Inhibitors of type II transmembrane serine proteases in the treatment of diseases of the respiratory tract – A review of patent literature
Bibliographic record
Abstract
INTRODUCTION: Type II transmembrane serine proteases (TTSPs) of the human respiratory tract generate high interest owing to their ability, among other roles, to cleave surface proteins of respiratory viruses. This step is critical in the viral invasion of coronaviruses, including SARS-CoV-2 responsible for COVID-19, but also influenza viruses and reoviruses. Accordingly, these cell surface enzymes constitute appealing therapeutic targets to develop host-based therapeutics against respiratory viral diseases. Additionally, their deregulated levels or activity has been described in non-viral diseases such as fibrosis, cancer, and osteoarthritis, making them potential targets in these indications. AREAS COVERED: Areas covered: This review includes WIPO-listed patents reporting small molecules and peptide-based inhibitors of type II transmembrane serine proteases of the respiratory tract. EXPERT OPINION: Expert opinion: Several TTSPs of the respiratory tract represent attractive pharmacological targets in the treatment of respiratory infectious diseases (notably COVID-19 and influenza), but also against idiopathic pulmonary fibrosis and lung cancer. The current emphasis is primarily on TMPRSS2, matriptase, and hepsin, yet other TTSPs await validation. Compounds listed herein are predominantly peptidomimetic inhibitors, some with covalent reversible mechanisms of action and high potencies. Their selectivity profile, however, are often only partially characterized. Preclinical data are promising and warrant further advancement in the above diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".