Tuesday, 27 September, 2022

What Rapid Computational Modelling Technologies Should be Applied for the Formulation Development of Anti-COVID19 Drugs or Vaccines?

Wuhan, a city residing in china reported few pneumonia cases with unknown cause in Deccember 2019. The researchers proved that the main cause of pneumonia in those patients is novel coronavirus which is officially called as severe acute respiratory syndrome related corona virus (SARS-CoV-2) and the WHO announced SARS-Cov-2 as Corona Virus Disease (COVID-19) and also declared an emergency for public health of international concern with provided short-term safety guidelines for disease spread controlling and management of patient health. According to WHO, till now, total confirmed cases are 2544792 while 175694 cases were died, and also confirmed total 213 countries, territories and areas with cases (1–3).

This virus is 26-32kb lengthy containing single stranded positive RNA genome and enveloped in an outer membrane with spikes of glycoprotein, responsible for crown-like appearance. It exists in alpha, beta, gamma and delta forms wherein beta form is the cause of COVID-19. Clinical symptom includes dry cough, fever, headache, tiredness, sore throat, diarrhoea. Severe cases reported occurrence of dyspnea while acute respiratory distress syndrome, refractory metabolic acidosis, septic shock and coagulation disorders can be other symptoms. Therapeutic strategies against COVID-19 can be diverse. Presently, evidence-based treatment and compassionate care in ICU is the foundation for COID-19 treatment with controlling nosocomial infection in hospitals. Critical parameters like evaluation of functioning of multi-organ, assessment of nutritional level also must be accounted along with preventing deep vein thrombosis in the bedriddens (1,4,5). As this virus attacks on various vital organs which includes lung, intestine, blood vessels and heart by attacking on the proteins associated with the organs. Thus, targeting to these proteins can provide the beneficial effect. Although several evidence based therapeutic agents are available for the treatment of COVID-19 infection but still potential treatment strategies or vaccines are required to make the whole world free from this situation (5). Various favourable drug moieties (antiviral, antibacterial, antimalarial and many more) stands out against the proteins involved in targeting disease and showed efficient effect but still selection of suitable molecule is unanswered question unless clinical trial takes place. Despite many scientists and researchers have engaged in studying these newly emerged COVID-19 virus and searching for the effective treatment as alternatives to solve this problem in a systematic manner which can fits in the FDA and other government bodies norms. Considering the emergency, the understanding of the COVID-19 target-ligand interactions with drug repurposing approach represents a key challenge.

Use of computational tools for this situation, can solve these problem and also fastens the process to the greater extent due to their applications in each and every field including pharmaceutical field and also use of it is advantageous over the experimental work. This computational tools helps in selecting the suitable drug molecule which acts on the multiple target proteins for the required effect and also selected molecule can be formulated in suitable dosage form for COVID-19 treatment (3).

In one of our article, we reviewed about how the various computational tools can be used for the successful invention of new molecules and development of various formulation such as tablets, liposomes, nanoparticles, in situ gel and many other formulations (6).

There are various computational tools nowadays which can be used in the pharmaceutical filed for discovering the acceptable molecule and developing effective formulation. This tools includes the Hansen solubility parameters prediction, quantitative structure activity relationship, quantitative structure property relationship, molecular modeling, molecular docking, molecular dynamic simulation, finite element method, computational fluid dynamics, discrete element modeling, ADMET prediction and also physiologically based pharmacokinetics models can be used successfully for the formulation development. This tools works separately in each and every step of the formulation development process like selection of potential molecule from the library of the drugs, solubility prediction, selection of suitable excipients like (solvents, polymers, penetration enhances, etc.), selection of suitable formulation design, the release mechanism of the drug from formulation, drug and formulation behaviour for is absorption, distribution, metabolism, excretion and toxicity prediction. The following are some studies which performed currently to select the potential molecules using different computational approaches mentioned above;

Kong et al. 2020, introduced an interactive, open access and free docking server which predicts the ligands (small molecules, antibodies and peptides) binding on the COVID-19 protein targets involved in the life cycle of the virus. Thus, it helps in the discovery as well as the development of novel molecules for the treatment of COVID-19 (7).

