Essential Role of Statistical Analysis in Preclinical Safety Evaluations
Before a new drug can proceed to clinical trials, rigorous safety assessments are required for regulatory approval. These evaluations, performed in Good Laboratory Practice (GLP)-certified laboratories, adhere to strict protocols to ensure data integrity and reliability.
Typically, study designs exhaustively detail factors like design, dosage, duration, and safety endpoints. However, the statistical analysis methods are often mentioned generically. Phrasing such as “data will be analyzed using suitable statistical techniques” leaves room for ambiguity, potentially impacting study outcome interpretation.
Audit reviews during GLP assessments often overlook statistical analysis methods. Ensuring the statistical methodologies used in GLP preclinical safety studies are meticulously audited is crucial for maintaining data fidelity and regulatory alignment. This practice guarantees that results are dependably applicable for further research or regulatory considerations.
The Organization for Economic Co-operation and Development (OECD) Test Guidelines (TGs) for toxicity evaluations highlight the necessity of clearly stating statistical methods in study plans. For instance, TGs for the 90-day repeated oral toxicity tests in rodents (TG No.408) and non-rodents (TG No.409) and carcinogenicity tests (TG No. 451) stipulate that statistical techniques and analyses should be pre-determined during the study’s conception.
The FDA’s Redbook emphasizes details on experimental design, including bias control methods and the anticipated statistical approaches outlined in study plans.
The OECD Guidance Document No.116 details statistical analysis in chronic toxicity and carcinogenicity studies, complementing TGs 451 (carcinogenicity studies), 452 (chronic toxicity studies), and 453 (combined chronic/carcinogenicity studies). Document No 35 offers further advice on various statistical tools applicable for toxicological data analysis.
Guidance Documents 116 and 35 introduce a statistical decision tree to aid in selecting suitable tools for toxicological data analysis.
Statistics in Clinical Trials
The International Council for Harmonization (ICH) recommends that clinical trials specify key design and analytical features in a pre-trial written protocol. An independent statistical analysis plan (SAP) should follow, providing detailed analysis instructions, and analyses must align with the SAP. Deviations need to be justified comprehensively to convince regulators of any changes.
Interpreting Toxicology Data with Statistics
Guidance Notes (ENV/JM/MONO (2002)) emphasize that toxicology studies aim to identify biologically meaningful responses, requiring robust biological interpretation alongside statistical insights. The objective is to produce data that informs risk assessments and regulatory choices, ensuring clarity on potential hazards against exposure contexts.
The notes further state that judgments based on statistical analysis should clearly communicate the validity and certainty levels of the tests chosen.
In toxicological studies lacking clear statistical methodologies, misinterpretations can negatively impact pharmaceutical advances.
A Practical Example
Consider a 13-week repeated dose study in mice assessing liver weight. The data was evaluated using the statistical decision tree from OECD Guidance Document No.116. Following normality and homogeneity tests, one-way ANOVA was performed due to more than two group comparisons, yielding insignificance (P=0.0804). However, re-evaluating the data with Dunnett’s Multiple Comparison Test revealed significant differences (P=0.0399) between the high-dose and control groups, indicating that ANOVA is not advised for such comparisons.
Document No. 116 cautions against treating statistical results as alternatives to biological assessments. Reporting exact p-values (e.g., P=0.051) instead of broad critical values helps highlight the report’s sufficiency to nullify hypotheses.
Purpose of Statistical Analysis in Toxicology
ICH S2 R1 guidelines define the role of statistics in augmenting data interpretation, highlighting that biological understanding is critical. Adequate grasping and selecting pertinent statistical methods underpin robust conclusions drawn by Study Directors.
Roles and Responsibilities
In GLP environments, a dedicated statistician may not be compulsory, unlike clinical settings where one must oversee all statistical dimensions.
Conclusions and Limitations
Articulating statistical analyses thoroughly in GLP preclinical studies is vital for data reliability and regulatory adherence. Though statistics are essential for interpretations, limitations exist, notably when data issues affect biological relevance and significance. Gad & Weil (1986) and Kobayashi & Pillai (2012) outline these challenges, emphasizing that poor data remain unchanged post-statistical application.