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| Main Author: | |
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| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2025
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.17744880 |
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Table of Contents:
- <p>This work presents a comprehensive, purely computational and non-clinical framework for designing optimized mRNA therapeutic blueprints for Hunter Syndrome (Mucopolysaccharidosis Type II, MPS II). Using integrated artificial intelligence, codon-usage modeling, RNA secondary structure prediction, immunogenicity profiling, chemical modification simulation, LNP delivery modeling, and pharmacoeconomic analysis, the study generates optimized mRNA constructs encoding human iduronate-2-sulfatase (IDS).</p> <p> </p> <p>The lead candidate (Variant E) demonstrates:</p> <p>• 2.6-fold predicted increase in IDS protein expression</p> <p>• 79% reduction in innate immunogenicity</p> <p>• 28% reduction in predicted adaptive immunogenicity</p> <p>• 35% improvement in RNA structural favorability</p> <p>• 96–99% modeled reduction in lifetime therapy cost compared to current enzyme replacement therapy (ERT)</p> <p> </p> <p>This research is intended strictly for scientific discussion and hypothesis generation. It involves no laboratory work, no biological agents, and no clinical or preclinical testing. All results represent theoretical predictions that require extensive experimental validation prior to any translational consideration.</p> <p> </p> <p>The full PDF includes:</p> <p>• AI-guided optimization pipeline</p> <p>• Five engineered IDS mRNA sequence variants</p> <p>• Regulatory element (UTR/Kozak) engineering</p> <p>• Chemical modification modeling (m1Ψ, 5-mC)</p> <p>• mRNA expression and decay modeling</p> <p>• LNP delivery simulation</p> <p>• Pharmacoeconomic modeling comparing mRNA therapy vs. ERT</p> <p>• Supplementary materials: sequences, parameters, extended results, code structure</p> <p> </p> <p>Keywords: Hunter Syndrome, MPS II, iduronate-2-sulfatase, mRNA therapeutics, computational biology, codon optimization, RNA structure prediction, immunogenicity modeling, AI-guided drug design, rare disease therapeutics.</p> <p> </p> <p>License: CC-BY 4.0</p>