A major burden of epilepsy is its inherent unpredictability, not only for seizures and lifetime course of the disease but also because the unpredictability in the effectiveness of antiepileptic drugs (AEDs). Patients often trial multiple drug combinations over many months or years and still fail to achieve full relief from seizures. Epilepsy is sometimes considered a disease of neuronal hyperexcitability, and many AEDs seem to work by decreasing neuronal excitability. Yet AEDs that are effective in some patients may be ineffective or indeed exacerbate seizures in others. In this article, we look specifically at carbamazepine and phenytoin. Both drugs are used primarily for management of complex partial seizures and less commonly for other seizure types. Both drugs reliably exacerbate absence seizures (Lerman, 1986; Perucca et al., 1998; Liu et al., 2006) and can also increase seizure frequency and type in other generalized epilepsy syndromes. In corticohippocampal slices from postnatal day 7 rats, in which seizure-like activity was induced by low magnesium, carbamazepine effects were highly labile. In some experiments, the drug had no effect, whereas in others the patterns of seizures were altered with tonic-like phases decreasing but with an increase in interictal discharges (Quilichini et al., 2003). In the same preparation, phenytoin was mostly without effect. In organotypic hippocampal slices, both carbamazepine and phenytoin also produced complex changes in seizure-like activity. The duration of tonic- and clonic-like events decreased but their frequency increased (Quilichini et al., 2003). These data demonstrate that effects of AEDs are complex and cannot be predicted by application of the simple idea that they reduce neuronal excitability. As we have shown when modeling the effects of epilepsy mutations (Thomas et al., 2009, 2010), there are multiple levels of positive and negative feedback that can be potentially modulated, and the net result on neuron and network activity cannot be predicted by intuitive arguments.
Sodium channels are critical for action potential (AP) initiation and propagation; they are expressed in higher quantities than other channel types, and mutations cause epilepsy (Escayg & Goldin, 2010). As a consequence, drugs that target sodium channels are among the most common AEDs prescribed. Carbamazepine and phenytoin, along with oxcarbazepine, eslicarbazepine, lamotrigine, valproate, zonisamide, furosemide, and lacosamide, all block sodium channels as part of their proposed mechanism of action. Most drugs seem to prolong recovery from inactivation (Kuo & Bean, 1994; Kuo et al., 1997). Definitive experiments studying the effects of carbamazepine and phenytoin in hippocampal neurons have shown that these drugs bind infrequently to channels at membrane potentials typical of resting neurons and bind frequently when neurons are depolarized (Kuo & Bean, 1994; Kuo et al., 1997). Block develops slowly compared to open times suggesting that the drugs bind preferentially to an inactivated state. Furthermore, the kinetics are distinguishably faster than slow inactivation suggesting that the drugs bind specifically to the fast inactivated state. Both carbamazepine and phenytoin have qualitatively similar effects on sodium channels but are quantitatively different.
Although the paradoxical nature of AEDs is well recognized, hypotheses as to why there is such patient to patient and seizure to seizure variability in the same patient are lacking. This variability will be due in part to the genetic makeup of the patient causing differences in ion channel responses to voltage and drugs. In this study, we explored nongenetic mechanisms that may cause paradoxical variability. Neuronal networks are complex, nonlinear dynamic systems with multiple levels of positive and negative feedback. Computer simulation provides a way to predict the consequences of manipulating aspects of these interactions (Lytton, 2008; Thomas & Petrou, 2008). Most biophysically realistic neuron and network models are based on the Hodgkin-Huxley formalism for describing ion channel kinetics (Hodgkin & Huxley, 1952). An assumption of these models is that activation and inactivation are independent processes. Although activation and inactivation are not independent, Hodgkin-Huxley models are able reproduce standard voltage clamp experimental data with high fidelity (Thomas et al., 2007). However, because carbamazepine and phenytoin bind exclusively to the inactivated state, independence of activation and inactivation can no longer be assumed. We developed a method to extend standard Hodgkin-Huxley models to incorporate state-dependent drug binding that can accurately reproduce published data. Because these drugs are used primarily in partial epilepsy, we incorporated these extended ion channel models into a previously published model of dentate gyrus (DG) neurons and networks (Santhakumar et al., 2005; Dyhrfjeld-Johnsen et al., 2007; Morgan & Soltesz, 2008; Thomas et al., 2009, 2010). We also introduced mossy fiber sprouting into the simulations as a model of environmental interaction.
