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

Bioinformatics. 2015 Jun 1;31(11):1866-8. doi: 10.1093/bioinformatics/btv067. Epub 2015 Jan 31.


Network pharmacology-based prediction of multi-targeted drug combinations is becoming a promising strategy to improve anticancer efficacy and safety. We developed a logic-based network algorithm, called Target Inhibition Interaction using Maximization and Minimization Averaging (TIMMA), which predicts the effects of drug combinations based on their binary drug-target 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/.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Antineoplastic Combined Chemotherapy Protocols / pharmacology*
  • Cell Line, Tumor
  • Drug Discovery
  • Drug Synergism
  • Humans
  • Neoplasms / drug therapy
  • Software*