Pharmaco-resistant Neonatal Seizures: Critical Mechanistic Insights from a Chemoconvulsant Model

Shivani C Kharod1, Brandon M Carter1, Shilpa D Kadam1,2

  • 1Neuroscience Laboratory, Hugo Moser Research Institute at Kennedy Krieger, Baltimore, Maryland, 21205.

Insights

Phenobarbital (PB) effectively treats pentylenetetrazole (PTZ)-induced neonatal seizures by upregulating KCC2. However, seizure induction methods significantly influence PB efficacy and resistance mechanisms in translational models.

Area of Science:

  • Neuroscience
  • Developmental Neuroscience
  • Pharmacology

Background:

  • Neonatal seizures, particularly those from hypoxic-ischemic encephalopathy (HIE), pose risks for mortality and long-term neurological issues.
  • Phenobarbital (PB) is the primary treatment for neonatal seizures but has a failure rate of approximately 50%.
  • Understanding PB resistance mechanisms is vital, necessitating reliable translational models.

Purpose of the Study:

  • To investigate the efficacy of phenobarbital (PB) in a pentylenetetrazole (PTZ)-induced neonatal seizure model.
  • To explore the underlying mechanisms of PB resistance in this model, comparing it to an ischemic seizure model.
  • To determine if seizure severity influences PB efficacy.

Main Methods:

  • Utilized a pentylenetetrazole (PTZ) model in postnatal day 7 (P7) CD-1 mice to induce neonatal seizures.
  • Administered a single dose of PB (25 mg/kg) to assess its anti-seizure effects.
  • Analyzed K-Cl cotransporter 2 (KCC2) and Na-K-Cl cotransporter 1 (NKCC1) expression, and TrkB pathway activation.
  • Compared seizure burden and PB efficacy with previously reported data from an ischemic seizure model.

Main Results:

  • PB significantly suppressed PTZ-induced seizures.
  • This suppression was linked to KCC2 upregulation and stable NKCC1 expression, without TrkB pathway activation.
  • PTZ seizure burdens were higher than in the ischemic model, suggesting seizure severity does not solely determine PB resistance.
  • Bumetanide (BTN) showed no anti-seizure effect, mirroring findings in the ischemic model.

Conclusions:

  • The method of seizure induction critically impacts the mechanisms underlying phenobarbital (PB) resistance in neonatal seizure models.
  • The pentylenetetrazole (PTZ) model demonstrates PB efficacy with KCC2 upregulation, contrasting with PB resistance observed in ischemic models.
  • Investigating seizure mechanisms requires careful consideration of the specific model used to induce seizures.

Related Concept Videos

Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
385
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
270
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
319
Critical Region, Critical Values and Significance Level01:16

Critical Region, Critical Values and Significance Level

The critical region, critical value, and significance level are interdependent concepts crucial in hypothesis testing.
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in  probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
13.4K
Critical Values01:31

Critical Values

A critical value is a definite value obtained from a particular probability distribution at a predecided confidence level (or a predecided significance level) for a given population parameter. The critical value provides demarcation that separates the sample statistics that are likely to occur from the ones that are unlikely to occur based on the given probability distribution and the population parameter to be estimated. The critical value for normal distribution is obtained from the z...
10.4K
Seizures: Classification01:13

Seizures: Classification

Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
1.6K