Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy

Abstract

Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

Figure 1. Schematic diagram of a systems pharmacology infrastructure for identification of new targets and anticancer indications of natural products

(A) Re-construction of drug-target network for natural products; (B) Building predictive network models via bSDTNBI for systematic prediction of new targets of natural products; (C) Performing network analyses for validating the new predicted drug-target interactions and for identifying testable hypothesis of new therapeutic effects of natural products; (D) Building the statistical network models for prioritizing new anticancer indication via integration of the computationally predicted (B) and known drug-target interaction network of natural products into the curated cancer-associated genes (proteins).

Figure 2. Analysis of target coverage and chemical diversity of natural products in the reconstructed global drug-target interaction network

Classification of drug targets (A) and drug-target interactions (B) across five types of target proteins annotated in TTD. (C) Chemical structure clustering of 2,388 natural products based on FCFP_6 fingerprint. (D) The representative structures of 10 cluster centers.

Figure 3. Receiver operating characteristic (ROC) curves of four models in 10-fold cross validation

For each model, the processes of 10-fold cross validation were repeated 10 times, and the mean values and standard deviation (mean±SD) of each evaluation indicator were calculated to measure the model performance. The shadow of each curve denotes the SD.

Figure 4. Precision-recall curves of four models evaluated by three independent drug-target networks: validation set A (A), validation set B (B), and validation set C (C)

The detailed information of three independent validation drug-target networks is described in Table 1 and Table S3.

Figure 5. Known and predicted drug-target network via the best model (bSDTNBI_KR) for 3 typical natural products (kaempherol, genistein and resveratrol)

This network includes 124 drug-target interactions connecting 3 natural products and 80 targets (48 cancer proteins and 32 non-cancer proteins [see Methods and Materials]).

Figure 6. Discovered drug-cancer indication networks

(A) The predicted drug-cancer indication network based on the experimentally validated drug-target interaction (ExpNet) only, containing 635 significant drug-cancer indications pairs (SDCs) between 124 natural products and 13 cancer types. (B) The predicted drug-cancer indication network based on both experimentally validated and computationally predicted drug-target interactions (Exp&ComNet), containing 993 SDCs between 196 natural products and 13 cancer types. The 13 major cancer types are: leukemia, bladder, breast, colon, glioblastoma multiforme (GBM), kidney, lung, ovarian, prostate, melanoma, stomach, thyroid, and uterine cancers.

Figure 7. Heat maps show the predicted indications for FDA-approved or clinical investigational natural products against 13 cancer types

(A) Predicted indications of 54 FDA-approved or clinical investigational natural products based on the experimentally validated drug-target interaction (ExpNet) only. (B) Predicted indications of 84 FDA-approved or clinical investigational natural products based on both experimentally validated and computationally prediction drug-target interactions (Exp&ComNet). The red asterisk in B reveals that a natural product does not show statistical significance based on ExpNet only (A). The area in gray represents the non-available value since no cancer proteins are overlapped with the known targets of a specific natural product. Color keys denote the predicted Z-scores. The area in red represents the natural product having the high Z-score across specific cancer indications. Abbreviates of 13 major cancer types are provided in the legend of Figure 6.

Figure 8. A discovered drug-target-disease network of 3 typical natural products

The predicted indications for 3 typical natural products (naringenin, disulfiram, and metformin) against 13 cancer types and their corresponding targets are shown. The predicted anticancer indications are based on the pooling data of the experimentally validated and computationally predicted drug-target interactions. The thickness of red line and dotted red line is proportional to the predicted Z-score. Abbreviates of 13 major cancer types are provides in the legend of Figure 6.