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Process-based toxicity analysis in risk assessment

Explore the process-based toxicity analysis using Dynamic Energy Budget (DEB) theory for comprehensive risk assessment. Understand the relation between DEB, toxicants, and individuals' resource allocation, with advanced examples and laboratory insights.

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Process-based toxicity analysis in risk assessment

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  1. Process-based toxicity analysisin risk assessment Tjalling Jager Bas Kooijman Dept. Theoretical Biology

  2. Contents • Dynamic Energy Budget (DEB) theory • Current procedures in (eco)tox • Introduction to DEBtox • Advanced examples • The DEB laboratory

  3. Why DEB theory? How do individuals acquire and allocate their resources?

  4. Relation DEB and toxicants ?

  5. Relation DEB and toxicants ?

  6. Relation DEB and toxicants ?

  7. food faeces reserves  1- maturity offspring structure Dynamic Energy Budgets assimilation somatic maint. maturity maint.

  8. DEB pillars • Quantitative theory; “first principles” • time, energy and mass balance • Life-cycle of the individual • links levels of organisation: molecule  ecosystems • Comparison of species • body-size scaling relationships; e.g. metabolic rate • Fundamental to biology; many practical applications • (bio)production,(eco)toxicity, climate change …

  9. Chemical-related projects at TB • Dutch government (RWS and RIVM) • biaccumulation metals in mussels; biomonitoring • toxicokinetics dioxin in humans • Dutch Technology Foundation STW • DEBdeg (bio)degradation of (toxic) compounds • DEBtum tumour induction/growth, analysis tox data • DEBtox indpop (reprod. modes in nematodes) • EU Projects • ModelKey effects on ecosystems and food chains • NoMiracle mixture toxicity More info: http://www.bio.vu.nl/thb/research/project/

  10. Current procedures in (eco)tox

  11. “RISK” Risk assessment EXPOSURE EFFECTS

  12. Process parameters at env. conditions Integrated model for system Exposure assessment Lab. experiments PEC

  13. Contr. NOEC * LOEC Standard approaches 1. Statistical testing Response log concentration

  14. What’s wrong with NOEC? • No statistically significant effect is not no effect • Effect at NOEC regularly 10-34%, up to >50% • Inefficient use of data • only last time point, only lowest doses • for non-parametric tests also values discarded OECD Braunschweig meeting 1996: NOEC is inappropriate and should be phased out!

  15. EC50 Standard approaches 1. Statistical testing 2. Curve fitting Response log concentration

  16. What’s wrong with ECx? Regression model is purely empirical • No estimation of process parameters • not possible to extrapolate to env. conditions • Inefficient use of data (last time point only) • ECx depends on exposure time

  17. 1 0.9 0.8 0.7 0.6 fraction surviving 24 hours 0.5 0.4 0.3 48 hours 0.2 0.1 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 concentration Effects change in time Nonylphenol, survival

  18. chemical B internal concentration chemical C time Why does LC50 decrease? Toxicokinetics • effects are related to internal concentrations • kinetics depend on chemical chemical A

  19. Daphnia chemical B small fish internal concentration large fish chemical C time Why does LC50 decrease? Toxicokinetics • effects are related to internal concentrations • kinetics depend on chemical • and species … chemical A

  20. carbendazim pentachlorobenzene 2.5 140 120 2 100 survival 1.5 80 body length body length 60 1 40 0.5 20 cumul. reproduction cumul. reproduction 0 0 0 5 10 15 20 0 2 4 6 8 10 12 14 16 time (days) time (days) Sub-lethal EC10 in time does not necessarily decrease in time …

  21. Consequences Procedures are inefficient • Test protocols yield more data than are used NOEC and LCx/ECx are not representative • Change in time, depending on species, body size, chemical and endpoint Standard exposure time leads to systematic error • in comparing effects • between chemicals (comparative RA, QSARs …?) • between species (SSDs … ?) OECD Braunschweig meeting 1996: Exposure time should be incorporated in data analysis

  22. Introduction to DEBtox

  23. DEBtox OECD Braunschweig meeting 1996: Exposure time should be incorporated in data analysis Mechanistic models should be favoured if they fit the data • Windows software, version 1.0 in 1996, version 2.0.1 in 2004 • Included in draft ISO/OECD guidance on statistical analysis of ecotox data

  24. Why process-based? Understand toxic effects • biology of organism and toxic mechanisms Match experimental set-up • e.g. degradation, pulse exposure Predictions for exposure situation • e.g. populations, food level, varying exposure

