Article

Quantitative drug interactions prediction system (Q-DIPS) - A dynamic computer-based method to assist in the choice of clinically relevant in vivo studies

Laboratory of Computer Assisted Therapeutics, Divisions of Clinical Pharmacology and Pharmacy, University Hospitals, Geneva, Switzerland.
Clinical Pharmacokinetics (Impact Factor: 5.49). 02/2001; 40(9):631-40. DOI: 10.2165/00003088-200140090-00001
Source: PubMed

ABSTRACT Metabolic drug interactions are a major source of clinical problems, but their investigation during drug development is often incomplete and poorly specific. In vitro studies give very accurate data on the interactions of drugs with selective cytochrome P450 (CYP) isozymes, but their interpretation in the clinical context is difficult. On the other hand, the design of in vivo studies is sometimes poor (choice of prototype substrate, doses, schedule of administration, number of volunteers), with the risk of minimising the real potential for interaction. To link in vitro and in vivo studies, several authors have suggested using extrapolation techniques, based on the comparison of in vitro inhibition data with the active in vivo concentrations of the inhibitor. However, the lack of knowledge of one or several important parameters (role of metabolites, intrahepatocyte accumulation) often limits the possibility for safe and accurate predictions. In consequence, these methods are useful to complement in vitro studies and help design clinically relevant in vivo studies, but they will not totally replace in vivo investigation in the future. We have developed a computerised application, the quantitative drug interactions prediction system (Q-DIPS), to make both qualitative deductions and quantitative predictions on the basis of a database containing updated information on CYP substrates, inhibitors and inducers, as well as pharmacokinetic parameters. We also propose a global approach to drug interactions problems--'good interactions practice--to help design rational drug interaction investigations, sequentially associating in vitro studies, in vitrolin vivo extrapolation and finally well-designed in vivo clinical studies.

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