Drugs or diet? – Developing novel therapeutic strategies targeting the free fatty acid family of <scp>GPCRs</scp>
Bibliographic record
Abstract
Free fatty acids (FFAs) are metabolic intermediates that may be obtained through the diet, synthesized endogenously, or produced via fermentation of carbohydrates by gut microbiota. In addition to serving as an important source of energy, FFAs are known to produce a variety of both beneficial and detrimental effects on metabolic and inflammatory processes. While historically, FFAs were believed to produce these effects only through intracellular targets such as peroxisome proliferator-activated receptors, it has now become clear that FFAs are also agonists for several GPCRs, including a family of four receptors now termed FFA1-4. Increasing evidence suggests that FFA1-4 mediate many of the beneficial properties of FFAs and not surprisingly, this has generated significant interest in the potential of these receptors as therapeutic targets for the treatment of a variety of metabolic and inflammatory disorders. In addition to the traditional strategy of developing small-molecule therapeutics targeting these receptors, there has also been some consideration given to alternate therapeutic approaches, specifically by manipulating endogenous FFA concentrations through alteration of either dietary intake, or production by gut microbiota. In this review, the current state of knowledge for FFA1-4 will be discussed, together with their potential as therapeutic targets in the treatment of metabolic and inflammatory disorders. In particular, the evidence in support of small molecule versus dietary and microbiota-based therapeutic approaches will be considered to provide insight into the development of novel multifaceted strategies targeting the FFA receptors for the treatment of metabolic and inflammatory disorders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".