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bioRxiv2025

Deep Learning to Predict Future Cognitive Decline: A Multimodal Approach Using Brain MRI and Clinical Data

Predicting the trajectory of clinical decline in aging individuals is a pressing challenge, especially for people with mild cognitive impairment, Alzheimers disease, Parkinsons disease, or vascular dementia. Accurate predictions can guide treatment decisions, identify risk factors, and optimize clinical trials. In this study, we compared two deep learning approaches for forecasting changes, over a 2-year interval, in the Clinical Dementia Rating scale sum of boxes score (sobCDR). This is a key metric in dementia research, and scores range from 0 (no impairment) to 18 (severe impairment). To predict decline, we trained a hybrid convolutional neural network that integrates 3D T1-weighted brain MRI scans with tabular clinical and demographic features (including age, sex, body mass index (BMI), and baseline sobCDR). We benchmarked its performance against AutoGluon, an automated multimodal machine learning framework that selects an appropriate neural network architecture. Our results demonstrate the importance of combining image and tabular data in predictive modeling for clinical applications. Deep learning algorithms can fuse image-based brain signatures and tabular clinical data, with potential for personalized prognostics in aging and dementia.

neuroscience↗

bioRxiv2018

Lead-DBS v2: Toward a comprehensive pipeline for deep brain stimulation imaging

Deep brain stimulation (DBS) is a highly efficacious treatment option for movement disorders and a growing number of other indications are investigated in clinical trials. To ensure optimal treatment outcome, exact electrode placement is required. Moreover, to analyze the relationship between electrode location and clinical results, a precise reconstruction of electrode placement is required, posing specific challenges to the field of neuroimaging. Since 2014 the open source toolbox Lead-DBS is available, which aims at facilitating this process. The tool has since become a popular platform for DBS imaging. With support of a broad community of researchers worldwide, methods have been continuously updated and complemented by new tools for tasks such as multispectral nonlinear registration, structural / functional connectivity analyses, brain shift correction, reconstruction of microelectrode recordings and orientation detection of segmented DBS leads. The rapid development and emergence of these methods in DBS data analysis require us to revisit and revise the pipelines introduced in the original methods publication. Here we demonstrate the updated DBS and connectome pipelines of Lead-DBS using a single patient example with state-of-the-art high-field imaging as well as a retrospective cohort of patients scanned in a typical clinical setting at 1.5T. Imaging data of the 3T example patient is co-registered using five algorithms and nonlinearly warped into template space using ten approaches for comparative purposes. After reconstruction of DBS electrodes (which is possible using three methods and a specific refinement tool), the volume of tissue activated is calculated for two DBS settings using four distinct models and various parameters. Finally, four whole-brain tractography algorithms are applied to the patients preoperative diffusion MRI data and structural as well as functional connectivity between the stimulation volume and other brain areas are estimated using a total of eight approaches and datasets. In addition, we demonstrate impact of selected preprocessing strategies on the retrospective sample of 51 PD patients. We compare the amount of variance in clinical improvement that can be explained by the computer model depending on the method of choice.\n\nThis work represents a multi-institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.

neuroscience↗

bioRxiv2018

BET inhibition induces HEXIM1- and RAD51-dependent conflicts between transcription and replication

BET bromodomain proteins are epigenetic readers required for oncogenic transcription activities, and BET inhibitors have been rapidly advanced into clinical trials. Understanding the effects of BET inhibition on other nuclear processes such as DNA replication will be important for future clinical applications. Here we show that BET inhibition causes replication stress in cancer and non-cancer cells due to a rapid burst in global RNA synthesis and interference of transcription with replication. We identify BRD4 as the main BET inhibitor target in this process and provide evidence that BRD4 inhibition causes transcription-replication interference through release of P-TEFb from its inhibitor HEXIM1, promoting RNA Polymerase II phosphorylation. Unusually, BET inhibitor-induced transcription-replication interference does not activate the classic ATM/ATR-dependent DNA damage response. We show however that they promote foci formation of the homologous recombination factor RAD51. Both HEXIM1 and RAD51 are required for BET inhibitor-induced fork slowing, but rescuing fork slowing by HEXIM1 or RAD51 depletion activate a DNA damage response. Our data support a new mechanism where BRD4 inhibition slows replication and suppresses DNA damage through concerted action of transcription and homologous recombination machineries. They shed new light on the roles of DNA replication and recombination in the action of this new class of cancer drugs.

