Biology subjects

Seashore-Ludlow, B.

Publications and source records attributed to Seashore-Ludlow, B..

9 recordsLinked to original sources

bioRxiv2026

Human-supervised Agentic AI for Hypothesis Generation and Experimental Assistance in Drug Repurposing

Computational drug repurposing has largely been focused on rapid hypothesis generation, yet real-world applications span a far broader lifecycle, from drug candidate suggestion to designing experiments, analyzing assay data, and iteratively refining candidates. Here, we demonstrate that agentic AI can operate throughout this lifecycle. To this end, we developed RepurAgent, a hierarchical multi-agent AI system comprising a supervisor agent and a planning agent that coordinate four specialized sub-agents (research, prediction, data, and report), through a human-in-the-loop design, with episodic memory and retrieval-augmented generation. The system is grounded in data, tools, and standard operating procedures specific for drug repurposing, developed within the REMEDi4ALL consortium. We validated the agentic system across three scenarios spanning the various stages within the repurposing lifecycle: in Acute Myeloid Leukemia, a blinded expert evaluation indicated that RepurAgent produced substantially more novel and mechanistically credible candidates compared to a vanilla LLM baseline; in a retrospective COVID-19 antiviral screen, RepurAgent acted as an adaptive experimental collaborator, prioritizing compounds with AUC-ROC up to 0.99 without predefined thresholds and flagging confounders missed in manual review; and for Multiple Sulfatase Deficiency, it prioritized 81 high-confidence candidates from 5000 compounds, which were further corroborated by domain experts. These results demonstrate that agentic AI can support across the drug repurposing lifecycle, from hypothesis generation to experimental analysis. RepurAgent is open source and deployed at https://repuragent.serve.scilifelab.se/.

bioinformatics↗

bioRxiv2025

Morphological cell profiling for drug repurposing against SARS-CoV-2 infection

Antiviral drug discovery has traditionally focused on targeting viral proteins, while host-directed strategies remain largely underexplored. Here, present a systematic drug repurposing strategy leveraging morphological profiling to identify host-targeting antivirals. Our image-based approach combines viral protein immunostaining with high-content Cell Painting analysis to simultaneously assess viral replication and provide in-depth analysis of host cell responses. By screening 5,275 repurposable drugs against SARS-CoV-2 infected cells, we identified compounds that reversed the infected cell phenotype, including ones not detected by conventional cytopathicity and antibody-based assays. A counter-screen excluded compounds whose antiviral activity was likely driven by drug-induced phospholipidosis (DIPL). Pathway enrichment analysis of compounds validated by both Cell Painting dose-response and DIPL assays, revealed host processes frequently hijacked by viruses, including innate immune responses and kinases. Among the top hits, both novel candidates, such as serdemetan, and previously reported broad-spectrum antivirals, such as sunitinib, were identified. Our approach constitutes an adaptable and scalable platform suited for diverse viral pathogens and cell systems. We provide a resource of open access screening data, images, and automated analysis pipelines to advance both antiviral discovery and pandemic preparedness.

microbiology↗

bioRxiv2025

From Library to Landscape: Integrative Annotation Workflows for Compound Libraries in Drug Repurposing

In the rapidly advancing landscape of drug discovery and repurposing, efficient access and integration of chemical and bioactivity data from public repositories has become essential. We implemented two complementary annotation pipelines (KNIME- and Python-based) designed to automate the extraction and integration of curated chemical and bioactivity data from public repositories. These pipelines are adaptable to any user-provided compound library, allowing reproducible workflows to integrate data from heterogeneous sources (e.g., ChEMBL and PubChem). As part of the REMEDi4ALL project, which aims to establish a European platform for drug repurposing, we validated our framework on a harmonized subset of the Specs repurposing collection (over 5000 compounds, available in-house). Additionally, we developed two interactive dashboards that support multilayered analyses and visualization by integrating chemical properties, bioactivity profiles, and relational data. We show how this framework streamlines the collection of harmonized data and facilitates analyses that are critical in drug repurposing efforts, while remaining versatile for broader applications in drug discovery. Both pipeline protocols are publicly available online, and the dashboards are open access.

bioinformatics↗

bioRxiv2025

Interdisciplinary Study on Drug-Induced-Phospholipidosis of Repurposing Libraries through Machine Learning and Experimental Evaluation in Different Cell Lines

