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diff --git a/190501_Genotox-PA.md b/190501_Genotox-PA.md new file mode 100644 index 0000000..acf64e9 --- /dev/null +++ b/190501_Genotox-PA.md @@ -0,0 +1,1325 @@ +Prediction of the mutagenic potential of different pyrrolizidine +alkaloids using LAZAR, Random Forest, Support Vector Machines, and Deep +Learning + +Authors + +Verena Schöning, Christoph Helma, Philipp Boss, Jürgen Drewe + +**Manuscript in preparation.** + +Corresponding author: + +Prof. Dr. Jürgen Drewe, MSc + +Abstract +======== + +Pyrrolizidine alkaloids (PAs) are secondary plant metabolites of some +plant families, which protect against predators and generally considered +as genotoxic and mutagenic. This mutagenicity is also the point of +concern in regulatory risk assessment of this substance group [EFSA +2011](#_ENREF_36)[EMA 2014](#_ENREF_38)[2016](#_ENREF_39)(; ; ). Several +investigations already showed that the mutagenic potential of PAs is +different, and largely depends on the structure. + +Since only very few of over 600 known PAs are available for *in vitro* +or *in vivo* experiments, the mutagenicity of PAs in this study was +estimated using four different machine learning techniques LAZAR and +Deep Learning, Random Forest and Support Vector Machines. However, all +models were not optimal for predicting the genotoxic potential of PAs +either due to problems with the applicability domain or due to low +performance. Therefore, no estimation regarding the genotoxic potential +of single PAs could be made. An analysis of the genotoxic potential of +different structural groups, showed promising results. For necine base +and necic acid, the results fitted well with literature for three +models. However, the prediction of the toxic principle of PAs, +dehydropyrrolizidine was only within expectation in one model +(TensorFlow-generated Deep Learning model), but not in the other four +models. This study shows convincingly the need to critically review and +assess the predictions obtained from machine learning approaches by +internal cross-validation, but also by external validation through +comparison with literature. + +Introduction +============ + +Pyrrolizidine alkaloids (PAs) are secondary plant ingredients found in +many plant species as protection against predators [Hartmann & Witte +1995](#_ENREF_59)[Langel et al. 2011](#_ENREF_76)(; ). PAs are ester +alkaloids, which are composed of a necine base (two fused five-membered +rings joined by a nitrogen atom) and one or two necic acid (carboxylic +ester arms). The necine base can have different structures and thereby +divides PAs into several structural groups, e.g. otonecine, platynecine, +and retronecine. The structural groups of the necic acid are macrocyclic +diester, open-ring diester and monoester [Langel et al. +2011](#_ENREF_76)(). + +PA are mainly metabolised in the liver, which is at the same time the +main target organ of toxicity [Bull & Dick 1959](#_ENREF_17)[Bull et al. +1958](#_ENREF_18)[Butler et al. 1970](#_ENREF_20)[DeLeve et al. +1996](#_ENREF_33)[Jago 1971](#_ENREF_65)[Li et al. +2011](#_ENREF_78)[Neumann et al. 2015](#_ENREF_99)(; ; ; ; ; ; ). There +are three principal metabolic pathways for 1,2-unsaturated PAs [Chen et +al. 2010](#_ENREF_26)(): (i) Detoxification by hydrolysis: the ester +bond on positions C7 and C9 are hydrolysed by non-specific esterases to +release necine base and necic acid, which are then subjected to further +phase II-conjugation and excretion. (ii) Detoxification by *N*-oxidation +of the necine base (only possible for retronecine-type PAs): the +nitrogen is oxidised to form a PA *N*-oxides, which can be conjugated by +phase II enzymes e.g. glutathione and then excreted. PA *N*-oxides can +be converted back into the corresponding parent PA [Wang et al. +2005](#_ENREF_134)(). (iii) Metabolic activation or toxification: PAs +are metabolic activated/ toxified by oxidation (for retronecine-type +PAs) or oxidative *N*-demethylation (for otonecine-type PAs [Lin +1998](#_ENREF_82)()). This pathway is mainly catalysed by cytochrome +P450 isoforms CYP2B and 3A [Ruan et al. 2014b](#_ENREF_115)(), and +results in the formation of dehydropyrrolizidines (DHP, also known as +pyrrolic ester or reactive pyrroles). DHPs are highly reactive and cause +damage in the cells where they are formed, usually hepatocytes. However, +they can also pass from the hepatocytes into the adjacent sinusoids and +damage the endothelial lining cells [Gao et al. 2015](#_ENREF_48)() +predominantly by reaction with protein, lipids and DNA. There is even +evidence, that conjugation of DHP to glutathione, which would generally +be considered a detoxification step, could result in reactive +metabolites, which might also lead to DNA adduct formation [Xia et al. +2015](#_ENREF_138)(). Due to the ability to form DNA adducts, DNA +crosslinks and DNA breaks 1,2-unsaturated PAs are generally considered +genotoxic and carcinogenic [Chen et al. 2010](#_ENREF_26)[EFSA +2011](#_ENREF_36)[Fu et al. 2004](#_ENREF_45)[Li et