Presented virtually at ASCPT Annual Meeting, March 2021
Lindsay M. Henderson, Claire Steinbronn, Jingjing Yu, Cathy Yeung, and Isabelle Ragueneau-Majlessi
This study’s objective was to evaluate the consistency in DDI labeling language of recently marketed drugs (2012-2020) when found to alter the exposure of coadministered digoxin, a clinical P-glycoprotein (P-gp) substrate and narrow therapeutic index (NTI) medication.
New drug application reviews contain critical drug interaction study results with newly approved drugs tested both as victims and as perpetrators of drug-drug interactions (DDIs). Pharmacokinetic-based DDI data for drugs approved by the US Food and Drug Administration in 2013–2017 (N = 137) were analyzed using the University of Washington Drug Interaction Database. For the largest metabolism- and transporter-based drug interactions, defined as a change in exposure ≥ 5-fold in victim drugs, the mechanisms and clinical relevance were characterized. Consistent with the major role of CYP3A in drug disposition, CYP3A inhibition and induction explained a majority of the observed interactions (new drugs as victims or as perpetrators). However, transporter-mediated interactions were also prevalent, with OATP1B1/1B3 playing a significant role. As victims, 17 and 4 new molecular entities (NMEs) were identified to be sensitive substrates of enzymes and transporters, respectively. When considered as perpetrators, three drugs showed strong inhibition of CYP3A, one was a strong CYP3A inducer, and two showed strong inhibition of transporters (OATP1B1/1B3 and/or BCRP). All DDIs with AUC changes ≥ 5-fold had labeling recommendations in their respective drug labels, contraindicating or limiting the coadministration with known substrates or perpetrators of the enzyme/transporter involved. The majority of sensitive substrates or strong inhibitors were oncology and antiviral treatments, suggesting a significant risk of DDIs in these patient populations for whom therapeutic management is already complex due to poly-therapy. Pharmacogenetic studies and physiologically based pharmacokinetic models were commonly used to assess the drug interaction potential in specific populations and clinical scenarios. Finally, absorption-based DDIs were evaluated in approximately 30% of drug applications, and 14 NMEs had label recommendations based on the results.
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.
Presented at ISSX conference, June 2019, Portland, OR, USA
Jingjing Yu, Ichiko Petrie, and Isabelle Ragueneau-Majlessi
The aim of the present work was to review pharmacokinetic drug-drug interaction (DDI) data available in New Drug Applications (NDAs) for drugs approved by the US Food and Drug Administration in 2018 and analyze the mechanisms mediating interactions that triggered label recommendations.
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.
Presented at Asia Pacific ISSX conference, May 2018, Hangzhou City, China
Jingjing Yu and Isabelle Ragueneau-Majlessi
The present work aimed to systematically review transporter-based in vitro and clinical inhibition evaluations of drugs approved by the U.S. Food and Drug Administration (FDA) from 2013 to 2016. In vitro inhibition parameters, pharmacokinetics, and clinical drug-drug interaction (DDI) studies available in the New Drug Application (NDA) reviews were analyzed using the University of Washington Drug Interaction Database. Following recommendations from the 2012 FDA DDI guidance, in vitro to in vivo prediction estimates were calculated for the transporters the most often studied.
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.
Presented at ISSX conference, September 2017, Providence, RI, USA
Jingjing Yu and Isabelle Ragueneau-Majlessi
The aim of the present work was to systematically review pharmacokinetic-based drug-drug interaction (DDI) data available in the most recent (2013-2016) New Drug Applications (NDAs) and highlight significant findings. The University of Washington Metabolism and Transport Drug Interaction Database was used to extract the results of metabolism, transport, and clinical DDI studies. All the DDI studies (new molecular entity (NME) as victim or perpetrator) with AUC changes ≥ 2-fold or < 2-fold but triggering dose recommendations were included in the analysis.
Presented at ASCPT conference, March 2017, Washington, DC, USA
Jingjing Yu, Zhu Zhou, Katie Owens, Tasha K. Ritchie, and Isabelle Ragueneau-Majlessi
The aim of the present work was to perform a systematic analysis of metabolism, transport, and drug interaction data available in New Drug Applications (NDAs) and Biologic License Applications (BLAs) of drugs approved in 2015, and highlight significant findings.
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.