Presented virtually at the 24th North American ISSX Meeting, September 2021
2021 ISSX Poster Presentation – 2020 NDA Clinical DDI Review
Jingjing Yu, Yan Wang, 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 2020 and analyze the mechanisms mediating interactions in order to facilitate an optimal management of DDIs in the clinic.
CPT Pharmacometrics Syst Pharmacol. 2021 Aug;10(8):953-961
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.
Clin Pharmacol Ther. 2021 Aug;110(2):452-463
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 drug‐drug interactions (DDI) were collected. 64% of the metabolites quantified in vivo had AUCM/AUCP≥25% and 75% of these metabolites were tested for cytochrome P450 (CYP) inhibition in vitro, resulting in 15 metabolites with potential DDI risk identification. While 50% of the metabolites with AUCM/AUCP<25% were also tested in vitro, none of them showed meaningful CYP inhibition potential. The metabolite % 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 ≥25% to warrant metabolite in vitro CYP screening to adequately characterize metabolite inhibitory DDI potential and support quantitative DDI predictions.
Presented virtually at ASCPT Annual Meeting, March 2021
2021 ASCPT Poster Presentation – Drug BCS classification and absorption-based DDIs
Katie Owens, Sophie Argon, Jingjing Yu, Isabelle Ragueneau-Majlessi, and colleagues at FDA
Food-effect (FE) and gastric pH-mediated drug-drug interactions (DDIs) are absorption-related. Here. we evaluated of the Biopharmaceutical Classification System (BCS) may be correlated with FE or pH-mediated DDI observed.
Presented virtually at ASCPT Annual Meeting, March 2021
2021 ASCPT Poster Presentation – DDI labeling language and clinical recommendations, digoxin as an example
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.
Identification and Quantification of Drugs, Metabolites, Drug Metabolizing Enzymes, and Transporters – Concepts, Methods, and Translational Sciences. Second Edition 2020, Chapter 11, 339-358.
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.
J Clin Pharmacol. 2020 Aug; 60(8); 1087-1098.
Published online 2020 Mar 20
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.
Clin Transl Sci.
Published online 2020 Jan 25
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.
Clin Transl Sci. 2020 Jan; 13(1): 47–52.
Published online 2019 Aug 29
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.
Presented at ISSX conference, June 2019, Portland, OR, USA
2019 ISSX Poster Presentation – Evaluation of OATP1B1/3 In Vitro Inhibition
Savannah J. McFeely, Yu, Tasha K. Ritchie, and Isabelle Ragueneau-Majlessi
The effect of inhibition of the organic anion transporting polypeptides (OATP) 1B1 and 1B3 has continued to grow in clinical significance and recognition. In the last five years, a signification portion of newly approved drugs in the US have been shown to be inhibitors of OATP1B1 and/or OATP1B3 in vitro. For this reason, it is critical to understand the effect of experimental variability on drug interaction predictions and how it impacts the decision for a clinical evaluation.