Role of Mechanistically-Based Pharmacokinetic/Pharmacodynamic Models in Drug Development

Abstract

Background and objective

This case study describes the pharmacokinetic and pharmacodynamic modelling undertaken during the development programme for UK-279,276 (neutrophil inhibitory factor), focusing on the transition from early empirical-based models to a final mechanistic-based model. UK-279,276 binds to the CD11b/CD18 (MAC-1) on neutrophils and was under development for the treatment of ischaemic stroke.

Methods

The aims, data, models, results and value-to-drug development processs across four stages of model development are described: (i) the validation of the pharmacokinetic assay; (ii) the development and application of an empirical patient pharmacokinetic/pharmacodynamic model; (iii) the development of a mechanistic-based model to bridge between patients and healthy volunteers; and (iv) propagation of the stage III model to a large efficacy study. The analyses utilised available concentration measurements (stages I–IV), CD11b receptor occupancy data (stages I–III) and neutrophil count data (stages III–IV) from three healthy volunteers (study 1, n = 51; study 2, n = 31; study 4, n = 15) and two patient studies (study 3, n = 169; study 5, n = 992). In studies 1–4, subjects received placebo or between three and six doses of UK-279,276 covering a range of 0.006 and 1.5 mg/kg as a single 15-minute intravenous infusion. In study 5, subjects received placebo or one of 15 possible doses of UK-279,276 (10–20mg) assigned through adaptive design and administered as a single 15-minute intravenous infusion. All model building was conducted using NONMEM version VI (beta).

The empirical pharmacokinetic/pharmacodynamic model developed during stage I was used to demonstrate that the pharmacokinetic assay was measuring biologically active drug. Simulations from the stage II model, developed from study 3, were used in the design of study 5. The model supported the switch to a fixed-dose regimen and the selection of the maximum dose and dosage increments. The common mechanistic-based model developed during stage III was used to support the ‘comparability strategy’ for UK-279,276 and provided insight into the underlying clearance mechanisms. At stage 4, the prior functionality available with NONMEM was used to successfully propagate the model from stage III in order to analyse the pharmacokinetic data from study 5. The analysis indicated that the exposure in study 5 was consistent with prior data. The role of empirical-based models in providing the learning for future mechanistic model development was highlighted. Similarly, the qualitative and quantitative aspects to knowledge propagation and the ultimate benefits from the development of the mechanistic-based model were demonstrated.

While the empirical-based models were used to guide some early drug development decisions for UK-279,276, the development of the mechanistic-based model was valuable in linking the complex pharmacokinetics/pharmacodynamics of UK-279,276 across the phases of drug development.