Elfiky et al. 2020, performed sequence analysis, molecular docking as well as the molecular dynamics simulation studies to assess the efficacy of the anti-HCV molecules on the RNA dependent RNA polymerase (RdRp) of COVID-19 using drug repurposing approach. Some drugs from anti-polymerase category also targeted against RdRp. The results showed that the stable binding affinity and stability of ligands towards the target COVID-19 RdRp. The ligands ribavirin, remidisvir, sofosbuvir and IDX-184 can be considered as the potential molecules for the COVID-19 treatment (8). Similar studies was performed by K. Sahu and team, who applied structure and ligand based approaches in virtual screening of drug using repurposing approach for drugs accepted for use in human being and present in clinical stages by targeting to 3CL protease of SARS-CoV-2 virus. Many compounds (from antiviral category) found to be potential hits against the 3CL protease protein based on the similarity of shape, pharmacophore modeling as well as the molecular docking studies (9).

Muralidharan et al. 2020, performed the molecular docking and molecular dynamic simulation studies to compare the efficiency of the individual and combination of antiviral drugs (ritonavir, lopinavir and oseltamavir) for controlling the virulence effect of COVID-19. The molecular docking studies and molecular dynamic simulation studies showed that the combination of three drug showed better binding affinity and energy as well as the stability with the target protein as compared to individual drugs, respectively (10). Similar studies was performed by J. Wang, who used the virtual molecular docking studies followed by molecular dynamic simulation studies for selection of potential inhibitor which can targets against the COVID-19. In addition to this, they have performed MM-PBSA-WSAS binding free energy calculations to further confirm the results. Based on the results, various potential inhibitor (valrubicin, lopinavir, carfilzomib, elbasvir and eravacycline) stands out against the COVID-19 from many promising drugs (11).

Adem et al. 2020, performed molecular docking studies for assessment of efficacy of plant based polyphenols bioactive agents against Main Protease (Mpro) COVID-19. Based on the binding energies results, rutin, apiin, hesperidin, diosmin and diacetylcurcumin showed better binding energy and thus found more effective than nelfinavir for the treatment of COVID-19 (12). Similar studies was performed by S. Khaerunnisa and team, in which they have selected medicinal agents obtained from the plants to assess the activity against the Mpro protein of COVID-19. Based on the results obtained, several molecules appeared as best which can acts against the Mpro protein (13). Although many computational studies were performed for selection of potential molecule by repurposing approach but the suitable drug delivery is also important criteria for treatment.

In one of our studies, we have used the computational approaches for the formulation development which includes the selection of suitable solvent for formulation preparation and using the selected solvents, the nanoparticle formulation was prepared computationally to predict or understand the mechanism of nanoparticle formation in nanoformulation. The obtained nanoparticle was evaluated for various computationally parameters which was validated by various experimental studies. With the current experiment, we could also establish that the simulations may not only help in reducing the number of lab experiments to be performed but also helps in identifying suitable excipients for nanoparticle formulation development by furnishing useful information on the micromolecular environment (14). Our other studies of solubility enhancement of poorly solubility enhancement and in-situ gel formation using computational approach, also proved that the computational tools are very helpful in different fields of the formulation development (15–17).

Based on this proof, the potential molecules selected for the COVID-19 treatment can be transformed into a suitable targeted formulation using computational tools. As this computational tools helps in selecting the excipients as well as in prediction of drug-excipient compatibility which fastens the formulation process and the prepared formulation can be used easily for the treating the infected patients. Also this computational tools can be used for the designing of suitable vaccine by predicting its affinity for required target in the body.

With this proof of studies, we can conclude that computational modeling approaches can be useful for selection of potential molecules and also in future for targeted formulation development of this potential molecules.


1.        Sinha Deepak Kumar. COVID-19: Vaccine development and therapeutic strategies  [Internet]. IndiaBioscience. 2020 [cited 2020 Apr 25]. Available from: https://covid-gyan.in/content/covid-19-vaccine-development-and-therapeutic-strategies

2.        W. H. O. Coronavirus disease 2019 [Internet]. [cited 2020 Apr 25]. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019

3.        Li L, Li R, Wu Z, Yang X, Zhao M, Liu J, et al. Therapeutic strategies for critically ill patients with COVID-19. Ann Intensive Care [Internet]. 2020 Apr 20 [cited 2020 Apr 25];10(1):45. Available from: http://www.ncbi.nlm.nih.gov/pubmed/32307593

4.        Zhang J, Litvinova M, Wang W, Wang Y, Deng X, Chen X, et al. Evolving epidemiology and transmission dynamics of coronavirus disease 2019 outside Hubei province, China: a descriptive and modelling study. Lancet Infect Dis [Internet]. 2020; Available from: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082768191&doi=10.1016%2FS1473-3099%2820%2930230-9&partnerID=40&md5=1a0f8c3bf417a8581680aafaefcd4602

5.        Ahmed SF, Quadeer AA, McKay MR. Preliminary Identification of Potential Vaccine Targets for the COVID-19 Coronavirus (SARS-CoV-2) Based on SARS-CoV Immunological Studies. Viruses [Internet]. 2020 Feb 25 [cited 2020 Apr 25];12(3):254. Available from: https://www.mdpi.com/1999-4915/12/3/254

6.        Mehta CH, Narayan R, Nayak UY. Computational modeling for formulation design. Drug Discov Today. 2019;24(3):781–8.

