Nonclinical Data Interconnectivity for Endpoint Predictivity (NICE)

Project Scope

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 interconnecting 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 Advance Hub site

• 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