PHUSE Wiki - PHUSE Wiki - Analyses and Displays ... · Web viewICH E7, Studies in Support of...

2nd DRAFT for Review

1. Analyses and Displays Associated to Non-Compartmental Pharmacokinetics – with a focus

on clinical trials

Draft - Version 0.2Created 30 Jan 2014

A White Paper by the FDA/PhUSE Development of Standard Scripts for Analysis and Programming Working Group

This white paper does not necessarily reflect the opinion of the institutions of those who have contributed.

1


2. Table of ContentsSection Page1. Analyses and Displays Associated to Non-

Compartmental Pharmacokinetics – with a focus on clinical trials........................................................................................................................1

2. Table of Contents....................................................................................................................2

3. Revision History......................................................................................................................4

4. Purpose....................................................................................................................................5

5. Introduction.............................................................................................................................6

6. General Considerations...........................................................................................................86.1. Reporting workflow............................................................................................................86.2. CDISC PK datasets creation workflow..............................................................................9

7. Calculation of PK parameters................................................................................................117.1. Main derived PK parameters............................................................................................117.2. NCA Checklist..................................................................................................................15

7.2.1. Missing sampling or concentration data..................................................................167.2.2. Concentration values below the limit of quantification...........................................167.2.3. Exclusion of outliers or influential data...................................................................167.2.4. Use of actual v.s. planned sampling timepoints.......................................................167.2.5. Reporting of missing PK parameters.......................................................................17

8. PK Tables, Figures and Listings for Individual Studies........................................................188.1. Standard List of Outputs...................................................................................................188.2. Annotated PK TFLs............................................................................................................18.3. PK TFLs Checklist.............................................................................................................1

8.3.1. Individual data handling in listings............................................................................18.3.2. Individual plots..........................................................................................................18.3.3. Descriptive statistics in tables....................................................................................2

8.3.3.1. Statistics in the presence of BQL data.............................28.3.4. Individual data handling in summary tables..............................................................28.3.5. Mean Plots..................................................................................................................38.3.6. Formats for individual data and statistics..................................................................3

9. Example SAP Language..........................................................................................................49.1. Data to be analysed.............................................................................................................49.2. Pharmacokinetic methods...................................................................................................4

10. References...............................................................................................................................5

11. Acknowledgements.................................................................................................................6

2


List of Tables

Table 6-1 Symbols and definition of terms used in single and multiple dose NCA......................12

Table 6-2. Main qualifiers for the determination of PK parameters..............................................13

Table 6-3 Main formulas for calculation for PK parameters.........................................................14

List of Figures

Figure 6-1 Reporting workflow for pharmacokinetic data..............................................................8

Figure 6-2 Process map for the creation of SDTM and ADaM PK datasets.................................10

Figure 8-1. Shell for individual PK concentration listing................................................................1

Figure 8-2. Shell for individual PK concentration listing................................................................3

Figure 8-3. Shell for overlaying PK concentration-time profiles....................................................4



Figure 8-6. Shell for summary of PK parameters..........................................................................10

Figure 8-7. Shell for summary of PK concentration......................................................................12

3


3. Revision HistoryVersion 1.0 was finalized xx XXXX 201x.

4


4. PurposeUnder CDISC, standards have been defined for data collection (CDASH), tabulation (SDTM), and analysis (ADaM) datasets. The next step is to develop standard tables, figures and listings. The Development of Standard Scripts for Analysis and Programming Working Group is leading an effort to create several white papers providing recommended analyses and displays for common measurements, and has developed a Script Repository as a place to store shared code.

The purpose of this white paper is to provide advice on displaying, summarizing, and/or analyzing measures of pharmacokinetic (PK) data in clinical trials. The intent is to begin the process of developing industry standards with respect to analysis and reporting for PK concentrations and non-compartmental PK parameters that are common across clinical trials. In particular, this white paper provides recommended processes for:

the calculation of PK parameters using non-compartmental analysis (NCA), the production of PK listings, tables and figures for inclusion in clinical study reports,

and the definition of statistical analysis plans (SAP) for PK data

Separate white papers address other types of data.

Model-based PK analyses are considered out-of-scope for this white paper.

This advice can be used when developing the analysis plan for individual clinical trials in which PK data are of interest. Although the focus of this white paper pertains to clinical trials where intense PK sampling is made, some of the content may apply to trials where only sparse samples are collected. Similarly, although the focus of this white paper pertains to clinical trials, some of the content may apply to pre-clinical studies where PK is being assessed.

Development of standard Tables, Figures, and Listings (TFLs) and associated analyses will lead to improved standardization from collection through data storage. (You need to know how you want to analyze and report results before finalizing how to collect and store data.) The development of standard TFLs will also lead to improved product lifecycle management by ensuring reviewers receive the desired analyses for the consistent and efficient evaluation of patient safety and drug exposure. Although having standard TFLs is an ultimate goal, this white paper reflects recommendations only and should not be interpreted as “required” by any regulatory agency.

