Perspectives on the past, present and future of CFAST therapeutic area data standards development

Jon Neville, Critical Path Institute

CJUG-PhUSE Single Day Event 14 April 2017

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Overview

•  Background- what is Critical Path Institute (C-Path) and how did we get involved in CDISC therapeutic area standards?

•  CFAST progress to date

•  Understanding what drives change (with examples)

•  Lessons learned/ future considerations

2

Disclaimers

The ideas shared in this presentation represent my perspective as a therapeutic area standards developer I cannot speak for CDISC, FDA, or PMDA

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Cri$cal  Path  Ins$tute  Created    

Memorandum  of  Understanding  created  between  the  FDA  and  C-­‐Path  in  2005    

Independent  501(c)3  founded  in  2004  “…  to  foster  development  of  new  evalua$on  tools  to  inform  medical  product  development”  

4

C-­‐Path:  A  Public-­‐Private  Partnership  

q Act  as  a  trusted,  neutral  third  party  q Convene  scien5fic  consor5a  of  industry,  academia,  and  

government  for  pre-­‐compe55ve  sharing  of  data/exper5se  

•  The  best  science  •  The  broadest  experience  •  Ac5ve  consensus  building  •  Shared  risk  and  costs  

q Enable  itera5ve  EMA/FDA/PMDA  par5cipa5on  in  developing  new  methods  to  assess  the  safety  and  efficacy  of  medical  products  

•  Official  regulatory  recogni5on  through  “qualifica5on”  of  Novel  Methodologies  /  Drug  Development  Tools  and  development  and  use  of  data  standards  

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Cri5cal  Path  Ins5tute  Consor5a  Twelve global consortia collaborating with 1,300+ scientists and 61 companies

Coalition Against Major Diseases Focusing on diseases of the brain

Coalition For Accelerating Standards and Therapies Data standards

Critical Path for Parkinson’s Consortium Enabling clinical trials in Parkinson’s Disease

Critical Path to TB Drug Regimens Accelerating the development of TB drug regimens and diagnostics

The Duchenne Regulatory Science Consortium Duchenne Muscular Dystrophy

International Neonatal Consortium Neonatal clinical trials

Polycystic Kidney Disease Outcomes Consortium

Multiple Sclerosis Outcomes Assessment Consortium

Patient-Reported Outcome Consortium Assessing treatment benefit

Electronic Patient-Reported Outcome Consortium Electronic capture of treatment benefit

Predictive Safety Testing Consortium Drug safety

Pediatric Trials Consortium Developing effective therapies for children

Biomarkers Clinical outcome assessment instruments

Clinical trial simulation tools Data standards In vitro tools

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CAMD: The Value of Data Sharing, Standards, and Integration

q  Nine member companies agreed to share data from 24 Alzheimer’s Disease trials

q  The data were not in a common format q  The data were remapped to CDISC

standards and pooled

q  A new clinical trial simulation tool was created

and has been the first model endorsed by the FDA and EMA

q  Researchers utilizing the model and the

database to advance research

Star5ng  Point  

Result  

24  studies,  >6500  pa5ents  

CFAST,  a  joint  ini$a$ve  of  Cri$cal  Path  Ins$tute  (C-­‐Path)  and  CDISC,  was  launched  to  accelerate  clinical  research  and  medical  product  development  by  facilita$ng  the  establishment  and  maintenance  of  data  standards,  tools  and  methods  for  conduc$ng  research  in  therapeu$c  areas  important  to  public  health.      CFAST  TA  project  collaborators  include  the  U.S.  Food  and  Drug  Administra$on  (FDA),  TransCelerate  BioPharma,  Inc.  (TCB)  and  the  Na$onal  Cancer  Ins$tute  Enterprise  Vocabulary  Services  (NCI-­‐EVS),  with  par$cipa$on  and  input  from  many  other  organiza$ons.    

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Coali5on  For  Accelera5ng  Standards  &  Therapies  (CFAST)  

© CDISC 2016

CFAST Accomplishments - Snapshot •  The CFAST Program is now in Year 5 •  Published 29 Standards to date •  Developed and piloted new processes, tools and

checklists •  Developed what a TAUG should contain over time

§  Examples, metadata tables and implementation advice

•  Utilized SHARE functionality as it became available

•  Partnered with many different organizations §  FDA, PMDA, C-Path Consortia, TransCelerate, NCI-

EVS, MS Society, One Mind, Gates, WWARN, CHDI, others..

