Structure. 2021 Aug 16:S0969-2126(21)00257-4. doi: 10.1016/j.str.2021.07.007. Online ahead of print.

ABSTRACT

The COVID-19 pandemic has resulted in 198 million reported infections and more than 4 million deaths as of July 2021 (covid19.who.int). Research to identify effective therapies for COVID-19 includes: (1) designing a vaccine as future protection; (2) de novo drug discovery; and (3) identifying existing drugs to repurpose them as effective and immediate treatments. To assist in drug repurposing and design, we determine two apo structures of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease at ambient temperature by serial femtosecond X-ray crystallography. We employ detailed molecular simulations of selected known main protease inhibitors with the structures and compare binding modes and energies. The combined structural and molecular modeling studies not only reveal the dynamics of small molecules targeting the main protease but also provide invaluable opportunities for drug repurposing and structure-based drug design strategies against SARS-CoV-2.

PMID:34403647 | DOI:10.1016/j.str.2021.07.007

bioRxiv. 2021 Aug 10:2021.06.25.449609. doi: 10.1101/2021.06.25.449609. Preprint.

ABSTRACT

The COVID-19 pandemic has accelerated the need to identify new therapeutics at pace, including through drug repurposing. We employed a Quadratic Unbounded Binary Optimization (QUBO) model, to search for compounds similar to Remdesivir (RDV), the only antiviral against SARS-CoV-2 currently approved for human use, using a quantum-inspired device. We modelled RDV and compounds present in the DrugBank database as graphs, established the optimal parameters in our algorithm and resolved the Maximum Weighted Independent Set problem within the conflict graph generated. We also employed a traditional Tanimoto fingerprint model. The two methods yielded different lists of compounds, with some overlap. While GS-6620 was the top compound predicted by both models, the QUBO model predicted BMS-986094 as second best. The Tanimoto model predicted different forms of cobalamin, also known as vitamin B12. We then determined the half maximal inhibitory concentration (IC 50 ) values in cell culture models of SARS-CoV-2 infection and assessed cytotoxicity. Lastly, we demonstrated efficacy against several variants including SARS-CoV-2 Strain England 2 (England 02/2020/407073), B.1.1.7 (Alpha), B.1.351 (Beta) and B.1.617.2 (Delta). Our data reveal that BMS-986094 and different forms of vitamin B12 are effective at inhibiting replication of all these variants of SARS-CoV-2. While BMS-986094 can cause secondary effects in humans as established by phase II trials, these findings suggest that vitamin B12 deserves consideration as a SARS-CoV-2 antiviral, particularly given its extended use and lack of toxicity in humans, and its availability and affordability. Our screening method can be employed in future searches for novel pharmacologic inhibitors, thus providing an approach for accelerating drug deployment.

PMID:34401881 | PMC:PMC8366797 | DOI:10.1101/2021.06.25.449609

Expert Syst Appl. 2021 Dec 15;185:115695. doi: 10.1016/j.eswa.2021.115695. Epub 2021 Aug 4.

ABSTRACT

During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.

PMID:34400854 | PMC:PMC8359727 | DOI:10.1016/j.eswa.2021.115695

Drug Discov Today. 2021 Aug 13:S1359-6446(21)00360-3. doi: 10.1016/j.drudis.2021.08.002. Online ahead of print.

ABSTRACT

Drug-repurposing technologies are growing in number and maturing. However, comparisons to each other and to reality are hindered because of a lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross-platform comparability, enabling us to continue to strive toward optimal repurposing by decreasing the time and cost of drug discovery and development.

PMID:34400352 | DOI:10.1016/j.drudis.2021.08.002

Med Hypotheses. 2021 Aug 9;155:110656. doi: 10.1016/j.mehy.2021.110656. Online ahead of print.

ABSTRACT

Currently, a plethora of information has been accumulated concerning COVID-19, including the transmission pathway of SARs-CoV-2. Thus, we retrieved targets associated with the development of COVID-19 via PubChem. A total of 517 targets were identified, and signaling pathways responded after infection of SARs-CoV-2 in humans constructed a bubble chart using RPackage. The bubble chart result suggested that the key signaling pathway against COVID-19 was the estrogen signaling pathway associated with AKT1, HSP90AB1, BCL2 targets. The three targets have the strongest affinity with three ligands-Akti-1/2, HSP990, S55746, respectively. In conclusion, this work provides three key elements to alleviate COVID-19 symptoms might be anti-inflammatory effects on SARs-CoV-2-infected lung cells.

