STAR Protoc. 2021 Jul 7;2(3):100653. doi: 10.1016/j.xpro.2021.100653. eCollection 2021 Sep 17.

ABSTRACT

Drug repositioning represents a cost- and time-efficient strategy for drug development. Artificial intelligence-based algorithms have been applied in drug repositioning by predicting drug-target interactions in an efficient and high throughput manner. Here, we present a workflow of in silico drug repositioning for host-based antivirals using specially defined targets, a refined list of drug candidates, and an easily implemented computational framework. The workflow described here can also apply to more general purposes, especially when given a user-defined druggable target gene set. For complete details on the use and execution of this protocol, please refer to Li et al. (2021).

PMID:34286288 | PMC:PMC8273420 | DOI:10.1016/j.xpro.2021.100653

Comput Struct Biotechnol J. 2021 Jun 10;19:3674-3681. doi: 10.1016/j.csbj.2021.06.013. eCollection 2021.

ABSTRACT

Mutations in leucine-rich repeat kinase 2 (LRRK2) are a frequent cause of autosomal dominant Parkinson's disease (PD) and have been associated with familial and sporadic PD. Reducing the kinase activity of LRRK2 is a promising therapeutic strategy since pathogenic mutations increase the kinase activity. Several small-molecule LRRK2 inhibitors are currently under investigation for the treatment of PD. However, drug discovery and development are always accompanied by high costs and a risk of late failure. The use of already approved drugs for a new indication, which is known as drug repositioning, can reduce the cost and risk. In this study, we applied a structure-based drug repositioning approach to identify new LRRK2 inhibitors that are already approved for a different indication. In a large-scale structure-based screening, we compared the protein-ligand interaction patterns of known LRRK2 inhibitors with protein-ligand complexes in the PDB. The screening yielded 6 drug repositioning candidates. Two of these candidates, Sunitinib and Crizotinib, demonstrated an inhibition potency (IC50) and binding affinity (Kd) in the nanomolar to micromolar range. While Sunitinib has already been known to inhibit LRRK2, Crizotinib is a novel LRRK2 binder. Our results underscore the potential of structure-based methods for drug discovery and development. In light of the recent breakthroughs in cryo-electron microscopy and structure prediction, we believe that structure-based approaches like ours will grow in importance.

PMID:34285770 | PMC:PMC8258795 | DOI:10.1016/j.csbj.2021.06.013

Mol Cell Neurosci. 2021 Jul 17:103656. doi: 10.1016/j.mcn.2021.103656. Online ahead of print.

ABSTRACT

Multiple sclerosis (MS) is a neuroinflammatory disorder leading to chronic disability. Brain lesions in MS commonly arise in normal-appearing white matter (NAWM). Genome-wide association studies (GWAS) have identified genetic variants associated with MS. Transcriptome alterations have been observed in case-control studies of NAWM. We developed a Cross-Dataset Evaluation (CDE) function for our network-based tool, Edge-Weighted Dense Module Search of GWAS (EW_dmGWAS). We applied CDE to integrate publicly available MS GWAS summary statistics of 41,505 cases and controls with collectively 38 NAWM expression samples, using the human protein interactome as the reference network, to investigate biological underpinnings of MS etiology. We validated the resulting modules with colocalization of GWAS and expression quantitative trait loci (eQTL) signals, using GTEx Consortium expression data for MS-relevant tissues: 14 brain tissues and 4 immune-related tissues. Other network assessments included a drug target query and functional gene set enrichment analysis. CDE prioritized a MS NAWM network containing 55 unique genes. The gene list was enriched (p-value = 2.34 × 10-7) with GWAS-eQTL colocalized genes: CDK4, IFITM3, MAPK1, MAPK3, METTL12B and PIK3R2. The resultant network also included drug signatures of FDA-approved medications. Gene set enrichment analysis revealed the top functional term "intracellular transport of virus", among other viral pathways. We prioritize critical genes from the resultant network: CDK4, IFITM3, MAPK1, MAPK3, METTL12B and PIK3R2. Enriched drug signatures suggest potential drug targets and drug repositioning strategies for MS. Finally, we propose mechanisms of potential MS viral onset, based on prioritized gene set and functional enrichment analysis.

