medRxiv [Preprint]. 2025 Mar 20:2025.03.19.25324270. doi: 10.1101/2025.03.19.25324270.

ABSTRACT

Antimicrobial resistance poses a major global threat due to the diminishing efficacy of current treatments and limited new therapies. Combination therapy with existing drugs offers a promising solution, yet current empirical methods often lead to suboptimal efficacy and inadvertent toxicity. The high cost of experimentally testing numerous combinations underscores the need for data-driven methods to streamline treatment design. We introduce CALMA, an approach that predicts the potency and toxicity of multi-drug combinations in Escherichia coli and Mycobacterium tuberculosis . CALMA identified synergistic antimicrobial combinations involving vancomycin and isoniazid that were antagonistic for toxicity, which were validated using in vitro cell viability assays in human cell lines and through mining of patient health records that showed reduced side effects in patients taking combinations identified by CALMA. By combining mechanistic modelling with deep learning, CALMA improves the interpretability of neural networks, identifies key pathways influencing drug interactions, and prioritizes combinations with enhanced potency and reduced toxicity.

PMID:40166569 | PMC:PMC11957163 | DOI:10.1101/2025.03.19.25324270

medRxiv [Preprint]. 2025 Mar 19:2025.03.18.25324215. doi: 10.1101/2025.03.18.25324215.

ABSTRACT

BACKGROUND: Accurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time, however manual assessment is time-consuming and can be imprecise. Artificial intelligence (AI) has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.

METHODS: We developed and validated open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography. The outputs of segmentation models were compared to sonographer measurements from two institutions to access accuracy and precision.

RESULTS: We utilized 877,983 echocardiographic measurements from 155,215 studies from Cedars-Sinai Medical Center (CSMC) to develop EchoNet-Measurements, an open-source deep learning model for echocardiographic annotation. The models demonstrated a good correlation when compared with sonographer measurements from held-out data from CSMC and an independent external validation dataset from Stanford Healthcare (SHC). Measurements across all nine B-mode and nine Doppler measurements had high accuracy (an overall R 2 of 0.967 (0.965 - 0.970) in the held-out CSMC dataset and 0.987 (0.984 - 0.989) in the SHC dataset). When evaluated end-to-end on a temporally distinct 2,103 studies at CSMC, EchoNet-Measurements performed well an overall R2 of 0.981 (0.976 - 0.984). Performance was consistent across patient characteristics including sex and atrial fibrillation status.

CONCLUSION: EchoNet-Measurement achieves high accuracy in automated echocardiographic measurement that is comparable to expert sonographers. This open-source model provides the foundation for future developments in AI applied to echocardiography.

CLINICAL PERSPECTIVE: What Is New?: We developed EchoNet-Measurements, the first publicly available deep learning framework for comprehensive automated echocardiographic measurements.We assessed the performance of EchoNet-Measurements, showing good precision and accuracy compared to human sonographers and cardiologists across multiple healthcare systems.What Are the Clinical Implications?: Deep-learning automated echocardiographic measurements can be conducted in a fraction of a second, reducing the time burden on sonographers and standardizing measurements, and potentially enhance reproducibility and diagnostic reliability.This open-source model provides broad opportunities for widespread adoption in both clinical use and research, including in resource-limited settings.

PMID:40166567 | PMC:PMC11957091 | DOI:10.1101/2025.03.18.25324215

medRxiv [Preprint]. 2025 Mar 20:2025.03.19.25324269. doi: 10.1101/2025.03.19.25324269.

ABSTRACT

Perivascular spaces (PVS) are cerebrospinal fluid-filled tunnels around brain blood vessels, crucial for the functions of the glymphatic system. Changes in PVS have been linked to vascular diseases and aging, necessitating accurate segmentation for further study. PVS segmentation poses challenges due to their small size, varying MRI appearances, and the scarcity of annotated data. We present a finely segmented PVS dataset from T2-weighted MRI scans, sourced from the Human Connectome Project Aging (HCP-Aging), encompassing 200 subjects aged 30 to 100. Our approach utilizes a combination of unsupervised and deep learning techniques with manual corrections to ensure high accuracy. This dataset aims to facilitate research on PVS dynamics across different ages and to explore their link to cognitive decline. It also supports the development of advanced image segmentation algorithms, contributing to improved medical imaging automation and the early detection of neurodegenerative diseases.

