Drug Synergy for Cancer using Computational Intelligence Techniques

Abstract

Cancer still remains a major health concern in the world. It is still a challenge for researchers to develop an effective cancer therapy in most types of cancers. Although cancer presents with a very high mortality rate, there are certain treatments available and the number is growing with new breakthroughs every day. A variety of cancer treatments exist, and the type of treatment a patient receives mainly depends upon the type of cancer they present with and its current stage. Various treatments of cancer exist such as radiation therapy, chemotherapy, drug synergy, hormonal therapy and surgery. Some patients need only one kind of treatment, but most of the patients will receive a combination of these. There are many side effects of aforementioned treatments including hair loss, vomiting, nausea, blood disorders, itchiness, constipation, diarrhea, vaginal dryness, enlarged and tender breast. Researchers are trying very hard to reduce the side effects caused by cancer treatment. Now days, more focus is being given by researchers towards targeted therapy such as drug synergism. It mainly targets the cancerous cells. Drug synergism is a branch which finds the optimal combination of two or more drugs on the basis of DDI (Drug Drug Interaction) to kill the cancerous cells in a more efficient manner as compared to their individual effects. However, there are huge number of drugs which leads to the combinatorial explosion problem. To handle the numerous amounts of drugs, machine learning (ML) plays a major role in this area. ML is categorized in supervised and unsupervised learning. In supervised learning, different problems can be categorized under classification and regression whereas in unsupervised learning, clustering problems are considered. Problem of drug synergy falls under the category of regression problems in which synergy score (considered as target) of different drugs is predicted from the given set of input parameters.