Comparison of Approaches for Determining Bioactivity Hits from High-Dimensional Profiling Data

Nyffeler, Johanna; Haggard, Derik E.; Willis, Clinton; Setzer, R. Woodrow; Judson, Richard S.; Friedman, Katie Paul; Everett, Logan J.; Harrill, Joshua

doi:10.1177/2472555220950245

Cited by 24 publications

(12 citation statements)

References 44 publications

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“…In a second step, we aimed to evaluate the relevance of our morphological feature selection in the development of a predictive DILI model that could be used to assess the potential of a compound to induce DILI based on its Cell Painting profile. So far, Cell Painting data have been used for cytotoxicity or for DILI prediction , and concentration-response modeling approaches to estimate drug bioactivity − thresholds. With the improvement of machine learning strategies observed these past years, many studies have shown that in silico models can be trained to predict the toxicity outcomes of hundreds of chemicals using image-based profiling .…”

Section: Discussionmentioning

confidence: 99%

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Lejal,

Cerisier,

Rouquié

et al. 2023

Chem. Res. Toxicol.

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Drug-induced liver injury (DILI) is a significant concern in drug development, often leading to drug withdrawal. Although many studies aim to identify biomarkers and gene/pathway signatures related to liver toxicity and aim to predict DILI compounds, this remains a challenge in drug discovery. With a strong development of high-content screening/imaging (HCS/HCI) for phenotypic screening, we explored the morphological cell perturbations induced by DILI compounds. In the first step, cell morphological signatures were associated with two datasets of DILI chemicals (DILIRank and eTox). The mechanisms of action were then analyzed for chemicals having transcriptomics data and sharing similar morphological perturbations. Signaling pathways associated with liver toxicity (cell cycle, cell growth, apoptosis, ...) were then captured, and a hypothetical relation between cell morphological perturbations and gene deregulation was illustrated within our analysis. Finally, using the cell morphological signatures, machine learning approaches were developed to predict chemicals with a potential risk of DILI. Some models showed relevant performance with validation set balanced accuracies between 0.645 and 0.739. Overall, our findings demonstrate the utility of combining HCI with transcriptomics data to identify the morphological and gene expression signatures related to DILI chemicals. Moreover, our protocol could be extended to other toxicity end points, offering a promising avenue for comprehensive toxicity assessment in drug discovery.

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Section: Discussionmentioning

confidence: 99%

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Lejal,

Cerisier,

Rouquié

et al. 2023

Chem. Res. Toxicol.

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“…Potential uses for this dataset include HCS and image-processing method development, MoA studies, informer set design, and compound triage 52,53 . These data can be re-analyzed by alternative methods such as focusing on individual features or more complex analyses such as the point-of-departure metric 20,54 .…”

Section: Discussionmentioning

confidence: 99%

Reference compounds for characterizing cellular injury in high-content cellular morphology assays

Dahlin

Hua

Zucconi

et al. 2022

Preprint

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Robust, generalizable approaches to identify compounds efficiently with undesirable mechanisms of action in complex cellular assays remain elusive. Such a process would be useful for hit triage during high-throughput screening and, ultimately, predictive toxicology during drug development. We generated cell painting and cellular health profiles for 218 prototypical cytotoxic and nuisance compounds in U-2 OS cells in a concentration-response format. A diversity of compounds causing cellular damage produced bioactive cell painting morphologies, including cytoskeletal poisons, genotoxins, nonspecific electrophiles, and redox-active compounds. Further, we show that lower quality lysine acetyltransferase inhibitors and nonspecific electrophiles can be distinguished from more selective counterparts. We propose that the purposeful inclusion of cytotoxic and nuisance reference compounds such as those profiled in this Resource will help with assay optimization and compound prioritization in complex cellular assays like cell painting.

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“…Feature-level and global concentration-response modeling of well-level CP data were performed as previously described ( Nyffeler et al, 2021 ). For feature-level modeling, the tcplfit2 package in R statistical software was used to model each of the 1,300 features to nine functions, constant, Hill, poly1, poly2, power, and exp2-5.…”

Section: Methodsmentioning

confidence: 99%

Optimization of Human Neural Progenitor Cells for an Imaging-Based High-Throughput Phenotypic Profiling Assay for Developmental Neurotoxicity Screening

et al. 2022

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Studies in in vivo rodent models have been the accepted approach by regulatory agencies to evaluate potential developmental neurotoxicity (DNT) of chemicals for decades. These studies, however, are inefficient and cannot meet the demand for the thousands of chemicals that need to be assessed for DNT hazard. As such, several in vitro new approach methods (NAMs) have been developed to circumvent limitations of these traditional studies. The DNT NAMs, some of which utilize human-derived cell models, are intended to be employed in a testing battery approach, each focused on a specific neurodevelopmental process. The need for multiple assays, however, to evaluate each process can prolong testing and prioritization of chemicals for more in depth assessments. Therefore, a multi-endpoint higher-throughput approach to assess DNT hazard potential would be of value. Accordingly, we have adapted a high-throughput phenotypic profiling (HTPP) approach for use with human-derived neural progenitor (hNP1) cells. HTPP is a fluorescence-based assay that quantitatively measures alterations in cellular morphology. This approach, however, required optimization of several laboratory procedures prior to chemical screening. First, we had to determine an appropriate cell plating density in 384-well plates. We then had to identify the minimum laminin concentration required for optimal cell growth and attachment. And finally, we had to evaluate whether addition of antibiotics to the culture medium would alter cellular morphology. We selected 6,000 cells/well as an appropriate plating density, 20 µg/ml laminin for optimal cell growth and attachment, and antibiotic addition in the culture medium. After optimizing hNP1 cell culture conditions for HTPP, it was then necessary to select appropriate in-plate assay controls from a reference chemical set. These reference chemicals were previously demonstrated to elicit unique phenotypic profiles in various other cell types. Aphidicolin, bafilomycin A1, berberine chloride, and cucurbitacin I induced robust phenotypic profiles as compared to dimethyl sulfoxide vehicle control in the hNP1 cells, and thus can be employed as in-plate assay controls for subsequent chemical screens. We have optimized HTPP for hNP1 cells, and consequently this approach can now be assessed as a potential NAM for DNT hazard evaluation and results compared to previously developed DNT assays.

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Comparison of Approaches for Determining Bioactivity Hits from High-Dimensional Profiling Data

Cited by 24 publications

References 44 publications

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Reference compounds for characterizing cellular injury in high-content cellular morphology assays

Optimization of Human Neural Progenitor Cells for an Imaging-Based High-Throughput Phenotypic Profiling Assay for Developmental Neurotoxicity Screening

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