The Pharmacogenomics Journal

Drug repurposing offers a practical strategy to identify new therapeutic uses for approved drugs, potentially reducing the time and cost associated with conventional drug development. We present a novel three-stage drug repurposing pipeline that integrates knowledge graph-based gene prediction, network-based drug-disease association analysis, and systematic classification of candidate drugs by therapeutic class. The pipeline integrates DGLinker to predict novel disease-associated genes, SAveRUNNER to identify drug repurposing candidates, and ATC Category Enrichment Analysis (ATCEA) to prioritise candidates by pharmacological class. We benchmarked the pipeline across twelve diseases using DrugBank and MEDI2-HPS as validation resources. Utilising DGLinker-expanded disease-gene sets as input increased the number of predicted repurposed drugs, while overall discriminative performance remained stable across diseases (AUROC 0.71-0.77). Application of ATCEA consistently improved precision, F1-score, and specificity, while reducing recall, reflecting a conservative prioritisation strategy that contracts the candidate space while retaining pharmacologically coherent drug-disease candidates. We further applied the pipeline to amyotrophic lateral sclerosis (ALS), a neurodegenerative disease with limited therapeutic options, and performed a deeper literature-based validation of the results. Incorporation of DGLinker-predicted genes substantially increased the number of significant candidate drugs and uncovered enriched ATC categories not identified using known ALS genes alone, including antidepressants and antipsychotics. Moreover, several drugs with supporting evidence available in the literature were identified only when DGLinker-predicted genes were used. Overall, 77 candidate drugs were prioritised within significantly enriched ATC categories, several of which are supported by previously published studies. To provide exploratory real-world support for these findings, we further evaluated candidate drugs in a longitudinal electronic health record (EHR) dataset of 2361 patients with ALS from King's College Hospital. Although the number of evaluable drugs was limited due to sample size, the EHR analysis provided additional clinically relevant context for selected prioritised drugs and pharmacological classes. Our pipeline demonstrates potential to accelerate drug repurposing by integrating complementary computational approaches to each step of the process, providing an end-to-end framework that showed robust performance across benchmarking experiments and use cases.

BackgroundAddressing the underlying causes of atrial fibrillation (AFib) is critically important. While potential AFib-related genes have been recognized, the impact of modifying these genes in humans remains poorly understood. ObjectiveWe assessed the cellular dependencies of 309 genes previously associated with AFib through genome-wide association studies using data from the Cancer Dependency Map project, aiming to prioritize potential therapeutic targets with minimal off-target effects. MethodsWe analyzed CRISPR-Cas9 knockout (CHRONOS scores) and RNA interference (RNAi) knockdown (DEMETER2 scores) screening data from 1,927 human cell lines across 24 tissue types, focusing on tissues associated with AFib initiation, presentation, and progression: autonomic ganglia, central nervous system (CNS), and soft tissue. We examined the expression and dependency scores of the AFib-associated genes, identifying significant correlations between gene expression and cellular dependency within specific tissues using Pearson correlation coefficients and controlling the false discovery rate (FDR) at 5%. ResultsOut of the 309 AFib-associated genes, 206 genes (66.7%) had CHRONOS dependency scores and 229 (74.1%) had DEMETER2 dependency scores available. Several genes showed significant negative dependency scores (CHRONOS < -0.5) across multiple tissues, indicating potential off-target effects if inhibited. In contrast, we identified 12 genes with significant expression-driven dependencies within AFib-associated tissues. In CNS cell lines, HAND2 (R = -0.456, FDR = 0.002) and VGLL2 (R = -0.434, FDR = 0.005) showed significant negative correlations between gene expression and cellular dependency. In soft tissue cell lines, BEST3 (R = -0.679, FDR = 0.001) and PITX2 (R = -0.679, FDR = 0.001) also demonstrated strong negative correlations. Additionally, ERBB4 in CNS lines showed a significant negative correlation (R = -0.361, FDR = 0.048). These findings suggest that inhibiting these genes may selectively affect high-expressing cells in AFib-associated tissues while minimizing effects on other tissues. ConclusionOur analysis identified HAND2, VGLL2, BEST3, and ERBB4 as potential therapeutic targets for AFib, demonstrating significant expression-driven dependencies in AFib-associated tissues with no pan-tissue essentiality. These results provide a quantitative basis for developing targeted therapies with reduced off-target effects. CONDENSED ABSTRACTAtrial fibrillation (AFIB) is one of the most common cardiac arrhythmias with numerous known risk factors. Although many AFIB-associated genes have been identified, the impact of screening or the effects of modifying these genes in humans remain poorly understood. We examined CRISPR knockout and RNAi knockdown screen data from nearly 2,000 human cell lines to assess the cellular dependencies of 309 genes associated with AFIB, previously identified through genome-wide association studies. Some genes demonstrate broad cell dependencies across various tissue types, indicating potential off-target effects if inhibited. Conversely, HAND2, VGLL2, BEST3, and ERBB4 were identified as genes of interest because their genetic knockouts specifically impacted high-expressing cells from tissue lineages pertinent to AFIB and/or were not pan-dependent. Overall, analyses of genetic screen data identified AFIB-associated genes whose knockout or knockdown selectively affected cell lines of relevant tissue lineages, prioritizing targets for potential AFIB treatments.

