Model-Informed Drug Development in Pulmonary Delivery: Semimechanistic Pharmacokinetic–Pharmacodynamic Modeling for Evaluation of Treatments against Chronic Pseudomonas aeruginosa Lung Infections

铜绿假单胞菌 药效学 药代动力学 药理学 医学 药品 药物开发 重症监护医学 生物 内科学 细菌 遗传学
作者
Tomás Sou,Irena Kukavica‐Ibrulj,Roger C. Lévesque,Lena E. Friberg,Christel A. S. Bergström
出处
期刊:Molecular Pharmaceutics [American Chemical Society]
卷期号:17 (5): 1458-1469 被引量:11
标识
DOI:10.1021/acs.molpharmaceut.9b00968
摘要

Antibiotic resistance is a major public health threat worldwide, and among others, about 80% of cystic fibrosis patients have chronic Pseudomonas aeruginosa (PA) lung infection resistant to many current antibiotics. Novel treatment strategies are therefore urgently needed. For lung infections, direct delivery of treatments to the site of action in the airway can achieve a higher local concentration with minimal systemic exposure and hence avoid risks of unwanted systemic adverse effects. Previously, a rat preclinical disease model for PA chronic lung infections has been reported. However, the role of this disease model in the development of new treatment has not been thoroughly evaluated. In this study, tobramycin (TOB) was used as a model antibiotic to evaluate the application of this preclinical disease model for PA treatments. The obtained data were used for pharmacokinetic–pharmacodynamic (PKPD) modeling. Plasma samples following pulmonary delivery of TOB via different dosing methods as well as growth and efficacy data from the chronic lung infection disease model following TOB treatments were collected for analysis and modeling. The developed PKPD model incorporates a semimechanistic description on biofilm development in chronic infections to allow the evaluation of drug action on bacteria in different states (i.e., planktonic, biofilm, and latent) and describes the available data from the efficacy study. The PKPD model can be used to support the application of the preclinical lung infection disease model by providing a quantitative description of the drug exposure–response relationship and a mechanistic platform to integrate all available PK and PKPD data with predictive capacity. With the support of appropriate experimental designs, the model can be further extended for other applications to, for instance, study the transition of bacteria between states and describe drug actions on biofilms.
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