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Background and objective Drug-drug interaction (DDI) is one of the main causes of toxicity and treatment inefficacy. This work focuses on non-communicable diseases (NCDs), the non-transmissible and long-lasting diseases since they are the leading cause of death globally. Drugs that are used in NCDs increase the probability of DDIs as a result of long time usage. This work proposes an Integrated Action Crossing (IAC) method that is effective in predicting the NCDs DDIs based on pharmacokinetic (PK) mechanism. Methods Drug-Enzyme (CYP450) and Drug-Transporter actions including substrate, inhibitor and inducer affect the PK mechanism of other drugs. Hence, this paper proposes an enzyme and transporter protein integrated action crossing method for DDIs prediction in NCDs. The NCDs Drugs information was retrieved from the DrugBank database and the actions of enzymes and transporter proteins that were crossed and integrated. The datasets were generated for machine training. Results Three machine learning approaches: Support Vector Machine, k-Nearest Neighbors, and Neural Networks were used for the assessment of the method. Performance evaluation was performed through five-fold cross validation and the different datasets and learning methods were compared. Two layers NNs achieved the best performance at the accuracy of 83.15% (F-Measure 85.23% and AUC 0.901). Conclusions The IAC method delivers better performance compared to the conventional method for the identification of NCDs DDIs.

Drug-Drug Interactions (DDI) is a cause of treatment inefficacy and toxicity. Many DDIs are caused by alterations in drug metabolism that related to drug-enzyme actions. Drugs can be substrate, inhibitor or inducer of enzymes which interfere their metabolism. Therefore, this paper proposes a method for simulating the DDI using agent based modeling (ABM). The study consists of three steps: data gathering, drug-enzyme action pairing and ABM generation. The ABM application was developed, allows user to adjust drug concentration, reaction rate parameters, and add others drugs to the system environment. To demonstrate the ABM application, the enzyme reaction of DDI example (simvastatin and amiodarone) was simulated. Simulation results demonstrate that the model could be an effective method to do the DDI simulation without the setting of the real experiment.

Drug-Drug Interactions (DDI) is a cause of treatment inefficacy and toxicity. The most DDI involve drug metabolism which related to enzyme and transporter protein. Drug-enzyme actions that alter the metabolism of other drugs consist of substrate, inhibitor and inducer. Non-communicable diseases (NCDs) are the leading cause of death, drugs that are used in NCDs can increase interaction probability because their long-term usage. This paper proposes Integrated Action Crossing (IAC), a new attribute generation method for DDIs prediction in NCDs. Drugs attributes in NCDs categories were extracted. The actions of enzymes and transporter proteins were crossed for generating dataset for prediction model creation. Neural network (NN) and others machine learning were investigated. Five-fold cross validation was performed for evaluaing the prediction model performance. The results showed that 2 layers NN obtained the best performance of NCDs DDIs prediction model at the accuracy of 83.15%.

Drug-Drug Interactions (DDIs) are major causes of morbidity and treatment inefficacy. The prediction of DDIs for avoiding the adverse effects is an important issue. There are many drug-drug interaction pairs, it is impossible to do in vitro or in vivo experiments for all the possible pairs. The limitation of DDIs research is the high costs. Many drug interactions are due to alterations in drug metabolism by enzymes. The most common among these enzymes are cytochrome P450 enzymes (CYP450). Drugs can be substrate, inhibitor or inducer of CYP450 which will affect metabolite of other drugs. This paper proposes enzyme action crossing attribute creation for DDIs prediction. Machine learning techniques, k-Nearest Neighbor (k-NN), Neural Networks (NNs), and Support Vector Machine (SVM) were used to find DDIs for simvastatin based on enzyme action crossing. SVM preformed the best providing the predictions at the accuracy of 70.40 % and of 81.85 % with balance and unbalance class label datasets respectively. Enzyme action crossing method provided the new attribute that can be used to predict drug-drug interactions.