J. Matthew Mahoney

The immune‐fibrotic axis functions as a vicious feedback loop where age‐related chronic, low‐grade systemic inflammation (inflammaging) drives maladaptive tissue repair and fibrosis, while senescent fibrotic cells in turn amplify systemic inflammation. In the aging heart, immune dysregulation and fibroblast activation drive tissue scarring and stiffening and a higher risk of heart failure and vascular disease. To identify cardiac regions and cell types affected with age, we monitored transcriptional changes across whole hearts of 6‐ and 24‐month‐old male and female mice. Spatially resolved signatures of macrophages and fibroblasts, non‐cardiomyocyte populations highly involved in inflammaging, were enriched predominantly in the valves and aorta. With age, two cell types simultaneously adopted maladaptive programs: Macrophages displayed reduced immune homeostasis together with increased chemotactic and stromal programs, while more widely distributed fibroblast‐lineage cells displayed alterations in extracellular matrix organization and intercellular signals, as well as upregulation of senescence‐associated Cdkn1a (encoding p21) and other activated features shared with human cardiac fibroblasts. The major biological significance of these findings is that cardiac immune‐fibrotic remodeling is not a uniform, heart‐wide process. Instead, it varies with cell type, with valves and aorta emerging as major sites where age‐associated programs converge to progressively compromise cardiac structure and function.

QT interval prolongation is associated with an increased risk of torsades de pointes (TdP), a potentially life-threatening ventricular arrhythmia, making assessment of cardiac repolarization a critical component of drug development. This work demonstrates how integrated in silico methodologies can be used to predict and quantitatively characterize QT liability across the drug discovery and development continuum. A proof-of concept translational workflow combining molecular structure analysis, in vitro pharmacology, animal data, and clinical exposure-response modeling was evaluated using the retrospective case examples verapamil and dofetilide. Molecular structure- and pharmacology-based assessments predicted hERG channel interaction and potential QT risk for both compounds. While initial in silico analyses suggested a high QT liability for verapamil and dofetilide, mechanistic electrophysiological modeling differentiated their effects. For verapamil, concurrent calcium channel inhibition offset hERG-mediated effects, resulting in predicted reductions in action potential duration and no clinically relevant QT prolongation. In contrast, dofetilide produced concentration-dependent increases in action potential duration, consistent with its known proarrhythmic potential. Translation of nonclinical findings to humans accurately predicted the absence of clinically meaningful QT effects for verapamil and identified QT prolongation exceeding the regulatory 10 ms threshold for dofetilide at therapeutic concentrations. Drug exposure and pharmacokinetic properties could be anticipated based solely on molecular structure. This conceptual workflow demonstrates how integrated quantitative in silico approaches can support earlier identification, characterization, and communication of QT risk, enabling informed decision-making and improved development efficiency from bench to bedside.

The target‐engagement predictor, implemented within the InSilicoTrials cloud‐based, AI‐powered platform for model‐informed drug discovery and development, enables early exploration of time‐dependent pharmacological responses. Predictions can be generated using inputs ranging from molecular structure to in vitro, animal, and clinical data. Scaling and translation across species are supported, accounting for differences in body size and plasma protein binding. This tutorial demonstrates the application of the target‐engagement predictor for benchmarking and decision‐support in the evaluation of new drug candidates. Pharmacokinetic (PK) and pharmacodynamic (PD) data for carvedilol, ivabradine, and sunitinib were used as illustrative examples. The ANDROMEDA and PLATO software were used to predict human clinical PK and target affinity parameters, respectively, based on molecular SMILES representations of the drugs. These predictions were subsequently incorporated into longitudinal PKPD simulation models. User‐friendly input wizards and interactive results dashboards were developed to facilitate clinical trial simulation for broader project teams, without requiring programming expertise. Predictions derived from molecular structure were compared with data generated from in vitro, rat, and clinical experimental settings. The target‐engagement predictor enables integration of heterogenous data sources to support drug development decisions, including early benefit–risk assessment and dose selection for Phase 1 studies. Direct prediction based on molecular structure alone represents a promising approach for early‐stage screening and evaluation of new drug candidates.

