Drug Targets Research Articles

Naturally acquired microchimerism: clinical, scientific and ethical issues

Microchimerism (MC) is understood as the presence in the body of cells genetically different from the population of the individual, capable of existence and persistence, that is, reproduction and differentiation. This process is associated with the exchange of cellular material between mother and fetus. The consequences of the intrauterine acquisition of maternal MC (MMC) by the fetus are essentially different from the acquisition by the mother of fetal CM (FMC) in the mature state of the organism. Microchimerism has been implicated in the development of autoimmune diseases, but it also helps the body limit a particular disease. Since all these processes take place during the early development of the fetal immune system, the initial response of the immune system is the development of specific tolerance to maternal antigens. MMС can modify immune functions and reactivity through the nongenetic acquisition of cellular and subcellular material. Both FMС and MMС are quite common phenomena, which affects the body of the child and mother, the differentiation and functionality of the host cells. All this allows us to consider foreign cells as a potential target for drugs in the fight against autoimmune diseases or, conversely, stimulation of regeneration processes of damaged tissues. Issues of evolution and prospects for the prevention of various pathological conditions are considered from the standpoint of taking into account maternal and fetal chimerism syndrome.

A three-way interface of the Nipah virus phosphoprotein X-domain coordinates polymerase movement along the viral genome.

Mononegaviruses comprise major human pathogens such as the Ebola virus, rabies virus, respiratory syncytial virus, measles virus, and Nipah virus (NiV). For replication and transcription, their polymerase complexes must negotiate a protein-encapsidated RNA genome, which requires the highly coordinated continuous formation and resolution of protein-protein interfaces as the polymerase advances along the template. The viral P protein assumes a central role in this process, but the molecular mechanism of ensuring polymerase mobility is poorly understood. Studying NiV polymerase complexes, we applied functional and biochemical assays to map three distinct interfaces in the NiV P XD and identified transient interactions between XD and the nucleocapsid core as instrumental in coordinating polymerase advance. These results define a conserved molecular principle regulating paramyxovirus polymerase dynamics and illuminate a promising druggable target for the structure-guided development of broad-spectrum polymerase inhibitors.

Identification of key signaling pathways and novel computational drug target for oral cancer, metabolic disorders and periodontal disease

AimDue to conventional endocrinological methods, there is presently no shared work available, and no therapeutic options have been demonstrated in oral cancer (OC) and periodontal disease (PD), type 2 diabetes (T2D), and obese patients. The aim of this study is to determine the similar molecular pathways and potential therapeutic targets in PD, OC, T2D, and obesity that may be used to anticipate the progression of the disease. MethodsFour Gene Expression Omnibus (GEO) microarray datasets (GSE29221, GSE15773, GSE16134, and GSE13601) are used for finding differentially expressed genes (DEGs) for T2D, obese, and PD patients with OC in order to explore comparable pathways and therapeutic medications. Gene ontology (GO) and pathway analysis were used to investigate the functional annotations of the genes. The hub genes were then identified using protein-protein interaction (PPI) networks, and the most significant PPI components were evaluated using a clustering approach. ResultsThese three gene expression-based datasets yielded a total of seven common DEGs. According to the GO annotation, the majority of the DEGs were connected with the microtubule cytoskeleton structure involved in mitosis. The KEGG pathways revealed that the concordant DEGs are connected to the cell cycle and progesterone-mediated oocyte maturation. Based on topological analysis of the PPI network, major hub genes (CCNB1, BUB1, TTK, PLAT, and AHNAK) and notable modules were revealed. This work additionally identified the connection of TF genes and miRNAs with common DEGs, as well as TF activity. ConclusionPredictive drug analysis yielded concordant drug compounds involved with T2D, OC, PD, and obesity disorder, which might be beneficial for examining the diagnosis, treatment, and prognosis of metabolic disorders and Oral cancer.

