From 7bbe4c444523f281d07f79aa8d0a4719668c3c80 Mon Sep 17 00:00:00 2001 From: Christoph Helma Date: Sat, 20 Mar 2021 00:14:10 +0100 Subject: manuscript update --- mutagenicity.md | 609 +++++++++++++++++++------------------------------------- 1 file changed, 203 insertions(+), 406 deletions(-) (limited to 'mutagenicity.md') diff --git a/mutagenicity.md b/mutagenicity.md index 3939d31..5a01ee9 100644 --- a/mutagenicity.md +++ b/mutagenicity.md @@ -1,5 +1,5 @@ --- -title: A comparison of nine machine learning models based on an expanded mutagenicity dataset and their application for predicting pyrrolizidine alkaloid mutagenicity +title: A comparison of nine machine learning mutagenicity models and their application for predicting pyrrolizidine alkaloids author: - Christoph Helma: @@ -8,23 +8,26 @@ author: correspondence: "yes" - Verena Schöning: institute: insel + - Jürgen Drewe: + institute: zeller, unibas - Philipp Boss: institute: sysbio - - Jürgen Drewe: - institute: zeller institute: - ist: name: in silico toxicology gmbh address: "Rastatterstrasse 41, 4057 Basel, Switzerland" - zeller: - name: Zeller AG + name: Max Zeller Söhne AG address: "Seeblickstrasse 4, 8590 Romanshorn, Switzerland" - sysbio: name: Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association address: "Robert-Rössle-Strasse 10, Berlin, 13125, Germany" + - unibas: + name: Clinical Pharmacology, Department of Pharmaceutical Sciences, University Hospital Basel, University of Basel + address: "Petersgraben 4, 4031 Basel, Switzerland" - insel: - name: Clinical Pharmacology and Toxicology, Department of General Internal Medicine, Bern University Hospital, University of Bern + name: Clinical Pharmacology and Toxicology, Department of General Internal Medicine, University Hospital Bern, University of Bern address: "Inselspital, 3010 Bern, Switzerland" bibliography: bibliography.bib @@ -44,16 +47,13 @@ Abstract Random forest, support vector machine, logistic regression, neural networks and k-nearest neighbor (`lazar`) algorithms, were applied to new *Salmonella* -mutagenicity dataset with {{cv.n_uniq}} unique chemical structures. - - -**TODO**: PA results +mutagenicity dataset with {{cv.n_uniq}} unique chemical structures utilizing +MolPrint2D and Chemistry Development Kit (CDK) descriptors. Crossvalidation +accuracies of all investigated models ranged from 80-85% which is comparable +with the interlaboratory variability of the *Salmonella* mutagenicity assay. +Pyrrolizidine alkaloid predictions showed a clear distinction between chemical +groups, where Otonecines had the highest proportion of positive mutagenicity +predictions and Monoester the lowest. Introduction ============ @@ -154,13 +154,13 @@ without further processing. To achieve consistency with 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 {{cv.n_uniq}} unique -structures and {{cv.n}} individual measurements. +Dataset merges were based on unique SMILES (*Simplified Molecular Input Line +Entry Specification*, @Weininger1989) 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 {{cv.n_uniq}} unique structures and {{cv.n}} +individual measurements. Source code for all data download, extraction and merge operations is publicly available from the git repository @@ -215,10 +215,10 @@ basically the chemical concept of functional groups. In contrast to predefined lists of fragments (e.g. FP3, FP4 or MACCs fingerprints) or descriptors (e.g CDK) they are generated dynamically from -chemical structures. This has the advantage that they can capture unknown substructures -of toxicological relevance that are not included in other descriptors. In addition they -allow the efficient calculation of -chemical similarities (e.g. Tanimoto indices) with simple set operations. +chemical structures. This has the advantage that they can capture unknown +substructures of toxicological relevance that are not included in other +descriptors. In addition they allow the efficient calculation of chemical +similarities (e.g. Tanimoto indices) with simple set operations. MolPrint2D fingerprints were calculated with the OpenBabel cheminformatics library (@OBoyle2011a). They can be obtained from the following locations: @@ -233,13 +233,6 @@ library (@OBoyle2011a). They can be obtained from the following locations: - sparse representation () - descriptor matrix () - - #### Chemistry Development Kit (*CDK*) descriptors Molecular 1D and 2D descriptors were calculated with the PaDEL-Descriptors @@ -259,28 +252,6 @@ The same procedure was applied for the pyrrolizidine dataset yielding {{pa.cdk.n_descriptors}} descriptors for {{pa.cdk.n_compounds}} compounds. CDK features for pyrrolizidine alkaloids are available at . - - Algorithms ---------- @@ -308,6 +279,14 @@ QSAR (*Quantitative structure--activity relationship*) modelling. Algorithms used within this study are described in the following sections. +#### Feature preprocessing + +MolPrint2D features were used without preprocessing. Near zero variance and +strongly correlated CDK descriptors were removed and the remaining descriptor +values were centered and scaled. Preprocessing was performed with the R `caret` +preProcess function using the methods "nzv","corr","center" and "scale" with +default settings. + #### Neighbour identification Utilizing this modularity, similarity calculations were based both on @@ -332,17 +311,18 @@ 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. This are predictions with *high confidence*. +- First a similarity threshold of 0.5 (MP2D/Tanimoto) or 0.9 (CDK/Cosine) is + used to collect neighbours, to create a local QSAR model and to make a + prediction for the query compound. This are predictions with *high + confidence*. -- 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 (*low confidence*). +- If any of these steps fails, the procedure is repeated with a similarity + threshold of 0.2 (MP2D/Tanimoto) or 0.7 (CDK/Cosine) and the prediction is + flagged with a warning that it might be out of the applicability domain of + the training data (*low confidence*). -- Similarity thresholds of 0.5 and 0.2 are the default values chosen - by the software developers and remained unchanged during the +- These Similarity thresholds are the default values chosen + by software developers and remained unchanged during the course of these experiments. Compounds with the same structure as the query structure are @@ -377,6 +357,17 @@ as more distant from the applicability domain (*low confidence*). Quantitative applicability domain information can be obtained from the similarities of individual neighbours. +#### Validation + +10-fold cross validation was performed for model evaluation. + +#### Pyrrolizidine alkaloid predictions + +For the prediction of pyrrolizidine alkaloids models were generated with the +MP2D and CDK training datasets. The complete feature set was used for MP2D +predictions, for CDK predictions the intersection between training and +pyrrolizidine alkaloid features was used. + #### Availability - Source code for this manuscript (GPL3): @@ -391,107 +382,56 @@ individual neighbours. - Public web interface: - - ### Tensorflow models -**TODO**: **Philipp** Kannst Du bitte die folgenden Absaetze ergaenzen und die Vorgangsweise fuer MP2D/CDK bzw CV/PA Vorhersagen beschreiben. +#### Feature Preprocessing - +For preprocessing of the CDK features we used a quantile transformation +to a uniform distribution. MP2D features were not preprocessed. #### Random forests (*RF*) +For the random forest classifier we used the parameters +n_estimators=1000and max_leaf_nodes=200. For the other parameters we +used the scikit-learn default values. + #### Logistic regression (SGD) (*LR-sgd*) +For the logistic regression we used an ensemble of five trained models. +For each model we used a batch size of 64 and trained for 50 epoch. As +an optimizer ADAM was chosen. For the other parameters we used the +tensorflow default values. + #### Logistic regression (scikit) (*LR-scikit*) +For the logistic regression we used as parameters the scikit-learn +default values. + #### Neural Nets (*NN*) +For the neural network we used an ensemble of five trained models. For +each model we used a batch size of 64 and trained for 50 epoch. As an +optimizer ADAM was chosen. The neural network had 4 hidden layers with +64 nodes each and a ReLu activation function. For the other parameters +we used the tensorflow default values. + #### Support vector