Motivation: Human liver microsome (HLM)-based metabolic stability assays are fundamental in early drug discovery, shaping pharmacokinetic profiles and oral bioavailability. However, these experimental assays are labor-intensive and time-consuming, limiting their application in large-scale virtual screening. Computational models can prioritize compounds at scale, yet most are classification-based, leaving quantitative and interpretable prediction of HLM half-life limited. Results: In this study, we developed a quantitative machine learning model for the direct prediction of HLM half-life (T1/2) by integrating 11,790 compounds combining in-house and curated public data. Among various combinations of molecular features and learning algorithms, the XGBoost model with RDKit 2D descriptors achieved the best predictive performance, with an RMSE of 0.507 and an R2 of 0.431 on an independent test set. Shapley Additive Explanations (SHAP) analysis identified lipophilicity and known metabolic soft-spot features as the primary contributors to the predictions. These results suggest that this quantitative approach provides a practical framework for defining metabolic stability margins, thereby supporting rapid Go/No-go decisions in preclinical drug discovery. Availability: The source code, data, and trained model are available at https://github.com/joshua-416/PredHLM.

Long COVID (LC) is a complex condition characterized by persistent, chronic multisystem manifestations, with a significant proportion of patients exhibiting neurological symptoms. Human ion channels (HICs), particularly potassium channels, are abundantly expressed in the nervous system and linked to key metabolic processes, making them potential candidates for understanding LC pathophysiology and drug repurposing. Meta-analysis of RNA-Seq datasets from COVID-19 recovered and LC patients was performed to identify altered HICs in LC. Differential gene expression analysis, functional enrichment analysis, and weighted gene co-expression network analysis (WGCNA) were performed to uncover key genes, pathways, and co-expression modules consisting of HICs, lipid metabolism-, and immune signaling-related genes. Drug-gene interaction analysis was performed to identify approved drugs targeting potential HICs. A total of 715 dysregulated genes, including eighteen HICs were identified, among which seven were potassium channels. Three significant modules containing HICs, lipid metabolism-, and immune signaling-related genes were identified and found to be associated with antigen processing and presentation, complement and coagulation cascades, and cytokine-related pathways. Approved drugs targeting KCNA6, KCNJ10, KCNN3, and KCNH4 were identified. With further experimental validation, these dysregulated potassium channels, supported by their co-expression networks and pathway associations, may act as potential candidates for drug repurposing in LC patients.

Drug repurposing -- the application of approved or shelved compounds to new therapeutic indications -- offers a cost- and time-efficient alternative to de novo drug discovery. However, the systematic identification of repurposing candidates from the rapidly expanding body of clinical trial data remains a significant challenge. Here I present a publicly accessible AI-powered tool that mines the ClinicalTrials.gov registry to identify approved drugs with under-explored therapeutic potential in high-value disease areas. The tool integrates natural language processing, mechanism-of-action pathway analysis, and trial density scoring to surface candidates where biological plausibility is high and clinical trial coverage is sparse. I demonstrate the tools utility across six cross-therapeutic case studies spanning oncology, cardiology, neurology, rare diseases, immunology, and infectious disease. Key findings include: the identification of Zonisamide as an under-explored combination candidate for obesity alongside GLP-1 receptor agonists; mechanistic validation of SGLT2 inhibitors in heart failure with preserved ejection fraction (HFpEF); and a novel cross-domain mapping of anti-TNF biologics to early-stage neurodegeneration via shared neuroinflammatory pathways. The tool is freely accessible and designed to lower the barrier for academic and industry researchers to systematically pursue repurposing opportunities.

Background: The Cilostazol Albumin Treatment in Subarachnoid Hemorrhage (CATS) trial evaluates eight active cilostazol-human albumin regimens plus control in patients with aneurysmal subarachnoid hemorrhage. We summarized the rationale for the primary statistical design, compared alternative Phase II methodologies, and evaluated reduced-arm sensitivity scenarios. Methods: The binary primary endpoint is Common Data Elements-defined delayed cerebral ischemia within 14 days after randomization. The selected design is Bayesian adaptive, with a burn-in phase, response-adaptive randomization among active arms while maintaining fixed control allocation, four interim analyses, early stopping for expected success or futility, and a two-dimensional normal dynamic linear model. Primary operating characteristics were obtained from 1,000 virtual trials per scenario using Fixed and Adaptive Clinical Trial Simulator version 7.0.0. Exploratory simulations evaluated six-, four-, and two-active-arm configurations and simplified alternative designs. Results: Compared with fixed equal allocation, the Bayesian adaptive design preserved an approximately 10% false-success probability under the global null while improving probability of success and efficiency in clinically relevant scenarios. Under the Realistic scenario, probability of success increased from 0.61 to 0.86, expected sample size decreased from 400 to 308, and expected duration decreased from 235 to 187 weeks. Under common thresholds, null probability of success was 0.098 for the full anchor and 0.073 for Reduced-6; Reduced-6 probabilities of success were 0.774 and 0.765 in the Realistic and Realistic2 scenarios. However, Reduced-6 omitted two monotherapy anchors and was less robust in Backwards2. In the comparator simulation, the selected design had probability of success of 0.858 and expected sample size of 308.3 under the Realistic scenario, compared with 0.624 to 0.845 and approximately 352 to 400 for simplified comparators. Conclusions: For identifying the most promising cilostazol-human albumin regimen for Phase III rather than confirming efficacy, the Bayesian response-adaptive design with two-dimensional normal dynamic linear model borrowing is more efficient and better aligned than simplified comparators. The full nine-arm design remains preferable because it preserves the complete therapeutic discovery space and is more robust to misspecified or non-smooth response surfaces.