Bigger PicturePhospholipidosis is a cellular condition characterized by the excessive accumulation of phospholipids within cells, that also can be induced by medications--especially those known as cationic amphiphilic drugs. While this phenomenon is not always harmful in itself, it can interfere with how cells respond in laboratory experiments and complicate the interpretation of drug screening results, leading to potential delays or failures in the development of new therapies. As the pharmaceutical industry increasingly relies on high-throughput screening and machine learning to accelerate early-stage drug discovery, there is growing interest in identifying compounds that may cause phospholipidosis before they move too far through the development pipeline. In response to this challenge, we at the EU-Horizon HLTH Remedi4ALL project compiled and analyzed one of the most comprehensive datasets to date--over 5,000 repurposed drugs tested across multiple cell lines--to better understand which compounds are likely to induce phospholipidosis. Using this dataset, we developed a machine learning model capable of predicting the risk of phospholipidosis based on a drugs chemical structure. By validating our approach across diverse experimental settings, our goal is to provide the broader scientific and pharmaceutical communities with a practical tool to flag problematic compounds early, ultimately helping bring safer, more effective treatments to patients faster. Highlights- A comprehensive dataset for a repurposing collection, comprising 5,228 compounds, for induction of phospholipidosis in A549-ACE2 and Vero-E6 cells -A machine learning model developed using in-house experimental phospholipidosis data across multiple cell lines -Exploration of chemical features that induce phospholipidosis and investigation of the relationships underlying different cell line susceptibilities -A case study: correlation of drug-induced phospholipidosis with antiviral compound effects against SARS-CoV-2 using phenotypic screening data Phospholipidosis (PLD), a cellular adverse effect that is, among others, caused by numerous cationic amphiphilic drugs. Interest is raised within pharma discovery to predict this phenomenon, as it can impact the outcome of phenotypic cellular screens and significantly delay drug development processes. The development of accurate and validated machine learning models for predicting drug-induced PLD across different cell lines and research centers could provide a valuable early application tool for the pharmaceutical industry, potentially accelerating drug discovery and reducing the risk of late-stage failures. We report here the assembly, curation, testing and modeling of one of the largest datasets of repurposed drugs (5K+) tested for PLD induction on different cell lines. A machine-learning classification method was developed and validated to predict whether molecules are prone to induce PLD effects when applied in cell-based screens. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=187 SRC="FIGDIR/small/663638v1_ufig1.gif" ALT="Figure 1"> View larger version (67K): org.highwire.dtl.DTLVardef@1c4760org.highwire.dtl.DTLVardef@92084dorg.highwire.dtl.DTLVardef@15effbaorg.highwire.dtl.DTLVardef@1e73f4d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG

molecular biology↗

bioRxiv2025

High-content morphological profiling by Cell Painting in 3D spheroids.

Cell Painting is a popular assay for morphological profiling of multi-labeled 2D monolayer cell cultures used in a wide range of applications. Culturing cells in 3D has potential for higher physiological relevance, such as when studying effects of perturbations. Robust and scalable 3D models can be challenging to characterize through imaging - particularly because light has difficulty penetrating cell multilayers. We introduce a scalable method where the Cell Painting assay is combined with tissue-clearing and applied to 3D spheroids generated in a ULA microplate format. Multi-channel images are acquired using confocal microscopy, and cells can be segmented inside those spheroids allowing for relevant morphological features to be extracted. Our end-to-end analysis pipeline comprises cell segmentation, morphological feature extraction, and between-spheroids and within-spheroid normalization. We demonstrate the method using spheroids cultured from two colorectal cancer cell lines and successfully detect distinct phenotypic changes upon compound treatments, on both spheroid-level using maximum intensity projections and on single cell-level. We show that drugs group by mechanism of action, with biologically relevant clusters especially evident with single-cell data. Finally, we contrast our method to results from 2D Cell Painting and discover a different pattern in DNA damaging drugs in HCT116 colorectal cancer cells. This work lays the foundation for multi-channel image-based screening in 3D spheroids.

pharmacology and toxicology↗

bioRxiv2024

SETD8 inhibition targets cancer cells with increased rates of ribosome biogenesis

