Drug Discovery Screening Research Articles

An interaction-based drug discovery screen explains known SARS-CoV-2 inhibitors and predicts new compound scaffolds

The recent outbreak of the COVID-19 pandemic caused by severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) has shown the necessity for fast and broad drug discovery methods to enable us to react quickly to novel and highly infectious diseases. A well-known SARS-CoV-2 target is the viral main 3-chymotrypsin-like cysteine protease (Mpro), known to control coronavirus replication, which is essential for the viral life cycle. Here, we applied an interaction-based drug repositioning algorithm on all protein-compound complexes available in the protein database (PDB) to identify Mpro inhibitors and potential novel compound scaffolds against SARS-CoV-2. The screen revealed a heterogeneous set of 692 potential Mpro inhibitors containing known ones such as Dasatinib, Amodiaquine, and Flavin mononucleotide, as well as so far untested chemical scaffolds. In a follow-up evaluation, we used publicly available data published almost two years after the screen to validate our results. In total, we are able to validate 17% of the top 100 predictions with publicly available data and can furthermore show that predicted compounds do cover scaffolds that are yet not associated with Mpro. Finally, we detected a potentially important binding pattern consisting of 3 hydrogen bonds with hydrogen donors of an oxyanion hole within the active side of Mpro. Overall, these results give hope that we will be better prepared for future pandemics and that drug development will become more efficient in the upcoming years.

Screening of Rhodomyrtus tomentosa (Aiton) Wight (Karamunting) Compounds that Have the Potential for Breast Cancer

Breast cancer is the highest disease in the world beating lung cancer, so various alternatives are needed to find a treatment. Screening drug discovery against breast cancer by targeting BCL-2 occupies the main position and is the goal of this study. The method used is insilico. the material used is 10 compounds from R. tomentose, 2w3l protein from RCSB, PLANT docking software, the docking coordinate used is X: 39.8057 Y: 26.9355 Z: -12.4145 with a radius of 11.1871, then to find out the type of bond and residue involved using discovery studio. The results of this study were docking scores from -91.3871(5'-Desgalloylstachyurin) to -65.4754 (Afrormosin), activity predictions from PASS online from 0.174 (Rhodomyrtosone C) to 0.799 (Lupeol). Based on the docking score, three compounds that have the potential to inhibit the work of BCL-2 were obtained namely 5'-Desgalloylstachyurin, Rhodomyrtosone B and Rhodomyrtosone D.

Identification of thrombopoiesis inducer based on a hybrid deep neural network model

Thrombocytopenia is a common haematological problem worldwide. Currently, there are no relatively safe and effective agents for the treatment of thrombocytopenia. To address this challenge, we propose a computational method that enables the discovery of novel drug candidates with haematopoietic activities. Based on different types of molecular representations, three deep learning (DL) algorithms, namely recurrent neural networks (RNNs), deep neural networks (DNNs), and hybrid neural networks (RNNs+DNNs), were used to develop classification models to distinguish between active and inactive compounds. The evaluation results illustrated that the hybrid DL model exhibited the best prediction performance, with an accuracy of 97.8 % and Matthews correlation coefficient of 0.958 on the test dataset. Subsequently, we performed drug discovery screening based on the hybrid DL model and identified a compound from the FDA-approved drug library that was structurally divergent from conventional drugs and showed a potential therapeutic action against thrombocytopenia. The novel drug candidate wedelolactone significantly promoted megakaryocyte differentiation in vitro and increased platelet levels and megakaryocyte differentiation in irradiated mice with no systemic toxicity. Overall, our work demonstrates how artificial intelligence can be used to discover novel drugs against thrombocytopenia.

Predicting compound activity from phenotypic profiles and chemical structures

Predicting assay results for compounds virtually using chemical structures and phenotypic profiles has the potential to reduce the time and resources of screens for drug discovery. Here, we evaluate the relative strength of three high-throughput data sources—chemical structures, imaging (Cell Painting), and gene-expression profiles (L1000)—to predict compound bioactivity using a historical collection of 16,170 compounds tested in 270 assays for a total of 585,439 readouts. All three data modalities can predict compound activity for 6–10% of assays, and in combination they predict 21% of assays with high accuracy, which is a 2 to 3 times higher success rate than using a single modality alone. In practice, the accuracy of predictors could be lower and still be useful, increasing the assays that can be predicted from 37% with chemical structures alone up to 64% when combined with phenotypic data. Our study shows that unbiased phenotypic profiling can be leveraged to enhance compound bioactivity prediction to accelerate the early stages of the drug-discovery process.

