Clinical Drug-drug Interactions Research Articles

Assessment of the drug-drug interaction potential for therapeutic proteins with pro-inflammatory activities.

It is well-recognized that therapeutic proteins (TPs) with pro-inflammatory activities elevate the pro-inflammatory cytokines and result in cytokine-drug interactions. In the current review, several pro-inflammatory cytokines, including IL-2, IL-6, IFN-γ, and TNF-α, as well as an anti-inflammatory cytokine IL-10, were summarized for their respective effect on major cytochrome P450 enzymes and efflux transporter PgP. Pro-inflammatory cytokines are generally associated with suppression of CYP enzymes across assay systems but have varied effect on Pgp expression levels and activities depending on the individual cytokines and assay systems, whereas IL-10 had no significant impact on CYP enzymes and P-gp. A cocktail drug-drug interaction (DDI) study design could be an ideal approach for simultaneously assess the impact of TPs with pro-inflammatory activities on multiple CYP enzymes. Clinical DDI studies using the cocktail approach have been conducted for several TPs with pro-inflammatory activities and for those TPs with pro-inflammatory activities which had no clinical DDI study conducted, languages for potential DDI risk due to cytokine-drug interaction were included in the label. Up to date drug cocktails, including clinically validated and unvalidated for DDI assessment, were summarized in this review. Most clinically validated cocktails focused either on CYP enzymes or transporters. Additional effort was needed to validate a cocktail to include both the major CYP enzymes and key transporters. In silico methods for assessment of the DDI for TPs with pro-inflammatory activities were also discussed.

Characterization of Elimination Pathways and the Feasibility of Endogenous Metabolites as Biomarkers of Organic Anion Transporter 1/3 (Oat1/3) Inhibition in Cynomolgus Monkeys.

Organic anion transporters 1 and 3 (OAT1/3) occupy a key role in mediating renal elimination. Kynurenic acid (KYNA) was previously discovered as an effective endogenous biomarker to assess drug-drug interaction (DDI) for OAT inhibitors. Here, further in vitro and in vivo investigation was performed to characterize the elimination routes and feasibility of KYNA, along with other reported endogenous metabolites, as biomarkers of Oat1/3 inhibition in bile duct-cannulated (BDC) cynomolgus monkeys. Our results suggested KYNA is a substrate of OAT1/3 and OAT2, but not OCT2, MATE1/2K, or NTCP, and it shares comparable affinities between OAT1 and OAT3. Renal and biliary excretions, and plasma concentration-time profiles of KYNA, pyridoxic acid (PDA), homovanillic acid (HVA), and coproporphyrin I (CP-I), were assessed in BDC monkeys dosed with either probenecid (PROB) at 100 mg/kg or the control vehicle. Renal excretion of KYNA, PDA, and HVA was determined to be the major elimination route. The maximum plasma concentration (Cmax) and AUC0-24h of KYNA were about 11.6 and 3.7-fold higher in the PROB group than in the vehicle group. Renal clearance (CLr) of KYNA decreased by 3.2-fold, but biliary clearance (CLbile) was not altered after PROB administration. A similar trend was observed for PDA and HVA. Interestingly, an elevation of plasma concentration and reduction of CP-I CLbile were observed after PROB treatment, which suggested inhibition of the CP-I Oatp-Mrp2 transport axis by PROB. Overall, our results indicated that KYNA could potentially facilitate early and reliable assessment of DDI liabilities of Oat inhibition in monkeys. Significance Statement This work reported renal excretion as the major elimination pathway for kynurenic acid, pyridoxic acid, and homovanillic acid. Administration of probenecid reduced renal clearance and increased plasma exposure of these biomarkers in monkeys, consistent with the observation in humans. These endogenous biomarkers discovered in monkeys could be potentially used to evaluate the clinical drug-drug interactions (DDIs) in the early phase of drug development.

Combined oral contraceptives as victims of drug interactions.

