Anidulafungin Levels in Breast Milk After Cessation of Treatment: A Case Report.

Background: Anidulafungin has poor oral bioavailability, with hardly any available information on how it affects breast milk, oral absorption, or gastrointestinal side effects in the infant. Case Presentation: A 40-year-old woman who recently gave birth to a healthy infant was treated for a period of 14 days for a Candida glabrata with 100 mg anidulafungin once a day. The department of clinical pharmacy was consulted to provide advice on how long the patient had to wait after ceasing anidulafungin before it was safe to start breastfeeding, with regard to preventing possible side effects of the drug to the infant, such as diarrhea or cholestasis and increase in liver enzyme values. The advice of the hospital pharmacist was pragmatic: to start breastfeeding within 2 days after the medication was discontinued based on half-time. Results: Owing to this lack of information, we measured anidulafungin concentrations in breast milk and found low levels. Conclusion: We concluded that anidulafungin is detectable in breast milk until 32 hours after anidulafungin treatment was stopped, and that no side effects were observed by the infant.

Predicting Beta-Lactam Target Non-Attainment in ICU Patients at Treatment Initiation: Development and External Validation of Three Novel (Machine Learning) Models.

In the intensive care unit (ICU), infection-related mortality is high. Although adequate antibiotic treatment is essential in infections, beta-lactam target non-attainment occurs in up to 45% of ICU patients, which is associated with a lower likelihood of clinical success. To optimize antibiotic treatment, we aimed to develop beta-lactam target non-attainment prediction models in ICU patients. Patients from two multicenter studies were included, with intravenous intermittent beta-lactam antibiotics administered and blood samples drawn within 12-36 h after antibiotic initiation. Beta-lactam target non-attainment models were developed and validated using random forest (RF), logistic regression (LR), and naïve Bayes (NB) models from 376 patients. External validation was performed on 150 ICU patients. We assessed performance by measuring discrimination, calibration, and net benefit at the default threshold probability of 0.20. Age, sex, serum creatinine, and type of beta-lactam antibiotic were found to be predictive of beta-lactam target non-attainment. In the external validation, the RF, LR, and NB models confirmed good discrimination with an area under the curve of 0.79 [95% CI 0.72-0.86], 0.80 [95% CI 0.73-0.87], and 0.75 [95% CI 0.67-0.82], respectively, and net benefit in the RF and LR models. We developed prediction models for beta-lactam target non-attainment within 12-36 h after antibiotic initiation in ICU patients. These online-accessible models use readily available patient variables and help optimize antibiotic treatment. The RF and LR models showed the best performance among the three models tested.