The progress of Mycobacterium tuberculosis drug targets

Tuberculosis (TB) has been troubling humans for hundreds of years, is a highly infectious disease caused by Mycobacterium tuberculosis (Mtb) infection, Mtb can infect almost all organs of the body and is one of the deadly infectious diseases in the world. At present, the first-line treatment regimen has a long treatment cycle and is prone to multiple drug resistance. Anti-tuberculosis drugs and latent tuberculosis infection (LTBI) resistance are increasing year by year, and new targets and new bioactive compounds are urgently needed to treat this disease. This review focuses on the latest reported anti-TB drug targets and related compounds in recent years, reviews the current TB drug regimen and major defects, outlines the key drug targets developed to date in Mtb, and the current situation of newly discovered anti-TB resistant forms of drugs. To provide a reference for the research and development of new anti-TB drugs and bring new treatment strategies for TB patients.

An open-access dashboard to interrogate the genetic diversity of Mycobacterium tuberculosis clinical isolates.

Tuberculosis (TB) remains one of the leading infectious disease killers in the world. The ongoing development of novel anti-TB medications has yielded potent compounds that often target single sites with well-defined mechanisms of action. However, despite the identification of resistance-associated mutations through target deconvolution studies, comparing these findings with the diverse Mycobacterium tuberculosis populations observed in clinical settings is often challenging. To address this gap, we constructed an open-access database encompassing genetic variations from > 50,000 clinical isolates, spanning the entirety of the M. tuberculosis protein-encoding genome. This resource offers a valuable tool for investigating the prevalence of target-based resistance mutations in any drug target within clinical contexts. To demonstrate the practical application of this dataset in drug discovery, we focused on drug targets currently undergoing phase II clinical trials. By juxtaposing genetic variations of these targets with resistance mutations derived from laboratory-adapted strains, we identified multiple positions across three targets harbouring resistance-associated mutations already present in clinical isolates. Furthermore, our analysis revealed a discernible correlation between genetic diversity within each protein and their predicted essentiality. This meta-analysis, openly accessible via a dedicated dashboard, enables comprehensive exploration of genetic diversity pertaining to any drug target or resistance determinant in M. tuberculosis.

NFAT5 governs cellular plasticity-driven resistance to KRAS-targeted therapy in pancreatic cancer.

Resistance to KRAS therapy in pancreatic ductal adenocarcinoma (PDAC) involves cellular plasticity, particularly the epithelial-to-mesenchymal transition (EMT), which poses challenges for effective targeting. Chronic pancreatitis, a known risk factor for PDAC, elevates TGFβ levels in the tumor microenvironment (TME), promoting resistance to KRAS therapy. Mechanistically, TGFβ induces the formation of a novel protein complex composed of SMAD3, SMAD4, and the nuclear factor NFAT5, triggering EMT and resistance by activating key mediators such as S100A4. Inhibiting NFAT5 attenuates pancreatitis-induced resistance to KRAS inhibition and extends mouse survival. Additionally, TGFβ stimulates PDAC cells to secrete CCL2, recruiting macrophages that contribute to KRAS bypass through paracrine S100A4. Our findings elucidate the role of TGFβ signaling in EMT-associated KRAS therapy resistance and identify NFAT5 as a druggable target. Targeting NFAT5 could disrupt this regulatory network, offering a potential avenue for preventing resistance in PDAC.

Comprehensive liquid biopsy analysis for monitoring NSCLC patients under second-line osimertinib treatment

