Phenotypic profiling methods for drug discovery have received revitalised interest due to the rapid adoption of novel computational methods, including artificial intelligence-based techniques. However, these methods predominantly analyse 2D images of 2D cell cultures, which can result in suboptimal predictive validity. This chapter highlights the transition to 4D morphological profiling, which integrates time-resolved 3D imaging of 3D cell cultures to capture cellular morphodynamics. We explore the evolution of morphological profiling and emphasise the pivotal role of deep learning in enhancing the resolution and depth of cellular analysis. Through detailed discussions on the historical background, current applications, and future directions of morphological profiling, we suggest how 4D phenotypic profiling offers a more accurate representation of cellular dynamics, thus potentially revolutionising drug discovery and therapeutic development.
There has been increasing interest in disease models with enhanced physiological fidelity. This has led to the development of new methods for generating advanced disease models utilizing primary cells and renewable sources, such as induced pluripotent stem cells and organoids. Furthermore, combining these types of models with high content imaging is expected to positively impact all stages of the drug discovery and development pipeline. Since data rich imaging assays can uncover nuanced cellular response to perturbation. In this review, we focus on the recent application of high content imaging to advanced disease models, covering general considerations in cell source, and culture format for screening, preclinical studies and translational applications, such as functional precision medicine approaches.
Despite the recent technological advancements in cancer research, there is still a major challenge to rapidly determine efficient chemotherapeutics to fight it. Traditional high-throughput drug screening was mainly focused on two-dimensional (2D) cell models, that were not physiologically relevant and were inadequately recapitulating the complexity of the tumors in vivo. Major advances in life science technology and oncology have enabled the 3D tissue culture models to be more amenable and affordable for high throughput screening. This yields more predictive compound response profiling for the translation of preclinical studies. These advances have proven that the use of 3D models is an important bridge to close the gap between drug discovery and personalized medicine. New small molecule approaches using 3D cell models have become an essential tool for the evaluation of new drug therapies. Advances by our group have enabled the implementation and screening of 3D models in a rapid and highly cost-effective manner that allows rapid and direct compound response profiling to be generated using a phenotypic approach. Creating these affordable and scalable methods to produce 3D culture models is an important achievement toward precision medicine while also providing novel methodologies for drug discovery.
This chapter delves into the latest advances in high-content imaging technologies for high-content screening (HCS) and drug discovery. It highlights innovations in light sheet fluorescence microscopy (LSFM) and label-based multiplexed imaging platforms such as imaging mass cytometry (IMC). The chapter emphasizes the role of optoelectronics in boosting imaging speed and multiplexing efficiency. It also discusses phenotypic drug discovery techniques, including Cell Painting and live cell imaging, alongside the transformative impact of artificial intelligence on these methods. Additionally, label-free techniques like Raman spectromicroscopy are explored. Advanced methods for probing protein interactions, particularly fluorescence resonance energy transfer (FRET) and its variations, are examined in detail. The chapter concludes by underscoring the potential of integrating optical, electronic, and AI tools to achieve high-content and high-throughput imaging in biomedical research.
High-content screening (HCS), which involves imaging at scale, relies on the use of appropriate cell models and automation systems, effective statistical assessment of assay quality and high-quality execution of the cell culture and plate preparation steps. The success of an HCS campaign will very much lie in the rigour applied in the assay planning and development phases, the robust assessment of the most appropriate imaging system and parameters to use and effective implementation of automation systems to enable large-scale screens which would not be feasible otherwise. In this chapter, we discuss key parameters and decisions that need to be made when developing an assay for HCS and when considering automation systems to deploy for the execution of the screen.
High content screening (HCS), a pivotal tool in drug discovery, involves extracting phenotypic characteristics from chemical or genetic treatments using microscopic imaging modalities. Traditionally, the development of these approaches has been impeded by two primary factors: the technical constraints of the image acquisition process and the challenge of deriving meaningful information from the complex imagery. These limitations have significantly hampered the ability to achieve an unbiased characterization of treatment effects, which is crucial for accurately classifying their mechanisms of action. This has, in turn, affected informed decision-making within the drug discovery pipeline. However, the field is currently undergoing a transformative shift. Advancements in imaging technology and data analysis are beginning to overcome these historical barriers, heralding a new era in HCS where the comprehensive and agnostic analysis of treatments is becoming increasingly feasible, promising to revolutionize the landscape of drug development and mechanistic classification.