PMID:34284104 | DOI:10.1016/j.mcn.2021.103656

Mol Divers. 2021 Jul 19. doi: 10.1007/s11030-021-10274-8. Online ahead of print.

ABSTRACT

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

PMID:34282519 | DOI:10.1007/s11030-021-10274-8

Int J Mol Sci. 2021 Jul 2;22(13):7164. doi: 10.3390/ijms22137164.

ABSTRACT

Recent reports suggest a link between positive regulation of the Hippo pathway with bipolar disorder (BD), and the Hippo pathway is known to interact with multiple other signaling pathways previously associated with BD and other psychiatric disorders. In this study, neuronal-like NT2 cells were treated with amisulpride (10 µM), aripiprazole (0.1 µM), clozapine (10 µM), lamotrigine (50 µM), lithium (2.5 mM), quetiapine (50 µM), risperidone (0.1 µM), valproate (0.5 mM), or vehicle control for 24 h. Genome-wide mRNA expression was quantified and analyzed using gene set enrichment analysis (GSEA), with genes belonging to Hippo, Wnt, Notch, TGF- β, and Hedgehog retrieved from the KEGG database. Five of the eight drugs downregulated the genes of the Hippo pathway and modulated several genes involved in the interacting pathways. We speculate that the regulation of these genes, especially by aripiprazole, clozapine, and quetiapine, results in a reduction of MAPK and NFκB pro-inflammatory signaling through modulation of Hippo, Wnt, and TGF-β pathways. We also employed connectivity map analysis to identify compounds that act on these pathways in a similar manner to the known psychiatric drugs. Thirty-six compounds were identified. The presence of antidepressants and antipsychotics validates our approach and reveals possible new targets for drug repurposing.

PMID:34281223 | DOI:10.3390/ijms22137164

J Interferon Cytokine Res. 2021 Jul;41(7):244-257. doi: 10.1089/jir.2020.0156.

ABSTRACT

Severe Acute Respiratory Syndrome-Coronavirus (SARS-CoV-2), which initiated as an endemic from China, converted into a pandemic disease worldwide within a couple of months' time. This has led researchers from all over the world to come together to find and develop possible curative or preventive strategies, including vaccine development, drug repurposing, plasma therapy, drug discovery, and cytokine-based therapies. Herein, we are providing, a summarized overview of immunopathology of the SARS-CoV-2 along with various therapeutic strategies undertaken to COVID-19 with a vision for their possible outcome. High levels of proinflammatory cytokines such as interleukin (IL)-7, G-CSF, IP-10, TNF-α, monocyte chemoattractant protein-1 (MCP-1), and IL-2 in severe cases of COVID-19 have been observed. Immune responses play significant roles in the determination of SARS-CoV-2 pathogenesis. Thus, exploring the underlying mechanism of the immune system response to SARS-CoV-2 infection would help in the prediction of disease course and selection of intensive care and therapeutic strategy. As an effort toward developing possible therapeutics for COVID-19, we highlighted different types of vaccines, which are under clinical trials, and also discussed the impact of genome variability on efficacy of vaccine under development.

PMID:34280026 | DOI:10.1089/jir.2020.0156

Ann Neurol. 2021 Jul 19. doi: 10.1002/ana.26169. Online ahead of print.

ABSTRACT

OBJECTIVE: To identify disease relevant genes and explore underlying immunological mechanisms that contribute to early and late onset forms of myasthenia gravis.

METHODS: We used a novel genomic methodology to integrate genome wide association study (GWAS) findings in myasthenia gravis with cell-type specific information such as gene expression patterns and promotor-enhancer interactions, in order to identify disease relevant genes. Subsequently, we conducted additional genomic investigations including an expression quantitative analysis of 313 healthy people to provide mechanistic insights.

