Identification Of Potential Drug Targets Research Articles

MMD-DTA: A multi-modal deep learning framework for drug-target binding affinity and binding region prediction.

The prediction of drug-target affinity (DTA) plays a crucial role in drug development and the identification of potential drug targets. In recent years, computer-assisted DTA prediction has emerged as a significant approach in this field. In this study, we propose a multi-modal deep learning framework called MMD-DTA for predicting drug-target binding affinity and binding regions. The model can predict DTA while simultaneously learning the binding regions of drug-target interactions through unsupervised learning. To achieve this, MMD-DTA first uses graph neural networks and target structural feature extraction network to extract multi-modal information from the sequences and structures of drugs and targets. It then utilizes the feature interaction and fusion modules to generate interaction descriptors for predicting DTA and interaction strength for binding region prediction. Our experimental results demonstrate that MMD-DTA outperforms existing models based on key evaluation metrics. Furthermore, external validation results indicate that MMD-DTA enhances the generalization capability of the model by integrating sequence and structural information of drugs and targets. The model trained on the benchmark dataset can effectively generalize to independent virtual screening tasks. The visualization of drug-target binding region prediction showcases the interpretability of MMD-DTA, providing valuable insights into the functional regions of drug molecules that interact with proteins.

A genome-scale metabolic model of a globally disseminated hyperinvasive M1 strain of Streptococcus pyogenes.

Streptococcus pyogenes is responsible for a range of diseases in humans contributing significantly to morbidity and mortality. Among more than 200 serotypes of S. pyogenes, serotype M1 strains hold the greatest clinical relevance due to their high prevalence in severe human infections. To enhance our understanding of pathogenesis and discovery of potential therapeutic approaches, we have developed the first genome-scale metabolic model (GEM) for a serotype M1 S. pyogenes strain, which we name iYH543. The curation of iYH543 involved cross-referencing a draft GEM of S. pyogenes serotype M1 from the AGORA2 database with gene essentiality and autotrophy data obtained from transposon mutagenesis-based and growth screens. We achieved a 92.6% (503/543 genes) accuracy in predicting gene essentiality and a 95% (19/20 amino acids) accuracy in predicting amino acid auxotrophy. Additionally, Biolog Phenotype microarrays were employed to examine the growth phenotypes of S. pyogenes, which further contributed to the refinement of iYH543. Notably, iYH543 demonstrated 88% accuracy (168/190 carbon sources) in predicting growth on various sole carbon sources. Discrepancies observed between iYH543 and the actual behavior of living S. pyogenes highlighted areas of uncertainty in the current understanding of S. pyogenes metabolism. iYH543 offers novel insights and hypotheses that can guide future research efforts and ultimately inform novel therapeutic strategies.IMPORTANCEGenome-scale models (GEMs) play a crucial role in investigating bacterial metabolism, predicting the effects of inhibiting specific metabolic genes and pathways, and aiding in the identification of potential drug targets. Here, we have developed the first GEM for the S. pyogenes highly virulent serotype, M1, which we name iYH543. The iYH543 achieved high accuracy in predicting gene essentiality. We also show that the knowledge obtained by substituting actual measurement values for iYH543 helps us gain insights that connect metabolism and virulence. iYH543 will serve as a useful tool for rational drug design targeting S. pyogenes metabolism and computational screening to investigate the interplay between inhibiting virulence factor synthesis and growth.

Identification of Potential Drug Targets for Myopia Through Mendelian Randomization.

