Modelling and simulation as research tools in paediatric drug development

Abstract

Purpose: Although practical and ethical constraints impose special requirements for the evaluation of treatment safety and efficacy in children, the main issue remains the empirical basis for patient stratification and dose selection at the early stage of the development of new chemical and biological entities. The aim of this review is to highlight the advantages and limitations of modelling and simulation (M&S) in supporting decision making during paediatric drug development.

Methods: A literature search on Pubmed's database Medical Subject Headings (MeSH) has been performed to retrieve relevant publications on the use of model-based approaches in paediatric drug development and therapeutics.

Results: M&S enable the assessment of the impact of different regimens as well as of different populations on a drug's safety and efficacy profile. It has been widely used in the last two decades to support pre-clinical and early clinical drug development. In fact, M&S have been applied to drug development as decision tools, as study optimization tools and as data analysis tools. In particular, this approach can be used to support dose adjustment in specific subgroups of a population. M&S may therefore allow the individualisation of drug therapy in children, improving the risk-benefit ratio in this population.

Conclusions: The lack of consensus on how to assess the impact of developmental factors on pharmacokinetics, pharmacodynamics, efficacy and safety has so far prevented a broader use of M&S. This problem is compounded by the limited collaboration between stakeholders, which prevents data sharing in this field. In this article, we emphasise the need for a concerted effort to promote the effective use of this technology in paediatric drug development and avoid unnecessary exposure of children to clinical trials.

Figures

Fig. 1

Simulations allow the assessment of a system’s performance under hypothetical and real-life scenarios (i.e. “what-if” scenarios), yielding information about the implications of different experimental designs and quantitative predictions about treatment outcome. In this example, a model of haematopoiesis is used to simulate the effects of darbepoetin alfa administered every 2 weeks in chemotherapy-induced anaemia based on weight-based fixed-dosing regimens. Adapted from Jumbe et al. [25]

Fig. 2

Physiologically-based pharmacokinetic (PBPK) models provide an integrated view of drug disposition in vivo. In contrast to empirical compartmental models, a PBPK model is aimed at describing the in vivo behaviour of the drug before the acquisition of in vivo data. PBPK relies primarily on describing drug disposition in terms of organ distribution, blood flow and metabolic capacity. Actual experiments become confirmatory and can therefore be optimised in terms of dose range, sample size, frequency and sampling intervals. Diagram adapted from Theil et al [28]

Fig. 3

Modelling and simulation can be used to support prediction and extrapolation of data in early clinical development. The graphs show the implications of pharmacokinetic differences for systemic exposure across different age groups in children. Based on pharmacokinetic parameter estimates systemic exposure can be simulated for a range of dosing regimens. Note the non-linearity in the dose range for the different age groups. Lines depict the fraction of patients categorised by body weight reaching target exposure criteria (a = AUC > 6.02 mg*h/L, b = plasma concentrations above IC80 for at least 3 h) following different doses of abacavir. circles 10 kg, crosses 20 kg, triangles 30 kg, squares 40 kg. From Cella et al [10]

Fig. 4

The diagram depicts the major components of a clinical trial simulation (CTS). In model-based drug development, a CTS can be used to characterise the interactions between drug and disease, enabling among other things the assessment of disease-modifying effects, dose selection and covariate effects (e.g. age, body weight). In conjunction with a trial model, CTS allows the evaluation of such interactions, taking into account uncertainty and trial design factors, including the implications of different statistical methods for the analysis of the data

Fig. 5

The concept of personalised medicines implies quantitative assessment of the risk–benefit ratio at the individual and patient population levels. M&S techniques are critical for such an evaluation. The use of a therapeutic utility index (TUI) illustrates how the safety–efficacy ratio of a treatment correlates with drug exposure. The graphs show a safety: probability of a major or minor bleeding event as a function of daily steady-state apixaban exposure (AUCss); b efficacy: probability of a venous thromboembolic event (VTE) as a function of daily apixaban AUCss and regimen. The shaded regions surrounding the regression lines represent the 90% bootstrap confidence intervals. The boxes at the bottom of each figure represent the distribution of apixaban exposures for the doses indicated. Exposure distributions are shown for the total daily dose (TDD) because the distributions of AUCss should be the same for b.i.d. and q.d. regimens for the same total daily dose. Subjects with moderate renal impairment are expected to have a 43% increase in apixaban exposure; however, apixaban’s therapeutic utility index suggests that dose adjustment is not needed in this population. From Leil et al. [53]