The Pharmacogenomics Journal

Introduction: The application of pharmacogenomics (PGx) in pediatrics is limited by the lack of age-oriented interpretation approaches, as algorithms developed for adults do not account for ontogenetic changes in the activity of drug-metabolizing enzymes and transport proteins. The aim of this study was to evaluate the clinical applicability of pharmacogenomic data in Russian children, assess the concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes, and develop recommendations for the generation of age-oriented PGx reports. Methods: We analyzed whole-exome sequencing (WES) data from 524 pediatric patients and 635 newborns, filtering pharmacogenomic annotations according to PharmGKB/ClinPGx evidence levels (1A-2B) and the presence of the 'Pediatrics' tag. The concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes was assessed in newborns. In a pediatric subgroup of 100 patients, a retrospective analysis of medical records was performed to evaluate the structure of pharmacotherapy and the frequency of adverse drug reactions (ADRs). A 'PGx-ADR-cost' database was created, and the relative population burden index was calculated for 27 gene-variant-drug-ADR associations. Results: Clinically relevant annotations (requiring drug avoidance or dose modification) accounted for only 5% of all initial pharmacogenomic annotations in both cohorts; 67.6% (pediatric cohort) and 67.2% (neonatal cohort) of these were related to alleles with altered function. Concordance between genotype-based recommendations and the ontogenetic status of drug-metabolizing enzymes in newborns was observed in only 5 of 14 (35.71%) gene-drug pairs. ADRs were identified in 21% of the 100 pediatric patients; however, only two cases could be explained by high-evidence PharmGKB/ClinPGx annotations. Ranking by relative population burden identified UGT1A1*28-irinotecan-induced neutropenia and HLA-A*31:01-carbamazepine-induced severe cutaneous reactions as priority associations. Conclusions: Age represents a critical factor in the interpretation of pharmacogenomic data in children, as current approaches to PGx reporting do not adequately incorporate the ontogenetic context. We propose a pediatric PGx interpretation model that includes mandatory reporting of patient age, ontogenetic adjustment, evidence-level stratification, and multidisciplinary clinical assessment. Prospective validation is required to confirm the clinical utility of the proposed approach.

Background. Pharmacogenetic (PGx) testing can guide drug prescribing but remains limited by the genomic assay used. Genotyping arrays are widely implemented yet limited to predefined variants, whereas low-pass whole-genome sequencing (LP-WGS) is not constrained by fixed probe design and may provide broader PGx variant availability after imputation. Methods. We compared Illumina Global Screening Array (GSA) v3 with ~1x LP-WGS for PGx profiling in 500 hospital biobank participants with electronic health record evidence of exposure to pharmacogenetically actionable drugs and reported adverse drug reactions. Concordance was evaluated genome-wide, at 20 actionable pharmacogenes for PharmCAT-derived star alleles and metabolizer phenotypes, and for HLA alleles. Results. Genome-wide concordance between imputed array and LP-WGS data was high (median 99.63%; interquartile range, 99.59%-99.64%). For pharmacogenetically relevant variants, LP-WGS captured a larger fraction, particularly rare alleles absent from the array data, whilst maintaining high concordance at shared sites. Predicted phenotype concordance exceeded 98% for most genes, although gene-specific differences in phenotype classification were observed. LP-WGS reduced missing phenotype assignments for selected loci, particularly CYP2C19 and NAT2, by improving resolution of star-allele structure. However, in structurally complex or incompletely characterized genes such as CYP2C9 and CYP2D6, broader variant recovery increased indeterminate classifications rather than consistently improving clinical interpretability. For HLA loci, concordance varied by imputation strategy, with SNP2HLA performing marginally better utilizing the GSA array compared to the LP-WGS approach. Conclusions. Overall, LP-WGS provides broader variant coverage and improved resolution for selected pharmacogenes but did not resolve all clinically important loci. These findings support further evaluation of LP-WGS as a scalable PGx screening approach, especially where long-term genomic data reuse is a priority.

