By Julia Milhet, Hedi Gharbi and Wenceslas Duveau, PhD

Electroencephalography (EEG) records neural activity inside the brain via electrodes placed on the scalp. As a real-time, dynamic recording method, EEG provides a direct window into neocortical electrical activity and is frequently used to monitor seizure activity and degenerative brain disorders.¹

The application of machine learning has improved the use of EEG and the data that can be derived from it; as a result, its use has extended upstream from clinical applications to preclinical studies. Because brain oscillations are highly conserved properties between humans and rodents, the technology can be used to predict the efficacy of therapeutic candidates. Some of the aberrant EEG oscillations characterized in human brain disorders have also been observed in animal models.

Preclinical EEG consists of recording local field potentials in the brain of freely moving animal models; in almost all brain structures, the electrical activity of tens of thousands of neurons can be recorded, which helps to clarify the processes underlying various neurological diseases. In addition to its direct access to brain function, EEG is an objective method that does not require the injection of a dye and allows the study of dynamic processes.

Translational EEG biomarkers

Aberrant neural oscillatory patterns can serve as accurate and unbiased biomarkers. SynapCell has developed methods and associated validation processes to qualify aberrant EEG patterns as disease-specific biomarkers for use in compound testing. As a quantitative measure of neural activity, EEG offers dynamic and robust predictive results and avoids observer bias. Aberrant EEG patterns must meet several key criteria to qualify as biomarkers:

• They must be well documented
in the clinic and found in the affected animal
patterns (eg, paroxysmal hippocampus
discharges in focal epilepsy).

• They must be modulated or inverted
by reference drugs (for example, beta oscillations
in suppressed Parkinson’s disease
by levodopa).

• They must be reproducible over time to
confirm the robustness (as determined by
animal cohorts, pharmacological supplement
challenges and in-depth statistical analyses).

Tail^® The in vivo platform encompasses high-level modeling methods, precise recording techniques, and deep learning-based signal processing to enable quantification of brain activity in healthy and pathological conditions. The high temporal resolution offered by EEG recordings makes it possible to assess the pharmacodynamics of pharmaceutical compounds in clinically relevant models using EEG biomarkers (Figure 1).

Cue accepts EEG inputs and supports a wide range of applications such as monitoring drug-induced oscillatory changes to determine if a compound reaches its target and crosses the blood-brain barrier (pharmacological EEG); testing of cognitive enhancers using steady-state auditory response; monitoring the antiepileptic properties of the compounds; or assess antiparkinsonian and antidyskinetic medications. The platform can also enable drug benchmarking; the discovery of mechanisms of action; screening of small compound libraries; and reporting of any adverse side effects.

The Cue platform can accommodate various dosing paradigms, including acute, subchronic, and chronic dosing paradigms, and it can be used with more complex protocols such as gene therapy-driven protein expression. Overall, Cue provides objective parameters for documenting the effects of test compounds on various brain diseases and can also be applied to characterize animal models (EEG phenotyping).

EEG phenotyping

The Cue platform can be used to phenotype preclinical animal models of central nervous system (CNS) disorders. Subtle oscillatory changes can be detected in a given model compared to wild-type controls to highlight a specific disease-related EEG phenotype. Once demonstrated, the EEG phenotype can be challenged with appropriate pharmacology to identify potential drug sensitivity. This process allows functional exploration using the EEG phenotype as a proxy for the disease or mechanism studied, as well as validation and target engagement with the ability to correlate a genotype (knock-in or knock-out, homozygous , heterozygous) to an EEG phenotype. This feature could be used to stratify subpopulations (eg, correlating mutations at different loci with different disease symptoms), to clarify disease mechanisms, and to support more precise and personalized treatment approaches. EEG phenotyping represents an important advance in drug discovery, as it increases the translational value of an animal model, highlighting a functional EEG biomarker of brain pathology.

An unmet need in the treatment of epilepsy

Although the seizures experienced by epileptic patients are well treated by drugs currently on the market, mesial temporal lobe epilepsy (mTLE), a form of focal epilepsy, remains difficult to treat, making it a need not satisfied high. Most pharmacological treatments have no effect on these nonconvulsive seizures, with 30% of patients being drug refractory. A major obstacle to the development of possible therapies for mTLE was the fact that none of the technologies available on the market could demonstrate a pathological and non-convulsive phenotype in the mTLE mouse model.

To help find a possible treatment for mTLE, SynapCell has incorporated a relevant model using the Cue platform to support the development of mTLE therapies (Figure 2). The model recapitulates a wide range of features observed in human mTLE, including disease symptomology, histopathological alterations, EEG data, and pharmacology.² When SynapCell’s Cue platform was used to characterize paroxysmal hippocampal discharges in this model, they were found to be very similar to those seen in patients, as well as being pharmacoresponsive to the same reference drugs tested. The mTLE mouse model therefore makes it possible to predict the efficacy of the drug from the preclinical stage.

Conclusion

The Cue platform takes advantage of the translational properties of EEG to closely replicate human disease in relevant animal models. Combined with relevant data analysis, EEG bridges the gap between preclinical research and clinical practice by accurately measuring the therapeutic potential of compounds in the brain. The platform can be applied to different therapeutic strategies, including gene therapies induced by adeno-associated viruses. Benefiting from the latest technological innovations, the platform enables more robust application of quantitative and predictive EEG measurements in CNS drug discovery.

Julia Milhet is Medical Technology Marketing Communications Coordinator, Hedi Gharbi is Head of Global Marketing, and Wenceslas Duveau, PhD, is Head of Sales and Product Development at Synap Cell.

References
1.Sutter R, Kaplan PW, Schomer DL. Historical aspects of electroencephalography. In: Schomer DL, Lopes da Silva FH, Eds. Niedermeyer electroencephalography: basic principles, clinical applications and related fields. 7th ed. Oxford University Press; 2017: 1–19. DOI: 10.1093/med/9780190228484.003.0001.

2. Duveau V, Roucard C. A mouse model of mesiotemporal lobe epilepsy. Neurochem. Res. 2017; 42(7):1919–1925. DOI: 10.1007/s11064-017-2239-3.