Metabolite Measurement in Index Substrate Drug Interaction Studies: A Review of the Literature and Recent New Drug Application Reviews.

BACKGROUND/OBJECTIVES: Index substrates are used to understand the processes involved in pharmacokinetic (PK) drug-drug interactions (DDIs). The aim of this analysis is to review metabolite measurement in clinical DDI studies, focusing on index substrates for cytochrome P450 (CYP) enzymes, including CYP1A2 (caffeine), CYP2B6 (bupropion), CYP2C8 (repaglinide), CYP2C9 ((S)-warfarin, flurbiprofen), CYP2C19 (omeprazole), CYP2D6 (desipramine, dextromethorphan, nebivolol), and CYP3A (midazolam, triazolam). METHODS: All data used in this evaluation were obtained from the Certara Drug Interaction Database. Clinical index substrate DDI studies with PK data for at least one metabolite, available from literature and recent new drug application reviews, were reviewed. Further, for positive DDI studies, a correlation analysis was performed between changes in plasma exposure of index substrates and their marker metabolites. RESULTS: A total of 3261 individual index DDI studies were available, with 45% measuring at least one metabolite. The occurrence of metabolite measurement in clinical DDI studies varied widely between index substrates and enzymes. DISCUSSION AND CONCLUSIONS: For substrates such as caffeine, bupropion, omeprazole, and dextromethorphan, the use of the metabolite/parent area under the curve ratio can provide greater sensitivity to DDI or reduce intrasubject variability. In some cases (e.g., omeprazole, repaglinide), the inclusion of metabolite measurement can provide mechanistic insights to understand complex interactions.

Risk of Enzyme- and Transporter-mediated Drug Interactions With Drugs Approved by the US Food and Drug Administration in 2022: A Detailed Analysis of In Vitro and Clinical Data Available in New Drug Application Reviews.

PURPOSE: This analysis aimed to provide mechanistic understanding and clinical relevance of pharmacokinetic drug-drug interactions (DDIs) associated with drugs approved by the Food and Drug Administration in 2022. METHODS: Drug metabolism, transport, and DDI data available in New Drug Applications (NDAs) of small molecular drugs approved (n = 22) was analyzed. The mechanism and clinical magnitude of these interactions were characterized based on in vitro, in silico, and clinical data. FINDINGS: As victims, 10 drugs were identified as clinical substrates. Of these, 7 drugs were substrates of CYP3A, including the sensitive substrates daridorexant and mitapivat. As perpetrators, 3 drugs (adagrasib, lenacapavir, and vonoprazan) were clinical inhibitors of CYP enzymes, and 2 drugs (mavacamten and mitapivat) showed induction. Regarding transporter data, abrocitinib and deucravacitinib were found to be substrates of OAT3 and P-gp/BCRP, respectively, and 4 drugs (abrocitinib, adagrasib, lenacapavir, and oteseconazole) were found to inhibit P-gp and/or BCRP. As expected, all clinical DDIs with AUC changes ≥ 2-fold triggered label recommendations. Over half of DDIs with an AUC change < 2 also had label recommendations, pertaining most often to the concomitant use of drugs with a narrow therapeutic index. Overall, CYP3A played a major role in the drug disposition of the drugs approved in 2022, mediating all strong drug interactions. IMPLICATIONS: The mechanistic information obtained from studying these new therapeutics with marker compounds can be extrapolated to common concomitant medications sharing the same pharmacokinetic properties, enhancing the safe and effective administration of these products in situations of polytherapy.

Seeing what is behind the smokescreen: A systematic review of methodological aspects of smoking interaction studies over the last three decades and implications for future clinical trials.

