Fingerprints of CNS drug effects: a plasma neuroendocrine reflection of D (2) receptor activation using multi‐biomarker pharmacokinetic/pharmacodynamic modelling

BACKGROUND AND PURPOSE: Because biological systems behave as networks, multi‐biomarker approaches increasingly replace single biomarker approaches in drug development. To improve the mechanistic insights into CNS drug effects, a plasma neuroendocrine fingerprint was identified using multi‐biomarker pharmacokinetic/pharmacodynamic (PK/PD) modelling. Short‐ and long‐term D(2) receptor activation was evaluated using quinpirole as a paradigm compound. EXPERIMENTAL APPROACH: Rats received 0, 0.17 or 0.86 mg·kg(−1) of the D(2) agonist quinpirole i.v. Quinpirole concentrations in plasma and brain extracellular fluid (brain(ECF)), as well as plasma concentrations of 13 hormones and neuropeptides, were measured. Experiments were performed at day 1 and repeated after 7‐day s.c. drug administration. PK/PD modelling was applied to identify the in vivo concentration–effect relations and neuroendocrine dynamics. KEY RESULTS: The quinpirole pharmacokinetics were adequately described by a two‐compartment model with an unbound brain(ECF)‐to‐plasma concentration ratio of 5. The release of adenocorticotropic hormone (ACTH), growth hormone, prolactin and thyroid‐stimulating hormone (TSH) from the pituitary was influenced. Except for ACTH, D(2) receptor expression levels on the pituitary hormone‐releasing cells predicted the concentration–effect relationship differences. Baseline levels (ACTH, prolactin, TSH), hormone release (ACTH) and potency (TSH) changed with treatment duration. CONCLUSIONS AND IMPLICATIONS: The integrated multi‐biomarker PK/PD approach revealed a fingerprint reflecting D(2) receptor activation. This forms the conceptual basis for in vivo evaluation of on‐ and off‐target CNS drug effects. The effect of treatment duration is highly relevant given the long‐term use of D(2) agonists in clinical practice. Further development towards quantitative systems pharmacology models will eventually facilitate mechanistic drug development.