ArticleBioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy2025
An Automated Tool for Glycosimilarity Assessment of mAb Therapeutic Biosimilars: Trastuzumab and Bevacizumab as Case Studies.
Article in BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Advances in Biosimilars: A Systematic Review of Machine Learning Applications.Pharmaceuticals (Basel, Switzerland) · 2026Review
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Authors and funding
4 authors.
Funding
Abstract
backgroundWith the expiration of patents for multiple biotherapeutics, biosimilars are gaining traction globally as cost-effective alternatives to the original products. Glycosylation, a critical quality attribute, makes glycosimilarity assessment pivotal for biosimilar development. Given the complexity of glycoanalytical profiles, assessing glycosimilarity is nontrivial.
objectiveThis study proposes a Python-based automated tool for rapid estimation of glycosimilarity index (GI). MATERIALS AND
methodsA comprehensive analytical glycosimilarity comparison of the trastuzumab originator product, Herclon (Roche), with five marketed biosimilars:Trasturel (Reliance Life Sciences), Canmab (Biocon), Vivitra (Zydus Ingenia), Hertraz (Mylan), and Biceltis (Cipla), has been performed. Similarly, a comparison between the bevacizumab originator product, Avastin (Roche), and its five biosimilars: Abevmy (Mylan), Krabeva (Biocon), Ivzumab (RPG LifeSciences), Bryxta (Zydus), and Advamab (Alkem Labs), is presented. Glycan profile has been assessed using liquid chromatography-fluorescence detection, and the data have been integrated using the XGBoost-machine learning algorithm to quantify glycan composition. The GI has been calculated by combining profile similarity and compositional similarity, estimated on the basis of the criticality and tolerance of each glycan.
resultsThe tool enabled rapid GI estimation (< 1 min/sample) with reduced errors compared with Excel (> 10 min/sample). Biosimilars exhibited high GI with several exceeding 95%, while the lowest GI observed were 87.80% for trastuzumab and 92.39% for bevacizumab.
conclusionsThe Python-based tool offers a high-throughput and a reliable platform for glycosimilarity assessment, outperforming traditional analysis. Minor variations in glycosylation patterns were observed among the biosimilars, suggesting a modest glycosimilarity variation (GI range between 80 and 100%). However, the limited number of innovator batches analyzed constrained the establishment of definitive tolerance limits. Future studies should focus on analyzing larger datasets to improve accuracy and define precise tolerance limits, enhancing the tool's reliability and its potential to accelerate biosimilar development.
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