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Record W2313571524 · doi:10.1139/y2012-110

Should we clean up the reputation of “dirty drugs”?

2012· editorial· en· W2313571524 on OpenAlexaffvenueabout
Grant N. Pierce

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2012
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA and protein synthesis mechanisms
Canadian institutionsSt. Boniface Hospital
Fundersnot available
KeywordsBusinessReputationMedicinePharmacologyChemistryInternet privacyNatural resource economicsAdvertisingEconomicsPolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.023
metaresearch head score (Gemma)0.052
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.052
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.003
Science and technology studies0.0100.037
Scholarly communication0.0200.036
Open science0.0030.008
Research integrity0.0190.031
Insufficient payload (model declined to judge)0.0350.022

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.

Opus teacher head0.015
GPT teacher head0.283
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations11
Published2012
Admission routes3
Has abstractyes

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