Drug-drug Interaction Simulations Research Articles

Simulation of drug-drug interactions between breast cancer chemotherapeutic agents and antiemetic drugs.

Chemotherapy-induced nausea and vomiting are commonly experienced side effects in breast cancer (BCa) patients. Antiemetic drugs used in BCa treatment are either inhibitors or inducers of cytochrome P450 (CYP) enzymes, while anticancer drugs are metabolized by CYPs. The purpose of the present work was to evaluate in silico drug-drug interaction (DDI) potential between BCa chemotherapeutic drugs and antiemetic agents. The Drug-Drug Interaction™ module of GastroPlus™ was employed to assess CYP-related interactions between antiemetic and anticancer therapy combinations. The CYP inhibitory or inducing parameters (IC50, Ki, EC50) used in simulations were obtained fromthe literature. Analyses of twenty-three BCa drugs indicated that 22% of the chemotherapeutic drugs do not need an antiemetic agent due to their low emetogenicity, whereas 30% of the anticancer drugs are not metabolized by CYPs. The remaining eleven anticancer drugs metabolized by CYPs generated ninety-nine combinations with nine antiemetics. Simulation of DDIs suggest that about half of the pairs did not demonstrate any potential for DDI, whereas 30%, 10%, and 9%of the pairs showed weak, moderate, and strong interaction potential, respectively. In the present study, netupitant was the only antiemetic that showed strong inhibitory interactions (predicted AUC ratio > 5) with CYP3A4-metabolzied anticancer therapies (e.g., docetaxel, ribociclib, olaparib). Moderate to no interactions were observed with ondansetron, aprepitant, rolapitant, and dexamethasone in combination with anticancer agents. It is critical to recognize that these interactions can get amplified in cancer patients because of the severity of the disease and chemotherapy toxicities. Clinicians need to be aware of the DDI likelihood of the drug combinations used in BCa treatment.

1136. No Change of Pharmacokinetics of Metformin by Concomitant Use of Ensitrelvir

Abstract Background Ensitrelvir is a novel oral SARS-CoV-2 3C-like protease inhibitor, and under late clinical development stage for COVID-19 diseases. Ensitrelvir exhibited an inhibition potency for organic cation transporter 1 (OCT1) and multidrug and toxin extrusion protein 1 (MATE1) in in vitro study and clinical drug-drug interaction (DDI) study is required judging from DDI guidance. Metformin is widely used for treatment of diabetes, and is a sensitive substrate for OCT1 and MATE1. We evaluated the effect of ensitrelvir on the pharmacokinetics (PK) of metformin with physiologically-based pharmacokinetic (PBPK) modeling and simulation and clinical DDI study. Methods The PBPK model of ensitrelvir was developed based on the physicochemical parameters, in vitro transporter inhibition parameters, and estimated PK parameters for human. DDI simulations between ensitrelvir and metformin were performed. Simcyp PBPK Simulator (Version 20, Certara UK Limited, UK) was used to develop PBPK model and simulate the DDIs. The in vitro 50% inhibitory concentration (IC50) values of each transporter were used as inhibition constant (Ki) for DDI simulations. Based on the PBPK analysis, the clinical DDI study planed. Results PBPK analysis: As the result of DDI simulation, ensitrelvir increased the area under the curve (AUC) of metformin by 12%. The result suggests that in vivo DDI potency of ensitrelvir via inhibition of OCT1 or MATE1 would be low at a single dose of ensitrelvir 1000 mg. Clinical DDI study: The plasma concentration-time profile of metformin and ensitrelvir were monitored after 96 hours from a single dose of metformin with or without ensitrelvir. Ensitrelvir does not have effect on the PK of metformin (a geometric mean of AUC ratio was 1.02, Japanese healthy subjects, N=14), suggesting no MATE1 and OCT1 inhibition by ensitrelvir at a clinical dose. The PBPK analysis could well predict the clinical DDI study result. Conclusion The results of PBPK analysis and the clinical DDI study suggest that no OCT1 and MATE1 inhibition by ensitrelvir is in the clinical dose. Therefore, ensitrelvir does not have a clinically meaningful effect on the pharmacokinetic profile of OCT1 and/or MATE1 substrates including metformin. Disclosures Kana Horiuchi, Shionogi & Co., Ltd.: employee Hiroki Koshimichi, n/a, Shionogi & Co., Ltd.: employee Takanobu Matsuzaki, Ph.D., Shionogi & Co., Ltd.: Employee Ryosuke Shimizu, Shionogi & Co., Ltd.: employee Shinpei Yoshida, Ph.D., Shionogi & Co., Ltd.: employee Ryuji Kubota, Ph.D., Shionogi & Co., Ltd.: employee|Shionogi & Co., Ltd.: employee Shingo Sakamoto, n/a, Shionogi & Co., Ltd.: employee.