RESULTS: We identified genes that were specifically linked to early onset myasthenia gravis based on GWAS associated risk variants including TNIP1, ORMDL3, GSDMB and TRAF3. We showed that regulators of toll-like receptor 4 signalling were enriched only among the early onset disease genes (fold enrichment = 3.85, p = 6.4 x 10-3 ). In contrast, T-cell regulators CD28 and CTLA4 were exclusively linked to late onset disease. We identified two causal genetic variants (rs231770 and rs231735; posterior probability= 0.98 and 0.91) near the CTLA4 gene. Subsequently, we demonstrated that these causal variants result in low expression of CTLA4 (rho =-0.66, p = 1.28 x 10-38 and rho = -0.52, p = 7.01 x 10-22 , for rs231735 and rs231770 respectively).

INTERPRETATION: The disease relevant genes identified in this study are a unique resource for many disciplines including clinicians, scientists and the pharmaceutical industry. The distinct immunological pathways linked to early and late onset myasthenia gravis carry important implications for drug repurposing opportunities and for future studies of drug development. This article is protected by copyright. All rights reserved.

PMID:34279044 | DOI:10.1002/ana.26169

J Biomol Struct Dyn. 2021 Jul 19:1-10. doi: 10.1080/07391102.2021.1947895. Online ahead of print.

ABSTRACT

Since the first appearance of a novel coronavirus pneumonia (NCP) caused by a novel human coronavirus, and especially after the infection started its rapid spread over the world causing the COVID-19 (coronavirus disease 2019) pandemics, a very substantial part of the scientific community is engaged in the intensive research dedicated to finding of the potential therapeutics to cure this disease. As repurposing of existing drugs represents the only instant solution for those infected with the virus, we have been working on utilization of the structure-based virtual screening method to find some potential medications. In this study, we screened a library of 646 FDA approved drugs against the receptor-binding domain of the SARS-CoV-2 spike (S) protein and the main protease of this virus. Scoring functions revealed that some of the anticancer drugs (such as Pazopanib, Irinotecan, and Imatinib), antipsychotic drug (Risperidone), and antiviral drug (Raltegravir) have a potential to interact with both targets with high efficiency. Further we performed molecular dynamics simulations to understand the evolution in protein upon interaction with drug. Also, we have performed a phylogenetic analysis of 43 different coronavirus strains infecting 12 different mammalian species.Communicated by Ramaswamy H. Sarma.

PMID:34278954 | DOI:10.1080/07391102.2021.1947895

Patterns (N Y). 2021 Jul 14:100325. doi: 10.1016/j.patter.2021.100325. Online ahead of print.

ABSTRACT

An effective monotherapy to target the complex and multifactorial pathology of SARS-CoV-2 infection poses a challenge to drug repositioning, which can be improved by combination therapy. We developed an online network pharmacology-based drug repositioning platform, COVID-CDR (http://vafaeelab.com/COVID19repositioning.html), that enables a visual and quantitative investigation of the interplay between the drug primary targets and the SARS-CoV-2-host interactome in the human protein-protein interaction network. COVID-CDR prioritizes drug combinations with potential to act synergistically through different, yet potentially complementary pathways. It provides the options for understanding multi-evidence drug-pair similarity scores along with several other relevant information on individual drugs or drug pairs. Overall, COVID-CDR is the first-of-its-kind online platform that provides a systematic approach for pre-clinical in silico investigation of combination therapies for treating COVID-19 at the fingertips of the clinicians and researchers.

PMID:34278363 | PMC:PMC8277549 | DOI:10.1016/j.patter.2021.100325

ACS Omega. 2021 Jun 25;6(27):17472-17482. doi: 10.1021/acsomega.1c01865. eCollection 2021 Jul 13.

