| Literature DB >> 34008957 |
Ajay Vikram Singh1, Vaisali Chandrasekar2, Poonam Janapareddy2, Divya Elsa Mathews2, Peter Laux1, Andreas Luch1, Yin Yang3, Beatriz Garcia-Canibano2, Shidin Balakrishnan2, Julien Abinahed2, Abdulla Al Ansari2, Sarada Prasad Dakua2.
Abstract
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.Entities:
Keywords: Blood−brain barrier; bioengineering; machine learning and artificial intelligence; nanoparticles; nanorobots; transcytosis
Year: 2021 PMID: 34008957 DOI: 10.1021/acschemneuro.1c00087
Source DB: PubMed Journal: ACS Chem Neurosci ISSN: 1948-7193 Impact factor: 4.418