A Mechanistic Physiologically Based Pharmacokinetic (PBPK) Drug Interaction Model for the Mouse Double Minute 2 (MDM2) Inhibitor KRT-232

Background: KRT-232 is a potent, selective, orally available, targeted inhibitor of human MDM2 homolog interactions with tumor suppressor protein 53 (p53). KRT-232 is under development for treatment of myeloproliferative neoplasms, acute myeloid leukemia and Merkel cell carcinoma. KRT-232 is a highly permeable 568.6 g/mol, monoprotic carboxylic acid (pKa 4.35); elimination involves primarily biliary-fecal excretion of an acyl glucuronide metabolite (M1) and enteric glucuronide hydrolysis with enterohepatic recirculation of parent drug. M1 is a 744.7 g/mol monoprotic acid with pKa 3.32 and 1/5th the MDM2 inhibitory potency of KRT-232. Plasma protein binding (fup) of KRT-232 and M1 were ~0.026 and ~0.006, respectively. Static model drug-drug interaction (DDI) calculations (FDA-CDER. 2020) indicated a potential minor DDI with substrates of cytochrome P450s (CYP) CYP3A4 and CYP2C8, UGT1A1, P-glycoprotein 1 (P-gp) and OATP1B1/3. Here, a 'fit-for-purpose' PBPK model for KRT-232 was developed using physicochemical, preclinical (in vitroandin vivo) and clinical pharmacokinetic (PK) data (KartosData on file; Ye et al.Xenobiotica. 2015; Gluck et al.Invest New Drugs. 2020 [Study A]). Methods:Simcyp Simulator V17 (release 1; 27/11/2017; 17.0.90.0) was used. In vitro kinetic parameters: In vitroKi for competitive inhibition of CYP2C8 was 3.8 and 0.17 µM for KRT-232 and M1, respectively.In vitroKI and kinact for mechanism-based inhibition of CYP3A4 by KRT-232 were 28 µM and 4.14 h-1, respectively. Estimatedin vitroKi values for KRT-232-mediated inhibition of UGT1A1, P-gp, OATP1B1 and OATP1B3, were 1.9, 14.8, 4.9 and 6.9 µM. For M1-mediated inhibition of UGT1A1, OATP1B1 and OATP1B3, values were 2.3, 0.393 and 0.606 µM. Model development and verification:KRT-232 absorption was described by a first-order process; fraction absorbed was assumed to be 100% and the absorption rate constant was manually optimized using observed mean plasma concentration data from Part 1 of Study A. KRT-232 clearance was based onin vitroformation clearance of M1 in human liver microsomes, scaled up 4-fold based on observed oral clearance of KRT-232 (Wood et al.Drug Metab Dispos. 2017). KRT-232 distribution volume was described by a minimal PBPK model. KRT-232 and M1 DDI parameters were based onin vitrodata. M1 clearance was manually optimized using mean observed data from Study A (Part 1, 240 mg dose), and distribution volume was predicted by the Simulator using Method 1 and a full PBPK model. PBPK simulated data were compared with patient PK data collected during Part 2 of Study A. Model application:The model predicted potential DDI after steady state (SS) 240 mg once daily KRT-232 administration with substrates of CYP3A4, CYP2C8, UGT1A1, P-gp and OATP1B1/3. DDI simulations included 100 virtual subjects assigned to 10 virtual trials (N=10 each). Population estimates of geometric mean and median Cmax and AUC0-∞ ratios were generated. A "worst case" sensitivity analysis was performed by using a 10-fold increase inin vitroCYP3A4 interaction potency (KI, EC50) relative to the measuredin vitrovalue. Results:Figure 1shows simulated SS mean and individual observed KRT-232 and M1 concentration data from Part 2 of Study A. SS KRT-232 Cmax, AUC0-24h and t1/2 were predicted within 0.80-1.25-fold of observed PK data and tmax was under-predicted (0.36-fold) by the model (Table 1). M1 Cmax and AUC0-24h were within 0.80-1.25-fold of observed PK data on Day 1 (not shown). M1 Cmax on Day 7 was within 0.80-1.25-fold of observed PK data and SS AUC0-24h, t1/2, and tmax were underpredicted (0.72, 0.79 and 0.61-fold) relative to observed PK data. In all simulated DDIs, no clinically significant interaction was predicted for SS 240 mg doses of KRT-232 since predicted Cmax and AUC0-∞ geometric mean ratio (GMR) changes were less than 1.25-fold (Table 2). The CYP3A4 DDI simulation gave a population GMR (90% CI) for Cmax and AUC0-∞ of 1.07 (1.06, 1.09) and 1.15 (1.12, 1.17), respectively. In a sensitivity check, predicted midazolam Cmax and AUC0-∞ after a 10-fold reduction ofin vitroKI and EC50 were 1.44-fold and 2.18-fold, respectively. Although unlikely, a weak CYP3A4 DDI could not be definitively excluded. Conclusions: Mean plasma exposure of KRT-232 and M1 and inter-individual variability were adequately described by the PBPK model. No clinically significant DDI were predicted based on Cmax and AUC0-∞ GMR changes of less than 1.25-fold. Disclosures Templeton: Certara UK, LTD.:Current Employment.Podoll:IV/PO, LLC:Consultancy.Krejsa:Kartos Therapeutics:Current Employment;AstraZeneca:Current equity holder in publicly-traded company;Seattle Genetics:Current equity holder in publicly-traded company;Acerta Pharma:Current equity holder in private company.Slatter:Kartos Therapeutics:Current Employment;AstraZeneca:Current equity holder in publicly-traded company;Amgen:Divested equity in a private or publicly-traded company in the past 24 months.

