Thresholds of Toxicological Concern – Setting a threshold for testing below which there is little concern

Hartung, Thomas

doi:10.14573/altex.1707011

Cited by 32 publications

(17 citation statements)

References 94 publications

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“…The RASAR represents an enabling technology with uses beyond regulatory toxicology ( Luechtefeld et al , 2018 ): Green Chemistry (or better here Green Toxicology), ie, the frontloading of toxicological considerations in the chemical and product development is one, the identification of problematic substances in the supply chain and the search for alternative chemicals is another one. Interestingly, these large databases also impact on the derivation of thresholds of toxicological concern (TTC) ( Hartung, 2017b ; van Ravenzwaay et al , 2017 ), which might synergize with the in silico approach of a RASAR: the RASAR would prioritize substances from the hazard properties side, while TTC bring in the relevant exposure for the respective toxicological space. This could be an integral part for the strategic development of a new safety sciences paradigm ( Busquet and Hartung, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility

Luechtefeld

Marsh²,

Rowlands

et al. 2018

Toxicological Sciences

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247

212

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Earlier we created a chemical hazard database via natural language processing of dossiers submitted to the European Chemical Agency with approximately 10 000 chemicals. We identified repeat OECD guideline tests to establish reproducibility of acute oral and dermal toxicity, eye and skin irritation, mutagenicity and skin sensitization. Based on 350–700+ chemicals each, the probability that an OECD guideline animal test would output the same result in a repeat test was 78%–96% (sensitivity 50%–87%). An expanded database with more than 866 000 chemical properties/hazards was used as training data and to model health hazards and chemical properties. The constructed models automate and extend the read-across method of chemical classification. The novel models called RASARs (read-across structure activity relationship) use binary fingerprints and Jaccard distance to define chemical similarity. A large chemical similarity adjacency matrix is constructed from this similarity metric and is used to derive feature vectors for supervised learning. We show results on 9 health hazards from 2 kinds of RASARs—“Simple” and “Data Fusion”. The “Simple” RASAR seeks to duplicate the traditional read-across method, predicting hazard from chemical analogs with known hazard data. The “Data Fusion” RASAR extends this concept by creating large feature vectors from all available property data rather than only the modeled hazard. Simple RASAR models tested in cross-validation achieve 70%–80% balanced accuracies with constraints on tested compounds. Cross validation of data fusion RASARs show balanced accuracies in the 80%–95% range across 9 health hazards with no constraints on tested compounds.

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Section: Discussionmentioning

confidence: 99%

Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility

Luechtefeld

Marsh²,

Rowlands

et al. 2018

Toxicological Sciences

Self Cite

247

212

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“…Placing stronger emphasis on exposure, or anticipating it before or concomitant to hazard assessment, gives opportunities to tailor and shortcut animal testing. An example is the concept of the threshold of toxicological concern (TTC) (Hartung, 2017), applicable where exposure assessment shows that expected doses are far below levels likely to produce a health effect. Hartung and Leist (2008), to the best of our knowledge, first suggested an internal TTC, where only the bioavailable fraction (e.g., blood levels) is used to establish whether a critical concentration is reached, "The idea might be carried further by identifying internal TTCs, i.e.…”

Section: What Is In the Box For Alternatives To Animal Testing?mentioning

confidence: 99%

The exposome – a new approach for risk assessment

Sillé

Karakitsios

Kleensang

et al. 2020

ALTEX

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Other 'omes existed even then, such as biome (collection of living things) and rhizome (system of roots), many of them based on the Greek suffix '-ome' -meaning, roughly, 'having the nature of.' But it was the glamorization of 'genome' by megabuck initiatives such as the Human Genome Project that really set the trend in motion, says Alexa McCray, a linguist and medical informatician at Harvard Medical School in Boston, Massachusetts. IntroductionClaiming a new 'ome has become fashionable in the post-human genome era. Monya Baker (2013), in her piece in Nature, claimed the number to be in the thousands. She wrote: "Botanist Hans Winkler had no idea what he was starting back in 1920, when he proposed the term 'genome' to refer to a set of chromosomes.

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“…Hartung [72] published a comprehensive review on the concept of TTCs. Risk assessments are based on defined doses (with and without the incorporation of uncertainty factors) below which nothing happens in test animals.…”

Section: How To Translate Data From Experimental Toxicology Into Humamentioning

confidence: 99%

Material Risks of Homeopathic Medicinal Products: Regulatory Frameworks, Results of Preclinical Toxicology, and Clinical Meta-Analyses and Their Implications

Koller

2020

Complement Med Res

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Homeopathy is widely used and broadly accepted by health care professionals and the general public but less in academic circles. To assess possible material health risks of homeopathic medicinal products, it is necessary to identify, select, and synthesize the findings of recent reviews of controlled homeopathic clinical trials. Matching these findings with experimental data from toxicological studies helps to clarify what is known and not known about the material risks of homeopathic medicines. Rules for toxicological risk assessment and management need to be applied independently of individual attitudes towards specific therapeutic options. European regulatory bodies have developed special protocols and decision trees to assure the safety of nonindividualized homeopathic remedies. This narrative review leads to suggestions that could ease and improve toxicological decision making. No homeopathy-specific type or pattern of side effects could be extracted from the meta-analysis data. No differences in the frequency of adverse reactions between homeopathic treatment and placebo treatment could be seen, no matter whether adverse events were reported in a quantitative or a qualitative manner. Some patterns of side effects show that adverse reactions do not necessarily correlate with treatment but with the condition of the patient. Overall, the controlled clinical data available for the material risk assessment of homeopathic remedies support the statement that, if a risk exists, it must be so small that it has not yet been established. To make our risk findings useful for personal decisions regarding homeopathy, we provide a thought experiment based on four different health situations and ask the question: is homeopathy as a monotherapy or as an add-on treatment an option or should it be rejected?

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Thresholds of Toxicological Concern – Setting a threshold for testing below which there is little concern

Cited by 32 publications

References 94 publications

Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility

Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility

The exposome – a new approach for risk assessment

Material Risks of Homeopathic Medicinal Products: Regulatory Frameworks, Results of Preclinical Toxicology, and Clinical Meta-Analyses and Their Implications

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