WG6 Nonclinical - Endpoint predictivity

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Endpoint Predictivity

Welcome to the Wiki for the Endpoint Predictivity project, part of the of the FDA/PhUSE Computational Sciences Nonclinical Working Group. Learn more about the larger working group at Non-Clinical Road-map and Impacts on Implementation.

At the 2013 FDA/PhUSE CSS, it was decided that the Endpoint Predictivity project and the Data Interconnectivity project would be able to take advantage of some obvious synergy between the groups by merging. This combined group was renamed Nonclinical Data Interconnectivity for Clinical Endpoint Predictivity (NICE).

Endpoint Predictivity: Understanding the rate at which a finding in a nonclinical study translates to a clinical finding. This does not require that the finding be the same in both species. Out of necessity, this understanding must include knowledge of exposure/effect relationships and other factors that affect this rate (e.g. age, gender, ethnicity, etc...)

Overview & Scope


Endpoint predictivity (Need/Challenge ID 0033). Nonclinical toxicologists have limited ability to look forward and determine the relative predictivity of nonclinical findings or pattern of findings for clinical adverse events or outcomes. For example, if a nonclinical toxicologist observes finding X in a chronic toxicology study in rats, will this finding translate to an adverse effect in humans? In some cases, comparative physiology might answer this question, but in many cases it cannot. This results in unacceptable type 1 and type 2 errors by industry and regulatory scientists (and many times the significance of findings is equivocal or simply unknown). There are multiple sub-issues here that may need to be separated out (some have to do with access to data, ability to merge with clinical data, and how to do the analysis (from both a scientific and an IT perspective)).


With the requirements for submission of nonclinical data in a standard electronic format (SEND) contained in PDUFA V, we expect that the problem of compilation and exploration of large sets of nonclinical data will become tractable in the near future. We hope to utilize this data in a number of ways, including (but not limited to):

  • Enable better prediction (informing both drug development studies and the clinical protocols and monitoring)
  • Build analysis panels for certain types of concerns
  • Enable better study design (endpoints, timing of collection, etc)
  • Reduce or streamline study endpoints or types based upon evidence


Identified Projects/Pilots/Activities

The current plan is to develop 3 scientific questions/use cases for piloting/further work. Our high level questions currently are:

  • Use Case 1: Do hERG changes in nonclinical models correlate with x, y or z clinical effects?
    • Refined: When a significant change in clinical BP (diastolic or systolic) at the maximum effect time point is observed, what is the mean BP change relative to the reference group in dog safety pharmacology studies at the maximum effect time point?
  • Use Case 2: Do transaminase changes in rodents correlate with changes in transaminases in humans?
    • Refined: For drugs or drug candidates for which transaminase data is available in both rats and humans, what is the exposure (in rats and humans) at the dose level at which the transaminase changes met the criteria for an increase in humans or rats?
  • Use Case 3: Are newer kidney biomarkers (e.g. KIM1) more predictive of clinical effects than classic renal injury biomarkers (e.g. creatinine, BUN)?
  • Use Case 4: Do / Which CNS findings in nonclinical species translate to clinical observations?

Next Steps (short term plans):

  1. High level use cases for specific relationships of nonclinical to clinical - Completed (see above)
  2. Refine high level use cases into a question that can be expressed as an algorithm - Completed for Use Cases 1-2 (see above); In progress for Use Cases 3-4
  3. Pass refined use cases to FDA for analysis
  4. Review results


  • August 2012 - Deliver refined initial use cases
  • December 2012 - Report results of analysis of initial use cases
  • February 2013 - Define central question
  • December 2013 - Complete analysis of central question
  • Early 2014 - Publish results of central question analysis



  • Define high level use cases


  • Complete analysis of initial use cases (see Plans section above)
  • Identify an innovative / seminal question that can be answered
  • Determine if additional data is needed
  • Answer seminal question
  • Publish results

Other Potential Tasks

  • Define criteria for questions in cardiovascular area or other diseases
  • Consider applicability of ILSI/HESI database


Participation Needs

We are looking for individuals with the following areas of expertise to contribute to this effort (current participants in this category):

  • Toxicologists (Cornwell, Wally, Kropp, Gartner, Seyler, Filler)
  • Pathologists (Ibanes)
  • Clinical Pathologists (Kimball)
  • Statisticians (Lee, Khan-Malek, Behrle)
  • Informatics Professionals (DeHaven, Nath, Smyrnios)
  • Clinical Pharmacologists

The total group will be limited to 12-20 people, with representation from all of the above areas of expertise.

What is the commitment?

  • Time - will vary widely (~4-8 hours / month, minimum of 1 hour every two weeks for team meetings + "homework")
  • Expected to contribute, not just be a spectator
  • Develop and review of use case scenarios
  • Contribute data (nonclinical and clinical)
  • Develop or contribute to analysis approach and execution
  • Contribution to presentations & publications, including white papers

If you would like to participate, please contact the co-leads for this group, Paul Cornwell (CornwellPD@lilly.com) and Jeremy Wally (Jeremy.Wally@fda.hhs.gov).


  • Past Teleconferences: See Conference Calls and Minutes section below
  • Teleconferences are planned for every 2 weeks
  • Quarterly updates to overall WG.
  • Co-Leads:
    • Paul Cornwell: CornwellPD@lilly.com
    • Jeremy Wally: Jeremy.Wally@fda.hhs.gov

Work Group Participants

Current Members

Paul Cornwell, Eli Lilly (co-lead)
Shree Nath, PointCross Life Sciences
Jon Kimball, PointCross Life Sciences
Sue DeHaven, Sanofi-Aventis
Tim Kropp, FDA
Martha Lee, FDA
Richard Khan-Malek, Sanofi
Jane Reed, Instem
Joelle Ibanes, MPI Research
Manfred Kansy, Roche
Paul Bradley, Instem
Ron Filler, Drug Development Consultants, Inc.
Jessica Behrle, Johnson and Johnson

Former Members

Bill Houser, Bristol-Myers Squibb
Troy Smyrnios, MPI Research
Jeremy Wally, FDA
Dave Seyler, Eli Lilly
Tim Gartner, Eli Lilly

Conference Calls and Minutes

6 June 2013 Meeting Minutes
7 March 2013 Meeting Minutes
25 February 2013 Meeting Minutes
24 January 2013 Meeting Minutes
3 January 2013 Meeting Minutes
29 November 2012 Meeting Minutes
18 October 2012 Meeting Minutes
27 September 2012 Meeting Minutes
6 September 2012 Meeting Minutes
19 July 2012 Meeting Minutes
28 June 2012 Meeting Minutes
7 June 2012 Meeting Minutes
17 May 2012 Meeting Minutes
3 May 2012 Meeting Minutes‎
12 April 2012 Meeting Minutes



FDA comments are an informal communication and represent the individual's best judgment. These comments do not bind or obligate FDA. The contents of this wiki are from the individual contributors and do not necessarily reflect the view and/or policies of the Food and Drug Administration, the employers of the individuals involved or any of their staff.

Last revision by Pacorn,10/2/2013