Accord ingly, the presence of potential drug drug interactions and the possibility of pharmacokinetic interven tions between the drugs could confound http://www.selleckchem.com/products/DAPT-GSI-IX.html the identifica tion of effective drug combinations. Furthermore, the number of possible combinations will increase expo nentially with the increasing availability of single drugs. For example, in the case of four drugs, there will be six possible combinations. This number would be enormous considering the fact that there are thousands of approved drugs. Due to the huge search space of possi ble combinations between known drugs, the identifica tion of optimal and effective drug combinations is a non trivial and challenging task. Therefore, it is necessary to develop effective in silico methods that are capable of discovering new drug com binations prior to combination synthesis and practical test in the lab.
Owing to the completion of human gen ome sequencing projects and the advancement of mole cular medicine, extensive system biology efforts have been made to discover new combinations based on molecular interaction networks in the past few years. Nevertheless, there is still a long way to go before we reach the stage of devising generally applicable and effective prediction models. Recently, there have been considerable progresses in developing new approaches for identifying drug drug interactions and even drug combinations. In this context, Geva Zatorsky et al. have recently found that the protein dynamics in response to drug combination can be accu rately described by a linear superposition of the dynamics under the corresponding individual drugs.
Their study indicated that protein dynamics of three and four drug combinations can be predicted based on the drug combination pairs, thereby providing a useful way for reducing the search space of possible drug com binations. Calzolari et al. devised an efficient search algorithm originated from information theory for opti mization of drug combinations based on the sequential decoding algorithms. More recently, researchers have also developed computational frameworks for pre dicting drug combinations and synergistic effects based AV-951 on high throughput data. In this work, we study the drug combinations in terms of their therapeutic similarity and the network topology of a drug cocktail network constructed from the effec tive drug combinations deposited in the Drug Combina tion Database.
We find that the drugs in an effective combination tend to have more similar ther apeutic effects and share more interaction partners in the context of drug cocktail network. We further develop a statistical approach called DCPred to predict possible drug combinations and validate this approach based on a benchmark dataset with all the known effective drug combinations. As a result, DCPred read FAQ achieves the overall best AUC score of 0.