molecular biology↗

bioRxiv2026

Talk2QSP: Deriving Executable Scenarios from Unstructured Literature via Human-in-the-Loop Agents

Quantitative Systems Pharmacology (QSP) models play an inherently interventional role in pharmaceutical research and development, functioning as executable causal systems for designing, evaluating, and replacing clinical trials. However, deploying QSP as an experimental planning engine remains constrained by the difficulty of translating unstructured literature descriptions of clinical or preclinical scenarios into reproducible, simulation-ready model interventions. Motivated by this issue, we propose an agent-based framework that operationalizes QSP models as intervention-ready experimental systems by automatically extracting and executing literature-derived scenarios. The framework combines semantic grounding of model entities with a large language model (LLM)-driven Scenario Extractor and a dual-agent Scenario Mapper. Rather than relying on opaque, single-shot reasoning, our pipeline converts free-text interventions into precise parameter configurations through discrete, verifiable work orders. Moreover, our dynamic Human-in-the-Loop (HITL) strategy empowers modelers to resolve biological ambiguities interactively. Across four diverse kinetic ordinary differential equation (ODE)/QSP models and seven Subject Matter Expert (SME)-curated literature scenarios, our model resolved all selected scenarios into correct executable parameter changes, including multi-dose interventions, unit conversions, no-op scenarios, and ambiguity-triggered HITL cases, demonstrating that structured collaboration between experts and agentic systems can resolve scenarios that standalone Systems Biology Markup Language (SBML) reasoning LLM calls or prior agentic efforts operating on processed SBML data handle unreliably.

systems biology↗

bioRxiv2026

Epithelial stem cell-derived chemokines track clinical remission in inflammatory bowel disease in a disease-specific manner

Background and AimsStem cell-derived organoids are promising platforms for therapeutic screening in inflammatory bowel disease (IBD), but identifying functional organoid readouts with translational utility is challenging. Colon epithelial organoids from patients with ulcerative colitis (UC) overexpress chemokines CXCL1, CXCL11, CCL2, and CCL28, yet whether these inflammatory signatures correlate with disease activity and treatment response is unknown. This short report investigates whether organoid-retained chemokines correlate with disease activity and therapeutic outcomes. MethodsWe interrogated three bulk and two single-cell transcriptomic datasets from IBD clinical trials encompassing anti-TNF and anti-integrin therapies to determine whether epithelial chemokines retained in UC organoids track clinical response and distinguish treatment responders from non-responders to biologic therapy across multiple IBD patient cohorts. ResultsIn bulk transcriptomic data, CXCL1, CXCL11, and CCL2 were elevated in active UC and normalized only in patients achieving clinical remission, independent of therapy class, with persistent chemokine overexpression in non-responders. Single-cell analysis demonstrated widespread chemokine overexpression in UC epithelial clusters, with consistent normalization of CXCL1, CXCL11, and CCL28 in LGR5-positive stem compartment of patients who achieved clinical remission, but not in non-responders. In Crohns disease, the resolution of these epithelial chemokines was not associated with clinical response. ConclusionsEpithelial chemokines, particularly CXCL1, CXCL11, and CCL28, track clinical remission in UC and represent candidate biomarkers and functional endpoints for epithelial-directed therapeutic strategies using stem cell-derived UC organoid models.

