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.

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.

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

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.

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/.

Background: Hypertension is a modifiable risk factor for dementia, yet the comparative effectiveness of angiotensin receptor blockers (ARBs) versus angiotensin converting enzyme inhibitors (ACEIs) on dementia risk remains uncertain. Objective: To compare the risk of dementia and dementia-free death of ARB versus ACEI initiation among US Veterans with incident hypertension. Methods: We conducted a retrospective target trial emulation using a new-user, active-comparator design among Veterans with incident hypertension. We analyzed longitudinal electronic health records from 2,577,000 individuals who initiated ARBs or ACEIs between 1/1/2000-12/31/2017, with up to five years of follow-up. The exposure was initiation of an ARB-based versus ACEI-based antihypertensive regimen. Co-primary outcomes were dementia, identified using natural language processing of clinical notes, and dementia-free death. We used inverse probability of treatment weights based on 66 pretreatment covariates to estimate the cumulative incidence of the outcomes for each treatment group. Weighted risk ratios and absolute risk differences through five years were computed with bootstrapped 95% CIs. Secondary outcomes included all-cause death and a composite of dementia or death, evaluated using a weighted Kaplan-Meier approach. Results: Among 2,577,000 Veterans (mean age, 63 years; 4.5% female; 65% White; 15% Black), 10% initiated ARBs and 90% initiated ACEIs. Over five years of follow up, 6% developed dementia, 12% died without dementia, and 13% died overall. ARB initiation yielded consistently lower risk of dementia (risk ratio, 0.88; 95% CI, 0.83-0.93 at 6 months to 0.92; 95% CI, 0.90-0.94 at 5 years) and dementia-free death (risk ratio, 0.90; 95% CI, 0.86-0.96 at 6 months to 1.00; 95% CI, 0.98-1.01 at 5 years) than ACEI initiation. Effects on secondary outcomes were similar to those for primary outcomes. Greater protective dementia effects were observed in older and male Veterans and non-statin users, with similar effects on dementia-free death. Discussion: Among US Veterans with incident treated hypertension, initiation of ARB versus ACEI antihypertensive regimen conveyed a modestly lower risk of dementia. Given the high prevalence of hypertension, these modest effects may confer meaningful population-level benefits on brain health. Future research estimating per-protocol effects using a more generalizable population is needed to confirm our findings. Key words: antihypertensive medication, dementia, natural language processing, target trial emulation, Veteran

Background: HIV infection has long been associated with increased incidence of severe cutaneous adverse reactions (SCAR). It remains unknown whether this increased incidence is a direct biological result of HIV infection, differences in drug exposure, or other demographic factors. Objective: To evaluate the association between HIV and SCAR and determine whether this relationship persists after adjusting for demographic factors and structured drug exposure. Methods: We analyzed reports from the FDA Adverse Event Reporting System (FAERS) from 2013-2023. SCAR outcomes included Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN), drug reaction with eosinophilia and systemic symptoms (DRESS), acute generalized exanthematous pustulosis (AGEP), and generalized bullous fixed drug eruption (GBFDE). HIV status was determined using antiretroviral exposure, indication text, and machine-learning imputation. Logistic regression models were constructed sequentially: unadjusted, demographic-adjusted, and fully adjusted with drug principal components to account for polypharmacy. Drug-level disproportionality and HIV-drug interaction analyses were also performed. Results: In unadjusted models, HIV was strongly associated with SCAR (OR ~2.0-2.7). Adjustment for demographics attenuated this association, and further adjustment for drug exposure reduced the effect to near null for overall SCAR and DRESS. A modest residual association persisted for SJS/TEN (OR ~1.3). Disproportionality analyses demonstrated enrichment of specific high-risk drugs in PLWH. Interaction modeling revealed drug-specific amplification of SCAR risk in HIV, notably for carbamazepine and clarithromycin, whereas other drugs showed minimal interaction. Conclusion: The association between HIV and SCAR is largely explained by differences in drug exposure and demographic factors. Residual risk is drug-specific rather than uniform, supporting a model in which HIV modifies susceptibility to select drug triggers rather than acting as a global risk factor. Further prospective and retrospective studies are required to quantify associations.