A physiologically based pharmacokinetic/pharmacodynamic modeling approach for drug-drug interaction evaluation of warfarin enantiomers with sorafenib

Sorafenib was suggested to cause drug-drug interaction (DDI) with the common anticoagulant, warfarin based on published studies. The inhibition on CYP2C9 enzyme was thought to be the mechanism, but further studies are warranted. Thus, a mechanistic PBPK/PD model for warfarin enantiomers was developed to predict DDI potential with sorafenib, aiming at providing reference for the rational use of both drugs.PBPK models of warfarin enantiomers were constructed by Simcyp software. A mechanistic PK/PD model was built in NONMEM software. PBPK model of sorafenib was fitted via a top-down method. The final PBPK/PD model of warfarin enantiomers was verified and validated by different dosing regimens, ethnicities and genetic polymorphisms, and used to perform DDI simulations between warfarin racemate and sorafenib among general populations and sub-populations with various CYP2C9 and VKORC1 genotypes. Results suggested low DDI risk between warfarin and sorafenib for general populations. Potentially serious consequence was seen for those carrying both CYP2C9 ∗2 and ∗3 and VKORC1 A/A genotypes.This PBPK/PD modeling approach for warfarin enantiomers enabled DDI evaluation with sorafenib. Close monitoring and warfarin dosage adjustment were recommended for patients carrying mutant genotypes. The novel model could be applied to investigate other drugs that may interact with warfarin.

Systematic Development and Verification of a Physiologically Based Pharmacokinetic Model of Rivaroxaban.

