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At least 19 recordsLinked to original sources
PubMed2026

Anti-psychotic drugs act synergistically in combination with antifungal drugs to inhibit drug-resistant Cryptococcus neoformans and Candida albicans.

UNLABELLED: Systemic fungal infections cause an estimated 3.8 million deaths annually, approximately 10% of which are caused by drug-resistant infections. With only five classes of antifungal drugs, treatment options are limited. Here, we explore synergistic drug combinations-when the efficacy of two drugs combined is greater than expected based on the sum of each individual drug's efficacy-to improve treatment of drug-resistant Cryptococcus neoformans and Candida albicans. Chlorpromazine acts synergistically with both amphotericin B and fluconazole against multiple fungal species, including azole-resistant C. neoformans and C. albicans. We then performed a genome-wide knockout mutant screen and found that ESCRT pathway mutants are resistant to chlorpromazine, while knockout mutants of genes involved in fatty acid biosynthesis are sensitive. Based on these data, we investigated sterol and fatty acid composition in chlorpromazine-treated cells and found only minor increases in sterol precursors, but a substantial increase in lipid droplet size and decreased lipid droplet numbers. This lipid droplet formation potentially sequesters lipid bioavailability and response to membrane stress. Together, these data suggest that chlorpromazine and its analogs are potentially promising treatments for systemic fungal infections that act via lipid homeostasis and stress response. IMPORTANCE: Fungal infections are a large and expensive health burden with high mortality rates. People with compromised immune systems from cancer, solid organ transplant, HIV infection, and other conditions are particularly affected. Systemic fungal infections are difficult to treat because there are few available drugs and treatment periods last months or years. Long treatment times increase the risk of treatment failure and can contribute to the rise of resistance. We identified an additional class of drugs, chlorpromazine and other phenothiazine drugs, that amplify the activity of existing antifungal drugs amphotericin B (AmB) and fluconazole (FLZ). AmB and FLZ act by targeting ergosterol, the fungal equivalent of cholesterol, which is required for a functional plasma membrane. Chlorpromazine increases the formation of lipid drops, which sequester lipids such as ergosterol. When chlorpromazine is combined with AmB, the fungal cell cannot respond to the plasma membrane damage caused by AmB, inhibiting the fungal cells. This work identifies new target processes and drugs that could treat deadly fungal infections.

antifungal resistance↗
PubMed2026

Surveilled subjectivation: narratives of drug policing among people who use prohibited drugs in Sweden.

In Sweden, possession and personal use of drugs are criminalized since 1988, resulting in police work being directed towards minor drug offenses. Despite this, police and other authorities are encouraged to protect the health and wellbeing of people who use prohibited drugs (PWUPD). Knowledge is scarce on how this drug policy plays out in practice. This study therefore analyzes interviews with 20 PWUPD who visited harm reduction services and interacted with policing agents in Stockholm, Sweden. The analysis is based on the participants' narratives of drug policing, and it concerns how they produced themselves as subjects through relations between materiality and discourse. We utilize the concept of surveilled subjectivation to elucidate what the participants could do, what they knew and who they could be or become under drug policing. Four themes were identified illustrating the link between discourse and materiality in PWUPD's surveilled subjectivation: "Material aspects of surveillance"; "Resisting the 'drug abuser' identity"; "Fighting power with power"; and "Crossing boundaries and becoming-other". The participants described nonstop efforts to prevent their bodies, activities, belongings and environments from being enfolded by drug law enforcement, which otherwise would fuel even more surveillance. They therefore disassociated themselves from the "drug abuser" identity, and managed encounters with policing agents by keeping a low profile or acting compliantly. While the study highlights the skills and knowledges the participants deployed to navigate omnipresent drug policing, we conclude that their production of autonomous and empowered subjectivities would be facilitated if possession and use of drugs were no longer criminalized.

Humans↗
PubMed2017

Drug target ontology to classify and integrate drug discovery data.

BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome. RESULTS: As part of that effort, we have developed a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships. CONCLUSIONS: DTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/ , Github ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://bioportal.bioontology.org/ontologies/DTO ). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

Biological Ontologies↗
PubMed2026

Patterns of Drug Resistance, Drug Resistance Conferring Mutations and Genomic DNA Methylation Revealed in Mycobacterium tuberculosis From South Africa.

