Bibliographic record
Abstract
In the May 2003 issue of The FASEB Journal (17, 788–789), an editorial article by Charles Smith and John Vane gives a somewhat skewed account of the development of angiotensin-converting enzyme (ACE) inhibitors as an example of drugs discovered by the pharmaceutical industry without input from NIH. Al¬though I do not wish to minimize the contribution of E. R. Squibb, without which this class of drugs would never have reached practical therapeutic application, I would like to set the record straight on a number of events that preceded and established the rationale for this therapy. Some of the most important steps in the late 1960s and early 1970s were: 1) the demonstration, independently, by John Laragh and Hans Brunner in New York and myself, working still in the United Kingdom (before I joined them in New York) that the renin-angiotensin system was responsible for myocardial and renal tissue damage and 2) that inhibition of this system in patients (achieved initially with the angiotensin II receptor blocker saralasin, which had preceded ACE inhibitors by 2 years) could reverse not only the high blood pressure, but also the hemodynamic aberrations of congestive heart failure. These studies established the “proof of concept” that inhibi¬tion of the renin-angiotensin system had therapeutic potential. The crucial connecting step between Sergio Ferreira's bradykinin potentiating factors and the renin-angiotensin system was, of course, the discovery by Ervin Erdos that the enzyme kininase II, which degrades bradykinin, and the ACE, which forms angioten¬sin II, is one and the same. All of this work had, up to that point, been funded by NIH and AHA grants. However, it is true that if Squibb had not stepped in, all this interesting scientific work would have remained just that and would have not evolved into a novel therapeutic modality. In fact, when I presented my heart failure results with saralasin (in my effort to obtain the ACE inhibitor teprotide for the same stud¬ies) to George Mackaness at Squibb in the mid 1970s, he exclaimed that now I had given him the justification that he needed to push for the development of ACE inhibitors. He graciously provided me with sufficient amounts of teprotide—a very expensive peptide in¬deed—free of charge, but could not provide other financial support at that stage. The point is that drug discovery usually does not happen “out of the blue.” Extensive basic, experimental and clinical research, funded mostly by public money, lays the scientific foundation that would justify further investment by a drug company; and unless the industry is willing to commit the time, effort and expenditure and take the risks, an important scientific finding does not translate into a potentially life-saving therapy.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.037 | 0.068 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".