Detailed specifications for TFL development are in the scope of this white paper. The hope is that code (utilizing SDTM and ADaM data structures) will be developed consistent with the concepts outlined in this white paper, and placed in the publicly available FDA/PhUSE Standard Scripts Repository.

5


5. Introduction Industry standards have evolved over time for data collection (CDASH), observed data (SDTM), and analysis datasets (ADaM). There is now recognition that the next step would be to develop standard TFLs for common measurements across clinical trials and across therapeutic areas. Some could argue that perhaps the industry should have started with creating standard TFLs prior to creating standards for collection and data storage (consistent with end-in-mind philosophy), however, having industry standards for data collection and analysis datasets provides a good basis for creating standard TFLs.

The beginning of the effort leading to this white paper came from the FDA computational statistics group (CBER and CDER). The FDA identified key priorities and teamed up with the Pharmaceuticals Users Software Exhange (PhUSE) to tackle various challenges using collaboration, crowd sourcing, and innovation (Rosario, et. al. 2012). The FDA and PhUSE created several working groups to address a number of these challenges. The working group titled “Development of Standard Scripts for Analysis and Programming” has led the development of this white paper, along with the development of a platform for storing shared code. Most contributors and reviewers of this white paper are industry statisticians, with input from non-industry statisticians (e.g., FDA and academia) and industry and non-industry clinicians. Hopefully additional input (e.g., other regulatory agencies) will be received for future versions of this white paper.

There are several existing documents that contain suggested TFLs for PK measurements. However, many of the documents are now relatively outdated, and generally lack sufficient detail to be used as support for the entire standardization effort. Nevertheless, these documents were used as a starting point in the development of this white paper. The documents include:

ICH E3: Structure and Content of Clinical Study Reports ICH E7, Studies in Support of Special Populations: Geriatrics US: Guideline for Industry: Structure and Content of Clinical Study Reports US: Study and Evaluation of Gender Differences in the Clinical Evaluation of Drugs US: General Considerations for Pediatric Pharmacokinetic Studies for Drugs and

Biological Products (draft) US: Pharmacokinetics in Patients with Impaired Renal Function: Study Design, Data

Analysis and Impact on Dosing and Labeling US: Pharmacokinetics in Patients with Hepatic Insufficiency: Study Design, Data

Analysis and Impact on Dosing and Labeling (draft) US: In Vivo Metabolism/Drug Interactions Studies: Study Design, Data Analysis and

Recommendations for Dosing and Labeling (draft) US: Population Pharmacokinetics US: Exposure-Response Relationships: Study Design, Data Analysis, and Regulatory

Applications Japan: Clinical Pharmacokinetic Studies of Pharmaceuticals EU: Pharmacokinetic Studies in man

6

http://www.ema.europa.eu/docs/en_GB/document_library/Scientific_guideline/2009/09/WC500003127.pdf

http://www.nihs.go.jp/phar/pdf/ClPkEng011122.pdf

http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/ucm072109.pdf



http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E3/E3_Guideline.pdf


EU: Questions & Answers: Positions on specific questions addressed to the EWP therapeutic subgroup on Pharmacokinetics

EU: Clinical Investigation of the Pharmacokinetics of Therapeutic Proteins EU: Points to Consider on Pharmacokinetics and Pharmacodynamics in the Development

of Antibacterial Medicinal Products

These guidance documents present high-level requirements for the collection, analysis and presentation of PK results in a variety of clinical trials. They do not provide, however, detailed information that would enable to standardize the presentation of PK results. This white paper tries to fill this gap and provides a set of standard rules and checklists to standardize the production of PK TFLs in clinical trials.

7

http://www.emea.europa.eu/pdfs/human/ewp/265599en.pdf

http://www.emea.europa.eu/pdfs/human/ewp/265599en.pdf




PK analysis checklist

PK Concentration PK Parameters

PK Datasets

PK Analysis

CDISC standardSDTM PC/PP domains => ADaM

PK TFL checklist


6. General Considerations

6.1. Reporting workflowThe general workflow for the analysis and reporting of PK data in clinical trials involves two major steps, as outlined in Figure 6-1:

1. Calculation of pharmacokinetic parameters 2. Production of PK TFLs

For each step, we shall define in subsequent sections, a checklist of standard rules that need to be followed. The SDTM to ADaM mapping for PK concentrations (PC) and parameters (PP) will also be discussed.

