Optimizing thein vitroand clinical assessment of drug interaction risk by understanding co-medications in patient populations

Identification of Organic Anion Transporter 2 Inhibitors: Screening, Structure-Based Analysis, and Clinical Drug Interaction Risk Assessment

Organic anion transporter 2 (OAT2 or SLC22A7) plays an important role in the hepatic uptake and renal secretion of several endogenous compounds and drugs. The goal of this work is to understand the structure activity of OAT2 inhibition and assess clinical drug interaction risk. A single-point inhibition assay using OAT2-transfected HEK293 cells was employed to screen about 150 compounds; and concentration-dependent inhibition potency (IC₅₀) was measured for the identified "inhibitors". Acids represented about 65% of all inhibitors, and the frequency of bases-plus-zwitterions approximately doubled for "non-inhibitors". Interestingly, 9 of 10 most potent inhibitors (low IC₅₀) are acids (pK_a ∼ 3-5). Additionally, inhibitors are significantly larger and lipophilic than non-inhibitors. In silico (binary) models were developed to identify inhibitors and non-inhibitors. Finally, in vivo risk assessed via static drug-drug interaction models identified several inhibitors with potential for renal and hepatic OAT2 inhibition at clinical doses. This is the first study assessing the global pattern of OAT2-ligand interactions.

Studying the right transporter at the right time: an in vitro strategy for assessing drug-drug interaction risk during drug discovery and development

INTRODUCTION

Transporters are significant in dictating drug pharmacokinetics, thus inhibition of transporter function can alter drug concentrations resulting in drug-drug interactions (DDIs). Because they can impact drug toxicity, transporter DDIs are a regulatory concern for which prediction of clinical effect from in vitro data is critical to understanding risk.

AREA COVERED

The authors propose in vitro strategies to assist mitigating/removing transporter DDI risk during development by frontloading specific studies, or managing patient risk in the clinic. An overview of clinically relevant drug transporters and observed DDIs are provided, alongside presentation of key considerations/recommendations for in vitro study design evaluating drugs as inhibitors or substrates. Guidance on identifying critical co-medications, clinically relevant disposition pathways and using mechanistic static equations for quantitative prediction of DDI is compiled.

EXPERT OPINION

The strategies provided will facilitate project teams to study the right transporter at the right time to minimise development risks associated with DDIs. To truly alleviate or manage clinical risk, the industry will benefit from moving away from current qualitative basic static equation approaches to transporter DDI hazard assessment towards adopting the use of mechanistic models to enable quantitative DDI prediction, thereby contextualising risk to ascertain whether a transporter DDI is simply pharmacokinetic or clinically significant requiring intervention.

Pharmacokinetic Drug-drug Interaction of Antibiotics Used in Sepsis Care in China

BACKGROUND

Many antibiotics have a high potential for interactions with drugs, as a perpetrator and/or victim, in critically ill patients, and particularly in sepsis patients.

METHODS

The aim of this review is to summarize the pharmacokinetic drug-drug interaction (DDI) of 45 antibiotics commonly used in sepsis care in China. Literature search was conducted to obtain human pharmacokinetics/ dispositions of the antibiotics, their interactions with drug-metabolizing enzymes or transporters, and their associated clinical drug interactions. Potential DDI is indicated by a DDI index ≥ 0.1 for inhibition or a treatedcell/ untreated-cell ratio of enzyme activity being ≥ 2 for induction.

RESULTS

The literature-mined information on human pharmacokinetics of the identified antibiotics and their potential drug interactions is summarized.

CONCLUSION

Antibiotic-perpetrated drug interactions, involving P450 enzyme inhibition, have been reported for four lipophilic antibacterials (ciprofloxacin, erythromycin, trimethoprim, and trimethoprim-sulfamethoxazole) and three antifungals (fluconazole, itraconazole, and voriconazole). In addition, seven hydrophilic antibacterials (ceftriaxone, cefamandole, piperacillin, penicillin G, amikacin, metronidazole, and linezolid) inhibit drug transporters in vitro. Despite no clinical PK drug interactions with the transporters, caution is advised in the use of these antibacterials. Eight hydrophilic antibiotics (all β-lactams; meropenem, cefotaxime, cefazolin, piperacillin, ticarcillin, penicillin G, ampicillin, and flucloxacillin), are potential victims of drug interactions due to transporter inhibition. Rifampin is reported to perpetrate drug interactions by inducing CYP3A or inhibiting OATP1B; it is also reported to be a victim of drug interactions, due to the dual inhibition of CYP3A4 and OATP1B by indinavir. In addition, three antifungals (caspofungin, itraconazole, and voriconazole) are reported to be victims of drug interactions because of P450 enzyme induction. Reports for other antibiotics acting as victims in drug interactions are scarce.

