Fingerprints of CNS drug effects: a plasma neuroendocrine reflection of D₂ receptor activation using multi‐biomarker pharmacokinetic/pharmacodynamic modelling

Abstract

Background and PurposeBecause 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 D2 receptor activation was evaluated using quinpirole as a paradigm compound.Experimental ApproachRats received 0, 0.17 or 0.86 mg·kg−1 of the D2 agonist quinpirole i.v. Quinpirole concentrations in plasma and brain extracellular fluid (brainECF), 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 ResultsThe quinpirole pharmacokinetics were adequately described by a two‐compartment model with an unbound brainECF‐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, D2 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 ImplicationsThe integrated multi‐biomarker PK/PD approach revealed a fingerprint reflecting D2 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 D2 agonists in clinical practice. Further development towards quantitative systems pharmacology models will eventually facilitate mechanistic drug development.

Blood‐Based Biomarkers of Quinpirole Pharmacology: Cluster‐Based PK/PD and Metabolomics to Unravel the Underlying Dynamics in Rat Plasma and Brain

“…The effects of 8-day QP administration Interestingly, although there was a significant response upon 8-day administration of QP in PK/PD parameters describing the neuroendocrine response, 19 no significant impact on basal biomarker levels was identified in the current study, although DA, DOPAC, and HVA were only analyzed for experiment day 1. Our hypothesis to see an effect after 8 days was based on a study in which behavioral tolerance and sensitization were observed within a period of 1 week after administration of a D 2 agonist in mice.…”

“…The PK model has been published previously and described the free QP concentrations in plasma and brain ECF with QP doses ranging from 0.17−2.14 mg/kg. 19 The visual predictive check and external validation have been added as Figures S2 and S3. Pharmacodynamic models.…”

“…Second, although it is not possible to determine whether the potency differences are related to off-target effects or different signal transduction efficiencies (see ref. 19 for discussion), the cluster-based PK/PD model can define a therapeutic range on basis of a system response in plasma and brain ECF . Elements of this model may be selected as input for mechanistic systems pharmacology models.…”

“…17 for discussion). In the current study, we set out to extend this methodology with a simultaneous evaluation of a metabolomic response in both plasma and brain ECF , using the selective dopamine D 2/3 receptor agonist quinpirole (QP) as paradigm compound with well-known PK/PD characteristics 18,19 to develop the methodology. Overall, the purpose is to develop a proof-ofconcept methodology to provide insight into the biochemical responses of CNS drugs in brain ECF and plasma, combined with PK/PD modeling as a new approach to discovering blood-based biomarkers of central responses.…”

Drug repurposing in neurological diseases: an integrated approach to reduce trial and error

“…Availability of such measures at this early stage has the potential to significantly reduce these timelines 44. While the identification and validation of predictive markers of treatment response remains challenging for CNS drugs, knowledge gained from compound selectivity, as well as the use of the drug in previous indications, may provide valuable insight into potential biomarkers 45 46. Biomarkers can be used to establish whether evidence of drug-target interaction leads to downstream effects, potentially saving time and guiding the design of clinical trials.…”