Background To build a clinically translatable neonatal seizure detection algorithm using amplitude-integrated electroencephalography (aEEG) and compressed spectral array (CSA). Methods Using a public dataset of annotated neonatal EEGs, features of the aEEG and CSA were extracted from the left and right centroparietal electrodes. These features were then used to train and test three machine learning classifiers, Random Forest (RF), Support Vector Machines (SVM), and Artificial Neural Networks (ANN). Results The trained RF, SVM, and ANN classifiers had areas under the curve (AUC) of 0.80, 0.69, and 0.79 for capturing seizure time periods and an average accuracy of 0.91, 0.90, and 0.92 respectively for capturing seizure and non-seizure time periods. Median accuracy scores were higher among patients without hypoxic-ischemic encephalopathy (HIE; median = 1 for all three classifiers) than HIE patients (median = 0.92, 0.93, 0.93). Conclusion A clinically interpretable aEEG-CSA algorithm is feasible for neonatal seizure detection by extracting standard EEG features and coupling these features with a supervised ML classifier.

Background: Whether a patient's pain improved after emergency department (ED) treatment is read from the record to benchmark EDs, compare drugs, and label research outcomes. It is interpretable only if a post-treatment score is recorded, appropriately timed, and chosen by a fixed rule; its stability across these choices is unknown. Methods: Retrospective measurement study of adult headache visits in a de-identified ED database (MIMIC-IV-ED, 2011-2019). Among treated visits, we quantified reassessment completeness by time window, estimated meaningful relief (a reduction of at least 2 points) under score-selection rules and missing-data assumptions, tested whether reassessment was predictable at treatment, and compared headache with other painful presentations. Results: Among 19,501 visits (15,273 patients), 13,682 (70.2%) were treated. A post-treatment pain score appeared at any time for 77.1% (95% CI, 76.4 to 77.8), but within 2 hours of the analgesic for only 47.9% and within 1 hour for 27.5%. Meaningful relief was 66.9% using the first post-treatment score but 81.0% and 83.4% using the last or lowest score; it was 67.5% under inverse-probability weighting and could be bounded only between 51.8% and 74.4%. Whether a score was recorded was weakly predictable at treatment (area under the curve, 0.566) and unrelated to baseline pain. Completeness was similar across headache strata and comparator painful presentations. In an independent ED (MC-MED, a different EHR), the score-selection effect replicated: relief rose from 71.1% (first) to 80.6% (last) and 83.4% (lowest). Conclusions: Documented pain relief after ED headache treatment was not a stable outcome: it varied with the reassessment window and score-selection rule, was not point-identified for unreassessed patients, and behaved like other painful ED presentations. Programs and research that use documented relief should prespecify the reassessment window, score-selection rule, completeness denominator, and a missing-data range, and favor protocol-timed reassessment.

Schizophrenia is a complex neuropsychiatric disorder in which genetic risk is thought to converge on cell type-specific regulatory mechanisms in the brain. We performed a cell type-resolved epigenome-wide association study (EWAS) of schizophrenia using fluorescence-activated nuclei sorting (FANS) to isolate neuron-enriched (NeuN+), oligodendrocyte-enriched (SOX10+) and other glial-enriched (NeuN-/SOX10-) nuclei populations alongside total prefrontal cortex nuclei fractions from 216 donors (104 schizophrenia cases and 112 controls). We identified 16 differentially methylated positions (DMPs) in neuron-enriched nuclei at experiment-wide significance and more than 400 additional neuronal DMPs at a discovery threshold. In contrast, no significant associations were identified in oligodendrocyte-enriched, glial-enriched or total nuclei fractions, demonstrating that schizophrenia-associated cortical methylomic variation is highly neuron-specific and largely masked in bulk tissue analyses. Neuronal DMPs exhibited a significant bias towards hypomethylation in schizophrenia and were enriched at loci implicated by genetic studies, including CACNA1C, CACNA1G and TRIO. Pathway analyses implicated genes involved in neurodevelopment, cell adhesion, synapse organisation, neurotransmission and synaptic plasticity. Schizophrenia-associated DNA methylation signatures identified in prefrontal cortex neurons showed correlated effects in neuronal nuclei isolated from the hippocampus and striatum, indicating partial conservation of disease-associated epigenetic alterations across brain regions. Together, these findings provide strong evidence for widespread neuron-specific epigenetic dysregulation in schizophrenia and highlight the importance of cell type-resolved approaches for elucidating the molecular mechanisms underlying psychiatric disease.