The purpose of this study was to identify potential drug targets for myopia and explore underlying mechanisms. Mendelian randomization (MR) was implemented to assess the effect of 2684 pharmacologically targetable genes in the blood and retina on the risk of myopia from a genomewide association study (GWAS) for age-at-onset of spectacle wearing-inferred mean spherical equivalent (MSE; discovery cohort, N = 287,448, European), which was further validated in a GWAS for autorefraction-measured MSE (replication cohort, N = 95,619, European). The reliability of the identified significant potential targets was strengthened by colocalization analysis. Additionally, enrichment analysis, protein-protein interaction network, and molecular docking were performed to explore the functional roles and the druggability of these targets. This systematic drug target identification has unveiled 6 putative genetically causal targets for myopia-CD34, CD55, Wnt3, LCAT, BTN3A1, and TSSK6-each backed by colocalization evidence in adult blood eQTL datasets. Functional analysis found that dopaminergic neuron differentiation, cell adhesion, Wnt signaling pathway, and plasma lipoprotein-associated pathways may be involved in myopia pathogenesis. Finally, drug prediction and molecular docking corroborated the pharmacological value of these targets with LCAT demonstrating the strongest binding affinity. Our study not only opens new avenues for the development of therapeutic interventions for myopia but may also help to understand the underlying mechanisms of myopia.

Multi-Omics Characterization of E3 Regulatory Patterns in Different Cancer Types.

Ubiquitination, a post-translational modification, refers to the covalent attachment of ubiquitin molecules to substrates. This modification plays a critical role in diverse cellular processes such as protein degradation. The specificity of ubiquitination for substrates is regulated by E3 ubiquitin ligases. Dysregulation of ubiquitination has been associated with numerous diseases, including cancers. In our study, we first investigated the protein expression patterns of E3 ligases across 12 cancer types. Our findings indicated that E3 ligases tend to be up-regulated and exhibit reduced tissue specificity in tumors. Moreover, the correlation of protein expression between E3 ligases and substrates demonstrated significant changes in cancers, suggesting that E3-substrate specificity alters in tumors compared to normal tissues. By integrating transcriptome, proteome, and ubiquitylome data, we further characterized the E3-substrate regulatory patterns in lung squamous cell carcinoma. Our analysis revealed that the upregulation of the SKP2 E3 ligase leads to excessive degradation of BRCA2, potentially promoting tumor cell proliferation and metastasis. Furthermore, the upregulation of E3 ubiquitin-protein ligase TRIM33 was identified as a biomarker associated with a favorable prognosis by inhibiting the cell cycle. This work exemplifies how leveraging multi-omics data to analyze E3 ligases across various cancers can unveil prognosis biomarkers and facilitate the identification of potential drug targets for cancer therapy.

Identification of Potential Drug Targets of Calix[4]arene by Reverse Docking.

Calixarenes are displaying great potential for the development of new drug delivery systems, diagnostic imaging, biosensing devices and inhibitors of biological processes. In particular, calixarene derivatives are able to interact with many different enzymes and function as inhibitors. By screening of the potential drug target database (PDTD) with a reverse docking procedure, we identify and discuss a selection of 100 proteins that interact strongly with calix[4]arene. We also discover that leucine (23.5 %), isoleucine (11.3 %), phenylalanines (11.3 %) and valine (9.5 %) are the most frequent binding residues followed by hydrophobic cysteines and methionines and aromatic histidines, tyrosines and tryptophanes. Top binders are peroxisome proliferator-activated receptors that already are targeted by commercial drugs, demonstrating the practical interest in calix[4]arene. Nuclear receptors, potassium channel, several carrier proteins, a variety of cancer-related proteins and viral proteins are prominent in the list. It is concluded that calix[4]arene, which is characterized by facile access, well-defined conformational characteristics, and ease of functionalization at both the lower and higher rims, could be a potential lead compound for the development of enzyme inhibitors and theranostic platforms.

Identification of potential drug targets for insomnia by Mendelian randomization analysis based on plasma proteomics.

