First-in-class small molecule drugs in 2022.

2022 is the third year of the global COVID-19 pandemic, and its troubles on new drug discovery are gradually apparent. 37 new drugs were approved by the FDA's Center for Drug Evaluation and Research (CDER) last year, down from the peak of 50 new drug approvals in 2021. Notably, first-in-class drugs still occupy a dominant position this year, with a total of 21 drugs. Among them, 7 are first-in-class small molecule drugs. Although the total number of new drug approvals in 2022 sharply decreased, some first-in-class small molecule drugs were regarded as significant, including mitapivat, the first oral activator targeting the pyruvate kinase (PK);mavacamten, the first selective allosteric inhibitor targeting the myocardial beta myosin ATPase;deucravacitinib, the first deuterated allosteric inhibitor targeting the tyrosine kinase 2 (TYK2);and lenacapavir, the first long-acting inhibitor targeting the HIV capsid. Generally, the research of first-in-class drugs needs to focus on difficult clinical problems and can treat some specific diseases through novel targets and biological mechanisms. There are tremendous challenges in the research processes of new drugs, including biological mechanism research, target selection, molecular screening, lead compound identification and druggability optimization. Therefore, the success of first-in-class drugs development has prominent guidance significance for new drug discovery. This review briefly describes the discovery background, research and development process and therapeutic application of 3 firstin- class small molecule drugs to provide research ideas and methods for more first-in-class drugs.Copyright © 2023, Chinese Pharmaceutical Association. All rights reserved.

Graph Neural Network Models for Chemical Compound Activeness Prediction For COVID-19 Drugs Discovery using Lipinski's Descriptors

In this study, we implemented graph neural network (GNN) methods to forecast in vitro inhibitory bioactivity or pharmacological concentration of chemical compounds against severe acute respiratory syndrome (SARS) coronaviruses from the graph representation amongst the compounds (i.e., nodes) and their respective features(i.e., node features) obtained by RDKit tool from their respectively SMILES (Simplified MolecularInput Line-Entry System), and we compared GNN models by experiments with our graph data of 375 nodes with 44,475 edges or links. This was done in response to the severe and significant consequences of the ongoing Coronavirus disease 2019 (COVID-19) disease. As a result, we discovered that implemented models, simple graph convolution (SGC), and graph convolution network (GCN) performed significantly well with comparable performance. © 2022 IEEE.

Drug discovery for coronavirus disease 2019 based on text mining and biomedical databases.

Objective Based on text mining technology and biomedical database, data mining and analysis of coronavirus disease 2019 (COVID-19) were carried out, and COVID-19 and its main symptoms related to fever, cough and respiratory disorders were explored. Methods The common targets of COVID-19 and its main symptoms cough, fever and respiratory disorder were obtained by GenCLiP 3 website, Gene ontology in metascape database (GO) and pathway enrichment analysis, then STRING database and Cytoscape software were used to construct the protein interaction network of common targets, the core genes were screened and obtained. DGIdb database and Symmap database were used to predict the therapeutic drugs of traditional Chinese and Western medicine for the core genes. Results A total of 28 gene targets of COVID-19 and its main symptoms were obtained, including 16 core genes such as IL2, IL1B and CCL2. Through the screening of DGIdb database, 28 chemicals interacting with 16 key targets were obtained, including thalidomide, leflunomide and cyclosporine et al. And 70 kinds of Chinese meteria medica including Polygonum cuspidatum, Astragalus membranaceus and aloe. Conclusion The pathological mechanism of COVID-19 and its main symptoms may be related to 28 common genes such as CD4, KNG1 and VEGFA, which may participate in the pathological process of COVID-19 by mediating TNF, IL-17 and other signal pathways. Potentially effective drugs may play a role in the treatment of COVID-19 through action related target pathway.Copyright © 2022 Tianjin Press of Chinese Herbal Medicines. All Rights Reserved.

Improving Antiviral Drug Research by Focusing on the Systems Theory Approach.