We examined whether polygenic risk for peripheral artery disease (PAD) is associated with severity among patients undergoing lower extremity revascularization at Yale New Haven Hospital. Patients were classified into European (EUR) and non-European (non-EUR) ancestry groups. Associations between the 19-variant polygenic score (PGS) and nine severity indicators were evaluated using linear and Cox regression models stratified by ancestry, followed by meta-analysis. Significant findings (p < 0.05) were assessed for replication in the UK Biobank (UKB). After quality control, 68 EUR and 59 non-EUR patients were included. In EUR patients, higher PGS was associated with increased risk for stroke (HR = 2.43, 95% CI 1.06-5.57). Meta-analysis revealed a significant association between higher PGS and younger age at surgery ({beta} = -2.90, SE = 1.28), which was replicated in the UKB ({beta} = -0.58, SE = 0.15). These results suggest genetic risk contributes to PAD severity.

Background: Fragile X-associated Tremor/Ataxia Syndrome (FXTAS) is a late-onset neurodegenerative disorder caused by FMR1 premutation CGG repeat expansions (55-200 repeats). The epigenetic landscape of the FXTAS brain remains uncharacterized. We performed genome-wide DNA methylation profiling of postmortem prefrontal cortex tissue to identify differentially methylated positions (DMPs) and candidate genes, and sought protein-level support for a neuroinflammatory signal. Methods: DNA methylation was profiled in postmortem prefrontal cortex (Brodmann area 9) from 27 male FXTAS cases and 29 male controls using the Illumina MethylationEPIC array (EPICv1 and EPICv2 platforms), merging 721,802 common probes. Surrogate variable analysis (SVA) controlled for confounders. DMPs were defined by p-value and FDR < 0.05; exploratory Reactome 2024 pathway analysis was performed on the DMP-associated gene list. Targeted proteomic profiling was performed in the same brain region using the Olink (proximity extension assay) Inflammation panel in 9 FXTAS cases and 12 controls, with SVA-adjusted differential abundance analysis, and concordance assessment against a prior mass spectrometry dataset. Results: We identified 108 significant cg-type DMPs mapping to 80 genes (50 hypermethylated, 58 hypomethylated in FXTAS). The strongest signal was CYP2E1 (7 concordant hypomethylated DMPs), an oxidative stress gene also implicated in Parkinsons disease. FTCD, a one-carbon cycle enzyme, carried 5 hypermethylated DMPs. A cluster of DMP-associated genes with established roles in innate immune and NF-kB signaling, TRAF3 (the single most significant DMP among the inflammation genes, hypermethylated), BATF, RCOR1, and MSI2; they pointed toward neuroinflammatory dysregulation. Additional genes included LINGO1 (myelination inhibitor), SYT3 (synaptic vesicle), and SLC39A4 (zinc transporter). Exploratory Reactome enrichment using the DMP-associated gene set nominated themes including neuroinflammation resolution, axonal growth inhibition, zinc homeostasis, and CYP2E1 metabolism at nominal significance (p<0.05); however, the gene-to-pathway mapping rate was low and no pathway survived correction for multiple testing. Olink proteomic analysis independently identified 60 significantly altered inflammation proteins (59 downregulated), including CXCL8, CXCL10, IL6, IL15, IL18, TLR3, IRAK1/4, and complement C1QA, which were directionally concordant with prior mass spectrometry data. Conclusions: This integrated study reveals a genome-wide epigenetic signature in the FXTAS prefrontal cortex implicating oxidative stress, myelination failure, zinc dysregulation, one-carbon cycle disruption, and most notably a coordinated set of epigenetically altered genes governing innate immune and NF-kB signaling. Convergence of TRAF3 hypermethylation with independent downregulation of TLR3 and NF-kB-pathway proteins at the protein level supports a coherent, cross-platform model of dysregulated neuroinflammatory signaling in FXTAS, identified here through individual gene- and protein-level convergence rather than formal pathway enrichment. FTCD hypermethylation proposes a self-reinforcing epigenetic loop via SAM depletion. These multi-omic findings establish FXTAS as a disorder of pervasive epigenetic reprogramming and nominate candidate genes for future mechanistic and therapeutic investigation.

