A phenoxy-bridged trinuclear Ni(ii) complex: synthesis, structural elucidation and molecular docking with viral proteins

A novel phenoxy-bridged trinuclear nickel(ii) complex [Ni3(mu-L)2(bipy)3](1) (where H3L= (E)-2-hydroxy-N-(2-hydroxy-3,5-diiodophenyl)-3,5-diiodobenzohydrazonic acid, bipy = 2,2'-bipyridyl) has been designed and synthesized as a potential antivirus drug candidate. The trinuclear Ni(ii) complex [Ni3(mu-L)2(bipy)3](1) was fully characterized via single crystal X-ray crystallography. The unique structure of the trinuclear nickel(ii) complex crystallized in a trigonal crystal system with P3221 space group and revealed distorted octahedral coordination geometry around each Ni(ii) ion. The X-ray diffraction analysis established the existence of a new kind of trinuclear metal system containing nickel(ii)-nickel(ii) interactions with an overall octahedral-like geometry about the nickel(ii) atoms. The non-bonded Ni-Ni distance seems to be 3.067 and 4.455 A from the nearest nickel atoms. The detailed structural analysis and non-covalent supramolecular interactions are also investigated by single crystal structure analysis and computational approaches. Hirshfeld surfaces (HSs) and 2D fingerprint plots (FPs) have been explored in the crystal structure to investigate the intermolecular interactions. The preliminary analysis of redox and magnetic characterization was conducted using cyclic voltammetry measurements and a vibrating sample magnetometer (VSM), respectively. This unique structure shows good inhibition performance for SARS-CoV-2, Omicron and HIV viruses. For insight into the potential application of the Ni(ii) coordination complex as an effective antivirus drug, we have examined the molecular docking of the trinuclear Ni(ii) complex [Ni3(mu-L)2(bipy)3](1) with the receptor binding domain (RBD) from SARS-CoV-2 (PDB ID: 7MZF), Omicron BA.3 variant spike (PDB ID: 7XIZ), and HIV protease (PDB ID: 7WCQ) viruses. This structure shows good inhibition performance for SARS-CoV-2, Omicron S protein and HIV protease viruses;the binding energies (DELTAG) and the respective Ki/Kd (inhibition/dissociation constants) correlation values are -8.9 (2.373 muM or 2373 nM), -8.1 (1.218 muM or 1218 nM) and -7.9 (0.874 muM or 874 nM), respectively. The results could be used for rational drug design against SARS-CoV-2 Omicron variant and HIV protease viruses.Copyright © 2023 The Royal Society of Chemistry.

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.

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.

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.

Novel Polymyxin-Inspired Peptidomimetics Targeting the SARS-CoV-2 Spike:hACE2 Interface.

Though the bulk of the COVID-19 pandemic is behind, the search for effective and safe anti-SARS-CoV-2 drugs continues to be relevant. A highly pursued approach for antiviral drug development involves targeting the viral spike (S) protein of SARS-CoV-2 to prevent its attachment to the cellular receptor ACE2. Here, we exploited the core structure of polymyxin B, a naturally occurring antibiotic, to design and synthesize unprecedented peptidomimetics (PMs), intended to target contemporarily two defined, non-overlapping regions of the S receptor-binding domain (RBD). Monomers 1, 2, and 8, and heterodimers 7 and 10 bound to the S-RBD with micromolar affinity in cell-free surface plasmon resonance assays (KD ranging from 2.31 µM to 2.78 µM for dimers and 8.56 µM to 10.12 µM for monomers). Although the PMs were not able to fully protect cell cultures from infection with authentic live SARS-CoV-2, dimer 10 exerted a minimal but detectable inhibition of SARS-CoV-2 entry in U87.ACE2+ and A549.ACE2.TMPRSS2+ cells. These results validated a previous modeling study and provided the first proof-of-feasibility of using medium-sized heterodimeric PMs for targeting the S-RBD. Thus, heterodimers 7 and 10 may serve as a lead for the development of optimized compounds, which are structurally related to polymyxin, with improved S-RBD affinity and anti-SARS-CoV-2 potential.

