In vivo evaluation of drug dialyzability in a rat model of hemodialysis.
Masaki FukunagaDaisuke KadowakiMika MoriSatomi HagiwaraYuki NaritaJunji SaruwatariRyota TanakaHiroshi WatanabeKeishi YamasakiKazuaki TaguchiHiroki ItoToru MaruyamaMasaki OtagiriSumio HirataPublished in: PloS one (2020)
It is important to calculate the drug removal by hemodialysis (HD) for drug dosing regimens in HD patients. However, there are limited and inconsistent information about the dialyzability of drugs by HD. Therefore, the aim of our study is to evaluate drug removal by utilizing a rat model of HD (HD rat) and to extrapolate this result to the drug removal rate in HD patients. HD rats received bilateral nephrectomy and HD for 2 h. The dialysis removal of 6 drugs was evaluated in HD rats. Dialysis efficiency, plasma protein binding rate (PBR) and distribution volume (Vd) of drugs were also measured. Furthermore, we examined the correlation between the dialyzability of drug in HD rats and humans and constructed the prediction formula of the drug dialyzability in HD patients. The clearance of urea and creatinine and normalized dialysis dose in HD rats were 0.83 ± 0.07 mL/min, 0.70 ± 0.08 mL/min, and 0.13 ± 0.06, respectively. The drug dialyzability in HD rats was similar to reported clinical data except for doripenem. A higher correlation was observed between drug dialyzability in reported clinical data and HD rats which were adjusted for PBR (r2 = 0.936; p < 0.001) compared to unadjusted (r2 = 0.812; p = 0.009). Therefore, we constructed the prediction formula of the drug dialyzability in HD patients by utilizing the HD rat model and PBR. This study is useful for evaluating the dialyzability of high-risk drugs in a clinical setting and might provide appropriate preclinical dialyzability data for new drug.
Keyphrases
- end stage renal disease
- chronic kidney disease
- peritoneal dialysis
- ejection fraction
- newly diagnosed
- drug induced
- adverse drug
- prognostic factors
- healthcare
- stem cells
- metabolic syndrome
- big data
- emergency department
- electronic health record
- machine learning
- artificial intelligence
- patient reported outcomes
- human milk
- health information