Insomnia, a common clinical disorder, significantly impacts the physical and mental well-being of patients. Currently, available hypnotic medications are unsatisfactory due to adverse reactions and dependency, necessitating the identification of new drug targets for the treatment of insomnia. In this study, we utilized 734 plasma proteins as genetic instruments obtained from genome-wide association studies to conduct a Mendelian randomization analysis, with insomnia as the outcome variable, to identify potential drug targets for insomnia. Additionally, we validated our results externally using other datasets. Sensitivity analyses entailed reverse Mendelian randomization analysis, Bayesian co-localization analysis, and phenotype scanning. Furthermore, we constructed a protein-protein interaction network to elucidate potential correlations between the identified proteins and existing targets. Mendelian randomization analysis indicated that elevated levels of TGFBI (OR = 1.01; 95% CI, 1.01-1.02) and PAM ((OR = 1.01; 95% CI, 1.01-1.02) in plasma are associated with an increased risk of insomnia, with external validation supporting these findings. Moreover, there was no evidence of reverse causality for these two proteins. Co-localization analysis confirmed that PAM (coloc.abf-PPH4 = 0.823) shared the same variant with insomnia, further substantiating its potential role as a therapeutic target. There are interactive relationships between the potential proteins and existing targets of insomnia. Overall, our findings suggested that elevated plasma levels of TGFBI and PAM are connected with an increased risk of insomnia and might be promising therapeutic targets, particularly PAM. However, further exploration is necessary to fully understand the underlying mechanisms involved.

Glucagon-like peptide (GLP)-1 regulation of lipid and lipoprotein metabolism.

Metabolic health is highly dependent on intestinal and hepatic handling of dietary and endogenous lipids and lipoproteins. Disorders of lipid and lipoprotein metabolism are commonly observed in patients with insulin resistant states such as obesity, metabolic syndrome, and type 2 diabetes. Evidence from both animal models and human studies indicates that a major underlying factor in metabolic or diabetic dyslipidemia is the overproduction of hepatic and intestinal apolipoprotein (apo)B-containing lipoprotein particles. These particles are catabolized down into highly proatherogenic remnants, which can be taken up into the arterial intima and promote plaque development. Several gut-derived peptides have been identified as key regulators of energy metabolism; one such peptide is the incretin hormone glucagon-like peptide (GLP)-1. Our laboratory has previously demonstrated that GLP-1 can signal both centrally and peripherally to reduce postprandial and fasting lipoprotein secretion. Moreover, we have demonstrated that GLP-1 receptor (GLP-1R) agonists can ameliorate diet-induced dyslipidemia. Recently, we published evidence for a novel vagal neuroendocrine signalling pathway by which native GLP-1 may exert its anti-lipemic effects. Furthermore, we demonstrated a novel role for other gut-derived peptides in regulating intestinal lipoprotein production. Overall, ample evidence supports a key role for GLP-1R on the portal vein afferent neurons and nodose ganglion in modulating intestinal fat absorption and lipoprotein production and identifies other gut-derived peptides as novel regulators of postprandial lipemia. Insights from these data may support identification of potential drug targets and the development of new therapeutics targeting treatment of diabetic dyslipidemia.

Identification of potential drug targets for allergic diseases from a genetic perspective: A mendelian randomization study.

