bioRxiv Science⌕ Search

bioRxiv · 10.1101/2024.04.25.591123

Cell Painting morphological profiles can complement QSAR models for rat acute oral toxicity prediction

Abstract

Early de-risking decisions in the development of new chemical compounds, enable the identification of novel chemical candidates with improved safety profiles. In vivo studies are traditionally conducted in the early assessment of acute oral toxicity of crop protection products to avoid compounds which are considered "very acutely toxic", with an in vivo Lethal Dose of 50% (LD50) [≤] 60 mg/kg bodyweight. Those studies are lengthy, costly, and raise ethical concerns, catalyzing the use of non-animal alternatives. The objective of our analysis was to assess the predictive efficacy of read-across approaches for acute oral toxicity in rats, comparing the use of chemical structure information, in vitro biological data derived from the Cell Painting profiling assay on U2OS cells or the combination of both. Our findings indicate that the classification of compounds as very acute oral toxic (LD50 [≤] 60 mg/kg) or not is possible using a read-across approach, with chemical structure information, morphological profiles, or a combination of both. When classifying compounds structurally similar to those in the training set, chemical structure was more predictive (balanced accuracy of 0.82). Conversely, when the compounds to be classified were structurally different from those in the training set, the morphological profiles were more predictive (balanced accuracy of 0.72). Combining the two models allowed for the classification of compounds structurally similar to those in the training set, to slightly improve the predictions (balanced accuracy of 0.85). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/591123v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@d7d4fcorg.highwire.dtl.DTLVardef@1e00a92org.highwire.dtl.DTLVardef@1d60fccorg.highwire.dtl.DTLVardef@a72961_HPS_FORMAT_FIGEXP M_FIG For Table of Contents Only C_FIG

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Camilleri, F., Wenda, J., Pecoraro-Mercier, C., Comet, J.-P., Rouquie, D.. 2024-04-28. Cell Painting morphological profiles can complement QSAR models for rat acute oral toxicity prediction. https://doi.org/10.1101/2024.04.25.591123

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Lipid-ASO therapeutics exhibit differential tissue targeted delivery upon systemic or local CNS administration

Antisense oligonucleotides (ASOs) are a powerful therapeutic modality, but their full potential is hindered by pharmacokinetic properties that affect tissue and cellular delivery. Lipid conjugation is increasingly used to modulate ASO's biodistribution and promote extrahepatic activity, yet lipid dependent effects on in vivo functional delivery, particularly in the central nervous system (CNS), remain less explored. Here, we performed a side by side in vivo comparison of cholesterol, palmitic acid (C16:0), docosanoic acid (C22:0), and eicosapentaenoic acid (C20:5) conjugated to a fully phosphorothioated 3 10 3 LNA gapmer ASO targeting the Malat1 long non coding RNA. Lipid-ASO conjugates were administered systemically or locally in the brain of mice and evaluated for tissue level and cellular level distribution by imaging, qPCR and single-cell RNA sequencing, simultaneously annotating cell origin and global transcriptional changes within the cell. Following systemic administration in mice, lipid conjugation improved overall multi organ efficacy compared to unconjugated ASO, but with pronounced tissue specific differences. Single cell sequencing of liver and heart transcriptomes revealed lipid dependent cellular uptake patterns and transcriptional responses distinct from administration of unconjugated ASO. After intracerebroventricular administration, selected fatty acid conjugates enhanced silencing in deep brain regions such as the striatum, whereas cholesterol conjugation impaired functional delivery despite increased CNS retention. Light-sheet microscopy showed restricted parenchymal penetration of cholesterol ASOs compared with broader but heterogeneous distribution of palmitic acid conjugate. Together, these findings demonstrate that lipid identity critically determines ASO efficacy, productive cellular uptake, and regional CNS engagement, emphasizing the need for context specific lipid design in ASO therapeutic development.

pharmacology and toxicology↗

Novel Dissymmetric Ionizable Lipid-Assembled Lipid Nanoparticles for Delivery of Ferroptosis-Related siRNA in Diabetic Treatment

Small interfering RNA (siRNA) enables precise post-transcriptional gene silencing for refractory diseases, yet its clinical translation remains limited by the lack of safe and efficient delivery vectors. Inspired by the dissymmetric alkyl chain architecture of natural membrane phospholipids, we designed and synthesized 34 novel ionizable lipids with dissymmetric hydrophobic tails and formulated them into lipid nanoparticles (LNPs). Through systematic physicochemical and biological assessments, we established clear structure-activity relationships and identified two lead LNPs (O14-LNP, H18a-LNP) with superior endosomal escape capacity, enhanced in vivo gene silencing potency, and favorable biosafety relative to the clinical benchmark MC3-LNP. In both streptozotocin-induced and spontaneous db/db type 2 diabetes (T2D) mouse models, lead LNPs delivering ferroptosis-related siRNAs effectively ameliorated glucose and lipid metabolic disorders, restored islet function, and alleviated hepatic steatosis. This study not only lays a theoretical foundation for the rational design of novel ionizable lipids, but also validates the therapeutic potential of siRNA therapy targeting ferroptosis, providing a versatile delivery platform and targeted therapeutic strategy for the treatment of T2D.

pharmacology and toxicology↗

AmesNet: A Deep Learning Model Enhancing Generalization in Ames Mutagenicity Prediction

Regulatory agencies require comprehensive genotoxicity assessments for all novel small-molecule therapeutics prior to human trials. Developers often delay these studies until a candidate is nearing regulatory submission because they are expensive and secondary to bioactivity. This timing creates a bottleneck where late-stage failures can jeopardize >$10 million in capital and multiple years of developmental progress per candidate. The Ames assay is used to detect a molecules mutagenic potential. Regulators now explicitly support the use of in silico Ames mutagenicity models through enabling legislation, dedicated FDA AI toxicology programs, internationally harmonized guidelines, and benchmark challenges. However, current Ames models suffer from a dramatic sensitivity drop-off when they evaluate molecules outside their training domain. Sensitivity is the most important metric in Ames prediction because false negatives allow mutagenic compounds to advance undetected and trigger the most costly late-stage failures. Attempts to fix this sensitivity drop-off often reduce overall model performance, which can be represented by balanced accuracy. For example, DeepAmes reports high levels of sensitivity only by sacrificing its balanced accuracy. We introduce AmesNet, a novel Task-Conditioned modeling paradigm that achieves both class-leading sensitivity and balanced accuracy in novel chemical spaces. AmesNet utilizes a dual branch architecture containing a molecular encoder and a dedicated channel to condition Ames assay context such as metabolic activation and bacterial strain type. In comparative benchmarks, AmesNet reached a sensitivity of 0.73 (95% confidence interval: 0.68-0.77) and a simultaneous balanced accuracy of 0.81 (95% confidence interval: 0.79-0.83) on the out-of-domain test data. This represents an improvement in sensitivity of up to 46% over existing approaches without a trade-off in balanced accuracy. Structural analysis demonstrates that AmesNet recovers difficult-to-detect mutagenic compounds missed by existing models. This framework provides a high-confidence filtering mechanism that enables drug developers to turn a costly late-stage safety bottleneck into a proactive decision-making edge.

pharmacology and toxicology↗