al. +2011](#_ENREF_78)[Takanashi et al. 1980](#_ENREF_126)[Yan et al. +2008](#_ENREF_140)[Zhao et al. 2012](#_ENREF_148)(; ; ; ; ; ; ). Still, +there is no evidence yet that PAs are carcinogenic in humans [ANZFA +2001](#_ENREF_4)[EMA 2016](#_ENREF_39)(; ). One general limitation of +studies with PAs is the number of different PAs investigated. Around 30 +PAs are currently commercially available, therefore all studies focus on +these PAs. This is also true for *in vitro* and *in vivo* tests on +mutagenicity and genotoxicity. To gain a wider perspective, in this +study over 600 different PAs were assessed on their mutagenic potential +using four different machine learning techniques. + +Materials and Methods +===================== + +Training dataset +---------------- + +For all methods, the same validated training dataset was used. The +training dataset was compiled from the following sources: + +- Kazius/Bursi Dataset (4337 compounds, [Kazius et al. + 2005](#_ENREF_71)()): + +> <http://cheminformatics.org/datasets/bursi/cas_4337.zip> + +- Hansen Dataset (6513 compounds, [Hansen et al. 2009](#_ENREF_57)()): + +> <http://doc.ml.tu-berlin.de/toxbenchmark/Mutagenicity_N6512.csv> + +- EFSA Dataset (695 compounds, [EFSA 2011](#_ENREF_36)()): + +> <https://data.europa.eu/euodp/data/storage/f/2017-0719T142131/GENOTOX%20data%20and%20dictionary.xls> + +Mutagenicity classifications from Kazius and Hansen datasets were used +without further processing. To achieve consistency between these +datasets, EFSA compounds were classified as mutagenic, if at least one +positive result was found for TA98 or T100 Salmonella strains. + +Dataset merges were based on unique SMILES (*Simplified Molecular Input +Line Entry Specification*) strings of the compound structures. +Duplicated experimental data with the same outcome was merged into a +single value, because it is likely that it originated from the same +experiment. Contradictory results were kept as multiple measurements in +the database. The combined training dataset contains 8281 unique +structures. + +Source code for all data download, extraction and merge operations is +publicly available from the git repository +<https://git.in-silico.ch/pyrrolizidine> under a GPL3 License. + +Testing dataset +--------------- + +The testing dataset consisted of 602 different PAs. The compilation of +the PA dataset is described in detail in [Schöning et al. +(2017)](#_ENREF_119). The PAs were assigned to groups according to +structural features of the necine base and necic acid. + +For the necine base, following groups were assigned: + +- Retronecine-type (1,2-unstaturated necine base) + +- Otonecine-type (1,2-unstaturated necine base) + +- Platynecine-type (1,2-saturated necine base) + +For the modification of necine base, following groups were assigned: + +- *N*-oxide-type + +- Tertiary-type (PAs which were neither from the *N*-oxide- nor + > DHP-type) + +- DHP-type (dehydropyrrolizidine, pyrrolic ester) + +For the necic acid, following groups were assigned: + +- Monoester-type + +- Open-ring diester-type + +- Macrocyclic diester-type + +For the Random Forest (RF), Support Vector Machines (SVM), and Deep +Learning (DL) models, molecular descriptors of the PAs were calculated +using the program PaDEL-Descriptors (version 2.21) [Yap +2011](#_ENREF_142)[2014](#_ENREF_143)(; ). From these descriptors were +chosen, which were actually used for the generation of the DL model. + +LAZAR +----- + +LAZAR (*lazy structure activity relationships*) is a modular framework +for read-across model development and validation. It follows the +following basic workflow: For a given chemical structure LAZAR: + +- searches in a database for similar structures (neighbours) with + experimental data, + +- builds a local QSAR model with these neighbours and + +- uses this model to predict the unknown activity of the query + compound. + +This procedure resembles an automated version of read across predictions +in toxicology, in machine learning terms it would be classified as a +k-nearest-neighbour algorithm. + +Apart from this basic workflow, LAZAR is completely modular and allows +the researcher to use any algorithm for similarity searches and local +QSAR (*Quantitative structure--activity relationship*) modelling. +Algorithms used within this study are described in the following +sections. + +### Neighbour identification + +Similarity calculations were based on MolPrint2D fingerprints [Bender et +al. 2004](#_ENREF_8)() from the OpenBabel cheminformatics library +[O\'Boyle et al. 2011](#_ENREF_104)(). The MolPrint2D fingerprint uses +atom environments as molecular representation, which resembles basically +the chemical concept of functional groups. For each atom in a molecule, +it represents the chemical environment using the atom types of connected +atoms. + +MolPrint2D fingerprints are generated dynamically from chemical +structures and do not rely on predefined lists of fragments (such as +OpenBabel FP3, FP4 or MACCs fingerprints or lists of +toxicophores/toxicophobes). This has the advantage that they may capture +substructures of toxicological relevance that are not included in other +fingerprints. + +From MolPrint2D fingerprints a feature vector with all atom environments +of a compound can be constructed that can be used to calculate chemical +similarities. + +The chemical similarity between two compounds a and b is expressed as +the proportion between atom environments common in both structures A ∩ B +and the total number of atom environments A U B (Jaccard/Tanimoto +index). + +$$sim = \frac{\left| A\ \cap B \right|}{\left| A\ \cup B \right|}$$ + +Threshold selection is a trade-off between prediction accuracy (high +threshold) and the number of predictable compounds (low threshold). As +it is in many practical cases desirable to make predictions even in the +absence of closely related neighbours, we follow a tiered approach: + +- First a similarity threshold of 0.5 is used to collect neighbours, + to create a local QSAR model and to make a prediction for the query + compound. + +- If any of these steps fails, the procedure is repeated with a + similarity threshold of 0.2 and the prediction is flagged with a + warning that it might be out of the applicability domain of the + training data. + +- Similarity thresholds of 0.5 and 0.2 are the default values chosen + > by the software developers and remained unchanged during the + > course of these experiments. + +Compounds with the same structure as the query structure are +automatically eliminated from neighbours to obtain unbiased predictions +in the presence of duplicates. + +### Local QSAR models and predictions + +Only similar compounds (neighbours) above the threshold are used for +local QSAR models. In this investigation, we are using a weighted +majority vote from the neighbour's experimental data for mutagenicity +classifications. Probabilities for both classes +(mutagenic/non-mutagenic) are calculated according to the following +formula and the class with the higher probability is used as prediction +outcome. + +$$p_{c} = \ \frac{\sum_{}^{}\text{sim}_{n,c}}{\sum_{}^{}\text{sim}_{n}}$$ + +$p_{c}$ Probability of class c (e.g. mutagenic or non-mutagenic)\ +$\sum_{}^{}\text{sim}_{n,c}$ Sum of similarities of neighbours with +class c\ +$\sum_{}^{}\text{sim}_{n}$ Sum of all neighbours + +### Applicability domain + +The applicability domain (AD) of LAZAR models is determined by the +structural diversity of the training data. If no similar compounds are +found in the training data no predictions will be generated. Warnings +are issued if the similarity threshold had to be lowered from 0.5 to 0.2 +in order to enable predictions. Predictions without warnings can be +considered as close to the applicability domain and predictions with +warnings as more distant from the applicability domain. Quantitative +applicability domain information can be obtained from the similarities +of individual neighbours. + +### Availability + +- LAZAR experiments for this manuscript: + [https://git.in-silico.ch/pyrrolizidine](https://deref-gmx.net/mail/client/Yn0laI8dUvs/dereferrer/?redirectUrl=https%3A%2F%2Fgit.in-silico.ch%2Fpyrrolizidine) + (source code, GPL3) + +- LAZAR framework: + [https://git.in-silico.ch/lazar](https://deref-gmx.net/mail/client/v26UgZbKEpE/dereferrer/?redirectUrl=https%3A%2F%2Fgit.in-silico.ch%2Flazar) + (source code, GPL3) + +- LAZAR GUI: + [https://git.in-silico.ch/lazar-gui](https://deref-gmx.net/mail/client/QstEPrpbcqQ/dereferrer/?redirectUrl=https%3A%2F%2Fgit.in-silico.ch%2Flazar-gui) + (source code, GPL3) + +- Public web interface: + [https://lazar.in-silico.ch](https://deref-gmx.net/mail/client/Gln3hLem0DY/dereferrer/?redirectUrl=https%3A%2F%2Flazar.in-silico.ch) + +Random Forest, Support Vector Machines, and Deep Learning in R-project +---------------------------------------------------------------------- + +In comparison to LAZAR, three other models (Random Forest (RF), Support +Vector Machines (SVM), and Deep Learning (DL)) were evaluated. + +For the generation of these models, molecular 1D and 2D descriptors of +the training dataset were calculated using PaDEL-Descriptors (version +2.21) [Yap 2011](#_ENREF_142)[2014](#_ENREF_143)(; ). + +As the training dataset contained over 8280 instances, it was decided to +delete instances with missing values during data pre-processing. +Furthermore, substances with equivocal outcome were removed. The final +training dataset contained 8080 instances with known mutagenic +potential. The RF, SVM, and DL models were generated using the R +software (R-project for Statistical Computing, +<https://www.r-project.org/>*;* version 3.3.1), specific R packages used +are identified for each step in the description below. During feature +selection, descriptor with near zero variance were removed using +'*NearZeroVar*'-function (package 'caret'). If the percentage of the +most common value was more than 90% or when the frequency ratio of the +most common value to the second most common value was greater than 95:5 +(e.g. 95 instances of the most common value and only 5 or less instances +of the second most common