BackgroundThe heterogeneous and complex genetic landscape of NSCLC impacts the clinical outcomes of patients who will eventually develop resistance to osimertinib. Liquid biopsy (LB) analysis as a minimally invasive approach is a key step to efficiently identify resistance mechanisms and adjust to proper subsequent treatments. Materials and methodsIn the present study, we combined plasma-cfDNA and CTC analysis from 30 NSCLC patients in samples collected before treatment and at the progression of disease (PD). We detected molecular alterations at the DNA mutation (EGFR, PIK3CA, KRAS G12C, BRAF V600E), DNA methylation (RASSF1A, BRMS1, FOXA1, SLFN1, SHISA3, RARβ,, WIF-1, RASSF10 and APC), gene expression (CK-19, CK-18, CK-8, AXL, TWIST-1, PD-L1, PIM-1, Vimentin, ALDH-1, and B2M) and chromosomal level (HER2 and MET amplification) as possible resistance mechanisms and druggable targets. We also studied the expression of PD-L1 in single CTCs using immunofluorescence.ResultsIn some cases, T790M resistance EGFR mutation was detected at baseline in CTCs but not in the corresponding plasma cfDNA. PIK3CA mutations were detected only in plasma-cfDNA but not in corresponding CTCs. KRAS G12C and BRAF V600E mutations were not detected in the samples analyzed. MET amplification was detected in the CTCs of two patients before treatment whereas HER2 amplification was detected in the CTCs of three patients at baseline and in one patient at PD. DNA methylation analysis revealed low concordance between CTCs and cfDNA, indicating the complementary information obtained through parallel LB analysis. Results from gene expression analysis indicated high rates of vimentin-positive CTCs detected at all time points during osimertinib. Moreover, there was an increased number of NSCLC patients at PD harboring CTCs positive in PD-L1. AXL and PIM-1 expression detected in CTCs during treatment suggesting new possible therapeutic strategies. DiscussionOur results reveal that comprehensive liquid biopsy analysis can efficiently represent the heterogeneous molecular landscape and provide prominent information on subsequent treatments for NSCLC patients at PD since druggable molecular alterations were detected in CTCs.

How to drug a cloud? Targeting intrinsically disordered proteins.

Biologically active proteins/regions without stable structure (i.e., intrinsically disordered proteins and regions (IDPs and IDRs)) are commonly found in all proteomes. They have a unique functional repertoire that complements the functionalities of ordered proteins and domains. IDPs/IDRs are multifunctional promiscuous binders capable of folding at interaction with specific binding partners on a template- or context-dependent manner, many of which undergo liquid-liquid phase separation, leading to the formation of membrane-less organelles and biomolecular condensates. Many of them are frequently related to the pathogenesis of various human diseases. All this defines IDPs/IDRs as attractive targets for the development of novel drugs. However, their lack of unique structures, multifunctionality, binding promiscuity, and involvement in unusual modes of action preclude direct use of traditional structure-based drug design approaches for targeting IDPs/IDRs, and make disorder-based drug discovery for these "protein clouds" challenging. Despite all these complexities there is continuing progress in the design of small molecules affecting IDPs/IDRs. This article describes the major structural features of IDPs/IDRs and the peculiarities of the disorder-based functionality. It also discusses the roles of IDPs/IDRs in various pathologies, and shows why the approaches elaborated for finding drugs targeting ordered proteins cannot be directly used for the intrinsic disorder-based drug design, and introduces some novel methodologies suitable for these purposes. Finally, it emphasizes that regardless of their multifunctionality, binding promiscuity, lack of unique structures, and highly dynamic nature, "protein clouds" are principally druggable. Significance Statement Intrinsically disordered proteins and regions are highly abundant in nature, have multiple important biological functions, are commonly involved in the pathogenesis of a multitude of human diseases, and are therefore considered as very attractive drug targets. Although dealing with these unstructured multifunctional protein/regions is a challenging task, multiple innovative approaches have been designed to target them by small molecules.