Cardiac safety assessment is an integral part of drug discovery and development. Drug candidates that adversely affect cardiac or hemodynamic function should be discontinued early unless a favorable benefit-risk ratio for patients can be justified. In this hypothesis-generating work, we aimed to develop a conceptual framework for informing early safety risk assessment based on in vitro drug affinities to pharmacological targets. For illustration, we used the drug-induced cardiotoxicity rank (DICTrank) data comprising 1318 drugs with cardiac safety concerns according to FDA labeling. The data was enriched with information on affinity to the most plausible mechanistic targets, clinical drug exposure, and human plasma protein binding. We descriptively identified 18 target classes potentially associated with elevated cardiovascular risk: potassium channels (accounting alone for 20% of the ‘most concern’ safety group); adrenergic, dopamine, serotonin, androgen, sex hormone, and opioid receptors; cyclooxygenase; sodium and calcium channels; muscarinic and glucocorticoid receptors; phosphodiesterase; topoisomerase; angiotensin-converting enzyme; angiotensin II type 1 receptor; monoamine transporters, and acetylcholinesterase. Overall, 80% of the ‘most concern’ drugs compared with only 12% of the ‘no concern’ drugs were associated with these targets in this exploratory descriptive analysis. Concentration–response analyses revealed differences in target potency and free drug exposure that appeared associated with variability in the severity of cardiotoxicity among drugs acting on the same target. This framework demonstrates how in vitro data can be used to benchmark new compounds early in development, enabling the timely discontinuation of candidates associated with substantial risk.

Background/Objectives: Fragile X Syndrome (FXS) is a neurodevelopmental disorder caused by a triplet repeat expansion in the Fmr1 gene leading to the loss of Fragile X Messenger Ribonucleoprotein (Fmr1 protein). The loss of Fmr1 protein modulates many cell biological processes and leads to the emergence of intellectual disability and autism. FXS is modeled in Fmr1-KO mice that display features consistent with human FXS, including hypersensitivity, cognitive and learning deficits, hyperactivity and audiogenic seizures. Here, we investigated the effect of auditory stimulation during a range of developmental stages on recognition memory and sociability deficits in Fmr1-KO mice. Methods: Fmr1-KO mice were subjected to auditory stimulation for 2 min three times a day at one-hour intervals for 5 days at the nursing, juvenile and adult stages. The animals were tested for social interaction and novel object recognition at 2 to 3 months old. Results: During auditory stimulation, the wild running phenotype was observed in the Fmr1-KO juvenile animals and two animals at the nursing stage experienced status epilepticus and died. Fmr1-KO animals showed social deficits compared to both the control and animals exposed to auditory stimulation at the juvenile stage. In the novel object recognition task, auditory stimulation was more effective at the nursing and juvenile stages. Conclusions: These data show that auditory stimulation may be an effective way to restore cognitive and social deficits in FXS.

The combined effects of thousands of genetic polymorphisms account for Alzheimer's disease (AD) genetic risk. Most AD polymorphisms affect gene expression. Thus, the transcriptome, the set of all gene expression levels for every gene in the genome, is a major mediator between the genotype to phenotype. This study uses genotypes, transcriptomes, and clinical phenotypes to identify the transcriptomic signature that mediates the causal effect of genotype on AD. By utilizing a causal inference method known as high dimensional mediation analysis (HDMA) on the Religious Orders Study/Memory and Aging Project (ROSMAP) longitudinal cohort, the genotype, transcriptome, and phenotype data were reduced to single scores encoding genotype, transcriptome, and phenotype correlations, and produce a ranked gene list based on putative causal importance of each gene for AD. Analysis of the up- and down-regulated genes prevalent in AD through Gene Ontology (GO) and KEGG databases reveals findings such as up-regulated functions which include angiogenesis and immune responses while down-regulated functions of genes include synaptic activity. Furthermore, utilizing Clue.io to identify candidate drugs to suppress AD-pathology reveals a plausible list of therapeutic candidates, including targeted genes and compounds such as SMAD3, TM7SF2 , and ABCB1 , which counteract the transcriptomic signature identified and may block the devastating effects of AD related to inflammatory responses, Aβ-induced toxicity, and neuronal death.

