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.

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.

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.

What Can Be Learned from Recent New Drug Applications? A Systematic Review of Drug Interaction Data for Drugs Approved by the US FDA in 2015.

As a follow up to previous reviews, the aim of the present analysis was to systematically examine all drug metabolism, transport, pharmacokinetics (PK), and drug-drug interaction (DDI) data available in the 33 new drug applications (NDAs) approved by the Food and Drug Administration (FDA) in 2015, using the University of Washington Drug Interaction Database, and to highlight the significant findings. In vitro, a majority of the new molecular entities (NMEs) were found to be substrates or inhibitors/inducers of at least one drug metabolizing enzyme or transporter. In vivo, 95 clinical DDI studies displayed positive PK interactions, with an area under the curve (AUC) ratio ≥ 1.25 for inhibition or ≤ 0.8 for induction. When NMEs were considered as victim drugs, 21 NMEs had at least one positive clinical DDI, with three NMEs shown to be sensitive substrates of CYP3A (AUC ratio ≥ 5 when coadministered with strong inhibitors): cobimetinib, isavuconazole (the active metabolite of prodrug isavuconazonium sulfate), and ivabradine. As perpetrators, nine NMEs showed positive inhibition and three NMEs showed positive induction, with some of these interactions involving both enzymes and transporters. The most significant changes for inhibition and induction were observed with rolapitant, a moderate inhibitor of CYP2D6 and lumacaftor, a strong inducer of CYP3A. Physiologically based pharmacokinetics simulations and pharmacogenetics studies were used for six and eight NMEs, respectively, to inform dosing recommendations. The effects of hepatic or renal impairment on the drugs' PK were also evaluated to support drug administration in these specific populations.

Key Findings from Preclinical and Clinical Drug Interaction Studies Presented in New Drug and Biological License Applications Approved by the Food and Drug Administration in 2014.

Regulatory approval documents contain valuable information, often not published, to assess the drug-drug interaction (DDI) profile of newly marketed drugs. This analysis aimed to systematically review all drug metabolism, transport, pharmacokinetics, and DDI data available in the new drug applications and biologic license applications approved by the U.S. Food and Drug Administration in 2014, using the University of Washington Drug Interaction Database, and to highlight the significant findings. Among the 30 new drug applications and 11 biologic license applications reviewed, 35 new molecular entities (NMEs) were well characterized with regard to drug metabolism, transport, and/or organ impairment and were fully analyzed in this review. In vitro, a majority of the NMEs were found to be substrates or inhibitors/inducers of at least one drug metabolizing enzyme or transporter. In vivo, when NMEs were considered as victim drugs, 16 NMEs had at least one in vivo DDI study with a clinically significant change in exposure (area under the time-plasma concentration curve or Cmax ratio ≥2 or ≤0.5), with 6 NMEs shown to be sensitive substrates of cytochrome P450 enzymes (area under the time-plasma concentration curve ratio ≥5 when coadministered with potent inhibitors): paritaprevir and naloxegol (CYP3A), eliglustat (CYP2D6), dasabuvir (CYP2C8), and tasimelteon and pirfenidone (CYP1A2). As perpetrators, seven NMEs showed clinically significant inhibition involving both enzymes and transporters, although no clinically significant induction was observed. Physiologically based pharmacokinetic modeling and pharmacogenetics studies were used for six and four NMEs, respectively, to optimize dosing recommendations in special populations and/or multiple impairment situations. In addition, the pharmacokinetic evaluations in patients with hepatic or renal impairment provided useful quantitative information to support drug administration in these fragile populations.

Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification.

Modeling and simulation of drug disposition has emerged as an important tool in drug development, clinical study design and regulatory review, and the number of physiologically based pharmacokinetic (PBPK) modeling related publications and regulatory submissions have risen dramatically in recent years. However, the extent of use of PBPK modeling by researchers, and the public availability of models has not been systematically evaluated. This review evaluates PBPK-related publications to 1) identify the common applications of PBPK modeling; 2) determine ways in which models are developed; 3) establish how model quality is assessed; and 4) provide a list of publically available PBPK models for sensitive P450 and transporter substrates as well as selective inhibitors and inducers. PubMed searches were conducted using the terms "PBPK" and "physiologically based pharmacokinetic model" to collect published models. Only papers on PBPK modeling of pharmaceutical agents in humans published in English between 2008 and May 2015 were reviewed. A total of 366 PBPK-related articles met the search criteria, with the number of articles published per year rising steadily. Published models were most commonly used for drug-drug interaction predictions (28%), followed by interindividual variability and general clinical pharmacokinetic predictions (23%), formulation or absorption modeling (12%), and predicting age-related changes in pharmacokinetics and disposition (10%). In total, 106 models of sensitive substrates, inhibitors, and inducers were identified. An in-depth analysis of the model development and verification revealed a lack of consistency in model development and quality assessment practices, demonstrating a need for development of best-practice guidelines.

