Accelerated Preclinical Paths to Support Rapid Development of COVID-19 Therapeutics

ACTIV-2: A Platform Trial for the Evaluation of Novel Therapeutics for the Treatment of Early COVID-19 in Outpatients

Clinical Trials Registration ClinicalTrials.gov Identifier: NCT04518410.

Accelerated CMC workflows to enable speed to clinic in the COVID-19 era: A multi-company view from the biopharmaceutical industry

The COVID-19 pandemic has placed unprecedented pressure on biopharmaceutical companies to develop efficacious preventative and therapeutic treatments, which is unlikely to abate in the coming years. The importance of fast progress to clinical evaluation for treatments, which tackle unmet medical needs puts strain on traditional product development timelines, which can take years from start to finish. Although previous work has been successful in reducing phase 1 timelines for recombinant antibodies, through utilization of the latest technological advances and acceptance of greater business risk or costs, substantially faster development is likely achievable without increased risk to patients during initial clinical evaluation. To optimize lessons learned from the pandemic and maximize multi-stakeholder (i.e., patients, clinicians, companies, regulatory agencies) benefit, we conducted an industry wide benchmarking survey in September/October 2021. The aims of this survey were to: (i) benchmark current technical practices of key process and product development activities related to manufacturing of therapeutic proteins, (ii) understand the impact of changes implemented in COVID-19 accelerated Ab programs, and whether any such changes can be retained as part of sustainable long-term business practices and (iii) understand whether any accelerative action(s) taken have (negatively) impacted the wider development process. This article provides an in-depth analysis of this data, ultimately highlighting an industry perspective of how biopharmaceutical companies can sustainably adopt new approaches to therapeutic protein development and production.

SARS-CoV-2: An Analysis of the Vaccine Candidates Tested in Combatting and Eliminating the COVID-19 Virus

Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, is a highly contagious virus, transferable via air droplets from close human-human contact. The pandemic has led to over 6.5 million deaths worldwide, making it the largest global health crisis since the influenza pandemic in 1918. SARS-CoV-2 rapidly spread around the world, forcing the World Health Organization (WHO) to deem it a global health pandemic after three months of its initiation. The virus has wreaked havoc on many countries worldwide, overwhelming healthcare systems, hence damaging many economies. Even though research has progressed the understanding of the SARS-CoV-2 virus, the information gathered about the vaccine trials and their findings have been scarcely distributed to the public in a single study. The information available to scientists has therefore given researchers a pathway to building an efficacious vehicle to substantially decrease the spread of the virus. The vaccines formulated had many challenges due to multiple factors such as viral mutations and clinical trial delays. This paper will aim to educate readers on the processes that the vaccine candidates took, and better understand the procedures; additionally, we'll look at all candidates' findings that went into clinical trials, assessing, analyzing, and evaluating the 27 vaccine candidates that went into phase III trials and the 13 candidates that went into either phase I/II trials.

BRET-Based Self-Cleaving Biosensors for SARS-CoV-2 3CLpro Inhibitor Discovery

The 3C-like protease (3CLpro) of SARS-CoV-2 is an attractive drug target for developing antivirals against SARS-CoV-2. A few small molecule inhibitors of 3CLpro are in clinical trials for COVID-19 treatments, and more inhibitors are under development. One limiting factor for 3CLpro inhibitors development is that the cellular activities of such inhibitors should be evaluated in Biosafety Level 3 (BSL-3) laboratories. Here, we design DNA-coded biosensors that can be used in BSL-2 laboratories to set up cell-based assays for 3CLpro inhibitor discovery. The biosensors were constructed by linking a green fluorescent protein (GFP2) to the N-terminus and a Renilla luciferase (RLuc8) to the C-terminus of SARS-CoV-2 3CLpro, with the linkers derived from the cleavage sequences of 3CLpro. After overexpression of the biosensors in human embryonic kidney (HEK) 293T cells, 3CLpro can be released from GFP2 and RLuc by self-cleavage, resulting in a decrease of the bioluminescence resonance energy transfer (BRET) signal. Using one of these biosensors, pBRET-10, we evaluated the cellular activities of several 3CLpro inhibitors. These inhibitors restored the BRET signal by blocking the proteolysis of pBRET-10, and their relative activities measured using pBRET-10 were consistent with their previously reported anti-SARS-CoV-2 activities. We conclude that the biosensor pBRET-10 is a useful tool for SARS-CoV-2 3CLpro inhibitor discovery. IMPORTANCE The virus proteases 3CLpro are validated drug targets for developing antivirals to treat coronavirus diseases, such as COVID-19. However, the development of 3CLpro inhibitors relies heavily on BSL-3 laboratories. Here, we report a series of BRET-based self-cleaving biosensors that can be used to set up cell-based assays to evaluate the cell permeability and cellular activity of SARS-CoV-2 3CLpro inhibitors in BSL-2 laboratories. The cell-based assay is suitable for high-throughput screening for 3CLpro inhibitors because of the simplicity and good reproducibility of our biosensors. The design strategy can also be used to design biosensors for other viral proteases for which the activation processes involve the self-cleavage of polyproteins.