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Completed Standards – through September, 2016

Pre-CFAST

CFAST

Therapeutic Area Publication

Alzheimer’s Disease v1 Sep, 2011Tuberculosis v1 Jun, 2012

Pain Aug, 2012Virology v1 Dec, 2012Parkinson’s Dec, 2012

Polycystic Kidney Disease Feb, 2013Asthma Nov, 2013

Alzheimer’s Disease v2 Dec, 2013Multiple Sclerosis May, 2014

Diabetes Sep, 2014CV Endpoints Oct ,2014

Influenza Nov,2014QT Studies Dec, 2014

Chronic Hepatitis C Virus May, 2015Schizophrenia Jun, 2015Dyslipidemia Jun, 2015Virology v2 Sep, 2015

Traumatic Brain Injury Dec, 2015Diabetes ADaM Supplement Dec, 2015

COPD Jan, 2016Tuberculosis v2 Feb, 2016Breast Cancer May, 2016

Kidney Transplant Oct, 2016 Rheumatoid Arthritis Nov, 2016

Major Depressive Disorder Dec, 2016 Diabetic Kidney Disease Dec, 2016

Pain v1.1 Dec, 2016 Ebola Dec, 2016

Malaria Jan, 2017

© CDISC 2016

CFAST TA Standards Pipeline

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Pre-CFAST 2012-2015 2015 2016 2017

Alzheimer’s v1 Asthma v1 Traumatic Brain Injury v1 One Mind

Kidney Transplant FDA-CFAST Yr3

Oncology – Lung FDA

Pain Univ of Rochester

Alzheimer’s v2 C-Path CAMD Consortium

Oncology - Breast Oncology – Colorectal FDA

CDAD FDA

Parkinson’s v1 Multiple Sclerosis MS Society

COPD Malaria Gates / WWARN

Polycystic Kidney Disease v1 PKD Foundation

Diabetes v1 Virology v2 FDA

Post Traumatic Stress Cohen Veterans Bioscience

TB v1 Gates Cardiology Endpoints v1 FDA

TB v2 (pediatrics) C-Path CPTR Consortium

Duchenne Muscular Dystrophy FDA – CFAST Yr3/C-Path D-RSC Consortium

Virology v1 Influenza FDA Rheum. Arthritis Ebola

Hepatitis-C FDA Cardiology Imaging Diabetic Kidney Disease

,

Schizophrenia FDA Oncology – Prostate FDA

HIV - FDA

Dyslipidemia Major Depression FDA CFAST Yr3

Huntington’s CHDI

http://www.cdisc.org/system/files/all/CFAST_ProjectPipeline.pdf

© CDISC 2016

TA Standards by Area Oncology

Infectious Diseases

Mental & Behavioral Disorders

Cardiovascular Neurological Chronic Respiratory Diseases

Autoimmune Diseases

Endocrinology Other

Breast Cancer v1 Tuberculosis v1 Tuberculosis v2, Gates

Schizophrenia FDA Dyslipidemia v1 Alzheimer’s v1,

v2

Asthma v1 Rheumatoid Arthritis v1

Polycystic Disease v1 University of Rochester

Pain v1 University of Rochester

Prostate Cancer v1 FDA

Influenza v1 Major Depressive Disorder v1 FDA

CV Endpoints v1 FDA

Traumatic Brain Injury v1 One Mind

COPD v1 Diabetes v1 Solid Organ (Kidney Transplant) v1 FDA

Colorectal Cancer v1 FDA

Hepatitis C, v1 FDA

Post Traumatic Stress Disorder v1 Cohen Veterans Bioscience

CV Imaging v1 Parkinson’s Disease v1

Diabetic Kidney Disease v1

Lung Cancer v1 FDA

Virology v1, v2 FDA

Bi-Polar v1 QT Studies v1

Multiple Sclerosis v1 MS Society

Malaria v1 Gates / WWARN

General Anxiety Disorder v1

Duchenne Muscular Dystrophy v1

Ebola v1 Huntington’s Disease v1

Vaccines v1

Parkinson’s v2

HIV v1 NIAID & FDA

CDAD FDA

4 9 8 4 5 2 1 3 2

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Bold - ongoing Planned

© CDISC 2016

FDA Data Standards Catalogue v4.4 http://www.fda.gov/ForIndustry/DataStandards/StudyDataStandards/default.htm

Requirement of Study Data Standards – Data Exchange Standards

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Studies that start after These dates.

