High content phenotypic screening identifies serotonin receptor modulators with selective activity upon breast cancer cell cycle and cytokine signaling pathways

In Vitro Predictions of Acute Fish Toxicity for Development of Sustainable Crop Protection Products

New agrochemicals must demonstrate safety to numerous ecological systems, including aquatic systems, and aquatic vertebrate toxicity is typically evaluated by using the in vivo acute fish toxicity (AFT) test. Here, we investigated two alternative in vitro assays using a cell line isolated from rainbow trout (Onchorhynchus mykiss) gill tissue: (i) adenosine triphosphate (ATP) luminescence and (ii) cell painting. The former assay measures cytotoxicity, while the latter measures changes in cellular morphology in response to chemical exposure. We assessed how well end points in these two assays predicted acute lethality (i.e., LC50 values) in independent in vivo AFT tests. When compared to results from OECD TG 249 (in vitro), we found that the ATP assay was not as predictive (R2 = 0.53) as the cell painting assay. Similarly, when compared to results from OECD TG 203 (in vivo), the cell painting was much more predictive (R2 = 0.67). Our results show that such in vitro assays are useful for fast and efficient screening alternatives to in vivo fish testing that can aid in the agrochemical discovery phase, where thousands of potential new actives are tested each year.

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting's ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other -omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting's ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

Improving the power of drug toxicity measurements by quantitative nuclei imaging

Imaging-based anticancer drug screens are becoming more prevalent due to development of automated fluorescent microscopes and imaging stations, as well as rapid advancements in image processing software. Automated cell imaging provides many benefits such as their ability to provide high-content data, modularity, dynamics recording and the fact that imaging is the most direct way to access cell viability and cell proliferation. However, currently most publicly available large-scale anticancer drugs screens, such as GDSC, CTRP and NCI-60, provide cell viability data measured by assays based on colorimetric or luminometric measurements of NADH or ATP levels. Although such datasets provide valuable data, it is unclear how well drug toxicity measurements can be integrated with imaging data. Here we explored the relations between drug toxicity data obtained by XTT assay, two quantitative nuclei imaging methods and trypan blue dye exclusion assay using a set of four cancer cell lines with different morphologies and 30 drugs with different mechanisms of action. We show that imaging-based approaches provide high accuracy and the differences between results obtained by different methods highly depend on drug mechanism of action. Selecting AUC metrics over IC50 or comparing data where significantly drugs reduced cell numbers noticeably improves consistency between methods. Using automated cell segmentation protocols we analyzed mitochondria activity in more than 11 thousand drug-treated cells and showed that XTT assay produces unreliable data for CDK4/6, Aurora A, VEGFR and PARP inhibitors due induced cell size growth and increase in individual mitochondria activity. We also explored several benefits of image-based analysis such as ability to monitor cell number dynamics, dissect changes in total and individual mitochondria activity from cell proliferation, and ability to identify chromatin remodeling drugs. Finally, we provide a web tool that allows comparing results obtained by different methods.

Morphological profiling in human neural progenitor cells classifies hits in a pilot drug screen for Alzheimer's disease