cell biology↗

bioRxiv2025

Agent-based modeling of cellular dynamics in adoptive cell therapy

Adoptive cell therapies (ACT) leverage tumor-immune interactions to cure cancer. Despite promising phase I/II clinical trials of chimeric-antigen-receptor natural killer (CAR-NK) cell therapies, molecular mechanisms and cellular properties required to achieve clinical benefits in broad cancer spectra remain underexplored. While in vitro and in vivo experiments are required in this endeavor, they are typically expensive, laborious, and limited to targeted investigations. Here, we present ABMACT (Agent-Based Model for Adoptive Cell Therapy), an in silico approach employing agent-based models (ABM) to simulate the continuous course and dynamics of an evolving tumor-immune ecosystem, consisting of heterogeneous "virtual cells" created based on knowledge and omics data observed in experiments and patients. Applying ABMACT in multiple therapeutic context indicates that to achieve optimal ACT efficacy, it is key to enhance immune cellular proliferation, cytotoxicity, and serial killing capacity. With ABMACT, in silico trials can be performed systematically to inform ACT product development and predict optimal treatment strategies.

bioinformatics↗

bioRxiv2024

GLP-1 receptor agonism ameliorates Parkinsons disease through modulation of neuronal insulin signalling and glial suppression

Neuronal insulin resistance is linked to the pathogenesis of Parkinsons disease through unclear, but potentially targetable, mechanisms. We delineated neuronal and glial mechanisms of insulin resistance and glucagon-like 1 peptide (GLP-1) receptor agonism in human iPSC models of synucleinopathy, and corroborated our findings in patient samples from a Phase 2 trial of a GLP-1R agonist in Parkinsons (NCT01971242). Human iPSC models of synucleinopathy exhibit neuronal insulin resistance and dysfunctional insulin signalling, which is associated with inhibition of the neuroprotective Akt pathways, and increased expression of the MAPK-associated p38 and JNK stress pathways. Ultimately, this imbalance is associated with cellular stress, impaired proteostasis, accumulation of -synuclein, and neuronal loss. The GLP-1R agonist exenatide led to restoration of insulin signalling, associated with restoration of Akt signalling and suppression of the MAPK pathways in neurons. GLP-1R agonism reverses the neuronal toxicity associated with the synucleinopathy, through reduction of oxidative stress, improved mitochondrial and lysosomal function, reduced aggregation of -synuclein, and enhanced neuronal viability. GLP-1R agonism further suppresses synuclein induced inflammatory states in glia, leading to neuroprotection through non cell autonomous effects. In the exenatide-PD2 clinical trial, exenatide treatment was associated with clinical improvement in individuals with higher baseline MAPK expression (and thus insulin resistance). Exenatide treatment led to a reduction of -synuclein aggregates, and a reduction in inflammatory cytokine IL-6. Taken together, our patient platform defines the mechanisms of GLP-1R action in neurons and astrocytes, identifies the population likely to benefit from GLP-1R agonism, and highlights the utility of GLP-1R agonism as a disease modifying strategy in synucleinopathies.

neuroscience↗

bioRxiv2021

Genomic analysis of patient-derived xenograft models reveals intra-tumor heterogeneity in endometrial cancer and can predict tumor growth inhibition with talazoparib

BackgroundEndometrial cancer (EC) is a major gynecological cancer with increasing incidence. It comprised of four molecular subtypes with differing etiology, prognoses, and response to chemotherapy. In the future, clinical trials testing new single agents or combination therapies will be targeted to the molecular subtype most likely to respond. Pre-clinical models that faithfully represent the molecular subtypes of EC are urgently needed, we sought to develop and characterize a panel of novel EC patient-derived xenograft (PDX) models. MethodsHere, we report whole exome or whole genome sequencing of 11 PDX models and the matched primary tumor. Analysis of multiple PDX lineages and passages was performed to study tumor heterogeneity across lineages and/or passages. Based on recent reports of frequent defects in the homologous recombination (HR) pathway in EC, we assessed mutational signatures and HR deficiency scores and correlated these with in vivo responses to the PARP inhibitor (PARPi) talazoparib in six PDXs representing the different molecular subtypes of EC. ResultsPDX models were successfully generated from all four molecular subtypes of EC and uterine carcinosarcomas, and they recapitulated morphology and the molecular landscape of primary tumors without major genomic drift. We also observed a wide range of inter-tumor and intra-tumor heterogeneity, well captured by different PDX lineages, which could lead to different treatment responses. An in vivo response to talazoparib was detected in two p53mut models consistent with stable disease, however both lacked the HR deficiency genomic signature. ConclusionsEC PDX models represent the four molecular subtypes of disease and can capture intra-tumoral heterogeneity of the original primary tumor. PDXs of the p53mut molecular subtype showed sensitivity to PARPi, however, deeper and more durable responses will likely require combination of PARPi with other agents.