machines (*SVM*) -Validation ----------- +We used the SVM implemented in scikit-learn. We used the parameters +kernel='rbf', gamma='scale'. For the other parameters we used the +scikit-learn default values. + +#### Validation 10-fold cross-validation was used for all Tensorflow models. +#### Pyrrolizidine alkaloid predictions + +For the prediction of pyrrolizidine alkaloids we trained the model described above on the +training data. For training and prediction only the features were used +that were in the intersection of features from the training data and the +pyrrolizidine alkaloids. + #### Availability Jupyter notebooks for these experiments can be found at the following locations @@ -548,32 +488,22 @@ https://git.in-silico.ch/mutagenicity-paper/tree/crossvalidations/confusion-matr individual predictions can be found in https://git.in-silico.ch/mutagenicity-paper/tree/crossvalidations/predictions/. -With exception of lazar/CDK all investigated algorithm/descriptor combinations +All investigated algorithm/descriptor combinations give accuracies between (80 and 85%) which is equivalent to the experimental variability of the *Salmonella typhimurium* mutagenicity bioassay (80-85%, @Benigni1988). Sensitivities and specificities are balanced in all of these models. - - Pyrrolizidine alkaloid mutagenicity predictions ----------------------------------------------- -Mutagenicity predictions from all investigated models for {{pa.n}} -pyrrolizidine alkaloids (PAs) can be downloaded from +Mutagenicity predictions of {{pa.n}} pyrrolizidine alkaloids (PAs) from all +investigated models can be downloaded from . A visual representation of all PA predictions can be found at . + -@fig:dhp - @fig:tert display the proportion of positive mutagenicity predictions from all models for the different pyrrolizidine alkaloid groups. +@fig:pa-groups displays the proportion of positive mutagenicity predictions +from all models for the different pyrrolizidine alkaloid groups. Tensorflow +models predicted all {{pa.n}} pyrrolizidine alkaloids, `lazar` MP2D models +predicted {{pa.mp2d_lazar_all.n}} compounds +({{pa.mp2d_lazar_high_confidence.n}} with high confidence) and `lazar` CDK +models {{pa.cdk_lazar_all.n}} compounds ({{pa.cdk_lazar_high_confidence.n}} +with high confidence). -![Summary of Dehydropyrrolizidine predictions](figures/Dehydropyrrolizidine.png){#fig:dhp} +![Summary of pyrrolizidine alkaloid predictions](figures/pa-groups.png){#fig:pa-groups} + For the visualisation of the position of pyrrolizidine alkaloids in respect to the training data set we have applied t-distributed stochastic neighbor -embedding (t-SNE, @Maaten2008) for MolPrint2D and CDK descriptors. t-SNE -maps each high-dimensional object (chemical) to a two-dimensional point, -maintaining the high-dimensional distances of the objects. Similar objects are -represented by nearby points and dissimilar objects are represented by distant -points. +embedding (t-SNE, @Maaten2008) for MolPrint2D and CDK descriptors. t-SNE maps +each high-dimensional object (chemical) to a two-dimensional point, maintaining +the high-dimensional distances of the objects. Similar objects are represented +by nearby points and dissimilar objects are represented by distant points. +t-SNE coordinates were calculated with the R `Rtsne` package using the default +settings (perplexity = 30, theta = 0.5, max_iter = 1000). + +@fig:tsne-mp2d shows the t-SNE of pyrrolizidine alkaloids (PA) and the +mutagenicity training data in MP2D space (Tanimoto/Jaccard similarity), which +resembles basically the structural diversity of the investigated compounds. + +![t-SNE visualisation of mutagenicity training data and pyrrolizidine alkaloids (PA) in MP2D space](figures/tsne-mp2d-mutagenicity.png){#fig:tsne-mp2d} + +@fig:tsne-cdk shows the t-SNE of pyrrolizidine alkaloids (PA) and the +mutagenicity training data in CDK space (Euclidean similarity), which resembles +basically the physical-chemical properties of the investigated compounds. + +![t-SNE visualisation of mutagenicity training data and pyrrolizidine alkaloids (PA) in CDK space](figures/tsne-cdk-mutagenicity.png){#fig:tsne-cdk} -@fig:tsne-mp2d shows the t-SNE of pyrrolizidine alkaloids (PA) and