Figure 6-1 Reporting workflow for pharmacokinetic data

8


6.2. CDISC PK datasets creation workflow

According the recommended CDISC process, SDTM Pharmacokinetic Concentration (PC) data, SDTM Pharmacokinetic Parameter (PP) data, ADaM Pharmacokinetic Concentration (ADPC) data and ADaM Pharmacokinetic Parameters (ADPP) are created based on SDTM/ADaM structure data, clinical data, bioanalytical data and the derived PK parameters calculated by scientists. Then, all the related listings, tables and figures can be generated based on ADPC and ADPP data sets.

The general process for creating SDTM and ADaM PK-related datasets is summarized in Figure 6-2.

9

Keys

Raw/CDASH

SDTM

ADaM

Analysis

Output

Clinical Bioanalysis

PC

ADPC

PP

ADPP

Statistics

ADSL

Structure

Structure

NCA

Parameters

PP TFLs

Statistics

PC TFLs


Figure 6-2 Process map for the creation of SDTM and ADaM PK datasets

It works as follows:

1. First, SDTM PC dataset is created based on SDTM structure dataset, clinical datasets, and bioanalytical dataset.

2. Second, based on the ADaM structure dataset, SDTM PC dataset and ADaM ADSL(Subject Level Analysis Dataset) are merged to create ADaM ADPC dataset. ADaM ADPC dataset supports PK parameters calculation. It also provides information to create PK concentration tables and figures.

3. Third, using specific software for non-compartmental analysis such as SAS or WinNonlin, PK parameters are calculated from ADaM ADPC. A derived dataset is created including all these calculated PK parameter information, and SDTM PP dataset is created based on this dataset.

10


4. Fourth, ADaM ADPP dataset is created based ADaM structure dataset and SDTM PP. ADPP dataset is the PK analysis dataset which is used for producing summary tables, statistical tables, and any other PK analysis.

11


7. Calculation of PK parameters

7.1. Main derived PK parametersIn Table 6-1, we present the main PK parameters and terms used for the non-compartmental analysis (NCA).

Table 6-1 Symbols and definition of terms used in single and multiple dose NCA

Symbol DefinitionAa Total amount of drug excreted in expired airAe Total amount of drug excreted in urineAe(t1-t2) Amount of drug excreted in urine from t1 to t2

Ae Amount of drug excreted in urine over a dosing intervalAf Total amount of drug excreted in fecesAf(t1-t2) Amount of drug excreted in feces from t1 to t2

At Total amount of drug excreted in expired air, feces and urineAUC Area Under the Curve from 0 to infinityAUC(0-t) Area under the curve from 0 to the time of the last quantifiable concentrationAUCextr Extrapolated AUCAUC(t1-t2) Partial Area Under the Curve between t1 and t2

AUC Area Under the Curve over a dosing intervalAUMC Area Under the first Moment Curve from 0 to infinityBLQ Below Limit of QuantificationCav Average concentration over a dosing intervalClast Last observed (quantifiable) concentration CL Total body clearanceCL/F Apparent total body clearanceCLCR Creatinine clearanceCLfm/F Apparent Formation clearance of a metaboliteCLNR Non-Renal Clearance CLR Renal Clearance CLss/F Apparent Total body clearance at steady stateCmax Maximum concentrationCmin Minimum concentration over a dosing intervalC(t) Drug concentration at any time tCtrough Measured concentration at the end of a dosing interval at steady stateD DoseF Absolute bioavailability. F= fD x fA x fI x fH where fD, fA, fI and fH

represent the fraction dissolved, the fraction absorbed, the fraction escaping intestinal first pass and the fraction escaping liver first pass respectively

Frel Relative bioavailabilityfe Fraction of the dose excreted (urine by default, add qualifier

for other fluids)

12


z First order terminal elimination rate constant or Apparent first order terminal elimination rate constant, for compounds presenting release/absorption as limiting steps

LF Linearity FactorLOQ Limit of QuantificationMRT Mean Residence TimePD Pharmacodynamic(s)PK Pharmacokinetic(s)PTF Peak to trough fluctuationR Accumulation ratioSwing Percentage of swing Dosing intervalTinf Infusion durationtlag Time delay between drug administration and the first measurable

(quantifiable) concentration.tmax Time of Cmax

t½ Terminal elimination half-life orApparent terminal elimination half-life, for compounds presenting release/absorption as limiting steps

Vss Volume of distribution at steady-stateVur Volume of urine Vz Volume of distributionVz/F Apparent volume of distribution CLHD Hemodialysis clearanceCLD Dialysis clearance or dialysanceCLUF Ultrafiltration clearanceE Extraction coefficientFr Fractional removalQP Plasma flow through the dialyzerQUF Ultrafiltration flow rate

Additional qualifier may be used when parameters need to be further defined for clarification purpose. They are often inserted as subscript. By default, the matrix will in general be plasma. A non-exhaustive list of qualifiers is presented in Table 6-2.

