Unmodified methodologies in target discovery for small molecule drugs:A rising star

Target discovery,involving target identification and validation,is the prerequisite for drug discovery and screening.Novel methodologies and technologies for the precise discovery and confirmation of drug targets are powerful tools in understanding the disease,looking for a drug and elucidating the mechanism of drug treatment.Among the common target identification and confirmation methods,the modified method is time-consuming and laborious,which may reduce or change the activity of natural products.The unmodified methods developed in recent years without chemical modification have gradually become an important means of studying drug targets.A wide range of unmodified approaches have been reported,introducing and analyzing the recent emerging methodologies and technologies.This review highlights the advantages and limitations of these methods for the application of drug target discovery and presents an overview of their contributions to the target discovery of small molecule drugs.The application and future development trends of methodologies in target discovery are also prospected to provide a reference for drug target research.

Comprehensive analysis of the role of ubiquitin-specific peptidases in colorectal cancer:A systematic review

BACKGROUND Colorectal cancer(CRC)is the third most frequent and the second most fatal cancer.The search for more effective drugs to treat this disease is ongoing.A better understanding of the mechanisms of CRC development and progression may reveal new therapeutic strategies.Ubiquitin-specific peptidases(USPs),the largest group of the deubiquitinase protein family,have long been implicated in various cancers.There have been numerous studies on the role of USPs in CRC;however,a comprehensive view of this role is lacking.AIM To provide a systematic review of the studies investigating the roles and functions of USPs in CRC.METHODS We systematically queried the MEDLINE(via PubMed),Scopus,and Web of Science databases.RESULTS Our study highlights the pivotal role of various USPs in several processes implicated in CRC:Regulation of the cell cycle,apoptosis,cancer stemness,epithelial–mesenchymal transition,metastasis,DNA repair,and drug resistance.The findings of this study suggest that USPs have great potential as drug targets and noninvasive biomarkers in CRC.The dysregulation of USPs in CRC contributes to drug resistance through multiple mechanisms.CONCLUSION Targeting specific USPs involved in drug resistance pathways could provide a novel therapeutic strategy for overcoming resistance to current treatment regimens in CRC.

Artificial Intelligence in Pharmaceutical Sciences

Drug discovery and development affects various aspects of human health and dramatically impacts the pharmaceutical market.However,investments in a new drug often go unrewarded due to the long and complex process of drug research and development(R&D).With the advancement of experimental technology and computer hardware,artificial intelligence(AI)has recently emerged as a leading tool in analyzing abundant and high-dimensional data.Explosive growth in the size of biomedical data provides advantages in applying AI in all stages of drug R&D.Driven by big data in biomedicine,AI has led to a revolution in drug R&D,due to its ability to discover new drugs more efficiently and at lower cost.This review begins with a brief overview of common AI models in the field of drug discovery;then,it summarizes and discusses in depth their specific applications in various stages of drug R&D,such as target discovery,drug discovery and design,preclinical research,automated drug synthesis,and influences in the pharmaceutical market.Finally,the major limitations of AI in drug R&D are fully discussed and possible solutions are proposed.

Integrated chromatin and transcriptomic profiling of patient-derived colon cancer organoids identifies personalized drug targets to overcome oxaliplatin resistance

Colorectal cancer is a leading cause of cancer deaths.Most colorectal cancer patients eventually develop chemoresistance to the current standard-of-care therapies.Here,we used patient-derived colorectal cancer organoids to demonstrate that resistant tumor cells undergo significant chromatin changes in response to oxaliplatin treatment.Integrated transcriptomic and chromatin accessibility analyses using ATAC-Seq and RNA-Seq identified a group of genes associated with significantly increased chromatin accessibility and upregulated gene expression.CRISPR/Cas9 silencing of fibroblast growth factor receptor 1(FGFR1)and oxytocin receptor(OXTR)helped overcome oxaliplatin resistance.Similarly,treatment with oxaliplatin in combination with an FGFR1 inhibitor(PD166866)or an antagonist of OXTR(L-368,899)suppressed chemoresistant organoids.However,oxaliplatin treatment did not activate either FGFR1 or OXTR expression in another resistant organoid,suggesting that chromatin accessibility changes are patient-specific.The use of patient-derived cancer organoids in combination with transcriptomic and chromatin profiling may lead to precision treatments to overcome chemoresistance in colorectal cancer.

Statistical considerations for high throughput screening data

High throughput screening(HTS)is a widely used effective approach in genome-wide association and large scale protein expression studies,drug discovery,and biomedical imaging research.How to accurately identify candidate‘targets’or biologically meaningful features with a high degree of confidence has led to extensive statistical research in an effort to minimize both false-positive and false-negative rates.A large body of literature on this topic with in-depth statistical contents is available.We examine currently available statistical methods on HTS and aim to summarize some selected methods into a concise,easy-tofollow introduction for experimental biologists.

Network Pharmacology in Research of Chinese Medicine Formula: Methodology, Application and Prospective

Chinese medicine(CM) is usually prescribed as CM formula to treat disease. The lack of effective research approach makes it difficult to elucidate the molecular mechanisms of CM formula owing to its complicated chemical compounds. Network pharmacology is increasingly applied in CM formula research in recent years, which is identified suitable for the study of CM formula. In this review, we summarized the methodology of network pharmacology, including network construction, network analysis and network verification. The aim of constructing a network is to achieve the interaction between the bioactive compounds and targets and the interaction between various targets, and then find out and validate the key nodes via network analysis and network verification. Besides, we reviewed the application in CM formula research, mainly including targets discovery, bioactive compounds screening, toxicity evaluation, mechanism research and quality control research. Finally, we proposed prospective in the future and limitations of network pharmacology, expecting to provide new strategy and thinking on study for CM formula.