Antimalarials: A Patents Landscape Study (2015-Present)
Bibliographic record
Abstract
Abstract: Plasmodium falciparum, Plasmodium vivax, Plasmodium ovale, Plasmodium malariae, and P. knowlesi are five Plasmodium species that cause malaria, a life-threatening parasitic disease. In the developing world, the rapid development of Plasmodium falciparum resistance to currently available treatments has become a serious health concern. This work reports a patent landscape analysis of patent documents related to antimalarial. The patent search was conducted using the commercially available CAS SciFinder database and the open-source patent database, The Lens. Seven hundred ninety-seven patents from The Lens and 1172 patent Sci-finder were exported using the antimalarial drug as a keyword. After the initial screening, 58 patent documents were shortlisted for in-depth analysis. After analysis, it was found that most of the top applicants come from the United States and Switzerland, which shows that market protection is more important in these two countries. The top applicants come from private companies, universities, and public-private partnerships. The United States, Europe, China, Canada, and the Republic of Korea lead the patent race in this area. The most-recorded IPC code is A61P33/06, related to the chemical substances or pharmaceutical formulations showing antimalarials activity. Most of the time, antimalarial drugs were made from quinine, artemisinin, trioxolane, naphthoquinones, and isoquinoline derivatives. Quinine and artemisinin are well-established classes of antimalarials with the maximum number of antimalarial drugs in the market. The research and innovations disclosed in most patents were focused on exploring or evaluating the new scaffolds and their mechanism of action against the normal and resistant malarial parasite. In conclusion, it has been found that various scaffolds are needed to be explored further in search of new antimalarial compounds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".