Background: Stress-induced childhood-onset neurodegeneration with variable ataxia and seizures (CONDSIAS) is a rare autosomal recessive disorder caused by biallelic variants in ADPRHL2, which encodes ADP-ribosylhydrolase 3 (ARH3), a key enzyme involved in poly (ADP-ribose) (PAR) metabolism. Although Minocycline has been reported to attenuate PAR-mediated neurotoxicity primarily through modulation of PARP-dependent pathways, whether it may also interact with ARH3 or influence the structural behavior of pathogenic ARH3 variants remains unknown. This study was designed to explore this possibility by integrating clinical observation with computational structural analyses. Methods: Comprehensive clinical evaluation, targeted Sanger sequencing, and in silico pathogenicity analyses were performed. Protein modeling, molecular docking, and 100-ns molecular dynamics simulations were conducted to evaluate the predicted structural consequences of the p.Thr79Pro variant and to explore potential interactions between ARH3 and Minocycline. Results: A homozygous ADPRHL2 variant (NM_017825.3:c.235A>C; p.Thr79Pro) was identified in a child with CONDSIAS. Computational analyses predicted reduced structural stability and increased conformational flexibility of the mutant ARH3 protein relative to the wild-type structure. MM-GBSA calculations estimated differences in binding free energies between the wild-type (-34.51 kcal/mol) and mutant (-39.76 kcal/mol) ARH3-Minocycline complexes, suggesting subtle differences in their predicted energetic profiles. Clinically, neurological progression appeared stable, with improved motor function observed during approximately one year of follow-up and no notable treatment-related adverse effects. Conclusions: By integrating clinical observations with computational structural analyses, this study provides preliminary computational support for the hypothesis that Minocycline may influence ARH3 conformational behavior in addition to its proposed effects on PARP-dependent pathways. Although these findings do not demonstrate direct molecular binding or therapeutic efficacy, they provide a biologically plausible framework for future biochemical, cellular, and functional investigations. Keywords: CONDSIAS; ADPRHL2; ARH3; Minocycline; molecular docking; molecular dynamics simulation; structural bioinformatics; translational medicine

Background: Cannabis is one of the most widely used psychoactive substances worldwide. {Delta}-tetrahydrocannabinol ({Delta}-THC) is the main contributor to cannabis-induced effects such as euphoria, anxiety, and psychotomimetic effects, and is metabolized by several hepatic enzymes, including CYP3A4. There are interindividual differences in how cannabis affects users, which have substantial genetic contributors. Methods: We examined how real-time effects of {Delta}-THC on psychotomimetic measures and on subjective effects of "high", sadness and anxiety in 188 healthy volunteers in a laboratory infusion paradigm, relate to polygenic risk scores (PRS) for cannabis lifetime use (CanLU), cannabis use disorder (CanUD), and CYP3A4 expression. Results: CYP3A4 expression PRS was significantly associated with {Delta}-THC-induced psychotomimetic effects. Genetic liability to use and misuse cannabis is potentially associated with lower {Delta}-THC-induced psychotomimetic symptoms. CanLU PRS nominally predicted enhanced {Delta}-THC-induced "high", while CanUD PRS predicted it to be lower. Conclusions: Our findings suggest that genetic liability to produce more CYP3A4 enzyme may be associated with faster {Delta}-THC degradation and the consequential diminution of the latter's effects. Nominal effects suggest that aversive outcomes may reduce cannabis use and use disorder genetic liability, and that CanUD subjects may need higher {Delta}-THC doses to experience euphoria ("high"). In total, this study provides novel insights regarding some of the specific genetic factors that influence interindividual variability in {Delta}-THC effects, mainly via {Delta}-THC metabolism.

