Notice bibliographique
Résumé
For more than 60 years, the general focus for the pharmaceutical industry has been to develop highly selective, very targeted drugs. The ideal goal for the drug industry has been to create a drug that specifically inhibits one molecule in one particular pathway. This kind of specificity will theoretically achieve the optimal biological effect with the added benefit of fewer, less significant unwanted side effects. The creation of statins to inhibit the 3-hydroxy-3-methylglutaryl-coenzyme A (HmG CoA) reductase enzyme is a good example. Statins block the synthesis of cholesterol by HmG CoA reductase activity (Yamamoto et al. 1980). Ultimately, this will lower circulating levels of total cholesterol and low-density lipoprotein (Kong et al. 1997) and result in a reduction in the incidence of myocardial infarctions and cardiovascular death (Ridker et al. 2009; Nicholls et al. 2010). This goal of drug specificity has been encouraged and supported by regulatory agencies like the US Food and Drug Administration (FDA) and Health Canada. Indeed, one may argue that the entire field of biomedical sciences has been strongly influenced by the philosophy of creating very specific pharmaceuticals. For example, the field of protein crystallography has been encouraged to identify how peptides, molecules, and drugs could interact with targets of interest to block unwanted pathological pathways/actions. The intense development of molecular biology techniques has been stimulated in part by the possibility of identifying a molecular basis for drug targeting. Decoding the human genome was thought to ultimately lead to the creation of better, more specific drugs to fight disease processes. Pharmacogenomics is a child of this way of thinking too. However, has the time come to pause and re-consider this general approach to the development and use of pharmaceutical agents in disease control? Is this approach that we have adopted and faithfully followed for more than half a century correct, or is it critically flawed? Would the use of less specific “dirty” drugs that affect more than one molecule or more than one signaling pathway be a better approach? Why would I even dare to offer this blasphemous suggestion? Take the issue of statins that I used above. Although we originally believed the cardiovascular benefits that these drugs produced were through a specific inhibition of HmG CoA reductase-mediated cholesterol synthesis, we are now recognizing that at least some of the beneficial effects of these drugs are through their anti-inflammatory actions (Ridker et al. 2005, 2009). The popularization of sildenafil (Viagra) was for its non-specific side-effects rather than its intended specificity on other pathways of interest. Even more important, we now know that the vast majority of chronic diseases are multifactorial diseases with more than one signaling pathway and more than one protein involved in the pathology. If we all agree with this, why does it come as a surprise that a “dirty” drug that attacks more than one pathway or influences the function of more than one protein is often the most beneficial therapeutically? Why do we increasingly see the appearance of functional foods or nutraceuticals to treat or prevent diseases (Kukongviriyapan et al. 2012; Pires et al. 2012)? They may be as potent as they are because they are less specific and instead attack more pathways and more molecules. Is that such a bad thing? Maybe we should clean up our approach to the development and use of pharmaceuticals and treat dirty drugs, foods, and nutraceuticals with less skepticism and more of a welcoming attitude.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,052 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,010 | 0,037 |
| Communication savante | 0,020 | 0,036 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,019 | 0,031 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,035 | 0,022 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».