PMID:40166557 | PMC:PMC11957161 | DOI:10.1101/2025.03.19.25324269

medRxiv [Preprint]. 2025 Mar 19:2025.03.18.25323918. doi: 10.1101/2025.03.18.25323918.

ABSTRACT

BACKGROUND: Accurate evaluation of aortic regurgitation (AR) severity is necessary for early detection and chronic disease management. AR is most commonly assessed by Doppler echocardiography, however limitations remain given variable image quality and need to integrate information from multiple views. This study developed and validated a deep learning model for automated AR severity assessment from multi-view color Doppler videos.

METHODS: We developed a video-based convolutional neural network (R2+1D) to classify AR severity using color Doppler echocardiography videos from five standard views: parasternal long-axis (PLAX), PLAX-aortic valve focus, apical three-chamber (A3C), A3C-aortic valve focus, and apical five-chamber (A5C). The model was trained on 47,638 videos from 32,396 studies (23,240 unique patients) from Cedars-Sinai Medical Center (CSMC) and externally validated on 3369 videos from 1504 studies (1493 unique patients) from Stanford Healthcare Center (SHC).

RESULTS: Combining assessments from multiple views, the EchoNet-AR model achieved excellent identification of both at least moderate AR (AUC 0.95, [95% CI 0.94-0.96]) and severe AR (AUC 0.97, [95% CI 0.96 - 0.98]). This performance was consistent in the external SHC validation cohort for both at least moderate AR (AUC 0.92, [95% CI 0.88-0.96]) and severe AR (AUC 0.94, [95% CI 0.89-0.98]). Subgroup analysis showed robust model performance across varying image quality, valve morphologies, and patient demographics. Saliency map visualizations demonstrated that the model focused on the proximal flow convergence zone and vena contracta, appropriately narrowing on hemodynamically significant regions.

CONCLUSION: The EchoNet-AR model accurately classifies AR severity and synthesizes information across multiple echocardiographic views with robust generalizability in an external cohort. The model shows potential as an automated clinical decision support tool for AR assessment, however clinical interpretation remains essential, particularly in complex cases with multiple valve pathologies or altered hemodynamics.

PMID:40166551 | PMC:PMC11957077 | DOI:10.1101/2025.03.18.25323918

Front Artif Intell. 2025 Mar 17;8:1496109. doi: 10.3389/frai.2025.1496109. eCollection 2025.

ABSTRACT

BACKGROUND: The diagnostic power of exercise stress electrocardiography (ExECG) remains limited. We aimed to construct an artificial intelligence (AI)-based method to enhance ExECG performance to identify patients with significant coronary artery disease (CAD).

METHODS: We retrospectively collected 818 patients who underwent both ExECG and coronary angiography (CAG) within 6 months. The mean age was 57.0 ± 10.1 years, and 614 (75%) were male patients. Significant coronary artery disease was seen in 369 (43.8%) CAG reports. We also included 197 individuals with normal ExECG and low risk of CAD. A convolutional recurrent neural network algorithm, integrating electrocardiographic (ECG) signals and features from ExECG reports, was developed to predict the risk of significant CAD. We also investigated the optimal number of inputted ECG signal slices and features and the weighting of features for model performance.

RESULTS: Using the data of patients undergoing CAG for training and test sets, our algorithm had an area under the curve, sensitivity, and specificity of 0.74, 0.86, and 0.47, respectively, which increased to 0.83, 0.89, and 0.60, respectively, after enrolling 197 subjects with low risk of CAD. Three ECG signal slices and 12 features yielded optimal performance metrics. The principal predictive feature variables were sex, maximum heart rate, and ST/HR index. Our model generated results within one minute after completing ExECG.

CONCLUSION: The multimodal AI algorithm, leveraging deep learning techniques, efficiently and accurately identifies patients with significant CAD using ExECG data, aiding clinical screening in both symptomatic and asymptomatic patients. Nevertheless, the specificity remains moderate (0.60), suggesting a potential for false positives and highlighting the need for further investigation.

PMID:40166362 | PMC:PMC11955648 | DOI:10.3389/frai.2025.1496109

bioRxiv [Preprint]. 2025 Mar 17:2025.03.14.643307. doi: 10.1101/2025.03.14.643307.

