Spatial artifact detection improves the reproducibility of drug screening experiments

Aleksandr Ianevski; Kristen Nader; Swapnil Potdar; Alexandra Gorbonos; Filipp Ianevski; Ziaurrehman Tanoli; Jani Saarela; Tero Aittokallio

doi:10.1016/j.isci.2025.113470

Spatial artifact detection improves the reproducibility of drug screening experiments

iScience. 2025 Aug 30;28(10):113470. doi: 10.1016/j.isci.2025.113470. eCollection 2025 Oct 17.

Authors

Aleksandr Ianevski^{1

2}, Kristen Nader^{1

2}, Swapnil Potdar¹, Alexandra Gorbonos¹, Filipp Ianevski¹, Ziaurrehman Tanoli¹, Jani Saarela¹, Tero Aittokallio^{1

2

3

4}

Affiliations

¹ Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
² iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
³ Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway.
⁴ Oslo Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway.

Abstract

Reliable and reproducible drug screening experiments are essential for drug discovery and personalized medicine. We demonstrate how systematic experimental errors in drug plates negatively impact data reproducibility, and that conventional quality control (QC) methods based on plate controls fail to detect these spatial errors. To address this limitation, we developed a control-independent QC approach that uses normalized residual fit error (NRFE) to identify systematic artifacts in drug screening experiments. Analysis of >100,000 duplicate measurements from the PRISM pharmacogenomic study revealed that NRFE-flagged experiments show 3-fold lower reproducibility among technical replicates. By integrating NRFE with QC methods to analyze 41,762 matched drug-cell line pairs between two datasets from the Genomics of Drug Sensitivity in Cancer project, we improved the cross-dataset correlation from 0.66 to 0.76. Available as an R package at https://github.com/IanevskiAleksandr/plateQC, plateQC provides a robust toolset for enhancing drug screening data reliability and consistency for basic research and translational applications.

Keywords: Bioinformatics; Biological sciences; Natural sciences; Pharmacoinformatics.