An Alternative Data Transformation Approach for ADA Cut Point Determination: Why Not Use a Weibull Transformation?
Open Access
- 13 August 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in The AAPS Journal
- Vol. 23 (5), 1-6
- https://doi.org/10.1208/s12248-021-00625-6
Abstract
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.Keywords
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