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More than 50% of women take at least one medication during lactation. However, 54% of drugs in the LactMed database lack lactation safety data, and only 2% have robust evidence. This highlights a significant gap in guidance for designing pharmacokinetic and safety studies characterizing infant safety following medication exposure during lactation, despite FDA guidelines recommending clinical lactation studies. Additional guidance is needed to select the most suitable study design for these studies. To address this, we identified key medication-related characteristics essential for designing lactation studies that assess infant safety following systemic exposure during lactation. This allowed us to develop a decision tree, named Milk4baby, to guide researchers in selecting the most appropriate methodological approach for each medication. Milk4baby was designed by reviewing the literature and iterative discussions with an interdisciplinary panel of experts in clinical pharmacology, lactation, and pharmacometrics on factors influencing the selection of the methodological approach and design of a lactation study. The decision tree first considers the prevalence of medication utilization in women of childbearing age. Next, the medication's safety profile in infants aged 0-2 years must be assessed using available safety data from infants, adults, and/or animals. Finally, the expected infant systemic exposure level is evaluated based on medication's oral bioavailability, transfer into human milk, risk of accumulation, and utilization patterns. After completing these steps, the decision tree recommends the most suitable methodological approach including case reports/case studies, population pharmacokinetic (popPK) modeling, physiologically based pharmacokinetic (PBPK) modeling and simulations, or pharmacoepidemiologic studies. Verification of the decision tree on 50 randomly selected medications from the LactMed and Le CRAT databases revealed that PBPK and case reports were the most appropriate approaches in 29 cases, primarily due to low prevalence of medication utilization. Designing popPK, PBPK, or pharmacoepidemiologic studies can be time-consuming and resource-intensive, while poorly designed case reports/case studies may yield limited or misleading information. Therefore, Milk4baby aims to help researchers enhance the efficiency and accuracy of determining infant safety following systemic exposure during lactation by choosing the most suitable strategy for lactation studies, ultimately supporting better-informed decisions for lactating women and their healthcare providers.
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http://dx.doi.org/10.3389/fphar.2025.1602018 | DOI Listing |
J Safety Res
September 2025
Department of Civil & Environmental Engineering, The University of Tennessee, Knoxville, United States. Electronic address:
Introduction: Pedestrian safety is a growing concern in the United States transportation sector, with around 7,500 pedestrian crash fatalities reported in the United States in recent years. Pedestrians are at an even higher risk of crashes at night due to limited visibility and alcohol impairment of the drivers or pedestrians. The U.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Computer Science, Osun State University, Osogbo, Nigeria.
Probabilistic Random Forest is an extension of the traditional Random Forest machine learning algorithm that is one of the frequently used machine learning algorithms employed for species distribution modeling. However, with the use of complex dataset for predicting the presence or absence of the species, It is essential that feature extraction is important to generate optimal prediction that can affect the model accuracy and AUC score of the model simulation. In this paper, we integrated the Genetic Algorithm Optimization technique, which is popular for its excellent feature extraction technique, to enhance the predictive performance of the PRF Model.
View Article and Find Full Text PDFJ Eval Clin Pract
September 2025
Pediatric Allergy and Immunology Department, Akdeniz University Hospital, Akdeniz University, Antalya, Türkiye.
Aims And Objectives: To evaluate the efficacy of YoungAsthma, a nurse-led, web-based mHealth intervention on asthma control and self-efficacy among adolescents with asthma utilizing decision tree analysis.
Background: Asthma is a prevalent chronic condition in pediatric populations, necessitating sustained management for optimal disease control.
Design: A randomized controlled clinical trial.
Front Rehabil Sci
August 2025
Department of Neurosurgery, David Geffen School of Medicine, University of California, Los Angeles, CA, United States.
Introduction: Spinal cord injury (SCI) presents a significant burden to patients, families, and the healthcare system. The ability to accurately predict functional outcomes for SCI patients is essential for optimizing rehabilitation strategies, guiding patient and family decision making, and improving patient care.
Methods: We conducted a retrospective analysis of 589 SCI patients admitted to a single acute rehabilitation facility and used the dataset to train advanced machine learning algorithms to predict patients' rehabilitation outcomes.
Bioinform Adv
August 2025
Department of CSE, BUET, Dhaka 1000, Bangladesh.
Motivation: Heavy usage of synthetic nitrogen fertilizers to satisfy the increasing demands for food has led to severe environmental impacts like decreasing crop yields and eutrophication. One promising alternative is using nitrogen-fixing microorganisms as biofertilizers, which use the nitrogenase enzyme. This could also be achieved by expressing a functional nitrogenase enzyme in the cells of the cereal crops.
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