Novel Strategies for the Treatment of COVID-19

On 4 September, 2020, the US National Institutes of Health launched a new clinical trial, “A Multicenter, Adaptive, Randomized Controlled Platform Trial of the Safety and Efficacy of Antithrombotic and Additional Strategies in Hospitalized Adults with COVID-19.” This open-label, placebo-controlled, multicenter, adaptive platform study was designed to evaluate therapeutic options for patients hospitalized with mild, moderate, or severe COVID-19. A variety of drugs and drug classes were selected, including heparin, the monoclonal antibody crizanlizumab, sodium-glucose cotransporter-2 inhibitors, and purinergic signaling receptor Y₁₂ inhibitors. These medications have been widely used in the treatment of other conditions, from sick cell disease to type 2 diabetes mellitus and some forms of cardiovascular disease, but their inclusion in a study of COVID-19 was somewhat unexpected. This article examines the rationale behind the use of these disparate agents in the treatment and prevention of adverse outcomes in patients with COVID-19 and explores how these strategies may be utilized in the future to address the severe acute respiratory syndrome coronavirus 2 pandemic.

Modeling SARS-CoV-2 and influenza infections and antiviral treatments in human lung epithelial tissue equivalents

There is a critical need for physiologically relevant, robust, and ready-to-use in vitro cellular assay platforms to rapidly model the infectivity of emerging viruses and develop new antiviral treatments. Here we describe the cellular complexity of human alveolar and tracheobronchial air liquid interface (ALI) tissue models during SARS-CoV-2 and influenza A virus (IAV) infections. Our results showed that both SARS-CoV-2 and IAV effectively infect these ALI tissues, with SARS-CoV-2 exhibiting a slower replication peaking at later time-points compared to IAV. We detected tissue-specific chemokine and cytokine storms in response to viral infection, including well-defined biomarkers in severe SARS-CoV-2 and IAV infections such as CXCL10, IL-6, and IL-10. Our single-cell RNA sequencing analysis showed similar findings to that found in vivo for SARS-CoV-2 infection, including dampened IFN response, increased chemokine induction, and inhibition of MHC Class I presentation not observed for IAV infected tissues. Finally, we demonstrate the pharmacological validity of these ALI tissue models as antiviral drug screening assay platforms, with the potential to be easily adapted to include other cell types and increase the throughput to test relevant pathogens.

Need for a Standardized Translational Drug Development Platform: Lessons Learned from the Repurposing of Drugs for COVID-19

In the absence of drugs to treat or prevent COVID-19, drug repurposing can be a valuable strategy. Despite a substantial number of clinical trials, drug repurposing did not deliver on its promise. While success was observed with some repurposed drugs (e.g., remdesivir, dexamethasone, tocilizumab, baricitinib), others failed to show clinical efficacy. One reason is the lack of clear translational processes based on adequate preclinical profiling before clinical evaluation. Combined with limitations of existing in vitro and in vivo models, there is a need for a systematic approach to urgent antiviral drug development in the context of a global pandemic. We implemented a methodology to test repurposed and experimental drugs to generate robust preclinical evidence for further clinical development. This translational drug development platform comprises in vitro, ex vivo, and in vivo models of SARS-CoV-2, along with pharmacokinetic modeling and simulation approaches to evaluate exposure levels in plasma and target organs. Here, we provide examples of identified repurposed antiviral drugs tested within our multidisciplinary collaboration to highlight lessons learned in urgent antiviral drug development during the COVID-19 pandemic. Our data confirm the importance of assessing in vitro and in vivo potency in multiple assays to boost the translatability of pre-clinical data. The value of pharmacokinetic modeling and simulations for compound prioritization is also discussed. We advocate the need for a standardized translational drug development platform for mild-to-moderate COVID-19 to generate preclinical evidence in support of clinical trials. We propose clear prerequisites for progression of drug candidates for repurposing into clinical trials. Further research is needed to gain a deeper understanding of the scope and limitations of the presented translational drug development platform.