Repositioning Cannabinoids and Terpenes as Novel EGFR-TKIs Candidates for Targeted Therapy Against Cancer: A virtual screening model using CADD and biophysical simulations

This study examines the potential of Cannabis sativa L. plants to be repurposed as therapeutic agents for cancer treatment through designing of hybrid Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). A set of 50 phytochemicals was taken from Cannabinoids and Terpenes and subjected for screening using Semi-flexible and Flexible Molecular Docking methods, MM-GBSA free binding energy computations, and pharmacokinetic/pharmacodynamic (ADME-Tox) predictions. Nine promising phytochemicals, Cannabidiolic acid (CBDA), Cannabidiol (CBD), Tetrahydrocannabivarin (THCV), Dronabinol (Δ-9-THC), Delta-8-Tetrahydrocannabinol (Δ-8-THC), Cannabicyclol (CBL), Delta9-tetrahydrocannabinolic acid (THCA), Beta-Caryophyllene (BCP), and Gamma-Elemene (γ-Ele) were identified as potential EGFR-TKIs natural product candidates for cancer therapy. To further validate these findings, a set of Molecular Dynamics simulations were conducted over a 200 ns trajectory. This hybrid early drug discovery screening strategy has the potential to yield a new generation of EGFR-TKIs based on natural cannabis products, suitable for cancer therapy. In addition, the application of this computational strategy in the virtual screening of both natural and synthetic chemical libraries could support the discovery of a wide range of lead drug agents to address numerous diseases.

A Novel Family of Lysosomotropic Tetracyclic Compounds for Treating Leukemia.

Acute myeloid leukemia (AML) is a heterogeneous hematological cancer characterized by poor prognosis and frequent relapses. Aside from specific mutation-related changes, in AML, the overall function of lysosomes and mitochondria is drastically altered to fulfill the elevated biomass and bioenergetic demands. On the basis of previous results, in silico drug discovery screening was used to identify a new family of lysosome-/mitochondria-targeting compounds. These novel tetracyclic hits, with a cationic amphiphilic structure, specifically eradicate leukemic cells by inducing both mitochondrial damage and apoptosis, and simultaneous lysosomal membrane leakiness. Lysosomal leakiness does not only elicit canonical lysosome-dependent cell death, but also activates the terminal differentiation of AML cells through the Ca2+-TFEB-MYC signaling axis. In addition to being an effective monotherapy, its combination with the chemotherapeutic arsenic trioxide (ATO) used in other types of leukemia is highly synergistic in AML cells, widening the therapeutic window of the treatment. Moreover, the compounds are effective in a wide panel of cancer cell lines and possess adequate pharmacological properties rendering them promising drug candidates for the treatment of AML and other neoplasias.

In vivo quantitative high-throughput screening for drug discovery and comparative toxicology.

Quantitative high-throughput screening (qHTS) pharmacologically evaluates chemical libraries for therapeutic uses, toxicological risk and, increasingly, for academic probe discovery. Phenotypic high-throughput screening assays interrogate molecular pathways, often relying on cell culture systems, historically less focused on multicellular organisms. Caenorhabditis elegans has served as a eukaryotic model organism for human biology by virtue of genetic conservation and experimental tractability. Here, a paradigm enabling C. elegans qHTS using 384-well microtiter plate laser-scanning cytometry is described, in which GFP-expressing organisms revealing phenotype-modifying structure-activity relationships guide subsequent life-stage and proteomic analyses, and Escherichia coli bacterial ghosts, a non-replicating nutrient source, allow compound exposures over two life cycles, mitigating bacterial overgrowth complications. We demonstrate the method with libraries of anti-infective agents, or substances of toxicological concern. Each was tested in seven-point titration to assess the feasibility of nematode-based in vivo qHTS, and examples of follow-up strategies were provided to study organism-based chemotype selectivity and subsequent network perturbations with a physiological impact. We anticipate that this qHTS approach will enable analysis of C. elegans orthologous phenotypes of human pathologies to facilitate drug library profiling for a range of therapeutic indications.

First person – Patricia Dranchak

ABSTRACT First Person is a series of interviews with the first authors of a selection of papers published in Disease Models & Mechanisms, helping researchers promote themselves alongside their papers. Patricia Dranchak is first author on ‘ In vivo quantitative high-throughput screening for drug discovery and comparative toxicology’, published in DMM. She is a staff scientist in the lab of James Inglese at the National Center for Advancing Translational Sciences (NCATS), Rockville, MD, USA. Patricia is interested in developing and integrating novel strategies and model systems to identify chemical modulators targeting the molecular basis of rare and neglected disease pathophysiologies.

