Are we ready to combat the ecotoxicity of COVID-19 pharmaceuticals? An in silico aquatic risk assessment.

To fight COVID-19 with uncountable medications and bioproducts throughout the world has taken us to another challenge of ecotoxicity. The indiscriminate usage followed by improper disposal of unused antibacterials, antivirals, antimalarials, immunomodulators, angiotensin II receptor blockers, corticosteroids, anthelmintics, anticoagulants etc. can lead us to an unimaginable ecotoxicity in the long run. A series of studies already identified active pharmaceutical ingredients (APIs) of the mentioned therapeutic classes and their metabolites in aquatic bodies as well as in wastewater treatment plants. Therefore, an initial ecotoxicity assessment of the majorly used pharmaceuticals is utmost requirement of the present time. The present in silico risk assessment study is focused on the aquatic toxicity prediction of 81 pharmaceuticals where 77 are most-used pharmaceuticals for COVID-19 throughout the world based on the literature along with one drug nirmatrelvir [PF-07321332] approved for emergency use by US-FDA and three other molecules under clinical trial. The ecotoxicity of the studied compounds were predicted based on the three aquatic species fish, algae and crustaceans employing the highest quality QSAR models available from the literature as well as using ECOSAR and QSAR Toolbox. To compare the toxicity thresholds, we have also used 4 control pharmaceuticals based on the worldwide occurrence from river, lake, STP, WWTPs, influent and effluent followed by high reported aquatic toxicity over the years as per the literature. Based on the statistical comparison, we have proposed top 3 pharmaceuticals used for the COVID-19 most toxic to the aquatic environment. The study will provide confident predictions of aquatic ecotoxicity data related to abundant use of COVID-19 drugs. The major aim of the study is to fill up the aquatic ecotoxicity data gap of major medications used for COVID-19.

Repurposing FDA approved drugs as possible anti-SARS-CoV-2 medications using ligand-based computational approaches: sum of ranking difference-based model selection.

The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prevailing, with more than 440 million infections and over 5.9 million deaths documented so far since the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The non-availability of treatment further aggravates the scenario, thereby demanding the exploration of pre-existing FDA-approved drugs for their effectiveness against COVID-19. The current research aims to identify potential anti-SARS-CoV-2 drugs using a computational approach and repurpose them if possible. In the present study, we have collected a set of 44 FDA-approved drugs of different classes from a previously published literature with their potential antiviral activity against COVID-19. We have employed both regression- and classification-based quantitative structure-activity relationship (QSAR) modeling to identify critical chemical features essential for anticoronaviral activity. Multiple models with the consensus algorithm were employed for the regression-based approach to improve the predictions. Additionally, we have employed a machine learning-based read-across approach using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home and linear discriminant analysis for the efficient prediction of potential drug candidate for COVID-19. Finally, the quantitative prediction ability of different modeling approaches was compared using the sum of ranking differences (SRD). Furthermore, we have predicted a true external set of 98 pharmaceuticals using the developed models for their probable anti-COVID activity and their prediction reliability was checked employing the "Prediction Reliability Indicator" tool available from https://dtclab.webs.com/software-tools. Though the present study does not target any protein of viral interaction, the modeling approaches developed can be helpful for identifying or screening potential anti-coronaviral drug candidates. Supplementary information: The online version contains supplementary material available at 10.1007/s11224-022-01975-3.

In silico modeling for quick prediction of inhibitory activity against 3CLpro enzyme in SARS CoV diseases.

As of 2 September 2020, the 2019 novel coronavirus or SARS CoV-2 has been responsible for more than 2,56,02,665 infections and 8,52,768 deaths worldwide. There has been an urgent need of newer drug discovery to tackle the situation. Severe acute respiratory syndrome-associated coronavirus 3C-like protease (or 3CLpro) is a potential target as anti-SARS agents as it plays a vital role in the viral life cycle. This study aims at developing a quantitative structure-activity relationship (QSAR) model against a group of 3CLpro inhibitors to study their structural requirements for their inhibitory activity. Further, molecular docking studies were carried out which helped in the justification of the QSAR findings. Moreover, molecular dynamics simulation study was performed for selected compounds to check the stability of interactions as suggested by the docking analysis. The current QSAR model was further used in the prediction and screening of large databases within a short time.Communicated by Ramaswamy H. Sarma.

