Addressing repeated-dose systemic toxicity using ToxCast and ToxRefDB data

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1 Addressing repeated-dose systemic toxicity using ToxCast and ToxRefDB data Gladys Ouédraogo, Ph.D, Pharm. D L Oréal R&I Predictive Model and Method Development Department Aulnay-sous-Bois Disclaimer: The views expressed in this presentation are those of the authors and do not necessarily reflect the ADVANCED RESEARCH views or policies of the US EPA and L Oréal

2 2 Repeated-dose systemic toxicity testing (banned in EU Cosmetic regulation EC 1233/2009) Purpose: Evaluate toxic effect(s) based on repeated exposure, Identify primary effect(s) on different organs in relation to dose and duration Define a no observed adverse effect level (NOAEL). Species: Relevant to intended use of test agent. Default rodent: rat, default non-rodent: dog Routes of exposure: Oral Dermal Inhalation OECD Test guideline documents addressing repeated dose toxicity: TG407: Repeated-dose 28-day oral toxicity study in rodents TG408: Repeated-dose 90-day oral toxicity study in rodents TG410: Repeated-dose dermal toxicity 21/28-day study TG411: Subchronic dermal toxicity : 90-day study TG412: Repeated-dose inhalation toxicity: 28-day or 14 day study TG413: Subchronic inhalation toxicity : 90-day study TG422: Combined repeated-dose toxicity study with the reproductive/developmental toxicity screening test TG452: Chronic toxicity studies TG453: Combined chronic toxicity/carcinogenicity studies ADVANCED RESEARCH

3 3 Repeated-dose systemic toxicity: evaluation for Cosmetics in Europe Before March 2013 Gavage (90 days) No observed adverse effect level (NOAEL) Derive a safe dose in use conditions After March 2013 Predictive approaches: research ongoing - Broad toxicity screening - Organ specific toxicity -? Derive a safe dose in use conditions

4 4 Objective Develop a quantitative model to predict systemic toxicity using chemical descriptors, ToxCast HTS in vitro and ToxRefDB in vivo data

5 5 The approach CHEMICAL SELECTION Chemicals filtered from ToxRefDB: only systemic endpoints, study type and species adjustment parameters. Lowest effect levels (LELs) were obtained and utilized in the modeling. LELs were derived using the following formula: All LELs were adjusted to a subchronic (90 day) rat study LEL using an adjustment factor for species- and time Species: Mouse, Rat, Primate, Dog, Rabbit, Hamster Route of administration: Oral MEASURING MODEL PREDICTIVITY Measure of the difference between predicted value to observed values RMSE n i 1 ( X obs i, X mo del, i) n 2

6 6 The approach DATA SOURCES: MOLECULAR DESCRIPTORS, PHYS-CHEM PARAMETERS, CHEMOTYPES Data Source Source Type Number of Features Link LeadScope Physico-Chemical Properties 6 ChemSpider Physico-Chemical Properties 15 ToxPrint Chemotype Chemotypes DRAGON Molecular Descriptors ToxCast Data in vitro data 821 (70 BG) ToxCast Summary Files

7 7 The approach CHEMICAL SET Total: 624 chemicals had in vitro and in vivo data Modeling set: 576 split into training (461) and test (115) set Validation set: 48 chemicals was

8 8 The approach ESTABLISHING BASELINES & BENCHMARKS: PERFORMANCE ANALYSIS BASED ON RMSE Simulated Baselines: LEL sampled from the test set (576), without replacement, 1000 times; For each iteration, error computed between sampled LELs and true LELs from ToxRefDB. Performance Baselines: population log-average LEL assigned to each chemical; then true LELs subtracted from population log-average LEL. Empirical Benchmark: in vivo-to-in vivo comparison using chronic mouse LEL to predict chronic rat LEL. Theoretical Benchmarck: Using a risk function that incorporates the variance of the estimator, Mean Square Error (MSE). Computed using the modeling chemical set with a log-average of 3.26 with an SD of To achieve a lower mean square error using a biased estimator, the following MSE of an estimator equation was used where σ= 3.26, n = 576. By taking the square root of the MSE value, the theoretical lower confidence bound was established.

9 9 BUILDING THE SYSTEMIC TOXICITY MODEL The caret R Package was used to build random forest (RF) models for each data set Consensus model built by taking the average values from all 3 independent models. The consensus model predictions were resampled to compute an overall RMSE.

