PKPD Modelling Of Cardiovascular Safety Pharmacology Data
Eleanor M. Howgate(1), Ahila Srinivasan(2), Kirsty Ash(2), Mark Dewhurst(2), Piet H. Van der Graaf(3), Leon Aarons(1)
(1) School of Pharmacy and Pharmaceutical Sciences, The University of Manchester, Stopford Building, Oxford Road, Manchester, M13 9PT, UK; (2) Global Safety Pharmacology, Pfizer Global Research and Development, Sandwich, Kent, CT13 9NJ, UK; (3) Department of Pharmacokinetics, Dynamics and Metabolism, Pfizer Global Research and Development, Sandwich, Kent, CT13 9NJ, UK.
Objectives: Modelling and simulation (M&S) techniques are now commonly used to analyse clinical pharmacokinetic (PK) and pharmacodynamic (PD) data as part of a model-based drug development framework. However, their use in the pre-clinical stages is limited and within the safety sciences is virtually non-existent. Application of such techniques to pre-clinical safety pharmacology data may help to improve the predictability of adverse effects compared to conventional methods. The aim of this project is to develop PKPD models using cardiovascular (CV) safety pharmacology data, which could eventually be used to predict CV effects in man.
Methods: PKPD relationships for the CV effects of NG-nitro-L-arginine methyl ester (L-NAME) were analysed using non-linear mixed effects modelling. Data were available for various pre-clinical species and analysis was performed using the MONOLIX software in a sequential manner. Various compartmental PK models, including ones modified to describe the conversion of L-NAME to its active metabolite NG-nitro-L-arginine (L-NOARG) were analysed. Indirect effect models with stimulation/inhibition of production of response were used to describe the effects of L-NAME on the haemodynamic parameters. Since haemodynamic parameters display circadian rhythms, various models were assessed to describe baseline data.
Results: The PK model that provided the best fit to the data was a 2-compartment, 1st order absorption model modified to describe the conversion of L-NAME to L-NOARG. This confirmed previous work that has shown the disposition of L-NOARG is best described by a 2-compartment model. Modelling of baseline haemodynamic data was best described by a simple cosine model. A model specifically produced to describe the circadian rhythm of CV parameters appeared too complex for this data set. Modelling of the haemodynamic response to L-NAME was adequately described by the indirect response model.
Conclusions: The work achieved to date has shown that PKPD models can be developed using typical CV safety pharmacology data and that non-linear mixed effect modelling is an appropriate method for such analysis.
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