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Results for “drug target prioritization”

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At least 19 recordsLinked to original sources

PubMed2006

Prioritizing genomic drug targets in pathogens: application to Mycobacterium tuberculosis.

We have developed a software program that weights and integrates specific properties on the genes in a pathogen so that they may be ranked as drug targets. We applied this software to produce three prioritized drug target lists for Mycobacterium tuberculosis, the causative agent of tuberculosis, a disease for which a new drug is desperately needed. Each list is based on an individual criterion. The first list prioritizes metabolic drug targets by the uniqueness of their roles in the M. tuberculosis metabolome ("metabolic chokepoints") and their similarity to known "druggable" protein classes (i.e., classes whose activity has previously been shown to be modulated by binding a small molecule). The second list prioritizes targets that would specifically impair M. tuberculosis, by weighting heavily those that are closely conserved within the Actinobacteria class but lack close homology to the host and gut flora. M. tuberculosis can survive asymptomatically in its host for many years by adapting to a dormant state referred to as "persistence." The final list aims to prioritize potential targets involved in maintaining persistence in M. tuberculosis. The rankings of current, candidate, and proposed drug targets are highlighted with respect to these lists. Some features were found to be more accurate than others in prioritizing studied targets. It can also be shown that targets can be prioritized by using evolutionary programming to optimize the weights of each desired property. We demonstrate this approach in prioritizing persistence targets.

Algorithms↗

PubMed2026

Leveraging the genetics of psychiatric disorders to prioritize potential drug targets and compounds.

Genetics can inform biologically relevant drug development and repurposing, which may improve patient care. Here, we leverage the genetics of psychiatric disorders to prioritize potential drug targets and compounds. We used the genome-wide association studies of four psychiatric disorders [attention deficit hyperactivity disorder (ADHD), bipolar disorder, depression, and schizophrenia] and genes encoding drug targets. We conducted drug enrichment analyses incorporating the novel and biologically specific GSA-MiXeR tool. We conducted multiple molecular trait analyses using large-scale transcriptomic and proteomic datasets sampled from brain and blood tissue. This included the novel use of the UK Biobank proteomic data for a proteome-wide association study of psychiatric disorders. With the accumulated evidence, we prioritize potential drug targets and compounds for each disorder. We reveal candidate drug targets associated with a single or multiple disorders that implicate glutamate signaling. Drug prioritization indicated genetic support for psychotropic medications, including several top-ranked antipsychotics for schizophrenia. We also observed genetic support for commonly used psychotropics for psychiatric treatment (e.g., clozapine, duloxetine, and lithium). Revealed opportunities for drug repurposing included cholinergic drugs for ADHD, estrogen modulators for depression, and matrix metalloproteinases for ADHD and depression. Our findings indicate the genetic liability to schizophrenia is associated with reduced brain and blood expression of CYP2D6, a gene encoding a metabolizer of drugs and neurotransmitters, suggesting a genetic risk for poor drug response and altered neurotransmission. Our extensive analyses highlight the utility of genetics for informing drug development and repurposing for psychiatric disorders, providing novel opportunities for improving patient outcomes. Depicted is the series of analyses conducted to generate a list of prioritized drug targets and compounds. First pairings of genome-wide association study (GWAS) traits with drugs are generated using enrichment analyses. Next, a series of molecular trait analyses is conducted to generate and rank a list of potential drug targets for each GWAS trait. Finally, enrichment and molecular trait results are combined to generate a ranked list of prioritized drugs for each GWAS trait based on supporting genetic evidence. ADHD = Attention deficit hyperactivity disorder, BIP = Bipolar disorder, DEP = Depression, SCZ = Schizophrenia, DBP = Diastolic blood pressure, T2D = Type 2 diabetes, RNA = ribonucleic acid, XWAS = both transcriptome and proteome-wide association studies, MR = Mendelian randomization, coloc = colocalization.

Humans↗

PubMed2026

Single-Cell Transcriptome-Wide Mendelian Randomization and Colocalization Uncover Potential Immunocytes-Related Therapeutic Targets for Obesity.