genomics↗

bioRxiv2023

RAPID RESISTANCE TO BET INHIBITORS IS MEDIATED BY FGFR1 IN GLIOBLASTOMA

Bromodomain and extra-terminal domain (BET) proteins are therapeutic targets in several cancers including the most common malignant adult brain tumor glioblastoma (GBM). Multiple small molecule inhibitors of BET proteins have been utilized in preclinical and clinical studies. Unfortunately, BET inhibitors have not shown efficacy in clinical trials enrolling GBM patients. One possible reason for this may stem from resistance mechanisms that arise after prolonged treatment within a clinical setting. However, the mechanisms and timeframe of resistance to BET inhibitors in GBM is not known. To identify the temporal order of resistance mechanisms in GBM we performed quantitative proteomics using multiplex-inhibitor bead mass spectrometry and demonstrated that intrinsic resistance to BET inhibitors in GBM treatment occurs rapidly within hours and involves the fibroblast growth factor receptor 1 (FGFR1) protein. Additionally, small molecule inhibition of BET proteins and FGFR1 simultaneously induces synergy in reducing GBM tumor growth in vitro and in vivo. Further, FGFR1 knockdown synergizes with BET inhibitor mediated reduction of GBM cell proliferation. Collectively, our studies suggest that co-targeting BET and FGFR1 may dampen resistance mechanisms to yield a clinical response in GBM.

cancer biology↗

bioRxiv2024

Rigor and Reproducibility of Digital Spatial Profiling on Clinically Sourced Human Tissues

Spatial transcriptomic profiling enables precise quantification of gene expression with simultaneous localization of expression profiles onto tissue structures. This new technology promises to improve our understanding of the disease mechanisms. Therefore, there is intense interest in applying these methods in clinical trials or as laboratory developed tests to aid in diagnosis of disease. Before these technologies can be more broadly deployed in clinical research and diagnostics, it is necessary to thoroughly understand their performance in real world conditions. In this study, we vet technical reproducibility, data normalization methods and assay sensitivity focusing predominantly on one widely used spatial transcriptomic methodology, digital spatial profiling. Using clinically sourced human tissue specimens, we find that digital spatial profiling exhibits high rigor and reproducibility. Our approach lays the foundation for incorporation of digital spatial profiling methods into clinical workflows.

pathology↗

bioRxiv2019

Liver-Chip: Reproducing Human and Cross-Species Toxicities

Nonclinical rodent and non-rodent toxicity models used to support clinical trials of candidate drugs may produce discordant results or fail to predict complications in humans contributing to drug failures in the clinic. Here we applied microengineered Organ-on-Chip (Organ-Chip) technology to design rat, dog, and human Liver-Chips containing species-specific primary hepatocytes interfaced with liver sinusoidal endothelial cells, with or without Kupffer cells and hepatic stellate cells, cultured under physiological fluid flow. The Liver-Chips detected diverse phenotypes of liver toxicity including hepatocellular injury, steatosis, cholestasis, and fibrosis as well as species-specific toxicities when treated with tool compounds. Multi-species Liver-Chips may provide a useful platform for prediction of liver toxicity and inform human relevance of liver toxicities detected in animal studies to better determine safety and human risk.\n\nOne Sentence SummaryMicroengineered Organ-Chip technology has been used to design rat, dog and human Liver-Chips that recapitulate species-specific liver toxicities.