PMID:34399157 | DOI:10.1016/j.mehy.2021.110656

Biosaf Health. 2021 Jul 21. doi: 10.1016/j.bsheal.2021.07.003. Online ahead of print.

ABSTRACT

The present pandemic has posed a crisis to the economy of the world and the health sector. Therefore, the race to expand research to understand some good molecular targets for vaccine and therapeutic development for SARS-CoV-2 is inevitable. The newly discovered coronavirus 2019 (COVID-19) is a positive sense, single-stranded RNA, and enveloped virus, assigned to the beta CoV genus. The virus (SARS-CoV-2) is more infectious than the previously detected coronaviruses (MERS and SARS). Findings from many studies have revealed that S protein and RdRp are good targets for drug repositioning, novel therapeutic development (antibodies and small molecule drugs), and vaccine discovery. Therapeutics such as chloroquine, convalescent plasma, monoclonal antibodies, spike binding peptides, and small molecules could alter the ability of S protein to bind to the ACE-2 receptor, and drugs such as remdesivir (targeting SARS-CoV-2 RdRp), favipir, and Emetine could prevent SASR-CoV-2 RNA synthesis. The novel vaccines such as mRNA1273 (Moderna), 3LNP-mRNAs (Pfizer/BioNTech), and ChAdOx1-S (University of Oxford/Astra Zeneca) targeting S protein have proven to be effective in combating the present pandemic. Further exploration of the potential of S protein and RdRp is crucial in fighting the present pandemic.

PMID:34396086 | PMC:PMC8346354 | DOI:10.1016/j.bsheal.2021.07.003

Front Genet. 2021 Jul 28;12:707836. doi: 10.3389/fgene.2021.707836. eCollection 2021.

ABSTRACT

Repurposing is an increasingly attractive method within the field of drug development for its efficiency at identifying new therapeutic opportunities among approved drugs at greatly reduced cost and time of more traditional methods. Repurposing has generated significant interest in the realm of rare disease treatment as an innovative strategy for finding ways to manage these complex conditions. The selection of which agents should be tested in which conditions is currently informed by both human and machine discovery, yet the appropriate balance between these approaches, including the role of artificial intelligence (AI), remains a significant topic of discussion in drug discovery for rare diseases and other conditions. Our drug repurposing team at Vanderbilt University Medical Center synergizes machine learning techniques like phenome-wide association study-a powerful regression method for generating hypotheses about new indications for an approved drug-with the knowledge and creativity of scientific, legal, and clinical domain experts. While our computational approaches generate drug repurposing hits with a high probability of success in a clinical trial, human knowledge remains essential for the hypothesis creation, interpretation, "go-no go" decisions with which machines continue to struggle. Here, we reflect on our experience synergizing AI and human knowledge toward realizable patient outcomes, providing case studies from our portfolio that inform how we balance human knowledge and machine intelligence for drug repurposing in rare disease.

PMID:34394194 | PMC:PMC8355705 | DOI:10.3389/fgene.2021.707836

Front Pharmacol. 2021 Jul 28;12:709856. doi: 10.3389/fphar.2021.709856. eCollection 2021.

ABSTRACT

The onset of the 2019 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated the identification of approved drugs to treat the disease, before the development, approval and widespread administration of suitable vaccines. To identify such a drug, we used a visual analytics workflow where computational tools applied over an AI-enhanced biomedical knowledge graph were combined with human expertise. The workflow comprised rapid augmentation of knowledge graph information from recent literature using machine learning (ML) based extraction, with human-guided iterative queries of the graph. Using this workflow, we identified the rheumatoid arthritis drug baricitinib as both an antiviral and anti-inflammatory therapy. The effectiveness of baricitinib was substantiated by the recent publication of the data from the ACTT-2 randomised Phase 3 trial, followed by emergency approval for use by the FDA, and a report from the CoV-BARRIER trial confirming significant reductions in mortality with baricitinib compared to standard of care. Such methods that iteratively combine computational tools with human expertise hold promise for the identification of treatments for rare and neglected diseases and, beyond drug repurposing, in areas of biological research where relevant data may be lacking or hidden in the mass of available biomedical literature.

PMID:34393789 | PMC:PMC8356560 | DOI:10.3389/fphar.2021.709856

Front Pharmacol. 2021 Jul 28;12:700776. doi: 10.3389/fphar.2021.700776. eCollection 2021.