Alzheimer's disease accounts for 60-70% of dementia cases. Current treatments are inadequate and there is a need to develop new approaches to drug discovery. Recently, in cancer, morphological profiling has been used in combination with high-throughput screening of small-molecule libraries in human cells in vitro. To test feasibility of this approach for Alzheimer's disease, we developed a cell morphology-based drug screen centred on the risk gene, SORL1 (which encodes the protein SORLA). Increased Alzheimer's disease risk has been repeatedly linked to variants in SORL1, particularly those conferring loss or decreased expression of SORLA, and lower SORL1 levels are observed in post-mortem brain samples from individuals with Alzheimer's disease. Consistent with its role in the endolysosomal pathway, SORL1 deletion is associated with enlarged endosomes in neural progenitor cells and neurons. We, therefore, hypothesized that multi-parametric, image-based cell phenotyping would identify features characteristic of SORL1 deletion. An automated morphological profiling method (Cell Painting) was adapted to neural progenitor cells and used to determine the phenotypic response of SORL1-/- neural progenitor cells to treatment with compounds from a small internationally approved drug library (TargetMol, 330 compounds). We detected distinct phenotypic signatures for SORL1-/- neural progenitor cells compared to isogenic wild-type controls. Furthermore, we identified 16 compounds (representing 14 drugs) that reversed the mutant morphological signatures in neural progenitor cells derived from three SORL1-/- induced pluripotent stem cell sub-clones. Network pharmacology analysis revealed the 16 compounds belonged to five mechanistic groups: 20S proteasome, aldehyde dehydrogenase, topoisomerase I and II, and DNA synthesis inhibitors. Enrichment analysis identified DNA synthesis/damage/repair, proteases/proteasome and metabolism as key pathways/biological processes. Prediction of novel targets revealed enrichment in pathways associated with neural cell function and Alzheimer's disease. Overall, this work suggests that (i) a quantitative phenotypic metric can distinguish induced pluripotent stem cell-derived SORL1-/- neural progenitor cells from isogenic wild-type controls and (ii) phenotypic screening combined with multi-parametric high-content image analysis is a viable option for drug repurposing and discovery in this human neural cell model of Alzheimer's disease.

The Neutrophil Dynamic Mass Redistribution Assay as a Medium throughput Primary Cell Screening Assay

In a typical G protein coupled receptor drug discovery campaign, an in vitro primary functional screening assay is often established in a recombinant system overexpressing the target of interest, which offers advantages with respect to overall throughput and robustness of compound testing. Subsequently, compounds are then progressed into more physiologically relevant but lower throughput ex vivo primary cell assays and finally in vivo studies. Here we describe a dynamic mass redistribution (DMR) assay that has been developed in a format suitable to support medium throughput drug screening in primary human neutrophils. Neutrophils are known to express both CXC chemokine receptor (CXCR) 1 and CXCR2 that are thought to play significant roles in various inflammatory disorders and cancer. Using multiple relevant chemokine ligands and a range of selective and nonselective small and large molecule antagonists that block CXCR1 and CXCR2 responses, we demonstrate distinct pharmacological profiles in neutrophil DMR from those observed in recombinant assays but predictive of activity in neutrophil chemotaxis and CD11b upregulation, a validated target engagement marker previously used in clinical studies of CXCR2 antagonists. The primary human neutrophil DMR cell system is highly reproducible, robust, and less prone to donor variability observed in CD11b and chemotaxis assays and thus provides a unique, more physiologically relevant, and higher throughput assay to support drug discovery and translation to early clinical trials. SIGNIFICANCE STATEMENT: Neutrophil dynamic mass redistribution assays provide a higher throughput screening assay to profile compounds in primary cells earlier in the screening cascade enabling a higher level of confidence in progressing the development of compounds toward the clinic. This is particularly important for chemokine receptors where redundancy contributes to a lack of correlation between recombinant screening assays and primary cells, with the coexpression of related receptors confounding results.

The Expression of Trace Amine-Associated Receptors (TAARs) in Breast Cancer Is Coincident with the Expression of Neuroactive Ligand-Receptor Systems and Depends on Tumor Intrinsic Subtype

Currently, the contribution of trace amine-associated receptors (TAARs) to breast cancer (BC) is recognized, but their associations with various pathological characteristics are not yet understood. There is accumulated transcriptomic data for BC tumors, which are represented in publicly accessible databases. We estimated TAARs' (including TAAR1, TAAR2, TAAR5, TAAR6, TAAR8, and TAAR9) associations with BC stage, grade, and molecular subtypes in these data and identified that the expression of all TAARs was associated with more unfavorable cancer subtypes, including basal-like and HER2-positive tumors. Also, the significant upregulation of all TAARs was demonstrated in circulating tumor cells compared to the metastatic lesions. Considering that co-expressed genes are more likely to be involved in the same biologic processes, we analyzed genes that are co-expressed with TAARs in BC. These gene sets were enriched with the genes of the olfactory transduction pathway and neuroactive ligand-receptor interaction participants. TAARs are co-expressed with G-protein-coupled receptors of monoamine neurotransmitters including dopamine, norepinephrine, and serotonin as well as with other neuroactive ligand-specific receptors. Since TAAR1 is able to modulate the activity of monoamine receptors that are involved in the regulation of BC growth, TAAR1 and potentially other TAARs may be regarded as prospective therapeutic targets for breast cancer.

