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Related Concept Videos

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Preclinical development consists of a series of tests that ensure the safety and efficacy of a new therapeutic compound before it is tested in humans. There are four main phases to this process. First, safety pharmacology tests are conducted to ensure the drug does not produce any acutely harmful effects. These tests examine parameters such as bronchoconstriction, cardiac dysrhythmias, blood pressure changes, and ataxia. Next, preliminary toxicological testing is performed to determine the...
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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Updated: Jun 18, 2025

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Deep Learning-based Modeling for Preclinical Drug Safety Assessment.

Guillaume Jaume1,2,3,4, Simone de Brot5,6,7, Andrew H Song1,2,3,4

  • 1Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.

Biorxiv : the Preprint Server for Biology
|August 2, 2024
PubMed
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Artificial intelligence (AI) enhances drug safety assessment by automating toxicological pathology. The TRACE model, trained on millions of images, accurately characterizes compound toxicity and outperforms human pathologists in concordance.

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Area of Science:

  • Toxicology
  • Computational Pathology
  • Drug Development

Background:

  • Assessing drug toxicity in preclinical studies is vital for clinical trial progression.
  • Manual histopathological analysis of animal tissues is time-consuming and subject to variability.
  • Artificial intelligence (AI) offers potential for accelerated and more objective pathology assessments.

Purpose of the Study:

  • To introduce TRACE, an AI model for automated toxicologic liver histopathology assessment.
  • To demonstrate TRACE's capability in handling diverse diagnostic tasks with limited labeled data.
  • To establish a novel computational framework for accelerating toxicological pathology.

Main Methods:

  • Developed TRACE, an AI model trained on 15 million histopathology images from preclinical studies in *Rattus norvegicus*.
  • Utilized digitized tissue sections from 157 preclinical studies.
  • Evaluated TRACE on tasks including response assessment, severity scoring, morphological retrieval, and dose-response characterization.

Main Results:

  • TRACE successfully performed multiple toxicology tasks, including automatic dose-response characterization.
  • In an independent reader study, TRACE demonstrated higher concordance with expert consensus than the average veterinary pathologist.
  • The AI model showed proficiency across various diagnostic scales and data limitations.

Conclusions:

  • TRACE represents a significant advancement in computational toxicology, offering an automated and accelerated approach.
  • The AI framework enhances consistency and reliability in toxicological pathology assessment.
  • This technology promises to expedite the drug development process by improving diagnostic efficiency.