A Review on Applications of Computational Methods in Drug Screening and Design

Discovery of novel RARα agonists using pharmacophore-based virtual screening, molecular docking, and molecular dynamics simulation studies

Nuclear retinoic acid receptors (RARs) are ligand-dependent transcription factors involved in various biological processes, such as embryogenesis, cell proliferation, differentiation, reproduction, and apoptosis. These receptors are regulated by retinoids, i.e., retinoic acid (RA) and its analogs, as receptor agonists. RAR agonists are promising therapeutic agents for the treatment of serious dermatological disorders, including some malignant conditions. By inducing apoptosis, they are able to inhibit the proliferation of diverse cancer cell lines. Also, RAR agonists have recently been identified as therapeutic options for some neurodegenerative diseases. These features make retinoids very attractive molecules for medical purposes. Synthetic selective RAR agonists have several advantages over endogenous ones, but they suffer poor pharmacokinetic properties. These compounds are normally lipophilic acids with unfavorable drug-like features such as poor oral bioavailability. Recently, highly selective, potent, and less toxic RAR agonists with proper lipophilicity, thus, good oral bioavailability have been developed for some therapeutic applications. In the present study, ligand and structure-based virtual screening technique was exploited to introduce some novel RARα agonists. Pharmacokinetic assessment was also performed in silico to suggest those compounds which have optimized drug-like features. Finally, two compounds with the best in silico pharmacological features are proposed as lead molecules for future development of RARα agonists.

Computational Guided Drug Targets Identification against Extended-Spectrum Beta-Lactamase-Producing Multi-Drug Resistant Uropathogenic Escherichia coli

Urinary tract infections (UTIs) are one of the most frequent bacterial infections in the world, both in the hospital and community settings. Uropathogenic Escherichia coli (UPEC) are the predominant etiological agents causing UTIs. Extended-spectrum beta-lactamase (ESBL) production is a prominent mechanism of resistance that hinders the antimicrobial treatment of UTIs caused by UPEC and poses a substantial danger to the arsenal of antibiotics now in use. As bacteria have several methods to counteract the effects of antibiotics, identifying new potential drug targets may help in the design of new antimicrobial agents, and in the control of the rising trend of antimicrobial resistance (AMR). The public availability of the entire genome sequences of humans and many disease-causing organisms has accelerated the hunt for viable therapeutic targets. Using a unique, hierarchical, in silico technique using computational tools, we discovered and described potential therapeutic drug targets against the ESBL-producing UPEC strain NA114. Three different sets of proteins (chokepoint, virulence, and resistance genes) were explored in phase 1. In phase 2, proteins shortlisted from phase 1 were analyzed for their essentiality, non-homology to the human genome, and gut flora. In phase 3, the further shortlisted putative drug targets were qualitatively characterized, including their subcellular location, broad-spectrum potential, and druggability evaluations. We found seven distinct targets for the pathogen that showed no similarity to the human proteome. Thus, possibilities for cross-reactivity between a target-specific antibacterial and human proteins were minimized. The subcellular locations of two targets, ECNA114_0085 and ECNA114_1060, were predicted as cytoplasmic and periplasmic, respectively. These proteins play an important role in bacterial peptidoglycan biosynthesis and inositol phosphate metabolism, and can be used in the design of drugs against these bacteria. Inhibition of these proteins will be helpful to combat infections caused by MDR UPEC.

Molecular Descriptors, Structure Generation, and Inverse QSAR/QSPR Based on SELFIES

For inverse QSAR/QSPR in conventional molecular design, several chemical structures must be generated and their molecular descriptors must be calculated. However, there is no one-to-one correspondence between the generated chemical structures and molecular descriptors. In this paper, molecular descriptors, structure generation, and inverse QSAR/QSPR based on self-referencing embedded strings (SELFIES), a 100% robust molecular string representation, are proposed. A one-hot vector is converted from SELFIES to SELFIES descriptors x, and an inverse analysis of the QSAR/QSPR model y = f(x) with the objective variable y and molecular descriptor x is conducted. Thus, x values that achieve a target y value are obtained. Based on these values, SELFIES strings or molecules are generated, meaning that inverse QSAR/QSPR is performed successfully. The SELFIES descriptors and SELFIES-based structure generation are verified using datasets of actual compounds. The successful construction of SELFIES-descriptor-based QSAR/QSPR models with predictive abilities comparable to those of models based on other fingerprints is confirmed. A large number of molecules with one-to-one relationships with the values of the SELFIES descriptors are generated. Furthermore, as a case study of inverse QSAR/QSPR, molecules with target y values are generated successfully. The Python code for the proposed method is available at https://github.com/hkaneko1985/dcekit.

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.

Virtual Screening of Nrf2 Dietary-Derived Agonists and Safety by a New Deep-Learning Model and Verified In Vitro and In Vivo

Nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is an essential regulatory target of antioxidants, but the lack of Nrf2 active site information has hindered discovery of new Nrf2 agonists from food-derived compounds by large-scale virtual screening. Two deep-learning models were separately trained to screen for Nrf2-agonists and safety. The trained models screened potentially active chemicals from approximately 70,000 dietary compounds within 5 min. Of the 169 potential Nrf2 agonists identified via deep-learning screening, 137 had not been reported before. Six compounds selected from the new Nrf2 agonists significantly increased (p < 0.05) the activity of Nrf2 on carbon tetrachloride (CCl₄)-intoxicated HepG2 cells (nicotiflorin (99.44 ± 18.5%), artemetin (97.91 ± 8.22%), daidzin (87.73 ± 3.77%), linonin (74.27 ± 5.73%), sinensetin (72.74 ± 10.41%), and tectoridin (77.78 ± 4.80%)), and their safety were demonstrated by an MTT assay. The safety and Nrf2 agonistic activity of nicotiflorin, artemetin, and daidzin were also reconfirm by a single-dose acute oral toxicity study and CCl₄-intoxicated rat assay.

