| CPC G16H 50/20 (2018.01) [G16B 25/10 (2019.02)] | 30 Claims |
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1. A method of using at least one hardware processor to train at least one machine learning algorithm being in a prediction model for drug response of a plurality of drugs to a cancer in a human patient diagnosed with the cancer, the method comprising:
using at least one cancer drug discovery data set comprising sub-structural features of the plurality of drugs comprising a descriptor from SMILES specification of the plurality of drugs, cancer biomarker data comprising gene expression data, and externally sourced drug response training dataset associated with the plurality of drugs and the cancer, to train the at least one machine learning algorithm in the prediction model,
using the prediction model to qualify a subset of drugs or drug combinations from the plurality of drugs in terms of their predicted biological response to a cancerous tissue of the human patient exhibiting gene expressions of a set of cancer biomarkers, wherein the cancerous tissue of the human patient is in the form of tumor fragments and not cancer cell lines;
implanting, in parallel, the cancerous tissue of the human patient exhibiting the gene expressions of the set of cancer biomarkers into multiple immune-deficient mice each subsequently administered a treatment with one of the subset of drugs or drug combinations; and
validating the prediction model by feeding back data corresponding to biological response of the treatment from the multiple immune-deficient mice, to the prediction model to further train the machine learning algorithm.
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