Smoking drug interaction studies represent a common approach for the clinical investigation of CYP1A2 induction. Despite this important role, they remain an "orphan topic" in the existing regulatory framework of drug interaction studies, and important methodological aspects remain unaddressed. The University of Washington Drug Interaction Database (DIDB) was used to systematically review the published literature on dedicated smoking pharmacokinetic interaction studies in healthy subjects (1990 to 2021, inclusive). Various methodological aspects of identified studies were reviewed. A total of 51 studies met all inclusion criteria and were included in the analysis. Our review revealed that methods applied in smoking interaction studies are heterogeneous and often fall short of established methodological standards of other interaction trials. Methodological deficiencies included incomplete description of study populations, poor definition and lack of objective confirmation of smoker and nonsmoker characteristics, under-representation of female subjects, small sample sizes, frequent lack of statistical sample size planning, frequent lack of use of existing markers of nicotine exposure and CYP1A2 activity measurements, and frequent lack of control of extrinsic CYP1A2 inducing or inhibiting factors. The frequent quality issues in the assessment and reporting of smoking interaction trials identified in this review call for a concerted effort in this area, if the results of these studies are meant to be followed by actionable decisions on dose optimization, when needed, for the effects of smoking on CYP1A2 victim drugs in smokers.

Strong Pharmacokinetic Drug-Drug Interactions With Drugs Approved by the US Food and Drug Administration in 2021: Mechanisms and Clinical Implications.

PURPOSE: This analysis aimed to identify all strong drug-drug interactions (DDIs) associated with drugs approved by the US Food and Drug Administration (FDA) in 2021. METHODS: DDI data for small molecular drugs approved by the FDA in 2021 (N = 36) were analyzed using the University of Washington Drug Interaction Database. The mechanism(s) and clinical magnitude of these interactions were characterized based on information available in the new drug application reviews. Clinical studies and simulation results with mean AUC ratios (AUCRs) ≥5 for inhibition DDIs and ≤0.2 for induction (ie, strong interactions) were then fully analyzed. A total of 7 drugs had an AUC change ≥5-fold as victim drugs, with inhibition and induction of cytochrome P450 (CYP) 3A explaining all interactions. FINDINGS: Six drugs, namely atogepant, finerenone, ibrexafungerp, infigratinib, mobocertinib, and voclosporin, were sensitive substrates of CYP3A, with AUCRs of 5.45 to 18.55 when co-administered with the strong inhibitors itraconazole or ketoconazole, whereas avacopan was a moderate sensitive substrate of CYP3A, most sensitive to induction (>5-fold change). Only 1 drug, viloxazine, was a strong perpetrator (CYP1A2 inhibition with caffeine AUCR of 5.83). No drug had strong inhibition of transporters and no strong induction of enzymes or transporters was detected. No dominant therapeutic class was identified. As expected, all these strong DDIs triggered strict labeling recommendations. IMPLICATIONS: Overall, identifying strong DDIs with newly approved drugs and understanding their mechanisms are critical to provide effective management strategies in patients who often receive multiple comedications.

Pharmacokinetic Drug-Drug Interactions with Drugs Approved by the US Food and Drug Administration in 2020: Mechanistic Understanding and Clinical Recommendations.

Drug-drug interaction (DDI) data for small molecular drugs approved by the US Food and Drug Administration in 2020 (N = 40) were analyzed using the University of Washington Drug Interaction Database. The mechanism(s) and clinical relevance of these interactions were characterized based on information available in the new drug application reviews. About 180 positive clinical studies defined as mean area under the curve ratios (AUCRs) ≥1.25 for inhibition DDIs or pharmacogenetic studies and ≤0.8 for induction DDIs were then fully analyzed. Oncology was the most represented therapeutic area, including 30% of 2020 approvals. As victim drugs, inhibition and induction of CYP3A explained most of all observed clinical interactions. Three sensitive substrates were identified: avapritinib (CYP3A), lonafarnib (CYP3A), and relugolix (P-glycoprotein), with AUCRs of 7.00, 5.07, and 6.25 when coadministered with itraconazole, ketoconazole, and erythromycin, respectively. As precipitants, three drugs were considered strong inhibitors of enzymes (AUCR ≥ 5): cedazuridine for cytidine deaminase and lonafarnib and tucatinib for CYP3A. No drug showed strong inhibition of transporters. No strong inducer of enzymes or transporters was identified. As expected, all DDIs with AUCRs ≥5 or ≤0.2 and almost all those with AUCRs of 2-5 and 0.2-0.5 triggered dosing recommendations in the drug label. Overall, all 2020 drugs found to be either sensitive substrates or strong inhibitors of enzymes or transporters were oncology treatments, underscoring the need for effective DDI management strategies in patients with cancer often receiving polytherapy. SIGNIFICANCE STATEMENT: This minireview provides a thorough and specific overview of the most significant pharmacokinetic-based DDI data observed (or expected) with small molecular drugs approved by the US Food and Drug Administration in 2020. It will help to better understand mitigation strategies to manage the DDI risks in the clinic.