Table 6-2. Main qualifiers for the determination of PK parameters.Matrices Routes of administrationbl Bloodcsf Cerebrospinal fluidfcs Fecesmlk Breast milk p Plasmarbc Red Blood Cellssal Salivaser Serumur Urine

im Intra-muscularnas Intra-nasaliv Intravenouspo Per osrec Rectalsc Subcutaneoussbl Sublingualtop Topical

13


Dosing regimen Bindingss Steady-statedayx If multiple administration, this qualifier can be used to specify the day at which the parameter is calculated

b Boundu Unbound

The formulas used for the calculation of the main PK parameters by NCA are presented in Table 6-3, below.

Table 6-3 Main formulas for calculation for PK parameters

Parameters DeterminationSingle Dose Multiple Dose (if different)

Ae Ae=∑Cur∗V urAe(t1-t2)

Ae( t1−t 2)=∑t1

t 2

Cur∗V ur

AUCAUC=AUC (0−t )+

Clast

λzAUC(t1-t2) AUC ( t1−t2)=∑

t1

t2 (C( t1 )+C( t 2 ))2

∗( t 2−t1)

AUMC 2

*0

z

last

z

lastlast CtCtAUMCAUMC

AUMC(t1-t2) AUMC ( t1−t2 )=∑

t 1

t 2 ( t1∗(C( t1)+ t2∗C ( t2 ))2

∗( t2−t1 )

Cav Cav=AUCτ

Cav=AUC ττ

CL CL=F∗DAUC

CLss=F∗DAUC τ

NB: after iv, F=1CL/F CL /F= D

AUCCLss /F= D

AUCτCLfm/F

CLfm /F=AemetaboliteAUC

= f e∗D

AUCClast Directly obtained from the observed concentration vs. time curves.

Cmax Directly obtained from the observed concentration vs. time curves

Cmin Directly obtained from the observed concentration vs. time curves.

CLNR CLNR=CL−CLR calculated only after iv or if F is known or if F is explicitly assumed to be 1

14


CLR CLR=AeAUC CLR=

Ae τAUC τ

Ctrough Directly obtained from the observed concentration vs. time curves.F

F=AUC po∗Div

AUC iv∗DpoF=

AUC τ po∗Div

AUC τ iv∗DpoFrel

poref

refporel DAUC

DAUCF

**

fe f e=Ae

D

LFLF=

AUCτ

AUCdose1z Estimated slope of the linear regression of ln concentration vs. time.MRT After single dose oral or iv bolus:

MRT= AUMCAUC

After infusion

MRT= AUMCAUC

−T inf2

After multiple po or iv bolus:

MRT=AUMC τ+τ∗(AUC−AUC τ )

AUC τ

PTF PTF=

Cmax−Cmin

Cav

R For mono-compartmental model:

R= 1

1−e−λZ∗τ at steady-state

R=1−e−n∗λz∗τ

1−e− λz∗τ at nth dose

After multiple dose administration:

Rmax=Cmax,ss

Cmax,dose1

Rmin=Cmin, ss

Cmin, dose 1

RAUC=AUC τss

AUC τdose 1Swing

Swing=Cmax−Cmin

Cmin

tlag Directly obtained from the observed concentrations

tmax Directly obtained from the observed concentrations. If two identical values are recorded for Cmax, the first one will be considered for tmax.

t½ t 1/2=ln 2λZ

Vss V ss=MRT∗CL= F∗D∗AUMCAUC2

15


Vz V Z=CLλZ

Vz/F V Z/F=CL /FλZ

NB: after iv, F=1CLHD CLHD =CLD+CLUFCLD

CLD=QPL

C i − Co

Ci where Ci is the concentration entering the dialyzer and Co is the concentration getting out of the dialyzer

CLUFCLUF = QUF

Co

C i where Ci is the concentration entering the dialyzer and Co is the concentration getting out of the dialyzer

EE=

C i − Co

Ci where Ci is the concentration entering the dialyzer and Co is the concentration getting out of the dialyzer

FrF r = 100 ∗

t1/2(1 )− t1 /2(2)

t1/2(1)

∗(1−e−λ Z( 2)

∗t)

where (1) refers to the period before the start of the dialysis and (2) to the period during the dialysis.

QPL QPL = blood¿ flow∗(1−hematocrit )

¿

QUFQUF =

W s −W e +W fl

dialysis time where Ws and We are the body weight at start and end of dialysis, and Wfl is the weight of the dialysate

7.2. NCA ChecklistA set of standard rules needs to be defined for the management of particular source data points in NCA. These include the following:

• Management of missing sampling or concentration data

• Management of concentration values below the lower limit of quantification

16


• Exclusion of outliers

• Use of actual v.s. planned sampling timepoints

• Reporting of missing PK parameters

We detail these rules in the following sub-sections.