pharmacology and toxicology↗

bioRxiv2025

Mechanoepigenetic Targeting of Histone Methyltransferase EZH2 Increases Potential of Scalable Manufacturing in Human Derived Primary Mesenchymal Stromal Cells

Manufacturing clinical grade mesenchymal stromal cells (MSCs) remains a major bottleneck for cell-based therapies, as extensive in-vitro expansion on standard tissue-culture plastic (TCP) drives loss of stemness, reduced immunomodulatory activity, and diminished therapeutic efficacy. Although substrate stiffness is known to influence MSC fate through mechanotransduction, the epigenetic mechanisms linking mechanical stress to progressive phenotypic drift remain poorly defined. A mechano-epigenetic pathway is identified in this work, centered on the histone methyltransferase EZH2 - that governs chromatin remodeling and loss of stemness during serial passaging. Multi-omics, high-resolution imaging, and functional assays show that MSCs expanded on mechanically stiff TCP accumulate H3K27me3 repressive chromatin mark, lose SWI/SNF -ARID1A chromatin- remodeling foci, and exhibit an altered chromatin accessibility profile. Pharmacological inhibition of EZH2 with GSK343 selectively reduced H3K27me3, restored ARID1A-containing SWI/SNF organization, and preserved MSC morphology and expression of canonical stemness markers (CD73, CD90, CD105) even at later passage. ATAC-seq analysis revealed that GSK343 rebalanced chromatin accessibility, reopening TEAD/YAP-responsive regulatory regions while repressing accessibility at lineage- priming and senescence-associated sites. RNA-seq demonstrated that GSK343 maintained transcriptional programs associated with immunomodulation, migration, and trophic signaling, while suppressing hyperproliferative and senescence-associated genes that are characteristic of late-passage MSCs. Proteomic profiling of MSC secretomes further showed that GSK343 attenuated pro-fibrotic ECM factors and senescence- linked proteins while enhancing angiogenic and reparative mediators. Functionally, conditioned media from GSK343-treated MSCs significantly increased primary chondrocyte proliferation, demonstrating preserved therapeutic potency. Together, these findings establish EZH2 as a central mediator of stiffness-induced epigenetic drift in human MSCs and demonstrate that EZH2 inhibition can maintain the stemness without compromising their expansion ability. This work provides a foundational strategy for mechano-epigenetic engineering of MSCs and highlights EZH2 inhibition as a scalable, manufacturing-compatible approach to preserve potency for regenerative medicine applications. SIGNIFIANCE STATEMENTPrimary mesenchymal stem/ stromal cells (MSC) derived from bone marrow are currently under numerous clinical trials for regenerative medicine and cancer treatment. Despite showing tremendous potential for their differentiation, trophic and immunomodulatory properties, their efficacy in clinical endpoint is limited. The traditional cell culture condition in cell culture flask limits their clinical translation by changing the MSC phenotype with serial passaging, a critical step required for harvesting a clinically appropriate number of cells. This work discovered the precise epigenetic mechanism involving histone methylation responsible for such changes in MSC phenotype. By inhibiting the histone methylation by GSK343, this study showed that MSC phenotype can be maintained over serial passaging as demonstrated by microscopy, multi-omics, and in vitro functional assays.

bioengineering↗

bioRxiv2026

Humanized FLT3 mice display enhanced tissue engraftment and support HIV-1 persistence and rebound