  25. toxicokinetics  DEBtox basics • Effect depends on internal concentration • one-compartment model

  26. target parameter  DEBtox basics • Chemical affects a parameter in DEB • e.g. maintenance rate toxicokinetics 

  27. DEB model  DEBtox basics • Change in target parameter affects endpoint • survival, reproduction, growth toxicokinetics  target parameter 

  28. assimilation  maintenance costs growth costs reproduction costs  hazard to embryo    hazard (lethal effects)  tumour induction  tumour endocrine disruption Modes of Action food faeces assimilation reserves  1- somatic maint. maturity maint. maturity offspring structure

  29. Windows version • User-friendly software, freely downloadable • Only for standard tests • acute survival • Daphnia reproduction • fish growth • algal population growth

  30. Example: survival dieldrin concentration (µg/L) time (d)

  31. Example: survival dieldrin

  32. 0 d 1 d 2 d 3 d 4 d 5 d 6 d 7 d Example: survival dieldrin NEC 5.2 (2.7-6.9) µg/L Killing rate 0.038 L/(µg d) Elim. rate 0.79 d-1 Blank haz. 0.0084 d-1

  33. Example: survival nonylphenol time concentration (mg/L)

  34. 0 hrs 24 hrs 48 hrs Example: survival nonylphenol NEC 0.14 (0.094-0.17) mg/L Killing rate 0.66 L/(mg h) Elim. rate 0.057 h-1

  35. Example: survival nonylphenol NEC LC50 LC0

  36. Example: repro cadmium Mode of action costs for repro NEC 3.3e-9 (0-0.017) mM Tolerance 4.7e-9 mM Max. repro 14 offspring/d Elim. rate 2.6e-9 d-1

  37. Example: repro cadmium EC0 EC50

  38. Advantages DEBtox For the standard software • Make efficient use of all data points • more accurate parameter estimates • reduce number of test animals … • More information obtained • ECx at any time point can be calculated • mode of action; crucial for population response • Characterisation of effects • time-independent NEC may replace NOEC and ECx

  39. Advanced examples

  40. DEBtox extensions Simultaneous fits on more data sets • endpoints, chemicals, species … Fit deviating experimental data • degradation, pulse exposure … Extrapolations • time, food level, temperature, (species) … At this moment only available as MatLab scripts

  41. Simultaneous fits Survival and body residues for cadmium (Heugens et al.) NEC on internal basis: 259 mg/kg dwt (202-321)

  42. 0 mg/L 1 0.8 3 mg/L 0.6 fraction surviving 0.4 4 mg/L 0.2 5 mg/L 10 mg/L 0 0 20 40 60 80 100 time (hours) Extrapolation From continuous exposure to a 20-hour pulse

  43. simultaneous fits Survival for 5 OP esters (data De Bruijn & Hermens) Same NEC, elim. rate, killing rate, receptor repair rate Different affinity for receptor

  44. 120 body size 1 100 0.8 80 0.6 60 0.4 40 0.2 20 0 0 0 2 4 6 8 10 12 14 16 0 5 10 15 survival reproduction simultaneous fits Reproduction test with cadmium (data Heugens et al.) Mode of action decrease assimilation

  45. 0.4 0.3 population growth rate (1/day) 0.2 0.1 0 0 0.05 0.1 0.15 0.2 concentration Extrapolations To populations and limiting food 90% food 80% food

  46. Body length Cumulative offspring Fraction surviving High food Low food Simultaneous fits Fenvalerate pulse at two food levels (data Pieters et al.) • Mode of action: assimilation • NEC survival: 0.42 µg/L • NEC growth/repro: 0.051 µg/L • Insights • intrinsic sensitivity independent of food • chemical effects fully reversible

  47. NEC impact population growth rate PEC concentration Opportunities 1:Relevant endpoint • ecologically relevant • time independent • integrate endpoints • comparable between chemicals

  48. impact impact PEC PEC Opportunities 1:Relevant endpoint • ecologically relevant • time independent • integrate endpoints • comparable between chemicals NEC population growth rate concentration

  49. exposure time survival time Opportunities 2:Match exposure scenario

  50. Opportunities 3:Reduce testing needs? • Use all of the data points • more data points per parameter • less animals needed • Less need to discard ‘poor’ data • disappearance of test compound • change in body weight of test organism • combine low-quality data sets • Less need for new tests • better extrapolations from lab data • opportunities for QSAR development …

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