A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening

In this study, the authors used AlphaScreen technology to develop a high-throughput screening method for interrogating small-molecule libraries for inhibitors of the Gαo–RGS17 interaction. RGS17 is implicated in the growth, proliferation, metastasis, and the migration of prostate and lung cancers. RGS17 is upregulated in lung and prostate tumors up to a 13-fold increase over patient-matched normal tissues. Studies show RGS17 knockdown inhibits colony formation and decreases tumorigenesis in nude mice. The screen in this study uses a measurement of the Gαo–RGS17 protein–protein interaction, with an excellent Z score exceeding 0.73, a signal-to-noise ratio >70, and a screening time of 1100 compounds per hour. The authors screened the NCI Diversity Set II and determined 35 initial hits, of which 16 were confirmed after screening against controls. The 16 compounds exhibited IC50 <10 µM in dose–response experiments. Four exhibited IC50 values <6 µM while inhibiting the Gαo–RGS17 interaction >50% when compared to a biotinylated glutathione-S-transferase control. This report describes the first high-throughput screen for RGS17 inhibitors, as well as a novel paradigm adaptable to many other RGS proteins, which are emerging as attractive drug targets for modulating G-protein-coupled receptor signaling.

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Accelerating organic solar cell material's discovery: high-throughput screening and big data

Energy & Environmental Science ◽

10.1039/d1ee00559f ◽

2021 ◽

Author(s):

Xabier Rodríguez-Martínez ◽

Enrique Pascual-San-José ◽

Mariano Campoy-Quiles

Keyword(s):

Machine Learning ◽

Big Data ◽

High Throughput ◽

Organic Solar Cells ◽

High Throughput Screening ◽

Organic Solar Cell ◽

State Of The Art ◽

Review Article ◽

Machine Learning Algorithms ◽

Device Optimization

This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms.

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A Microfluidic Spheroid Culture Device with a Concentration Gradient Generator for High-Throughput Screening of Drug Efficacy

Molecules ◽

10.3390/molecules23123355 ◽

2018 ◽

Vol 23 (12) ◽

pp. 3355 ◽

Cited By ~ 14

Author(s):

Wanyoung Lim ◽

Sungsu Park

Keyword(s):

High Throughput ◽

Concentration Gradient ◽

High Throughput Screening ◽

3D Cell Culture ◽

Drug Efficacy ◽

Cancer Drug ◽

Spheroid Culture ◽

Gradient Generator ◽

Micro Channels

Three-dimensional (3D) cell culture is considered more clinically relevant in mimicking the structural and physiological conditions of tumors in vivo compared to two-dimensional cell cultures. In recent years, high-throughput screening (HTS) in 3D cell arrays has been extensively used for drug discovery because of its usability and applicability. Herein, we developed a microfluidic spheroid culture device (μFSCD) with a concentration gradient generator (CGG) that enabled cells to form spheroids and grow in the presence of cancer drug gradients. The device is composed of concave microwells with several serpentine micro-channels which generate a concentration gradient. Once the colon cancer cells (HCT116) formed a single spheroid (approximately 120 μm in diameter) in each microwell, spheroids were perfused in the presence of the cancer drug gradient irinotecan for three days. The number of spheroids, roundness, and cell viability, were inversely proportional to the drug concentration. These results suggest that the μFSCD with a CGG has the potential to become an HTS platform for screening the efficacy of cancer drugs.

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Cytotoxic Profiling of Annotated and Diverse Chemical Libraries Using Quantitative High-Throughput Screening

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/2472555219873068 ◽

2019 ◽

Vol 25 (1) ◽

pp. 9-20 ◽

Cited By ~ 2

Author(s):

Olivia W. Lee ◽

Shelley Austin ◽

Madison Gamma ◽

Dorian M. Cheff ◽

Tobie D. Lee ◽

...

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

High Throughput ◽

High Throughput Screening ◽

Drug Targets ◽

Large Scale ◽

Early Stage ◽

Cancer Cell Line ◽

Hek 293 ◽

Cytotoxic Compounds

Cell-based phenotypic screening is a commonly used approach to discover biological pathways, novel drug targets, chemical probes, and high-quality hit-to-lead molecules. Many hits identified from high-throughput screening campaigns are ruled out through a series of follow-up potency, selectivity/specificity, and cytotoxicity assays. Prioritization of molecules with little or no cytotoxicity for downstream evaluation can influence the future direction of projects, so cytotoxicity profiling of screening libraries at an early stage is essential for increasing the likelihood of candidate success. In this study, we assessed the cell-based cytotoxicity of nearly 10,000 compounds in the National Institutes of Health, National Center for Advancing Translational Sciences annotated libraries and more than 100,000 compounds in a diversity library against four normal cell lines (HEK 293, NIH 3T3, CRL-7250, and HaCat) and one cancer cell line (KB 3-1, a HeLa subline). This large-scale library profiling was analyzed for overall screening outcomes, hit rates, pan-activity, and selectivity. For the annotated library, we also examined the primary targets and mechanistic pathways regularly associated with cell death. To our knowledge, this is the first study to use high-throughput screening to profile a large screening collection (>100,000 compounds) for cytotoxicity in both normal and cancer cell lines. The results generated here constitute a valuable resource for the scientific community and provide insight into the extent of cytotoxic compounds in screening libraries, allowing for the identification and avoidance of compounds with cytotoxicity during high-throughput screening campaigns.

