Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

Chemical Research in Toxicology, Journal Year: 2024, Volume and Issue: 37(6), P. 827 - 849

Published: May 17, 2024

The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% fail to make it through the process. identification unexpected toxicity issues during preclinical stages a significant factor contributing this high failure. These can have major impact on success drug and must be carefully considered throughout development late-stage rejections or withdrawals candidates significantly increase costs associated development, particularly when detected after market release. Understanding drug-biological target interactions essential for evaluating compound safety, as well predicting therapeutic effects potential off-target could lead toxicity. This will enable scientists predict assess safety profiles more accurately. Evaluation critical aspect biomolecules, proteins, play vital roles complex biological networks often serve targets various chemicals. Therefore, better understanding these crucial advancement development. computational methods protein–ligand emerging promising approach adheres 3Rs principles (replace, reduce, refine) has garnered attention recent years. In review, we present thorough examination latest breakthroughs prediction, highlighting significance drug-target binding affinity anticipating mitigating possible adverse effects. doing so, aim contribute effective secure drugs.

Language: Английский

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Progress in toxicogenomics to protect human health

Nature Reviews Genetics, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 2, 2024

Language: Английский

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Evaluating the synergistic use of advanced liver models and AI for the prediction of drug-induced liver injury

Expert Opinion on Drug Metabolism & Toxicology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 15

Published: Feb. 2, 2025

Drug-induced liver injury (DILI) is a leading cause of acute failure. Hepatotoxicity typically occurs only in subset individuals after prolonged exposure and constitutes major risk factor for the termination drug development projects. We provide an overview available human models DILI research discuss how they have been used to aid early assessments mitigate project closures due clinical stages. summarize different data that can be provided by such illustrate these diverse types interfaced with machine learning strategies improve predictions safety liabilities. Advanced closely mimic phenotypes functions many weeks, allowing recapitulation hepatotoxicity events vitro. Integration biochemical, histological, toxicogenomic output from physicochemical compound properties using architectures holds promise enhance preclinical predictions. However, realize this aim, it important benchmark on test sets positive negative compounds carefully annotate share resulting data.

Language: Английский

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Complex in vitro models positioned for impact to drug testing in pharma: a review

Biofabrication, Journal Year: 2024, Volume and Issue: 16(4), P. 042006 - 042006

Published: Aug. 27, 2024

Abstract Recent years have seen the creation and popularization of various complex in vitro models (CIVMs), such as organoids organs-on-chip, a technology with potential to reduce animal usage pharma while also enhancing our ability create safe efficacious drugs for patients. Public awareness CIVMs has increased, part, due recent passage FDA Modernization Act 2.0. This visibility is expected spur deeper investment adoption models. Thus, end-users model developers alike require framework both understand readiness current enter drug development process, assess upcoming same. review presents selection based on comparative -omics data (which we term model-omics), metrics qualification specific test assays that may support context-of-use (COU) assays. We surveyed existing healthy tissue ten development-critical organs body, provide evaluations suggestions improving model-omics COU each. In whole, this comes from perspective, seeks an evaluation where are poised maximum impact roadmap realizing potential.

Language: Английский

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Unleashing the potential of cell painting assays for compound activities and hazards prediction

Frontiers in Toxicology, Journal Year: 2024, Volume and Issue: 6

Published: July 17, 2024

The cell painting (CP) assay has emerged as a potent imaging-based high-throughput phenotypic profiling (HTPP) tool that provides comprehensive input data for in silico prediction of compound activities and potential hazards drug discovery toxicology. CP enables the rapid, multiplexed investigation various molecular mechanisms thousands compounds at single-cell level. resulting large volumes image provide great opportunities but also pose challenges to analysis routines well property models. This review addresses integration CP-based together with or substitute structural information from into machine (ML) deep learning (DL) models predict human-relevant disease endpoints identify underlying modes-of-action (MoA) while avoiding unnecessary animal testing. successful application combination powerful ML/DL promises further advances understanding responses cells guiding therapeutic development risk assessment. Therefore, this highlights importance unlocking assays when combined fingerprints evaluation discusses current are associated approach.

Language: Английский

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Attention-based deep learning for accurate cell image analysis

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 8, 2025

Language: Английский

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Optimization of the drug-induced cholestasis index based on advanced modeling for predicting liver toxicity

Toxicology, Journal Year: 2025, Volume and Issue: unknown, P. 154119 - 154119

Published: March 1, 2025

Language: Английский

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Drug-induced cytotoxicity prediction in muscle cells, an application of the Cell Painting assay

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(3), P. e0320040 - e0320040

Published: March 31, 2025

In silico toxicity prediction offers the chance of reducing or replacing most animal testing through integration large experimental assay datasets with appropriate computational approaches. The use Cell Painting to detect various phenotypic changes induced by chemicals is emerging as a powerful technique in prediction. However, approaches cancer cells that are less relevant for many toxicological endpoints, which may limit usefulness this data. study, myoblast cell line used characterize cellular responses panel 30 known myotoxicants. place traditional structural descriptors, here each perturbation described fingerprint calculated properties, deducted from intensity, shape, texture individual cells. We show these kinds descriptors convey information allow viability and fate myoblasts differentiated myotubes C2C12 line, clustering drugs their cytotoxicity responses.

Language: Английский

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Beyond Images: Data Extraction, Analysis and Interpretation

Royal Society of Chemistry eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 75 - 98

Published: April 30, 2025

High content screening (HCS), a pivotal tool in drug discovery, involves extracting phenotypic characteristics from chemical or genetic treatments using microscopic imaging modalities. Traditionally, the development of these approaches has been impeded by two primary factors: technical constraints image acquisition process and challenge deriving meaningful information complex imagery. These limitations have significantly hampered ability to achieve an unbiased characterization treatment effects, which is crucial for accurately classifying their mechanisms action. This has, turn, affected informed decision-making within discovery pipeline. However, field currently undergoing transformative shift. Advancements technology data analysis are beginning overcome historical barriers, heralding new era HCS where comprehensive agnostic becoming increasingly feasible, promising revolutionize landscape mechanistic classification.

Language: Английский

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Morphological Profiling in 2D High Content Screening Assays – Case Studies and Applications

Royal Society of Chemistry eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 125 - 146

Published: April 30, 2025

In order to capture a complete picture of cellular phenotypes in high content assay, broad morphological information is required. To address this need, 2013 the Cell Painting assay was developed as an unbiased, cell based, profiling capable capturing subtle changes morphology. Since then, there has been explosion studies using across large number drug discovery related applications. This chapter will include discussion on various applications and how technology can be used answer specific questions relevant such hit identification, target activity mapping, mechanism-of-action classification, toxicity prediction patient stratification. Further, look at recent surge move from aggregated image level analysis single insights, highlight different data types that being compared combined with (including gene expression profiling), finally give overview largest dataset generated date, Joint Undertaking Morphological Profiling – (JUMP-CP), may leveraged discovery.

Language: Английский

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