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In Silico Prediction of DILI - Extraction of Histopathology Data from Preclinical Toxicity Studies of the eTOX Database for new In Silico Models of Hepatotoxicity Alexander Amberg 1 , Lennart T. Anger 1 , Manuela Stolte 1 , Jennifer Hemmerich 1 , Hans Matter 2 , Lilia Fisk 3 , Inga Tluczkiewicz 4 , Kevin Pinto-Gil 5 , Oriol López-Massaguer 5 , Manuel Pastor 5 1 Sanofi, Preclinical Safety, Frankfurt, Germany; 2 Sanofi, Integrated Drug Discovery, Frankfurt, Germany; 3 Lhasa Limited, Leeds, United Kingdom; 4 Fraunhofer ITEM, Hannover, Germany; 5 IMIM, GRIB, Barcelona, Spain IN SILICO MODEL DEVELOPMENT AND RESULTS CONCLUSION The eTOX consortium extracted in vivo data from unpublished preclinical toxicity studies of 13 EFPIA partners. This new database contains high quality toxicity results in high detail level from 1,947 drug candidates (8,196 studies) supplemented with 1,286 chemicals from the RepDose database (2,695 studies). Different compilation steps were applied to transform these data into usable in silico model training datasets: initially, all toxicity findings were extracted from study reports (paper/PDF). Then the verbatim terms for all treatment-related hepatotoxicity findings were harmonized using special ontologies. Finally, to receive model training sets with sufficient compound numbers and chemical space coverage, all primary histopathology terms were combined and grouped to different 1st and then 2nd level clusters of similar toxicity mechanisms: e.g. primary necrosis terms such as “centrilobular”, “periportal” etc. were grouped to 1st level cluster “necrosis”, then clusters such as “necrosis”, “vacuolization” etc. were grouped to 2nd level cluster “degenerative lesions”. With this approach, various training datasets were compiled depending on the species (rat, dog and monkey), treatment durations (2 weeks - 2 years) and administrations routes. Then, different modeling approaches were applied on these datasets, including structural alerts, fragment-based and molecular descriptor-based machine learning approaches (e.g. random forest, decision tree, k nearest neighbor). Models were validated and optimized, first by internal validation (test set 10%) then by external validation using Sanofi’s confidential data. For example, best external validation results (n=66) were achieved for the 1st cluster rat necrosis models (229 positives, 198 negatives) using fragment-based (Sensitivity: 0.80, Specificity: 0.77) and a molecular descriptor-based decision tree approach (Sensitivity: 0.81, Specificity: 0.88). These validation results show that by reasonable clustering histopathology data from eTOX, it is possible to develop highly predictive in silico models for drug-induced liver injury (DILI). METHODS Structural Alerts – Validation and Improvement of existing and Identification of new alerts eTOX Training Dataset Compilation Steps to transform the eTOX in vivo data into usable in silico model training datasets 1) Data extraction of treatment-/compound-related hepatotoxicity findings from study reports #2118/P455 ABSTRACT INTRODUCTION A) EFPIA preclinical toxicity studies (paper/PDF) • Reports/studies: 8,196 • Compounds: 1,947 B) RepDose DB (http://fraunhofer-repdose.de/) • Reports/studies: 2,695 • Compounds: 1,286 Work performed at eTOX Hackathon (“Hack Marathon”) of toxicologist, pathologist, in silico modeler, data manager 2) Harmonization of the verbatim terms from study reports using special ontologies and combination of the data to different model trainings datasets Rat • Histopathology • Clinical chemistry • Liver weight Dog • Histopathology • Clinical chemistry • Liver weight Monkey • Histopathology • Clinical chemistry • Liver weight No. of compounds EFPIA oral RepDose gavage, feed EFPIA oral RepDose gavage, feed EFPIA oral RepDose gavage, feed ≤ 28 days (~ 1 month) 889 284 380 - 62 - 28 – 120 days (~ 3 month) 114 562 96 91 16 - ≥ 120 days (~ 6-24 month) 87 382 98 189 18 - Data matrix available for all of these training sets primary