scheme and who signed informed consent were consecutively included. Anthropometric and clinical information was retrieved from medical records. Single nucleotide polymorphisms in MDR1 (C3435T) and SLCO1B1 (A388G and T521C) genes were evaluated. RMP plasma concentrations and time data were assessed with NONMEM software. A total of 71 Mexican TB patients from 18 to 72 years old were included for RMP quantification from 0.3 to 12 h after dose; 329 and 97 plasma concentrations were available for model development and validation, respectively. Sequential process includes a typical lag time of 0.25 h prior to absorption start with a Ka of 1.24 h-1 and a zero-order absorption of 0.62 h to characterize the gradual increase in RMP plasma concentrations. Final model includes total body weight in volume of distribution (0.7 L/kg, CV = 26.8%) and a total clearance of 5.96 L/h (CV = 38.5%). Bioavailability was modified according to time under treatment and generic formulation administration. In conclusion, a population pharmacokinetic model was developed to describe the variability in RMP plasma concentrations in Mexican TB patients. Genetic variants evaluated did not showed significant influence on pharmacokinetic parameters. Final model will allow therapeutic drug monitoring at early stages.

Validation of a Drug Transporter Probe Cocktail Using the Prototypical Inhibitors Rifampin, Probenecid, Verapamil, and Cimetidine.

BACKGROUND AND OBJECTIVE: A novel cocktail containing four substrates of key drug transporters was previously optimized to eliminate mutual drug-drug interactions between the probes digoxin (P-glycoprotein substrate), furosemide (organic anion transporter 1/3), metformin (organic cation transporter 2, multidrug and toxin extrusion protein 1/2-K), and rosuvastatin (organic anion transporting polypeptide 1B1/3, breast cancer resistance protein). This clinical trial investigated the effects of four commonly employed drug transporter inhibitors on cocktail drug pharmacokinetics. METHODS: In a randomized open-label crossover trial in 45 healthy male subjects, treatment groups received the cocktail with or without single oral doses of rifampin, verapamil, cimetidine or probenecid. Concentrations of the probe drugs in serial plasma samples and urine fractions were measured by validated liquid chromatography-tandem mass spectrometry assays to assess systemic exposure. RESULTS: The results were generally in accordance with known in vitro and/or clinical drug-drug interaction data. Single-dose rifampin increased rosuvastatin area under the plasma concentration-time curve up to the last quantifiable concentration (AUC0-tz) by 248% and maximum plasma concentration (Cmax) by 1025%. Probenecid increased furosemide AUC0-tz by 172% and Cmax by 23%. Cimetidine reduced metformin renal clearance by 26%. The effect of single-dose verapamil on digoxin systemic exposure was less than expected from multiple-dose studies (AUC0-tz unaltered, Cmax + 22%). CONCLUSIONS: Taking all the interaction results together, the transporter cocktail is considered to be validated as a sensitive and specific tool for evaluating transporter-mediated drug-drug interactions in drug development. CLINICAL TRIAL REGISTRATION: EudraCT number 2017-001549-29.

Effect of Rifampin-Mediated OATP1B1 and OATP1B3 Transporter Inhibition on the Pharmacokinetics of the P2Y12 Receptor Antagonist Selatogrel.

In vitro studies have indicated that the P2Y12 receptor antagonist selatogrel is a substrate of organic anion-transporting-polypeptide (OATP)1B1 and OATP1B3 that are known to mediate hepatic uptake. Selatogrel is primarily eliminated via the biliary route. Therefore, the study aim was to investigate the effect of rifampin-mediated OATP1B1 and OATP1B3 inhibition on the pharmacokinetics (PK) of selatogrel. This was a randomized, double-blind, placebo-controlled, two-period, crossover study in 14 healthy subjects. In each period, a single subcutaneous dose of 4 mg selatogrel was administered, either immediately after a single intravenous 30 minutes infusion of 600 mg rifampin or after placebo. Plasma samples were collected for 36 hours and analyzed using a validated liquid chromatography-tandem mass spectrometry method. PK parameters of selatogrel were calculated using noncompartmental analysis. The effect of rifampin was explored based on geometric mean peak plasma concentration (Cmax ) and area under the concentration curve from zero to infinity (AUC0-∞ ) ratios and for time of maximum plasma concentration (Tmax ) by Wilcoxon signed rank test. In addition, the safety and tolerability of the study treatments were evaluated. The geometric mean ratios of Cmax and AUC0-∞ were 1.19 (90% confidence interval (CI) 1.11-1.28) and 1.43 (90% CI 1.36-1.51), respectively, indicating a minor selatogrel exposure increase when administered after an infusion of rifampin compared with placebo. Rifampin administration did not affect terminal half-life (t½ ) or Tmax of selatogrel. All study treatments were safe and well-tolerated. A single dose of 600 mg rifampin, a potent OATP1B1/1B3 inhibitor, did not impact the PK of selatogrel to a clinically relevant extent suggesting that OATP1B1 and OATP1B3 transporters do not play a major role in the elimination of selatogrel.