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A nanodroplet cell processing platform facilitating drug synergy evaluations for anti-cancer treatments.

ABSTRACT: Therapeutic drug synergism intervened in cancer treatments has been demonstrated to be more effective than using a single effector. However, it remains inherently challenging, with a limited cell count from tumor samples, to achieve potent personalized drug cocktails. To address the issue above, we herein present a nanodroplet cell processing platform. The platform incorporates an automatic nanodroplet dispenser with cell array ParaStamp chips, which were fabricated by a new wax stamping approach derived from laser direct writing. Such approach enables not only the on-demand de-wetting with hydrophobic wax films on substrates but also the mask-less fabrication of non-planar microstructures (i.e. no photolithography process). The ParaStamp chip was pre-occupied with anti-cancer drugs and their associate mixtures, enabling for the spatially addressable screening of optimal drug combinations simultaneously. Each droplet with a critical volume of 200 nl containing with 100 cells was utilized. Results revealed that the optimal combination reduces approximate 28-folds of conducted doses compared with single drugs. Tumor inhibition with the optimally selected drug combination was further confirmed by using PC-3 tumor-bearing mouse models. Together, the nanodroplet cell processing platform could therefore offer new opportunities to power the personalized cancer medicine at early-stage drug screening and discovery.

SUBMITTER: Kuo CT

PROVIDER: S-EPMC6625988 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Motivation:While drug combination therapies are a well-established concept in cancer treatment, identifying novel synergistic combinations is challenging due to the size of combinatorial space. However, computational approaches have emerged as a time- and cost-efficient way to prioritize combinations to test, based on recently available large-scale combination screening data. Recently, Deep Learning has had an impact in many research areas by achieving new state-of-the-art model performance. However, Deep Learning has not yet been applied to drug synergy prediction, which is the approach we present here, termed DeepSynergy. DeepSynergy uses chemical and genomic information as input information, a normalization strategy to account for input data heterogeneity, and conical layers to model drug synergies. Results:DeepSynergy was compared to other machine learning methods such as Gradient Boosting Machines, Random Forests, Support Vector Machines and Elastic Nets on the largest publicly available synergy dataset with respect to mean squared error. DeepSynergy significantly outperformed the other methods with an improvement of 7.2% over the second best method at the prediction of novel drug combinations within the space of explored drugs and cell lines. At this task, the mean Pearson correlation coefficient between the measured and the predicted values of DeepSynergy was 0.73. Applying DeepSynergy for classification of these novel drug combinations resulted in a high predictive performance of an AUC of 0.90. Furthermore, we found that all compared methods exhibit low predictive performance when extrapolating to unexplored drugs or cell lines, which we suggest is due to limitations in the size and diversity of the dataset. We envision that DeepSynergy could be a valuable tool for selecting novel synergistic drug combinations. Availability and implementation:DeepSynergy is available via www.bioinf.jku.at/software/DeepSynergy. Contact:klambauer@bioinf.jku.at. Supplementary information:Supplementary data are available at Bioinformatics online.

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A nanodroplet cell processing platform facilitating drug synergy evaluations for anti-cancer treatments.

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