ABSTRACT

Due to the favorable chemical properties of mirrored chiral centers (such as improved stability, bioavailability, and membrane permeability) the computational design of D-peptides targeting biological L-proteins is a valuable area of research. To design these structures in silico , a computational workflow should correctly dock and fold a peptide while maintaining chiral centers. The latest AlphaFold 3 (AF3) from Abramson et al. (2024) enforces a strict chiral violation penalty to maintain chiral centers from model inputs and is reported to have a low chiral violation rate of only 4.4% on a PoseBusters benchmark containing diverse chiral molecules. Herein, we report the results of 3,255 experiments with AF3 to evaluate its ability to predict the fold, chirality, and binding pose of D-peptides in heterochiral complexes. Despite our inputs specifying explicit D-stereocenters, we report that the AF3 chiral violation for D-peptide binders is much higher at 51% across all evaluated predictions; on average the model is as accurate as chance (random chirality choice, L or D, for each peptide residue). Increasing the number of seeds failed to improve this violation rate. The AF3 predictions exhibit incorrect folds and binding poses, with D-peptides commonly oriented incorrectly in the L-protein binding pocket. Confidence metrics returned by AF3 also fail to distinguish predictions with low chirality violation and correct docking vs. predictions with high chirality violation and incorrect docking. We conclude that AF3 is a poor predictor of D-peptide chirality, fold, and binding pose.

SUMMARY: A crucial task in computational protein design is predicting fold, as this property determines the structure and function of a protein. Abramson et al. 1 published in Nature on AlphaFold 3 (AF3), a powerful deep learning framework for predicting chemical structures in both bound and unbound states. This architecture is tuned to respect chiral centers, which are atoms (in proteins, backbone α -carbons) covalently bound to four different chemical species 2 . These centers adopt two non-superposable forms, often called "handedness," termed L (all biological proteins adopt this form) and D (the mirror image of L). L and D chiral centers exert significant influence on chemical function; changing the chirality of even a single residue can dramatically alter chemical properties such as enantioselective binding (e.g., antifolate resistance 3 ) and stability 4 . Additionally, D-peptides (small proteins containing exclusively D chiral centers) exhibit many advantages compared to their L-peptide counterparts, such as protease evasion 5 , and are therefore therapeutically relevant modalities. Due to vastly differing chemical properties, an algorithm should respect chiral center inputs and exhibit an error rate of 0%. Although Abramson et al. 1 reports a low 4.4% chirality violation across diverse chiral centers, we have found that the chiral violation rate for D-peptides with D chiral center inputs explicitly specified is much higher at 51%. Increasing the number of seeds fails to improve this rate. Our data highlights a crucial structural prediction error in AF3 and demonstrates the model is as accurate on average as chance (random chirality choice, L or D, for each peptide residue). Compared to empirical structures, AF3 is also highly inaccurate when folding and docking D-peptide:L-protein complexes. The failure of AF3 to accurately predict these chemical interactions indicates more work is need for high-quality prediction of D-peptides.

PMID:40166350 | PMC:PMC11956919 | DOI:10.1101/2025.03.14.643307

bioRxiv [Preprint]. 2025 Mar 21:2025.03.21.644611. doi: 10.1101/2025.03.21.644611.

ABSTRACT

Recording activity from large cell populations in deep neural circuits is essential for understanding brain function. Three-photon (3P) imaging is an emerging technology that allows for imaging of structure and function in subcortical brain structures. However, increased tissue heating, as well as the low repetition rate sources inherent to 3P imaging, have limited the fields of view (FOV) to areas of ≤0.3 mm 2 . Here we present a Large Imaging Field of view Three-photon (LIFT) microscope with a FOV of [gt]3 mm 2 . LIFT combines high numerical aperture (NA) optimized sampling, using a custom scanning module, with deep learning-based denoising, to enable population imaging in deep brain regions. We demonstrate non-invasive calcium imaging in the mouse brain from >1500 cells across CA1, the surrounding white matter, and adjacent deep layers of the cortex, and show population imaging with high signal-to-noise in the rat cortex at a depth of 1.2 mm. The LIFT microscope was built with all off-the-shelf components and allows for a flexible choice of imaging scale and rate.

PMID:40166349 | PMC:PMC11957121 | DOI:10.1101/2025.03.21.644611

bioRxiv [Preprint]. 2025 Mar 20:2025.01.14.633078. doi: 10.1101/2025.01.14.633078.