7.2.1. Missing sampling or concentration dataUnless otherwise specified below, missing sampling or concentration values should not be imputed and left missing in the calculation of derived PK parameters. If the actual sampling time is missing but a valid concentration value has been measured, the protocol time is generally used for the calculation of derived PK parameters.

A missing pre-dose value for single-dose study is usually replaced by 0 for the PK calculations.

7.2.2. Concentration values below the limit of quantificationFor plasma concentrations, all BLQ (Below the Limit of Quantification (LOQ)) values occurring prior to Cmax are replaced by “0” (i.e., for lag-time characterization), except for embedded BLQ values (between two measurable data points) which are treated as missing. Post-Cmax BLQ values are treated as missing.

For urine, when calculating individual amounts and cumulative amounts excreted, BLQ urine levels are set to zero.

7.2.3. Exclusion of outliers On a case by case basis, it may be necessary to exclude individual PK concentration values for the calculation of derived PK parameters, because they are abnormal. Any excluded data should be flagged in the individual data listings. If known, the reason for exclusion should also be documented. For chemical entities, it may be necessary to exclude a subject from all pharmacokinetic evaluations if the pre-dose concentration is significantly non-null (a value larger than 5% of the subject’s Cmax may be used as a threshold). For biological entities, predose concentration can be included in all pharmacokinetic measurements and calculations.

7.2.4. Use of actual v.s. planned sampling timepointsIf possible, actual post-dose time should be used in calculation of PK parameters and in the generation of individual concentration-time profiles.

Planned sampling times may be used for pre single-dose values and as a replacement for unknown or missing actual times.

17


7.2.5. Reporting of missing PK parametersThe percentage of extrapolated AUC should not exceed 20 % for each individual profile. If the percentage of extrapolated AUC is more than 20 %, the individual result should be flagged for exclusion in the report, as well as the parameters depending on AUC.

Terminal half-life should be determined over a time interval equal to at least 2 x t½, using at least 3 data points and with Adj_RSq2 should be greater or equal to 0.85. If at least one of these three conditions is not fulfilled, the terminal half-life should be flagged for exclusion and mentioned in the report, as well as the parameters depending on t½.

18


8. PK Tables, Figures and Listings for Individual Studies

8.1. Standard List of Outputs In individual studies where PK data is collected, the following list of PK outputs are commonly produced:

• Listing of individual PK concentrations

• Listing of individual PK parameters

• Summary table of PK concentrations

• Summary table of PK parameters

• Figures for PK concentration-time profiles:

– Individual plots (separate and/or overlaying)

– Mean plots with or without error bars

In addition, statistical TFLs are created in trials where a statistical analysis of PK data is planned.

Section 8.2 provides illustrative shells for the main types of PK TFLs and Section 8.3 presents a set of standard rules for the reporting of PK data in TFLs.

The proposed standard PK TFLs contain 3 parts: the title, body and footnote, that can be adapted to match individual company standard or study-specific requirements.

In our standard template, the title part contains the following pieces of information:

1. Sponsor/Protocol/Product information, such as the name of the company, the protocol numbar and or the compound name/code.

2. Listing/Table/Figure label to identify the type of output3. The output number according to ICH E3 guidance document.4. The output title5. A page numbering indicator for the page number and the total number of pages.6. The analysis population

The body part is broken up into two parts:

1. An optional headline defining the information displayed on any particular page (the by-lines).

2. The actual output content presented in a tabular grid.

The footnote contains the following pieces of information:

1. The definition of all abbreviations2. Annotations for flagged data values. Usually, flags are used for exclusion of individual

data.

19


3. A description of how BQL values are reported (optional). 4. Information about source dataset, program and output path5. Information about data and program status (development/test/production)6. Production date and time.

20


8.2. Annotated PK TFLs

SPONSOR/PROTOCOL/PRODUCT INFO (page x of x)

Listing 16.2.5-x.x Individual PK parameters by compound, matrix, analyte and [actual/randomised] [treatments/group]

Analysis Set : All subjects

Compound: XXX, Matrix: YYY, Analyte: ZZZ [Actual/Randomised] [treatment/group] [sequence]: AAAAAA

Country/Site/Subject

Age/Sex/Race

PeriodProfil

eday

Parameter (unit) Value

CNTR/ST1/XXXXX

YY/M/Ca

1 1 AUCinf (hr*ng/mL) xxx

AUClast (hr*ng/mL) xxx Tmax (hr)

xx.x*

- Value * was not considered for summary and inferential procedures.- Age/Sex/Race: M=Male, F=Female, Ca=Caucasian, … PATH DATA/PROGRAM/OUTPUT PRODUCTION STATUS/RUN DMMMYYYY: HHMM

Figure 8-3. Shell for individual PK concentration listing

Annotations:

If the actual treatment is known and if it differs from the randomised treatment, it is recommended to report according to actual treatment. Otherwise, the randomised treatment is reported.