Rivaroxaban is indicated for stroke prevention in nonvalvular atrial fibrillation (AF). Its elimination is mediated by both hepatic metabolism and renal excretion. Consequently, its clearance is susceptible to both intrinsic (pathophysiological) and extrinsic (concomitant drugs) variabilities that in turn implicate bleeding risks. Upon systematic model verification, physiologically based pharmacokinetic (PBPK) models are qualified for the quantitative rationalization of complex drug-drug-disease interactions (DDDIs). Hence, this study aimed to develop and verify a PBPK model of rivaroxaban systematically. Key parameters required to define rivaroxaban's disposition were either obtained from in vivo data or generated via in vitro metabolism and transport kinetic assays. Our developed PBPK model successfully predicted rivaroxaban's clinical pharmacokinetic parameters within predefined success metrics. Consideration of basolateral organic anion transporter 3 (OAT3)-mediated proximal tubular uptake in tandem with apical P-glycoprotein (P-gp)-mediated efflux facilitated mechanistic characterization of the renal elimination of rivaroxaban in both healthy and renal impaired patients. Retrospective drug-drug interaction (DDI) simulations, incorporating in vitro metabolic inhibitory parameters, accurately recapitulated clinically observed attenuation of rivaroxaban's hepatic clearance due to enzyme-mediated DDIs with CYP3A4/2J2 inhibitors (verapamil and ketoconazole). Notably, transporter-mediated DDI simulations between rivaroxaban and the P-gp inhibitor ketoconazole yielded minimal increases in rivaroxaban's systemic exposure when P-gp-mediated efflux was solely inhibited, but were successfully characterized when concomitant basolateral uptake inhibition was incorporated in the simulation. In conclusion, our developed PBPK model of rivaroxaban is systematically verified for prospective interrogation and management of untested yet clinically relevant DDDIs pertinent to AF management using rivaroxaban. SIGNIFICANCE STATEMENT: Rivaroxaban is susceptible to DDDIs comprising renal impairment and P-gp and CYP3A4/2J2 inhibition. Here, systematic construction and verification of a PBPK model of rivaroxaban, with the inclusion of a mechanistic kidney component, provided insight into the previously arcane role of OAT3-mediated basolateral uptake in influencing both clinically observed renal elimination of rivaroxaban and differential extents of transporter-mediated DDIs. The verified model holds potential for investigating clinically relevant DDDIs involving rivaroxaban and designing dosing adjustments to optimize its pharmacotherapy in atrial fibrillation.

Physiologically-based pharmacokinetic modeling and simulation of drug-drug interactions between azole antifungals, macrolide antibiotics or proton pump inhibitors and tacrolimus for provision of quantitative information to clinical practice

Physiologically-based pharmacokinetic modeling and simulation of drug-drug interactions between azole antifungals, macrolide antibiotics or proton pump inhibitors and tacrolimus for provision of quantitative information to clinical practice

A physiologically based pharmacokinetic (PBPK) parent-metabolite model of the chemotherapeutic zoptarelin doxorubicin—integration of in vitro results, Phase I and Phase II data and model application for drug–drug interaction potential analysis

Zoptarelin doxorubicin is a fusion molecule of the chemotherapeutic doxorubicin and a luteinizing hormone-releasing hormone receptor (LHRHR) agonist, designed for drug targeting to LHRHR positive tumors. The aim of this study was to establish a physiologically based pharmacokinetic (PBPK) parent-metabolite model of zoptarelin doxorubicin and to apply it for drug-drug interaction (DDI) potential analysis. The PBPK model was built in a two-step procedure. First, a model for doxorubicin was developed, using clinical data of a doxorubicin study arm. Second, a parent-metabolite model for zoptarelin doxorubicin was built, using clinical data of three different zoptarelin doxorubicin studies with a dosing range of 10-267mg/m2, integrating the established doxorubicin model. DDI parameters determined in vitro were implemented to predict the impact of zoptarelin doxorubicin on possible victim drugs. In vitro, zoptarelin doxorubicin inhibits the drug transporters organic anion-transporting polypeptide 1B3 (OATP1B3) and organic cation transporter 2 (OCT2). The model was applied to evaluate the in vivo inhibition of these transporters in a generic manner, predicting worst-case scenario decreases of 0.5% for OATP1B3 and of 2.5% for OCT2 transport rates. Specific DDI simulations using PBPK models of simvastatin (OATP1B3 substrate) and metformin (OCT2 substrate) predict no significant changes of the plasma concentrations of these two victim drugs during co-administration. The first whole-body PBPK model of zoptarelin doxorubicin and its active metabolite doxorubicin has been successfully established. Zoptarelin doxorubicin shows no potential for DDIs via OATP1B3 and OCT2.

