The lists of sensitive substrates, inhibitors, and inducers, including the file combining all lists, have been updated and are available in the DIDB Resource Center.
A total of 18 drug interactions, involving 13 drugs, were added to the October version or were updated. Nine of these drugs were cancer treatments, including 7 kinase inhibitors, being identified as sensitive substrates or perpetrators of CYP enzymes or the P-gp transporter.
As always, feel free to contact us if you have any questions or comments.
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.
Clin Ther. 2021 Nov;43(11):2032-2039
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).
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
2021 ISSX Poster Presentation – Drug Disposition Characteristics and Hepatic Impairment
Abstract
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 24th North American ISSX Meeting, September 2021
Jingjing Yu,Yan Wang, Cheryl Wu, and Isabelle Ragueneau-Majlessi
2021 ISSX Poster Presentation – Anti-Infective Knowledgbase
Abstract
Patients with infectious diseases in low-income countries (LICs) are often at risk of pharmacokinetic (PK) drug-drug interactions (DDIs). To assist in silico mechanistic modeling and simulations to predict DDI liability and guide optimal management of DDIs, a knowledgebase of anti-infective drugs, specifically treatments for malaria and tuberculosis, has been established.
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.
We have recorded two videos to show how our DIDB editorial team manually and carefully curate data from citations and FDA drug approval packages and present them in DIDB.
The aim of those videos is to provide you with a better understanding of DIDB’s scientific and editorial processes.
The videos are available in DIDB Resource Center. Please note that you must be signed in to access.
The June Newsletter is now available. You can see this and past newsletters (published in 2020 and 2021) in the DIDB Resource Center. This is our last issue as you can now access a list of the latest citations published which have been entered into DIDB (please note that you must be signed in to access).
Do not hesitate to contact us with comments or suggestions.
Abstract
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.