the mutagenicity training data in MP2D space (Tanimoto/Jaccard similarity). +@fig:tsne-mp2d-rf and @fig:tsne-cdk-lazar-all depict two example pyrrolizidine alkaloid +mutagenicity predictions in the context of training data. t-SNE visualisations of all investigated models can be downloaded from . -![t-SNE visualisation of mutagenicity training data and pyrrolizidine alkaloids (PA)](figures/tsne-mp2d-mutagenicity.png){#fig:tsne-mp2d} + -@fig:tsne-cdk shows the t-SNE of pyrrolizidine alkaloids (PA) and the mutagenicity training data in CDK space (Euclidean similarity). +![t-SNE visualisation of MP2D random forest predictions](figures/tsne-mp2d-rf-classifications.png){#fig:tsne-mp2d-rf} + + + +![t-SNE visualisation of all CDK lazar predictions](figures/tsne-cdk-lazar-all-classifications.png){#fig:tsne-cdk-lazar-all} + + Discussion ========== @@ -657,63 +637,39 @@ Algorithms structures for a given compound and calculates the prediction based on the experimental data for these structures. The QSAR literature calls such models frequently *local models*, because models are generated specifically for each -query compound. The investigated tensorflow models are in contrast *global models*, i.e. a -single model is used to make predictions for all compounds. It has been -postulated in the past, that local models are more accurate, because they can -account better for mechanisms, that affect only a subset of the training data. - -@tbl:cv-mp2d, @tbl:cv-cdk and @fig:roc show that all models with the exception -of lazar-CDK have similar crossvalidation accuracies that are comparable to the -experimental variability of the *Salmonella typhimurium* mutagenicity bioassay -(80-85% according to @Benigni1988). All of these models have balanced -sensitivity (true position rate) and specificity (true negative rate) and -provide highly significant concordance with experimental data (as determined by -McNemar's Test). This is a clear indication that *in-silico* predictions can be -as reliable as the bioassays. Given that the variability of experimental data -is similar to model variability it is impossible to decide which model gives -the most accurate predictions, as models with higher accuracies (e.g. NN-CDK) -might just approximate experimental errors better than more robust models. - -`lazar` predictions with CDK descriptors are a notable exception, as it has a -much lower overall accuracy ({{lazar_all_cdk.acc}}) than all other models. -`lazar` uses basically a k-nearest-neighbor (with variable k) and it seems that -CDK descriptors are not very well suited for chemical similarity calculations. -We have confirmed this independently by validating k-nn models from the `R -caret` package, which give also sub-par accuracies (data not shown). - -@fig:tsne-cdk is another indication that similarity calculations with CDK -descriptors are not as useful as fingerprint based similarities, because it -shows a less clearer separation between chemical classes and -mutagens/non-mutagens than @fig:tsne-mp2d. It seems that more complex models -than simple k-nn are required to utilize CDK descriptors efficiently. +query compound. The investigated tensorflow models are in contrast *global +models*, i.e. a single model is used to make predictions for all compounds. It +has been postulated in the past, that local models are more accurate, because +they can account better for mechanisms, that affect only a subset of the +training data. + +@tbl:cv-mp2d, @tbl:cv-cdk and @fig:roc show that the crossvalidation accuracies +of all models are comparable to the experimental variability of the *Salmonella +typhimurium* mutagenicity bioassay (80-85% according to @Benigni1988). All of +these models have balanced sensitivity (true position rate) and specificity +(true negative rate) and provide highly significant concordance with +experimental data (as determined by McNemar's Test). This is a clear indication +that *in-silico* predictions can be as reliable as the bioassays. Given that +the variability of experimental data is similar to model variability it is +impossible to decide which model gives the most accurate predictions, as models +with higher accuracies might just approximate experimental errors