Establishing Robust Sample Re-Analysis Procedures for Regulatory Compliance

Introduction: Why Re-Analysis Is Critical in Bioanalytical Testing

Bioanalytical laboratories supporting clinical trials routinely encounter scenarios where test results may be flagged for potential re-analysis. These may include unexpected values, assay drift, out-of-specification (OOS) results, or stability-related deviations. Re-analysis refers to repeating the sample test using the same validated method to confirm or clarify the original result.

Regulatory agencies such as the FDA, EMA, and MHRA closely scrutinize re-analysis practices, especially during inspections. Improper justification or lack of documentation for re-analysis can lead to serious data integrity concerns. Therefore, laboratories must define strict, transparent, and auditable procedures to govern sample re-analysis throughout the trial lifecycle.

Regulatory Expectations for Sample Re-Analysis

Several global regulatory bodies offer guidance on how and when sample re-analysis should be performed:

• FDA’s Bioanalytical Method Validation Guidance (2018): Re-analysis must be scientifically justified and pre-defined in SOPs or protocol.
• EMA Guideline on Bioanalytical Method Validation: Emphasizes documenting reasons for re-testing and ensuring integrity of the original result.
• MHRA GCP Inspection Manual: Flags unapproved or excessive re-analysis as a major data integrity risk.

Agencies expect a clear decision-making tree for determining when re-analysis is permitted, and how results are handled and reported.

Triggers for Sample Re-Analysis

The following conditions may justify re-analysis:

• Instrument failure or run interruption during original analysis
• Out-of-range QC samples in the same batch
• Suspected carryover contamination
• Data transcription or calculation errors (prior to reporting)
• Subject sample result inconsistent with clinical context
• Stability-related concerns due to freeze-thaw cycles
• Missing or unreadable chromatographic peaks

Re-analysis should not be used to obtain desired results or artificially remove outliers. It is a scientific and quality assurance mechanism — not a data manipulation tool.

Decision Flowchart for Re-Analysis (Illustrative)

Re-Analysis SOP Elements

Laboratories must maintain a dedicated SOP for re-analysis, including:

• Definitions: Clarify difference between re-analysis, repeat testing, and re-injection
• Pre-authorization: Define when analyst must seek QA approval
• Documentation: Maintain audit trail including chromatograms, reason for re-analysis, and raw data
• Data Handling: Define whether original or repeat value is reported, and justification
• QC Inclusion: Whether re-analysis requires full batch or single sample processing

CAPA for Improper Re-Analysis

Regulatory audits have identified several compliance issues related to improper re-analysis:

• Missing documentation for re-analysis rationale
• Multiple retests until desired value is achieved
• Unapproved SOP deviations
• Analyst-initiated re-tests without QA review

In such cases, CAPA should include:

• Retraining of analysts on re-analysis criteria
• Revision of SOP to strengthen approval workflow
• Retrospective audit of historical re-analyses
• Inclusion of re-analysis justification in sponsor audit trail

Sample Case: MHRA Audit Observation on Re-Analysis

During an MHRA inspection of a Phase III oncology trial, the auditor noted that 18% of plasma samples were re-analyzed due to “unexpectedly high concentration.” Upon review, no scientific or protocol-based rationale was documented, and analysts had used their own discretion.

The site received a critical finding. CAPA included a ban on discretionary re-analysis, mandatory documentation fields in the LIMS system, and routine QA review of all flagged samples.

Inspection Readiness Tips

To demonstrate compliance with sample re-analysis practices, labs should:

• Maintain centralized logs of all re-analyzed samples, linked to original results
• Ensure that re-analysis is traceable in audit trail and includes date, analyst, method, and batch ID
• Flag re-analysis in sponsor data listings (e.g., eCRF or central lab file)
• Retain original data — never overwrite with repeat value unless SOP permits

Role of Data Management in Re-Analysis Tracking

Integration with data management platforms (e.g., CDMS or LIMS) can streamline re-analysis monitoring. Automated triggers for re-analysis and electronic CAPA workflows allow:

• Faster resolution of flagged results
• Documentation of justification in real time
• Preventing unauthorized retesting through system validation

Conclusion: Making Re-Analysis Defensible and Audit-Proof

In regulated clinical trials, re-analysis is a necessary quality control tool but must be executed with extreme discipline. It cannot be used to “fix” data or support bias-driven decisions. Instead, it should be a structured, documented, and statistically defensible activity that strengthens overall data quality.

