bibliography updated, text corrections

5 files changed, 223 insertions, 71 deletions

diff --git a/Makefile b/Makefile
index 8025943..8b30f57 100644
--- a/Makefile
+++ b/Makefile
@@ -73,7 +73,7 @@ DATA = data.yaml
 all: mutagenicity.pdf $(PA_DIR)pa-predictions.pdf $(CV_PREDICTIONS) $(CONFUSION_MATRICES) $(PA_PREDICTIONS)
 include $(PANDOC_SCHOLAR_PATH)/Makefile
 
-mutagenicity.mustache.md: $(DATA) mutagenicity.md $(FIGURES)
+mutagenicity.mustache.md: $(DATA) mutagenicity.md $(FIGURES) bibliography.bib
 	mustache $^ > $@
 
 # manuscript data
diff --git a/bibliography.bib b/bibliography.bib
index ca5c3da..0fea5e0 100644
--- a/bibliography.bib
+++ b/bibliography.bib
@@ -1,6 +1,104 @@
+@incollection{Piegorsch1991,
+  author = {Piegorsch, W.W. and Zeiger, E.},
+  booktitle = {Statistical Methods in Toxicology, Lecture Notes in Medical Informatics},
+  editor = {Hotorn, L.},
+  publisher = {Springer-Verlag},
+  year = 1991,
+  title = {Measuring intra-assay agreement for the Ames salmonella assay},
+  pages = {35–41},
+}
+
+@article{Dunkel1984,
+author = {Dunkel, Virginia C. and Zeiger, Errol and Brusick, David and McCoy, Elena and McGregor, Douglas and Mortelmans, Kristien and Rosenkranz, Herbert S. and Simmon, Vincent F.},
+title = {Reproducibility of microbial mutagenicity assays: I. Tests with Salmonella typhimurium and Escherichia coli using a standardized protocol},
+journal = {Environmental Mutagenesis},
+volume = {6},
+number = {S2},
+pages = {1-50},
+keywords = {mutagenicity, Salmonella, E. coli, interlaboratory reproducibility, collaborative study, standardized protocol},
+doi = {https://doi.org/10.1002/em.2860060702},
+url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/em.2860060702},
+eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/em.2860060702},
+year = {1984}
+}
+
+
+
+@article{Kamber2009,
+    author = {Kamber, Markus and Flückiger-Isler, Sini and Engelhardt, Günter and Jaeckh, Rudolf and Zeiger, Errol},
+    title = "{Comparison of the Ames II and traditional Ames test responses with respect to mutagenicity, strain specificities, need for metabolism and correlation with rodent carcinogenicity}",
+    journal = {Mutagenesis},
+    volume = {24},
+    number = {4},
+    pages = {359-366},
+    year = {2009},
+    month = {05},
+    abstract = "{The Ames II Salmonella mutagenicity assay procedure was used to test 71 chemicals, and the results were compared with those from the traditional Ames Salmonella test using the NTP database as the reference. All Ames II tests were performed using a fluctuation procedure in microplate format, using TAMix for the detection of base pair substitutions and TA98 to detect frameshift mutations. There was 84\\% agreement between the two procedures in identifying mutagens and non-mutagens, which is equivalent to the intra- and interlaboratory reproducibility of 87\\% for the traditional test. The two tests also performed similarly in their predictions of rodent carcinogenicity.}",
+    issn = {0267-8357},
+    doi = {10.1093/mutage/gep017},
+    url = {https://doi.org/10.1093/mutage/gep017},
+    eprint = {https://academic.oup.com/mutage/article-pdf/24/4/359/3787533/gep017.pdf},
+}
+
+@article{Rutz2020,
+  author = {Rutz, L. and Gao, L. and Küpper, J.H. and others},
+  title = {Structure-dependent genotoxic potencies of selected pyrrolizidine alkaloids in metabolically competent HepG2 cells},
+  journal = {Arch. Toxicol.},
+  number = 94,
+  pages = {4159–4172},
+  year = 2020,
+  doi = {https://doi.org/10.1007/s00204-020-02895-z},
+}
+
+@article{Merz2016,
+title = {Interim relative potency factors for the toxicological risk assessment of pyrrolizidine alkaloids in food and herbal medicines},
+journal = {Toxicology Letters},
+volume = {263},
+pages = {44-57},
+year = {2016},
+issn = {0378-4274},
+doi = {https://doi.org/10.1016/j.toxlet.2016.05.002},
+url = {https://www.sciencedirect.com/science/article/pii/S0378427416300911},
+author = {Karl-Heinz Merz and Dieter Schrenk},
+keywords = {Pyrrolizidine alkaloids, Relative potency factors, Risk assessment, Toxicity},
