Model‐Informed Drug Development for Antimicrobials: Translational PK and PK/PD Modeling to Predict an Efficacious Human Dose for Apramycin. Issue 4 (28th November 2020) - Sou, Tomás; Hansen, Jon; Liepinsh, Edgars; Backlund, Maria; Ercan, Onur; Grinberga, Solveiga; Cao, Sha; Giachou, Paraskevi; Petersson, Anna; Tomczak, Magdalena; Urbas, Malgorzata; Zabicka, Dorota; et al.
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 f AUC/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.