Phytochemical investigation of Asarum sieboldii var. seoulense and the phenotypic profiles of its constituents against a Parkinson's Disease olfactory cell line

Asarum sieboldii var. seoulense is a plant species under the family Aristolochiaceae and has been used for centuries as an ingredient in a well-known Traditional Chinese medicine (TCM), "Xixin", to treat symptoms of the neurodegenerative condition Parkinson's Disease (PD). Although there have been studies on the neuroprotective effect of this TCM, the phenotypic profiles of its chemical constituents against PD-implicated cellular organelles have not been reported. This research investigated the chemistry of A. sieboldii var. seoulense extract to identify the active small molecules that exhibited perturbation to the cellular compartments related to PD, potentially supporting its traditional application in treating this condition. 1H NMR-guided chemical investigation of this plant yielded twenty secondary metabolites which belong to isobutylamides, lignans and phenolics. The compounds were evaluated against an olfactory cell line derived from a PD patient using phenotypic assay. Several isolates, 2, 3, 7, 11, 13-16 and 18-20, were found to induce moderate perturbation to the staining of mitochondria, autophagosome and α-tubulin of the cells. Considering that PD pathogenesis is closely related to these cellular compartments, the results provided a rationale for the traditional application of Xixin in the treatment of PD.

Eco-friendly synthesis of new olanzapine derivatives and evaluation of their anticancer potential

New derivatives of the known antipsychotic drug olanzapine have been obtained as potential compounds with anticancer activity in two metabolically different breast cancer cell lines: MCF-7 and triple negative MDA-MB-231. The compounds were obtained under phase transfer catalysis (PTC) in the presence of microwave irradiation (MW) or ultrasound (")))"), evaluating the effect of solvents such as dimethylformamide, water, or choline chloride/urea (natural deep eutectic solvent, NaDES). In the best option, the compounds were obtained within 2 minutes with a yield of 57-86% in MW. Two of the obtained compounds which have a naphthalimide moiety and a pentyl (7) or hexyl chain (8) show pronounced cytotoxicity. Interestingly, neither olanzapine nor desmethylolanzapine (DOLA), which was one of the substrates for the synthesis reaction, showed any significant activity in the study.

Selection of Optimal Cell Lines for High-Content Phenotypic Screening

High-content microscopy offers a scalable approach to screen against multiple targets in a single pass. Prior work has focused on methods to select "optimal" cellular readouts in microscopy screens. However, methods to select optimal cell line models have garnered much less attention. Here, we provide a roadmap for how to select the cell line or lines that are best suited to identify bioactive compounds and their mechanism of action (MOA). We test our approach on compounds targeting cancer-relevant pathways, ranking cell lines in two tasks: detecting compound activity ("phenoactivity") and grouping compounds with similar MOA by similar phenotype ("phenosimilarity"). Evaluating six cell lines across 3214 well-annotated compounds, we show that optimal cell line selection depends on both the task of interest (e.g., detecting phenoactivity vs inferring phenosimilarity) and distribution of MOAs within the compound library. Given a task of interest and a set of compounds, we provide a systematic framework for choosing optimal cell line(s). Our framework can be used to reduce the number of cell lines required to identify hits within a compound library and help accelerate the pace of early drug discovery.

