Skip to main content

Table 2 Summaries of reviewed systems approaches for identifying drug combinations

From: Systems biology approaches for advancing the discovery of effective drug combinations

Disease models Method Key findings Validation Reference
Computational models of cell signaling networks
Breast cancer Mass-action model Combined inhibition of MEK and PI3K optimally decreased cell viability. in vitro [25]
Ovarian cancer Mass-action model the ratio of PTEN to activated PI3K predicts RTK inhibitor resistance in vitro [26]
Ovarian cancer Mass-action model ErbB3 inhibition inhibits the ErbB-PI3K network more potently than current therapies. in vivo (rodent) [27]
Breast cancer Logic-based Combined inhibition of c-MYC and ERBB2 improved treatment for trastuzumab resistant breast cancer. in vitro [30]
T cell large granular lymphocyte leukemia Logic-based Sphingosine kinase 1 and NFKB are essential for survival of leukemic T cell large granular lymphocytes. in vitro [31]
Colorectal cancer Fuzzy Logic MK2 and MEK are co-regulators of ERK and EGF induced IKK inhibition. in vitro [32]
Cardiac hypertrophy Normalized-Hill model Ras had the greatest influence on hypertrophy and correlation between node degree and influence is low. in vitro [35]
Various 3-node enzymatic models Identified consistent synergistic and antagonistic motifs. in silico [41]
Various 4-node enzymatic models Synergy is more prevalent in motifs with negative feedback between the target and an upstream effector or mutual inhibition between parallel pathways. in silico [42]
Cardiac hypertrophy Statistical association model Maladaptive and adaptive hypertrophy features were in separate modules in the simplified hypertrophy network map generated by k-means clustering of ligands and phenotypic outputs. in vitro [45]
Melanoma Statistical association model PLK1 inhibition increases cytotoxicity of RAF inhibitor resistant melanoma cells. in vitro [47]
Various Statistical association model Reconstructed classic T cell signaling network using multiparameter single-cell data and Bayesian network inference. in vitro [48]
Signature-based approaches
Lung cancer CMap PI3K inhibition enhanced docetaxel-induced cytotoxicity in vitro [55]
Lymphoblastic Leukemia CMap mTor inhibition induced glucocorticoid sensitivity by decreasing MCL1 in vitro [52]
Lung cancer K-Map The combination of bosutinib and gefitinib has synergistic effects in EGFR mutant non-small cell lung cancer in vitro [57]
Network-based approaches
Osteosarcoma Target Inhibition Map (TIM) Developed an algorithm using a training set of drug sensitivities with known targets to predict responses to new drugs and combinations. in vitro [58,59]
Breast and pancreatic cancer TIMMA Target Inhibition inference using Maximization and Minimization Averaging (TIMMA). Improved computational cost and accuracy of the above TIM approach. Predicted kinase pairs that could be inhibited to prevent cancer survival. in vitro [60]
Various Elastic Net Regularization Performed phenotypic screen using an optimal set of 32 kinase inhibitors. They used an elastic net regulatization algorithm to deconvolute the polypharmacology and identify key kinases regulating cell migration. in vitro [61]
Lung and breast cancer DrugComboRanker Created drug and disease functional networks based on genomic profiles and interactome data. Drug combinations are predicted by identifying drugs whose targets are enriched in the disease network. Literature support [62]
Various Mixed integer linear programming Built a network of drug-target interactions from DrugBank. Given an input gene set, the algorithm selects drug combinations that maximize on target effects and minimize off target effects Literature support [63]
Various Systems analysis of Drug Combinations Drugs with similar therapeutic effects cluster together in a network of successful drug combinations produced using the Drug Combination Database [59]. Network observations were used to develop a statistical approach for predicting drug combinations (DCPred) Literature support [65]
Drug-drug interactions Drug-drug interaction network Applied five machine learning models to a data set of drug-drug pair similarities including 721 approved drugs to predict drug-drug interactions. Literature support [66]
Integration of functional genomics and computational methods
Breast cancer RNAi screen PTEN downregulation with active PI3K signaling induce trastuzumab resistance in vitro [68]
Colorectal cancer RNAi screen EGFR inhibition synergizes with BRAF(V600E) inhibition in vivo (rodent) [69]
Lymphoma 8-gene RNAi signature Drug combination signatures were usually a weighted composite of single drug effects in vitro [70]
Colorectal cancer RNAi screen The combination of Selumetinib (MEK1/2 inhibitor) and CsA (Wnt inhibitor) has synergistic anti-proliferative effects in vivo (rodent) [71]
High-throughput drug combination screens
HIV Pooled screen Used pools of 10 drugs in 384-well plates to study all possibly pairs of 1000 compounds in the minimum number of wells possible in vitro [72]
Melanoma Drug combination screen Sorafenib (a multi-kinase inhibitor) and diclofenac (NSAID) had synergistic effects across all nine tested melanoma cell lines. in vitro [73]
Lymphoma Drug combination screen Screen of 500 compounds with ibrutinib revealed favorable combinations with inhibitors of PI3K signaling, the Bcl2 family, and B-cell receptor pathway in vitro [74]
Various cancers Drug combination screen Screen of 5,000 combinations of FDA-approved drugs in the NCI-60 cancer cell line panel. in vitro [75]
Lymphoma RNAi-modeled tumor heterogeneity Intatumor heterogeneity influences the prediction of effective drug combinations. in vivo (rodent) [77,78]