ABSTRACT

Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate their signaling pathways. Defects in PTMs have been linked to numerous developmental disorders and human diseases, including cancer, diabetes, heart, neurodegenerative and metabolic diseases. PTMs are important targets in drug discovery, as they can significantly influence various aspects of drug interactions including binding affinity. The structural consequences of PTMs, such as phosphorylation-induced conformational changes or their effects on ligand binding affinity, have historically been challenging to study on a large scale, primarily due to reliance on experimental methods. Recent advancements in computational power and artificial intelligence, particularly in deep learning algorithms and protein structure prediction tools like AlphaFold3, have opened new possibilities for exploring the structural context of interactions between PTMs and drugs. These AI-driven methods enable accurate modeling of protein structures including prediction of PTM-modified regions and simulation of ligand-binding dynamics on a large scale. In this work, we identified small molecule binding-associated PTMs that can influence drug binding across all human proteins listed as small molecule targets in the DrugDomain database, which we developed recently. 6,131 identified PTMs were mapped to structural domains from Evolutionary Classification of Protein Domains (ECOD) database. Scientific contribution. Using recent AI-based approaches for protein structure prediction (AlphaFold3, RoseTTAFold All-Atom, Chai-1), we generated 14,178 models of PTM-modified human proteins with docked ligands. Our results demonstrate that these methods can predict PTM effects on small molecule binding, but precise evaluation of their accuracy requires a much larger benchmarking set. We also found that phosphorylation of NADPH-Cytochrome P450 Reductase, observed in cervical and lung cancer, causes significant structural disruption in the binding pocket, potentially impairing protein function. All data and generated models are available from DrugDomain database v1.1 ( http://prodata.swmed.edu/DrugDomain/ ) and GitHub ( https://github.com/kirmedvedev/DrugDomain ). This resource is the first to our knowledge in offering structural context for small molecule binding-associated PTMs on a large scale.

PMID:40166291 | PMC:PMC11956905 | DOI:10.1101/2025.01.14.633078

3 Biotech. 2025 Apr;15(4):102. doi: 10.1007/s13205-025-04272-y. Epub 2025 Mar 29.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a lung disease with an unknown etiology and a short survival rate. There is no accurate method of early diagnosis, and it involves computed tomography (CT) or lung biopsy. Since diagnostic methods are not accurate due to their similarity to other lung pathologies, discovering new biomarkers is a key issue for diagnosticians. Currently, the use of ccf-DNA (circulating cell-free deoxyribonucleic acid) is an important focus due to its association with IPF-induced alterations in metabolic pathways, such as amino acid metabolism, energy metabolism, and lipid metabolism pathways. Other biomarkers associated with metabolic changes have been found, and they are related to changes in type II/type I alveolar epithelial cells (AECs I/II), changes in extracellular matrix (ECM), and inflammatory processes. Currently, IPF pathogenetic treatment remains unknown, and the mortality rates are increasing, and the patients are diagnosed at a late stage. Signaling pathways and metabolic dysfunction have a significant role in the disease occurrence, particularly the transforming growth factor-β (TGF-β) signaling pathway, which plays an essential role. TGF-β, Wnt, Hedgehog (Hh), and integrin signaling are the main drivers of fibrosis. These pathways activate the transformation of fibroblasts into myofibroblasts, extracellular matrix (ECM) deposition, and tissue remodeling fibrosis. Therapy targeting diverse signaling pathways to slow disease progression is crucial in the treatment of IPF. Two antifibrotic medications, including pirfenidone and nintedanib, are Food and Drug Administration (FDA)-approved for treatment. ccf-DNA could become a new biomarker for IPF diagnosis to detect the disease at the early stage, while FDA-approved therapies could help to prevent late conditions from forming and decrease mortality rates.

PMID:40165930 | PMC:PMC11954786 | DOI:10.1007/s13205-025-04272-y

Clin Respir J. 2025 Apr;19(4):e70072. doi: 10.1111/crj.70072.

ABSTRACT

BACKGROUND: This study aims to summarize the similarities and differences in immune cell characteristics, and potential therapeutic targets between systemic sclerosis-associated interstitial lung disease (SSc-ILD) and idiopathic pulmonary fibrosis (IPF).

METHODS: This study included SSc-ILD and SSc-nonILD patients who were admitted to Beijing Chaoyang Hospital between April 4th, 2013, to June 30th, 2023. Publicly available datasets, including peripheral blood monocular cell (pbmc) single-cell data, SSc, SSc-ILD pbmc transcriptome data, and SSc-ILD, IPF lung tissue transcriptome data were analyzed. Statistical analyses were conducted using the SPSS and R software, employing standard statistical methods and bioinformatics packages such as Seurat, DESeq2, enrichR, and CellChat.

