Pre-clinical delivery of gene therapy products to the cerebrospinal fluid: challenges and considerations for clinical translation

While the majority of gene therapy studies in neurological indications have focused on direct gene transfer to the central nervous system (CNS), there is growing interest in the delivery of therapeutics using the cerebrospinal fluid (CSF) as a conduit. Historically, direct CNS routes-of-administration (RoAs) have relied on tissue dynamics, displacement of interstitial fluid, and regional specificity to achieve focal delivery into regions of interest, such as the brain. While intraparenchymal delivery minimizes peripheral organ exposure, one perceived drawback is the relative invasiveness of this approach to drug delivery. In this mini review, we examine the CSF as an alternative RoA to target CNS tissue and discuss considerations associated with the safety of performing such procedures, biodistribution of therapeutics following single administration, and translation of findings given differences between small and large animals. These factors will help delineate key considerations for translating data obtained from animal studies into clinical settings that may be useful in the treatment of neurological conditions.

Real-Time MRI-Guided Stereotactic Aspiration of Spontaneous Intracerebral Hematoma: A Preclinical Feasibility Study

BACKGROUND

Minimally invasive surgical techniques have reinvigorated the role of surgical options for spontaneous intracranial hematomas; however, they are limited by the lack of real-time feedback on the extent of hematoma evacuation.

OBJECTIVE

To describe the development of a MRI-guided catheter-based aspiration system, the ClearPoint Pursuit Neuroaspiration Device (ClearPoint Neuro) and validation in phantom models.

METHODS

In this preclinical experimental trial, 8 phantom brains with skull models were created to simulate an intracranial hematoma with 2 clot sizes, 30 cc (small clot) and 60 cc (large clot). After registration, the aspiration catheter (Pursuit device) was aligned to the desired planned trajectory. The aspiration of the clot was performed under real-time MRI scan in 3 orthogonal views. The primary end point was reduction of the clot volume to less than 15 cc or 70% of the original clot volume.

RESULTS

Successful completion of clot evacuation was achieved in all models. The average postaspiration clot volume was 9.5 cc (8.7 cc for small clots and 10.2 cc for large clots). The average percentage reduction of clot volume was 76.3% (range 58.7%-85.2%). The average total procedure time (from frame registration to final postaspiration clot assessment) was 50 min. The average aspiration time was 6.9 min.

CONCLUSION

This preclinical trial confirms the feasibility and efficacy of MRI-guided aspiration under real-time image guidance in simulation models for intracranial hematoma. Clinical use of the system in patients would further validate its efficacy and safety.

Neurophysiological Biomarkers of Parkinson's Disease

BACKGROUND

There is a need for reliable and robust Parkinson's disease biomarkers that reflect severity and are sensitive to disease modifying investigational therapeutics.

OBJECTIVE

To demonstrate the utility of EEG as a reliable, quantitative biomarker with potential as a pharmacodynamic endpoint for use in clinical assessments of neuroprotective therapeutics for Parkison's disease.

METHODS

A multi modal study was performed including aquisition of resting state EEG data and dopamine transporter PET imaging from Parkinson's disease patients off medication and compared against age-matched controls.

RESULTS

Qualitative and test/retest analysis of the EEG data demonstrated the reliability of the methods. Source localization using low resolution brain electromagnetic tomography identified significant differences in Parkinson's patients versus control subjects in the anterior cingulate and temporal lobe, areas with established association to Parkinson's disease pathology. Changes in cortico-cortical and cortico-thalamic coupling were observed as excessive EEG beta coherence in Parkinson's disease patients, and correlated with UPDRS scores and dopamine transporter activity, supporting the potential for cortical EEG coherence to serve as a reliable measure of disease severity. Using machine learning approaches, an EEG discriminant function analysis classifier was identified that parallels the loss of dopamine synapses as measured by dopamine transporter PET.

CONCLUSION

Our results support the utility of EEG in characterizing alterations in neurophysiological oscillatory activity associated with Parkinson's disease and highlight potential as a reliable method for monitoring disease progression and as a pharmacodynamic endpoint for Parkinson's disease modification therapy.

Modeling temporal sequences of cognitive state changes based on a combination of EEG-engagement, EEG-workload, and heart rate metrics

The objective of this study was to investigate the feasibility of physiological metrics such as ECG-derived heart rate and EEG-derived cognitive workload and engagement as potential predictors of performance on different training tasks. An unsupervised approach based on self-organizing neural network (NN) was utilized to model cognitive state changes over time. The feature vector comprised EEG-engagement, EEG-workload, and heart rate metrics, all self-normalized to account for individual differences. During the competitive training process, a linear topology was developed where the feature vectors similar to each other activated the same NN nodes. The NN model was trained and auto-validated on combat marksmanship training data from 51 participants that were required to make "deadly force decisions" in challenging combat scenarios. The trained NN model was cross validated using 10-fold cross-validation. It was also validated on a golf study in which additional 22 participants were asked to complete 10 sessions of 10 putts each. Temporal sequences of the activated nodes for both studies followed the same pattern of changes, demonstrating the generalization capabilities of the approach. Most node transition changes were local, but important events typically caused significant changes in the physiological metrics, as evidenced by larger state changes. This was investigated by calculating a transition score as the sum of subsequent state transitions between the activated NN nodes. Correlation analysis demonstrated statistically significant correlations between the transition scores and subjects' performances in both studies. This paper explored the hypothesis that temporal sequences of physiological changes comprise the discriminative patterns for performance prediction. These physiological markers could be utilized in future training improvement systems (e.g., through neurofeedback), and applied across a variety of training environments.