Quantum machine learning framework for virtual screening in drug discovery: a prospective quantum advantage

Machine Learning for ligand based virtual screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases such as COVID-19. In this paper, we propose a general-purpose framework combining a classical Support Vector Classifier algorithm with quantum kernel estimation for LB-VS on real-world databases, and we argue in favor of its prospective quantum advantage. Indeed, we heuristically prove that our quantum integrated workflow can, at least in some relevant instances, provide a tangible advantage compared to state-of-art classical algorithms operating on the same datasets, showing strong dependence on target and features selection method. Finally, we test our algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing that hardware simulations provide results in line with the predicted performances and can surpass classical equivalents.

Protein engineering approaches to determine adenosine receptor-G protein interactions.

The adenosine receptor subfamily of G protein-coupled receptors is an important family of membrane receptors that modulate blood pressure and more recently have been implicated in cancer, neurodegenerative diseases, and diabetes, making them a significant fraction of drug discovery efforts. Adenosine A2A receptor (A2AR), one of four subfamily members, has a longer C-terminus than the other adenosine receptor subtypes, which may contribute to its exceptional trafficking to the plasma membrane, but its flexibility has been attributed to protein-protein interactions. Chimeric A1 and A3 receptors with A2AR C-termini showed improved localization to the plasma membrane and are functionally active, with greater than three-fold higher yields than previously reported from other heterologous expression systems. Interestingly, these chimeras are able to bind to their native G proteins, suggesting the specificity of interaction does not rely on the C-terminus. To investigate this further using purified A2AR and Gαs, we determined the affinity and binding rates via surface plasmon resonance (SPR). Together with in vivo signaling data, our results suggest that loss of downstream signaling for the truncation may result from a reduced GPCR-G protein complex interaction, which may be important for interpretation of structure-based drug discovery screening. Current studies are focused on the effect of lipid nanodiscs on A2AR-G protein interactions.

Integration of fingerprint-based similarity searching and kernel-based partial least squares analysis to predict inhibitory activity against CSK, HER2, JAK1, JAK2, and JAK3.

Fingerprint-based similarity searching is an important strategy for virtual screening in drug discovery. In the present study, we carried out a systematic virtual screening study, followed by the establishment of kernel-based partial least square (KPLS) analysis prediction models for five tyrosine kinase drug targets, C-terminal SRC kinase (CSK), human epidermal growth factor 2 (HER2), and Janus kinases 1, 2, and 3 (JAK1, JAK2, and JAK3), using a dataset of 3688 compounds. These kinases are important drug discovery targets, particularly as HER2 has been validated for the treatment of metastatic breast cancer, JAK inhibitors have been validated for the clinical management of arthritis and autoimmune diseases, and CSK has been found to play an important role in bone remodeling in arthritis. We conducted similarity screenings with the most active molecule for each target in the dataset as a query using eight (8) types of two-dimensional (2D) molecular fingerprints, comprising seven Hashed fingerprints, Linear, Dendritic, Radial, Pairwise, Triplet, Torsion, and MOLSPRINT2D, and one Structural keys fingerprint, MACCS. The top ranked 1% of compounds from each target's similarity screening results was used to set up kernel-based partial least square (KPLS) prediction models, with q2 values up to 0.8. The best KPLS model for each target was selected based on its predictive ability and boot strapping results and used for prediction. This integrated study approach combining similarity screening with KPLS analysis has a high potential to enhance the accuracy and efficiency of virtual screening and thus improve the drug discovery process.

Computational strategies for PROTAC drug discovery

Proteolysis-targeting chimeras (PROTACs), a novel targeted protein degradation technology for potential clinical drug discovery, is composed of a protein-targeting ligand covalently linked to an E3 ligase ligand. Through recruiting E3 ligase to target proteins, PROTACs elicit ubiquitination and subsequent degradation of targets via the ubiquitin-proteasome system. In the past few decades, molecular docking and virtual screening have emerged as an efficient strategy in drug discovery for identifying compounds from a large database of chemical structures. For PROTACs, molecular docking accurately simulates the protein-PROTAC-E3 ternary complex, thus greatly accelerating structure-activity-relationship analysis, and improving ligand affinity and selectivity. In this review, we summarize recent efforts in the application of molecular docking and virtual screening for PROTAC drug discovery. To date, approximately nine target proteins and twelve PROTACs have been successfully developed through molecular docking and virtual screening. Finally, the potential challenges of molecular docking and virtual screening-based PROTACs are discussed.