Linking chemicals, genes and morphological perturbations to diseases

The progress in image-based high-content screening technology has facilitated high-throughput phenotypic profiling notably the quantification of cell morphology perturbation by chemicals. However, understanding the mechanism of action of a chemical and linking it to cell morphology and phenotypes remains a challenge in drug discovery. In this study, we intended to integrate molecules that induced transcriptomic perturbations and cellular morphological changes into a biological network in order to assess chemical-phenotypic relationships in humans. Such a network was enriched with existing disease information to suggest molecular and cellular profiles leading to phenotypes. Two datasets were used for this study. Firstly, we used the "Cell Painting morphological profiling assay" dataset, composed of 30,000 compounds tested on human osteosarcoma cells (named U2OS). Secondly, we used the "L1000 mRNA profiling assay" dataset, a collection of transcriptional expression data from cultured human cells treated with approximately 20,000 bioactive small molecules from the Library of Integrated Network-based Cellular Signatures (LINCS). Furthermore, pathways, gene ontology terms and disease enrichments were performed on the transcriptomics data. Overall, our study makes it possible to develop a biological network combining chemical-gene-pathway-morphological perturbation and disease relationships. It contains an ensemble of 9989 chemicals, 732 significant morphological features and 12,328 genes. Through diverse examples, we demonstrated that some drugs shared similar genes, pathways and morphological profiles that, taken together, could help in deciphering chemical-phenotype observations.

Meeting the Challenge 2: Identification of Potential Chemical Probes for Parkinson's Disease from Ligusticum chuanxiong Hort Using Cytological Profiling

Traditional Chinese medicine (TCM) has been around for thousands of years and is increasingly gaining popularity in the Western world to treat various complex disorders including the incurable neurodegenerative condition, Parkinson's Disease (PD). One of the many directions in recent studies of PD is utilizing the phenotypic assay, or cytological profiling, to evaluate the phenotypic changes of PD-implicated cellular components in patient-derived olfactory neuroepithelial (hONS) cells, upon treating the cells with extracts or pure compounds. To obtain small molecules for studies utilizing PD phenotyping assays, Ligusticum chuanxiong Hort was selected for analysis as it is a popular Chinese herbal medicine used for treating PD-like symptoms. Fifty-three secondary metabolites, including six new compounds, were isolated from the ethanolic extract of L. chuanxiong; their structures were elucidated based on several spectroscopic techniques such as NMR, MS, Fourier transform infrared (FTIR), UV, and theoretical density functional theory (DFT) calculations. Cytological profiling of the afforded natural products against PD hONS cells revealed 34 compounds strongly perturbated the staining of several cellular organelles. In fact, greaterthan 1.5-fold change was observed compared to the control (dimethyl sulfoxide; DMSO), with early endosome, lysosome, and autophagosome (LC3b) being particularly affected. Given these biological compartments are closely related to PD pathogenesis, the results helped rationalize the traditional medicinal use of L. chuanxiong in PD treatment. Further, the hit compounds can serve as chemical probes to map the molecular pathways underlying PD, potentially leading to new therapeutic targets for PD.

Morphological profiling of environmental chemicals enables efficient and untargeted exploration of combination effects

Environmental chemicals are commonly studied one at a time, and there is a need to advance our understanding of the effect of exposure to their combinations. Here we apply high-content microscopy imaging of cells stained with multiplexed dyes (Cell Painting) to profile the effects of Cetyltrimethylammonium bromide (CTAB), Bisphenol A (BPA), and Dibutyltin dilaurate (DBTDL) exposure on four human cell lines; both individually and in all combinations. We show that morphological features can be used with multivariate data analysis to discern between exposures from individual compounds, concentrations, and combinations. CTAB and DBTDL induced concentration-dependent morphological changes across the four cell lines, and BPA exacerbated morphological effects when combined with CTAB and DBTDL. Combined exposure to CTAB and BPA induced changes in the ER, Golgi apparatus, nucleoli and cytoplasmic RNA in one of the cell lines. Different responses between cell lines indicate that multiple cell types are needed when assessing combination effects. The rapid and relatively low-cost experiments combined with high information content make Cell Painting an attractive methodology for future studies of combination effects. All data in the study is made publicly available on Figshare.