Abstract Background Non-cardiac chest pain (NCCP) is commonly regarded as a low-risk condition. However, long-term mortality, cause-specific death, and high-risk subgroup characteristics remain poorly defined. Methods In this multicentre registry-linked cohort study, we linked the Chest Pain Center Registry from 101 hospitals in Hunan, China, with the Mortality and Cause of Death Registry. Adults diagnosed with NCCP from Jan 1, 2017, to Dec 31, 2021, were included. We assessed 3-year all-cause, cardiovascular, and non-cardiovascular mortality using Cox, restricted cubic spline, and Fine-Gray models. Findings Among 160,245 patients, 4674 deaths occurred within 3 years (2.9%). Mortality increased sharply after 60.5 years. Age [&ge;] 60.5 years (adjusted hazard ratio [aHR] 7.49 [95% CI 6.89-8.14]), rural residence (time-varying aHR 1.46 [1.35-1.57] in year 1 and 1.66 [1.46-1.89] in years 1-3), and male sex (aHR 1.47 [1.38-1.57]) independently predicted death. Three-year mortality ranged from 0.3% in younger urban women to 8.4% in older rural men. Cardiovascular diseases accounted for 56.4% of deaths among older patients, whereas other non-cardiovascular causes (22.8%) and malignancy (20.8%) were the largest categories among younger decedents. Interpretation NCCP is not uniformly benign. Age, rural residence, and sex identify patients who could benefit from risk-stratified follow-up, with cardiovascular prevention prioritised for older rural men and broader non-cardiovascular assessment considered for younger patients.

Background: Osimertinib is the standard first-line treatment for EGFR- mutant non-small cell lung cancer (NSCLC) harboring common activating mutations, including exon 19 deletions and L858R. It is also active against tumors with acquired T790M resistance. However, the EGFR L858R+T790M compound mutation, where both variants co-occur within the same tumor, may confer distinct drug-sensitivity profiles not predicted by either mutation alone. Limited data exist on comparative treatment outcomes in this rare genotype. Methods: Using the MSK-CHORD clinicogenomic dataset (n=24,950), we identified patients with concurrent EGFR L858R and T790M mutations receiving erlotinib (Erlo) or osimertinib (Osi) monotherapy. Real-world progression-free survival (rwPFS) per treatment line was calculated using a strict definition requiring confirmed radiological progression events (rwPFS-strict), excluding lines with null endpoint data. Kaplan-Meier analysis, log-rank testing, Cox proportional hazards regression, and cross-cohort heterogeneity testing (Cochran's Q statistic) were performed. Two control cohorts, L858R-only (n=372) and T790M-only (n=76), were analyzed in parallel to assess mutation-context specificity of treatment response. Results: Thirty-one patients with EGFR L858R+T790M were identified; 21 contributed evaluable monotherapy lines, yielding 23 Erlo and 15 Osi treatment lines (14 unique patients per treatment group, 7 contributing to both). Median rwPFS numerically favored Erlo over Osi (7.10 vs 5.32 months; HR 1.29, 95% CI 0.66-2.52; log-rank p=0.46). This directional trend was reversed in the L858R-only control cohort, where Osi demonstrated significant superiority (9.03 vs 5.75 months; HR 0.70, 95% CI 0.55-0.89; p=0.003). The T790M-only cohort showed no significant difference (HR 1.32, p=0.12). An exploratory post-hoc heterogeneity test confirmed a significant cross-cohort interaction (Q=9.94, df=2, p=0.007). Conclusions: The expected osimertinib advantage was absent in L858R+T790M compound-mutant NSCLC. The opposing hazard ratio directions across mutation contexts (HR 1.29 vs 0.70), with a significant exploratory cross-cohort interaction (p=0.007), suggest that the EGFR L858R+T790M compound mutation may represent a pharmacologically distinct entity with differential TKI sensitivity. These hypothesis-generating findings warrant prospective validation.

Objective: To develop and validate clinically relevant polygenic scores (PGS) for alcohol (AUD), cannabis (CanUD), opioid (OUD), tobacco (TUD), and polysubstance use disorders (polySUD) across African (AA), European (EA), and Latinx (LA) ancestry populations. Methods: Using multiple genome-wide association study summary statistics and PGS methods, substance use disorder PGS were developed and evaluated in Indiana Biobank samples (IB, N: 1,356-24,989), then top-performing PGS were validated in All of Us Research Program samples (AOU, N: 62,389-209,952). Case and controls were defined using ICD-9/10 codes. All participants were aged 18 years or older (>=21 years for AUD controls). Clinical relevance was defined as an odds ratio (OR) >=2 for individuals with the highest PGS determined based on disorder prevalence compared to everyone else. Results: In EA and LA, all PGS achieved clinically relevant performance in both IB and AOU (ORs: 2.00-9.10; P <= 3.87E-4). In AA, PGS met this threshold in IB (ORs: 2.02-2.71; P <= 2.20E-4) but not in AOU (ORs: 1.28-1.56; P <=0.03). Overall, OUD PGS showed the strongest associations in most analyses, followed by CanUD and polySUD. Generally, compared to female PGS, male PGS had higher or comparable ORs, but the differences were not significant except AUD PGS in AOU LA. Conclusions: PGS demonstrated clinically meaningful risk prediction for substance use disorders in EA and LA, supporting the feasibility of future clinical implementation for population-level screening. However, reduced performance in AA underscores the urgent need for more genetic studies in that population.