Tuberculosis remains a major public health threat globally, with drug-resistant strains undermining treatment efficacy. We analyzed 126 Mycobacterium tuberculosis (M. tuberculosis) isolates with diverse drug resistance spectra and selected 35 for whole genome sequencing (WGS) using Illumina NextSeq, SMRT PacBio Onso and SMRT PacBio Revio sequencing platforms. The study aimed to characterize drug resistance profiles, compare short- and long-read sequencing performance, identify lineages among South African isolates, detect known drug resistance mutations and their lineage-specific patterns, and utilize long-read SMRT platforms for epigenetic profiling. Multiple drug resistance mutations were identified, some lineage-specific, and notably, East-African-Indian (EAI) Lineage 1 isolates often considered less pathogenic, showed significant potential for multidrug-resistance development, including higher fluoroquinolone resistance as compared to other lineages. Three DNA motifs with methylated adenines, namely CACGCaG, CtCCaG and GaTNNNNRtAC, were detected, with methylation patterns varying by lineage and strain due to mutations in the corresponding methyltransferases (MTases). A particularly notable finding was the stable maintenance of a genetic heterogeneity in the mamB MTase, performing methylation at CACGCaG motifs. These results highlight the combined role of genetic and epigenetic variation in M. tuberculosis adaptive evolution and underscore the value of integrating long-read sequencing into TB surveillance and research.

Mycobacterium tuberculosis↗
PubMed2026

The impact of drug decriminalization policy on mental health- and substance-related service utilization among people who use drugs with prior care in British Columbia.

BACKGROUND: In 2023, British Columbia implemented a pilot illicit drug decriminalization policy aimed at addressing the high burden of illicit drug toxicity deaths, creating a need to examine its health system impacts. Although research on decriminalization has largely focused only on substance-related outcomes, broader mental health service utilization, including substance-related mental health care, among people who use drugs remains an insufficiently studied domain that this study seeks to address. METHODS: In this single interrupted time series analysis of prevalent people who use drugs with prior care (PWUD-PC) in BC, we examined the proportion of people who accessed mental health and substance-related (MH-SR) services and their average monthly MH-SR visits one year before and after the decriminalization policy, stratified by physician visits, emergency department visits, and hospitalizations. RESULTS: The population prevalence of PWUD-PC remained stable at 1.8% before and after decriminalization, and no statistically significant changes were observed in the proportion of PWUD-PC accessing MH-SR physician services and hospitalizations. The early period following decriminalization did not produce large shifts in overall MH-SR service use among PWUD-PC. Some movements in trends were seen in emergency department use, while several other outcomes, particularly in hospitalizations and physician visits, continued pre-existing trends. CONCLUSION: Overall, we found stable patterns of MH-SR service engagement across the intervention period and small shifts in the post-intervention trends of average MH-SR service visits. These findings suggest that the early period following decriminalization did not lead to abrupt or large shifts in MH-SR service use among PWUD-PC.

Humans↗
PubMed2025

The causal relationship between antihypertensive drugs and knee osteoarthritis: A drug target Mendelian randomization study.

Recently studies have revealed a robust association between hypertension and knee osteoarthritis (KOA), with patients likely to suffer from both conditions. We employed Mendelian randomization (MR) analysis to assess the impact of antihypertensive medications on KOA, aiming to offer clinical guidance for concomitant drug therapy and identify potential therapeutic targets for KOA. We obtained exposure instruments (instrumental variables) by locating Single-nucleotide polymorphisms related to systolic blood pressure near drug target genes. We then conducted Mendelian randomization analyses between the exposure data and genome-wide association studies data on KOA to evaluate the impact of antihypertensive drugs on KOA. We observed a significant association between decreased expression of the SLC12A2 target gene and a reduced risk of KOA (odds ratio: 0.915, 95% confidence interval: 0.869-0.964, P&#x2005;<&#x2005;.001). In this study, we find that SLC12A2 inhibitors can have a beneficial effect on KOA, and that the SLC12A2 gene may be a potential therapeutic target for KOA. These findings suggest that when treating patients with both hypertension and KOA, clinicians may consider prioritizing the use of SLC12A2 inhibitors.

