Thus far, this gene therapy approach has been well tolerated and shows PET evidence of sustained gene expression. These initial findings demonstrate the safety of the therapy; higher doses of adeno-associated viral vector containing the human aromatic l-amino acid decarboxylase gene in the next cohort of patients may further increase dopamine production in the putamen and provide more profound clinical benefit.

AAV2-neurturin delivery to the putamen and substantia nigra bilaterally in PD was not superior to sham surgery. The procedure was well tolerated, and there were no clinically significant adverse events related to AAV2-neurturin.

Convection-enhanced delivery (CED) has recently entered the clinic and represents a promising new delivery option for targeted gene therapy in Parkinson’s disease (PD). The prime stereotactic target for the majority of recent gene therapy clinical trials has been the human putamen. The stereotactic delivery of therapeutic agents into putamen (or other subcortical structures) via CED remains problematic due to the difficulty in knowing what volume of therapeutic agent to deliver. Preclinical studies in non-human primates (NHP) offer a way to model treatment strategies prior to clinical trials. Understanding more accurately the volumetric differences in striatum, especially putamen, between NHP and humans is essential in predicting convective volume parameters in human clinical trials. In this study, magnetic resonance images (MRI) were obtained for volumetric measurements of striatum (putamen and caudate nucleus) and whole brain from 11 PD patients, 13 aged healthy human subjects, as well as 8 parkinsonian and 30 normal NHP. The human brain is 13–18 times larger than the monkey brain. However, this ratio is significantly smaller for striatum (5.7–6.5), caudate nucleus (4.6–6.6) and putamen (4.4–6.6). Size and species of the monkeys used for this comparative study are responsible for differences in ratios for each structure between monkeys and humans. This volumetric ratio may have important implications in the design of clinical therapies for PD and Huntington’s disease and should be considered when local therapies such as gene transfer, local protein administration or cellular replacement are translated based on non-human primate research.

Objective. Deep brain stimulation (DBS) is an effective treatment for Parkinson’s disease (PD) but its success depends on a time-consuming process of trial-and-error to identify the optimal stimulation settings for each individual patient. Data-driven optimization algorithms have been proposed to efficiently find the stimulation setting that maximizes a quantitative biomarker of symptom relief. However, these algorithms cannot efficiently take into account stimulation settings that may control symptoms but also cause side effects. Here we demonstrate how multi-objective data-driven optimization can be used to find the optimal trade-off between maximizing symptom relief and minimizing side effects. Approach. Cortical and motor evoked potential data collected from PD patients during intraoperative stimulation of the subthalamic nucleus were used to construct a framework for designing and prototyping data-driven multi-objective optimization algorithms. Using this framework, we explored how these techniques can be applied clinically, and characterized the design features critical for solving this optimization problem. Our two optimization objectives were to maximize cortical evoked potentials, a putative biomarker of therapeutic benefit, and to minimize motor potentials, a biomarker of motor side effects. Main Results. Using this in silico design framework, we demonstrated how the optimal trade-off between two objectives can substantially reduce the stimulation parameter space by 61 ± 19%. The best algorithm for identifying the optimal trade-off between the two objectives was a Bayesian optimization approach with an area under the receiver operating characteristic curve of up to 0.94 ± 0.02, which was possible with the use of a surrogate model and a well-tuned acquisition function to efficiently select which stimulation settings to sample. Significance. These findings show that multi-objective optimization is a promising approach for identifying the optimal trade-off between symptom relief and side effects in DBS. Moreover, these approaches can be readily extended to newly discovered biomarkers, adapted to DBS for disorders beyond PD, and can scale with the development of more complex DBS devices.