Modes-of-action of antifungal compounds: Stressors and (target-site-specific) toxins, toxicants, or Toxin-stressors

Fungi and antifungal compounds are relevant to the United Nation's Sustainable Development Goals. However, the modes-of-action of antifungals-whether they are naturally occurring substances or anthropogenic fungicides-are often unknown or are misallocated in terms of their mechanistic category. Here, we consider the most effective approaches to identifying whether antifungal substances are cellular stressors, toxins/toxicants (that are target-site-specific), or have a hybrid mode-of-action as Toxin-stressors (that induce cellular stress yet are target-site-specific). This newly described 'toxin-stressor' category includes some photosensitisers that target the cell membrane and, once activated by light or ultraviolet radiation, cause oxidative damage. We provide a glossary of terms and a diagrammatic representation of diverse types of stressors, toxic substances, and Toxin-stressors, a classification that is pertinent to inhibitory substances not only for fungi but for all types of cellular life. A decision-tree approach can also be used to help differentiate toxic substances from cellular stressors (Curr Opin Biotechnol 2015 33: 228-259). For compounds that target specific sites in the cell, we evaluate the relative merits of using metabolite analyses, chemical genetics, chemoproteomics, transcriptomics, and the target-based drug-discovery approach (based on that used in pharmaceutical research), focusing on both ascomycete models and the less-studied basidiomycete fungi. Chemical genetic methods to elucidate modes-of-action currently have limited application for fungi where molecular tools are not yet available; we discuss ways to circumvent this bottleneck. We also discuss ecologically commonplace scenarios in which multiple substances act to limit the functionality of the fungal cell and a number of as-yet-unresolved questions about the modes-of-action of antifungal compounds pertaining to the Sustainable Development Goals.

Artificial Intelligence Technologies in Cardiology

As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital transformation of vast quantities of information. The implementation of AI has already achieved success in the domains of molecular chemistry and drug discoveries. The reduction in costs and in the time needed for experiments to predict the pharmacological activities of new molecules is a milestone in science. These successful applications of AI algorithms provide hope for a revolution in healthcare systems. A significant part of artificial intelligence is machine learning (ML), of which there are three main types-supervised learning, unsupervised learning, and reinforcement learning. In this review, the full scope of the AI workflow is presented, with explanations of the most-often-used ML algorithms and descriptions of performance metrics for both regression and classification. A brief introduction to explainable artificial intelligence (XAI) is provided, with examples of technologies that have developed for XAI. We review important AI implementations in cardiology for supervised, unsupervised, and reinforcement learning and natural language processing, emphasizing the used algorithm. Finally, we discuss the need to establish legal, ethical, and methodical requirements for the deployment of AI models in medicine.

MSGNN-DTA: Multi-Scale Topological Feature Fusion Based on Graph Neural Networks for Drug-Target Binding Affinity Prediction

The accurate prediction of drug-target binding affinity (DTA) is an essential step in drug discovery and drug repositioning. Although deep learning methods have been widely adopted for DTA prediction, the complexity of extracting drug and target protein features hampers the accuracy of these predictions. In this study, we propose a novel model for DTA prediction named MSGNN-DTA, which leverages a fused multi-scale topological feature approach based on graph neural networks (GNNs). To address the challenge of accurately extracting drug and target protein features, we introduce a gated skip-connection mechanism during the feature learning process to fuse multi-scale topological features, resulting in information-rich representations of drugs and proteins. Our approach constructs drug atom graphs, motif graphs, and weighted protein graphs to fully extract topological information and provide a comprehensive understanding of underlying molecular interactions from multiple perspectives. Experimental results on two benchmark datasets demonstrate that MSGNN-DTA outperforms the state-of-the-art models in all evaluation metrics, showcasing the effectiveness of the proposed approach. Moreover, the study conducts a case study based on already FDA-approved drugs in the DrugBank dataset to highlight the potential of the MSGNN-DTA framework in identifying drug candidates for specific targets, which could accelerate the process of virtual screening and drug repositioning.

Galaxolide and Irgacure 369 are novel environmental androgens

Endocrine disruptors are environmental chemicals that can interfere with the endocrine system. However, research on endocrine disruptors that interfere with androgen's actions is still limited. The purpose of this study is to use in silico computation, i.e., molecular docking to facilitate the identification of environmental androgens. Computational docking was used to study the binding interactions of environmental/industrial compounds with the three dimensional structure of human androgen receptor (AR). Then reporter assay and cell proliferation assay using AR-expressing LNCaP prostate cancer cells were used to determine their in vitro androgenic activity. Animal studies using immature male rats were also carried out to test their in vivo androgenic activity. Two novel environmental androgens were identified. As a photoinitiator, 2-benzyl-2-(dimethylamino)-4'-morpholinobutyrophenone (Irgacure 369, abbreviated as IC-369) is widely used in the packaging and electronics industries. Galaxolide (HHCB) is widely used in the production of perfume, fabric softeners and detergents. We found that both IC-369 and HHCB could activate AR transcriptional activity and promote cell proliferation in AR-sensitive LNCaP cells. Furthermore, IC-369 and HHCB could induce cell proliferation and histological changes of seminal vesicles in immature rats. RNA sequencing and qPCR analysis showed that androgen-related genes in seminal vesicle tissue were up-regulated by IC-369 and HHCB. In conclusion, IC-369 and HHCB are new environmental androgens that bind AR and induce AR transcriptional activity, thereby exerting toxicological effects on the development of male reproductive organs.

Structure of the Spring Viraemia of Carp Virus Ribonucleoprotein Complex Reveals Its Assembly Mechanism and Application in Antiviral Drug Screening

Spring viremia of carp virus (SVCV) is a highly pathogenic Vesiculovirus infecting the common carp, yet neither a vaccine nor effective therapies are available to treat spring viremia of carp (SVC). Like all negative-sense viruses, SVCV contains an RNA genome that is encapsidated by the nucleoprotein (N) in the form of a ribonucleoprotein (RNP) complex, which serves as the template for viral replication and transcription. Here, the three-dimensional (3D) structure of SVCV RNP was resolved through cryo-electron microscopy (cryo-EM) at a resolution of 3.7 Å. RNP assembly was stabilized by N and C loops; RNA was wrapped in the groove between the N and C lobes with 9 nt nucleotide per protomer. Combined with mutational analysis, our results elucidated the mechanism of RNP formation. The RNA binding groove of SVCV N was used as a target for drug virtual screening, and it was found suramin had a good antiviral effect. This study provided insights into RNP assembly, and anti-SVCV drug screening was performed on the basis of this structure, providing a theoretical basis and efficient drug screening method for the prevention and treatment of SVC. IMPORTANCE Aquaculture accounts for about 70% of global aquatic products, and viral diseases severely harm the development of aquaculture industry. Spring viremia of carp virus (SVCV) is the pathogen causing highly contagious spring viremia of carp (SVC) disease in cyprinids, especially common carp (Cyprinus carpio), yet neither a vaccine nor effective therapies are available to treat this disease. In this study, we have elucidated the mechanism of SVCV ribonucleoprotein complex (RNP) formation by resolving the 3D structure of SVCV RNP and screened antiviral drugs based on the structure. It is found that suramin could competitively bind to the RNA binding groove and has good antiviral effects both in vivo and in vitro. Our study provides a template for rational drug discovery efforts to treat and prevent SVCV infections.