ABSTRACT

Since the beginning of the COVID-19 pandemic, pharmaceutical treatment hypotheses have abounded, each requiring careful evaluation. A randomized controlled trial generally provides the most credible evaluation of a treatment, but the efficiency and effectiveness of the trial depend on the existing evidence supporting the treatment. The researcher must therefore compile a body of evidence justifying the use of time and resources to further investigate a treatment hypothesis in a trial. An observational study can provide this evidence, but the lack of randomized exposure and the researcher's inability to control treatment administration and data collection introduce significant challenges. A proper analysis of observational health care data thus requires contributions from experts in a diverse set of topics ranging from epidemiology and causal analysis to relevant medical specialties and data sources. Here we summarize these contributions as 10 rules that serve as an end-to-end introduction to retrospective pharmacoepidemiological analyses of observational health care data using a running example of a hypothetical COVID-19 study. A detailed supplement presents a practical how-to guide for following each rule. When carefully designed and properly executed, a retrospective pharmacoepidemiological analysis framed around these rules will inform the decisions of whether and how to investigate a treatment hypothesis in a randomized controlled trial. This work has important implications for any future pandemic by prescribing what we can and should do while the world waits for global vaccine distribution.

PMID:34393782 | PMC:PMC8357144 | DOI:10.3389/fphar.2021.700776

Biomed Pharmacother. 2021 Aug 12;142:112020. doi: 10.1016/j.biopha.2021.112020. Online ahead of print.

ABSTRACT

Chagas disease, caused by Trypanosoma cruzi, is treated with only two drugs; benznidazole and nifurtimox. These drugs have some disadvantages, including their efficacy only in the acute or early infection phases, adverse effects during their use, and the resistance that the parasite has developed to their activity. Therefore, it is necessary to identify new, safe and effective therapeutic alternatives to treat Chagas disease, though governments and the pharmaceutical industry have shown a lack of interest in contributing to this solution. Institutions and research groups on the other hand have worked on some strategies that can help to address the problem. Some of these include the modification of conventional drug dosages, drug repurposing, and combined therapy. Plants and derived compounds with antiparasitic effects have also been studied, taking advantage of traditional medicinal knowledge. Others have studied the parasite to identify essential genes that can be used as therapeutic targets to design new, targeted drugs. Some of these studies have generated promising results, but few reach clinical phase studies. Institutions and research groups should be encouraged to unify efforts and cover all aspects of drug development according to resources and knowledge availability. In the end, this exchange of knowledge would lead to the development of new therapeutic alternatives to treat Chagas disease and benefit the populations it affects.

PMID:34392087 | DOI:10.1016/j.biopha.2021.112020

Int J Pharm. 2021 Aug 11:121002. doi: 10.1016/j.ijpharm.2021.121002. Online ahead of print.

ABSTRACT

3D printing has revolutionized pharmaceutical research, with applications encompassing tissue regeneration and drug delivery. Adopting 3D printing for pharmaceutical drug delivery personalization via nanoparticle-reinforced hydrogel scaffolds promises great regenerative potential. Herein, we engineered novel core/shell, bio-inspired, drug-loaded polymeric hydrogel scaffolds for pharmaceutically personalized drug delivery and superior osteoregeneration. Scaffolds were developed using biopolymeric blends of gelatin, polyvinyl alcohol and hyaluronic acid and integrated with composite doxycycline/hydroxyapatite/polycaprolactone nanoparticles (DX/HAp/PCL) innovatively via 3D printing. The developed scaffolds were optimized for swelling pattern and in-vitro drug release through tailoring the biphasic microstructure and wet/dry state to attain various pharmaceutical personalization platforms. Freeze-dried scaffolds with nanoparticles reinforcing the core phase (DX/HAp/PCL-LCS-FD) demonstrated favorably controlled swelling, preserved structural integrity and controlled drug release over 28 days. DX/HAp/PCL-LCS-FD featured double-ranged pore size (90.4 ± 3.9 and 196.6 ± 38.8 µm for shell and core phases, respectively), interconnected porosity and superior mechanical stiffness (74.5 ± 6.8 kPa) for osteogenic functionality. Cell spreading analysis, computed tomography and histomorphometry in a rabbit tibial model confirmed osteoconduction, bioresorption, immune tolerance and bone regenerative potential of the original scaffolds, affording complete defect healing with bone tissue. Our findings suggest that the developed platforms promise prominent local drug delivery and bone regeneration.