Long-read whole genome sequencing (lrWGS) shows promise as an all-in-one test to detect clinically relevant variants and variants difficult to detect by current short-read whole genome sequencing (srWGS) pipelines. Comparisons between lrWGS and srWGS (or exome sequencing) pipelines will become commonplace as lrWGS is more widely adopted for clinical testing, particularly for individuals not diagnosed by srWGS. However, the sensitivity of lrWGS for detecting variants previously identified and prioritized by clinical srWGS has yet to be assessed. As part of the SeqFirst-neo study, a subset of critically ill newborns and their parents who underwent clinical srWGS also underwent lrWGS on the Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio) platforms. In total, 134 families were sequenced across multiple technologies including 128 families with clinical srWGS who were sequenced on both lrWGS platforms. We compared the variants reported by clinical testing with the variants identified by lrWGS. Among the 128 families sequenced on all three platforms, 89 SNV/indels and 14 SV/CNVs clinically reported by the srWGS testing pipeline were evaluated. All variants assessed in probands were ultimately detected by both lrWGS platforms, although three events were not detected prior to application of an updated variant caller, highlighting the rapid evolution of lrWGS variant calling. Additionally, breakpoint coordinates and event sizes often differed substantially between calls from srWGS and events called in lrWGS data. Our work demonstrates that while most clinically reported variants from srWGS can be detected by lrWGS pipelines, challenges remain when attempting direct comparisons, particularly for SV/CNVs.

Globally, approximately 75% of sickle cell disease (SCD) cases occur in sub-Saharan Africa, yet empirical data on its natural history, clinical burden, and modifiers remain scarce in the region. This retrospective study describes the demographic characteristics, complications, and routine care and examines how non-genetic factors and blood markers relate to disease severity. We analysed 8402 medical records from 840 SCD patients with confirmed HbSS genotype registered in MRCG Keneba and Fajara clinics (NKeneba=148; NFajara=692). A generalised linear model was employed to estimate the association of non-genetic correlates, blood biomarkers, and routine care medications with disease severity. Here, we showed 67% of patients in the Keneba cohort and 92% of those in the Fajara cohort had no documented SCD-related chronic complication. Despite no documented evidence of hydroxyurea use, rates of SCD crises (Keneba=0.57, Fajara=0.63) and infections (Keneba=0.53, Fajara=0.35), expressed per patient-year, were low in both cohorts, with 99% of patients experiencing less than or equal to 3 SCD crises per patient-year. Age at diagnosis, gender and seasonality were not significantly associated with SCD crises or other clinical outcomes/events rates. Each additional folic acid prescription was associated with higher haemoglobin(g/dL) (total folic acid prescriptions: Beta-Fajara=1.31, P=0.005; Beta-Keneba=1.20, P<0.001). Penicillin prophylaxis was associated with a reduced rate of infection (total Pen V prescriptions: IRR-Fajara=0.85, P=0.002; IRR-Keneba=0.93, P=0.002) and SCD crises (IRR-Fajara=0.67, P=0.001; IRR-Keneba=0.87, P=0.001). This study found low acute event rates and chronic complications prevalence in the absence of hydroxyurea use. No significant associations were observed between non-genetic correlates and clinical events, but the study highlighted the need for continued folic acid supplementation and penicillin prophylaxis due to their observed beneficial effects.