Physiologic Targets and Modes of Action for CBL0137, a Lead for Human African Trypanosomiasis Drug Development

CBL0137 is a lead drug for human African trypanosomiasis, caused by Trypanosoma brucei Herein, we use a four-step strategy to 1) identify physiologic targets and 2) determine modes of molecular action of CBL0137 in the trypanosome. First, we identified fourteen CBL0137-binding proteins using affinity chromatography. Second, we developed hypotheses of molecular modes of action, using predicted functions of CBL0137-binding proteins as guides. Third, we documented effects of CBL0137 on molecular pathways in the trypanosome. Fourth, we identified physiologic targets of the drug by knocking down genes encoding CBL0137-binding proteins and comparing their molecular effects to those obtained when trypanosomes were treated with CBL0137. CBL0137-binding proteins included glycolysis enzymes (aldolase, glyceraldehyde-3-phosphate dehydrogenase, phosphofructokinase, phosphoglycerate kinase) and DNA-binding proteins [universal minicircle sequence binding protein 2, replication protein A1 (RPA1), replication protein A2 (RPA2)]. In chemical biology studies, CBL0137 did not reduce ATP level in the trypanosome, ruling out glycolysis enzymes as crucial targets for the drug. Thus, many CBL0137-binding proteins are not physiologic targets of the drug. CBL0137 inhibited 1) nucleus mitosis, 2) nuclear DNA replication, and 3) polypeptide synthesis as the first carbazole inhibitor of eukaryote translation. RNA interference (RNAi) against RPA1 inhibited both DNA synthesis and mitosis, whereas RPA2 knockdown inhibited mitosis, consistent with both proteins being physiologic targets of CBL0137. Principles used here to distinguish drug-binding proteins from physiologic targets of CBL0137 can be deployed with different drugs in other biologic systems. SIGNIFICANCE STATEMENT: To distinguish drug-binding proteins from physiologic targets in the African trypanosome, we devised and executed a multidisciplinary approach involving biochemical, genetic, cell, and chemical biology experiments. The strategy we employed can be used for drugs in other biological systems.

Drug Repositioning and Subgroup Discovery for Precision Medicine Implementation in Triple Negative Breast Cancer

Breast cancer (BC) is the leading cause of death among female patients with cancer. Patients with triple-negative breast cancer (TNBC) have the lowest survival rate. TNBC has substantial heterogeneity within the BC population. This study utilized our novel patient stratification and drug repositioning method to find subgroups of BC patients that share common genetic profiles and that may respond similarly to the recommended drugs. After further examination of the discovered patient subgroups, we identified five homogeneous druggable TNBC subgroups. A drug repositioning algorithm was then applied to find the drugs with a high potential for each subgroup. Most of the top drugs for these subgroups were chemotherapy used for various types of cancer, including BC. After analyzing the biological mechanisms targeted by these drugs, ferroptosis was the common cell death mechanism induced by the top drugs in the subgroups with neoplasm subdivision and race as clinical variables. In contrast, the antioxidative effect on cancer cells was the common targeted mechanism in the subgroup of patients with an age less than 50. Literature reviews were used to validate our findings, which could provide invaluable insights to streamline the drug repositioning process and could be further studied in a wet lab setting and in clinical trials.

A novel high-content phenotypic screen to identify inhibitors of mitochondrial DNA maintenance in trypanosomes

Kinetoplastid parasites cause diverse neglected diseases in humans and livestock, with an urgent need for new treatments. The survival of kinetoplastids depends on their uniquely structured mitochondrial genome (kDNA), the eponymous kinetoplast. Here, we report the development of a high-content screen for pharmacologically induced kDNA loss, based on specific staining of parasites and automated image analysis. As proof of concept, we screened a diverse set of ∼14,000 small molecules and exemplify a validated hit as a novel kDNA-targeting compound.

