جامعة الكوت - University of kut - بحوث

السنة	عنوان البحث	نشر البحث
2026	Precision dosing in neonates: AI as a pharmacological tool	Pharmacia
The pharmacological therapy of neonates is extremely complicated due to the immature organ systems of the body, high water content, and low number of clinical studies in children of this age. Conventional dosing strategies, usually based on weight or age, are often inaccurate and may result in either underexposure or toxicity. A structured literature search was performed in PubMed/MEDLINE and Scopus, as well as Web of Science, from 2000 to September 2025. The search was based on neonatal pharmacokinetics, model-informed precision dosing (MIPD), Bayesian dosing, and artificial intelligence/machine learning. Following the removal of duplicates and two-stage screening (title/abstract and full-text review), 82 studies were used in the end to conduct this review. This review follows the PRISMA 2020 flow selection style. Pharmacokinetic and pharmacodynamic (popPK/PD) models, with the help of Bayesian strategies, can offer a context for therapy individualization, but they are still limited by the absence of complete information and high intersubjective differences. Artificial intelligence (AI) can also be used to provide an extra level of support by processing vast and multifaceted data, discovering latent trends, and improving dose predictions in real time. This review concluded that applications of AI-driven tools are now emerging in neonatal intensive care, particularly for antibiotics and analgesics, where accurate dosing is critical. Combining AI with known pharmacometric practices could assist clinicians in minimizing adverse drug reactions, enhancing treatment outcomes, and getting closer to actual personalized pharmacotherap