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 protect the safety of patients, it is vital that researchers find methods for representing drug mechanism knowledge that support making clinically relevant drug-drug interaction (DDI) predictions. Our research aims to identify the challenges of representing and reasoning with drug mechanism knowledge and to evaluate potential informatics solutions to these challenges through the process of developing a knowledge-based system capable of predicting clinically relevant DDIs that occur via metabolic mechanisms. In previous work, we designed a simple, rule-based, model of metabolic inhibition and induction and applied it to a database containing assertions about 267 drugs. This pilot system taught us that drug mechanism knowledge is often dynamic, missing, or uncertain. In this paper, we propose methods to address these properties of mechanism knowledge and describe a new prototype system, the Drug Interaction Knowledge-base (DIKB), that implements our proposed methods so that we can explore their strengths and limitations. A novel feature of the DIKB is its use of a truth maintenance system to link changes in the evidence support for assertions about drug properties to the set of interactions and non-interactions the system predicts.IEEE Transactions on Information Technology in Biomedicine 08/2007; 11(4):386-97. DOI:10.1109/TITB.2007.890842 · 2.07 Impact Factor
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ABSTRACT: Potential drug-drug interactions as well as drug-xenobiotic interactions are a major source of clinical problems, sometimes with dramatic consequences. Investigation of drug-drug interactions during drug development is a major concern for the drug companies while developing new drugs. Our knowledge of the drug-metabolising enzymes, their mechanism of action, and their regulation has made considerable progress during the last decades. Various efficient in vitro approaches have been developed during recent years and powerful computer-based data handling is becoming widely available. All these tools allow us to initiate, early in the development of new chemical entities, large-scale studies on the interactions of drugs with selective cytochrome P-450 (CYP) isozymes, drug receptors, and other cellular entities. Standardisation and validation of these methodological approaches significantly improve the quality of the data generated and the reliability of their interpretation. The simplicity and the low costs associated with the use of in vitro techniques have made them a method of choice to investigate drug-drug interactions. Promising successes have been achieved in the extrapolation of in vitro data to the in vivo situation and in the prediction of drug-drug interaction. Nevertheless, linking in vitro and in vivo studies still remains fraught with difficulties and should be made with great caution.The Scientific World Journal 04/2002; 2:751-66. DOI:10.1100/tsw.2002.144 · 1.73 Impact Factor