Gonçalves Pereira João, Fernandes Joana, Mendes Tânia, Gonzalez Filipe André, Fernandes Susana M
Grupo de Investigação e Desenvolvimento em Infeção e Sépsis, Clínica Universitária de Medicina Intensiva, Faculdade de Medicina, Universidade de Lisboa, 1649-004 Lisbon, Portugal.
Serviço de Medicina Intensiva, Hospital Vila Franca de Xira, 2600-009 Vila Franca de Xira, Portugal.
Antibiotics (Basel). 2024 Sep 6;13(9):853. doi: 10.3390/antibiotics13090853.
Antimicrobial dosing can be a complex challenge. Although a solid rationale exists for a link between antibiotic exposure and outcome, conflicting data suggest a poor correlation between pharmacokinetic/pharmacodynamic targets and infection control. Different reasons may lead to this discrepancy: poor tissue penetration by β-lactams due to inflammation and inadequate tissue perfusion; different bacterial response to antibiotics and biofilms; heterogeneity of the host's immune response and drug metabolism; bacterial tolerance and acquisition of resistance during therapy. Consequently, either a fixed dose of antibiotics or a fixed target concentration may be doomed to fail. The role of biomarkers in understanding and monitoring host response to infection is also incompletely defined. Nowadays, with the ever-growing stream of data collected in hospitals, utilizing the most efficient analytical tools may lead to better personalization of therapy. The rise of artificial intelligence and machine learning has allowed large amounts of data to be rapidly accessed and analyzed. These unsupervised learning models can apprehend the data structure and identify homogeneous subgroups, facilitating the individualization of medical interventions. This review aims to discuss the challenges of β-lactam dosing, focusing on its pharmacodynamics and the new challenges and opportunities arising from integrating machine learning algorithms to personalize patient treatment.
抗菌药物给药可能是一项复杂的挑战。尽管抗生素暴露与治疗结果之间存在联系有充分的理论依据，但相互矛盾的数据表明药代动力学/药效学目标与感染控制之间的相关性较差。导致这种差异的原因可能有多种：炎症和组织灌注不足导致β-内酰胺类药物组织穿透性差；细菌对抗生素和生物膜的反应不同；宿主免疫反应和药物代谢的异质性；细菌在治疗期间的耐受性和耐药性获得。因此，固定剂量的抗生素或固定的目标浓度都可能注定失败。生物标志物在理解和监测宿主对感染的反应中的作用也尚未完全明确。如今，随着医院收集的数据量不断增加，使用最有效的分析工具可能会使治疗更加个性化。人工智能和机器学习的兴起使得大量数据能够被快速获取和分析。这些无监督学习模型可以理解数据结构并识别同质亚组，从而促进医疗干预的个性化。本综述旨在讨论β-内酰胺类药物给药的挑战，重点关注其药效学以及将机器学习算法整合到个性化患者治疗中所带来的新挑战和机遇。