The conventional drug design method focuses on the reductionist approach of simplifying complex things. Pharmaceutical development following this approach is thorough and detailed. However, it does not guarantee satisfactory results for all drugs. Systems theory, which explores the nature of things from a holistic perspective based on their integrity and relevance, has played a vital role in the prevention and treatment of major viral diseases. Based on the interpretations of examples of the holistic approach in drug designs at home and abroad, novel coronavirus infection demonstrates the advantages of combining reductionist and systemic theories in the research of antiviral drugs, with a view to providing guidance for the design and development of antiviral drugs as well as scientific solutions for the prevention and treatment of viral diseases.Copyright © 2023 Chinese Pharmaceutical Association. All rights reserved.

In silico assessment of diterpenes as potential inhibitors of SARS-COV-2 main protease

Aim: We aimed to investigate the potential inhibitory effects of diterpenes on SARS-CoV-2 main protease (Mpro). Materials & methods: We performed a virtual screening of diterpenoids against Mpro using molecular docking, molecular dynamics simulation and absorption, distribution, metabolism and excretion) analysis. Results: Some tested compounds followed Lipinski's rule and showed drug-like properties. Some diterpenoids possessed remarkable binding affinities with SARS-CoV-2 Mpro and drug-like pharmacokinetic properties. Three derivatives exhibited structural deviations lower than 1 angstrom. Conclusion: The findings of the study suggest that some of the diterpenes could be candidates as potential inhibitors for Mpro of SARS-CoV-2.

Recent Advances in Covalent Drug Discovery.

In spite of the increasing number of biologics license applications, the development of covalent inhibitors is still a growing field within drug discovery. The successful approval of some covalent protein kinase inhibitors, such as ibrutinib (BTK covalent inhibitor) and dacomitinib (EGFR covalent inhibitor), and the very recent discovery of covalent inhibitors for viral proteases, such as boceprevir, narlaprevir, and nirmatrelvir, represent a new milestone in covalent drug development. Generally, the formation of covalent bonds that target proteins can offer drugs diverse advantages in terms of target selectivity, drug resistance, and administration concentration. The most important factor for covalent inhibitors is the electrophile (warhead), which dictates selectivity, reactivity, and the type of protein binding (i.e., reversible or irreversible) and can be modified/optimized through rational designs. Furthermore, covalent inhibitors are becoming more and more common in proteolysis, targeting chimeras (PROTACs) for degrading proteins, including those that are currently considered to be 'undruggable'. The aim of this review is to highlight the current state of covalent inhibitor development, including a short historical overview and some examples of applications of PROTAC technologies and treatment of the SARS-CoV-2 virus.

Bichromonol, a dimeric coumarin with anti-HIV activity from the stem bark of Hypericum roeperianum.

Infectious diseases caused by viruses like HIV and SARS-COV-2 (COVID-19) pose serious public health threats. In search for new antiviral small molecules from chemically underexplored Hypericum species, a previously undescribed atropisomeric C8-C8' linked dimeric coumarin named bichromonol (1) was isolated from the stem bark of Hypericum roeperianum. The structure was elucidated by MS data and NMR spectroscopy. The absolute configuration at the biaryl axis was determined by comparing the experimental ECD spectrum with those calculated for the respective atropisomers. Bichromonol was tested in cell-based assays for cytotoxicity against MT-4 (CC50 = 54 µM) cells and anti-HIV activity in infected MT-4 cells. It exhibits significant activity at EC50 = 6.6-12.0 µM against HIV-1 wild type and its clinically relevant mutant strains. Especially, against the resistant variants A17 and EFVR, bichromonol is more effective than the commercial drug nevirapine and might thus have potential to serve as a new anti-HIV lead.

Looking for SARS-CoV-2 therapeutics through computational approaches.

