Assessing the effect of genetic markers on drug immunogenicity from a mechanistic model-based approach.
Drug immunogenicity
Genetic
Semi-continuous data
Semi-parametric
Two-part improper survival model
Journal
BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545
Informations de publication
Date de publication:
20 03 2020
20 03 2020
Historique:
received:
19
06
2019
accepted:
25
02
2020
entrez:
21
3
2020
pubmed:
21
3
2020
medline:
25
6
2021
Statut:
epublish
Résumé
With the growth in use of biotherapic drugs in various medical fields, the occurrence of anti-drug antibodies represents nowadays a serious issue. This immune response against a drug can be due either to pre-existing antibodies or to the novel production of antibodies from B-cell clones by a fraction of the exposed subjects. Identifying genetic markers associated with the immunogenicity of biotherapeutic drugs may provide new opportunities for risk stratification before the introduction of the drug. However, real-world investigations should take into account that the population under study is a mixture of pre-immune, immune-reactive and immune-tolerant subjects. In this work, we propose a novel test for assessing the effect of genetic markers on drug immunogenicity taking into account that the population under study is a mixed one. This test statistic is derived from a novel two-part semiparametric improper survival model which relies on immunological mechanistic considerations. Simulation results show the good behavior of the proposed statistic as compared to a two-part logrank test. In a study on drug immunogenicity, our results highlighted findings that would have been discarded when considering classical tests. We propose a novel test that can be used for analyzing drug immunogenicity and is easy to implement with standard softwares. This test is also applicable for situations where one wants to test the equality of improper survival distributions of semi-continuous outcomes between two or more independent groups.
Sections du résumé
BACKGROUND
With the growth in use of biotherapic drugs in various medical fields, the occurrence of anti-drug antibodies represents nowadays a serious issue. This immune response against a drug can be due either to pre-existing antibodies or to the novel production of antibodies from B-cell clones by a fraction of the exposed subjects. Identifying genetic markers associated with the immunogenicity of biotherapeutic drugs may provide new opportunities for risk stratification before the introduction of the drug. However, real-world investigations should take into account that the population under study is a mixture of pre-immune, immune-reactive and immune-tolerant subjects.
METHOD
In this work, we propose a novel test for assessing the effect of genetic markers on drug immunogenicity taking into account that the population under study is a mixed one. This test statistic is derived from a novel two-part semiparametric improper survival model which relies on immunological mechanistic considerations.
RESULTS
Simulation results show the good behavior of the proposed statistic as compared to a two-part logrank test. In a study on drug immunogenicity, our results highlighted findings that would have been discarded when considering classical tests.
CONCLUSION
We propose a novel test that can be used for analyzing drug immunogenicity and is easy to implement with standard softwares. This test is also applicable for situations where one wants to test the equality of improper survival distributions of semi-continuous outcomes between two or more independent groups.
Identifiants
pubmed: 32192445
doi: 10.1186/s12874-020-00941-z
pii: 10.1186/s12874-020-00941-z
pmc: PMC7082907
doi:
Substances chimiques
Antibodies
0
Genetic Markers
0
Pharmaceutical Preparations
0
Banques de données
ClinicalTrials.gov
['NCT02116504']
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
69Références
AAPS J. 2013 Jul;15(3):893-6
pubmed: 23761225
Stat Med. 2015 May 20;34(11):1904-11
pubmed: 25630496
Annu Rev Stat Appl. 2017 Mar;4:283-315
pubmed: 28890906
Bioinformatics. 2005 Mar 1;21(5):660-8
pubmed: 15479710
J Am Stat Assoc. 2007 Jun 1;102(478):560-572
pubmed: 21031152
J Am Stat Assoc. 2003 Dec 1;98(464):1063-1078
pubmed: 21151838
Clin Exp Immunol. 2015 Sep;181(3):385-400
pubmed: 25959571
BioDrugs. 2016 Jun;30(3):195-206
pubmed: 27097915
Stat Med. 2001 Apr 30;20(8):1215-34
pubmed: 11304737
AAPS J. 2014 Jul;16(4):658-73
pubmed: 24764037