Omega-3 polyunsaturated fatty acid derived lipid mediators: a comprehensive update on their application in anti-cancer drug discovery.

INTRODUCTION: ω-3 Polyunsaturated fatty acids (PUFAs) have a range of health benefits, including anticancer activity, and are converted to lipid mediators that could be adapted into pharmacological strategies. However, the stability of these mediators must be improved, and they may require formulation to achieve optimal tissue concentrations. AREAS COVERED: Herein, the author reviews the literature around chemical stabilization and formulation of ω-3 PUFA mediators and their application in anticancer drug discovery. EXPERT OPINION: Aryl-urea bioisosteres of ω-3 PUFA epoxides that killed cancer cells targeted the mitochondrion by a novel dual mechanism: as protonophoric uncouplers and as inhibitors of electron transport complex III that activated ER-stress and disrupted mitochondrial integrity. In contrast, aryl-ureas that contain electron-donating substituents prevented cancer cell migration. Thus, aryl-ureas represent a novel class of agents with tunable anticancer properties. Stabilized analogues of other ω-3 PUFA-derived mediators could also be adapted into anticancer strategies. Indeed, a cocktail of agents that simultaneously promote cell killing, inhibit metastasis and angiogenesis, and that attenuate the pro-inflammatory microenvironment is a novel future anticancer strategy. Such regimen may enhance anticancer drug efficacy, minimize the development of anticancer drug resistance and enhance outcomes.

A dual-target SPR screening system for simultaneous ligand discovery of SARS-CoV-2 spike protein and its receptor ACE2 from Chinese herbs.

Traditional Chinese Medicine (TCM) is a supremely valuable resource for the development of drug discovery. Few methods are capable of hunting for potential molecule ligands from TCM towards more than one single protein target. In this study, a novel dual-target surface plasmon resonance (SPR) biosensor was developed to perform targeted compound screening of two key proteins involved in the cellular invasion process of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): the spike (S) protein receptor binding domain (RBD) and the angiotensin-converting enzyme 2 (ACE2). The screening and identification of active compounds from six Chinese herbs were conducted taking into consideration the multi-component and multi-target nature of Traditional Chinese Medicine (TCM). Puerarin from Radix Puerariae Lobatae was discovered to exhibit specific binding affinity to both S protein RBD and ACE2. The results highlight the efficiency of the dual-target SPR system in drug screening and provide a novel approach for exploring the targeted mechanisms of active components from Chinese herbs for disease treatment.

C. elegans: a prominent platform for modeling and drug screening in neurological disorders.

INTRODUCTION: Human neurodevelopmental and neurodegenerative diseases (NDevDs and NDegDs, respectively) encompass a broad spectrum of disorders affecting the nervous system with an increasing incidence. In this context, the nematode C. elegans, has emerged as a benchmark model for biological research, especially in the field of neuroscience. AREAS COVERED: The authors highlight the numerous advantages of this tiny worm as a model for exploring nervous system pathologies and as a platform for drug discovery. There is a particular focus given to describing the existing models of C. elegans for the study of NDevDs and NDegDs. Specifically, the authors underscore their strong applicability in preclinical drug development. Furthermore, they place particular emphasis on detailing the common techniques employed to explore the nervous system in both healthy and diseased states. EXPERT OPINION: Drug discovery constitutes a long and expensive process. The incorporation of invertebrate models, such as C. elegans, stands as an exemplary strategy for mitigating costs and expediting timelines. The utilization of C. elegans as a platform to replicate nervous system pathologies and conduct high-throughput automated assays in the initial phases of drug discovery is pivotal for rendering therapeutic options more attainable and cost-effective.

Consideration of vendor-related differences in hepatic metabolic stability data to optimize early ADME screening in drug discovery.

Hepatic metabolic stability is a crucial determinant of oral bioavailability and plasma concentrations of a compound, and its measurement is important in early drug discovery. Preliminary metabolic stability estimations are commonly performed in liver microsomal fractions. At the National Center for Advancing Translational Sciences, a single-point assay in rat liver microsomes (RLM) is employed for initial stability assessment (Tier I) and a multi-point detailed stability assay is employed as a Tier II assay for promising compounds. Although the in vitro and in vivo metabolic stability of compounds typically exhibit good correlation, conflicting results may arise in certain cases. While investigating one such instance, we serendipitously found vendor-related RLM differences in metabolic stability and metabolite formation, which had implications for in vitro and in vivo correlations. In this study, we highlight the importance of considering vendor differences in hepatic metabolic stability data and discuss strategies to avoid these pitfalls.

