Information Quality in Regulatory Decision Making: Peer Review versus Good Laboratory Practice

By McCarty, Lynn S.; Borgert, Christopher J. et al. | Environmental Health Perspectives, July 2012 | Go to article overview
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Information Quality in Regulatory Decision Making: Peer Review versus Good Laboratory Practice


McCarty, Lynn S., Borgert, Christopher J., Mihaich, Ellen M., Environmental Health Perspectives


BACKGROUND: There is an ongoing discussion on the provenance of toxicity testing data regarding how best to ensure its validity and credibility. A central argument is whether journal peer-review procedures are superior to Good Laboratory Practice (GLP) standards employed for compliance with regulatory mandates.

OBJECTIVE: We sought to evaluate the rationale for regulatory decision making based on peer-review procedures versus GLP standards.

METHOD: We examined pertinent published literature regarding how scientific data quality and validity are evaluated for peer review, GLP compliance, and development of regulations.

DISCUSSION: Some contend that peer review is a coherent, consistent evaluative procedure providing quality control for experimental data generation, analysis, and reporting sufficient to reliably establish relative merit, whereas GLP is seen as merely a tracking process designed to thwart investigator corruption. This view is not supported by published analyses pointing to subjectivity and variability in peer-review processes. Although GLP is not designed to establish relative merit, it is an internationally accepted quality assurance, quality control method for documenting experimental conduct and data.

CONCLUSIONS: Neither process is completely sufficient for establishing relative scientific soundness. However, changes occurring both in peer-review processes and in regulatory guidance resulting in clearer, more transparent communication of scientific information point to an emerging convergence in ensuring information quality. The solution to determining relative merit lies in developing a well-documented, generally accepted weight-of-evidence scheme to evaluate both peer-reviewed and GLP information used in regulatory decision making where both merit and specific relevance inform the process.

KEY WORDS: data quality, GLP, peer review, regulatory decision making, toxicity tests. Environ Health Perspect 120:927-934 (2012). http://dx.doi.org/10.1289/ehp.1104277 [Online 17 February 2012]

The validity and credibility of scientific data is central to all scientific endeavors, as well as to decision structures that use such data (Schreider et al. 2010). Principal among those are risk assessments, safety assessments, and regulatory decisions routinely made by federal agencies such as the U.S. Environmental Protection Agency (EPA), the Food and Drug Administration (FDA), and the U.S. Department of Agriculture (USDA) in the United States or in similar agencies in other jurisdictions. Regulatory decisions are often questioned because either the type or the source of the data relied upon comes under scrutiny. Regulatory decisions have been challenged for relying on data that allegedly lack relevance or sensitivity for the protection of public health and the environment and for relying on data generated by scientists or laboratories perceived to have a conflict of interest regarding the outcome of the decision (e.g., Myers et al. 2009). Some proposed solutions argue for transparency and stress the availability of raw data and methodological details as the principal means of enhancing credibility (Borgert 2007; Schreider a al. 2010).

More transparency may increase the credibility of decisions because it enhances the perceived honesty of the process. On the other hand, transparency and honesty, in and of themselves, do not address underlying questions about data quality. Peer-review requirements for scientific journals and data acceptance requirements for regulatory programs both acknowledge that a rigorous evaluation of data quality is essential, yet the practices and procedures for addressing it differ across the spectrum of bodies that deal with scientific data. These differences may arise from disparate definitions of data quality but more likely relate to the reasons for adjudicating data quality, which differ according to the purview of these bodies.

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