This study enrolled 152 hypertensive patients with an ARR > 3.7 to assess the relationship between the traditional hypokalemia cutoff (3.5 mmol/L) and primary aldosteronism (PA) screening, and to establish a new cutoff. Under the traditional cutoff, only 35.7% of PA patients presented with hypokalemia. ROC curve analysis identified a new cutoff of 4.22 mmol/L, which increased sensitivity from 35.7% to 77.5%, with a specificity of 91.1% and an AUC of 0.897. The findings indicate that the traditional cutoff is insufficiently sensitive, while the new cutoff markedly improves screening sensitivity and facilitates early detection of PA.

Context Polyendocrine metabolic ovarian syndrome (PMOS), formerly known as polycystic ovary syndrome (PCOS), is a biologically heterogeneous disorder, yet previous clustering studies have reported inconsistent phenotype structures. Whether these discrepancies reflect methodological variability or genuine multidimensional disease biology remains unknown. Objective To determine whether independently derived endocrine, metabolic, inflammatory, and thyroid phenotypes represent the same underlying biological structure or capture distinct dimensions of PMOS heterogeneity. Design Cross-sectional observational study using a cross-space phenotyping framework. Setting Tertiary referral outpatient endocrinology and gynecology clinic. Participants A total of 1,286 women were diagnosed with PCOS according to the Rotterdam criteria. Methods Four predefined biological spaces (endocrine, metabolic, inflammatory, and thyroid) were analyzed independently. Within each space, standardized preprocessing, dimensionality reduction, and unsupervised clustering were performed. Cluster robustness was evaluated using bootstrap resampling, while agreement between independently derived phenotypes was quantified using the adjusted Rand index (ARI). Biological relevance was assessed using independent non-circular validation with variables excluded from phenotype derivation. Sensitivity analyses compared complete-case and imputed datasets. Results All four biological spaces produced highly stable clustering solutions (bootstrap ARI: endocrine 0.915, metabolic 0.964, inflammatory 0.930, thyroid 0.990). Despite this robustness, agreement between independently derived phenotypes remained consistently low. The highest concordance was observed between metabolic and inflammatory phenotypes (ARI = 0.208), followed by endocrine and metabolic phenotypes (ARI = 0.159), whereas agreement involving thyroid phenotypes was close to zero. Independent non-circular validation confirmed that all identified phenotypes represented biologically coherent patient subgroups beyond the variables used for clustering. Sensitivity analyses demonstrated high agreement between complete-case and imputed solutions, supporting the robustness of the findings. Conclusions Stable biological phenotypes exist within individual physiological domains of PMOS but do not converge into a single overarching biological phenotype. These findings support a multidimensional model of PMOS heterogeneity in which endocrine, metabolic, inflammatory, and thyroid systems describe complementary rather than interchangeable aspects of disease biology. Cross-space phenotyping provides a general framework for investigating biological heterogeneity in complex disorders and may facilitate future precision medicine approaches.

BACKGROUND: Despite effectiveness as a once-weekly antimalarial prophylaxis, mefloquine has fallen out of favor due to its neuropsychiatric side effects. While possible genetic susceptibilities have been identified in preliminary studies, pharmacogenomic testing guidance is not available for mefloquine. METHODS: Volunteers with a history of mefloquine exposure were recruited to a cross-sectional case-control study. Pharmacogenomic analysis was performed on 7 candidate genes of interest with 16 missense variants including ORM1, MTHFR, MDR1, PYK2, HT2A, ADA, and ADORA2A. RESULTS: Fifty participants enrolled including those who had mefloquine exposure and chronic adverse effects (AEs) lasting 6 months or longer (n = 23); with subsequent AEs less than 6 months (n = 12); no AEs (n = 8); and a control group with a history of post-traumatic stress disorder (PTSD) but no mefloquine exposure (n = 7). Psychometric testing showed that mefloquine users with AEs lasting 6 months or more and PTSD patients who had not used mefloquine reported more evidence of sleep impairment, balance and equilibrium disorders, and lower levels of psychological well-being than mefloquine users who reported without AEs or with AEs but lasted less than 6 months. The ADORA2A gene was found to carry a higher burden of variation among volunteers exposed to mefloquine with AEs compared to those who did not. The variant rs141942830 within ADORA2A was observed to be higher among cases compared to the reference allele frequency listed in the gnomAD database but was found to not be significantly enriched. In addition, MTFHR gene was found to be enriched for variation in volunteers with long-term side effects compared to those with short-term or no side effects. CONCLUSIONS: Volunteers who reported long-term adverse events after exposure to mefloquine had excess rare variation within the ADORA2A gene compared to those without adverse events and those with short term adverse events. The ADORA2A rs141942830 was identified as a new variant of interest, as it was elevated but not significantly enriched among cases of long-term AEs, compared to the population frequency reported by gnomAD. These non-silent variants may serve as mediators to alternate pathways for signal transduction or drug metabolism.