ABSTRACT

The drug-resistant strains of Mycobacterium tuberculosis (M.tb) are evolving at an alarming rate, and this indicates the urgent need for the development of novel antitubercular drugs. However, genetic mutations, complex cell wall system of M.tb, and influx-efflux transporter systems are the major permeability barriers that significantly affect the M.tb drugs activity. Thus, most of the small molecules are ineffective to arrest the M.tb cell growth, even though they are effective at the cellular level. To address the permeability issue, different machine learning models that effectively distinguish permeable and impermeable compounds were developed. The enzyme-based (IC50) and cell-based (minimal inhibitory concentration) data were considered for the classification of M.tb permeable and impermeable compounds. It was assumed that the compounds that have high activity in both enzyme-based and cell-based assays possess the required M.tb cell wall permeability. The XGBoost model was outperformed when compared to the other models generated from different algorithms such as random forest, support vector machine, and naïve Bayes. The XGBoost model was further validated using the validation data set (21 permeable and 19 impermeable compounds). The obtained machine learning models suggested that various descriptors such as molecular weight, atom type, electrotopological state, hydrogen bond donor/acceptor counts, and extended topochemical atoms of molecules are the major determining factors for both M.tb cell permeability and inhibitory activity. Furthermore, potential antimycobacterial drugs were identified using computational drug repurposing. All the approved drugs from DrugBank were collected and screened using the developed permeability model. The screened compounds were given as input in the PASS server for the identification of possible antimycobacterial compounds. The drugs that were retained after two filters were docked to the active site of 10 different potential antimycobacterial drug targets. The results obtained from this study may improve the understanding of M.tb permeability and activity that may aid in the development of novel antimycobacterial drugs.

PMID:34278133 | PMC:PMC8280707 | DOI:10.1021/acsomega.1c01865

Front Microbiol. 2021 Jun 30;12:653562. doi: 10.3389/fmicb.2021.653562. eCollection 2021.

ABSTRACT

With a continuous threat of antimicrobial resistance on human health worldwide, efforts for new alternatives are ongoing for the management of bacterial infectious diseases. Natural products of land and sea, being conceived to be having fewer side effects, pose themselves as a welcome relief. In this respect, we have taken a scaffolded approach to unearthing the almost unexplored chemical constituents of Malaysian red seaweed, Gracilaria edulis. Essentially, a preliminary evaluation of the ethyl acetate and acetone solvent extracts, among a series of six such, revealed potential antibacterial activity against six MDR species namely, Klebsiella pneumoniae, Pseudomonas aeruginosa, Salmonella enterica, methicillin-resistant Staphylococcus aureus (MRSA), Streptococcus pyogenes, and Bacillus subtilis. Detailed analyses of the inlying chemical constituents, through LC-MS and GC-MS chromatographic separation, revealed a library of metabolic compounds. These were led for further virtual screening against selected key role playing proteins in the virulence of the aforesaid bacteria. To this end, detailed predictive pharmacological analyses added up to reinforce Eplerenone as a natural alternative from the plethora of plausible bioactives. Our work adds the ongoing effort to re-discover and repurpose biochemical compounds to combat the antimicrobial resistance offered by the Gram-positive and the -negative bacterial species.

PMID:34276590 | PMC:PMC8279767 | DOI:10.3389/fmicb.2021.653562

Front Pharmacol. 2021 Jun 30;12:617537. doi: 10.3389/fphar.2021.617537. eCollection 2021.

ABSTRACT

Background: Traditional therapeutics targeting Alzheimer's disease (AD)-related subpathologies have so far proved ineffective. Drug repurposing, a more effective strategy that aims to find new indications for existing drugs against other diseases, offers benefits in AD drug development. In this study, we aim to identify potential anti-AD agents through enrichment analysis of drug-induced transcriptional profiles of pathways based on AD-associated risk genes identified from genome-wide association analyses (GWAS) and single-cell transcriptomic studies. Methods: We systematically constructed four gene lists (972 risk genes) from GWAS and single-cell transcriptomic studies and performed functional and genes overlap analyses in Enrichr tool. We then used a comprehensive drug repurposing tool Gene2Drug by combining drug-induced transcriptional responses with the associated pathways to compute candidate drugs from each gene list. Prioritized potential candidates (eight drugs) were further assessed with literature review. Results: The genomic-based gene lists contain late-onset AD associated genes (BIN1, ABCA7, APOE, CLU, and PICALM) and clinical AD drug targets (TREM2, CD33, CHRNA2, PRSS8, ACE, TKT, APP, and GABRA1). Our analysis identified eight AD candidate drugs (ellipticine, alsterpaullone, tomelukast, ginkgolide A, chrysin, ouabain, sulindac sulfide and lorglumide), four of which (alsterpaullone, ginkgolide A, chrysin and ouabain) have shown repurposing potential for AD validated by their preclinical evidence and moderate toxicity profiles from literature. These support the value of pathway-based prioritization based on the disease risk genes from GWAS and scRNA-seq data analysis. Conclusion: Our analysis strategy identified some potential drug candidates for AD. Although the drugs still need further experimental validation, the approach may be applied to repurpose drugs for other neurological disorders using their genomic information identified from large-scale genomic studies.

