- On 17 Jul 2002 at 20:28:27, dorothee.krone.at.viatris.de sent the message

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Dear all,

Does anyone use the LIMS Watson in connection with the PK Software

Kinetica, both from Innaphase ?

We need contact to persons experienced with the data transfer and blq

flagging within the LIMS.

Thanks for your help!

Dorothee Krone

Scientist Pharmacokinetics

Viatris GmbH & Co. KG

Early Phase Development

Bioanalytics & Pharmacokinetics

Weismüllerstrasse 45

60314 Frankfurt/Main

Germany

e-mail: Dorothee.krone.at.viatris.de - On 25 Jul 2002 at 12:27:29, "Dan Hirshout" (dhirshout.-a-.innaphase.com) sent the message

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THE ANSWER:

Watson automatically flags values that are above the ULOQ or below the LLOQ.

Watson has the capability to export data directly to Kinetica--just choose

Kinetica from the PK menu and all the study data is exported. Any data that

is BLQ is marked as "<" (i.e., BLQ) by Kinetica.

Does that help? If not, then what is it that you are trying to do?

Best Regards,

Dan Hirshout

InnaPhase Corporation

dhirshout.-a-.innaphase.com - On 26 Jul 2002 at 12:22:41, Paul Hutson (prhutson.at.pharmacy.wisc.edu) sent the message

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Dan:

There was some recent traffic on the NONMEM listserve about how to

handle BLQ data. How is this handled by Kinetica?

Paul

Paul Hutson, Pharm.D.

Associate Professor (CHS)

UW School of Pharmacy

777 Highland Avenue

Madison, WI 53705-2222 - On 26 Jul 2002 at 16:06:10, Roger Jelliffe (jelliffe.aaa.usc.edu) sent the message

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Dear All:

Concerning BLQ data and lab assay errors and sensitivity. We feel

that the best thing is for each lab to do is first to determine its own

assay error pattern. Using our USC*PACK software, the assay error

polynomial for the assay data is that the

assay SD = I + J x C + K x Csq, where C is the concentration, Csq is the

square of the concentration, and I, J, and K are the coefficients in the

polynomial describing the usually nonlinear relationship between the assay

concentration and the SD with which it is measured. This is an easy and

cost-effective way to get an estimate of the SD with which any single

sample is measured. It permits fitting the data by its Fisher information,

the reciprocal of the assay variance.

I am following this with a repeat of what I mentioned some time

earlier. About accuracy and precision. They ARE important. We are modeling

population data so we can act on it OPTIMALLY, that is, to develop dosage

regimens to achieve desired target goals with maximal precision. It is not

just that the assay should be acceptably precise over its working range,

but also that the error be carefully determined so it can be used to fit

data by its Fisher information. Different weighting schemes clearly yield

different population model parameter values. This is one of the reasons

that linear regression on the logs of the levels, with its inappropriate

weighting scheme built into the fit, often yields significantly different

parameter values compared to weighted nonlinear least squares or the MAP

Bayesian fitting procedure, as these can take the correct weighting scheme

based on the assay error polynomial, and linear regression cannot.

The issue of LOQ is also important here. When we have no other

info about the specimen except the measured value itself, then there most

certainly IS a LOQ. However, when we do most PK work, that is not the case.

We know, with reasonable precision, when the doses were given and when the

samples were obtained. So we know the drug is really present. Even simple

linear models show us that the last molecule is theoretically never

excreted. So, instead of having to ask, as we must in toxicological work,

if the drug is PRESENT OR NOT, and having therefore to develop a LOQ in

that situation, in PK/PD work we know the drug really is present. The

question being asked is not the same as in toxicology. It is instead - HOW

MUCH drug is present?

Most people agree that weighting data by its Fisher information is

appropriate - the reciprocal of the variance of the data point. It works

quite well. The point is that when you determine the assay error and

express it as a polynomial function of the concentration, that important

relationship continues over the entire range of the assay, down to and

including the blank, if you set it up correctly. This point is discussed in

more detail in an article in Therap Drug Monit 15:380-393, 1993, especially

the section on Evaluating the Credibility of Population Parameter Values

and Serum Level Data, pp. 386-391. Thus, not only should one determine if

the assay is sufficiently precise or not, but even after that decision is

made, there remains the issue of fitting the data correctly by its Fisher

information. Determining the assay error polynomial in this way is a cost

effective way to do this. It has the fringe benefit that there is no LOQ

for PK work.

Finally, there comes the issue of the remaining part of the

intraindividual variability - that due to the errors with which the various

doses have been prepared and administered, the errors in recording when the

doses were given, the errors in recording when the various serum samples

(or other responses) were obtained, the misspecification of the structural

model, and any unsuspected changes in parameter values that have taken

place during the period of the data analysis. All these are remaining

sources of intraindividual variability. They can be computed as an overall

single parameter, if you use the iterative 2 stage Bayesian (IT2B)

population modeling program in the USC*PACK collection, as a parameter

which we call gamma. In this way it is possible to have a reasonable

estimate of the relative amount of noise due to the assay error, and that

due to the other sources of error.

This computation of gamma is also starting to be implemented in

our new nonparametric adaptive grid (NPAG) population modeling software, so

that eventually it will not be necessary to use parametric modeling

software for this purpose any more. Parametric software using the FOCE

approximation for the log-likelihood function was recently compared with

NPAG by Bob Leary at the PAGE meeting in Paris in June. He showed, in a

carefully simulated population study, that the FOCE approximation was

associated with loss of statistical consistency and significant errors,

while nonparametric NPAG method did not have this problem, as the

likelihood calculations are exact. In addition, the FOCE approximation was

associated with a significant loss of statistical efficiency and

statistical convergence, while NPAG was much more efficient. Dr. Leary's

data and the graphs of the results can be seen, under "New developments in

population modeling", on our web site, www.lapk.org.

Once again, in PK/PD work, there does not have to be any BLQ or

LOQ. I look forward to discussing this more with you.

Very best regards,

Roger Jelliffe

Also see: http://www.boomer.org/pkin/

Roger W. Jelliffe, M.D. Professor of Medicine,

Laboratory of Applied Pharmacokinetics,

USC Keck School of Medicine

2250 Alcazar St, Los Angeles CA 90033, USA

email= jelliffe.-a-.hsc.usc.edu

Our web site= http://www.lapk.org - On 30 Jul 2002 at 13:08:14, "Dan Hirshout" (dhirshout.-at-.innaphase.com) sent the message

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Paul,

FOR AUC Calculations

When linear rule is applied to AUC calculation, BLQ or zero data points are

not included in AUClast (AUC from t=0 to last sampling time) calculation if

there are no normal status data following the BLQ or zero data points.

BLQ Data (Can be set as Default, 0 or as Missing)

Default- the default will take whatever the LQ value (e.g. <0.2 will be

calculated as 0.2).

Set as 0 - will calculate all BLQ data as 0.

Set as missing- will skip the BLQ data and will not use the BLQ in the

calculation.

Note: In order to flag data as BLQ, identify each undetectable data point

with a "less than" sign (<) before the data.

Moreover, BLQ before first non-zero normal data = 0

(Select the check box to treat all BLQ before the first quantifiable data as

0 under the AUC* method options).

Best Regards,

Dan Hirshout

InnaPhase Corp.

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