Combined oral contraceptives (COCs) are widely used in women of reproductive age in the United States. Metabolism plays an important role in the elimination of estrogens and progestins contained in COCs. It is unavoidable that a woman using COCs may need to take another drug to treat a disease. If the concurrently used drug induces enzymes responsible for the metabolism of progestins and/or estrogens, unintended pregnancy or irregular bleeding may occur. If the concurrent drug inhibits the metabolism of these exogenous hormones, there may be an increased safety risk such as thrombosis. Therefore, for an investigational drug intended to be used in women with reproductive potential, evaluating its effects on the pharmacokinetics of COCs is important to determine if additional labeling is necessary for managing drug-drug interactions between the concomitant product and the COCs. It is challenging to determine when this clinical drug interaction study is needed, whether an observed exposure change of progestin/estrogen is clinically meaningful, and if the results of a clinical drug interaction study with one COC can predict exposure changes of unstudied COCs to inform labeling. In this review, we summarize the current understanding of metabolic pathways of estrogens and progestins contained in commonly used COCs, known interactions of these COCs as victim drugs, and discuss possible mechanisms of interactions for unexpected results. We also discuss recent advances, knowledge gaps, and future perspectives on this important topic. The review will enhance the understanding of DDIs with COCs and improve the safe and effective use of COCs. Significance Statement This minireview provides an overview of the metabolic pathways of ethinyl estradiol (EE) and progestins contained in commonly used COCs and significant drug interactions of these COCs as victims. It also discusses recent advances, knowledge gaps, and future perspectives and provides insights on potential mechanisms for unexpected results of clinical drug interaction studies of COCs. This minireview will help the reader understand considerations when evaluating the drug interaction potential with COCs for drugs that are expected to be used concurrently.

Inhibition of human UDP-glucuronosyltransferase enzyme by ripretinib: Implications for drug-drug interactions

Ripretinib, a tyrosine kinase inhibitor (TKI), is the first FDA approved fourth-line therapy for adults with advanced gastrointestinal stromal tumor (GIST). Studies have shown that several TKIs for treating GIST were potent inhibitors of human UDP-glucosyltransferase (UGTs) enzymes. However, whether ripretinib affects the activity of UGTs remains unclear. The aim of this study was to investigate the effects of ripretinib on major UGT isoforms, as well as to evaluate its potential drug-drug interactions (DDIs) risk caused by the inhibition of UGTs activities. The inhibitory effects and inhibition modes of ripretinib on UGTs were systematically evaluated using high-performance liquid chromatography (HPLC) and enzyme kinetic studies, respectively. Our data showed that ripretinib exhibited potent inhibition against UGT1A1, UGT1A3, UGT1A4, UGT1A7 and UGT1A8. Enzyme kinetic studies indicated that ripretinib was not only a competitive inhibitor of UGT1A1, UGT1A4 and UGT1A7, but also a noncompetitive inhibitor of UGT1A3, as well as a mixed inhibitor of UGT1A8. The prediction results of in vitro-in vivo extrapolation (IVIVE) demonstrated that ripretinib might bring the potential risk of DDIs when combined with substrates of UGT1A1, UGT1A3, UGT1A4, UGT1A7 or UGT1A8. Therefore, special attention should be paid when ripretinib is used in conjunction with other drugs metabolized by UGTs to avoid risk of DDIs in clinic.

Tiered approach to evaluate the CYP3A victim and perpetrator drug-drug interaction potential for vonoprazan using PBPK modeling and clinical data to inform labeling.

Vonoprazan is metabolized extensively through CYP3A and is an in vitro time-dependent inhibitor of CYP3A. A tiered approach was applied to understand the CYP3A victim and perpetrator drug-drug interaction (DDI) potential for vonoprazan. Mechanistic static modeling suggested vonoprazan is a potential clinically relevant CYP3A inhibitor. Thus, a clinical study was conducted to evaluate the impact of vonoprazan on the exposure of oral midazolam, an index substrate for CYP3A. A physiologically-based pharmacokinetic (PBPK) model for vonoprazan was also developed using in vitro data, drug- and system-specific parameters, and clinical data and observations from a [14 C] human absorption, distribution, metabolism, and excretion study. The PBPK model was refined and verified using data from a clinical DDI study with the strong CYP3A inhibitor, clarithromycin, to confirm the fraction metabolized by CYP3A, and the oral midazolam clinical DDI data assessing vonoprazan as a time-dependent inhibitor of CYP3A. The verified PBPK model was applied to simulate the anticipated changes in vonoprazan exposure due to moderate and strong CYP3A inducers (efavirenz and rifampin, respectively). The clinical midazolam DDI study indicated weak inhibition of CYP3A, with a less than twofold increase in midazolam exposure. PBPK simulations projected a 50% to 80% reduction in vonoprazan exposure when administered concomitantly with moderate or strong CYP3A inducers. Based on these results, the vonoprazan label was revised and states that lower doses of sensitive CYP3A substrates with a narrow therapeutic index should be used when administered concomitantly with vonoprazan, and co-administration with moderate and strong CYP3A inducers should be avoided.

Mechanistic Study of Icaritin-Induced Inactivation of Cytochrome P450 2C9.

