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Modeling the competition between aggregation and self‐assembly during virus‐like particle processing

Overview of attention for article published in Biotechnology & Bioengineering, August 2010
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102 Mendeley
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Title
Modeling the competition between aggregation and self‐assembly during virus‐like particle processing
Published in
Biotechnology & Bioengineering, August 2010
DOI 10.1002/bit.22821
Pubmed ID
Authors

Yong Ding, Yap Pang Chuan, Lizhong He, Anton P.J. Middelberg

Abstract

Understanding and controlling aggregation is an essential aspect in the development of pharmaceutical proteins to improve product yield, potency and quality consistency. Even a minute quantity of aggregates may be reactogenic and can render the final product unusable. Self-assembly processing of virus-like particles (VLPs) is an efficient method to quicken the delivery of safe and efficacious vaccines to the market at low cost. VLP production, as with the manufacture of many biotherapeutics, is susceptible to aggregation, which may be minimized through the use of accurate and practical mathematical models. However, existing models for virus assembly are idealized, and do not predict the non-native aggregation behavior of self-assembling viral subunits in a tractable nor useful way. Here we present a mechanistic mathematical model describing VLP self-assembly that accounts for partitioning of reactive subunits between the correct and aggregation pathways. Our results show that unproductive aggregation causes up to 38% product loss by competing favorably with the productive nucleation of self-assembling subunits, therefore limiting the availability of nuclei for subsequent capsid growth. The protein subunit aggregation reaction exhibits an apparent second-order concentration dependence, suggesting a dimerization-controlled agglomeration pathway. Despite the plethora of possible assembly intermediates and aggregation pathways, protein aggregation behavior may be predicted by a relatively simple yet realistic model. More importantly, we have shown that our bioengineering model is amenable to different reactor formats, thus opening the way to rational scale-up strategies for products that comprise biomolecular assemblies.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
India 1 <1%
Pakistan 1 <1%
Unknown 97 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 29%
Student > Master 19 19%
Researcher 11 11%
Student > Bachelor 10 10%
Other 7 7%
Other 13 13%
Unknown 12 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 34 33%
Biochemistry, Genetics and Molecular Biology 14 14%
Engineering 11 11%
Chemistry 8 8%
Chemical Engineering 6 6%
Other 13 13%
Unknown 16 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 January 2022.
All research outputs
#8,534,528
of 25,373,627 outputs
Outputs from Biotechnology & Bioengineering
#2,527
of 6,450 outputs
Outputs of similar age
#38,377
of 104,661 outputs
Outputs of similar age from Biotechnology & Bioengineering
#23
of 48 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,450 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 20th percentile – i.e., 20% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 104,661 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.