PMID:34390809 | DOI:10.1016/j.ijpharm.2021.121002

Cancer Lett. 2021 Aug 11:S0304-3835(21)00398-0. doi: 10.1016/j.canlet.2021.08.009. Online ahead of print.

ABSTRACT

Multiple myeloma (MM) is incurable and the second most common hematologic malignancy in plasma cells. Multiple myeloma stem-like cells (MMSCs), a rare population of MM cells, are believed to be the major cause of drug resistance and high recurrence rates in patients with MM. Therefore, developing novel strategies to eradicate MMSCs may favor myeloma treatment. In this study, based on the drug repositioning strategy, we found that albendazole (ABZ), a broad-spectrum antiparasitic drug, selectively suppresses the proliferation of multiple myeloma cells in vitro and in vivo and decreases number of aldehyde dehydrogenase (ALDH)-positive MMSCs in MM. Furthermore, RNA-seq of MM cells after ABZ treatment revealed that inhibition of the nuclear factor kappa-B (NF-κB) pathway is a key mediator of ABZ against MM. Moreover, we demonstrated that ABZ can resensitize cells resistant to bortezomib and overcome MMSCs-induced bortezomib resistance by decreasing ALDH1+ MMSCs numbers. Our findings provide preclinical evidence for utilizing the previously known pharmacologically active drug albendazole for the treatment of multiple myeloma.

PMID:34390764 | DOI:10.1016/j.canlet.2021.08.009

Biomed Pharmacother. 2021 Aug 5;142:112015. doi: 10.1016/j.biopha.2021.112015. Online ahead of print.

ABSTRACT

COVID-19, an infectious disease, has emerged as one of the leading causes of death worldwide, making it one of the severe public health issues in recent decades. nCoV, the novel SARS coronavirus that causes COVID-19, has brought together scientists in the quest for possible therapeutic and preventive measures. The development of new drugs to manage COVID-19 effectively is a challenging and time-consuming process, thus encouraging extensive investigation of drug repurposing and repositioning candidates. Several medications, including remdesivir, hydroxychloroquine, chloroquine, lopinavir, favipiravir, ribavirin, ritonavir, interferons, azithromycin, capivasertib and bevacizumab, are currently under clinical trials for COVID-19. In addition, several medicinal plants with considerable antiviral activities are potential therapeutic candidates for COVID-19. Statistical data show that the pandemic is yet to slow down, and authorities are placing their hopes on vaccines. Within a short period, four types of vaccines, namely, whole virus, viral vector, protein subunit, and nucleic acid (RNA/DNA), which can confer protection against COVID-19 in different ways, were already in a clinical trial. SARS-CoV-2 variants spread is associated with antibody escape from the virus Spike epitopes, which has grave concerns for viral re-infection and even compromises the effectiveness of the vaccines. Despite these efforts, COVID-19 treatment is still solely based on clinical management through supportive care. We aim to highlight the recent trends in COVID-19, relevant statistics, and clinical findings, as well as potential therapeutics, including in-line treatment methods, preventive measures, and vaccines to combat the prevalence of COVID-19.

PMID:34388532 | PMC:PMC8339548 | DOI:10.1016/j.biopha.2021.112015

Comput Biol Med. 2021 Aug 8;136:104748. doi: 10.1016/j.compbiomed.2021.104748. Online ahead of print.

ABSTRACT

The outbreak of the triple mutant strain of severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) was more virulent and pathogenic than its original strain. The viral triple mutant strain of SARS-COV-2 is extremely adaptive and increases penetrability into the host. The triple mutant viral strain was first reported in Brazil and South Africa and then communicated to different countries responsible for the second wave of the coronavirus disease (COVID-19) global pandemic with a high mortality rate. The reported genomic mutations are responsible for the alterations in the viral functional and structural proteins, causing the ineffectiveness of the existing antiviral therapy targeting these proteins. Thus, in current research, molecular docking simulation-based virtual screening of a ligand library consisting of FDA-approved existing drugs followed by molecular dynamics simulation-based validation of leads was performed to develop a potent inhibitor molecule for the triple mutant viral strain SARS-CoV-2. Based on the safety profile, tamibarotene was selected as a safe and effective drug candidate for developing therapy against the triple mutant viral spike protein of SARS-CoV-2.