Allergic diseases typically refer to a heterogeneous group of conditions primarily caused by the activation of mast cells or eosinophils, including atopic dermatitis (AD), allergic rhinitis (AR), and asthma. Asthma, AR, and AD collectively affect approximately one-fifth of the global population, imposing a significant economic burden on society. Despite the availability of drugs to treat allergic diseases, they have been shown to be insufficient in controlling relapses and halting disease progression. Therefore, new drug targets are needed to prevent the onset of allergic diseases. We employed a Mendelian randomization approach to identify potential drug targets for the treatment of allergic diseases. Leveraging 1798 genetic instruments for 1537 plasma proteins from the latest reported Genome-Wide Association Studies (GWAS), we analyzed the GWAS summary statistics of Ferreira MA etal. (nCase=180,129, nControl=180,709) using the Mendelian randomization method. Furthermore, we validated our findings in the GWAS data from the FinnGen and UK Biobank cohorts. Subsequently, we conducted sensitivity tests through reverse causal analysis, Bayesian colocalization analysis, and phenotype scanning. Additionally, we performed protein-protein interaction analysis to determine the interaction between causal proteins. Finally, based on the potential protein targets, we conducted molecular docking to identify potential drugs for the treatment of allergic diseases. At Bonferroni significance (p<3.25×10-5), the Mendelian randomization analysis revealed 11 significantly associated protein-allergic disease pairs. Among these, the increased levels of TNFAIP3, ERBB3, TLR1, and IL1RL2 proteins were associated with a reduced risk of allergic diseases, with corresponding odds ratios of 0.82 (0.76-0.88), 0.74 (0.66-0.82), 0.49 (0.45-0.55), and 0.81 (0.75-0.87), respectively. Conversely, increased levels of IL6R, IL1R1, ITPKA, IL1RL1, KYNU, LAYN, and LRP11 proteins were linked to an elevated risk of allergic diseases, with corresponding odds ratios of 1.04 (1.03-1.05), 1.25 (1.18-1.34), 1.48 (1.25-1.75), 1.14 (1.11-1.18), 1.09 (1.05-1.12), 1.96 (1.56-2.47), and 1.05 (1.03-1.07), respectively. Bayesian colocalization analysis suggested that LAYN (coloc.abf-PPH4=0.819) and TNFAIP3 (coloc.abf-PPH4=0.930) share the same variant associated with allergic diseases. Our study demonstrates a causal association between the expression levels of TNFAIP3 and LAYN and the risk of allergic diseases, suggesting them as potential drug targets for these conditions, warranting further clinical investigation.

Obesity and depression: Review on common neurobiological mechanisms and identification of potential drug targets.

IntroductionThis review aimed to identify common pathophysiological mechanisms that exist between depression and obesity, as well as pharmacological strategies used in clinical trials and animal models. It is necessary to carry out larger studies that integrate the multiple neurobiological processes of these phenomena and the search for therapeutic targets that affect these pathways.ObjectivesConduct a literature review on the common neurobiological aspects that exist between depression and obesity. Compare pharmacological and therapeutic strategies in the management of depressive patients by means of common neurobiological mechanisms.MethodsWe used the Pubmed search engine to search for the keywords: ((depression and obesity) OR (common pathways for depression and obesity) OR (therapeutic targets for depression and obesity) OR (neurobiology of depression and obesity) OR (treatments for depression and obesity))Once the predefined screening was carried out, 68 studies were identified. 54 articles were left for review and analysis. At the end of the review, 25 studies were discarded, including 29 studies with relevance to the objectives described in the study. These articles were selected when they provided information with adequate, concrete and specific reasoning towards the scientific and methodological elements of the review.Results Table 1: Findings related to the NEGR1 gene and its involvement in processes associated with alterations associated with depression and obesity. Table 2: Findings of inflammation associated with depression and obesity in clinical trials and animal models.Image:Image 2:ConclusionsAlterations in the NEGR1 gene, inflammatory markers, HPA axis and microbiota demonstrate multiple pathophysiological mechanisms in the clinical pictures associated with obesity and depression.Infliximab, pioglitazone, ondansentron, BVT.2733 and palmitoylethanolamide showed anti-inflammatory regulatory effects with reduction in depressive symptoms and multiple anti-inflammatory markers.Animal models for obesity and depression present ample and reliable evidence regarding the use of drugs that direct their therapeutic profile towards the pathophysiological mechanisms involved in pathologies involving depressive and metabolic disorders.Disclosure of InterestNone Declared

Advancing Breast Cancer Drug Delivery: The Transformative Potential of Bioinformatics and Artificial Intelligence

