Use of pharmacokinetic/ pharmacodynamic modelling for starting dose selection in first-in-human trials of high-risk biologics

Abstract

What is already known about this subject: Recent regulatory guidance has highlighted the importance of using pharmacokinetic-pharmacodynamic (PK-PD) modelling in the selection of starting doses in first-in-human trials of high-risk biologics. However, limited examples exist in literature illustrating this procedure.

What this study adds: An interpretation of the recommended dose-selection methodology and the minimum anticipated biological effect level (MABEL) principle, contained in the updated European Medicines Agency guidance on risk-mitigation strategies for first-in-human studies, is presented. Some literature and simulation-based examples of the application of PK-PD modelling principles to starting dose selection using in vitro and in vivo data under the MABEL paradigm are highlighted, along with the advantages and limitations of this approach.

Aims: To illustrate the use of pharmacokinetic-pharmacodynamic (PK-PD) models to select rational starting doses in clinical trials within the minimum anticipated biological effect level (MABEL) principle using literature data and through simulations.

Methods: The new European Medicines Agency guidance on starting dose selection of high-risk biologics was analysed considering the basic pharmacological properties and preclinical testing limitations of many biologics. The MABEL approach to dose selection was illustrated through simulations and through literature-reported examples on the selection of starting doses for biologics such as antibodies based on in vitro biomarker data, in vivo PK and PK-PD data.

Results: Literature reports indicating the use of preclinical pharmacological and toxicological data to select successfully safe starting doses in line with the MABEL principle are summarized. PK-PD model-based simulations of receptor occupancy for an anti-IgE antibody system indicate that the relative abundance of IgE in animal models and patients and the turnover rate of the IgE-antibody complex relative to the off-rate of the antibody from IgE are important determinants of in vivo receptor occupancy.

Conclusions: Mechanistic PK-PD models are capable of integrating preclinical in vitro and in vivo data to select starting doses rationally in first-in-human trials. Biological drug-receptor interaction dynamics is complex and multiple factors affect the dose-receptor occupancy relationship. Thus, these factors should be taken into account when selecting starting doses.

Figure 1

Schematic representation of the process of selecting starting doses in a first-in-human trial of a MAB. Eff1–3 could be predicted human effects such as biomarkers, toxicological effects, etc. HED, human equivalent no adverse event level dose; RO, receptor occupancy

Figure 3

Pharmacokinetic–pharmacodynamic model-based prediction of human dose (mg kg⁻¹)–response (%RO) based on cynomolgus monkey dose–response using target-mediated drug disposition model

Figure 2

Schematic of a mechanism-based target-mediated drug disposition (TMD) model. Model accounts for antibody and receptor dynamics as well as complex formation and turnover. The broken arrows are processes that are assumed to be at equilibrium in the K_d-based receptor occupancy calculation

Figure 4

Comparison of predicted receptor occupancy based on K_d-based calculation (K_d/2000) and pharmacokinetic–pharmacodynamic (PK–PD) method. Evaluation of the impact of koff on the dose required for 10% receptor occupancy. (•), PK–PD model-based prediction; (▪), equilibrium-based prediction (K_d/2000)