Pharmacodynamic analysis of the interaction between tiagabine and midazolam with an allosteric model that incorporates signal transduction

Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics

INTRODUCTION

CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.

Benzodiazepines: electron affinity, receptors and cell signaling - a multifaceted approach

This report entails a multifaceted approach to benzodiazepine (BZ) action, involving electron affinity, receptors, cell signaling and other aspects. Computations of the electron affinities (EAs) of different BZs have been carried out to establish the effect of various substituents on their EA. These computations were undertaken to serve as a first step in determining what role electron transfer (ET) plays in BZ activity. The calculations were conducted on the premise that the nature of the substituent will either decrease or increase the electron density of the benzene ring, thus altering the ability of the molecule to accept an electron. Investigations were performed on the effect of drug protonation on EA. Similarities involving substituent effects in prior electrochemical studies are also discussed. As part of the multifaceted approach, EA is linked to ET, which appears to play a role in therapeutic activity and toxicity. There is extensive literature dealing with the role of receptors in BZ activity. Significant information on receptor involvement was reported more than 40 years ago. Gamma-aminobutyric acid (GABA) is known to be importantly involved. GABA is a probable mediator of BZ effects. BZ and GABA receptors, although not identical, are physiologically linked. Cell signaling is known to play a part in the biochemistry of BZ action. Various factors participated, such as gene expression, allosteric influence, toxic effects and therapeutic action. Evidence points to involvement of EA and ET in the mode of action in cell signaling. Oxidative stress and antioxidant effects are also addressed.

Electroencephalogram-based pharmacodynamic measures: a review

Pharmacokinetics and pharmacodynamics can provide a useful modeling framework for predicting drug activity and can serve as a basis for dose optimization. Determining a suitable biomarker or surrogate measure of drug effect for pharmacodynamic models can be challenging. The electroencephalograph is a widely-available and non-invasive tool for recording brainwave activity simultaneously from multiple brain regions. Certain drug classes (such as drugs that act on the central nervous system) also generate a reproducible electroencephalogram (EEG) effect. Characterization of such a drug-induced EEG effect can produce pharmacokinetic/pharmacodynamic models useful for titrating drug levels and expediting development of chemically-similar drug analogs. This paper reviews the relevant concepts involved in pharmacokinetic/pharmacodynamic modeling using EEG-based pharmacodynamic measures. In addition, examples of such models for various drugs are organized by drug activity as well as chemical structure and presented.

Mechanism-Based Pharmacokinetic-Pharmacodynamic Modeling: Biophase Distribution, Receptor Theory, and Dynamical Systems Analysis

Mechanism-based PK-PD models differ from conventional PK-PD models in that they contain specific expressions to characterize, in a quantitative manner, processes on the causal path between drug administration and effect. This includes target site distribution, target binding and activation, pharmacodynamic interactions, transduction, and homeostatic feedback mechanisms. As the final step, the effects on disease processes and disease progression are considered. Particularly through the incorporation of concepts from receptor theory and dynamical systems analysis, important progress has been made in the field of mechanism-based PK-PD modeling. This has yielded models with much-improved properties for extrapolation and prediction. These models constitute a theoretical basis for rational drug discovery and development.

Population pharmacokinetic model of fluvoxamine in rats: utility for application in animal behavioral studies

The limitations of blood sampling in pharmacokinetic (PK)/pharmacodynamic (PD) studies in behavioral animal models could in part be overcome by a mixed effects modeling approach. This analysis characterizes and evaluates the population PK of fluvoxamine in rat plasma using nonlinear mixed effects modeling. The model is assessed for its utility in animal behavioral PK/PD studies. In six studies with a different experimental setup, study site and/or sampling design, rats received an intravenous infusion of 1, 3.7 or 7.3mg/kg fluvoxamine. A population three-compartment PK model adequately described the fluvoxamine plasma concentrations. Body weight was included as a covariate and mean population PK parameters for CL, V(1), V(2), Q(2), V(3) and Q(3) were 25.1 ml/min, 256 ml, 721 ml, 30.3 ml/min, 136 ml and 1.0 ml/min, respectively. Inter-individual variability was identified on CL (39.5%), V(1) (43.5%), V(2) (50.1%) and Q(2) (25.7%). A predictive check and bootstrap analysis confirmed the predictive ability, model stability and precision of the parameter estimates. Body weight was identified as a significant covariate of the inter-compartmental clearance Q(2). The pharmacokinetics was independent of factors such as dose, surgery (for instrumentation) and study site. The utility of the model in animal behavioral studies was demonstrated in a PK/PD analysis of the effects on REM sleep in which a sparse PK sampling design was used. By using the pertinent information from the population PK model, individual PK profiles and the PK/PD correlation could be adequately described.

