Utility of TMS and EEG in Phase 1 Drug Trials for Alzheimer’s Disease

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterised by cognitive decline, memory loss, and behavioural changes. Despite decades of research, the development of effective treatments remains challenging. One of the main problems is the complexity of the disease and the difficulty in assessing the efficacy of new drug candidates in the early stages of clinical trials. This is where advanced neurophysiological techniques such as Transcranial Magnetic Stimulation (TMS) and Electroencephalography (EEG) can be invaluable in providing early indicators that can then inform and support both later stages in the pipeline, other potential drug candidates within programme and highlight other potential indications that may benefit.

Understanding Phase I Trials

 Phase I trials are the first human clinical trials for a new drug. They focus on assessing safety, tolerability, pharmacokinetics, and pharmacodynamics. These trials are crucial for establishing the foundation for further clinical testing. Traditional methods in Phase I trials in CNS indications, such as corticospinal fluid sampling and subjective outcome measures, provide limited insight into the direct effects of a drug on brain function. This limitation has spurred the integration of TMS and EEG (and other neuroscience techniques) to bridge the gap.

The Role of TMS in Alzheimer’s Disease Research

TMS is a non-invasive technique that uses a magnetic field to stimulate specific areas of the brain. In AD research, TMS can measure cortical excitability and the functional connectivity of different brain regions, both of which are often altered in AD. This method provides insight into whether a new drug is modulating brain activity as intended. A measure of target engagement.

TMS also holds potential for biomarker discovery. Reduced cortical inhibition, as measured by paired-pulse TMS protocols, has been linked to AD. Drugs that normalise these patterns could serve as early markers of therapeutic efficacy. Importantly, TMS has been explored as a treatment option in Alzheimer’s. For example, Bagattini et al. (2020) showed that repetitive TMS (rTMS) applied over the dorsolateral prefrontal cortex (DLPFC) enhanced memory performance in AD patients when combined with cognitive training. Patients who received rTMS experienced more significant improvements in memory and other cognitive functions than those who received cognitive training alone. Ferreri et al. (2021) further highlighted the neurophysiological biomarkers detected through TMS-EEG that may predict the progression of mild cognitive impairment (MCI) to AD. Their study showed that disrupted EEG synchronisation, particularly in beta and gamma frequency bands, could predict which MCI patients would convert to AD, making it a potential early biomarker for future trials.

EEG’s Contribution to Alzheimer’s Drug Trials

EEG records the brain’s electrical activity through sensors placed on the scalp and provides several advantages in AD drug trials. EEG’s high temporal resolution allows researchers to monitor brain activity in real time, helping detect immediate drug effects on brain oscillations. For instance, abnormal EEG patterns such as increased theta power and reduced alpha and beta power are linked to cognitive decline in AD. Recent advancements, like those by Cejnek et al. (2021), have introduced novel approaches for using EEG as a diagnostic tool. Their study proposed an innovative method for detecting AD and mild cognitive impairment (MCI) by measuring the “novelty” in EEG signals. This technique yielded high sensitivity and specificity in identifying patients with AD, demonstrating the potential of EEG as an early diagnostic tool. In Phase I trials, EEG can offer early indications of a drug’s impact on neural networks, potentially guiding decisions about dose adjustments and long-term efficacy.

Combining TMS and EEG

The combination of TMS and EEG offers a powerful approach to studying brain function. TMS-EEG enables a direct assessment of the brain’s response to magnetic stimulation in real time, providing insights into cortical reactivity, connectivity, and plasticity – factors often impaired in AD. Tǎuțan et al. (2023) demonstrated that TMS-EEG can be used to capture relevant features of AD pathophysiology using machine learning. Their study extracted over 150 time-domain features from TMS-EEG data and achieved high classification accuracy in distinguishing AD patients from healthy controls. Features like the amplitude of the TMS-evoked potential (TEP) and the complexity of the EEG signal were crucial in identifying AD pathology, suggesting that these metrics could aid in patient classification and disease tracking. Nardone et al. (2021) reviewed TMS-EEG studies in dementia, highlighting that TMS-induced responses can reveal altered connectivity between brain regions. For example, AD patients exhibited reduced TMS-evoked responses in the temporo-parietal and fronto-central areas, reflecting disruptions in sensorimotor networks. Such findings emphasise the utility of TMS-EEG for understanding brain network changes in AD and other dementias. These integrated approaches are already showing promise. Studies combining TMS with EEG, such as using TMS to stimulate the DLPFC while simultaneously recording EEG, help researchers assess functional connectivity in real time. Drugs that enhance DLPFC connectivity through this method may show early therapeutic benefit.

Challenges and Future Directions

Despite the utility of TMS and EEG, their implementation in AD drug trials comes with challenges. Variability in neurophysiological measures and the need for specialised equipment and expertise can complicate their widespread use. Moreover, disease heterogeneity in AD patients can make it difficult to generalise findings across populations. However, advances in technology, such as improved EEG signal analysis techniques like those developed by Cejnek et al. (2021), and machine learning approaches as shown by Tǎuțan et al. (2023), are addressing these challenges.

TMS and EEG are valuable tools in Phase I drug trials for Alzheimer’s disease. They provide unique insights into brain function, facilitate biomarker development, and help predict clinical outcomes. As demonstrated by recent research, including the work of Bagattini et al. (2020), Camera et al. (2024), and Nardone et al. (2021), these techniques offer promise not only for understanding the disease but also for accelerating the development of effective treatments for this devastating condition.

How We Can Help

Working closely with our MHRA accredited Phase I CRO partners and our Sponsor clients, The Science Behind specialises in bringing together the specific expertise and equipment to design, augment and implement these techniques into Phase I clinical trials.

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References

Bagattini, C. et al. (2020). Enhancing cognitive training effects in Alzheimer’s disease: rTMS as an add-on treatment. Brain Stimulation 13(6): 1655-1664. DOI: https://doi.org/10.1016/j.brs.2020.09.010

Camera, F. et al. (2024). Dosimetry for repetitive transcranial magnetic stimulation: a translational study from Alzheimer’s disease patients to controlled in vitro investigations. Physics in Medicine and Biology 69(18). DOI: 10.1088/1361-6560/ad6f69

Cejnek, M. et al. (2021). Novelty detection – based approach for Alzheimer’s disease and mild cognitive impairment diagnosis from EEG. Medical & Biological Engineering & Computing 59: 2287-2296. DOI: https://doi.org/10.1007/s11517-021-02427-6

Ferreri, F. et al. (2021). TMS-EEG biomarkers of amnestic mild cognitive impairment due to Alzheimer’s disease: a proof-of-concept six years prospective study. Front. Aging Neurosci. 13. DOI: https://doi.org/10.3389/fnagi.2021.737281

Nardone, R. et al. (2021). TMS-EEG co-registration in patients with mild cognitive impairment, Alzheimer’s disease and other dementias: a systematic review. Brain Sciences 11(3): 303. DOI: 10.3390/brainsci11030303

Tǎuțan, A.M. et al. (2023). TMS-EEG perturbation biomarkers for Alzheimer’s disease patients’ classification. Scientific Reports 13. DOI: 10.1038/s41598-022-22978-4