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Joint analysis of PK and immunogenicity outcomes using factorization model − a powerful approach for PK similarity study
Biological products, whether they are innovator products or biosimilars, can incite an immunogenic response ensuing in the development of anti-drug antibodies (ADA). The presence of ADA’s often affects the drug clearance, resulting in an increase in the variability of pharmacokinetic (PK) analysis a...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9547438/ https://www.ncbi.nlm.nih.gov/pubmed/36209046 http://dx.doi.org/10.1186/s12874-022-01742-2 |
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author | Haliduola, Halimu N. Berti, Fausto Stroissnig, Heimo Guenzi, Eric Otto, Hendrik Sattar, Abid Mansmann, Ulrich |
author_facet | Haliduola, Halimu N. Berti, Fausto Stroissnig, Heimo Guenzi, Eric Otto, Hendrik Sattar, Abid Mansmann, Ulrich |
author_sort | Haliduola, Halimu N. |
collection | PubMed |
description | Biological products, whether they are innovator products or biosimilars, can incite an immunogenic response ensuing in the development of anti-drug antibodies (ADA). The presence of ADA’s often affects the drug clearance, resulting in an increase in the variability of pharmacokinetic (PK) analysis and challenges in the design and analysis of PK similarity studies. Immunogenic response is a complex process which may be manifested by product and non-product-related factors. Potential imbalances in non-product-related factors between treatment groups may lead to differences in antibodies formation and thus in PK outcome. The current standard statistical approaches dismiss any associations between immunogenicity and PK outcomes. However, we consider PK and immunogenicity as the two correlated outcomes of the study treatment. In this research, we propose a factorization model for the simultaneous analysis of PK parameters (normal variable after taking log-transformation) and immunogenic response subgroup (binary variable). The central principle of the factorization model is to describe the likelihood function as the product of the marginal distribution of one outcome and the conditional distribution of the second outcome given the previous one. Factorization model captures the additional information contained in the correlation between the outcomes, it is more efficient than models that ignore potential dependencies between the outcomes. In our context, factorization model accounts for variability in PK data by considering the influence of immunogenicity. Based on our simulation studies, the factorization model provides more accurate and efficient estimates of the treatment effect in the PK data by taking into account the impact of immunogenicity. These findings are supported by two PK similarity clinical studies with a highly immunogenic biologic. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01742-2. |
format | Online Article Text |
id | pubmed-9547438 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-95474382022-10-09 Joint analysis of PK and immunogenicity outcomes using factorization model − a powerful approach for PK similarity study Haliduola, Halimu N. Berti, Fausto Stroissnig, Heimo Guenzi, Eric Otto, Hendrik Sattar, Abid Mansmann, Ulrich BMC Med Res Methodol Research Biological products, whether they are innovator products or biosimilars, can incite an immunogenic response ensuing in the development of anti-drug antibodies (ADA). The presence of ADA’s often affects the drug clearance, resulting in an increase in the variability of pharmacokinetic (PK) analysis and challenges in the design and analysis of PK similarity studies. Immunogenic response is a complex process which may be manifested by product and non-product-related factors. Potential imbalances in non-product-related factors between treatment groups may lead to differences in antibodies formation and thus in PK outcome. The current standard statistical approaches dismiss any associations between immunogenicity and PK outcomes. However, we consider PK and immunogenicity as the two correlated outcomes of the study treatment. In this research, we propose a factorization model for the simultaneous analysis of PK parameters (normal variable after taking log-transformation) and immunogenic response subgroup (binary variable). The central principle of the factorization model is to describe the likelihood function as the product of the marginal distribution of one outcome and the conditional distribution of the second outcome given the previous one. Factorization model captures the additional information contained in the correlation between the outcomes, it is more efficient than models that ignore potential dependencies between the outcomes. In our context, factorization model accounts for variability in PK data by considering the influence of immunogenicity. Based on our simulation studies, the factorization model provides more accurate and efficient estimates of the treatment effect in the PK data by taking into account the impact of immunogenicity. These findings are supported by two PK similarity clinical studies with a highly immunogenic biologic. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-022-01742-2. BioMed Central 2022-10-08 /pmc/articles/PMC9547438/ /pubmed/36209046 http://dx.doi.org/10.1186/s12874-022-01742-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Haliduola, Halimu N. Berti, Fausto Stroissnig, Heimo Guenzi, Eric Otto, Hendrik Sattar, Abid Mansmann, Ulrich Joint analysis of PK and immunogenicity outcomes using factorization model − a powerful approach for PK similarity study |
title | Joint analysis of PK and immunogenicity outcomes using factorization model − a powerful approach for PK similarity study |
title_full | Joint analysis of PK and immunogenicity outcomes using factorization model − a powerful approach for PK similarity study |
title_fullStr | Joint analysis of PK and immunogenicity outcomes using factorization model − a powerful approach for PK similarity study |
title_full_unstemmed | Joint analysis of PK and immunogenicity outcomes using factorization model − a powerful approach for PK similarity study |
title_short | Joint analysis of PK and immunogenicity outcomes using factorization model − a powerful approach for PK similarity study |
title_sort | joint analysis of pk and immunogenicity outcomes using factorization model − a powerful approach for pk similarity study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9547438/ https://www.ncbi.nlm.nih.gov/pubmed/36209046 http://dx.doi.org/10.1186/s12874-022-01742-2 |
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