Exploring the Relationship of Drug BCS Classification, Food Effect, and Gastric pH-Dependent Drug Interactions.

Food effect (FE) and gastric pH-dependent drug-drug interactions (DDIs) are both absorption-related. Here, we evaluated if Biopharmaceutics Classification System (BCS) classes may be correlated with FE or pH-dependent DDIs. Trends in FE data were investigated for 170 drugs with clinical FE studies from the literature and new drugs approved from 2013 to 2019 by US Food and Drug Administration. A subset of 38 drugs was also evaluated to determine whether FE results can inform the need for a gastric pH-dependent DDI study. The results of FE studies were defined as no effect (AUC ratio 0.80-1.25), increased exposure (AUC ratio ≥1.25), or decreased exposure (AUC ratio ≤0.8). Drugs with significantly increased exposure FE (AUC ratio ≥2.0; N=14) were BCS Class 2 or 4, while drugs with significantly decreased exposure FE (AUC ratio ≤0.5; N=2) were BCS Class 1/3 or 3. The lack of FE was aligned with the lack of a pH-dependent DDI for all 7 BCS Class 1 or 3 drugs as expected. For the 13 BCS Class 2 or 4 weak base drugs with an increased exposure FE, 6 had a pH-dependent DDI (AUC ratio ≤0.8). Among the 16 BCS Class 2 or 4 weak base drugs with no FE, 6 had a pH-dependent DDI (AUC ratio ≤0.8). FE appears to have limited correlation with BCS classes except for BCS Class 1 drugs, confirming that multiple physiological mechanisms can impact FE. Lack of FE does not indicate absence of pH-dependent DDI for BCS Class 2 or 4 drugs. Graphical Abstract.

Analysis of Drug-Drug Interaction Labeling Language and Clinical Recommendations for Newly Approved Drugs Evaluated With Digoxin, Midazolam, and S-Warfarin.

PURPOSE: To best promote drug tolerability and efficacy in the clinic, data from drug-drug interaction (DDI) evaluations and subsequent translation of the results to DDI prevention and/or management strategies must be incorporated into the US Food and Drug Administration (FDA) product labeling in a consistent manner because differences in language might result in varied interpretations. This analysis aimed to assess the consistency in DDI labeling language in New Drug Applications (NDAs). METHODS: NDAs of recently approved drugs (2012-2020) that increase the exposure of digoxin, midazolam, and S-warfarin, index substrates of P-glycoprotein, cytochrome P450 (CYP) 3A, and CYP2C9 activity, respectively, were fully reviewed. Noninhibitors were also evaluated to appreciate the extent of mechanistic extrapolation in case of negative index studies. FINDINGS: After a systematic review of the DDI studies available in NDAs, FDA-approved labeling, and commonly used clinical tertiary resources, differences in DDI results presentation and resulting clinical recommendations were found, even for inhibitors that affect similarly the exposure of the same index substrate. Studies with negative results were often reported in the labels without providing mechanistic interpretation, thus limiting the possible extrapolation of this information to other known substrates. IMPLICATIONS: The variability in language affects how the information was presented to clinicians in tertiary resources. Strategies that aim to improve the translation of mechanistic DDI index studies into consistent labeling recommendations are briefly discussed in this review.

Excipient knowledgebase: Development of a comprehensive tool for understanding the disposition and interaction potential of common excipients.

