BACKGROUND: IgA vasculitis is the most common form of systemic vasculitis in children but can occur in adults. Inciting antigens include infections, drugs, foods, insect bites, and immunizations. Antibiotics and tumor necrosis factor (TNF) alpha inhibitors are the most common class of drugs that cause IgA vasculitis. Although sotalol and rivaroxaban have been documented to cause leukocytoclastic vasculitis, we have never come across any literature attributing IgA vasculitis to either drug. Additionally, Rocky Mountain spotted fever has not been associated with IgA vasculitis despite being described in cutaneous and systemic vasculitis cases. Here, we present a case of IgA vasculitis triggered by sotalol with challenging differentials, including a recent infection with Rocky Mountain spotted fever, malignancy, and rivaroxaban as possible triggers.
METHODS: 68 yr old male with a history of lung cancer treated with resection and chemotherapy 5 years ago is currently in remission, and recently was started on sotalol and rivaroxaban for new-onset paroxysmal atrial fibrillation. He presented with diffuse petechial/purpural rash on the lower limbs, multiple joint pain, severe abdominal pain and rectal bleeds, hemoptysis, and renal dysfunction. IgG titers for RMSF were high. Punch biopsy of skin and renal biopsy were consistent with IgA vasculitis. Sotalol and rivaroxaban were stopped. The patient was treated with oral prednisone, and his condition relatively improved.
CONCLUSIONS: Ig A vasculitis is mostly a self-limiting disease, but adults tend to have a severe course. It is important to diagnose early and identify a trigger. Removing the offending agent or treating the underlying infection is an important aspect of management.

BACKGROUND: Antibiotic resistance is increasing rapidly in pathogenic organisms, creating more complications for treatment of diseases. Rocky Mountain spotted fever (RMSF) is a neglected tropical disease in humans caused by Rickettsia rickettsii for which no effective therapeutic is available. Subtractive genomics methods facilitate the characterization of non-homologous essential proteins that could be targeted for the discovery of potential therapeutic compounds against R. rickettsii to combat RMSF. Present study followed an in-silico based methodology, involving scanning and filtering the complete proteome of Rickettsia rickettsii by using several prioritization parameters in the search of potential candidates for drug development. Further the putative targets were subjected to series of molecular dockings with ligands obtained from PDB ligand database to identify suitable potential inhibitors. The comparative genomic analysis revealed 606 non-homologous proteins and 233 essential non-homologous proteins of R. rickettsii. The metabolic pathway analysis predicted 120 proteins as putative drug targets, out of which 56 proteins were found to be associated with metabolic pathways unique to the bacteria and further subcellular localization analysis revealed that 9 proteins as potential drug targets which are secretion proteins, involved in peptidoglycan biosynthesis, folate biosynthesis and bacterial secretion system. As secretion proteins are more feasible as vaccine candidates, we have selected a most potential target i.e. tolC, an outer membrane efflux protein that belongs to type I secretion system and has major role in pathogen survival as well as MDR persistence. So for case study, we have modelled the three dimensional structure of tolC (tunnel protein). The model was further subjected to virtual screening and in-silico docking. The study identified three potential inhibitors having PDB Id 19V, 6Q8 and 39H. Further we have suggested that the above study would be most important while considering the selection of candidate targets and drug or vaccine designing against R. rickettsii.

Computational and structure guided methods can make significant contributions to the development of solutions for difficult protein engineering problems, including the optimization of next generation of engineered antibodies. In this paper, we describe a contemporary industrial antibody engineering program, based on hypothesis-driven in silico protein optimization method. The foundational concepts and methods of computational protein engineering are discussed, and an example of a computational modeling and structure-guided protein engineering workflow is provided for the design of best-in-class heterodimeric Fc with high purity and favorable biophysical properties. We present the engineering rationale as well as structural and functional characterization data on these engineered designs.