Acanthamoeba spp: Dose Response Models

Acanthamoeba Spp.

Author: Sushil Tamrakar

General overview of Acanthamoeba spp.

Acanthamoeba is a microscopic, free-living amoeba that can cause rare, but severe infections of the eye, skin, and central nervous system. This amoeba is found worldwide in the environment in water and soil. It can be spread to the eyes through contact lens use, cuts, bruises or inhalation. Most people will be exposed to Acanthamoeba during their lifetime, but very few will become sick from this exposure

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Summary Data

Culbertson et al. (1966) studied pathogenicity of HN-3 strain of Hartmannlelid (Acanthamoeba) amoeba. The amoebae were inoculated intranasally to groups of mice and responses were recorded in terms of deaths, brain invasion, Acute Meningoencephalitis (AME) and Granulotomous Encephalitis

Experiment serial number Reference Host type Agent strain Route # of doses Dose units Response Best fit model Optimized parameter(s) LD50/ID50 270 mice intranasal 3 no of trophozoites death exponential k = 1.31E-04 5.28E+03 271 mice intranasal 3 no of trophozoites brain invasion exponential k = 9.26E-04 7.49E+02 272 mice intranasal 3 no of trophozoites acute meningoencephalitis exponential k = 2.6E-04 2.67E+03 *This model is preferred in most circumstances. However, consider all available models to decide which one is most appropriate for your analysis.

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Optimization Output for experiment 270

Dose response data Dose Dead Survived Total 100 2 78 80 500 4 76 80 1000 10 70 80

Goodness of fit and model selection Model Deviance Δ Degrees of freedom χ20.95,1 p-value χ20.95,m-k p-value Exponential 0.969 0.104 2 3.84 0.747 5.99 0.616 Beta Poisson 0.864 1 3.84 0.353 Exponential is preferred to beta-Poisson; cannot reject good fit for exponential.

Optimized k parameter for the exponential model, from 10000 bootstrap iterations Parameter MLE estimate Percentiles 0.5% 2.5% 5% 95% 97.5% 99.5% k 1.31E-04 5.59E-05 7.23E-05 8.06E-05 1.92E-04 2.03E-04 2.29E-04 ID50/LD50/ETC* 5.28E+03 3.03E+03 3.42E+03 3.62E+03 8.60E+03 9.58E+03 1.24E+04 *Not a parameter of the exponential model; however, it facilitates comparison with other models.

Parameter histogram for Exponential model (uncertainty of the parameter)

Exponential model plot, with confidence bounds around optimized model

Optimization Output for experiment 271

Dose response data Dose Brain invasion Non brain invasion Total 100 7 73 80 500 35 45 80 1000 44 36 80

Goodness of fit and model selection Model Deviance Δ Degrees of freedom χ20.95,1 p-value χ20.95,m-k p-value Exponential 2.46 1.15 2 3.84 0.284 5.99 0.292 Beta Poisson 1.31 1 3.84 0.252 Exponential is preferred to beta-Poisson; cannot reject good fit for exponential.

Optimized k parameter for the exponential model, from 10000 bootstrap iterations Parameter MLE estimate Percentiles 0.5% 2.5% 5% 95% 97.5% 99.5% k 9.26E-04 6.87E-04 7.39E-04 7.67E-04 1.11E-03 1.15E-03 1.22E-03 ID50/LD50/ETC* 7.49E+02 5.66E+02 6.05E+02 6.25E+02 9.04E+02 9.38E+02 1.01E+03 *Not a parameter of the exponential model; however, it facilitates comparison with other models.

Parameter histogram for exponential model (uncertainty of the parameter)

Exponential model plot, with confidence bounds around optimized model

Optimization Output for experiment 272

Dose response data Dose Acute meningoencephalitis NOT acute meningoencephalitis Total 100 2 78 80 500 8 72 80 1000 20 60 80

Goodness of fit and model selection Model Deviance Δ Degrees of freedom χ20.95,1 p-value χ20.95,m-k p-value Exponential 0.577 -0.00258 2 3.84 1 5.99 0.749 Beta Poisson 0.58 1 3.84 0.446 Exponential is preferred to beta-Poisson; cannot reject good fit for exponential.

Optimized k parameter for the exponential model, from 10000 bootstrap iterations Parameter MLE estimate Percentiles 0.5% 2.5% 5% 95% 97.5% 99.5% k 2.6E-04 1.49E-04 1.75E-04 1.85E-04 3.40E-04 3.58E-04 3.91E-04 ID50/LD50/ETC* 2.67E+03 1.77E+03 1.94E+03 2.04E+03 3.74E+03 3.96E+03 4.64E+03 *Not a parameter of the exponential model; however, it facilitates comparison with other models.

Parameter histogram for exponential model (uncertainty of the parameter)

Exponential model plot, with confidence bounds around optimized model

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Summary

The recommended model will be based on the endpoint of response. There were three different types of responses.

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