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A deep neural network model for packing density predictions and its application in the study of 1.5 million organic molecules

Chemical Science ◽

10.1039/c9sc02677k ◽

2019 ◽

Vol 10 (36) ◽

pp. 8374-8383 ◽

Cited By ~ 1

Author(s):

Mohammad Atif Faiz Afzal ◽

Aditya Sonpal ◽

Mojtaba Haghighatlari ◽

Andrew J. Schultz ◽

Johannes Hachmann

Keyword(s):

Neural Network ◽

Machine Learning ◽

Refractive Index ◽

High Throughput ◽

Neural Network Model ◽

High Throughput Screening ◽

Deep Neural Network ◽

Organic Molecules ◽

High Refractive Index ◽

Computational Pipeline

Computational pipeline for the accelerated discovery of organic materials with high refractive index via high-throughput screening and machine learning.

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103 High-throughput screening against intractable targets: the use of target disruption gene signatures for rapid cancer drug discovery

European Journal of Cancer Supplements ◽

10.1016/s1359-6349(04)80111-3 ◽

2004 ◽

Vol 2 (8) ◽

pp. 34

Author(s):

P.E. Young ◽

J. Castaneda ◽

M. Chakiath ◽

K. Evans ◽

S.K. Horrigan ◽

...

Keyword(s):

Drug Discovery ◽

High Throughput ◽

High Throughput Screening ◽

Cancer Drug ◽

Gene Signatures ◽

Cancer Drug Discovery

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Platforms for High-Throughput Screening and Force Measurements on Fungi and Oomycetes

Micromachines ◽

10.3390/mi12060639 ◽

2021 ◽

Vol 12 (6) ◽

pp. 639

Author(s):

Yiling Sun ◽

Ayelen Tayagui ◽

Sarah Sale ◽

Debolina Sarkar ◽

Volker Nock ◽

...

Keyword(s):

High Throughput ◽

High Throughput Screening ◽

Drug Targets ◽

Control Strategies ◽

Pathogenic Fungi ◽

Antifungal Drugs ◽

Plant Diseases ◽

Force Measurements ◽

Technical Developments ◽

Alternative Drug

Pathogenic fungi and oomycetes give rise to a significant number of animal and plant diseases. While the spread of these pathogenic microorganisms is increasing globally, emerging resistance to antifungal drugs is making associated diseases more difficult to treat. High-throughput screening (HTS) and new developments in lab-on-a-chip (LOC) platforms promise to aid the discovery of urgently required new control strategies and anti-fungal/oomycete drugs. In this review, we summarize existing HTS and emergent LOC approaches in the context of infection strategies and invasive growth exhibited by these microorganisms. To aid this, we introduce key biological aspects and review existing HTS platforms based on both conventional and LOC techniques. We then provide an in-depth discussion of more specialized LOC platforms for force measurements on hyphae and to study electro- and chemotaxis in spores, approaches which have the potential to aid the discovery of alternative drug targets on future HTS platforms. Finally, we conclude with a brief discussion of the technical developments required to improve the uptake of these platforms into the general laboratory environment.

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Uncertainty-Informed Deep Transfer Learning of PFAS Toxicity

10.26434/chemrxiv.14397140 ◽

2021 ◽

Author(s):

Jeremy Feinstein ◽

ganesh sivaraman ◽

Kurt Picel ◽

Brian Peters ◽

Alvaro Vazquez-Mayagoitia ◽

...

Keyword(s):

Machine Learning ◽

Transfer Learning ◽

High Throughput ◽

High Throughput Screening ◽

Organic Chemical ◽

Learning Method ◽

Toxicity Data ◽

Chemical Structures ◽

Machine Learning Methods ◽

Computational Methodology

In this article, we present our recent study on computational methodology for predicting the toxicity of PFAS known as “forever chemicals” based on chemical structures through evaluation of multiple machine learning methods. To address the scarcity of PFAS toxicity data, a deep “transfer learning” method has been investigated by leveraging toxicity information over the entire organic chemical domain and an uncertainty-informed workflow by incorporating SelectiveNet architecture, which can support future guidance of high throughput screening with knowledge of chemical structures, has been developed.

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Complex Tumor Spheroids, a Tissue-Mimicking Tumor Model, for Drug Discovery and Precision Medicine

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/24725552211038362 ◽

2021 ◽

pp. 247255522110383

Author(s):

Gurmeet Kaur ◽

David M. Evans ◽

Beverly A. Teicher ◽

Nathan P. Coussens

Keyword(s):

Drug Discovery ◽

Precision Medicine ◽

Cell Lines ◽

High Throughput ◽

High Throughput Screening ◽

Cell Types ◽

Response To Therapy ◽

Cancer Drug ◽

Malignant Cells ◽

Pancreatic Cell

Malignant tumors are complex tissues composed of malignant cells, vascular cells, structural mesenchymal cells including pericytes and carcinoma-associated fibroblasts, infiltrating immune cells, and others, collectively called the tumor stroma. The number of stromal cells in a tumor is often much greater than the number of malignant cells. The physical associations among all these cell types are critical to tumor growth, survival, and response to therapy. Most cell-based screens for cancer drug discovery and precision medicine validation use malignant cells in isolation as monolayers, embedded in a matrix, or as spheroids in suspension. Medium- and high-throughput screening with multiple cell lines requires a scalable, reproducible, robust cell-based assay. Complex spheroids include malignant cells and two normal cell types, human umbilical vein endothelial cells and highly plastic mesenchymal stem cells, which rapidly adapt to the malignant cell microenvironment. The patient-derived pancreatic adenocarcinoma cell line, K24384-001-R, was used to explore complex spheroid structure and response to anticancer agents in a 96-well format. We describe the development of the complex spheroid assay as well as the growth and structure of complex spheroids over time. Subsequently, we demonstrate successful assay miniaturization to a 384-well format and robust performance in a high-throughput screen. Implementation of the complex spheroid assay was further demonstrated with 10 well-established pancreatic cell lines. By incorporating both human stromal and tumor components, complex spheroids might provide an improved model for tumor response in vivo.

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