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 | |
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) | |