Prediction of cancer cell sensitivity to natural products based on genomic and chemical properties

Abstract

Natural products play a significant role in cancer chemotherapy. They are likely to provide many lead structures, which can be used as templates for the construction of novel drugs with enhanced antitumor activity. Traditional research approaches studied structure-activity relationship of natural products and obtained key structural properties, such as chemical bond or group, with the purpose of ascertaining their effect on a single cell line or a single tissue type. Here, for the first time, we develop a machine learning method to comprehensively predict natural products responses against a panel of cancer cell lines based on both the gene expression and the chemical properties of natural products. The results on two datasets, training set and independent test set, show that this proposed method yields significantly better prediction accuracy. In addition, we also demonstrate the predictive power of our proposed method by modeling the cancer cell sensitivity to two natural products, Curcumin and Resveratrol, which indicate that our method can effectively predict the response of cancer cell lines to these two natural products. Taken together, the method will facilitate the identification of natural products as cancer therapies and the development of precision medicine by linking the features of patient genomes to natural product sensitivity.

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

“…Alongside the traditional chemoinformatics-based approaches described earlier, the newer generation of drug discovery pipelines are utilising concepts like network pharmacology and ML in the drug discovery process [ 21 , 90 , 91 ]. AI has progressed from mostly theoretical studies to more real-world applications in recent years, including different stages of drug research and development.…”

Integration of Phytochemicals and Phytotherapy into Cancer Precision Medicine

“…The results obtained with these sophisticated techniques are evaluated by methods of computational biology and bioinformatics to extract and model the relevant knowledge gained from a vast plethora of generated data [14,15]. The potential of this new technological dimension of technology lies in its translation from the laboratory to practical routine for diagnosis and treatment and comprehensive approach to diagnosing tumors and tumor subtypes, to predict response to treatment and occurrence of unwanted side effects), Individualized treatment regimens may be planned based on a patient's (or tumor's) individual expression profiles to optimize the survival prognosis of cancer patients [16][17][18]. Several conditions determine the setup of patienttailored therapies, e.g.…”

Algorithms for Drug Sensitivity Prediction

“…For instance, [113] considered the design of machine learning models (neural networks and random forests) for drug sensitivity prediction that utilized both genomic characterizations and chemical descriptors as input features. The work in [114] considers the design of decision trees, support vector machines, random forests and rotation forests [115] for anti-cancer drug response prediction from both genomic characterizations and chemical descriptors. The rotation forest technique applies PCA to K subsets of the feature set to generate K separate trees from the extracted features for creating an ensemble.…”