Interconnectivity of Disparate Nonclinical Data Silos for Drug Discovery and Development

Abstract

Pharmaceutical research and development generates enormous amounts of nonclinical and clinical data related to safety and efficacy, and the ability to manage and utilize these data is critical for discovering and developing new drugs. Information systems exist that store and analyze relationships among seemingly disparate data sets (ie, data silos); however, to fully utilize the potential of these informatics systems, it is necessary to define basic parameters about the data and to develop concepts regarding “interconnectivity,” or relationships among disparate data sets. To explore these issues, the Nonclinical Data Interconnectivity Sub-Group was chartered: a component of the Non-Clinical Road-Map and Impacts on Implementation Working Group associated with the US FDA–PhUSE (Pharmaceutical Users Software Exchange) Computational Sciences initiative. As a starting point, the group defined the meaning of data interconnectivity. Nonclinical data types were then identified and challenges and opportunities for interconnectivity explored. Specific-use cases were identified to provide examples of the value for interconnecting data across disciplines or silos.

Keywords

informatics data analysis drug safety nonclinical data interconnectivity data silos

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References

Kropp

Rosario

DeHaven

. FDA engages collaborators to address nonclinical data challenges. Therapeutic Innovation & Regulatory Science. 2013;47:41.

Cases

Furlong

Albanell

. Improving data and knowledge management to better integrate health care and research. J Int Med. 2013;274:321–328.

Valerio

Jr . In silico toxicology for the pharmaceutical sciences. Toxicol Appl Pharmacol. 2009;241:356–370.

Slater

Bouton

Huang

. Beyond data integration. Drug Discovery Today. 2008;13:584–589.

Briggs

Cases

Heard

. Inroads to predict in vivo toxicology: an introduction to the eTOX Project. Int J Mol Sci. 2012;13:3820–3846.

Dorato

Engelhardt

. The no-observed-effect-level in drug safety evaluations: use, issues and definition(s). Regul Toxicol Pharmacol. 2005;42:265–274.

US Food and Drug Administration. Guidance for Industry: Providing Regulatory Submissions in Electronic Format—Standardized Study Data. Washington, DC: US Department of Health and Human Services; 2012 [Revision 1, 2014].

Kramer

Wood

Kropp

. SEND: Electronic submission of nonclinical data [abstract P604]. Int J Toxicol. 2012;31:121.

Keenan

Goodman

. Regulatory forum commentary: through the looking glass—SENDing the pathology data we have INHAND [published online April 18, 2013]. Toxicol Pathol.

10.

Parchment

Gordon

Grieshaber

. Predicting hematological toxicity (myelosuppression) of cytotoxic drug therapy from in vitro tests. Annal Oncol 1998;9:357–364.

11.

Moneta

Geroni

Valota

. Predicting the maximum-tolerated dose of PNU-159548 (4-demethoxy-3′-deamino-3′-aziridinyl-4′-methylsulphonyl-daunorubicin) in humans using CFU-GM clonogenic assays and prospective validation. Eur J Cancer 2003;39:675–683.

12.

Center for Drug Evaluation and Research. Guidance for Industry, Reproductive and Developmental Toxicities: Integrating Study Results to Assess Concerns. Washington, DC: US Food and Drug Administration; 2011.

13.

Redfern

Carlsson

Davis

. Relationships between preclinical cardiac electrophysiology, clinical QT interval prolongation and torsade de pointes for a broad range of drugs: evidence for a provisional safety margin in drug development. Cardiovasc Res. 2003;58:32–45.

14.

Roden

. Drug-induced prolongation of the QT interval. N Engl J Med. 2004;350:1013–1022.

15.

US Food and Drug Administration. Guidance for Industry and Review Staff, Labeling for Human Prescription Drugs and Biological Products: Determining Established Pharmacologic Class for Use in the Highlights of Prescribing Information. Washington, DC: US Department of Health and Human Services; 2009.