In order to capture a complete picture of cellular phenotypes in a high content assay, broad morphological information is required. To address this need, in 2013 the Cell Painting assay was developed as an unbiased, cell based, morphological profiling assay capable of capturing subtle changes in morphology. Since then, there has been an explosion of studies using Cell Painting across a large number of drug discovery related applications. This chapter will include discussion on the various applications of morphological profiling and how the technology can be used to answer specific questions relevant to drug discovery such as hit identification, target activity mapping, mechanism-of-action classification, toxicity prediction and patient stratification. Further, this chapter will look at the recent surge to move from aggregated image level analysis to single cell insights, highlight the different data types that morphological profiling is being compared to and combined with (including gene expression profiling), and finally give an overview of the largest Cell Painting dataset generated to date, the Joint Undertaking in Morphological Profiling – Cell Painting (JUMP-CP), and how this may be leveraged in drug discovery.
Screening of genetic or chemical perturbations is a pragmatic approach to answering research questions that makes minimal prior assumptions. It is an approach that finds application in both academic discovery research and within pharmaceutical research and development. High content screening specifically utilises cell-based biology, and all of the complexity that affords, coupled with microscopy and data analysis to enable a huge range of questions to be addressed in all manner of inventive ways. With the increasing availability of automation, it has never been easier for research groups, institutes and companies to undertake large image-based screening campaigns. So what could go wrong? Let us count the ways…
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly pathogenic and a cause of the recent global health pandemic. Millions of people across the world were dramatically impacted by the coronavirus disease 2019 (COVID-19) and this resulted in long lasting socioeconomic losses. Early on, there was no information available about the virus and therefore robust methods were urgently required to develop therapeutic interventions. In this chapter, we describe how a high-content screening (HCS) approach was devised against SARS-CoV-2 to rapidly identify inhibitors and modulators of infection. The assay monitored inhibition of compounds on virus replication by means of immunofluorescence staining of nucleocapsid (N) protein while simultaneously evaluating cellular toxicity. The assay was adapted into a microtiter plate format, validated against known reference compounds, and then screened against a large compound collection. The systematic use of this technology platform is a blueprint for pandemic response offering several advantages in the drug discovery pipeline including efficiency, time, and costs that ultimately led to the successful identification of promising candidates against SARS-CoV-2.
Cellular high content imaging provides a rich phenotype for characterizing the mechanism-of-action of compounds. Together with a high-quality chemical probe collection (the MoA Box), we demonstrate imaging readouts such as Cell Painting can be used to group compounds by a similar mechanism and target, and to uncover novel biology in high-content screens. Further, cell image phenotypes combined with Transformer models are used to prospectively predict compound activity in unrelated assays. Finally, we demonstrate that, with generative AI, cellular staining can be replaced with in silico labelling, with promising future applications at scale.
DNA-encoded library (DEL) selection is typically an affinity-based process that encompasses the incubation of DELs with a target, separation of compounds that bind the target from those that do not bind, amplification and sequencing of the DNA barcodes, and decoding to reveal the chemical structures of binders. DEL technology has had a notable impact in drug discovery with various projects progressing into different stages of development and clinical trials. DEL methodology allows for ultra-high throughput screening, permitting exploration of broad chemical diversity and rapid identification of hits that exhibit desired effects with specific targets. DELs have been successfully employed in the discovery of small molecules targeting a variety of pharmaceutical targets, including proteins and nucleic acids. This approach has expedited the identification of tool compounds to probe biological processes and the discovery of hit compounds that have progressed to clinical candidates, thereby facilitating the drug discovery process. In this chapter, we provide an overview of different DEL affinity selection strategies and the achievements of DEL selections on different target types.
Currently, research on the application of covalent DNA-encoded compound libraries (covalent DELs, or CoDELs) is experiencing rapid growth, with numerous advancements and diverse applications. CoDELs provide several advantages over traditional discovery methods for discovering covalent inhibitors and have successfully overcome some of their limitations. This section provides an overview of the current state and major applications in CoDELs. It will briefly introduce the design principles and selection methods, and provide a detailed analysis of the compound properties exhibited by these libraries.
Targeted protein degradation (TPD) provides new therapeutic opportunities beyond traditional inhibitors. TPD relies on the ability to induce proximity between an E3 ligase and the target of interest, harnessing the ubiquitin proteasome system to ubiquitylate and degrade the target. This proximity can be induced by either monofunctional ligands (molecular glues) or bifunctional molecules that tether ligases and target ligands together. DNA encoded libraries (DELs) provide rapid access to diverse chemical space for ligand discovery and, by their design, facilitate the development of both molecular glues and bifunctional degraders.
