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

Predicting relapsing-remitting dynamics in multiple sclerosis using discrete distribution models: a population approach

BACKGROUND

Relapsing-remitting dynamics are a hallmark of autoimmune diseases such as Multiple Sclerosis (MS). A clinical relapse in MS reflects an acute focal inflammatory event in the central nervous system that affects signal conduction by damaging myelinated axons. Those events are evident in T1-weighted post-contrast magnetic resonance imaging (MRI) as contrast enhancing lesions (CEL). CEL dynamics are considered unpredictable and are characterized by high intra- and inter-patient variability. Here, a population approach (nonlinear mixed-effects models) was applied to analyse of CEL progression, aiming to propose a model that adequately captures CEL dynamics.

METHODS AND FINDINGS

We explored several discrete distribution models to CEL counts observed in nine MS patients undergoing a monthly MRI for 48 months. All patients were enrolled in the study free of immunosuppressive drugs, except for intravenous methylprednisolone or oral prednisone taper for a clinical relapse. Analyses were performed with the nonlinear mixed-effect modelling software NONMEM 7.2. Although several models were able to adequately characterize the observed CEL dynamics, the negative binomial distribution model had the best predictive ability. Significant improvements in fitting were observed when the CEL counts from previous months were incorporated to predict the current month's CEL count. The predictive capacity of the model was validated using a second cohort of fourteen patients who underwent monthly MRIs during 6-months. This analysis also identified and quantified the effect of steroids for the relapse treatment.

CONCLUSIONS

The model was able to characterize the observed relapsing-remitting CEL dynamic and to quantify the inter-patient variability. Moreover, the nature of the effect of steroid treatment suggested that this therapy helps resolve older CELs yet does not affect newly appearing active lesions in that month. This model could be used for design of future longitudinal studies and clinical trials, as well as for the evaluation of new therapies.

Two-drug antimicrobial chemotherapy: a mathematical model and experiments with Mycobacterium marinum

Multi-drug therapy is the standard-of-care treatment for tuberculosis. Despite this, virtually all studies of the pharmacodynamics (PD) of mycobacterial drugs employed for the design of treatment protocols are restricted to single agents. In this report, mathematical models and in vitro experiments with Mycobacterium marinum and five antimycobacterial drugs are used to quantitatively evaluate the pharmaco-, population and evolutionary dynamics of two-drug antimicrobial chemotherapy regimes. Time kill experiments with single and pairs of antibiotics are used to estimate the parameters and evaluate the fit of Hill-function-based PD models. While Hill functions provide excellent fits for the PD of each single antibiotic studied, rifampin, amikacin, clarithromycin, streptomycin and moxifloxacin, two-drug Hill functions with a unique interaction parameter cannot account for the PD of any of the 10 pairs of these drugs. If we assume two antibiotic-concentration dependent functions for the interaction parameter, one for sub-MIC and one for supra-MIC drug concentrations, the modified biphasic Hill function provides a reasonably good fit for the PD of all 10 pairs of antibiotics studied. Monte Carlo simulations of antibiotic treatment based on the experimentally-determined PD functions are used to evaluate the potential microbiological efficacy (rate of clearance) and evolutionary consequences (likelihood of generating multi-drug resistance) of these different drug combinations as well as their sensitivity to different forms of non-adherence to therapy. These two-drug treatment simulations predict varying outcomes for the different pairs of antibiotics with respect to the aforementioned measures of efficacy. In summary, Hill functions with biphasic drug-drug interaction terms provide accurate analogs for the PD of pairs of antibiotics and M. marinum. The models, experimental protocols and computer simulations used in this study can be applied to evaluate the potential microbiological and evolutionary efficacy of two-drug therapy for any bactericidal antibiotics and bacteria that can be cultured in vitro.

