A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials

Detection of Adverse Drug Reactions in COVID-19 Hospitalized Patients in Saudi Arabia: A Retrospective Study by ADR Prompt Indicators

Seeking an alternative approach for detecting adverse drug reactions (ADRs) in coronavirus patients (COVID-19) and enhancing drug safety, a retrospective study of six months was conducted utilizing an electronic medical record (EMR) database to detect ADRs in hospitalized patients for COVID-19, using "ADR prompt indicators" (APIs). Consequently, confirmed ADRs were subjected to multifaceted analyses, such as demographic attribution, relationship with specific drugs and implication for organs and systems of the body, incidence rate, type, severity, and preventability of ADR. The incidence rate of ADRs is 37%, the predisposition of organs and systems to ADR is observed remarkably in the hepatobiliary and gastrointestinal systems at 41.8% vs. 36.2%, p < 0.0001, and the classes of drugs implicated in the ADRs are lopinavir-ritonavir 16.3%, antibiotics 24.1%, and hydroxychloroquine12.8%. Furthermore, the duration of hospitalization and polypharmacy are significantly higher in patients with ADRs at 14.13 ± 7.87 versus 9.55 ± 7.90, p < 0.001, and 9.74 ± 5.51 versus 6.98 ± 4.36, p < 0.0001, respectively. Comorbidities are detected in 42.5% of patients and 75.2%, of patients with DM, and HTN, displaying significant ADRs, p-value < 0.05. This is a symbolic study providing a comprehensive acquaintance of the importance of APIs in detecting hospitalized ADRs, revealing increased detection rates and robust assertive values with insignificant costs, incorporating the hospital EMR database, and enhancing transparency and time effectiveness.

The IDentif.AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens

IDentif.AI-x, a clinically actionable artificial intelligence platform, was used to rapidly pinpoint and prioritize optimal combination therapies against COVID-19 by pairing a prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus and Vero E6 assay with a quadratic optimization workflow. A starting pool of 12 candidate drugs developed in collaboration with a community of infectious disease clinicians was first narrowed down to a six-drug pool and then interrogated in 50 combination regimens at three dosing levels per drug, representing 729 possible combinations. IDentif.AI-x revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived, and pinpointed a number of clinically actionable drug interactions, which were further reconfirmed in SARS-CoV-2 variants B.1.351 (Beta) and B.1.617.2 (Delta). IDentif.AI-x prioritized promising drug combinations for clinical translation and can be immediately adjusted and re-executed with a new pool of promising therapies in an actionable path towards rapidly optimizing combination therapy following pandemic emergence.

Potential of Microneedle Systems for COVID-19 Vaccination: Current Trends and Challenges

To prevent the coronavirus disease 2019 (COVID-19) pandemic and aid restoration to prepandemic normality, global mass vaccination is urgently needed. Inducing herd immunity through mass vaccination has proven to be a highly effective strategy for preventing the spread of many infectious diseases, which protects the most vulnerable population groups that are unable to develop immunity, such as people with immunodeficiencies or weakened immune systems due to underlying medical or debilitating conditions. In achieving global outreach, the maintenance of the vaccine potency, transportation, and needle waste generation become major issues. Moreover, needle phobia and vaccine hesitancy act as hurdles to successful mass vaccination. The use of dissolvable microneedles for COVID-19 vaccination could act as a major paradigm shift in attaining the desired goal to vaccinate billions in the shortest time possible. In addressing these points, we discuss the potential of the use of dissolvable microneedles for COVID-19 vaccination based on the current literature.

DeepVir: Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning-Application to COVID-19

This study formulates antiviral repositioning as a matrix completion problem wherein the antiviral drugs are along the rows and the viruses are along the columns. The input matrix is partially filled, with ones in positions where the antiviral drug has been known to be effective against a virus. The curated metadata for antivirals (chemical structure and pathways) and viruses (genomic structure and symptoms) are encoded into our matrix completion framework as graph Laplacian regularization. We then frame the resulting multiple graph regularized matrix completion (GRMC) problem as deep matrix factorization. This is solved by using a novel optimization method called HyPALM (Hybrid Proximal Alternating Linearized Minimization). Results of our curated RNA drug-virus association data set show that the proposed approach excels over state-of-the-art GRMC techniques. When applied to in silico prediction of antivirals for COVID-19, our approach returns antivirals that are either used for treating patients or are under trials for the same.