10 TRAIN CHEMICALS LELS CHEM PROPS CHEMOTYPES BGS RESAMPLE WORKFLOW OF MODEL DEVELOPMENT Three data types (Chemical properties, chemotypes, and in vitro biological groups [BGs]) considered Feature selection applied to the train set of chemicals to remove zero/near zero variance, and highly correlated features. Independent models were built using 5-fold cross validation, 10 repeats for the train chemical set, and applied to the test chemical set. Consensus model built by taking the mean predicted LEL and resampling. From the consensus model, a lowest effect level (LEL) was predicted. DATA TYPES FEATURE SELECTION RANDOM FOREST MODELS MODEL PREDICTION CHEMICAL PROPERTIES (1687) BUILD APPLY MODELS PHYSICO-CHEMICAL PROPERTIES (21) MOLECULAR DESCRIPTORS (1666) DATA SOURCES CHEMICAL PROPERTIES CHEMICAL PROPERTIES CHEMOTYPES (729) TOXPRINT FINGERPRINTS CHEMOTYPES CHEMOTYPES CONSENSUS MODEL LOWEST EFFECT LEVEL (LEL) IN VITRO BGS (70) TOXCAST HTS IN VITRO ASSAY ENDPOINTS (820) INTO 70 BIOLOGICAL GROUPS REMOVE SOURCES WITH: - ZERO/NEAR ZERO VARIANCE - HIGHLY CORRELATED (>0.8) 5-FOLD CROSS VALIDATION 10 REPEATS IN VITRO BGS IN VITRO BGS MEAN OF PREDICTED LEL TRAIN CHEMICAL SET TEST CHEMICAL SET

11 11 THE MODEL PREDICTION A TRAIN CHEMICAL SET (461) B TEST CHEMICAL SET (115) C SUMMARY QSAR Chemotypes HTS-BG Consensus Train Chemical Set (461) 0.99 (0.061) 1.01 (0.068) 1.02 (0.065) - Test Chemical Set (115) Model Predictions for (a) train and (b) test chemical sets and (c) summary results: (a) 461 chemicals from train set were used for a QSAR model (blue, open circles), Chemotypes (green, open circle), HTS-BG (pink, open circles). A consensus model was built from the 3 independent models (gray, filled-in circles).

12 12 CASE STUDY CHEMICALS RELEVANT TO COSMETICS CASRN ChemName LEL Model Model: Model: Model: LEL (log) (mg/kg/day) QSAR Chemotype HTS-BG consensus Residuals ,2-Propylene glycol Propyl gallate Diethylene glycol monomethyl ether Myrcene Ethylene glycol ,7-Dimethyl-2,6-octadienal Ethylhexanoic acid Piperonyl butoxide Caprolactam Butoxyethanol Ethyl-1-hexanol Coumarin Genistein Triclosan Diethanolamine Dibutyl phthalate Isoeugenol Benzophenone Methyleugenol Chemicals with negative residuals indicate a more conservative prediction.

13 13 CONCLUSIONS - PERSPECTIVES A systemic toxicity model is established that can predict a LEL (within 1 order of magnitude uncertainty) using chemical property, chemotypes, and in vitro data by building independent models and taking a consensus approach Establishing baselines and benchmarks provides realistic expectations for model development Different possibilities can be explored for optimizing the model at the level of the in vivo data, the in vitro data, cheminformatics and modeling techniques.

14 14 Repeated-dose systemic toxicity: which alternatives to in vivo testing? TTC: threshold of toxicological concern Read-across (when analog(s) with testing data available) Ab initio/de novo approach (when no relevant analog can be identified) Some initiatives useful towards developing tools for repeated-dose toxicity (nonexhaustive): ToxCast/Tox21 SEURAT1 HESS RISK21 e-tox EU-ToxRisk21 Cosmetics Europe Long range science program

15 15 Do the best you can until you know better. Then when you know better, do better. Maya Angelou

16 16 ACKNOWLEDGMENTS US EPA Matt Martin LyLy Pham Richard Judson Keith Houck Ann Richard John Wambaugh Kevin Crofton Rusty Thomas David Dix Bob Kavlock Oregon State University Lisa Truong L Oréal Hicham Noçairi Laurence Le Capitaine Charles Gomes Delphine Blanchet Françoise Gautier Sophie Loisel-Joubert Jacques Clouzeau Reine Note Silvia Teissier Ann Detroyer Stéphanie Ringeissen Pascal Berthe José Cotovio

17 17 BARKA (Thank you)

18 18 Back-up

19 19 Predictive approaches: where are we?

20 20 TOXCAST BIOLOGICAL GROUPINGS