A phenomics approach for antiviral drug discovery

BACKGROUND

The emergence and continued global spread of the current COVID-19 pandemic has highlighted the need for methods to identify novel or repurposed therapeutic drugs in a fast and effective way. Despite the availability of methods for the discovery of antiviral drugs, the majority tend to focus on the effects of such drugs on a given virus, its constituent proteins, or enzymatic activity, often neglecting the consequences on host cells. This may lead to partial assessment of the efficacy of the tested anti-viral compounds, as potential toxicity impacting the overall physiology of host cells may mask the effects of both viral infection and drug candidates. Here we present a method able to assess the general health of host cells based on morphological profiling, for untargeted phenotypic drug screening against viral infections.

RESULTS

We combine Cell Painting with antibody-based detection of viral infection in a single assay. We designed an image analysis pipeline for segmentation and classification of virus-infected and non-infected cells, followed by extraction of morphological properties. We show that this methodology can successfully capture virus-induced phenotypic signatures of MRC-5 human lung fibroblasts infected with human coronavirus 229E (CoV-229E). Moreover, we demonstrate that our method can be used in phenotypic drug screening using a panel of nine host- and virus-targeting antivirals. Treatment with effective antiviral compounds reversed the morphological profile of the host cells towards a non-infected state.

CONCLUSIONS

The phenomics approach presented here, which makes use of a modified Cell Painting protocol by incorporating an anti-virus antibody stain, can be used for the unbiased morphological profiling of virus infection on host cells. The method can identify antiviral reference compounds, as well as novel antivirals, demonstrating its suitability to be implemented as a strategy for antiviral drug repurposing and drug discovery.

Critical assessment of AI in drug discovery

Introduction: Artificial Intelligence (AI) has become a component of our everyday lives, with applications ranging from recommendations on what to buy to the analysis of radiology images. Many of the techniques originally developed for other fields such as language translation and computer vision are now being applied in drug discovery. AI has enabled multiple aspects of drug discovery including the analysis of high content screening data, and the design and synthesis of new molecules.Areas covered: This perspective provides an overview of the application of AI in several areas relevant to drug discovery including property prediction, molecule generation, image analysis, and organic synthesis planning.Expert opinion: While a variety of machine learning methods are now being routinely used to predict biological activity and ADME properties, methods of representing molecules continue to evolve. Molecule generation methods are relatively new and unproven but hold the potential to access new, unexplored areas of chemical space. The application of AI in drug discovery will continue to benefit from dedicated research, as well as AI developments in other fields. With this pairing algorithmic advancements and high-quality data, the impact of AI in drug discovery will continue to grow in the coming years.

Morphological profiling of small molecules

Profiling approaches such as gene expression or proteome profiling generate small-molecule bioactivity profiles that describe a perturbed cellular state in a rather unbiased manner and have become indispensable tools in the evaluation of bioactive small molecules. Automated imaging and image analysis can record morphological alterations that are induced by small molecules through the detection of hundreds of morphological features in high-throughput experiments. Thus, morphological profiling has gained growing attention in academia and the pharmaceutical industry as it enables detection of bioactivity in compound collections in a broader biological context in the early stages of compound development and the drug-discovery process. Profiling may be used successfully to predict mode of action or targets of unexplored compounds and to uncover unanticipated activity for already characterized small molecules. Here, we review the reported approaches to morphological profiling and the kind of bioactivity that can be detected so far and, thus, predicted.

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug's activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.

Chemical constituents from Macleaya cordata (Willd) R. Br. and their phenotypic functions against a Parkinson's disease patient-derived cell line