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.

Drug discovery for heart failure targeting myosin-binding protein C.

Cardiac MyBP-C (cMyBP-C) interacts with actin and myosin to fine-tune cardiac muscle contractility. Phosphorylation of cMyBP-C, which reduces the binding of cMyBP-C to actin and myosin, is often decreased in patients with heart failure (HF) and is cardioprotective in model systems of HF. Therefore, cMyBP-C is a potential target for HF drugs that mimic its phosphorylation and/or perturb its interactions with actin or myosin. We labeled actin with fluorescein-5-maleimide (FMAL) and the C0-C2 fragment of cMyBP-C (cC0-C2) with tetramethylrhodamine (TMR). We performed two complementary high-throughput screens (HTS) on an FDA-approved drug library, to discover small molecules that specifically bind to cMyBP-C and affect its interactions with actin or myosin, using fluorescence lifetime (FLT) detection. We first excited FMAL and detected its FLT, to measure changes in fluorescence resonance energy transfer (FRET) from FMAL (donor) to TMR (acceptor), indicating binding. Using the same samples, we then excited TMR directly, using a longer wavelength laser, to detect the effects of compounds on the environmentally sensitive FLT of TMR, to identify compounds that bind directly to cC0-C2. Secondary assays, performed on selected modulators with the most promising effects in the primary HTS assays, characterized the specificity of these compounds for phosphorylated versus unphosphorylated cC0-C2 and for cC0-C2 versus C1-C2 of fast skeletal muscle (fC1-C2). A subset of identified compounds modulated ATPase activity in cardiac and/or skeletal myofibrils. These assays establish the feasibility of the discovery of small-molecule modulators of the cMyBP-C-actin/myosin interaction, with the ultimate goal of developing therapies for HF.

Contrastive learning in protein language space predicts interactions between drugs and protein targets.

Sequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and scalable while remaining sensitive to subtle variations in the inputs. However, current computational techniques fail to simultaneously meet these goals, often sacrificing performance of one to achieve the others. We develop a deep learning model, ConPLex, successfully leveraging the advances in pretrained protein language models ("PLex") and employing a protein-anchored contrastive coembedding ("Con") to outperform state-of-the-art approaches. ConPLex achieves high accuracy, broad adaptivity to unseen data, and specificity against decoy compounds. It makes predictions of binding based on the distance between learned representations, enabling predictions at the scale of massive compound libraries and the human proteome. Experimental testing of 19 kinase-drug interaction predictions validated 12 interactions, including four with subnanomolar affinity, plus a strongly binding EPHB1 inhibitor (KD = 1.3 nM). Furthermore, ConPLex embeddings are interpretable, which enables us to visualize the drug-target embedding space and use embeddings to characterize the function of human cell-surface proteins. We anticipate that ConPLex will facilitate efficient drug discovery by making highly sensitive in silico drug screening feasible at the genome scale. ConPLex is available open source at https://ConPLex.csail.mit.edu.

ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy.

Drug discovery, which plays a vital role in maintaining human health, is a persistent challenge. Fragment-based drug discovery (FBDD) is one of the strategies for the discovery of novel candidate compounds. Computational tools in FBDD could help to identify potential drug leads in a cost-efficient and time-saving manner. The Auto Core Fragment in silico Screening (ACFIS) server is a well-established and effective online tool for FBDD. However, the accurate prediction of protein-fragment binding mode and affinity is still a major challenge for FBDD due to weak binding affinity. Here, we present an updated version (ACFIS 2.0), that incorporates a dynamic fragment growing strategy to consider protein flexibility. The major improvements of ACFIS 2.0 include (i) increased accuracy of hit compound identification (from 75.4% to 88.5% using the same test set), (ii) improved rationality of the protein-fragment binding mode, (iii) increased structural diversity due to expanded fragment libraries and (iv) inclusion of more comprehensive functionality for predicting molecular properties. Three successful cases of drug lead discovery using ACFIS 2.0 are described, including drugs leads to treat Parkinson's disease, cancer, and major depressive disorder. These cases demonstrate the utility of this web-based server. ACFIS 2.0 is freely available at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS2/.

Optimization of Cell Viability Assays for Drug Sensitivity Screens.

During the preclinical stages of the drug discovery process, cell viability assays are fundamental tools for studying the phenotypic properties and overall health of cells following in vitro drug sensitivity screens. Therefore, it is important to optimize your viability assay of choice to obtain reproducible and replicable results, as well as use relevant drug response metrics (e.g., IC50, AUC, GR50, and GRmax) to identify candidate drugs for further evaluation in vivo. Herein, we used the resazurin reduction assay which is a quick, cost-effective, simple-to-use, and sensitive method for examining the phenotypic properties of cells. Using the MCF7 breast cancer cell line, we provide a detailed step-by-step protocol for optimizing drug sensitivity screens using the resazurin assay.