Background: Myotonic dystrophy type 1 (DM1) is caused by a CTG repeat expansion in the DMPK gene and represents the most common adult-onset myopathy. Current molecular diagnostics rely on labor-intensive assays that limit accessibility and scalability. Haplotype-based approaches offer a promising alternative for detecting pathogenic expansions indirectly. Methods: We performed genome-wide genotyping in 226 genetically confirmed DM1 patients from the Saguenay-Lac-Saint-Jean founder population and reconstructed haplotypes surrounding the DMPK pathogenic repeat expansion. Based on these haplotypes, we performed a phylogenetic analysis that was further integrated with genealogical reconstruction from the BALSAC database to investigate the origin and transmission of DM1 haplotypes. To evaluate epidemiological utility, we implemented gene dropping simulations within the SLSJ extended genealogies (>80,000 starting individuals) to estimate DM1 incidence at birth. Results: A DM1-associated haplotype was identified in all patients (226/226), consistent with a single major ancestral origin in the SLSJ population. This complete concordance supports the robustness of haplotype-based approaches to infer carrier status without direct repeat sizing. Integrating phylogenetic analysis and genealogical data identified a single couple as the most likely entry point of DM1 in Quebec. Simulation-based estimates of incidence at birth exceeded observed prevalence, suggesting underdiagnosis in the region. Marked geographic heterogeneity in the SLSJ is also observed. Conclusions: Our results demonstrate that haplotype-based approaches can provide a reliable, cost-effective alternative to conventional pathogenic DM1 repeat carriers identification and familial screening strategies.

Background: Motor complications, such as motor fluctuations and Levodopa-induced dyskinesias (LID), significantly impair quality of life in persons with Parkinson's disease (PD) on long-term Levodopa treatment. Predicting their onset is crucial for tailored patient care. Objectives: To develop and evaluate machine learning (ML) models to forecast the onset of new motor fluctuations and LID in PD patients within three years from baseline assessment, and to assess how training cohort composition influences performance. Methods: A comprehensive ML workflow with repeated Nested Grid Search Cross-Validation was applied to real-world clinical data from a multicentric cohort of 247 PD patients. ML models were rigorously evaluated on the clinically relevant subgroup free of motor complications at baseline. SHAP analysis provided model explainability. Results: Models achieved moderate predictive power for both LID (SVC: MCC 0.28 {+/-} 0.14) and motor fluctuations (Voting MCC = 0.32 {+/-} 0.18). For LID prediction, the strongest predictors were the Levodopa Equivalent Daily Dose (LEDD), baseline motor fluctuations, and duration of Levodopa therapy, with risk increasing significantly above a LEDD threshold of 300-400 mg. A critical ablation study revealed that excluding patients with pre-existing complications caused a collapse in model sensitivity, highlighting their essential role in defining the upper bound of predicted risk. Conclusions: The model-based risk assessment is consistent with established clinical factors. Inclusion of the full spectrum of disease severity, including patients with pre-existing motor complications, in the training set is essential for achieving a robust probabilistic risk scale and reliable model calibration for new-onset prediction.

Background: Sphingosine 1-phosphate receptor (S1PR) modulators can cause transient, dose-related negative chronotropic effects. Mocravimod is an oral S1PR modulator that is developed as a maintenance therapy in allogenic haematopoietic cell transplantation (allo-HCT). This phase I study evaluated whether two dose-titration regimens attenuate early bradycardia when initiating mocravimod while preserving pharmacokinetic (PK) and pharmacodynamic (PD) activity. Patients and methods: In this randomized, double-blind, placebo-controlled, parallel-group study, healthy adults received once-daily oral mocravimod using either dose titration (DT) regimen DT1 (0.3-2.0 mg with 4-day stepwise escalation) or regimen DT2 (0.5 mg to Day 14, 1.2 mg Days 15-18, then 2 mg), a fixed 2 mg regimen, or placebo for 21 days. The primary endpoint was the number of bradycardia episodes on treatment initiation and dose-escalation days derived from 24-hour Holter monitoring; PK of mocravimod and mocravimod-phosphate (whole blood) and PD effects (absolute lymphocyte count [ALC]) were assessed. Results: Fifty-six participants were randomized and 53 completed the study. Both titration regimens resulted in fewer bradycardia episodes than fixed initiation at 2 mg during the first week of treatment. Differences between titration and fixed dosing were no longer evident after Day 9, consistent with tolerance development. PK profiles were consistent with prior phase I data. By Day 21, DT1 achieved exposures close to the fixed 2 mg regimen, whereas DT2 yielded lower exposures, reflecting slower escalation. Peripheral lymphopenia developed in all active treatment groups and was comparable between regimens by Day 21, returning toward baseline by study end. Safety was similar between titration regimens and placebo, with similar distribution and incidence of adverse events. No serious adverse events occurred. Conclusion: Two practical titration regimens mitigated the early negative chronotropic effect observed with fixed-dose initiation of mocravimod at 2 mg once daily. Importantly, titration preserved the expected PK and PD profile, supporting dose escalation as an effective initiation strategy to improve early cardiac tolerability.