Although the use of excipients is widespread, a thorough understanding of the drug interaction potential of these compounds remains a frequent topic of current research. Not only can excipients alter the disposition of coformulated drugs, but it is likely that these effects on co-administered drugs can reach to clinical significance leading to potential adverse effects or loss of efficacy. These risks can be evaluated through use of in silico methods of mechanistic modeling, including approaches, such as population pharmacokinetic (PK) and physiologically-based PK modeling, which require a comprehensive understanding of the compounds to ensure accurate predictions. We established a knowledgebase of the available compound (or substance) and interaction-specific parameters with the goal of providing a single source of physiochemical, in vitro, and clinical PK and interaction data of commonly used excipients. To illustrate the utility of this knowledgebase, a model for cremophor EL was developed and used to hypothesize the potential for CYP3A- and P-gp-based interactions as a proof of concept.

Do Inhibitory Metabolites Impact DDI Risk Assessment? Analysis of in vitro and in vivo Data from NDA Reviews Between 2013 and 2018.

Evaluating the potential of new drugs and their metabolites to cause drug-drug interactions (DDIs) is critical for understanding drug safety and efficacy. Although multiple analyses of proprietary metabolite testing data have been published, no systematic analyses of metabolite data collected according to current testing criteria have been conducted. To address this knowledge gap, 120 new molecular entities approved between 2013 and 2018 were reviewed. Comprehensive data on metabolite-to-parent area under the curve ratios (AUCM /AUCP ), inhibitory potency of parent and metabolites, and clinical DDIs were collected. Sixty-four percent of the metabolites quantified in vivo had AUCM /AUCP  ≥ 0.25 and 75% of these metabolites were tested for cytochrome P450 (CYP) inhibition in vitro, resulting in 15 metabolites with potential DDI risk identification. Although 50% of the metabolites with AUCM /AUCP  < 0.25 were also tested in vitro, none of them showed meaningful CYP inhibition potential. The metabolite percentage of plasma total radioactivity cutoff of ≥ 10% did not appear to add value to metabolite testing strategies. No relationship between metabolite versus parent drug polarity and inhibition potency was observed. Comparison of metabolite and parent maximum concentration (Cmax ) divided by inhibition constant (Ki ) values suggested that metabolites can contribute to in vivo DDIs and, hence, quantitative prediction of clinical DDI magnitude may require both parent and metabolite data. This systematic analysis of metabolite data for newly approved drugs supports an AUCM /AUCP cutoff of ≥ 0.25 to warrant metabolite in vitro CYP screening to adequately characterize metabolite inhibitory DDI potential and support quantitative DDI predictions.

Inhibitors of Organic Anion-Transporting Polypeptides 1B1 and 1B3: Clinical Relevance and Regulatory Perspective.

Organic anion-transporting polypeptides (OATPs) 1B1 and 1B3 are the primary hepatic transporters responsible for uptake of drugs into the liver and, as such, an area of growing research focus. Currently, evaluation of these transporters as potential mediators of drug-drug interactions (DDIs) is recommended by regulatory agencies worldwide during the drug development process. Despite the growing focus on OATP1B1/1B3 as mediators of DDIs, only 2 drugs are recommended as index inhibitors for use in clinical studies, single-dose rifampin and cyclosporine, each with limitations for the utility of the resulting data. In this study a thorough analysis of the available in vitro and clinical data was conducted to identify drugs that are clinically relevant inhibitors of OATP1B1/1B3 and, from those, to select any novel index inhibitors. A total of 13 drugs and 16 combination products were identified as clinical inhibitors of OATP1B1/1B3, showing significant changes in exposure for sensitive substrates of the transporters, with strong supporting in vitro evidence. Although none of the identified inhibitors qualified as index inhibitors, this study confirmed the utility of cyclosporine and single-dose rifampin as index inhibitors to evaluate the effect of broad, multiple-pathway inhibition and more selective OATP1B1/1B3 inhibition, respectively.

In Vitro-to-In Vivo Extrapolation of Transporter Inhibition Data for Drugs Approved by the US Food and Drug Administration in 2018.

