4.6 Review

Emerging Application of Nanorobotics and Artificial Intelligence To Cross the BBB: Advances in Design, Controlled Maneuvering, and Targeting of the Barriers

期刊

ACS CHEMICAL NEUROSCIENCE
卷 12, 期 11, 页码 1835-1853

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acschemneuro.1c00087

关键词

Blood-brain barrier; nanorobots; transcytosis; machine learning and artificial intelligence; bioengineering; nanoparticles

资金

  1. BfR SFP [1322-725, 1322-735]
  2. Hamad Medical Corporation [IRGC-05-SI-18-360]

向作者/读者索取更多资源

This review focuses on the use of nanoparticles for bimolecular engineering to manipulate, control, target, and deliver therapeutic payloads across cellular barriers, particularly the blood-brain barrier (BBB). Specific case studies on payload delivery in brain tumors and neurological disorders are outlined, along with discussions on the opportunities and challenges in nanorobot development and design. The integration of computationally powered machine learning tools and artificial intelligence with robotics is also discussed for predicting and designing next-generation nanorobots for safe delivery across the BBB.
The blood-brain barrier (BBB) is a prime focus for clinicians to maintain the homeostatic function in health and deliver the theranostics in brain cancer and number of neurological diseases. The structural hierarchy and in situ biochemical signaling of BBB neurovascular unit have been primary targets to recapitulate into the in vitro modules. The microengineered perfusion systems and development in 3D cellular and organoid culture have given a major thrust to BBB research for neuropharmacology. In this review, we focus on revisiting the nanoparticles based bimolecular engineering to enable them to maneuver, control, target, and deliver the theranostic payloads across cellular BBB as nanorobots or nanobots. Subsequently we provide a brief outline of specific case studies addressing the payload delivery in brain tumor and neurological disorders (e.g., Alzheimer's disease, Parkinson's disease, multiple sclerosis, etc.). In addition, we also address the opportunities and challenges across the nanorobots' development and design. Finally, we address how computationally powered machine learning (ML) tools and artificial intelligence (AI) can be partnered with robotics to predict and design the next generation nanorobots to interact and deliver across the BBB without causing damage, toxicity, or malfunctions. The content of this review could be references to multidisciplinary science to clinicians, roboticists, chemists, and bioengineers involved in cutting-edge pharmaceutical design and BBB research.

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