AAV Gene Therapy Drug Development and Translation of Engineered Ocular and Neurotropic Capsids: A Systematic Review Using Natural Language Processing

Abstract

Natural AAV serotypes often lack specificity and efficiency, leading to off-target effects and a low therapeutic index. To overcome these limitations of naturally occurring serotypes, there has been a keen interest in the field to engineer novel capsids to enhance tissue and cell-specific targeting, resulting in a high number of published literature reports over the past few years. To ensure a systematic review and illustrate advances in engineered capsids that enhance specificity and efficiency, we used Natural Language Processing with Linguamatics i2E to identify neurotropic and ocular AAV capsids tested in non-human primates. By querying PubMed abstracts for specific mentions of AAVs, administration routes, and organ/tissue/species, we obtained 5907 hits, refined through an optimized process to 36 relevant and unique abstracts. Notable findings include numerous novel capsids summarized by route of administration: (1) systemic administration, targeting the central nervous system (e.g., AAV-PHP.eB, AAV X1.1, and AAV.PAL2), (2) direct central nervous system injection (e.g., AAV2.Retro, Olig001, and AAV2.1A), and (3) ocular administration (e.g., AAV.44.9 (E531D), rAAV2tYF, and Anc80L65). Such engineered capsids exhibit enhanced tissue specificity, improved pharmacokinetics and pharmacodynamics, or reduced off-target effects compared to the parent serotypes. Our study provides insight into state-of-the-art translational and drug-development considerations for engineered neurotropic and ocular capsids. We also highlight the effectiveness of Natural Language Processing and Large Language Models as tools in identifying and characterizing engineered neurotropic and ocular AAV capsids to summarize this rapidly growing class of drugs and area of therapeutics.

Keywords: CNS; adeno‐associated virus; drug development; engineered capsids; gene therapy; natural language processing; ocular.

FIGURE 1

Summarized NLP workflow. NLP, natural language processing, LLM, large language model (1) NLP Criteria—The NLP software searches PubMed abstracts with the following criteria: [AAV] AND [Route of Administration] AND [Organ OR Tissue OR Species]. (2) This workflow produced 5907 hits, where each hit is an instance of the previous criteria within 150 words and/or 5 sentences. AAVs mentioned in the same abstract were typically grouped. (3) The data was further filtered into unique abstracts by removing duplicates, and then filtered by any mentions of an NHP species in its respective column, yielding 458 unique abstracts. (4a) The NLP‐generated data were filtered by researcher‐identified engineered AAV capsids. (5a) Remaining abstracts were finally filtered by NLP‐generated data in the tissue column, producing a total of 33 abstracts fitting the filtration criteria. (4b) The NHP abstracts were revisited, and the exact engineered AAV capsids mentioned in the abstracts were manually identified, producing 28 abstracts in addition to those from 4A. (5b) Manually identified abstracts and NLP‐generated data abstracts were combined into 65 total engineered capsid abstracts relating to NHPs. (6) The full‐text papers from all 65 engineered capsid abstracts were manually screened to identify papers that targeted the CNS and the eye. (7) These papers were finally filtered to ensure that they injected engineered capsids into NHPs and to remove cases where NHPs were simply mentioned. Genentech's internal LLM software aided in producing the data in steps 4B and 7 through the identification of engineered capsids and the validation of papers fitting all the criteria.

FIGURE 2

NLP generated results.