BACKGROUND: In the last few years in silico tools, including drug repurposing coupled with structure-based virtual screening, have been extensively employed to look for anti-COVID-19 agents. OBJECTIVE: The present review aims to provide readers with a portrayal of computational approaches that could conduct more quickly and cheaply to novel anti-viral agents. Particular attention is given to docking-based virtual screening. METHOD: The World Health Organization website was consulted to gain the latest information on SARS-CoV-2, its novel variants and their interplay with COVID-19 severity and treatment options. The Protein Data Bank was explored to look for 3D coordinates of SARS-CoV-2 proteins in their free and bound states, in the wild-types and mutated forms. Recent literature related to in silico studies focused on SARS-CoV-2 proteins was searched through PubMed. RESULTS: A large amount of work has been devoted thus far to computationally targeting viral entry and searching for inhibitors of the S-protein/ACE2 receptor complex. Another large area of investigation is linked to in silico identification of molecules able to block viral proteases -including Mpro- thus avoiding maturation of proteins crucial for virus life cycle. Such computational studies have explored the inhibitory potential of the most diverse molecule databases (including plant extracts, dietary compounds, FDA approved drugs). CONCLUSION: More efforts need to be dedicated in the close future to experimentally validate the therapeutic power of in silico identified compounds in order to catch, among the wide ensemble of computational hits, novel therapeutics to prevent and/or treat COVID-19.

An Elusive Target: Inhibitors of JC Polyomavirus Infection and Their Development as Therapeutics for the Treatment of Progressive Multifocal Leukoencephalopathy.

Progressive multifocal leukoencephalopathy (PML) is a rare demyelinating disease caused by infection with JC Polyomavirus (JCPyV). Despite the identification of the disease and isolation of the causative pathogen over fifty years ago, no antiviral treatments or prophylactic vaccines exist. Disease onset is usually associated with immunosuppression, and current treatment guidelines are limited to restoring immune function. This review summarizes the drugs and small molecules that have been shown to inhibit JCPyV infection and spread. Paying attention to historical developments in the field, we discuss key steps of the virus lifecycle and antivirals known to inhibit each event. We review current obstacles in PML drug discovery, including the difficulties associated with compound penetrance into the central nervous system. We also summarize recent findings in our laboratory regarding the potent anti-JCPyV activity of a novel compound that antagonizes the virus-induced signaling events necessary to establish a productive infection. Understanding the current panel of antiviral compounds will help center the field for future drug discovery efforts.

Drug Discovery and Big Data: From Research to the Community

Technology and artificial intelligence, alongside the COVID-19 pandemic vastly increasing technology use in health care, have precipitated an escalation of big data. Although real-world data (RWD) and real-world evidence (RWE) have contributed to determining outcomes outside the scope of randomized clinical trials (RCTs), RWD and RWE are underutilized in demonstrating drug effectiveness. Utilizing RWD may enhance the ability of regulatory agencies to approve drugs, provide drug effectiveness insight to payers, and improve personalized medicine. Additionally, RWD and RWE may assist in overcoming the limitations of RCT data such as treatment adherence and underrepresented patient subgroups and may support and expedite drug repositioning. Even though the limitations of using RWE and RWD include fragmented data context, poor data quality, and information governance, healthcare analytics hubs such as the European Health Data Space are designed to foster synergy among private and public healthcare players and may assist in overcoming these potential limitations. Such healthcare analytics hubs may enhance the utilization of RWE and/or RWD, which could ultimately result in better patient outcomes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

First-in-class small molecule drugs in 2022

2022 is the third year of the global COVID-19 pandemic, and its troubles on new drug discovery are gradually apparent. 37 new drugs were approved by the FDA's Center for Drug Evaluation and Research (CDER) last year, down from the peak of 50 new drug approvals in 2021. Notably, first-in-class drugs still occupy a dominant position this year, with a total of 21 drugs. Among them, 7 are first-in-class small molecule drugs. Although the total number of new drug approvals in 2022 sharply decreased, some first-in-class small molecule drugs were regarded as significant, including mitapivat, the first oral activator targeting the pyruvate kinase (PK);mavacamten, the first selective allosteric inhibitor targeting the myocardial beta myosin ATPase;deucravacitinib, the first deuterated allosteric inhibitor targeting the tyrosine kinase 2 (TYK2);and lenacapavir, the first long-acting inhibitor targeting the HIV capsid. Generally, the research of first-in-class drugs needs to focus on difficult clinical problems and can treat some specific diseases through novel targets and biological mechanisms. There are tremendous challenges in the research processes of new drugs, including biological mechanism research, target selection, molecular screening, lead compound identification and druggability optimization. Therefore, the success of first-in-class drugs development has prominent guidance significance for new drug discovery. This review briefly describes the discovery background, research and development process and therapeutic application of 3 firstin- class small molecule drugs to provide research ideas and methods for more first-in-class drugs.Copyright © 2023, Chinese Pharmaceutical Association. All rights reserved.

