Lipogenesis inhibitors: therapeutic opportunities and challenges
Bibliographic record
Abstract
Fatty acids are essential for survival, acting as bioenergetic substrates, structural components and signalling molecules. Given their vital role, cells have evolved mechanisms to generate fatty acids from alternative carbon sources, through a process known as de novo lipogenesis (DNL). Despite the importance of DNL, aberrant upregulation is associated with a wide variety of pathologies. Inhibiting core enzymes of DNL, including citrate/isocitrate carrier (CIC), ATP-citrate lyase (ACLY), acetyl-CoA carboxylase (ACC) and fatty acid synthase (FAS), represents an attractive therapeutic strategy. Despite challenges related to efficacy, selectivity and safety, several new classes of synthetic DNL inhibitors have entered clinical-stage development and may become the foundation for a new class of therapeutics. De novo lipogenesis (DNL) is vital for the maintenance of whole-body and cellular homeostasis, but aberrant upregulation of the pathway is associated with a broad range of conditions, including cardiovascular disease, metabolic disorders and cancers. Here, Steinberg and colleagues provide an overview of the physiological and pathological roles of the core DNL enzymes and assess strategies and agents currently in development to therapeutically target them.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".