Importance Pharmacogenomic (PGx) guidelines can improve medication efficacy and reduce toxicity, but their application in pediatric intensive care units (PICUs) remains largely unexplored. Objective To determine the frequency of medications with established PGx guidelines administered in the PICU and assess the capacity of exome sequencing to capture PGx phenotypes for these medications. Design Retrospective cohort study integrating electronic medical record and exome sequencing data. Setting Morgan Stanley Children's Hospital of NewYork-Presbyterian, a single center tertiary care children's hospital. Participants A total of 4,939 children admitted to the PICU (2020 - 2024), and 192 children admitted to the PICU who underwent exome sequencing for research purposes (2015 - 2023). Exposure Critical illness requiring PICU admission. Main Outcomes and Measures Frequencies of administration of medications with established PGx guidelines in the PICU and the proportion of individuals with exome sequencing with identifiable PGx phenotypes. Results Among 4,939 PICU patients, 37.2% (n=1,837) received at least one medication with established PGx guidelines and 14.4% (n=712) received two or more such medications. Twenty PGx genes were implicated; CYP2C9 was most common (17.3%, n=853). An estimated 8.2% of patients received medications for which PGx-guided recommendations would have altered clinical management. Among 192 patients who underwent exome sequencing, at least one metabolizer phenotype was identified in 62% (n=119). Conclusions and Relevance Many critically ill children receive medications with established PGx guidelines. This study highlights an opportunity for more personalized medicine for critically ill children admitted to a tertiary care hospital and assesses the strengths and weaknesses of exome sequencing to uncover pertinent PGx phenotypes.

Sub-Saharan African populations carry pharmacogenomic alleles poorly represented in the European-derived reference panels underlying most clinical genotyping tools. We present a curated, machine-readable catalog of nine actionable alleles across six pharmacogenes (CYP2D6, CYP2B6, CYP2C9, CYP2C19, CYP3A5, NAT2) with African-specific frequency ranges, functional annotations, and evidence levels derived from reanalysis of 661 high-coverage whole-genome sequences across seven 1000 Genomes Project African populations. Direct comparison against PharmCAT v3.4.0 shows that CYP2D6 produces zero diplotype calls (0/661 samples callable) due to monomorphic reference positions absent from standard variant-only VCF output, a known limitation whose consequences for African allele carriers had not been reported. afripharmagen's reduced-position strategy identifies 243 CYP2D617 and 134 CYP2D629 carriers from the same input. For CYP2B6, CYP2C9, CYP2C19, and NAT2, both tools show concordance of 95-100%. Frequency gradients (CYP2B66: 30-50%; CYP2D617: 15-35% in West Africa; CYP3A5*1: 60-95%) translate directly into prescribing risk for efavirenz, tramadol, tacrolimus, and isoniazid. Pharmacogenomic decision support in African settings must incorporate population-specific allele definitions and input-format-aware strategies.

Pharmacogenomics (PGx) can improve safety and effectiveness of commonly dispensed medicines, but its value at the population level depends on how often clinically actionable PGx phenotypes co-occur with the medicines they affect. We assessed this co-occurrence in a cross-sectional analysis of Our Future Health (OFH), a new UK national biobank, by applying Pharmacogenomics Clinical Annotation Tool (PharmCAT v3.1.1) to imputed genotypes from 738,531 participants across 17 pharmacogenes with established PGx prescribing guidelines. Every participant had at least one actionable PGx phenotype, with a mean of 6.1 (SD 1.3). The number of actionable PGx phenotypes was similar across genetically inferred ancestry groups, although the pharmacogenes contributing to that count differed between groups. Using linked primary care dispensing records, 36.8% (95% CI 36.7-36.9) had been dispensed at least one medicine between April 2018 and June 2025 matched to a gene for which they carried an actionable PGx phenotype. Co-occurrence rose with age, ranging from 43.7% to 58.9% across ancestry groups among those aged [≥]70 years. Participants carried an actionable PGx phenotype for a mean of 13.8 (SD 6.5) of the 33 medicines dispensed in English primary care with PGx prescribing guidance, of which a mean of 0.6 (SD 1.0) had been dispensed. Co-occurrence was concentrated in a few widely dispensed classes, principally proton-pump inhibitors and antidepressants acting through CYP2C19 and statins through SLCO1B1. These findings highlight opportunities to optimise treatment for a large proportion of patients receiving routine medications and identify where pre-emptive PGx testing could have the greatest clinical benefit.

