Sinopharm COVID-19 vaccine-induced Stevens-Johnson syndrome.
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ABSTRACT:
SUBMITTER: Boualila L
PROVIDER: S-EPMC8733279 | biostudies-literature | 2022 Apr
REPOSITORIES: biostudies-literature
Publications
Sinopharm COVID-19 vaccine-induced Stevens-Johnson syndrome.
Boualila L L Mrini B B Tagmouti A A El Moubarik N N Benchekroun Belabbes M M Boutimzine N N Cherkaoui L O LO
Journal francais d'ophtalmologie 20220106 4
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Project description:BackgroundAlpelisib is a recently approved treatment for hormone receptor-positive, HER2-negative, PIK3CA-mutated advanced breast cancer. It has been associated with alopecia and rash, but there are no documented cases of Stevens-Johnson Syndrome (SJS) associated with this drug. Here, we detail the first case of SJS associated with alpelisib.Case descriptionOur patient is a 60-year-old woman with a past medical history of metastatic hormone receptor-positive (ER+ 80% and PR+ 1%), HER2-negative metastatic breast cancer who presented with acute odynophagia, fevers, and diffuse body rash after receiving her first doses of alpelisib and fulvestrant in the preceding days. She presented to the emergency department after developing a whole-body rash and severe ulceration of her buccal mucosa. She was started on methylprednisolone with remarkable improvement in symptoms.ConclusionThis case report details the only report of SJS following alpelisib treatment. Immediate cessation of drugs and initiation of steroids are the cornerstone of treatment. Patients who experience such side effects will have to be monitored closely for long-term sequelae associated with SJS, including cutaneous, ocular, and oral sequelae, all of which can profoundly affect the quality of life for cancer patients.
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Project description:The ocular surface microbiome is an essential factor that maintains ocular surface homeostasis. Since the ocular surface is continuously exposed to the external environment, its microbiome, tears, and local immunity are vital for maintaining normal conditions. Additionally, this microbiome helps prevent pathogen colonization, which commonly leads to opportunistic infection. The abnormal ocular surface microbiome has previously been reported in several conditions, including dry eyes, allergy, blepharitis, graft-versus-host disease (GVHD), and Stevens-Johnson syndrome (SJS). Several approaches were applied to identify the ocular microbiome, including conventional culture techniques and molecular sequencing techniques. By using 16s rRNA sequencing, alterations in the type, proportion, and composition of bacterial communities, described by alpha (α)-and beta (β)-diversity, were observed in SJS patients compared to the healthy group. Conventional culture techniques indicated a higher number of positive bacterial cultures in the SJS group, with a predominance of gram-positive cocci and gram-positive bacilli. Besides, there are increased variations and multiple detections of bacterial genera. Taken together, SJS causes structural changes in the ocular surface and significantly affects its microbiome. Further studies into the area of temporal relationship, metagenomics, proteomics, and metabolomics analysis of the microbiome will lead to a better understanding of this disease. Finally, the treatment using prebiotics and probiotics to re-establish the normal ocular ecosystem and bring back a healthy ocular surface await confirmation.
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Project description:OBJECTIVE:Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. MATERIALS AND METHODS:Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). RESULTS:We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%-81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. DISCUSSION:Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. CONCLUSIONS:We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations.
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