RESULTS: The results revealed that the CD4+/CD8+ T cell ratio of pbmc in SSc-ILD patients was significantly higher than in SSc-nonILD patients. In IPF patients, an elevated CD4+/CD8+ T cell ratio was also observed in progressive group, and Treg and mature CD4+ T cells might cause this change. JAK-STAT pathway and the cytokine-cytokine receptor interaction pathway were activated in peripheral blood T cells of IPF patients. The CD30, CD40, and FLT3 signaling pathways were found to play crucial roles in T cell interactions with other immune cells among IPF patients. SPA17 as a commonly upregulated gene among SSc, SSc-ILD, and IPF pbmc and lung, with its expression correlating positively with disease severity and lung function progression.

CONCLUSION: CD4+/CD8+ T cell ratio might associate with ILD initiation and progression; Treg cells and mature CD4+ T cells play key roles of it. SPA17 might serve as a pan-ILD marker and associated with lung function progression.

PMID:40165483 | DOI:10.1111/crj.70072

J Gerontol A Biol Sci Med Sci. 2025 Mar 28:glaf053. doi: 10.1093/gerona/glaf053. Online ahead of print.

ABSTRACT

Ageing-associated cognitive decline affects more than half of those in long-term residential aged care. Emerging evidence suggests that gut microbiome-host interactions influence the effects of modifiable risk factors. We investigated the relationship between gut microbiome characteristics and severity of cognitive impairment CI in 159 residents of long-term aged care. Severe CI was associated with a significantly increased abundance of proinflammatory bacterial species, including Methanobrevibacter smithii and Alistipes finegoldii, and decreased relative abundance of beneficial bacterial clades. Severe CI was associated with increased microbial capacity for methanogenesis, and reduced capacity for synthesis of short-chain fatty acids, neurotransmitters glutamate and gamma-aminobutyric acid, and amino acids required for neuro-protective lysosomal activity. These relationships were independent of age, sex, antibiotic exposure, and diet. Our findings implicate multiple gut microbiome-brain pathways in ageing-associated cognitive decline, including inflammation, neurotransmission, and autophagy, and highlight the potential to predict and prevent cognitive decline through microbiome-targeted strategies.

PMID:40166866 | DOI:10.1093/gerona/glaf053

Fundam Res. 2023 Feb 19;5(1):215-227. doi: 10.1016/j.fmre.2023.01.009. eCollection 2025 Jan.

ABSTRACT

There are critical transition phenomena during the progression of many diseases. Such critical transitions are usually accompanied by catastrophic disease deterioration, and their prediction is of significant importance for disease prevention and treatment. However, predicting disease deterioration solely based on a single sample is a difficult problem. In this study, we presented the network information gain (NIG) method, for predicting the critical transitions or disease state based on network flow entropy from omics data of each individual. NIG can not only efficiently predict disease deteriorations but also detect their dynamic network biomarkers on an individual basis and further identify potential therapeutic targets. The numerical simulation demonstrates the effectiveness of NIG. Moreover, our method was validated by successfully predicting disease deteriorations and identifying their potential therapeutic targets from four real omics datasets, i.e., an influenza dataset and three cancer datasets.

PMID:40166114 | PMC:PMC11955047 | DOI:10.1016/j.fmre.2023.01.009

Plant Cell Environ. 2025 Mar 31. doi: 10.1111/pce.15494. Online ahead of print.

ABSTRACT

As the most prevalent RNA modification in eukaryotes, N6-methyladenosine (m6A) plays a crucial role in regulating various biological processes in plants, including embryonic development and flowering. However, the function of m6A RNA methyltransferase in moso bamboo remains poorly understood. In this study, we identified two m6A methyltransferases in moso bamboo, PheMTA1 and PheMTA2. Overexpression of PheMTA1 and PheMTA2 significantly promoted root development and enhanced salt tolerance in rice. Using the HyperTRIBE method, we fused PheMTA1 and PheMTA2 with ADARcdE488Q and introduced them into rice. RNA sequencing (RNA-seq) of the overexpressing rice identified the target RNAs bound by PheMTA1 and PheMTA2. PheMTA1 and PheMTA2 bind to OsATM3 and OsSF3B1, which were involved in the development of root and salt resistance. Finally, we revealed the effects of transcription or alternative splicing on resistance-related genes like OsRS33, OsPRR73, OsAPX2 and OsHAP2E, which are associated with the observed phenotype. In conclusion, our study demonstrates that the m6A methyltransferases PheMTA1 and PheMTA2 from moso bamboo are involved in root development and enhance plant resistance to salt stress.