Antibiotic discovery against Piscirickettsia salmonis using a combined in silico and in vitro approach

Piscirickettsia salmonis is one of the main pathogens causing considerable economic losses in salmonid farming. The DNA gyrase of several pathogenic bacteria has been the target of choice for antibiotic design and discovery for years, due to its key function during DNA replication. In this study, we carried out a combined in silico and in vitro approach to antibiotic discovery targeting the GyrA subunit of Piscirickettsia salmonis. The in silico results of this work showed that flumequine (-6.6 kcal/mol), finafloxacin (-7.2 kcal/mol), rosoxacin (-6.6 kcal/mol), elvitegravir (-6.4 kcal/mol), sarafloxacin (-8.3 kcal/mol), orbifloxacin (-7.9 kcal/mol), and sparfloxacin (-7.2 kcal/mol) are docked with good affinities in the DNA binding domain of the Piscirickettsia salmonis GyrA subunit. In the in vitro inhibition assay, it was observed that most of these molecules inhibit the growth of Piscirickettsia salmonis, except for elvitegravir. We believe that this methodology could help to significantly reduce the time and cost of antibiotic discovery trials to combat Piscirickettsia salmonis within the salmonid farming industry.

Multitargeted Virtual Screening and Molecular Simulation of Natural Product-like Compounds against GSK3β, NMDA-Receptor, and BACE-1 for the Management of Alzheimer's Disease

The complexity of Alzheimer's disease (AD) and several side effects of currently available medication inclined us to search for a novel natural cure by targeting multiple key regulatory proteins. We initially virtually screened the natural product-like compounds against GSK3β, NMDA receptor, and BACE-1 and thereafter validated the best hit through molecular dynamics simulation (MDS). The results demonstrated that out of 2029 compounds, only 51 compounds exhibited better binding interactions than native ligands, with all three protein targets (NMDA, GSK3β, and BACE) considered multitarget inhibitors. Among them, F1094-0201 is the most potent inhibitor against multiple targets with binding energy -11.7, -10.6, and -12 kcal/mol, respectively. ADME-T analysis results showed that F1094-0201 was found to be suitable for CNS drug-likeness in addition to their other drug-likeness properties. The MDS results of RMSD, RMSF, Rg, SASA, SSE and residue interactions indicated the formation of a strong and stable association in the complex of ligands (F1094-0201) and proteins. These findings confirm the F1094-0201's ability to remain inside target proteins' binding pockets while forming a stable complex of protein-ligand. The free energies (MM/GBSA) of BACE-F1094-0201, GSK3β-F1094-0201, and NMDA-F1094-0201 complex formation were -73.78 ± 4.31 kcal mol^-1, -72.77 ± 3.43 kcal mol^-1, and -52.51 ± 2.85 kcal mol^-1, respectively. Amongst the target proteins, F1094-0201 have a more stable association with BACE, followed by NMDA and GSK3β. These attributes of F1094-0201 indicate it as a possible option for the management of pathophysiological pathways associated with AD.

Molecular mechanisms augmenting resistance to current therapies in clinics among cervical cancer patients

Cervical cancer (CC) is the fourth leading cause of cancer death (~ 324,000 deaths annually) among women internationally, with 85% of these deaths reported in developing regions, particularly sub-Saharan Africa and Southeast Asia. Human papillomavirus (HPV) is considered the major driver of CC, and with the availability of the prophylactic vaccine, HPV-associated CC is expected to be eliminated soon. However, female patients with advanced-stage cervical cancer demonstrated a high recurrence rate (50-70%) within two years of completing radiochemotherapy. Currently, 90% of failures in chemotherapy are during the invasion and metastasis of cancers related to drug resistance. Although molecular target therapies have shown promising results in the lab, they have had little success in patients due to the tumor heterogeneity fueling resistance to these therapies and bypass the targeted signaling pathway. The last two decades have seen the emergence of immunotherapy, especially immune checkpoint blockade (ICB) therapies, as an effective treatment against metastatic tumors. Unfortunately, only a small subgroup of patients (< 20%) have benefited from this approach, reflecting disease heterogeneity and manifestation with primary or acquired resistance over time. Thus, understanding the mechanisms driving drug resistance in CC could significantly improve the quality of medical care for cancer patients and steer them to accurate, individualized treatment. The rise of artificial intelligence and machine learning has also been a pivotal factor in cancer drug discovery. With the advancement in such technology, cervical cancer screening and diagnosis are expected to become easier. This review will systematically discuss the different tumor-intrinsic and extrinsic mechanisms CC cells to adapt to resist current treatments and scheme novel strategies to overcome cancer drug resistance.

QSAR and molecular docking studies of isatin and indole derivatives as SARS 3CLpro inhibitors

The 3C-like protease (3CL^pro), known as the main protease of SARS-COV, plays a vital role in the viral replication cycle and is a critical target for the development of SARS inhibitor. Comparative sequence analysis has shown that the 3CL^pro of two coronaviruses, SARS-CoV-2 and SARS-CoV, show high structural similarity, and several common features are shared among the substrates of 3CL^pro in different coronaviruses. The goal of this study is the development of validated QSAR models by CORAL software and Monte Carlo optimization to predict the inhibitory activity of 81 isatin and indole-based compounds against SARS CoV 3CL^pro. The models were built using a newer objective function optimization of this software, known as the index of ideality correlation (IIC), which provides favorable results. The entire set of molecules was randomly divided into four sets including: active training, passive training, calibration and validation sets. The optimal descriptors were selected from the hybrid model by combining SMILES and hydrogen suppressed graph (HSG) based on the objective function. According to the model interpretation results, eight synthesized compounds were extracted and introduced from the ChEMBL database as good SARS CoV 3CL^pro inhibitor. Also, the activity of the introduced molecules further was supported by docking studies using 3CL^pro of both SARS-COV-1 and SARS-COV-2. Based on the results of ADMET and OPE study, compounds CHEMBL4458417 and CHEMBL4565907 both containing an indole scaffold with the positive values of drug-likeness and the highest drug-score can be introduced as selected leads.