Machine learning (ML) has begun to realize its promise in many domains in the last several years. While small molecule drug discovery has lagged in comparison to other areas, developments in computing capabilities, data generation, and algorithms have enabled significant progress in molecule prediction. DNA-encoded libraries (DELs) represent an efficient way to generate the quantity of data required for effective model building, providing a mechanism for protein-target specific prediction with economics that permit individual organizations to operate. DEL-based machine learning (DEL-ML) has been demonstrated to work for a variety of targets and continues to expand in its usage in the industry and in the approaches reported. With this initial success, a number of challenges and considerations faced by the DEL-ML practitioner have been identified including denoising of DEL data, choice of ML algorithm, hyperparameters and molecule representations, and the need for relevant metrics for assessment, particularly given the high resource and time costs of testing predictions. In order to fully realize the potential of DEL-ML, key improvements in drug discovery infrastructure and broad availability of DEL data are needed.
Solid-phase DNA-encoded library (DEL) technology introduces advanced activity-based screening capabilities by virtue of its “one-bead-one-compound” (OBOC) format. In this review, we first describe the design and construction of so-called “OBOC-DELs.” We then explore the engineering of a microfluidic screening platform that integrates and automates high-throughput bead-based screening, highlighting examples of fluorescence-based functional assay development and miniaturization to microfluidic droplets. Additionally, we detail the statistical framework of OBOC-DEL screening experimental design and data interpretation. Finally, we summarize the numerous applications that have spawned since OBOC-DEL technology’s inception, including screening by biochemical activity, dose-response, cellular activity, competition binding affinity, and pharmacokinetic properties. Looking forward, there are likely further opportunities to employ bead-based synthesis and screening strategies to other encoded library modalities.
Three naturally isolated isoflavones (2, 4 and 5), two synthetically modified isoflavones (3 and 6) and four synthesised isoflavones (10, 12, 13 and 15) were tested in-vitro to access their activities against Chang liver (normal cell line), JURKAT (leukemia), MCF-17 (breast), HEP-G2 (liver), PC3 (prostate) and LNCap (prostate) cell lines.In the test against liver cancer, the synthetically modified isoflavone 3 was better than the curcumin (CUR) control with IC50 value of 5.14 µM.Synthesised isoflavone 12 showed promising activity against prostate cancer (PC3) (IC50 = 6.11 µM).Whereas intermediate 2,4-dihydroxydeoxybenzoin (10) showed appreciable to moderate activity against breast, liver and prostate (PC3) cancer cell lines (IC50 values of 14.59, 31.79 and 65.20 µM, respectively).By comparison, the synthesised isoflavones showed better activity than the naturally isolated isoflavones against the same cancer cell lines.These results could serve as the basis of a structure activity relationship (SAR) study to identify potential anticancer drug candidates, with improved biological properties based on the isoflavone scaffold.
Introduction: Malaria remains a significant public health challenge in sub-saharan Africa.As a result, the high cost of conventional antimalarial drugs, poor quality drugs, and the emergence of drug resistance have necessitated the need for alternative sources of medicine to treat and prevent malaria.Safety concerns have also been raised in regard to herbal remedies, which have made it necessary for the screening of two antimalarial polyherbal remedies namely CtA and CtB prepared from a defined mixture of hot water extracts of 6 plants (Cymbopogon citratus Stapf, Curcuma longa L., Enantia chlorantha Oliv., Mangifera indica L., Carica papaya L., Alstonia boonei De Wild.).Materials and methods: Scientific justification was reported as to the assumed efficacy of the plant cocktails.On that basis, the reprotoxic impact of antimalarial treatments of CtA and CtB on the male reproductive system of mice is investigated in this study.This is done by evaluating testiculosomatic index, histopathological changes of testes, sperm morphology, and enzyme immunoassays for testosterone and luteinizing hormone.Analyses of data were done by using the software version 23 of SPSS.This was followed by Dunnett's multiple post hoc test with significance considered at p<0.05.Results: The data analyzed showed that testiculosomatic index significantly decreased in the suppressive group, but increased in the prophylactic and treated/unparasitized groups.Histology of the testes revealed interstitial oedema, erosion of the germinal epithelium.The number of abnormal sperm cells was significantly increased in the curative, suppressive, prophylactic and treated/unparasitized groups.Sperm cells with folded tail occurred more prominently, while knobbed sperm cells had fewer occurrence.Testosterone and luteinizing hormone concentrations were significantly decreased in the suppressive and prophylactic groups.In the curative groups, concentrations had a significant increase for testosterone, but there was a decrease in luteinizing hormone concentration.Conclusions: The results generally showed treatmentassociated damage to the mice DNA.Therefore, it is noted in this study that excessive consumption of these antimalarial cocktail should be regulated.
