Towards De Novo Drug Design for the Coronavirus: A Drug-Target Interaction Prediction Approach using Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism

For humans, the COVID-19 pandemic and Coronavirus have undeniably been a nightmare. Although there are effective vaccines, specific drugs are still urgent. Normally, to identify potential drugs, one needs to design and then test interactions between the drug and the virus in an in silico manner for determining candidates. This Drug-Target Interaction (DTI) process, can be done by molecular docking, which is too complicated and time-consuming for manual works. Therefore, it opens room for applying Artificial Intelligence (AI) techniques. In particular, Graph Neural Network (GNN) attracts recent attention since its high suitability for the nature of drug compounds and virus proteins. However, to introduce such a representation well-reflecting biological structures of biological compounds is not a trivial task. Moreover, since available datasets of Coronavirus are still not highly popular, the recently developed GNNs have been suffering from overfitting on them. We then address those issues by proposing a novel model known as Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism. On one hand, our model can learn more precise features of compounds and proteins. On the other hand, we introduce a new gating mechanism to create better atom representation from non-neighbor information. Once applying transfer learning from very large databanks, our model enjoys promising performance, especially when experimenting with Coronavirus. © 2022 IEEE.

In silico study to find a potent inhibitor, Vilazodone, to cure COVID-19 using molecular docking

The pandemic caused by the coronavirus SARS-COVID-2 has had devastating impact on the world. It has caused a significant number of deaths across the world. Fast spread and lack of vaccine prompted academia to adopt new, fast and reliable methodologies to design new drugs. A combined approach of direct drug design and indirect drug design has been used for molecular docking. In the study, we found a compound, Vilazodone, with a binding energy of -8.40 kcal/mol. The druglikeness properties of this compound are investigated through SWISS ADMET analysis. In this in-silico study, we confirmed this compound is a potential drug candidate against SARS-CoV-2.However, in-vitro and in-vivo studies are required to prove its efficacy. © 2022 IEEE.

Rational identification of small molecules derived from 9,10-dihydrophenanthrene as potential inhibitors of 3CLpro enzyme for COVID-19 therapy: a computer-aided drug design approach.

Small molecules such as 9,10-dihydrophenanthrene derivatives have remarkable activity toward inhibition of SARS-CoV-2 3CLpro and COVID-19 proliferation, which show a strong correlation between their structures and bioactivity. Therefore, these small compounds could be suitable for clinical pharmaceutical use against COVID-19. The objective of this study was to remodel the structures of 9,10-dihydrophenanthrene derivatives to achieve a powerful biological activity against 3CLpro and favorable pharmacokinetic properties for drug design and discovery. Therefore, by the use of bioinformatics techniques, we developed robust 3D-QSAR models that are capable of describing the structure-activity relationship for 46 molecules based on 9,10-dihydrophenanthrene derivatives using CoMFA/SE (R 2 = 0.97, Q 2 = 0.81, R 2 pred = 0.95, c R 2 p = 0.71) and CoMSIA/SEHDA (R 2 = 0.94, Q 2 = 0.76, R 2 pred = 0.91, c R 2 p = 0.65) techniques. Accordingly, 96 lead compounds were generated based on a template molecule that showed the highest observed activity in vitro (T40, pIC50 = 5.81) and predicted their activities and bioavailability in silico. The rational screening outputs of 3D-QSAR, Molecular docking, ADMET, and MM-GBSA led to the identification of 9 novel modeled molecules as potent noncovalent drugs against SARS-CoV-2-3CLpro. Finally, by molecular dynamics simulations, the stability and structural dynamics of 3CLpro free and complex (PDB code: 6LU7) were discussed in the presence of samples of 9,10-dihydrophenanthrene derivative in an aqueous environment. Overall, the retrosynthesis of the proposed drug compounds in this study and the evaluation of their bioactivity in vitro and in vivo may be interesting for designing and discovering a new drug effective against COVID-19. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-022-02004-z.

THE USE OF MEDICINAL PLANTS IN LANDSCAPE DESIGN IN THE STEPPE ZONE OF UKRAINE

The article provides data on the use of medicinal plants in landscaping of the Steppe zone of Ukraine, special attention is paid to the creation of a rock garden. Medicinal plants have been widely used by man throughout his entire existence. Today, scientists are very interested in this group of plants due to the emergence of a pandemic caused by the deadly COVID-19 virus. It has been established that the phytochemicals of these plants are potential drugs against this virus. When creating a rock garden from herbaceous medicinal plants, the generally accepted methods were used in gardening art. And also the landscape-ecological method was used, which takes into account the relationship between vegetation and biotope. Despite the fact that rock gardens have long been used in landscape design, their popularity in the design of various landscape objects does not lose its relevance today. A rock garden is a composition of a combination of plants with stones organically located at different levels. The analysis of scientific publications has shown that the study of the use of medicinal plants in this method of gardening is insufficiently covered. In our proposed version of the rock garden, plant species that are adapted to the specific climate conditions of the Steppe zone will be used. At the same time, the flowering time, height, seasonal decorativeness and harmonious combination were taken into account. The range of medicinal rock garden proposed in this article is represented by the following species and their hybrid forms: Teucrium chamaedrys L., Mentha piperita L., Mentha spicata L., Mentha longifolia (L.) Huds., Mentha suaveolens Ehrh., Melissa officinalis L., Lavandula angustifolia Mill., Echinacea purpurea (L.) Moench., Salvia sclarea L., Perilla frutescens (L.) Britton, Monarda didyma L., Origanum vulgare L., Hyssopus officinalis L., Thymus serpyllum L., Thymus vulgaris L., Thymus × citriodorus (Pers.) Schreb, Satureja montana L. Perovskia atriplicifolia Benth. © 2021 International Multidisciplinary Scientific Geoconference. All rights reserved.