CETSA and thermal proteome profiling strategies for target identification and drug discovery of natural products.

BACKGROUND: Monitoring target engagement at various stages of drug development is essential for natural product (NP)-based drug discovery and development. The cellular thermal shift assay (CETSA) developed in 2013 is a novel, broadly applicable, label-free biophysical assay based on the principle of ligand-induced thermal stabilization of target proteins, which enables direct assessment of drug-target engagement in physiologically relevant contexts, including intact cells, cell lysates and tissues. This review aims to provide an overview of the work principles of CETSA and its derivative strategies and their recent progress in protein target validation, target identification and drug lead discovery of NPs. METHODS: A literature-based survey was conducted using the Web of Science and PubMed databases. The required information was reviewed and discussed to highlight the important role of CETSA-derived strategies in NP studies. RESULTS: After nearly ten years of upgrading and evolution, CETSA has been mainly developed into three formats: classic Western blotting (WB)-CETSA for target validation, thermal proteome profiling (TPP, also known as MS-CETSA) for unbiased proteome-wide target identification, and high-throughput (HT)-CETSA for drug hit discovery and lead optimization. Importantly, the application possibilities of a variety of TPP approaches for the target discovery of bioactive NPs are highlighted and discussed, including TPP-temperature range (TPP-TR), TPP-compound concentration range (TPP-CCR), two-dimensional TPP (2D-TPP), cell surface-TPP (CS-TPP), simplified TPP (STPP), thermal stability shift-based fluorescence difference in 2D gel electrophoresis (TS-FITGE) and precipitate supported TPP (PSTPP). In addition, the key advantages, limitations and future outlook of CETSA strategies for NP studies are discussed. CONCLUSION: The accumulation of CETSA-based data can significantly accelerate the elucidation of the mechanism of action and drug lead discovery of NPs, and provide strong evidence for NP treatment against certain diseases. The CETSA strategy will certainly bring a great return far beyond the initial investment and open up more possibilities for future NP-based drug research and development.

Computational approaches streamlining drug discovery.

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.

Advances in receptor chromatography for drug discovery and drug-receptor interaction studies.

Receptor chromatography involves high-throughput separation and accurate drug screening based on specific drug-receptor recognition and affinity, which has been widely used to screen active compounds in complex samples. This review summarizes the immobilization methods for receptors from three aspects: random covalent immobilization methods, site-specific covalent immobilization methods and dual-target receptor chromatography. Meanwhile, it focuses on its applications from three angles: screening active compounds in natural products, in natural-product-derived DNA-encoded compound libraries and drug-receptor interactions. This review provides new insights for the design and application of receptor chromatography, high-throughput and accurate drug screening, drug-receptor interactions and more.

Natural Products Drug Discovery: On Silica or In-Silico?

Natural products have been the most important source for drug development throughout the human history. Over time, the formulation of drugs has evolved from crude drugs to refined chemicals. In modern drug discovery, conventional natural products lead-finding usually uses a top-down approach, namely bio-guided fractionation. In this approach, the crude extracts are separated by chromatography and resulting fractions are tested for activity. Subsequently, active fractions are further refined until a single active compound is obtained. However, this is a painstakingly slow and expensive process. Among the alternatives that have been developed to improve this situation, metabolomics has proved to yield interesting results having been applied successfully to drug discovery in the last two decades. The metabolomics-based approach in lead-finding comprises two steps: (1) in-depth chemical profiling of target samples, e.g. plant extracts, and bioactivity assessment, (2) correlation of the chemical and biological data by chemometrics. In the first step of this approach, the target samples are chemically profiled in an untargeted manner to detect as many compounds as possible. So far, NMR spectroscopy, LC-MS, GC-MS, and MS/MS spectrometry are the most common profiling tools. The profile data are correlated with the biological activity with the help of various chemometric methods such as multivariate data analysis. This in-silico analysis has a high potential to replace or complement conventional on-silica bioassay-guided fractionation as it will greatly reduce the number of bioassays, and thus time and costs. Moreover, it may reveal synergistic mechanisms, when present, something for which the classical top-down approach is clearly not suited. This chapter aims to give an overview of successful approaches based on the application of chemical profiling with chemometrics in natural products drug discovery.

