ArticleNature communications2024
Systematic multi-trait AAV capsid engineering for efficient gene delivery.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
51 citing papers in PubMed.
- In vivo immune cell engineering from bench to clinical reality.Pharmaceutical science advances · 2026Review
- Beyond Precision: A Multidimensional Framework for Selecting Genetic Medicine Platforms.Cells · 2026Review
- AI-engineered AAV capsid enables intravitreal delivery for the treatment of diverse retinal degenerations.Molecular therapy. Advances · 2026Article
- Recent advancements in improving cross-species applicability of bioengineered AAV capsids.Gene therapy · 2026Review
- Applications of synthetic biology in biomedicine.Molecular biomedicine · 2026Review
- Epigenetic editing approaches maturity: AI-driven precision design, delivery innovation, and the road to clinical translation.Clinical epigenetics · 2026Review
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- Trends in the Engineering of Adeno-Associated Virus (AAV) for Precision Gene Delivery to the Central Nervous System (CNS).International journal of molecular sciences · 2026Review
- Engineering challenges and translational opportunities in emerging gene delivery platforms.Nature biomedical engineering · 2026Review
- Tissue-specific gene delivery approaches.Bioengineering & translational medicine · 2026Review
- Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability.Scientific reports · 2026Article
- Ultrasound-guided left heart injection: a safer and more efficient strategy for mouse thoracic aortic gene delivery.Gene therapy · 2026Article
- Gene modification: Exploring the potential in treating kidney diseases.Pharmacological research · 2026Review
- In vivo engineering of CAR-T cells: delivery strategies and clinical translation.Biomarker research · 2026Review
- Viral vector-based gene therapies in the clinic: An update.Bioengineering & translational medicine · 2026Review
- The delivery challenge of adeno-associated virus vector-based gene therapies for neurological diseases.Frontiers in neuroscience · 2026Review
- Advancing Gene Therapy for Pancreatitis: From Genetic Insights to Clinical Translation.Research (Washington, D.C.) · 2026Review
- Therapeutic in vivo genome editing: innovations and challenges in rAAV vector-based CRISPR delivery.Gene therapy · 2026Review
- Bridging science and hope: the evolving story of gene therapy for neuromuscular diseases.Frontiers in cell and developmental biology · 2026Review
- The amazing AAV capsids: Into the structure-verse.Molecular therapy. Methods & clinical development · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
Broadening gene therapy applications requires manufacturable vectors that efficiently transduce target cells in humans and preclinical models. Conventional selections of adeno-associated virus (AAV) capsid libraries are inefficient at searching the vast sequence space for the small fraction of vectors possessing multiple traits essential for clinical translation. Here, we present Fit4Function, a generalizable machine learning (ML) approach for systematically engineering multi-trait AAV capsids. By leveraging a capsid library that uniformly samples the manufacturable sequence space, reproducible screening data are generated to train accurate sequence-to-function models. Combining six models, we designed a multi-trait (liver-targeted, manufacturable) capsid library and validated 88% of library variants on all six predetermined criteria. Furthermore, the models, trained only on mouse in vivo and human in vitro Fit4Function data, accurately predicted AAV capsid variant biodistribution in macaque. Top candidates exhibited production yields comparable to AAV9, efficient murine liver transduction, up to 1000-fold greater human hepatocyte transduction, and increased enrichment relative to AAV9 in a screen for liver transduction in macaques. The Fit4Function strategy ultimately makes it possible to predict cross-species traits of peptide-modified AAV capsids and is a critical step toward assembling an ML atlas that predicts AAV capsid performance across dozens of traits.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.