PMID:34276354 | PMC:PMC8277916 | DOI:10.3389/fphar.2021.617537

Hum Mol Genet. 2021 Jul 19:ddab207. doi: 10.1093/hmg/ddab207. Online ahead of print.

ABSTRACT

Charcot-Marie-Tooth (CMT) disease is a neuropathy that lacks effective therapy. CMT patients show degeneration of peripheral nerves, leading to muscle weakness and loss of proprioception. Loss of mitochondrial oxidative phosphorylation proteins and enzymes of the antioxidant response accompany degeneration of nerves in skin biopsies of CMT patients. Herein, we followed a drug-repurposing approach to find drugs in an FDA-approved library that could prevent development of CMT disease in the Gdap1-null mouse model. We found that the antibiotic florfenicol is a mitochondrial uncoupler that prevents the production of reactive oxygen species and activates respiration in human GDAP1-knockdown neuroblastoma cells and in dorsal root ganglion neurons of Gdap1-null mice. Treatment of CMT-affected Gdap1-null mice with florfenicol has no beneficial effect in the course of the disease. However, administration of florfenicol, or the antioxidant MitoQ to pre-symptomatic GDAP1-null mice prevented weight gain and ameliorated the motor coordination deficiencies that developed in the Gdap1-null mice. Interestingly, both florfenicol and MitoQ halted the decay in mitochondrial and redox proteins in sciatic nerves of Gdap1-null mice supporting that oxidative damage is implicated in the etiology of the neuropathy. These findings support the development of clinical trials for translation of these drugs for treatment of CMT patients.

PMID:34274972 | DOI:10.1093/hmg/ddab207

Eur J Med Chem. 2021 Jul 13;224:113698. doi: 10.1016/j.ejmech.2021.113698. Online ahead of print.

ABSTRACT

Over recent years, many outbreaks caused by (re)emerging RNA viruses have been reported worldwide, including life-threatening Flaviviruses, such as Dengue (DENV) and Zika (ZIKV). Currently, there is only one licensed vaccine against Dengue, Dengvaxia®. However, its administration is not recommended for children under nine years. Still, there are no specific inhibitors available to treat these infectious diseases. Among the flaviviral proteins, NS5 RNA-dependent RNA polymerase (RdRp) is a metalloenzyme essential for viral replication, suggesting that it is a promising macromolecular target since it has no human homolog. Nowadays, several NS5 RdRp inhibitors have been reported, while none inhibitors are currently in clinical development. In this context, this review constitutes a comprehensive work focused on RdRp inhibitors from natural, synthetic, and even repurposing sources. Furthermore, their main aspects associated with the structure-activity relationship (SAR), proposed mechanisms of action, computational studies, and other topics will be discussed in detail.

PMID:34274831 | DOI:10.1016/j.ejmech.2021.113698

Curr Opin Chem Biol. 2021 Jul 15;65:74-84. doi: 10.1016/j.cbpa.2021.06.001. Online ahead of print.

ABSTRACT

Drug repurposing aims to find new uses for already existing and approved drugs. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. We also highlight several applications for cancer using kinase inhibitors, Alzheimer's disease as well as COVID-19.

PMID:34274565 | DOI:10.1016/j.cbpa.2021.06.001

Rev Endocr Metab Disord. 2021 Jul 17. doi: 10.1007/s11154-021-09675-9. Online ahead of print.

