Recombinant adeno-associated virus (AAV) is widely recognized as the most promising viral vector for clinical
gene therapy. Despite fatal adverse events with FDA and EMA-approved AAV gene therapies, more AAV gene
therapies are expected to be approved. A critical challenge in the field remains the production of sufficient
vectors with optimal tropism to target specific tissues, compounded by the lack of clear prospects for
standardized quality assurance. AAV tropism is highly species-dependent, which limits the relevance of
preclinical studies in small animals to human applications. Recent advancements in Artificial Intelligence (AI)
and machine learning have begun to transform problem-solving approaches in AAV research. AI-driven
applications, particularly in capsid design, offer promising solutions to these challenges. This emerging
technology is poised to revolutionize not only vector development and production but also the treatments
available to patients receiving AAV-based therapies.

Acta Biochimica Polonica is planning a Special Issue devoted to AI-assisted vector improvements,
manufacturing, and quality assurance to facilitate this field moving forward. We are soliciting original papers,
review articles, and minireviews addressing AAV manufacturing. Preferred manuscripts include, but are not
limited to:
•Either comprehensive or brief, focused review articles on AAV’s species barriers, manufacturing, or
quality assurance
• Studies of the species barriers of AAV tropism
• Innovative methods to improve vector production and quality assurance
• Applications of AI technology, including but not limited to capsid design

For authors, please review the journal's information regarding Author Guidelines and Article Processing Charges, or direct any questions to the Editorial Office: abp@frontierspartnerships.org.

Keywords: Adeno-associated virus (AAV), vector production, AAV tropism, quality control assay, artificial intelligence (AI)