The pivotal role of artificial intelligence in enhancing experimental animal model research: A machine learning perspective

Indian Journal of Pharmacology, Journal Year: 2024, Volume and Issue: 56(1), P. 1 - 3

Published: Jan. 1, 2024

Artificial intelligence (AI) refers to a computer imitating "intellectual processes characteristic of humans, such as the ability reason, discover meaning, generalize, or learn from past experience."[1] Machine learning (ML) is core area AI create predictive models by data and gradually enhancing capacity for prediction through experience.[2] The integration ML into animal research has shown promising potential enhance translation reproducibility, complementing traditional approaches models. can optimize preclinical studies using analyzing complex datasets, improving experimental design, predicting outcomes. This enables researchers extract more meaningful information experiments.[3] Combining AI/ML analyses model with human clinical allows better findings. integrated approach helps bridge gap between studies, increasing relevance findings disease. A combination transcriptomic analysis (studying gene expression patterns) in postmortem brain tissue Alzheimer's disease patients mouse was done help identify dysregulated pathways associated excitatory neurotransmission, process crucial function.[4] contribute standardization protocols promoting reproducibility. Automated tools reduce variability results reliability across different laboratories.[5] Using algorithms drug dosing epilepsy able regimens that reduced seizure frequency severity while minimizing toxicities.[6] Integrating big both commonalities differences species.[7] employed automatically track analyze movements their natural controlled environments. understand behavior patterns, social interactions, responses environmental changes.[8] Various platforms, including MoSeq, DeepHL, DeepPoseKit, SLEAP, DeepLabCut, employ deep techniques pose estimation. particularly, stands out widely utilized platform behavioral analysis, addressing numerous limitations posture providing robust tracking diverse environments.[9] be trained recognize individual animals based on facial features unique markings. particularly useful studying structures interactions within groups.[10] It also accelerates discovery efficacy side effects pharmaceutical compounds.[11] Advancements microfluidics-supported chemical synthesis, biological testing, systems iterative design improvement are laying foundation increased automation these processes.[12] creation alcohol-preferring apomorphine-susceptible rat strains selective breeding aimed mimic alcohol use disorder schizophrenia. These phenotypes, influenced genetic factors, necessitate thorough analysis. this method pinpointing most pertinent behaviors, streamlining before embarking extensive task multiple generations animals.[13] In involving symbolic language, decipher meaning behind signals.[14] Bayesian high-throughput screening indicated repurposing nicardipine similar dihydropyridine calcium channel inhibitors treatment Pitt–Hopkins syndrome, rare hereditary illness presenting autism spectrum disorders (ASDs).[15] have been used predict functional consequences voltage-gated well sodium ion channels, which connected developmental encephalopathy, schizophrenia, ASD.[16,17] application toxicity aligns advancements availability algorithm capabilities.[18] Integrated hold promise transforming toxicology hazards new entities reducing reliance testing. Early rule-based expert evolved statistical machine-learning Quantitative Structure Activity Relationship (QSAR).[19] current era embraces learning, utilizing neural networks predictions. One DeepTox standardizes representations compounds, followed computation descriptors input methods. Subsequently, undergoes training, evaluation, assembly effective ensembles. Ultimately, pipeline predicts compounds.[20] Deep framework simultaneously vitro, vivo, data. Pretrained SMILES embeddings Morgan fingerprints two distinct molecular-input representations. multitask demonstrates high accuracy various endpoints, toxicity, evidenced performance metrics. More specifically, compared MoleculeNet benchmark, pretrained molecular improves predictions.[21] novel hybrid network (HNN), named HNN-Tox, introduced at doses. innovative combines frameworks, namely convolutional multilayer perceptron-type feed-forward network.[22] Another eToxPred customized libraries virtual screening. facilitates exclusion candidates may risks prove challenging synthesize.[23] combined addresses challenges. challenges include working together fields organizations gather complete datasets clinical, neuroimaging, genetic, biochemical groups. collaborative will facilitate development capable extracting insights sources.[24] As technologies continue advance, it prioritize ethical considerations tools, when sensitive Ensuring privacy responsible handling practices essential maintaining public trust integrity endeavors.[25] developed should undergo validation settings. Validation populations real-world conditions ensure generalizability models.[26] conclusion, holds immense transformative discoveries. field maximize impact advancing our understanding developing strategies diagnosis treatment.

Language: Английский

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Standing the test of COVID-19: charting the new frontiers of medicine