SETD8 is a methyltransferase that is overexpressed in several cancers, which monomethylates H4K20 as well as other non-histone targets such as PCNA or p53. We here report novel SETD8 inhibitors, which were discovered while trying to identify chemicals that prevent 53BP1 foci formation, an event mediated by H4K20 methylation. Consistent with previous reports, SETD8 inhibitors induce p53 expression, although they are equally toxic for p53-deficient cells. Thermal stability proteomics revealed that the compounds had a particular impact on nucleoli, which was confirmed by fluorescent and electron microscopy. Similarly, Setd8 deletion generated nucleolar stress and impaired ribosome biogenesis, confirming that this was an on-target effect of SETD8 inhibitors. Furthermore, a genome-wide CRISPR screen identified an enrichment of nucleolar factors among those modulating the toxicity of SETD8 inhibitors. Accordingly, the toxicity of SETD8 inhibitors correlated with MYC or mTOR activity, key regulators of ribosome biogenesis. Together, our study provides a new class of SETD8 inhibitors and a novel biomarker to identify tumors most likely to respond to this therapy.

cancer biology↗

bioRxiv2024

SMAC mimetics overcome apoptotic resistance in ovarian cancer through MSLN-TNF alpha axis

Resistance to chemotherapy and PARPi inhibitors remains a critical challenge in the treatment of epithelial ovarian cancer, mainly due to disabled apoptotic responses in tumor cells. Given mesothelins pivotal role in ovarian cancer and its restricted expression in healthy tissues, we conducted a drug-screening discovery analysis across a range of genetically modified cancer cells to unveil mesothelins therapeutic impact. We observed enhanced cell death in cancer cells with low mesothelin expression when exposed to a second mitochondria-derived activator of caspases (SMAC) mimetics, and demonstrated a compelling synergy when combined with chemotherapy in ex vivo patient-derived cultures and zebrafish tumor xenografts. Mechanistically, the addition of the SMAC mimetics drug birinapant to either carboplatin or paclitaxel triggered the activation of the Caspase 8-dependent apoptotic program facilitated by TNFLJ signaling. Multimodal analysis of neoadjuvant-treated patient samples further revealed an association between tumor-associated macrophages and the activation of TNFLJ-related pathways. Our proposed bimodal treatment shows promise in enhancing the clinical management of patients by harnessing the potential of SMAC mimetics alongside conventional chemotherapy.

cancer biology↗

bioRxiv2022

High-throughput cell spheroid production and assembly analysis by microfluidics and deep learning

3D cell culture models are an important tool in translational research but have been out of reach for high-throughput screening due to complexity, requirement of large cell numbers and inadequate standardization. Here, we present a high-throughput workflow to produce and characterize the formation of miniaturized spheroids using deep learning. We train a convolutional neural network (CNN) for cell ensemble morphology classification, benchmark it against more conventional image analysis, and characterize spheroid assembly determining optimal surfactant concentrations and incubation times for spheroid production for three cell lines with different spheroid formation properties. Notably, this format is compatible with large-scale spheroid production and screening. The presented workflow and CNN offer a template for large scale minispheroid production and analysis and can be extended and re-trained to characterize morphological responses in spheroids to additives, culture conditions and large drug libraries.

bioengineering↗

bioRxiv2022

Subcellular mapping of the protein landscape of SARS-CoV-2 infected cells for target-centric drug repurposing

The COVID-19 pandemic has resulted in millions of deaths and affected socioeconomic structure worldwide and the search for new antivirals and treatments are still ongoing. In the search for new drug target and to increase our understanding of the disease, we used large scale immunofluorescence to explore the host cell response to SARS-CoV-2 infection. Among the 602 host proteins studied in this host response screen, changes in abundance and subcellular localization were observed for 97 proteins, with 45 proteins showing increased abundance and 10 reduced abundances. 20 proteins displayed changed localization upon infection and an additional 22 proteins displayed altered abundance and localization, together contributing to diverse reshuffling of the host cell protein landscape. We then selected existing and approved small-molecule drugs (n =123) against our identified host response proteins and identified 3 compounds - elesclomol, crizotinib and rimcazole, that significantly reduced antiviral activity. Our study introduces a novel, targeted and systematic approach based on host protein profiling, to identify new targets for drug repurposing. The dataset of [~]75,000 immunofluorescence images from this study are published as a resource available for further studies.

cell biology↗