ReviewFrontiers in bioengineering and biotechnology2022
The use of predictive models to develop chromatography-based purification processes.
Review in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed.
- Toward Rational Design of Precision-Fermented Milk Proteins: Integrating Cross-Species Selection, Post-Translational Modification, and AI Optimization.Comprehensive reviews in food science and food safety · 2026Review
- Hydraulic Pressure-Programmed Molecular Transport in Tough Hydrogels.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Modern Optimization Strategies in High-Performance Liquid Chromatography Analysis.Journal of separation science · 2026Review
- Developing downstream processes for the purification of recombinant proteins and small molecules from Nicotiana benthamiana biomass.Plant biotechnology journal · 2026Review
- An overview of descriptors to capture protein properties - Tools and perspectives in the context of QSAR modeling.Computational and structural biotechnology journal · 2023Review
- Product safety aspects of plant molecular farming.Frontiers in bioengineering and biotechnology · 2023Review
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions
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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.