Despite advances in antiretroviral therapy (ART), HIV-1 cure efforts remain hindered by viral reservoirs in long-lived myeloid cells and immune-privileged tissues that are less accessible and therefore unlikely to be assessed in human clinical trials. Consequently, there is a critical need for robust research platforms such as immune cell humanized mice to bridge preclinical and clinical HIV research. However, previously described humanized mouse models have demonstrated incomplete hematopoietic development, particularly showing low levels of NK or myeloid cells. Herein, we present the novel humanized FLT3 mouse model that develops NK cells, myeloid progenitors, monocytes, and both functional conventional (cDCs) and plasmacytoid dendritic cells (pDCs) to support HIV-1 infection. Human cord blood derived CD34+ hematopoietic stem cells (HSC) were engrafted in the FLT3 (Hu-FLT3) and NSG (Hu-NSG) mouse strains for comparison. Our data showed that while Hu-NSG and Hu-FLT3 mice have comparable human lymphocyte levels, the proportion of myeloid cells (including monocytes, pDCs and cDCs) in Hu-FLT3 mice (16.2 %) was three-fold higher than in Hu-NSG mice (5.6 %) and the proportion of NK cells was six-fold higher (12.8 % and 1.9 %, respectively). Both strains successfully supported HIV-1 infection, maintain viral replication for 17 weeks in untreated mice, and proviral DNA was detectable in peripheral blood, bone marrow and spleen. While ART effectively reduced viral load to undetectable levels in four weeks in both strains, we observed viral rebound after treatment discontinuation within 3 weeks, reaching the same levels of viral load pre-ART and mimicking what is observed in people living with HIV (PLWH).Human immune cells and HIV-1 RNA were higher in tissues of Hu-FLT3 mice compared to Hu-NSG mice, mirroring features reported in human tissue reservoirs. Our findings demonstrated that Hu-FLT3 mice support enhanced development of human innate immune cells in blood and tissues, which are associated with higher levels of HIV-1 replication compared to Hu-NSG mice. This study establishes a novel, robust and accessible in vivo platform to investigate potential HIV cure and persistence-targeting interventions with translational relevance to human therapeutic development thanks to the improved and more complete human immune repertoire in Hu-FLT3.

immunology↗

bioRxiv2026

Biomarker-Targeted O6-Guanine Alkylation Potentiates ATR Inhibitor Response in De Novo and Relapsed/Refractory AML

Acute myeloid leukemia (AML) remains limited by high relapse rates and a scarcity of biomarker-directed therapies, underscoring the need to identify actionable vulnerabilities and mechanisms of therapeutic resistance. O6-methylguanine-DNA methyltransferase (MGMT), a DNA repair enzyme that directly reverses mutagenic O6-alkylguanine lesions, is epigenetically silenced in multiple cancers. Although MGMT silencing is predictive of temozolomide (TMZ) response in glioblastoma with significant survival advantage, prior clinical trials of TMZ in AML have shown only modest responses. Therefore, the prevalence and therapeutic relevance of MGMT silencing, as well as genetic factors that modify its therapeutic response in AML, remain to be comprehensively investigated. Here, we first profiled MGMT status across a diverse cohort of 23 de novo and 16 relapsed/refractory (R/R) primary AMLs using an integrated analysis of MGMT mRNA expression, promoter methylation, and protein expression. We found that approximately 25-30% of AMLs harbor MGMT silencing, in contrast to consistent MGMT expression in healthy CD34+ hematopoietic stem and progenitor cells. We further identified frequent loss of mismatch repair (MMR) proteins in these AML cohorts, a known resistance mechanism against TMZ in glioblastoma. Using CRISPR knockout screening and genetically engineered AML models, we found that MMR loss also drives both upfront and acquired TMZ resistance in MGMT-silenced AML. These findings identify MMR deficiency as an important and previously underappreciated genetic contributor to the modest responses observed in prior trials of TMZ in AML. To therapeutically exploit MGMT silencing while bypassing genetic determinants of resistance, including MMR deficiency, we evaluated clinically and preclinically characterized MGMT-dependent DNA-alkylating agents and identified a fluoroethylating analog of TMZ, N3-(2-fluoroethyl) imidazotetrazine (KL50), with pronounced and selective activity in MGMT-silenced AML. KL50 retained antileukemic activity irrespective of MMR status, significantly prolonging survival in humanized MISTRG6 mice harboring primary AML patient-derived xenografts. Mechanistically, KL50 induced DNA damage through the time-dependent formation of interstrand DNA crosslinks, bypassing MMR dependence and triggering a replication stress response dominated by ATR signaling. Pharmacologic ATR inhibition synergized with KL50, producing marked antileukemic activity and significantly extending survival across AML models without compromising hematologic safety. Together, these findings establish MGMT silencing as a prevalent and therapeutically actionable biomarker in AML, define MMR status as a key determinant of TMZ response in AML but not KL50 sensitivity, and provide a translational rationale for combining low-dose O6-fluoroethylating imidazotetrazines with ATR inhibitors to target both de novo and R/R AML across diverse genetic backgrounds.