Abstract: Breast cancer remains a significant global health challenge, necessitating innovative approaches to improve treatment efficacy while minimizing side effects. This review explores the promising advancements in breast cancer drug delivery driven by the transformative potential of bioinformatics and Artificial Intelligence (AI). Bioinformatics plays a pivotal role in unraveling the intricate genomic landscape of breast cancer, enabling the identification of potential drug targets and biomarkers. The integration of multi-omics data facilitates a comprehensive understanding of the disease, guiding personalized treatment strategies. Moreover, bioinformatics-driven approaches aid in biomarker discovery and prediction, offering novel tools for prognosis and treatment response assessment. AI, particularly machine learning and deep learning, has revolutionized breast cancer research. Machine learning models empower accurate diagnosis through image analysis, improve survival prediction, and enhance risk assessment. Deep learning algorithms, such as convolutional neural networks, enable precise tumor detection and classification from medical imaging data, notably mammograms and MRI scans. Additionally, natural language processing techniques facilitate the mining of vast scientific literature, uncovering hidden insights and identifying potential drug targets. Network-based approaches integrated with AI algorithms facilitate the identification of central proteins as promising drug targets within complex biological networks. This review also examines AIoptimized nanoformulations designed to enhance targeted drug delivery. AI-guided design of drugloaded nanoparticles improves drug encapsulation efficiency, release kinetics, and site-specific delivery, offering promising solutions to overcome the challenges of conventional drug delivery.

Identification of gene variation feature for targeted therapy of non-small cell lung cancer through combined method of DNA and RNA sequencing

Next generation sequencing (NGS) is typically used to reveal tumor gene variation feature for targeted therapy of various types of human cancers, including non-small cell lung cancer (NSCLC). Here, we report the role and potential applicable value of combining DNA and RNA sequencing in gene variation detection in NSCLC. 386 NSCLC patients with stage II-IV were enrolled and detected using NGS sequencing of DNA and RNA panels that covered all well-documented target driver genes from the Chinese Society of Clinical Oncology (CSCO). The rate of epidermal growth factor receptor (EGFR) single nucleotide variation (SNV)/indel, mesenchymal-epithelial transition factor (MET) copy number variation (CNV) and anaplastic lymphoma kinase (ALK) fusion were 52.1%, 4.1% and 6.0% in the NSCLC cohort. The landscapes of SNV/indel, CNV and gene fusion in the cohort were depicted as well. Meanwhile, we assessed detection efficacy of DNA and RNA sequencing in gene fusion. Detected number and types of gene fusion using the RNA sequencing were better than those using the DNA sequencing. Gene fusion with intergenic region was only detected by DNA sequencing and MET exon 14 skipping (METΔex14) was more easily identified by RNA sequencing. Finally, we investigated clinical correlations of SNV/indel/CNV/fusion with clinicopathologic features in the NSCLC cohort. Taken together, RNA sequencing significantly complements deficiency of DNA sequencing for gene fusion, which cooperatively presents comprehensive and reliable gene variation features and facilitate the identification of potential drug targets for NSCLC patients.

Endoplasmic reticulum stress in breast cancer: a predictive model for prognosis and therapy selection.

Breast cancer (BC) is a leading cause of mortality among women, underscoring the urgent need for improved therapeutic predictio. Developing a precise prognostic model is crucial. The role of Endoplasmic Reticulum Stress (ERS) in cancer suggests its potential as a critical factor in BC development and progression, highlighting the importance of precise prognostic models for tailored treatment strategies. Through comprehensive analysis of ERS-related gene expression in BC, utilizing both single-cell and bulk sequencing data from varied BC subtypes, we identified eight key ERS-related genes. LASSO regression and machine learning techniques were employed to construct a prognostic model, validated across multiple datasets and compared with existing models for its predictive accuracy. The developed ERS-model categorizes BC patients into distinct risk groups with significant differences in clinical prognosis, confirmed by robust ROC, DCA, and KM analyses. The model forecasts survival rates with high precision, revealing distinct immune infiltration patterns and treatment responsiveness between risk groups. Notably, we discovered six druggable targets and validated Methotrexate and Gemcitabine as effective agents for high-risk BC treatment, based on their sensitivity profiles and potential for addressing the lack of active targets in BC. Our study advances BC research by establishing a significant link between ERS and BC prognosis at both the molecular and cellular levels. By stratifying patients into risk-defined groups, we unveil disparities in immune cell infiltration and drug response, guiding personalized treatment. The identification of potential drug targets and therapeutic agents opens new avenues for targeted interventions, promising to enhance outcomes for high-risk BC patients and paving the way for personalized cancer therapy.