better than +more robust models. Our results do not support the assumption that local models are superior to global models for classification purposes. For regression models (lowest observed effect level) we have found however that local models may outperform global models (@Helma2018) with accuracies similar to experimental variability. - +As all investigated algorithms give similar accuracies the selection will +depend more on practical considerations than on intrinsic properties. Nearest +neighbor algorithms like `lazar` have the practical advantage that the +rationales for individual predictions can be presented in a straightforward +manner that is understandable without a background in statistics or machine +learning (@fig:lazar). This allows a critical examination of individual +predictions and prevents blind trust in models that are intransparent to users +with a toxicological background. + +![Lazar screenshot of 12,21-Dihydroxy-4-methyl-4,8-secosenecinonan-8,11,16-trione mutagenicity prediction](figures/lazar-screenshot.png){#fig:lazar} Descriptors ----------- @@ -728,31 +684,15 @@ descriptors are used to determine chemical similarities in the default `lazar` settings, and previous experiments have shown, that they give more accurate results than predefined fingerprints (e.g. MACCS, FP2-4). - - *Chemistry Development Kit* (CDK, @Willighagen2017) descriptors were calculated with the PaDEL graphical interface (@Yap2011). They include 1D and 2D topological descriptors as well as physical-chemical properties. -With exception of `lazar` all investigated algorithms obtained models within -the experimental variability for both types of descriptors. As discussed before -CDK descriptors seem to be less suitable for chemical similarity calculations -than MolPrint2D descriptors. +All investigated algorithms obtained models within the experimental variability +for both types of descriptors (@tbl:cv-mp2d, @tbl:cv-cdk, @fig:roc). Given that similar predictive accuracies are obtainable from both types of -descriptors the choice depends more on practical considerations: +descriptors the choice depends once more on practical considerations: MolPrint2D fragments can be calculated very efficiently for every well defined chemical structure with OpenBabel (@OBoyle2011a). CDK descriptor calculations @@ -771,43 +711,12 @@ efficient. Due to the large number of substructures present in training compounds, they lead however to large and sparsely populated datasets, if they have to be expanded to a binary matrix (e.g. as input for tensorflow models). CDK descriptors contain in contrast in every case matrices with -{{cv.cdk.n_descriptors}} columns. - - +{{cv.cdk.n_descriptors}} columns which can cause substantial computational overhead. Pyrrolizidine alkaloid mutagenicity predictions ----------------------------------------------- -@fig:dhp - @fig:tert show a clear differentiation between the different +@fig:pa-groups shows a clear differentiation between the different pyrrolizidine alkaloid groups. The largest proportion of mutagenic predictions was observed for Otonecines {{pa.groups.Otonecine.mut_perc}}% ({{pa.groups.Otonecine.mut}}/{{pa.groups.Otonecine.n_pred}}), the lowest for @@ -821,24 +730,26 @@ specificities in crossvalidation experiments some of the models (MPD-RF, CDK-RF and CDK-SVM) predict a lower number of mutagens ({{pa.cdk_rf.mut_perc}}-{{pa.mp2d_rf.mut_perc}}%) than the majority of the models ({{pa.mp2d_svm.mut_perc}}-{{pa.mp2d_lazar_high_confidence.mut_perc}}% -@tbl:pa-mp2d, @tbl:pa-cdk, @fig:dhp - @fig:tert). +(@fig:pa-groups). lazar-CDK on the other hand +predicts the largest number of mutagens for all groups with exception of +Otonecines. -From a practical point we still have to face the question, how to choose model predictions, if no experimental data is available (we found two PAs in the training data, but this number is too low, to draw any general conclusions). +These differences between predictions from different algorithms and descriptors +were not expected based on crossvalidation results. - -**TODO**: **Verena** Hier ist ein alter Text von Dir zum Recylen: - -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 =========== -- cgit v1.2.3