+abstract = {Pyrrolizidine alkaloids (PAs) are among the most potent natural toxins occurring in a broad spectrum of plant species from various families. Recently, findings of considerable contamination of teas/herbal infusions prepared from non-PA plants have been reported. These are obviously due to cross-contamination with minor amounts of PA plants and can affect both food and herbal medicines. Another source of human exposure is honey collected from PA plants. These findings illustrate the requirement for a comprehensive risk assessment of PAs, hampered by the enormous number of different PA congeners occurring in nature. Up to now, risk assessment is based on the carcinogenicity of certain PAs after chronic application to rats using the sum of detected PAs as dose metric. Because of the well-documented large structure-dependent differences between sub-groups of PA congeners with respect to their genotoxicity and (cyto)toxicity, however, this procedure is inadequate. Here we provide an overview of recent attempts to assess the risk of PA exposure and the available literature on the toxic effects and potencies of different congeners. Based on these considerations, we have derived interim Relative Potency (REP) factors for a number of abundant PAs suggesting a factor of 1.0 for cyclic di-esters and open-chain di-esters with 7S configuration, of 0.3 for mono-esters with 7S configuration, of 0.1 for open-chain di-esters with 7R configuration and of 0.01 for mono-esters with 7R configuration. For N-oxides we suggest to apply the REP factor of the corresponding PA. We are confident that the use of these values can provide a more scientific basis for PA risk assessment until a more detailed experimental analysis of the potencies of all relevant congeners can be carried out.}
+}
+
+@article{Yan2008,
+  author = {J. Yan and Q. Xia and M.W. Chou and P.P. Fu},
+  year = 2008,
+  title =   {Metabolic activation of retronecine and retronecine {N-oxide} - formation of {DHP}-derived {DNA} adducts},
+  journal = {Toxicol. Ind. Health},
+  number = {24(3)},
+  pages = {181-8},
+  doi =  {https://doi.org/https://doi.org/10.1177/0748233708093727},
+}
+
+@misc{HMPC2014,
+  author = {HMPC},
+  year = 2014, 
+  title = {Public statement on the use of herbal medicinal products 5 containing toxic, unsaturated pyrrolizidine alkaloids (PAs), European Medicines Agency, Committee on Herbal Medicinal Products (HMPC) EMA/HMPC/8931082011},
+}
+
+@misc{EMA2020,
+  author = {EMA},
+  year = 2020,
+  title = {Public statement on the use of herbal medicinal products containing toxic, unsaturated pyrrolizidine alkaloids (PAs) including recommendations regarding contamination of herbal medicinal products with pyrrolizidine alkaloids. European Medicines Agency, Committee on Herbal Medicinal Products (HMPC), EMA/HMPC/893108/2011 Rev.1},
+}
+
+@article{Forsch2018,
+  author = {Forsch, K. and V. Schöning and L. Disch and B. Siewert and M. Unger and J. Drewe},
+  year = 2018,
+  title = {Development of an in vitro screening method of acute cytotoxicity of the pyrrolizidine alkaloid lasiocarpine in human and rodent hepatic cell lines by increasing susceptibility},
+  journal = {Journal of Ethnopharmacology},
+  number = 217,
+  pages = {134-139},
+  doi = {https://doi.org/10.1016/j.jep.2018.02.018},
+}
+
 @misc{ICH2017,
-  author = {International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH)},
-  title = {Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk M7(R1)},
+  author = {{International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH)}},
+  title = {Assessment and control of {DNA} reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk M7(R1)},
   year = 2017,
   note = {\url{https://database.ich.org/sites/default/files/M7_R1_Guideline.pdf}},
   note = {Accessed: 29-03-2021},
@@ -21,7 +119,8 @@
   title = {Mutagenicity of pyrrolizidine alkaloids in the Salmonella typhimurium/mammalian microsome system},
   journal = {Mutation research},
   number = 281, 
-  pages = {143–147}
+  pages = {143–147},
+  doi = {https://doi.org/https://doi.org/10.1016/0165-7992(92)90050-r}
 }
 
