Abstract
Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes.
Highlights
In the past decades, targeted therapies modulating specific targets were considerably successful
Both glyburide and metformin are indicated for type 2 diabetes but work in different ways: glyburide reduces insulin resistance while metformin increases insulin secretion, and the combination of these two drugs can improve therapeutic efficacy due to their complementary mechanisms [5]
The overlap between our predictions and those reported in the literature demonstrate that this approach can effectively identify new drug combinations with the enriched feature patterns as an indicator for the mode of action underlying both marketed and predicted drug combinations
Summary
In the past decades, targeted therapies modulating specific targets were considerably successful. The rate of new drug approvals is slowing down despite increasing research budgets for drug discovery One reason for this is that most human diseases are caused by complex biological processes that are redundant and robust to drug perturbations of a single molecular target. Saracatinib can overcome the resistance of breast cancer to trastuzumab when both drugs are used together, thereby improving the efficacy of trastuzumab [4] Both glyburide and metformin are indicated for type 2 diabetes but work in different ways: glyburide reduces insulin resistance while metformin increases insulin secretion, and the combination of these two drugs can improve therapeutic efficacy due to their complementary mechanisms [5]. Drugs are combined based on their mechanisms of action, which is characterized by the properties of drugs, such as
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