Icaritin (ICT) is a prenylflavonoid derivative that has been approved by National Medical Products Administration for the treatment of hepatocellular carcinoma. This study aims to evaluate the potential inhibitory effect of ICT against cytochrome P450 (CYP) enzymes and to elucidate the inactivation mechanisms. Results showed that ICT inactivated CYP2C9 in a time-, concentration-, and NADPH-dependent manner with Ki = 1.896 μM, Kinact = 0.02298 minutes-1, and Kinact/Ki = 12 minutes-1 mM-1, whereas the activities of other CYP isozymes was minimally affected. Additionally, the presence of CYP2C9 competitive inhibitor, sulfaphenazole, superoxide dismutase/catalase system, and GSH all protected CYP2C9 from ICT-induced activity loss. Moreover, the activity loss was neither recovered by washing the ICT-CYP2C9 preincubation mixture nor the addition of potassium ferricyanide. These results, collectively, implied the underlying inactivation mechanism involved the covalent binding of ICT to the apoprotein and/or the prosthetic heme of CYP2C9. Furthermore, an ICT-quinone methide (QM)-derived GSH adduct was identified, and human glutathione S-transferases (GST) isozymes GSTA1-1, GSTM1-1, and GSTP1-1 were shown to be substantially involved in the detoxification of ICT-QM. Interestingly, our systematic molecular modeling work predicted that ICT-QM was covalently bound to C216, a cysteine residue located in the F-G loop downstream of substrate recognition site (SRS) 2 in CYP2C9. The sequential molecular dynamics simulation confirmed the binding to C216 induced a conformational change in the active catalytic center of CYP2C9. Lastly, the potential risks of clinical drug-drug interactions triggered by ICT as a perpetrator were extrapolated. In summary, this work confirmed that ICT was an inactivator of CYP2C9. SIGNIFICANCE STATEMENT: This study is the first to report the time-dependent inhibition of CYP2C9 by icaritin (ICT) and the intrinsic molecular mechanism behind it. Experimental data indicated that the inactivation was via irreversible covalent binding of ICT-quinone methide to CYP2C9, while molecular modeling analysis provided additional evidence by predicting C216 as the key binding site which influenced the structural confirmation of CYP2C9's catalytic center. These findings suggest the potential of drug-drug interactions when ICT is co-administered with CYP2C9 substrates clinically.

Preincubation Time-Dependent, Long-Lasting Inhibition of Drug Transporters and Impact on the Prediction of Drug-Drug Interactions.

Transporter-mediated drug-drug interaction (DDI) is of clinical concern, and the quantitative prediction of DDIs is an indispensable part of drug development. Cell-based inhibition assays, in which a representative probe substrate and a potential inhibitor are coincubated, are routinely performed to assess the inhibitory potential of new molecular entities on drug transporters. However, the inhibitory effect of cyclosporine A (CsA) on organic anion transporting polypeptide (OATP) 1B1 is substantially potentiated with CsA preincubation, and this effect is both long-lasting and dependent on the preincubation time. This phenomenon has also been reported with transporters other than OATP1Bs, but it is considered more prevalent among OATP1Bs and organic cation transporters. Regulatory agencies have also noted this preincubation effect and have recommended that pharmaceutical companies consider inhibitor preincubation when performing in vitro OATP1B1 and OATP1B3 inhibition studies. Although the underlying mechanisms responsible for the preincubation effect are not fully understood, a trans-inhibition mechanism was recently demonstrated for OATP1B1 inhibition by CsA, in which CsA inhibited OATP1B1 not only extracellularly (cis-inhibition) but also intracellularly (trans-inhibition). Furthermore, the trans-inhibition potency of CsA was much greater than that of cis-inhibition, suggesting that trans-inhibition might be a key driver of clinical DDIs of CsA with OATP1B substrate drugs. Although confidence in transporter-mediated DDI prediction is generally considered to be low, the predictability might be further improved by incorporating the trans-inhibition mechanism into static and dynamic models for preincubation-dependent inhibitors of OATP1Bs and perhaps other transporters. SIGNIFICANCE STATEMENT: Preincubation time-dependent, long-lasting inhibition has been observed for OATP1B1 and other solute carrier transporters in vitro. Recently, a trans-inhibition mechanism for the preincubation effect of CsA on OATP1B1 inhibition was identified, with the trans-inhibition potency being greater than that of cis-inhibition. The concept of trans-inhibition may allow us to further understand the mechanism of transporter-mediated DDIs not only for OATP1B1 but also for other transporters and to improve the accuracy and confidence of DDI predictions.

Mechanistic in vitro studies indicate that the clinical drug-drug interactions between protease inhibitors and rosuvastatin are driven by inhibition of intestinal BCRP and hepatic OATP1B1 with minimal contribution from OATP1B3, NTCP and OAT3.

