Equivalency Comparison of Drug Product Validation Batch Data to Reference Batches
Introduction
his guidance addresses the equivalency comparison of manufacturing process data from drug product (DP) validation batches to previous batches (called “reference” batches), when applicable. A new or modified drug products should be demonstrated to be equivalent to previously produced product. Comparisons must be done as part of process validation studies for new product and significantly modified processes that require validation. For new products, equivalency of validation data (e.g. finished product, critical in-process tests or critical parameters) to biobatch(es) or pivotal clinical batches is shown. For all equivalency studies, it is expected that the results of the validation batch testing be within registered specifications.
In cases where the specifications may not be reflective of recent process capability, it is recommended that additional criteria such as meeting the upper statistical limit of historical data, be considered for validation equivalency criteria. Guidance is provided on
A) Selection of reference batches for the comparison,
B) Types of data that are compared for the most common dosage forms,
C) General acceptance criteria, and
D) Conclusion.
Guidance on types of statistical methods one can use to compare data is shown (Appendix) and examples are given. Reference batches are those batches that form a clinical or marketed- product basis (e.g. bioequivalence, bioavailability or production).
Recommendations & Rationale
General Recommendations:
It is recommended that determination of equivalence criteria includes consideration of the number of reference batches available, the statistical distribution and the confidence that data are representative of the process:
Tabulated and/or graphical analyses are suggested to review larger sets of data points. Example D shows a tabulated comparison. Trends can be visualized graphically and related to regulatory, alert or proposed specifications. Appropriate statistical hypothesis/tests of equivalence (e.g. interval hypothesis or equivalency test-Reference) with confidence intervals may be used. Consulting a statistical expert may be useful. See Example E.
Selection of reference batches:
New Products (i.e. new drug product at first commercial manufacturing site)
The most recent reference batches made by the same process are recommended from the following sources:
Plant batches (Qualification, Pre-Validation or Scale-up batches), and/or
R&D batches supporting regulatory submission (Stability, Biobatch or pivotal Phase III clinical batch(es)) In example B in the Appendix, two new submission batches were selected for the comparison to three validation batches. In this particular case, high process capability had been shown. Typically, a larger number of reference batches are recommended for statistical purposes, if they are available. If available, data from at least 10 lots usually provide enough data to perform statistical analysis with a high degree of confidence. Potential reference batches may be excluded if deviations or failures are shown by root cause investigation to not be representative of normal processing. These exclusions should be explained with rationales that include why the batches are not representative of normal processing.
Existing Drug Products
Site Transfers (e.g. Site A to Site B)
For manufacturing site transfers, the reference batches could be selected from those prepared at the originating (sending) site (e.g. Commercial, Validation). Consecutive reference batches are suggested for reference data from the sending site provided the same process without major changes has been used. Alternatively, original regulatory submission batch(s) may be selected, if applicable, such as if there is little production history in commercial manufacturing but ample R&D data. See Example A in the Appendix.
Major Changes undergoing revalidation.
The most recent reference batches, before the change is made, are suggested from batches prepared by the process used to produce commercially released material {e.g. Production history, original validation, stability, or prevalidation batches)
Other considerations for selecting historical batches for the reference data set are:
Batches should be selected from those prepared at commercial scale, and from consecutively prepared acceptable batches that represent the process as it has most recently been run.
If available, data from at least 10 lots usually provide enough data to perform statistical analysis with a high degree of confidence.
The impact of past changes to the process or to analytical methods should be considered when selecting historical batches for the data set.
Rejected, reprocessed, and reworked batches generally should be excluded from the historical set because they are generally not representative of the results expected from normal processing. Removing such batches for cause and with documented rationales is permissible (thus breaking the consecutive nature of the batches), but batches should never be arbitrarily added to or excluded for the purpose of influencing the historical ranges of analytical results. Potential reference batches may be excluded if deviations or failures are shown by root cause investigation to not be representative of normal processing. These exclusions should be explained with rationales that include why the batches are not representative of normal processing.
The historical reference batches should be predetermined and included or referenced in the validation protocol. Reference batches are usually the most recent (e.g. last 30 production batches). A link to original clinical batches is typically unnecessary if it is existing product that has already been validated. One does not need to repeat information in other documents such as regulatory submissions, Technology Transfer, Change Control or other Comparability or Equivalency studies. These studies can simply be referenced in the protocol.
There are critical quality attributes (e.g. impurities) which often have relatively small values. These may result in a variance of less than 1. In these cases, an F- test (ratio of variances) may be significant in a statistical sense, but not in a practical sense. Therefore, in these cases, the data should be reviewed for practical significance.
Type of Data for Comparison, from common dosage forms:
Results from routine analytical release testing should be examined when performing the equivalence comparison. Results from testing of the validation batches will be compared to historical results obtained using the same analytical methods. A change in an assay method thus requires careful consideration, unless it has already been shown to give equivalent results to the earlier method.
Select tests that provide quantitative results. Tests that provide qualitative results (“Meets Test”, “Positive”) are generally of less value to equivalence evaluations.
Critical Quality Attributes comparison- Examples of potential critical quality attributes (CQAs) are shown below. Drug product attributes that are identified as critical need to be evaluated for equivalency.
Tablets -assay, degradation impurities, dissolution, content uniformity, friability, hardness, moisture, film-coated tablets -inspection attributes.
Capsules-assay, impurities, dissolution, weight variation, content uniformity, moisture, microbial limits. Softgels may include leakage, appearance for precipitation/cloudiness.
Powder Blends-particle size distributions, density, API uniformity, moisture content, flowability.
Suspensions/solutions – assay, pH, viscosity, specific gravity, sedimentation volume /redispersibility/mean particle size, preservative content, microbial content.
Oral Powders/Suspensions for Reconstitution-API uniformity, reconstitution times
Emulsions-assay, impurities, content uniformity, pH, viscosity, rheology (pourability), preservative content, mean particle size of dispersed phase
Ointments/Creams/Pastes/Lotions/Gels/Solutions,-
Topicals-appearance, pH, clarity, viscosity, specific gravity, assay, preservative
Ophthalmic/Otic-assay, impurities, sterility, particulate matter, specific gravity
Lyophilization products-assay, impurities, moisture content, reconstitution time, appearance/durability/cake breakage.
Dry Powder Inhalers-API uniformity, delivered dose, aerodynamic assessment of particles, moisture, inhaler component extractables/leachables, microbial content.
Metered Dose Inhalers (MDIs)-API uniformity, valve performance/ delivery, assay, aerodynamic assessment of particles, delivered dose, water content, inhaler component extractables/leachables, microbial content,.
Drug injection products-assay, preservatives, degradation, particulate matter, pH, sterility and pyrogencity.
Conclusion:
A conclusion as to the data equivalence and comparability between the validation batches and the reference batches is made.
Appendix:
- Example A: Example of Oral Tablet, 10, 20, and 40 mg, Transferred Products
- Example B: Example of Oral Suspension, 40 mg/ml, New Drug Product
- Example C: Example of Comparison of Critical tablet Hardness
- Example D: Example of Comparison of Blend Uniformity
- Example E: Interval Hypothesis Test (Equivalency Test)