Prolyl isomerase Pin1 promotes autophagy and cancer cell viability through activating FoxO3 signalling.

Pin1-directed prolyl isomerization is a central common oncogenic mechanism to drive tumorigenic processes. However, the role of Pin1 in cellular autophagy is still poorly understood. Here we report that pharmacological inhibition of Pin1 decreased the formation of autophagosome/autolysosomes upon nutrient starvation. Inhibition of Pin1 reduced, whereas forced expression of Pin1 increased, the level of LC3 and viability of U2OS and PANC-1 cells. Pin1 could augment the accumulation of LC3 upon chloroquine treatment, while chloroquine also disturbed its function on cell viability. RNA-Seq and qPCR identified altered autophagic pathway upon Pin1 silencing. Mechanistically, FoxO3 was identified critical for Pin1-mediated autophagy. Knockdown of FoxO3 could rescue the changes of LC3 level and cellular viability caused by Pin1 overexpression. In xenograft mouse model, Pin1 reduced the sensitivity of PANC-1 to chloroquine while FoxO3 silencing could inhibit Pin1's function. Moreover, Pin1 could bind FoxO3 via its pS284-P motif, reduce its phosphorylation at T32, facilitate its nuclear retention, and therefore increased its transcriptional activity. S284A mutation of FoxO3 interfered with its T32 phosphorylation, reduced its nuclear localization and disrupted its function to support cell viability upon nutrient starvation. Furthermore, the protein level of Pin1 positively correlated with FoxO3 nuclear localization and LC3 level in pancreatic adenocarcinoma and osteosarcoma samples. Together, this study highlights an important role for Pin1-FoxO3 axis in regulating autophagy and cancer cell viability. Intervening in the Pin1-FoxO3 interaction would serve as an effective therapeutic strategy and the pS284-P motif of FoxO3 provides a potential target for drug design.

TRIF-dependent signaling and its role in liver diseases.

TIR domain-containing adaptor inducing IFN-β (TRIF) is a crucial adaptor molecule downstream of toll-like receptors 3 (TLR3) and 4 (TLR4). TRIF directly binds to TLR3 through its TIR domain, while it associates with TLR4 indirectly through the bridge adaptor molecule TRIF-related adaptor molecule (TRAM). TRIF plays a pivotal role in regulating interferon beta 1 (IFN-β) response, nuclear factor kappa B (NF-κB) signaling, apoptosis, and necroptosis signaling mediated by TLR3 and TLR4. It accomplishes these by recruiting and activating various kinases or transcription factors via its distinct domains. In this review, we comprehensively summarize the TRIF-dependent signaling pathways mediated by TLR3 and TLR4, elucidating key target molecules and downstream pathways. Furthermore, we provide an overview of TRIF's impact on several liver disorders, including drug-induced liver injury, ischemia-reperfusion liver injury, autoimmune hepatitis, viral hepatitis, alcohol-associated liver disease (ALD), metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH). We also explore its effects on liver steatosis, inflammation, fibrosis, and carcinogenesis. A comprehensive understanding of the TRIF-dependent signaling pathways, as well as the intricate relationship between TRIF and liver diseases, can facilitate the identification of potential drug targets and the development of novel and effective therapeutics against hepatic disorders.

