Characteristics of indirect pharmacodynamic models and applications to clinical drug responses

This review describes four basic physiologic indirect pharmacodynamic response (IDR) models which have been proposed to characterize the pharmacodynamics of drugs that act by indirect mechanisms such as inhibition or stimulation of the production or dissipation of factors controlling the measured effect. The principles underlying IDR models and their response patterns are described. The applicability of these basic IDR models to characterize pharmacodynamic responses of diverse drugs such as inhibition of gastric acid secretion by nizatidine and stimulation of MX protein synthesis by interferon α-2a is demonstrated. A list of other uses of these models is provided. These models can be readily extended to accommodate additional complexities such as nonstationary or circadian baselines, equilibration delay, depletion or accumulation of a precursor pool, sigmoidicity, or other mechanisms. Indirect response models which have a logical mechanistic basis account for time-delays in many responses and are widely applicable in clinical pharmacology

Predictions of in vivo prolactin levels from in vitro k I values of d 2 receptor antagonists using an agonist-antagonist interaction model

Item does not contain fulltextProlactin elevation is a side effect of all currently available D2 receptor antagonists used in the treatment of schizophrenia. Prolactin elevation is the result of a direct antagonistic D2 effect blocking the tonic inhibition of prolactin release by dopamine. The aims of this work were to assess the correlation between in vitro estimates of D2 receptor affinity and pharmacokinetic-pharmacodynamic model-based estimates obtained from analysis of clinical data using an agonist-antagonist interaction (AAI) model and to assess the value of such a correlation in early prediction of full prolactin time profiles. A population model describing longitudinal prolactin data was fitted to clinical data from 16 clinical phases 1 and 3 trials including five different compounds. Pharmacokinetic data were modeled for each compound and the prolactin model was both fitted in per-compound fits as well as simultaneously to all prolactin data. Estimates of prolactin elevating potency were compared to corresponding in vitro values and their predictability was evaluated through model-based simulations. The model successfully described the prolactin time course for all compounds. Estimates derived from experimental preclinical data and the model fit of the clinical data were strongly correlated (p < 0.001), and simulations adequately predicted the prolactin elevation in five out of six compounds. The AAI model has the potential to be used in drug development to predict prolactin response for a given exposure of D2 antagonists using routinely produced preclinical data