Design, synthesis, and evaluation of novel Indole-Based small molecules as sirtuin inhibitors with anticancer activities.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, driven mainly by chronic hepatitis infections and metabolic disorders, which highlights the urgent need for novel therapeutic strategies. Sirtuins, particularly SIRT1 are crucial in HCC pathogenesis, making it a promising drug target. Indole-based molecules show potential as therapeutic agents by interacting with key proteins like sirtuins involved in cancer progression. In this study, we designed and synthesized novel indole-based small molecules and investigated their potential sirtuin inhibitory action and anticancer activity on HCC cell lines. Four of the twenty-eight tested small molecules on different cancer types were selected (4 g, 4 h, 4o, and 7j) based on their structure-activity relationship and studied on a panel of HCC cell lines. Compounds had active drug-target interactions with SIRT1 or SIRT2 based on DEEPScreen DTI predictions and docking studies which confirmed that 4o, 4 g, and 7j were most potent in their interaction with SIRT1. Compound 4 g caused the highest sirtuin activity inhibition in vitro and induced G1 arrest and apoptosis in HCC cell lines.

Integrating metabolomics and proteomics to identify novel drug targets for heart failure and atrial fibrillation

BackgroundAltered metabolism plays a role in the pathophysiology of cardiac diseases, such as atrial fibrillation (AF) and heart failure (HF). We aimed to identify novel plasma metabolites and proteins associating with cardiac disease.MethodsMendelian randomisation (MR) was used to assess the association of 174 metabolites measured in up to 86,507 participants with AF, HF, dilated cardiomyopathy (DCM), and non-ischemic cardiomyopathy (NICM). Subsequently, we sourced data on 1567 plasma proteins and performed cis MR to identify proteins affecting the identified metabolites as well as the cardiac diseases. Proteins were prioritised on cardiac expression and druggability, and mapped to biological pathways.ResultsWe identified 35 metabolites associating with cardiac disease. AF was affected by seventeen metabolites, HF by nineteen, DCM by four, and NCIM by taurine. HF was particularly enriched for phosphatidylcholines (p = 0.029) and DCM for acylcarnitines (p = 0.001). Metabolite involvement with AF was more uniform, spanning for example phosphatidylcholines, amino acids, and acylcarnitines. We identified 38 druggable proteins expressed in cardiac tissue, with a directionally concordant effect on metabolites and cardiac disease. We recapitulated known associations, for example between the drug target of digoxin (AT1B2), taurine and NICM risk. Additionally, we identified numerous novel findings, such as higher RET values associating with phosphatidylcholines and decreasing AF and HF. RET is targeted by drugs such as regorafenib which has known cardiotoxic side-effects. Pathway analysis implicated involvement of GDF15 signalling through RET, and ghrelin regulation of energy homeostasis in cardiac pathogenesis.ConclusionsThis study identified 35 plasma metabolites involved with cardiac diseases and linked these to 38 druggable proteins, providing actionable leads for drug development.

Identification of Potential Inhibitors of TGFβR1 for the treatment of Cancer through Structure-based virtual screening and Molecular dynamics simulations.

Globally, cancer is one of the leading causes of death. Resistance to conventional medications, such as chemotherapy and radiation, continues to be a significantchallenge in the treatment of cancerdespite the availability of numerous medicines. Therefore, the highest priority is to hunt for new therapeutic agents. Transforming growth factor-beta is a pivotal regulatory cytokine that exerts significant influence over cellular processes, particularly emphasizing its role in facilitating and modulating cell proliferation. TGFβ1,identified asmost promising active site of the TGF-β signaling, is a potent drug target site that has garnered wide attention for developing new anticancer agents. The present investigation investigates the potential phytochemicals as TGFβR1 inhibitors. The SB431542 complexed TGFβR1 protein model was used to screen the natural product database to obtain a compound with high binding potential. NPC247629 has emerged as the best-scored compound among all the screened compounds, demonstrating the highest affinity towards the TGFβR1 regarding docking score -17.54 kcal/mol. TheMD simulation study indicated that all proposed hits are retained inside the receptor in dynamic states. The best-screened hits, NPC247629 and NPC60735, have excellent binding affinity and hold a massive potential for TGFβR1 inhibition, paving the way for promising future investigations in cancer treatment.

Causal inference can lead us to modifiable mechanisms and informative archetypes in sepsis.