Retro Drug Design: From Target Properties to Molecular Structures

To deliver more therapeutics to more patients more quickly and economically is the ultimate goal of pharmaceutical researchers. The advent and rapid development of artificial intelligence (AI), in combination with other powerful computational methods in drug discovery, makes this goal more practical than ever before. Here, we describe a new strategy, retro drug design, or RDD, to create novel small-molecule drugs from scratch to meet multiple predefined requirements, including biological activity against a drug target and optimal range of physicochemical and ADMET properties. The molecular structure was represented by an atom typing based molecular descriptor system, optATP, which was further transformed to the space of loading vectors from principal component analysis. Traditional predictive models were trained over experimental data for the target properties using optATP and shallow machine learning methods. The Monte Carlo sampling algorithm was then utilized to find the solutions in the space of loading vectors that have the target properties. Finally, a deep learning model was employed to decode molecular structures from the solutions. To test the feasibility of the algorithm, we challenged RDD to generate novel kinase inhibitors from random numbers with five different ADMET properties optimized at the same time. The best Tanimoto similarity score between the generated valid structures and the available 4,314 kinase inhibitors was < 0.50, indicating a high extent of novelty of the generated compounds. From the 3,040 structures that met all six target properties, 20 were selected for synthesis and experimental measurement of inhibition activity over 97 representative kinases and the ADMET properties. Fifteen and eight compounds were determined to be hits or strong hits, respectively. Five of the six strong kinase inhibitors have excellent experimental ADMET properties. The results presented in this paper illustrate that RDD has the potential to significantly improve the current drug discovery process.

Current and emerging immunomodulators for treatment of SARS-CoV2 infection (COVID-19)

INTRODUCTION

SARS-CoV-2, the virus that causes COVID-19, elicits a variety of host responses ranging from asymptomatic or mild illness in most people, to severe disease and critical illness in a subset of patients with systemic inflammation and hypoxemic respiratory failure.

AREAS COVERED

Heterogeneous clinical presentations are often driven by disparate responses of the host immune system, with severe disease associated with aberrant interferon signaling or cytokine storm syndrome. This manuscript examines current therapeutic approaches, including the use of immunomodulators such as corticosteroids, interleukin inhibitors, kinase inhibitors, fluvoxamine, and ivermectin, and also explores the ways that these therapies and others may be used to treat COVID-19 in the future.

EXPERT OPINION

Modulation of the immune response has become a mainstay of treatment of COVID-19, although the optimal mechanism has not yet been defined and there is considerable controversy regarding clinical management. As time progresses, the therapeutic approach to COVID-19 will undoubtedly change, particularly as we learn more about the pathophysiology of SARS-CoV-2 infection.

Non-clinical safety assessment and in vivo biodistribution of CoviFab, an RBD-specific F(ab')2 fragment derived from equine polyclonal antibodies

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has required the urgent development of new therapies, among which passive immunotherapy is contemplated. CoviFab (INM005) is a RBD-specific F(ab′)2 fragment derived from equine polyclonal antibodies. We investigate their preclinical security and biodistribution by in vivo and ex vivo NIR imaging after intravenous administration of a dose of 4 mg/kg at time 0 and 48 h. Images were taken at 1, 12, 24, 36, 48, 49, 60, 72, 84, 96, 108, 120, 132 and 144 h after the first intravenous injection. At 96 and 144 h, mice were sacrificed for haematology, serum chemistry, clinical pathology, histopathology and ex vivo imaging. The biodistribution profile was similar in all organs studied, with the highest fluorescence at 1 h after each injection, gradually decreasing after that each one and until the end of the study (144 h). The toxicology study revealed no significant changes in the haematology and serum chemistry parameters. Further, there were no changes in the gross and histological examination of organs. Nonclinical data of the current study confirm that CoviFab is safe, without observable adverse effects in mice. Furthermore, we confirm that bioimaging studies are a useful approach in preclinical trials to determine biodistribution.