Objectives: To assess how patients with non-valvular atrial fibrillation (NVAF) receiving oral anticoagulants are managed in routine primary care practice in Spain. Methods: This observational, descriptive study included patients with NVAF treated with oral anticoagulants for at least 6 months before enrolment and managed in primary care settings in Spain. The Barthel and ACTS questionnaires were administered to evaluate functional autonomy and treatment satisfaction, respectively. Results: A total of 1,901 patients were included: 428 received vitamin K antagonists (VKAs) and 1,473 direct oral anticoagulants (DOACs). Compared with patients receiving DOACs, those treated with VKAs were significantly older and had a higher prevalence of more hypertension. Mean treatment duration was 7.6 years for VKAs and 3.8 years for DOACs. Among patients receiving VKAs, 56.2% and 59.0% achieved good anticoagulation control according to the direct and Rosendaal methods, respectively. Patients in the DOAC group reported greater satisfaction across several domains, including perceived treatment benefits, lower impact on daily life, and overall positive treatment effect. Event incidence rates (per 1,000 person-years) were higher with DOACs than with VKAs for stroke (1.75; 95% CI 1.05-2.92), ischemic stroke (1.78; 95% CI 1.06-3.00), acute myocardial infarction (1.64; 95% CI 0.97-2.79), and major bleeding (3.68; 95% CI 1.81-7.46). Conclusions: In routine primary care practice in Spain, patient profiles and treatment duration varied by oral anticoagulant type. These differences may partly explain the higher rates of stroke and major bleeding observed with DOACs versus VKAs, despite greater treatment satisfaction among patients receiving DOACs.

Background Racial and ethnic minority populations face disproportionate rates of uncontrolled blood pressure (BP) and hypertension-related mortality. Remote hypertension monitoring (RHM) with active clinician-led medication titration has shown promise for improving BP control, but real-world evidence in majority-minority primary care settings remains limited. Methods This retrospective cohort study (January 2022-December 2024) enrolled adults with hypertension in a Bluetooth-integrated RHM program at a single urban academic primary care clinic. Of 550 patients enrolled, 503 with evaluable follow-up data were included. Patients transmitted daily home BP readings; clinicians reviewed readings monthly and titrated anti-hypertensive regimens per 2017 ACC/AHA guidelines. BP control was assessed at baseline and 3, 6, and 9 months. Factors associated with longitudinal BP control were examined using multivariable generalized estimating equations (GEE), with outcomes defined as strict control (<130/80 mmHg), at-least-moderate control (<140/90 mmHg), and uncontrolled (>140/90 mmHg). Results Among 503 participants (mean age 58.3 [SD 12.1] years; 63.6% African American; 52.9% male), BP control increased from 10.1% at baseline to 37.1% at 9 months. Each additional month of enrollment was associated with reduced odds of uncontrolled BP (adjusted odds ratio [aOR] 0.82; 95% CI, 0.80-0.85; P<.001). White race was associated with lower odds of uncontrolled BP versus African American race (aOR 0.57, at-least-moderate control; aOR 0.40, strict control; both P<.001). Male sex (aOR 1.46; P=.02) and congestive heart failure (aOR 2.09, strict control; aOR 2.05, at-least-moderate control; both P<.05) were associated with higher odds of uncontrolled BP. Conclusion Bluetooth-integrated RHM with active clinician-led medication titration was associated with a nearly 4-fold increase in BP control over 9 months in a majority-minority primary care population. Persistent within-program racial disparities underscore the need for equity-centered strategies beyond technology adoption alone. Prospective studies with concurrent usual-care comparators are needed to establish causal inference.

Accurately predicting the effects of pharmacogenomic variants is essential for the development of personalized therapeutic strategies, as genetic variability can influence drug response differently across patients. Here, we assessed several computational approaches using a dataset of pharmacogenomic variants with either clinical annotations or functional characterization by deep mutational scanning, compiled from the literature, with an additional focus on CYP2C9, a clinically relevant drug-metabolizing enzyme. Our results show that, despite recent methodological advances, substantial room for improvement remains. In particular, current methods struggle to distinguish gain-of-function variants associated with increased drug clearance and fast-metabolizer phenotypes from neutral variants, whereas loss-of-function variants that reduce drug clearance are predicted more accurately. The integration of structural and evolutionary information appears to be a key strategy for improving performance, with the coevolution-based StructureDCA method achieving the highest accuracy compared with classical genetic variant-effect predictors and recent deep learning approaches, including the pathogenic-variant predictor AlphaMissense and general protein language model-based methods. Finally, our results indicate that computational models can complement in vitro experiments in clinical variant interpretation, as StructureDCA predictions showed better agreement with clinically annotated phenotypes than large-scale deep mutational scanning data in several cases.

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.