Blood group antigens, defined by epitopes on the erythrocyte surface, are central to transfusion safety and maternal-fetal compatibility. While the genetic basis of many clinically relevant blood group antigens is well established, which structural and biophysical parameters determine whether a single-nucleotide variant gives rise to an antigenic phenotype remains unclear. Here, we integrate structural, biophysical, and evolutionary analyses to systematically evaluate features associated with single amino acid substitutions across 24 human protein-based blood group systems. We analyse 319 variants with curated phenotypic annotations alongside 481 control variants, identifying key determinants of null and antigenic phenotypes. Null variants are characterized by high evolutionary conservation, burial within the protein core, loss of hydrophobicity, increased polarity, and a propensity for arginine substitutions. Antigenic variants are also enriched in arginine; however, in contrast to null variants, they tend to occur at less conserved, more solvent-accessible, and structurally flexible sites. Supervised machine learning models trained on structural and biophysical descriptors were applied to distinguish (i) null and (ii) antigenic variants from controls, achieving balanced accuracies of 0.82 and 0.63, respectively. Feature importance analysis identified predicted pathogenicity, solvent accessibility, and evolutionary conservation as the most predictive determinants of null variants, whereas hydrophobicity, conservation, and flexibility dominated antigen prediction. This work establishes a framework linking molecular variation to blood group phenotypes and provides a foundation for predicting the impact of novel missense mutations in transfusion medicine and beyond.

Background Large-scale functional assays, including multiplex assays of variant effect, have substantial potential to resolve variants of uncertain significance (VUS), particularly for rare missense variants where clinical and population evidence are limited. The ClinGen assay-level clinical validation framework described by Brnich et al provided baseline guidance for the use of functional data for variant classification. However, clear consensus regarding construction of variant 'truthsets' by which to clinically validate functional data remains lacking. Methods CanVIG-UK developed consensus recommendations for truthset construction through an iterative national consultation process involving the CanVIG Steering Advisory Group (CStAG), wider CanVIG-UK membership, and engagement with international functional genomics experts. Consultation was based on previous analyses of 2,120 truthset constructions examining the impact of truthset composition on evidence point allocation within the ClinGen assay-level clinical validation framework. Results Across several consultations, CanVIG-UK established nine guiding principles and seven best-practice recommendations for assay-level clinical validation, using the assumed context of an assay for a cancer susceptibility gene where loss-of-function is the mechanism of pathogenicity. The principal recommendation stipulates, where assays are intended for use in interpretation of largely missense variants, the truthset used to validate should comprise only missense variants. Rather than mixtures of different variant types which may serve to over-estimate assay performance. Additional recommendations support option for relaxation of truthset stringency to improve power, augmentation of benign missense truthsets with systematically derived 'proxy-clinical' benign variants, independent clinical validation separate from assayist-defined validation, and careful evaluation of missense score distributions against that of protein-truncating and synonymous variants. Guidance is also provided for scenarios with limited pathogenic truthset availability and for assays reporting multiple deleterious zones or readouts. Conclusions The CanVIG-UK principles and recommendations for truthset construction upon the ClinGen assay-level clinical validation framework, while aiming to form a baseline for future discussion regarding other functional and disease contexts and helping to address the gap between publication of new data and routine clinical implementation.