Effect of enzyme inhibition on perampanel pharmacokinetics: Why study design matters

ObjectivesPerampanel, a selective, noncompetitive AMPA receptor antagonist, is indicated as adjunctive therapy for the treatment of partial seizures with or without secondarily generalized seizures and primary generalized tonic-clonic seizures in patients with epilepsy aged 12years and older. In vitro studies and Phase I trials indicate that perampanel is metabolized almost exclusively by CYP3A, with an elimination half-life (t1/2) averaging approximately 105h. Understanding of pharmacokinetic (PK) interactions—enzyme inhibition or induction—and anticipating their occurrence are important for management of patients with epilepsy. Here we report PK results from a Phase I drug-drug interaction (DDI) study (Study 005) combining perampanel with the CYP3A inhibitor ketoconazole, as well as supplementary in silico predictions further exploring this interaction. MethodsA Phase I, randomized, open-label, two-period, two-treatment, two-way crossover study was conducted in 26 healthy adult male volunteers. Subjects were randomized to 1 of 2 treatment sequences. In one period, subjects received a single 1-mg fasting dose of perampanel (Day1); in the other period, subjects received ketoconazole 400mg once daily for 10days with a single 1-mg perampanel dose while fasting (Day3). Blood samples were drawn at multiple time points up to 288h after the perampanel dose. Pharmacokinetic parameters of perampanel were calculated by noncompartmental analysis, and safety was recorded. An integrated, physiologically based PK model built in Simcyp® provided additional insight into this interaction. Drug-drug interaction intensity was measured by the ratio of systemic exposure (area under plasma concentration-time curve [AUC]) of perampanel in the presence or absence of concomitant ketoconazole. ResultsSingle oral doses of 1mg perampanel and once-daily oral doses of ketoconazole 400mg were safe and well tolerated. Maximum perampanel plasma concentration (Cmax) and time to Cmax showed no apparent differences when perampanel was administered alone versus with ketoconazole. Ketoconazole co-administration resulted in an approximate 20% increase in perampanel AUC (P<0.001). This increase, although statistically significant, was a<2.0-fold AUC change and alone would suggest a modest effect of ketoconazole. To further explore these results, DDI simulations were performed to query the findings and test additional study conditions. Using the actual trial conditions of Study 005, the simulations also predicted an AUC ratio increase <2-fold, providing verification of the simulation assumptions and the modest effect of ketoconazole for 10days. Simulations further suggested that an interaction effect of ketoconazole on perampanel exposure (>2-fold) of potential clinical significance could be predicted when using larger doses of ketoconazole (e.g., 200mg every 6h) coadministered for a greater time period (e.g., 30days), with AUC ratio as high as 3.36. Additionally, simulations suggested that a significant interaction with co-administration of perampanel and an inhibitor more potent than ketoconazole (such as itraconazole) could not be ruled out. ConclusionsSelecting an appropriate study design is critical to fully characterize the PK interaction for drugs such as perampanel that have a long t1/2. Although a negligible effect on perampanel PK was observed following co-administration of ketoconazole 400mg/day for 10days, this is likely due in part to the relatively brief co-administration period of ketoconazole and perampanel (<3 times the t1/2 of perampanel). While short-term administration of a CYP3A inhibitor may not significantly increase perampanel exposure, such increases may be expected following chronic and larger dosing or with a more potent inhibitor.

