Artificial lipidation of proteins and peptides: from mechanism to clinical applications
Bibliographic record
Abstract
The landscape of modern medicine has been transformed by protein-based therapeutics, offering targeted treatments for complex disorders with remarkable specificity and efficacy. However, these biologics face significant limitations in clinical settings, including rapid clearance, vulnerability to enzymatic degradation, poor absorption across biological membranes and inefficient distribution within target tissues. Artificial lipidation provides an innovative solution to these challenges, by the deliberate attachment of lipid groups to proteins and peptide structures. This biomimetic approach harnesses principles observed in natural post-translational modifications to create therapeutics with superior pharmacological profiles. By strategically incorporating lipid moieties, researchers can significantly prolong circulation half-life through albumin binding, protect against proteolytic breakdown, facilitate cellular uptake, customize pharmacokinetic parameters and enhance tissue-specific targeting. This Review provides a comprehensive analysis of current lipidation technologies, contrasting covalent modification strategies with noncovalent complexation approaches. We examine the molecular mechanisms underlying the therapeutic benefits, survey successful clinical applications and explore emerging opportunities across diverse therapeutic areas. Through this analysis, we offer insights to guide rational design decisions for developing optimized lipidated biotherapeutics with enhanced clinical performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".