Weight-loss treatment is crucial for individuals with obesity to prevent various complications. The role of Immune cells in obesity has been recently recognized, whereas its translation into therapy requires identifying key target genes. We performed Mendelian randomization (MR) analysis to assess causal relationships between expression quantitative trait loci (eQTL) of 14 immune cells and obesity-related traits (obesity, body mass index and body fat percentage), and validated the results in colocalization analysis. For the putative causal genes identified by the MR and colocalization analyses, we conducted pathway enrichment, differential expressed gene (DEG) analysis and search of druggable evidence, and utilized a Tier system to prioritize drug targets for obesity. MR and colocalization evidence was observed for 1630 genes associated with one or more obesity-related traits, mainly expressed in CD4+ naive/central memory T cells and enriched in antigen processing and presentation pathways. Forty-one genes showed causal relationship with all three outcomes, among which 19 genes have not been reported for obesity previously. DEG analysis using single-cell RNA sequencing data of blood or adipose tissue indicated that the differential expression of UBE2Z in monocytes, ZCCHC7 in T cells, and FNBP4 in B cells between lean and obese individuals were consistent with the MR results. By searching drug-gene interaction databases, we found targeted drugs for PYGB and PRUNE1, and PYGB was the top gene ranked in the Tier system. This study provides evidence for the involvement of immune cells in obesity, and the potential cell-specific, immune-related targets for obesity treatment.

Obesity↗

PubMed2026

Decoding Primary Open-Angle Glaucoma: A Multi-Omics Approach to Identify Druggable Effector Genes.

PURPOSE: Genomewide association studies (GWAS) have identified numerous primary open angle glaucoma (POAG) risk loci, yet most reside in non-coding regions with unclear function. Mapping these loci to effector genes can elucidate disease mechanisms, identify functionally conserved variants, improve cross-ancestry risk prediction by reducing population-specific noise, and uncover shared therapeutic targets. METHODS: Here, we integrate European POAG GWAS with six types of multi-omics molecular Quantitative Trait Locis (xQTLs) using multi-trait colocalization to identify candidate effector variants and evaluate their cross-population relevance using genetic risk score (GRS) analysis, and their therapeutic potential through drug target prioritization. RESULTS: We identified 25 POAG effector variants colocalized with at least one xQTLs. In non-European populations, effector variants showed stronger effect size correlations with Europeans than non-colocalized variants (Pearson r2 = African 0.85 vs. 0.71; East Asian 0.81 vs. 0.69; and Latin American 0.91 vs. 0.75). Effector variants also had smaller allele frequency variations across populations (average interquartile range [IQR] = 0.15 vs. 0.20). The genetic risk score based on effector variants performed comparably to the genome-wide significant single-nucleotide polymorphism (SNP)-based GRS in non-European populations. Drug prioritization identified zinc, copper, sunitinib, probucol, and astemizole as potential common therapeutic agents for POAG and its subtypes. CONCLUSIONS: Our findings offer deeper insight into the molecular mechanisms underlying glaucoma and effector variants for developing more robust GRS models and broadly effective therapeutic strategies for POAG.

Humans↗

PubMed2025

Multi-omic underpinnings of heterogeneous aging across multiple organ systems.

Aging is the main determinant of chronic diseases and mortality, yet organ-specific aging trajectories vary, and the molecular basis underlying this heterogeneity remains unclear. To elucidate this, we integrated genomic, epigenomic, transcriptomic, proteomic, and metabolomic data, employing post-genome-wide association study methodologies to systematically investigate the molecular mechanisms of nine organ-specific aging clocks and four blood-based epigenetic clocks. We uncovered genetic correlations and specific phenotypic clusters among these aging-related traits, identified prioritized genetic drug targets for heterogeneous aging, and elucidated downstream proteomic and metabolomic effects mediated by heterogeneous aging. We constructed a cross-layer molecular interaction network of heterogeneous aging across multiple organ systems and characterized detectable biomarkers of this heterogeneity. Integrating these findings, we developed an R/Shiny-based framework that provides a comprehensive multi-omic molecular landscape of heterogeneous aging, thereby advancing the understanding of aging heterogeneity and informing precision medicine strategies to delay organ-specific aging and prevent or treat its associated chronic diseases.

Aging↗

PubMed2001

The dynamics of molecular networks: applications to therapeutic discovery.

