Suggestive Strategies for Mitigating Adverse Effects in Drug-Drug Interactions

Abstract

The purpose of this study is to investigate the Drug-Drug Interactions (DDIs) remains a thorny issue in clinical practice since it drastically affects efficacy of treatments and triggers adverse effects. As disease states have either become more complex, or treatment has become more aggressive, there has been a marked up-tick in DDIs; consequently, they remain a prime area of focus in modern medicine. DDIs can change the pharmacologic actions of others, produce new or manufacturing effects which may be perilous; the results might range from treatment failure to deaths. These interactions can range from alteration of hormones, enzymes and over complex organs and other organ systems. For instance, they can alter the metabolism of drugs or physiologic functions , which results in direct toxicity or lack of drug efficacy. Some of the health related consequences of DDIs include neurological adverse effects, endocrine and organ diseases, nervous system pathology and cardiovascular diseases. It is therefore important to develop an understanding of the factors involved in DDIs so as to address them. Some of the strategies are; performing well-coordinated drug trial, prescribing drugs carefully, checking on the levels of therapeutic drugs, and medication review. These approaches are essential in the assessment, minimization, and control of the adverse impacts of DDIs, thus enhancing the safety of patients and their treatment responses. In this study, we broadened the focus of the analysis by employing data from the DrugBank database to predict 65 distinct types of DDI- related events. For DDI prediction we used two deep models including CNN and DNN. Four categories of drug-related features, including SMILES, enzymes, pathways, and targets were considered as input of these models and obtained from DrugBank. The output of the models is the prediction of certain of occurrence of DDI related events. Various tests were carried out to compare different number of layers, activation functions for the CNN and drug features. Moreover, the identified drug interactions were selected to target the enzymes that should be affected according to the names provided as the model’s output. Additionally, a key goal of our study is to improve performance metrics such as recall, ensuring that our models are more effective at identifying true positive interactions, which is critical for enhancing patient safety and treatment outcomes.