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Because of their high expression and importance in AP generation, sodium channels are attractive drug targets for a range of diseases (Meisler & Kearney, 2005; Nardi et al., 2012). Effective drugs must be able to reduce symptoms while continuing to allow normal function. Ideally, drugs could be chosen based on an individual patient's symptoms and history. In the case of phenytoin, carbamazepine, and other AEDs it is notoriously difficult to predict whether a drug will be effective, produce unacceptable side-effects, or indeed exacerbate seizures. State-dependent sodium channel blockers like phenytoin and carbamazepine are thought be effective anticonvulsants because they inhibit high frequency AP firing while allowing low frequency firing to continue unimpeded (Ayala et al., 1977; McLean & Macdonald, 1983; Adler et al., 1986; Kuo & Bean, 1994; Kuo et al., 1997). However, the clinical observation that these drugs are only effective in some patients and the observation that they exacerbate seizures in some cases belies this simple view.
In order to examine how state-dependent sodium channel block impacts neuron and network behavior, we extended standard models of voltage gating to include drug gating. The overwhelming majority of voltage gating models are based on the Hodgkin-Huxley formalism because of the modest experimental data required to build them and their ability to reproduce experimental findings (Thomas et al., 2007). The technique we have developed here allows Hodgkin-Huxley models to incorporate drug-bound states and, at least in the case of phenytoin and carbamazepine, reproduce experimental data. These drugs bind preferentially to the fast-inactivated states rather than the closed state, providing an apparent voltage dependence. Unbinding is slow, holding the channel in the inactivated state and thus reducing channel availability. Binding is also slow and so occurs predominantly when the neuron is depolarized for many seconds. These two observations have to led to the view that these drugs will only block sustained high frequency firing. Because these drugs also bind to the fast-inactivated states in preference to slow-inactivated states we did not include slow inactivation in our model.
Our data predict that the effects of state-dependent block depend on more than just firing rate. In the case of a single neuron, responses to AP firing invoke negative feedback by opening voltage gated calcium channels, which increases intracellular calcium, and which in turn opens calcium activated potassium channels. We have found in the current (Fig. 3) and in a previous study (Thomas et al., 2010) that inhibiting sodium channels has a larger effect on AP firing through this mechanism than through a reduction in sodium channel availability. Furthermore, we have previously predicted that this increased firing leads to increased transmitter release, despite reduced AP amplitude, and hence increased excitation.
We also incorporated our modified sodium channel model into a DG network model and tested the effects of drug binding in normal networks and networks with pathologic recurrent excitatory connections. In normal networks, drugs had no effect. Synaptic inputs, even when driven at high frequency, differ from current injections in that the membrane spends more time near rest. This in turn reduces the time in the inactivated state, thereby reducing the opportunity for drug to bind. This was true even for networks in which the resting membrane potential was held at −50 mV.
Networks with mossy fiber sprouting, modeling epilepsy disease conditions, showed very different responses depending on network state. When neurons in the networks had resting membrane potentials of −70 mV, phenytoin and carbamazepine were able to reduce both the duration of the response to a brief stimulus and the extent to which it propagated through the network. On the other hand, in networks in which neurons were depolarized to −50 mV, phenytoin and carbamazepine slightly increased the duration of responses.
These data demonstrate that the effects of drugs are more complex than simply blocking high frequency firing. As we have discussed, by disrupting negative feedback, state-dependent blockers may increase firing. This is likely to be highly dependent on the neuron type. Neurons in the DG express large calcium-dependent potassium conductances (Staley et al., 1992; Dudek & Sutula, 2007; Howard et al., 2007), whereas in other networks these conductances do not play such a large role. Both the degree of recurrent feedback and resting membrane potential are important for determining the effects of drugs in these networks. Without recurrent excitatory connections, drugs had no effect, whereas the presence of recurrent excitatory drive depolarizes neurons sufficiently that drug block can occur and this drive is able to overcome the effects of calcium-activated potassium channels. The effects of resting membrane potential on network activity were also contradictory. The simple model predicts that increased resting membrane potentials increases drug binding and should decrease AP firing. This has potential consequences for how drugs can influence seizure activity. Drugs are more likely to be effective when epileptiform activity rises rapidly from quiescent network states or when development of the seizures requires pathologic activity to propagate through a quiescent network. These observations may account for clinical variability of drug efficacy.
Resting membrane potential is determined by network state (Destexhe et al., 2001, 2003; Fellous et al., 2003). Neurons in active networks undergo constant synaptic bombardment and as a result are significantly more depolarized than neurons in quiescent networks. The degree of activity in the network is, in turn, determined by behavioral state, preictal dynamic trajectory at the focus, and the nature of the networks that seizures must propagate through as they evolve. Our observations are consistent with drugs reducing seizures, for example in networks with low resting membrane potential, or having no effect and indeed exacerbating seizures in networks with high resting membrane potential. Taken together, understanding the effects of drugs depends on the precise networks involved both in initiation and spread and their dynamical history.