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Modeling the effect of many component mixtures on survival in time (RP 4.1)

Modeling the effect of many component mixtures on survival in time (RP 4.1). Jan Baas Vrije Universiteit Amsterdam dept. of Theoretical Biology In cooperation with: Mieke Broerse, Tjalling Jager, You Song, Kees van Gestel, Bas Kooijman. Approach.

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Modeling the effect of many component mixtures on survival in time (RP 4.1)

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  1. Modeling the effect of many component mixtures on survival in time (RP 4.1) Jan Baas Vrije Universiteit Amsterdam dept. of Theoretical Biology In cooperation with: Mieke Broerse, Tjalling Jager, You Song, Kees van Gestel, Bas Kooijman

  2. Approach We developed a process based model to interpret the effects of binary mixtures on survival in time Environmental Toxicology and Chemistry, Vol. 26, No. 6, pp. 1320–1327, 2007 This model was extended to more components in the mixture

  3. Many compound mixture Aim: Predict the effect of a many compound mixture with the same working mechanism at any point in time! with as little information as possible Application: eg Narcotics like PAH, (chlorinated) hydrocarbons, aromatic compounds, larger aldehydes

  4. Principles of survival modeling Model based on internal concentrations and consists of two parts: - One compartment model for uptake and elimination - Hazard model giving the survival probability Per component 3 parameters are needed to describe the effect on survival in time - No Effect Concentration (mol/l) - Killing rate (1/(mol/l)) - Elimination rate (1/t)

  5. Survival probability NEC, k† and ke determine the survival probability (S) for each component. Smix equals the product of survival probability of all components in the mixture (corrected for the blank (Sb)). For a mixture with n compounds: Smix = S1 * S2 * S3 *…. Sn * Sb

  6. How to get the parameters needed Theoretical relationships for the parameters needed: log C0b = log C0a Pbow / Paow relation NEC log bb = log ba Paow / Pbow relation killing rate log keb = log kea√(Paow / Pbow) relation elimination rate If one parameter for one component in the mixture is known. The other parameters can be calculated. QSARS to check

  7. QSAR for NEC

  8. Application A pilot experiment was carried out by Mieke Broerse and You Song for three PAH. -) Folsomia Candida -) Measurements at different time points The mixture effect was calculated on the basis of the effect of 1 PAH.

  9. Preliminary conclusions • The pilot experiment showed a good agreement between measured and predicted mixture effect • The results suggest that the Effect Cancelling Capacity (i.e. NEC) must be “divided” over the components in the mixture • More mixture data are needed to further “validate” the modeling results and to describe the behavior of the NEC

  10. Future work Extension of the current models to different endpoints. Survival, growth, reproduction will be handled together using the updated DEB framework. Data collection in co-operation with: Mieke Broerse/Kees van Gestel VU Netherlands Claus Svendsen/Steve Sturzenbaum/ Suresh Swain/Dave Spurgeon Kings College UK Others??

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