Revolution of AAV in Drug Discovery: From Delivery System to Clinical Application.

Journal: Journal of medical virology
Published Date:

Abstract

Adeno-associated virus (AAV) is a non-enveloped DNA virus infecting a wide variety of species, tissues, and cell types, which is recognized as a safe and effective method for delivering therapeutic transgenes. AAV vector is the most popular viral gene delivery system in clinical delivery systems with unique and multiple advantages, such as tissue tropism, transduction specificity, long-lasting gene expression, low immune responses, and without host chromosome incorporation. Till now, four AAV-based gene therapy drugs have already been approved by the US Food and Drug Administration (FDA) or European Medicines Agency (EMA). Despite the success of AAV vectors, there are still some remaining challenges that limit further usage, such as poor packaging capacity, low organ specificity, pre-existing humoral immunity, and vector dose-dependent toxicity. In the present review, we address the different approaches to optimize AAV vector delivery system with a focus on capsid engineering, packaging capacity, and immune response at the clinical level. The review further investigates the potential of manipulating AAV vectors in preclinical applications and clinical translation, which emphasizes the challenges and prospects in viral vector selection, drug delivery strategies, immune reactions in cancer, neurodegenerative disease, retinal disease, SARS-CoV-2, and monkeypox. Finally, it forecasts future directions and potential challenges of artificial intelligence (AI), vaccines, and nanobodies, which emphasizes the need for ethical and secure approaches in AAV application.

Authors

  • Ling Yin
    National Population Health Data Center, Changping, China.
  • Hongliang He
    Laboratory of Structural Immunology, National Key Laboratory of Immune Response and Immunotherapy, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China.
  • Hongliang Zhang
    Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, 325035, China. Electronic address: zhang1hongliang@163.com.
  • Yuhua Shang
    Anhui Genebiol Biotech. Ltd., Hefei, Anhui, China.
  • Chengbo Fu
    Institute of Health and Medicine, Hefei Comprehensive National Science Center, Hefei, Anhui, China.
  • Songquan Wu
    Center of Disease Immunity and Intervention, College of Medicine, Lishui University, Lishui, China.
  • Tengchuan Jin
    Department of Obstetrics and Gynecology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China. jint@ustc.edu.cn.