Humans↗
PubMed2026

Novel potential treatment options for infections caused by multi-drug and extensively drug-resistant Neisseria gonorrhoeae strains.

INTRODUCTION: Neisseria gonorrhoeae has evolved antimicrobial resistance (AMR) since antimicrobial treatment of gonorrhea was introduced. The AMR development is driven by the bacterium's high capacity for genetic adaptation, antimicrobial overuse and misuse, and insufficient surveillance. Novel therapeutic options are urgently needed. AREAS COVERED: This review summarizes novel gonorrhea treatment options, with special emphasis on the novel oral antimicrobials zoliflodacin and gepotidacin that obtained US FDA-approval for treatment of uncomplicated urogenital gonorrhea in December 2025. It also highlights compounds in early clinical or preclinical development that have demonstrated promising in vitro activity against N. gonorrhoeae. EXPERT OPINION: Zoliflodacin and gepotidacin have the potential to optimize gonorrhea management as oral alternatives to current injectable ceftriaxone. Their successful long-term use will depend on optimized use strategies, including indications, evidence-based approved dosing, adherence, surveillance, and population-specific considerations. Public-health agencies and clinicians will need to balance broad clinical access with antimicrobial stewardship measures to delay the AMR emergence. Looking ahead, gonorrhea management will hopefully shift from empirical, syndromic treatment toward etiology-guided and AMR-informed therapy, driven by advances in rapid point-of-care testing and whole-genome sequencing technologies. Continuous phenotypic and genomic surveillance remains essential to detect early AMR signals, transmission of AMR strains, and inform treatment guidelines.

AMR↗
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

Cardioprotective glucose-lowering drugs, statins, and secondary major adverse cardiovascular events: a nationwide cohort study of individuals with type 2 diabetes and cardiovascular disease.

BACKGROUND: In type 2 diabetes, cardioprotective glucose-lowering drugs, including sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists, and statins reduce the risk of secondary major adverse cardiovascular events. No trials examined the combination of these drugs as withholding statins in high-risk individuals would be unethical. We tested the hypothesis that cardioprotective glucose-lowering drug and statin combined is associated with lower risk of secondary major adverse cardiovascular events than either drug alone. METHODS: Individuals with type 2 diabetes and established cardiovascular disease from December 2012 through 2021 were identified via national Danish health registers. They were analyzed in an active comparator cohort including 15,404 individuals followed from treatment intensification with cardioprotective glucose-lowering drug or dipeptidyl peptidase-4 inhibitor and additionally in a time-varying cohort including 76,853 individuals with yearly updated treatment and covariate status. The treatment groups were: (i) no cardioprotective drug (no use of cardioprotective glucose-lowering drug or statin), (ii) cardioprotective glucose-lowering drug, (iii) statin, and (iv) cardioprotective glucose-lowering drug and statin. The primary outcome was a new major adverse cardiovascular event (myocardial infarction, stroke, or cardiovascular death). RESULTS: During mean 2.7 and 4.7&#xa0;years of follow-up, 1,843 and 23,051 had major adverse cardiovascular events in the active comparator and time-varying cohorts. In the active comparator cohort, when compared to nonusers of cardioprotective glucose-lowering drug or statin, multivariable adjusted hazard ratios of major adverse cardiovascular events were 0.82 (95% confidence interval: 0.67 to 1.02) for cardioprotective glucose-lowering drug, 0.85 (0.74 to 0.97) for statin, and 0.71 (0.60 to 0.84) for cardioprotective glucose-lowering drug and statin combined. Corresponding hazard ratios in the time-varying cohort were 0.77 (0.69 to 0.86), 0.73 (0.70 to 0.75), and 0.57 (0.54 to 0.60), respectively. When restricting the active comparator cohort to individuals entering observation between 2019 and 2021 with reduced statistical power, the corresponding hazard ratios were 0.86 (0.57 to 1.31), 0.89 (0.60 to 1.30), and 0.79 (0.54 to 1.14), respectively. In both cohorts p for interaction between cardioprotective glucose-lowering drug and statin was&#x2009;>&#x2009;0.05. CONCLUSIONS AND RELEVANCE: In individuals with type 2 diabetes and established cardiovascular disease, treatment with a cardioprotective glucose-lowering drug and statin in combination was associated with lower risk of secondary major adverse cardiovascular events than using either drug alone. This is important given the persistently suboptimal uptake of both drug classes in real-world practice. Some biases can never be completely excluded in real-world pharmacotherapy use studies such as this one, including confounding by indication, time-related or immortal time biases, and shifting standards of care, rendering causal interpretation unattainable; however, our results seemed consistent across numerous sensitivity analyses and designs.

