Warning: Missing argument 2 for wpdb::prepare(), called in /home4/bquality/public_html/wp-content/plugins/membership/membershipincludes/classes/membershippublic.php on line 2303 and defined in /home4/bquality/public_html/wp-includes/wp-db.php on line 1290

Warning: Missing argument 2 for wpdb::prepare(), called in /home4/bquality/public_html/wp-content/plugins/membership/membershipincludes/classes/membershippublic.php on line 2303 and defined in /home4/bquality/public_html/wp-includes/wp-db.php on line 1290
Flurry of Papers Published on Unwanted Immunogenicity | BioQuality.biz

Unwanted immunogenicity is a significant problem for many biopharmaceuticals, and its detection is crucial for development, approval, and successful commercialization of these products. Several papers have been published recently on this important topic. Following are brief synopses of two of these papers. For references to 10 more recent publications, see our upcoming April-May issue.

Preclinical Models Used for Immunogenicity Prediction of Therapeutic Proteins.
Brinks V, Weinbuch D, Baker M, Dean Y, Stas P, Kostense S, Rup B, Jiskoot W.  Department of Pharmaceutics Utrecht Institute for Pharmaceutical Sciences (UIPS), Utrecht University, Universiteitsweg 99, 3584 CG, Utrecht, The Netherlands.  Pharm Res. 2013 May 7. [Epub ahead of print]

All therapeutic proteins are potentially immunogenic. Antibodies formed against these drugs can decrease efficacy, leading to drastically increased therapeutic costs and in rare cases to serious and sometimes life threatening side-effects. Many efforts are therefore undertaken to develop therapeutic proteins with minimal immunogenicity. For this, immunogenicity prediction of candidate drugs during early drug development is essential. Several in silico, in vitro and in vivo models are used to predict immunogenicity of drug leads, to modify potentially immunogenic properties and to continue development of drug candidates with expected low immunogenicity. Despite the extensive use of these predictive models, their actual predictive value varies. Important reasons for this uncertainty are the limited/insufficient knowledge on the immune mechanisms underlying immunogenicity of therapeutic proteins, the fact that different predictive models explore different components of the immune system and the lack of an integrated clinical validation. In this review, we discuss the predictive models in use, summarize aspects of immunogenicity that these models predict and explore the merits and the limitations of each of the models.

Impact of Anti-Drug Antibodies in Preclinical Pharmacokinetic Assessment.
Thway TM, Magana I, Bautista A, Jawa V, Gu W, Ma M. Department of Pharmacokinetic and Drug Metabolism, Amgen Inc., One Amgen Center Drive, Thousand Oaks, California, 91320, USA.  AAPS J. 2013 May 8. [Epub ahead of print]

The administration of human biotherapeutics is often associated with a higher incidence of immunogenicity in preclinical species. The presence of anti-drug antibodies (ADAs) in the test samples can affect the accurate measurement of therapeutic protein (TP) in bioanalytical methods designed to support pharmacokinetic (PK) and toxicokinetic (TK) assessments. The impact can vary depending on the bioanalytical method platform and study dosing design. The goal of this study is to evaluate the impact of ADA response on the bioanalytical methods in support of PK/TK and the associated study data interpretation. Sprague Dawley rats were administered with four weekly doses of 50 mg/kg TP, a humanized monoclonal antibody. The TP in serum samples was measured using three bioanalytical methods that quantified bound and/or unbound TP to ADA. The ADA response in the animals was classified into negative, low, medium, and high based on the magnitude of the response. The presence of ADA in samples led to discrepant TP measurements between the methods, especially at time points where the TP concentrations were low. This could be due to ADA interference to the accurate measurement of ADA-bound TP concentrations. The TP concentration at last time point (C last) was reduced by 82.8%, 98.6%, and 99.8%, respectively, for samples containing low, medium, and high levels of ADA. The interfering effects of the ADA on bioanalytical methods and exposure were evident as early as 2 weeks post-dosing. This modeling approach can provide the better understanding of ADA impact on PK exposure in multiple doses.

Comments

Post a comment

You must be logged in to post a comment.

BioQuality.com. Copyright © 2015. All rights reserved.
Turning Information into Practical Knowledge