Data-Driven Feedback Identifies Focused Ultrasound Exposure Regimens for Improved Nanotheranostic Targeting of the Brain

Hohyun Lee; Victor Menezes; Shiqin Zeng; Chulyong Kim; Cynthia M Baseman; Jae Hyun Kim; Samhita Padmanabhan; Pranav Premdas; Naima Djeddar; Anton Bryksin; Nikhil Pandey; Pavlos Anastasiadis; Anthony J Kim; Tobey J MacDonald; Chetan Bettegowda; Graeme F Woodworth; Felix J Herrmann; Costas Arvanitis

doi:10.1002/advs.202517834

Data-Driven Feedback Identifies Focused Ultrasound Exposure Regimens for Improved Nanotheranostic Targeting of the Brain

Adv Sci (Weinh). 2026 Jan 7:e17834. doi: 10.1002/advs.202517834. Online ahead of print.

Authors

Hohyun Lee¹, Victor Menezes¹, Shiqin Zeng², Chulyong Kim¹, Cynthia M Baseman³, Jae Hyun Kim¹, Samhita Padmanabhan⁴, Pranav Premdas⁵, Naima Djeddar⁶, Anton Bryksin⁶, Nikhil Pandey⁷, Pavlos Anastasiadis^{7

8

9

10}, Anthony J Kim^{7

10}, Tobey J MacDonald¹¹, Chetan Bettegowda¹², Graeme F Woodworth^{7

8

9

10}, Felix J Herrmann^{2

5

13}, Costas Arvanitis^{1

4}

Affiliations

¹ Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.
² School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.
³ School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, United States.
⁴ Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, United States.
⁵ School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States.
⁶ Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia, United States.
⁷ Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, Maryland, United States.
⁸ Brain Tumor Program, Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, Maryland, United States.
⁹ Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States.
¹⁰ Department of Bioengineering, University of Maryland, Baltimore, Maryland, United States.
¹¹ Aflac Cancer & Blood Disorders Center, Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States.
¹² Department of Neurosurgery, John Hopkins University School of Medicine, Baltimore, Maryland, United States.
¹³ School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States.

PMID: 41498583
DOI: 10.1002/advs.202517834

Abstract

The blood-brain barrier (BBB) renders the delivery of nanomedicine in the brain ineffective and the detection of circulating disease-related DNA from the brain unreliable. Here, we demonstrate that microbubble-enhanced focused ultrasound (MB-FUS) mediated BBB opening, supported by large-data models predict sonication regimens for safe and effective BBB opening. Importantly, a closed-loop MB-FUS controller augmented by machine learning (ML-CL) expands the treatment window, as compared to conventional controllers, by persistently and proactively maximizing the BBB permeability while preventing tissue damage. By successfully scaling up from mice to rats and from healthy to diseased brains (glioma), ML-CL rendered the BBB permeable to large nanoparticles and markedly improved the release and detection of reporter gene DNA from tumors in blood. Together, our findings reveal the potential of data-driven feedback to support the development of next-generation AI-powered ultrasound systems for safe, robust, and efficient nanotheranostic targeting and treatment of brain diseases.

Keywords: blood‐brain barrier; brain cancer; focused ultrasound; liquid biopsy; machine learning; nanoparticle; theranostics.

Abstract

Grants and funding