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Classification model of amino acid sequences prone to aggregation of therapeutic proteins
BACKGROUND: Total body clearance of biological drugs is for the most part dependent on the receptor mechanisms (receptor mediated clearance) and the concentration of antibodies aimed at administered drug – anti-drug-antibodies (ADA). One of the significant factors that induces the increase of ADA le...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937009/ https://www.ncbi.nlm.nih.gov/pubmed/27388622 http://dx.doi.org/10.1186/s40203-016-0019-4 |
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author | Marczak, Monika Okoniewska, Krystyna Grabowski, Tomasz |
author_facet | Marczak, Monika Okoniewska, Krystyna Grabowski, Tomasz |
author_sort | Marczak, Monika |
collection | PubMed |
description | BACKGROUND: Total body clearance of biological drugs is for the most part dependent on the receptor mechanisms (receptor mediated clearance) and the concentration of antibodies aimed at administered drug – anti-drug-antibodies (ADA). One of the significant factors that induces the increase of ADA level after drug administration could be the aggregates present in the finished product or formed in the organism. Numerous attempts have been made to identify the sequence fragments that could be responsible for forming the aggregates – aggregate prone regions (APR). PURPOSE: The aim of this study was to find physiochemical parameters specific to APR that would differentiate APR from other sequences present in therapeutic proteins. METHODS: Two groups of amino acid sequences were used in the study. The first one was represented by the sequences separated from the therapeutic proteins (n = 84) able to form APR. A control set (CS) consisted of peptides that were chosen based on 22 tregitope sequences. RESULTS: Classification model and four classes (A, B, C, D) of sequences were finally presented. For model validation Cooper statistics was presented. CONCLUSIONS: The study proposes a classification model of APR. This consists in a distinction of APR from sequences that do not form aggregates based on the differences in the value of physicochemical parameters. Significant share of electrostatic parameters in relation to classification model was indicated. |
format | Online Article Text |
id | pubmed-4937009 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-49370092016-07-20 Classification model of amino acid sequences prone to aggregation of therapeutic proteins Marczak, Monika Okoniewska, Krystyna Grabowski, Tomasz In Silico Pharmacol Original Research BACKGROUND: Total body clearance of biological drugs is for the most part dependent on the receptor mechanisms (receptor mediated clearance) and the concentration of antibodies aimed at administered drug – anti-drug-antibodies (ADA). One of the significant factors that induces the increase of ADA level after drug administration could be the aggregates present in the finished product or formed in the organism. Numerous attempts have been made to identify the sequence fragments that could be responsible for forming the aggregates – aggregate prone regions (APR). PURPOSE: The aim of this study was to find physiochemical parameters specific to APR that would differentiate APR from other sequences present in therapeutic proteins. METHODS: Two groups of amino acid sequences were used in the study. The first one was represented by the sequences separated from the therapeutic proteins (n = 84) able to form APR. A control set (CS) consisted of peptides that were chosen based on 22 tregitope sequences. RESULTS: Classification model and four classes (A, B, C, D) of sequences were finally presented. For model validation Cooper statistics was presented. CONCLUSIONS: The study proposes a classification model of APR. This consists in a distinction of APR from sequences that do not form aggregates based on the differences in the value of physicochemical parameters. Significant share of electrostatic parameters in relation to classification model was indicated. Springer Berlin Heidelberg 2016-07-07 /pmc/articles/PMC4937009/ /pubmed/27388622 http://dx.doi.org/10.1186/s40203-016-0019-4 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Research Marczak, Monika Okoniewska, Krystyna Grabowski, Tomasz Classification model of amino acid sequences prone to aggregation of therapeutic proteins |
title | Classification model of amino acid sequences prone to aggregation of therapeutic proteins |
title_full | Classification model of amino acid sequences prone to aggregation of therapeutic proteins |
title_fullStr | Classification model of amino acid sequences prone to aggregation of therapeutic proteins |
title_full_unstemmed | Classification model of amino acid sequences prone to aggregation of therapeutic proteins |
title_short | Classification model of amino acid sequences prone to aggregation of therapeutic proteins |
title_sort | classification model of amino acid sequences prone to aggregation of therapeutic proteins |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4937009/ https://www.ncbi.nlm.nih.gov/pubmed/27388622 http://dx.doi.org/10.1186/s40203-016-0019-4 |
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