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Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo
Published on: February 6, 2015
Chemoinformatic analysis of NCI preclinical tumor data: evaluating compound efficacy from mouse xenograft data,
Anders Wallqvist1, Ruili Huang, David G Covell
1Laboratory of Computational Technologies, SAIC-Frederick, Inc., NCI-Frederick, Frederick, Maryland 21702, USA. wallqvist@ncifcrf.gov
Abstract:
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.
Insights
Chemoinformatic analysis of tumor xenografts reveals that compound chemistry and in vitro cell growth inhibition are key predictors of efficacy and toxicity. This approach effectively identifies promising oncology drug candidates with improved therapeutic potential.
Area of Science:
- Computational chemistry and pharmacology
- Translational oncology research
- Drug discovery and development
Background:
- Human tumor xenografts are crucial preclinical models for evaluating anti-cancer drug efficacy and toxicity.
- Predicting xenograft outcomes remains challenging, necessitating advanced analytical approaches.
- Chemoinformatics offers powerful tools for analyzing complex biological and chemical data.
Purpose of the Study:
- To perform a chemoinformatic examination of NCI human tumor xenograft data.
- To explore relationships between small molecules, treatment, efficacy (tumor weight reduction, survival time increase), and toxicity.
- To develop predictive models for xenograft endpoints and identify key predictive factors.
Main Methods:
- Utilized NCI public human tumor xenograft data for chemoinformatic analysis.
- Defined efficacy endpoints (tumor weight reduction, survival time increase) and a toxicity measure (survival advantage).
- Constructed linear predictive models using treatment modality, chemoinformatic descriptors, and NCI-60 cell assay data; validated using cross-validation and ROC analysis.
Main Results:
- Successfully constructed predictive linear models for xenograft endpoints with good predictive power (0.67 < r(2) < or = 0.74).
- Chemoinformatic descriptors and in vitro cell growth inhibition were stronger predictors of xenograft outcome than treatment modality alone.
- Achieved a 70% positive predictive value in distinguishing FDA-approved oncology agents from tested compounds.
Conclusions:
- Chemoinformatic analysis integrating xenograft data and in vitro preclinical testing is effective for identifying drug candidates with superior efficacy and reduced toxicity.
- Strong relationships were found between xenograft efficacy and biological pathways associated with correlated mRNA expressions.
- This approach aids in understanding compound mechanisms of action and optimizing drug development strategies.
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