Beyond the Exscalate4CoV Project: LIGATE and REMEDI4ALL Projects

In the last 2 years, the SARS-CoV-2 (COVID-19) pandemic demonstrated that rapid response to outbreaks with readily effective treatments represents a primary health and societal priority. At the same time, we became conscious that technological resources are often not used in the most efficient manner. The LIGATE and REpurposing MEDIcines For All (REMEDI4ALL) projects started on the large-scale mobilization efforts of the EXaSCale smArt pLatform Against paThogEns (Exscalate4Cov) project with the aim to apply cutting-edge technologies in drug discovery, sustain the fight against future pandemics, and promote the everyday fight against rare diseases. In particular, the LIGATE project, using the drug-discovery platform Exscalate, intends to boost the virtual screening of drug campaigns at an extreme scale in terms of performance and streamline the drug-development process. The aim of the REMEDI4ALL project is to collect sciQ1entific expertise and innovative technology platforms for the repurposing of medicines to treat rare diseases or other pathologic conditions with no current therapy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A European Drug-Discovery Platform: From In Silico to Experimental Validation

The COVID-19 pandemic highlighted an urgent need for streamlined drug development processes. Enhanced virtual screening methods could expedite drug discovery via rapid screening of large virtual compound libraries to identify high-priority drug candidates. The EXSCALATE4CoV (EXaSCale smArt pLatform Against paThogEns for CoronaVirus) consortium (E4C) research team developed EXSCALATE (EXaSCale smArt pLatform Against paThogEns), the most complex screening simulation to date, containing a virtual library of >500 billion compounds and a high-throughput docking software, LiGen (Ligand Generator). Additionally, E4C developed a smaller virtual screen of a "safe-in-man” drug library to identify optimal candidates for drug repurposing. To identify compounds targeting SARS-CoV-2, EXSCALATE performed >1 trillion docking simulations to optimize the probability of identifying successful drug candidates. Ligands identified in simulations underwent subsequent in vitro experimentation to determine drug candidates that have anti-SARS-CoV-2 agency and have probable in-human efficacy. While many compound candidates were validated to have anti-SARS-CoV-2 properties, raloxifene had the best outcome and subsequently demonstrated efficacy in a phase 2 clinical trial in patients with early mild-to-moderate COVID-19, providing proof of concept that the in silico approaches used here are a valuable resource during emergencies. After its emergence in 2019, the SARS-CoV-2 coronavirus spread internationally at a rapid pace, leading to the designation of COVID-19 as a pandemic in March 2020. In addition to a devastating impact on public health, COVID-19 has resulted in extensive negative social and economic effects in every corner of the globe. When the pandemic arrived, the medical and scientific communities identified an urgent need to establish more rapid therapeutic and vaccine development processes for COVID-19. However, it was clear that any new measures needed to be implemented in a way that also supported rapid mobilization to fight potential future pandemics. Therapeutic discovery is a complicated and prolonged process, often taking 10–15 years to complete all stages, and typically involves a linear workflow starting with in silico investigations, followed by increasingly complex and correspondingly expensive in vitro, in vivo, and clinical studies. In the context of the pandemic, the importance of the in silico stage increased because of the capacity of exascale computational methods to identify and prioritize small molecule (and biological) agents with the greatest therapeutic potential. Better in silico-generated starting points for drug-discovery efforts increase the likelihood of success in downstream laboratory-based experimental stages and can contribute to vitally needed reductions in costs and time to market for new therapies. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Production of the Receptor-binding Domain of the Viral Spike Proteins from 2003 and 2019 SARS CoVs and the Four Common Human Coronaviruses for Serologic Assays and Inhibitor Screening.