Recent advances in biomedical research, genomics and bioinformatics have given the pharmaceutical and biotechnology industries new promises and expectations: providing effective cures for complex diseases, discovering and prioritizing drug targets more efficiently, eliminating toxic and ineffective compounds earlier and delivering the right drug therapy to the appropriate patients. Ultimately, the biomedical information generated today must be transformed into integrated predictive models that can be consulted for decision-making in drug discovery, efficacy and toxicity screening and individualized therapy. Here we describe how models that capture different aspects of network dynamics can be generated and applied in disease pathway identification, drug screening, diagnostics and individualized therapy.

Journal Article↗

PubMed1999

Computer-aided target selection-prioritizing targets for antifungal drug discovery.

The entire DNA sequence of the Saccharomyces cerevisiae genome was completed in 1996 and represents the first entirely decoded eukaryotic genome. Because major human pathogenic fungi such as Candida albicans are closely related to S. cerevisiae on a molecular level, the question arises as to how this new information can be used to identify and prioritize those genes that are most suitable as targets for antimycotic drug discovery. To tackle this challenge, a software tool called CATS (computer-aided target selection) was developed. The authors describe how it allows an automated and periodically updated assessment of all S. cerevisiae genes to be carried out with regard to their suitability as antifungal targets.

Journal Article↗

PubMed2026

Genetic evidence supports the prioritization of CD40 among prespecified immune-related candidate drug targets in myasthenia gravis.

AIM: To prioritize prespecified immune-related candidate drug targets in myasthenia gravis for further validation based on integrated genetic evidence. METHODS: We integrated drug-target Mendelian randomization (MR) using cis-expression quantitative trait loci (cis-eQTLs), protein-level MR of plasma CD40 abundance using plasma protein quantitative trait loci (pQTLs), and colocalization analyses to evaluate genetically proxied associations with overall MG, early-onset myasthenia gravis (EOMG), and late-onset myasthenia gravis (LOMG). RESULTS: In this study, CD40 showed the most consistent genetic evidence among the six prespecified targets. Effect estimates are reported as odds ratios (ORs) with 95% confidence intervals (CIs). Higher CD40 expression proxied by cis-eQTLs was associated with increased risk of overall MG (OR = 1.14, 95% CI: 1.05-1.24, Bonferroni-adjusted p = 0.022) and EOMG (OR = 1.32, 95% CI: 1.12-1.56, Bonferroni-adjusted p = 0.015). Genetically predicted higher plasma CD40 protein abundance was associated with increased overall MG risk (OR = 1.31, 95% CI: 1.08-1.57, Bonferroni-adjusted p = 0.010), whereas the protein-level MR result for EOMG was directionally consistent but not statistically significant. Colocalization analysis provided suggestive but not definitive evidence of colocalization between CD40 expression and EOMG risk. FCGRT, IL2RA, and SYK showed additional exploratory MR signals requiring further validation. CONCLUSION: CD40 showed the most consistent genetic support among the prespecified targets, supporting its prioritization for functional validation and further therapeutic investigation in MG.

CD40↗

PubMed2025

Identifying novel protein biomarkers with cross-psychiatric disorders effects and potential intervention targets: Evidence from proteomic-Mendelian randomization.

Plasma proteins are the potential therapeutic targets for psychiatric disorders due to their important roles in signal transduction. We aimed to explore the plasma protein biomarkers with cross-psychiatric disorders effects. Proteome-wide Mendelian randomization (MR) and colocalization analyses were performed to investigate the potential causal relationship between plasma protein biomarkers and 12 psychiatric disorders and further identify the potential proteins with cross-effects. To assess the directionality and exclude potential reverse causation, Steiger directionality tests and reverse MR analyses were additionally conducted. Then, validation analysis was performed by employing summary data from cross-psychiatric disorder GWAS to validate the cross-psychiatric effects of proteins. Protein-protein interactions were conducted to evaluate the interaction between candidate proteins and druggability assessment was used to prioritize potential drug targets for psychiatric disorders. We identified novel plasma proteins that possessed cross-psychiatric disorder effects, especially BTN2A1 and BTN3A2 associated with major depressive disorder (MDD), schizophrenia (SCZ), and bipolar disorder (BIP); ITIH1, ITIH3, ITIH4 and FES associated with SCZ and BIP, and the cross-effects of these proteins on SCZ and BIP were confirmed by validation analyses. Steiger tests and reverse MR supported causal directionality. Besides, the protein-protein interactions (PPI) analysis indicated cross-effects proteins had significant interaction, especially ITIH1-ITIH3. The druggability assessment prioritized eight proteins, two of which (ITIH3 and NCAM1) has been targeted by antipsychotic drugs. Our findings provided insights into shared biological mechanisms underlying these conditions.