Sponsors, CROs, and laboratories must invest in clear SOPs, QA oversight, analyst training, and audit trail integrity. When these systems are in place, sample re-analysis becomes a regulatory strength — not a vulnerability.

Regulatory Guide to Sample Reanalysis in BA/BE Studies

Introduction: Why Sample Reanalysis Is a Critical Topic

Sample reanalysis is an essential component of bioanalytical integrity in bioavailability and bioequivalence (BA/BE) studies. It ensures the accuracy and reproducibility of drug concentration measurements in biological matrices, often plasma or serum. However, reanalyzing samples is not a casual activity — regulatory agencies have placed stringent controls and expectations around it to prevent selective or biased data reporting.

In this guide, we explore the criteria, scenarios, and documentation requirements for sample reanalysis in BA/BE trials as defined by agencies such as FDA, EMA, and CDSCO (India).

Types of Reanalysis in BA/BE Studies

Sample reanalysis can be broadly categorized into two types:

1. Incurred Sample Reanalysis (ISR): A regulatory requirement to assess the reproducibility of real subject samples.
2. Investigative Reanalysis: Triggered when QC or sample results fall outside predefined acceptance limits or due to analytical anomalies.

While ISR is part of planned study design, investigative reanalysis must follow strict procedural and documentation protocols to avoid regulatory findings.

When Is Sample Reanalysis Justified?

Reanalysis is acceptable under specific conditions only. Examples include:

• Unexpected concentration-time profile deviations
• Chromatographic issues like peak splitting, broadening, or interference
• Out-of-specification QC or calibration curve failures
• Instrument malfunction during injection
• Suspected sample degradation (e.g., due to thawing)

Reanalysis should not be used for adjusting results based on sponsor expectations or outlier removal unless scientifically justified and documented.

Acceptance Criteria for Incurred Sample Reanalysis (ISR)

ISR is the gold standard for evaluating method reproducibility. According to regulatory guidelines:

• Minimum of 10% of study samples (usually from both C_max and elimination phase) must be reanalyzed.
• Acceptance criteria: At least two-thirds of the repeated samples should be within ±20% of the original result.

Example of ISR assessment:

ISR failures may prompt revalidation or further investigation. Agencies may reject studies with systemic ISR failure.

Regulatory Guidance and Key Expectations

• FDA: Emphasizes ISR for assessing reproducibility and prohibits arbitrary sample reanalysis.
• EMA: Requires ISR for all pivotal studies and discourages reanalysis unless justified and documented.
• CDSCO: Requires ISR plans to be pre-approved and deviations must be reported with justifications.

All reanalysis must be pre-defined in bioanalytical SOPs and validation protocols, and any deviation must be recorded as part of the study deviation log.

Investigative Reanalysis and Documentation

Unlike ISR, investigative reanalysis is initiated when data anomalies arise during the course of sample analysis. The analyst must notify QA and follow the reanalysis decision tree described in internal SOPs.

Essential documentation includes:

• Reason for reanalysis (e.g., chromatogram anomaly, instrument alert)
• Approval from bioanalytical lead and QA
• Chromatograms and raw data from both original and reanalyzed runs
• Justification memo and reanalysis report

Any attempt to reanalyze without documented rationale or QA oversight can result in a critical audit finding.

Case Study: ISR Failure Triggers Revalidation

In a pivotal BE study of a BCS Class II antihypertensive drug, ISR showed only 50% of reanalyzed samples within ±20% criteria. Root cause analysis revealed inconsistent autosampler temperatures. A full method revalidation was conducted including revised stability studies. The final report was updated in CTD Module 5.3.1.4 and accepted by the EMA after clarifications.

How to Avoid Regulatory Non-Compliance

To prevent findings related to reanalysis:

• Establish a well-defined SOP on sample reanalysis and ISR
• Include ISR plan in study protocol and method validation report
• Engage QA in every reanalysis decision
• Limit reanalysis to scientifically justified cases only
• Maintain transparency in deviation logs and raw data submissions

Explore additional ISR trends and guidance on EU Clinical Trials Register.

Conclusion: Treat Reanalysis as a Scientific, Not Corrective, Tool

Reanalysis plays a crucial role in ensuring the integrity and reliability of bioanalytical results in BA/BE trials. However, without robust SOPs, justified decision-making, and regulatory alignment, it can quickly become a point of scrutiny. Incurred Sample Reanalysis (ISR) is not a formality—it’s a statistical assurance of your method’s reliability. Similarly, investigative reanalysis must be limited, transparent, and defensible. With proper planning and documentation, reanalysis strengthens your study; without it, it invites regulatory trouble.