Drug Interactions for Low-Dose Inhaled Nemiralisib: A Case Study Integrating Modeling, In Vitro, and Clinical Investigations

In vitro data for low-dose inhaled phosphoinositide 3-kinase delta inhibitor nemiralisib revealed that it was a substrate and a potent metabolism-dependent inhibitor of cytochrome P450 (P450) 3A4 and a P-glycoprotein (P-gp) substrate. An integrated in silico, in vitro, and clinical approach including a clinical drug interaction study as well as a bespoke physiologically based pharmacokinetic (PBPK) model was used to assess the drug-drug interaction (DDI) risk. Inhaled nemiralisib (100 µg, single dose) was coadministered with itraconazole, a potent P4503A4/P-gp inhibitor, following 200 mg daily administrations for 10 days in 20 male healthy subjects. Systemic exposure to nemiralisib (AUC0-inf) increased by 2.01-fold versus nemiralisib alone. To extrapolate the clinical data to other P4503A4 inhibitors, an inhaled PBPK model was developed using Simcyp software. Retrospective simulation of the victim risk showed good agreement between simulated and observed data (AUC0-inf ratio 2.3 vs. 2.01, respectively). Prospective DDI simulations predicted a weak but manageable drug interaction when nemiralisib was coadministered with other P4503A4 inhibitors, such as the macrolides clarithromycin and erythromycin (simulated AUC0-inf ratio of 1.7), both common comedications in the intended patient populations. PBPK and static mechanistic models were also used to predict a negligible perpetrator DDI effect for nemiralisib on other P4503A4 substrates, including midazolam (a sensitive probe substrate of P4503A4) and theophylline (a narrow therapeutic index drug and another common comedication). In summary, an integrated in silico, in vitro, and clinical approach including an inhalation PBPK model has successfully discharged any potential patient DDI risks in future nemiralisib clinical trials. SIGNIFICANCE STATEMENT: This paper describes the integration of in silico, in vitro, and clinical data to successfully discharge potential drug-drug interaction risks for a low-dose inhaled drug. This work featured assessment of victim and perpetrator risks of drug transporters and cytochrome P450 enzymes, utilizing empirical and mechanistic approaches combined with clinical data (drug interaction and human absorption, metabolism, and pharmacokinetics) and physiologically based pharmacokinetic modeling approaches to facilitate bespoke risk assessment in target patient populations.

Simultaneous Assessment of Transporter-Mediated Drug-Drug Interactions Using a Probe Drug Cocktail in Cynomolgus Monkey

We aim to establish an in vivo preclinical model to enable simultaneous assessment of inhibition potential of an investigational drug on clinically relevant drug transporters, organic anion-transporting polypeptide (OATP)1B, breast cancer resistance protein (BCRP), P-glycoprotein (P-gp), and organic anion transporter (OAT)3. Pharmacokinetics of substrate cocktail consisting of pitavastatin (OATP1B substrate), rosuvastatin (OATP1B/BCRP/OAT3), sulfasalazine (BCRP), and talinolol (P-gp) were obtained in cynomolgus monkey-alone or in combination with transporter inhibitors. Single-dose rifampicin (30 mg/kg) significantly (P < 0.01) increased the plasma exposure of all four drugs, with a marked effect on pitavastatin and rosuvastatin [area under the plasma concentration-time curve (AUC) ratio ∼21-39]. Elacridar, BCRP/P-gp inhibitor, increased the AUC of sulfasalazine, talinolol, as well as rosuvastatin and pitavastatin. An OAT1/3 inhibitor (probenecid) significantly (P < 0.05) impacted the renal clearance of rosuvastatin (∼8-fold). In vitro, rifampicin (10 µM) inhibited uptake of pitavastatin, rosuvastatin, and sulfasalazine by monkey and human primary hepatocytes. Transport studies using membrane vesicles suggested that all probe substrates, except talinolol, are transported by cynoBCRP, whereas talinolol is a cynoP-gp substrate. Elacridar and rifampicin inhibited both cynoBCRP and cynoP-gp in vitro, indicating potential for in vivo intestinal efflux inhibition. In conclusion, a probe substrate cocktail was validated to simultaneously evaluate perpetrator impact on multiple clinically relevant transporters using the cynomolgus monkey. The results support the use of the cynomolgus monkey as a model that could enable drug-drug interaction risk assessment, before advancing a new molecular entity into clinical development, as well as providing mechanistic insights on transporter-mediated interactions.

Mechanistic in vitro studies confirm that inhibition of the renal apical efflux transporter multidrug and toxin extrusion (MATE) 1, and not altered absorption, underlies the increased metformin exposure observed in clinical interactions with cimetidine, trimethoprim or pyrimethamine

