1 DMPK & BAC Department, African Institute of Biomedical Science and Technology (AiBST), Harare, Zimbabwe
2 University of Zimbabwe School of Pharmacy, Harare, Zimbabwe
Polypharmacy as a result of combating co-infections, or combination therapy for better efficacy and reducing the emergency of drug resistance, is on the increase in the African clinical setting in the advent of HIV/AIDS, and tuberculosis (TB) co-infections, and increasing incidences of malaria and other tropical infections. The clinicians and pharmacists are therefore faced with the challenge of prescribing drugs in combinations that are likely to result in severe adverse effects or compromising treatment success. The aim of this study was, therefore, to develop a simple stand alone or network based experimental computational tool to assist doctors and pharmacists in detecting drug combinations likely to result in undesirable metabolism based drug-drug interactions (DDIs) and offer alternate safe prescription options. The mechanism of most drug-drug interactions is through inhibition and induction of drug metabolising enzymes. Models for the prediction of reversible and irreversible inhibitors of the major drug metabolising enzyme system, cytochrome P450, were used in developing the pharmacoinformatic tool. These models enable the prediction of likely in vivo drug-drug interactions from in vitro data. In vivo drug-drug interaction data from the literature was also loaded into the software to validate the system and to give clinical guidance on specific drug-drug interactions. In this first phase of the project, focus was on medications used in the treatment of HIV/AIDS, TB, malaria and other diseases common in Africa. The prototypic tool was based on a Standard Query Language (SQL) database with DELPHI 6.0 as the user interface. Its user friendly pages lead the doctor or pharmacist through drug combination entry functions and gives warning if an interaction is likely. Subsequent actions enable the operator to retrieve more information on the mechanism of interactions, the quantitative measure of the interaction, access to published abstracts on studies, and possible prescription options to minimise DDIs. The software currently has data for 50 drugs used in the design and focuses on the treatment of tropical diseases in addition to classical cases of drug-drug interactions involving other general classes of drugs. The tool can be distributed on Compaq Disk (CD) and be run on any Personal Computer (PC) on windows. We have successfully developed a pharmacokinetic- based tool with a potential to assist clinicians and pharmacists in detecting and rationalizing DDIs. The tool has proved very useful as a teaching tool on DDIs by using the more advanced functions that explore the performance of current drug-drug interactions prediction models. From the available literature, it is clear that more studies need to be done to establish the prevalence and mechanisms of DDIs in the treatment of infectious diseases. We are now adding more data, validating the tool and finally testing the acceptability of this tool among clinicians and pharmacists for routine use.
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