Stress and Anxiety Management During the COVID-19 Pandemic (Lessons Learnt from a Cohort of Orthopaedic Registrars Redeployed to ITU).

BACKGROUND: Working during the coronavirus pandemic has had a significant impact on health care workers. A group of orthopaedic trainees at Royal Gwent Hospital, UK, were redeployed to intensive therapy unit for four weeks during COVID-19 pandemic. This study reviews our experience; focusing on causes of stress and anxiety, and how they were managed. The lessons learnt could be used as a framework for pre-emptive me-asures during future challenges. MATERIAL AND METHODS: Orthopaedic registrars were divided into two groups. Seven trainees (Redeployed group) moved to ITU for four weeks to support the critical care team. The other group (Retained group) of eight registrars continued to cover orthopaedic rota. A survey was done for anxiety levels comparing the two groups at three time points during these four weeks. RESULTS: Anxiety and stress in the ITU-redeployed group was comparatively less than the continuing group as time progressed during the redeployment. CONCLUSIONS: 1. The disruptive impact of the COVID-19 pandemic has been a source of massive stress and an-xiety for health care workers. 2. Our experience shows that stress is controllable with the correct strategies. 3. The main points are early identification of vulnerable groups, proper induction, active involvement, adequate explanation, appreciation, good communication, and available psychological support whenever needed. 4. These are essential to maintain a resilient workforce against upcoming waves of COVID-19.

The role of orthopaedic trainees during the COVID-19 pandemic and impact on post-graduate orthopaedic education: a four-nation survey of over 100 orthopaedic trainees.

AIMS: The COVID-19 pandemic has had a significant impact on the provision of orthopaedic care across the UK. During the pandemic orthopaedic specialist registrars were redeployed to "frontline" specialties occupying non-surgical roles. The impact of the COVID-19 pandemic on orthopaedic training in the UK is unknown. This paper sought to examine the role of orthopaedic trainees during the COVID-19 and the impact of COVID-19 pandemic on postgraduate orthopaedic education. METHODS: A 42-point questionnaire was designed, validated, and disseminated via e-mail and an instant-messaging platform. RESULTS: A total of 101 orthopaedic trainees, representing the four nations (Wales, England, Scotland, and Northern Ireland), completed the questionnaire. Overall, 23.1% (23/101) of trainees were redeployed to non-surgical roles. Of these, 73% (17/23) were redeployed to intensive treatment units (ITUs), 13% (3/23) to A/E, and 13%(3/23%) to general medicine. Of the trainees redeployed to ITU 100%, (17/17) received formal induction. Non-deployed or returning trainees had a significant reduction in sessions. In total, 42.9% (42/101) % of trainees were not timetabled into fracture clinic, 53% (53/101) of trainees had one allocated theatre list per week, and 63.8%(64/101) of trainees did not feel they obtained enough experience in the attached subspecialty and preferred repeating this. Overall, 93% (93/101) of respondents attended at least one weekly online webinar, with 79% (79/101) of trainees rating these as useful or very useful, while 95% (95/101) trainees attended online deanery teaching which was rated as more useful than online webinars (p = 0.005). CONCLUSION: Orthopaedic specialist trainees occupied an important role during the COVID-19 pandemic. COVID-19 has had a significant impact on orthopaedic training. It is imperative this is properly understood to ensure orthopaedic specialist trainees achieve competencies set out in the training curriculum.Cite this article: Bone Joint Open 2020;1-11:676-682.

Trauma surgery at a designated COVID-19-free site during the pandemic: a safe model and a possible way to restart routine elective surgery.

