Abstract
Assessment of immunogenicity is a major aspect in evaluating the safety of biological therapeutic proteins. It is important to evaluate the immunogenic potential of the biologics in an appropriate fashion using clearly defined strategy and clinical trials. The studies must include the appropriate risk assessment procedures using validated methods. The immune responses against the therapeutic biologics can be studied using various methodologies. These include enzyme linked immunoassays (ELISA), surface plasmon resonance (SPR), chemiluminescence, and flowcytometry assays for binding antibodies and cell based assays for neutralizing antibodies. The immune responses to the biologics can widely vary in various cross section of the population, thus a combination of techniques are necessary to fully evaluate the immunogenic potential of the biologics. This review outlines various commonly used technology platforms, its merits and shortcomings for the evaluation of the immune responses.
Call for Papers in Thematic Issues
Artificial Intelligence in Bioinformatics
Bioinformatics is an interdisciplinary field that analyzes and explores biological data. This field combines biology and information system. Artificial Intelligence (AI) has attracted great attention as it tries to replicate human intelligence. It has become common technology for analyzing and solving complex data and problems and encompasses sub-fields of machine ...read more
Latest Advancements in Biotherapeutics.
The scope of this thematic issue is to comprehensively explore the rapidly evolving landscape of biotherapeutics, emphasizing breakthroughs in precision medicine. Encompassing diverse therapeutic modalities, the issue will delve into the latest developments in monoclonal antibodies, CRISPR/Cas gene editing, CAR-T cell therapies, and innovative drug delivery systems, such as nanoparticle-based ...read more
Machine Learning and Artificial Intelligence for Medical Data Analysis and Human Information Analysis in Healthcare
The intersection of machine learning (ML) and artificial intelligence (AI) with the pharmaceutical industry is revolutionizing traditional paradigms in drug discovery and development. These technologies have introduced innovative approaches to analyzing complex datasets and predicting chemical properties, leading to more efficient identification and optimization of drug candidates. By employing sophisticated ...read more
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