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Programmatic Generation of Health Canada's Comparative Bioavailability Information, Appendix B

Michael S. RimlerSA11 – 2020 PHUSE US Connect

Background– Therapeutic Products Directorate (TPD) of Health Canada (HC)– If relying on comparative bioavailability studies to establish

safety and efficacy– Preparation of Comparative Bioavailability Information for Drug

Submissions in the CTD Format (2004)

– Five modules in CTD– Module 1:

1. Table of Contents

2. Application information3. Product labelling

Abbreviated New Drug Submission (ANDS)

Introduction / Overview

2020 / PHUSE US ConnectSA11 2

https://www.canada.ca/content/dam/hc-sc/migration/hc-sc/dhp-mps/alt_formats/hpfb-dgpsa/pdf/prodpharma/draft_ebauche_ctdbe-

eng.pdf4. Health Canada summaries5. An environmental assessment statement6. Electronic review documents

Bioequivalence Datasets

https://www.canada.ca/content/dam/hc-sc/migration/hc-sc/dhp-mps/alt_formats/hpfb-dgpsa/pdf/prodpharma/draft_ebauche_ctdbe-eng.pdf

Objective


Facilitates validationwith independent

programming

Illustrate the value of programmatic generation of BE Datasets

Facilitates replication the process for future

submissions

Legal Bit


This presentation contains a lot of SAS code.I promise to make it as painless as possible.

You may become weary from all the SAS code. You may begin to feel sleepy from all the SAS code.

Disclaimer

If you begin to feel dizzy or fatigued (from all the SAS code), please feel free to get up and move around. J

Bioequivalence (BE) Datasets– ASCII format– Appendix B of the guidance– Two components

– Data file (DAT file)– Information file (INF file)

Module 1.6 – Electronic Review Documents

Elements to be produced


The DAT File

Characteristics– Measured and uncorrected drug concentrations from a bioequivalence study– Extension *.dat– One record per subject per study period, sorted by treatment/formulation and subject

The DAT file


Example

– Space delimited (not required)– BLQ set to zero; missing data entered as a period (.)– Uncorrected concentrations should fall within nominal concentrations of the standard curve– May require presenting in units different CSR analyses

The DAT file


Additional Characteristics Implemented in SAS Code– Presents concentration data in desired units of measure, if different from analysis units– Allows the number of timepoints to be data driven– Allows the length of concentration fields to be data driven– Creates a SAS dataset to be used for electronic compare by QC– Creates a data definition file used as an input to the INF file and for electronic compare by QC

Input Parameters– Path of input data– Path to write output– Conversion factor– Filename

SAS sample code

The DAT file


Full code in Appendix of paper

%ru_cr8_dat(pathin =,pathout =,convfact=,fname =);

Why generate with (SAS) code?– Code is replicable, can be copied (and updated) to the next effort– Allows flexibility such as

– Adjusting units– Data driven characteristics

– Creation of permanent (SAS) dataset for validation compare– Generation of INF data definition file

– For production use

– For validation compare

The DAT file


First step

Data driven concentration fields

– Facilitates construction of SAS format of the form z7.3 to display concentrations with leading zeros (e.g., such as 012.345).

SAS sample code

The DAT file


where paramcd="CONC_P1C";

if find(avalc,"NQ")>0 and aval=. then aval=0;

aval_conv = aval*%eval(&convfact.);

proc sql noprint;select max(TotalLength) into : len from &prefix.DS1; select max(NumDPS) into : dps from &prefix.DS1;

quit;

z7.3

Convert missing concentrations to a period (with alignment)

Output DAT file

SAS sample code

The DAT file


if C&ii. = "" then C&ii. = tranwrd(put(0,z&xfmt.),"0"," ");

data _null_;set &prefix.DS5;by SORT_BY_TRT SUBJECT;file "&pathout./&fname..dat" ;put SUBJECT SEQUENCE PERIOD TREATMENT%do ii = 1 %to &num_tpts.;

C&ii. $char%eval(&len. + 1 + &dps. + 1).%end; ;

run;proc sort data=&prefix.DS5

out=dddlib.DAT_FILE(drop=SORT_BY_TRT);by TREATMENT SUBJECT;

run;

Output SAS dataset for compare

Create Data Definition File

The DAT file


proc contents data=work.&prefix.DS5out=&prefix.DATA1(keep=NAME TYPE LENGTH VARNUM)directory details varnum noprint;

run;