AIMS: Elective operating was halted during the COVID-19 pandemic to increase the capacity to provide care to an unprecedented volume of critically unwell patients. During the pandemic, the orthopaedic department at the Aneurin Bevan University Health Board restructured the trauma service, relocating semi-urgent ambulatory trauma operating to the isolated clean elective centre (St. Woolos' Hospital) from the main hospital receiving COVID-19 patients (Royal Gwent Hospital). This study presents our experience of providing semi-urgent trauma care in a COVID-19-free surgical unit as a safe way to treat trauma patients during the pandemic and a potential model for restarting an elective orthopaedic service. METHODS: All patients undergoing surgery during the COVID-19 pandemic at the orthopaedic surgical unit (OSU) in St. Woolos' Hospital from 23 March 2020 to 24 April 2020 were included. All patients that were operated on had a telephone follow-up two weeks after surgery to assess if they had experienced COVID-19 symptoms or had been tested for COVID-19. The nature of admission, operative details, and patient demographics were obtained from the health board's electronic record. Staff were assessed for sickness, self-isolation, and COVID-19 status. RESULTS: A total of 58 surgical procedures were undertaken at the OSU during the study period; 93% (n = 54) of patients completed the telephone follow-up. Open reduction and internal fixation of ankle and wrist fractures were the most common procedures. None of the patients nor members of their households had developed symptoms suggestive of COVID-19 or required testing. No staff members reported sick days or were advised by occupational health to undergo viral testing. CONCLUSION: This study provides optimism that orthopaedic patients planned for surgery can be protected from COVID-19 nosocomial transmission at separate COVID-19-free sites.Cite this article: Bone Joint Open 2020;1-6:302-308.

In Silico Modeling of Small Molecule Carboxamides as Inhibitors of SARS-CoV 3CL Protease: An Approach Towards Combating COVID-19.

BACKGROUND: The quantitative structure-activity relationship (QSAR) approach is most widely used for the prediction of biological activity of potential medicinal compounds. A QSAR model is developed by correlating the information obtained from chemical structures (numerical descriptors/ independent variables) with the experimental response values (the dependent variable). METHODS: In the current study, we have developed a QSAR model to predict the inhibitory activity of small molecule carboxamides against severe acute respiratory syndrome coronavirus (SARS-- CoV) 3CLpro enzyme. Due to the structural similarity of this enzyme with SARS-CoV-2, the causative organism of the recent pandemic, the former may be used for the development of therapies against coronavirus disease 19 (COVID-19). RESULTS: The final multiple linear regression (MLR) model was based on four two-dimensional descriptors with definite physicochemical meaning. The model was strictly validated using different internal and external quality metrics. The model showed significant statistical quality in terms of determination coefficient (R2=0.748, adjusted R2 or R2 adj = 0.700), cross-validated leave-one-out Q2 (Q2=0.628) and external predicted variance R2 pred = 0.723. The final validated model was used for the prediction of external set compounds as well as to virtually design a new library of small molecules. We have also performed a docking analysis of the most active and least active compounds present in the dataset for comparative analysis and to explain the features obtained from the 2D-QSAR model. CONCLUSION: The derived model may be useful to predict the inhibitory activity of small molecules within the applicability domain of the model only based on the chemical structure information prior to their synthesis and testing.

Therapeutics for COVID-19: from computation to practices-where we are, where we are heading to.

After the 1918 Spanish Flu pandemic caused by the H1N1 virus, the recent coronavirus disease 2019 (COVID-19) brought us to the time of serious global health catastrophe. Although no proven therapies are identified yet which can offer a definitive treatment of the COVID-19, a series of antiviral, antibacterial, antiparasitic, immunosuppressant drugs have shown clinical benefits based on repurposing theory. However, these studies are made on small number of patients, and, in majority of the cases, have been carried out as nonrandomized trials. As society is running against the time to combat the COVID-19, we present here a comprehensive review dealing with up-to-date information of therapeutics or drug regimens being utilized by physicians to treat COVID-19 patients along with in-depth discussion of mechanism of action of these drugs and their targets. Ongoing vaccine trials, monoclonal antibodies therapy and convalescent plasma treatment are also discussed. Keeping in mind that computational approaches can offer a significant insight to repurposing based drug discovery, an exhaustive discussion of computational modeling studies is performed which can assist target-specific drug discovery.