The goal of this investigation is the development and a priori evaluation of multi-drug treatment regimes that are effective in clearing long-term bacterial infections like tuberculosis, and also minimize the likelihood of multi-drug resistance arising during therapy. To achieve this end, we use mathematical models and in vitro experiments with Mycobacterium marinum (a close relative of M. tuberculosis) and five different antimycobacterial agents to develop and validate realistic analogues of the pharmacodynamics of two-drug chemotherapy. All ten drug pairs examined exhibited the same general biphasic drug-drug interaction properties: at low concentrations (subMICs), the two drugs together were less effective than anticipated from their independent pharmacodynamics (were antagonistic), but as their concentrations increased, the interactions between the antibiotics became relatively more synergistic. Using computer simulations with these empirically estimated two-drug pharmacodynamic functions, we evaluated the relative efficacy of the different antibiotic combinations in terms of the anticipated rate of clearance of infections and the likelihood of resistance arising with and without non-adherence to a treatment regime. The simulations predict different outcomes for each of the drug combinations. The models and experimental methods used in this study can be applied to characterize any combinations of bactericidal antibiotics and evaluate their potential efficacy.

Modeling longitudinal daily seizure frequency data from pregabalin add-on treatment

The purpose of this study was to describe longitudinal daily seizure count data with respect to the effects of time and pregabalin add-on therapy. Models were developed in a stepwise manner: base model, time effect model, and time and drug effect (final) model, using a negative binomial distribution with Markovian features. Mean daily seizure count (λ) was estimated to be 0.385 (relative standard error [RSE] 3.09%) and was further increased depending on the seizure count on the previous day. An overdispersion parameter (OVDP), representing extra-Poisson variation, was estimated to be 0.330 (RSE 11.7%). Interindividual variances on λ and OVDP were 84.7% and 210%, respectively. Over time, λ tended to increase exponentially with a rate constant of 0.272 year⁻¹ (RSE 26.8%). A mixture model was applied to classify responders/nonresponders to pregabalin treatment. Within the responders, λ decreased exponentially with respect to dose with a constant of 0.00108 mg⁻¹ (RSE 11.9%). The estimated responder rate was 66% (RSE 27.6%). Simulation-based diagnostics showed the model reasonably reproduced the characteristics of observed data. Highly variable daily seizure frequency was successfully characterized incorporating baseline characteristics, time effect, and the effect of pregabalin with classification of responders/nonresponders, all of which are necessary to adequately assess the efficacy of antiepileptic drugs.

Interaction of tiagabine with valproate in the mouse pentylenetetrazole-induced seizure model: an isobolographic analysis for non-parallel dose-response relationship curves

PURPOSE

To characterize the interaction between tiagabine (TGB) and valproate (VPA)--two antiepileptic drugs in the mouse pentylenetetrazole (PTZ)-induced clonic seizure model, type I isobolographic analysis for non-parallel dose-response relationship curves (DRRCs) was used.

MATERIAL AND METHODS

Clonic seizures were evoked in albino Swiss mice by subcutaneous injection of PTZ at its CD97 (100 mg/ kg). To ascertain the nature of interaction between TGB and VPA administered in combination, total brain concentrations of TGB and VPA were estimated by using high-performance liquid chromatography (HPLC) and fluorescence polarization immunoassay (FPIA).

RESULTS

TGB and VPA produced clear-cut anticonvulsant effects against PTZ-induced clonic seizures in mice and their DRRCs were not parallel to one another. The type I isobolographic analysis for non-parallel DRRCs revealed that the combination of TGB with VPA at the fixed-ratio of 1:1 exerted additive interaction against PTZ-induced clonic seizures in mice. With FPIA, it was found that TGB did not affect total brain VPA concentrations in experimental animals. Moreover, VPA had no significant impact on total brain concentrations of TGB in mice, as measured with HPLC.

CONCLUSION

The additive interaction between TGB and VPA at the fixed-ratio of 1:1 in the mouse PTZ model was pharmacodynamic in nature.

