RivivcA: 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 mode variable it represents either in vitro dissolution profile (mode = "deconv") or PK profile after oral administration of the drug (mode="conv")

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 mode variable it represents either PK profile after oral administration of the drug (mode = "deconv") or a drug cumulative absorption profile (mode="conv"), sometimes substituted directly by the in vitro dissolution profile

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: mode="conv" for numerical convolution method or mode="deconv" for numerical deconvolution (default)

explicit.interp

convolution and deconvolution explicit interpolation parameter, namely number of the curve interpolation points

implicit.interp

implicit interpolation - a factor multiplying explicit.interp for better accuracy; applies to the deconvolution procedure only

optimization.maxit

maximum number of iterations used by optim() method; applies to the deconvolution procedure only

Value

$regression

returns a whole object of the linear regression - a result from the lm() procedure

$numeric

returns results from NumConv() or NumDeconv() functions

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.

Examples

Run this code

# NOT RUN {
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")

# }