*** Start and stop positions ***;if _n_ = 1 then StartPos = 1;else StartPos = EndPos + 2;EndPos = StartPos + Length - 1;IDX = IDX + LENGTH;

length POSITION $10;POSITION = compress(

put(StartPos,best.)||"-"||put(EndPos,best.));

The INF File

Specific information about the concentrations in the DAT filei. Sampling timesii. Drug name, drug strength, dose form, potency, and dose level administerediii. Limit of quantitationiv. Standard curve rangev. Study periodvi. Treatment labellingvii. Company name of the sponsor and firm performing the studyviii. Contact personix. Date the file was generatedx. Description of the record layout in the data file (DAT)

The INF file


Example from Guidance

The INF file


N = # Subjects ??N = # SampTimes ??

Why generate with (SAS) code– Input template reviewed once layout issues, subsequent focus on the data driven elements– Selected components are data driven, instead of hard coded– The data definition table is data driven (via DAT code), instead of hard coded– Code is replicable, can be copied (and updated) to next effort– Creation of (SAS) dataset for validation compare

The INF file


The INF file – Input Template


#i. SAMPLING TIMES: 0 / 0.5 / 1.0 / 2.0 / 3.0 / 4.0 / 6.0 / 8.0 / 12.0 hours (N=<bigN>)##ii.#(a) DRUG NAME: FAKENAME #(b) DRUG STRENGTH COMPARED: Fakedrugimab 30.0 mg#(c) DOSAGE FORM: tablet#(d) POTENCY: Fakedrugimab 99.2% #(e) DOSE ADMINISTERED: Fakedrugimab 30.0 mg##iii. LIMIT OF QUANTITATION (LOQ): LOQ for Fakedrugimab is 10.0 ng/mL ##iv. STANDARD CURVE RANGE: 10.0 to 10000 ng/mL for Fakedrugimab##v. STUDY PERIOD: First subject first visit (screening start): June 27, 2017# Last subject last visit (Last follow-up visit): January 18, 2019##vi. TREATMENT LABELLING: <TRTA># <TRTB># #vii.#a. SPONSOR COMPANY'S NAME: GlaxoSmithKline#b. FIRM PERFORMING STUDY: Rimler Pharmacology, 1600 Pennsylvania Ave NW, Washington, DC 20500##viii. CONTACT PERSON: George Washington# Phone (615) 555-1212# Fax (615) 555-1213##ix. DATE FILE GENERATED: <insert date>##x. Description of the record layout in the data file##DESCRIPTOR POSITION LENGTH TYPE EXAMPLE

Structured per guidance

Four fields to be data driven

Leading ‘#’ symbols in Column 1

Last line providing header for data definition table

Data driven fields

SAS sample code

The INF file


data _null;x = put(today(),worddate.);call symput("today",strip(x));

run;

proc sql noprint;select count(distinct USUBJID) into : bigN from admlib.adsl where PKCFL="Y";select TRTP into : trta from admlib.adpc where TRTPN=1;select TRTP into : trtb from admlib.adpc where TRTPN=2;

quit;

if find(_line,"<insert date>")>0 then line = tranwrd(_line,"<insert date>","&today.");else if find(_line,"<bigN>")>0 then line = tranwrd(_line,"<bigN>","%eval(&bigN.)");else if find(_line,"<TRTA>")>0 then line = tranwrd(_line,"<TRTA>","&trta.");else if find(_line,"<TRTB>")>0 then line = tranwrd(_line,"<TRTB>","&trtb.");else line = _line;

Read in template

Output INF file

SAS sample code

The INF file


filename _in_file "&pathout./refdata/&in_file.";data &prefix.INF1;

length _line $200;infile _in_file lrecl=32767 truncover;input _line $1-200;_line = substr(_line,2); *** Ignore character in column 1 ***;

run;

data _null_;set &prefix.INF2 end=eof;file "&pathout./&fname..inf" ;put LINE $char200.;

run;

Perform validation with independent programming– Dataset with formatted fields supporting the DAT file– Dataset containing DAT file record layout for the INF file– Data driven elements of the INF file that can be independently derived

Replicate the process for future submissions to Health Canada (HC)– Code should be stable from one HC BE submission to another– Leverages a standard input template with data-driven fields

– Uses PROC CONTENTS from DAT generation (keeps components in sync)– Contains “date generated” field

– Elements of the input template file which are submission specific and not data driven can be updated easily

Programmatic generation facilitates the ability to

Summary


Questions?


Michael S. RimlerGlaxoSmithKline

[email protected]

http://gsk.com

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