cancer biology↗

bioRxiv2023

DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling

Drug-induced cardiotoxicity (DICT) is one of the leading causes of drug attrition in clinical trials or withdrawal from the market. Many studies have been conducted to detect DICT in the early stage of drug development and clinical diagnosis, but the success is limited, as evident by the high attrition rate at all clinical phases due to DICT. Most of these efforts, if not all, have focused on specific adverse events and/or mechanisms associated with DICT, such as QT prolongation and hERG-related cardiotoxicity. However, given the broad spectrum of cardiotoxicity, it is necessary to develop a reference drug list with a systematic annotation of DICT potential across a large number of drugs and drug classes. Such a list is essential for developing effective DICT diagnostic biomarkers and early prediction strategies using new approach methods (NAMs), including artificial intelligence (AI). By utilizing labeling documents for FDA (U.S. Food and Drug Administration)-approved drugs, we developed a drug classification scheme to prioritize drugs based on their DICT potential. This resulted in DICTrank, which is the largest dataset of drugs annotated with ranked DICT risk in humans. DICTrank categorizes drugs into four categories of DICT concerns by integrating DICT severity and labeling content. The dataset consists of 1318 drugs, classified as follows: Most-DICT-Concern (341), Less-DICT-Concern (528), No-DICT-Concern (343), and Ambiguous-DICT-Concern (106; lacking sufficient information in the labeling document to determine cardiotoxicity potential). DICTrank covers a wide range of drug therapeutic categories. Using this extensive DICT dataset, we discovered that several therapeutic categories were significantly enriched with drugs of Most-DICT-Concern as well as the association of daily dose with drug class. These categories include antineoplastic agents, sex hormones and modulators of the genital system, anti-inflammatory and antirheumatic products, beta-blocking agents, and cardiac therapy. DICTrank represents the largest drug list for DICT to date, and it could contribute to the development of NAMs and AI models for the early identification of DICT risk during drug development and beyond.

pharmacology and toxicology↗

bioRxiv2024

Temporal dynamics of fluorescence and photoacoustic signals of a Cetuximab-IRDye800 conjugate in EGFR-overexpressing tumors

Molecular fluorescence-guided surgery has shown promise for tumor margin delineation but is limited by its depth profiling capability. Interestingly, most fluorophores, either clinically approved or in clinical trials, can also be used as photoacoustic contrast agents, yet their use is limited due to the low light fluence permitted for clinical use and the limited sensitivity of current photoacoustic imaging systems. There is therefore an urgent unmet need to establish methods for enhancing contrast in molecular targeted PA imaging which could potentially complement and overcome limitations in molecular fluorescence guided therapies. In this study, we compare the photoacoustic (PA) and fluorescence imaging capabilities of a cetuximab-IRDye800 conjugate in a subcutaneous tumor xenograft model. We demonstrate that while the fluorescence signal increases steadily over time after administration of cetuximab-IRDye800, PA signal peaks early ([~]2 fold higher at 6-hour as compared to pre-injection controls) and then decreases ([~]1.3 fold higher at 24-hour as compared to pre-injection controls). This pattern aligns with previous findings using other antibody-conjugated PA contrast agents. Mechanistically, we demonstrate that the formation of H-aggregates upon antibody conjugation enhances PA contrast of the IRDye800. The disruption of these H-aggregates, as the antibody-dye conjugate is degraded post receptor-mediated endocytosis, decreases PA signal intensity. The timeframe of maximum PA signal and decrease thereafter is consistent with the time frame of receptor-mediated endocytosis of cetuximab-IRDye800. Our data suggests that tumor cell surface binding results in peak PA signal while lysosomal localization and degradation results in a significant drop in PA signal. Our study sheds light on the distinct temporal dynamics of PA and fluorescence signals of Cetuximab-IRDye800 conjugate and we propose that optimizing IRDye800 conjugation to antibodies can further enhance PA signal intensity when timed to precisely to capture IRDye800 in an H-aggregate form.

