| Title: | Modeling and Assessing Similarity of Drug Dissolutions Profiles |
|---|---|
| Description: | Implementation of a model-based bootstrap approach for testing whether two formulations are similar. The package provides a function for fitting a pharmacokinetic model to time-concentration data and comparing the results for all five candidate models regarding the Residual Sum of Squares (RSS). The candidate set contains a First order, Hixson-Crowell, Higuchi, Weibull and a logistic model. The assessment of similarity implemented in this package is performed regarding the maximum deviation of the profiles. See Moellenhoff et al. (2018) <doi:10.1002/sim.7689> for details. |
| Authors: | Kathrin Moellenhoff |
| Maintainer: | Kathrin Moellenhoff <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.0 |
| Built: | 2025-01-30 02:35:41 UTC |
| Source: | https://github.com/cran/SimDissolution |
(Artificial) Concentration data for 2 formulas, each including 12 tablets. Concentrations are measured at six points in time.
data(example_data)data(example_data)
A data frame with 24 rows and 8 variables including tablet, group and measurements at six points in time.
Function for computing the f2, time points have to be identical. Validity criteria of the f2 have to be be checked in advance. See Moellenhoff et al. (2018) <doi:10.1002/sim.7689>
f2(conc1, conc2)f2(conc1, conc2)
conc1, conc2
|
data frames containing the concentrations obtained for each of the two formulations |
a single value for the f2
Moellenhoff et al. (2018) <doi:10.1002/sim.7689>
data(example_data) conc1<-select(filter(example_data,Group=="1"),-Tablet,-Group) conc2<-select(filter(example_data,Group=="2"),-Tablet,-Group) f2(conc1=conc1,conc2=conc2)data(example_data) conc1<-select(filter(example_data,Group=="1"),-Tablet,-Group) conc2<-select(filter(example_data,Group=="2"),-Tablet,-Group) f2(conc1=conc1,conc2=conc2)
This function fits a pharmacokinetic model (dissolution profile) to time-concentration data using non-linear least squares regression. The model can be chosen from a candidate set containing a First order, Hixson-Crowell,Higuchi, Weibull and a logistic model. See Moellenhoff et al. (2018) <doi:10.1002/sim.7689> for details.
fit_pharm_mod(time, conc, m, plot = TRUE)fit_pharm_mod(time, conc, m, plot = TRUE)
time |
a vector containing the time points of measurements |
conc |
data frame or matrix containing the concentrations (see the example) |
m |
model type. Built-in models are "firstorder", "hixson", "higuchi", "weibull" and "logistic" |
plot |
plot of the model, default is TRUE. |
A list containing the model type and the obtained parameters, further the RSS for all possible models. Furthermore a plot is given.
Moellenhoff et al. (2018) <doi:10.1002/sim.7689>
data(example_data) conc1 <- select(filter(example_data,Group=="1"),-Tablet,-Group) time <- c(10,15,20,30,45,60) fit_pharm_mod(time,conc1,m="logistic")data(example_data) conc1 <- select(filter(example_data,Group=="1"),-Tablet,-Group) time <- c(10,15,20,30,45,60) fit_pharm_mod(time,conc1,m="logistic")
Function for testing whether two dissolution profiles are similar concerning the
hypotheses
$m_1$ and $m_2$ are pharmacokinetic models chosen from a candidate set containing a First order, Hixson-Crowell,Higuchi, Weibull and a logistic model.
See Moellenhoff et al. (2018) <doi:10.1002/sim.7689> for details.
sim_test(time1, time2 = time1, conc1, conc2, m1, m2, epsilon = 10, B = 1000, plot = FALSE)sim_test(time1, time2 = time1, conc1, conc2, m1, m2, epsilon = 10, B = 1000, plot = FALSE)
time1, time2
|
vectors containing the time points of measurements for each of the two formulations; if not further specified the time points are identical in both groups |
conc1, conc2
|
data frames or matrices containing the concentrations obtained for each of the two formulations (see the example) |
m1, m2
|
model types. Built-in models are "firstorder", "hixson", "higuchi", "weibull" and "logistic" |
epsilon |
positive argument specifying the equivalence threshold (in %), default is 10% corresponding to an f2 of 50 according to current guidelines |
B |
number of bootstrap replications. If missing, default value of B is 1000 |
plot |
if TRUE, a plot of the absolute difference curve of the two estimated models will be given. The default is FALSE. |
A list containing the p.value, the types of models, the f2, the maximum absolute difference of the models, the estimated model parameters, the number of bootstrap replications and a summary of the bootstrap test statistic. Furthermore plots of the two models are given.
Moellenhoff et al. (2018) <doi:10.1002/sim.7689>
EMA (2010) <https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-investigation-bioequivalence-rev1_en.pdf>
data(example_data) conc1 <- select(filter(example_data,Group=="1"),-Tablet,-Group) conc2 <- select(filter(example_data,Group=="2"),-Tablet,-Group) time <- c(10,15,20,30,45,60) sim_test(time1=time,time2=time,conc1=conc1,conc2=conc2,m1="logistic",m2="logistic",B=500,plot=TRUE)data(example_data) conc1 <- select(filter(example_data,Group=="1"),-Tablet,-Group) conc2 <- select(filter(example_data,Group=="2"),-Tablet,-Group) time <- c(10,15,20,30,45,60) sim_test(time1=time,time2=time,conc1=conc1,conc2=conc2,m1="logistic",m2="logistic",B=500,plot=TRUE)