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Development of QSAR models for in silico screening of antibody solubility
Although monoclonal antibodies (mAbs) have been shown to be extremely effective in treating a number of diseases, they often suffer from poor developability attributes, such as high viscosity and low solubility at elevated concentrations. Since experimental candidate screening is often materials and...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9037471/ https://www.ncbi.nlm.nih.gov/pubmed/35442164 http://dx.doi.org/10.1080/19420862.2022.2062807 |
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author | Han, Xuan Shih, James Lin, Yuhao Chai, Qing Cramer, Steven M. |
author_facet | Han, Xuan Shih, James Lin, Yuhao Chai, Qing Cramer, Steven M. |
author_sort | Han, Xuan |
collection | PubMed |
description | Although monoclonal antibodies (mAbs) have been shown to be extremely effective in treating a number of diseases, they often suffer from poor developability attributes, such as high viscosity and low solubility at elevated concentrations. Since experimental candidate screening is often materials and labor intensive, there is substantial interest in developing in silico tools for expediting mAb design. Here, we present a strategy using machine learning-based QSAR models for the a priori estimation of mAb solubility. The extrapolated protein solubilities of a set of 111 antibodies in a histidine buffer were determined using a high throughput PEG precipitation assay. 3D homology models of the antibodies were determined, and a large set of in house and commercially available molecular descriptors were then calculated. The resulting experimental and descriptor data were then used for the development of QSAR models of mAb solubilities. After feature selection and training with different machine learning algorithms, the models were evaluated with external test sets. The resulting regression models were able to estimate the solubility values of external test set data with R(2) of 0.81 and 0.85 for the two regression models developed. In addition, three class and binary classification models were developed and shown to be good estimators of mAb solubility behavior, with overall test set accuracies of 0.70 and 0.95, respectively. The analysis of the selected molecular descriptors in these models was also found to be informative and suggested that several charge-based descriptors and isotype may play important roles in mAb solubility. The combination of high throughput relative solubility experimental techniques in concert with efficient machine learning QSAR models offers an opportunity to rapidly screen potential mAb candidates and to design therapeutics with improved solubility characteristics. |
format | Online Article Text |
id | pubmed-9037471 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-90374712022-04-26 Development of QSAR models for in silico screening of antibody solubility Han, Xuan Shih, James Lin, Yuhao Chai, Qing Cramer, Steven M. MAbs Report Although monoclonal antibodies (mAbs) have been shown to be extremely effective in treating a number of diseases, they often suffer from poor developability attributes, such as high viscosity and low solubility at elevated concentrations. Since experimental candidate screening is often materials and labor intensive, there is substantial interest in developing in silico tools for expediting mAb design. Here, we present a strategy using machine learning-based QSAR models for the a priori estimation of mAb solubility. The extrapolated protein solubilities of a set of 111 antibodies in a histidine buffer were determined using a high throughput PEG precipitation assay. 3D homology models of the antibodies were determined, and a large set of in house and commercially available molecular descriptors were then calculated. The resulting experimental and descriptor data were then used for the development of QSAR models of mAb solubilities. After feature selection and training with different machine learning algorithms, the models were evaluated with external test sets. The resulting regression models were able to estimate the solubility values of external test set data with R(2) of 0.81 and 0.85 for the two regression models developed. In addition, three class and binary classification models were developed and shown to be good estimators of mAb solubility behavior, with overall test set accuracies of 0.70 and 0.95, respectively. The analysis of the selected molecular descriptors in these models was also found to be informative and suggested that several charge-based descriptors and isotype may play important roles in mAb solubility. The combination of high throughput relative solubility experimental techniques in concert with efficient machine learning QSAR models offers an opportunity to rapidly screen potential mAb candidates and to design therapeutics with improved solubility characteristics. Taylor & Francis 2022-04-20 /pmc/articles/PMC9037471/ /pubmed/35442164 http://dx.doi.org/10.1080/19420862.2022.2062807 Text en © 2022 Eli Lilly and Company. Published with license by Taylor & Francis Group, LLC. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Report Han, Xuan Shih, James Lin, Yuhao Chai, Qing Cramer, Steven M. Development of QSAR models for in silico screening of antibody solubility |
title | Development of QSAR models for in silico screening of antibody solubility |
title_full | Development of QSAR models for in silico screening of antibody solubility |
title_fullStr | Development of QSAR models for in silico screening of antibody solubility |
title_full_unstemmed | Development of QSAR models for in silico screening of antibody solubility |
title_short | Development of QSAR models for in silico screening of antibody solubility |
title_sort | development of qsar models for in silico screening of antibody solubility |
topic | Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9037471/ https://www.ncbi.nlm.nih.gov/pubmed/35442164 http://dx.doi.org/10.1080/19420862.2022.2062807 |
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