Sustained Delivery of SARS-CoV-2 RBD Subunit Vaccine Using a High Affinity Injectable Hydrogel Scaffold

The receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) spike protein that mediates viral entry into host cells is a good candidate immunogen for vaccine development against coronavirus disease 2019 (COVID‐19). Because of its small size, most preclinical and early clinical efforts have focused on multimerizing RBD on various formats of nanoparticles to increase its immunogenicity. Using an easily administered injectable hydrogel scaffold that is rationally designed for enhanced retainment of RBD, an alternative and facile approach for boosting RBD immunogenicity in mice is demonstrated. Prolonged delivery of poly (I:C) adjuvanted RBD by the hydrogel scaffold results in sustained exposure to lymphoid tissues, which elicits serum IgG titers comparable to those induced by three bolus injections, but more long‐lasting and polarized toward T_H1‐mediated IgG2b. The hydrogel scaffold induces potent germinal center (GC) reactions, correlating with RBD‐specific antibody generation and robust type 1 T cell responses. Besides being an enduring RBD reservoir, the hydrogel scaffold becomes a local inflammatory niche for innate immune cell activation. Collectively, the injectable hydrogel scaffold provides a simple, practical, and inexpensive means to enhance the efficacy of RBD‐based subunit vaccines against COVID‐19 and may be applicable to other circulating and emerging pathogens.

SARS-CoV-2 Antiviral Therapy

The development of effective antiviral therapy for COVID-19 is critical for those awaiting vaccination, as well as for those who do not respond robustly to vaccination. This review summarizes 1 year of progress in the race to develop antiviral therapies for COVID-19, including research spanning preclinical and clinical drug development efforts, with an emphasis on antiviral compounds that are in clinical development or that are high priorities for clinical development. The review is divided into sections on compounds that inhibit SARS-CoV-2 enzymes, including its polymerase and proteases; compounds that inhibit virus entry, including monoclonal antibodies; interferons; and repurposed drugs that inhibit host processes required for SARS-CoV-2 replication. The review concludes with a summary of the lessons to be learned from SARS-CoV-2 drug development efforts and the challenges to continued progress.

Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives

Molecular modeling in pharmacology is a promising emerging tool for exploring drug interactions with cellular components. Recent advances in molecular simulations, big data analysis, and artificial intelligence (AI) have opened new opportunities for rationalizing drug interactions with their pharmacological targets. Despite the obvious utility and increasing impact of computational approaches, their development is not progressing at the same speed in different fields of pharmacology. Here, we review current in silico techniques used in cardiovascular diseases (CVDs), cardiological drug discovery, and assessment of cardiotoxicity. In silico techniques are paving the way to a new era in cardiovascular medicine, but their use somewhat lags behind that in other fields.

Accelerating Coronavirus Disease 2019 Therapeutic Interventions and Vaccines-Selecting Compounds for Clinical Evaluation in Coronavirus Disease 2019 Clinical Trials

Given the urgent need for coronavirus disease 2019 therapeutics, early in the pandemic the Accelerating Coronavirus Disease 2019 Therapeutic Interventions and Vaccines (ACTIV) public-private partnership rapidly designed a unique therapeutic agent intake and assessment process for candidate treatments of coronavirus disease 2019. These treatments included antivirals, immune modulators, severe acute respiratory syndrome coronavirus 2 neutralizing antibodies, and organ-supportive treatments at both the preclinical and clinical stages of development. The ACTIV Therapeutics-Clinical Working Group Agent Prioritization subgroup established a uniform data collection process required to perform an assessment of any agent type using review criteria that were identified and differentially weighted for each agent class. The ACTIV Therapeutics-Clinical Working Group evaluated over 750 therapeutic agents with potential application for coronavirus disease 2019 and prioritized promising candidates for testing within the master protocols conducted by ACTIV. In addition, promising agents among preclinical candidates were selected by ACTIV to be matched with laboratories that could assist in executing rigorous preclinical studies. Between April 14, 2020, and May 31, 2021, the Agent Prioritization subgroup advanced 20 agents into the Accelerating Coronavirus Disease 2019 Therapeutic Interventions and Vaccines master protocols and matched 25 agents with laboratories to assist with preclinical testing.

