A new drug combinatory effect prediction algorithm on the cancer cell based on gene expression and dose-response curve

C. Pankaj Goswami, L. Cheng, P. S. Alexander, A. Singal, L. Li

Research output: Contribution to journalArticle

11 Scopus citations


Gene expression data before and after treatment with an individual drug and the IC20 of dose-response data were utilized to predict two drugs' interaction effects on a diffuse large B-cell lymphoma (DLBCL) cancer cell. A novel drug interaction scoring algorithm was developed to account for either synergistic or antagonistic effects between drug combinations. Different core gene selection schemes were investigated, which included the whole gene set, the drug-sensitive gene set, the drug-sensitive minus drug-resistant gene set, and the known drug target gene set. The prediction scores were compared with the observed drug interaction data at 6, 12, and 24 hours with a probability concordance (PC) index. The test result shows the concordance between observed and predicted drug interaction ranking reaches a PC index of 0.605. The scoring reliability and efficiency was further confirmed in five drug interaction studies published in the GEO database.

Original languageEnglish (US)
Pages (from-to)80-90
Number of pages11
JournalCPT: Pharmacometrics and Systems Pharmacology
Issue number2
StatePublished - Feb 1 2015


ASJC Scopus subject areas

  • Modeling and Simulation
  • Pharmacology (medical)

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