Humans↗

PubMed2002

Applications of antisense and siRNAs during preclinical drug development.

A significantly greater number of candidate drug targets and compounds are now being generated during preclinical drug development. To date, however, such increases have not led to improvements in clinical success rates or reduced times to market. There is a need for better strategies to prioritize targets and drug candidates. Antisense and siRNA technologies offer exceptional speed and specificity to address this need. In particular, antisense and siRNAs are beginning to be used in combination with expression profiling to evaluate drug specificity and mechanism-of-action, aiding in the identification of better candidates earlier in the drug development process.

Drug Evaluation, Preclinical↗

PubMed2024

An integrated in-silico approach for drug target identification in human pathogen Shigella dysenteriae.

Shigella dysenteriae, is a Gram-negative bacterium that emerged as the second most significant cause of bacillary dysentery. Antibiotic treatment is vital in lowering Shigella infection rates, yet the growing global resistance to broad-spectrum antibiotics poses a significant challenge. The persistent multidrug resistance of S. dysenteriae complicates its management and control. Hence, there is an urgent requirement to discover novel therapeutic targets and potent medications to prevent and treat this disease. Therefore, the integration of bioinformatics methods such as subtractive and comparative analysis provides a pathway to compute the pan-genome of S. dysenteriae. In our study, we analysed a dataset comprising 27 whole genomes. The S. dysenteriae strain SD197 was used as the reference for determining the core genome. Initially, our focus was directed towards the identification of the proteome of the core genome. Moreover, several filters were applied to the core genome, including assessments for non-host homology, protein essentiality, and virulence, in order to prioritize potential drug targets. Among these targets were Integration host factor subunit alpha and Tyrosine recombinase XerC. Furthermore, four drug-like compounds showing potential inhibitory effects against both target proteins were identified. Subsequently, molecular docking analysis was conducted involving these targets and the compounds. This initial study provides the list of novel targets against S. dysenteriae. Conclusively, future in vitro investigations could validate our in-silico findings and uncover potential therapeutic drugs for combating bacillary dysentery infection.

Shigella dysenteriae↗

PubMed2003

Human inter-individual DNA sequence variation in candidate genes, drug targets, the importance of haplotypes and pharmacogenomics.

The identification of genes predisposing to human diseases is of paramount importance for understanding the molecular basis of the disease and individually different drug response, and will establish new routes to diagnosis and therapeutic advances of immense medical benefit. A key step common to all strategies for disease gene identification is the systematic analysis of candidate gene sequences to identify specific sequence variations associated with disease or any other phenotype of pharmaceutical relevance. In this article, current concepts and approaches to haplotype-based candidate gene analysis are reviewed. Moreover, a comprehensive summary of recent studies and data on the amount, nature, pattern and structure of genetic variation in candidate genes is given. These data demonstrate altogether remarkable gene sequence and haplotype diversity. Numerous individually different forms of a gene may exist. This presents challenges to the traditional views of the concept of "a" gene with far-reaching implications on the functional analysis of candidate gene variation, on the establishment of "sequence"-"structure"-"function" and complex haplotype/genotype-phenotype relationships, on the identification, evaluation and prioritization of drug targets and the concept of a "personalized medicine" in general. Moreover, present and future approaches to the identification of candidate and disease genes will be addressed. These include whole genome-based approaches such as integrative genomics as well as functional genomics-based approaches to analyze and model complex biological and medical processes. The analysis of whole complex systems in particular will provide the basis to make "maximally informed" guesses on candidate genes and address complex variability patterns in genes as well as complex genotype-phenotype relationships comprehensively at an advanced level.

