In clinical practice, drug prescription error is a common occurrence [1,2] and it results in sub-therapeutic dosing or adverse drug reactions (ADRs). Studies carried out in the United States of America (USA) by the Committee on Quality of Health Care in America (CQHCA) revealed that 44 000 to 98 000 deaths occurring annually are due to medication errors and 7 000 of the total deaths are due to ADRs [3]. It was also estimated that, over 350 000 ADRs occur yearly in United States nursing homes [4]. Again, a retrospective analysis at two London hospitals found that 11% of admitted patients experienced adverse events where 48% were judged to be preventable and 8% led to death [5]. All these studies have given us a hint that the situation might be worse in developing nations where medical expertise and infrastructure are underdeveloped.

The increased demand for pharmacotherapeutic agents in infectious disease co-infections has introduced complexity in the treatment and management of patients in resource limited nations [6]. In the treatment and management of such cases, drug combinations are inevitable as they help in the rapid eradication of causative pathogen, minimize emergence of drug resistant parasites and lead to quick patient recovery. However, each drug provides both therapeutic and toxic effects, thus making physicians worry about safety in the simultaneous use of many drugs. Polypharmacy predisposes patients to adverse drug reactions (ADRs) or adverse drug events (ADEs) emanating from drug-drug interactions (DDIs) [7,8].

Scientifically, a number of clinically significant DDIs occur at the metabolic level. Metabolic drug-drug interaction results in decreased therapeutic effect, generation of toxic metabolites due to enzyme induction or increased substrate plasma concentration above its therapeutic index to toxic levels due to enzyme inhibition. The impact of DDIs is revealed through withdrawals of mibefradil, sirovudine, astemizole and cisapride from the pharmaceutical market [9].

Increased understanding of biochemical pathways of drug metabolism and development of in vitro methods to identify chemical entities that may cause clinically significant DDIs has aided in the screening for safer lead compounds at preclinical stage. Even though this is the case, drug combinations are unavoidable but some drug-drug interactions can be prevented. Recent investigations in the use of drugs have shown that most of DDIs can be prevented through identification of compounds that have in vitro inhibition constant (K_i) of less than 1µM. These chemical entities result in clinically significant DDIs due to enzyme inhibition [10]. Categorically, compounds with K_i value above 10 µM were considered as weak inhibitors. Furthermore, efforts are being made to quantitatively predict the magnitude of in vivo DDIs using in vitro data. However, although very useful, these predictions are being implemented at preclinical levels of drug discovery. Most antiparasitic drugs where discovered and introduced on to the market without knowledge of this data and continue to be used without caution with respect to the potential for drug-drug interactions. Towards addressing this shortcoming, retrospective in vitro screening of antiparasitic drugs for inhibitory and inductory effects on CYP450s were carried out in our laboratory [11-13]. There is, however, still a big gap in clinical evaluation of some of the potential DDI that might arise in the use of various combinations of drugs in the treatment and/or prophylaxis of TB, malaria, HIV/AIDS and other infectious diseases.

Though efforts have been made to assist physicians in handling large patient data and decision making, the available computer based decision support systems like CPOEs offer fragmented data [14,15]. In order to address these deficiencies in health care, the aim of this work was to develop a computer based pharmacoinformatic tool for use in the prediction of drug-drug interaction. Our target groups are clinicians and pharmacists. In this work, CYP450-drug interactions were used as the basis of predicting in vivo DDIs after oral drug administration. Published in vivo data that supports interaction between particular drug combinations was incorporated. Doctors and related users will be assisted in the selection of safer alternative drugs if their first option is not suitable. In addition, the tool will also be used for teaching purposes in preclinical pharmacokinetic concepts towards the prediction of DDIs and appropriate dose estimate utilizing in vitro and in vivo data. Other studies with the same scope exist and they focus on the compilation of a DDI databases [16-18]. The major differences with our study are algorithms used and inclusion of drugs mostly used Africa, e.g. antiparasitic drugs.

