Systems Modeling: a perspective from analysis of ToxCast assays

Transcription

1 Systems Modeling: a perspective from analysis of ToxCast assays Thomas B. Knudsen National Center for Computational Toxicology The views expressed in this presentation are those of the author[s] and do not necessarily reflect the views or policies of the U.S. Environmental Protection Agency. Officeof Research and Development NationalCenter for Computational Toxicology NAS Workshop, Mixtures and Cumulative Risk Assessment: approaches using the latest science and thinking about pathways Washington DC, July 27-28, 2011

2 Predictive Toxicology v National Academy of Sciences Report (2007) Toxicity Testing in the Twenty-first Century: A Vision and a Strategy v move away from testing products on animals and towards a more relevant, scalable, cost-effective and humane approach v assess chemicals by early changes in molecular function, biological pathways and cellular processes v Tox21: partnership of EPA (Computational Toxicology Program), NIEHS (National Toxicology Program), NCGC (NIH National Chemical Genomics Center ), and FDA 2

3 HTS paradigm NCGC vast data on many chemicals high-throughput screening fast screening of chemical libraries SCALE high-content screening detailed imaging, arrays PARALLELISM challenge interpreting all of the data -pathway profiling - text-mining, data-mining bioinformatics, pathways systems modeling 3

4 Chemical property dimension ToxCast_Phase I_v2_293 4 LOG P = octanol/water partition coefficient 3 TPSA = log (Total Polar Surface Area) COMPLEXITY 2 Complexity = log (complexity based on paths, branching, atoms) TPSA 1 0 LOGP SOURCE: Ann Richard, NCCT 4

5 Chemical property dimension ToxCast_Phase I_v2_293 ToxCast_Phase II_667 4 LOG P = octanol/water partition coefficient 3 TPSA = log (Total Polar Surface Area) COMPLEXITY 2 Complexity = log (complexity based on paths, branching, atoms) TPSA 1 0 LOGP SOURCE: Ann Richard, NCCT 5

6 Chemical property dimension LOG P = octanol/water partition coefficient TPSA = log (Total Polar Surface Area) Complexity = log (complexity based on paths, branching, atoms) COMPLEXITY TPSA 1 0 ToxCast_Phase I_v2_293 ToxCast_Phase II_667 LOGP SOURCE: Ann Richard, NCCT ToxCast_111 pharma 6

7 Chemical property dimension LOG P = octanol/water partition coefficient TPSA = log (Total Polar Surface Area) Complexity = log (complexity based on paths, branching, atoms) COMPLEXITY TPSA 1 0 ToxCast_Phase I_v2_293 ToxCast_Phase II_667 LOGP Tox21_ SOURCE: Ann Richard, NCCT ToxCast_111 pharma 7

8 ToxCast: Phase-I assays QSAR exposure metrics Predictive Model RTK Q-dose response In vivo endpoints ToxRefDB_rat ToxRefDB_rabbit human 1 o cell co-culture Complex culture / SMOs murine ES cells zebrafish embryos cellular impedance Cell-based assays cellular properties transactivation NPAs Biochemical HTS CYP450s GPCRs NRs Kin-Phs proteases TRs / ICs 8

9 ToxCast: Phase-I data analysis Assays Emax AC50 Chemicals 828 Assay-Chemical Pairs had AC50s of less than 1µM : Assay-Chemical Hit Concentration ~500 Assays X 320 Chemicals X {5-18} conc X {1-3} Replicates X {1-3} Time Points 3.2 Million Data Points 9

10 ToxCast: predictive models v In vivo effects models (ToxRefDB) liver tumors: Judson et al. 2010, Env Hlth Persp 118: rat fertility: Martin et al. 2011, Biol Reprod 85: rat-rabbit prenatal devtox: Sipes et al (submitted) v Mechanistic models (Pathways) endocrine disruption: Reif et al. 2010, Env Hlth Persp nuclear receptor pathway: Shah et al. 2011, PLoS One 6(2): e14584 mesc differentiation: Chandler et al. 2011, PLoS One 6(6): e18540 angiogenesis: Kleinstreuer et al. 2011, Env Hlth Persp (in press) ToxCast data and a running list of publications can be accessed from the NCCT website 10

11 Predictive model: rat fertility v continuous exposure of M/F rats from 10-wks premating through lactation in 2 nd generation [OPPTS & OECD 416] v adverse effects on M/F fertility & generational comparisons in ToxRefDB [Martin et al. (2009) Toxicol Sci 110: ] v OECD evaluation of proposed Extended One-Generation Reproductive Toxicity Study (EOGRTS) and retrospective analysis to evaluate impact of the 2 nd generation on risk assessments, and classification & labeling (C&L) in Europe [Piersma et al. (2011) Reprod Toxicol PMID: ] 11

12 Fertility class data: ToxRefDB 650 unique effects on 353 chemicals o o o reproductive performance (fertility, mating, gestation interval) reproductive tract effects (testis, epididymis, ovary, uterus, sperm metrics) developmental landmarks (preputial separation, vaginal opening, AGD) SOURCE: Martin et al. (2011) Biol Reprod 85:

