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-GeoTox
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-Installation
-You can install the development version of GeoTox from GitHub with:
-# install.packages("devtools")
-devtools::install_github("Spatiotemporal-Exposures-and-Toxicology/GeoTox")
-
-Example
-library(GeoTox)
-library(dplyr, warn.conflicts = FALSE)
-
-Estimate chemical concentration-response curves
-conc <- 10^rep(-2:2, each = 3)
-tp <- 100 # top asymptote
-ga <- 1.6 # AC50
-gw <- 1.2 # slope
-resp <- tp / (1 + (ga / conc)^gw) + rnorm(length(conc), sd = 5)
-
-fit_2param <- fit_hill(log10(conc), resp) # slope fixed at 1
-fit_3param <- fit_hill(log10(conc), resp, fixed_slope = FALSE)
-
-rbind(
- "inputs" = c(tp, log10(ga), gw, NA),
- "3-param" = c(fit_3param$par),
- "2-param" = c(fit_2param$par[1:2], 1, fit_2param$par[3])
-)
-#> tp logAC50 slope t-error
-#> inputs 100.00000 0.2041200 1.200000 NA
-#> 3-param 97.63578 0.1516374 1.485236 1.177891
-#> 2-param 101.53494 0.2360917 1.000000 1.417567
-
-Estimate population dose-response
-Input data
-# Number of samples to simulate
-MC_iter <- 10
-
-# Number of chemicals to simulate
-n_chem <- 4
-
-# Create age groups and group sizes
-age <- data.frame(
- AGEGRP = 0:18,
- TOT_POP = c(0, round(runif(18, max = 1000)))
-)
-age$TOT_POP[1] <- sum(age$TOT_POP[-1])
-
-# Create chemical exposure mean and sd
-exposure <- data.frame(
- mean = (1 + runif(n_chem))*1e-6,
- sd = (1 + runif(n_chem))*1e-7
-)
-
-# Create chemical concentration-response data
-conc_resp <- lapply(1:n_chem, function(idx) {
- conc <- 10^rep(-2:2, each = 3)
- tp <- 100 + rnorm(1, sd = 15)
- ga <- 10^(2 * runif(1) - 1)
- gw <- 1 + rnorm(1)/5
- resp <- tp / (1 + (ga / conc)^gw) + rnorm(length(conc))
- resp[resp < 0] <- 0
- data.frame(
- logc = log10(conc),
- resp = resp
- )
-})
-fits <- lapply(conc_resp, function(df) {
- fit_hill(df$logc, df$resp)
-})
-chem_params <- do.call(
- rbind,
- lapply(fits, function(fit) {
- as_tibble(t(unlist(fit))) %>%
- rename(
- tp = par.tp,
- tp.sd = sds.tp,
- logAC50 = par.logAC50,
- logAC50.sd = sds.logAC50
- ) %>%
- select(
- tp, tp.sd, logAC50, logAC50.sd,
- logc_min, logc_max, resp_min, resp_max, AIC
- ) %>%
- mutate(across(tp:AIC, ~ as.numeric(.x)))
- })
-)
-
-# Steady-state concentration (will be generated from httk)
-C_ss <- matrix(runif(n_chem * MC_iter), nrow = MC_iter)Simulate data
-# Simulate age based on relative population group sizes
-simulated_age <- simulate_age(
- age,
- n = MC_iter
-)
-
-# Simulate inhalation rate using default params
-simulated_IR <- simulate_inhalation_rate(
- simulated_age
-)
-
-# Simulate external exposure
-simulated_exposure <- simulate_exposure(
- exposure$mean,
- exposure$sd,
- n = MC_iter
-)Computations
-internal_dose <- calc_internal_dose(
- C_ext = simulated_exposure,
- IR = simulated_IR
-)
-
-invitro_concentration <- calc_invitro_concentration(
- D_int = internal_dose,
- C_ss = C_ss
-)
-
-concentration_response <- calc_concentration_response(
- resp = chem_params,
- concentration = invitro_concentration
-)
-
-concentration_response
-#> GCA.Eff IA.eff GCA.HQ.10 IA.HQ.10
-#> 1 1.316500e-04 1.316519e-04 1.197871e-05 1.197851e-05
-#> 2 1.928066e-04 1.928077e-04 1.756148e-05 1.756120e-05
-#> 3 1.509427e-04 1.509448e-04 1.339909e-05 1.339880e-05
-#> 4 1.931396e-04 1.931407e-04 1.742350e-05 1.742319e-05
-#> 5 2.000661e-04 2.000678e-04 1.748640e-05 1.748673e-05
-#> 6 9.090331e-05 9.090415e-05 8.634445e-06 8.634337e-06
-#> 7 6.336773e-05 6.336758e-05 5.870044e-06 5.869961e-06
-#> 8 1.501012e-04 1.501024e-04 1.365014e-05 1.364991e-05
-#> 9 7.009742e-05 7.009701e-05 6.962928e-06 6.962838e-06
-#> 10 7.310190e-05 7.310279e-05 6.848001e-06 6.847910e-06