cancer biology↗

bioRxiv2025

Apolipoprotein E ε4 exacerbates microglia-mediated complement-dependent synapse loss caused by neuronal Tpk deficiency

Thiamine pyrophosphokinse-1 (TPK) is a key enzyme that converts thiamine to functional thiamine diphosphate (TDP). TPK insufficiency and hence TDP reduction in neurons induced by amyloid-{beta} deposition and diabetes, an independent risk factor of Alzheimers disease (AD), recapitulate multi-pathophysiological features in the brain of mice, similar to those in human AD. Apolipoprotein E {varepsilon}4 allele (APOE4) is the most well-known genetic risk factor for AD. Clinical trials by boosting TDP using benfotiamine, a derivative of thiamine that significantly elevates TDP level in human encrythrocytes, have shown the inferior clinical efficacies in APOE4 carriers compared to non-APOE4 carriers. Clarifying the relationship between APOE4 and TPK expression and multi-pathophysiological characteristics of AD induced by Tpk deficiency in neurons is imperative. Here, we find that humanized APOE4 didnt directly affect Tpk expression of mice, but markedly aggravates behavior abnormalities of Tpk-cKO mice. Pathologically, the Tpk-cKO mice with humanized APOE4 knock-in (AE-cKO mice) exhibit more synapse loss than the mice with only humanized APOE4 knock-in and the Tpk-cKO mice. Transcriptomics and pathologic analysis identified that APOE4 promoted the overactivation of microglia and the transition of microglia to a disease-associated and phagocytosis state via a complement-mediated pathway. Further, the C3aR antagonist significantly repressed microglia phagocytosis and synaptic elimination of the AE-cKO mice. Our results demonstrate that APOE4 exacerbates behavior dysfunction of Tpk-cKO mice through microglia-mediated complement-dependent synaptic elimination. These findings provide important insights into the role of APOE4 in the pathogenesis of AD.

neuroscience↗

bioRxiv2025

A Tonic Signaling Code Predicts CAR-T Cell Efficacy in Diffuse Midline Glioma

Diffuse midline glioma (DIPG/DMG) is a uniformly fatal pediatric brain tumor with no effective cure. Although CAR T-cell therapy shows promise, clinical outcomes remain inconsistent due to limited persistence and premature exhaustion. Reliable predictive biomarkers are lacking, and proposed exhaustion or stemness markers provide limited utility. Here, we systematically compare multiple CAR-T constructs targeting clinically-relevant antigen B7-H3 and identify antigen-independent CAR activation, or tonic signaling, as a key determinant of therapeutic performance. We find that B7-H3 CAR-T cells with restrained tonic signaling display superior tumor killing, persistence, and resistance to exhaustion, along with reduced CAR membrane clustering, in patient-derived DIPG models. Integrated multi-omics and single-cell profiling further reveal a CAR-T tonic signaling-associated gene signature that outperforms conventional exhaustion or stemness markers in predicting therapeutic efficacy across multiple clinical trials, including DIPG and other tumor types. Together, these findings define a mechanistic and predictive framework to guide CAR design and improve clinical outcomes.

cancer biology↗