ABSTRACT

Developed as an antidiabetic drug, recent evidence suggests that several sodium-glucose co-transporter 2 inhibitors (SGLT2i), especially canagliflozin and dapagliflozin, may exhibit in vitro and in vivo anticancer activities in selected cancer types, including an inhibition of tumor growth and induction of cell death. When used in combination with chemotherapy or radiotherapy, SGLT2i may offer possible synergistic effects in enhancing their treatment efficacy while alleviating associated side effects. Potential mechanisms include a reduction of glucose uptake into cancer cells, systemic glucose restriction, modulation of multiple signaling pathways, and regulation of different gene and protein expression. Furthermore, preliminary clinical findings have reported potential anticancer properties of canagliflozin and dapagliflozin in patients with liver and colon cancers respectively, with reference to decreases in their tumor marker levels. Given its general tolerability and routine use in diabetes management, SGLT2i may be a good candidate for drug repurposing in cancer treatment and as adjunct to conventional therapies. While current evidence reveals that only certain SGLT2i appear to be effective against selected cancer types, further studies are needed to explore the antitumor abilities of each SGLT2i in various cancers. Moreover, clinical trials are called for to evaluate the safety and feasibility of introducing SGLT2i in the treatment regimen of patients with specific cancers, and to identify the preferred route of drug administration for targeted delivery to selected tumor sites.

PMID:34272645 | DOI:10.1007/s11154-021-09675-9

Comput Biol Chem. 2021 Jul 5;93:107536. doi: 10.1016/j.compbiolchem.2021.107536. Online ahead of print.

ABSTRACT

BACKGROUND: Discover possible Drug Target Interactions (DTIs) is a decisive step in the detection of the effects of drugs as well as drug repositioning. There is a strong incentive to develop effective computational methods that can effectively predict potential DTIs, as traditional DTI laboratory experiments are expensive, time-consuming, and labor-intensive. Some technologies have been developed for this purpose, however large numbers of interactions have not yet been detected, the accuracy of their prediction still low, and protein sequences and structured data are rarely used together in the prediction process.

METHODS: This paper presents DTIs prediction model that takes advantage of the special capacity of the structured form of proteins and drugs. Our model obtains features from protein amino-acid sequences using physical and chemical properties, and from drugs smiles (Simplified Molecular Input Line Entry System) strings using encoding techniques. Comparing the proposed model with different existing methods under K-fold cross validation, empirical results show that our model based on ensemble learning algorithms for DTI prediction provide more accurate results from both structures and features data.

RESULTS: The proposed model is applied on two datasets:Benchmark (feature only) datasets and DrugBank (Structure data) datasets. Experimental results obtained by Light-Boost and ExtraTree using structures and feature data results in 98 % accuracy and 0.97 f-score comparing to 94 % and 0.92 achieved by the existing methods. Moreover, our model can successfully predict more yet undiscovered interactions, and hence can be used as a practical tool to drug repositioning. A case study of applying our prediction model on the proteins that are known to be affected by Corona viruses in order to predict the possible interactions among these proteins and existing drugs is performed. Also, our model is applied on Covid-19 related drugs announced on DrugBank. The results show that some drugs like DB00691 and DB05203 are predicted with 100 % accuracy to interact with ACE2 protein. This protein is a self-membrane protein that enables Covid-19 infection. Hence, our model can be used as an effective tool in drug reposition to predict possible drug treatments for Covid-19.

PMID:34271420 | DOI:10.1016/j.compbiolchem.2021.107536

bioRxiv. 2021 Jul 5:2021.07.03.450938. doi: 10.1101/2021.07.03.450938. Preprint.

ABSTRACT

Drug development for specific antiviral agents against coronavirus disease 2019 (COVID-19) is still an unmet medical need as the pandemic continues to spread globally. Although huge efforts for drug repurposing and compound screens have put forth, only few compounds remain in late stage clinical trials. New approaches and assays are needed to accelerate COVID-19 drug discovery and development. Here we report a time-resolved fluorescence resonance energy transfer-based assay that detects the severe acute respiratory syndrome coronavirus 2 (SARS-CoV‑2) nucleocapsid protein (NP) produced in infected cells. It uses two specific anti-NP monoclonal antibodies (MAbs) conjugated to donor and acceptor fluorophores that produces a robust ratiometric signal for high throughput screening of large compound collections. Using this assay, we measured a half maximal inhibitory concentration (IC 50 ) for Remdesivir of 9.3 μM against infection with SARS-CoV-2 USA/WA1/2020 (WA-1). The assay also detected SARS-CoV-2 South African (Beta, β), Brazilian/Japanese variant P.1 (Gamma, γ), and Californian (Epsilon, ε), variants of concern or interest (VoC). Therefore, this homogeneous SARS-CoV-2 NP detection assay can be used for accelerating lead compound discovery for drug development and for evaluating drug efficacy against emerging SARS-CoV-2 VoC.