© CDISC 2016

•  Required e-Study Data for NDA’s using CDISC Oct 1st, 2016

•  CDISC TA Standards may be used in NDA applications

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(Slide courtesy of Hiroshi Sakaguchi, PMDA)

© CDISC 2016

Why so many changes? •  Foundational standards (e.g., SDTM) were

created from relatively limited use cases

•  Therapeutic areas take a closer look at more specific uses cases, which require specific strategies

•  The next couple examples highlight where developers encountered a need for change in order to accommodate these use cases

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EXAMPLE 1: VIROLOGY A NEW APPROACH TO MICROORGANISM NOMENCLATURE

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What is Taxonomic Nomenclature?

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Sub-species?

Sub-sub-species?

Sub-sub-sub-species?

Virus taxonomy at the sub-species levels is not standardized

Virus Nomenclature- Virology v1.0

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5. SPECIES and STRAIN were added to the domains to allow for the separation of genetic and genomic data from pathogens, such as viruses (that are the subject of this user guide) from genetic data on their human hosts (whose species and stain, if not human, would be submitted in the Demographics domain). 6. It was suggested that SUBSTRAIN and CLADE be added to the domains. However, because of ambiguous definitions and because the hierarchy used seems to differ, these potential additions were deferred until a future version.

© CDISC 2016

Virus Nomenclature Issue

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HIV   Influenza  A   Hep  C   Hep  B   HPV  

SPECIES  level   Species   Species   Species   Species   Species  Subspecies  Level  1   Type   Subtype   Genotype   Genotype   Type  

Subspecies  Level  2   Group   Strain     Subtype   Sub-­‐genotype  

Subspecies  Level  3  

 Subtype  (or  Clade)  

Recombina$on  Type  

Subspecies  Level  4     Subclade  

--NSPCES

--NSTRN?

© CDISC 2016

NHOID and Non-host Organism Identifiers (OI) Domain

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© CDISC 2016

Advantages of NHOID/OI approach to Nomenclature •  Agnostic with regard to pathogen nomenclature

terminology •  Works for any non-host organism •  It’s modular – take what you need:

§  NHOID provides a “snapshot” of bug identity §  OI dataset provides parsed details of taxonomy when

needed without the need for countless new variables like –NSPCES and –NSTRN

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EXAMPLE 2: TUBERCULOSIS A HARMONIZED APPROACH TO PHENOTYPIC DRUG SENSITIVITY TESTING (DST)

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Phenotypic Drug Sensitivity Testing Results

SR SRR

ug/ml antibiotic

Microbiology Susceptibility Examples in SDTMIG 3.2: Why They Don’t Work

1.  Sometimes the drug concentration is the result of the test and sometimes it is a pre-defined part of the test; the current model cannot support both structures.

2.  Drug name is the test: a.  Doesn’t tell you what the test is (i.e. MIC) b.  Controlled terminology team will not control drug names

3.  No where to represent information on the pathogen that is being tested. Must link back to the identification record in MB.

© CDISC 2016

Row STUDYID DOMAIN USUBJID MBSEQ MBLNKID MBTESTCD MBTEST MBORRES

1 ABC MB ABC-01-01 1 LKN03 MTBINH M. Tuberculosis INH Resistant

NEGATIVE

Row STUDYID DOMAIN USUBJID MSSEQ MSLNKID MSTESTCD MSTEST MSCAT MSORRES

1 ABC MS ABC-01-01 1 LKN03 GROWTH Growth DST NEGATIVE

2 ABC MS ABC-01-01 2 LKN03 DSTDRUG Drug Susceptibility Test Name

DST Isoniazid

3 ABC MS ABC-01-01 3 LKN03 DSTCONC Drug Susceptibility Test Concentration

DST 0.1

Drug Sensitivity Testing – TB v1.0

But… •  Phenotypic drug susceptibility data is challenging to model as four separate

rows across two domains. •  Susceptibility records need to be linked back to the identification in order to

determine what organism is the subject of the test.