7.        Kong R, Yang G, Xue R, Liu M, Wang F, Hu J, et al. COVID-19 Docking Server: An interactive server for docking small molecules, peptides and antibodies against potential targets of COVID-19 | Request PDF. arXiv Prepr [Internet]. 2020 Feb 29 [cited 2020 Apr 25]; Available from: https://www.researchgate.net/publication/339642283_COVID-19_Docking_Server_An_interactive_server_for_docking_small_molecules_peptides_and_antibodies_against_potential_targets_of_COVID-19

8.        Elfiky AA. Anti-HCV, nucleotide inhibitors, repurposing against COVID-19. Life Sci. 2020 May 1;248:117477.

9.        Sahu K, Tuszynski J, Houghton M, Tyrrell L, Noskov S. Computational Screening of Molecules Approved in Phase-I Clinical Trials to Identify 3CL Protease Inhibitors to Treat COVID-19. Preprints. 2020 Apr 2;

10.      Muralidharan N, Sakthivel R, Velmurugan D, Gromiha MM. Computational studies of drug repurposing and synergism of lopinavir, oseltamivir and ritonavir binding with SARS-CoV-2 Protease against COVID-19. J Biomol Struct Dyn [Internet]. 2020 Apr 6 [cited 2020 Apr 25];1–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/32248766

11.      Wang J. Fast Identification of Possible Drug Treatment of Coronavirus Disease -19 (COVID-19) Through Computational Drug Repurposing Study. J Chem Inf Model [Internet]. 2020 Feb 21

12.      Adem S, Eyupoglu V, Sarfraz I, Rasul A, Ali M. Identification of Potent COVID-19 Main Protease (Mpro) Inhibitors from Natural Polyphenols: An in Silico Strategy Unveils a Hope against CORONA. Preprints [Internet]. 2020 Mar 23 [cited 2020 Apr 25]; Available from: https://www.preprints.org/manuscript/202003.0333/v1

13.      Khaerunnisa S, Kurniawan H, Awaluddin R, Suhartati S, Soetjipto S. Potential Inhibitor of COVID-19 Main Protease (Mpro) From Several Medicinal Plant Compounds by Molecular Docking Study . Prepr [Internet]. 2020 Mar 13 [cited 2020 Apr 25];1–4. Available from: https://pubchem.ncbi.nlm.nih.gov/

14.      Mehta CH, Narayan R, Aithal G, Pandiyan S, Bhat P, Dengale S, et al. Molecular simulation driven experiment for formulation of fixed dose combination of Darunavir and Ritonavir as anti-HIV nanosuspension. J Mol Liq. 2019 Nov 1;293.

15.      Sherje AP, Kulkarni V, Murahari M, Nayak UY, Bhat P, Suvarna V, et al. Inclusion Complexation of Etodolac with Hydroxypropyl-beta-cyclodextrin and Auxiliary Agents: Formulation Characterization and Molecular Modeling Studies. Mol Pharm [Internet]. 2017 Mar 1 [cited 2018 Jan 22];14(4):1231–42. Available from: http://pubs.acs.org/doi/10.1021/acs.molpharmaceut.6b01115

16.      Suvarna V, Thorat S, Nayak U, Sherje A, Murahari M. Host-guest interaction study of Efavirenz with hydroxypropyl‑β‑cyclodextrin and l‑arginine by computational simulation studies: Preparation and characterization of supramolecular complexes. J Mol Liq [Internet]. 2018 Jun 1 [cited 2018 Mar 18];259:55–64. Available from: https://www.sciencedirect.com/science/article/pii/S0167732218302125

17.      Aithal G, Nayak U, Mehta C, Narayan R, Gopalkrishna P, Pandiyan S, et al. Localized In Situ Nanoemulgel Drug Delivery System of Quercetin for Periodontitis: Development and Computational Simulations. Molecules [Internet]. 2018 Jun 6 [cited 2018 Jul 28];23(6):1–15. Available from: http://www.ncbi.nlm.nih.gov/pubmed/29882751

Name of Blogger: Chetan Mehta
ARPB Blogger ID: ARPBGZ234

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