A systematic analysis of the inhibition transporter data available in New Drug Applications of drugs approved by the US Food and Drug Administration (FDA) in 2018 (N = 42) was performed. In vitro-to-in vivo predictions using basic models were available for the nine transporters currently recommended for evaluation. Overall, 29 parents and 16 metabolites showed in vitro inhibition of at least one transporter, with the largest number of drugs found to be inhibitors of P-gp followed by BCRP. The most represented therapeutic areas were oncology drugs and anti-infective agents, each comprising 31%. Among drugs with prediction values greater than the FDA recommended cutoffs and further evaluated in vivo, 56% showed positive clinical interactions (area under the concentration-time curve ratio (AUCRs) ≥ 1.25). Although all the observed or simulated inhibitions were weak (AUCRs < 2), seven of the nine interactions (involving five drugs) resulted in labeling recommendations. Interestingly, more than half of the drugs with predictions greater than the cutoffs had no further evaluations, highlighting that current basic models represent a useful, simple first step to evaluate the clinical relevance of in vitro findings, but that multiple other factors are considered when deciding the need for clinical studies. Four drugs had prediction values less than the cutoffs but had clinical evaluations or physiologically-based pharmacokinetic simulations available. Consistent with the predictions, all of them were confirmed not to inhibit these transporters in vivo (AUCRs of 0.94-1.09). Overall, based on the clinical evaluations available, drugs approved in 2018 were found to have a relatively limited impact on drug transporters, with all victim AUCRs < 2.

Variability in In Vitro OATP1B1/1B3 Inhibition Data: Impact of Incubation Conditions on Variability and Subsequent Drug Interaction Predictions.

As the research into the organic anion transporting polypeptides (OATPs) continues to grow, it is important to ensure that the data generated are accurate and reproducible. In the in vitro evaluation of OATP1B1/1B3 inhibition, there are many variables that can contribute to variability in the resulting inhibition constants, which can then, in turn, contribute to variable results when clinical predictions (R-values) are performed. Currently, the only experimental condition recommended by the US Food and Drug Administration (FDA) is the inclusion of a pre-incubation period.1 To identify other potential sources of variability, a descriptive analysis of available in vitro inhibition data was completed. For each of the 21 substrate/inhibitor pairs evaluated, cell type and pre-incubation were found to have the greatest effect on half-maximal inhibitory concentration (IC50 ) variability. Indeed, when only HEK293 cells and co-incubation conditions were included, the observed variability for the entire data set (highest IC50 /lowest) was reduced from 12.4 to 5.2. The choice of probe substrate used in the study also had a significant effect on inhibitor constant variability. Interestingly, despite the broad range of inhibitory constants identified, these two factors showed little effect on the calculated R-values relative to the FDA evaluation cutoff of 1.1 triggering a clinical evaluation for the inhibitors evaluated. However, because of the small data set available, further research is needed to confirm these preliminary results and define best practice for the study of OATPs.

Drug-Drug Interactions of Infectious Disease Treatments in Low-Income Countries: A Neglected Topic?

Despite recent advances in recognizing and reducing the risk of drug-drug interactions (DDIs) in developed countries, there are still significant challenges in managing DDIs in low-income countries (LICs) worldwide. In the treatment of major infectious diseases in these regions, multiple factors contribute to ineffective management of DDIs that lead to loss of efficacy or increased risk of adverse events to patients. Some of these difficulties, however, can be overcome. This review aims to evaluate the inherent complexities of DDI management in LICs from pharmacological standpoints and illustrate the unique barriers to effective management of DDIs, such as the challenges of co-infection and treatment settings. A better understanding of comprehensive drug-related properties, population-specific attributes, such as physiological changes associated with infectious diseases, and the use of modeling and simulation techniques are discussed, as they can facilitate the implementation of optimal treatments for infectious diseases at the individual patient level.

Identification and Evaluation of Clinical Substrates of Organic Anion Transporting Polypeptides 1B1 and 1B3.