Retrospective use of PBPK modelling to understand a clinical drug–drug interaction between dextromethorphan and GSK1034702

1. In a clinical trial, a strong drug–drug interaction (DDI) was observed between dextromethorphan (DM, the object or victim drug) and GSK1034702 (the precipitant or perpetrator drug), following single and repeat doses. This study determined the inhibition parameters of GSK1034702 in vitro and applied PBPK modelling approaches to simulate the clinical observations and provide mechanistic hypotheses to understand the DDI.2. In vitro assays were conducted to determine the inhibition parameters of human CYP2D6 by GSK1034702. PBPK models were populated with the in vitro parameters and DDI simulations conducted and compared to the observed data from a clinical study with DM and GSK1034702.3. GSK1034702 was a potent direct and metabolism-dependent inhibitor of human CYP2D6, with inhibition parameters of: IC50 = 1.6 μM, Kinact = 3.7 h−1 and KI = 0.8 μM. Incorporating these data into PBPK models predicted a DDI after repeat, but not single, 5 mg doses of GSK1034702.4. The DDI observed with repeat administration of GSK1034702 (5 mg) can be attributed to metabolism-dependent inhibition of CYP2D6. Further, in vitro data were generated and several potential mechanisms proposed to explain the interaction observed following a single dose of GSK1034702.

Antiretroviral Use in the CEASE Cohort Study and Implications for Direct-Acting Antiviral Therapy in Human Immunodeficiency Virus/Hepatitis C Virus Coinfection.

Background. Interferon-free direct-acting antiviral (DAA) regimens for hepatitis C virus (HCV) provide a major advance in clinical management, including in human immunodeficiency virus (HIV)/HCV coinfection. Drug-drug interactions (DDIs) with combination antiretroviral therapy (cART) require consideration. This study aimed to characterize the cART regimens in HIV/HCV-coinfected individuals and assess the clinical significance of DDIs with DAAs in a real-world cohort. Methods. This analysis included participants enrolled in CEASE-D, a prospective cohort of HIV/HCV-coinfected individuals in Sydney, Australia, between July 2014 and December 2015. A simulation of potential DDIs between participants' cART and interferon-free DAA regimens was performed using www.hep-druginteractions.org and relevant prescribing information. Results. In individuals on cART with HCV genotype (GT) 1 and 4 (n = 128), category 3 DDIs (contraindicated or not recommended) were noted in 0% with sofosbuvir/ledipasvir, 0% with sofosbuvir plus daclatasvir, 17% with sofosbuvir/velpatasvir, 36% with ombitasvir/paritaprevir/ritonavir ± dasabuvir, 51% with grazoprevir/elbasvir, and 51% with sofosbuvir plus simeprevir; current cART regimens were suitable for coadministration in 100%, 100%, 73%, 64%, 49%, and 49%, respectively. In individuals with HCV GT 2 or 3 (n = 53), category 3 DDIs were evident in 0% with sofosbuvir plus daclatasvir, 0% with sofosbuvir and ribavirin, and 13% with sofosbuvir/velpatasvir; current cART regimens were suitable in 100%, 100%, and 81%, respectively. Conclusions. Potential DDIs are expected and will impact on DAA prescribing in HIV/HCV coinfection. Sofosbuvir in combination with an NS5A inhibitor or ribavirin appeared to be the most suitable regimens in this cohort. Evaluation of potential DDIs is required to prevent adverse events or treatment failure.

Potential for Drug-Drug Interactions between Antiretrovirals and HCV Direct Acting Antivirals in a Large Cohort of HIV/HCV Coinfected Patients.

ObjectivesDevelopment of direct acting antivirals (DAA) offers new benefits for patients with chronic hepatitis C. The combination of these drugs with antiretroviral treatment (cART) is a real challenge in HIV/HCV coinfected patients. The aim of this study was to describe potential drug-drug interactions between DAAs and antiretroviral drugs in a cohort of HIV/HCV coinfected patients.MethodsCross-sectional study of all HIV/HCV coinfected patients attending at least one visit in 2012 in the multicenter French Dat’AIDS cohort. A simulation of drug-drug interactions between antiretroviral treatment and DAAs available in 2015 was performed.ResultsOf 16,634 HIV-infected patients, 2,511 had detectable anti-HCV antibodies, of whom 1,196 had a detectable HCV-RNA and were not receiving HCV treatment at the time of analysis. 97.1% of these patients were receiving cART and 81.2% had a plasma HIV RNA <50 copies/mL. cART included combinations of nucleoside reverse transcriptase inhibitors with a boosted protease inhibitor in 43.6%, a non-nucleoside reverse transcriptase inhibitor in 17.3%, an integrase inhibitor in 15.4% and various combinations or antiretroviral drugs in 23.7% of patients. A previous treatment against HCV had been administered in 64.4% of patients. Contraindicated associations/potential interactions were expected between cART and respectively sofosbuvir (0.2%/0%), sofosbuvir/ledipasvir (0.2%/67.6%), daclatasvir (0%/49.4%), ombitasvir/boosted paritaprevir (with or without dasabuvir) (34.4%/52.2%) and simeprevir (78.8%/0%).ConclusionsSignificant potential drug-drug interactions are expected between cART and the currently available DAAs in the majority of HIV/HCV coinfected patients. Sofosbuvir/ledipasvir and sofosbuvir/daclatasvir with or without ribavirin appeared the most suitable combinations in our population. A close collaboration between hepatologists and HIV/AIDS specialists appears necessary for the management of HCV treatment concomitantly to cART.

