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Disease modeling involves the use of mathematical functions to describe quantitatively the time course of disease progression. In order to characterize the natural progression of disease, these models generally incorporate longitudinal data for some biomarker(s) of disease severity or can incorporate more direct measures of disease severity. Disease models are also often linked to pharmacokinetic–pharmacodynamic models so that the influence of drug treatment on disease progression can be quantified and evaluated. Regulatory agencies have embraced disease progression models as powerful tools that can be used to improve drug development productivity. This article provides a brief overview of key concepts in disease progression modeling followed by illustrative examples from models for Alzheimer’s disease. Finally, recent novel applications in which disease progression models have been linked to cost-effectiveness analysis and genomic analysis are described.

Objectives The aims of this study were to develop a population pharmacokinetic model for intravenous paracetamol in preterm and term neonates and to assess the generalizability of the model by testing its predictive performance in an external dataset. Methods Nonlinear mixed-effects models were constructed from paracetamol concentration–time data in NONMEM 7.2. Potential covariates included body weight, gestational age, postnatal age, postmenstrual age, sex, race, total bilirubin, and estimated glomerular filtration rate. An external dataset was used to test the predictive performance of the model through calculation of bias, precision, and normalized prediction distribution errors. Results The model-building dataset included 260 observations from 35 neonates with a mean gestational age of 33.6 weeks [standard deviation (SD) 6.6]. Data were well-described by a one-compartment model with first-order elimination. Weight predicted paracetamol clearance and volume of distribution, which were estimated as 0.348 L/h (5.5 % relative standard error; 30.8 % coefficient of variation) and 2.46 L (3.5 % relative standard error; 14.3 % coefficient of variation), respectively, at the mean subject weight of 2.30 kg. An external evaluation was performed on an independent dataset that included 436 observations from 60 neonates with a mean gestational age of 35.6 weeks (SD 4.3). The median prediction error was 10.1 % [95 % confidence interval (CI) 6.1–14.3] and the median absolute prediction error was 25.3 % (95 % CI 23.1–28.1). Conclusions Weight predicted intravenous paracetamol pharmacokinetics in neonates ranging from extreme preterm to full-term gestational status. External evaluation suggested that these findings should be generalizable to other similar patient populations.

Objectives This study aimed to model the population pharmacokinetics of intravenous paracetamol and its major metabolites in neonates and to identify influential patient characteristics, especially those affecting the formation clearance (CLformation) of oxidative pathway metabolites. Methods Neonates with a clinical indication for intravenous analgesia received five 15-mg/kg doses of paracetamol at 12-h intervals (<28 weeks’ gestation) or seven 15-mg/kg doses at 8-h intervals (≥28 weeks’ gestation). Plasma and urine were sampled throughout the 72-h study period. Concentration-time data for paracetamol, paracetamol-glucuronide, paracetamol-sulfate, and the combined oxidative pathway metabolites (paracetamol-cysteine and paracetamol-N-acetylcysteine) were simultaneously modeled in NONMEM 7.2. Results The model incorporated 259 plasma and 350 urine samples from 35 neonates with a mean gestational age of 33.6 weeks (standard deviation 6.6). CLformation for all metabolites increased with weight; CLformation for glucuronidation and oxidation also increased with postnatal age. At the mean weight (2.3 kg) and postnatal age (7.5 days), CLformation estimates (bootstrap 95% confidence interval; between-subject variability) were 0.049 L/h (0.038–0.062; 62 %) for glucuronidation, 0.21 L/h (0.17–0.24; 33 %) for sulfation, and 0.058 L/h (0.044–0.078; 72 %) for oxidation. Expression of individual oxidation CLformation as a fraction of total individual paracetamol clearance showed that, on average, fractional oxidation CLformation increased <15 % when plotted against weight or postnatal age. Conclusions The parent-metabolite model successfully characterized the pharmacokinetics of intravenous paracetamol and its metabolites in neonates. Maturational changes in the fraction of paracetamol undergoing oxidation were small relative to between-subject variability.