PMID:34388463 | PMC:PMC8349365 | DOI:10.1016/j.compbiomed.2021.104748

J Chem Inf Model. 2021 Aug 13. doi: 10.1021/acs.jcim.1c00683. Online ahead of print.

ABSTRACT

With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for in vitro testing by several groups. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein, we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA-approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, lumefantrine, an antimalarial was selected for testing and showed limited antiviral activity in cell-based assays while demonstrating binding (Kd 259 nM) to the spike protein using microscale thermophoresis. Several other compounds which we prioritized have since been tested by others and were also found to be active in vitro. This combined machine learning and in vitro testing approach can be expanded to virtually screen available molecules with predicted activity against SARS-CoV-2 reference WIV04 strain and circulating variants of concern. In the process of this work, we have created multiple iterations of machine learning models that can be used as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 with over 500 compounds is now freely available at www.assaycentral.org.

PMID:34387990 | DOI:10.1021/acs.jcim.1c00683

Front Microbiol. 2021 Jul 27;12:707629. doi: 10.3389/fmicb.2021.707629. eCollection 2021.

ABSTRACT

It has been demonstrated that some non-steroidal anti-inflammatory drugs (NSAIDs), like acetylsalicylic acid, diclofenac, and ibuprofen, have anti-biofilm activity in concentrations found in human pharmacokinetic studies, which could fuel an interest in repurposing these well tolerated drugs as adjunctive therapies for biofilm-related infections. Here we sought to review the currently available data on the anti-biofilm activity of NSAIDs and its relevance in a clinical context. We performed a systematic literature review to identify the most commonly tested NSAIDs drugs in the last 5 years, the bacterial species that have demonstrated to be responsive to their actions, and the emergence of resistance to these molecules. We found that most studies investigating NSAIDs' activity against biofilms were in vitro, and frequently tested non-clinical bacterial isolates, which may not adequately represent the bacterial populations that cause clinically-relevant biofilm-related infections. Furthermore, studies concerning NSAIDs and antibiotic resistance are scarce, with divergent outcomes. Although the potential to use NSAIDs to control biofilm-related infections seems to be an exciting avenue, there is a paucity of studies that tested these drugs using appropriate in vivo models of biofilm infections or in controlled human clinical trials to support their repurposing as anti-biofilm agents.

PMID:34385992 | PMC:PMC8353384 | DOI:10.3389/fmicb.2021.707629

Nat Genet. 2021 Aug 12. doi: 10.1038/s41588-021-00909-9. Online ahead of print.

ABSTRACT

The functional interpretation of genome-wide association studies (GWAS) is challenging due to the cell-type-dependent influences of genetic variants. Here, we generated comprehensive maps of expression quantitative trait loci (eQTLs) for 659 microdissected human kidney samples and identified cell-type-eQTLs by mapping interactions between cell type abundances and genotypes. By partitioning heritability using stratified linkage disequilibrium score regression to integrate GWAS with single-cell RNA sequencing and single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing data, we prioritized proximal tubules for kidney function and endothelial cells and distal tubule segments for blood pressure pathogenesis. Bayesian colocalization analysis nominated more than 200 genes for kidney function and hypertension. Our study clarifies the mechanism of commonly used antihypertensive and renal-protective drugs and identifies drug repurposing opportunities for kidney disease.

PMID:34385711 | DOI:10.1038/s41588-021-00909-9

Trials. 2021 Aug 11;22(1):530. doi: 10.1186/s13063-021-05475-3.

ABSTRACT

BACKGROUND: Systemic lupus erythematosus (SLE) is an autoimmune disease that can involve multiple organs or systems. Lupus nephritis (LN) is associated with high mortality and morbidity. However, plenty of patients do not respond to present treatment or relapse. Iguratimod (IGU) is a new small molecular, anti-rheumatic drug and has shown the potential for drug repurposing from rheumatoid arthritis (RA) to LN treatment. It has been approved for treating RA in northeast Asia. Beyond expectation in a recent observational study, over 90% of thirteen refractory LN patients responded to iguratimod monotherapy in 24 weeks, with no steroids dose increasing or any other medication add-on during the entire follow-up.

METHODS/DESIGN: This study is a multi-center, randomized, 52-week parallel positive drug-controlled study. The study was designed as a head-to-head comparison between the iguratimod and present first-line therapy on LN patients. A total of 120 patients (60 patients each group) is in the enrolling plan. All enrolled patients are assigned randomly into trial and control groups. The patients will be selected from six study sites in China and will all have biopsy-proven active lupus nephritis. In the first 24 weeks of the trial, IGU is compared with cyclophosphamide as an induction therapy, and in the second 24 weeks, IGU is compared with azathioprine as a maintenance therapy. The primary outcome is renal remission rate including both complete remission and partial remission at week 52, which will be analyzed using a non-inferiority hypothesis test.

