Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation.

Adaptive deep brain stimulation (aDBS) shows promise for improving treatment for neurological disorders such as Parkinson's disease (PD). aDBS uses symptom-related biomarkers to adjust stimulation parameters in real-time to target symptoms more precisely. To enable these dynamic adjustments, parameters for an aDBS algorithm must be determined for each individual patient. This requires time-consuming manual tuning by clinical researchers, making it difficult to find an optimal configuration for a single patient or to scale to many patients. Furthermore, the long-term effectiveness of aDBS algorithms configured in-clinic while the patient is at home remains an open question. To implement this therapy at large scale, a methodology to automatically configure aDBS algorithm parameters while remotely monitoring therapy outcomes is needed. In this paper, we share a design for an at-home data collection platform to help the field address both issues. The platform is composed of an integrated hardware and software ecosystem that is open-source and allows for at-home collection of neural, inertial, and multi-camera video data. To ensure privacy for patient-identifiable data, the platform encrypts and transfers data through a virtual private network. The methods include time-aligning data streams and extracting pose estimates from video recordings. To demonstrate the use of this system, we deployed this platform to the home of an individual with PD and collected data during self-guided clinical tasks and periods of free behavior over the course of 1.5 years. Data were recorded at sub-therapeutic, therapeutic, and supra-therapeutic stimulation amplitudes to evaluate motor symptom severity under different therapeutic conditions. These time-aligned data show the platform is capable of synchronized at-home multi-modal data collection for therapeutic evaluation. This system architecture may be used to support automated aDBS research, to collect new datasets and to study the long-term effects of DBS therapy outside the clinic for those suffering from neurological disorders.

A Fiber Optic Interface Coupled to Nanosensors: Applications to Protein Aggregation and Organic Molecule Quantification.

Fluorescent nanosensors hold promise to address analytical challenges in the biopharmaceutical industry. The monitoring of therapeutic protein critical quality attributes such as aggregation is a long-standing challenge requiring low detection limits and multiplexing of different product parameters. However, general approaches for interfacing nanosensors to the biopharmaceutical process remain minimally explored to date. Herein, we design and fabricate a integrated fiber optic nanosensor element, measuring sensitivity, response time, and stability for applications to the rapid process monitoring. The fiber optic-nanosensor interface, or optode, consists of label-free nIR fluorescent single-walled carbon nanotube transducers embedded within a protective yet porous hydrogel attached to the end of the fiber waveguide. The optode platform is shown to be capable of differentiating the aggregation status of human immunoglobulin G, reporting the relative fraction of monomers and dimer aggregates with sizes 5.6 and 9.6 nm, respectively, in under 5 min of analysis time. We introduce a lab-on-fiber design with potential for at-line monitoring with integration of 3D-printed miniaturized sensor tips having high mechanical flexibility. A parallel measurement of fluctuations in laser excitation allows for intensity normalization and significantly lower noise level (3.7 times improved) when using lower quality lasers, improving the cost effectiveness of the platform. As an application, we demonstrate the capability of the fully integrated lab-on-fiber system to rapidly monitor various bioanalytes including serotonin, norepinephrine, adrenaline, and hydrogen peroxide, in addition to proteins and their aggregation states. These results in total constitute an effective form factor for nanosensor-based transducers for applications in industrial process monitoring.

Characterization of Protein Aggregation Using Hydrogel-Encapsulated nIR Fluorescent Nanoparticle Sensors.

The monitoring of biopharmaceutical critical quality attributes in-process, at both the process development and manufacturing stages, is necessary for the implementation of process analytical technology and quality-by-design principles. Among these attributes, it is important to monitor and control protein aggregation during the manufacturing of biological therapeutics to prevent adverse immunogenic responses and minimize negative impacts on drug deliverability. In this work, we explore hydrogel-encapsulated, label-free fluorescent nanosensors for the characterization of protein aggregation. A mathematical model is used to describe the diffusion and binding of a series of stressed pharmaceutical samples to such sensors, describing their dynamic response. We use mathematical modeling to map the influence of hydrogel properties on the separation performance, given the composition of UV-stressed IgG1 samples. Using this modified model, the compositions of light-stressed IgG1 samples were fit to experimental data and correlated with size-exclusion chromatography data. The results demonstrate the ability to detect the presence of high-molecular-weight protein species at a concentration as low as 1%. This work represents a significant step toward the development and deployment of rapid process analytical technologies for biopharmaceutical characterization.