 @article{Chen2010,
@@ -30,38 +129,54 @@
   title = {Genotoxicity of pyrrolizidine alkaloids},
   journal = {J. Appl. Toxicol.},
   numbr = 30,
-  pages = {183-96}
+  pages = {183-96},
+  doi = {https://doi.org/10.1002/jat.1504}
 }
 
 @article{Li2013,
-  authors = {Li YH, Kan WL, Li N, Lin G},
-  year = 2013,
-  title = {Assessment of pyrrolizidine alkaloid-induced toxicity in an in vitro screening model},
-  journal = {J. Ethnopharmacol.},
-  number = 150,
-  pages = {560-7}
+title = {Assessment of pyrrolizidine alkaloid-induced toxicity in an in vitro screening model},
+journal = {Journal of Ethnopharmacology},
+volume = {150},
+number = {2},
+pages = {560-567},
+year = {2013},
+issn = {0378-8741},
+doi = {https://doi.org/10.1016/j.jep.2013.09.010},
+url = {https://www.sciencedirect.com/science/article/pii/S0378874113006430},
+author = {Yan Hong Li and Winnie Lai Ting Kan and Na Li and Ge Lin},
+keywords = {Pyrrolizidine alkaloids,  model, Cytotoxicity, HepG2 cell},
+abstract = {Ethnopharmacological relevance
+Pyrrolizidine alkaloids (PAs) are a group of heterocyclic phytotoxins present in a wide range of plants. The consumption of PA-containing medicinal herbs or PA-contaminated foodstuffs has long been reported to cause human hepatotoxicity. However, the degrees of hepatotoxicity of different PAs are unknown, which makes it difficult to determine a universal threshold of toxic dose of individual PAs for safe regulation of PA-containing natural products. The aim of the present study is to develop a simple and convenient in vitro model to assess the hepatotoxicity of different PAs.
+Material and methods
+Six common cytotoxicity assays were used to evaluate the hepatotoxicity of different PAs in human hepatocellular carcinoma HepG2 cells.
+Results
+The combination of MTT and bromodeoxyuridine incorporation (BrdU) assays demonstrated to be a suitable method to evaluate the toxic potencies of various PAs in HepG2 cells, and the results indicated that otonecine-type PA (clivorine: IC20=0.013±0.004mM (MTT), 0.066±0.031mM (BrdU)) exhibited significantly higher cytotoxic and anti-proliferative effects than retronecine-type PA (retrorsine: IC20=0.27±0.07mM (MTT), 0.19±0.03mM (BrdU)). While as expected, the known less toxic platyphylline-type PA (platyphylline: IC20=0.85±0.11mM (MTT), 1.01±0.40mM (BrdU)) exhibited significantly less toxicity. The different cytotoxic and anti-proliferative potencies of various PAs in the same retronecine-type could also be discriminated by using the combined MTT and BrdU assays. In addition, the developed assays were further utilized to test alkaloid extract of Gynura segetum, a senecionine and seneciphylline-containing herb, the overall cytotoxicity of two PAs in the extract was comparable to that of these two PAs tested individually.
+Conclusion
+Using the developed in vitro model, the cytotoxicity of different PAs and the extract of a PA-containing herb were investigated in parallel in one system, and their different hepatotoxic potencies were determined and directly compared for the first time. The results suggested that the developed model has a great potential to be applied for the quick screening of the toxicity of PAs and PA-containing natural products.}
 }
 