Virtual screening and zebrafish models in tandem, for drug discovery and development.

INTRODUCTION: The combination of Virtual Screening (VS) techniques with in vivo screening in the zebrafish model is currently being used in tandem for drug development in a faster and more efficient way. AREAS COVERED: We review the different virtual screening techniques, the use of zebrafish as a vertebrate model for drug discovery and the synergy that exists between them. EXPERT OPINION: We highlight the advantages of combining virtual and zebrafish larvae screening for drug discovery. On the one hand, VS is a faster and cheaper tool for searching active compounds and possible candidates for therapy than in vivo screening when processing large compound libraries. On the other hand, zebrafish larvae form a vertebrate model that allows in vivo screening of large amounts of the compounds. Importantly, physiology and chemical response are mostly conserved between zebrafish and mammals. The availability of the transgenic and mutant zebrafish lines allows an analysis of a specific phenotype upon treatment, along with toxicity, off-target effect, side effects, and dosage. The advantages of VS, in vivo whole animal approach screening, and the screening combinations are also reviewed.

Drug vector representation and potential efficacy prediction based on graph representation learning and transcriptome data: Acacetin from traditional Chinese Medicine model.

ETHNOPHARMACOLOGICAL RELEVANCE: Acacetin is widely distributed in traditional Chinese medicine and traditional herbs, with strong biological activity. Perhaps there are many potential effects that have not been explored. In the field of drug discovery, Mainstream methods focus on chemical structure. Traditional medicine cannot adapt to the mainstream prediction methods due to its complex composition. AIM OF THE STUDY: Our aim is that provide a prediction method more suitable for traditional medicine by graph representation learning and transcriptome data. And use this method to predict acacetin. MATERIALS AND METHODS: Our method mainly consists of two parts. The first part is to use the method of graph representation learning to vectorize drugs as a database. The original data of this part comes from transcriptome data on Gene Expression Omnibus. The method of graph representation learning is an unsupervised learning. If there is no prior knowledge as the label data, the training effect cannot be analyzed. Therefore, we define a standard score to evaluate our results through the idea of Jaccard index. The second part is to put the target drug into our database. The potential similarity between drugs was evaluated by the Euclidean distance between vectors, and the potential efficacy of the target drug is predicted by combining the chemical-disease relationship data in the Comparative Toxicogenomics Database. The target drug in this paper uses acacetin. We compared the predicted results with existing reports, and we also experimentally verified the efficacy of improving insulin resistance in the predicted results. RESULTS: The prediction results are relatively consistent with the existing reports, which demonstrated that our method has a certain degree of predictive performance. And for the efficacy of improving insulin resistance in the predicted result, we verified it through experiments. CONCLUSIONS: We propose a method to predict the potential efficacy of drugs based on transcriptome data, using Graph representation learning, which is very suitable for traditional medicine. Through this method, we predicted the efficacy of acacetin, and the results are relatively consistent with the current reports. This provides a new idea for unsupervised learning to apply medical information.

Ayurveda and in silico Approach: A Challenging Proficient Confluence for Better Development of Effective Traditional Medicine Spotlighting Network Pharmacology.

Coalescence of traditional medicine Ayurveda and in silico technology is a rigor for supplementary development of future-ready effective traditional medicine. Ayurveda is a popular traditional medicine in South Asia, emanating worldwide for the treatment of metabolic disorders and chronic illness. Techniques of in silico biology are not much explored for the investigation of a variety of bioactive phytochemicals of Ayurvedic herbs. Drug repurposing, reverse pharmacology, and polypharmacology in Ayurveda are areas in silico explorations that are needed to understand the rich repertoire of herbs, minerals, herbo-minerals, and assorted Ayurvedic formulations. This review emphasizes exploring the concept of Ayurveda with in silico approaches and the need for Ayurinformatics studies. It also provides an overview of in silico studies done on phytoconstituents of some important Ayurvedic plants, the utility of in silico studies in Ayurvedic phytoconstituents/formulations, limitations/challenges, and prospects of in silico studies in Ayurveda. This article discusses the convergence of in silico work, especially in the least explored field of Ayurveda. The focused coalesce of these two domains could present a predictive combinatorial platform to enhance translational research magnitude. In nutshell, it could provide new insight into an Ayurvedic drug discovery involving an in silico approach that could not only alleviate the process of traditional medicine research but also enhance its effectiveness in addressing health care.