Previous use of a mechanistic static model to accurately quantify the increased rosuvastatin exposure due to drug-drug interaction (DDI) with coadministered atazanavir underpredicted the magnitude of area under the plasma concentration-time curve ratio (AUCR) based on inhibition of breast cancer resistance protein (BCRP) and organic anion transporting polypeptide (OATP) 1B1. To reconcile the disconnect between predicted and clinical AUCR, atazanavir and other protease inhibitors (darunavir, lopinavir and ritonavir) were evaluated as inhibitors of BCRP, OATP1B1, OATP1B3, sodium taurocholate cotransporting polypeptide (NTCP) and organic anion transporter (OAT) 3. None of the drugs inhibited OAT3, nor did darunavir and ritonavir inhibit OATP1B3 or NTCP. All drugs inhibited BCRP-mediated estrone 3-sulfate transport or OATP1B1-mediated estradiol 17β-D-glucuronide transport with the same rank order of inhibitory potency (lopinavir>ritonavir>atazanavir>>darunavir) and mean IC50 values ranging from 15.5 ± 2.80 μM to 143 ± 14.7 μM or 0.220 ± 0.0655 μM to 9.53 ± 2.50 μM, respectively. Atazanavir and lopinavir also inhibited OATP1B3- or NTCP-mediated transport with a mean IC50 of 1.86 ± 0.500 μM or 65.6 ± 10.7 μM and 5.04 ± 0.0950 μM or 20.3 ± 2.13 μM, respectively. Following integration of a combined hepatic transport component into the previous mechanistic static model using the in vitro inhibitory kinetic parameters determined above for atazanavir, the newly predicted rosuvastatin AUCR reconciled with the clinically observed AUCR confirming additional minor involvement of OATP1B3 and NTCP inhibition in its DDI. The predictions for the other protease inhibitors confirmed inhibition of intestinal BCRP and hepatic OATP1B1 as the principal pathways involved in their clinical DDI with rosuvastatin.

Best practices for documentation of psychotropic drug-drug interactions in an adult psychiatric clinic.

Psychotropic drug-drug interactions (DDIs) contribute to adverse drug events, but many go undetected or unmanaged. Thorough documentation of potential DDIs can improve patient safety. The primary objective of this study is to determine the quality of and factors associated with documentation of DDIs in an adult psychiatric clinic run by postgraduate year 3 psychiatry residents (PGY3s). A list of high-alert psychotropic medications was identified by consulting primary literature on DDIs and clinic records. Charts of patients prescribed these medications by PGY3 residents from July 2021 to March 2022 were reviewed to detect potential DDIs and assess documentation. Chart documentation of DDIs was noted as none, partial, or complete. Chart review identified 146 DDIs among 129 patients. Among the 146 DDIs, 65% were not documented, 24% were partially documented, and 11% had complete documentation. The percentage of pharmacodynamic interactions documented was 68.6% with 35.3% of pharmacokinetic interactions documented. Factors associated with partial or complete documentation included diagnosis of psychotic disorder (p = .003), treatment with clozapine (p = .02), treatment with benzodiazepine-receptor agonist (p < .01), and assumption of care during July (p = .04). Factors associated with no documentation include diagnosis of "other (primarily impulse control disorder)" (p < .01) and taking an enzyme-inhibiting antidepressant (p < .01). Investigators propose best practices for psychotropic DDI documentation: (1) description and potential outcome of DDI, (2) monitoring and management, (3) Patient education on DDI, and (4) patient response to DDI education. Strategies to improve DDI documentation quality include targeted provider education, incentives, and electronic medical record "DDI smart phrases."

Industry Perspective on Therapeutic Peptide Drug-Drug Interaction Assessments During Drug Development: A European Federation of Pharmaceutical Industries and Associations White Paper.

Drug-drug interaction (DDI) assessments are well defined in health authority guidelines for small molecule drugs, and US Food and Drug Administration (FDA) draft guidance is now available for therapeutic proteins. However, there are currently no regulatory guidelines outlining DDI assessments for therapeutic peptides, which poses significant uncertainty and challenges during drug development for this heterogenous class of molecules. A cross-industry peptide DDI working group consisting of experts from 10 leading companies was formed under the sponsorship of the European Federation of Pharmaceutical Industries and Associations. We aimed to capture the range of DDI studies undertaken for peptide drugs by (i) anonymously surveying relevant companies involved in peptide drug development to better understand DDI study type/timing currently performed and (ii) compiling a database containing in vitro / clinical DDI data from submission packages for recently approved peptide drugs. Our analyses highlight significant gaps and uncertainty in the field. For example, the reported timing of in vitro peptide DDI studies, if performed, vary substantially across responding companies from early research to phase III. Nearly all in vitro cytochrome P450 / transporter inhibition studies reported in the survey were negative. For the few positive hits reported, no clinical follow-up studies were performed, questioning the clinical relevance of these findings. Furthermore, available submission packages reveal DDI likelihood is low for peptides >2kDa, making it reasonable to adopt a risk-based approach during drug development for larger peptides. By benchmarking the landscape of peptide DDI activities across the industry, we set the stage for future discussions with health authorities on harmonizing peptide DDI approaches.