© CDISC 2016

Drug Sensitivity Testing – Virology v1.0

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7. Representing viral resistance data in an SDTM-based domain model is a challenge. An initial attempt was made to model these data in the Microbiology domains, but this approach was abandoned because the current MB/MS domain structures are limited to resistance based on only one result. Virology data, on the other hand, includes multiple results, and a net assessment that summarizes these results. The use of the LB domain, which already includes examples of viral test data, was next considered but this approach was felt to create too high a burden for creating test codes which would have included the virus as part of the test name. After considering these alternatives, the team chose to create a Viral Resistance (VR) domain that includes the species and strain variables, eliminating the need to maintain pathogen-specific test names.

© CDISC 2016

1.  Virology team felt that viral resistance data could not be adequately supported by the MS domain so the Viral Resistance (VR) domain was created.

2.  Created: a. Descriptive test codes without the drug name b. New variables for: species and drug name 3.  Now have a structure works great for MIC, IC50 etc.!

But… •  Now we have a VR domain and an MS domain. Does it make sense to

create a new resistance domain for each non-host organism ....fungi, parasites, worms etc.?

•  The virology group did not create a corresponding virus identification domain analogous to MB. Can MB be used for all pathogen identification?

•  How do we harmonize efforts and make one set of domains work for all relevant data?

YES!

Drug Sensitivity Testing – Virology v1.0

© CDISC 2016

Proposal for one all-encompassing MS domain

NHOID

•  From Virology v1.0 •  Use MSTESTCD/TEST to represent the name of the test. •  Variable to represent the name of the drug being used.

•  From Virology v2.0 •  Use the NHOID variable to represent the organism that is the subject of the

test. •  From TB v2.0

•  Add two new variables to accommodate test results when the drug concentration is a pre-specified part of the test.

MSAGENT Microbial

Susceptibility Microbial

Susceptibility Microbial

Susceptibility

MICROSUS

MICROSUS

MICROSUS

© CDISC 2016

Advantages of a Unified Approach for Microbiology Domains

•  One domain for all DST, regardless of non-host organism

•  Allows results to represented in one domain as they are reported

•  Provides unambiguous home for individual concepts (Test, drug, concentration and units, organism)

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© CDISC 2016

Summary of changes discussed •  --NSPCES and –NSTRN deprecated

(Addition of NHOID to be formalized in SDTM v1.6)

•  VR domain deprecated in favor of a unified MS domain (Additional required variables MSAGENT, MSCONC, MSCONCU to be formalized in SDTM v1.6)

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© CDISC 2016

What are We Getting at? •  All early-version standards guides are “stakes in the

ground.” •  Standards are continuously evolving as we learn more

and examine more use-cases •  Updates and changes may appear abrupt, but only occur

after much vetting, discussion and feedback from the community, including implementers

•  The good news: The more TAs we develop, the more overlapping concepts emerge (=REUSABILITY!)

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© CDISC 2016

General Observations & Lessons Learned

•  Focus has been on: §  the number of standards developed §  meeting timelines §  SDTM and Terminology

•  CDISC user community slow to implement TA standards §  Difficult to implement, need to refer to other standards, hard to keep

up with the number of standards produced, user unwillingness to adopt due to provisional status

§  Clear request from CDISC community and Regulatory Authorities to slow down

•  Cross-team dependencies, not fully considered §  Resulted in re-work, more time needed, adjusted timelines §  Contributed to inconsistency between standards; Foundational

standards not keeping pace

•  Impact on Staff workload (C-PATH & CDISC, NCI-EVS), not fully considered

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© CDISC 2016

Map of Teams – Foundational Teams are mostly Volunteer

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© CDISC 2016

General Observations & Lessons Learned •  No planning for maintenance of standards

§  now focusing on an update effort, increased work

•  Addition of documents and deliverables by FDA §  FDA Recommendations documents

•  May not get these in the future §  TA Specifications §  FDA request for a “change log” capturing the differences

between public review and publication §  Extend Public Review from 30 to 60 days

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© CDISC 2016

Map of CDISC Products

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© CDISC 2016

CFAST Collaboration – Next Steps •  Move to “collaborative integration” model for project

organization: §  Animal Rule (collaboration between CDISC/C-Path) §  Already collaborating in this integrative manner on HIV v1 §  The product will be a complete co-developed HIV v1.

