Quantitative analysis of the CD4+ T cell response to therapeutic antibodies in healthy donors using a novel T cell:PBMC assay

Many biopharmaceuticals (BPs) are known to be immunogenic in the clinic, which can result in modified pharmacokinetics, reduced efficacy, allergic reactions and anaphylaxis. During recent years, several technologies to predict immunogenicity have been introduced, but the predictive value is still considered low. Thus, there is an unmet medical need for optimization of such technologies. The generation of T cell dependent high affinity anti-drug antibodies plays a key role in clinical immunogenicity. This study aimed at developing and evaluating a novel in vitro T cell:PBMC assay for prediction of the immunogenicity potential of BPs. To this end, we assessed the ability of infliximab (anti-TNF-α), rituximab (anti-CD20), adalimumab (anti-TNF-α) and natalizumab (anti-α4-integrin), all showing immunogenicity in the clinic, to induce a CD4+ T cells response. Keyhole limpet hemocyanin (KLH) and cytomegalovirus pp65 protein (CMV) were included as neo-antigen and recall antigen positive controls, respectively. By analyzing 26 healthy donors having HLA-DRB1 alleles matching the European population, we calculated the frequency of responding donors, the magnitude of the response, and the frequency of BP-specific T cells, as measured by 3[H]-thymidine incorporation and ELISpot IL-2 secretion. KLH and CMV demonstrated a strong T cell response in all the donors analyzed. The frequency of responding donors to the BPs was 4% for infliximab, 8% for adalimumab, 19% for rituximab and 27% for natalizumab, which is compared to and discussed with their respective observed clinical immunogenicity. This study further complements predictive immunogenicity testing by quantifying the in vitro CD4+ T cell responses to different BPs. Even though the data generated using this modified method does not directly translate to the clinical situation, a high sensitivity and immunogenic potential of most BPs is demonstrated

Image pattern recognition in big data: taxonomy and open challenges: survey

Image pattern recognition in the field of big data has gained increasing importance and attention from researchers and practitioners in many domains of science and technology. This paper focuses on the usage of image pattern recognition for big data applications. In this context, the taxonomy of image pattern recognition and big data is revealed. The applications of image pattern recognition for big data, including multimedia, biometrics, and biology/biomedical, are also highlighted. Moreover, the significance of using pattern-based feature reduction in big data is discussed, and machine-learning techniques in pattern recognition applications are presented. A comparison based on the objectives of the approaches is presented to underline the taxonomy. This paper provides a novel review in exploring image recognition approaches for big data, which can be used in future research