Compared with traditional clinical trials, the real-world studies set higher requirements on the authenticity (reality), applicability, and timeliness of the evidence obtained. In this paper, we brought up a hypothesis that creating synergies between observational and experimental studies may meet these requirements. And then it was discussed in three aspects including providing evidence, research design and execution. In addition, data analysis facilitated generating efficient and robust evidence which was in support of decision making. Finally, some enlightenment may be offered for Traditional Chinese Medicine evaluation methods based on the synergies of both study types.
The synergistic effect of drug combinations can solve the problem of acquired resistance to single drug therapy and has great potential for the treatment of complex diseases such as cancer. In this study, to explore the impact of interactions between different drug molecules on the effect of anticancer drugs, we proposed a Transformer-based deep learning prediction model—SMILESynergy. First, the drug text data—simplified molecular input line entry system (SMILES) were used to represent the drug molecules, and drug molecule isomers were generated through SMILES Enumeration for data augmentation. Then, the attention mechanism in the Transformer was used to encode and decode the drug molecules after data augmentation, and finally, a multi-layer perceptron (MLP) was connected to obtain the synergy value of the drugs. Experimental results showed that our model had a mean squared error of 51.34 in regression analysis, an accuracy of 0.97 in classification analysis, and better predictive performance than the DeepSynergy and MulinputSynergy models. SMILESynergy offers improved predictive performance to assist researchers in rapidly screening optimal drug combinations to improve cancer treatment outcomes.