Modelling overdispersion and Markovian features in count data

The number of counts (events) per unit of time is a discrete response variable that is generally analyzed with the Poisson distribution (PS) model. The PS model makes two assumptions: the mean number of counts (lambda) is assumed equal to the variance, and counts occurring in non-overlapping intervals are assumed independent. However, many counting outcomes show greater variability than predicted by the PS model, a phenomenon called overdispersion. The purpose of this study was to implement and explore, in the population context, different distribution models accounting for overdispersion and Markov patterns in the analysis of count data. Daily seizures count data obtained from 551 subjects during the 12-week screening phase of a double-blind, placebo-controlled, parallel-group multicenter study performed in epileptic patients with medically refractory partial seizures, were used in the current investigation. The following distribution models were fitted to the data: PS, Zero-Inflated PS (ZIP), Negative Binomial (NB), and Zero-Inflated Negative Binomial (ZINB) models. Markovian features were introduced estimating different lambdas and overdispersion parameters depending on whether the previous day was a seizure or a non-seizure day. All analyses were performed with NONMEM VI. All models were successfully implemented and all overdispersed models improved the fit with respect to the PS model. The NB model resulted in the best description of the data. The inclusion of Markovian features in lambda and in the overdispersion parameter improved the fit significantly (P < 0.001). The plot of the variance versus mean daily seizure count profiles, and the number of transitions, are suggested as model performance tools reflecting the capability to handle overdispersion and Markovian features, respectively.

Isobolographic characterization of interactions of levetiracetam with the various antiepileptic drugs in the mouse 6 Hz psychomotor seizure model

The aim of this study was to characterize the anticonvulsant effects of levetiracetam (LEV) in combination with the various antiepileptic drugs (clonazepam [CZP], oxcarbazepine [OXC], phenobarbital [PB], tiagabine [TGB], and valproate [VPA]), in the mouse 6 Hz psychomotor seizure model. Limbic (psychomotor) seizure activity was evoked in albino Swiss mice by a current (32 mA, 6 Hz, 3s stimulus duration) delivered via ocular electrodes and isobolographic analysis for parallel and non-parallel dose-response effects was used to characterize the consequent anticonvulsant interactions between the various drug combinations. Potential concurrent adverse-effect profiles of interactions between LEV and CZP, OXC, PB, TGB, and VPA at the fixed-ratio of 1:1 were evaluated in the chimney (motor performance), passive avoidance (long-term memory), and grip-strength (muscular strength) tests. LEV administered singly was associated with a dose-response relationship curve (DRRC) that was parallel to that for CZP and non-parallel to that for OXC, PB, TGB and VPA. With isobolography for parallel DRRCs, the combination of LEV with CZP at three fixed-ratios of 1:3, 1:1 and 3:1 was additive in nature. With isobolography for non-parallel DRRCs the combinations of LEV with OXC, TGB and VPA at the fixed-ratio of 1:1 were also additive. In contrast, the isobolography for non-parallel DRRCs revealed that the interaction for the combination of LEV with PB at the fixed-ratio of 1:1 was supra-additive (synergistic). None of the combinations were associated with any concurrent adverse effects with regards to motor coordination, long-term memory or muscular strength. LEV is associated with favorable anticonvulsant synergism with PB and is additive with regards to CZP, OXC, TGB and VPA in the mouse 6 Hz psychomotor seizure model.

Modulatory effects of nitric oxide-active drugs on the anticonvulsant activity of lamotrigine in an experimental model of partial complex epilepsy in the rat

Background

The effects induced by administering the anticonvulsant lamotrigine, the preferential inhibitor of neuronal nitric oxide synthase 7-nitroindazole and the precursor of NO synthesis L-arginine, alone or in combination, on an experimental model of partial complex seizures (maximal dentate gyrus activation) were studied in urethane anaesthetized rats. The epileptic activity of the dentate gyrus was obtained through the repetitive stimulation of the angular bundle and maximal dentate gyrus activation latency, duration and post-stimulus afterdischarge duration were evaluated.