PMID:40165397 | DOI:10.1111/pce.15494

Plant Genome. 2025 Jun;18(2):e70023. doi: 10.1002/tpg2.70023.

ABSTRACT

Ascochyta blight (AB) is one of the most devastating fungal diseases of chickpea (Cicer arietinum L.). Conventional breeding has focused on exploiting and introgressing major genes (qualitative effect) to improve AB resistance in released varieties. However, such approaches are time-consuming and prone to the breakdown of disease resistance due to the fast evolution of AB pathogen. Genomic selection (GS) offers a promising alternative by predicting breeding values using genome-wide single nucleotide polymorphisms (SNPs), regardless of major or minor effects. To our knowledge, this is the first study to develop and implement GS to improve AB resistance in chickpea. Over 4 years, 2790 chickpea lines, representing a broad range of germplasm collections primarily sourced from the Australian Grains Genebank, were evaluated for AB disease response in the field and in an outdoor pot-based facility. Plants were genotyped with the Illumina multispecies pulse 30K SNP array, resulting in 23,239 high-quality SNPs distributed across the genome. Intermediate-to-high genomic prediction accuracies (0.40-0.90) were achieved across validation scenarios. Bayesian modeling identified six major QTL explaining 33% of the genetic variance for AB resistance, with the remaining variance explained by minor effect genes. Using genomic estimated breeding values (GEBVs), 462 lines of the 2790 lines were predicted to have higher resistance compared to the released check varieties, revealing the potential of further improvement of AB resistance for the industry. The desirable genomic prediction accuracy obtained in the study supports the applicability of GS to breed for AB resistance in chickpea.

PMID:40164996 | DOI:10.1002/tpg2.70023

BMC Pharmacol Toxicol. 2025 Mar 31;26(1):73. doi: 10.1186/s40360-025-00912-4.

ABSTRACT

BACKGROUND: Melasma is a common hyperpigmentation disorder that causes significant distress to patients. In the real world, it is closely associated with various medications, making the timely identification and discontinuation of causative drugs an important aspect of clinical management. This study investigates the relationship between melasma and drug exposure based on data from the FDA Adverse Event Reporting System (FAERS) database.

METHODS: This study includes reports from the first quarter of 2004 to the second quarter of 2024, focusing on cases related to melasma. We employed four statistical methods to analyze the association between suspected drugs and adverse events related to melasma.

RESULTS: Within a specific timeframe, we extracted a total of 408 adverse reaction reports related to melasma. The result shows that a higher number of cases in female patients compared to male patients. The United States had the highest number of reported cases. We identified 22 drugs that were notably associated with melasma. Among these, the contraceptive "Ethinylestradiol and norethindrone" demonstrated the strongest signal of association.

CONCLUSIONS: Melasma is associated with exposure to various medications, with a notable proportion of cases coincided with contraceptive use. The mechanisms involved include hormonal disturbances and oxidative stress.

PMID:40165336 | DOI:10.1186/s40360-025-00912-4

PLoS Genet. 2025 Mar 31;21(3):e1011638. doi: 10.1371/journal.pgen.1011638. eCollection 2025 Mar.

ABSTRACT

Safety failures are an important factor in low drug development success rates. Human genetic evidence can select drug targets causal in disease and enrich for successful programs. Here, we sought to determine whether human genetic evidence can also enrich for labeled side effects (SEs) of approved drugs. We combined the SIDER database of SEs with human genetic evidence from genome-wide association studies, Mendelian disease, and somatic mutations. SEs were 2.0 times more likely to occur for drugs whose target possessed human genetic evidence for a trait similar to the SE. Enrichment was highest when the trait and SE were most similar to each other, and was robust to removing drugs where the approved indication was also similar to the SE. The enrichment of genetic evidence was greatest for SEs that were more drug specific, affected more people, and were more severe. There was significant heterogeneity among disease areas the SEs mapped to, with the highest positive predictive value for cardiovascular SEs. This supports the integration of human genetic evidence early in the drug discovery process to identify potential SE risks to be monitored or mitigated in the course of drug development.

PMID:40163513 | PMC:PMC11977994 | DOI:10.1371/journal.pgen.1011638