Clinical assessment of gepotidacin (GSK2140944) as a victim and perpetrator of drug-drug interactions via CYP3A metabolism and transporters.

Gepotidacin is a novel triazaacenaphthylene antibiotic in phase III development. Based on nonclinical in vitro characterization of gepotidacin metabolism, two phase I studies were conducted in healthy participants to investigate clinical drug-drug interactions (DDIs). We assessed gepotidacin as a DDI victim with a potent cytochrome P450 (CYP) 3A4/P-glycoprotein (P-gp) inhibitor (itraconazole), potent CYP3A4 inducer (rifampicin), and nonspecific organic cation transporter (OCT)/multidrug and toxic extrusion transporter (MATE) renal transport inhibitor (cimetidine) via single doses of gepotidacin before and after co-administration with multiple doses of the modulator drugs. Gepotidacin DDI perpetrator potential for P-gp inhibition (digoxin) and CYP3A4 inhibition (midazolam) was evaluated via single doses of the two-drug cocktail without and with gepotidacin. The DDI magnitudes were interpreted based on area under the concentration-time curve (AUC). A weak DDI (AUC increase 48%-50%) was observed for gepotidacin co-administered with itraconazole. A clinically significant decrease in gepotidacin plasma AUC (52%) was observed with rifampicin coadministration, indicating a moderate DDI. There was no DDI for gepotidacin with cimetidine; a unique biomarker approach showed increased serum creatinine (24%), decreased renal clearance of creatinine (21%), and N1-methylnicotinamide (39%), which confirmed extensive MATE inhibition and partial OCT2 inhibition. Gepotidacin was not a P-gp DDI perpetrator, although the maximum plasma concentration of digoxin increased (53%) and is potentially clinically relevant given its narrow therapeutic index. Gepotidacin demonstrated weak CYP3A4 inhibition with midazolam (<2-fold AUC increase). There were no new safety-risk profile findings. These results will inform the safe and efficacious clinical use of gepotidacin when co-administered with other drugs.

Translatability of in vitro Inhibition Potency to in vivo P-Glycoprotein Mediated Drug Interaction Risk

P-glycoprotein (P-gp) may limit oral drug absorption of substrate drugs due to intestinal efflux. Therefore, regulatory agencies require investigation of new chemical entities as possible inhibitors of P-gp in vitro. Unfortunately, inter-laboratory and inter-assay variability have hindered the translatability of in vitro P-gp inhibition data to predict clinical drug interaction risk. The current study was designed to evaluate the impact of potential IC50 discrepancies between two commonly utilized assays, i.e., bi-directional Madin-Darby Canine Kidney-MDR1 cell-based and MDR1 membrane vesicle-based assays. When comparing vesicle- to cell-based IC50 values (n = 28 inhibitors), non-P-gp substrates presented good correlation between assay formats, whereas IC50s of P-gp substrates were similar or lower in the vesicle assays. The IC50s obtained with a cell line expressing relatively low P-gp aligned more closely to those obtained from the vesicle assay, but passive permeability of the inhibitors did not appear to influence the correlation of IC50s, suggesting that efflux activity reduces intracellular inhibitor concentrations. IC50s obtained between two independent laboratories using the same assay type showed good correlation. Using the G-value (i.e., ratio of estimated gut concentration-to-inhibition potency) >10 cutoff recommended by regulatory agencies resulted in minimal differences in predictive performance, suggesting this cutoff is appropriate for either assay format.

Prediction of the Renal Organic Anion Transporter 1 (OAT1)- Mediated Drug Interactions for LY404039, the Active Metabolite of Pomaglumetad Methionil.

