Curr Med Chem. 2021 Aug 20. doi: 10.2174/0929867328666210820114025. Online ahead of print.

ABSTRACT

BACKGROUND: SARS-CoV-2, which emerged in Wuhan, China, is a new global threat that has killed millions of people and continues to do so. This pandemic has not only threatened human life but has also triggered economic downturns across the world. Researchers have made significant strides in discovering molecular insights into SARS-CoV-2 pathogenesis and developing vaccines, but there is still no successful cure for SARS-CoV-2 infected patients.

OBJECTIVE: The present study has proposed a drug-repositioning pipeline for the design and discovery of an effective fungal-derived bioactive metabolite as a drug candidate against SARS-CoV-2.

METHODS: Fungal derivative "Cordycepin" was selected for this study to investigate the inhibitory properties against RNA-dependent RNA polymerase (RdRp) (PDB ID: 6M71) of SARS-CoV-2. The pharmacological profile, intermolecular interactions, binding energy, and stability of the compound were determined utilizing cheminformatic approaches. Subsequently, molecular dynamic simulation was performed to better understand the binding mechanism of cordycepin to RdRp.

RESULTS: The pharmacological data and retrieved molecular dynamics simulations trajectories suggest excellent drug-likeliness and greater structural stability of cordycepin, while the catalytic residues (Asp760, Asp761), as well as other active site residues (Trp617, Asp618, Tyr619, Trp800, Glu811) of RdRp, showed better stability during the overall simulation span.

CONCLUSION: Promising results of pharmacological investigation along with molecular simulations revealed that cordycepin exhibited strong inhibitory potential against SARS-CoV-2 polymerase enzyme (RdRp). Hence, cordycepin should be highly recommended to test in a laboratory to confirm its inhibitory potential against the SARS-CoV-2 polymerase enzyme (RdRp).

PMID:34420502 | DOI:10.2174/0929867328666210820114025

Diabetes. 2021 Aug 20:dbi200046. doi: 10.2337/dbi20-0046. Online ahead of print.

ABSTRACT

Completion of the Human Genome Project enabled a novel systems- and network-level understanding of biology, but this remains to be applied for understanding the pathogenesis of type 1 diabetes (T1D). We propose that defining the key gene regulatory networks that drive β-cell dysfunction and death in T1D might enable the design of therapies that target the core disease mechanism, namely, the progressive loss of pancreatic β-cells. Indeed, many successful drugs do not directly target individual disease genes but, rather, modulate the consequences of defective steps, targeting proteins located one or two steps downstream. If we transpose this to the T1D situation, it makes sense to target the pathways that modulate the β-cell responses to the immune assault-in relation to signals that may stimulate the immune response (e.g., HLA class I and chemokine overexpression and/or neoantigen expression) or inhibit the invading immune cells (e.g., PDL1 and HLA-E expression)-instead of targeting only the immune system, as it is usually proposed. Here we discuss the importance of a focus on β-cells in T1D, lessons learned from other autoimmune diseases, the "alternative splicing connection," data mining, and drug repurposing to protect β-cells in T1D and then some of the initial candidates under testing for β-cell protection.

PMID:34417266 | DOI:10.2337/dbi20-0046

Eur J Med Chem. 2021 Aug 14;225:113778. doi: 10.1016/j.ejmech.2021.113778. Online ahead of print.

ABSTRACT

Histone lysine-specific demethylase 1 (LSD1) is an important epigenetic modulator, and is implicated in malignant transformation and tumor pathogenesis in different ways. Therefore, the inhibition of LSD1 provides an attractive therapeutic target for cancer therapy. Based on drug repurposing strategy, we screened our in-house chemical library toward LSD1, and found that the EGFR inhibitor erlotinib, an FDA-approved drug for lung cancer, possessed low potency against LSD1 (IC50 = 35.80 μM). Herein, we report our further medicinal chemistry effort to obtain a highly water-soluble erlotinib analog 5k (>100 mg/mL) with significantly enhanced inhibitory activity against LSD1 (IC50 = 0.69 μM) as well as higher specificity. In MGC-803 cells, 5k suppressed the demethylation of LSD1, indicating its cellular activity against the enzyme. In addition, 5k had a remarkable capacity to inhibit colony formation, suppress migration and induce apoptosis of MGC803 cells. Furthermore, in MGC-803 xenograft mouse model, 5k treatment resulted in significant reduction in tumor size by 81.6% and 96.1% at dosages of 40 and 80 mg/kg/d, respectively. Our findings indicate that erlotinib-based analogs provide a novel structural set of LSD1 inhibitors with potential for further investigation, and may serve as novel candidates for the treatment of LSD1-overexpressing cancers.