Breast cancer resistance protein (ABCG2) in clinical pharmacokinetics and drug interactions: practical recommendations for clinical victim and perpetrator drug-drug interaction study design.

Breast cancer resistance protein (BCRP; ABCG2) limits intestinal absorption of low-permeability substrate drugs and mediates biliary excretion of drugs and metabolites. Based on clinical evidence of BCRP-mediated drug-drug interactions (DDIs) and the c.421C>A functional polymorphism affecting drug efficacy and safety, both the US Food and Drug Administration and European Medicines Agency recommend preclinical evaluation and, when appropriate, clinical assessment of BCRP-mediated DDIs. Although many BCRP substrates and inhibitors have been identified in vitro, clinical translation has been confounded by overlap with other transporters and metabolic enzymes. Regulatory recommendations for BCRP-mediated clinical DDI studies are challenging, as consensus is lacking on the choice of the most robust and specific human BCRP substrates and inhibitors and optimal study design. This review proposes a path forward based on a comprehensive analysis of available data. Oral sulfasalazine (1000 mg, immediate-release tablet) is the best available clinical substrate for intestinal BCRP, oral rosuvastatin (20 mg) for both intestinal and hepatic BCRP, and intravenous rosuvastatin (4 mg) for hepatic BCRP. Oral curcumin (2000 mg) and lapatinib (250 mg) are the best available clinical BCRP inhibitors. To interrogate the worst-case clinical BCRP DDI scenario, study subjects harboring the BCRP c.421C/C reference genotype are recommended. In addition, if sulfasalazine is selected as the substrate, subjects having the rapid acetylator phenotype are recommended. In the case of rosuvastatin, subjects with the organic anion-transporting polypeptide 1B1 c.521T/T genotype are recommended, together with monitoring of rosuvastatin's cholesterol-lowering effect at baseline and DDI phase. A proof-of-concept clinical study is being planned by a collaborative consortium to evaluate the proposed BCRP DDI study design.

Drug disposition and drug-drug interaction data in 2013 FDA new drug applications: a systematic review.

The aim of the present work was to perform a systematic review of drug metabolism, transport, pharmacokinetics, and DDI data available in the NDAs approved by the FDA in 2013, using the University of Washington Drug Interaction Database, and to highlight significant findings. Among 27 NMEs approved, 22 (81%) were well characterized with regard to drug metabolism, transport, or organ impairment, in accordance with the FDA drug interaction guidance (2012) and were fully analyzed in this review. In vitro, a majority of the NMEs were found to be substrates or inhibitors/inducers of at least one drug metabolizing enzyme or transporter. However, in vivo, only half (n = 11) showed clinically relevant drug interactions, with most related to the NMEs as victim drugs and CYP3A being the most affected enzyme. As perpetrators, the overall effects for NMEs were much less pronounced, compared with when they served as victims. In addition, the pharmacokinetic evaluation in patients with hepatic or renal impairment provided useful information for further understanding of the drugs' disposition.

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.

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.

Predicting substrates of the human breast cancer resistance protein using a support vector machine method.

BACKGROUND: Human breast cancer resistance protein (BCRP) is an ATP-binding cassette (ABC) efflux transporter that confers multidrug resistance in cancers and also plays an important role in the absorption, distribution and elimination of drugs. Prediction as to if drugs or new molecular entities are BCRP substrates should afford a cost-effective means that can help evaluate the pharmacokinetic properties, efficacy, and safety of these drugs or drug candidates. At present, limited studies have been done to develop in silico prediction models for BCRP substrates. In this study, we developed support vector machine (SVM) models to predict wild-type BCRP substrates based on a total of 263 known BCRP substrates and non-substrates collected from literature. The final SVM model was integrated to a free web server. RESULTS: We showed that the final SVM model had an overall prediction accuracy of ~73% for an independent external validation data set of 40 compounds. The prediction accuracy for wild-type BCRP substrates was ~76%, which is higher than that for non-substrates. The free web server (http://bcrp.althotas.com) allows the users to predict whether a query compound is a wild-type BCRP substrate and calculate its physicochemical properties such as molecular weight, logP value, and polarizability. CONCLUSIONS: We have developed an SVM prediction model for wild-type BCRP substrates based on a relatively large number of known wild-type BCRP substrates and non-substrates. This model may prove valuable for screening substrates and non-substrates of BCRP, a clinically important ABC efflux drug transporter.

Application of receiver operating characteristic analysis to refine the prediction of potential digoxin drug interactions.

