A Glance at the Development and Patent Literature of Tecovirimat: The First-in-Class Therapy for Emerging Monkeypox Outbreak
Bibliographic record
Abstract
Monkeypox disease (MPX) is currently considered a global threat after COVID-19. European Medicines Agency (EMA) approved Tecovirimat in capsule dosage form (200 mg) as the first treatment for MPX in January 2022. This article highlights Tecovirimat's development and patent literature review and is believed to benefit the scientists working on developing MPX treatments. The literature for Tecovirimat was gathered from the website of SIGA Technologies (developer of Tecovirimat), regulatory agencies (EMA, United States Food and Drug Administration (USFDA), and Health Canada), PubMed, and freely accessible clinical/patent databases. Tecovirimat was first recognized as an anti-orthopoxvirus molecule in 2002 and developed by SIGA Technologies. The USFDA and Health Canada have also recently approved Tecovirimat to treat smallpox in 2018 and 2021, respectively. The efficacy of Tecovirimat was verified in infected non-human primates (monkeys) and rabbits under the USFDA's Animal Rule. Most clinical studies have been done on Tecovirimat's safety and pharmacokinetic parameters. The patent literature has revealed inventions related to the capsule, injection, suspension, crystalline forms, amorphous form, and drug combinations (Tecovirimat + cidofovir) and process for preparing Tecovirimat. The authors foresee the off-label use of Tecovirimat in the USA and Canada for MPX and other orthopoxvirus infections. The authors also trust that there is immense scope for developing new Tecovirimat-based treatments (new drug combinations with other antivirals) for orthopoxvirus and other viral diseases. Drug interaction studies and drug resistance studies on Tecovirimat are also recommended. Tecovirimat is believed to handle the current MPX outbreak and is a new hope of biosecurity against smallpox or orthopoxvirus-related bioterrorism attack.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".