Medical progress is reflected in the advance from broad clinical syndromes to mechanistically coherent diagnoses. By this metric, research in sepsis is far behind other areas of medicine-the word itself conflates multiple different disease mechanisms, whilst excluding noninfectious syndromes (e.g.,trauma, pancreatitis) with similar pathogenesis. New technologies, both for deep phenotyping and data analysis, offer the capability to define biological states with extreme depth. Progress is limited by a fundamental problem: observed groupings of patients lacking shared causal mechanisms are very poor predictors of response to treatment. Here, we discuss concrete steps to identify groups of patients reflecting archetypes of disease with shared underlying mechanisms of pathogenesis. Recent evidence demonstrates the role of causal inference from host genetics and randomised clinical trials to inform stratification analyses. Genetic studies can directly illuminate drug targets, but in addition they create a reservoir of statistical power that can be divided many times among potential patient subgroups to test for mechanistic coherence, accelerating discovery of modifiable mechanisms for testing in trials. Novel approaches, such as subgroup identification in-flight in clinical trials, will improve efficiency. Within the next decade, we expect ongoing large-scale collaborative projects to discover and test therapeutically relevant sepsis archetypes.

New Evidence for the Role of the Blood-Brain Barrier and Inflammation in Stress-Associated Depression: A Gene-Environment Analysis Covering 19,296 Genes in 109,360 Humans.

Mounting evidence supports the key role of the disrupted integrity of the blood-brain barrier (BBB) in stress- and inflammation-associated depression. We assumed that variations in genes regulating the expression and coding proteins constructing and maintaining this barrier, along with those involved in inflammation, have a predisposing or protecting role in the development of depressive symptoms after experiencing severe stress. To prove this, genome-by-environment (GxE) interaction analyses were conducted on 6.26 M SNPS covering 19,296 genes on PHQ9 depression in interaction with adult traumatic events scores in the UK Biobank (n = 109,360) in a hypothesis-free setup. Among the 63 genes that were significant in stress-connected depression, 17 were associated with BBB, 23 with inflammatory processes, and 4 with neuroticism. Compared to all genes, the enrichment of significant BBB-associated hits was 3.82, and those of inflammation-associated hits were 1.59. Besides some sex differences, CSMD1 and PTPRD, encoding proteins taking part in BBB integrity, were the most significant hits in both males and females. In conclusion, the identified risk genes and their encoded proteins could provide biomarkers or new drug targets to promote BBB integrity and thus prevent or decrease stress- and inflammation-associated depressive symptoms, and possibly infection, e.g., COVID-19-associated mental and neurological symptoms.

Recent strategies in target identification of natural products: Exploring applications in chronic inflammation and beyond.

Natural products are a treasure trove for drug discovery, especially in the areas of infection, inflammation and cancer, due to their diverse bioactivities and complex, and varied structures. Chronic inflammation is closely related to many diseases, including complex diseases such as cancer and neurodegeneration. Improving target identification for natural products contributes to elucidating their mechanism of action and clinical progress. It also facilitates the discovery of novel druggable targets and the elimination of undesirable ones, thereby significantly enhancing the productivity of drug discovery and development. Moreover, the rise of polypharmacological strategies, considered promising for the treatment of complex diseases, will further increase the demand for target deconvolution. This review underscores strategies for identifying natural product targets (NPs) in the context of chronic inflammation over the past 5 years. These strategies encompass computational methodologies for early target discovery and the anticipation of compound binding sites, proteomics-driven approaches for target delineation and experimental biology techniques for target validation and comprehensive mechanistic exploration.

