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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 1 of 1 Research Studies DisplayedLiu S, Matvekas A, Naimi T
Morphomics-informed population pharmacokinetic and physiologically-based pharmacokinetic modeling to optimize cefazolin surgical prophylaxis.
This study’s objective was to use algorithms that repurpose radiologic data into body composition (morphomics) to aid in informing dosing decisions for the antibiotic cefazolin for patients undergoing colorectal surgery who have obesity. This prospective study measured cefazolin plasma, fat, and colon tissue concentrations in these patients to develop a morphomics-informed population pharmacokinetic (PopPK) model to guide dose adjustments. A physiologically-based pharmacokinetic (PBPK) model was also constructed to inform tissue partitioning in 21 morbidly obese patients (body mass index ≥35 kg/m2 with one or more co-morbid conditions). Morphomics and pharmacokinetic data were available in 58 patients with a median weight of 95.9 kg and and 55 years, respectively. The plasma-to-subcutaneous fat partition coefficient was predicted to be 0.072 for the PopPK model and 0.060 for the PBPK model. Covariates of cefazolin exposure were identified as the estimated creatinine clearance (eCL(cr) ) and body depth at the third lumbar vertebra (body depth_L3). The authors concluded that kidney function and morphomics were more informative than body weight as covariates of cefazolin target site exposure. They advised that data from more diverse populations, consensus on target cefazolin exposure, and comparative studies are needed before a change in practice can be implemented.
AHRQ-funded; HS027183.
Citation: Liu S, Matvekas A, Naimi T .
Morphomics-informed population pharmacokinetic and physiologically-based pharmacokinetic modeling to optimize cefazolin surgical prophylaxis.
Pharmacotherapy 2024 Jan; 44(1):77-86. doi: 10.1002/phar.2878..
Keywords: Surgery, Antibiotics, Medication, Prevention, Obesity, Healthcare-Associated Infections (HAIs)