Interlaboratory Evaluation of a Multiplexed High Information Content In Vitro Genotoxicity Assay

S.M. Bryce1, D.T. Bernacki1, J.C. Bemis1, R.A. Spellman2, M.E. Engel2, M. Schuler2, E. Lorge3, P.T. Heikkinen4, U. Hemmann5, V. Thybaud6, S. Wilde7, N. Queisser7, A. Sutter7, A. Zeller8, M Guérard8, D. Kirkland9, S.D. Dertinger1

1 Litron Laboratories
2 Pfizer Worldwide Research and Development
3 Servier Group
4 Orion Pharma
5 Sanofi-Aventis Deutschland GmbH
6 Sanofi
7 Bayer AG
8 Roche Pharma Research and Early Development
9 Kirkland Consulting

Environmental and Molecular Mutagenesis
Volume 58, Issue 3, 01 April 2017, Pages 146-161

Historically, determining genotoxic mode of action (MoA) using the traditional testing battery has been challenging. This is partially because the three main types of DNA damage (gene mutation, structural chromosome damage, and aneuploidy) cannot currently be measured by a single assay. Also, the variety of organisms, treatment schedules and test article concentrations used by current methods complicate MoA calls.

Litron's new MultiFlow® method addresses this by simultaneously providing high throughput analysis of multiple genotoxic biomarkers. Based on this high content method, an interlab trial was conducted with six pharmaceutical companies using a prototype MultiFlow DNA Damage kit. Scientists at each lab treated TK6 cells with numerous genotoxic and nongenotoxic compounds. Flow cytometric data were obtained from the following endpoints:

·      γH2AX
·      p53
·      phospho-histone H3
·      polyploidization

Data were sent to Litron for additional interpretation and analysis. Machine learning algorithms and threshold cut-off values were developed to classify compounds as clastogenic, aneugenic or nongenotoxic.

Results showed excellent transferability of the method across labs and strong agreement with expected MoA classification. This trial demonstrates that the MultiFlow assay can provide MoA information as part of routine safety assessment in a more efficient manner than traditional methods.

We'd like to thank the editors of EMM for selecting this paper as Editor's Choice and providing it as Open Access. We also want to acknowledge our collaborators for the important work they contributed to this investigation.