The High-Performance Computing Resources for the EXSCALATE4CoV Project

In order to repurpose currently available therapeutics for novel diseases, druggable targets have to be identified and matched with small molecules. In the case of a public health emergency, such as the ongoing coronavirus disease 2019 (COVID-19) pandemic, this identification needs to be accomplished quickly to support the rapid initiation of effective treatments to minimize casualties. The utilization of supercomputers, or more generally High-Performance Computing (HPC) facilities, to accelerate drug design is well established, but when the pandemic emerged in early 2020, it was necessary to activate a process of urgent computing, i.e., prioritized and immediate access to the most powerful computing resources available. Thanks to the close collaboration of the partners in the HPC activity, it was possible to rapidly deploy an urgent computing infrastructure of world-class supercomputers, massive cloud storage, efficient simulation software, and analysis tools. With this infrastructure, the project team performed very long molecular dynamics simulations and extreme-scale virtual drug screening experiments, eventually identifying molecules with potential antiviral activity. In conclusion, the EXaSCale smArt pLatform Against paThogEns for CoronaVirus (EXSCALATE4CoV) project successfully brought together Italian computing resources to help identify effective drugs to stop the spread of the SARS-CoV-2 virus. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The research progress of SARS-CoV-2 main protease inhibitors from 2020 to 2022.

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide. The main protease (Mpro) of SARS-CoV-2 plays a central role in viral replication and transcription and represents an attractive drug target for fighting COVID-19. Many SARS-CoV-2 Mpro inhibitors have been reported, including covalent and noncovalent inhibitors. The SARS-CoV-2 Mpro inhibitor PF-07321332 (Nirmatrelvir) designed by Pfizer has been put on the market. This paper briefly introduces the structural characteristics of SARS-CoV-2 Mpro and summarizes the research progress of SARS-CoV-2 Mpro inhibitors from the aspects of drug repurposing and drug design. These information will provide a basis for the drug development of treating the infection of SARS-CoV-2 and even other coronaviruses in the future.

Optimizing the dynamics of bone turnover with genetic algorithm.

Control systems and the modeling strategies are not only limited to engineering problems. These approaches can be used in the field of bio-mathematics as well and modern studies have promoted this approach to a great extent. The computational modeling and simulation of bone metastasis is painful yet critical after cancer invades the body. This vicious cycle is complex, and several research centers worldwide are devoted to understanding the dynamics and setting up a treatment strategy for this life-threatening behavior of cancer. Cancerous cells activation and the corresponding process of metastasis is reported to boost during the periodic waves of COVID-19, due to the inflammatory nature of the infection associated with SARS-2 and its variants. The bone cells are comprised of two types of cells responsible for bone formation and resorption. The computational framework of such cells, in spatial form, can help the researchers forecast the bone dynamics in a robust manner where the impact of cancer is incorporated into the computational model as a source of perturbation. A series of computational models are presented to explore the complex behavior of bone metastasis with COVID-19 induced infection. The finite difference algorithm is used to simulate the nonlinear computational model. The results obtained are in close agreement with the experimental findings. The computational results can help explore the vicious cycle's fate and help set up control strategies through drug therapies.

Drug Design and Disease Diagnosis: The Potential of Deep Learning Models in Biology

Early prediction and detection enable reduced transmission of human diseases and provide healthcare professionals ample time to make subsequent diagnoses and treatment strategies. This, in turn, aids in saving more lives and results in lower medical costs. Designing small chemical molecules to treat fatal disorders is also urgently needed to address the high death rate of these diseases worldwide. A recent analysis of published literature suggested that deep learning (DL) based models apply more potential algorithms to hybrid databases of chemical data. Considering the above, we first discussed the concept of DL architectures and their applications in drug development and diagnostics in this review. Although DL-based approaches have applications in several fields, in the following sections of the arti-cle, we focus on recent developments of DL-based techniques in biology, notably in structure predic-tion, cancer drug development, COVID infection diagnostics, and drug repurposing strategies. Each review section summarizes several cutting-edge, recently developed DL-based techniques. Additionally, we introduced the approaches presented in our group, whose prediction accuracy is relatively compara-ble with current computational models. We concluded the review by discussing the benefits and draw-backs of DL techniques and outlining the future paths for data collecting and developing efficient computational models.Copyright © 2023 Bentham Science Publishers.

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.

Molecular Factors and Pathways of Hepatotoxicity Associated with HIV/SARS-CoV-2 Protease Inhibitors.

Antiviral protease inhibitors are peptidomimetic molecules that block the active catalytic center of viral proteases and, thereby, prevent the cleavage of viral polyprotein precursors into maturation. They continue to be a key class of antiviral drugs that can be used either as boosters for other classes of antivirals or as major components of current regimens in therapies for the treatment of infections with human immunodeficiency virus (HIV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, sustained/lifelong treatment with the drugs or drugs combined with other substance(s) often leads to severe hepatic side effects such as lipid abnormalities, insulin resistance, and hepatotoxicity. The underlying pathogenic mechanisms are not fully known and are under continuous investigation. This review focuses on the general as well as specific molecular mechanisms of the protease inhibitor-induced hepatotoxicity involving transporter proteins, apolipoprotein B, cytochrome P450 isozymes, insulin-receptor substrate 1, Akt/PKB signaling, lipogenic factors, UDP-glucuronosyltransferase, pregnane X receptor, hepatocyte nuclear factor 4α, reactive oxygen species, inflammatory cytokines, off-target proteases, and small GTPase Rab proteins related to ER-Golgi trafficking, organelle stress, and liver injury. Potential pharmaceutical/therapeutic solutions to antiviral drug-induced hepatic side effects are also discussed.

