pkpd.Release (version 0.1.0)

first_order_release: First-Order Drug Release Kinetic Model

Description

Fits experimental cumulative drug release data to a first-order kinetic model using linear regression on the log-transformed unreleased fraction. The function supports optional grouping (formulation/batch) and pH-dependent analysis. It can generate plots with straight lines and annotations for first-order rate constant (k1), intercept, coefficient of determination (R^2), and time required for 50-percent drug release (t50).

Value

A list containing:

fitted_parameters

Data frame with k1, intercept, R^2, and t50 values for each group or pH condition.

data

The processed data used for model fitting and plotting.

Arguments

data

A data frame containing experimental drug release data.

time_col

Character string specifying the column name for time.

log_remain_col

Column name for log cumulative percent drug remaining.

group_col

Optional character string specifying a column for grouping (e.g., formulation/batch).

pH_col

Optional character string specifying a column containing pH values.

plot

Logical; if TRUE, generates a plot of experimental data with first-order fitted curves.

annotate

Logical; if TRUE, annotates the plot with k1, intercept, R^2, and t50 (only if <= 2 groups).

Author

Paul Angelo C. Manlapaz

References

Ostwald, W. (1884) <doi:10.1002/prac.18840290139> Studien zur chemischen Dynamik. Journal für Praktische Chemie, 29(1), 385–408.

Noyes, A. A., & Whitney, W. R. (1897) <doi:10.1021/ja02086a003> The rate of solution of solid substances in their own solutions. Journal of the American Chemical Society, 19(12), 930–934.

Examples

# Example I: Single formulation
df_1 <- data.frame(
  time = c(0, 15, 30, 45, 60, 90, 120, 150, 180),
  log_remain = c(2, 1.947, 1.899, 1.840, 1.780, 1.625, 1.447, 1.182, 0.813)
)
first_order_release(
  data = df_1,
  time_col = "time",
  log_remain_col = "log_remain"
)

# Example II: Two formulations (grouped, not pH-dependent)
df_2 <- data.frame(
  time = rep(c(0, 30, 60, 90, 120, 150), 2),
  log_remain = c(
    2.00, 1.84, 1.73, 1.53, 1.37, 1.25,  # Formulation A
    2.00, 1.88, 1.76, 1.67, 1.53, 1.39   # Formulation B
  ),
  formulation = rep(c("Formulation A", "Formulation B"), each = 6)
)
first_order_release(
  data = df_2,
  time_col = "time",
  log_remain_col = "log_remain",
  group_col = "formulation"
)

# Example III: pH-dependent first-order release
df_pH <- data.frame(
  time = rep(c(0, 60, 120, 180), 2),
  log_remain = c(
    2.00, 1.74, 1.38, 1.11,  # pH 7.4
    2.00, 1.84, 1.66, 1.48   # pH 4.5
  ),
  pH = rep(c(7.4, 4.5), each = 4)
)
first_order_release(
  data = df_pH,
  time_col = "time",
  log_remain_col = "log_remain",
  pH_col = "pH"
)

# Example IV: Two formulations under two pH conditions
df1 <- data.frame(
  time = rep(c(0, 30, 60, 90, 120, 150, 180), 2),
  log_remain = c(
    2.000, 1.918, 1.842, 1.755, 1.685, 1.598, 1.520,
    2.000, 1.865, 1.748, 1.612, 1.488, 1.352, 1.225
  ),
  pH = rep(c(4.5, 7.6), each = 7)
)
df2 <- data.frame(
  time = rep(c(0, 20, 40, 60, 80, 100, 120), 2),
  log_remain = c(
    2.000, 1.936, 1.872, 1.806, 1.742, 1.675, 1.610,
    2.000, 1.882, 1.760, 1.645, 1.522, 1.408, 1.295
  ),
  pH = rep(c(4.5, 7.6), each = 7)
)
df_all <- rbind(
  cbind(formulation = "Dataset 1", df1),
  cbind(formulation = "Dataset 2", df2)
)
first_order_release(
  data = df_all,
  time_col = "time",
  log_remain_col = "log_remain",
  group_col = "formulation",
  pH_col = "pH"
)