value), a descriptor was classified as having +a near zero variance. After that, highly correlated descriptors were +removed using the '*findCorrelation*'-function (package 'caret') with a +cut-off of 0.9. This resulted in a training dataset with 516 +descriptors. These descriptors were scaled to be in the range between 0 +and 1 using the '*preProcess*'-function (package 'caret'). The scaling +routine was saved in order to apply the same scaling on the testing +dataset. As these three steps did not consider the outcome, it was +decided that they do not need to be included in the cross-validation of +the model. To further reduce the number of features, a LASSO (*least +absolute shrinkage and selection operator*) regression was performed +using the '*glmnet*'-function (package '*glmnet*'). The reduced dataset +was used for the generation of the pre-trained models. + +For the RF model, the '*randomForest*'-function (package +'*randomForest*') was used. A forest with 1000 trees with maximal +terminal nodes of 200 was grown for the prediction. + +The '*svm*'-function (package 'e1071') with a *radial basis function +kernel* was used for the SVM model. + +The DL model was generated using the '*h2o.deeplearning*'-function +(package '*h2o*'). The DL contained four hidden layer with 70, 50, 50, +and 10 neurons, respectively. Other hyperparameter were set as follows: +l1=1.0E-7, l2=1.0E-11, epsilon = 1.0E-10, rho = 0.8, and quantile\_alpha += 0.5. For all other hyperparameter, the default values were used. +Weights and biases were in a first step determined with an unsupervised +DL model. These values were then used for the actual, supervised DL +model. + +To validate these models, an internal cross-validation approach was +chosen. The training dataset was randomly split in training data, which +contained 95% of the data, and validation data, which contain 5% of the +data. A feature selection with LASSO on the training data was performed, +reducing the number of descriptors to approximately 100. This step was +repeated five times. Based on each of the five different training data, +the predictive models were trained and the performance tested with the +validation data. This step was repeated 10 times. Furthermore, a +y-randomisation using the RF model was performed. During +y-randomisation, the outcome (y-variable) is randomly permuted. The +theory is that after randomisation of the outcome, the model should not +be able to correlate the outcome to the properties (descriptor values) +of the substances. The performance of the model should therefore +indicate a by change prediction with an accuracy of about 50%. If this +is true, it can be concluded that correlation between actual outcome and +properties of the substances is real and not by chance [Rücker et al. +2007](#_ENREF_117)(). + +{width="6.26875in" +height="5.486111111111111in"} + +Figure 1: Flowchart of the generation and validation of the models +generated in R-project + +Deep Learning in TensorFlow +--------------------------- + +Alternatively, a DL model was established with Python-based TensorFlow +program (<https://www.tensorflow.org/>) using the high-level API Keras +(<https://www.tensorflow.org/guide/keras>) to build the models. + +Data pre-processing was done by rank transformation using the +'*QuantileTransformer*' procedure. A sequential model has been used. +Four layers have been used: input layer, two hidden layers (with 12, 8 +and 8 nodes, respectively) and one output layer. For the output layer, a +sigmoidal activation function and for all other layers the ReLU +('*Rectified Linear Unit*') activation function was used. Additionally, +a L^2^-penalty of 0.001 was used for the input layer. For training of +the model, the ADAM algorithm was used to minimise the cross-entropy +loss using the default parameters of Keras. Training was performed for +100 epochs with a batch size of 64. The model was implemented with +Python 3.6 and Keras. For training of the model, a 6-fold +cross-validation was used. Accuracy was estimated by ROC-AUC and +confusion matrix. + +Results +======= + +LAZAR +----- + +For 46 PAs, no prediction could be made. 26 PAs had no neighbours and 20 +PAs had only one neighbour. For additional 396 PAs, the similarity +threshold had to be reduced from 0.5 to 0.2 to obtain enough neighbours +for a prediction. This means that these substances might not be within +the applicability domain (AD). Therefore, only 160 of 602 PAs were well +within the stricter AD with the similarity threshold of 0.5 and 556 PAs +in the AD with the similarity threshold of 0.2. + +{width="5.905511811023622in" +height="3.868241469816273in"} + +Figure 2: Genotoxic potential of the different PA groups as predicted by +LAZAR, using the **similarity threshold** **of 0.5**. + +*Genotoxic*: percentage number of compounds per group, which were +predicted to be genotoxic.