COVID-19 Research: Lessons from Non-Human Primate Models

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the coronavirus disease 2019 (COVID-19). It emerged from China in December 2019 and rapidly spread across the globe, causing a pandemic with unprecedented impacts on public health and economy. Therefore, there is an urgent need for the development of curative treatments and vaccines. In humans, COVID-19 pathogenesis shows a wide range of symptoms, from asymptomatic to severe pneumonia. Identifying animal models of SARS-CoV-2 infection that reflect the clinical symptoms of COVID-19 is of critical importance. Nonhuman primates (NHPss) correspond to relevant models to assess vaccine and antiviral effectiveness. This review discusses the use of NHPs as models for COVID-19 research, with focus on the pathogenesis of SARS-CoV-2 infection, drug discovery and pre-clinical evaluation of vaccine candidates.

Targeting Viral Surface Proteins through Structure-Based Design

The emergence of novel viral infections of zoonotic origin and mutations of existing human pathogenic viruses represent a serious concern for public health. It warrants the establishment of better interventions and protective therapies to combat the virus and prevent its spread. Surface glycoproteins catalyzing the fusion of viral particles and host cells have proven to be an excellent target for antivirals as well as vaccines. This review focuses on recent advances for computational structure-based design of antivirals and vaccines targeting viral fusion machinery to control seasonal and emerging respiratory viruses.

N-Acetylcysteine as Adjuvant Therapy for COVID-19 - A Perspective on the Current State of the Evidence

The looming severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a long-lasting pandemic of coronavirus disease 2019 (COVID-19) around the globe with substantial morbidity and mortality. N-acetylcysteine, being a nutraceutical precursor of an important antioxidant glutathione, can perform several biological functions in mammals and microbes. It has consequently garnered a growing interest as a potential adjunctive therapy for coronavirus disease. Here, we review evidence concerning the effects of N-acetylcysteine in respiratory viral infections based on currently available in vitro, in vivo, and human clinical investigations. The repurposing of a known drug such as N-acetylcysteine may significantly hasten the deployment of a novel approach for COVID-19. Since the drug candidate has already been translated into the clinic for several decades, its established pharmacological properties and safety and side-effect profiles expedite preclinical and clinical assessment for the treatment of COVID-19. In vitro data have depicted that N-acetylcysteine increases antioxidant capacity, interferes with virus replication, and suppresses expression of pro-inflammatory cytokines in cells infected with influenza viruses or respiratory syncytial virus. Furthermore, findings from in vivo studies have displayed that, by virtue of immune modulation and anti-inflammatory mechanism, N-acetylcysteine reduces the mortality rate in influenza-infected mice animal models. The promising in vitro and in vivo results have prompted the initiation of human subject research for the treatment of COVID-19, including severe pneumonia and acute respiratory distress syndrome. Albeit some evidence of benefits has been observed in clinical outcomes of patients, precision nanoparticle design of N-acetylcysteine may allow for greater therapeutic efficacy.

Time to 'Mind the Gap' in novel small molecule drug discovery for direct-acting antivirals for SARS-CoV-2

A pipeline of effective direct-acting antivirals (DAAs) remains a critical gap in addressing the current pandemic given vaccination hesitancy, the emergence of viral variants of concern, susceptible populations for which vaccination is ineffective or unavailable, and the possibility that coronavirus disease 2019 (COVID-19) is here to stay. Since the start of the pandemic, global efforts in small molecule drug discovery have focused largely on testing of FDA-approved drugs to accelerate evaluation in clinical trials in hospitalized patients. With 80% of the population who test positive for SARS-CoV-2 having asymptomatic to mild COVID-19, early stage, DAAs would be of enormous benefit to reduce spread, duration of symptoms and quarantine length. We highlight a few of the most promising DAAs in clinical trials and discuss considerations in how to navigate the challenges and pitfalls of novel small molecule discovery and thereby accelerate the advancement of new, safe, and oral DAAs.

