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@@ -5,9 +5,11 @@ author: - David Vorgrimmler^1^ - Denis Gebele^1^ - Martin Gütlein^2^ - - Benoit Schilter^3^ - - Elena Lo Piparo^3^ -include-before: ^1^ in silico toxicology gmbh, Basel, Switzerland\newline^2^ Inst. f. Computer Science, Johannes Gutenberg Universität Mainz, Germany\newline^3^ Chemical Food Safety Group, Nestlé Research Center, Lausanne, Switzerland + - Barbara Engeli^3^ + - Jürg Zarn^3^ + - Benoit Schilter^4^ + - Elena Lo Piparo^4^ +include-before: ^1^ in silico toxicology gmbh, Basel, Switzerland\newline^2^ Inst. f. Computer Science, Johannes Gutenberg Universität Mainz, Germany\newline^3^ Federal Food Safety and Veterinary Office (FSVO) , Risk Assessment Division , Bern , Switzerland\newline^4^ Chemical Food Safety Group, Nestlé Research Center, Lausanne, Switzerland keywords: (Q)SAR, read-across, LOAEL, experimental variability date: \today abstract: | @@ -52,8 +54,8 @@ prioritization in research and development (safety by design) is a big challenge mainly because of the time and cost constraints associated with the generation of relevant animal data. In this context, alternative approaches to obtain timely and fit-for-purpose toxicological information are being -developed. Amongst others, non-testing, structure-activity based *in silico* -toxicology methods (also called computational toxicology) are considered highly +developed. Amongst others *in silico* +toxicology methods are considered highly promising. Importantly, they are raising more and more interests and getting increased acceptance in various regulatory (e.g. [@ECHA2008, @EFSA2016, @EFSA2014, @HealthCanada2016, @OECD2015]) and industrial (e.g. @@ -62,13 +64,12 @@ and getting increased acceptance in various regulatory (e.g. For a long time already, computational methods have been an integral part of pharmaceutical discovery pipelines, while in chemical food safety their actual potentials emerged only recently [@LoPiparo2011]. -In this later field, an application considered critical is in the +In this field, an application considered critical is in the establishment of levels of safety concern in order to rapidly and efficiently manage toxicologically uncharacterized chemicals identified in food. This requires a risk-based approach to benchmark exposure with a quantitative value of toxicity relevant for risk assessment [@Schilter2014]. -Since most of the time chemical food safety deals with -life-long exposures to relatively low levels of chemicals, and because +Since chronic studies have the highest power (more animals per group and more endpoints than other studies) and because long-term toxicity studies are often the most sensitive in food toxicology databases, predicting chronic toxicity is of prime importance. Up to now, read-across and Quantitative Structure Activity @@ -334,7 +335,7 @@ data (Nestlé and FSVO databases combined). ## Availability Public webinterface - ~ <https://lazar.in-silico.ch> + ~ <https://lazar.in-silico.ch> (see [@fig:screenshot]) `lazar` framework ~ <https://github.com/opentox/lazar> (source code) @@ -348,6 +349,7 @@ Manuscript Docker image ~ <https://hub.docker.com/r/insilicotox/loael-paper/> (container with manuscript, validation experiments, `lazar` libraries and third party dependencies) +{#fig:screenshot} Results ======= @@ -383,9 +385,9 @@ used with different kinds of features. We have investigated structural as well as physico-chemical properties and concluded that both databases are very similar, both in terms of chemical structures and physico-chemical properties. -The only statistically significant difference between both databases, is that +The only statistically significant difference between both databases is that the Nestlé database contains more small compounds (61 structures with less than -11 atoms) than the FSVO-database (19 small structures, p-value 3.7E-7). +11 non-hydrogen atoms) than the FSVO-database (19 small structures, chi-square test: p-value 3.7E-7). <!-- [@fig:ches-mapper-pc] shows an embedding that is based on physico-chemical (PC) @@ -424,7 +426,7 @@ same experiments. Both databases contain substances with multiple measurements, which allow the determination of experimental variabilities. -For this purpose we have calculated the mean standard deviation of compounds with multiple measurements. Mean standard deviations and thus experimental variabilities are similar for both databases. +For this purpose we have calculated the mean LOAEL standard deviation of compounds with multiple measurements. Mean standard deviations and thus experimental variabilities are similar for both databases. The Nestlé database has 567 LOAEL values for 445 unique structures, 93 compounds have @@ -449,7 +451,7 @@ The combined test set has a mean standard deviation (-log10 transformed values) 0.55 mmol/kg_bw/day) ([@fig:intra]). -{#fig:intra} +{#fig:intra} ##### Inter database variability @@ -570,7 +572,7 @@ Discussion It is currently acknowledged that there is a strong need for toxicological information on the multiple thousands of chemicals to -which human may be exposed through food. These include for examples many +which human may be exposed through food. These include for example many chemicals in commerce, which could potentially find their way into food [@Stanton2016, @Fowler2011], but also substances migrating from food contact materials [@Grob2006], chemicals @@ -596,8 +598,8 @@ exposure estimates. The level of safety concern of a chemical is then determined by the size of the MoE and its suitability to cover the uncertainties of the assessment. To be applicable, such an approach requires quantitative predictions of toxicological endpoints relevant -for risk assessment. The present work focuses on prediction of chronic -toxicity, a major and often pivotal endpoints of toxicological databases +for risk assessment. The present work focuses on the prediction of chronic +toxicity, a major and often pivotal endpoint of toxicological databases used for hazard identification and characterization of food chemicals. In a previous study, automated read-across like models for predicting @@ -608,7 +610,7 @@ observed in these models were within the published estimation of experimental variability [@LoPiparo2014]. In the present study, a similar approach was applied to build models generating quantitative predictions of long-term toxicity. Two databases compiling -chronic oral rat lowest adverse effect levels (LOAEL) as endpoint were +chronic oral rat lowest adverse effect levels (LOAEL) as reference value were available from different sources. Our investigations clearly indicated that the Nestlé and FSVO databases are very similar in terms of chemical structures and properties as well as distribution of experimental LOAEL @@ -713,11 +715,9 @@ Finally there is a substantial number of compounds (37), where no predictions can be made, because there are no similar compounds in the training data. These compounds clearly fall beyond the applicability domain of the training dataset and in such cases it is preferable to avoid predictions instead of random guessing. ---> - -TODO: GUI screenshot <!-- +TODO: GUI screenshot is covered in prediction interval shows that `lazar` read across predictions fit well into the experimental variability of LOAEL values. @@ -754,13 +754,5 @@ data. In such cases experimental investigations can be substituted with still give usable results, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended. -<!-- -We could demonstrate that `lazar` predictions within the applicability domain of the training data have the same variability as the experimental training data. In such cases experimental investigations can be substituted with in silico predictions. -Predictions with a lower similarity threshold can still give usable results, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended. - -- beware of over-optimisations and the race for "better" validation results -- reproducible research ---> - References ========== |