PMID:34416665 | DOI:10.1016/j.ejmech.2021.113778

Biomed Pharmacother. 2021 Aug 17;142:112029. doi: 10.1016/j.biopha.2021.112029. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) is a leading cause of cancer related deaths worldwide. It was suggested that albendazole (ABZ) is a powerful inhibitor of several carcinoma types. However, the bioavailability of ABZ is very poor. Additionally, the mechanisms underlying the antitumor effects of ABZ may go beyond its tubulin-inhibiting activity. Therefore, we aimed to examine the effects of ABZ suspension (i.p. and p.o.) and ABZ-loaded cubosomes (LC) on the diethylnitrosamine-induced HCC in mice. ABZ-loaded nanoparticles exhibited a mean particle size of 48.17 ± 0.65 nm and entrapped 93.26 ± 2.48% of ABZ. The in vivo absorption study confirmed a two-fold improvement in the relative bioavailability compared with aqueous ABZ suspension. Furthermore, the oral administration of ABZ cubosomal dispersion demonstrated regression of tumor production rates that was comparable with ABZ (i.p.). ABZ relieved oxidative stress, improved liver function, and decreased necroinflammation score. The antiangiogenic activity was evident as ABZ effectively downregulated tissue expression of CD34, mRNA expression of CD309 and VEGF at the protein expression level. Besides, lower levels of MMP-9 and CXCR4 indicated antimetastatic activity. ABZ showed a considerable level of apoptotic activity as indicated by increased mRNA expression level of p53 and the increased Bax/BCL-2 ratio and active caspase-3. Additionally, Ki-67 expression levels were downregulated showing an antiproliferative potential. These protective effects contributed to increasing survival rate of diethylnitrosamine-treated mice. These effects found to be mediated via interrupting ERK1/2-HIF-1α-p300/CREB interactions. Therefore, our findings revealed that disrupting ERK1/2-HIF-1α-p300/CREB interplay might create a novel therapeutic target for the management of HCC.

PMID:34416629 | DOI:10.1016/j.biopha.2021.112029

Int J Pharm. 2021 Aug 17:121023. doi: 10.1016/j.ijpharm.2021.121023. Online ahead of print.

ABSTRACT

Up to date, there were no approved drugs against coronavirus (COVID-19) disease that dangerously affects global health and the economy. Repurposing the existing drugs would be a promising approach for COVID-19 management. The antidepressant drugs, selective serotonin reuptake inhibitors (SSRIs) class, have antiviral, anti-inflammatory, and anticoagulant effects, which makes them auspicious drugs for COVID 19 treatment. Therefore, this study aimed to predict the possible therapeutic activity of SSRIs against COVID-19. Firstly, molecular docking studies were performed to hypothesize the possible interaction of SSRIs to the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-COV-2) main protease. Secondly, the candidate drug was loaded in lipid polymer hybrid (LPH) nanoparticles to enhance its activity. The studied SSRIs were Fluoxetine hydrochloride (FH), Atomoxteine, Paroxetine, Nisoxteine, Repoxteine RR, and Repoxteine SS. Interestingly, FH could effectively bind with SARS-COV-2 main protease via hydrogen bond formation with low binding energy (-6.7 kcal/mol). Moreover, the optimization of FH-LPH formulation achieved 65.1±2.7% encapsulation efficiency, 10.3±0.4% loading efficiency, 98.5±3.5 nm particle size, and -10.5±0.45 mV zeta potential. Additionally, it improved cellular internalization in a time-dependent manner with good biocompatibility on Human lung fibroblast (CCD-19Lu) cells. Therefore, the study suggested the potential activity of FH-LPH nanoparticles against the COVID-19 pandemic.