All‐in‐Human Target Discovery in Alzheimer’s Disease and Resilient Brains

AbstractBackgroundAlzheimer’s disease (AD) is characterized by neuropathological hallmarks including amyloid plaques and tau tangles and neurodegeneration in the frontal cortex. While there is a strong association of tau pathology with neurodegeneration and cognitive decline, around 30% of amyloid plaque‐carrying seniors remain cognitively healthy. We hypothesize that protective cellular mechanisms upstream of tau phosphorylation and neurodegeneration may underlie this resilience.MethodWe applied high resolution spatial transcriptomics and single nuclei RNA sequencing on postmortem frontal lobes from 10 demented and 10 non‐demented individuals with comparable degrees of amyloid pathology. We integrated the generated transcriptomics data with proteomics, genomics and drug action databases and interpret cellular transcriptome responses to pathology in the context of human specific protein‐protein interaction networks.ResultUsing spatial and single nuclei transcriptomics, we identify specific transcriptomic signatures and regulatory networks in cellular microenvironments around amyloid plaques and pTau pathology niches. Our results suggest that specific neuronal and glial subpopulations carry enriched vulnerability towards amyloid plaque and pTau depositions. We further find that certain cell death signaling pathways related to neurodegeneration are overrepresented in the context of pTau pathology. Finally, our multi‐omics database integration approach has led to the identification of potential drug targets for therapeutics addressing cellular mechanisms of neuroprotection.ConclusionSpatial transcriptomics from human brain tissue is a powerful approach to reveal cellular mechanisms of pathogenic lesions and enables us to identify neuroprotective mechanisms that may prevent the development of tauopathy and ultimately neuronal loss. The combination of single cell and spatial transcriptomics together with protein‐protein interaction networks provides a promising approach for the identification of novel therapeutic targets for AD.

Lipoprotein lipids and apolipoproteins in primary immune thrombocytopenia: Results from a clinical characteristics and causal relationship verification, potential drug target identification by Mendelian randomization analyses.

Primary immune thrombocytopenia (ITP) is an acquired autoimmune disease. Cellular and systemic lipid metabolism plays a significant role in the regulation of immune cell activities. However, the role of lipoprotein lipids and apolipoproteins in ITP remains elusive. The automatic biochemistry analyser was used to measure the levels of serum total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), apolipoprotein A-I (apoA-I), apoB, apoE and lipoprotein a [LP(a)]. Genetic variants strongly associated with circulating lipoprotein lipids and apolipoproteins (LDL-C, apoB, TG, HDL-C and apoA-I) were extracted to perform Mendelian randomization (MR) analyses. Finally, drug-target MR and passive ITP mice model was used to investigate the potential druggable targets of ITP. Levels of HDL-C, apoA-I, decreased and LP(a) increased in ITP patients compared with healthy controls. Low HDL-C was causally associated with ITP susceptibility. Through drug-target MR and animal modelling, ABCA1 was identified as a potential target to design drugs for ITP. Our study found that lipid metabolism is related to ITP. The causative association between HDL-C and the risk of ITP was also established. The study provided new evidence of the aetiology of ITP. ABCA1 might be a potential drug target for ITP.

Identification of Potential Drug Targets for Antiplatelet Therapy Specifically Targeting Platelets of Old Individuals through Proteomic Analysis.

Aging is a growing problem worldwide, and the prevalence and mortality of arterial and venous thromboembolism (VTE) are higher in the elderly than in the young population. To address this issue, various anticoagulants have been used. However, no evidence can confirm that antithrombotic agents are suitable for the elderly. Therefore, this study aims to investigate the platelet proteome of aged mice and identify antithrombotic drug targets specific to the elderly. Based on the proteome analysis of platelets from aged mice, 308 increased or decreased proteins were identified. Among these proteins, three targets were selected as potential antithrombotic drug targets. These targets are membrane proteins or related to platelet function and include beta-2-glycoprotein 1 (β2GP1, ApolipoproteinH (ApoH)), alpha-1-acid glycoprotein2 (AGP2, Orosomucoid-2 (Orm2)), and Ras-related protein (Rab11a).