From preclinical to human – prediction of oral absorption and drug–drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach in an industrial setting: a workflow by using case example

A case example is presented in which the physiologically based modeling approach has been used to model the absorption of a lipophilic BCS Class II compound predominantly metabolized by CYP3A4, and to assess the interplay of absorption related parameters with the drug-drug interaction (DDI) potential. The PBPK model was built in the rat using Gastroplus® to study the absorption characteristics of the compound. Subsequently relevant model parameters were used to predict the non-linear human PK observed during first-in-human study after optimizing the absorption model for colonic absorption, bile micelle solubilization and unbound fraction in gut enterocytes (fu(gut)) using SIMCYP® simulator. The model fitted absorption parameters were then used to assess the drug-drug interaction (DDI) potential of the test compound when administered along with multiple doses of a potent CYP 3A4 inhibitor, ketoconazole. The impact of fu(gut) in the extent of DDI was assessed using parameter sensitivity analysis. After optimizing the preclinical model and taking into consideration bile micelle solubilization and colonic absorption, the non-linear pharmacokinetics of the test compound was satisfactorily predicted in man. Sensitivity analysis performed with the absorption parameter fu(gut) indicated that it could be an important parameter in predicting oral absorption. In addition, DDI simulations using SIMCYP® suggest that C(max) and AUC ratios may also be sensitive to the fu(gut) input in the model. Since fu(gut) cannot be measured experimentally, sensitivity analysis may help in assessing the importance of fu(gut) in human PK and DDI prediction using SIMCYP®.

Maraviroc: in vitro assessment of drug–drug interaction potential

To characterize the cytochrome P450 enzyme(s) responsible for the N-dealkylation of maraviroc in vitro, and predict the extent of clinical drug-drug interactions (DDIs). Human liver and recombinant CYP microsomes were used to identify the CYP enzyme responsible for maraviroc N-dealkylation. Studies comprised enzyme kinetics and evaluation of the effects of specific CYP inhibitors. In vitro data were then used as inputs for simulation of DDIs with ketoconazole, ritonavir, saquinavir and atazanvir, using the Simcyptrade mark population-based absorption, distribution, metabolism and elimination (ADME) simulator. Study designs for simulations mirrored those actually used in the clinic. Maraviroc was metabolized to its N-dealkylated product via a single CYP enzyme characterized by a K(m) of 21 microM and V(max) of 0.45 pmol pmol(-1) min(-1) in human liver microsomes and was inhibited by ketoconazole (CYP3A4 inhibitor). In a panel of recombinant CYP enzymes, CYP3A4 was identified as the major CYP responsible for maraviroc metabolism. Using recombinant CYP3A4, N-dealkylation was characterized by a K(m) of 13 microM and a V(max) of 3 pmol pmol(-1) CYP min(-1). Simulations therefore focused on the effect of CYP3A4 inhibitors on maraviroc pharmacokinetics. The simulated median AUC ratios were in good agreement with observed clinical changes (within twofold in all cases), although, in general, there was a trend for overprediction in the magnitude of the DDI. Maraviroc is a substrate for CYP3A4, and exposure will therefore be modulated by CYP3A4 inhibitors. Simcyptrade mark has successfully simulated the extent of clinical interactions with CYP3A4 inhibitors, further validating this software as a good predictor of CYP-based DDIs.