PREDICTING POTENTIAL CYP450 ENZYME INHIBITION BASED DRUG-DRUG INTERACTION DURING DRUGS PRESCRIPTION USING A COMPUTER AID
Zvada, Simbarashe Peter
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Introduction: The increase in the number of drugs on the market and concomitant treatment of co-infections has increased the potential for drug interactions making it difficult for healthcare professionals to minimize the potential adverse effects of every drug. Fortunately, Medical Informatics has been evolving to match this increase in complexity in medical delivery. Pharmacoinformatics has become particularly relevant in addressing some of the undesirable effects associated with the increased practice of polypharmacy. Therefore, the major aim of this study was to develop a computer based pharmacoinformatic tool for use by clinicians and pharmacists in the prediction of in vivo drug-drug interactions (DDIs) using in vitro data. Materials and methods: The prototypic tool was developed using Standard Query Language (SQL) database and Delphi 6.0 as the programming language. Literature sources were assembled, both as databases and symposia abstracts, original publications of drug-enzyme or drug-drug interactions for competitive and mechanism-based inhibition. Sources with validated in vitro methods and having the following parameters: inhibition constant (Ki); maximum enzyme velocity (Vmax); substrate concentration needed to reach half maximal velocity (Km); fraction metabolized by cytochrome P450 (fm) and fraction cleared by cytochrome P450 (fh), were considered. Different plasma concentrations of the inhibitor available to the enzyme site for interaction were tested with and without taking into account protein binding. The concentrations included the average maximum plasma concentration (Cmax) and the estimated of maximum concentration of the inhibitor at entrance to the liver (Iin.max), both bound and unbound. A pilot study was carried out among 10 doctors and 10 pharmacists to test the medical relevance of the tool using a questionnaire with scores ranging from 1 (best) to 6 (worst). Results and discussion: Various drug combinations were tested. The best predictions of in vivo drug-drug interactions were achieved when the concentration of inhibitor was set at the unbound maximum concentration at entrance to the liver enzymes with better overall geometric mean fold error (GMFE) values of 0.68 and overall root mean square error (RMSE) of 3.13 without considering mechanism-based inhibition (MBI). There was improvement in overall GMFE (0.49) and RMSE (1.71) for steady-state unbound Cmax when MBI was incorporated. A preliminary evaluation of the tool by medical professionals has highly recommended application in private practice and in academia as a teaching tool, and with mixed reactions in public sector. The survey recommended that modifications be made on details captured under product composition. Conclusion: The pharmacoinformatic tool developed during this work is likely to be well received by the medical community starting as a teaching tool. More drugs used routinely need to be added, and a high sample size evaluation of relevance and acceptability conducted. The predictive capacity of the tool had low levels of bias when the concentration of inhibitor was set at the unbound maximum concentration at entrance to the liver enzymes. However more work needs to be done to include Drug-Drug Interactions (DDIs) due to induction and irreversible enzyme inhibition or through inhibition of other enzymes not considered in this study.