Regulatory Affairs – Communicating with Health Authorities

Fuelle, Hans‐Juergen; Lanctin, Valerie

doi:10.1002/9783527824014.ch10

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Chemical safety is a significant factor leading to the loss of new chemicals during their discovery phase and even after they've been introduced to the market, especially concerning regulatory matters. The potential side effects of chemicals pose significant challenges in the clinical realm as they find new applications in the consumer market (Fuelle & Lanctin, 2022). Determining how to anticipate these negative chemical/toxicological responses remains crucial.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Powered Insights into Nanotoxicology

Sriram T,

Chakraborty,

Prasanna

2024

IJALSR

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The application of nanomaterials in medicine necessitates a thorough assessment of their toxicity to ensure their safe use in living organisms. Advanced technologies like artificial intelligence (AI) and machine learning (ML) are instrumental in processing vast datasets in toxicology, encompassing toxicological databases and image-based screening results. Nanomaterials exhibit significant variability in their physical and chemical properties, making their toxicological assessment unique. The potential adverse effects of these materials, as they find new applications in the consumer market, raise crucial concerns in clinical settings. Understanding the toxicological mechanisms of these substances is vital. Traditionally, the pharmaceutical industry employs animal models to evaluate compound toxicity before human trials, guided by various regulatory legislations. Modern toxicology increasingly relies on computational methods. ML techniques, especially decision tree algorithms, are pivotal for categorizing nanomaterials in nanotoxicology. These algorithms identify essential input parameters and extract meaningful information from extensive datasets. This overview emphasizes the role of AI algorithms in nanotoxicology, quantitative toxicology, nano data collection, predictive models, essential NP properties, image-based databases, and various challenges in the field.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Powered Insights into Nanotoxicology

Sriram T,

Chakraborty,

Prasanna

2024

IJALSR

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Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review

et al. 2023

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The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in order to ensure safe application on living organisms. Artificial intelligence (AI) and machine learning (MI) can be used to analyze and interpret large amounts of data in the field of toxicology, such as data from toxicological databases and high-content image-based screening data. Physiologically based pharmacokinetic (PBPK) models and nano-quantitative structure–activity relationship (QSAR) models can be used to predict the behavior and toxic effects of nanomaterials, respectively. PBPK and Nano-QSAR are prominent ML tool for harmful event analysis that is used to understand the mechanisms by which chemical compounds can cause toxic effects, while toxicogenomics is the study of the genetic basis of toxic responses in living organisms. Despite the potential of these methods, there are still many challenges and uncertainties that need to be addressed in the field. In this review, we provide an overview of artificial intelligence (AI) and machine learning (ML) techniques in nanomedicine and nanotoxicology to better understand the potential toxic effects of these materials at the nanoscale.

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Regulatory Affairs – Communicating with Health Authorities

Cited by 2 publications

References 10 publications

Artificial Intelligence Powered Insights into Nanotoxicology

Artificial Intelligence Powered Insights into Nanotoxicology

Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review

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