 @article{Xia2013,
-  authors = {Xia, Q. and Zhao, Y. and Von Tungeln, L.S. and Doerge, D.R. and Lin, G. and et al.},
+  author = {Q. Xia and Y. Zhao and L.S. Von Tungeln and D.R. Doerge and G. Lin and G. Cai and P.P. Fu},
   year = 2013,
-  title = {Pyrrolizidine alkaloid-derived DNA adducts as a common biological biomarker of pyrrolizidine alkaloid-induced tumorigenicity},
+  title = {Pyrrolizidine alkaloid-derived {DNA} adducts as a common biological biomarker of pyrrolizidine alkaloid-induced tumorigenicity},
   journal = {Chem Res. Toxicol.},
   number = 26,
-  pages = {1384-96}
+  pages = {1384-96},
+  doi = {https://doi.org/https://doi.org/10.1021/tx400241c}
 }
 
 @article{Fu2004,
-  authors = {Fu, P.P. and Xia, Q. and, Lin, G. and Chou, M.W.},
+  author = {P. P. Fu and Xia, Q. and Lin, G. and Chou, M. W.},
   year = 2004,
   title = {Pyrrolizidine alkaloids--genotoxicity, metabolism enzymes, metabolic activation, and mechanisms},
   journal = {Drug Metab. Rev.},
   number = 36,
-  pages = {1-55}
+  pages = {1-55},
+  doi = {https://doi.org/https://doi.org/10.1081/dmr-120028426}
 }
 
 @article{Louisse2019,
-  title = {Determination of genotoxic potencies of pyrrolizidine alkaloids in HepaRG cells using the γH2AX assay},
+  title = {Determination of genotoxic potencies of pyrrolizidine alkaloids in {HepaRG} cells using the {γH2AX} assay},
   journal = {Food and Chemical Toxicology},
   volume = {131},
   pages = {110532},
@@ -75,7 +190,7 @@
 }
 
 @article{Allemang2018,
-  title = {Relative potency of fifteen pyrrolizidine alkaloids to induce DNA damage as measured by micronucleus induction in HepaRG human liver cells},
+  title = {Relative potency of fifteen pyrrolizidine alkaloids to induce {DNA} damage as measured by micronucleus induction in {HepaRG} human liver cells},
   journal = {Food and Chemical Toxicology},
   volume = {121},
   pages = {72-81},
@@ -88,7 +203,7 @@
   abstract = {Plant-based 1,2-unsaturated Pyrrolizidine Alkaloids (PAs) can be found as contaminants in foods like teas, herbs and honey. PAs are responsible for liver genotoxicity/carcinogenicity following metabolic activation, making them a relevant concern for safety assessment. Current regulatory risk assessments take a precautionary approach and assume all PAs are as potent as the known most potent representatives: lasiocarpine and riddelliine. Our study investigated whether genotoxicity potency differed as a consequence of structural differences, assessing micronuclei in vitro in HepaRG cells which express metabolising enzymes at levels similar to primary human hepatocytes. Benchmark Dose (BMD) analysis was used to calculate the critical effect dose for 15 PAs representing 6 structural classes. When BMD confidence intervals were used to rank PAs, lasiocarpine was the most potent PA and plotted distinctly from all other PAs examined. PA-N-oxides were least potent, notably less potent than their corresponding parent PA's. The observed genotoxic potency compared favorably with existing in vitro data when metabolic competency was considered. Although further consideration of biokinetics will be needed to develop a robust understanding of relative potencies for a realistic risk assessment of PA mixtures, these data facilitate understanding of their genotoxic potencies and affirm that not all PAs are created equal.}
 }
 @article{Hadi2021,
-  title = {Genotoxicity of selected pyrrolizidine alkaloids in human hepatoma cell lines HepG2 and Huh6},
+  title = {Genotoxicity of selected pyrrolizidine alkaloids in human hepatoma cell lines {HepG2} and {Huh6}},
   journal = {Mutation Research/Genetic Toxicology and Environmental Mutagenesis},
   volume = {861-862},
   pages = {503305},
@@ -120,10 +235,10 @@ The widely available human hepatoma cell lines HepG2 and Huh6 were suitable for
 }
 