BACKGROUNDSpontaneous preterm birth (sPTB) remains the foremost cause of neonatal morbidity and mortality worldwide. Although histologic chorioamnionitis (HCA) and placental vascular abnormalities are frequently observed in sPTB, the molecular cascades linking these lesions to labor initiation remain poorly understood. Emerging evidence implicates circadian dysregulation and trophoblast dysfunction as additional drivers of sPTB. OBJECTIVEThis study aims to map placental pathology to distinct transcriptomic functional signatures that may precipitate sPTB, delineate the contribution of circadian regulation - both core-clock genes and circadian transcription-factor target sets (TFTs) - to sPTB, and identify placental cell-type-enriched and developmental pathway signatures that differ between sPTB and term deliveries. STUDY DESIGNWe performed bulk RNA sequencing on 32 formalin fixed, paraffin embedded placental specimens from 12 selected women (9 sPTB and 3 Term) in the POUCH Study cohort. Samples were selected for white ethnicity, maternal age 23-33years, and parity 1-4 to reduce heterogeneity within groups. An extraction-free HTG transcriptome panel assayed 19,398 protein-coding genes. Log2-fold changes of all genes were computed with limma adjusted for maternal age, gestational age, parity, placental region, placental pathology, and POUCHID (a clustering variable) for sPTB vs. Term and HCA/vascular lesion vs. no pathology (no placental pathology adjustment). Gene-set enrichment used 50 Hallmark sets (MSigDB) plus curated placental circadian, circadian TFT, cell-type, and developmental pathways or gene sets. RESULTSsPTB placentas displayed a global suppression of metabolic, secretory, and immune pathways (e.g., protein secretion, oxidative phosphorylation, Interferon responses, Complement, ROS, MYC Targets, TGF {beta}, mTORC1, and Coagulation) while KRAS Signaling Down and EMT were up-regulated. HCA-enriched sets (TNF/NF-{kappa}B, ROS, KRAS Up, IL-2/STAT5, Hypoxia, Interferon-{gamma}) were up-regulated, with EMT and Notch remaining down. Vascular abnormalities alone showed up-regulation of 12 Hallmark sets - including TGF-{beta}, TNF/NF-{kappa}B, ROS, pancreatic {beta}-cell stress, Hypoxia, Oxidative Phosphorylation, EMT, and mTORC1 - while Notch was down-regulated. When HCA co-exists with vascular abnormalities, the Hallmark profile becomes more inflammatory highlighting a synergistic exacerbation of innate immunity, oxidative stress, and programmed cell death with the 12 up-regulated sets (Complement, Interferon /{gamma}, TNF, ROS, Apoptosis, and Heme Metabolism). The exclusive downregulation of DNA Repair suggests compromised genomic integrity. Circadian gene-sets analysis revealed an up-regulated Regulation of Circadian Sleep Wake Cycle in sPTB but down-regulation of core clock pathway and suppressed circadian TF targets. Cell-type enrichment reveals increased trophoblast giant cells and IGFBP1-DKK1 positive fetal cells, with marked suppression of extravillous trophoblasts, syncytiotrophoblasts, villous cytotrophoblasts, and fetal myeloid cells. Placental developmental pathways were downregulated, indicating arrested trophoblast maturation. CONCLUSIONOur pilot analysis demonstrates sPTB placentas exhibit a global suppression of metabolic, secretory, and immune-modulatory programs and maladaptive trophoblast remodeling, whereas HCA and vascular abnormalities drove distinct inflammatory or hypoxic signatures. The shared and opposing Hallmark pathways across phenotypes highlight distinct yet overlapping pathogenic mechanisms. Dysregulated circadian pathways, consistent downregulated transcription factor target gene sets, and trophoblast-specific signatures implicate circadian misalignment and impaired placental maturation as key contributors to preterm parturition. These findings provide a mechanistic atlas linking placental pathology to sPTB and highlight potential targets for chronotherapeutic and cell-type-specific interventions. AJOG at a GlanceO_ST_ABSWhy was this study conducted?C_ST_ABSSpontaneous preterm birth remains a leading cause of neonatal morbidity. Histopathologic lesions of the placenta, particularly chorioamnionitis and vascular abnormalities, are common in preterm deliveries, yet the underlying molecular pathways are poorly understood. We sought to integrate functioning pathway profiles of placental histology, circadian biology, and cell types to identify mechanistic drivers of sPTB. Key findingsO_LIsPTB placentas showed widespread down-regulation of oxidative phosphorylation, mTORC1, hypoxia, interferon, and TNF/NF-{kappa}B pathways. C_LIO_LIHCA placentas up-regulated the same pathways (except androgen response), revealing a reciprocal inflammatory-hypoxic signature. C_LIO_LIVascular abnormalities displayed a distinct mix of up- and down-regulated pathways, suggesting divergent reparative responses. C_LIO_LIPlacentas with co-existing HCA and vascular abnormalities enriched more inflammatory Hallmark pathways: the 12 up-regulated sets (Complement, Interferon /{gamma}, TNF, ROS, Apoptosis, and Heme Metabolism) highlight a synergistic exacerbation of innate immunity, oxidative stress, and programmed cell death and the exclusive down-regulation of DNA Repair suggests compromised genomic integrity, which can contribute to premature placental senescence and preterm labor. C_LIO_LICircadian clock and multiple transcription-factor targets were enriched in sPTB, and trophoblast-specific signatures (giant, extravillous, syncytiotrophoblast) were prominent. C_LI What does this add to what is known?The study demonstrates a clear dichotomy between inflammatory and hypoxic molecular programs in sPTB and HCA, identifies circadian dysregulation as a potential contributor, and highlights trophoblast subpopulations as key players. These insights open avenues for targeted biomarkers and chronotherapy in preterm birth prevention.

