ArticleScientific reports2020
Automated Flow Synthesis of Tumor Neoantigen Peptides for Personalized Immunotherapy.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
12 citing papers in PubMed, 30 citations in OpenAlex.
- Artificial intelligence in peptide cancer vaccine design: from neoantigen discovery to immunogenicity prediction.Frontiers in genetics · 2026Review
- Recent Review on Biological Barriers and Host-Material Interfaces in Precision Drug Delivery: Advancement in Biomaterial Engineering for Better Treatment Therapies.Pharmaceutics · 2024Review
- Design of Cytotoxic T Cell Epitopes by Machine Learning of Human Degrons.ACS central science · 2024Article
- Acid-Modulated Peptide Synthesis for Application on Oxide Biosensor Interfaces.Nanomaterials (Basel, Switzerland) · 2023Article
- Peptide Vaccines in Melanoma: Chemical Approaches towards Improved Immunotherapeutic Efficacy.Pharmaceutics · 2023Review
- Cell-penetrating peptides enhance peptide vaccine accumulation and persistence in lymph nodes to drive immunogenicity.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Continuous Flow Synthesis of Anticancer Drugs.Molecules (Basel, Switzerland) · 2021Review
- Personal neoantigen vaccines induce persistent memory T cell responses and epitope spreading in patients with melanoma.Nature medicine · 2021Article
- Glycoproteogenomics: Setting the Course for Next-generation Cancer Neoantigen Discovery for Cancer Vaccines.Genomics, proteomics & bioinformatics · 2021Review
- Engineering precision nanoparticles for drug delivery.Nature reviews. Drug discovery · 2021Review
- Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis.ACS central science · 2020Article
- Efficient flow synthesis of human antimicrobial peptides.Australian journal of chemistry · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 5 institutions in 1 country.
Funding
Abstract
High-throughput genome sequencing and computation have enabled rapid identification of targets for personalized medicine, including cancer vaccines. Synthetic peptides are an established mode of cancer vaccine delivery, but generating the peptides for each patient in a rapid and affordable fashion remains difficult. High-throughput peptide synthesis technology is therefore urgently needed for patient-specific cancer vaccines to succeed in the clinic. Previously, we developed automated flow peptide synthesis technology that greatly accelerates the production of synthetic peptides. Herein, we show that this technology permits the synthesis of high-quality peptides for personalized medicine. Automated flow synthesis produces 30-mer peptides in less than 35 minutes and 15- to 16-mer peptides in less than 20 minutes. The purity of these peptides is comparable with or higher than the purity of peptides produced by other methods. This work illustrates how automated flow synthesis technology can enable customized peptide therapies by accelerating synthesis and increasing purity. We envision that implementing this technology in clinical settings will greatly increase capacity to generate clinical-grade peptides on demand, which is a key step in reaching the full potential of personalized vaccines for the treatment of cancer and other diseases.
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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.