 @article{Langel2011,
-  author = {Langel, D. and Ober, D. and Pelser P.B.},
+  author = {Langel, D. and Ober, D. and Pelser, P.B.},
   year = 2011,
   title = {The evolution of pyrrolizidine alkaloid biosynthesis and diversity in the Senecioneae},
-  jounrnal = {Phytochemistry Reviews},
+  journal = {Phytochemistry Reviews},
   number = 10,
   pages = {3-74}
 }
@@ -154,15 +269,7 @@ The widely available human hepatoma cell lines HepG2 and Huh6 were suitable for
   year = 2017,
   number = 160,
   pages = {361-370},
-}
-
-@article{EFSA2011,
-  author = {EFSA},
-  title = {Scientific Opinion on Pyrrolizidine alkaloids in food and feed},
-  journal = {EFSA Journal},
-  year = 2011,
-  number = 9,
-  pages = {1-134},
+  doi = {https://doi.org/https://doi.org/10.1093/toxsci/kfx187},
 }
 
 @book{Mattocks1986,
@@ -174,7 +281,7 @@ The widely available human hepatoma cell lines HepG2 and Huh6 were suitable for
 
 @article{Maaten2008,
   author = {van der Maaten, L.J.P. and Hinton, G.E.},
-  title = {Visualizing Data Using t-SNE},
+  title = {Visualizing Data Using {t-SNE}},
   journal = {Journal of Machine Learning Research},
   year = 2008,
   number = 9,
@@ -238,12 +345,13 @@ eprint = {
 }
 
 @article{Yap2011,
-  author = "Yap, CW.",
+  author = "Yap, C.W.",
   year = 2011,
-  title = "PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints",
+  title = {{PaDEL-descriptor}: an open source software to calculate molecular descriptors and fingerprints},
   journal = "Journal of computational chemistry",
   number = 32,
-  pages = "1466-74"
+  pages = "1466-74",
+  doi = {https://doi.org/10.1002/jcc.21707}
 }
 
 @Article{Bender2004,
@@ -504,15 +612,24 @@ eprint = {
   pages =        "215--220",
 }
 
-@Article{EFSA2016,
-  author =       {EFSA},
-  year =         "2016",
-  title =        "Guidance on the establishment of the residue
-                  definition for dietary assessment: {EFSA} panel on Plant
-                  Protect Products and their Residues ({PPR})",
-  journal =      "EFSA Journal",
-  number =       "14",
-  pages =        "1--12",
+@article{EFSA2011,
+  author = {EFSA},
+  title = {Scientific Opinion on Pyrrolizidine alkaloids in food and feed},
+  journal = {EFSA Journal},
+  year = 2011,
+  number = 9,
+  pages = {1-134},
+  doi = {https://doi.org/10.2903/j.efsa.2011.2406},
+}
+
+@article{EFSA2016,
+  author =       {{EFSA}},
+  title =        {Guidance on the establishment of the residue definition for dietary assessment: {EFSA} panel on Plant Protect Products and their Residues ({PPR})},
+  journal =      {EFSA Journal},
+  year =         2016,
+  number =       14,
+  pages =        {1-12},
+  doi = {https://doi.org/10.2903/j.efsa.2016.4549},
 }
 
 @TechReport{ECHA2008,
diff --git a/mutagenicity.md b/mutagenicity.md
index 9c5f427..c80bdf1 100644
--- a/mutagenicity.md
+++ b/mutagenicity.md
@@ -10,6 +10,8 @@ author:
       institute: insel
   - Jürgen Drewe:
       institute: zeller, unibas
+      email: juergendrewe@zellerag.ch
+      correspondence: "yes"
   - Philipp Boss:
       institute: sysbio
 
@@ -33,11 +35,12 @@ institute:
 bibliography: bibliography.bib
 keywords: mutagenicity, QSAR, lazar, random forest, support vector machine, linear regression, neural nets, deep learning, pyrrolizidine alkaloids, OpenBabel, CDK
 
+#documentclass: frontiersHLTH
 documentclass: scrartcl
 tblPrefix: Table
 figPrefix: Figure
 header-includes:
-    - \usepackage{lineno, setspace, color, colortbl, longtable}
+    - \usepackage{lineno, color, setspace}
     - \doublespacing
     - \linenumbers
 ...
@@ -69,13 +72,14 @@ Computer based (*in silico*) mutagenicity predictions can be used in the early
 screening of novel compounds (e.g. drug candidates), but they are also gaining
 regulatory acceptance e.g. for the registration of industrial chemicals within
 REACH (@ECHA2017) or the assessment of impurities in pharmaceuticals (ICH M7
-guideline, @ICH2017).
+guideline, Harmonisation of Technical Requirements for Pharmaceuticals for
+Human Use @ICH2017).
 