•  See concept map •  TA projects scoped to reach B2E goals

§  (Protocol), CDASH, SDTM, ADaM, Terminology, QRS

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CDISC C-PATH

© CDISC 2016

Integrative Standards Development Model

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CDISC – Prevention C-Path – Treatment TAUG - HIV v1

© CDISC 2016

Next Steps for Future Projects •  Aligned timelines

§  When there is more than on grant awarding organization and be transparent about the community it takes to develop a CDISC standard

•  Redefine “success” §  Greater focus on quality and completeness vs. quantity §  Adoption of TAUGs Regulatory Authorities

•  by user community – difficult to measure (TAC, CAC, TLC, Members) §  Strive for a better balance between the CFAST project pipeline and foundational

standards development •  Evaluate “topic guides” inclusion in the pipeline discussions.

•  Revise grant writing process & template language to include B2E approach

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© CDISC 2016 39

Approving Body

Approving Body Modeling

Consultation Team

MCT

Approval by MCT to start

Internal Review

SRC & Copy-Editing

SRC & Publication Committee

TLC-GC or TAPSC – Technical Leadership Team – Governance Committee or TA Steering Committee MCT- SR - Modeling Consultation Team - Super Reviewers Modeling Forum

Consider Translation Requirements

Copy-Editing, GSP and formatting

check

60-day Public Review

© CDISC 2016

Revised Process

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© CDISC 2016

CDISC Review Councils

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Modeling Review Council (MRC) Standards Review Council (SRC)

Provide early input on technical approaches (Concept Maps, CDASH, SDTM, ADaM, Controlled Terminology, XML Tech)

Review packages of documents/standards after internal review and prior to public review to ensure harmonization with existing standards

Review packages of documents/standards prior to internal review to ensure harmonization with existing standards

Ensure that all public review comments have been appropriately addressed

Review modeling issues that have been escalated for resolution from internal CDISC teams

Other future considerations

•  Many concepts addressed in TAs apply to multiple disease areas

•  The data modeling approaches to these concepts should be easier to find

•  A new type of cross-cutting topic area user guide has been proposed

•  Address a given concept with examples from multiple use cases

•  Published as a standalone mini-guide •  TAUGs could point to these guides

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Examples of Cross-cutting Topics

Topic Teams Involved Description Combination Therapies / Drug Regimens  

SDTM, CDASH, ADaM, SHARE*   Representation of regimens that combine multiple products or even medications with other treatment modalities (e.g., radiation, surgery). Representation of active ingredient doses for combination products.  

Patient Diaries SDTM, CDASH, SHARE*   Agree how to represent timing of diary data., The occasions when diary data are collected are not visits defined in the Trial Visits domain, and there is disagreement about how they time periods evaluated should be represented (e.g., as intervals around time points or as retrospective questions relative to dates) Issues related to transcription or extraction of diary data into CRFs.  

Imaging   SDTM, CDASH, ADaM, SHARE*   Consolidation of examples? Explaining representation of methods?  

Assessment Intervals   SDTM, ADaM, SHARE*

Epidemiology-based Findings

SDTM, CDASH, ADaM, SHARE* Trial design for epidemiological studies? Applicability of informed consent, disposition.

   

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Pilot case: currently in

development

Conclusion

•  CFAST has accomplished significant work in 5 years, and is the single biggest driver of change to foundational standards

•  Changes to foundational models only happen after careful consideration of multiple stakeholder needs and use cases (though we acknowledge change can be disruptive)

•  The process continues to evolve as we apply lessons learned in support of a higher quality output and broader impact analyses

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© CDISC 2014 45