A Review on Artificial Intelligence Approaches and Rational Approaches in Drug Discovery.

Artificial intelligence (AI) speeds up the drug development process and reduces its time, as well as the cost which is of enormous importance in outbreaks such as COVID-19. It uses a set of machine learning algorithms that collects the available data from resources, categorises, processes and develops novel learning methodologies. Virtual screening is a successful application of AI, which is used in screening huge drug-like databases and filtering to a small number of compounds. The brain's thinking of AI is its neural networking which uses techniques such as Convoluted Neural Network (CNN), Recursive Neural Network (RNN) or Generative Adversial Neural Network (GANN). The application ranges from small molecule drug discovery to the development of vaccines. In the present review article, we discussed various techniques of drug design, structure and ligand-based, pharmacokinetics and toxicity prediction using AI. The rapid phase of discovery is the need of the hour and AI is a targeted approach to achieve this.

Drug Design against Covid-19: A Remote Teaching Experience in Pharmaceutical Chemistry

Infront of with the difficulties faced in making a new drug available to the population, it is essential to seek ways to simplify the process. In silico methodologies are alternatives to benchtop experiments, being frequently used due to their speed and low cost. The present study aimed to formulate a theoretical-practical activity in the Pharmaceutical Chemistry course, where students applied their knowledge of structural modeling and molecular docking to propose bioactive compounds against molecular targets of the SARS-CoV-2 virus. The class was divided, and each group presented a drug candidate, the precursors being natural molecules. In total, seven derivatives were designed and tested against different macromolecules, and then an in silico prediction of their physicochemical characteristics was performed. The docking results were positive for all derivatives, in terms of binding energy, mainly GEND with -9.0 kcal mol-1. In addition, the prototypes exhibited good interactions with the amino acids of the respective targets, mainly KAED, QUED and GEND, in addition to presenting adequate physicochemical properties for meeting the Lipinski restrictions. Therefore, this study presented at least three potential inhibitors of SARS-CoV-2, showing the importance of using computational tools in drug design and development, as well as in teaching practice.

In silico disease modeling for COVID-19

Coronavirus disease 2019 has resulted in a global pandemic with ∼ 3.8 million deaths, resulting in one of the most deadly pandemics in human history. The high mortality rate of the severe acute respiratory syndromes caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the lack of effective therapeutic regimens necessitate the generation of knowledge for the biology of the infection, the pathophysiology of the respiratory disease, and effective preventive and therapeutic approaches. In silico modeling is a promising approach for the rapid and cost-effective progress of research, especially in the context of the especially demanding situation and the restrictions caused by the pandemic. This chapter reviews the in silico modeling approaches that have been applied in the field of SARS-CoV-2 research focusing on the design of these models, on the clinical implications of the data derived, and on the limitations of these models. © 2023 Elsevier Inc. All rights reserved.

Predictive analysis, diagnosis of COVID-19 through computational screening and validation with spectro photometrical approach

Objective: To develop Favipiravir, based predictive models of coronavirus disease 2019 (COVID-19) from small molecule databases such as PubChem, Drug Bank, Zinc Database, and literature. Method(s): High Throughput Virtual Screening (HTVS) using different computational screening methods is used to identify the target and lead molecules. CoMFA (Comparative Molecular Field Analysis) is a 3D-QSAR procedure depending on information from known dynamic atoms and eventually permits one to plan and anticipate exercises of particles. These two analysis is used to train predictive models. Result(s): The predictive model achieved the highest accuracy score with a relatively small dataset size can be a subject of overfitting. Datasets with over 500 samples demonstrate an accuracy of about 85-95%, that can be considered as very good. Conclusion(s): From the result it is observed that Increasing level of potassium, sodium and nitrogen will lead to burst lipid bilayer membrane of virus which cause RNA replication rapidly. However, low level of sodium, potassium and nitrogen will help in the DNA polymerase inhibition and replication can be stopped. The best developed QSAR model in terms of the druggability and activity relation has been selected over the parent Favipiravir molecule for designing COVID-19 drugs may lead towards pharmaceutical development in future.Copyright © 2023, The Author(s), under exclusive licence to Korean Society of Environmental Risk Assessment and Health Science.

Superoxide Dismutase Inhibitors against Malaria, Leishmaniasis, and Chagas Disease: Systematic Review.