\ +*Not genotoxic*: percentage number of compounds per group, which were +predicted to be not genotoxic\ +*Outside AD*: percentage number of compounds per group, which were +outside the applicability domain (AD). + +{width="5.905511811023622in" +height="3.868241469816273in"} + +Figure 3: Genotoxic potential of the different PA groups as predicted by +LAZAR, using the **similarity threshold of 0.2** + +*Genotoxic*: percentage number of compounds per group, which were +predicted to be genotoxic.\ +*Not genotoxic*: percentage number of compounds per group, which were +predicted to be not genotoxic\ +*Outside AD*: percentage number of compounds per group, which were +outside the applicability domain (AD). + +Interestingly, using both similarity thresholds (e.g. 0.2 and 0.5), the +majority of PAs in all groups except otonecine, were predicted to be not +genotoxic. + +The following rank order for genotoxicity probability can be deduced +from the results of both similarity thresholds: + +- Necine base: platynecine ≤ retronecine \<\< otonecine + +- Necic acid: monoester \< diester \< macrocyclic diester + +- Modification of necine base: *N*-oxide \< DHP \< tertiary PA + +Random Forest, Support Vector Machines, and Deep Learning +--------------------------------------------------------- + +Applicability domain + +The AD of the training dataset and the PA dataset was evaluated using +the Jaccard distance. A Jaccard distance of '0' indicates that the +substances are similar, whereas a value of '1' shows that the substances +are different. The Jaccard distance was below 0.2 for all PAs relative +to the training dataset. Therefore, PA dataset is within the AD of the +training dataset and the models can be used to predict the genotoxic +potential of the PA dataset. + +y-randomisation + +After y-randomisation of the outcome, the accuracy and CCR are around +50%, indicating a chance in the distribution of the results. This shows, +that the outcome is actually related to the predictors and not by +chance. + +Random Forest + +The validation showed that the RF model has an accuracy of 64%, a +sensitivity of 66% and a specificity of 63%. The confusion matrix of the +model, calculated for 8080 instances, is provided in Table 1. + +Table 1: Confusion matrix of the RF model + + Predicted genotoxicity + ----------------------- ------------------------ ---------- ---------- ------------- + Measured genotoxicity ***PP*** ***PN*** ***Total*** + ***TP*** 2274 1163 3437 + ***TN*** 1736 2907 4643 + ***Total*** 4010 4070 8080 + +PP: Predicted positive; PN: Predicted negative, TP: True positive, TN: +True negative + +In general, the majority of PAs were considered to be not genotoxic by +the RF model (Figure 4). + +{width="6.063194444444444in" +height="3.8756944444444446in"} + +Figure 4: Genotoxic potential of the different PA groups as predicted by +**RF model** + +*Genotoxic*: percentage number of compounds per group, which was +predicted to be genotoxic.\ +*Not genotoxic*: percentage number of compounds per group, which was +predicted to be not genotoxic. + +From the results, the following rank orders of genotoxic potential could +be deduced: + +- Necine base: platynecine \< retronecine \< otonecine + +- Necic acid: monoester (= 0%) \< diester \< macrocyclic diester + +- Modification of necine base: *N*-oxide = dehydropyrrolizidine (0%) + \< tertiary PA + +Support Vector Machines + +The validation showed that the SVM model has an accuracy of 62%, a +sensitivity of 65% and a specificity of 60%. The confusion matrix of SVM +model, calculated for 8080 instances, is provided in Table 2. + +Table 2: Confusion matrix of the SVM model + + Predicted genotoxicity + ----------------------- ------------------------ ---------- ---------- ------------- + Measured genotoxicity ***PP*** ***PN*** ***Total*** + ***TP*** 2057 1107 3164 + ***TN*** 1953 2963 4916 + ***Total*** 4010 4070 8080 + +PP: Predicted positive; PN: Predicted negative, TP: True positive, TN: +True negative + +In the SVM model, also the majority of PAs were considered to be not +genotoxic (Figure 5). + +{width="6.063194444444444in" +height="3.9694444444444446in"} + +Figure 5: Genotoxic potential of the different PA groups as predicted by +**SVM model** + +*Genotoxic*: percentage number of compounds per group, which was +predicted to be genotoxic.\ +*Not genotoxic*: percentage number of compounds per group, which was +predicted to be not genotoxic + +From the results, the following rank orders of genotoxic potential could +be deduced: + +- Necine base: otonecine \< platynecine = retronecine + +- Necic acid: macrocyclic diester \< monoester = diester + +- Modification of necine base: dehydropyrrolizidine \< tertiary + PA \< *N*-oxide + +Deep Learning (R-project) + +The validation showed that the DL model generated in R has an accuracy +of 59%, a sensitivity of 89% and a specificity of 30%. The confusion +matrix of the model, normalised to 8080 instances, is provided in Table +3. + +Table 3: Confusion matrix of the DL model (R-project) + + Predicted genotoxicity + ----------------------- ------------------------ ---------- ---------- ------------- + Measured genotoxicity ***PP*** ***PN*** ***Total*** + ***TP*** 3575 435 4010 + ***TN*** 2853 1217 4070 + ***Total*** 6428 1652 8080 + +PP: Predicted positive; PN: Predicted negative, TP: True positive, TN: +True negative + +In contrast, the majority of PAs were considered to be genotoxic by the +DL model in R (Figure 6). + +{width="6.063194444444444in" +height="3.982638888888889in"} + +Figure 6: Genotoxic potential of the different PA groups as predicted by +**DL model (R-project)** + +*Genotoxic*: percentage number of compounds per group, which was +predicted to be genotoxic.