The recombinant receptor-binding domain (RBD) of the viral spike protein from SARS-CoV-1 and 2 are reliable antigens for detecting viral-specific antibodies in humans. We and others have shown that the levels of RBD-binding antibodies and SARS-CoV-2 neutralizing antibodies in patients are correlated. Here, we report the expression and purification of properly folded RBD proteins from SARS and common-cold HCoVs in mammalian cells. RBD proteins were produced with cleavable tags for affinity purification from the cell culture medium and to support multiple immunoassay platforms and drug discovery efforts. Graphic abstract: High-Yield Production of Viral Spike RBDs for Diagnostics and Drug Discovery.

Intelligent medicine and beyond

With the developments of medical artificial intelligence (AI), meta-data analysis, intelligence-aided drug design and discovery, surgical robots and image-navigated precision treatments, intelligent medicine (IM) as a new era evolved from ancient medicine and biomedical medicine, has become an emerging topic and important criteria for clinical applications. It is fully characterized by fundamental research-driven, new-generation technique-directed as well as state-of-the-art paradigms for advanced disease diagnosis and therapy leading to an even broader future of modern medicine. As a fundamental subject and also a practice-oriented field, intelligent medicine is highly trans-disciplinary and cross-developed, which has emerged the knowledge of modern medicine, basic sciences and engineering. Basically, intelligent medicine has three domains of intelligent biomaterials, intelligent devices and intelligent techniques. Intelligent biomaterials derive from traditional biomedical materials, and currently are endowed with multiple functionalities for medical uses. For example, micro-/nanorobots, smart responsive biomaterials and digital drugs are representative intelligent biomaterials which have been already commercialized and applied to clinical uses. Intelligent devices, such as surgical robots, rehabilitation robots and medical powered exoskeleton, are an important majority in the family of intelligent medicine. Intelligent biomaterials and intelligent devices are more and more closely integrated with each other especially on the occasions of intelligence acquisition, remote transmission, AI-aided analysis and management. In comparison, intelligent techniques are internalized in the former two domains and are playing a critical role in the development of intelligent medicine. Representative intelligent techniques of telemedicine, image-navigated surgery, virtual/augmented reality and AI-assisted image analysis for early-stage disease assessments have been employed in nowadays clinical operations which to a large extent relieved medical labors. In the past decades, China has been in the leading groups compared to international colleagues in the arena of intelligent medicine, and a series of eminent research has been clinically translated for practical uses in China. For instance, the first 5G-aided remote surgery has been realized in Fujian Province in January 2019, which for the first time validated their applicability for human uses. The surgical robots have found China as the most vigorous market, and more than 10 famous Chinese companies are developing versatile surgical robots for both Chinese people and people all over the world. China also applied AI techniques to new drug developments especially in early 2020 when COVID-19 epidemic roared, and several active molecules and drug motifs have been discovered for early-stage COVID-19 screening and treatments. Based on the significance of intelligent medicine and its rapid developments in both basic research and industrials, this review summarized the comprehensive viewpoints of the Y6 Xiangshan Science Conferences titled with Fundamental Principles and Key Technologies of Intelligent Medicine, and gave an in-depth discussion on main perspectives of future developments of the integration of biomaterial and devices, the integration of bioinformatics and medical hardware, and the synergy of biotechnology and intelligence information. It is expected that this featuring article will further promote intelligent medicine to an even broader community not only for scientists but also for industrials, and in the long run embrace a perspective future for its blooming and rich contributions in China in the coming 5 years. © 2023 Chinese Academy of Sciences. All rights reserved.

