This is a PDF file of a peer-reviewed paper that has been accepted for publication. Although unedited, the content has been subjected to preliminary formatting. Nature is providing this early version of the typeset paper as a service to our authors and readers. The text and figures will undergo copyediting and a proof review before the paper is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers apply.

This is a PDF file of a peer-reviewed paper that has been accepted for publication. Although unedited, the content has been subjected to preliminary formatting. Nature is providing this early version of the typeset paper as a service to our authors and readers. The text and figures will undergo copyediting and a proof review before the paper is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers apply.

cancer drug development has been riddled with high attrition rates, in part, due to poor reproducibility of preclinical models for drug discovery. Poor experimental design and lack of scientific transparency may cause experimental biases that in turn affect data quality, robustness and reproducibility. Here, we pinpoint sources of experimental variability in conventional 2D cell-based cancer drug screens to determine the effect of confounders on cell viability for MCF7 and HCC38 breast cancer cell lines treated with platinum agents (cisplatin and carboplatin) and a proteasome inhibitor (bortezomib). Variance component analysis demonstrated that variations in cell viability were primarily associated with the choice of pharmaceutical drug and cell line, and less likely to be due to the type of growth medium or assay incubation time. Furthermore, careful consideration should be given to different methods of storing diluted pharmaceutical drugs and use of DMSO controls due to the potential risk of evaporation and the subsequent effect on dose-response curves. Optimization of experimental parameters not only improved data quality substantially but also resulted in reproducible results for bortezomib-and cisplatin-treated HCC38, MCF7, MCF-10A, and MDA-MB-436 cells. Taken together, these findings indicate that replicability (the same analyst re-performs the same experiment multiple times) and reproducibility (different analysts perform the same experiment using different experimental conditions) for cell-based drug screens can be improved by identifying potential confounders and subsequent optimization of experimental parameters for each cell line. Cancer drug candidates currently have the lowest overall success rates and are 23% less likely to succeed in phase III clinical trials compared with other therapeutic areas 1-3. At a cost of approximately $3 billion per approved drug, over a decade may have passed from target discovery to drug approval (long development time) and tens of thousands of drug candidates would have likely been dropped due to drug safety and/or efficacy issues 4. Cell-based pharmacogenomics screens are commonly used during the preclinical drug screening process to identify druggable targets by characterizing the biological effects associated with drug response and toxicity. However, low interlaboratory reproducibility of cell-based pharmacogenomics screens, lack of robust disease models that recapitulate the natural progression of human cancers, and drug-associated toxicity issues contribute to the high drug attrition rates in oncology 5-13. Biomedical researchers and the pharmaceutical industry are, therefore, developing strategies to improve early-phase drug screening, e.g. novel preclinical models of disease and drug repurposing 9,14. Drug-dose response assays (e.g. MTT assay) performed in two-dimensional (2D) cell culture are typically used to evaluate drug efficacy and potency in cells exposed to a drug for up to 72 hours 15-17. However, it has been challenging to develop robust drug sensitivi...

No abstract