Multivariate Models for Skin Sensitization Hazard and Potency
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1 Multivariate Models for Skin Sensitization Hazard and Potency Judy Strickland Integrated Laboratory Systems, Inc. Contractor Supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) OpenTox USA 2017 July 13, 2017
2 NICEATM NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), organized as an office under the NTP Division, part of NIEHS ILS provides technical support for NICEATM under an NIEHS contract ICCVAM support Tox21 validation support International harmonization efforts Disclaimer: ILS staff provide technical support for NICEATM, but do not represent NIEHS, NTP, or the official positions of any federal agency 2
3 3 ICCVAM Skin Sensitization Working Group
4 Allergic Contact Dermatitis Accounts for 10-15% of all occupational disease Most common adverse effect of topical drugs 25% of the European general population has a skin allergy to 1/30 allergens in the European baseline series U.S. regulatory requirements for testing to provide hazard information and labeling 4 Decyl glucoside
5 Skin Sensitization Testing Data required by EPA (for pesticides), FDA, CPSC, OSHA, REACh EPA requires hazard; CPSC, REACh, FDA, and OSHA require potency Acceptable stand-alone tests: Murine local lymph node assay (LLNA) (OECD TG 429) Guinea pig tests: Buehler and guinea pig maximization tests (OECD TG 406) Non-animal tests for weight-of-evidence approach: 5 Direct peptide reactivity assay (DPRA) (OECD TG 442C) KeratinoSens TM (OECD TG 442D) Human cell line activation test (h-clat) (OECD TG 442E), U-SENS, and IL-8 Luc
6 Alignment of Non-animal Methods to AOP Chemical Structure & Properties Molecular Initiating Event Cellular Response Organ Response Organism Response Metabolism Penetration Electrophilic substance DPRA Key Event 1 Covalent interaction with skin proteins Key Event 3 Dendritic Cells (DCs) Induction of inflammatory cytokines and surface molecules Mobilisation of DCs Key Event 2 Keratinocytes responses h-clat Activation of inflammatory cytokines Induction of cytoprotective genes Key Event 4 T-cell proliferation Histocompatibility complexes presentation by DCs Activation of T cells Proliferation of activated T-cells Adverse Outcome Inflammation upon challenge with allergen KeratinoSens 1 For sensitization that is initiated by covalent binding to proteins 6 Graphic adapted from OECD Guidance Document No. 168: The Adverse Outcome Pathway for Skin Sensitisation Initiated by Covalent Binding to Proteins: Part 1, Part 2.
7 Objectives Produce and test approaches that integrate non-animal data to predict skin sensitization hazard and potency Three in chemico or in vitro assays (OECD TG) DPRA, KeratinoSens, and h-clat Physicochemical properties An in silico method (read-across using OECD QSAR Toolbox) Built 4 types of models: LLNA and human hazard and potency 7 Hazard Sensitizer Nonsensitizer Potency 1A (strong) 1B (other or weak ) Nonsensitizer
8 In Chemico and In Vitro Tests Unreacted peptide Test chemical Reaction mixture h-clat Measurements EC200 (CD54), EC150 (CD86) % viability KeratinoSens Measurements Fold induction EC1.5 % viability 24 h DPRA Measurements %Cys peptide depletion %Lys peptide depletion %Ave.Lys.Cys depletion 8 THP-1 1x10 6 cells /ml Chemical exposure at 8 doses based on CV75 Flow cytometric analysis Cell staining (CD86 & CD54)
9 1 Machine Learning Approaches Support vector machine (SVM) Logistic regression Classification and regression tree Linear discriminant analysis Artificial neural network Naïve Bayes algorithm Evaluated test (~20%) and training sets (~80%) and performed leave-one-out cross-validation 9
10 Variables for Hazard Models 10 In chemico/in vitro/in silico data DPRA: [binary] and [continuous] (Avg.Lys.Cys, Cys, and Lys) KeratinoSens: [binary] h-clat: [binary] Read-across from OECD QSAR Toolboxv3.2: [binary] Six physicochemical properties Partition coefficient (LogP) [continuous] Water solubility (LogS) [continuous] Vapor pressure (LogVP) [continuous] Melting point (MP) [continuous] Boiling point (BP) [continuous] Molecular weight (MW) [continuous]