terms with at least one treatment-related finding (values = LOEL) Example data matrix histopathology Substance ID min dose max dose accumu- lation lipid bile duct hyperplasia congestion hyperplasia hypertrophy hypertrophy kupffer cells Inflammation necrosis centrilobular necrosis periportal single cell necrosis Compound 1 500 1500 Compound 2 50 1000 50 250 250 Compound 3 0.69 13.8 0.69 Compound 4 100 1750 750 100 Compound 5 38 510 Compound 6 50 2000 250 1000 Compound 7 60 500 60 Compound 8 100 2000 2000 100 Compound 9 5 651 Compound 10 1 100 5 100 No. of positives 3 35 7 47 111 6 40 6 8 33 all individual compounds Primary terms - preferred ontology Positive cpds. 1 st level cluster Primary terms Positive cpds. 1 st level cluster 1 st level cluster 2 nd level cluster hypertrophy, epithelial | hypertrophy, hepatocyte | hypertrophy 142 cell enlargement | enlargement | hypertrophy | peroxisome proliferation | swelling 380 hypertrophy intracellular vacuolation | vacuolation, biliary epithelium | vacuolation, epithelial 140 vacuolization 134 vacuolation degenerative lesions accumulation, lipid | increased, lipid content | intracellular increase of lipids | vacuolation, lipidic (fatty change) | vacuolation, lipidic 131 fatty degeneration | lipidosis 114 steatosis degenerative lesions necrosis, fibrinoid | necrosis, focal/multifocal | necrosis, hepato- cellular | necrosis, centrilobular | necrosis, midzonal | necrosis, periportal | necrosis, zonal | single cell necrosis … 126 apoptosis | necrosis 165 necrosis degenerative lesions abscess(es) | chronic inflammatory/proliferative/ metaplastic changes | inflammation, granulomatous | inflammatory processes … 124 inflammation 67 inflammation inflammatory changes inflammatory cell infiltration | granuloma | histiocytic inflammatory cell infiltrate | histiocytic proliferation | increased, histiocyte number | increased plasma cell number ... 94 granulocytes | granuloma | histiocytosis | infiltration | leukocytes | macrophages | polynuclear cells 78 infiltration inflammatory changes RepDose: histopathology findings EFPIA legacy reports: histopathology findings • Many compilation steps had to be applied to transform the in vivo hepatotoxicity data from the eTOX database into usable model training datasets • Data extraction of treatment-related findings from unpublished preclinical toxicity study reports • Harmonization of the verbatim terms using special ontologies and combination of the data • Grouping of primary histopathology terms to 1 st and 2 nd level cluster of similar findings (and mechanisms) to receive trainings data with sufficient compounds numbers and chemical space coverage • Various in silico models were developed from these training datasets using approaches like • Statistical structural fragment / fingerprint based models • Molecular descriptor based machine learning models (QSAR), like Decision Tree (DT), Partial Least Square (PLS), Random Forest (RF), k-Nearest Neighbor (kNN) etc. • Systems biology models • Structural alerts • Internal & external validation showed sensitivities up to 81% and specificities up to 88% • Case study on tienilic acid demonstrated how these in silico models can be used for the prediction of DILI and for elucidation of potential mechanisms of hepatotoxicity • Mulliner et al. models [7] prediction results • Leadscope & SVM prediction summary • eTOX models prediction results • Leadscope & C5 DT summary structural feature contribution structural feature contribution Tienilic acid Withdrawn from the market due to idiosyncratic autoimmune mediated hepatotoxicity findings in patients, characterized by covalent CYP2C9 binding (antibodies of acylated CYP2C9 hapten found in patients) [6] Structural alert: Thiophene c) metabolic CYP soft spot analysis (MetaSite) • Drug Induced Liver Injury (DILI) • still of great concern for patient safety and major cause for drug candidate attrition and drug withdrawal from the market • Data about liver toxicity • scattered and available from many sources public, proprietary, consortia (eTOX), commercial eSafety in vitro, preclinical in vivo, clinical, post-market • Chen et al. [1] reviewed several in silico models for human DILI (since 2012) • Models trained with 74 - 1087 compounds from different data sources using various modeling approaches • Performance: higher accuracy for small datasets: 70-84% (13-53 compounds) lower accuracy for larger datasets: 60-75% (73-1087 compounds) high specificity (90-95%), but poorer sensitivity (~50%) Conclusion • Sufficiently harmonized hepatotoxicity datasets from all the different sources in high detail level missing • Consequently, satisfying in silico models for generation of reliable predictions of hepatotoxicity lacking using these harmonized, detailed training data • In the eTOX consortium framework in vivo data were extracted from unpublished preclinical toxicity studies of 13 EFPIA partners from pharmaceutical industry [2] eTOX database: high quality preclinical toxicity results in high detail level Consortium (www.e-tox.net) • Objectives A) Setup eTOX database and pre-competitive data sharing • Internal EFPIA toxicity reports plus public toxicity data • Using standardized ontologies and database schema B) Development of new prediction models • Use of eTOX data as model training data • Participants Pharma (13) Academia (11) SME (6) The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement nº 115002 (eTOX), resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contributions • Validation of alerts using different datasets [2 , 7] • drugs, (pre)clinical, chemicals, individual species (rat, dog, monkey) Validation results: example eTOX drugs (% correct positive predictions) [1] Chen M, Bisgin H, Tong L, Hong H, Fang H, Borlak J, Tong W “Towards predictive models for drug-induced liver injury in humans: are we there yet?” Biomarkers Med. 8(2), 201-213, 2014 [2] Sanz F, Pognan F, Steger-Hartmann T, Díaz C “Legacy data sharing to improve drug safety assessment: the eTOX project” Nat. Rev. Drug Discov. 16(12), 811-812, 2017 [3] Carbonell P, Lopez O, Amberg A, Pastor M, Sanz F “Hepatotoxicity Prediction by Systems Biology Modeling of Disturbed Metabolic Pathways using Gene Expression Data” ALTEX 34(2), 219-234, 2017 [4] López-Massaguer O, Pinto-Gil K, Sanz F, Amberg A, Anger LT, Stolte M, Ravagli C, Marc P, Pastor M “Generating modelling data from repeat-dose toxicity reports” Toxicol. Sci., 1–14, 2017 [5] Enoch SJ, Ellison CM, Schultz TW, Cronin MT “A review of the electrophilic reaction chemistry involved in covalent protein binding relevant to toxicity” Crit. Rev. Toxicol. 41(9), 783-802, 2011 [6] Kalgutkar AS, Gardner I, Obach RS, Shaffer CL, Callegari E, Henne KR et al. „A comprehensive listing of bioactivation pathways of organic functional groups” Curr. Drug Metab. 6(3), 161-225, 2005 [7] Mulliner D, Schmidt F, Stolte M, Spirkl HP, Czich A, Amberg A. “Computational Models for Human and Animal Hepatotoxicity with a Global Application Scope” Chem. Res. Toxicol. 29(5), 757-767, 2016 Quinoline Derek alert 685 PP = 78% (14/18 positive) Thiopene Kalgutkar PP = 37% (15/41 positive) Phenoxyacetic acid Derek alert 690 PP = 53% (10/19 positive) Aromatic sulphonamide Kalgutkar PP = 9.5% (2/21 positive) LOEL Boxplot summary Quinoline Kalgutkar [6] Enoch/Cronin [5] Alert source Derek Nexus 5.0.2 • Translational assessment • preclinical vs. clinical predictivity metabolic soft spot analysis (MetaSite) Tienilic acid (6/21) (31/34) (12/17) (5/18) (positives/total) Statistical structural fragment / fingerprint based models • Model approach (www.leadscope.com) • Use of predefined fragments for statistical correlation to toxicological activity from training dataset • Model output • Predicted probability (0-100%) for model endpoint • Information in / out of domain of training data • Individual structural fragments analysis • Link to the respective training data QSAR - Molecular descriptor based machine learning models • QSAR Modeling approach A • Cubist c5.0 Decision Tree model • Molecular descriptors • MOE, CATS, Crippen packages • Model output • Classification model (50-60 rules): positive / negative for respective model endpoint • Validation results • External validation using Sanofi’s confidential data (n=66) Systems biology models Model training data 77 overlapping compounds extracted from Modeling approach on the example statins • Validation results • Internal validation (test dataset: 4% of original dataset) 80.2 79.2 81.3 77.7 73.3 80.6 78.5 78.8 78.1 78.8 80.3 76.8 2 nd level cluster 1 st level cluster 74.3 72.4 76.9 79.8 75.0 82.8 78.1 75.0 79.8 79.4 78.1 80.3 80.6 81.7 79.3 78.4 78.0 79.2 78.0 80.0 77.0 out of domain predictions 20 29 85.0 81.0 88.0 • Results [3] • Most frequent disturbed metabolic pathways: mitochondrial β-oxidation of fatty acids and amino acid metabolism • ROC AUC up to 0.9 for some hepatotox endpoints • Quantitative assessment • use of LOEL of finding, box plot analysis • Implementation of mechanistic information • identification of responsible reactive metabolite • likelihood for reactive metabolite formation by analysis of metabolic soft spots using MetaSite (www.moldiscovery.com/software/metasite) • Identification of new structural alerts • Fragment analysis by Leadscope • Fragment analysis by SARpy (www.vegahub.eu/portfolio-item/sarpy) • Molecular descriptors • Adriana, GRIND2 descriptors using AdrianaCode and Pentacle software • Model output • Qualitative and quantitative scores • Validation results [4] • Qualitative models DEG, INF, PRO CASE STUDY structural feature contribution R R R Training compounds Training compounds Training compounds a) quantitative assessment Training compounds Training compounds External validation results: rat liver necrosis (NEC) • QSAR Modeling approach B • PLS-R/ PLS-DA (Partial Least Square Regression/ Discriminant Analysis), RF-R/RF-C (Random Forest Regressor/ Classifier) • Validation results [4] • Quantitative model PRO • Aim: increase mechanistic understanding: pathway, cell, tissue, organ level Endpoint (mg/kg) Number of points 50 100 500 Hepatotoxicity 14 0.89 0.89 0.54 1. Clinical chemistry findings 11 0.92 0.92 0.83 1.1 Hepatobiliary injury 3 1 1 0.5 1.2 Hepatocellular injury 10 0.90 0.90 0.81 2. Morphological findings 8 1.0 1.0 - 2.1 Hepatobiliary injury 1 - - - 2.2 Hepatocellular injury 8 1.0 1.0 - Bioactivation of Thiophene [6] Pyrazole PP = 63% (10/16 positives) Triflourmethylbenzene PP = 61% (40/61 positives) Acetamidobenzene PP = 51% (26/51 positives) Halogenated benzylamine PP = 73% (24/33 positives) in total 78 primary terms b) Metabolic activation / toxicification step SME = Small Medium Enterprises 200 100 0 Dose [mg/kg/day] Dog Monkey Rat 12/17 1/4 1/2 1,000 100 10 1 LOEL Dog Monkey Rat . . . Median . . . . . . . . . . . . . . . Dose [mg/kg/day] Tested Doses Compound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 3) Grouping of histopathology terms - to cluster of similar findings (and mechanism) Dose [mg/kg/day] 300 100 0 200 400 500 600 5 / 10 2 / 3 Dog Rat In Silico Models Cluster level No. training compounds Positives 1 Negatives 2 Necrosis NEC 1 st 427 229 198 Steatosis STE 1 st 406 208 198 Inflammation IFM 1 st 322 124 198 Infiltration IFT 1 st 302 104 198 Proliferation PLF 1 st 345 147 198 Hyperplasia HYP 1 st 439 241 198 Hypertrophy HYT 1 st 652 455 197 Degenerative lesions DEG 2 nd 602 355 247 Inflammatory changes INF 2 nd 412 165 247 Non-neoplastic proliferative changes PRO 2 nd 558 311 247 Training datasets • Histopathology data oral rat studies up to 2 years used for in silico models 1 Positives: Compounds with findings in respective endpoint 2 Negatives: Compounds without liver findings in histopathology, clinical chemistry, liver weight and tested up to 1000mg/kg Hepatotoxicity: eTOX (preclinical) Degenerative lesions Necrosis Inflammatory changes Steatosis Inflammation Infiltration 2 nd level cluster Non-neoplastic proliferative changes 1 st level cluster Proliferation Hyperplasia … … … Hypertrophy S O Cl Cl O OH O S O R metabolic activation/ toxification N N H R N H Cl N H F R R NH O R R F F F R N R O OH O R S R S N O O R R R
