Tissue-type Differences in Focused Ultrasound and Microbubble-mediated Drug Delivery to the Brain Exist at Vessel Level
Bibliographic record
Abstract
Rationale:The efficacy of drug delivery to the brain is constrained by the impermeability of the blood-brain barrier (BBB) in healthy tissues and the heterogeneous permeability of the blood-tumor barrier (BTB) in gliomas.Focused ultrasound (FUS) has emerged as a promising technique to transiently modulate vascular permeability, however its effects vary across different brain tissues.This study systematically evaluates the effects of FUS-induced vascular permeability modulation in the gray matter (GM), white matter (WM), and brain tumors, considering their distinct tissue architectures, vascular densities, and permeability profile.Additionally, we compare the delivery of bevacizumab (antiangiogenic monoclonal antibody) and methotrexate (small-molecule chemotherapeutic) to determine how molecular size influences vascular-level permeability and extravasation distances.Methods: A total of n = 48 Fischer-344 rats, including both healthy and tumor-bearing cohorts, underwent magnetic resonance imaging (MRI)-guided FUS using a feedback-controlled algorithm to modulate microbubble pressure based on microbubble emissions.Tumors were either untreated or received a single FUS exposure, while healthy tissues, including GM and WM, were treated with either a single exposure, or a repeated exposure administered 30 minutes after the first one.MR images were used to assess contrast enhancement before and after sonication.Drug deposition was quantified via fluorescence microscopy in terms of local signal intensities and distances of extravasation.Tissue-specific vascular characteristics, including vessel diameters, densities, and inter-vessel distances, were also analysed. Results:The lack of MRI contrast enhancement in untreated tissues suggested a healthy permeability status of the BBB in GM and WM, while a compromised BTB was observed in tumors.Following FUS treatments, contrast enhancement significantly increased in all tissues, with tumors exhibiting the most pronounced effects.Repeated FUS further enhanced permeability in GM and WM, achieving drug deposition levels comparable to those observed in tumors after a single treatment.At the vascular level, FUS exposure led to significant increases in drug extravasation distances, particularly in tumors.Vascular densities were approximately threefold higher in GM, compared to WM and tumors (GM:WM:Tumor 3.2:1:1), yet both drug signal intensities and extravasation distance correlated more strongly with the number of treatments than with baseline vascularity.Fluorescence microscopy revealed that bevacizumab extravasation was primarily localized near vessel lumens, whereas methotrexate exhibited significantly greater extravascular diffusion, reaching distances spanning entire inter-vessel spaces, consistent with its lower molecular weight.At the individual vessel level, white matter showed significantly lower drug signal intensity than gray matter following a single treatment. Conclusion:This study provides vascular-level insights into how FUS-mediated drug delivery is influenced by tissue architecture, vascular properties, treatment regimen, and drug molecular weight.Notably, at the individual vessel level, drug extravasation varies between the different tissue types, and thus vascular density is not the sole driver of differences in drug deposition in these tissues.The study findings highlight the potential of repeated FUS exposures for enhancing the deposition of therapeutics across the physiologically intact BBB of both the gray and white matter, reaching levels comparable to those observed in the pathologically compromised BTB of gliomas.Thus, sonications prescribed over previously permeabilized tissues facilitate deeper drug penetration into interstitial compartments, allowing therapeutics to reach cells further from vessel lumens despite inherent tissue-specific differences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".