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Diagonal Method to Measure Synergy Among Any Number of Drugs
Published on: June 21, 2018
Prediction model for synergistic anti-tumor multi-compound combinations from traditional Chinese medicine based on
Mengqiu Sun1, Shengnan She1, Hengwei Chen1
1Department of Pharmaceutics, School of Pharmacy, Jiangsu University, Zhenjiang 212013, P. R. China.
Abstract:
Traditional Chinese medicine (TCM) is characterized by synergistic therapeutic effect involving multiple compounds and targets, which provide potential new therapy for the treatment of complex cancer conditions. However, the main contributors and the underlying mechanisms of synergistic TCM cancer therapies remain largely undetermined. Machine learning now provides a new approach to determine synergistic compound combinations from complex components of TCM. In this study, a prediction model based on extreme gradient boosting (XGBoost) algorithm was constructed by integrating gene expression data of different cancer cell lines, targets information of natural compounds and drug response data. Radix Paeoniae Rubra (RPR) was selected as a model herbal sample to evaluate the reliability of the constructed model. The optimal XGBoost prediction model achieved a good performance with Mean Square Error (MSE) of 0.66, Mean Absolute Error (MAE) of 0.61, and the Root Mean Squared Error (RMSE) of 0.81 on test dataset. The superior synergistic anti-tumor combinations of D15 (Paeonol[Formula: see text][Formula: see text][Formula: see text]Ethyl gallate) and D13 (Paeoniflorin[Formula: see text][Formula: see text][Formula: see text]Paeonol) were successfully predicted from RPR and experimentally validated on MCF-7 cells. Moreover, the combination of D13 could work as a main contributor to a synergistic anti-proliferative activity in the compatibility of RPR and Cortex Moutan (CM). Our XGBoost model could be a reliable tool for the efficient prediction of synergistic anti-tumor multi-compound combinations from TCM.
Insights
Machine learning accurately predicts synergistic Traditional Chinese Medicine (TCM) compounds for cancer therapy. An XGBoost model identified potent combinations from Radix Paeoniae Rubra, validating their anti-tumor effects.
Area of Science:
- Computational biology and bioinformatics
- Pharmacology and natural product chemistry
- Oncology and cancer therapeutics
Background:
- Traditional Chinese Medicine (TCM) offers synergistic multi-compound, multi-target therapeutic potential for complex cancers.
- Identifying key contributors and mechanisms in TCM synergistic anti-cancer effects remains a significant challenge.
- Machine learning presents a novel computational approach to decipher complex TCM formulations.
Purpose of the Study:
- To develop and validate a machine learning model for predicting synergistic anti-cancer compound combinations from TCM.
- To identify specific synergistic combinations within Radix Paeoniae Rubra (RPR) with potential anti-tumor activity.
- To evaluate the model's reliability and predictive accuracy using experimental validation.
Main Methods:
- Construction of an extreme gradient boosting (XGBoost) prediction model.
- Integration of diverse datasets including cancer cell line gene expression, natural compound targets, and drug response data.
- Model evaluation using metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) on a test dataset.
Main Results:
- The optimal XGBoost model demonstrated robust performance with MSE=0.66, MAE=0.61, and RMSE=0.81.
- Predicted synergistic anti-tumor combinations D15 (Paeonol-Ethyl gallate) and D13 (Paeoniflorin-Paeonol) from RPR were identified.
- Experimental validation confirmed the anti-tumor efficacy of D15 and D13 on MCF-7 cells, with D13 showing significant contribution in RPR-Cortex Moutan compatibility.
Conclusions:
- The developed XGBoost model is a reliable tool for efficiently predicting synergistic anti-tumor multi-compound combinations from TCM.
- This approach facilitates the discovery of novel therapeutic strategies by elucidating the synergistic mechanisms of TCM.
- The findings pave the way for integrating computational methods into TCM research for modern drug discovery.
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