Base Sequence↗

PubMed2025

Genetic pleiotropy underlying obesity and autoimmune disorders: a large-scale cross-trait gwas analysis in European ancestry populations.

BACKGROUND: Obesity and autoimmune disorders represent a significant comorbidity burden, yet their shared genetic architecture is not fully understood. Elucidating the pleiotropic genetic basis underlying both conditions is crucial for unraveling the mechanisms driving their co-occurrence and advancing therapeutic strategies. METHODS: We conducted a large-scale cross-trait analysis integrating genome-wide association study (GWAS) summary data for obesity and 17 autoimmune diseases. Genetic correlations were assessed using LD score regression and high-definition likelihood. Cross-trait pleiotropic analysis was performed using Stratified Pleiotropic Locus Mapping (PLACO) to identify shared loci, followed by Bayesian colocalization to confirm shared causal variants. Gene-level and tissue-specific heritability analyses were conducted, and drug targets were prioritized via summary-based Mendelian randomization (SMR). Finally, immune co-localization and bidirectional Mendelian randomization were employed to elucidate immunological mechanisms and causal relationships. RESULTS: Our analysis identified eight autoimmune diseases with significant genetic correlations to obesity. We discovered 10,324 pleiotropic SNPs, which mapped to 52 independent risk loci, with nine loci confirmed as shared causal variants by colocalization. Gene-level analysis revealed 133 unique pleiotropic genes, including CLN3, SH2B1, and MMEL1, enriched in pathways of hematopoietic cell differentiation and immune homeostasis. Tissue-specific heritability was most prominent in the spleen, whole blood, and EBV-transformed lymphocytes. Immuno-co-localization implicated six IgD+ CD38- %B cell-related traits as key pathological conduits. Bidirectional Mendelian randomization established a causal role of obesity in hypothyroidism, psoriasis, and multiple sclerosis, while revealing an inverse causal association of type 1 diabetes with obesity risk. CONCLUSIONS: This study demonstrates a robust shared genetic foundation between obesity and multiple autoimmune diseases, pinpointing specific pleiotropic loci, genes, and immune cell subsets. Our findings provide a mechanistic framework for their comorbidity and highlight potential targets for therapeutic intervention.

Humans↗

PubMed2004

G protein-coupled receptors in hypothalamic circuits involved in metabolic diseases.

G protein-coupled receptors (GPCRs) are cellular surface proteins that span seven transmembrane domains and constitute the largest family of receptors in the genome. They are highly expressed in the central nervous system, and particularly in the hypothalamus, a structure known to be involved in the regulation of energy balance. This review describes the cellular distribution of GPCRs within hypothalamic regions involved in the control of energy balance, and their signaling pathways. An approach which uses knowledge of these features might help to identify and prioritize targets for drug development and strengthen our understanding of the neurobiology of eating disorders.

Animals↗

PubMed2004

Genomic filtering: an approach to discovering novel antiparasitics.

Genomic filtering is a rapid approach to identifying and prioritizing molecular targets for drug discovery. For infectious disease applications, comparative genomics filters allow the selection of pathogen-specific gene products, whereas functional genomics filters, such as RNA interference (RNAi), allow the selection of gene products essential for pathogen survival. The approach is especially applicable to antiparasitic drug discovery where the phylogenetic distance between parasite and host make the likelihood of drug cross-toxicity due to conservation of molecular targets greater than for more distantly related pathogens such as prokaryotes. This article discusses some of the inherent challenges of applying genomics to the early steps of drug discovery and describes one successful comparative and functional genomics filtering strategy that has been implemented to prioritize molecular targets and identify chemical leads for nematode control.

Animals↗

PubMed2026

Genetic Contributors to Postoperative Delirium and Their Implications for Dementia Outcomes.

