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.

Informational inefficiency on bitcoin futures

This paper investigates the dynamics and drivers of informational inefficiency in the Bitcoin futures market. To quantify the adaptive pattern of informational inefficiency, we leverage two groups of statistics which measure long memory and fractal dimension to construct a global-local market inefficiency index. Our findings validate the adaptive market hypothesis, and the global and local inefficiency exhibits different patterns and contributions. Regarding the driving factors of the time-varying inefficiency, our results suggest that trading activity of retailers (hedgers) increases (decreases) informational inefficiency. Compared to hedgers and retailers, the role played by speculators is more likely to be affected by the COVID-19 crisis. Extremely bullish and bearish investor sentiment has more significant impact on the local inefficiency. Arbitrage potential, funding liquidity, and the pandemic exert impacts on the global and local inefficiency differently. No significant evidence is found for market liquidity and policy uncertainty related to cryptocurrency.

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.

Interpersonal Distance Tracking with mmWave Radar and IMUs

Tracking interpersonal distances is essential for real-time social distancing management and ex-post contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack's decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacy-intrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach. © 2023 Owner/Author.

Identification of host factors that bind to the 5 ' end of the MERS-CoV RNA genome

Plain language summaryMERS-CoV is a virus that causes a severe illness in the nose, mouth and throat of humans. It is a zoonotic virus, which means that it can spread from animals to humans. MERS-CoV was first found in Saudi Arabia in 2012 and continues to pose a threat to public health. Interactions between the virus and human cells and proteins are important to establishing infection. Understanding these interactions is important for the development of drugs to treat viral infections. Here, we have identified some proteins that interact with MERS-CoV. Tweetable A proteomic approach for the identification of cellular proteins that interact with the 5 '-terminal region of MERS-CoV RNA genome. #MERS-CoV #RNA_viruses. Aim: The aim of this study was to identify host factors that interact with the 5 ' end of the MERS-CoV RNA genome. Materials & methods: RNA affinity chromatography followed by mass spectrometry analysis was used to identify the binding of host factors in Vero E6 cells. Results: A total of 59 host factors that bound the MERS-CoV RNA genome in non-infected Vero E6 cells were identified. Most of the identified cellular proteins were previously reported to interact with the genome of other RNA viruses. We validated our mass spectrometry results using western blotting. Conclusion: These data enhance our knowledge about the RNA-host interactions of coronaviruses, which could serve as targets for developing antiviral therapeutics against MERS-CoV.

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.

Assessment of Anti-SARS-CoV-2 antibody levels among university students vaccinated with different COVID-19 primary and booster doses - fall 2021, Wisconsin.

BACKGROUND: University students commonly received COVID-19 vaccinations before returning to U.S. campuses in the Fall of 2021. Given likely immunologic variation among students based on differences in type of primary series and/or booster dose vaccine received, we conducted serologic investigations in September and December 2021 on a large university campus in Wisconsin to assess anti-SARS-CoV-2 antibody levels. METHODS: We collected blood samples, demographic information, and COVID-19 illness and vaccination history from a convenience sample of students. Sera were analyzed for both anti-spike (anti-S) and anti-nucleocapsid (anti-N) antibody levels using World Health Organization standardized binding antibody units per milliliter (BAU/mL). Levels were compared across categorical primary COVID-19 vaccine series received and binary COVID-19 mRNA booster status. The association between anti-S levels and time since most recent vaccination dose was estimated by mixed-effects linear regression. RESULTS: In total, 356 students participated, of whom 219 (61.5%) had received a primary vaccine series of Pfizer-BioNTech or Moderna mRNA vaccines and 85 (23.9%) had received vaccines from Sinovac or Sinopharm. Median anti-S levels were significantly higher for mRNA primary vaccine series recipients (2.90 and 2.86 log [BAU/mL], respectively), compared with those who received Sinopharm or Sinovac vaccines (1.63 and 1.95 log [BAU/mL], respectively). Sinopharm and Sinovac vaccine recipients were associated with a significantly faster anti-S decline over time, compared with mRNA vaccine recipients (P <.001). By December, 48/172 (27.9%) participants reported receiving an mRNA COVID-19 vaccine booster, which reduced the anti-S antibody discrepancies between primary series vaccine types. CONCLUSIONS: Our work supports the benefit of heterologous boosting against COVID-19. COVID-19 mRNA vaccine booster doses were associated with increases in anti-SARS-CoV-2 antibody levels; following an mRNA booster dose, students with both mRNA and non-mRNA primary series receipt were associated with comparable levels of anti-S IgG.