Since copy number variations (CNVs) in pharmacogenes can cause significant alterations in drug metabolism, their reliable detection is of high importance both for large-scale studies and personalized medicine. Whole-genome sequencing, and specifically long-read sequencing, is the gold standard for CNV detection. Despite increasing availability of these technologies, genotyping arrays are still widely used as cost-effective alternatives in biobank and clinical settings, yet calling CNVs based on array intensity signals is challenging due to low base pair resolution. In this work, we developed a neural network model, nnCNV, to predict deletions in the CYP2C19 pharmacogene region from array intensity signals. We compared our method to the most widely used algorithm, PennCNV, and demonstrated better performance reaching 100% accuracy in the test dataset. Furthermore, we predicted probe-by-probe CYP2C19 deletion coordinates for all Estonian Biobank samples using nnCNV and PennCNV, and validated these predictions using an identity-by-descent (IBD) sharing method, which also demonstrated superior nnCNV performance. For the deletion samples with conflicting PennCNV and nnCNV predictions, we performed PCR analysis for validation, which showed 97% precision for nnCNV compared to 23% for PennCNV. Finally, we assessed the gradient-based feature importance maps and showed that nnCNV utilizes signal intensity information not only from deletion probes, but also from probes in flanking regions. Our results demonstrate that long-range information, which cannot be utilized by hidden Markov models, can improve CNV calling.

Purpose In deprescribing studies, a prescription-free gap is typically used to determine if patients discontinued their treatment. An appropriate gap depends on the typical time between prescriptions during continued use. This work aims to characterise the interval between prescriptions of chronic drugs using different methods for a cohort of older people in primary care in Ireland. Methods The empirical prescription interval was analysed for 38,154 patients for the twenty most common drug classes and the association between covariates and the interval was analysed using a multi-level model. Estimates were also compared to those obtained from the parametric waiting time distribution (pWTD) approach. Results Available covariates had consistent relationships with prescription intervals across drug classes. For example, each additional prescription issue was associated with an increase in the interval by 5.0 (NSAIDs) to 19.7 days ("Other antidepressants"). Full public health cover was associated with a -29.0 day (inhaled adrenergics) to -11.0 day (opioids) change relative to partial cover, while other/private cover had a -17.9 day (benzodiazepines and associated drugs) to -7.1 day (SSRI and SNRIs) change relative to partial cover. The pWTD also produced consistent estimates of the population interval for most drugs. Conclusions The interval varied substantially within drug classes, due to a mixture of patient, practice and unmodelled factors. Variation between practices was effectively explained, with residual variation between patients and within patients. The pWTD approach is useful for describing complex distributions of intervals, and may be more appropriate for inferring a gap than summarising truncated data.

Persons with epilepsy (PWE) experience high rates of psychiatric comorbidity, yet the population-scale pharmacoepidemiology of psychiatric medication (PM) use alongside antiseizure medications (ASMs) has not been previously characterized. Using Epic Cosmos, a federated electronic health record network spanning >300 million patients across >2,000 hospital systems, we examined patterns of ASM and PM co-prescriptions in PWE (ICD- 10 G40.x) every year between 2018-2025. PM prescriptions were similarly assessed in patients with asthma (J45.x). We found that despite the introduction of several newer ASMs, the overall prescribing landscape remained stable, with little change in the relative use of individual ASMs over time. Compared with asthma patients, PWE were more likely to receive prescriptions for opiates, antidepressant and antipsychotic medications across the age spectrum. 17-year-old or younger PWE were more likely to receive ADHD/stimulant medications, whereas adults and older adults exhibited a shift toward cognitive enhancing agents. We did not observe a preponderance of PM co- prescribing with any specific ASM or ASM class. Together, these results provide a population-scale, age-specific survey of psychiatric medication co-prescriptions in epilepsy, establishing a framework to monitor surrogate markers of psychiatric comorbidity and to support pharmacovigilance of potential drug-drug interactions.

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.

