Accurate prediction of drug responses in cancer cell lines (CCLs) and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine. Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response (CDR) prediction, challenges remain regarding the generalization of new drugs that are unseen in the training set. Herein, we propose a multimodal fusion deep learning (DL) model called drug-target and single-cell language based CDR (DTLCDR) to predict preclinical and clinical CDRs. The model integrates chemical descriptors, molecular graph representations, predicted protein target profiles of drugs, and cell line expression profiles with general knowledge from single cells. Among these features, a well-trained drug-target interaction (DTI) prediction model is used to generate target profiles of drugs, and a pretrained single-cell language model is integrated to provide general genomic knowledge. Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods. Further ablation studies verified the effectiveness of each component of our model, highlighting the significant contribution of target information to generalizability. Subsequently, the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments, demonstrating its potential for real-world applications. Moreover, DTLCDR was transferred to the clinical datasets, demonstrating satisfactory performance in the clinical data, regardless of whether the drugs were included in the cell line dataset. Overall, our results suggest that the DTLCDR is a promising tool for personalized drug discovery.

Objective To develop the UHPLC-MS/MS method for the determination of amygdalin, paeoniflorin, ferulic acid, calycosin glucosidase, quercetin and formononetin in Buyang-Huanwu decoction. Methods Isocratic elution was carried out with mobile phase consisting of methanol- 2 mM ammonium formate. The separation was performed on Agilent ZORBAX SB-C18 maintained at 35 ℃. The flow rate was 200 μl/min, and the injection volume was 2 μl. The mass spectrometer was operated in the positive and negative ionization electrospray (ESI) mode using multiple monitoring (MRM) for analysis of six components. The mass spectrometric conditions were that ion source temperature 400 ℃, dry gas flow 500 L/h, atomization gas flow rate 75.8 Kpa, spray voltage 4000 V, dry gas temperature 400 ℃. Results The amygdalin, paeoniflorin, ferulic acid, calycosin glucosidase, quercetin and formononetin were all analyzed exactly, and the linear ranges were 0.5-32, 0.2-12.8, 0.1-6.4, 0.8-51.2, 0.4-25.6, 0.08-5.12 ng, respectively. The r were 0.9921, 0.9945, 0.9928, 0.9958, 0.9947, 0.9966, respectively. The recoveries of six analytes ranged from 99.21％ to 101.44％ and the relative standard deviations were all below 2.05％. Conclusions A sensitive, accuracy and suitable UHPLC-MS/MS method has been developed, and the method could be applied for the determination of amygdalin, paeoniflorin, ferulic acid, calycosin glucosidase, quercetin and formononetin in Buyang-Huanwu decoction.