TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples

TY - JOUR

T1 - TIMMA-R

T2 - an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples

AU - He, Liye

AU - Wennerberg, Krister

AU - Aittokallio, Tero

AU - Tang, Jing

N1 - © The Author 2015. Published by Oxford University Press.

PY - 2015/6/1

Y1 - 2015/6/1

N2 - Summary: Network pharmacology-based prediction of multi-targeted drug combinations is becoming a promising strategy to improve anticancer efficacy and safety. We developed a logicbased network algorithm, called Target Inhibition Interaction using Maximization and Minimization Averaging (TIMMA), which predicts the effects of drug combinations based on their binary drugtarget interactions and single-drug sensitivity profiles in a given cancer sample. Here, we report the R implementation of the algorithm (TIMMA-R), which is much faster than the original MATLAB code. The major extensions include modeling of multiclass drug-target profiles and network visualization. We also show that the TIMMA-R predictions are robust to the intrinsic noise in the experimental data, thus making it a promising high-throughput tool to prioritize drug combinations in various cancer types for follow-up experimentation or clinical applications. Availability and implementation: TIMMA-R source code is freely available at http://cran.r-project. org/web/packages/timma/.

AB - Summary: Network pharmacology-based prediction of multi-targeted drug combinations is becoming a promising strategy to improve anticancer efficacy and safety. We developed a logicbased network algorithm, called Target Inhibition Interaction using Maximization and Minimization Averaging (TIMMA), which predicts the effects of drug combinations based on their binary drugtarget interactions and single-drug sensitivity profiles in a given cancer sample. Here, we report the R implementation of the algorithm (TIMMA-R), which is much faster than the original MATLAB code. The major extensions include modeling of multiclass drug-target profiles and network visualization. We also show that the TIMMA-R predictions are robust to the intrinsic noise in the experimental data, thus making it a promising high-throughput tool to prioritize drug combinations in various cancer types for follow-up experimentation or clinical applications. Availability and implementation: TIMMA-R source code is freely available at http://cran.r-project. org/web/packages/timma/.

KW - Algorithms

KW - Antineoplastic Combined Chemotherapy Protocols/pharmacology

KW - Cell Line, Tumor

KW - Drug Discovery

KW - Drug Synergism

KW - Humans

KW - Neoplasms/drug therapy

KW - Software

U2 - 10.1093/bioinformatics/btv067

DO - 10.1093/bioinformatics/btv067

M3 - Journal article

C2 - 25638808

SN - 1367-4811

VL - 31

SP - 1866

EP - 1868

JO - Bioinformatics (Online)

JF - Bioinformatics (Online)

IS - 11

ER -