NICE

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Nonclinical Data Interconnectivity for Endpoint Predictivity (NICE)


Overall Goals of the project

Explore means by which nonclinical and clinical data can be interconnected in ways that facilitate their use for identifying hazards and risks of pharmaceuticals. Key parameters for inteconnecting clinical with nonclinical data include drug class, pharmacologic class, pharmacologic target and/or mechanism of action. An outcome of this project could be guidance for the US FDA and the pharmaceutical industry regarding design/analysis of SEND datasets to allow for nonclinical data to be associated with metadata that accurately describes the drug’s mechanism of action, target and/or pharmacologic class. In addition, strategies for interconnecting nonclinical with clinical data, based on pharmacologic class, could be developed. In addition, strategies for searching for and identifying pharmacologic class effects for adverse events and toxicities could be developed.

Specific topics to consider include:

  • What are accepted terminologies/nomenclatures for pharmacologic class and/or mechanism of action ? How are these developed ?
  • What in silico tools/databases are available for interconnecting clinical adverse events/toxicities with nonclinical toxicity data using pharmacologic class ?
  • Can guidance be developed regarding the use of in silico/computational tools for interconnecting pharmacologic class with clinical and nonclinical data ?
  • What resources are available regarding this topic ?
  • Useful databases and references for data analysis/searching will be included on this wiki site

Project Activities Completed in 2014

  • Various case examples of pharmacologic class/targets were selected in which clinical and nonclinical safety data were likely available in the public domain
  • Publicly available databases (eg, DailyMed, Drugs@FDA, Drug Bank)
  • Two different commercial analytical tools were evaluated (Instem's Safety Intelligence Program and Elsevier's Pharmapendium)
  • The following questions were asked. What types of data are available and unavailable ? Can relationships be developed between clinical and nonclinical safety data ? What limitations exist regarding these analyses ?

Summary and Suggestions

  • Availability of nonclinical and clinical safety data in public databases is highly variable and difficult to search
  • Publicly available data are oftentimes summary level, versus primary datasets
  • FDA NDA summaries of approved drugs have the greatest amount of nonclinical and clinical data. However, the data are not organized by pharmacologic class, the data are difficult to develop relationships between nonclinical and clinical data sets, automation with analysis/searching is not apparent, and data available based on FDA assessment of primary source data
  • Commercially available software tools provide significant advantages for searching and data analysis; address disadvantages indicated above to varying degrees of success
  • Key challenges for evaluating relationships between nonclinical and clinical safety data include the following: 1) relating doses in toxicology studies with human doses, 2) comparing drug exposures between animal and human studies, 3) differences in terminology/ontology between nonclinical and clinical data, 4) variances/inconsistency in terminology across studies, 5) mapping animal toxicology findings (especially pathology) to human adverse events and vice-versa, and 6) ability to search based on pharmacologic class/mechanism of action is not always apparent as some pharmacologic classes/mechanisms of action are too broad for effective searching (eg, kinase inhibitors)
  • Associating nonclinical safety data with pharmacologic class information (MOA = mechanism of action, PE = physiologic effect, CS = chemical structure, and/or EPC = established pharmacologic class) or targets could aid with the following: 1) identifying common toxicities with a pharmacologic class, 2) identifying relationships between nonclinical and clinical data, 3) provide an alternative to therapeutic area for categorizing drugs, and 4) provide a means to anticipate undesirable effects that may be associated with the drug or pharmacologic class
  • Pharmacologic class information (MOA, PE, CS and/or EPC) should be included as metadata associated with SEND datasets; 1) use the Veterans Administration National Drug File - Reference Terminology (NDF-RT) as a guide, and 2) include known pharmacologic target for the drug

Note: this project is derived from the combination of 2 former projects in the Nonclinical WG: Endpoint Predictivity and Data Interconnectivity


NICE Members

  • Alan P. Brown (co-lead; Novartis)
  • Paul Brown (co-lead; US FDA)
  • Carol Rivera-Lopez (US FDA)
  • Philip Judson (Lhasa Ltd.)
  • Lee Geiger (GlaxoSmithKline)
  • Randall Smith (GlaxoSmithKline)

Accomplishments

  • The following article was recently published by members of the Nonclinical Working Group:

Kasturi J, Brown AP, Brown P, Madhavan S, Prabakar L, Wally JL. Interconnectivity of disparate nonclinical data silos for drug discovery and development. Therapeutic Innovation & Regulatory Science, OnlineFirst version 22 April 2014

http://dij.sagepub.com/content/early/2014/04/21/2168479014531421.abstract.

  • The following slide set summarizes the NICE project and provides suggestions from the project team

Resources

Relevant Publications
Links to Select Drug Databases

Conference Calls and Minutes

14 January 2015 Meeting Minutes
14 November 2014 Meeting Minutes
17 September 2014 Meeting Minutes
29 July 2014 Meeting Minutes
9 July 2014 Meeting Minutes
16 June 2014 Meeting Minutes
29 April 2014 Meeting Minutes - 2nd TC
29 April 2014 Meeting Minutes
11 December 2013 Meeting Minutes
16 October 2013 Meeting Minutes
30 September 2013 Meeting Minutes



Disclaimer

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 APBrown,03/19/2015