Quantifying the effect of kidney disease on glomerular filtration rate (GFR) is important when describing variability in clearance of drugs eliminated by the kidney. We aimed to develop a continuous model for renal function (RF) from prematurity to adulthood based on consistent models for fat free mass (FFM), creatinine production rate (CPR) and GFR. A model for fractional FFM in premature neonates to adults was developed using pooled data from 4462 subjects and 2847 FFM observations. It was found that girls have a FFM higher than that predicted from adult women based on height, total body mass and sex, and boys have a FFM lower than adult men until around the onset of puberty, when it approaches adult male values. Data from 108 subjects with measurements of Scr and GFR were used to construct a model for CPR. CLcr was predicted using a model for CPR (based on FFM, postmenstrual age and sex) and serum creatinine (Scr), and avoids discontinuous predictions between neonates, children, and adults. Individual CPR may then be used with individual Scr to predict estimated GFR (eGFR=CPR/Scr). A previously published model for human GFR based on 1153 GFR observations in 923 subjects without known kidney disease was updated using the model for fractional FFM to predict individual size and age consistent values for the expected normal GFR (nGFR). Individual renal function was then calculated using RF=eGFR/nGFR.

Quantitative systems pharmacology (QSP) is a relatively new discipline within modelling and simulation that has gained wide attention over the past few years. The application of QSP models spans drug-target identification and validation, through all drug development phases as well as clinical applications. Due to their detailed mechanistic nature, QSP models are capable of extrapolating knowledge to predict outcomes in scenarios that have not been tested experimentally making them an important resource in experimental and clinical pharmacology. However, these models are complicated to work with due to their size and inherent complexity. This makes many applications of QSP models for simulation, parameter estimation and trial design computationally intractable. A number of techniques have been developed to simplify QSP models into smaller models that are more amenable to further analyses while retaining their accurate predictive capabilities. Different simplification techniques have different strengths and weaknesses and hence different utilities. Understanding the utilities of different methods is essential for selection of the best method for a particular situation. In this paper, we have created an overall framework for model simplification techniques that allows a natural categorisation of methods based on their utility. We provide a brief description of the concept underpinning the different methods and example applications. A summary of the utilities of methods is intended provide a guide to modellers in their model endeavours to simplify these complicated models.