Public bioactivity databases are heterogeneous not only in measurement type, where binding affinities and functional potencies are reported on different scales, but in pharmacology: the same compound and target can carry agonist, antagonist, or inhibitor records measured through binding displacement, cAMP, {beta}-arrestin, or [35S]GTP{gamma}S readouts that quantify different biological events. Pooling these records produces models whose output is detached from any coherent pharmacological claim. Prior work has standardized bioactivity at scale and quantified the noise from mixing measurement types, but pharmacological mechanism and assay-readout class have not been treated as a primary axis of large-scale curation. This study presents an auditable, OECD-anchored framework that stratifies public records by action type and assay readout before modeling, converting heterogeneous data into externally validated, interpretable QSAR tasks that compose with existing standardization resources rather than replacing them. The framework is demonstrated on the four opioid receptors (MOR, DOR, KOR, and nociceptin/orphanin FQ, NOP). Four public sources were reconciled into 72,148 merged records and 50,977 curated measurements spanning 19,585 compounds, each carrying auditable attributes for source agreement, endpoint meaning, pharmacology class, assay readout, and trust tier. Receptor-level binding tasks formed a compact benchmark with strong locked external performance, including KOR pK (R2 = 0.79, n = 798) and DOR pK (R2 = 0.77, n = 736). Pharmacology- and readout-resolved functional endpoints yielded externally validated strata that pooled labels would obscure, including a MOR antagonist functional-inhibition endpoint (R2 = 0.86, n = 110) and agonist potency endpoints for DOR, KOR, and MOR (R2 up to 0.81). Comparison against a fully pooled baseline shows that pooled models either match stratified models on coherent endpoints or reach a deceptively high R2 on functional-IC50 endpoints by training predominantly on binding-displacement records, so the pooled number predicts affinity rather than functional activity. SHAP attribution indicates that binding and functional potency encode partially distinct structure-activity signals. The dataset contract, not model performance alone, defines the validity and scope of a QSAR claim, and stratification is a precondition for a functional model to support a defensible claim. Curation logic, derived tables, frozen data, and reproducibility artifacts are released.

Genetic testing for inherited cardiovascular conditions is recommended by multiple national guidelines to inform medical management. However, access to genetic counseling and testing is often limited particularly in the inpatient setting. Cardiologists cite lack of access to genetic counselors as a reason for not pursuing genetic testing. Genetic test education videos have been successfully implemented in the outpatient setting to increase patient volumes and decrease wait times, but they have not been studied in the inpatient setting.

Objectives To determine whether boys with VUS detected through newborn screening (NBS) for adrenoleukodystrophy (ALD) develop adrenal insufficiency (aiALD) and cerebral ALD (cALD) at rates comparable to those with pathogenic variants, and to evaluate the relationship between C26:0-lysophosphatidylcholine (C26:0-LPC) levels and clinical outcomes. Methods We conducted a retrospective multicenter cohort study (2013 - 2025) across six US centers, including 201 males identified through NBS in 19 states. Variants were classified as pathogenic (n=65), likely pathogenic (n=45), or VUS (n=88). Primary outcomes were development of aiALD and cALD; secondary outcomes included C26:0-LPC levels. Statistical analyses included Kaplan-Meier, mixed-effects regression, and Cox models. Results 201 males with ABCD1 variants identified through NBS for ALD. Median age at last follow-up was 4.2 years (IQR 2.5 - 7.9). Overall, 26% developed aiALD (54% pathogenic, 16% likely pathogenic, 11% VUS), and 8% developed cALD (11%, 9%, and 4.5%, respectively). Pathogenic/likely pathogenic variants were associated with higher odds of aiALD than VUS (OR 5.8; 95% CI 2.16 - 15.58; p=0.001). At 150 months, 39% of individuals with pathogenic/likely pathogenic variants remained free of aiALD versus 85% with VUS. C26:0-LPC levels were higher in pathogenic variants and correlated with genotype (p=0.0006). Higher levels were associated with increased aiALD risk and earlier onset (HR 1.38 per 0.1 umol/L; 95% CI 1.20 - 1.59; p<0.0001). Conclusions Boys with VUS had lower rates of aiALD and lower C26:0-LPC levels than those with pathogenic variants, although some developed disease. C26:0-LPC correlates with genotype and risk, supporting its role in variant classification and risk-stratified surveillance.