BACKGROUND: Postoperative delirium (POD) is a perioperative neurocognitive disorder that substantially impairs patient recovery. Unfortunately, its genetic risk profile and relationship with subsequent dementia remain unclear. This study aimed to elucidate genetic contributors to POD identified via Hospital Episode Statistics codes and to examine its association with subsequent dementia. METHODS: The study included 230,179 noncardiac and 21,254 cardiac surgery subjects from the UK Biobank, defining POD using delirium codes from the International Classification of Diseases (10th revision) recorded within the first 7 postoperative days. Genome-wide association studies were performed in the noncardiac and cardiac cohorts and their prespecified subgroups, followed by functional annotation, gene prioritization and drug-target analyses. Associations between POD and subsequent dementia were estimated using Cox models. RESULTS: In the noncardiac cohort, one genome-wide significant locus was identified at the APOE region, with rs429358 as the lead variant ( P = 5.00 × 10 -28 ). Integrative gene prioritization analyses highlighted multiple genes within this locus. Exploratory drug-target analyses suggested potential subgroup-specific drug-target enrichment. In the cardiac cohort, no genome-wide significant signals were detected. POD was associated with all-cause dementia after both noncardiac (hazard ratio, 6.45; 95% CI, 5.45 to 7.63) and cardiac (hazard ratio, 2.95; 95% CI, 1.71 to 5.08) surgeries. CONCLUSIONS: This study demonstrates APOE as a genetic risk locus for International Classification of Diseases-coded POD in the noncardiac surgery setting and confirms an association between POD and subsequent dementia.

Humans↗

PubMed2026

The tissue-specific effects of glucose-lowering drug targets on aging mediated through DNA methylation: a multi-omics genetic study.

BACKGROUND: DNA methylation plays a key role in mediating the anti-aging effects of glucose-lowering drugs. This study aims to systematically explore the potential anti-aging effects of target genes of FDA-approved glucose-lowering drugs and the underlying epigenetic mediators. METHODS: We conducted a two-sample Mendelian randomization (MR) study to investigate the putative causal relationships between the gene expression levels of glucose-lowering drug targets and 10 aging-related phenotypes, followed by a two-step MR to estimate the mediation effect of DNA methylation. Drug candidates were selected according to the latest review of clinical drug use for type 2 diabetes, and their target genes were obtained from the DGIdb. Tissue-specific cis-expression quantitative trait loci (eQTLs) from GTEx Consortium were selected as genetic instruments to proxy the expression level of drug-target genes. Glycemic phenotypes were used as positive controls to validate the instruments. The cis- and trans-methylation QTLs of Cytosine-phosphate-Guanine sites near the drug target genes were obtained from GoDMC Consortium. Additionally, we performed enrichment analyses focused on tissue specificity and aging pathways to further corroborate our findings. RESULTS: We obtained 194 target genes interacting with 36 FDA-approved anti-diabetic drugs, of which the tissue-specific eQTLs were used to proxy the drug target effects. MR showed strong evidence that nine interacting genes of six glucose-lowering drugs showed anti-aging potential on one or more aging-related phenotypes mediated by DNA methylation: EHMT2, HSPA4, IGF2BP2, IRS1, LPL, NDUFAF1, NDUFS3, SLC22A3, and TCF7L2. These genes were distributed in 17 tissues, especially in the central nervous system, suggesting a potential neural component in their anti-aging effects. For instance, expression of EHMT2 in several brain basal ganglia regions, where the gene interacted with Tolazamide, showed a protective effect on frailty (odds ratio (OR) in caudate = 1.02, 95%CI = 1.01-1.04, FDR adjusted P = 1.69 × 10-2; OR in putamen = 1.02, 95% CI = 1.01-1.03, PFDR = 3.37 × 10-2, OR in nucleus accumbens = 1.02, 95% CI = 1.01-1.04, PFDR = 3.37 × 10-2). These associations were externally validated by searching literature evidence in existing EWAS and TWAS studies, as well as evidence from enrichment analyses. CONCLUSIONS: This study prioritizes nine glucose-lowering genes as anti-aging drug targets in specific tissues and prioritizes their epigenetic regulation through DNA methylation for future drug development.

DNA Methylation↗

PubMed2003

Prioritizing the human genome: knowledge management for drug discovery.

This review covers recent methods to create a manageable subset of drug targets for development by prioritizing novel genes from the Human Genome Project. The ability to organize genomic data into a distinct set of drug discovery assets can be viewed as a form of knowledge management. While bioinformatics systems have been built to manage genomics-based data, the central theme in creating any bioinformatics infrastructure should be organization-specific knowledge management.

Animals↗