Humans↗
PubMed2025

Whole genome sequencing-based detection of extensively drug-resistant tuberculosis from Ethiopia.

BACKGROUND: Rapid and accurate detection of extensively drug-resistant tuberculosis is crucial for effective intervention. Next-generation sequencing technologies have been recommended to rapidly and accurately detect resistance to second-line anti-TB drugs. We deployed whole-genome sequencing to detect mutations associated with drug resistance in pre-extensively drug-resistant tuberculosis and extensively drug-resistant tuberculosis strains in Ethiopia. METHODS: This report is part of the routine laboratory-based drug-resistance surveillance in Ethiopia. Among 15 pre-extensively drug-resistant tuberculosis and extensively drug-resistant tuberculosis isolates identified during the study period, eleven isolates were retrieved by Whole-genome sequencing. Illumina NextSeq 550 instruments were used to generate genomic data. Lineage and drug-resistance prediction were performed with Tuberculosis Profiler, while phylogeny was conducted by IQ-tree. RESULTS: Of the genotyped isolates, whole-genome sequencing identifies five extensively drug-resistant tuberculosis and four pre-extensively drug-resistant tuberculosis strains. It detects fluoroquinolone resistance mutations gyrA (Ala90Val, Asp94Tyr, Asp94Gly). Bedaquiline resistance mutations are found in atpE (Glu61Asp) and Rv0678 (139dupG, 141 and 142dupTC). Cross-resistance is identified between bedaquiline and clofazimine (n&#x2009;=&#x2009;4) and delamanid and pretomanid (n&#x2009;=&#x2009;1). Concordance result is observed between phenotypic drug-susceptibility testing and whole-genome sequencing for eight cases, while three cases are discordant (fluoroquinolones, delamanid, and pretomanid). Phylogenetic analysis reveals three major lineages: Lineage 4 (Euro-American, n&#x2009;=&#x2009;6 isolates), Lineage 3 (East African-Indian, n&#x2009;=&#x2009;3 isolates), and Lineage 1 (Indo-Oceanic, n&#x2009;=&#x2009;2 isolates). CONCLUSIONS: Whole-genome sequencing identifies dominant mutations in genes such as gyrA, atpE, and Rv067 that are associated with resistance to second-line anti-tuberculosis drugs. Significant cross-resistance is observed between key second-line drugs, bedaquiline and clofazimine, as well as delamanid and pretomanid. This finding highlights the need for routine genomic surveillance to detect drug resistance early, improve treatment outcomes, and prevent transmission.

Journal Article↗
PubMed2024

Shared etiology of Mendelian and complex disease supports drug discovery.