In some particular multi-arm trials, all groups receive the same treatment but they differ by other characteristics (such as gender, disease status or stage, administration with food, etc…). In these cases, data are reported according to the group and not the treatment.

1


In sequential and cross-over trials, the sequence and period are reported. In parallel trials, these data are usually skipped. In multi-part trials, the part is displayed either in the title, headline or column.

A footnote indicates how BQL values are reported.

2



Listing 16.2.5-x.x Pharmacokinetic concentration by compound, matrix, analyte and [actual/randomised] [treatments/group]

Analysis Set : PK analysis set

Compound: XXX, Matrix: YYY, Analyte: ZZZ [Actual/Randomised] [treatment/group] [sequence]: AAAAAA

Country/Site/Subject

Age/Sex/Race

PeriodProfil

eday

Scheduled Sampling Time (uom)

Date/Time of collection

Elapsed Time (uom)

Concentration (uom)

CNTR/ST1/XXXXX

YY/M/Ca

1 1 0.5 2000-02-12Txx:xx xx.x xxx.xx *

1.0 2000-02-12Txx:xx xx.x xxx.xx 1.5 2000-02-12Txx:xx xx.x xxx.xx- Value * was not considered for summary and inferential procedures.- Values <LLOQ were considered as [zero;missing;LLOQ; LLOQ/Z;…]. - Age/Sex/Race: M=Male, F=Female, Ca=Caucasian, … PATH DATA/PROGRAM/OUTPUT PRODUCTION STATUS/RUN DMMMYYYY: HHMM

Figure 8-4. Shell for individual PK concentration listing

Annotations:


In some particular multi-arm trials, all groups receive the same treatment but they differ by other characteristics (such as gender, disease status or stage, administration with food, etc…). In these cases, data are reported according to the groups and not the treatment.

In sequential and cross-over trials, the sequence and period are reported. In parallel trials, these data are skipped. In multi-part trials, the part is displayed either in the title, headline or column.


3



Figure 16.2.5-x.x Overlaying individual concentration-time profiles by compound, matrix, analyte and [actual/randomised] [treatments/group]


Compound: XXX, Matrix: YYY, Analyte: ZZZ[Actual/Randomised] [treatment/group] : AAAAAA

- Values <LLOQ were considered as [zero;missing;LLOQ; LLOQ/Z;…]. PATH DATA/PROGRAM/OUTPUT PRODUCTION STATUS/RUN DMMMYYYY: HHMM

Figure 8-5. Shell for overlaying PK concentration-time profiles

Annotations:


4

LLOQ

Author, 01/03/-1,

We could also include dose normalized plots




5



Figure 16.2.5-x.x Individual concentration-time profiles by compound, matrix, analyte and [actual/randomised] [treatments/group]


Compound: XXX, Matrix: YYY, Analyte: ZZZCountry/Site/Subject: CNTR/ST1/XXXXX



Annotations:


6

LLOQ




Notes to programmer:

Plot against actual time since last dosing, if possible. Otherwise, use protocol times. For multiple dose trials, display the entire time course or split into different panels by dosing occasion, as most appropriate For multi-period trials, display overlay treatments separately for each analyte Scale of Y axis may be either identical across all subjects or subject-specific, as most relevant.

7



Figure 14.2-x.x [Arithmetic/Geometric] mean (SD) concentration-time plot per treatment

(overlaying) and analyte (separately) Analysis Set : PK analysis set



Annotations:

It is customary to produce arithmetic mean plots for PK data. Given the

8

LLOQ



A footnote indicates how BQL values are managed for the computation of the statistics.

Notes to programmer:

Plot against protocol time since last dosing. Individual data may have been flagged for exclusion if actual time differs significantly from scheduled ones.

One- or two-sided error bars may be used. For one-sided, either use the same side or chose the side depending on the mean values (upward for higher and downward for smaller).

9


SPONSOR/PROTOCOL/PRODUCT INFO (page x of x)Table 14.2-x.x Summary statistics for PK parameters

by compound, matrix, analyte and [actual/randomised] [treatments/group]Analysis Set : PK analysis set

Compound: XXX, Matrix: YYY, Analyte: ZZZ

Actual

treatment

Period

day Statistic

<Parameter 1>

<unit>

<Parameter 2>

<unit>

<Parameter 3>

<unit>

TRTA 1 n xx xx xx

Mean (SD) xxx (xxx) xxx (xxx) xxx (xxx)

CV% mean xx.x xx.x xx.x

Geo-mean xxx xxx xxx

CV% geo-mean xx.x xx.x xx.x

Median xxx xxx xxx

[Min; Max] [xxx;xxx] [xxx;xxx] [xxx;xxx]

CV% = coefficient of variation (%)=sd/mean*100; CV% geo-mean=(sqrt (exp (variance for log transformed data)-1))*100

Geo-mean: Geometric mean.