In-silico assay of a dosing vehicle based on chitosan-TiO2 and modified benzofuran-isatin molecules against Pseudomonas aeruginosa

A high priority of the World Health Organization (WHO) is the study of drugs against Pseudomonas aeruginosa, which has developed antibiotic resistance. In this order, recent research is analyzing biomaterials and metal oxide nanoparticles, such as chitosan (QT) and TiO2 (NT), which can transport molecules with biological activity against bacteria, to propose them as drug carrier candidates. In the present work, 10 modified benzofuran-isatin molecules were studied through computational simulation using density functional theory (DFT) and molecular docking assays against Hfq and LpxC (proteins of P. aeruginosa). The results show that the ligand efficiency of commercial drugs C-CP and C-AZI against Hfq is low compared with the best-designed molecule MOL-A. However, we highlight that the influence of NT promotes a better interaction of some molecules, where MOL-E generates a better interaction by 0.219 kcal/mol when NT is introduced in Hfq, forming the system Hfq-NT (Target-NT). Similar behavior is observed in the LpxC target, in which MOL-J is better at 0.072 kcal/mol. Finally, two pharmacophoric models for Hfq and LpxC implicate hydrophobic and aromatic-hydrophobic fragments.

A New and an Eco-Friendly Approach for the Construction of Multi-Functionalized Benzenes with Computational Studies

The new path chosen is more appropriate in the context of green chemistry. This research aims to construct 5,6,7,8-tetrahydronaphthalene-1,3-dicarbonitrile (THNDC) and 1,2,3,4-tetrahydroisoquinoline-6,8-dicarbonitrile (THIDC) derivatives via the cyclization of three easily obtainable reactants under an environmentally benign mortar and pestle grinding technique. Notably, the robust route offers an esteemed opportunity for the introduction of multi-substituted benzenes and ensures the good compatibility of bioactive molecules. Furthermore, the synthesized compounds are investigated using docking simulations with two representative drugs (6c and 6e) for target validation. The physicochemical, pharmacokinetic, drug-like properties (ADMET), and therapeutic friendliness characteristics of these synthesized compounds are computed.

An overview of compound properties, multiparameter optimization, and computational drug design methods for PARP-1 inhibitor drugs

Breast cancer treatment with PARP-1 inhibitors remains challenging due to emerging toxicities, drug resistance, and unaffordable costs of treatment options. How do we invent strategies to design better anti-cancer drugs? A part of the answer is in optimized compound properties, desirability functions, and modern computational drug design methods that drive selectivity and toxicity and have not been reviewed for PARP-1 inhibitors. Nonetheless, comparisons of these compound properties for PARP-1 inhibitors are not available in the literature. In this review, we analyze the physchem, PKPD space to identify inherent desirability functions characteristic of approved drugs that can be valuable for the design of better candidates. Recent literature utilizing ligand, structure-based drug design strategies and matched molecular pair analysis (MMPA) for the discovery of novel PARP-1 inhibitors are also reviewed. Thus, this perspective provides valuable insights into the medchem and multiparameter optimization of PARP-1 inhibitors that might be useful to other medicinal chemists.

Docking and Molecular Dynamics Identify Leads against 5 Alpha Reductase 2 for Benign Prostate Hyperplasia Treatment

Steroid 5 alpha-reductase 2 (5αR-2) is a membrane-embedded protein that together with other isoforms plays a key role in the metabolism of steroids. This enzyme catalyzes the reduction of testosterone to the more potent ligand, dihydrotestosterone (DHT) in the prostate. Androgens, testosterone, and DHT play important roles in prostate growth, development, and function. At the same time, both testosterone and DHT have been implicated in the pathogenesis of benign prostate hyperplasia (BPH). Inhibition of the DHT formation, therefore, provides a therapeutic strategy that offers the possibility of preventing, delaying, or treating BPH. Currently, two steroidal drugs that inhibit 5αR-2, dutasteride and finasteride, have been approved for clinical use. These two come at a high cost and also portray undesirable sexual side effects which necessitate the need to find new chemotherapeutic alternatives for the disease. Based on the aforementioned, finasteride and dutasteride were subjected to scaffold hopping, fragment-based de novo design, molecular docking, and molecular dynamics simulations employing databases like ChEMBL, DrugBank, PubChem, ChemSpider, and Zinc15 in the identification of potential hits targeting 5αR-2. Altogether, ten novel compounds targeting 5αR-2 were identified with binding energies lower or comparable to finasteride and dutasteride, the main inhibitors for this target. Molecular docking and molecular dynamics simulations studies identify amino acid residues Glu57, Phe219, Phe223, and Leu224 to be critical for ligand binding and complex stability. The physicochemical and pharmacological profiling suggests the potential of the hit compounds to be drug-like and orally active. Similarly, the quality parameter assessments revealed the hits possess LELP greater than 3 implying their promise as lead-like molecules. The compounds A5, A9, and A10 were, respectively, predicted to treat prostate disorders with Pa (0.188, 0.361, and 0.270) and Pi (0.176, 0.050, and 0.093), while A8 and A9 were found to be associated with BPH treatment with Pa (0.09 and 0.127) and Pi (0.077 and 0.033), respectively. Structural similarity searches via DrugBank identified the drugs faropenem, acemetacin, estradiol valerate, and yohimbine to be useful for BPH treatment suggesting the de novo designed ligands as potential chemotherapeutic agents for treating this disease.

