Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates

Targeting cancer-related inflammation with non-steroidal anti-inflammatory drugs: Perspectives in pharmacogenomics

Inflammatory processes are essential for innate immunity and contribute to carcinogenesis in various malignancies, such as colorectal cancer, esophageal cancer and lung cancer. Pharmacotherapies targeting inflammation have the potential to reduce the risk of carcinogenesis and improve therapeutic efficacy of existing anti-cancer treatment. Non-steroidal anti-inflammatory drugs (NSAIDs), comprising a variety of structurally different chemicals that can inhibit cyclooxygenase (COX) enzymes and other COX-independent pathways, are originally used to treat inflammatory diseases, but their preventive and therapeutic potential for cancers have also attracted researchers’ attention. Pharmacogenomic variability, including distinct genetic characteristics among different patients, can significantly affect pharmacokinetics and effectiveness of NSAIDs, which might determine the preventive or therapeutic success for cancer patients. Hence, a more comprehensive understanding in pharmacogenomic characteristics of NSAIDs and cancer-related inflammation would provide new insights into this appealing strategy. In this review, the up-to-date advances in clinical and experimental researches targeting cancer-related inflammation with NSAIDs are presented, and the potential of pharmacogenomics are discussed as well

A review of artificial intelligence applications for antimicrobial resistance

The wide use and abuse of antibiotics could make antimicrobial resistance (AMR) an increasingly serious issue that threatens global health and imposes an enormous burden on society and the economy. To avoid the crisis of AMR, we have to fundamentally change our approach. Artificial intelligence (AI) represents a new paradigm to combat AMR. Thus, various AI approaches to this problem have sprung up, some of which may be considered successful cases of domain-specific AI applications in AMR. However, to the best of our knowledge, there is no systematic review illustrating the use of these AI-based applications for AMR. Therefore, this review briefly introduces how to employ AI technology against AMR by using the predictive AMR model, the rational use of antibiotics, antimicrobial peptides (AMPs) and antibiotic combinations, as well as future research directions

Metformin attenuates chronic lung allograft dysfunction: evidence in rat models

Abstract Background Chronic lung allograft dysfunction (CLAD) directly causes an abysmal long-term prognosis after lung transplantation (LTx), but effective and safe drugs are not available. Metformin exhibits high therapeutic potential due to its antifibrotic and immunomodulatory effects; however, it is unclear whether metformin exerts a therapeutic effect in CLAD. We sought to investigate the effect of metformin on CLAD based on rat models. Methods Allogeneic LTx rats were treated with Cyclosporin A (CsA) in the first week, followed by metformin, CsA, or vehicle treatment. Syngeneic LTx rats received only vehicles. All rats were sacrificed on post-transplant week 4. Pathology of lung graft, spleen, and thymus, extent of lung fibrosis, activity of profibrotic cytokines and signaling pathway, adaptive immunity, and AMPK activity were then studied. Results Allogeneic recipients without maintenance CsA treatment manifested CLAD pathological characteristics, but these changes were not observed in rats treated with metformin. For the antifibrotic effect, metformin suppressed the fibrosis extent and profibrotic cytokine expression in lung grafts. Regarding immunomodulatory effect, metformin reduced T- and B-cell infiltration in lung grafts, spleen and thymus weights, the T- and B-cell zone areas in the spleen, and the thymic medullary area. In addition, metformin activated AMPK in lung allografts and in α-SMA+ cells and T cells in the lung grafts. Conclusions Metformin attenuates CLAD in rat models, which could be attributed to the antifibrotic and immunomodulatory effects. AMPK activation suggests the potential molecular mechanism. Our study provides an experimental rationale for further clinical trials

Cationic nanocarriers induce cell necrosis through impairment of Na+/K+-ATPase and cause subsequent inflammatory response

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Enhanced antitumor activity and mechanism of biodegradable polymeric micelles-encapsulated chetomin in both transgenic zebrafish and mouse models

Chetomin is a promising molecule with anti-tumor activities in the epipolythiodioxopiperazine family of fungal secondary metabolites; however, strong hydrophobicity has limited its further applications. In this work, chetomin was encapsulated into polymeric micelles to obtain an aqueous formulation, and the chetomin loaded micelles (Che-M) exhibited small particle size and high encapsulation efficiency. When the concentration of copolymer was higher than the critical gelation concentration, the Che-M could form a thermosensitive hydrogel (Che-H), which was free-flowing sol at ambient temperature and converted into a non-flowing gel at body temperature. The molecular modeling study has indicated that chetomin interacted with PCL as a core, which was embraced by PEG as a shell. Che-M showed equal cytotoxicity with free chetomin, but the apoptosis inducing effects of Che-M were more significant. Besides, Che-M could increase the GSSG level, decrease the GSH level, and increase the ROS in CT26 cells. Furthermore, stronger inhibitory effects of Che-M were observed on embryonic angiogenesis, tumor-induced angiogenesis and tumor growth in transgenic zebrafish models. In addition, Che-M was effective in inhibiting tumor growth and prolonging survival in a subcutaneous CT26 tumor model. In a colorectal peritoneal carcinomatosis model, both Che-M and Che-H showed excellent therapeutic effects, but Che-H was more effective. In conclusion, Che-M and Che-H may serve as candidates for cancer therapy