-*Salmonella* mutagenicity is at the moment the toxicological endpoint with the
+Currently, *Salmonella* mutagenicity is the toxicological endpoint with the
 largest amount of public data for almost 10000 structures, whereas datasets for
 other endpoints contain typically only a few hundred compounds. The Ames test
 itself is relatively reproducible with an interlaboratory variability of 80-85%
-(@Benigni1988).
+(@Piegorsch1991).
 
 This makes the development of mutagenicity models also interesting from a
 computational chemistry and machine learning point of view.  The relatively
@@ -148,8 +152,8 @@ under a GPL3 License. The new combined dataset can be found at
 The pyrrolizidine alkaloid dataset was created from five independent, necine
 base substructure searches in PubChem (https://pubchem.ncbi.nlm.nih.gov/) and
 compared to the PAs listed in the EFSA publication @EFSA2011 and the book by
-Mattocks @Mattocks1986, to ensure, that all major PAs were included. PAs
-mentioned in these publications which were not found in the downloaded
+@Mattocks1986, to ensure, that all major PAs were included. PAs
+mentioned in these publications, which were not found in the downloaded
 substances were searched individually in PubChem and, if available, downloaded
 separately.  Non-PA substances, duplicates, and isomers were removed from the
 files, but artificial PAs, even if unlikely to occur in nature, were kept. The
@@ -193,7 +197,7 @@ 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
+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
@@ -297,7 +301,7 @@ absence of closely related neighbours, we follow a tiered approach:
     flagged with a warning that it might be out of the applicability domain of
     the training data (*low confidence*).
 
--   These Similarity thresholds are the default values chosen
+-   These similarity thresholds are the default values chosen
     by software developers and remained unchanged during the
     course of these experiments.
 
@@ -368,13 +372,13 @@ 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 
+n_estimators=1000 and 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 
+For each model we used a batch size of 64 and trained for 50 epochs. As 
 an optimizer ADAM was chosen. For the other parameters we used the 
 tensorflow default values.
 
@@ -386,7 +390,7 @@ 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 
+each model we used a batch size of 64 and trained for 50 epochs. 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.
@@ -467,7 +471,7 @@ https://git.in-silico.ch/mutagenicity-paper/tree/crossvalidations/predictions/.
 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
+@Piegorsch1991). Sensitivities and specificities are balanced in all of
 these models.
 
 Pyrrolizidine alkaloid mutagenicity predictions 
@@ -638,16 +642,16 @@ 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
+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
+typhimurium* mutagenicity bioassay (80-85% according to @Piegorsch1991). All of
+these models have balanced sensitivity (true positive 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
+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
@@ -663,11 +667,16 @@ 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.
+learning (a screenshot of the mutagenicity prediction for
+12,21-Dihydroxy-4-methyl-4,8-secosenecinonan-8,11,16-trione can be found at
+https://git.in-silico.ch/mutagenicity-paper/tree/figures/lazar-screenshot.png).
+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}
+<!--
+![`lazar` screenshot of 12,21-Dihydroxy-4-methyl-4,8-secosenecinonan-8,11,16-trione mutagenicity prediction](figures/lazar-screenshot.png){#fig:lazar}
+-->
 
 Descriptors
 -----------
@@ -776,27 +785,30 @@ retronecine-type (@Li2013). 
 ### Modifications of necine base
 
 The group-specific results reflect the expected relationship between the
-groups: the low mutagenic potential of N-oxides and the high potential of
-Dehydropyrrolizidines (DHP) (@Chen2010). 
+groups: the low mutagenic potential of *N*-oxides and the high potential of
+dehydropyrrolizidines (DHP) (@Chen2010). 
+However, *N*-oxides may be *in vivo* converted back to their parent toxic/tumorigenic parent PA (@Yan2008),  on the other hand they are highly water soluble and generally considered as detoxification products, which are *in vivo* quickly renally eliminated (@Chen2010).
 