Drug–Target Interaction Deep Learning-Based Model Identifies the Flavonoid Troxerutin as a Candidate TRPV1 Antagonist

Based on the advances made by artificial intelligence (AI) technologies in drug discovery, including target identification, hit molecule identification, and lead optimization, this study investigated natural compounds that could act as transient receptor potential vanilloid 1 (TRPV1) channel protein antagonists. Using a molecular transformer drug–target interaction (MT-DTI) model, troxerutin was predicted to be a TRPV1 antagonist at IC50 582.73 nM. In a TRPV1-overexpressing HEK293T cell line, we found that troxerutin antagonized the calcium influx induced by the TRPV1 agonist capsaicin in vitro. A structural modeling and docking experiment of troxerutin and human TRPV1 confirmed that troxerutin could be a TRPV1 antagonist. A small-scale clinical trial consisting of 29 participants was performed to examine the efficacy of troxerutin in humans. Compared to a vehicle lotion, both 1% and 10% w/v troxerutin lotions reduced skin irritation, as measured by skin redness induced by capsaicin, suggesting that troxerutin could ameliorate skin sensitivity in clinical practice. We concluded that troxerutin is a potential TRPV1 antagonist based on the deep learning MT-DTI model prediction. The present study provides a useful reference for target-based drug discovery using AI technology and may provide useful information for the integrated research field of AI technology and biology.

Updates on drug designing approach through computational strategies: a review.

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

An extensive survey of technological applications in drug discovery and development, encompassing offline and online approaches, is presented in this review. The span of research issues that can be tackled using these advances is vast, opening new horizons for future innovations. The article is designed to incite further research investments into drug development procedures and bridge existing research voids by outlining multiple pharmaceutical products that resulted from employing systematic computational methodologies.

New Ways to Protect the Host from SARS-CoV-2? Lung Microbiome Metabolites Inhibit STAT3 and Modulate the Immunological Network.

COVID-19 caused by the SARS-CoV-2 infection is a systemic disease that affects multiple organs, biological pathways, and cell types. A systems biology approach would benefit the study of COVID-19 in the pandemic as well as the endemic state. Notably, patients with COVID-19 have dysbiosis of lung microbiota whose functional relevance to the host is largely unknown. We carried out a systems biology investigation of the impact of lung microbiome-derived metabolites on host immune system during COVID-19. RNAseq was performed to identify the host-specific pro- and anti-inflammatory differentially expressed genes (DEGs) in bronchial epithelium and alveolar cells during SARS-CoV-2 infection. The overlapping DEGs were harnessed to construct an immune network while their key transcriptional regulator was deciphered. We identified 68 overlapping genes from both cell types to construct the immune network, and Signal Transducer and Activator of Transcription 3 (STAT3) was found to regulate the majority of the network proteins. Furthermore, thymidine diphosphate produced from the lung microbiome had the highest affinity with STAT3 (-6.349 kcal/mol) than the known STAT3 inhibitors (n = 410), with an affinity ranging from -5.39 to 1.31 kcal/mol. In addition, the molecular dynamic studies showed distinguishable changes in the behavior of the STAT3 complex when compared with free STAT3. Overall, our results provide new observations on the importance of lung microbiome metabolites that regulate the host immune system in patients with COVID-19, and may open up new avenues for preventive medicine and therapeutics innovation.

Small molecule antiviral compound collection (SMACC): A comprehensive, highly curated database to support the discovery of broad-spectrum antiviral drug molecules.

Diseases caused by new viruses cost thousands if not millions of human lives and trillions of dollars. We have identified, collected, curated, and integrated all chemogenomics data from ChEMBL for 13 emerging viruses that hold the greatest potential threat to global human health. By identifying and solving several challenges related to data annotation accuracy, we developed a highly curated and thoroughly annotated database of compounds tested in both phenotypic and target-based assays for these viruses that we dubbed SMACC (Small Molecule Antiviral Compound Collection). The pilot version of the SMACC database contains over 32,500 entries for 13 viruses. By analyzing data in SMACC, we have identified ∼50 compounds with polyviral inhibition profile, mostly covering flavi- and coronaviruses. The SMACC database may serve as a reference for virologists and medicinal chemists working on the development of novel BSA agents in preparation for future viral outbreaks. SMACC is publicly available at https://smacc.mml.unc.edu.