Prognostic model for gastric cancer patients with COVID-19 and network pharmacology study on treatment by lentinan

Patients with gastric cancer (GC) are more susceptible to coronavirus disease 2019 (COVID-19), which further worsens their already challenging prognosis. However, there are no effective treatment options for these patients. Lentinan is a potent bioactive component with antiviral and antitumor effects. We hypothesized that lentinan might exert powerful pharmacological effects in patients with GC and COVID-19. In this study, a prognostic model of patients with GC/COVID-19 was constructed and used to apply a network pharmacology approach to reveal biological functions, drug targets, and molecular mechanisms of the action of lentinan against GC/COVID-19. Clinical analysis revealed key prognostic genes in patients with GC/COVID-19. The results of network pharmacology analysis suggested that the therapeutic effect of lentinan on GC/COVID-19 mainly involves the modulation of several neutrophil-related biological processes, as well as the nucleotide-binding and oligomerization domain-like receptor and interleukin-17 signaling pathways. In addition, C-X-C motif chemokine ligand 8, vascular endothelial growth factor A, ribonuclease 3, and F2 were identified as key genes of lentinan against GC/COVID-19. Key prognostic genes were identified in patients with GC/COVID-19 through the construction of a prognostic model. Pharmacological functions and signaling pathways of lentinan against GC/COVID-19 were revealed. These included the regulation of neutrophils and NOD-like receptor signaling pathways. The findings provide the first published evidence of the potential value of lentinan as a complementary therapy for GC/COVID-19.

Benchmarking machine learning methods for synthetic lethality prediction in cancer

Synthetic lethality (SL) is a gold mine of anticancer drug targets, exposing cancer-specific dependencies of cellular survival. To complement resource-intensive experimental screening, many machine learning methods for SL prediction have emerged recently. However, a comprehensive benchmarking is lacking. This study systematically benchmarks 12 recent machine learning methods for SL prediction, assessing their performance across diverse data splitting scenarios, negative sample ratios, and negative sampling techniques, on both classification and ranking tasks. We observe that all the methods can perform significantly better by improving data quality, e.g., excluding computationally derived SLs from training and sampling negative labels based on gene expression. Among the methods, SLMGAE performs the best. Furthermore, the methods have limitations in realistic scenarios such as cold-start independent tests and context-specific SLs. These results, together with source code and datasets made freely available, provide guidance for selecting suitable methods and developing more powerful techniques for SL virtual screening.

Ensemble Docking as a Tool for the Rational Design of Peptidomimetic Staphylococcus aureus Sortase A Inhibitors.

Sortase A (SrtA) of Staphylococcus aureus has long been shown to be a relevant molecular target for antibacterial development. Moreover, the designed SrtA inhibitors act via the antivirulence mechanism, potentially causing less evolutional pressure and reduced antimicrobial resistance. However, no marketed drugs or even drug candidates have been reported until recently, despite numerous efforts in the field. SrtA has been shown to be a tough target for rational structure-based drug design (SBDD), which hampers the regular development of small-molecule inhibitors using the available arsenal of drug discovery tools. Recently, several oligopeptides resembling the sorting sequence LPxTG (Leu-Pro-Any-Thr-Gly) of the native substrates of SrtA were reported to be active in the micromolar range. Despite the good experimental design of those works, their molecular modeling parts are still not convincing enough to be used as a basis for a rational modification of peptidic inhibitors. In this work, we propose to use the ensemble docking approach, in which the relevant SrtA conformations are extracted from the molecular dynamics simulation of the LPRDA (Leu-Pro-Arg-Asp-Ala)-SrtA complex, to effectively represent the most significant and diverse target conformations. The developed protocol is shown to describe the known experimental data well and then is applied to a series of new peptidomimetic molecules resembling the active oligopeptide structures reported previously in order to prioritize structures from this work for further synthesis and activity testing. The proposed approach is compared to existing alternatives, and further directions for its development are outlined.

Phosphoproteomic Analysis of Signaling Pathways in Lymphomas.

