Inborn Errors of Amino Acid Metabolism Revisited: Clinical Implications and Insights into Current Therapies
Bibliographic record
Abstract
Background/Objectives: Inborn errors of amino acid metabolism (IEAAMs) are inherited disorders caused by defects in amino acid catabolism, biosynthesis, or transport. In this review, we aimed to synthesise recent evidence on the clinical manifestations and current and future therapeutic strategies for major IEAAMs. Methods: A narrative review was undertaken on studies published up to November 2025. No fixed start date was set. Instead, earlier studies were included if historically significant or frequently cited in contemporary guidelines, and emphasis was placed on recent developments over the last 5–10 years. Evidence was identified through structured searches of PubMed, clinical trial registries, and public communications on selected IEAAMs, which were synthesised in textual and tabular form. Results: Management across IEAAMs involves the restriction of amino acids or natural proteins, disease-specific dietary formulations, micronutrient optimisation, cofactor or enzyme replacement, and pharmacological chaperones. This is supported by structured monitoring and emergency regimens to prevent catabolic crises. Organ transplantation remains crucial for select indications, such as liver transplantation in hereditary tyrosinaemia with liver disease. Novel approaches include substrate reduction, the pharmacological targeting of upstream pathways, viral vector gene transfer, and liver-directed mRNA therapy. Several of these novel approaches have entered clinical trials, but many remain in the preclinical stage. Conclusions: Despite advances in the treatment of IEAAMs, many patients still experience significant morbidity. Future focus should be on further refining emerging molecular and gene-based treatments and optimising neuroprotective and metabolic targets. The equitable implementation of personalised, life-spanning treatments within multidisciplinary rare disease services will be essential.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".