AChEI-EL:Prediction of Acetylcholinesterase Inhibitors Based on Ensemble Learning Model

With the outbreak of the COVID-19, people are eager to develop potential drugs for specific diseases through efficient technological means. Alzheimer's disease (AD) has become one of the top ten causes of death in the world and is a typical neurodegenerative disease. When acetylcholinesterase (AChE) is inhibited, it improves the transmission of cholinergic neurotransmitters in patients and restores cognitive function, so acetylcholinesterase inhibitors (AChEIs) are often considered by researchers as potential drugs for the treatment of AD. Machine learning algorithms and data mining techniques can accelerate drug development and reduce the cost of biological experiments, so it is of great significance to develop models that can accurately predict AChEIs. However, few studies have applied efficient and mature ensemble learning methods to the problem of predicting potential inhibitors of AChE. In this study, we constructed a dataset from a publicly available biological experiment database, and for the first time established an ensemble learning model based on CatBoost and XGBoost to predict potential AChEIs. We demonstrate the advantages of ensemble learning models in building AChEIs predictor based on imbalanced, heterogeneous data through a comprehensive evaluation. Afterwards, we also combined the best-performing models into a blending model AChEI-EL for case studies, and obtained the top-ranked potential inhibitors that have been shown to have the potential to inhibit the AChE. These results suggest that our method has a promising application in the field of AD. Finally, we developed a WEB online prediction platform based on the best model for the use and reference of researchers. © 2022 IEEE.

Trends and concerns of potentially inappropriate medication use in patients with cardiovascular diseases.

Introduction: The use of potentially inappropriate medications (PIM) is an alarming social risk factor in cardiovascular patients. PIM administration may result in iatrogenic disorders and adverse consequences may be attenuated by limiting PIM intake.Areas covered: The goal of this review article is to discuss the trends, risks, and concerns regarding PIM administration with focus on cardiovascular patients. To find data, we searched literature using electronic databases (Pubmed/Medline 1966-2021 and Web of Science 1975-2021). The data search terms were cardiovascular diseases, potentially inappropriate medication, potentially harmful drug-drug combination, potentially harmful drug-disease combination, drug interaction, deprescribing, and electronic health record.Expert opinion: Drugs for heart diseases are the most commonly prescribed medications in older individuals. Despite the availability of explicit and implicit PIM criteria, the incidence of PIM use in cardiovascular patients remains high ranging from 7 to 85% in different patient categories. Physician-induced disorders often occur when PIM is administered and adverse effects may be reduced by limiting PIM intake. Main strategies promising for addressing PIM use include deprescribing, implementation of systematic electronic records, pharmacist medication review, and collaboration among cardiologists, internists, geriatricians, clinical pharmacologists, pharmacists, and other healthcare professionals as basis of multidisciplinary assessment teams.

Potential drug-drug interactions in COVID 19 patients in treatment with lopinavir/ritonavir.

OBJECTIVES: To determine the prevalence of potential interactions in COVID19 patients receiving lopinavir/ritonavir (LPV/r). The secondary objective was to develop recommendations and identify the risk factors associated with presenting potential interactions with LPV/r. SUBJECTS AND METHODS: Cross-sectional and multicenter study with the participation of 2 hospitals. COVID 19 patients over 18 years of age, admitted to hospital and under treatment with LPV/r were included. A screening of potential interactions related to LPV/r and home and hospital medication was carried out. Lexicomp® (Uptodate), HIV-drug interactions and COVID-drug interactions were used as the query database. RESULTS: 361 patients with a mean age of 62.77 ±â€¯14.64 years were included, where 59.6% (n = 215) were men. 62.3% (n = 225) had 1 or more potential interactions and 26, 87% (n = 97) 2 or more. The independent variables associated with presenting ≥1 potential interactions were age (>65) (OR 1.95; 95% CI 1.06-3.59, P = .033), ICU admission (OR 9.22; CI 95% 1.98-42.93; P = .005), previous respiratory pathology (OR 2.90; 95% CI 1.15-7.36; P = .024), psychiatric (OR 4.14; 95 CI% 1.36-12.61; P = .013), dyslipidemia (OR 3.21; 95% CI 1.63-6.35; P = .001) and the number of drugs prescribed (OR 4.33; 95% CI 2.40-7.81; P = .000). CONCLUSION: The prevalence of potential interactions in COVD 19 patient undergoing treatment with LPV/r is high, with age (>65), ICU admission, previous respiratory and psychiatric pathology, dyslipidemia and the number of prescribed drugs acting as risk factors.

