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Record W2789917827 · doi:10.1002/9781118801512.ch8

Plant Recombinant Lysosomal Enzymes as Replacement Therapeutics for Lysosomal Storage Diseases

2018· other· en· W2789917827 on OpenAlexaff
Allison R. Kermode, Grant McNair, Owen Pierce

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicTransgenic Plants and Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsEnzyme replacement therapyRecombinant DNALysosomeEndocytosisAlpha-galactosidaseLysosomal storage diseaseLysosomal storage disordersEnzymeGlucuronidaseBiologyIntracellularBiochemistryComputational biologyBiotechnologyMedicineGeneFabry diseaseDiseaseReceptor

Abstract

fetched live from OpenAlex

Lysosomal storage diseases (LSDs) are a broad class of over 70 genetic diseases that are caused by mutations in proteins critical for lysosomal function; collectively these diseases have an estimated prevalence of 1 in 5000 live births. LSDs can be treated by enzyme replacement therapy (ERT) in which the purified recombinant enzyme is delivered intravenously to patients by weekly infusions. Plant-based platforms for ERT drug production are being pursued; these may enable more economical and safe treatments than traditional production platforms. Yet the use of plant platforms poses significant technical challenges – one of which is associated with generating a therapeutically efficacious product. The administered recombinant enzyme must have suitable targeting signals for endocytosis into patient cells and for intracellular delivery to the lysosome. For many enzymes this requires the mannose-6-phosphate (M6P) tag on the replacement protein. Elaboration of this trafficking motif requires post-translational enzymatic machinery that is not present in plant cells. We discuss approaches to effect M6P elaboration onto plant-made recombinant lysosomal hydrolases, as well as emerging alternative modifications for improved biodistribution of ERT.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.270
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2018
Admission routes1
Has abstractyes

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