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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.

Projecting trees species growth into future climate is subject to large uncertainties and it is of importance to quantify the different sources (e.g., site, climate model) to prioritize research efforts. This study quantifies and compares sites and climate model-induced uncertainties in projected Norway spruce growth from Denmark. We analyzed tree-rings from 340 Norway spruce trees sampled in 14 planted stands (1. Plantation; period 1950–1987) and additionally 36 trees from six trials in a common garden experiment (2. Common garden; period 1972–2012). Growth-climate correlations were estimated and multiple linear and nonlinear regression models relating growth with climate were tested. Tree growth was projected up to 2100 applying multiple linear or quadratic regression models based on the 15 Atmosphere-Ocean General Circulation Models (AOGCMs) of the Coupled Model Inter-comparison Project Phase 5 (CMIP5). The climate-growth models showed that summer drought and warm previous-year late-summer and early-autumn constrain growth. In some stands, warm springs affected growth positively. The projections of growth under future climates on average showed from no to slightly negative changes in growth compared to present growth rates. However, projections showed a very large variation, ranging from highly positive to highly negative growth changes. The uncertainties due to variation in site responses and in climate models were substantial. A lesser degree of uncertainty was related to the emission scenarios. Even though our projections on average suggest that Norway spruce may experience a growth reduction in the future, the tremendous variation in growth predictions due to differences between stands and climate models calls for further research and caution when projections are interpreted. These results also suggest that forest managers in general should avoid the use of Norway spruce on exposed and drought prone sites and as an additional resilience measur