Precise mapping of single-stranded DNA breaks by sequence-templated erroneous DNA polymerase end-labelling.

The ability to analyze whether DNA contains lesions is essential in identifying mutagenic substances. Currently, the detection of single-stranded DNA breaks (SSBs) lacks precision. To address this limitation, we develop a method for sequence-templated erroneous end-labelling sequencing (STEEL-seq), which enables the mapping of SSBs.

Extensive analysis of pollutant interactions: Identification of deleterious synergistic effects at environmentally relevant dose levels using Drosophila and mammalian cells.

Environmental pollutants are commonly present in low concentrations, often as complex mixtures that can lead to various interaction phenomena, including synergism, antagonism, or additive effects. These interactions can alter the overall toxicity or biological impact of the mixture when compared to the effects of individual pollutants. While regulatory agencies typically assess the safety of individual pollutants, the cumulative and interactive effects of pollutant mixtures are less well understood.

The intracellular free concentration of endocrine disrupting chemicals enables translation between cell-free and cell-based estrogenic activity assays.

Many environmental toxicants can activate estrogen receptor α (ERα), disrupting normal endocrine function. While these activities are predicted across in silico, in vitro, and in vivo models, translating active concentrations between these systems remains challenging. We hypothesized that cellular uptake and the resulting free intracellular toxicant concentration could bridge this gap.

Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.

Machine learning (ML) is increasingly valuable for predicting molecular properties and toxicity in drug discovery. However, toxicity-related end points have always been challenging to evaluate experimentally with respect to translation due to the required resources for human and animal studies; this has impacted data availability in the field. ML can augment or even potentially replace traditional experimental processes depending on the project phase and specific goals of the prediction.

Conformal prediction enables disease course prediction and allows individualized diagnostic uncertainty in multiple sclerosis.

Accurate assessment of progression and disease course in multiple sclerosis (MS) is vital for timely and appropriate clinical intervention. The gradual transition from relapsing-remitting MS (RRMS) to secondary progressive MS (SPMS) is often diagnosed retrospectively with a typical delay of three years. To address this diagnostic delay, we developed a predictive model that uses electronic health records to distinguish between RRMS and SPMS at each individual visit.

Computational drug repurposing: approaches, evaluation of in silico resources and case studies.

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information.

Associations of PFAS and OH-PCBs with risk of multiple sclerosis onset and disability worsening.

Exposure to per- and polyfluorinated substances (PFAS) and hydroxylated polychlorinated biphenyls (OH-PCBs) is associated with adverse human health effects, including immunosuppression. It is unknown if these substances can affect the course of autoimmune diseases. This study was based on 907 individuals with multiple sclerosis (MS) and 907 matched controls, where the MS cases were followed longitudinally using the Swedish MS register.

Cell Painting: a decade of discovery and innovation in cellular imaging.

Modern quantitative image analysis techniques have enabled high-throughput, high-content imaging experiments. Image-based profiling leverages the rich information in images to identify similarities or differences among biological samples, rather than measuring a few features, as in high-content screening. Here, we review a decade of advancements and applications of Cell Painting, a microscopy-based cell-labeling assay aiming to capture a cell's state, introduced in 2013 to optimize and standardize image-based profiling.

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted and Data.

Drug-induced liver injury (DILI) has been a significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. Over the last decade, the existing suite of proxy-DILI assays has generally improved at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing the prediction of DILI because it allows for evaluating large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects.

CPSign: conformal prediction for cheminformatics modeling.

Conformal prediction has seen many applications in pharmaceutical science, being able to calibrate outputs of machine learning models and producing valid prediction intervals. We here present the open source software CPSign that is a complete implementation of conformal prediction for cheminformatics modeling. CPSign implements inductive and transductive conformal prediction for classification and regression, and probabilistic prediction with the Venn-ABERS methodology.

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted and Data.

Drug-induced liver injury (DILI) has been significant challenge in drug discovery, often leading to clinical trial failures and necessitating drug withdrawals. The existing suite of in vitro proxy-DILI assays is generally effective at identifying compounds with hepatotoxicity. However, there is considerable interest in enhancing in silico prediction of DILI because it allows for the evaluation of large sets of compounds more quickly and cost-effectively, particularly in the early stages of projects.

Artificial intelligence for high content imaging in drug discovery.

Artificial intelligence (AI) and high-content imaging (HCI) are contributing to advancements in drug discovery, propelled by the recent progress in deep neural networks. This review highlights AI's role in analysis of HCI data from fixed and live-cell imaging, enabling novel label-free and multi-channel fluorescent screening methods, and improving compound profiling. HCI experiments are rapid and cost-effective, facilitating large data set accumulation for AI model training.

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting.

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction.

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting.

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction.

Corrigendum: Development of new approach methods for the identification and characterization of endocrine metabolic disruptors-a PARC project.

[This corrects the article DOI: 10.3389/ftox.2023.

From pixels to phenotypes: Integrating image-based profiling with cell health data as BioMorph features improves interpretability.

Cell Painting assays generate morphological profiles that are versatile descriptors of biological systems and have been used to predict in vitro and in vivo drug effects. However, Cell Painting features extracted from classical software such as CellProfiler are based on statistical calculations and often not readily biologically interpretable. In this study, we propose a new feature space, which we call BioMorph, that maps these Cell Painting features with readouts from comprehensive Cell Health assays.

Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank.

Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of various chemical and biological data to predict cardiotoxicity, using the recently released Drug-Induced Cardiotoxicity Rank (DICTrank) dataset from the United States FDA. We analyzed a diverse set of data sources, including physicochemical properties, annotated mechanisms of action (MOA), Cell Painting, Gene Expression, and more, to identify indications of cardiotoxicity.

ELIXIR and Toxicology: a community in development.

Toxicology has been an active research field for many decades, with academic, industrial and government involvement. Modern omics and computational approaches are changing the field, from merely disease-specific observational models into target-specific predictive models. Traditionally, toxicology has strong links with other fields such as biology, chemistry, pharmacology and medicine.

Evaluating the utility of brightfield image data for mechanism of action prediction.

Fluorescence staining techniques, such as Cell Painting, together with fluorescence microscopy have proven invaluable for visualizing and quantifying the effects that drugs and other perturbations have on cultured cells. However, fluorescence microscopy is expensive, time-consuming, labor-intensive, and the stains applied can be cytotoxic, interfering with the activity under study. The simplest form of microscopy, brightfield microscopy, lacks these downsides, but the images produced have low contrast and the cellular compartments are difficult to discern.

Development of new approach methods for the identification and characterization of endocrine metabolic disruptors-a PARC project.

In past times, the analysis of endocrine disrupting properties of chemicals has mainly been focused on (anti-)estrogenic or (anti-)androgenic properties, as well as on aspects of steroidogenesis and the modulation of thyroid signaling. More recently, disruption of energy metabolism and related signaling pathways by exogenous substances, so-called metabolism-disrupting chemicals (MDCs) have come into focus. While general effects such as body and organ weight changes are routinely monitored in animal studies, there is a clear lack of mechanistic test systems to determine and characterize the metabolism-disrupting potential of chemicals.

Disease phenotype prediction in multiple sclerosis.

Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work toward a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n = 15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC = 0.