Discovery of novel S6K1 inhibitors by an ensemble-based virtual screening method and molecular dynamics simulation

Ribosomal protein S6 kinase beta-1 (S6K1) is considered a potential target for the treatment of various diseases, such as obesity, type II diabetes, and cancer. Development of novel S6K1 inhibitors is an urgent and important task for the medicinal chemists. In this research, an effective ensemble-based virtual screening method, including common feature pharmacophore model, 3D-QSAR pharmacophore model, naïve Bayes classifier model, and molecular docking, was applied to discover potential S6K1 inhibitors from BioDiversity database with 29,158 compounds. Finally, 7 hits displayed considerable properties and considered as potential inhibitors against S6K1. Further, carefully analyzing the interactions between these 7 hits and key residues in the S6K1 active site, and comparing them with the reference compound PF-4708671, it was found that 2 hits exhibited better binding patterns. In order to further investigate the mechanism of the interactions between 2 hits and S6K1 at simulated physiological conditions, the molecular dynamics simulation was performed. The ΔG_bind energies for S6K1-Hit1 and S6K1-Hit2 were - 111.47 ± 1.29 and - 54.29 ± 1.19 kJ mol^-1, respectively. Furthermore, deep analysis of these results revealed that Hit1 was the most stable complex, which can stably bind to S6K1 active site, interact with all of the key residues, and induce H1, H2, and M-loop regions changes. Therefore, the identified Hit1 may be a promising lead compound for developing new S6K1 inhibitor for various metabolic diseases treatment.

Developing novel antifungals: lessons from G protein-coupled receptors

Up to 1.5 million people die yearly from fungal disease, but the repertoire of antifungal drug classes is minimal and the incidence of drug resistance is rising rapidly. This dilemma was recently declared by the World Health Organization as a global health emergency, but the discovery of new antifungal drug classes remains excruciatingly slow. This process could be accelerated by focusing on novel targets, such as G protein-coupled receptor (GPCR)-like proteins, that have a high likelihood of being druggable and have well-defined biology and roles in disease. We discuss recent successes in understanding the biology of virulence and in structure determination of yeast GPCRs, and highlight new approaches that might pay significant dividends in the urgent search for novel antifungal drugs.

Co-creation environment with cloud virtual reality and real-time artificial intelligence toward the design of molecular robots

This paper describes the design philosophy for our cloud-based virtual reality (VR) co-creation environment (CCE) for molecular modeling. Using interactive VR simulation can provide enhanced perspectives in molecular modeling for intuitive live demonstration and experimentation in the CCE. Then the use of the CCE can enhance knowledge creation by bringing people together to share and create ideas or knowledge that may not emerge otherwise. Our prototype CCE discussed here, which was developed to demonstrate our design philosophy, has already enabled multiple members to log in and touch virtual molecules running on a cloud server with no noticeable network latency via real-time artificial intelligence techniques. The CCE plays an essential role in the rational design of molecular robot parts, which consist of bio-molecules such as DNA and protein molecules.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects

Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed.

Building a knowledge graph to enable precision medicine

Developing personalized diagnostic strategies and targeted treatments requires a deep understanding of disease biology and the ability to dissect the relationship between molecular and genetic factors and their phenotypic consequences. However, such knowledge is fragmented across publications, non-standardized repositories, and evolving ontologies describing various scales of biological organization between genotypes and clinical phenotypes. Here, we present PrimeKG, a multimodal knowledge graph for precision medicine analyses. PrimeKG integrates 20 high-quality resources to describe 17,080 diseases with 4,050,249 relationships representing ten major biological scales, including disease-associated protein perturbations, biological processes and pathways, anatomical and phenotypic scales, and the entire range of approved drugs with their therapeutic action, considerably expanding previous efforts in disease-rooted knowledge graphs. PrimeKG contains an abundance of 'indications', 'contradictions', and 'off-label use' drug-disease edges that lack in other knowledge graphs and can support AI analyses of how drugs affect disease-associated networks. We supplement PrimeKG's graph structure with language descriptions of clinical guidelines to enable multimodal analyses and provide instructions for continual updates of PrimeKG as new data become available.

Comparative Proteomics and Genome-Wide Druggability Analyses Prioritized Promising Therapeutic Targets against Drug-Resistant Leishmania tropica

Leishmania tropica is a tropical parasite causing cutaneous leishmaniasis (CL) in humans. Leishmaniasis is a serious public health threat, affecting an estimated 350 million people in 98 countries. The global rise in antileishmanial drug resistance has triggered the need to explore novel therapeutic strategies against this parasite. In the present study, we utilized the recently available multidrug resistant L. tropica strain proteome data repository to identify alternative therapeutic drug targets based on comparative subtractive proteomic and druggability analyses. Additionally, small drug-like compounds were scanned against novel targets based on virtual screening and ADME profiling. The analysis unveiled 496 essential cellular proteins of L. tropica that were nonhomologous to the human proteome set. The druggability analyses prioritized nine parasite-specific druggable proteins essential for the parasite's basic cellular survival, growth, and virulence. These prioritized proteins were identified to have appropriate binding pockets to anchor small drug-like compounds. Among these, UDPase and PCNA were prioritized as the top-ranked druggable proteins. The pharmacophore-based virtual screening and ADME profiling predicted MolPort-000-730-162 and MolPort-020-232-354 as the top hit drug-like compounds from the Pharmit resource to inhibit L. tropica UDPase and PCNA, respectively. The alternative drug targets and drug-like molecules predicted in the current study lay the groundwork for developing novel antileishmanial therapies.

Modern drug discovery for inflammatory bowel disease: The role of computational methods

Inflammatory bowel diseases (IBDs) comprising ulcerative colitis, Crohn’s disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract. IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized. Cell therapy, intestinal microecology, apheresis therapy, exosome therapy and small molecules are emerging therapeutic options for IBD. Currently, it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD. Several small molecule inhibitors are being developed as a promising alternative for IBD therapy. The use of highly efficient and time-saving techniques, such as computational methods, is still a viable option for the development of these small molecule drugs. The computer-aided (in silico) discovery approach is one drug development technique that has mostly proven efficacy. Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner. This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods. Some computational approaches to IBD genomic studies, target identification, and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.