\ +*Not genotoxic*: percentage number of compounds per group, which was +predicted to be not genotoxic + +From the results, the following rank orders of genotoxic potential could +be proposed: + +- Necine base: platynecine \< retronecine \< otonecine + +- Necic acid: monoester \< diester \< macrocyclic diester + +- Modification of necine base: tertiary PA = dehydropyrrolizidine \< + *N*-oxide. + +DL model (TensorFlow) + +The validation showed that the DL model generated in TensorFlow has an +accuracy of 68%, a sensitivity of 70% and a specificity of 46%. The +confusion matrix of the model, normalised to 8080 instances, is provided +in Table 4. + +Table 4: Confusion matrix of the DL model (TensorFlow) + + Predicted genotoxicity + ----------------------- ------------------------ ---------- ---------- ------------- + Measured genotoxicity ***PP*** ***PN*** ***Total*** + ***TP*** 2851 1227 4078 + ***TN*** 1825 2177 4002 + ***Total*** 4676 3404 8080 + +PP: Predicted positive; PN: Predicted negative, TP: True positive, TN: +True negative + +The ROC curves from the 6-fold validation are shown in Figure 7. + +{width="3.825in" +height="2.7327045056867894in"} + +Figure 7: Six-fold cross-validation of TensorFlow DL model show an +average area under the ROC-curve (ROC-AUC; measure of accuracy) of 68%. + +In contrast to the DL generated in R, the DL model generated in +TensorFlow predicted the majority of PAs as not genotoxic. + +{width="6.26875in" +height="3.6993055555555556in"} + +Figure 8: Genotoxic potential of the different PA groups as predicted by +**DL model (TensorFlow)** + +*Genotoxic*: percentage number of compounds per group, which was +predicted to be genotoxic.\ +*Not genotoxic*: percentage number of compounds per group, which was +predicted to be not genotoxic + +The following rank orders of genotoxic potential could be proposed based +on the results: + +- Necine base: platynecine \< otonecine \< retronecine + +- Necic acid: monoester \< diester \< macrocyclic diester + +- Modification of necine base: tertiary PA \< *N*-oxide \<\< + dehydropyrrolizidine. + +In summary, the validation results of the four methods are presented in +the following table. + +Table 5 Results of the cross-validation of the four models and after +y-randomisation + + ---------------------------------------------------------------------- + Accuracy CCR Sensitivity Specificity + ----------------------- ---------- ------- ------------- ------------- + RF model 64.1% 64.4% 66.2% 62.6% + + SVM model 62.1% 62.6% 65.0% 60.3% + + DL model\ 59.3% 59.5% 89.2% 29.9% + (R-project) + + DL model (TensorFlow) 68% 62.2% 69.9% 45.6% + + y-randomisation 50.5% 50.4% 50.3% 50.6% + ---------------------------------------------------------------------- + +CCR (correct classification rate) + +Discussion +========== + +General model performance + +Based on the results of the cross-validation for all models, LAZAR, RF, +SVM, DL (R-project) and DL (TensorFlow) it can be state that the +prediction results are not optimal due to different reasons. The +accuracy as measured during cross-validation of the four models (RF, +SVM, DL (R-project and TensorFlow)) was partly low with CCR values +between 59.3 and 68%, with the R-generated DL model and the +TensorFlow-generated DL model showing the worst and the best +performance, respectively. The validation of the R-generated DL model +revealed a high sensitivity (89.2%) but an unacceptably low specificity +of 29.9% indicating a high number of false positive estimates. The +TensorFlow-generated DL model, however, showed an acceptable but not +optimal accuracy of 68%, a sensitivity of 69.9% and a specificity of +45.6%. The low specificity indicates that both DL models tends to +predict too many instances as positive (genotoxic), and therefore have a +high false positive rate. This allows at least with the TensorFlow +generated DL model to make group statements, but the confidence for +estimations of single PAs appears to be insufficiently low. + +Several factors have likely contributed to the low to moderate +performance of the used methods as shown during the cross-validation: + +1. The outcome in the training dataset was based on the results of AMES + tests for genotoxicity [ICH 2011](#_ENREF_63)(), an *in vitro* test + in different strains of the bacteria *Salmonella typhimurium*. In + this test, mutagenicity is evaluated with and without prior + metabolic activation of the test substance. Metabolic activation + could result in the formation of genotoxic metabolites from + non-genotoxic parent compounds. However, no distinction was made in + the training