Mechanistic Studies Supporting the Evaluation of Pharmacokinetic-Drug Interactions with Drugs Approved by the U.S. Food and Drug Administration in 2023: a Systemic Review of New Drug Applications

Presented at the 26th North American ISSX and JSSX Meeting, September 2024
Jingjing Yu, Yan Wang, and Isabelle Ragueneau-Majlessi

2024 ISSX/JSSX Poster Presentation – 2023 NDA Reviews

Abstract

The mechanistic evaluation of enzyme- and transporter-based drug-drug interactions (DDIs) is an integral part of the drug development process and supports the safe and effective clinical use of new therapies. In the present work, DDI data for small molecular drugs approved by the U.S. Food and Drug Administration in 2023 (N = 38) were analyzed using the Certara Drug Interaction Database. The mechanism(s) and clinical magnitude of the observed interactions were characterized based on information available in the new drug application reviews.

Understanding the role of CYP3A in the metabolism of kinase inhibitors marketed in the past decade to better manage the risk of clinical drug-drug interactions.

Presented at the 25th North American ISSX Meeting, September 2023
Jingjing Yu, Sophie Argon, Katie Owens, Ichiko Petrie, and Isabelle Ragueneau-Majlessi

2023 ISSX Poster Presentation – CYP3A-Kinase inhibitors

Abstract

Kinase inhibitors (KIs) are among the most represented therapeutic areas for novel drugs approved in recent years. In the present analysis, drug metabolism data for all KIs approved in the US since 2011 were reviewed. Mechanistic clinical drug interaction studies with CYP3A perpetrators were fully analyzed using Certara’s Drug Interaction Database (DIDB®). The mechanism(s) and clinical relevance of these interactions were characterized based on information available in the new drug application reviews.

Risk of pharmacokinetic drug-drug interactions with novel drugs approved by the US FDA in 2022: a detailed review of DDI data from NDA documentation.

Presented at the 25th North American ISSX Meeting, September 2023
Jingjing Yu, Yan Wang, and Isabelle Ragueneau-Majlessi

2023 ISSX Poster Presentation – 2022 NDA Reviews

Abstract

Understanding the ADME processes involved in pharmacokinetic-based drug-drug interactions (DDIs) is critical to facilitate an optimal management of DDIs in the clinic. In the present work, drug metabolism and transport in vitro and in vivo data for small molecular drugs approved by the U.S. Food and Drug Administration in 2022 (N=22) were analyzed using the Certara Drug Interaction Database. The mechanism(s) and clinical relevance of these interactions were characterized based on information available in the new drug application (NDA) reviews.

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

Clin Ther. 2022 Oct6;S0149-2918(22)00323-X

Abstract

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.

Enzyme- and Transporter-Mediated Clinical Drug Interactions with Drugs by the U.S. Food and Drug Administration in 2021: What Can be Learned from New Drug applications Reviews?

Presented at the ISSX/MDO Meeting, September 2022
Jingjing Yu, Yan Wang, and Isabelle Ragueneau-Majlessi

2022 ISSX Poster Presentation – 2021 NDA Reviews

Abstract

 The mechanistic evaluation of enzyme- and transporter-based drug-drug interactions (DDIs) during drug development is critical to support management strategies in the clinic.

The objectives of the study were to review pharmacokinetic-based clinical DDI data available in the new drug application (NDA) reviews for drugs approved by the FDA in 2021, and to understand the main mechanisms that mediate interactions resulting in label recommendations. 

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

Abstract

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.

Pharmacokinetic Drug-Drug Interactions With Drugs Approved by the U.S. Food and Drug Administration in 2020: Mechanistic Understanding and Clinical Recommendations

Drug Metab Dispos. 2021 Oct7; 47(2); 135-144

Abstract

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 mechaniDrug-drug interaction (DDI) data for small molecular drugs approved by the U.S. 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) {greater than or equal to} 1.25 for inhibition DDIs or pharmacogenetic studies and {less than or equal to} 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-gp), with AUCRs of 7.00, 5.07, and 6.25 when co-administered with itraconazole, ketoconazole, and erythromycin, respectively. As precipitants, three drugs were considered strong inhibitors of enzymes (AUCR {greater than or equal to} 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 {greater than or equal to} 5 or {less than or equal to} 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 cancer patients often receiving poly-therapy. 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 U.S. Food and Drug Administration in 2020. It will help to better understand mitigation strategies to manage the DDI risks in the clinic.

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

Abstract

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).

Mechanisms and clinical significance of pharmacokinetic-based drug-drug interactions with drugs approved by the U.S. Food and Drug Administration in 2020

Presented virtually at the 24th North American ISSX Meeting, September 2021
Jingjing Yu, Yan Wang, and Isabelle Ragueneau-Majlessi

2021 ISSX Poster Presentation – 2020 NDA Clinical DDI Review

Abstract

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.

Do inhibitory metabolites impact DDI risk assessment? Analysis of in vitro and in vivo data from NDA reviews between 2013 and 2018

Abstract

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.