A Case Presentation on Ofloxacin Induced Dermal Hypersensitivity Reaction

Abstract

Ofloxacin is a second-generation fluoroquinolone and is highly effective against a wide range of bacterial infections. Fluoroquinolones are well-tolerated drugs with mild-to-moderate adverse effects such as gastrointestinal disturbances, skin reactions, and neurological reactions. These are widely used antimicrobials, which can cause cutaneous ADRs in about 1%–2% of patients. Hypersensitivity reactions due to ofloxacin are found rarely, ranging in frequency from 0.4% to 2%, respectively. This case report highlights one such event of cutaneous ADR by the use of ofloxacin injection causing Injection site reaction after a few minutes of Ofloxacin infusion. Based on history and clinical examination patient was diagnosed as ofloxacin induced hypersensitivity reaction and was successfully treated with antihistamines and corticosteroids. General management of ADR includes withdrawal/suspension, dose reduction of the suspected drug, and administration of supportive therapy. In this case, the suspected drugs were withdrawn and the patient was treated symptomatically. Pharmacovigilance should be a part of patient care to reduce the occurrence of adverse drug reactions and also encourage practitioners in reporting to gather more and more data regarding adverse drug reactions. Due to the rarity of Ofloxacin induced ADRs this article sheds light on one of such cases to help in pharmacovigilance

Predicting pharmaceutical powder flow from microscopy images using deep learning

“…large experimental error, low reproducibility, and time and resource consumption. 14 These disadvantages highlight the need for alternative methods to predict powder flowability to save time and resources, especially at the beginning of the development of a new pharmaceutical ingredient, when the amount of material available is at its premium. By compiling and using training data that is the result of this rigorous process of repeat testing, we ensure that the ground truth labels are accurate and hence the predictions made by the Deep Learning (DL) models and the trends which the networks capture are too.…”