Discovery and optimization of natural-based nanomolar c-Kit inhibitors via in silico and in vitro studies

c-Kit is a receptor tyrosine kinase which is involved in intracellular signaling and mutations of c-Kit have been associated with various types of cancers. Investigations have shown that inhibition of c-Kit, using tyrosine kinase inhibitors, yielded promising results in cancer treatment marking it as a promising target for cancer therapy. However, the emerging resistance for the current therapy necessitates the development of more potent inhibitors which are not affected by these mutations. Herein, virtual screening of a library of natural-based compounds yielded three hits (2, 5 and 6) which possessed nanomolar inhibitory (2.02, 4.33 and 2.80 nM, respectively) activity when tested in vitro against c-Kit. Single point mutation docking studies showed the hits to be unaffected by the most common resistance mutation in imatinib-resistant cells, mutation of Val654. Although, the top hits exhibited around 3000 higher inhibitory potency toward c-Kit when compared to imatinib (5.4 µM), previous studies have shown that they are metabolically unstable. Fragment-based drug design approaches were then employed to enhance binding affinity of the top hit and make it more metabolically stable. Screening of the generated fragments yielded a new derivative, F1, which demonstrated stronger binding affinity, stability and binding free energy when compared to the hit compound 2.Communicated by Ramaswamy H. Sarma.

High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior

Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of highthroughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti- SARS-CoV-2 agents.

Traditional Machine and Deep Learning for Predicting Toxicity Endpoints

Molecular structure property modeling is an increasingly important tool for predicting compounds with desired properties due to the expensive and resource-intensive nature and the problem of toxicity-related attrition in late phases during drug discovery and development. Lately, the interest for applying deep learning techniques has increased considerably. This investigation compares the traditional physico-chemical descriptor and machine learning-based approaches through autoencoder generated descriptors to two different descriptor-free, Simplified Molecular Input Line Entry System (SMILES) based, deep learning architectures of Bidirectional Encoder Representations from Transformers (BERT) type using the Mondrian aggregated conformal prediction method as overarching framework. The results show for the binary CATMoS non-toxic and very-toxic datasets that for the former, almost equally balanced, dataset all methods perform equally well while for the latter dataset, with an 11-fold difference between the two classes, the MolBERT model based on a large pre-trained network performs somewhat better compared to the rest with high efficiency for both classes (0.93-0.94) as well as high values for sensitivity, specificity and balanced accuracy (0.86-0.87). The descriptor-free, SMILES-based, deep learning BERT architectures seem capable of producing well-balanced predictive models with defined applicability domains. This work also demonstrates that the class imbalance problem is gracefully handled through the use of Mondrian conformal prediction without the use of over- and/or under-sampling, weighting of classes or cost-sensitive methods.

Recent PELE Developments and Applications in Drug Discovery Campaigns

Computer simulation techniques are gaining a central role in molecular pharmacology. Due to several factors, including the significant improvements of traditional molecular modelling, the irruption of machine learning methods, the massive data generation, or the unlimited computational resources through cloud computing, the future of pharmacology seems to go hand in hand with in silico predictions. In this review, we summarize our recent efforts in such a direction, centered on the unconventional Monte Carlo PELE software and on its coupling with machine learning techniques. We also provide new data on combining two recent new techniques, aquaPELE capable of exhaustive water sampling and fragPELE, for fragment growing.

Predicting chemical structure using reinforcement learning with a stack-augmented conditional variational autoencoder

In this paper, a reinforcement learning model is proposed that can maximize the predicted binding affinity between a generated molecule and target proteins. The model used to generate molecules in the proposed model was the Stacked Conditional Variation AutoEncoder (Stack-CVAE), which acts as an agent in reinforcement learning so that the resulting chemical formulas have the desired chemical properties and show high binding affinity with specific target proteins. We generated 1000 chemical formulas using the chemical properties of sorafenib and the three target kinases of sorafenib. Then, we confirmed that Stack-CVAE generates more of the valid and unique chemical compounds that have the desired chemical properties and predicted binding affinity better than other generative models. More detailed analysis for 100 of the top scoring molecules show that they are novel ones not found in existing chemical databases. Moreover, they reveal significantly higher predicted binding affinity score for Raf kinases than for other kinases. Furthermore, they are highly druggable and synthesizable.

Inhibitory effect of lupeol, quercetin, and solasodine on Rhizopus oryzae: A molecular docking and dynamic simulation study

BACKGROUND

Mucormycosis is an infection caused by fungi belonging to the order Mucorales. Rhizopus oryzae is one of the most prevalent organisms identified in mucormycosis patients. Because it spreads quickly through the blood vessels, this opportunistic illness has an exceptionally high fatality rate, even when vigorous treatment is administered. Nonetheless, it has a high tolerance to antifungal medicines, limiting treatment options. As a result, improved methods for preventing and treating mucormycosis are desperately needed. Hence, this study was aimed at assessing the effect of lupeol, quercetin, and solasodine against mucormycosis based on computational approaches.

METHODS

The Rhizopus oryzae RNA-dependent RNA polymerase (RdRp) was the target for the design of drugs against the deadly mucormycosis. The three-dimensional structure of the RdRp was modelled with a Swiss model and validated using PROCHECK, VERIFY 3D, and QMEAN. Using the Schrodinger maestro module, a molecular docking study was performed between RdRp and the antimicrobial phytochemicals lupeol, quercetin, and solasodine. A molecular dynamics (MD) simulation study was used to assess the stability and interaction of the RdRp with these phytochemicals.

RESULTS

The RdRp protein binds strongly to lupeol (-7.2 kcal/mol), quercetin (-9.1 kcal/mol), and solasodine (-9.6 kcal/mol), according to molecular docking assessment based on the lowest binding energy, confirmation, and bond interaction. Simulations suggest that lupeol, quercetin, and solasodine complexes with RdRp and showed stable confirmation with minimal fluctuation throughout the 200 nanoseconds based on the RMSD and RMSF trajectory assessments.

CONCLUSION

The molecular docking and MD simulation investigation improved our understanding of phytochemical-RdRp interactions. Due to its high affinity for RdRp, solasodine may be a better treatment option for mucormycosis.

