| Type: | Package |
| Title: | In Vitro in Vivo Correlation Linear Level "A" |
| Version: | 0.9.1 |
| Date: | 2022-04-24 |
| Author: | Aleksander Mendyk <mfmendyk@cyf-kr.edu.pl>, with contributions from Sebastian Polak <mfpolak@@cyf-kr.edu.pl>. |
| Maintainer: | Aleksander Mendyk <mfmendyk@cyf-kr.edu.pl> |
| Depends: | signal, compiler |
| Suggests: | graphics |
| Description: | It is devoted to the IVIVC linear level A with numerical deconvolution method. The latter is working for inequal and incompatible timepoints between impulse and response curves. A numerical convolution method is also available. Application domains include pharamaceutical industry QA/QC and R&D together with academic research. |
| License: | GPL (≥ 3) |
| NeedsCompilation: | no |
| Packaged: | 2022-04-23 23:01:17 UTC; olo |
| Repository: | CRAN |
| Date/Publication: | 2022-04-23 23:50:10 UTC |
IVIVC LEVEL A
Description
This package performs linear iv vitro in vivo correlation of linear level A. It provides numerical convolution/deconvolution procedures with unequal time steps and no assumptions about the function shapes.
Details
| Package: | Rivivc |
| Type: | Package |
| Version: | 0.9 |
| Date: | 2012-10-03 |
| License: | GPLv3 |
Author(s)
Aleksander Mendyk and Sebastian Polak
Maintainer: Aleksander Mendyk <mfmendyk@cyf-kr.edu.pl>
References
Langenbucher (2003) F. Handling of computational in vitro/in vivo correlation problems by Microsoft Excel: III. Convolution and deconvolution. Eur J Pharm Biopharm. 56, 429-37.
Numerical convolution
Description
Performs numerical convolution independent of the sampling points but requiring the same timescale of the input and impulse profiles.
Usage
NumConv(impulse.matrix,input.matrix,conv.timescale = NULL,
explicit.interpolation = 1000)
Arguments
impulse.matrix |
matrix of the PK profile after the drug intravenous (i.v.) administration |
input.matrix |
cumulative in vivo absorption profile |
conv.timescale |
a timescale of convolution defined either as a whole vector with specific timepoints |
explicit.interpolation |
sampling accuracy used by the interpolation method to find the same timepoints for input and impulse profiles |
Value
Output values are:
$par |
convolved time profile based on the original timescale |
$par_explicit |
provides convolution with the explicit interpolation |
Author(s)
Aleksander Mendyk and Sebastian Polak
See Also
Examples
require(Rivivc)
require(graphics)
#i.v. data
data("impulse")
#p.o. PK profile
data("resp")
#in vitro dissolution for correlation purposes
data("input")
#preparing data matrices
input_mtx<-as.matrix(input)
impulse_mtx<-as.matrix(impulse)
resp_mtx<-as.matrix(resp)
#setting interpolation accuracy
accur_explic<-1000
#run convolution
result<-NumConv(impulse_mtx,input_mtx,explicit.interp=accur_explic)
print("Raw results")
print(result$par)
print("Raw results explicit")
print(result$par_explicit)
dev.new()
plot(resp_mtx)
lines(result$par, type="l", col="blue")
dev.new()
plot(resp_mtx)
lines(result$par_explicit, type="l", col="blue")
Numerical deconvolution method
Description
Numerical deconvolution method based on the convolution and the optim() BFGS method to find in vivo absorption profile through the convolution approach. The function works iteratively with the cumulative in vivo absorption profile optimization performed by the BFGS method in regard to the convolved PK profile and its proximity to the real known p.o. profile.
Usage
NumDeconv(impulse.matrix,resp.matrix,dose_iv=NULL,dose_po=NULL,
deconv.timescale = NULL, explicit.interpolation = 20,
implicit.interpolation = 10, optim.maxit = 200)
Arguments
impulse.matrix |
matrix of the PK profile after the drug intravenous (i.v.) administration |
resp.matrix |
PK profile after oral (p.o.) administration of the drug |
dose_iv |
drug dose after i.v. administration; not obligatory but if provided must be in the same units like the dose p.o. |
dose_po |
drug dose after p.o. administration; not obligatory but if provided must be in the same units like the dose i.v. |
deconv.timescale |
a timescale of deconvolution defined either as a whole vector with specific timepoints |
explicit.interpolation |
deconvolution explicit interpolation parameter, namely number of the curve interpolation points used directly by the |
implicit.interpolation |
implicit interpolation - a factor multiplying |
optim.maxit |
maximum number of iterations used by |
Details
This method is an empirical approach to the deconvolution method with minimum mechanistic assumptions. Yet the latter involve kinetics linearity when the doses of i.v. and p.o. are different, thus the i.v. profile is scaled by multiplication with the factor of dose_po/dose_iv. It is also important to know that large values of explicit and/or implicit accuracy lead to the long execution times. The recommended values are explicit = 20 and implicit = 10, however this is only a rule of thumb used here. When looking for higher accuracy it is advisable to increase implicit interpolation prior to the explicit.