DISCUSSION: Most patients diagnosed with SLE will develop LN within 5 years and LN remains a major cause of morbidity and death for SLE patients. Although some medications are proven effective for the treatment of this condition, at least 20-35% LN patients have to suffer from relapse or ineffective treatment and medication intolerance is also frequent. This trial is designed to demonstrate whether iguratimod can be used as an alternative induction or maintenance therapy in subjects who have lupus nephritis. Data from this study will provide an evidence on whether or not iguratimod should be recommended to active LN patients.

TRIAL REGISTRATION: ClinicalTrials.gov NCT02936375 . Registered on October 18, 2016.

PMID:34380536 | DOI:10.1186/s13063-021-05475-3

Microbiol Spectr. 2021 Aug 11:e0047221. doi: 10.1128/Spectrum.00472-21. Online ahead of print.

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a causative agent of the coronavirus disease 2019 (COVID-19) pandemic, and the development of therapeutic interventions is urgently needed. So far, monoclonal antibodies and drug repositioning are the main methods for drug development, and this effort was partially successful. Since the beginning of the COVID-19 pandemic, the emergence of SARS-CoV-2 variants has been reported in many parts of the world, and the main concern is whether the current vaccines and therapeutics are still effective against these variant viruses. Viral entry and viral RNA-dependent RNA polymerase (RdRp) are the main targets of current drug development; therefore, the inhibitory effects of transmembrane serine protease 2 (TMPRSS2) and RdRp inhibitors were compared among the early SARS-CoV-2 isolate (lineage A) and the two recent variants (lineage B.1.1.7 and lineage B.1.351) identified in the United Kingdom and South Africa, respectively. Our in vitro analysis of viral replication showed that the drugs targeting TMPRSS2 and RdRp are equally effective against the two variants of concern. IMPORTANCE The COVID-19 pandemic is causing unprecedented global problems in both public health and human society. While some vaccines and monoclonal antibodies were successfully developed very quickly and are currently being used, numerous variants of the causative SARS-CoV-2 are emerging and threatening the efficacy of vaccines and monoclonal antibodies. In order to respond to this challenge, we assessed antiviral efficacy of small-molecule inhibitors that are being developed for treatment of COVID-19 and found that they are still very effective against the SARS-CoV-2 variants. Since most small-molecule inhibitors target viral or host factors other than the mutated sequence of the viral spike protein, they are expected to be potent control measures against the COVID-19 pandemic.

PMID:34378968 | DOI:10.1128/Spectrum.00472-21

Biomater Sci. 2021 Aug 11. doi: 10.1039/d1bm00486g. Online ahead of print.

ABSTRACT

Duloxetine (DLX) is a selective serotonin and noradrenaline reuptake inhibitor (SNRI) used for the treatment of pain, but it has been reported to show side effects in 10-20% of patients. Its analgesic efficacy in central pain is putatively related to its influence on descending inhibitory neuronal pathways. However, DLX can also affect the activation of microglia. This study was performed to investigate whether PLGA nanoparticles (NPs), which are expected to enhance targeting to microglia, can improve the analgesic efficacy and limit the side effects of DLX. PLGA NPs encapsulating a low dose of DLX (DLX NPs) were synthesized and characterized and their localization was determined. The analgesic and anti-inflammatory effects of DLX NPs were evaluated in a spinal nerve ligation (SNL)-induced neuropathic pain model. The analgesic effect of DLX lasted for only a few hours and disappeared within 1 day. However, DLX NPs alleviated mechanical allodynia, and the effect was maintained for 1 week. DLX NPs were localized to the spinal microglia and suppressed microglial activation, phosphorylation of p38/NF-κB-mediated pathways and the production of inflammatory cytokines in the spinal dorsal horn of SNL rats. We demonstrated that DLX NPs can provide a prolonged analgesic effect by enhanced targeting of microglia. Our observations imply that DLX delivery through nanoparticle encapsulation allows drug repositioning with a prolonged analgesic effect, and reduces the potential side effects of abuse and overdose.

PMID:34378557 | DOI:10.1039/d1bm00486g