Quantitative drug interactions prediction system (Q-DIPS): a dynamic computer-based method to assist in the choice of clinically relevant in vivo studies.
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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ABSTRACT: To cope with increasing numbers of compounds and much higher demand on early ADME data, higher-throughput absorption, distribution, metabolism, and elimination (ADME) in vitro screens have been developed. However, the realisation that screening is a costly business urged pharma and biotech companies to investigate in silico prediction and simulation of pharmacokinetic data and processes. Good progress has been made in simulating the extent and rate of absorption, plasma concentration–time profiles, the site of metabolism within the compound and formed metabolites, and the effect of drug–drug interactions. Further development of robust in silico tools, in combination with high-throughput in vitro screening, will lead to an in combo approach towards drug discovery.Drug Discovery Today BIOSILICO 01/2004; 2(1):38-45.
Article: Computing with evidence[Show abstract] [Hide abstract]
ABSTRACT: We describe a novel experiment that we conducted with the Drug Interaction Knowledge-base (DIKB) to determine which combinations of evidence enable a rule-based theory of metabolic drug-drug interactions to make the most optimal set of predictions. The focus of the experiment was a group of 16 drugs including six members of the HMG-CoA-reductase inhibitor family (statins). The experiment helped identify evidence-use strategies that enabled the DIKB to predict significantly more interactions present in a validation set than the most rigorous strategy developed by drug experts with no loss of accuracy. The best-performing strategies included evidence types that would normally be of lesser predictive value but that are often more accessible than more rigorous types. Our experimental methods represent a new approach to leveraging the available scientific evidence within a domain where important evidence is often missing or of questionable value for supporting important assertions.Journal of Biomedical Informatics 12/2009; 42(6):990-1003. · 2.48 Impact Factor