Crystallographic fragment screening in academic cancer drug discovery.

Fragment-based drug discovery (FBDD) has brought several drugs to the clinic, notably to target proteins once considered to be challenging, or even undruggable. Screening in FBDD relies upon observing and/or measuring weak (millimolar-scale) binding events using biophysical techniques or crystallographic fragment screening. This latter structural approach provides no information about binding affinity but can reveal binding mode and atomic detail on protein-fragment interactions to accelerate hit-to-lead development. In recent years, high-throughput platforms have been developed at synchrotron facilities to screen thousands of fragment-soaked crystals. However, using accessible manual techniques it is possible to run informative, smaller-scale screens within an academic lab setting. This chapter describes general protocols for home laboratory-scale fragment screening, from fragment soaking through to structure solution and, where appropriate, signposts to background, protocols or alternatives elsewhere.

High-throughput determination of enantiopurity in atroposelective synthesis of aryl triazoles.

Atropisomeric scaffolds are a common design element found in pharmaceuticals, many deriving from an N-C axis of chirality. The handedness associated with atropisomeric drugs is oftentimes crucial for their efficacy and/or safety. With the increased use of high-throughput screening (HTS) for drug discovery, the need for rapid enantiomeric excess (ee) analysis is needed to keep up with the fast workflow. Here, we describe a circular dichroism (CD) based assay that could be applied to the ee determination of N-C axially chiral triazole derivatives. Analytical samples for CD were prepared from crude mixtures by three sequential steps: liquid-liquid extraction (LLE), a wash-elute, and complexation with Cu(ii) triflate. The initial ee measurement of five samples of atropisomer 2 was conducted by the use of a CD spectropolarimeter with a 6-position cell changer, resulting in errors of less than 1% ee. High-throughput ee determination was performed on a CD plate reader using a 96-well plate. A total of 28 atropisomeric samples (14 for 2 and 14 for 3) were screened for ee. The CD readings were completed in 60 seconds with average absolute errors of ±7.2% and 5.7% ee for 2 and 3, respectively.

Biofabrication of vascularized adipose tissues and their biomedical applications.

Recent advances in adipose tissue engineering and cell biology have led to the development of innovative therapeutic strategies in regenerative medicine for adipose tissue reconstruction. To date, the many in vitro and in vivo models developed for vascularized adipose tissue engineering cover a wide range of research areas, including studies with cells of various origins and types, polymeric scaffolds of natural and synthetic derivation, models presented using decellularized tissues, and scaffold-free approaches. In this review, studies on adipose tissue types with different functions, characteristics and body locations have been summarized with 3D in vitro fabrication approaches. The reason for the particular focus on vascularized adipose tissue models is that current liposuction and fat transplantation methods are unsuitable for adipose tissue reconstruction as the lack of blood vessels results in inadequate nutrient and oxygen delivery, leading to necrosis in situ. In the first part of this paper, current studies and applications of white and brown adipose tissues are presented according to the polymeric materials used, focusing on the studies which could show vasculature in vitro and after in vivo implantation, and then the research on adipose tissue fabrication and applications are explained.

Human-Induced Pluripotent Stem Cell-Based Differentiation of Cardiomyocyte Subtypes for Drug Discovery and Cell Therapy.

Drug attrition rates have increased over the past few years, accompanied with growing costs for the pharmaceutical industry and consumers. Lack of in vitro models connecting the results of toxicity screening assays with clinical outcomes accounts for this high attrition rate. The emergence of cardiomyocytes derived from human pluripotent stem cells provides an amenable source of cells for disease modeling, drug discovery, and cardiotoxicity screening. Functionally similar to to embryonic stem cells, but with fewer ethical concerns, induced pluripotent stem cells (iPSCs) can recapitulate patient-specific genetic backgrounds, which would be a huge revolution for personalized medicine. The generated iPSC-derived cardiomyocytes (iPSC-CMs) represent different subtypes including ventricular-, atrial-, and nodal-like cardiomyocytes. Purifying these subtypes for chamber-specific drug screening presents opportunities and challenges. In this chapter, we discuss the strategies for the purification of iPSC-CMs, the use of iPSC-CMs for drug discovery and cardiotoxicity test, and the current limitations of iPSC-CMs that should be overcome for wider and more precise cardiovascular applications.