Immunoinformatic-guided designing of multi-epitope vaccine construct against Brucella Suis 1300

Brucella suis mediates the transmission of brucellosis in humans and animals and a significant facultative zoonotic pathogen found in livestock. It has the capacity to survive and multiply in a phagocytic environment and to acquire resistance under hostile conditions thus becoming a threat globally. Antibiotic resistance is posing a substantial public health threat, hence there is an unmet and urgent clinical need for immune-based non-antibiotic methods to treat brucellosis. Hence, we aimed to explore the whole proteome of Brucella suis to predict antigenic proteins as a vaccine target and designed a novel chimeric vaccine (multi-epitope vaccine) through subtractive genomics-based reverse vaccinology approaches. The applied subsequent hierarchical shortlisting resulted in the identification of Multidrug efflux Resistance-nodulation-division (RND) transporter outer membrane subunit (gene BepC) that may act as a potential vaccine target. T-cell and B-cell epitopes have been predicted from target proteins using a number of immunoinformatic methods. Six MHC I, ten MHC II, and four B-cell epitopes were used to create a 324-amino-acid MEV construct, which was coupled with appropriate linkers and adjuvant. To boost the immunological response to the vaccine, the vaccine was combined with the TLR4 agonist HBHA protein. The MEV structure predicted was found to be highly antigenic, non-toxic, non-allergenic, flexible, stable, and soluble. To confirm the interactions with the receptors, a molecular docking simulation of the MEV was done using the human TLR4 (toll-like receptor 4) and HLAs. The stability and binding of the MEV-docked complexes with TLR4 were assessed using molecular dynamics (MD) simulation. Finally, MEV was reverse translated, its cDNA structure was evaluated, and then, in silico cloning into an E. coli expression host was conducted to promote maximum vaccine protein production with appropriate post-translational modifications. These comprehensive computer calculations backed up the efficacy of the suggested MEV in protecting against B. suis infections. However, more experimental validations are needed to adequately assess the vaccine candidate's potential. HIGHLIGHTS: • Subtractive genomic analysis and reverse vaccinology for the prioritization of novel vaccine target • Examination of chimeric vaccine in terms of allergenicity, antigenicity, MHC I, II binding efficacy, and structural-based studies • Molecular docking simulation method to rank based vaccine candidate and understand their binding modes.

Intrinsically Disordered Proteins: An Overview

Many proteins and protein segments cannot attain a single stable three-dimensional structure under physiological conditions; instead, they adopt multiple interconverting conformational states. Such intrinsically disordered proteins or protein segments are highly abundant across proteomes, and are involved in various effector functions. This review focuses on different aspects of disordered proteins and disordered protein regions, which form the basis of the so-called "Disorder-function paradigm" of proteins. Additionally, various experimental approaches and computational tools used for characterizing disordered regions in proteins are discussed. Finally, the role of disordered proteins in diseases and their utility as potential drug targets are explored.

Recent developments on UDP-N-acetylmuramoyl-L-alanine-D-gutamate ligase (Mur D) enzyme for antimicrobial drug development: An emphasis on in-silico approaches

Introduction

The rapid emergence of antibiotic resistance among various bacterial pathogens has been one of the major concerns of health organizations across the world. In this context, for the development of novel inhibitors against antibiotic-resistant bacterial pathogens, UDP-N-Acetylmuramoyl-L-Alanine-D-Glutamate Ligase (MurD) enzyme represents one of the most apposite targets.

Body

The present review focuses on updated advancements on MurD-targeted inhibitors in recent years along with genetic regulation, structural and functional characteristics of the MurD enzyme from various bacterial pathogens. A concise account of various crystal structures of MurD enzyme, submitted into Protein Data Bank is also discussed.

Discussion

MurD, an ATP dependent cytoplasmic enzyme is an important target for drug discovery. The genetic organization of MurD enzyme is well elucidated and many crystal structures of MurD enzyme are submitted into Protein Data bank. Various inhibitors against MurD enzyme have been developed so far with an increase in the use of in-silico methods in the recent past. But cell permeability barriers and conformational changes of MurD enzyme during catalytic reaction need to be addressed for effective drug development. So, a combination of in-silico methods along with experimental work is proposed to counter the catalytic machinery of MurD enzyme.

In silico investigations identified Butyl Xanalterate to competently target CK2α (CSNK2A1) for therapy of chronic lymphocytic leukemia

Chronic lymphocytic leukemia (CLL) is an incurable malignancy of B-cells. In this study, bioinformatics analyses were conducted to identify possible pathogenic roles of CK2α, which is a protein encoded by CSNK2A1, in the progression and aggressiveness of CLL. Furthermore, various computational tools were used to search for a competent inhibitor of CK2α from fungal metabolites that could be proposed for CLL therapy. In CLL patients, high-expression of CSNK2A1 was associated with early need for therapy (n = 130, p < 0.0001) and short overall survival (OS; n = 107, p = 0.005). Consistently, bioinformatics analyses showed CSNK2A1 to associate with/play roles in CLL proliferation and survival-dependent pathways. Furthermore, PPI network analysis identified interaction partners of CK2α (PPI enrichment p value = 1 × 10^-16) that associated with early need for therapy (n = 130, p < 0.003) and have been known to heavily impact on the progression of CLL. These findings constructed a rational for targeting CK2α for CLL therapy. Consequently, computational analyses reported 35 fungal metabolites out of 5820 (filtered from 19,967 metabolites) to have lower binding energy (ΔG: - 10.9 to - 11.7 kcal/mol) and better binding affinity (Kd: 9.77 × 10⁷ M^-1 to 3.77 × 10⁸ M^-1) compared with the native ligand (ΔG: - 10.8, Kd: 8.3 × 10⁷ M-^-1). Furthermore, molecular dynamics simulation study established that Butyl Xanalterate-CK2α complex continuously remained stable throughout the simulation time (100 ns). Moreover, Butyl Xanalterate interacted with most of the catalytic residues, where complex was stabilized by more than 65% hydrogen bond interactions, and a significant hydrophobic interaction with residue Phe113. Here, high-expression of CSNK2A1 was implicated in the progression and poor prognosis of CLL, making it a potential therapeutic target in the disease. Butyl Xanalterate showed stable and strong interactions with CK2α, thus we propose it as a competitive inhibitor of CK2α for CLL therapy.