dataset between substances that needed metabolic + activation before being mutagenic and those that were mutagenic + without metabolic activation. LAZAR is able to handle this + 'inaccuracy' in the training dataset well due to the way the + algorithm works: LAZAR predicts the genotoxic potential based on the + neighbours of substances with comparable structural features, + considering mutagenic and not mutagenic neighbours. Based on the + structural similarity, a probability for mutagenicity and no + mutagenicity is calculated independently from each other (meaning + that the sum of probabilities does not necessarily adds up to 100%). + The class with the higher outcome is then the overall outcome for + the substance. + +> In contrast, the other models need to be trained first to recognise +> the structural features that are responsible for genotoxicity. +> Therefore, the mixture of substances being mutagenic with and without +> metabolic activation in the training dataset may have adversely +> affected the ability to separate the dataset in two distinct classes +> and thus explains the relatively low performance of these models. + +2. Machine learning algorithms try to find an optimized solution in a + high-dimensional (one dimension per each predictor) space. Sometimes + these methods do not find the global optimum of estimates but only + local (not optimal) solutions. Strategies to find the global + solutions are systematic variation (grid search) of the + hyperparameters of the methods, which may be very time consuming in + particular in large datasets. + +Mutagenicity of PAs + +Due to the low to moderate predictivity of all models, quantitative +statement on the genotoxicity of single PAs cannot be made with +sufficient confidence. + +The predictions of the SVM model did not fit with the other models or +literature, and are therefore not further considered in the discussion. + +Necic acid + +The rank order of the necic acid is comparable in the four models +considered (LAZAR, RF and DL (R-project and TensorFlow). PAs from the +monoester type had the lowest genotoxic potential, followed by PAs from +the open-ring diester type. PAs with macrocyclic diesters had the +highest genotoxic potential. The result fit well with current state of +knowledge: in general, PAs, which have a macrocyclic diesters as necic +acid, are considered more toxic than those with an open-ring diester or +monoester [EFSA 2011](#_ENREF_36)[Fu et al. 2004](#_ENREF_45)[Ruan et +al. 2014b](#_ENREF_115)(; ; ). + +Necine base + +The rank order of necine base is comparable in LAZAR, RF, and DL +(R-project) models: with platynecine being less or as genotoxic as +retronecine, and otonecine being the most genotoxic. In the +TensorFlow-generate DL model, platynecine also has the lowest genotoxic +probability, but are then followed by the otonecines and last by +retronecine. These results partly correspond to earlier published +studies. Saturated PAs of the platynecine-type are generally accepted to +be less or non-toxic and have been shown in *in vitro* experiments to +form no DNA-adducts [Xia et al. 2013](#_ENREF_139)(). Therefore, it is +striking, that 1,2-unsaturated PAs of the retronecine-type should have +an almost comparable genotoxic potential in the LAZAR and DL (R-project) +model. In literature, otonecine-type PAs were shown to be more toxic +than those of the retronecine-type [Li et al. 2013](#_ENREF_80)(). + +Modifications of necine base + +The group-specific results of the TensorFlow-generated DL model appear +to reflect the expected relationship between the groups: the low +genotoxic potential of *N*-oxides and the highest potential of +dehydropyrrolizidines [Chen et al. 2010](#_ENREF_26)(). + +In the LAZAR model, the genotoxic potential of dehydropyrrolizidines +(DHP) (using the extended AD) is comparable to that of tertiary PAs. +Since, DHP is regarded as the toxic principle in the metabolism of PAs, +and known to produce protein- and DNA-adducts [Chen et al. +2010](#_ENREF_26)(), the LAZAR model did not meet this expectation it +predicted the majority of DHP as being not genotoxic. However, the +following issues need to be considered. On the one hand, all DHP were +outside of the stricter AD of 0.5. This indicates that in general, there +might be a problem with the AD. In addition, DHP has two unsaturated +double bounds in its necine base, making it highly reactive. DHP and +other comparable molecules have a very short lifespan, and usually +cannot be used in *in vitro* experiments. This might explain the absence +of suitable neighbours in LAZAR. + +Furthermore, the probabilities for this substance groups needs to be +considered, and not only the consolidated prediction. In the LAZAR +model, all DHPs had probabilities for both outcomes (genotoxic and not +genotoxic) mainly below 30%. Additionally, the probabilities for both +outcomes were close together, often within 10% of each other. The fact +that for both outcomes, the probabilities were low and close together, +indicates a lower confidence in the prediction of the