Geo-mean and CV% geo-mean not presented when the minimum concentration is zero at respective timepoint.

PATH DATA/PROGRAM/OUTPUT PRODUCTION STATUS/RUN DMMMYYYY: HHMM

Figure 8-8. Shell for summary of PK parameters

Annotations:

10






11


SPONSOR/PROTOCOL/PRODUCT INFO (page x of x)Table 14.2-x.x Summary statistics for PK concentration

by compound, matrix, analyte and [actual/randomised] [treatments/group]Analysis Set : PK analysis set

Compound: XXX, Matrix: YYY, Analyte: ZZZ, Unit : uom

Profile

day

Dose

referenc

e id

Scheduled

time point

(hrs)

Statistic TRTA TRTB

1 1 0.0 n xx xx

Mean (SD) xxx (xxx) xxx (xxx)

CV% mean xx.x xx.x

Geo-mean xxx xxx

CV% geo-mean xx.x xx.x

Median xxx xxx

[Min; Max] [xxx;xxx] [xxx;xxx]

CV% = coefficient of variation (%)=sd/mean*100; CV% geo-mean=(sqrt (exp (variance for log transformed data)-1))*100

Geo-mean: Geometric mean.

Geo-mean and CV% geo-mean not presented when the minimum concentration is zero at respective timepoint. BLQ Values considered as zero in descriptive statistics calculation.

PATH DATA/PROGRAM/OUTPUT PRODUCTION STATUS/RUN DMMMYYYY: HHMM

Figure 8-9. Shell for summary of PK concentration

Annotations:

12






13

Author, 01/03/-1,

Need to add additional shells, eg for urine PK


8.3. PK TFLs Checklist

8.3.1. Individual data handling in listings Concentration data below the lower limit of quantification (BLQ) should be labelled as such in the data listings. Several flagging options are possible for BLQ data, including:

the actual numerical data with a flag (eg, ”*”), a missing value, an imputed value such as zero, the LLOQ, the LLOQ/2, etc… the labels “BQL” or “<X” where X is the numerical value of the LOQ established by the

laboratory. It is useful to add a footnote to the listing in order to indicate how BLQ data were reported.

Missing values should also be labelled as such in the data listings. Label such as “NV” (no value) or “.” (dot) may be used.

Any missing sampling or concentration data that was imputed should be flagged in the concentration data listing.

Any individual data excluded from NCA or statistical analysis should be flagged in the listings.

8.3.2. Individual plots

Depending on the aim of the study (crossover, parallel design), individual graphs can be presented per treatment (spaghetti plots) and/or by subject.

Plasma concentration vs. time profiles are often reported both on linear scale and on semi-logarithmic scale.

For urine, amount excreted, cumulative or not, are usually presented in linear scale.

The actual times are most often reported in individual plots. The protocol time may be used as an alternative when actual times are missing or if that presentation is more relevant.The label of the X-axis should match the time scale that was used to avoid any confusion.

Regarding scales, the axes can be optimized per treatment, for the entire study, per subject or per occasion, as deemed appropriate.

Most often, individual plots present all available data. Datapoint flagged for exclusion or that have been imputed may be identified using different symbols and/or colors in the plots. For BQL values, a footnote is often added that details how these data were imputed or managed in the plots. An horizontal reference line at the BQL numerical value may also be added to indicate the threshold in the plots.

1


8.3.3. Descriptive statistics in tables Otherwise specify in the protocol or SAP, the following descriptive statistics are often calculated for PK concentrations and PK parameters: N, arithmetic mean, SD, CV%, minimum, median, maximum, geometric mean and geometric CV%. The geometric CV% is computed as:

CV% geo-mean=(sqrt (exp (variance for log transformed data)-1))*100.

For Tmax and Tlag, it is customary to report only the N, median, minimum and maximum statistics.

Additional statistics such as the number of missing observations, quartiles (Q1, Q3), specific percentiles, the standard error, %SEM and confidence intervals are less frequently reported.

The CV% will be reported as missing if the arithmetic mean is zero.

If the non-missing data values are not all positive, then the geometric mean and CV% cannot be calculated and should be reported as missing.

The measures of precision (SD, CV%, geometric CV%, etc…) are not reported when there is only one non-missing data.

8.3.3.1. Statistics in the presence of BQL dataIn case of values below the LLOQ or above the upper limit of quantification (ULOQ), the frequency (n, %) of values below the LLOQ and above the ULOQ, respectively, may be reported.