The objective of this work was to demonstrate that clinical OAT1-mediated DDIs can be predicted using physiologically based pharmacokinetic (PBPK) modeling. LY404039 is a metabotropic glutamate receptor 2/3 agonist and the active moiety of the prodrug pomaglumetad methionil (LY2140023). After oral administration, pomaglumetad methionil is rapidly taken up by enterocytes via PEPT1 and once absorbed, converted to LY404039 via membrane dehydropeptidase 1 (DPEP1). LY404039 is renally excreted by both glomerular filtration and active secretion and in vitro studies showed that the active secretion of LY404039 was mediated by the organic anion transporter 1 (OAT1). Both clinical and in vitro data were used to build a PBPK model to predict OAT1-mediated DDIs. In vitro inhibitory potencies (IC50) of the known OAT inhibitors, probenecid and ibuprofen, were determined to be 4.00 and 2.63µM, respectively. Subsequently, clinical drug-drug interaction (DDI) study showed probenecid reduced the renal clearance of LY404039 by 30 to 40%. The PBPK bottom-up model, predicted a renal clearance that was approximately 20% lower than the observed one. The middle-out model, using an OAT1 relative activity factor (RAF) of 3, accurately reproduced the renal clearance of LY404039 and pharmacokinetic (PK) changes of LY404039 in the presence of probenecid. OAT1- mediated DDIs can be predicted using in vitro measured IC50 and PBPK modeling. The effect of ibuprofen was predicted to be minimal (AUC ratio of 1.15) and not clinically relevant.

Verifying in vitro-determined enzyme contributions to cannabidiol clearance for exposure predictions in human through physiologically-based pharmacokinetic modeling.

Cannabidiol (CBD) is approved for treatment of seizures associated with two forms of epilepsy that become apparent in infancy or early childhood. To consider an adult physiologically-based pharmacokinetic (PBPK) model for pediatric scaling, we assessed in vitro-derived cytochrome P450 (CYP) and uridine 5'-diphospho-glucuronosyltransferase (UGT) enzyme contributions to CBD clearance in human. An i.v. PBPK model was constructed using CBD physicochemical properties and knowledge of disposition. The i.v. datasets were used for model building and evaluation. Oral PBPK models for CBD administered in fasted and fed states were developed using single dose oral datasets and parameters optimized from the i.v. model and evaluated with multiple dose datasets. Relative contributions of CBD metabolizing enzymes were partitioned according to in vitro studies. Clinical drug-drug interaction (DDI) studies were simulated using CBD fed state, itraconazole, fluconazole, and rifampicin PBPK models. Linear mixed effect modeling was used to estimate area under the concentration-time curve from zero to infinity (AUC0-∞ ) perpetrator + CBD versus CBD alone. The i.v. and oral datasets used in model evaluation produced acceptable average fold error (AFE) of 1.28 and absolute AFE of 1.65. Relative contributions of drug-metabolizing enzymes to CBD clearance were proposed from in vitro data: UGT1A7 4%, UGT1A9 16%, UGT2B7 10%, CYP3A4 38%, CYP2C19 21%, and CYP2C9 11%. The simulated DDI studies using the in vitro-derived values produced AUC0-∞ treatment ratios comparable to observed: itraconazole 1.24 versus 1.07, fluconazole 1.45 versus 1.22, and rifampicin 0.49 versus 0.69. The constructed CBD PBPK models can predict adult exposures and have potential for use in pediatrics where exposure estimates are limited.

High-throughput cytochrome P450 loss and metabolic intermediate complex assays to aid in designing out of CYP3A inactivation.

Time-dependent inactivation (TDI) of cytochrome P450 (CYP) enzymes may result in clinical drug-drug interactions (DDIs). Therefore, designing out of CYP TDI prior to advancing a compound to clinical development is highly desirable. As TDI of CYP3A is a common occurrence in small molecule drug discovery, high-throughput methods are sought to help identify the mechanism of inactivation and enable design strategies to mitigate CYP3A TDI. CYP inactivation via modification or destruction of the prosthetic heme group results in loss of the ability of the enzyme to bind carbon monoxide. Additionally, formation of a tight binding complex with the heme iron, referred to as a metabolic intermediate (MI) complex, also results in enzyme inactivation. The methods described herein provide a high-throughput means of identifying and comparing compounds for their ability to inactivate via destruction/modification of the heme via loss of the ability to bind carbon monooxide, as well as via formation of an MI complex.

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 &amp; Co., Ltd.: employee Hiroki Koshimichi, n/a, Shionogi &amp; Co., Ltd.: employee Takanobu Matsuzaki, Ph.D., Shionogi &amp; Co., Ltd.: Employee Ryosuke Shimizu, Shionogi &amp; Co., Ltd.: employee Shinpei Yoshida, Ph.D., Shionogi &amp; Co., Ltd.: employee Ryuji Kubota, Ph.D., Shionogi &amp; Co., Ltd.: employee|Shionogi &amp; Co., Ltd.: employee Shingo Sakamoto, n/a, Shionogi &amp; Co., Ltd.: employee.

