Constructing Acceptance Limits for Multiple Stage Tests

Bergum, James

doi:10.3109/03639049009023647

Cited by 23 publications

(20 citation statements)

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“…However, the probability of meeting the acceptance criteria (P a ) for a future batch at a particular stage can be estimated by comparing the pooled dissolution statistics (average and standard deviation) of the measured batches against with data derived from a Monte Carlo simulation of the USP acceptance criteria guidelines with defined batch averages and variability. More detail of this approach and other similar strategies are described by Bergum (9)(10)(11)(12) and other publications (13)(14)(15).…”

Section: Example Ii: Dissolution-determining Probability Of Acceptancmentioning

confidence: 99%

Acceptance Probability (P a) Analysis for Process Validation Lifecycle Stages

et al. 2015

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Abstract. This paper introduces an innovative statistical approach towards understanding how variation impacts the acceptance criteria of quality attributes. Because of more complex stage-wise acceptance criteria, traditional process capability measures are inadequate for general application in the pharmaceutical industry. The probability of acceptance concept provides a clear measure, derived from specific acceptance criteria for each quality attribute. In line with the 2011 FDA Guidance, this approach systematically evaluates data and scientifically establishes evidence that a process is capable of consistently delivering quality product. The probability of acceptance provides a direct and readily understandable indication of product risk. As with traditional capability indices, the acceptance probability approach assumes that underlying data distributions are normal. The computational solutions for dosage uniformity and dissolution acceptance criteria are readily applicable. For dosage uniformity, the expected AV range may be determined using the s lo and s hi values along with the worst case estimates of the mean. This approach permits a risk-based assessment of future batch performance of the critical quality attributes. The concept is also readily applicable to sterile/non sterile liquid dose products. Quality attributes such as deliverable volume and assay per spray have stage-wise acceptance that can be converted into an acceptance probability. Accepted statistical guidelines indicate processes with C pk >1.33 as performing well within statistical control and those with C pk <1.0 as Bincapable^(1). A C pk >1.33 is associated with a centered process that will statistically produce less than 63 defective units per million. This is equivalent to an acceptance probability of >99.99%.

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Section: Example Ii: Dissolution-determining Probability Of Acceptancmentioning

confidence: 99%

Acceptance Probability (P a) Analysis for Process Validation Lifecycle Stages

et al. 2015

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“…The chance that a particular product will meet the overall stage-wise criteria can be defined by the probability of acceptance (P a ). The stage-wise rules essentially create a complicated equation that transforms the mean and standard deviation of the acquired dissolution data into an acceptance probability (8)(9)(10). Among other approaches, this equation can be solved using a Monte Carlo computation wherein the results are stored into a series of lookup tables (dissolution Monte Carlo transformation).…”

Section: Dissolutionmentioning

confidence: 99%

Stage 2 Process Performance Qualification (PPQ): a Scientific Approach to Determine the Number of PPQ Batches

Pazhayattil

Alsmeyer²,

Chen

et al. 2015

AAPS PharmSciTech

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Abstract. The approach documented in this article reviews data from earlier process validation lifecycle stages with a described statistical model to provide the Bbest estimate^on the number of process performance qualification (PPQ) batches that should generate sufficient information to make a scientific and risk-based decision on product robustness. This approach is based upon estimation of a statistical confidence from the current product knowledge (Stage 1), historical variability for similar products/ processes (batch-to-batch), and label claim specifications such as strength. The analysis is to determine the confidence level with the measurements of the product quality attributes and to compare them with the specifications. The projected minimum number of PPQ batches required will vary depending on the product, process understanding, and attributes, which are critical input parameters for the current statistical model. This new approach considers the critical finished product CQAs (assay, dissolution, and content uniformity), primarily because assay/content uniformity and dissolution as well as strength are the components of the label claim. The key CQAs determine the number of PPQ batches. This approach will ensure that sufficient scientific data is generated to demonstrate process robustness as desired by the 2011 FDA guidance.

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“…Bergum [2] provided the following lower bound for the probability of passing the USP/NF dissolution test:…”

Section: Dissolution Testingmentioning

confidence: 99%

“…Bergum [2] and Chow and Liu [3] provided some lower bounds for the probability of passing USP/NF tests such as the dissolution test. These lower bounds, however, are sometimes too low to be of practical use.…”

Section: Introductionmentioning

confidence: 99%

“…Although computer simulation (Monte Carlo) methods can be used to evaluate this function for a given set of population mean and variance, it may be too time consuming to compute this function in establishing in-house specification limits for critical decision making. [2,3] Thus, it is useful to find a lower bound for the probability of passing the USF/NF test that can be easily and quickly evaluated when the population mean and variance are known (or estimated). Lower bounds are considered in the interest of conservative decision for quality assurance.…”

Section: Introductionmentioning

confidence: 99%

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Probability lower bounds for USP/NF tests

Chow

Shao

Wang

2002

Journal of Biopharmaceutical Statistics

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In the pharmaceutical industry, a number of tests such as content uniformity and dissolution testing are usually performed at various stages of drug manufacturing process to ensure that the drug product meets standards for identity, strength, quality, purity, and stability of the drug product as specified in the United States Pharmacopedia and National Formulary (USP/NF). The USP/NF provides requirements for sampling plans, testing procedures, and acceptance criteria for these tests. To ensure that there is a high probability of passing the USP/NF tests, the sponsors usually establish in-house specification limits based on some lower bounds of the probabilities of passing USP/NF tests for future samples. In this article, we derive some probability lower bounds for USP/NF tests. It is shown that the proposed probability lower bounds are better than the existing ones and are very close to the true probabilities in a broad range of the population mean and variance of the test sample.

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Constructing Acceptance Limits for Multiple Stage Tests

Cited by 23 publications

References 0 publications

Acceptance Probability (P a) Analysis for Process Validation Lifecycle Stages

Acceptance Probability (P a) Analysis for Process Validation Lifecycle Stages

Stage 2 Process Performance Qualification (PPQ): a Scientific Approach to Determine the Number of PPQ Batches

Probability lower bounds for USP/NF tests

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