Chemoinformatic analysis of NCI preclinical tumor data: evaluating compound efficacy from mouse xenograft data, NCI-60 screening data, and compound descriptors.
We provide a chemoinformatic examination of the NCI public human tumor xenograft data to explore relationships between small molecules, treatment modality, efficacy, and toxicity. Efficacy endpoints of tumor weight reduction (TW) and survival time increase (ST) compared to tumor bearing control mice were augmented by a toxicity measure, defined as the survival advantage of treated versus control animals (TX). These endpoints were used to define two independent therapeutic indices (TIs) as the ratio of efficacy (TW or ST) to toxicity (TX). Linear models predictive of xenograft endpoints were successfully constructed (0.67 < r(2) < or = 0.74)(observed_versus_predicted) using a model comprised of variables in treatment modality, chemoinformatic descriptors, and in vitro cell growth inhibition in the NCI 60-cell assay. Cross-validation analysis based on randomly chosen training subsets found these predictive correlations to be robust. Model-based sensitivity analysis found chemistry and growth inhibition to provide the best, and treatment modality the worst, indicators of xenograft endpoint. The poor predictive power derived from treatment alone appears to be of less importance to xenograft outcome for compounds having strongly similar chemical and biological features. ROC-based model validation found a 70% positive predictive value for distinguishing FDA approved oncology agents from available xenograft tested compounds. Additional chemoinformatic applications are provided that relate xenograft outcome to biological pathways and putative mechanism of compound action. These results find a strong relationship between xenograft efficacy and pathways comprised of genes having highly correlated mRNA expressions. Our analysis demonstrates that chemoinformatic studies utilizing a combination of xenograft data and in vitro preclinical testing offer an effective means to identify compound classes with superior efficacy and reduced toxicity.
Wallqvist
Anders
A
Laboratory of Computational Technologies, SAIC-Frederick, Inc., NCI-Frederick, Frederick, Maryland 21702, USA. wallqvist@ncifcrf.gov
Huang
Ruili
R
Covell
David G
DG
D000818:Animals; D000970:Antineoplastic Agents / Q000494:pharmacology / Q000627:therapeutic use*; D004354:Drug Screening Assays, Antitumor; D051379:Mice; D009316:National Institutes of Health (U.S.); D009374:Neoplasms, Experimental / Q000188:drug therapy*; D014183:Transplantation, Heterologous; D014481:United States
D000970:Antineoplastic Agents