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F Ajayi

Publications and source records attributed to F Ajayi.

5 recordsLinked to original sources

In vivo drug-drug interaction studies--a survey of all new molecular entities approved from 1987 to 1997.

Ninety-eight new molecular entities applications approved between 1987 to 1991 (period I) and 193 applications for new molecular entities between 1992 to 1997 (period II) were surveyed for drug-drug interaction studies. In period I (used as a comparator), 32 applications contained drug-drug interaction studies for a total of 117 studies. In period II, 106 applications reported drug-drug interaction studies, and the number of studies per new molecular entity ranged from 0 to 15. Most studies (77%) were performed in healthy subjects, with 44% using crossover designs, 7% using parallel designs, and the remaining using fixed sequence designs. The most common dosing scheme for new molecular entities/interacting drug was multiple dose (47%), whereas single dose/multiple dose was used in 31% of studies, and single dose/single dose was used in 18% of studies. Of the 540 drug-drug interaction studies submitted in period II, 80 (15%) resulted in clinically significant labeling statements. Submissions for new molecular entities to the Center for Drug Evaluation and Research divisions most likely to include drug-drug interaction studies were neuropharmacology, cardiorenal, antiviral, and antiinfective drugs. Some drug classes such as oncology drug products and radioimaging products were least likely to include drug-drug interaction studies in their submissions. We conclude that the use of drug-drug interaction studies in the drug development process has increased between the two periods.

Biopharmaceutics↗

In vitro metabolic interaction studies: experience of the Food and Drug Administration.

A total of 194 new molecular entities approved by the Food and Drug Administration between 1992 and 1997 were surveyed to determine the role of in vitro metabolic interactions in the conduct of drug-drug interaction studies and to examine the methods used in these studies. Approximately 30% of the submissions were found to have in vitro metabolism-based interaction studies, most of which were inhibitory in nature. Chemical inhibition was the most commonly used approach in studying drug interactions in vitro. In this article, an attempt to assess the quality of the chemical inhibition approach was made. Four areas were found to be often overlooked: (1) incubation time and concentrations of the drug, (2) the difference between inhibition constant (k(i)) and 50% inhibitory concentration (IC50) values, (3) the substrate-dependent inhibition potential, and (4) the metabolic genotype or phenotype of the liver donor. We discuss the pitfalls in estimating drug interactions when these four areas are overlooked.

Cytochrome P-450 Enzyme Inhibitors↗

FDA evaluations using in vitro metabolism to predict and interpret in vivo metabolic drug-drug interactions: impact on labeling.

Recent advances in in vitro metabolism methods have led to an improved ability to predict clinically relevant metabolic drug-drug interactions. To address the relationships of in vitro metabolism data and in vivo metabolism outcomes, the Office of Clinical Pharmacology and Biopharmaceutics in the Center for Drug Evaluation and Research, Food and Drug Administration, evaluated a number of recently approved new drug applications. The goal of these evaluations was to determine the contribution of in vitro metabolism data in (1) predicting in vivo drug-drug interactions, (2) determining the need to conduct an in vivo drug-drug interaction study, and (3) incorporating findings into drug product labeling. Ten cases are presented in this article. They fall into two major groups: (1) in vitro data were predictive of in vivo results, and (2) in vitro data were not predictive of in vivo results. Discussion of these cases highlights factors limiting predictability of in vivo metabolic interactions from in vitro metabolism data. The integration of these findings into drug product labeling is also discussed.

Data Collection↗