11 Variable Importance 13 Independent Variables hclat: hclat majority call [0/1] DPRA: DPRA majority call [0/1] Keratino: Keratino majority call [0/1] OECD: QSAR Toolbox [0/1] Ranked by Random Forest Assays Avg.Lys.Cys: avg Depletion Lys & Cys [0, 95.0] Lys: avg % Depletion Lys [0, 91.0] Cys: avg % Depletion Cys [0, 100] LogP: partition coefficient [-8.28, 6.46] LogS: water solubility [-6.39, 1.92] LogVP: vapor pressure [-28.47, 5.89] MW: molecular weight [30.03, ] MP: melting point [ , ] BP: boiling point [-19.1, 932.2] Properties 11
12 Evaluation of Prediction Performance Predicted Outcome Positive Negative Actual Classes Positive True positive False negative Negative False positive True negative Sensitivity = TP TP + FN Specificity = Accuracy = TN TN + FP TP + TN TP + FN + TN + FP 12 Models evaluated against reference data (54 models for LLNA and 36 for human) Individual in chemico, in vitro, and in silico results against reference data
13 Model Results for Hazard (Pos/Neg) Support vector machine had the best performance; logistic regression was very similar for the human models For LLNA, best 7 models had accuracy of 89-96% For human, best 6 models had accuracy of 92% DPRA (83% for LLNA and human) and h-clat (84% for LLNA and 81% for human) had the highest accuracy. LLNA accuracy for predicting human hazard was 84%. 13
14 Potency Models 120 chemicals with LLNA and 87 with human potency category data Approximately 1/3 strong sensitizers, 1/3 weak sensitizers, and 1/3 nonsensitizers Evaluated test (~20%) and training sets (~80%) and performed leave-one-out cross-validation 14
15 Variables for Potency Models In chemico/in vitro data DPRA: Avg.Lys.Cys [continuous] KeratinoSens: EC1.5 [continuous] h-clat: Minimum induction threshold for CD54 EC200 and CD86 EC150 [continuous] Six physicochemical properties [continuous] Four variable sets Six physicochemical properties + three assays 2. Six physicochemical properties 3. Three assays 4. Log P + three assays
16 Modeling Strategy One Tier Chemicals Nonsensitizers Sensitizers (weak) Sensitizers (strong) Accuracy LLNA: 78% for SVM with all assays and physicochemical properties Human: 75% for SVM with all assays and physicochemical properties and SVM with all assays + Log P For predicting human categories LLNA = 69% 16
17 Modeling Strategy - Two Tiers Chemicals Tier 1 Nonsensitizers Sensitizers 17 Strong Sensitizers Tier 2 Weak Sensitizers Accuracy LLNA: 88% for SVM with all assays and physicochemical properties Human: 81% for SVM with all assays and physicochemical properties and SVM with all assays + Log P For predicting human categories LLNA = 69%
18 Conclusions 18 The performance of the best support vector machine models were superior to Individual non-animal test methods The LLNA, the preferred and accepted stand-alone animal test For hazard prediction (binary), good performance could be obtained without using all of the in chemico and in vitro assays For human hazard prediction, Log P performed better than analogous models with all six physicochemical properties For potency category prediction (multi-class), physicochemical properties were less important than for the hazard models
19 Where Are These Models Now? Best model for predicting LLNA hazard was one of 12 case studies in OECD Guidance Document 256 Guidance Document on the Reporting of Defined Approaches and Individual Information Sources to be Used within Integrated Approaches to Testing and Assessment (IATA) for Skin Sensitisation, Annex 1 Cosmetics Europe evaluation of the case studies showed that performance for predicting human outcomes was equivalent or better than the LLNA OECD recently approved a project for development of a performance-based test guideline for defined approaches and test methods for skin sensitization 19
20 Acknowledgments - 1 ILS-NICEATM 1 Qingda (Dan) Zang Michael Paris Neepa Choksi Shannon Bell David Allen NTP Warren Casey Nicole Kleinstreuer ILS staff are supported by NIEHS contract HHSN C. 20
21 Acknowledgments - 2 ICCVAM Skin Sensitization Working Group Moiz Mumtaz (ATSDR) Patricia Ruiz (ATSDR) John Gordon (CPSC) Joanna Matheson (CPSC) Emily N. Reinke (DOD) Evisabel Craig (EPA) David Lehmann (EPA) Anna Lowit (EPA) Timothy McMahon (EPA) Todd Stedeford (EPA) Simona Bancos (FDA) Paul C. Brown (FDA) Rakhi M. Dalal-Panguluri (FDA) Wei Ding (FDA) Robert Heflich (FDA) Abigail C. Jacobs (FDA) Diego Rua (FDA) Nakissa Sadrieh (FDA) Stanislav Vukmanovic (FDA) Jeffrey Yourick (FDA) Warren Casey (NIEHS) Dori Germolec (NIEHS) Nicole Kleinstreuer (NIEHS) Elijah Petersen (NIST) ICATM Liaison Member Silvia Casati (EURL ECVAM) 21
22 22 Thank You!
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