Silymarin as a preventive or therapeutic measure for chemotherapy and radiotherapy-induced adverse reactions: a comprehensive review of preclinical and clinical data.

Thus far, silymarin has been examined in several studies for prevention or treatment of various chemotherapy or radiotherapy-induced adverse reactions. In this review, we try to collect all available human, animal, and pre-clinical data in this field. The search was done in Scopus, PubMed, Medline, and systematic reviews in the Cochrane database, using the following keywords: "Cancer," "Chemotherapy," "Radiotherapy," "Mucositis," "Nephrotoxicity," "Dermatitis," "Ototoxicity," "Cardiotoxicity," "Nephrotoxicity," "Hepatotoxicity," "Reproductive system," "Silybum marianum," "Milk thistle," and "Silymarin" and "Silybin." We included all relevant in vitro, in vivo, and human studies up to the date of publication. Based on 64 included studies in this review, silymarin is considered a safe and well-tolerated compound, with no known clinical drug interaction. Notably, multiple adverse reactions of chemotherapeutic agents are effectively managed by its antioxidant, anti-apoptotic, anti-inflammatory, and anti-immunomodulatory properties. Clinical trials suggest that oral silymarin may be a promising adjuvant with cancer treatments, particularly against hepatotoxicity (n = 10), nephrotoxicity (n = 3), diarrhea (n = 1), and mucositis (n = 3), whereas its topical formulation can be particularly effective against radiodermatitis (n = 2) and hand-foot syndrome (HFS) (n = 1). Further studies are required to determine the optimal dose, duration, and the best formulation of silymarin to prevent and/or manage chemotherapy and radiotherapy-induced complications.

Evaluating the performance of machine-learning regression models for pharmacokinetic drug-drug interactions.

Combination therapy or concomitant drug administration can be associated with pharmacokinetic drug-drug interactions, increasing the risk of adverse drug events and reduced drug efficacy. Thus far, machine-learning models have been developed that can classify drug-drug interactions. However, to enable quantification of the pharmacokinetic effects of a drug-drug interaction, regression-based machine learning should be explored. Therefore, this study investigated the use of regression-based machine learning to predict changes in drug exposure caused by pharmacokinetic drug-drug interactions. Fold changes in exposure relative to substrate drug monotherapy were collected from 120 clinical drug-drug interaction studies extracted from the Washington Drug Interaction Database and SimCYP compound library files. Drug characteristics (features) were collected such as structure, physicochemical properties, in vitro pharmacokinetic properties, cytochrome P450 metabolic activity, and population characteristics. Three different regression-based supervised machine-learning models were then applied to the prediction task: random forest, elastic net, and support vector regressor. Model performance was evaluated using fivefold cross-validation. Strongest performance was observed with support vector regression, with 78% of predictions within twofold of the observed exposure changes. The results show that changes in drug exposure can be predicted with reasonable accuracy using regression-based machine-learning models trained on data available early in drug discovery. This has potential applications in enabling earlier drug-drug interaction risk assessment for new drug candidates.

Population Pharmacokinetics in Hepatic and Renal Impairment and PBPK Drug-Drug Interaction Simulations of Asciminib, a Novel Tyrosine Kinase Inhibitor in Chronic Myeloid Leukemia in Chronic Phase

