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@@ -37,32 +37,31 @@ Introduction Relying on standard animal toxicological testing for chemical hazard identification and characterization is increasingly questioned on both -scientific and ethical grounds. In addition, it appears obvious that -from a resource perspective, the capacity of standard toxicology to -address the safety of thousands of untested chemicals (Fowler et al., -2011) to which human may be exposed is very limited. It has also been -recognized that getting rapid insight on toxicity of chemicals in case -of emergency safety incidents or for early 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 promising. Importantly, they are raising more and more interests -and getting increased acceptance in various regulatory (e.g. ECHA, 2008; -EFSA, 2016, 2014; Health Canada, 2016; OECD, 2015) and industrial (e.g. -Stanton and Kruszewski, 2016; Lo Piparo et al., 2011) frameworks. +scientific and ethical grounds. In addition, it appears obvious that from +a resource perspective, the capacity of standard toxicology to address the +safety of thousands of untested chemicals [@Fowler2011] to which human may be +exposed is very limited. It has also been recognized that getting rapid insight +on toxicity of chemicals in case of emergency safety incidents or for early +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 +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. +[@Stanton2016, @LoPiparo2011]) frameworks. 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 (Lo Piparo et al., -2011). In this later field, an application considered critical is in the +safety their actual potentials emerged only recently [@LoPiparo2011]. +In this later 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 (Schilter -et al., 2014a). Since most of the time chemical food safety deals 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 long-term toxicity studies are often the most sensitive in food toxicology databases, predicting chronic toxicity is of prime @@ -136,13 +135,11 @@ The Nestlé database can be obtained from the following GitHub links: [original ### Swiss Food Safety and Veterinary Office (FSVO) database Publicly available data from pesticide evaluations of chronic rat -toxicity studies from the European Food Safety Authority (EFSA) (EFSA, -2014), the Joint FAO/WHO Meeting on Pesticide Residues (JMPR) (WHO, -2011) and the US EPA (US EPA, 2011) were compiled to form the +toxicity studies from the European Food Safety Authority (EFSA) [@EFSA2014], the Joint FAO/WHO Meeting on Pesticide Residues (JMPR) [@WHO2011] and the US EPA [@EPA2011] were compiled to form the FSVO-database. Only studies providing both an experimental NOAEL and an experimental LOAEL were included. The LOAELs were taken as they were reported in the evaluations. Further details on the database are -described elsewhere (Zarn et al., 2011; Zarn et al., 2013). The +described elsewhere [@Zarn2011, @Zarn2013]. The FSVO-database consists of `r length(s$SMILES)` rat LOAEL values for `r length(unique(s$SMILES))` unique chemical structures. It can be obtained from the following GitHub links: @@ -172,8 +169,8 @@ dataset](https://github.com/opentox/loael-paper/blob/submission/data/test_log10. contains data from compounds that occur in both databases. LOAEL values equal at five significant digits were considered as duplicates originating from the same study/publication and only one instance was kept in the test dataset. The -test dataset has `r length(t$SMILES)` LOAEL values for `r -length(unique(t$SMILES))` unique chemical structures and was used for +test dataset has `r length(t$SMILES)` LOAEL values for `r length(unique(t$SMILES))` +unique chemical structures and was used for - evaluating experimental variability - comparing model predictions with experimental variability. @@ -439,8 +436,8 @@ c.mg = read.csv("data/all_mg_dup.csv",header=T) c.mg$sd <- ave(c.mg$LOAEL,c.mg$SMILES,FUN=sd) ``` -The Nestlé database has `r length(m$SMILES)` LOAEL values for `r -length(levels(m$SMILES))` unique structures, `r m.dupnr` compounds have +The Nestlé database has `r length(m$SMILES)` LOAEL values for +`r length(levels(m$SMILES))` unique structures, `r m.dupnr` compounds have multiple measurements with a mean standard deviation (-log10 transformed values) of `r round(mean(m.dup$sd),2)` (`r round(mean(10^(-1*m.mg$sd)),2)` mg/kg_bw/day, `r round(mean(10^(-1*m.dup$sd)),2)` mmol/kg_bw/day) @@ -483,8 +480,8 @@ datasets. As both datasets contain duplicates medians were used for the correlation plot and statistics. It should be kept in mind that the aggregation of duplicated measurements into a single median value hides a substantial portion of the experimental variability. Correlation analysis shows a significant (p-value < 2.2e-16) -correlation between the experimental data in both datasets with r\^2: `r -round(median.r.square,2)`, RMSE: `r round(median.rmse,2)` +correlation between the experimental data in both datasets with r\^2: +`r round(median.r.square,2)`, RMSE: `r round(median.rmse,2)` ![Correlation of median LOAEL values from Nestlé and FSVO databases. Data with identical values in both databases was removed from @@ -510,8 +507,8 @@ correct_predictions = length(training$SMILES)-incorrect_predictions In order to compare the performance of *in silico* read across models with experimental variability we are using compounds that occur in both datasets as a test set (`r length(t$SMILES)` measurements, `r length(unique(t$SMILES))` -compounds). `lazar` read across predictions were obtained for `r -length(unique(t$SMILES))` compounds, `r length(unique(t$SMILES)) - length(training$SMILES)` +compounds). `lazar` read across predictions were obtained for +`r length(unique(t$SMILES))` compounds, `r length(unique(t$SMILES)) - length(training$SMILES)` predictions failed, because no similar compounds were found in the training data (i.e. they were not covered by the applicability domain of the training data). @@ -632,11 +629,11 @@ 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 chemicals in commerce, which could potentially find their way into food -(Stanton and Kruszewski, 2016; Fowler et al., 2011), but also substances -migrating from food contact materials (Grob et al., 2006), chemicals -generated over food processing (Cottererill et al., 2008), environmental -contaminants as well as inherent plant toxicants (Schilter et al., -2014b). For the vast majority of these chemicals, no toxicological data +[@Stanton2016, @Fowler2011], but also substances +migrating from food contact materials [@Grob2006], chemicals +generated over food processing [@Cotterill2008], environmental +contaminants as well as inherent plant toxicants [@Schilter2013]. +For the vast majority of these chemicals, no toxicological data is available and consequently insight on their potential health risks is very difficult to obtain. It is recognized that testing all of them in standard animal studies is neither feasible from a resource perspective @@ -650,7 +647,7 @@ toxicology is thought to play an important role for that. In order to establish the level of safety concern of food chemicals toxicologically not characterized, a methodology mimicking the process of chemical risk assessment, and supported by computational toxicology, -was proposed (Schilter et al., 2014a). It is based on the calculation of +was proposed [@Schilter2014]. It is based on the calculation of margins of exposure (MoE) between predicted values of toxicity and 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 @@ -665,7 +662,7 @@ carcinogenic potency were developed. In these models, substances in the training dataset similar to the query compounds are automatically identified and used to derive a quantitative TD50 value. The errors observed in these models were within the published estimation of -experimental variability (Lo Piparo, et al., 2014). In the present +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 @@ -726,7 +723,7 @@ than chonic studies should be studied. It is likely that more substances reflecting a wider chemical domain may be available. To predict such shorter duration endpoints would also be valuable for chronic toxicy since evidence suggest that exposure duration has little impact on the -levels of NOAELs/LOAELs (Zarn et al., 2011, 2013). +levels of NOAELs/LOAELs [@Zarn2011, @Zarn2013]. <!-- Elena + Benoit |