Drug evaluation has always been an important area of research in the pharmaceutical industry. However, animal welfare protection and other shortcomings of traditional drug development models pose obstacles and challenges to drug evaluation. Organ-on-a-chip (OoC) technology, which simulates human organs on a chip of the physiological environment and functionality, and with high fidelity reproduction organ-level of physiology or pathophysiology, exhibits great promise for innovating the drug development pipeline. Meanwhile, the advancement in artificial intelligence (AI) provides more improvements for the design and data processing of OoCs. Here, we review the current progress that has been made to generate OoC platforms, and how human single and multi-OoCs have been used in applications, including drug testing, disease modeling, and personalized medicine. Moreover, we discuss issues facing the field, such as large data processing and reproducibility, and point to the integration of OoCs and AI in data analysis and automation, which is of great benefit in future drug evaluation. Finally, we look forward to the opportunities and challenges faced by the coupling of OoCs and AI. In summary, advancements in OoCs development, and future combinations with AI, will eventually break the current state of drug evaluation.

Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.

Chlorogenic acid, an important active component of coffee with anti-tumor activities, has been found for a hundred years. However, the lack of understanding about its target proteins greatly limits the exploration of its anti-tumor molecular mechanisms and clinical applications. Here, in vitro and animal experiments showed that chlorogenic acid had a significant inhibitory effect on the proliferation of A549 cells. The ability of chlorogenic acid to naturally emit fluorescence was exploited to screen its target proteins while avoiding false positives brought about by chemical modifications when using fluorescent tags. Consequently, we identified and verified annexin A2 as a covalent binding target of chlorogenic acid in A549 cells. We also discovered that chlorogenic acid inhibits the binding of annexin A2 to p50 subunit thereby inhibiting the expression of downstream anti-apoptotic genes cIAP1 and cIAP2 of the NF-κB signaling pathway in A549 cells in vitro and in vivo. Moreover, we found that chlorogenic acid hindered the binding of annexin A2 to actin possibly causing inhibition of tumor cell cycle and migration. Thus, this work demonstrates that chlorogenic acid binds annexin A2, causing a decrease in the expression of NF-κB downstream anti-apoptotic genes, and inhibiting the proliferation of A549 cells in vivo and in vitro.

Purpose: The study comprehensively evaluated the prognostic roles of the platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), basophil-to-lymphocyte ratio (BLR), and eosinophil-to-lymphocyte ratio (ELR) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Patients and Methods: Six hundred and nineteen patients with AECOPD and 300 healthy volunteers were retrospectively included into the study. The clinical characteristics of the patients with AECOPD and the complete blood counts (CBCs) of the healthy volunteers were collected. The associations of PLR, NLR, MLR, BLR, and ELR with airflow limitation, hospital length of stay (LOS), C-reactive protein (CRP), and in-hospital mortality in patients with AECOPD were analyzed. Results: Compared with the healthy volunteers, PLR, NLR, MLR, BLR, and ELR were all elevated in COPD patients under stable condition. PLR, NLR, MLR, and BLR were further elevated while ELR was lowered during exacerbation. In the patients with AECOPD, PLR, NLR, and MLR were positively correlated with hospital LOS as well as CRP. In contrast, ELR was negatively correlated with hospital LOS as well as CRP. Elevated PLR, NLR, and MLR were all associated with more severe airflow limitation in AECOPD. Elevated PLR, NLR, and MLR were all associated with increased in-hospital mortality while elevated ELR was associated with decreased in-hospital mortality. Binary logistic regression analysis showed that smoking history, FEV1% predicted, pneumonia, pulmonary heart disease (PHD), uric acid (UA), albumin, and MLR were significant independent predictors ofin-hospital mortality. These predictors along with ELR were used to construct a nomogram for predicting in-hospital mortality in AECOPD. The nomogram had a C-index of 0.850 (95% CI: 0.799– 0.901), and the calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) further demonstrated its good predictive value and clinical applicability. Conclusion: In summary, PLR, NLR, MLR, and ELR served as useful biomarkers in patients with AECOPD.

Some studies have reported that activated ribosomes are positively associated with malignant tumors, especially in hepatocellular carcinoma (HCC). The RNA-binding protein PNO1 is a critical ribosome rarely reported in human tumors. This study aimed to explore the molecular mechanisms of PNO1 in HCC. Using 150 formalin-fixed and paraffin-embedded samples and 8 fresh samples, we found high PNO1 expression in HCC tumor tissues through Western blotting and RT-PCR. Moreover, the higher PNO1 expression was associated with poor HCC prognosis patients. In vitro and in vivo experiments indicated that PNO1 overexpression promoted the proliferation and depressed the apoptosis of HCC cells. High PNO1 expression also increased the autophagy of HCC cells. The molecular mechanisms underlying PNO1 were examined by RNA-seq analysis and a series of functional experiments. Results showed that PNO1 promoted HCC progression through the MAPK signaling pathway. Therefore, PNO1 was overexpressed in HCC, promoted autophagy, and inhibited the apoptosis of HCC cells through the MAPK signaling pathway.

Natural active products (NAPs) are derived from chemical substances found in nature that have biological activity and medicinal potential. Screening and revealing the protein targets of NAPs is an indispensable link in the pharmacological and toxicological understanding of NAPs. Proteins are the main factors executing cell functions, and cells rely on the function of proteins to complete various activities in the life cycle. The important mechanism of action of drugs is to regulate cell biological activities by interacting with proteins and other macromolecules. At present, the classic way to screen protein targets is based on the molecular label tracing method, which has a long cycle and changes the molecular structure and pharmacological effects of NAPs. Due to the shortcomings of molecular labelling methods, in recent years, scientists have tried to develop a variety of label-free protein target identification methods for NAPs and have made a certain amount of progress. This article reviews the current protein target identification methods for NAPs with the aim of providing a reference for research on NAP protein targets.