13 Fertility model 13

14 External Validation Set External validation set Comparison to EU Repro C&L 14

15 Predictive Model: prenatal DevTox v gestational exposure (rat, rabbit) [OPPTS & OECD 414] 15

16 Feature Selection o linear discriminant analysis (LDA) and five-fold cross-validation on 80/20 split o BA: balanced accuracy (sensitivity-specificity) SOURCE: Sipes et al (submitted) 16

17 Features in the model 17

18 DevTox Feature map univariate model multivariate model 18

19 Why we need systems models INPUT (I) OUPUT (O) STIMULUS RESPONSE components network graph functional model

20 Replicating cellular behavior macrophage navigating RBCs toward a microbial pathogen simple CC3D model 20

21 Blood Vessel Development SOURCE: Kleinstreuer et al Env Hlth Persp (in press) 21

22 CC3D Vascular Model: Cellular Behavior Molecular Signal Expression Chemotaxis Proliferation VEGF165 MC IC ECt ECs MC Proteases ECs ECt VEGF121 ECs ECt MC IC ECt ECs Flt1 (VEGFR1) ECs CCL2 ECs IC IC MC IC CXCL10 IC ECs Ang1 MC Tie2 ECs MC upar ECs ECt KDR (VEGFR2) ECs ECt ECs Endothelial Stalk (ECs) Endothelial Tip (ECt) Mural Cell (MC) Inflammatory Cell (IC) 22

23 Vascular Plexus ECs ECt MC IC 23

24 Vascular Plexus ECs MC VEGF 165 ECt IC Ang1/ Tie2 MMPs CXCL10 VEGF 121 CCL2 24 sflt1

25 Reconstructing Toxicity: Utilizing in vitro HTS data ToxCast Assays Cell behavior Model Parameters Tie2 CXCL10 upar Endothelial Proliferation VCAM1 Ang1 receptor expressed by ECs, controls MC adhesion and proliferation competitive binding with VEGF on heparanglycan site of ECs, ECt EC receptor controlling interaction and movement along extra-cellular matrix Increase in EC volume and mitosis in response to growth factors Cellular adhesion molecule influencing IC,MC, ECs adherence Tie2.secretion (ECs), chemotactic strength (MC) CXCL10.secretion (IC, MC) Motility (ECs, ECt) ECs.targetVolume, mitotic threshold J:MC, ECs; J:IC, ECs Flt1 (VEGFR1) Decoy receptor expressed by ECs, increased expression adjacent to ECt, sequesters VEGF PBMC Cytotox Increase in apoptosis rate of IC IC.apoptosisrate CCL2 Mural Proliferation Growth factor and chemoattractantfor ECs, ECt, MC Increase in MC volume and mitosis in response to growth factors Flt1.secretion (ECs), VEGF.fieldcoupling CCL2.secretion, chemotaxis(ic, MC, ECs, ECt) MC.targetVolume, mitotic threshold KDR (VEGFR2) Major signallingreceptor for VEGF: controls cell growth, chemotaxis, secretion, tip cell expression ECs.targetVolume, VEGFsol.secretion (ECs), chemotacticstrength (ECt), ECt.celltype, J:ECt,ECs 25

26 HTS in vitro data: Thalidomide & 5HPP-33 >2-fold change 4 µm 40 µm Up Down 26

27 Thalidomide In silico simulations 5HPP-33 4 µm control 4 µm 40 µm 40 µm 27

28 Endothelial cell number & VEGF Thalidomide 5HPP-33 EC num CoV 4µM : 3.5% 40µM : 5.9% CoV 4µM: 2.6% 40µM : 0.3% VEGF conc CoV 4µM : 7.1% 40µM : 9.3% CoV 4µM : 5.4% 40µM : 6.2% 28

29 In vitro In silico Control Control Thalidomide (100 µm) Thalidomide (40 µm) 5HPP-33 (100 µm) 5HPP-33 (40 µm) Noguchi et al. (2005) Virtual Embryo simulation 29

30 In silico predictions: ToxCast chemicals endothelial connectivity (plexus), in-degree (branching), vessel uniformity (width), sprouting control weakened mural adhesion to nascent vessels; altered endothelial growth and spreading behavior, lack of sprouting due to Flt1 inhibition endothelial hyperplasia with decreased cell migration and polarization possibly due to increased upar enhances ECM locking little to no vessel formation TxC-P TxC-T TxC-Y 30

31 Mixed effects model 31

32 Mixed effects simulation 32

33 Effect Quantification Total Cell Number Average (multiple simulations) Control PFOS Triclosan Mixture 33

34 NCCT credits Bob Kavlock David Dix Richard Judson Tom Knudsen Amar Singh (LHM) Michael Rountree (SSC) Richard Spencer (LHM) Nikal Kleinstreuer Nisha Sipes Kelly Chandler (NHEERL) Keith Houck Matt Martin David Reif Imran Shah Ann Richard Jim Rabinowitz John Wambaugh Woody Setzer John Jack Monica Linnenbrink Stephen Little) 34