In the African setting, internet connectivity is generally slow and expensive making web-based applications unsuitable and unfriendly for some users. However, this can be rectified through our efforts to make the software suitable for both standalone and local area network applications with the possibility of data synchronizations. In order to appreciate most of the functionalities, we recommend hands on use of the prototypic tool both in preclinical and clinical settings.

Development of the software was done successfully. When the user logs into the system, a default page appears. The only difference from Fig. (1) is that, the small popped up windows where the physician or pharmacist captures drugs and dosage details will not be displayed but are only prompted by the user.

Fig. (1) a). An illustration of the screen from AiBST’s pharmacoinformatic tool that appears during drug prescription. b). An illustration of the screen from AiBST’s pharmacoinformatic that appears during capturing of dosage details.

During routine drug prescription or dispensing by doctors and pharmacist respectively, if they add at least two potentially interacting drugs, a warning is given (Fig. 2) followed by a detailed description of interaction (Fig. 3). The interaction is one-to-many. This means, interaction will be tested on all the drugs already prescribed. The pair in which we have major interactions will be shown in test outcome table. Details of interactions of the added drug with the drug already prescribed can be retrieved through clicking page header, test details, which is next to page header - test outcome. Analysis of different inhibitor concentration was also done to come up with the one that give better prediction on the fold increase in exposure of affected drug when compared with in vivo published data (Fig. 4). Maneuvering of all the pages as highlighted in all figures was done by 5 pharmacists and 5 doctors who expressed support for the enormous potential of the tool in the medical community. At this stage, they recommended that the tool be used as a teaching tool awaiting further validation and capturing of commonly used drug combinations. Should this tool finally find its way into the medical field the best targets recommended are doctors.

Fig. (2) An illustration of a warning given when two potentially interacting drugs are co-prescribed.

Fig. (3) A page that details mechanism of interaction, alternatives, fold-increase in exposure of affected drug.

Fig. (4) a). Illustration of how different inhibitor concentrations can be analyzed and their corresponding effect on the accuracy of prediction. b). Different fold increase in exposure of affected drug due to different inhibitor concentrations.

The harnessing of the growing field of information technology into various disciplines has gained much recognition and popularity over the past decades. Large amounts of data generated in the medical fraternity from drug discovery to clinical practice often leaves physicians with a tough task to handle. It is documented that only a small percentage of clinically relevant data that is churned is utilized during routine practice [29]. A number of decision support systems have been developed to match the increase in the amount of data although it calls for a well articulated plan and distinct leadership to achieve success. Most medical informatics tools available are mainly to capture and analyze patient medical data. The few that capture drug-drug interactions, act as electronic version of the hard copies and have been shown to be medically useful in minimizing medication errors [30,31]. Software developed to predict these DDIs exist but are utilized by pharmaceutical companies where they are mainly for drug discovery and development [32]. Most of the software is password protected, and the available trial versions do not give a full understanding the pharmaco-kinetics and pharmacodynamic basis of how the interaction is picked.

In this study, we are mainly focusing on the clinical application rather than early phases of drug discovery. Furthermore, we have addressed the issue of both published and unpublished drug-drug interaction since there is a growing need for evidence-based therapeutic approach (Fig. 3; Fig. 5). These issues make our software different from those already available on the market. Even though a bigger study is yet to be done to test the user friendliness of the interface, savings in time to access DDI data , relevance of warnings and adoption recommendations, tests by a few selected doctors and pharmacists is showing a positive result in that direction. It doesn’t require wide knowledge and expertise in computers, though basic literacy is important. Information retrieval can simply be done through clicking page headers. When the physician or pharmacist logs on in the software, during addition of prescription drugs, dosage details are captured through a small activated window as shown in Fig. (1). Because the user can make mistakes, which is common in the medical field [1,2], the captured drug prescription details are shown on the left corner of the page output allowing for quick and easy editing to make corrections.