Accurate prediction of blood-brain barrier permeability (BBBP) is essential for central nervous system drug discovery, yet existing models are often limited by their reliance on predefined physicochemical descriptors, small-molecule-centered training sets, or conformation-dependent representations, which restricts their transferability across chemically diverse modalities especially peptides. In addition, publicly available BBBP datasets remain fragmented, inconsistently standardized, and weakly controlled for molecular redundancy, increasing the risk of data leakage and overestimated model performance. In this study, we propose BBBP-Atlas, a structure-aware BBB permeability prediction model designed for unified modeling of small molecules and peptides with the first cross-modal dataset OmniBBBP. Designed to bypass descriptor and conformation dependencies, our model represents standardized molecular structures as atom-level graphs to capture local atom-bond environments and long-range topological dependencies associated with BBB transport. This design enables direct learning of structure-permeability relationships from molecular topology. For model training and evaluation, we curated a cross-modal, redundancy-filtered database OmniBBBP that seamlessly unifies small molecules and complex peptides, containing 10,218 unique compounds with 9,316 small molecules and 902 peptides. BBBP-Atlas achieved an accuracy of 0.8914 and an MCC of 0.7678 on the independent test set. On a balanced external benchmark of 200 compounds, our model reached an AUC of 0.9108, an accuracy of 0.8500, and an MCC of 0.7000, outperforming LightBBB by an absolute MCC gain of 6%. Case studies further showed that BBBP-Atlas captured clinically meaningful BBB permeability patterns, correctly identifying lorlatinib as BBB-permeable and vancomycin as BBB-impermeable with high confidence. The OmniBBBP-backed BBBP-Atlas offers a versatile and cross-modal approach for single-compound prediction, batch screening, and dataset exploration for CNS drug discovery. BBBP-Atlas is available at https://cadd.drugflow.com/bbbp/.

BackgroundMYORG (myogenesis-regulating glycosidase) and STRADB (STE20-related kinase adapter protein beta) were previously identified as activity-mediated skeletal muscle genes with potential roles in frailty and sarcopenia. We hypothesized that, if these genes are sustained by neuromuscular contractile activity, their expression should be consistently downregulated in muscular dystrophies, conditions defined by progressive muscle degeneration and secondary functional disuse. MethodsWe performed a systematic cross-dataset transcriptomic analysis of five publicly available GEO microarray datasets of human skeletal muscle. Discovery analysis was conducted in GSE3307 (Affymetrix HG-U133A/B; samples spanning DMD, LGMD2A/B/I, BMD, FSHD, JDM, ALS, AQM versus healthy controls). Independent external validation was performed in GSE38417 (HG-U133 Plus 2.0, DMD; n=16/6), GSE11681 (HG-U133A/B, LGMD2A; n=8-10/9-10), GSE465 (HG-U95Av2/B/C, multi-disease), and GSE1007 (HG-U95B/C/E, DMD; n=10-11/11). Raw CEL files underwent array-level quality assessment using NUSE and RLE diagnostics prior to normalization. Seven poor-quality arrays were excluded (none from Control, DMD, or LGMD groups). Remaining arrays were processed by robust multi-array average (RMA) normalization, and differential expression was assessed by limma with Benjamini-Hochberg FDR correction. ResultsMYORG was significantly downregulated in DMD (log2 fold-change [logFC] = -0.93, adj.P<0.001), LGMD2A (logFC = -0.82, adj.P<0.01), LGMD2B (logFC = -1.01, adj.P<0.01), and LGMD2I (logFC = -1.03, adj.P<0.01) in GSE3307. STRADB was significantly reduced in DMD (logFC = -0.33, adj.P<0.05) and showed a near-significant trend in LGMD2I (logFC = - 0.42, adj.P = 0.061). MYORG downregulation in DMD was independently replicated in GSE38417 (logFC = -1.40, adj.P<0.001) and GSE1007 (logFC = -0.80, adj.P<0.001). STRADB was also significantly downregulated in GSE38417 DMD (logFC = -0.45, adj.P<0.001). Deoxygalactonojirimycin, an iminosugar and an FDA/EMA-approved pharmacological chaperone (migalastat/Galafold) for Fabry disease, has been reported to be a specific molecular interactor that stabilizes MYORG protein in skeletal muscle. ConclusionsThis multi-dataset study further supports the role of MYORG and STRADB as activity-sensitive muscle genes that are robustly downregulated in DMD and LGMD. The pharmacological interaction between migalastat and MYORG provides a mechanistically grounded rationale for investigating this approved agent as an adjunct therapy in muscular dystrophies, in combination with the existing standard of care. This also supports active investigation of iminosugar analogs to target MYORG as potential therapeutics for improving skeletal muscle function in dystrophies, frailty, and sarcopenia.