Grant: Project Grant
University of Toronto
$1,690,650.00
A strategy for using machine intelligence to design high-capacity viral vectors that evade pre-existing immunity and enable gene therapies for diseases involving defects in large genes.
EF 2025-26
Résumé
THE PROBLEM: Correcting large gene monogenic diseases using gene therapy is prevented by the pre-existing immune response to large cargo viral delivery systems (adenovirus).
PROPOSED SOLUTION: A machine intelligence capable of active learning to design and refine synthetic adenovirus-based large capacity vectors that evade pre-existing neutralizing antibodies.
MAJOR DELIVEABLES: (a) Combined active learning-generative model for large capacity viral vector design (aim 1); (b) Core resource comprising a large human serum and PBMC bank representing the human population--as widely as we can--for immunological testing (aim 2); (c) Panel of large capacity, synthetic AdV with low immunogenicity and high transduction efficiency; (d) Preclinically validated AdVs for DMD, RYR1 and NEB gene delivery (aim 3). Deliverables in each aim are completely (a & b) or partially (c & d) independent of success in other aims.
Programme
Project Grant
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
CIHR Funding Decisions Database
Tel que publié
Id
532483
Name
Garton Michael J
Region
Ontario
Country
Canada
Orgtype
Institution Paid; Research Institution
Instname2
Genetics
Theme2
Biomedical
Prcname2
Genomics: Systems and computational biology
Projecttitle
A strategy for using machine intelligence to design high-capacity viral vectors that evade pre-existing immunity and enable gene therapies for diseases involving defects in large genes.
Programtype2
Operating Grants
Orgnameinp2
University of Toronto
Cihramount2
$1,690,650
Cihrequipment2
$0
Programname2
Project Grant
Keyworddelim
Viral Vector Design
Abstract
THE PROBLEM: Correcting large gene monogenic diseases using gene therapy is prevented by the pre-existing immune response to large cargo viral delivery systems (adenovirus).
PROPOSED SOLUTION: A machine intelligence capable of active learning to design and refine synthetic adenovirus-based large capacity vectors that evade pre-existing neutralizing antibodies.
MAJOR DELIVEABLES: (a) Combined active learning-generative model for large capacity viral vector design (aim 1); (b) Core resource comprising a large human serum and PBMC bank representing the human population--as widely as we can--for immunological testing (aim 2); (c) Panel of large capacity, synthetic AdV with low immunogenicity and high transduction efficiency; (d) Preclinically validated AdVs for DMD, RYR1 and NEB gene delivery (aim 3). Deliverables in each aim are completely (a & b) or partially (c & d) independent of success in other aims.
Competitiondate
202509
Approvedterm2
5 yrs 0 mth
Pinamesdelim
Garton, Michael J
Approvedterm
5 ans 0 mois
Cihrcontribution2
~ $1,690,650~ $0~