BACKGROUND: Drugs targeting disease causal genes are more likely to succeed for that disease. However, complex disease causal genes are not always clear. In contrast, Mendelian disease causal genes are well-known and druggable. Here, we seek an approach to exploit the well characterized biology of Mendelian diseases for complex disease drug discovery, by exploiting evidence of pathogenic processes shared between monogenic and complex disease. One way to find shared disease etiology is clinical association: some Mendelian diseases are known to predispose patients to specific complex diseases (comorbidity). Previous studies link this comorbidity to pleiotropic effects of the Mendelian disease causal genes on the complex disease. METHODS: In previous work studying incidence of 90 Mendelian and 65 complex diseases, we found 2,908 pairs of clinically associated (comorbid) diseases. Using this clinical signal, we can match each complex disease to a set of Mendelian disease causal genes. We hypothesize that the drugs targeting these genes are potential candidate drugs for the complex disease. We evaluate our candidate drugs using information of current drug indications or investigations. RESULTS: Our analysis shows that the candidate drugs are enriched among currently investigated or indicated drugs for the relevant complex diseases (odds ratio&#x2009;=&#x2009;1.84, p&#x2009;=&#x2009;5.98e-22). Additionally, the candidate drugs are more likely to be in advanced stages of the drug development pipeline. We also present an approach to prioritize Mendelian diseases with particular promise for drug repurposing. Finally, we find that the combination of comorbidity and genetic similarity for a Mendelian disease and cancer pair leads to recommendation of candidate drugs that are enriched for those investigated or indicated. CONCLUSIONS: Our findings suggest a novel way to take advantage of the rich knowledge about Mendelian disease biology to improve treatment of complex diseases.

Humans↗
PubMed2026

Proteomics-Driven Strategies for Proximity-Inducing Drug Discovery.

In recent years, proximity-inducing drugs have emerged as a novel therapeutic modality that induces or stabilizes protein-protein interactions, especially by recruiting effector proteins to specific target proteins, thereby achieving functions beyond traditional inhibitors. The potential of proximity-inducing drugs extends beyond targeted protein degradation (TPD), as studies have demonstrated their ability to regulate biological processes such as signal transduction, gene transcription, chromatin regulation, and protein trafficking by modulating protein interaction networks. Rational discovery of proximity-inducing drugs requires clarifying their effects on protein-protein interactions, determining drug selectivity, and developing suitable ligands for drug construction. Proteomics has become a central technology in drug discovery, enabling global identification of the direct drug targets and systematic characterization of proteome-wide downstream responses. This provides a more refined map of drug mechanisms. In parallel, advances in machine learning applied to proteomic data, together with the expansion of proteome-wide ligandability maps, are further accelerating the discovery and optimization of proximity-inducing drugs. This review summarizes recent advances of proximity-inducing drugs, with a particular emphasis on how proteomics facilitates target space expansion, drug efficacy optimization, and ligandability discovery, alongside the emerging contributions of machine learning. Collectively, these insights aim to support the rational development of next-generation proximity-inducing drugs.

Drug Discovery↗
PubMed2025

Transfer learning with multiomics integration and deep neural networks reveals drug resistance mechanisms in cancer.

Drug resistance remains one of the primary challenges in effective cancer therapy. In this study, we employed a deep neural network (DNN)-based transfer learning (TL) approach to predict drug response and uncover drug resistance mechanisms. We integrated gene expression, somatic mutation, and copy number aberration (CNA) data with drug response profiles using multi-omics integration (MI). We used the Genomics of Drug Sensitivity in Cancer (GDSC) data for training and incorporated drugs with same pathways into the training models. We then evaluated drug response predictions on independent in-vivo PDX Encyclopedia (PDX) and ex-vivo the Cancer Genome Atlas (TCGA) datasets. In addition, we conducted pathway enrichment analyses to elucidate the mechanisms underlying drug resistance for paclitaxel, 5-fluorouracil (5-FU), gemcitabine, and cetuximab. We also applied Fisher's exact test (FET) to assess potential associations between drug resistance and the presence of mutations or CNAs. Our pan-drug models outperformed other methods based on the area under the precision-recall curve (AUCPR). Our pathway enrichment analyses revealed LDHB-mediated pyruvate metabolism and FYN-mediated focal adhesion might have pivotal roles in paclitaxel resistance, while PINK1-mediated mitophagy might be critical in 5-FU resistance. In addition to transcriptional activation, FET suggested that CNAs in LDHB and PINK1 may also be associated with resistance to paclitaxel and 5-FU, respectively. Furthermore, enrichment results for paclitaxel and cetuximab indicated shared resistance mechanisms between the two drugs. Importantly, our findings are consistent with prior experimental studies, providing literature-based validation of our results. Overall, our DNN-based TL approach achieved strong predictive performance across PDX & TCGA datasets and enrichment analyses provided valuable biological insights into drug resistance mechanisms.