Value
Three matrices are returned at the output of the function:
$par |
represents original timescale provided at the input |
$par_explicit |
provides deconvolution with the explicit interpolation |
$par_implicit |
provides deconvolution with the implicit interpolation |
Author(s)
Aleksander Mendyk and Sebastian Polak
See Also
Examples
require(Rivivc)
require(graphics)
#i.v. data
data("impulse")
#p.o. PK profile
data("resp")
#in vitro dissolution for correlation purposes
data("input")
#preparing data matrices
input_mtx<-as.matrix(input)
impulse_mtx<-as.matrix(impulse)
resp_mtx<-as.matrix(resp)
#setting accuracy for both interpolation modes
accur_explic<-10
accur_implic<-5
#for deconvolution
result<-NumDeconv(impulse_mtx,resp_mtx,explicit.interp=accur_explic,implicit.interp=accur_implic)
print("Raw results")
print(result$par)
print("Explicit interpolation")
print(result$par_explicit)
print("Implicit interpolation")
print(result$par_implicit)
#let's compare the deconvolved curve with known input
dev.new()
plot(input_mtx)
lines(result$par, type="l", col="blue")
Level A linear correlation for a single formulation
Description
This is the major function to be called where numerical convolution ad/or deconvolution might be used for a linear in vitro in vivo correlation level A. It performes either numerical convolution via /codeNumConv() or deconvolution via /codeNumDeconv() and correlates their results with the known.data object via linear regression lm(). If you just want raw results of convolution/deconvolution then call explicitely NumConv or link{NumDeconv}
Usage
RivivcA(known.data, impulse.data, second.profile.data,dose_iv=NULL,dose_po=NULL,
mode = "deconv", explicit.interp = 20, implicit.interp = 10,
optimization.maxit = 200)
Arguments
known.data |
the data matrix to be correlated with; depending on the state of the |
impulse.data |
matrix of the PK profile after the drug i.v. administration |
second.profile.data |
matrix of the second PK profile; depending on the |
dose_iv |
drug dose after i.v. administration; not obligatory but if provided must be in the same units like the dose p.o. |
dose_po |
drug dose after p.o. administration; not obligatory but if provided must be in the same units like the dose i.v. |
mode |
represents the method used here; two states are allowed: |
explicit.interp |
convolution and deconvolution explicit interpolation parameter, namely number of the curve interpolation points |
implicit.interp |
implicit interpolation - a factor multiplying |
optimization.maxit |
maximum number of iterations used by |
Details
The function represents either convolution or deconvolution data together with linear regression of the above functions outputs and known data supplied as a parameter. Please bear in mind that NumDeconv() procedure is iterative and therefore depending on the parameters might require substantial amount of time to converge. Please refer to the NumDeconv description.
Value
$regression |
returns a whole object of the linear regression - a result from the |
$numeric |
returns results from |
Author(s)
Aleksander Mendyk and Sebastian Polak
See Also
Examples
require(Rivivc)
require(graphics)
#i.v. data
data("impulse")
#p.o. PK profile
data("resp")
#in vitro dissolution for correlation purposes
data("input")
#preparing data matrices
input_mtx<-as.matrix(input)
impulse_mtx<-as.matrix(impulse)
resp_mtx<-as.matrix(resp)
#setting accuracy
accur_explic<-20
accur_implic<-5
#run deconvolution
result<-RivivcA(input_mtx,impulse_mtx,resp_mtx,
explicit.interp=accur_explic,implicit.interp=accur_implic)
summary(result$regression)
print("Raw results of deconvolution")
print(result$numeric$par)
predicted<-predict(result$regression)
deconvolved_data<-unname(predicted)
orig_data<-input_mtx[,2]
dev.new()
plot(orig_data,result$numeric$par[,2])
lines(orig_data,deconvolved_data, type="l", col="blue")
dev.new()
plot(input_mtx)
lines(result$numeric$par, type="l", col="blue")
PK profile after drug intravenous administration
Description
This data set gives the time and concentration of the hypothetical drug after its intravenous administration. This is the simulated data set.
Usage
data(impulse)Format
matrix
In vivo absorption of the drug
Description
This data set gives the time and cumulative amount of the hypothetical drug absorbed. It is also used as in vitro dissolution for Rivivc example of IVIVC level A. This is the simulated data set.
Usage
data(input)Format
matrix
PK profile after drug oral administration
Description
This data set gives the time and concentration of the hypothetical drug after its oral administration. This is the simulated data set.
Usage
data(resp)Format
matrix