Age-Related Seroprevalence of Antibodies Against AAV-LK03 in a UK Population Cohort
Bibliographic record
Abstract
Recombinant adeno-associated virus (rAAV) vectors are a promising platform for in vivo gene therapy. The presence of neutralizing antibodies (Nab) against AAV capsids decreases cell transduction efficiency and is a common exclusion criterion for participation in clinical trials. Novel engineered capsids are being generated to improve gene delivery to the target cells and facilitate success of clinical trials; however, the prevalence of antibodies against such capsids remains largely unknown. We therefore assessed the seroprevalence of antibodies against a novel synthetic liver-tropic capsid AAV-LK03. We measured seroprevalence of immunoglobulin (Ig)G (i.e., neutralizing and nonneutralizing) antibodies and Nab to AAV-LK03 in a cohort of 323 UK patients (including 260 pediatric) and 52 juvenile rhesus macaques. We also performed comparative analysis of seroprevalence of Nab against wild-type AAV8 and AAV3B capsids. Overall IgG seroprevalence for AAV-LK03 was 39% in human samples. The titer increased with age. Prevalence of Nab was 23%, 35%, and 18% for AAV-LK03, AAV3B, and AAV8, respectively, with the lowest seroprevalence between 3 and 17 years of age for all serotypes. Presence of Nab against AAV-LK03 decreased from 36% in the youngest cohort (birth to 6 months) to 7% in older primary school-age children (9-11 years) and then progressively increased to 54% in late adulthood. Cross-reactivity between serotypes was >60%. Nab seroprevalence in macaques was 62%, 85%, and 40% for AAV-LK03, AAV3B, and AAV8, respectively. When planning for AAV gene therapy clinical trials, knowing the seropositivity of the target population is critical. In the population studied, AAV seroprevalence for AAV serotypes tested was low. However, high cross-reactivity between AAV serotypes remains a barrier for re-injection. Shifts in Nab seroprevalence during the first decade need to be confirmed by longitudinal studies. This possibility suggests that pediatric patients could respond differently to AAV therapy according to age. If late childhood is an ideal age window, intervention at an early age when maternal Nab levels are high may be challenging. Nab-positive children excluded from trials could be rescreened for eligibility at regular intervals because this status may change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".