Immunologic evaluation of patients with cefotetan-induced anaphylaxis

ChAlPred: A web server for prediction of allergenicity of chemical compounds

BACKGROUND

Allergy is the abrupt reaction of the immune system that may occur after the exposure to allergens such as proteins, peptides, or chemicals. In the past, various methods have been generated for predicting allergenicity of proteins and peptides. In contrast, there is no method that can predict allergenic potential of chemicals. In this paper, we described a method ChAlPred developed for predicting chemical allergens as well as for designing chemical analogs with desired allergenicity.

METHOD

In this study, we have used 403 allergenic and 1074 non-allergenic chemical compounds obtained from IEDB database. The PaDEL software was used to compute the molecular descriptors of the chemical compounds to develop different prediction models. All the models were trained and tested on the 80% training data and evaluated on the 20% validation data using the 2D, 3D and FP descriptors.

RESULTS

In this study, we have developed different prediction models using several machine learning approaches. It was observed that the Random Forest based model developed using hybrid descriptors performed the best, and achieved the maximum accuracy of 83.39% and AUC of 0.93 on validation dataset. The fingerprint analysis of the dataset indicates that certain chemical fingerprints are more abundant in allergens that include PubChemFP129 and GraphFP1014. We have also predicted allergenicity potential of FDA-approved drugs using our best model and identified the drugs causing allergic symptoms (e.g., Cefuroxime, Spironolactone, Tioconazole). Our results agreed with allergenicity of these drugs reported in literature.

CONCLUSIONS

To aid the research community, we developed a smart-device compatible web server ChAlPred (https://webs.iiitd.edu.in/raghava/chalpred/) that allows to predict and design the chemicals with allergenic properties.

Serum Specific IgE to Thyroid Peroxidase Activates Basophils in Aspirin Intolerant Urticaria

Thyroid antibodies are frequently observed in urticaria patients, but their roles in urticaria are not clearly elucidated. We investigated the role of serum specific IgE to thyroid peroxidase (TPO) in patients with aspirin intolerant acute urticaria (AIAU) and aspirin intolerant chronic urticaria (AICU). We recruited 59 AIAU and 96 AICU patients with 69 normal controls (NC). Serum specific IgE to TPO was measured by manual direct ELISA, and CD203c expressions on basophil with additions of TPO were measured to prove a direct role of TPO in effector cells. The prevalences of serum specific IgE to TPO were significantly higher in AIAU (15.2%) and AICU groups (7.5%) compared to NC (0%, P=0.018: P=0.013, respectively). Flow cytometry showed CD203c induction in a dose dependent manner with serial additions of TPO in some AIAU and AICU patients having high specific IgE to TPO. Our findings show that the prevalence of serum specific IgE to TPO was significantly higher in both AIAU and AICU patients than in NC. It is suggested that specific IgE to TPO play a pathogenic role in AIAU and AICU.