model for DHPs. + +In the DL (R-project) and RF model, *N*-oxides have a by far more +genotoxic potential that tertiary PAs or dehydropyrrolizidines. As PA +*N*-oxides are easily conjugated for extraction, they are generally +considered as detoxification products, which are *in vivo* quickly +renally eliminated [Chen et al. 2010](#_ENREF_26)(). On the other hand, +*N*-oxides can be also back-transformed to the corresponding tertiary PA +[Wang et al. 2005](#_ENREF_134)(). Therefore, it may be questioned, +whether *N*-oxides themselves are generally less genotoxic than the +corresponding tertiary PAs. However, in the groups of modification of +the necine base, dehydropyrrolizidine, the toxic principle of PAs, +should have had the highest genotoxic potential. Taken together, the +predictions of the modifications of the necine base from the LAZAR, RF +and R-generated DL model cannot -- in contrast to the TensorFlow DL +model - be considered as reliable. + +Overall, when comparing the prediction results of the PAs to current +published knowledge, it can be concluded that the performance of most +models was low to moderate. This might be contributed to the following +issues: + +1. In the LAZAR model, only 26.6% PAs were within the stricter AD. With + the extended AD, 92.3% of the PAs could be included in the + prediction. Even though the Jaccard distance between the training + dataset and the PA dataset for the RF, SVM, and DL (R-project and + TensorFlow) models was small, suggesting a high similarity, the + LAZAR indicated that PAs have only few local neighbours, which might + adversely affect the prediction of the mutagenic potential of PAs. + +2. All above-mentioned models were used to predict the mutagenicity of + PAs. PAs are generally considered to be genotoxic, and the mode of + action is also known. Therefore, the fact that some models predict + the majority of PAs as not genotoxic seems contradictory. To + understand this result, the basis, the training dataset, has to be + considered. The mutagenicity of in the training dataset are based on + data of mutagenicity in bacteria. There are some studies, which show + mutagenicity of PAs in the AMES test [Chen et al. + 2010](#_ENREF_26)(). Also, [Rubiolo et al. (1992)](#_ENREF_116) + examined several different PAs and several different extracts of + PA-containing plants in the AMES test. They found that the AMES test + was indeed able to detect mutagenicity of PAs, but in general, + appeared to have a low sensitivity. The pre-incubation phase for + metabolic activation of PAs by microsomal enzymes was the + sensitivity-limiting step. This could very well mean that this is + also reflected in the QSAR models. + +Conclusions +=========== + +In this study, an attempt was made to predict the genotoxic potential of +PAs using five different machine learning techniques (LAZAR, RF, SVM, DL +(R-project and TensorFlow). The results of all models fitted only partly +to the findings in literature, with best results obtained with the +TensorFlow DL model. Therefore, modelling allows statements on the +relative risks of genotoxicity of the different PA groups. Individual +predictions for selective PAs appear, however, not reliable on the +current basis of the used training dataset. + +This study emphasises the importance of critical assessment of +predictions by QSAR models. This includes not only extensive literature +research to assess the plausibility of the predictions, but also a good +knowledge of the metabolism of the test substances and understanding for +possible mechanisms of toxicity. + +In further studies, additional machine learning techniques or a modified +(extended) training dataset should be used for an additional attempt to +predict the genotoxic potential of PAs. + +References +========== + +[]{#_ENREF_4 .anchor} + +[]{#_ENREF_8 .anchor} + +[]{#_ENREF_17 .anchor} + +[]{#_ENREF_18 .anchor} + +[]{#_ENREF_20 .anchor} + +[]{#_ENREF_26 .anchor} + +[]{#_ENREF_33 .anchor} + +[]{#_ENREF_36 .anchor} + +[]{#_ENREF_38 .anchor} + +[]{#_ENREF_39 .anchor} + +[]{#_ENREF_45 .anchor} + +[]{#_ENREF_48 .anchor} + +[]{#_ENREF_57 .anchor} + +[]{#_ENREF_59 .anchor} + +[]{#_ENREF_63 .anchor} + +[]{#_ENREF_65 .anchor} + +[]{#_ENREF_71 .anchor} + +[]{#_ENREF_76 .anchor} + +[]{#_ENREF_78 .anchor} + +[]{#_ENREF_80 .anchor} + +[]{#_ENREF_82 .anchor} + +[]{#_ENREF_99 .anchor} + +[]{#_ENREF_104 .anchor} + +<https://openbabel.org/docs/dev/Fingerprints/intro.html> + +[]{#_ENREF_115 .anchor} + +[]{#_ENREF_116 .anchor} + +[]{#_ENREF_117 .anchor} + +[]{#_ENREF_119 .anchor} + +[]{#_ENREF_126 .anchor} + +[]{#_ENREF_134 .anchor} + +[]{#_ENREF_138 .anchor} + +[]{#_ENREF_139 .anchor} + +[]{#_ENREF_140 .anchor} + +[]{#_ENREF_142 .anchor} + +[]{#_ENREF_143 +.anchor}<http://www.yapcwsoft.com/dd/padeldescriptor/Descriptors.xls> + +[]{#_ENREF_148 .anchor} + +Aguer C, Gambarotta D, Mailloux RJ, Moffat C, Dent R, et al. 2011. +Galactose enhances oxidative metabolism and reveals mitochondrial +dysfunction in human primary muscle cells. 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