Frontiers in Science, Journal Year: 2024, Volume and Issue: 2

Published: May 23, 2024

The COVID-19 pandemic accelerated research and innovation across numerous fields of medicine. It emphasized how disease concepts must reflect dynamic heterogeneous interrelationships between physical characteristics, genetics, co-morbidities, environmental exposures, socioeconomic determinants health throughout life. This article explores scientists other stakeholders collaborate in novel, interdisciplinary ways at these new frontiers medicine, focusing on communicable diseases, precision/personalized systems data science. highlighted the critical protective role vaccines against current emerging threats. Radical efficiency gains vaccine development (through mRNA technologies, public private investment, regulatory measures) be leveraged future together with continued area monoclonal antibodies, novel antimicrobials, multisectoral, international action diseases. Inter-individual heterogeneity pathophysiology prompted targeted therapeutics. Beyond COVID-19, medicine will become increasingly personalized via advanced omics-based technologies biology—for example targeting gut microbiome specific mechanisms underlying immunoinflammatory diseases genetic conditions. Modeling proved to strengthening risk assessment supporting decision-making. Advanced computational analytics artificial intelligence (AI) may help integrate epidemic modeling, clinical features, genomics, immune factors, data, anthropometric measures into a “systems medicine” approach. also digital giving telehealth therapeutics roles system resilience patient care. New methods employed during including decentralized trials, could benefit evidence generation decision-making more widely. In conclusion, shaped by multistakeholder collaborations that address complex molecular, clinical, social interrelationships, fostering precision while improving health. Open science, innovative partnerships, patient-centricity key success.

Language: Английский

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Developing a pragmatic consensus procedure supporting the ICH S1B(R1) weight of evidence carcinogenicity assessment

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

Published: April 5, 2024

The ICH S1B carcinogenicity global testing guideline has been recently revised with a novel addendum that describes comprehensive integrated Weight of Evidence (WoE) approach to determine the need for 2-year rat study. In present work, experts from different organizations have joined efforts standardize as much possible procedural framework integration evidence associated S1B(R1) WoE criteria. uses pragmatic consensus procedure hazard assessment facilitate transparent, consistent, and documented decision-making it discusses best-practices both organization studies presentation data in format suitable regulatory review. First, is acknowledged six factors described form an network within holistic used synergistically analyze explain safety signals. Second, proposed standardized builds upon considerations related primary sources evidence, mechanistic analysis, alternative methodologies investigative approaches, metabolites, reliability other acquired information. Each highlighting how they can contribute overall assessment. A suggested reporting summarize cross-integration also presented. This work notes even if study ultimately required, creating valuable understanding specific levels human carcinogenic risk better than identified previously bioassay alone.

Language: Английский

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The European Partnership PARC’s Role in Actively Promoting the Uptake of New Approach Methodologies and Next-Generation Risk Assessment into Regulatory Risk Assessment Practice

Current Opinion in Toxicology, Journal Year: 2025, Volume and Issue: unknown, P. 100517 - 100517

Published: Jan. 1, 2025

Language: Английский

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Artificial Intelligence in Experimental Surgery: Ethical Breakthroughs and Technological Innovations within In Silico Models

Published: Feb. 10, 2025

Integrating artificial intelligence (AI) into experimental surgery represents a transformative shift in biomedical research, offering innovative alternatives to traditional animal-based preclinical models. AI-driven methodologies, including computerized models and surgical simulations, enhance precision, reproducibility, ethical compliance while reducing reliance on _in vivo_ experimentation. This review systematically explores the role of AI optimizing procedures, operative techniques, technology, analyzing its impact decision-making, predictive modeling, training simulations. A comprehensive search was conducted across PubMed, Embase, Scopus, Web Science, SciELO, identifying studies AI-enhanced strategies, silico models, validation techniques. The findings highlight AI's potential replace animal testing, refine training, improve research accuracy. However, challenges remain, data standardization, regulatory adaptation, considerations related methodologies. Addressing these requires interdisciplinary collaboration development validated frameworks support widespread implementation surgery. Future should focus standardizing applications, ensuring methodological transparency, integrating clinical translation pathways. underscores revolutionary shaping future path more ethical, precise,

Language: Английский

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Artificial intelligence in experimental studies and in drug design

Russian Journal for Personalized Medicine, Journal Year: 2025, Volume and Issue: 5(1), P. 58 - 65

Published: March 7, 2025

The paper addresses the role of Artificial intelligence (A) in modern drug design and experimental work biomedicine. It is shown how AI technologies can accelerate discovery innovations decrease time translational cycle. Advantages approaches are presented.

Language: Английский

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Advanced in vitro systems available for respiratory toxicology

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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NAMazing: Déjà Vu at the lab bench - Why animal-free science is the new automobile

NAM journal., Journal Year: 2025, Volume and Issue: 1, P. 100022 - 100022

Published: Jan. 1, 2025

Language: Английский

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Developmental toxicity: artificial intelligence-powered assessments

Trends in Pharmacological Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Language: Английский

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Prediction of reproductive and developmental toxicity using an attention and gate augmented graph convolutional network

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

Published: May 25, 2025

Due to the diverse molecular structures of chemical compounds and their intricate biological pathways toxicity, predicting reproductive developmental toxicity remains a challenge. Traditional Quantitative Structure-Activity Relationship models that rely on descriptors have limitations in capturing complexity achieve high predictive performance. In this study, we developed descriptor-free deep learning model by constructing Graph Convolutional Network designed with multi-head attention gated skip-connections predict toxicity. By integrating structural alerts directly related into model, enabled more effective toxicologically relevant substructures. We built dataset 4,514 compounds, including both organic inorganic substances. The was trained validated using stratified 5-fold cross-validation. It demonstrated excellent performance, achieving an accuracy 81.19% test set. To address interpretability identified subgraphs corresponding known alerts, providing insights model's decision-making process. This study conducted accordance OECD principles for reliable modeling contributes development robust silico prediction.

Language: Английский

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