The design of a potent inhibitor of the hepatitis C virus NS3 protease: <i>BILN 2061</i>—From the NMR tube to the clinic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The virally encoded serine protease NS3/NS4A is essential to the life cycle of the hepatitis C virus (HCV), an important human pathogen causing chronic hepatitis, cirrhosis of the liver, and hepatocellular carcinoma. Until very recently, the design of inhibitors for the HCV NS3 protease was limited to large peptidomimetic compounds with poor pharmacokinetic properties, making drug discovery an extremely challenging endeavor. In our quest for the discovery of a small-molecule lead that could block replication of the hepatitis C virus by binding to the HCV NS3 protease, the critical protein-polypeptide interactions between the virally encoded NS3 serine protease and its polyprotein substrate were investigated. Lead optimization of a substrate-based hexapeptide, guided by structural data, led to the understanding of the molecular dynamics and electronic effects that modulate the affinity of peptidomimetic ligands for the active site of this enzyme. Macrocyclic beta-strand scaffolds were designed that allowed the discovery of potent, highly selective, and orally bioavailable compounds. These molecules were the first HCV NS3 protease inhibitors reported that inhibit replication of HCV subgenomic RNA in a cell-based replicon assay at low nanomolar concentrations. Optimization of their biopharmaceutical properties led to the discovery of the clinical candidate BILN 2061. Oral administration of BILN 2061 to patients infected with the hepatitis C genotype 1 virus resulted in an impressive reduction of viral RNA levels, establishing proof-of-concept for HCV NS3 protease inhibitors as therapeutic agents in humans.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it