Cytotoxic Potential of Alternaria tenuissima AUMC14342 Mycoendophyte Extract: A Study Combined with LC-MS/MS Metabolic Profiling and Molecular Docking Simulation

Breast, cervical, and ovarian cancers are among the most serious cancers and the main causes of mortality in females worldwide, necessitating urgent efforts to find newer sources of safe anticancer drugs. The present study aimed to evaluate the anticancer potency of mycoendophytic Alternaria tenuissima AUMC14342 ethyl acetate extract on HeLa (cervical cancer), SKOV-3 (ovarian cancer), and MCF-7 (breast adenocarcinoma) cell lines. The extract showed potent effect on MCF-7 cells with an IC50 value of 55.53 μg/mL. Cell cycle distribution analysis of treated MCF-7 cells revealed a cell cycle arrest at the S phase with a significant increase in the cell population (25.53%). When compared to control cells, no significant signs of necrotic or apoptotic cell death were observed. LC-MS/MS analysis of Alternaria tenuissima extract afforded the identification of 20 secondary metabolites, including 7-dehydrobrefeldin A, which exhibited the highest interaction score (-8.0156 kcal/mol) in molecular docking analysis against human aromatase. Regarding ADME pharmacokinetics and drug-likeness properties, 7-dehydrobrefeldin A, 4'-epialtenuene, and atransfusarin had good GIT absorption and water solubility without any violation of drug-likeness rules. These findings support the anticancer activity of bioactive metabolites derived from endophytic fungi and provide drug scaffolds and substitute sources for the future development of safe chemotherapy.

Development of machine learning models for the screening of potential HSP90 inhibitors

Heat shock protein 90 (Hsp90) is a molecular chaperone playing a significant role in the folding of client proteins. This cellular protein is linked to the progression of several cancer types, including breast cancer, lung cancer, and gastrointestinal stromal tumors. Several oncogenic kinases are Hsp90 clients and their activity depends on this molecular chaperone. This makes HSP90 a prominent therapeutic target for cancer treatment. Studies have confirmed the inhibition of HSP90 as a striking therapeutic treatment for cancer management. In this study, we have utilized machine learning and different in silico approaches to screen the KCB database to identify the potential HSP90 inhibitors. Further evaluation of these inhibitors on various cancer cell lines showed favorable inhibitory activity. These inhibitors could serve as a basis for future development of effective HSP90 inhibitors.

Lone J, Giresha AS, Krishna Ram H, Manjunatha JG, El-Serehy HA, Narayanappa M, Shankar J, Bhardwaj R, Ahmad Guru S, Dharmappa KK. Quercitrin neutralizes sPLA2IIa activity, reduces the inflammatory IL-6 level in PC3 cell lines, and exhibits anti-tumor activity in the EAC-bearing mice model

Human phospholipase A2 group IIa (sPLA2IIa) is an inflammatory enzyme that plays a significant role in tumorigenesis. Inhibiting the sPLA2IIa enzyme with an effective molecule can reduce the inflammatory response and halt cancer progression. The present study evaluates quercitrin, a biflavonoid, for sPLA2IIa inhibition and anticancer activity. Quercitrin inhibited sPLA2IIa activity to a greater extent-at 86.24% ± 1.41 with an IC50 value of 8.77 μM ± 0.9. The nature of sPLA2IIa inhibition was evaluated by increasing calcium concentration from 2.5 to 15 µM and substrate from 20 to 120 nM, which did not alter the level of inhibition. Intrinsic fluorescence and far UV-CD studies confirmed the direct interaction of quercitrin with the sPLA2IIa enzyme. This significantly reduced the sPLA2IIa-induced hemolytic activity and mouse paw edema from 97.32% ± 1.23-16.91% ± 2.03 and 172.87% ± 1.9-118.41% ± 2.53, respectively. As an anticancer activity, quercitrin reduced PC-3 cell viability from 98.66% ± 2.51-18.3% ± 1.52 and significantly decreased the IL-6 level in a dose-dependent manner from 98.35% ± 2.2-37.12% ± 2.4. It increased the mean survival time (MST) of EAC-bearing Swiss albino mice from 30 to 35 days. It obeyed Lipinski's rule of five, suggesting a druggable property. Thus, all the above experimental results were promising and encouraged further investigation into developing quercitrin as a therapeutic drug for both inflammatory diseases and cancers.

The use of predictive models to develop chromatography-based purification processes

Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

Novel Drug Design for Treatment of COVID-19: A Systematic Review of Preclinical Studies

Background

Since the beginning of the novel coronavirus (SARS-CoV-2) disease outbreak, there has been an increasing interest in discovering potential therapeutic agents for this disease. In this regard, we conducted a systematic review through an overview of drug development (in silico, in vitro, and in vivo) for treating COVID-19.

Methods

A systematic search was carried out in major databases including PubMed, Web of Science, Scopus, EMBASE, and Google Scholar from December 2019 to March 2021. A combination of the following terms was used: coronavirus, COVID-19, SARS-CoV-2, drug design, drug development, In silico, In vitro, and In vivo. A narrative synthesis was performed as a qualitative method for the data synthesis of each outcome measure.

Results

A total of 2168 articles were identified through searching databases. Finally, 315 studies (266 in silico, 34 in vitro, and 15 in vivo) were included. In studies with in silico approach, 98 article study repurposed drug and 91 studies evaluated herbal medicine on COVID-19. Among 260 drugs repurposed by the computational method, the best results were observed with saquinavir (n = 9), ritonavir (n = 8), and lopinavir (n = 6). Main protease (n = 154) following spike glycoprotein (n = 62) and other nonstructural protein of virus (n = 45) was among the most studied targets. Doxycycline, chlorpromazine, azithromycin, heparin, bepridil, and glycyrrhizic acid showed both in silico and in vitro inhibitory effects against SARS-CoV-2.

Conclusion

The preclinical studies of novel drug design for COVID-19 focused on main protease and spike glycoprotein as targets for antiviral development. From evaluated structures, saquinavir, ritonavir, eucalyptus, Tinospora cordifolia, aloe, green tea, curcumin, pyrazole, and triazole derivatives in in silico studies and doxycycline, chlorpromazine, and heparin from in vitro and human monoclonal antibodies from in vivo studies showed promised results regarding efficacy. It seems that due to the nature of COVID-19 disease, finding some drugs with multitarget antiviral actions and anti-inflammatory potential is valuable and some herbal medicines have this potential.