Metformin is a common co‐medication for many diseases and the victim of clinical drug‐drug interactions (DDIs) perpetrated by cimetidine, trimethoprim and pyrimethamine, resulting in decreased active renal clearance due to inhibition of organic cation transport proteins and increased plasma exposure of metformin. To understand whether area under the plasma concentration–time curve (AUC) increases relate to absorption, in vitro inhibitory potencies of these drugs against metformin transport by human organic cation transporter (OCT) 1, and the apical to basolateral absorptive permeability of metformin across Caco‐2 cells in the presence of therapeutic intestinal concentrations of cimetidine, trimethoprim or pyrimethamine, were determined. Whilst all inhibited OCT1, none enhanced metformin's absorptive permeability (~0.5 × 10⁻⁶ cm/sec) suggesting that DDI AUC changes are not related to absorption. Subsequently, to understand whether inhibition of renal transporters are responsible for AUC increases, in vitro inhibitory potencies against metformin transport by human OCT2, multidrug and toxin extrusion (MATE) 1 and MATE2‐K were determined. Ensuing IC₅₀ values were incorporated into mechanistic static equations, alongside unbound maximal plasma concentration and transporter fraction excreted values, in order to calculate theoretical increases in metformin AUC due to inhibition by cimetidine, trimethoprim or pyrimethamine. Calculated theoretical fold‐increases in metformin exposure confirmed solitary inhibition of renal MATE1 to be the likely mechanism underlying the observed exposure changes in clinical DDIs. Interestingly, clinically observed increases in metformin AUC were predicted more closely when the renal transporter fraction excreted value derived from oral metformin administration, rather than intravenous, was utilized in theoretical calculations, likely reflecting the “flip‐flop” pharmacokinetic profile of the drug.

Projecting ADME Behavior and Drug-Drug Interactions in Early Discovery and Development: Application of the Extended Clearance Classification System

PURPOSE

To assess the utility of Extended Clearance Classification System (ECCS) in understanding absorption, distribution, metabolism, and elimination (ADME) attributes and enabling victim drug-drug interaction (DDI) predictions.

METHODS

A database of 368 drugs with relevant ADME parameters, main metabolizing enzymes, uptake transporters, efflux transporters, and highest change in exposure (%AUC) in presence of inhibitors was developed using published literature. Drugs were characterized according to ECCS using ionization, molecular weight and estimated permeability.

RESULTS

Analyses suggested that ECCS class 1A drugs are well absorbed and systemic clearance is determined by metabolism mediated by CYP2C, esterases, and UGTs. For class 1B drugs, oral absorption is high and the predominant clearance mechanism is hepatic uptake mediated by OATP transporters. High permeability neutral/basic drugs (class 2) showed high oral absorption, with metabolism mediated generally by CYP3A, CYP2D6 and UGTs as the predominant clearance mechanism. Class 3A/4 drugs showed moderate absorption with dominant renal clearance involving OAT/OCT2 transporters. Class 3B drugs showed low to moderate absorption with hepatic uptake (OATPs) and/or renal clearance as primary clearance mechanisms. The highest DDI risk is typically seen with class 2/1B/3B compounds manifested by inhibition of either CYP metabolism or active hepatic uptake. Class 2 showed a wider range in AUC change likely due to a variety of enzymes involved. DDI risk for class 3A/4 is small and associated with inhibition of renal transporters.

CONCLUSIONS

ECCS provides a framework to project ADME profiles and further enables prediction of victim DDI liabilities in drug discovery and development.

Evaluation of drug-drug interactions for oncology therapies: in vitro-in vivo extrapolation model-based risk assessment

AIMS

Understanding drug-drug interactions (DDI) is a critical part of the drug development process as polypharmacy has become commonplace in many therapeutic areas including the cancer patient population. The objectives of this study were to investigate cytochrome P450 (CYP)-mediated DDI profiles available for therapies used in the oncology setting and evaluate how models based on in vitro-in vivo extrapolation performed in predicting CYP-mediated DDI risk.

METHODS

A dataset of 125 oncology therapies was collated using drug label and approval history information, incorporating in vitro and clinical PK data. The predictive accuracy of the basic and net effect mechanistic static models was assessed using this oncology drug dataset, for both victim and perpetrator potential of CYP3A-mediated DDI.

RESULTS

The incidence of CYP3A-mediated interaction potential was 47%, 22% and 11% for substrates, inhibitors and inducers, respectively. The basic models for precipitants gave conservative predictions with no false negatives, whilst the mechanistic static models provided reasonable quantitative predictions (2.3-3-fold error). Further analysis revealed that incorporating DDI at the level of the intestine was in most cases over-predicting interaction magnitude due to overestimates of the rate and extent of oral absorption of the precipitant. Quantifying victim DDI potential was also demonstrated using fmCYP3A estimates from ketoconazole clinical DDI studies to predict the magnitude of interaction on co-administration with the CYP3A inducer, rifampicin (1.6-3.3 fold error).

CONCLUSIONS

This work illustrates the utility and limitations of current DDI risk assessment approaches applied to a range of contemporary anti-cancer agents, and discusses the implications for therapeutic combination strategies.

A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development

Prediction of human pharmacokinetics of new drugs, as well as other disposition attributes, has become a routine practice in drug research and development. Prior to the 1990s, drug disposition science was used in a mostly descriptive manner in the drug development phase. With the advent of in vitro methods and availability of human-derived reagents for in vitro studies, drug-disposition scientists became engaged in the compound design phase of drug discovery to optimize and predict human disposition properties prior to nomination of candidate compounds into the drug development phase. This has reaped benefits in that the attrition rate of new drug candidates in drug development for reasons of unacceptable pharmacokinetics has greatly decreased. Attributes that are predicted include clearance, volume of distribution, half-life, absorption, and drug-drug interactions. In this article, we offer our experience-based perspectives on the tools and methods of predicting human drug disposition using in vitro and animal data.