You are presented with a paragraph from a COVID-19 literature. Classify the topics of this piece of text by choosing from the following categories:

- Epidemic Forecasting: The content relates to predictions about the spread of COVID-19.
- Treatment: The paragraph discusses therapeutic methods or medication for COVID-19.
- Prevention: The content covers measures to prevent the spread of COVID-19.
- Mechanism: The paragraph explains the biological mechanisms of the virus.
- Case Report: The content is a report of a COVID-19 case.
- Transmission: The paragraph discusses how COVID-19 is transmitted.
- Diagnosis: The content is about diagnosing COVID-19 in individuals.

Your classification should be provided as an element within a JSON array. The array should be formatted with topics enclosed in double quotes and separated by commas, following the JSON format, for example: ["Topic1", "Topic2", ...]. Include all applicable topics within the array. Your output should be a valid JSON array, and no additional text should be included in your response.

Article:

Since December 2019, coronavirus disease (COVID-19) has claimed the lives of millions of people across the globe. To date, no medicine is available for the responsible virus SARS-CoV-2. 3CLpro, that is, 3-chymotrypsin-like protease, the main protease (M(pro) ), has an important role in cleaving pp1a and pp1ab polyproteins. This M(pro) serves as an important target in drug designing against COVID-19. Herein, the study includes the investigation, screening, and identification of potent leads from (Withania sps.), against SARS-CoV-2, using virtual screening, molecular docking, and molecular dynamics (MD) simulations. Seventy-three natural compounds from this important medicinal plant were screened. The Binding affinity was used to identify the most probable target to inhibit the M(pro) , compounds 27-hydroxywithanolide F (W32, -11.5 kcal/mol), withanolide A (W56, -11.4 kcal/mol), and withacoagulin H (W30, -11.1 kcal/mol) showed highest binding energy. Lipinski's rule, followed by drug-likability and likeness screening, resulted in 36 molecules. Further, MD simulation of 50 ns predicted withacoagulin H possessing strong binding affinity and hydrogen-bonding interactions with the active site. The binding free energy calculation showed the most negative energy of withacoagulin H (-63.463 KJ/mol) compared to other selected compounds. The study also compared the bonding energy of already reported repurposed and newly synthesized drugs. Further, absorption, distribution, metabolism, and excretion predictions were made to found a good balance of potency. Hence the following screened compounds from Withania sps. could serve as the potential leads for drug development against COVID-19.
["Mechanism", "Treatment"]