Identification of potential drug targets for rheumatoid arthritis from genetic insights: a Mendelian randomization study

IntroductionRheumatoid arthritis (RA) is a chronic inflammatory illness that mostly affects the joints of the hands and feet and can reduce life expectancy by an average of 3 to 10 years. Although tremendous progress has been achieved in the treatment of RA, a large minority of patients continue to respond poorly to existing medications, owing in part to a lack of appropriate therapeutic targets.MethodsTo find therapeutic targets for RA, a Mendelian randomization (MR) was performed. Cis-expression quantitative trait loci (cis-eQTL, exposure) data were obtained from the eQTLGen Consortium (sample size 31,684). Summary statistics for RA (outcome) were obtained from two largest independent cohorts: sample sizes of 97,173 (22,350 cases and 74,823 controls) and 269,377 (8279 cases and 261,098), respectively. Colocalisation analysis was used to test whether RA risk and gene expression were driven by common SNPs. Drug prediction and molecular docking was further used to validate the medicinal value of drug targets.ResultsSeven drug targets were significant in both cohorts in MR analysis and supported by localization. PheWAS at the gene level showed only ATP2A1 associated with other traits. These genes are strongly associated with immune function in terms of biological significance. Molecular docking showed excellent binding for drugs and proteins with available structural data.ConclusionThis study identifies seven potential drug targets for RA. Drugs designed to target these genes have a higher chance of success in clinical trials and is expected to help prioritise RA drug development and save on drug development costs.

The Role of Deep Learning in Pharma: Revolutionizing Drug Discovery and Development.

- In recent years, the pharmaceutical industry has witnessed a transformative shift in the way drugs are discovered and developed, thanks to the advent of deep learning. This paper explores the profound impact of deep learning techniques on various stages of drug discovery and development, from target identification and lead optimization to clinical trials and personalized medicine. Deep learning, a subset of artificial intelligence, has demonstrated exceptional capabilities in handling complex biological data, including genomics, proteomics, and chemical informatics. It enables the integration of vast and diverse datasets, facilitating the identification of potential drug targets with unprecedented accuracy. Moreover, deep learning models can predict the binding affinity of drug candidates to specific target proteins, expediting the lead optimization process and reducing the need for costly experimental iterations. Deep learning algorithms enhance patient stratification and biomarker discovery, ultimately leading to more successful trials with higher patient response rates. Additionally, the ability to analyze real-world patient data aids in the identification of adverse events and the development of safer drugs.&#x0D; Perrsonalized medicine is another area greatly influenced by deep learning, as it allows for tailoring treatments to individual patients based on their unique genetic and clinical profiles. This promises to revolutionize patient care, optimizing therapeutic outcomes while minimizing adverse effects. Despite the remarkable advancements facilitated by deep learning, there are challenges to address, such as data privacy, interpretability of models, and regulatory considerations. This paper discusses these challenges and potential solutions. Deep learning has emerged as a powerful tool in the pharmaceutical industry, driving innovation, efficiency, and precision in drug discovery and development. Its integration into the drug development pipeline holds the promise of accelerating the delivery of safer and more effective therapies to patients worldwide, marking a significant milestone in the evolution of pharmaceutical science.

COVID Variants, Villain and Victory: A Bioinformatics Perspective.

The SARS-CoV-2 virus, a novel member of the Coronaviridae family, is responsible for the viral infection known as Coronavirus Disease 2019 (COVID-19). In response to the urgent and critical need for rapid detection, diagnosis, analysis, interpretation, and treatment of COVID-19, a wide variety of bioinformatics tools have been developed. Given the virulence of SARS-CoV-2, it is crucial to explore the pathophysiology of the virus. We intend to examine how bioinformatics, in conjunction with next-generation sequencing techniques, can be leveraged to improve current diagnostic tools and streamline vaccine development for emerging SARS-CoV-2 variants. We also emphasize how bioinformatics, in general, can contribute to critical areas of biomedicine, including clinical diagnostics, SARS-CoV-2 genomic surveillance and its evolution, identification of potential drug targets, and development of therapeutic strategies. Currently, state-of-the-art bioinformatics tools have helped overcome technical obstacles with respect to genomic surveillance and have assisted in rapid detection, diagnosis, and delivering precise treatment to individuals on time.