Application of mechanism-based population PKPD modelling in the rational selection of clinical candidates: an anti-IgE antibody example.
Balaji Agoram, Steven Martin, Piet van der Graaf.
Pfizer, Inc. UK
Objectives: Design, selection and development of clinical candidates should, optimally, incorporate mechanistic PKPD knowledge gained from previous experience with the same therapeutic targets. We have illustrated this idea using reported PKPD analyses on omalizumab, a humanised monoclonal antibody for the treatment of asthma and allergic rhinitis.
To characterise the relationship between in vitro potency and the in vivo efficacy profile of anti IgE antibody omalizumab using simulated data generated from reported PKPD models for omalizumab. To evaluate possible in vitro changes to the molecule to improve its in vivo PD profile.
Methods: A PKPD model of omalizumab was gathered from literature1 and implemented in the nonlinear mixed effects modelling package, NONMEM. With this PKPD model, deterministic and stochastic simulations were performed using mean and uncertainty distributions of the parameters to characterise the relationship between in vitro affinity and PK parameters on the in vivo time-course of effect profile.
Results: Sensitivity analysis indicated that a 5-fold increase in in vitro affinity is likely to translate into increased efficacy and/or reduced dose size. Beyond this limit, further increases in affinity are unlikely to result in additional clinical benefit. The clinical efficacy appears limited by serum half-life of the compound. This was confirmed by the sensitivity analysis on the serum half-life. Increasing half-life at same potency resulted in increased efficacy.
Conclusions: The mechanism-based PKPD approach has provided a framework to quantify the nonlinear relationship between in vitro/in vivo affinity and clinical potency/efficacy for anti-IgE antibodies and can be used for efficient selection of follow-on candidates. The model allows for prediction of in vivo dose-response relationships on the basis of in vitro characteristics and hence for rational and efficient compound design. For example, the model predicts that large (>10-fold) increases in in vitro affinity (which may be difficult to achieve) do not necessarily translate into increased clinical efficacy. This suggests that additional, alternative, improvements, such as decreased susceptibility to non-specific clearance, might be worth exploring.
References:
[1]. Meno-tetang, and Lowe, 2005, Basic & Clin Pharmacol. Toxicol. 96 182-192
