Safety by Design: Pre-IND Risk Considerations for In Vivo Gene Editing Programs
Genotoxicity, immunogenicity, off-target editing — a framework for building safety considerations into the LNP formulation screening process before animal studies begin.
Technical writing from the Gendelivr research team on formulation science, delivery mechanics, and computational screening. 12 articles.
Genotoxicity, immunogenicity, off-target editing — a framework for building safety considerations into the LNP formulation screening process before animal studies begin.
Base editors and prime editors are larger than standard SpCas9 constructs. How larger cargo size reshapes the LNP formulation optimization problem.
Only ~1-2% of endocytosed LNPs successfully escape to the cytoplasm. An examination of proton sponge effect, membrane fusion models, and formulation levers.
RNA integrity starts degrading before the LNP reaches its target. How ionizable lipid pKa, internal pH, and aqueous core composition affect cargo stability.
Natural AAV serotypes miss the target cells for many CNS and hepatic programs. A technical review of rational capsid engineering approaches for re-directing tropism.
Running 10,000 virtual formulations costs a fraction of bench synthesis. A breakdown of the computational cost structure versus wet lab equivalent.
Payload size, immunogenicity, redosing, manufacturing cost — a structured comparison to help gene therapy programs choose the right delivery modality.
Higher PEG density reduces protein corona formation but suppresses cellular uptake. How to model the PEG tradeoff computationally before running a single mouse study.
Apolipoprotein E adsorption onto LNP surfaces is the dominant driver of liver-targeting efficiency — here's how formulation parameters modulate it.
A phase-by-phase dissection of the bench-to-IND timeline for LNP delivery programs, identifying where computational screening creates the most leverage.
Serum protein adsorption, endosomal entrapment, and immune clearance — a technical breakdown of the delivery challenges every gene editing program faces.
Why ionizable lipid chemistry dominates transfection efficiency, and how computational pre-screening narrows the design space from thousands to dozens of candidates.