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Model-Informed Drug Development for Antimicrobials: Translational PK and PK/PD Modeling to Predict an Efficacious Human Dose for Apramycin


Sou, Tomás; Hansen, Jon; Liepinsh, Edgars; Backlund, Maria; Ercan, Onur; Grinberga, Solveiga; Cao, Sha; Giachou, Paraskevi; Petersson, Anna; Tomczak, Magdalena; Urbas, Malgorzata; Zabicka, Dorota; Vingsbo Lundberg, Carina; Hughes, Diarmaid; Hobbie, Sven N; Friberg, Lena E (2021). Model-Informed Drug Development for Antimicrobials: Translational PK and PK/PD Modeling to Predict an Efficacious Human Dose for Apramycin. Clinical Pharmacology and Therapeutics, 109(4):1063-1073.

Abstract

Apramycin represents a subclass of aminoglycoside antibiotics that has been shown to evade almost all mechanisms of clinically relevant aminoglycoside resistance. Model-informed drug development may facilitate its transition from preclinical to clinical phase. This study explored the potential of pharmacokinetic/pharmacodynamic (PK/PD) modeling to maximize the use of in vitro time-kill and in vivo preclinical data for prediction of a human efficacious dose (HED) for apramycin. PK model parameters of apramycin from four different species (mouse, rat, guinea pig, and dog) were allometrically scaled to humans. A semimechanistic PK/PD model was developed from the rich in vitro data on four Escherichia coli strains and subsequently the sparse in vivo efficacy data on the same strains were integrated. An efficacious human dose was predicted from the PK/PD model and compared with the classical PK/PD index methodology and the aminoglycoside dose similarity. One-compartment models described the PK data and human values for clearance and volume of distribution were predicted to 7.07 L/hour and 26.8 L, respectively. The required fAUC/MIC (area under the unbound drug concentration-time curve over MIC ratio) targets for stasis and 1-log kill in the thigh model were 34.5 and 76.2, respectively. The developed PK/PD model predicted the efficacy data well with strain-specific differences in susceptibility, maximum bacterial load, and resistance development. All three dose prediction approaches supported an apramycin daily dose of 30 mg/kg for a typical adult patient. The results indicate that the mechanistic PK/PD modeling approach can be suitable for HED prediction and serves to efficiently integrate all available efficacy data with potential to improve predictive capacity.

Abstract

Apramycin represents a subclass of aminoglycoside antibiotics that has been shown to evade almost all mechanisms of clinically relevant aminoglycoside resistance. Model-informed drug development may facilitate its transition from preclinical to clinical phase. This study explored the potential of pharmacokinetic/pharmacodynamic (PK/PD) modeling to maximize the use of in vitro time-kill and in vivo preclinical data for prediction of a human efficacious dose (HED) for apramycin. PK model parameters of apramycin from four different species (mouse, rat, guinea pig, and dog) were allometrically scaled to humans. A semimechanistic PK/PD model was developed from the rich in vitro data on four Escherichia coli strains and subsequently the sparse in vivo efficacy data on the same strains were integrated. An efficacious human dose was predicted from the PK/PD model and compared with the classical PK/PD index methodology and the aminoglycoside dose similarity. One-compartment models described the PK data and human values for clearance and volume of distribution were predicted to 7.07 L/hour and 26.8 L, respectively. The required fAUC/MIC (area under the unbound drug concentration-time curve over MIC ratio) targets for stasis and 1-log kill in the thigh model were 34.5 and 76.2, respectively. The developed PK/PD model predicted the efficacy data well with strain-specific differences in susceptibility, maximum bacterial load, and resistance development. All three dose prediction approaches supported an apramycin daily dose of 30 mg/kg for a typical adult patient. The results indicate that the mechanistic PK/PD modeling approach can be suitable for HED prediction and serves to efficiently integrate all available efficacy data with potential to improve predictive capacity.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Institute of Medical Microbiology
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Scopus Subject Areas:Life Sciences > Pharmacology
Health Sciences > Pharmacology (medical)
Language:English
Date:April 2021
Deposited On:08 Jun 2021 15:51
Last Modified:26 May 2024 01:40
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:0009-9236
OA Status:Hybrid
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1002/cpt.2104
PubMed ID:33150591
  • Content: Published Version
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)