In the 2012 Food and Drug Administration (FDA) draft guidance on drug-drug interactions (DDIs), a new molecular entity that inhibits P-glycoprotein (P-gp) may need a clinical DDI study with a P-gp substrate such as digoxin when the maximum concentration of inhibitor at steady state divided by IC50 ([I1]/IC50) is ≥0.1 or concentration of inhibitor based on highest approved dose dissolved in 250 ml divide by IC50 ([I2]/IC50) is ≥10. In this article, refined criteria are presented, determined by receiver operating characteristic analysis, using IC50 values generated by 23 laboratories. P-gp probe substrates were digoxin for polarized cell-lines and N-methyl quinidine or vinblastine for P-gp overexpressed vesicles. Inhibition of probe substrate transport was evaluated using 15 known P-gp inhibitors. Importantly, the criteria derived in this article take into account variability in IC50 values. Moreover, they are statistically derived based on the highest degree of accuracy in predicting true positive and true negative digoxin DDI results. The refined criteria of [I1]/IC50 ≥ 0.03 and [I2]/IC50 ≥ 45 and FDA criteria were applied to a test set of 101 in vitro-in vivo digoxin DDI pairs collated from the literature. The number of false negatives (none predicted but DDI observed) were similar, 10 and 12%, whereas the number of false positives (DDI predicted but not observed) substantially decreased from 51 to 40%, relative to the FDA criteria. On the basis of estimated overall variability in IC50 values, a theoretical 95% confidence interval calculation was developed for single laboratory IC50 values, translating into a range of [I1]/IC50 and [I2]/IC50 values. The extent by which this range falls above the criteria is a measure of risk associated with the decision, attributable to variability in IC50 values.

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.

e-PKGene: a knowledge-based research tool for analysing the impact of genetics on drug exposure.

e-PKGene (www.pharmacogeneticsinfo.org) is a manually curated knowledge product developed in the Department of Pharmaceutics at the University of Washington, USA. The tool integrates information from the literature, public repositories, reference textbooks, product prescribing labels and clinical review sections of new drug approval packages. The database's easy-to-use web portal offers tools for visualisation, reporting and filtering of information. The database helps scientists to mine pharmacokinetic and pharmacodynamic information for drug-metabolising enzymes and transporters, and provides access to available quantitative information on drug exposure contained in the literature. It allows in-depth analysis of the impact of genetic variants of enzymes and transporters on pharmacokinetic responses to drugs and metabolites. This review gives a brief description of the database organisation, its search functionalities and examples of use.

Importance of multi-p450 inhibition in drug-drug interactions: evaluation of incidence, inhibition magnitude, and prediction from in vitro data.

Drugs that are mainly cleared by a single enzyme are considered more sensitive to drug-drug interactions (DDIs) than drugs cleared by multiple pathways. However, whether this is true when a drug cleared by multiple pathways is coadministered with an inhibitor of multiple P450 enzymes (multi-P450 inhibition) is not known. Mathematically, simultaneous equipotent inhibition of two elimination pathways that each contribute half of the drug clearance is equal to equipotent inhibition of a single pathway that clears the drug. However, simultaneous strong or moderate inhibition of two pathways by a single inhibitor is perceived as an unlikely scenario. The aim of this study was (i) to identify P450 inhibitors currently in clinical use that can inhibit more than one clearance pathway of an object drug in vivo and (ii) to evaluate the magnitude and predictability of DDIs caused by these multi-P450 inhibitors. Multi-P450 inhibitors were identified using the Metabolism and Transport Drug Interaction Database. A total of 38 multi-P450 inhibitors, defined as inhibitors that increased the AUC or decreased the clearance of probes of two or more P450s, were identified. Seventeen (45%) multi-P450 inhibitors were strong inhibitors of at least one P450, and an additional 12 (32%) were moderate inhibitors of one or more P450s. Only one inhibitor (fluvoxamine) was a strong inhibitor of more than one enzyme. Fifteen of the multi-P450 inhibitors also inhibit drug transporters in vivo, but such data are lacking on many of the inhibitors. Inhibition of multiple P450 enzymes by a single inhibitor resulted in significant (>2-fold) clinical DDIs with drugs that are cleared by multiple pathways such as imipramine and diazepam, while strong P450 inhibitors resulted in only weak DDIs with these object drugs. The magnitude of the DDIs between multi-P450 inhibitors and diazepam, imipramine, and omeprazole could be predicted using in vitro data with similar accuracy as probe substrate studies with the same inhibitors. The results of this study suggest that inhibition of multiple clearance pathways in vivo is clinically relevant, and the risk of DDIs with object drugs may be best evaluated in studies using multi-P450 inhibitors.

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.

A useful tool for drug interaction evaluation: the University of Washington Metabolism and Transport Drug Interaction Database.

The Metabolism and Transport Drug Interaction Database (http://www.druginteractioninfo.org) is a web-based research and analysis tool developed in the Department of Pharmaceutics at the University of Washington. The database has the largest manually curated collection of data related to drug interactions in humans. The tool integrates information from the literature, public repositories, reference textbooks, guideline documents, product prescribing labels and clinical review sections of new drug approval (NDA) packages. The database's easy-to-use web portal offers tools for visualisation, reporting and filtering of information. The database helps scientists to mine kinetics information for drug-metabolising enzymes and transporters, to assess the extent of in vivo drug interaction studies, as well as case reports for drugs, therapeutic proteins, food products and herbal derivatives. This review provides a brief description of the database organisation, its search functionalities and examples of use.

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.

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.