Introduction: Asciminib, a first-in-class BCR::ABL1 inhibitor that works by specifically targeting the ABL myristoyl pocket (STAMP), has been recently approved by the FDA for adult patients with Philadelphia chromosome-positive chronic myeloid leukemia in chronic phase (Ph+ CML-CP) treated with ≥2 prior tyrosine kinase inhibitors (TKIs), as well as adult patients with Ph+ CML-CP with the T315I mutation. The recommended asciminib doses for patients with Ph+ CML-CP without the T315I mutation are 40 mg twice daily (BID), which is the dose tested in the pivotal phase III study (ASCEMBL), and a more patient-centric dose of 80 mg once daily (QD). For patients with the T315I mutation, the approved dose of asciminib is 200 mg BID. All asciminib drug-drug interaction (DDI) and hepatic and renal impairment studies were conducted at single or multiple doses of 40 mg. Here, we aim to assess the clinical impact of hepatic and renal impairment and of DDIs on the pharmacokinetics (PK) of asciminib and provide recommendations for eventual necessary dose adjustments. Methods: Organ impairment effect was quantified using population pharmacokinetics (PopPK), while DDIs were predicted using physiologically-based pharmacokinetics (PBPK). A 2-compartment PopPK model with a delayed first-order absorption and first-order elimination was developed for asciminib (Li et al. Clin Pharmacokinet, 2022). The original analysis included pooled PK data from patients receiving asciminib at 10−200 mg BID (in the phase I dose-finding study, NCT02081378) or 40 mg BID (in the phase III study ASCEMBL, NCT03106779), and identified baseline absolute glomerular filtration rate (aGFR), nominal total daily dose, body weight and formulation as significant covariates affecting asciminib PK. The model has been further extended to describe patients with renal (based on aGFR) or hepatic (based on Child-Pugh class) impairment, using data from two phase I PK studies in patients with renal (NCT03605277) or hepatic (NCT02857868) impairment. A PBPK model was developed and validated for asciminib using SimCYP v19.1. A stepwise "middle out” approach was used leveraging in vitro, in silico and in vivo data. The PBPK model was validated using single and multiple dose PK at 20‒400 mg daily dose as well as clinical DDI and PK data in patients with organ impairment. Comprehensive DDI scenarios and PK in patients with hepatic and renal impairment, which had not been clinically tested, were simulated at the different recommended doses of asciminib (40 mg BID, 80 mg QD, 200 mg BID). Results: There was no significant impact of hepatic function on asciminib exposure; a non-significant trend to lower clearance was observed in patients with severe hepatic impairment. The PopPK derived steady-state exposures (measured as area under the curve [AUC]0-24h) for patients with severe hepatic impairment were almost identical for both dosing regimens (40 mg BID: 13,018 ng*h/mL, 80 mg QD: 13,020 ng*h/mL), and only marginally higher than those of patients with normal hepatic function (12,638 ng*h/mL and 12,646 ng*h/mL, respectively), well within the therapeutic window of asciminib. Renal function assessment showed that clearance was slightly decreased in patients with renal impairment, although this was not clinically relevant. For both 80 mg QD and 40 mg BID, the median predicted AUC0-24h and maximum plasma concentration (Cmax) for severe renal impairment were 60% and 40% higher, respectively, than those of a typical individual with normal renal function. However, this difference is not considered to be clinically relevant given the relatively flat exposure-safety relationship over a 5-fold difference in exposure. The PBPK model predicted no drug interaction that would significantly impact the PK of asciminib (as a victim) or of other substrates (as a perpetrator). Overall, asciminib exposure was weakly affected by interactions with inducers and inhibitors as a victim (Figure 1B) and was predicted to be a weak inhibitor of CYP3A4 and P-gp at all tested doses and a weak to moderate inhibitor of CYP2C9 as a perpetrator (Figure 1A). Conclusions: Hepatic or renal impairment does not have a clinically significant impact on the PK of asciminib. Hence, dose adaptations for patients with hepatic or renal impairment are not warranted. Overall, asciminib has a manageable DDI risk at all recommended doses. Sponsor: Novartis. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Management of Drug-Drug Interactions Between Long-Acting Cabotegravir and Rilpivirine and Comedications With Inducing Properties: A Modeling Study.

Long-acting (LA) intramuscular cabotegravir and rilpivirine are prone to drug-drug interactions (DDI). However, given the long dosing interval, the conduct of clinical DDIs studies with LA antiretrovirals is challenging. We performed virtual clinical DDI studies using physiologically based pharmacokinetic (PBPK) modeling to provide recommendations for the management of DDIs with strong or moderate inducers such as rifampicin or rifabutin. Each DDI scenario included a cohort of virtual individuals (50% female) between 20 and 50 years of age with a body mass index of 18-30 kg/m2. Cabotegravir and rilpivirine were given alone and in combination with rifampicin or rifabutin. The predictive performance of the PBPK model to simulate cabotegravir and rilpivirine pharmacokinetics after oral and intramuscular administration and to reproduce DDIs with rifampicin and rifabutin was first verified against available observed clinical data. The verified model was subsequently used to simulate unstudied DDI scenarios. At steady state, the strong inducer rifampicin was predicted to decrease the area under the curve (AUC) of LA cabotegravir by 61% and rilpivirine by 38%. An increase in the dosing frequency did not overcome the DDI with rifampicin. The moderate inducer rifabutin was predicted to reduce the AUC of LA cabotegravir by 16% and rilpivirine by 18%. The DDI with rifabutin can be overcome by administering LA cabotegravir/rilpivirine monthly together with a daily oral rilpivirine dose of 25 mg. LA cabotegravir/rilpivirine should be avoided with strong inducers but coadministration with moderate inducers is possible by adding oral rilpivirine daily dosing to the monthly injection.