Objective. Electroencephalograms (EEGs) are time-series records of the electrical potential from collective neural activity in the brain. EEG waveform patterns—rhythmic and irregular oscillations and transient patterns of sharp waves or spikes—are potential phenotypical biomarkers, reflecting genotype-specific neural activity. This is especially relevant to diagnosing epilepsy without direct seizure observations, which is common in clinical settings, as well as in animal models, which often have subtle neurological phenotypes without overt epilepsy. Herein, we investigate genotypic prediction from long-term EEG signals of freely behaving mice belonging to six groups defined by the presence or absence of a neurological disease-genotype (TSC1 gene knockout) in three different inbred strains with distinct genetic backgrounds. Approach. We propose a machine learning approach to predict the genotypes of individual mice from the occurrence counts of waveforms that approximate short windows of the EEG. That is, a dictionary of waveforms is optimized to approximate windows from each genotype, and the vectors of waveform occurrence counts are the features for predicting genotypes via logistic regression models. Main results. Across two-fold cross-validation of the waveform dictionary learning, and leave-one-individual-out genotype prediction, we find that waveform counts pooled over multiple hour segments enable reliable prediction of mouse strain with an accuracy of 70% (95% CI 62–78) compared to chance rate of 38%. For two of the three strains, DBA2 and C57B6, strain-specific classifiers reliably determined the epilepsy-genotype (TSC1 gene knockout) with accuracies of 86% (95% CI 70–101) and 67% (95% 55–79), respectively. None of the mice of these strains had evidence of overt seizures or EEG-based seizure detection. In comparison, a state-of-the-art time-series classification approach (Hydra) enables higher strain classification at 98%, comparable TSC1-genotype prediction for the two strains (86% and 71% respectively), but the method is not interpretable. Significance. The methodologies and results show the potential of EEG waveforms as interpretable phenotypes and bag-of-waves as a feature representation for identifying epilepsy genotypes.

Objective: Despite decades of development in anti‐seizure medications, ~30% of individuals remain refractory to all treatments, and none of the existing therapies are disease modifying. Identifying targets outside the current preclinical paradigm is critically important. This study aimed to characterize the landscape of current epilepsy treatments at the level of gene interaction networks and identify novel genetic modifiers of epilepsy as potential novel therapeutic targets. Methods: We performed a functional network analysis to score genes based on their interactions with known epilepsy genes, and we integrated these functional scores with population genetics data and drug tractability information. In parallel, we performed a meta‐analysis of genome‐wide association studies of epilepsy‐related phenotypes in genetically diverse mice using a large compendium of historical phenotyping data. Genes within mapped loci were prioritized based on functional rankings, and genomic evolutionary rate profiling (GERP) was used to identify highly single‐nucleotide polymorphisms at evolutionarily constrained positions. Results: Functional network analyses of known epilepsy genes revealed a strong involvement of neurodevelopmental processes in epilepsy pathogenesis, which are not targeted by existing or emerging treatments. Meta‐analysis of seizure traits in mice identified 118 non‐overlapping loci harboring potential seizure phenotype modifiers. Using functional rankings, we prioritized 168 candidate genes within these loci and used GERP scores to filter down to 75 SNPs as candidate variants within these genes. Among them, five genes— Ephb2 , En2 , Cadps2 , Igsf21 , and Cep170 —contain regulatory variants in evolutionarily constrained sites. Four of these genes are validated as modifiers of neurological traits, including epilepsy susceptibility. Significance: This study prioritized epilepsy modifier genes that are strongly predicted to influence neurodevelopmental processes, which are underrepresented among current therapeutic targets. Furthermore, the identified genes represent novel candidate modifiers with potential clinical relevance. Our systems‐level analysis offers a novel view into the potential target landscape, pointing toward promising new directions for disease‐modifying treatments.

Mass spectrometry imaging (MSI) with ion mobility improves the specificity of spatially quantifying lipids and small molecule metabolites. However, existing computational frameworks for analyzing this high-dimensional data are limited by proprietary software, compound annotation, scalability issues on high-performance computing systems, inability to incorporate ion mobility, and lack of support for investigating spatial reactions among small molecules. Paired mass distance based reactomics, on the other hand, can reveal reaction level changes and has not been applied in MSI data analysis. In this work, a R based open-source spatial reactomics (OSSpRe) workflow is developed to analyze MSI data with ion mobility. This workflow provides a scalable and reproducible solution to handle high-resolution MSI data, compounds annotation based on publicly available ion mobility databases, as well as the investigation of spatial biochemical reactions within tissues based on reactomics.

Although many genes are subject to local regulation, recent evidence suggests that complex distal regulation may be more important in mediating phenotypic variability. To assess the role of distal gene regulation in complex traits, we combine multi-tissue transcriptomes with physiological outcomes to model diet-induced obesity and metabolic disease in a population of Diversity Outbred mice. Using a novel high-dimensional mediation analysis, we identify a composite transcriptome signature that summarizes genetic effects on gene expression and explains 30% of the variation across all metabolic traits. The signature is heritable, interpretable in biological terms, and predicts obesity status from gene expression in an independently derived mouse cohort and multiple human studies. Transcripts contributing most strongly to this composite mediator frequently have complex, distal regulation distributed throughout the genome. These results suggest that trait-relevant variation in transcription is largely distally regulated, but is nonetheless identifiable, interpretable, and translatable across species.