-Dehydropyrrolizidines are regarded as the toxic principle in the metabolism of
-PAs, and known to produce protein- and DNA-adducts (@Chen2010). None of the
-models did not meet this expectation and predicted the majority of DHP as
+DHP are regarded as the toxic principle in the metabolism of
+PAs, and are known to produce protein- and DNA-adducts (@Chen2010). None of our investigated
+models did meet this expectation and all of them predicted the majority of DHP as
 non-mutagenic. However, the following issues need to be considered. On the one
-hand, all DHP were outside of the stricter applicability domain of MP2D lazar.
+hand, all DHP were outside of the stricter applicability domain of MP2D `lazar`.
 This indicates that they are structurally very different than the training data
 and might be out of the applicability domain of all models based on this
 training set. 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. 
+short lifespan *in vivo*, and usually cannot be used in *in vitro* experiments. 
 
 <!--
 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. 
 -->
 
+<!--
 PA N-oxides are easily conjugated for extraction, they are generally considered
 as detoxification products, which are *in vivo* quickly renally eliminated
 (@Chen2010).
+-->
 
 Overall the low number of positive mutagenicity predictions was unexpected.
 PAs are generally considered to be genotoxic, and the mode of action is also known.
@@ -812,6 +824,26 @@ 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 the low sensitivity of the Ames test for PAs is also reflected in the investigated models.
 
+A *in vitro* screen of cellular PA effects (metabolic activation and mutagenic
+effects) in human and rodent hepatocytes (HepG2 and H-4-II-E) showed that
+results may also critically depend on the cellular model and cell culture
+conditions and may underestimate the effects of PAs (@Forsch2018).
+
+In summary, we found marked differences in the predicted genotoxic potential
+between the PA groups: most toxic appeared the otonecines and macrocyclic
+diesters, least toxic the platynecines and the mono- and diesters. These
+results are comparable with *in vitro* measurements in hepatic HepaRG cells
+(@Louisse2019), where relative potencies (RP) were determined: for otonecines
+and cyclic diesters RP = 1, for open diesters RP = 0.1 and for monoesters RP =
+0.01.
+
+Due to a lack of
+differential data, European authorities based their risk assessment in a
+worst-case approach on lasiocarpine, for which sufficient data on genotoxicity
+and carcinogenicity were available (@HMPC2014, @EMA2020). Our data further support a tiered risk assessment
+based on *in silico* and experimental data on the relative potency of
+individual PAs as already suggested by other authors (@Merz2016, @Rutz2020, @Louisse2019). 
+
 <!--
 non-conflicting CIDs
 43040
@@ -894,6 +926,8 @@ in this context, because they present rationales for predictions (similar
 compounds with experimental data) which can be accepted or rejected by
 toxicologists and provide validated applicability domain estimations. 
 
+Our data show that large difference exist with regard to genotoxic potential between different pyrrolizidine subgroups. These results may allow to adjust risk assessment of pyrrolizidine contamination.
+
 <!---
 in a form that is understandable and criticiseable by toxicologists without a machine learning background.
 
diff --git a/mutagenicity.pdf b/mutagenicity.pdf
index e46da4d..44aeec5 100644
--- a/mutagenicity.pdf
+++ b/mutagenicity.pdf
diff --git a/shell.nix b/shell.nix
index ab0ed26..e1ffe4d 100644
--- a/shell.nix
+++ b/shell.nix
@@ -3,11 +3,12 @@ with import (fetchTarball "https://github.com/NixOS/nixpkgs/archive/d88cdc7bc1a7
 let
   R-packages = rWrapper.override { packages = with rPackages; [ ggplot2 Rtsne ]; };
   gems = bundlerEnv { name = "mustache"; gemdir = ./.; };
-  latex-authblk = texlive.combine { inherit (texlive) scheme-medium preprint; };
+  latex-authblk = texlive.combine { inherit (texlive) scheme-medium preprint sttools changepage datetime fmtcount; };
 in mkShell {
   name = "mutagenicity-paper";
   buildInputs = [
     # paper
+    unzip
     git
     latex-authblk
     fontconfig