Criteria to Reevaluate Anti-drug Antibody Assay Cut Point Suitability in the Target Population

Tan, Charles; Steeno, Gregory S.; You, Zhiping; Gaitonde, Puneet; Cai, Chun-Hua; Kamerud, John; Gorovits, Boris; Baltrukonis, Daniel

doi:10.1208/s12248-019-0400-4

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…The CP of 8 out of 10 data sets were within 2–11% ( 3 , 10 ) with the Weibull transformation, whereas only 5 were in that range with the decision tree-based CP approach. A recent publication highlighted that the false positive rate is related to the number of samples included in the CP calculation ( 11 ). Therefore the expected false positive rate for 100 data points for data set 10 should rather be between 2 and 9%.…”

Section: Discussionmentioning

confidence: 99%

“…However, the more data that is excluded (assuming these were not caused by pre-existing ADA (pADA)), the greater the risk of observing an artificially high false positive rate ( 6 ) in the study samples because the recommended 5% false positive rate ( 2 ) is calculated based on the remaining data points. This could make an in-study CP necessary ( 3 , 10 , 11 ) which is not considered optimal, as it could slow down data reporting although it is however scientifically sound. The impact of the in-study cut point has to be fully assessed before implementation ( 4 ) taking challenges into account that in some rare disease clinical trials, only a limited number of patient are enrolled.…”

Section: Introductionmentioning

confidence: 99%

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An Alternative Data Transformation Approach for ADA Cut Point Determination: Why Not Use a Weibull Transformation?

Jordan

Staack

2021

AAPS J

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The testing of protein drug candidates for inducing the generation of anti-drug antibodies (ADA) plays a fundamental role in drug development. The basis of the testing strategy includes a screening assay followed by a confirmatory test. Screening assay cut points (CP) are calculated mainly based on two approaches, either non-parametric, when the data set does not appear normally distributed, or parametric, in the case of a normal distribution. A normal distribution of data is preferred and may be achieved after outlier exclusion and, if necessary, transformation of the data. The authors present a Weibull transformation and a comparison with a decision tree-based approach that was tested on 10 data sets (healthy human volunteer matrix, different projects). Emphasis is placed on a transformation calculation that can be easily reproduced to make it accessible to non-mathematicians. The cut point value and the effect on the false positive rate as well as the number of excluded samples of both methods are compared.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Alternative Data Transformation Approach for ADA Cut Point Determination: Why Not Use a Weibull Transformation?

Jordan

Staack

2021

AAPS J

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“…An in-study cut point may be needed, if the putative positive rate among the pre-dose samples for the patient population being evaluated is outside of the expected false positive rate, which is usually preferred to be within 2-11% range (5,11,12), after the exclusion of confirmed positive samples. It should be noted that a reasonable size population is needed to reliably assess the false positive rate.…”

Section: In-study Cut Pointsmentioning

confidence: 99%

“…Consultation with a statistician is advised. Additionally, alternative ranges may be set and justified based on the variability and other characteristics of the data on a case by case basis (12). The pre-dose sample results for the current patient population can be compared to those from the pre-study validation (healthy human), or from a previously evaluated disease, using similar methods as described previously for the validation and analysis of clinical subpopulations.…”

Section: In-study Cut Pointsmentioning

confidence: 99%

Anti-drug Antibody Validation Testing and Reporting Harmonization

Myler

Pedras-Vasconcelos

Phillips

et al. 2021

AAPS J

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Evolving immunogenicity assay performance expectations and a lack of harmonized anti-drug antibody validation testing and reporting tools have resulted in significant time spent by health authorities and sponsors on resolving filing queries. Following debate at the American Association of Pharmaceutical Sciences National Biotechnology Conference, a group was formed to address these gaps. Over the last 3 years, 44 members from 29 organizations (including 5 members from Europe and 10 members from FDA) discussed gaps in understanding immunogenicity assay requirements and have developed harmonization tools for use by industry scientists to facilitate filings to health authorities. Herein, this team provides testing and reporting strategies and tools for the following assessments: (1) pre-study validation cut point; (2) in-study cut points, including procedures for applying cut points to mixed populations; (3) system suitability control criteria for in-study plate acceptance; (4) assay sensitivity, including the selection of an appropriate low positive control; (5) specificity, including drug and target tolerance; (6) sample stability that reflects sample storage and handling conditions; (7) assay selectivity to matrix components, including hemolytic, lipemic, and disease state matrices; (8) domain specificity for multi-domain therapeutics; (9) and minimum required dilution and extraction-based sample processing for titer reporting. Graphical Abstract

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Method validation of a bridging immunoassay in combination with acid-dissociation and bead treatment for detection of anti-drug antibody

Yang

Zhu

et al. 2023

Heliyon

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Criteria to Reevaluate Anti-drug Antibody Assay Cut Point Suitability in the Target Population

Cited by 5 publications

References 3 publications

An Alternative Data Transformation Approach for ADA Cut Point Determination: Why Not Use a Weibull Transformation?

An Alternative Data Transformation Approach for ADA Cut Point Determination: Why Not Use a Weibull Transformation?

Anti-drug Antibody Validation Testing and Reporting Harmonization

Method validation of a bridging immunoassay in combination with acid-dissociation and bead treatment for detection of anti-drug antibody

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