## differences in lambda_z calculation between PHX and PK package in R [Software]

Dear mittyri,

The methods implemented in the R package PK are based on methods published in peer reviewed journals such as Non-compartmental estimation of pharmacokinetic parameters in serial sampling designs or here Non-compartmental estimation of pharmacokinetic parameters for flexible sampling designs based on log-transformation of individual values to estimate lambda_z.

The rationale for log-transforming the indivdual values is that based on this approach the variance-covariance matrix can account for values both used for derving the AUC0-t as well lambda_z used to estimate the AUC from t to infinity.

I am not aware of a publication which justifies calculation of lambda_z in case of sparse sampling based on means only as implemented in PHX.

In addition, attention should be paid to handling to values <LLOQ where you may find this paper of interest Methods for Non-Compartmental Pharmacokinetic Analysis With Observations Below the Limit of Quantification.

best regards & hope this helps

martin

PS.: I would like to use the opportunity to illustrate how important adequate handling of BLQ values are by using a theoretical example. Consider a serial sampling design (N=5 animals per time point) where all but one value is BLQ at the last time point and think about estimation of t1/2. Ignoring BLQ values at the last time point for 4 out of 5 animals will lead to a overestimated population t1/2 as the last time point is just driven by a single animal. The same is when you set BLQ values to zero as estimation of t1/2 requires some log-transformation and log of 0 is not defined and is therefore equivalent to omitting those BLQ values.

The methods implemented in the R package PK are based on methods published in peer reviewed journals such as Non-compartmental estimation of pharmacokinetic parameters in serial sampling designs or here Non-compartmental estimation of pharmacokinetic parameters for flexible sampling designs based on log-transformation of individual values to estimate lambda_z.

The rationale for log-transforming the indivdual values is that based on this approach the variance-covariance matrix can account for values both used for derving the AUC0-t as well lambda_z used to estimate the AUC from t to infinity.

I am not aware of a publication which justifies calculation of lambda_z in case of sparse sampling based on means only as implemented in PHX.

In addition, attention should be paid to handling to values <LLOQ where you may find this paper of interest Methods for Non-Compartmental Pharmacokinetic Analysis With Observations Below the Limit of Quantification.

best regards & hope this helps

martin

PS.: I would like to use the opportunity to illustrate how important adequate handling of BLQ values are by using a theoretical example. Consider a serial sampling design (N=5 animals per time point) where all but one value is BLQ at the last time point and think about estimation of t1/2. Ignoring BLQ values at the last time point for 4 out of 5 animals will lead to a overestimated population t1/2 as the last time point is just driven by a single animal. The same is when you set BLQ values to zero as estimation of t1/2 requires some log-transformation and log of 0 is not defined and is therefore equivalent to omitting those BLQ values.

### Complete thread:

- differences in lambda_z calculation between PHX and PK package in R mittyri 2020-09-27 18:25 [Software]
- differences in lambda_z calculation between PHX and PK package in Rmartin 2020-09-28 08:40
- PK package suggestions mittyri 2020-10-06 12:31

- differences in lambda_z calculation between PHX and PK package in Rmartin 2020-09-28 08:40