Organic anion transporting polypeptides (OATPs) 1B1 and 1B3 facilitate the uptake of drugs and endogenous compounds into the liver. In recent years, the impact of these transporters on drug-drug interactions (DDIs) has become a focus of research, and the evaluation of their role in drug disposition is recommended by regulatory agencies worldwide.1-3 Although sensitive substrates of OATP1B1/1B3 have been identified in the literature and probe drugs have been proposed by regulatory agencies, there is no general consensus on the ideal in vivo substrate for clinical DDI studies as analysis may be confounded by contribution from other metabolic and/or transport pathways.1-3 A thorough analysis of the available in vitro and in vivo data regarding OATP1B1/1B3 substrates was performed using the in vitro, clinical, and pharmacogenetic modules in the University of Washington Drug Interaction Database. A total of 34 compounds were identified and further investigated as possible clinical substrates using a novel indexing system. By analyzing the compounds for in vivo characteristics, including sensitivity to inhibition by known OATP1B1/1B3 inhibitors, selectivity for OATP1B1/1B3 compared with other transport and metabolic pathways, and safety profiles, a total of six compounds were identified as potential clinical markers of OATP1B1/1B3 activity.

Mechanisms and Clinical Significance of Pharmacokinetic-Based Drug-Drug Interactions with Drugs Approved by the U.S. Food and Drug Administration in 2017.

Pharmacokinetic-based drug-drug interaction (DDI) data for drugs approved by the U.S. Food and Drug Administration in 2017 (N = 34) were analyzed using the University of Washington Drug Interaction Database. The mechanisms and clinical relevance of these interactions were characterized based on information from new drug application reviews. CYP3A inhibition and induction explained most of the observed drug interactions (new drugs as victims or as perpetrators), and transporters mediated about half of all DDIs, alone or with enzymes. Organic anion transporting polypeptide (OATP)1B1/1B3 played a significant role, mediating more than half of the drug interactions with area under the time-plasma curve (AUC) changes ≥5-fold. As victims, five new drugs were identified as sensitive substrates: abemeciclib, midostaurin, and neratinib for CYP3A and glecaprevir and voxilaprevir for OATP1B1/1B3. As perpetrators, three drugs were considered strong inhibitors: ribociclib for CYP3A, glecaprevir/pibrentasvir for OATP1B1/1B3, and sofosbuvir/velpatasvir/voxilaprevir for OATP1B1/1B3 and breast cancer resistance protein. No strong inducer of enzymes or transporters was identified. DDIs with AUC changes ≥5-fold and almost all DDIs with AUC changes 2- to 5-fold had dose recommendations in their respective drug labels. A small fraction of DDIs with exposure changes <2-fold had a labeling impact, mostly related to drugs with narrow therapeutic indices. As with drugs approved in recent years, all drugs found to be sensitive substrates or strong inhibitors of enzymes or transporters were among oncology or antiviral treatments, suggesting a serious risk of DDIs in these patient populations for whom effective therapy is already complex because of polytherapy.

Risk of Clinically Relevant Pharmacokinetic-Based Drug-Drug Interactions with Drugs Approved by the U.S. Food and Drug Administration Between 2013 and 2016.

A total of 103 drugs (including 14 combination drugs) were approved by the U.S. Food and Drug Administration from 2013 to 2016. Pharmacokinetic-based drug interaction profiles were analyzed using the University of Washington Drug Interaction Database, and the clinical relevance of these observations was characterized based on information from new drug application reviews. CYP3A was involved in approximately two-thirds of all drug-drug interactions (DDIs). Transporters (alone or with enzymes) participated in about half of all interactions, but most of these were weak-to-moderate interactions. When considered as victims, eight new molecular entities (NMEs; cobimetinib, ibrutinib, isavuconazole, ivabradine, naloxegol, paritaprevir, simeprevir, and venetoclax) were identified as sensitive substrates of CYP3A, two NMEs (pirfenidone and tasimelteon) were sensitive substrates of CYP1A2, one NME (dasabuvir) was a sensitive substrate of CYP2C8, one NME (eliglustat) was a sensitive substrate of CYP2D6, and one NME (grazoprevir) was a sensitive substrate of OATP1B1/3 (with changes in exposure greater than 5-fold when coadministered with a strong inhibitor). Approximately 75% of identified CYP3A substrates were also substrates of P-glycoprotein. As perpetrators, most clinical DDIs involved weak-to-moderate inhibition or induction. Only idelalisib showed strong inhibition of CYP3A, and lumacaftor behaved as a strong CYP3A inducer. Among drugs with large changes in exposure (≥5-fold), whether as victim or perpetrator, the most-represented therapeutic classes were antivirals and oncology drugs, suggesting a significant risk of clinical DDIs in these patient populations.