OBJETIVOS: Determinar la prevalencia de interacciones potenciales en pacientes COVID19 en tratamiento con lopinavir/ritonavir (LPV/r). El objetivo secundario fue elaborar recomendaciones e identificar los factores de riesgo asociados a presentar interacciones potenciales con LPV/r. SUJETOS Y MÉTODOS: Estudio transversal y multicéntrico con la participación 2 hospitales. Se incluyeron pacientes COVID 19 mayores de 18 años, con ingreso hospitalario y en tratamiento con LPV/r. Se realizó un cribado de las interacciones potenciales relacionadas con LPV/r y la medicación domiciliaria y hospitalaria. Se utilizó como base de datos de consulta Lexicomp® (Uptodate), HIV-drug interacctions y COVID-drug interacctions. RESULTADOS: Se incluyeron 361 pacientes con una media de edad de 62,77 ±â€¯14,64 años, donde el 59,6% (n = 215) fueron hombres. El 62,3% (n = 225) tuvieron 1 o más interacciones potenciales y el 26, 87% (n = 97) 2 o más. Las variables independientes asociadas a presentar ≥ 1 interacciones potenciales fueron la edad (> 65) (OR 1,95; IC 95% 1,06­3,59; P = ,033), el ingreso en UCI (OR 9,22; IC 95% 1,98­42,93; P = ,005), la patología previa respiratoria (OR 2,90; IC 95% 1,15­7,36; P = ,024), psiquiátrica (OR 4,14; IC 95% 1,36­12,61; P = ,013), la dislipemia (OR 3,21; IC 95% 1.63­6,35; P = ,001) y el número de fármacos prescrito (OR 4,33; IC 95% 2,40­7,81; P = ,000). CONCLUSIÓN: La prevalencia de interacciones potenciales en paciente COVD 19 en tratamiento con LPV/r es elevada, comportándose como factores de riesgo asociados la edad (>65), el ingreso en UCI, la patología previa respiratoria, psiquiátrica y la dislipemia y el número de fármacos prescritos.

[Potential drug-drug interactions in COVID 19 patients in treatment with lopinavir/ritonavir]. / Interacciones medicamentosas potenciales en pacientes COVID 19 en tratamiento con lopinavir/ritonavir.

OBJECTIVES: To determine the prevalence of potential interactions in COVID-19 patients receiving lopinavir/ritonavir (LPV/r). The secondary objective was to develop recommendations and identify the risk factors associated with presenting potential interactions with LPV/r. SUBJECTS AND METHODS: Cross-sectional and multicenter study with the participation of 2 hospitals. COVID-19 patients over 18 years of age, admitted to hospital and under treatment with LPV/r were included. A screening of potential interactions related to LPV/r and home and hospital medication was carried out. Lexicomp® (Uptodate), HIV-drug interactions and COVID-drug interactions were used as the query database. RESULTS: 361 patients with a mean age of 62.77 ± 14.64 years were included, where 59.6% (n = 215) were men. 62.3% (n = 225) had 1 or more potential interactions and 26, 87% (n = 97) 2 or more. The independent variables associated with presenting ≥1 potential interactions were age (> 65) (OR 1.95; 95% CI 1.06-3.59, P =.033), ICU admission (OR 9.22; CI 95% 1.98-42.93; P =.005), previous respiratory pathology (OR 2.90; 95% CI 1.15-7.36; P =.024), psychiatric (OR 4.14; 95 CI % 1.36-12.61; P =.013), dyslipidemia (OR 3.21; 95% CI 1.63-6.35; P =.001) and the number of drugs prescribed (OR 4.33; 95% CI 2.40-7.81; P =.000). CONCLUSION: The prevalence of potential interactions in COVD-19 patient undergoing treatment with LPV/r is high, with age (> 65), ICU admission, previous respiratory and psychiatric pathology, dyslipidemia and the number of prescribed drugs acting as risk factors.

Major Neurologic Adverse Drug Reactions, Potential Drug-Drug Interactions and Pharmacokinetic Aspects of Drugs Used in COVID-19 Patients with Stroke: A Narrative Review.

Stroke has been considered as one of the underlying diseases that increases the probability of severe infection and mortality. Meanwhile, there are ongoing reports of stroke subsequent to COVID-19 infection. In this narrative paper, we reviewed major neurologic adverse drug reactions (ADRs) and pharmacokinetics of drugs which are routinely used for COVID-19 infection and their potential drug-drug interactions (PDDIs) with common drugs used for the treatment of stroke. It is highly recommended to monitor patients on chloroquine (CQ), hydroxychloroquine (HCQ), antiviral drugs, and/or corticosteroids about initiation or progression of cardiac arrhythmias, delirium, seizure, myopathy, and/or neuropathy. In addition, PDDIs of anti-COVID-19 drugs with tissue plasminogen activator (tPA), anticoagulants, antiaggregants, statins, antihypertensive agents, and iodine-contrast agents should be considered. The most dangerous PDDIs were interaction of lopinavir/ritonavir or atazanavir with clopidogrel, prasugrel, and new oral anticoagulants (NOACs).