It may not be relevant to report standard empirical statistics when the total number of BLQ values is large (eg, when it exceeds 1/3rd of the total). Instead, the summary statistics (mean, standard deviation) may be adapted to the presence of censored values (values below the LLOQ and/or values above the ULOQ), by reporting the maximum likelihood estimates from a parametric model for data that can be right censored and left censored (e.g., using SAS PROC LIFEREG). In the case of censoring, the empirical median may not be reported. Likewise, the empirical minimum (maximum) may not be reported if there are values below the LLOQ (above the ULOQ).

8.3.4. Individual data handling in summary tables

When the actual sampling time differs significantly from the protocol time (eg., when the deviation is greater than 10%), then the concentration should be excluded from descriptive statistics calculation but kept in the PK parameters determination. A flag and a footnote should be presented in the table.

The method used to handle BQL data prior to the calculation of summary statistics should be presented in a footnote on the summary tables. If BQL data have been imputed, the imputation

2


value (eg, 0, LLOQ or LLOQ/2) should be indicated. If data has been left missing, if the actual values BQL or if a censoring method has been used, this should be indicated in the footnote.

In urine, only descriptive statistics on amount excreted/fraction or cumulative amount excreted over time are usually computed (no descriptive statistics on concentration or volume). When these amounts are not estimable they should be considered as missing.

In specific situations, it may not be possible to calculate PK parameters, either because some required input data are not available or for some justifiable calculation reason. If the proportion of missing PK parameter is large (eg, larger than 1/3 of all data), descriptive statistics may not be calculated. In that case, the specific reason for not reporting the statistics should be indicated in the table.

8.3.5. Mean Plots When generating the mean concentration-time plots from the average dataset, replace the early (pre-Cmax) not calculated values by zero in order to capture the lag-time, if any. Protocol times are used for generating the mean concentration-time data.

Considering the inherently log-normal distribution of concentrations, plots of geometric mean concentration versus time may be generated in addition to or in replacement of the arithmetic mean plots. Linear and log-linear displays are often produced side by side to clearly delineate the concentration-time profiles.

Error bars are usually added to the mean display in the linear-linear scale to characterise the data distribution in the population. In that case the standard deviation (SD) is often reported. Error bars may also be used in particualr situations to characterize the precision on the mean. Then, either standard errors (SE) or confidence intervals (CI) are reported. In general two-sided error bars are reported, unless the figures becomes too busy. In that case, one-sided bars may be considered. Different sides (upper/lower) may also be considered in multi-line plots to improve rendering of the graph. For instance, use a lower bar for groups having low concentrations and vice-versa.

8.3.6. Formats for individual data and statisticsIf possible, the individual PK concentrations and parameters should be formatted to 3 significant figures in the individual data listings. Other formats or rounding presentations may be considered as deemed appropriate. For statistical tables, the descriptive statistics are often rounded to one additional digit (eg, 4 significant figures) for the mean, and median, to two additional digits (eg, 5 significant figures) for the SD, and to the same number of digits (eg, 3 significant figures) for the minimum and maximum values. The CV values are often reported in percent unit and using one decimal place.

3


9. Example SAP Language

9.1. Data to be analysedAll subjects with evaluable PK parameter data and no major protocol deviation with an impact on PK data will be included in the PK analysis set.

9.2. Pharmacokinetic methodsAll patients within the PK analysis set will be included in the pharmacokinetic data analysis.Individual PK data for all randomised subjects will be listed.

Biofluid concentrations will be expressed in [UOM]. All concentrations below the limit of quantification (LLOQ) or missing data will be labeled as such in the concentration data listings. Concentrations below the LLOQ will be treated as [zero or LLOQ or LLOQ/2] in summary statistics for concentration data only. They will not be considered for calculation of PK parameters (with the exception of the pre-dose samples). PK concentration profiles will be summarized by treatment and over time in tabular and graphical formats. Arithmetic mean (+/-SD) concentration-time plots will be produced.

The following pharmacokinetic parameters will be determined using non-compartmental methods:

Primary PK parameters: AUC, AUC(0-t), Cmax.

Secondary PK parameters: tmax, t½.

Descriptive statistics of pharmacokinetic parameters and concentrations will include mean, SD, and CV, min and max. When a geometric mean will be presented it will be stated as such. Since Tmax is generally evaluated by a nonparametric method, median values and ranges will be given for this parameter.

4


10. ReferencesTO BE COMPLETED

5


11. AcknowledgementsThe key contributors include: Francois Vandenhende, Ingrid Burton, Sascha Ahrweiler, and Vincent Buchheit.

Additional contributors and members of the white paper project within the FDA/PhUSE Development of Standard Scripts for Analysis and Programming Working Group include: …

Acknowledgement to others who provided text for various sections, review comments, and/or participated in discussions related to methodology: …

6

Date post:	14-Jul-2020
Category:	Documents
Upload:	others
View:	7 times
Download:	0 times

PHUSE Wiki - PHUSE Wiki - Analyses and Displays ... · Web viewICH E7, Studies in Support of...

Documents