Ayurveda and in silico Approach: A Challenging Proficient Confluence for Better Development of Effective Traditional Medicine Spotlighting Network Pharmacology / 中国结合医学杂志

Coalescence of traditional medicine Ayurveda and in silico technology is a rigor for supplementary development of future-ready effective traditional medicine. Ayurveda is a popular traditional medicine in South Asia, emanating worldwide for the treatment of metabolic disorders and chronic illness. Techniques of in silico biology are not much explored for the investigation of a variety of bioactive phytochemicals of Ayurvedic herbs. Drug repurposing, reverse pharmacology, and polypharmacology in Ayurveda are areas in silico explorations that are needed to understand the rich repertoire of herbs, minerals, herbo-minerals, and assorted Ayurvedic formulations. This review emphasizes exploring the concept of Ayurveda with in silico approaches and the need for Ayurinformatics studies. It also provides an overview of in silico studies done on phytoconstituents of some important Ayurvedic plants, the utility of in silico studies in Ayurvedic phytoconstituents/formulations, limitations/challenges, and prospects of in silico studies in Ayurveda. This article discusses the convergence of in silico work, especially in the least explored field of Ayurveda. The focused coalesce of these two domains could present a predictive combinatorial platform to enhance translational research magnitude. In nutshell, it could provide new insight into an Ayurvedic drug discovery involving an in silico approach that could not only alleviate the process of traditional medicine research but also enhance its effectiveness in addressing health care.

Actives-Based Receptor Selection Strongly Increases the Success Rate in Structure-Based Drug Design and Leads to Identification of 22 Potent Cancer Inhibitors.

Computer-aided drug design, an important component of the early stages of the drug discovery pipeline, routinely identifies large numbers of false positive hits that are subsequently confirmed to be experimentally inactive compounds. We have developed a methodology to improve true positive prediction rates in structure-based drug design and have successfully applied the protocol to twenty target systems and identified the top three performing conformers for each of the targets. Receptor performance was evaluated based on the area under the curve of the receiver operating characteristic curve for two independent sets of known actives. For a subset of five diverse cancer-related disease targets, we validated our approach through experimental testing of the top 50 compounds from a blind screening of a small molecule library containing hundreds of thousands of compounds. Our methods of receptor and compound selection resulted in the identification of 22 novel inhibitors in the low µM-nM range, with the most potent being an EGFR inhibitor with an IC50 value of 7.96 nM. Additionally, for a subset of five independent target systems, we demonstrated the utility of Gaussian accelerated molecular dynamics to thoroughly explore a target system's potential energy surface and generate highly predictive receptor conformations.

A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia.

Nowadays, the complexity of disease mechanisms and the inadequacy of single-target therapies in restoring the biological system have inevitably instigated the strategy of multi-target therapeutics with the analysis of each target individually. However, it is not suitable for dealing with the conflicts between targets or between drugs. With the release of high-precision protein structure prediction artificial intelligence, large-scale high-precision protein structure prediction and docking have become possible. In this article, we propose a multi-target drug discovery method by the example of therapeutic hypothermia (TH). First, we performed protein structure prediction for all protein targets of each group by AlphaFold2 and RoseTTAFold. Then, QuickVina 2 is used for molecular docking between the proteins and drugs. After docking, we use PageRank to rank single drugs and drug combinations of each group. The ePharmaLib was used for predicting the side effect targets. Given the differences in the weights of different targets, the method can effectively avoid inhibiting beneficial proteins while inhibiting harmful proteins. So it could minimize the conflicts between different doses and be friendly to chronotherapeutics. Besides, this method also has potential in precision medicine for its high compatibility with bioinformatics and promotes the development of pharmacogenomics and bioinfo-pharmacology.

Small Molecular Drug Screening Based on Clinical Therapeutic Effect.

Virtual screening can significantly save experimental time and costs for early drug discovery. Drug multi-classification can speed up virtual screening and quickly predict the most likely class for a drug. In this study, 1019 drug molecules with actual therapeutic effects are collected from multiple databases and documents, and molecular sets are grouped according to therapeutic effect and mechanism of action. Molecular descriptors and molecular fingerprints are obtained through SMILES to quantify molecular structures. After using the Kennard-Stone method to divide the data set, a better combination can be obtained by comparing the combined results of five classification algorithms and a fusion method. Furthermore, for a specific data set, the model with the best performance is used to predict the validation data set. The test set shows that prediction accuracy can reach 0.862 and kappa coefficient can reach 0.808. The highest classification accuracy of the validation set is 0.873. The more reliable molecular set has been found, which could be used to predict potential attributes of unknown drug compounds and even to discover new use for old drugs. We hope this research can provide a reference for virtual screening of multiple classes of drugs at the same time in the future.