Presented at the 14th ISSX International Meeting, September 2025
Oliver Hatley, Olha Shuklinova, Anna Murphy, Roz Southall, Eman El-Khateeb, Jingjing Yu, Isabelle
Ragueneau-Majlessi, and Iain Gardner
The application of Physiologically Based Pharmacokinetic (PBPK) modelling in FDA NDAs was assessed across 2023 and 2024 approvals.
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.
Presented at the 14th ISSX International Meeting, September 2025
Jingjing Yu, Sophie Argon, Katie Owens, Yan Wang, and Isabelle Ragueneau-Majlessi
In the present work, drug metabolism, drug transport, and drug interaction in vitro, in silico, and clinical data for small molecular drugs approved by the U.S. Food and Drug Administration in 2024 (N = 34) were analyzed using Certara Drug Interaction Database (DIDB®; https://www.druginteractionsolutions.org/). The mechanism(s) and clinical relevance of these pharmacokinetic interactions were characterized based on information available in the new drug application reviews.
Presented at the 26th North American ISSX and JSSX Meeting, September 2024
Jingjing Yu, Sophie Argon, Yan Wang, and Isabelle Ragueneau-Majlessi
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.
Presented at the 25th North American ISSX Meeting, September 2023
Jingjing Yu, Yan Wang, and Isabelle Ragueneau-Majlessi
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.
Presented at the ISSX/MDO Meeting, September 2022
Jingjing Yu, Yan Wang, and Isabelle Ragueneau-Majlessi
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.
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.
Presented virtually at 24th North American ISSX Meeting, September 2021
Jessica Sontheimer, Zoé Borgel, Jingjing Yu, William Copalu, Catherine K. Yeung, Eva Berglund, and Isabelle Ragueneau-Majlessi
The aim of the study was to systematically review the disposition parameters of drugs most affected by hepatic impairment(HI) and investigate whether there are elimination characteristics (such as enzyme or transporter involvement in drug elimination) that predisposed for a large effect of HI on drug exposure.
Presented virtually at the 24th North American ISSX Meeting, September 2021
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.
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.