Expanding the chemical space of 3(5)-functionalized 1,2,4-triazoles

An efficient approach to the gram-scale synthesis of 3(5)-substituted, 1,3- and 1,5-disubstituted 1,2,4-triazole-derived building blocks is described. The key synthetic precursors - 1,2,4-triazole-3(5)-carboxylates (20 examples, 35-89% yield) were prepared from readily available acyl hydrazides and ethyl 2-ethoxy-2-iminoacetate hydrochloride. Further transformations were performed following the convergent synthetic strategy and allowed the preparation of 1,3- and 1,5-disubstituted 1,2,4-triazole-derived esters (16 examples, 25-75% yield), 3(5)-substituted, 1,3- and 1,5-disubstituted carboxylate salts (18 examples, 78-93% yield), amides (5 examples, 82-93% yield), nitriles (5 examples, 30-85% yield), hydrazides (6 examples, 84-89% yield), and hydroxamic acids (3 examples, 73-78% yield). Considering wide applications of the 1,2,4-triazole motif in medicinal chemistry, these compounds are valuable building blocks for lead-oriented synthesis; they have also great potential for coordination chemistry.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10593-022-03064-z.

Repurposing FDA-approved Drugs Targeting SARS-CoV2 3CLpro: a study by applying Virtual Screening, Molecular Dynamics, MM-PBSA Calculations and Covalent Docking

Background:

Since the end of 2019, the etiologic agent SAR-CoV-2 responsible for one of the most significant epidemics in history has caused severe global economic, social, and health damages. The drug repurposing approach and application of Structure-based Drug Discovery (SBDD) using in silico techniques are increasingly frequent, leading to the identification of several molecules that may represent promising potential.

Method:

In this context, here we use in silico methods of virtual screening (VS), pharmacophore modeling (PM), and fragment-based drug design (FBDD), in addition to molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM -PBSA) calculations, and covalent docking (CD) for the identification of potential treatments against SARS-CoV-2. We initially validated the docking protocol followed by VS in 1,613 FDA-approved drugs obtained from the ZINC database. Thus, we identified 15 top hits, of which three of them were selected for further simulations. In parallel, for the compounds with a fit score value ≤ of 30, we performed the FBDD protocol, where we designed 12 compounds

Result:

By applying a PM protocol in the ZINC database, we identified three promising drug candidates. Then, the 9 top hits were evaluated in simulations of MD, MM-PBSA, and CD. Subsequently, MD showed that all identified hits showed stability at the active site without significant changes in the protein's structural integrity, as evidenced by the RMSD, RMSF, Rg, SASA graphics. They also showed interactions with the catalytic dyad (His41 and Cys145) and other essential residues for activity (Glu166 and Gln189) and high affinity for MM-PBSA, with possible covalent inhibition mechanism.

Conclution:

Finally, our protocol helped identify potential compounds wherein ZINC896717 (Zafirlukast), ZINC1546066 (Erlotinib), and ZINC1554274 (Rilpivirine) were more promising and could be explored in vitro, in vivo, and clinical trials to prove their potential as antiviral agents.

Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.

An Examination of COVID-19 Medications' Effectiveness in Managing and Treating COVID-19 Patients: A Comparative Review

Background: The review seeks to shed light on the administered and recommended COVID-19 treatment medications through an evaluation of their efficacy. Methods: Data were collected from key databases, including Scopus, Medline, Google Scholar, and CINAHL. Other platforms included WHO and FDA publications. The review's literature search was guided by the WHO solidarity clinical trials for COVID-19 scope and trial-assessment parameters. Results: The findings indicate that the use of antiretroviral drugs as an early treatment for COVID-19 patients has been useful. It has reduced hospital time, hastened the clinical cure period, delayed and reduced the need for mechanical and invasive ventilation, and reduced mortality rates. The use of vitamins, minerals, and supplements has been linked to increased immunity and thus offering the body a fighting chance. Nevertheless, antibiotics do not correlate with improving patients' wellbeing and are highly discouraged from the developed clinical trials. Conclusions: The review demonstrates the need for additional clinical trials with a randomized, extensive sample base and over a more extended period to examine the potential side effects of the medications administered. Critically, the findings underscore the need for vaccination as the only viable medication to limit the SARS-CoV-2 virus spread.