Fig. (5) A page that shows a retrieved referenced abstract of in vivo drug-drug interaction and fold changes in exposure of affected drug.

If more than one drug is captured and there is a potential for interaction, an interrupting warning message is displayed (Fig. 2). This is important function because it alerts the physician who would otherwise fail to recognize the message if it is shelved or highlighted on other parts of the interface. The message is followed by description of mechanism of interaction, alternative drugs and approaches, and the interacting drugs coupled with the fold increase in the exposure of the affected drug (Fig. 3). It is hoped that physicians as well as pharmacist will benefit from this output both as a teaching tool and in optimal patient health care delivery. Displaying of alternative drugs is based on the magnitude of increase in exposure of affected drug due to the effector. If an alternative to the effector drug causes a higher magnitude, it can not be displayed. This helps in eliminating the trial-an-error approach in choosing alternatives, which is a common practice during routine drug prescription. Detection and minimization of potential DDIs is extremely important in patient care since it reduces chances of adverse drug reactions and events which have been demonstrated to cause ADR, death and result in increased health care costs [5]. Further developments and the use of this tool is hoped to lead to improved interaction between doctors and pharmacists when potential DDIs have been detected in order to give correct drug doses and/or drug combinations to patients.

There is still an on going debate on the best estimate of the correct concentration of the inhibitor available to the drug metabolizing enzyme to use in the models for the prediction of DDIs [27]. Preliminary analysis of different concentrations was done as highlighted (Fig. 4). Recent studies and so far indicated in our prediction tool (Fig. 4), show that better predictive accuracy can be achieved when the unbound concentration of the inhibitor entering the liver from the portal vein was used. We have therefore used this as a default concentration for calculations. The user can also learn how the interaction varies with change in inhibitor concentration as highlighted (Fig. 4) through the use of drop down menu. This also educates users on the possibility of under-prediction or over-prediction when protein binding is taken or not taken into account for the various models. The tool will therefore be responsive to the ongoing advances in research where the role of drug transporters could be important for making better and successful DDI predictions.

In fact, in the medical community pharmacists are not obliged or mandated by any law to alter prescribed drugs but can communicate with the prescriber. Under normal circumstances, patients can either send a relative or a child to purchase medications on their behalf, it becomes difficult for a pharmacist to know the drugs the patient might also be taking in addition to the one being dispensed. With this in mind, pharmacist may not be able to detect potential drug-drug interaction. So the best targets are doctors who came face to face with the patient during disease diagnosis and subsequent drug prescription, and this has been recommended by physicians and doctors. This recommendation supports our hope of leading to improved interaction between doctors and pharmacists when potential DDIs have been detected in order to give correct drug doses and/or drug combinations to patients. In addition, the sparse antiparasitic data that we have uploaded undermines proper validation and routine clinical use.

In conclusion, the tool has the potential to quantitatively predict potential DDIs and could be used by pharmacists and doctors in the minimization of potential DDIs based adverse drug reactions if more in vitro and in vivo data has been added and validated. However more work needs to be done to include DDIs due to induction and irreversible enzyme inhibition or through inhibition of other enzymes not considered in this study.

What was known before our work

• The qualitative and quantitative nature of drug-drug interaction.
• The contribution of drug prescription error to adverse drug events.
• Need for simple, reliable and easily accessible drug interaction databases.

Contribution of our work

• Detection of potential drug-drug interaction among ARVs, antitubercular and antiparasitic drugs. Most databases do not include most antiparasitic drugs.
• The continued need for simple computer-based decision support systems with ability to predict potential interaction among given combinations of drugs at clinical level.
• Clinical decision support systems with functions on how the decision is reached help in teaching physicians and pharmacist on how to detect potential drug-drug interaction and this improves their knowledge.
• Assistance in picking alternative drugs during drug prescription minimizes the trial-and-error approach, and saves time.
• Electronic systems which enable clinicians to easily access published scientific data improve interaction between researchers and medical personnel.