scholarly journals Prioritisation of Compounds for 3CLpro Inhibitor Development on SARS-CoV-2 Variants

Molecules ◽  
2021 ◽  
Vol 26 (10) ◽  
pp. 3003
Author(s):  
Marko Jukič ◽  
Blaž Škrlj ◽  
Gašper Tomšič ◽  
Sebastian Pleško ◽  
Črtomir Podlipnik ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Cysteine Protease ◽  
Chemical Space ◽  
Structural Data ◽  
Biological Evaluation ◽  
Chemical Library ◽  
Screening Experiment ◽  
Life Threatening Illness ◽  
Virtual Screening Experiment ◽  
Life Threatening

COVID-19 represents a new potentially life-threatening illness caused by severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 pathogen. In 2021, new variants of the virus with multiple key mutations have emerged, such as B.1.1.7, B.1.351, P.1 and B.1.617, and are threatening to render available vaccines or potential drugs ineffective. In this regard, we highlight 3CLpro, the main viral protease, as a valuable therapeutic target that possesses no mutations in the described pandemically relevant variants. 3CLpro could therefore provide trans-variant effectiveness that is supported by structural studies and possesses readily available biological evaluation experiments. With this in mind, we performed a high throughput virtual screening experiment using CmDock and the “In-Stock” chemical library to prepare prioritisation lists of compounds for further studies. We coupled the virtual screening experiment to a machine learning-supported classification and activity regression study to bring maximal enrichment and available structural data on known 3CLpro inhibitors to the prepared focused libraries. All virtual screening hits are classified according to 3CLpro inhibitor, viral cysteine protease or remaining chemical space based on the calculated set of 208 chemical descriptors. Last but not least, we analysed if the current set of 3CLpro inhibitors could be used in activity prediction and observed that the field of 3CLpro inhibitors is drastically under-represented compared to the chemical space of viral cysteine protease inhibitors. We postulate that this methodology of 3CLpro inhibitor library preparation and compound prioritisation far surpass the selection of compounds from available commercial “corona focused libraries”.

scholarly journals Drug discovery by integration of pharmacophore modeling, virtual screening and biological evaluation by means of bioinformatics resources

2016 ◽  
Author(s):  
Serena Dotolo ◽  
Angelo Facchiano
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Development Process ◽  
Chemical Space ◽  
Pharmacophore Modeling ◽  
Biological Evaluation ◽  
Lead Compounds ◽  
Computational Screening ◽  
Computational Strategy

Drug discovery is a step-by-step process very important in biopharmaceutical field. We are interested in identifying new investigational drug-likes as potential inhibitors of determinate biological-therapeutic targets, trying to decrease the side effects and to safeguard the human health. However, it is a long and very expensive process. Therefore, we are using a new computational strategy, based on Pharmacophore modeling, to select bioactive substances (natural or synthetic), through the integration of bioinformatics online tools and local resource and platforms, in order to include into the strategy also knowledge from high-throughput studies, for new potential lead compounds generation-optimization, trying to accelerate the early phase of the drug development process. The protocol of this new computational strategy is characterized by a multi-step design focused on: 1) screening in RCSB-PDB for a crystal structure of a specific biological target, suitable for the following steps; 2) pharmacophore modeling and virtual computational screening, by using public domain databases of bioactive compounds, as the ZINC12 database [5], in order to find a promising molecule that could become a new potential medicine. 3) molecular and biological evaluation, to check the compounds selected by virtual screening, for their biological properties through public databases, as PubChem Compound, SciFinder, and Chemicalize to trace their origin and underline their most important physical-chemical features, PathPred (an enzyme-catalyzed metabolic pathway predictor server) to highlight and identify their biosynthetic-metabolic pathways and investigating the biotransformation of best candidates, analyzing their metabolites and their potential biological activity. Moreover, ADMET/toxicity predictor server applying the Lipinski-Veber filter are used to calculate the bioavailability the ADMET/toxicity properties. After this check, only molecules with good bioavailability, good predicted activity and good ADMET properties are considered as hits compounds or drug-likes to direct the design of next experimental assays [6]. Finally, the lead compounds selected are analyzed through molecular dynamics simulations. 4) simulations of molecular dynamics on the best lead compounds, to investigate atomic details of protein-compound molecular interactions in different conditions (different organic solutions, organisms and systems). REFERENCES [1] Dubey A, Facchiano A, Ramteke PW, Marabotti A. “In silico approach to find chymase inhibitors among biogenic compounds.” Future Med Chem. 2016; 8(8):841-51 [2] Dubey A, Marabotti A, Ramteke PW, Facchiano A. "Interaction of human chymase with ginkgolides, terpene trilactones of Ginkgo biloba investigated by molecular docking simulations.” Biochem Biophys Res Commun. 2016; 473(2):449-54. [3] Katara P. “Role of bioinformatics and pharmacogenomics in drug discovery and development process”. Netw Model Anal Health Inform Bioinforma 2013; 2: 225-230. [4] Sunseri J. and Koes D. R. “Pharmit: Interactive Exploration of Chemical Space”.Nucl. Acids Res. 2016; 44(W1): W442-448. [5] Irwin J.J. and Shoichet B.K. “ZINC- A free database of Commercially Available Compounds for Virtual Screening”. J.Chem.Inf.Model. 2005; 45: 177-182. [6] Kaserer T., Beck K. R., Akram M., Odermatt A., Schuster D. “Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Application Exemplified on Hydroxysteroid Dehydrogenases”.Molecules 2015; 20: 22799–22832.