Advances of the Target-Based and Phenotypic Screenings and Strategies in Drug Discovery

Review Advances of the Target-Based and Phenotypic Screenings and Strategies in Drug Discovery Shoubao Wang 1,*, Zihan Wang1, Lianhua Fang1, Yang Lv2, and Guanhua Du1,* 1 Beijing Key Laboratory of Drug Targets Identification and Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100050, China. 2 Beijing Key Laboratory of Polymorphic Drugs, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100050, China. * Correspondence: shoubaowang@imm.ac.cn (Shoubao Wang); dugh@imm.ac.cn (Guanhua Du). Received: 8 November 2022 Accepted: 15 November 2022 Published: 21 December 2022 Abstract: Drug discovery and development is a complex and expensive process. There are two approaches, phenotypic and target-based approaches, each holding different advantages for screening novel drug candidates when pursuing successful marketing authorization. However, the attrition rates of drug candidates continue to increase. In this review, we discuss recent successes and ongoing advances in phenotypic screening and target-based screening for drug discovery. We also explore how strategic and technological innovations may fuel new approaches in drug discovery. There are two approaches in drug discovery.

A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery in the Past Fifteen Years.

Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in the early stage of drug discovery. In this review, we comprehensively surveyed the prospective applications of molecular docking judged by solid experimental validations in the literature over the past fifteen years. Herein, we systematically analyzed the novelty of the targets and the docking hits, practical protocols of docking screening, and the following experimental validations. Among the 419 case studies we reviewed, most virtual screenings were carried out on widely studied targets, and only 22% were on less-explored new targets. Regarding docking software, GLIDE is the most popular one used in molecular docking, while the DOCK 3 series showed a strong capacity for large-scale virtual screening. Besides, the majority of identified hits are promising in structural novelty and one-quarter of the hits showed better potency than 1 μM, indicating that the primary advantage of SBVS is to discover new chemotypes rather than highly potent compounds. Furthermore, in most studies, only in vitro bioassays were carried out to validate the docking hits, which might limit the further characterization and development of the identified active compounds. Finally, several successful stories of SBVS with extensive experimental validations have been highlighted, which provide unique insights into future SBVS drug discovery campaigns.

Advances in high-throughput mass spectrometry in drug discovery.

High-throughput (HT) screening drug discovery, during which thousands or millions of compounds are screened, remains the key methodology for identifying active chemical matter in early drug discovery pipelines. Recent technological developments in mass spectrometry (MS) and automation have revolutionized the application of MS for use in HT screens. These methods allow the targeting of unlabelled biomolecules in HT assays, thereby expanding the breadth of targets for which HT assays can be developed compared to traditional approaches. Moreover, these label-free MS assays are often cheaper, faster, and more physiologically relevant than competing assay technologies. In this review, we will describe current MS techniques used in drug discovery and explain their advantages and disadvantages. We will highlight the power of mass spectrometry in label-free invitro assays, and its application for setting up multiplexed cellular phenotypic assays, providing an exciting new tool for screening compounds in cell lines, and even primary cells. Finally, we will give an outlook on how technological advances will increase the future use and the capabilities of mass spectrometry in drug discovery.

RGO-PANI composite Au microelectrodes for sensitive ECIS analysis of human gastric (MKN-1) cancer cells

Microelectrode-based cell chip studies for cellular responses often require improved adhesion and growth conditions for efficient cellular diagnosis and high throughput screening in drug discovery. Cell-chip studies are often performed on gold electrodes due to their biocompatibility, and stability, but the electrode–electrolyte interfacial capacitance is the main drawback to the overall sensitivity of the detection system. Thus, here, we developed reduced graphene oxide-polyaniline-modified gold microelectrodes for real-time impedance-based monitoring of human gastric adenocarcinoma cancer (MKN-1) cells. The impedance characterization on modified electrodes showed 28-fold enhanced conductivity than the bare electrodes, and the spectra were modeled with proper equivalent circuits to extrapolate the values of circuit elements. The impedance of both time-and frequency-dependent measurements of cell-covered modified electrodes with equivalent model circuits was analyzed to achieve cellular behavior, such as adhesion, spreading, proliferation, and influence of anti-cancer agents. The normalized impedance at 41.5 kHz (|Z|norm 41 kHz) was selected to monitor the cell growth analysis, which was found linear with the proliferation of adherent cells along with the influence of the anticancer drug agent on the MKN-1 cells. The synergistic effects and biocompatible nature of PANI-RGO modifications improved the overall sensitivity for the cell-growth studies of MKN-1 cells.