INTRODUCTION: Diseases caused by protozoa are one of the leading causes of death worldwide, especially in tropical regions such as Brazil. Chagas disease, leishmaniasis, and malaria are responsible for around 234 million cases and more than 400,000 deaths worldwide. Despite this scenario, drugs for these diseases have several limitations, which justifies the search for new treatments. Iron superoxide dismutase is a promising target for the drug design to treat patients with these diseases. It is a validated target and protects against oxidative stress. AIM: Thus, this systematic review aimed to synthesize evidence on the importance of superoxide dismutase in the drug design to treat patients with this protozoosis. METHODS: A search was performed for in vitro and in vivo studies, without publication and language restrictions, in MEDLINE (PubMed), LILACS (BVS), Science Direct, and EMBASE (Elsevier). Studies that pointed to the relationship between the reduction or increase in superoxide dismutase activity and the diseases were included. 23 studies were selected for the qualitative synthesis. RESULTS: The results showed that the inhibition or reduction of the enzyme activity decreases the degree of infection and reinfection and improves the results in treating these diseases. In contrast, the increase in activity caused a high degree of survival and resistance of the parasites. CONCLUSION: However, the overall quality of evidence is low and more studies with methodological rigor are provided.

In-silico guided design, screening, and molecular dynamic simulation studies for the identification of potential SARS-CoV-2 main protease inhibitors for the targeted treatment of COVID-19.

COVID-19, the disease responsible for the recent pandemic, is caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The main protease (Mpro) of SARS-CoV-2 is an essential proteolytic enzyme that plays a number of important roles in the replication of the virus in human host cells. Blocking the function of SARS-CoV-2 Mpro offers a promising and targeted, therapeutic option for the treatment of the COVID-19 infection. Such an inhibitory strategy is currently successful in treating COVID-19 under FDA's emergency use authorization, although with limited benefit to the immunocompromised along with an unfortunate number of side effects and drug-drug interactions. Current COVID vaccines protect against severe disease and death but are mostly ineffective toward long COVID which has been seen in 5-36% of patients. SARS-CoV-2 is a rapidly mutating virus and is here to stay endemically. Hence, alternate therapeutics to treat SARS-CoV-2 infections are still needed. Moreover, because of the high degree of conservation of Mpro among different coronaviruses, any newly developed antiviral agents should better prepare us for potential future epidemics or pandemics. In this paper, we first describe the design and computational docking of a library of novel 188 first-generation peptidomimetic protease inhibitors using various electrophilic warheads with aza-peptide epoxides, α-ketoesters, and ß-diketones identified as the most effective. Second-generation designs, 192 compounds in total, focused on aza-peptide epoxides with drug-like properties, incorporating dipeptidyl backbones and heterocyclic ring motifs such as proline, indole, and pyrrole groups, yielding 8 hit candidates. These novel and specific inhibitors for SARS-CoV-2 Mpro can ultimately serve as valuable alternate and broad-spectrum antivirals against COVID-19.Communicated by Ramaswamy H. Sarma.

Structural insights into SARS-CoV-2 main protease conformational plasticity.

The spread of different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants underscores the need for insights into the structural properties of its structural and non-structural proteins. The highly conserved homo-dimeric chymotrypsin-like protease (3CL MPRO ), belonging to the class of cysteine hydrolases, plays an indispensable role in processing viral polyproteins that are involved in viral replication and transcription. Studies have successfully demonstrated the role of MPRO as an attractive drug target for designing antiviral treatments because of its importance in the viral life cycle. Herein, we report the structural dynamics of six experimentally solved structures of MPRO (i.e., 6LU7, 6M03, 6WQF, 6Y2E, 6Y84, and 7BUY including both ligand-free and ligand-bound states) at different resolutions. We have employed a structure-based balanced forcefield, CHARMM36m through state-of-the-art all-atoms molecular dynamics simulations at µ-seconds scale at room temperature (303K) and pH 7.0 to explore their structure-function relationship. The helical domain-III responsible for dimerization mostly contributes to the altered conformational states and destabilization of MPRO . A keen observation of the high degree of flexibility in the P5 binding pocket adjoining domain II-III highlights the reason for observation of conformational heterogeneity among the structural ensembles of MPRO . We also observe a differential dynamics of the catalytic pocket residues His41, Cys145, and Asp187, which may lead to catalytic impairment of the monomeric proteases. Among the highly populated conformational states of the six systems, 6LU7 and 7M03 forms the most stable and compact MPRO conformation with intact catalytic site and structural integrity. Altogether, our findings from this extensive study provides a benchmark to identify physiologically relevant structures of such promising drug targets for structure-based drug design and discovery of potent drug-like compounds having clinical potential.