Targets in allergy-directed immunotherapy

Critical appraisal of the clinical utility of sublingual immunotherapy in allergy

Since it was introduced by Noon in 1911, allergen-specific immunotherapy or desensitization has been widely prescribed in the management of allergic diseases. Aimed at the etiology, it represents the only effective treatment for allergy. The basic mechanisms of immunotherapy are becoming better understood and allow us to improve this technique in the future. The sublingual immunotherapy as an alternative to subcutaneous route has been widely studied. Several clinical trials confirmed that sublingual immunotherapy is efficient in reducing allergic respiratory symptoms. The sublingual immunotherapy reduces the risk of developing serious side effects due to desensitization. We performed a literature review in order to remind the mechanisms of action and to demonstrate efficacy and tolerability of the sublingual immunotherapy in the treatment of allergic rhinoconjunctivitis and asthma and its impact on the quality of life.

Prediction of supertype-specific HLA class I binding peptides using support vector machines

Experimental approaches for identifying T-cell epitopes are time-consuming, costly and not applicable to the large scale screening. Computer modeling methods can help to minimize the number of experiments required, enable a systematic scanning for candidate major histocompatibility complex (MHC) binding peptides and thus speed up vaccine development. We developed a prediction system based on a novel data representation of peptide/MHC interaction and support vector machines (SVM) for prediction of peptides that promiscuously bind to multiple Human Leukocyte Antigen (HLA, human MHC) alleles belonging to a HLA supertype. Ten-fold cross-validation results showed that the overall performance of SVM models is improved in comparison to our previously published methods based on hidden Markov models (HMM) and artificial neural networks (ANN), also confirmed by blind testing. At specificity 0.90, sensitivity values of SVM models were 0.90 and 0.92 for HLA-A2 and -A3 dataset respectively. Average area under the receiver operating curve (A(ROC)) of SVM models in blind testing are 0.89 and 0.92 for HLA-A2 and -A3 datasets. A(ROC) of HLA-A2 and -A3 SVM models were 0.94 and 0.95, validated using a full overlapping study of 9-mer peptides from human papillomavirus type 16 E6 and E7 proteins. In addition, a large-scale experimental dataset has been used to validate HLA-A2 and -A3 SVM models. The SVM prediction models were integrated into a web-based computational system MULTIPRED1, accessible at antigen.i2r.a-star.edu.sg/multipred1/.