Isobolographic characterisation of interactions among selected newer antiepileptic drugs in the mouse pentylenetetrazole-induced seizure model

The aim of this study was to characterise the types of interactions between gabapentin (GBP), tiagabine (TGB) and three second-generation antiepileptic drugs (AEDs) with different mechanisms of action (felbamate [FBM], loreclezole [LCZ], and oxcarbazepine [OXC]) by isobolographic analysis. Anticonvulsant and acute neurotoxic adverse effect profiles of combinations of GBP and TGB with other AEDs at fixed ratios of 1:3, 1:1 and 3:1 were investigated in pentylenetetrazole (PTZ)-induced seizures and the chimney test (as a measure of motor impairment) in mice so as to identify optimal combinations. Protective indices (PIs) and benefit indices (BIs) were calculated for each combination in order to properly classify the investigated interactions. Isobolographic analysis revealed that only the combination of GBP with OXC at the fixed ratio of 1:1 exerted supra-additive (synergistic) interaction (P<0.05) against PTZ-induced seizures. The other combinations tested between GBP and OXC (1:3 and 3:1), as well as all combinations of GBP with FBM or LCZ (1:3, 1:1 and 3:1) were additive in the PTZ test. Similarly, all combinations of TGB with FBM LCZ, and OXC (at the fixed ratios of 1:3, 1:1 and 3:1) were associated with additive interactions against PTZ-induced seizures in mice. In the chimney test, the isobolographic analysis revealed that the combinations of GBP and OXC (at the fixed ratios of 1:3 and 1:1), GBP and LCZ (at 1:1), as well as TGB and OXC (at 1:3 and 1:1) were sub-additive (antagonistic; P<0.05 and P<0.01). In contrast, only one combination tested (TGB and LCZ at the fixed ratio of 1:1) was supra-additive (synergistic; P<0.05) in the chimney test, whereas the other combinations of GBP and TGB with OXC, FBM, and LCZ displayed barely additivity. Based upon the current preclinical data, GBP and OXC appear to be a particularly favourable combination. Also, the combinations of GBP with FBM, GBP with LCZ, and TGB with OXC are beneficial. In contrast, during the combining of TGB with FBM, or TGB with LCZ, the utmost caution is advised because of their unfavourable profiles in this preclinical study.

The interaction between vigabatrin and diazepam on the electroencephalogram during active behaviour in rats: an isobolic analysis

To test whether polytherapy with two gamma-aminobutyric acid (GABA) -ergic drugs might be clinically relevant for epilepsy treatment, effects on spike and wave discharges, the fraction of time spent being behaviourally active, and the background electroencephalogram (EEG) during behavioural activity of vigabatrin (15-500 mg/kg i.p.) and diazepam (1.25-10 mg/kg i.p.) were compared with their combination (dose ratio 1:25) in rats. Isobolic analyses were performed to describe the interactions. Unfortunately, no conclusions can be drawn concerning the interaction of both drugs on the spike and wave discharge activity because the effect of diazepam was shown to be dominant. Only vigabatrin decreased the behavioural activity, whereas there was a trend towards a decrease after diazepam. All treatments dose dependently increased the power in the beta frequency band. Unfortunately, the dose ratio was not optimal to describe the interaction. The theta peak frequency was dose dependently decreased after all treatments. There was a synergistic interaction between the two drugs on this variable. These data support both the idea that an increase in power in the beta frequency band can serve as a biomarker for GABAergic inhibition and the suggestion that clinically effective anxiolytics decrease the theta peak frequency. Furthermore, we show that on different variables, there might be different optimal dosage combinations, which might complicate the clinical application of polytherapy.

Pharmacodynamic Analysis of the Anticonvulsant Effects of Tiagabine and Lamotrigine in Combination in the Rat

PURPOSE

The pharmacodynamic interaction between the antiepileptic drugs (AEDs) tiagabine (TGB) and lamotrigine (LTG) was characterized on basis of the anticonvulsant effect in the cortical stimulation model in the rat.

METHODS

The study was conducted according to a partial crossover design, in which both drugs were infused intravenously to achieve linear increases in the plasma concentration in the absence and presence of a steady-state concentration of the second drug. The anticonvulsant effect was quantified by counts of four specific ictal signs (eye closure, forelimb clonus, forelimb extension, and head jerk). A potential pharmacokinetic interaction was accounted for by determination of total plasma concentrations of both drugs.

RESULTS

When given separately, both TGB and LTG suppressed all ictal signs in a concentration-dependent manner, with the exception of eye closure, which was not suppressed by LTG. The interaction between both drugs was estimated by response surface analysis by using the difference between the observed effect and the additive effect to identify synergistic drug concentrations. This analysis showed that the pharmacodynamic interaction between TGB and LTG is synergistic for the ictal signs of eye closure and head jerk. In contrast, the interaction was additive for the ictal signs of forelimb clonus and forelimb tonus.

CONCLUSIONS

This study demonstrates the usefulness of ictal-component analysis for studying the pharmacodynamic interaction between AEDs. Quantification of both the nature and the magnitude of the interaction between TGB and LTG led to the identification of two ictal signs that were synergistically suppressed. This approach offers a theoretical basis to identify and optimize drug combinations that are useful to treat refractory epilepsy.