Measurement and models accounting for cell death capture hidden variation in compound response

Abstract

Cancer cell sensitivity or resistance is almost universally quantified through a direct or surrogate measure of cell number. However, compound responses can occur through many distinct phenotypic outcomes, including changes in cell growth, apoptosis, and non-apoptotic cell death. These outcomes have divergent effects on the tumor microenvironment, immune response, and resistance mechanisms. Here, we show that quantifying cell viability alone is insufficient to distinguish between these compound responses. Using an alternative assay and drug-response analysis amenable to high-throughput measurement, we find that compounds with identical viability outcomes can have very different effects on cell growth and death. Moreover, additive compound pairs with distinct growth/death effects can appear synergistic when only assessed by viability. Overall, these results demonstrate an approach to incorporating measurements of cell death when characterizing a pharmacologic response.

Discovery and Preclinical Characterization of BIIB129, a Covalent, Selective, and Brain-Penetrant BTK Inhibitor for the Treatment of Multiple Sclerosis

“…We also observed that when tested using immortalized TMD8 cells, some compounds were unexpectedly inactive or exhibited only partial maximum effect ( E max ) , in preventing cellular proliferation (i.e., compound 13 , TMD8 IC 50 = 0.60 nM, E max = 30%). These findings were rationalized by a reduction in cell viability, although experiments were not performed to establish the structure–activity relationship (SAR) contributing to the differences between a reduction in cellular growth versus an increase in cell death. Instead, we opted to include the TMD8 assay as an additional filter to exclude compounds exhibiting E max < 100%, resulting in the deprioritization of compounds such as 13 and the investigation of other constrained bicyclic linkers.…”

Logical modeling: Combining manual curation and automated parameterization to predict drug synergies

“…In addition, drug synergy is an elusive concept itself, with different mathematical reference models producing different synergy scores 43 . Finally, high throughput drug screens, as employed here, typically reports drug responses based on measured residual ATP content after drug exposure, which is known not to capture all growth-reducing drug responses 44 , 45 , 46 . Despite these limitations, which must be expected to reduce the performance of any drug synergy prediction approach, our logical simulation-based in-silico pre-selection approach performs immensely better than a blinded screen that would assay the same numbers of candidates: at a sensitivity of 50%, roughly 35-40% of a pre-selected set of predicted synergies will be observed in follow up drug synergy experiments in drug screen where only 4% of drug combinations acted synergistically overall.…”

Materials-driven approaches to understand extrinsic drug resistance in cancer

“…These unique features of the TME make important contributions to tumorigenesis, tumor growth, metastasis, and immune evasion. Unsurprisingly, the TME can also have strong effects on drug therapy, including altering drug pharmacokinetics, 36 generating chemoresistance signals, 37,38 and promoting broad spectrum drug resistance by modulating apoptotic priming. 39,40 These phenotypes are more pronounced in metastatic microenvironments.…”

“…Labs must quantify differences in growth rates across their cells and materials before choosing one of the metrics above, or they risk presenting data that is difficult to translate to a preclinical model or to another lab's biomaterial system. Systems biologists have developed both experimental and computational tools to go even further, with ways to measure not only whether cells die, but how, 37,86 and how quickly. 87–89…”