Modeling SARS-CoV-2 and Influenza Infections and Antiviral Treatments in Human Lung Epithelial Tissue Equivalents

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is the third coronavirus in less than 20 years to spillover from an animal reservoir and cause severe disease in humans. High impact respiratory viruses such as pathogenic beta-coronaviruses and influenza viruses, as well as other emerging respiratory viruses, pose an ongoing global health threat to humans. There is a critical need for physiologically relevant, robust and ready to use, in vitro cellular assay platforms to rapidly model the infectivity of emerging respiratory viruses and discover and develop new antiviral treatments. Here, we validate in vitro human alveolar and tracheobronchial tissue equivalents and assess their usefulness as in vitro assay platforms in the context of live SARS-CoV-2 and influenza A virus infections. We establish the cellular complexity of two distinct tracheobronchial and alveolar epithelial air liquid interface (ALI) tissue models, describe SARS-CoV-2 and influenza virus infectivity rates and patterns in these ALI tissues, the viral-induced cytokine production as it relates to tissue-specific disease, and demonstrate the pharmacologically validity of these lung epithelium models as antiviral drug screening assay platforms.

Retro Drug Design: From Target Properties to Molecular Structures

To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefined requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate μ opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. Out of the 42,000 structures, only 267 chemicals were commercially available, indicating a high extent of novelty of the AI-generated compounds. We purchased and assayed 96 compounds, and 25 of which were found to be MOR agonists. These compounds also have excellent BBB scores. The results presented in this paper illustrate that RDD has potential to revolutionize the current drug discovery process and create novel structures with multiple desired properties, including biological functions and ADMET properties. Availability of an AI-enabled fast track in drug discovery is essential to cope with emergent public health threat, such as pandemic of COVID-19.

Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids

Purpose: Coronavirus disease–2019 (COVID-19) continues to be a global threat and remains a significant cause of hospitalizations. Recent clinical guidelines have supported the use of corticosteroids or remdesivir in the treatment of COVID-19. However, uncertainty remains about which patients are most likely to benefit from treatment with either drug; such knowledge is crucial for avoiding preventable adverse effects, minimizing costs, and effectively allocating resources. This study presents a machine-learning system with the capacity to identify patients in whom treatment with a corticosteroid or remdesivir is associated with improved survival time.

Methods: Gradient-boosted decision-tree models used for predicting treatment benefit were trained and tested on data from electronic health records dated between December 18, 2019, and October 18, 2020, from adult patients (age ≥18 years) with COVID-19 in 10 US hospitals. Models were evaluated for performance in identifying patients with longer survival times when treated with a corticosteroid versus remdesivir. Fine and Gray proportional-hazards models were used for identifying significant findings in treated and nontreated patients, in a subset of patients who received supplemental oxygen, and in patients identified by the algorithm. Inverse probability-of-treatment weights were used to adjust for confounding. Models were trained and tested separately for each treatment.

Findings: Data from 2364 patients were included, with men comprising slightly more than 50% of the sample; 893 patients were treated with remdesivir, and 1471 were treated with a corticosteroid. After adjustment for confounding, neither corticosteroids nor remdesivir use was associated with increased survival time in the overall population or in the subpopulation that received supplemental oxygen. However, in the populations identified by the algorithms, both corticosteroids and remdesivir were significantly associated with an increase in survival time, with hazard ratios of 0.56 and 0.40, respectively (both, P = 0.04).

Implications: Machine-learning methods have the capacity to identify hospitalized patients with COVID-19 in whom treatment with a corticosteroid or remdesivir is associated with an increase in survival time. These methods may help to improve patient outcomes and allocate resources during the COVID-19 crisis.