Developing User Personas to Aid in the Design of a User-Centered Natural Product-Drug Interaction Information Resource for Researchers.

Pharmacokinetic interactions between natural products and conventional drugs can adversely impact patient outcomes. These complex interactions present unique challenges that require clear communication to researchers. We are creating a public information portal to facilitate researchers' access to credible evidence about these interactions. As part of a user-centered design process, three types of intended researchers were surveyed: drug-drug interaction scientists, clinical pharmacists, and drug compendium editors. Of the 23 invited researchers, 17 completed the survey. The researchers suggested a number of specific requirements for a natural product-drug interaction information resource, including specific information about a given interaction, the potential to cause adverse effects, and the clinical importance. Results were used to develop user personas that provided the development team with a concise and memorable way to represent information needs of the three main researcher types and a common basis for communicating the design's rationale.

Physiologically Based Pharmacokinetic Model of the CYP2D6 Probe Atomoxetine: Extrapolation to Special Populations and Drug-Drug Interactions.

Physiologically based pharmacokinetic (PBPK) modeling of drug disposition and drug-drug interactions (DDIs) has become a key component of drug development. PBPK modeling has also been considered as an approach to predict drug disposition in special populations. However, whether models developed and validated in healthy populations can be extrapolated to special populations is not well established. The goal of this study was to determine whether a drug-specific PBPK model validated using healthy populations could be used to predict drug disposition in specific populations and in organ impairment patients. A full PBPK model of atomoxetine was developed using a training set of pharmacokinetic (PK) data from CYP2D6 genotyped individuals. The model was validated using drug-specific acceptance criteria and a test set of 14 healthy subject PK studies. Population PBPK models were then challenged by simulating the effects of ethnicity, DDIs, pediatrics, and renal and hepatic impairment on atomoxetine PK. Atomoxetine disposition was successfully predicted in 100% of healthy subject studies, 88% of studies in Asians, 79% of DDI studies, and 100% of pediatric studies. However, the atomoxetine area under the plasma concentration versus time curve (AUC) was overpredicted by 3- to 4-fold in end stage renal disease and hepatic impairment. The results show that validated PBPK models can be extrapolated to different ethnicities, DDIs, and pediatrics but not to renal and hepatic impairment patients, likely due to incomplete understanding of the physiologic changes in these conditions. These results show that systematic modeling efforts can be used to further refine population models to improve the predictive value in this area.

Intestinal Drug Interactions Mediated by OATPs: A Systematic Review of Preclinical and Clinical Findings.

In recent years, an increasing number of clinical drug-drug interactions (DDIs) have been attributed to inhibition of intestinal organic anion-transporting polypeptides (OATPs); however, only a few of these DDI results were reflected in drug labels. This review aims to provide a thorough analysis of intestinal OATP-mediated pharmacokinetic-based DDIs, using both in vitro and clinical investigations, highlighting the main mechanistic findings and discussing their clinical relevance. On the basis of pharmacogenetic and clinical DDI results, a total of 12 drugs were identified as possible clinical substrates of OATP2B1 and OATP1A2. Among them, 3 drugs, namely atenolol, celiprolol, and fexofenadine, have emerged as the most sensitive substrates to evaluate clinical OATP-mediated intestinal DDIs when interactions with P-glycoprotein by the test compound can be ruled out. With regard to perpetrators, 8 dietary or natural products and 1 investigational drug, ronacaleret (now terminated), showed clinical intestinal inhibition attributable to OATPs, producing ≥20% decreases in area under the plasma concentration-time curve of the co-administered drug. Common juices, such as apple juice, grapefruit juice, and orange juice, are considered potent inhibitors of intestinal OATP2B1 and OATP1A2 (decreasing exposure of the co-administered substrate by ∼85%) and may be adequate prototype inhibitors to investigate intestinal DDIs mediated by OATPs.