PMID:34416332 | DOI:10.1016/j.ijpharm.2021.121023

ACS Med Chem Lett. 2021 Aug 2;12(8):1333-1341. doi: 10.1021/acsmedchemlett.1c00328. eCollection 2021 Aug 12.

ABSTRACT

In the context of drug repositioning and expanding the existing structure-activity relationship around astemizole (AST), a new series of analogues were designed, synthesized, and evaluated for their antiplasmodium activity. Among 46 analogues tested, compounds 21, 30, and 33 displayed high activities against asexual blood stage parasites (PfNF54 IC50 = 0.025-0.043 μM), whereas amide compound 46 additionally showed activity against late-stage gametocytes (stage IV/V; PfLG IC50 = 0.6 ± 0.1 μM) and 860-fold higher selectivity over hERG (46, SI = 43) compared to AST. Several analogues displaying high solubility (Sol > 100 μM) and low cytoxicity in the Chinese hamster ovary (SI > 148) cell line have also been identified.

PMID:34413963 | PMC:PMC8366009 | DOI:10.1021/acsmedchemlett.1c00328

Sci Rep. 2021 Aug 19;11(1):16814. doi: 10.1038/s41598-021-96110-3.

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has emerged as a pandemic. Paucity of information concerning the virus and therapeutic interventions have made SARS-CoV-2 infection a genuine threat to global public health. Therefore, there is a growing need for understanding the molecular mechanism of SARS-CoV-2 infection at cellular level. To address this, we undertook a systems biology approach by analyzing publicly available RNA-seq datasets of SARS-CoV-2 infection of different cells and compared with other lung pathogenic infections. Our study identified several key genes and pathways uniquely associated with SARS-CoV-2 infection. Genes such as interleukin (IL)-6, CXCL8, CCL20, CXCL1 and CXCL3 were upregulated, which in particular regulate the cytokine storm and IL-17 signaling pathway. Of note, SARS-CoV-2 infection strongly activated IL-17 signaling pathway compared with other respiratory viruses. Additionally, this transcriptomic signature was also analyzed to predict potential drug repurposing and small molecule inhibitors. In conclusion, our comprehensive data analysis identifies key molecular pathways to reveal underlying pathological etiology and potential therapeutic targets in SARS-CoV-2 infection.

PMID:34413339 | DOI:10.1038/s41598-021-96110-3

Proc Natl Acad Sci U S A. 2021 Sep 7;118(36):e2105815118. doi: 10.1073/pnas.2105815118.

ABSTRACT

The global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the associated disease COVID-19, requires therapeutic interventions that can be rapidly identified and translated to clinical care. Traditional drug discovery methods have a >90% failure rate and can take 10 to 15 y from target identification to clinical use. In contrast, drug repurposing can significantly accelerate translation. We developed a quantitative high-throughput screen to identify efficacious agents against SARS-CoV-2. From a library of 1,425 US Food and Drug Administration (FDA)-approved compounds and clinical candidates, we identified 17 hits that inhibited SARS-CoV-2 infection and analyzed their antiviral activity across multiple cell lines, including lymph node carcinoma of the prostate (LNCaP) cells and a physiologically relevant model of alveolar epithelial type 2 cells (iAEC2s). Additionally, we found that inhibitors of the Ras/Raf/MEK/ERK signaling pathway exacerbate SARS-CoV-2 infection in vitro. Notably, we discovered that lactoferrin, a glycoprotein found in secretory fluids including mammalian milk, inhibits SARS-CoV-2 infection in the nanomolar range in all cell models with multiple modes of action, including blockage of virus attachment to cellular heparan sulfate and enhancement of interferon responses. Given its safety profile, lactoferrin is a readily translatable therapeutic option for the management of COVID-19.

PMID:34413211 | DOI:10.1073/pnas.2105815118

Front Artif Intell. 2021 Aug 2;4:711467. doi: 10.3389/frai.2021.711467. eCollection 2021.

