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Development of the Fathead Minnow Narcosis Toxicity Data Base

Development of the Fathead Minnow Narcosis Toxicity Data Base. Larry Brooke 1 , Gilman Veith 2 , Daniel Call 3 , Dianne Geiger 1 , and Christine Russom 4 1 University of Wisconsin-Superior, 2 QSAR foundation, 3 University of Dubuque, and 4 U.S. EPA Mid-Continent Ecology Laboratory.

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Development of the Fathead Minnow Narcosis Toxicity Data Base

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  1. Development of the Fathead Minnow Narcosis Toxicity Data Base Larry Brooke1, Gilman Veith2, Daniel Call3, Dianne Geiger1, and Christine Russom4 1University of Wisconsin-Superior, 2QSAR foundation, 3University of Dubuque, and 4U.S. EPA Mid-Continent Ecology Laboratory

  2. Applying Predictive Data Mining to Predictive ToxicologyFrom Narcosis to McKim Conference Chihae Yang 28th June, 2006

  3. Acknowledgment • Gilman Veith, International QSAR Foundation • J.F. Rathman, The Ohio State University • Leadscope team • Ohio Technology Action Fund

  4. From Meyer-Overtone to McKim Conference • Narcosis • …”toxicity of neutral organics is related to their ability to partition between water and a lipophilic biphase where molecules exert their activity…” • Model system for partition: olive oil/water. • Evolution Narcosis Non-polar and polar narcosis Reactivity ……

  5. Paradigm shift • How do we strategically leverage? • How do we read across the species, endpoints, structural classes, different knowledge domains? In silico In vitro In vivo Omics

  6. Predictive data mining strategies structural descriptions analogs chemical stressor profile biological/environmental fate Yang, C.; Richard, A.M., Cross, K.P. Current Computer-Aided Drug Design,2006, 2, 1-19.

  7. Steps in predictive data mining Structure, data, graphs, models Visualization SAR & QSAR Profiling Grouping Analysis Hypothesis driven queries Analog searching Read across Searching Chemistry Biology integration Knowledge addition Relational database Platform

  8. Data mining analysis methods Focused Data Sets Compound grouping Analysis QSAR Prediction Classification Rule Extraction Classification Clustering Expert Grouping Pattern Recognition Profiling Large diverse Data Sets

  9. Applying to predictive tox • Profiling “chem-bio” domain • Cut across different knowledge domains • Find hidden signals and relationships from data • Qualify/quantify read-across • Complementary to (Q)SAR • Build hypothesis driven models • Go beyond Yes/No question and answer

  10. Predictive data mining examples • Biological profile • Relationships between fish narcosis and toxicological findings in rat inhalation studies? • Fathead minnow EPA dataset • Rat acute toxicity dataset from RTECS • Thermodynamics consideration

  11. Theoretical bases:Vapor-liquid equilibrium • Non-ideal Raoult’s law: • - The equilibrium distribution between liquid and vapor phases for a chemical species i gi : activity coefficient xi : mole fraction of i in the liquid phase piv : vapor pressure of pure liquid i at the same temperature T yi : mole fraction in the vapor phase.

  12. Study sources for rat and FHM correlations - rat exposure time 2-8 hours - narcosis RTECS 2006 2341 921 • single dose • inhalation • chamber EPA FHM 617 76 • dose unit (mg/mL) • defined LD50 179 LC50 at 96 hr

  13. Profiling examples

  14. N N N Representing structures with Leadscope molecular descriptors Benzenes Functional groups Heterocycles Pharmacophores Spacers User defined features

  15. Read-across using structural descriptors profiles of rat organ lesions LC50 FHM Structural descriptors

  16. 23 structural descriptors were selected.

  17. Quantitative read-across Liver kidney ubl Lung GI pLC50 Rat pLC50 FHM

  18. From a surface scientist point of view • Passive diffusion through lipid bilayer • Headgroup interaction • Hydrophobic tail interaction • Hydrophilic to lipophilic balance (HLB) • Partition model of molecules in lipid layer :

  19. UNIFAC activity coefficient model “residual” term “combinatorial” term molecular volume and surface area effects (size, shape, packing) intermolecular energy effects (interaction) The properties of Gases & Liquids, 4th ed., R. Reid, J. Prausnitz, B. Poling, McGraw Hill, 1987

  20. Advantages of UNIFAC model • Group contribution method • Molecular descriptors-based activity coefficients • Flexibility to vary liquid phases compositions • octanol/water • octanol-water solution/water • hexadecane/water • lipid/water • etc.

  21. Example: Lipid as a solvent phase

  22. O O H Example of activity coefficients in various environment Activity coefficients at infinite dilution can be used to model solubility in various phases.

  23. measured LogP LogP (ow/w) LogP (o/w) LogP (h/w) LogP (dppc/w)

  24. Reflection …We’re committed to nothing less than a point-for-point transcript of everything there is. Only one problem: the index is harder to use than the book. We’ll live to see the day when retrieving from the catalog becomes more difficult than extracting from the world that catalog condenses…. “The gold bug variations, Richard Powers”, 2004

  25. Distribution of LC50s for FHM and rats pLD50 of rats pLC50 of FHM Mean: 1.52 Mean: 0.669

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