Preeclampsia (PE) is a hypertensive pregnancy-specific disorder and a leading cause of maternal and fetal mortality. A common feature of PE placentas and maternal plasma is dyslipidemia, or abnormal lipid levels, which can increase oxidative stress and endothelial dysfunction. However, the precise transcriptional, post-transcriptional, and epigenetic mechanisms underlying these abnormalities remain poorly characterized. Identifying such changes may clarify disease mechanisms and identify lipid-related PE biomarkers. We conducted a large-scale meta-analysis integrating public placental datasets from NCBI GEO, comprising four DNA methylation (DNAm) datasets (n = 172), three RNA-sequencing datasets (n = 92), and an independent RNA microarray validation cohort (n =146). We evaluated differential DNAm (limma), gene expression (DESeq2), transcript-level shifts (Swish), and alternative splicing (rMATS) in PE versus control placentas, with all analyses stratified by fetal sex via an interaction term model. We also performed placental cell-type deconvolution to quantify PE-associated cell-type proportion changes. Our results demonstrated that lipid-related regulation changes in PE placentas occur primarily at the gene and transcript level, with DNAm showing no changes. We also identified significant isoform switching in PE that were undetected by differential gene expression analysis, and primarily driven by alternative transcription initiation and termination sites rather than alternative splicing. A subset of these isoform switches mapped to pathways dysregulated in PE and were predicted to cause functional protein changes. An interaction term model identified several sex-specific differentially expressed genes (DEGs) in PE, including a subset of male-specific downregulated genes involved in oxidative metabolism. However, many of the remaining sex-specific DEGs across both sexes were previously uncharacterized in the literature. These findings suggest that transcriptional and isoform-level regulation play a role in PE-associated dyslipidemia, with certain regulatory pathways displaying fetal sex-specific patterns. Highlights- Preeclampsia-associated dyslipidemia manifests at the gene and transcript level - Reciprocal isoform switches were missed by standard gene-level analyses - Alternative transcript initiation and termination drove isoform switching - Sex-interaction modeling identified sex-specific transcriptional shifts in PE

In February 2026, the FDA announced that a single pivotal phase 3 (P3) trial would become the new default standard for drug approval - a regulatory direction that had been legally enabled since the FDA Modernization Act of 1997. This announcement has strategic, scientific, and economic implications for drug developers, contract research organizations (CROs), and biotech investors. We argue that the expansion of this framework, originally reserved for various niche submissions, represents a paradigm change, dramatically increasing the value of rigorous early phase (P1 and P2) trial design, requiring sponsors to establish both statistical efficacy signals and mechanistic biological understanding before entering phase 3. Using a CNS indication cost model, we show that single P3 approval can reduce total development expenditure from approximately $447 million over 14 years to $297 million over 12 years - a savings of $150 million and providing two years of additional commercial runway for a modeled CNS drug. Case examples including lecanemab, omaveloxolone, and tofersen illustrate how biomarker-informed early phase strategies can establish the confirmatory evidence necessary for single-trial approval. We provide practical guidance for maximizing the value of P1 and P2 under this evolving framework.

ONTOSIGHT(R) ADMETron is a high-performance SaaS based AI platform designed for the rapid profiling and visualization of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties. The platform integrates a highly interactive web interface with a robust predictive engine, enabling the batch processing of compounds for high-throughput virtual screening. The core engine employs an ensemble model that combines recurrent neural network (RNN)-derived embeddings from SMILES strings with physicochemical descriptors, which are fed into gradient boosting machines (GBMs). This architecture provides accurate predictions across 34 distinct ADMET endpoints, encompassing critical categories such as physicochemical properties, absorption, CYP450 inhibition, hERG toxicity, and mutagenicity. The platforms superior performance is quantitatively validated by its top-tier ranking on the Therapeutics Data Commons (TDC) ADMET Benchmark Group, demonstrating robustness and generalizability with notable results including 2nd place for Ames mutagenicity (AUROC 0.870) and 2nd place for LD50 (MAE 0.573). In addition to its predictive capabilities, ADMETron introduces a novel SAR analysis framework that enables real-time comparison of multiple compounds and approved drugs through an interactive radar graph visualization. Comparative evaluation against widely used online ADMET platforms demonstrated broader endpoint coverage, including pharmacokinetic, physicochemical, and medicinal chemistry assessments within a unified environment. The combination of benchmark-validated predictive performance, comprehensive ADMET profiling, and advanced visualization tools positions ADMETron as a next-generation platform for virtual screening, lead optimization, and data-driven decision-making in modern drug discovery (https://admetron.partex.ai/).