Human-centered design as a tool to improve employee experience: The case of a US plant-based food manufacturer

U.S. organizations are facing a self-resignation problem. Employees are leaving their jobs in rates never seen before mainly due to human dissatisfactions. The high staff turnover rates put organizations in jeopardy compromising their entire operation. Organizations that are not able to retain and attract local talent can incur in ergonomics, quality, productivity problems and additional business risks. We propose an empirical study conducted in a plant-based food company located in the Northwest of the United States, facing a turnover problem, to bring new knowledge in the field of employee experience. Based on the Human-Centered Design (HCD) and case study methodologies, we conducted 152 in-depth interviews with the operational workforce from the company of the case study to discover the employee needs. Later, we evaluated data obtained from the interviewees applying HCD principles (convergent and divergent techniques). After grouping our data, we identified ten themes "communication", "training", "accountability", "management", "trust", 'safety", "equipment functionality", "favoritism", "food safety", "recognition", "culture", and "work environment". We propose the themes can help decision makers to ideate organizational strategies to improve the employee experience perspective by aiming the satisfaction of human needs and human's role in the manufacturing setting.

NSF REU entrepreneurially minded applied energy program evaluation: traditional delivery versus alternative delivery (implemented during COVID-19)

Purpose: The National Science Foundation (NSF) Research Experience for Undergraduates (REU) programs are traditionally delivered in-person and full-time (40 h per week) for 10 weeks during the summer. However, this type of format has the potential to limit broader student participation. This study aims to compare learning assessment data between a traditional NSF REU (10 weeks of summer, full-time, in-person) to an alternative NSF REU delivered virtually, part-time and over 10 months as a result of the coronavirus disease 2019 (COVID-19) pandemic. Design/methodology/approach: A retrospective pre-then-post survey was completed to assess perceived learning gains for each REU program. Three learning gains categories were assessed: entrepreneurial competencies, career goals and research skill development. T-tests were used to evaluate a difference in means between pre and post. Findings: Findings show the greatest quantity of learning gains within the alternative program delivery. Moreover, a larger quantity of learning gains was perceived within the first semester of the alternative program delivery compared to the second semester. Practical implications: The authors propose the NSF should be intentional about trying new approaches to REU programs delivery, including duration and format, as a way to broaden participation in engineering. Originality/value: This study is original in that it is the first of its kind to assess an alternative REU program delivery (allowed only because of the COVID-19 pandemic) in comparison to traditional REU program delivery. © 2023, Emerald Publishing Limited.

Analyst Research Activity During the COVID-19 Pandemic

This paper documents that, in response to the COVID-19 pandemic, analysts increase their research activity and significantly revise their forecasts when compared to the pre-pandemic period. Uncertainty-adjusted forecast errors are either comparable or smaller during the pandemic compared to the pre-pandemic period. Investor attention and price reactions to analyst forecast revisions are higher during the pandemic and the effect is stronger in periods where investors actively search for information about firms. During the pandemic, investors value analyst price discovery role more than their role in interpreting public information. Jointly, the results suggest that analysts play an important information intermediation role during the COVID-19 pandemic. © 2023 The Author. Abacus published by John Wiley & Sons Australia, Ltd on behalf of Accounting Foundation, The University of Sydney.

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.

Price leadership in China's oil futures market: take two

This study is the first to conduct a comprehensive analysis of the price discovery and market liquidity aspects of China's crude oil futures market compared to WTI and Brent. With intraday-day data consolidated into 1-second intervals and three measures of price discovery, we find that China's crude oil futures market reports encouraging signs in terms of price discovery and efficiency, also showing great resilience during the COVID-19 pandemic. The market has obtained a dominant role in price discovery relative to WTI and Brent during its day trading hours, and has almost caught up with Brent in terms of market liquidity. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

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.