Treffer: Multi-laboratory comparisons of manual patch clamp hERG data generated using standardized protocols and following ICH S7B Q&A 2.1 best practices.

Acute block of hERG channels is the most common mechanism underlying drug-induced QT<subscript>C</subscript> prolongation and potentially fatal Torsade de Pointes arrhythmia. Updates to ICH E14 Q&As now allow for using negative nonclinical data, including hERG, to support QT<subscript>C</subscript> risk assessment in late-stage clinical development. To interpret the hERG results, understanding hERG assay reproducibility or hERG data variability is pivotal. Protocol and best practice recommendations have been provided with the goal of minimizing lab-to-lab data differences, but the impact remains unclear. To fill this knowledge gap, hERG data from a HESI-coordinated multi-laboratory study were leveraged. Using standardized protocol and following best practices for patch clamp studies, five laboratories tested 28 drugs using the manual patch clamp technique. Systematic differences in block potencies were observed for data generated by one laboratory for the first 21 drugs, and these differences disappeared for the last seven drugs. Exposure, pharmacological sensitivity of the cell lines, and cell/data qualities were ruled out as the factors underlying systematic differences. All laboratories retested two drugs and obtained results within 1.6X of the initial testings, except for another laboratory that obtained data for one drug that differed from its initial testing by 7.6X. Descriptive statistics and meta-analysis were applied to the dataset to estimate what the distribution in hERG block potencies would be if a laboratory were to test the same drug repeatedly. This measure, or hERG data variability, was ~ 5X. Based on these results, hERG block potency values within 5X of each other should not be considered different, since these values are within the natural data distribution of the hERG assay; laboratory-specific safety margin threshold may be required to account for systematic data differences. [ABSTRACT FROM AUTHOR]

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