Congenital adrenal hyperplasia (CAH) is a rare inherited disorder requiring lifelong hormone replacement therapy. Excessive hormone replacement poses a significant risk for long-term complications, such as hypertension; however, quantitative approaches for optimizing dosing remain underdeveloped. This study aimed to identify factors associated with hypertension in patients with CAH and to develop a predictive model to support longitudinal fludrocortisone dose adjustment in pediatric patients who were already receiving mineralocorticoid replacement. We first employed generalized linear mixed models (GLMM) to evaluate the relationships among therapeutic agents, biochemical markers, and hypertension. Our results indicated a significant positive association between the dose of fludrocortisone (FC) and diastolic hypertension, whereas no such association was observed for the dose of hydrocortisone (HC). Using expert curated data, we subsequently constructed multiple predictive models, including CatBoost, XGBoost, and LightGBM, to enable individualized adjustment of FC dosage. All models were evaluated on an independent test set, with CatBoost, XGBoost, and LightGBM demonstrating comparably strong performance (R^2: 0.75 to 0.77). Subgroup analyses revealed that predictive accuracy was highest in children aged 0 to 2 years, where the top-performing model achieved a mean ideal prediction rate of 59.6%. This study not only confirms the significant link between FC dosing and hypertension in CAH patients but also provides a machine learning based decision support tool to assist individualized longitudinal dose adjustment. The model shows promise as a clinical decision-support instrument to facilitate personalized and precise management of CAH therapy.

BackgroundLMNA codes a widely expressed nuclear cytoskeletal protein (lamin A/C) with multiple important functions. Pathogenic LMNA genetic variation may lead to autosomal dominant cardiomyopathy, though the severity and rate of progression can vary with the specific nucleotide change and location. Prior studies showed that induced pluripotent stem cells (iPSC)-derived cardiomyocytes (iCMs) with LMNA R541C exhibited reduced LMNA protein abundance, increased sarcomere disorganization, and abnormal electrophysiology. MethodsWe investigated the LMNA-R541C variant that exhibits a highly penetrant and severe clinical cardiomyopathy phenotype using transcriptomic analysis of iCMs. Patient-derived iPSCs with CRISPR-corrected (clustered regularly interspersed short palindromic repeats) isogenic control cells and CRISPR knock-in LMNA-R541C heterozygous iPSCs were generated for isogenic controlled experiments. ResultsIn differential gene expression analyses we observed that LMNAR541C/WT iPSC-derived cardiomyocytes had consistent perturbations in 123 genes across CRISPR-corrected and knock-in experiments compared to controls. Pathway analysis identified that the G2M checkpoint and oxidative phosphorylation processes were consistently dysregulated and confirm these findings in previously published iPSC and murine models. DiscussionThese results implicate perturbed gene expression and pathways that may contribute to the severe phenotypes in LMNA-R541C. Informatic analysis of pathways suggests several drug classes including multiple cardiac glycosides as potential targeted therapeutic candidates to be explored.

Accurate prediction of ex vivo drug sensitivity in acute myeloid leukemia (AML) patients from transcriptomic data is a critical challenge for precision oncology. Existing computational approaches have explored uncertainty quantification in cancer drug response prediction primarily using cell line data, while patient-level AML models typically rely on heuristic confidence measures rather than statistically calibrated uncertainty estimates. Here, we present a framework applying split conformal prediction to patient-level AML drug response modeling using the BeatAML 2.0 cohort. We trained Elastic Net and XGBoost regressors on bulk RNA-seq gene expression profiles from 318 AML patients, analyzing 34,764 patient-drug observations across 122 compounds. Baseline models achieved median Pearson R values of 0.291 (Elastic Net) and 0.281 (XGBoost) across 122 drugs. Wrapping these models with split conformal prediction yielded well-calibrated prediction intervals across three confidence levels: empirical coverages of 81.4%, 90.7%, and 95.5% against nominal targets of 80%, 90%, and 95%, respectively. Analysis of prediction interval widths revealed substantial drug-class-specific uncertainty patterns, with HDAC and BCL-2 inhibitors exhibiting markedly higher uncertainty than MDM2 inhibitors, suggesting a potential association between transcriptomic predictability and drug mechanism of action, although several drug classes were represented by only a small number of compounds. Predictive uncertainty was not significantly associated with ELN2017 molecular risk classification (Kruskal-Wallis p=0.395) or NPM1 mutation status (p=0.788). These results demonstrate that statistically valid uncertainty quantification can be achieved for patient-level AML drug response prediction despite substantial biological heterogeneity. to the best of our knowledge, no published study has applied split conformal prediction to patient-level ex vivo drug sensitivity prediction in the BeatAML cohort, providing a principled alternative to heuristic confidence scoring approaches.

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.