PMID:34268508 | PMC:PMC8282096 | DOI:10.1101/2021.07.03.450938

Cost Eff Resour Alloc. 2021 Jul 15;19(1):42. doi: 10.1186/s12962-021-00296-2.

ABSTRACT

BACKGROUND: A core evaluation framework that captures the health care and societal benefits of value added medicines (VAMs, also often called repurposed medicines) was proposed in Report 1, aiming to reduce the heterogeneity in value assessment processes across countries and to create incentives for manufacturers to invest into incremental innovation. However, this can be impactful only if the framework can be adapted to heterogeneous health care financing systems in different jurisdictions, and the cost of evidence generation necessitated by the framework takes into account the anticipated benefits for the health care system and rewards for the developers.

AREAS COVERED: The framework could potentially improve the pricing and reimbursement decisions of VAMs by adapting it to different country specific decision-contexts such as deliberative processes, augmented cost-effectiveness frameworks or formal multi-criteria decision analysis (MCDA); alternatively, some of its domains may be added to current general evaluation frameworks of medicines. The proposed evaluation framework may provide a starting point for practices based on which VAMs can be exempted from generic pricing mechanisms or can be integrated into the reimbursement and procurement system, allowing for price differentiation according to their added value. Besides evidence from RCTs, pricing and reimbursement decision processes of VAMs should allow for ex-ante non-RCT evidence for certain domains. Alternatively, relying on ex-post evidence agreements-such as outcome guarantee or coverage with evidence development-can also reduce decision uncertainty.

CONCLUSIONS: The core evaluation framework for VAMs could trigger changes in the existing pricing, reimbursement and procurement practices by improving the appraisal of the added value created by incremental innovation.

PMID:34266465 | DOI:10.1186/s12962-021-00296-2

J Basic Clin Physiol Pharmacol. 2021 Jul 15. doi: 10.1515/jbcpp-2020-0369. Online ahead of print.

ABSTRACT

OBJECTIVES: The new Coronavirus (SARS-CoV-2) created a pandemic in the world in late 2019 and early 2020. Unfortunately, despite the increasing prevalence of the disease, there is no effective drug for the treatment. A computational drug repurposing study would be an appropriate and rapid way to provide an effective drug in the treatment of the coronavirus disease of 2019 (COVID-19) pandemic. In this study, the inhibitory potential of more than 50 antiviral drugs on three important proteins of SARS-CoV-2, was investigated using the molecular docking method.

METHODS: By literature review, three important proteins, including main protease, RNA-dependent RNA polymerase (RdRp), and spike, were selected as the drug targets. The three-dimensional (3D) structure of protease, spike, and RdRp proteins was obtained from the Protein Data Bank. Proteins were energy minimized. More than 50 antiviral drugs were considered as candidates for protein inhibition, and their 3D structure was obtained from Drug Bank. Molecular docking settings were defined using Autodock 4.2 software and the algorithm was executed.

RESULTS: Based on the estimated binding energy of docking and hydrogen bond analysis and the position of drug binding, five drugs including, indinavir, lopinavir, saquinavir, nelfinavir, and remdesivir, had the highest inhibitory potential for all three proteins.

CONCLUSIONS: According to the results, among the mentioned drugs, saquinavir and lopinavir showed the highest inhibitory potential for all three proteins compared to the other drugs. This study suggests that saquinavir and lopinavir could be included in the laboratory phase studies as a two-drug treatment for SARS-CoV-2 inhibition.

PMID:34265888 | DOI:10.1515/jbcpp-2020-0369