pkpd.Release (version 0.1.0)

ld50_model: Lethal Dose 50 (LD50) Pharmacodynamic Model

Description

Fits quantal mortality data to a logistic dose-response model to estimate the Lethal Dose 50 (LD50), defined as the dose expected to cause death in 50

The model uses binomial logistic regression and supports optional grouping (e.g., species, sex, strain, formulation) and stratification by experimental conditions (e.g., exposure route, duration).

In addition to LD50 estimation, the model provides the following interpretable parameters:

• Slope: Represents the steepness of the dose-mortality relationship. A larger slope indicates a rapid transition from survival to lethality with increasing dose (high sensitivity and narrow safety margin), whereas a smaller slope reflects a more gradual increase in mortality, suggesting greater inter-individual variability in response.

• Intercept: Represents the baseline log-odds of mortality at zero dose. A strongly negative intercept indicates negligible background mortality, while a positive intercept suggests mortality occurring in the absence of administered dose, which may reflect experimental bias, underlying disease, or study design limitations.

• LD50 95% Confidence Interval: An approximate 95 interval for the LD50, computed using the delta method. This provides an uncertainty range around the estimated dose causing 50

• McFadden Pseudo-R\(^2\): A likelihood-based measure of model goodness-of-fit that quantifies the improvement of the fitted model over a null (intercept-only) model. Values between 0.1 and 0.2 indicate acceptable biological fit, while values above 0.3 suggest a strong and reliable dose-mortality relationship.

The function can generate dose-response plots with fitted curves and annotate LD50, slope, intercept, LD50 confidence intervals, and McFadden pseudo-R\(^2\) values.

Value

A list containing:

fitted_parameters

Data frame with LD50, 95% confidence intervals, slope, intercept, and pseudo-R2 values for each group.

data

The processed data used for model fitting and plotting.

Arguments

data

A data frame containing mortality response data.

dose_col

Character string specifying the administered dose column.

response_col

Character string specifying the binary mortality outcome (0 = alive, 1 = dead).

group_col

Optional character string specifying a grouping variable.

condition_col

Optional character string specifying an experimental condition.

plot

Logical; if TRUE, generates dose-mortality plots.

annotate

Logical; if TRUE, annotates the plot with LD50, confidence intervals, and model parameters (only if <=2 groups).

Author

Paul Angelo C. Manlapaz

References

Bliss, C. I. (1935) <doi:10.1111/j.1744-7348.1935.tb07713.x> The calculation of the dosage-mortality curve. Annals of Applied Biology, 22(1), 134–167.

Finney, D. J. (1971) <isbn:9780521080415> Probit Analysis, 3rd Edition. Cambridge University Press, Cambridge.

Examples

# Example I: Single-species acute toxicity study
df1 <- data.frame(
  dose = c(10, 25, 50, 100, 200),
  dead = c(0, 0, 1, 1, 1)
)
ld50_model(
  data = df1,
  dose_col = "dose",
  response_col = "dead"
)

# Example II: Sex-dependent LD50 analysis
df2 <- data.frame(
  dose = rep(c(10, 25, 50, 100), 2),
  dead = c(0, 0, 1, 1, 0, 0, 0, 1),
  sex = rep(c("Male", "Female"), each = 4)
)
ld50_model(
  data = df2,
  dose_col = "dose",
  response_col = "dead",
  group_col = "sex"
)

# Example III: Species and exposure route comparison
df3 <- data.frame(
  dose = rep(c(20, 40, 80, 160), 4),
  dead = c(0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1),
  species = rep(c("Rat", "Mouse"), each = 8),
  route = rep(c("Oral", "IP"), each = 4, times = 2)
)
ld50_model(
  data = df3,
  dose_col = "dose",
  response_col = "dead",
  group_col = "species",
  condition_col = "route"
)