Summary data of potency and parameter information from semi-mechanistic PKPD modeling of prolactin release following administration of the dopamine D2 receptor antagonists risperidone, paliperidone and remoxipride in rats

We provide the reader with relevant data related to our recently published paper, comparing two mathematical models to describe prolactin turnover in rats following one or two doses of the dopamine D2 receptor antagonists risperidone, paliperidone and remoxipride, “A comparison of two semi-mechanistic models for prolactin release and prediction of receptor occupancy following administration of dopamine D2 receptor antagonists in rats” (Taneja et al., 2016) [1]. All information is tabulated. Summary level data on the in vitro potencies and the physicochemical properties is presented in Table 1. Model parameters required to explore the precursor pool model are presented in Table 2. In Table 3, estimated parameter comparisons for both models are presented, when separate potencies are estimated for risperidone and paliperidone, as compared to a common potency for both drugs. In Table 4, parameter estimates are compared when the drug effect is parameterized in terms of drug concentration or receptor occupancy

Population pharmacokinetic modeling of tapentadol extended release (ER) in healthy subjects and patients with moderate or severe chronic pain

Background and Objective: Tapentadol is a centrally acting analgesic with two mechanisms of action, mu-opioid receptor agonism and noradrenaline reuptake inhibition. The objectives were to describe the pharmacokinetic behavior of tapentadol after oral administration of an extended-release (ER) formulation in healthy subjects and patients with chronic pain and to evaluate covariate effects. Methods: Data were obtained from 2276 subjects enrolled in five phase I and nine phase II and III studies. Nonlinear mixed-effects modeling was conducted using NONMEM. Results: The population estimates of apparent oral clearance and apparent central volume of distribution were 257 L/h and 1870 L, respectively. The complex absorption was described with a transit compartment for the first input. The second input function embraces saturable "binding" in the "absorption compartment", and a time-varying rate constant. Covariate evaluation demonstrated that age, aspartate aminotransferase, and health (painful diabetic neuropathy or not) had a statistically significant effect on apparent clearance, and bioavailability appeared to be dependent on body weight. The pcVPC indicted that the model provided a robust and unbiased fit to the data. Conclusions: A one-compartment disposition model with two input functions and first-order elimination adequately described the pharmacokinetics of tapentadol ER. The dose-dependency in the pharmacokinetics of tapentadol ER is adequately described by the absorption model. None of the covariates were considered as clinically relevant factors that warrant dose adjustments

A comparison of two semi-mechanistic models for prolactin release and prediction of receptor occupancy following administration of dopamine D-2 receptor antagonists in rats

Pharmacolog

Correlation between in vitro and in vivo concentration–effect relationships of naproxen in rats and healthy volunteers

1. Understanding the mechanisms underlying the analgesic effect of new cyclooxygenase inhibitors is essential to identify dosing requirements in early stages of drug development. Accurate extrapolation to humans of in vitro and in vivo findings in preclinical species is needed to optimise dosing regimen in inflammatory conditions. 2. The current investigation characterises the inhibition of prostaglandin E2 (PGE(2)) and thromboxane B2 (TXB(2)) by naproxen in vitro and in vivo in rat and human blood. The inhibition of PGE(2) in the absence or presence of increasing concentrations of naproxen (10(−8)–10(−1) M) was measured by ex vivo whole blood stimulation with LPS, whereas inhibition of TXB(2) was measured in serum following blood clotting. In further experiments, inhibition of PGE(2) and TXB(2) levels was also assessed ex vivo in animals treated with naproxen (2.5, 10, 25 mg kg(−1)). Subsequently, pharmacokinetic (PK)/pharmacodynamics (PD) modelling of in vitro and in vivo data was performed using nonlinear mixed effects in NONMEM (V). 3. Inhibition of PGE(2) and TXB(2) was characterised by a sigmoid E(max) model. The exposure–response relationships in vitro and in vivo were of the same order of magnitude in both species. IC(80) estimates obtained in vitro were similar for PGE(2) inhibition (130.8±11 and 131.9±19 10(−6) M, mean±s.d. for humans and rats, respectively), but slightly different for TXB(2) inhibition (103.9±15 and 151.4±40 10(−6) M, mean±s.d. for humans and rats, respectively, P< 0.05). These differences, however, may not be biologically relevant. 4. The results confirm the value of exposure–effect relationships determined in vitro as a means to predict the pharmacological activity in vivo. This analysis also highlights the need to parameterise concentration–effect relationships in early drug development, as indicated by the estimates of IC(80) for PGE(2) and TXB(2) inhibition

Prediction of human CNS pharmacokinetics using a physiologically-based pharmacokinetic modeling approach

Knowledge of drug concentration-time profiles at the central nervous system (CNS) target-site is critically important for rational development of CNS targeted drugs. Our aim was to translate a recently published comprehensive CNS physiologically-based pharmacokinetic (PBPK) model from rat to human, and to predict drug concentration-time profiles in multiple CNS compartments on available human data of four drugs (acetaminophen, oxycodone, morphine and phenytoin). Values of the system-specific parameters in the rat CNS PBPK model were replaced by corresponding human values. The contribution of active transporters for the four selected drugs was scaled based on differences in expression of the pertinent transporters in both species. Model predictions were evaluated with available pharmacokinetic (PK) data in human brain extracellular fluid and/or cerebrospinal fluid, obtained under physiologically healthy CNS conditions (acetaminophen, oxycodone, and morphine) and under pathophysiological CNS conditions where CNS physiology could be affected (acetaminophen, morphine and phenytoin). The human CNS PBPK model could successfully predict their concentration-time profiles in multiple human CNS compartments in physiological CNS conditions within a 1.6-fold error. Furthermore, the model allowed investigation of the potential underlying mechanisms that can explain differences in CNS PK associated with pathophysiological changes. This analysis supports the relevance of the developed model to allow more effective selection of CNS drug candidates since it enables the prediction of CNS target-site concentrations in humans, which are essential for drug development and patient treatment