Cellular fate is regulated by intricate signal transduction mediated by posttranslational protein modifications like phosphorylation to transmit information. As other cancer types, lymphomas frequently show dysregulation of signaling pathways that contribute to malignant transformation and tumor progression. For example, in diffuse large B-cell lymphoma the B-cell antigen receptor was identified as an oncogenic driver mediating cellular growth and survival signals. Thus, the elucidation of these complex signaling networks is crucial to gain insight into the mechanisms underlying tumorigenesis and to identify target proteins for innovative therapeutic approaches.Here, we describe a mass spectrometry-based phosphoproteomic approach for the global analysis of intracellular signaling events and their dynamics. The workflow combines phosphopeptide enrichment and fractionation with liquid chromatography-coupled mass spectrometry for the amino acid site-specific identification and quantification of thousands of phosphorylation events. Such global signaling analyses have great potential for the elucidation of oncogenic pathomechanisms, diagnostic biomarkers, and drug targets.

MECOM Locus classical transcript isoforms affect tumor immune microenvironment and different targets in ovarian cancer

The MECOM locus is a gene frequently amplified in high-grade serous ovarian carcinoma (HGSOC). Nevertheless, the body of research examining the associations among MECOM transcripts, patient prognosis, and their role in modulating the tumor immune microenvironment (TIME) remains sparse, particularly in large cohorts. This study assessed the expression of MECOM transcripts in 352 HGSOC patients and 88 normal ovarian tissues from the combined GTEx/TCGA database. Using resources such as the UCSC Genome Browser, Ensembl, and NextProt, two transcripts corresponding to classical protein isoforms from MECOM were identified. Cox proportional hazards regression analysis, Kaplan-Meier survival curves, and a comprehensive TIME evaluation algorithm were employed to elucidate the connections between the expression levels of these transcripts and both patient prognosis and TIME status. Chromatin Immunoprecipitation sequencing (ChIP-seq) data for the two protein isoforms, as well as RNA sequencing data post-targeted silencing, were analyzed to identify potential regulatory targets of the different transcription factors. Elevated expression of the MECOM isoform transcripts was correlated with poorer survival in HGSOC patients, potentially through the modulation of cancer-associated fibroblasts (CAFs) and immunosuppressive cell populations. In contrast, higher levels of EVI1 isoform transcripts were linked to enhanced survival, possibly due to the regulation of CD8+ T cells, macrophages, and a reduction in the expression of JUN protein, or its DNA-binding activity on downstream genes. Diverse protein isoforms derived from MECOM were found to differentially affect the survival and tumor development in ovarian cancer patients through specific mechanisms. Investigating the molecular mechanisms underlying disease pathogenesis and identifying potential drug target proteins at the level of splice variant isoforms were deemed crucial.

Single-cell technology for drug discovery and development

The success rate of drug development today remains low, with long development cycles and high costs, especially in areas such as oncology, neurology, immunology, and infectious diseases. Single-cell omics, encompassing transcriptomics, genomics, epigenomics, proteomics, and metabolomics enable the analysis of gene expression profiles and cellular heterogeneity from the perspective of individual cells, offering a high-resolution view of their functional diversity. These technologies can help reveal disease mechanisms, drug target identification and validation, selection of preclinical models and candidate drugs, and clinical decision-making based on disease response to drugs, all at the single-cell level. The development of deep learning technology has provided a powerful tool for research in drug discovery based on single-cell techniques, which has evolved with the advent of large-scale public databases to predict drug responses and targets. In addition, traditional Chinese medicine (TCMs) research has also entered the era of single-cell technology. Single-cell omics technologies offer an alternative way in deciphering the mechanisms of TCMs in disease treatment, revealing drug targets, screening new drugs, and designing combinations of TCMs. This review aims to explore the application of single-cell omics technologies in drug screening and development comprehensively, highlighting how they accelerate the drug development process and facilitate personalized medicine by precisely identifying therapeutic targets, predicting drug responsiveness, deciphering mechanisms of action. It is also concluded that drug development process and therapeutic efficacy of drugs can be improved by combining single-cell omics and artificial intelligence techniques.