ABSTRACT

Drug labeling contains an 'INDICATIONS AND USAGE' that provides vital information to support clinical decision making and regulatory management. Effective extraction of drug indication information from free-text based resources could facilitate drug repositioning projects and help collect real-world evidence in support of secondary use of approved medicines. To enable AI-powered language models for the extraction of drug indication information, we used manual reading and curation to develop a Drug Indication Classification and Encyclopedia (DICE) based on FDA approved human prescription drug labeling. A DICE scheme with 7,231 sentences categorized into five classes (indications, contradictions, side effects, usage instructions, and clinical observations) was developed. To further elucidate the utility of the DICE, we developed nine different AI-based classifiers for the prediction of indications based on the developed DICE to comprehensively assess their performance. We found that the transformer-based language models yielded an average MCC of 0.887, outperforming the word embedding-based Bidirectional long short-term memory (BiLSTM) models (0.862) with a 2.82% improvement on the test set. The best classifiers were also used to extract drug indication information in DrugBank and achieved a high enrichment rate (>0.930) for this task. We found that domain-specific training could provide more explainable models without performance sacrifices and better generalization for external validation datasets. Altogether, the proposed DICE could be a standard resource for the development and evaluation of task-specific AI-powered, natural language processing (NLP) models.

PMID:34409286 | PMC:PMC8366025 | DOI:10.3389/frai.2021.711467

Genet Test Mol Biomarkers. 2021 Aug;25(8):563-570. doi: 10.1089/gtmb.2021.0031.

ABSTRACT

Aims: HbE/β-thalassemia is the most prevalent form of severe β-thalassemia in Asian countries. Hydroxyurea (HU) is the most common drug used for the management of sickle-cell anemia but not thalassemia. In this study, we aimed to assess clinical HU response among the Bengali HbE/β-thalassemia patients with respect to the XmnI γGglobin polymorphism and elucidate the association between this polymorphism and HU response efficacy. Materials and Methods: We enrolled 49 transfusion-dependent patients with HbE/β-thalassemia. Fetal hemoglobin levels were measured using high-performance liquid chromatography and complete blood counts were determined pre- and post-HU therapy. Polymerase chain reaction-restriction fragment length polymorphism analyses were performed for genotyping the XmnI γGglobin polymorphism. Results: A total of 30 (61.22%) patients were found to be responders, whereas the remaining 19 (38.78%) were nonresponders. We found 33 patients with the heterozygous (C/T) and three with the homozygous mutant (T/T) genotype status. We obtained a statistically significant correlation (p < 0.001) between the XmnI polymorphism genotype and transfusion-free interval. Patients with the XmnI polymorphism were found to be good responders for HU therapy and showed increased hemoglobin levels. Conclusions: Our findings indicate that HU is a potential drug candidate for thalassemia management, particularly for HbE/β-thalassemia. These results hold implications in repurposing HU as an effective and efficient therapy for HbE/β-thalassemia.

PMID:34406845 | DOI:10.1089/gtmb.2021.0031

Future Microbiol. 2021 Aug 18. doi: 10.2217/fmb-2020-0310. Online ahead of print.

ABSTRACT

Aim: This study was conducted to explore the antibacterial potential of selective estrogen receptor modulators (SERMs). Materials & methods: The percentage growth retardation, bacterial growth kinetics, biofilm, checkerboard and bacterial burden assays were conducted to check antibacterial potential of SERMs. Finally, docking study was also conducted to predict possible antibacterial mechanism of SERMs. Results: In vitro and in vivo studies have shown the antibacterial activity of SERMs against different tested strains of bacteria. The synergistic activity of SERMs in combination with standard antibacterial agents was also observed and tested further under in vivo conditions. In vivo results have shown decreased bacterial bioburden. Docking studies have predicted the multimodal antibacterial mechanism of SERMs. Conclusion: SERMs can be considered as promising broad-spectrum antibacterial agents.

PMID:34406075 | DOI:10.2217/fmb-2020-0310

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