Targeting the DEAD-Box RNA Helicase eIF4A with Rocaglates-A Pan-Antiviral Strategy for Minimizing the Impact of Future RNA Virus Pandemics

The increase in pandemics caused by RNA viruses of zoonotic origin highlights the urgent need for broad-spectrum antivirals against novel and re-emerging RNA viruses. Broad-spectrum antivirals could be deployed as first-line interventions during an outbreak while virus-specific drugs and vaccines are developed and rolled out. Viruses depend on the host’s protein synthesis machinery for replication. Several natural compounds that target the cellular DEAD-box RNA helicase eIF4A, a key component of the eukaryotic translation initiation complex eIF4F, have emerged as potential broad-spectrum antivirals. Rocaglates, a group of flavaglines of plant origin that clamp mRNAs with highly structured 5′ untranslated regions (5′UTRs) onto the surface of eIF4A through specific stacking interactions, exhibit the largest selectivity and potential therapeutic indices among all known eIF4A inhibitors. Their unique mechanism of action limits the inhibitory effect of rocaglates to the translation of eIF4A-dependent viral mRNAs and a minor fraction of host mRNAs exhibiting stable RNA secondary structures and/or polypurine sequence stretches in their 5′UTRs, resulting in minimal potential toxic side effects. Maintaining a favorable safety profile while inducing efficient inhibition of a broad spectrum of RNA viruses makes rocaglates into primary candidates for further development as pan-antiviral therapeutics.

Experimental Models of SARS-CoV-2 Infection: Possible Platforms to Study COVID-19 Pathogenesis and Potential Treatments

In December 2019, a novel coronavirus crossed species barriers to infect humans and was effectively transmitted from person to person, leading including vaccines and antiviral drugs that could prevent or limit the burden or transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global health priority. It is thus of utmost importance to assess possible therapeutic strategies against SARS-CoV-2 using experimental models that recapitulate aspects of the human disease. Here, we review available models currently being developed and used to study SARS-CoV-2 infection and highlight their application to screen potential therapeutic approaches, including repurposed antiviral drugs and vaccines. Each identified model provides a valuable insight into SARS-CoV-2 cellular tropism, replication kinetics, and cell damage that could ultimately enhance understanding of SARS-CoV-2 pathogenesis and protective immunity. Expected final online publication date for the Annual Review of Pharmacology and Toxicology, Volume 62 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Epigallocatechin-3-gallate, an active ingredient of Traditional Chinese Medicines, inhibits the 3CLpro activity of SARS-CoV-2

SARS-CoV-2 is the etiological agent responsible for the ongoing pandemic of coronavirus disease 2019 (COVID-19). The main protease of SARS-CoV-2, 3CLpro, is an attractive target for antiviral inhibitors due to its indispensable role in viral replication and gene expression of viral proteins. The search of compounds that can effectively inhibit the crucial activity of 3CLpro, which results to interference of the virus life cycle, is now widely pursued. Here, we report that epigallocatechin-3-gallate (EGCG), an active ingredient of Chinese herbal medicine (CHM), is a potent inhibitor of 3CLpro with half-maximum inhibitory concentration (IC50) of 0.874 ± 0.005 μM. In the study, we retrospectively analyzed the clinical data of 123 cases of COVID-19 patients, and found three effective Traditional Chinese Medicines (TCM) prescriptions. Multiple strategies were performed to screen potent inhibitors of SARS-CoV-2 3CLpro from the active ingredients of TCMs, including network pharmacology, molecular docking, surface plasmon resonance (SPR) binding assay and fluorescence resonance energy transfer (FRET)-based inhibition assay. The SPR assay showed good interaction between EGCG and 3CLpro with KD ~6.17 μM, suggesting a relatively high affinity of EGCG with SARS-CoV-2 3CLpro. Our results provide critical insights into the mechanism of action of EGCG as a potential therapeutic agent against COVID-19.

ACTIVating Resources for the COVID-19 Pandemic: In Vivo Models for Vaccines and Therapeutics

The Preclinical Working Group of Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV), a public-private partnership spearheaded by the National Institutes of Health, has been charged with identifying, prioritizing, and communicating SARS-CoV-2 preclinical resources. Reviewing SARS-CoV-2 animal model data facilitates standardization and harmonization and informs knowledge gaps and prioritization of limited resources. To date, mouse, hamster, ferret, guinea pig, and non-human primates have been investigated. Several species are permissive for SARS-CoV-2 replication, often exhibiting mild disease with resolution, reflecting most human COVID-19 cases. More severe disease develops in a few models, some associated with advanced age, a risk factor for human disease. This review provides a snapshot that recommends the suitability of models for testing vaccines and therapeutics, which may evolve as our understanding of COVID-19 disease biology improves. COVID-19 is a complex disease, and individual models recapitulate certain aspects of disease; therefore, the coordination and assessment of animal models is imperative.