Cytological profiling (CP) assay against a human olfactory neuroshpere-derived (hONS) cell line using a library of traditional Chinese medicinal plant extracts gave indications that the ethanolic extract of Macleaya cordata (Willd) R. Br. elicited strong perturbations to various cellular components. Further chemical investigation of this extract resulted in the isolation of two new benzo[c]phenanthridine alkaloids, (6R)-10-methoxybocconoline (1) and 6-(1-hydroxyethyl)-10-methoxy-5,6-dihydrochelerythrine (2). Their planar structures were elucidated by extensive 1D and 2D NMR studies, together with MS data. The absolute configuration for position C-6 of 1 and relative configurations for position C-6 and C-1' of 2 were assigned by density functional theory (DFT) calculations of ECD and NMR data, respectively. Also isolated were fourteen known metabolites, including ten alkaloids (3-12) and four coumaroyl-containing compounds (13-16). Cytological profiling of the isolates against Parkinson's Disease (PD) patient-derived olfactory cells revealed bocconoline (3) and 6-(1-hydroxyethyl)-5,6-dihydrochelerythrine (4) significantly perturbated the features of cellular organelles including early endosomes, mitochondria and autophagosomes. Given that hONS cells from PD patients model some functional aspects of the disease, the results suggested that these phenotypic profiles may have a role in the mechanisms underlying PD and signified the efficacy of CP in finding potential chemical tools to study the biological pathways in PD.

Phenotypic Profiling of Reference Chemicals across Biologically Diverse Cell Types Using the Cell Painting Assay

Cell Painting is a high-throughput phenotypic profiling assay that uses fluorescent cytochemistry to visualize a variety of organelles and high-content imaging to derive a large number of morphological features at the single-cell level. Most Cell Painting studies have used the U-2 OS cell line for chemical or functional genomics screening. The Cell Painting assay can be used with many other human-derived cell types, given that the assay is based on the use of fluoroprobes that label organelles that are present in most (if not all) human cells. Questions remain, however, regarding the optimization steps required and overall ease of deployment of the Cell Painting assay to novel cell types. Here, we used the Cell Painting assay to characterize the phenotypic effects of 14 phenotypic reference chemicals in concentration-response screening mode across six biologically diverse human-derived cell lines (U-2 OS, MCF7, HepG2, A549, HTB-9 and ARPE-19). All cell lines were labeled using the same cytochemistry protocol, and the same set of phenotypic features was calculated. We found it necessary to optimize image acquisition settings and cell segmentation parameters for each cell type, but did not adjust the cytochemistry protocol. For some reference chemicals, similar subsets of phenotypic features corresponding to a particular organelle were associated with the highest-effect magnitudes in each affected cell type. Overall, for certain chemicals, the Cell Painting assay yielded qualitatively similar biological activity profiles among a group of diverse, morphologically distinct human-derived cell lines without the requirement for cell type-specific optimization of cytochemistry protocols.

Pharmacogenomics of breast cancer: highlighting CYP2D6 and tamoxifen

PURPOSE

To review recent pharmacogenomics studies on breast cancer patients undergoing tamoxifen therapy, highlighting how our knowledge on cytochrome P450 2D6 (CYP2D6) can help to guide the development of adjuvant therapies for these patients.

METHODS

A comprehensive literature search was conducted. Articles reporting findings pertaining to the effect of CYP2D6 on the therapeutic efficacy of tamoxifen, those reporting how targeting CYP2D6 could inform tamoxifen-based therapy development, and those on the tamoxifen effects on cell lines and animal models were included in the review.

RESULTS

With CYP2D6 being the primary enzyme for tamoxifen metabolism, single-nucleotide polymorphisms (SNPs) in this gene were one of the determinants in the rate of tamoxifen metabolism, thereby potentially having an effect on the efficacy of tamoxifen-based therapies. Our review indicates the potential effectiveness of targeting these SNPs, including those for the CYP2D6*10 allele (c. 100C > T), in modifying the level of tamoxifen metabolism. These findings suggest the importance of pharmacogenomics research in our understanding of the efficacy of adjuvant therapies. However, the involvement of multiple enzymes in tamoxifen metabolism, dietary factors, ethnic differences in gene frequencies, and patients' compliance to tamoxifen therapies in studies do present challenges in pharmacogenomics research.

CONCLUSIONS

Pharmacogenomics could play important roles in mediating the advancement of tamoxifen-based adjuvant therapies. Research efforts should be directed towards the exploration of further SNPs of CYP2D6 that affect tamoxifen metabolism, as well as epigenetic changes in CYP2D6, enabling the design of precision medicine and confirming clinical validity in the use of pharmacogenomics for tamoxifen.