PK IN DRUG DEVELOPMENT. CDISC management of PK data. Matteo Rossini Milan, 9 February 2010

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1 Matteo Rossini Milan, 9 February 2010

2 Introduction to CDISC CDISC: Clinical Data Interchange Standards Consortium CDISC is a global, open, multidisciplinary, non-profit organization that has established standards to support the acquisition, exchange, submission and archive of clinical research data and metadata

3 The CDISC mission is to develop and support global, platformindependent data standards that enable information system interoperability to improve medical research and related areas of healthcare CDISC standards are vendorneutral, platform-independent and freely available via the CDISC website

4 FDA and CDISC FDA stated that SDTM will be used for electronic submission of data tabulations FDA announced on 6 November 2009 that they will accept SDTM version submissions

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9 Introduction to SDTM SDTM: Study Data Tabulation Model SDTM provides a general framework for information collected during human and animal studies and submitted to regulatory authorities Observation described by a series of variables classified according to its Role

10 SDTM variables Identifier variables, which identify the study, the subject involved in the study, the domain, and the sequence number of the record Topic variables, which specify the focus of the observation (such as the name of a lab test)

11 SDTM variables Timing variables, which describe the timing of an observation Qualifier variables, which include additional illustrative text, or numeric values that describe the results or additional traits of the observation (such as units or descriptive adjectives)

12 SDTM variables Rule variables, which express an algorithm or executable method to define start, end, or looping conditions in the Trial Design model

13 Qualifier variables classification Grouping Qualifiers are used to group together a collection of observations within the same domain. Examples include --CAT and --SCAT

14 Qualifier variables classification Result Qualifiers describe the specific results associated with the topic variable in a Findings dataset. They answer the question raised by the topic variable. Result Qualifiers are --ORRES, -- STRESC, and --STRESN

15 Qualifier variables classification Synonym Qualifiers specify an alternative name for a particular variable in an observation. Examples include --MODIFY and - -DECOD, which are equivalent terms for a --TRT or --TERM topic variable, --TEST and --LOINC which are equivalent terms for a -- TESTCD

16 Qualifier variables classification Record Qualifiers define additional attributes of the observation record as a whole (rather than describing a particular variable within a record). Examples include --REASND, AESLIFE, and all other SAE flag variables in the AE domain; AGE, SEX, and RACE in the DM domain; and --BLFL, -- POS, --LOC, --SPEC and --NAM in a Findings domain

17 Qualifier variables classification Variable Qualifiers are used to describe a specific variable within an observation and are only meaningful in the context of the variable they qualify. Examples include --ORRESU, --ORNRHI, and --ORNRLO, all of which are Variable Qualifiers of --ORRES; and --DOSU, which is a Variable Qualifier of --DOSE

18 PC and PP domains foreseen but not implemented in SDTM version PC and PP domains added as standard in SDTM version PC domain: Pharmacokinetic Concentrations PP domain: Pharmacokinetic Parameters (derived from PC)

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35 Problems Data transfer from analytical laboratories Merge of data from CRFs and analytical laboratory Mapping of data structure used for data capture into SDTM domains

36 Controlled and significant terminology within the constraints imposed by SDTM requirements (e.g. 8 characters for --TESTCD variable and 40 characters for -- TEST) QC versus CRFs and SDs is difficult since SDTM domains do not follow the CRF/SD flow

37 CROSS ALLIANCE experience SDTM implemented since studies managed with SDTM Internal SDTM standard (structure of domains and terminology) Management of Sponsors SDTM standard Cooperation with several analytical laboratory

38 Example Single dose, open, randomised, two way cross-over, pilot bioavailability study 16 blood samples for each period PK concentration data and PK parameters of a subject

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53 CDISC Italian Speaking User Group Support of companies for CDISC implementation Organization of meetings Sharing of experience Help for problem solving

54 Thank you! Matteo Rossini Biostatistician & Senior Data Manager

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