Development of a Whole-Body Physiologically Based Pharmacokinetic Approach to Assess the Pharmacokinetics of Drugs in Elderly Individuals

Abstract

Background: Because of the vulnerability and frailty of elderly adults, clinical drug development has traditionally been biased towards young and middle-aged adults. Recent efforts have begun to incorporate data from paediatric investigations. Nevertheless, the elderly often remain underrepresented in clinical trials, even though persons aged 65 years and older receive the majority of drug prescriptions. Consequently, a knowledge gap exists with regard to pharmacokinetic (PK) and pharmacodynamic (PD) responses in elderly subjects, leaving the safety and efficacy of medicines for this population unclear.

Objectives: The goal of this study was to extend a physiologically based pharmacokinetic (PBPK) model for adults to encompass the full course of healthy aging through to the age of 100 years, to support dose selection and improve pharmacotherapy for the elderly age group.

Methods: For parameterization of the PBPK model for healthy aging individuals, the literature was scanned for anthropometric and physiological data, which were consolidated and incorporated into the PBPK software PK-Sim^®. Age-related changes that occur from 65 to 100 years of age were the main focus of this work. For a sound and continuous description of an aging human, data on anatomical and physiological changes ranging from early adulthood to old age were included. The capability of the PBPK approach to predict distribution and elimination of drugs was verified using the test compounds morphine and furosemide, administered intravenously. Both are cleared by a single elimination pathway. PK parameters for the two compounds in younger adults and elderly individuals were obtained from the literature. Matching virtual populations-with regard to age, sex, anthropometric measures and dosage-were generated. Profiles of plasma drug concentrations over time, volume of distribution at steady state (V _ss) values and elimination half-life (t _½) values from the literature were compared with those predicted by PBPK simulations for both younger adults and the elderly.

Results: For most organs, the age-dependent information gathered in the extensive literature analysis was dense. In contrast, with respect to blood flow, the literature study produced only sparse data for several tissues, and in these cases, linear regression was required to capture the entire elderly age range. On the basis of age-informed physiology, the predicted PK profiles described age-associated trends well. The root mean squared prediction error for the prediction of plasma concentrations of furosemide and morphine in the elderly were improved by 32 and 49 %, respectively, by use of age-informed physiology. The majority of the individual V _ss and t _½ values for the two model compounds, furosemide and morphine, were well predicted in the elderly population, except for long furosemide half-lifes.

Conclusion: The results of this study support the feasibility of using a knowledge-driven PBPK aging model that includes the elderly to predict PK alterations throughout the entire course of aging, and thus to optimize drug therapy in elderly individuals. These results indicate that pharmacotherapy and safety-related control of geriatric drug therapy regimens may be greatly facilitated by the information gained from PBPK predictions.

Fig. 1

Proposed workflow for a knowledge-driven physiologically based pharmacokinetic (PBPK) aging approach. A solid line represents the work of the current study; a broken line depicts the potential usage of an aging PBPK model. ADME absorption, distribution, metabolism and excretion processes, PK pharmacokinetic

Fig. 2

Distributions of mean organ weights in females (left) and males (right), from newborns to individuals up to 100 years of age. GI gastrointestinal

Fig. 3

Age-dependent changes in body height: comparison between simulations (grey dots) [N = 5000] and observations (reference mean data; black circles) in females and males. The reference data were all gathered from anthropometric studies or studies where they appeared as covariates. The sizes of the black circles indicate the relative numbers of subjects

Fig. 4

Distribution of body water in males (left) and females (right) over the course of aging. Extracellular body water (dark grey lines) and total body water (light grey lines) are shown as percentages of body weight. Values reported in the literature for extracellular water (triangles) and total body water (circles) are shown for comparison

Fig. 5

Changes in kidney function in females (left) and males (right) over the course of aging. To describe the influence of aging on the glomerular filtration rate (GFR), a new reverse sigmoid hyperbolic maturation function was developed and applied to both sexes, starting at the age of 30 years. Changes in the GFR were investigated by analysis of exogenous markers in prospective renal transplant donors. A stepwise regression analysis of the gathered data, normalized to body surface area (BSA), was performed for 1213 females (upper panel) and 1081 males (lower panel). A solid black line represents the new GFR function in relation to kidney weight, and a dashed black line represents the maturation of GFR function according to Rhodin et al. [127]. The grey shaded areas represent the predicted 95 % percentile range based on kidney size variability. The black circles depict the observed GFR rates [–126]

Fig. 6

Upper panel: predicted mean plasma concentration–time profiles of furosemide and morphine in younger adults (grey dashed line) versus the elderly (black line). The concentrations are normalized to a 40 mg dose for furosemide and to a 10 mg dose for morphine. Observed data are superimposed (black open symbols represent values for the elderly [130, 134, 137]; grey open symbols represent values for younger adults [, , , –138, 141, 142]). Lower panel: goodness-of-fit plots for furosemide and morphine model predictions in the adult population (black dots) and the elderly population (grey dots). A solid line represents the line of identity; dashed lines represent the 1.25-fold error range and dotted lines represent the 2-fold error range

Fig. 7

Observed versus predicted values for the volume of distribution at steady state (V _ss) and elimination half-life (t _½), as well as the corresponding fold over-/underprediction, for the test compound furosemide. A solid line represents the line of unity; dashed black lines represent the 1.25-fold level and grey lines represent the 2-fold level of the predicted accuracy

Fig. 8

Observed versus predicted values for the volume of distribution at steady state (V _ss) and elimination half-life (t _½), as well as the corresponding fold over-/underprediction, for the test compound morphine. A solid line represents the line of unity; dashed black lines represent the 1.25-fold level and grey lines represent the 2-fold level of the predicted accuracy