Results

Either Lamotrigine (10 mg kg^-1) or 7-nitroindazole (75 mg kg^-1) i.p. administration had an anticonvulsant effect, significantly reducing the number of animals responding to angular bundle stimulation. On the contrary, i.p. injection of L-arginine (1 g kg^-1) induced an aggravation of the epileptiform phenomena, demonstrated by the significant augmentation of the duration of both maximal dentate activation and afterdischarge. Furthermore, the injection of lamotrigine and 7-nitroindazole in combination significantly increased the anticonvulsant effects induced by the same drugs separately, either reducing the number of responding animals or decreasing both maximal dentate gyrus activation and afterdischarge durations. On the contrary, the combined treatment with L-arginine and lamotrigine did not modify the maximal dentate gyrus activation parameters suggesting an adversative effect of L-arginine-increased nitric oxide levels on the lamotrigine-induced anticonvulsant action.

Conclusion

The present results indicate that the nitrergic neurotransmission exerts a significant modulatory role in the control of the development of paroxystic phenomena in the maximal dentate gyrus activation model of epilepsy. Finally, our data suggest a functional relationship between the nitric oxide system and the anticonvulsant effect of lamotrigine which could be enhanced by reducing nitric oxide levels and, conversely, dampened by an increased nitrergic activity.

Synergistic combinations of anticonvulsant agents: what is the evidence from animal experiments?

PURPOSE

Combination therapy is often used in the treatment of seizures refractory to monotherapy. At the same time, the pharmacodynamic mechanisms that determine the combined efficacy of antiepileptic drugs (AEDs) are unknown, and this prevents a rational use of these drug combinations. We critically evaluate the existing evidence for pharmacodynamic synergism between AEDs from preclinical studies in animal models of epilepsy to identify useful combinations of mechanisms and to determine whether study outcome depends on the various research methods that are in use.

METHODS

Published articles were included if the studies were placebo-controlled, in vivo, or ex vivo animal studies investigating marketed or experimental AEDs. The animal models that were used in these studies, the primary molecular targets of the tested drugs, and the methods of interpretation were recorded. The potential association of these factors with the study outcome (synergism: yes or no) was assessed through logistic regression analysis.

RESULTS

In total, 107 studies were identified, in which 536 interaction experiments were conducted. In 54% of these experiments, the possibility of a pharmacokinetic interaction was not investigated. The majority of studies were conducted in the maximal electroshock model, and other established models were the pentylenetetrazole model, amygdala kindling, and the DBA/2 model. By far the most widely used method for interpretation of the results was evaluation of the effect of a threshold dose of one agent on the median effective dose (ED50) of another agent. Experiments relying on this method found synergism significantly more often compared with experiments relying on other methods (p<0.001). Furthermore, experiments including antagonists of the AMPA receptor were more likely to find synergism in comparison with all other experiments (p<0.001).

CONCLUSIONS

Intensive preclinical research into the effects of AED combinations has not led to an understanding of the pharmacodynamic properties of AED combinations. Specifically, the majority of the preclinical studies are not adequately designed to distinguish between additive, synergistic, and antagonistic interactions. Quantitative pharmacokinetic-pharmacodynamic studies of selectively acting AEDs in a battery of animal models are necessary for the development of truly synergistic drug combinations.

Principles: mechanisms and modeling of synergism in cellular responses

Cells can be considered as integrators of simultaneous stimuli, in which cross-talk between transduction pathways can eventually produce responses that are significantly different from simply additive responses. Synergism represents an efficient means of increasing the amplitude of cellular responses induced by low levels of stimulation. Recently, several kinetic and physicochemical models have been developed to describe and predict synergistic responses. In this article, the mechanisms that control the magnitude and timing of cellular synergism are discussed. We suggest that the analysis of theoretical models could enable a general prediction of synergism despite the presence of signal-specific synergistic responses. In addition, application of the proposed concepts should aid understanding of the wide occurrence of synergism induced by interacting transduction pathways in multi-drug clinical treatment.