Humans↗
PubMed2025

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations between within and outside biological pathways, and utilized these pathway difference values as cell line features, combined with the drugs' SMILES chemical structure information as inputs to the model. Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. Finally, a multilayer perceptron (MLP) outputs the final predictions of drug response. Our model exhibits higher accuracy in predicting the sensitivity to anticancer drugs comparing with other methods proposed recently. It is found that PARP inhibitors, and Topoisomerase I inhibitors were particularly sensitive to SCLC when analyzing the drug response predictions for lung cancer cell lines. Additionally, the model is capable of highlighting biological pathways related to cancer and accurately capturing critical parts of the drug's chemical structure. We also validated the model's clinical utility using clinical data from The Cancer Genome Atlas. In summary, the PASO model suggests potential as a robust support in individualized cancer treatment. Our methods are implemented in Python and are freely available from GitHub (https://github.com/queryang/PASO).

Deep Learning↗
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&#x2009;=&#x2009;1.02, 95%CI&#x2009;=&#x2009;1.01-1.04, FDR adjusted P&#x2009;=&#x2009;1.69&#x2009;&#xd7;&#x2009;10-2; OR in putamen&#x2009;=&#x2009;1.02, 95% CI&#x2009;=&#x2009;1.01-1.03, PFDR&#x2009;=&#x2009;3.37&#x2009;&#xd7;&#x2009;10-2, OR in nucleus accumbens&#x2009;=&#x2009;1.02, 95% CI&#x2009;=&#x2009;1.01-1.04, PFDR&#x2009;=&#x2009;3.37&#x2009;&#xd7;&#x2009;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↗
PubMed2026

First-line drug-resistant tuberculosis among children under 15 years in Ethiopia: insights from phenotypic and whole-genome sequencing approaches.

BACKGROUND: Childhood drug-resistant tuberculosis is often underdiagnosed and inadequately characterized due to the paucibacillary nature of the disease. This study aimed to assess resistance to first-line anti-tuberculosis drugs in children using phenotypic drug susceptibility testing and whole-genome sequencing. METHODS: A retrospective-prospective study was conducted on culture-confirmed childhood tuberculosis cases in Ethiopia (2017&#x2013;2023). Phenotypic drug susceptibility testing was performed on 110 Mycobacterium tuberculosis complex isolates. Whole-genome sequencing was completed for 85 of these isolates, which were analyzed using the TB-Profiler and MTBSeq pipelines. We assessed the sensitivity, specificity, predictive values, and kappa agreement of whole-genome sequencing compared with phenotypic drug susceptibility testing. RESULTS: Phenotypic resistance to at least one first-line anti-TB drug was observed in 26/110 (23.6%) of the examined isolates, with isoniazid resistance being the most frequent, 23/110 (20.9%), followed by rifampicin resistance, 18/110 (16.4%). TB-Profiler showed almost perfect agreement with phenotypic drug susceptibility testing for rifampicin (sensitivity 94.4%, kappa&#x2009;=&#x2009;0.96) and isoniazid (sensitivity 91.3%, kappa&#x2009;=&#x2009;0.91), whereas MTBSeq showed slightly lower performance. Both pipelines demonstrated moderate to weak agreement with phenotypic drug susceptibility testing for detecting resistance to ethambutol, pyrazinamide, and streptomycin. The most frequently observed resistance mutations among phenotypically resistant isolates were rpoB (Ser450Leu), katG (S315Thr), embB (Met306Ile), and pncA (C-11&#xa0;A&#x2009;>&#x2009;G) for rifampicin, isoniazid, ethambutol, and pyrazinamide, respectively. Discrepancies between genotypic and phenotypic drug susceptibility testing were observed across all first-line anti-TB drug-resistant isolates, particularly for ethambutol and pyrazinamide. CONCLUSION: We found a high prevalence of isoniazid resistance, along with rifampicin resistance, underscoring the need for early detection in vulnerable groups. Whole-genome sequencing showed good accuracy for these drugs, with TB-Profiler performing best. CLINICAL TRIAL NUMBER: Not applicable.

Humans↗