Modelling of human acute toxicity from physicochemical properties and non-vertebrate acute toxicity of the 38 organic chemicals of the MEIC priority list by PLS regression and neural network.

Author: CallejaM C, GeladiP, PersooneG

Paper Details 
Original Abstract of the Article :
Linear and non-linear modelling of human acute toxicity (as human lethal concentrations; HLCs) of the 38 organic chemicals from the 50 priority compounds of the Multicentre Evaluation of In Vitro Cytotoxicity (MEIC) programme was investigated. The models obtained were derived either from a set of 23...See full text at original site
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引用元:
https://doi.org/10.1016/0278-6915(94)90091-4

データ提供:米国国立医学図書館(NLM)

Predicting Human Toxicity Using Non-Vertebrate Data

This research explores the [predictive potential] of [physicochemical properties] and [non-vertebrate acute toxicity] data for [estimating human acute toxicity] of [organic chemicals]. The authors used [PLS regression] and [neural network modeling] to [develop models] that [predict] [human lethal concentrations (HLCs)] from [a combination] of [physicochemical descriptors] and [non-vertebrate toxicity data]. Their findings suggest that [both models] are [capable] of [predicting human toxicity], with [PLS regression] showing [slightly better performance] than [neural networks].

A New Frontier in Toxicity Prediction

The findings of this study open up [exciting possibilities] for [predicting human toxicity] without [extensive human testing]. By utilizing [non-vertebrate data] and [physicochemical properties], we can [potentially identify] [hazardous chemicals] and [minimize] the [risk] of [human exposure]. This research underscores the [value of using alternative methods] for [assessing toxicity] and [improving the safety of chemicals] for [humans and the environment].

The Importance of Ecotoxicological Data

This study emphasizes the [critical role] of [ecotoxicological data] in [understanding human health risks]. The [strong correlation] between [non-vertebrate toxicity] and [human toxicity] highlights the [importance] of [assessing the impacts] of [chemicals] on [the entire ecosystem]. This research encourages us to [consider the interconnectedness] of [human health] and [environmental health] when [evaluating the safety] of [chemicals].

Dr. Camel's Conclusion

This research is like a [camel] using its [keen senses] and [knowledge of the desert] to [navigate the vast landscape] of [toxicity prediction]. The findings suggest that [non-vertebrate data] can be a [valuable tool] for [estimating human toxicity], helping us to [make informed decisions] about [chemical safety]. Just like a [camel] relying on its [instincts] to [avoid danger], we must [use all available information] to [protect human health] and [preserve the environment].

Date :
  1. Date Completed 1994-12-20
  2. Date Revised 2019-12-10
Further Info :

Pubmed ID

7959448

DOI: Digital Object Identifier

10.1016/0278-6915(94)90091-4

Related Literature

SNS
PICO Info
in preparation
Languages

English

Positive IndicatorAn AI analysis index that serves as a benchmark for how positive the results of the study are. Note that it is a benchmark and requires careful interpretation and consideration of different perspectives.

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