Focused ultrasound increases gene delivery to deep brain structure following the administration of a recombinant adeno-associated virus in the cerebrospinal fluid
Bibliographic record
Abstract
Abstract Gene delivery via adeno-associated viral vectors can provide lasting clinical benefits following a one-time treatment. Delivery throughout the brain is needed for the treatment of neurological disorders with widespread pathology, including Alzheimer and Parkinson diseases, and amyotrophic lateral sclerosis. Most gene vectors have poor diffusion in the brain tissue. Furthermore, it is only at high intravenous doses that gene vectors can overcome the blood-brain barrier. In contrast, relatively lower doses of gene vectors injected in the cerebrospinal fluid enable significant transduction of superficial brain regions. The remaining challenge and unmet need of gene therapy is to deliver gene vectors to deep brain structures using a minimally invasive strategy. Here, we demonstrate that non-invasive focused ultrasound blood-brain barrier modulation can increase the delivery of recombinant adeno-associated virus by 5-fold to deep brain structures following injection in the cisterna magna. Delivery of adeno-associated viral vectors to the central nervous system, via administration in the cerebrospinal fluid, is being evaluated in several clinical trials for treating beta-galactosidase-1 deficiency, Batten disease, Alzheimer disease, Parkinson disease, amyotrophic lateral sclerosis, and spinal muscular atrophy. Our findings suggest that the efficacy of gene therapies delivered in the cerebrospinal fluid can be enhanced by targeting brain areas of interest with focused ultrasound. Significance statement Administration of viral vectors in the cerebrospinal fluid through the cisterna magna is being evaluated in patients with neurological disorders. Focused ultrasound combined with intravenous microbubbles safely increases the permeability of the blood-brain barrier in humans and enables delivery of intravenous adeno-associated virus in non-human primates. Here, we demonstrate that combining these two clinically relevant gene delivery methods, intracisterna magna administration and focused ultrasound with microbubbles, can facilitate gene delivery to superficial and deep brain structures. Our findings have the potential to increase the efficacy of gene therapies, particularly for disorders with brain regions that have remained difficult to reach.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".