scholarly journals Drug discovery by integration of pharmacophore modeling, virtual screening and biological evaluation by means of bioinformatics resources

2016 ◽  
Author(s):  
Serena Dotolo ◽  
Angelo Facchiano
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Development Process ◽  
Chemical Space ◽  
Pharmacophore Modeling ◽  
Biological Evaluation ◽  
Lead Compounds ◽  
Computational Screening ◽  
Computational Strategy

Drug discovery is a step-by-step process very important in biopharmaceutical field. We are interested in identifying new investigational drug-likes as potential inhibitors of determinate biological-therapeutic targets, trying to decrease the side effects and to safeguard the human health. However, it is a long and very expensive process. Therefore, we are using a new computational strategy, based on Pharmacophore modeling, to select bioactive substances (natural or synthetic), through the integration of bioinformatics online tools and local resource and platforms, in order to include into the strategy also knowledge from high-throughput studies, for new potential lead compounds generation-optimization, trying to accelerate the early phase of the drug development process. The protocol of this new computational strategy is characterized by a multi-step design focused on: 1) screening in RCSB-PDB for a crystal structure of a specific biological target, suitable for the following steps; 2) pharmacophore modeling and virtual computational screening, by using public domain databases of bioactive compounds, as the ZINC12 database [5], in order to find a promising molecule that could become a new potential medicine. 3) molecular and biological evaluation, to check the compounds selected by virtual screening, for their biological properties through public databases, as PubChem Compound, SciFinder, and Chemicalize to trace their origin and underline their most important physical-chemical features, PathPred (an enzyme-catalyzed metabolic pathway predictor server) to highlight and identify their biosynthetic-metabolic pathways and investigating the biotransformation of best candidates, analyzing their metabolites and their potential biological activity. Moreover, ADMET/toxicity predictor server applying the Lipinski-Veber filter are used to calculate the bioavailability the ADMET/toxicity properties. After this check, only molecules with good bioavailability, good predicted activity and good ADMET properties are considered as hits compounds or drug-likes to direct the design of next experimental assays [6]. Finally, the lead compounds selected are analyzed through molecular dynamics simulations. 4) simulations of molecular dynamics on the best lead compounds, to investigate atomic details of protein-compound molecular interactions in different conditions (different organic solutions, organisms and systems). REFERENCES [1] Dubey A, Facchiano A, Ramteke PW, Marabotti A. “In silico approach to find chymase inhibitors among biogenic compounds.” Future Med Chem. 2016; 8(8):841-51 [2] Dubey A, Marabotti A, Ramteke PW, Facchiano A. "Interaction of human chymase with ginkgolides, terpene trilactones of Ginkgo biloba investigated by molecular docking simulations.” Biochem Biophys Res Commun. 2016; 473(2):449-54. [3] Katara P. “Role of bioinformatics and pharmacogenomics in drug discovery and development process”. Netw Model Anal Health Inform Bioinforma 2013; 2: 225-230. [4] Sunseri J. and Koes D. R. “Pharmit: Interactive Exploration of Chemical Space”.Nucl. Acids Res. 2016; 44(W1): W442-448. [5] Irwin J.J. and Shoichet B.K. “ZINC- A free database of Commercially Available Compounds for Virtual Screening”. J.Chem.Inf.Model. 2005; 45: 177-182. [6] Kaserer T., Beck K. R., Akram M., Odermatt A., Schuster D. “Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Application Exemplified on Hydroxysteroid Dehydrogenases”.Molecules 2015; 20: 22799–22832.

Review of Adapting to Life-Threatening Illness.

1987 ◽  
Vol 32 (10) ◽  
pp. 906-906
Author(s):  
No authorship indicated
Keyword(s):  
Life Threatening Illness ◽  
Life Threatening

Life-threatening Illness as Precursor to Posttraumatic Growth

2013 ◽  
Author(s):  
Lawrence G. Calhoun ◽  
◽  
Jay Azarow ◽  
Tzipi Weiss ◽  
Joel Millam
Keyword(s):  
Posttraumatic Growth ◽  
Life Threatening Illness ◽  
Life Threatening

Sophistry and Circumstance at the End of Life

2008 ◽  
Vol 5 (1) ◽  
pp. 81-88
Author(s):  
Philip Berry
Keyword(s):  
Liver Failure ◽  
Medical Personnel ◽  
Medical Decision ◽  
Loss Of Consciousness ◽  
Supportive Treatment ◽  
Medical Decisions ◽  
Life Threatening Illness ◽  
Two Factors ◽  
Life Threatening ◽  
Counselling Process

When life-threatening illness robs a patient of the ability to express their desires, medical personnel must work through the issues of management and prognosis with relatives. Management decisions are guided by medical judgement and the relatives’ account of the patient’s wishes, but difficulties occur when distance grows between these two factors. In these circumstances the counselling process may turn into a doctor-led justification of the medical decision. This article presents two strands of dialogue, in which a doctor, counselling for and against continuation of supportive treatment in two patients with liver failure, demonstrates selectivity and inconsistency in constructing an argument. The specific issues of loss of consciousness (with obscuration of personal identity), statistical ‘futility’ and removal of autonomy are explored and used to bolster diametrically opposed medical decisions. By examining the doctor’s ability to interpret these issues according to circumstance, the author demonstrates how it is possible to shade medical facts depending on the desired outcome.

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