Dataset Information

An interdisciplinary computational/experimental approach to evaluate drug-loaded gold nanoparticle tumor cytotoxicity.

ABSTRACT:

Aim

Clinical translation of cancer nanotherapy has largely failed due to the infeasibility of optimizing the complex interaction of nano/drug/tumor/patient parameters. We develop an interdisciplinary approach modeling diffusive transport of drug-loaded gold nanoparticles in heterogeneously-vascularized tumors.

Materials & methods

Evaluated lung cancer cytotoxicity to paclitaxel/cisplatin using novel two-layer (hexadecanethiol/phosphatidylcholine) and three-layer (with high-density-lipoprotein) nanoparticles. Computer simulations calibrated to in-vitro data simulated nanotherapy of heterogeneously-vascularized tumors.

Results

Evaluation of free-drug cytotoxicity between monolayer/spheroid cultures demonstrates a substantial differential, with increased resistance conferred by diffusive transport. Nanoparticles had significantly higher efficacy than free-drug. Simulations of nanotherapy demonstrate 9.5% (cisplatin) and 41.3% (paclitaxel) tumor radius decrease.

Conclusion

Interdisciplinary approach evaluating gold nanoparticle cytotoxicity and diffusive transport may provide insight into cancer nanotherapy.

SUBMITTER: Curtis LT

PROVIDER: S-EPMC4910950 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An interdisciplinary computational/experimental approach to evaluate drug-loaded gold nanoparticle tumor cytotoxicity.

Curtis Louis T LT England Christopher G CG Wu Min M Lowengrub John J Frieboes Hermann B HB

Nanomedicine (London, England) 20160201 3

<h4>Aim</h4>Clinical translation of cancer nanotherapy has largely failed due to the infeasibility of optimizing the complex interaction of nano/drug/tumor/patient parameters. We develop an interdisciplinary approach modeling diffusive transport of drug-loaded gold nanoparticles in heterogeneously-vascularized tumors.<h4>Materials & methods</h4>Evaluated lung cancer cytotoxicity to paclitaxel/cisplatin using novel two-layer (hexadecanethiol/phosphatidylcholine) and three-layer (with high-density ...[more]

PMID: 26829163

Similar Datasets

Project description:Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications.

Dataset Information

An interdisciplinary computational/experimental approach to evaluate drug-loaded gold nanoparticle tumor cytotoxicity.

Aim

Materials & methods

Results

Conclusion

Publications

An interdisciplinary computational/experimental approach to evaluate drug-loaded gold nanoparticle tumor cytotoxicity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets