Trecento: in silico network-driven identification of target combinations for combination therapy

In recent years, there has been a gradual paradigm shift in the drug industry towards combination therapyafter the “one-target one-drug” (mono-therapy) approach fails to increase the number of successful drugs. Although, the use of multiple drugs in a therapy is potentially useful for addressing diseases ( .g., cancer) that implicate multiple genes and pathways, there is also a higher possibility of drug toxicity that is caused by various factors including the compounding ofoff-target effectsof individual drugs. Careful selection of target combination is an important step towards reducing attrition rate of new drug combinations during preclinical and clinical phases. Although various techniques have been suggested for target selection, they generally apply to mono-therapies. Many of these approaches ( e.g., affinity matrix) cannot be applied directly to target combination identification because they lack consideration of interaction between targets that affects efficacy and toxicity profiles of drug combinations hitting these targets. In addition, the few proposed approach for target combination identification all suffer from certain key limitations. First, they assume that all targets are equally probable for a combination. However, targets influence effects of the combinations differently and appropriate choice of targets can potentially improve the target combinations. Second, they demand specific technical expertise from users ( e.g., knowledge of what off-target effects to optimize) and a lack of the required expertise can undermine the effectiveness of the method. In this dissertation, we seek to address these limitations by proposing a framework called TRECENTO(In SilicoNeTwork-dRiven IdEntifiCation of TargEt CombiNaTions for COmbination Therapy). TRECENTO uses a network-based approach for identifying target combinations in xix ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library