Advancing CNS Therapeutics with Proteomics

The quest for neurological and neurodegenerative therapies is still going strong. In 2023, the number of FDA neurology approvals was once again second only to oncology and active neuroscience trials continue to increase.  However, despite heavy investment, this therapeutic area remains challenging for R&D¹. Central nervous system (CNS) indications in particular—including neurodegenerative diseases such as Alzheimer’s and psychiatric disorders such as depression, autism spectrum disorder, and schizophrenia—have both longer development timelines and lower likelihood of approval. How can inroads be made with these difficult diseases? 

Proteomics approaches could be one way. Modern proteomics has a significant role to play in drug development, from elucidating disease biology and helping find effective drug candidates to identifying and utilizing biomarkers for predicting and monitoring efficacy and safety. However, proteomic data poses special analysis and data management challenges. 

How is proteomics transforming R&D and translational medicine for CNS therapeutics? We highlight the importance of a scalable data integration and analytics platform in leveraging this approach.

What is Proteomics

Applying Proteomics in CNS Drug Discovery

Interest in using proteomics for CNS drug discovery has rapidly risen in the past few years. Two areas in which proteomics (or neuroproteomics) has shown significant value are target identification and biomarker development (Figure 1).

Target Identification

For CNS therapeutic development, protein targets are a major focus. This includes targeting proteins that aggregate in neurodegenerative diseases, such as amyloid-beta (Aβ) or p-tau in Alzheimer’s disease, for clearance or degradation. Other drugs modulate neurotransmitter receptors in a range of neuropsychiatric, neurological, and neurodegenerative disorders—for example, serotonin reuptake inhibitors for depression or the recently approved schizophrenia treatment Cobenfy, which targets cholinergic receptors. With proteins as major targets, a better understanding of the proteome is crucial in uncovering new CNS therapies. 

However, proteomic analysis of brain tissue poses special challenges² due to the rarity of cell types in certain brain regions and the role of membrane-associated proteins, both of which limit the amount of input material available for experiments. Nevertheless, advances in sample preparation techniques, protein tagging methods, and disease models have enabled interrogation of CNS disease using proteomics. As one example, tau protein forms tangles during Alzheimer's disease and is the focus of ongoing clinical trials. Proteomics studies have recently demonstrated³ that various phosphorylated, ubiquitinated, and acetylated forms of the tau protein correlate with clinical stages of Alzheimer’s disease and may even explain the process by which tau aggregates. This work suggests that perhaps different forms of tau need to be targeted at different stages of the disease. Proteomics approaches are also yielding insight—and potential drug targets—for psychiatric and developmental disorders such as depression⁴, schizophrenia⁵, and autism⁶. 

Biomarker Development

Protein-based biomarkers (Figure 2) have long benefited the oncology field, where they have been used for decades to diagnose and stratify patients, as well as to monitor drug response and safety. For example, prostate-specific antigen (PSA) is widely used for screening (diagnosis) and risk stratification (prognosis) of prostate cancer, while HER2 is a predictive biomarker that can identify patients most likely to respond to HER2-targeted therapies. Meanwhile, other biomarkers such as ALT are used to monitor drug-induced toxicities. Notably, proteomic methods played a direct role in the development⁷ of the first multivariate index assay, OVA1, for assessing ovarian cancer risk, and proteomic discovery of cancer biomarkers continues to be an active area of interest. 

Discovery of analogous biomarkers using proteomics for the CNS therapeutic area, where clinician-rated motor and cognitive measures remain common, is a major goal. Existing protein biomarkers⁸ include tau and Aβ for Alzheimer’s and frontotemporal dementia, α-synuclein for Parkinson’s, and neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) for a range of neurodegenerative and neuroinflammatory disorders. However, for CNS disorders, precision medicine⁹ is still—compared to oncology—a relatively young field, leaving a vast opportunity for diagnostic, prognostic, and predictive biomarkers to transform treatment. 

Particularly challenging in the case of CNS disorders is uncovering clinically usable biomarkers from the brain: aside from PET or MRI-based imaging, clinicians currently rely on cerebrospinal fluid (CSF), collected using invasive lumbar puncture procedures. To address the limitations of these methods, in addition to digitally captured biomarkers that have proven to successfully detect and track subtle sensory and motor changes, there is also increasing interest in blood-based biomarkers. For instance, proteomics¹⁰ was recently used to uncover novel blood-based biomarkers for multiple sclerosis; this set of biomarkers has now undergone clinical validation¹¹ as a multivariate assay that could be used to monitor disease progression in multiple sclerosis patients. Similar work is ongoing for indications such as stroke¹² and Alzheimer’s¹³.

Data Management and Analysis Needs

Proteomics, multi-omics, or even real-world data collected from digital devices could greatly benefit therapeutic development for CNS disorders – such as biomarker discovery for more accurate diagnosis, earlier intervention to prevent complications, and identification of the most suitable patient population for a given treatment. However, scientists still face significant pain points in integrating and deriving relevant insights from such complex datasets.  

Despite the increased utility of mass spectrometry (MS), working with MS-derived proteomics datasets is associated with unique data management and analytical challenges. The data is typically widely distributed across different sources and available in a variety of formats, hampering their accessibility and usability.  Overcoming these difficulties could dramatically increase the efficiency of neuroproteomic analytical approaches in addressing complex research questions and advancing care for CNS diseases.

A Safe, Centralized, Scalable Environment

Bringing experimental results into a single point of access is fundamental for their increased usability by diverse data consumers, and thus, enhanced insight generation. Yet, improved data democratization cannot compromise its security, especially while working with sensitive patient data. Therefore, the ideal data storage and management system should be able to grant fine-grained permissions to different collaborators to ensure that only authorized users can access and work with data.

Standardized Data Operations

Once data is brought to such a secure system, it needs to be readily usable and interoperable with other data types. Since the diverse range of proteomic technologies and protocols generate complex, heterogeneous outputs, this data requires proper preparation including processing, quality control, harmonization – to create analysis-ready datasets for further operations. High-throughput proteomic data contains substantial noise and irrelevant features, which can obscure true indicators. Thus, the processing step is critical in isolating robust, accurate information so proteomics data can be used to their full potential. Yet, processing methods vary significantly across the data science community. The range of bioinformatics tools and pipelines used result in different data formats that need further re-structuring to fit standardized data models for downstream statistical analysis and integration with other data types.

Improved Data Discoverability & Reusability

A quality neuroproteomics dataset can provide value outside its primary use—potentially holding answers to other questions, but only if it is findable and accessible.  

Effective data discoverability and retrieval rely on well-thought-out data organization and standardized metadata across the organization. Yet, consistent data attributes are usually missing from analysis methods – and even if included, they often do not align with all internal conventions. Efficient data sharing within and across biopharma companies for knowledge dissemination and secondary analyses requires quality-controlled, well-annotated, structured data. Moreover, there is a need for facilitated access to self-service tools for analysis and insight generation.

Unlock Value from Proteomics with A Purpose-Built Platform

Used by biopharma organizations leading precision medicine development, Genedata Profiler^® empowers scientists to identify therapeutic targets, uncover biomarkers and develop biomarker-based solutions (e.g., companion diagnostics) from proteomics and other high-dimensional data.  In the case of CNS diseases this can include other omics data, but also summary information from structural and functional neuroimaging data (PET, fMRI, EEG), or from behavioral data (for example, tremor for Parkinson’s, gait for multiple sclerosis, and sleep, activity, and phone usage for depression). These tools ultimately can accelerate clinical development through smarter clinical trials and improve healthcare outcomes.

Optimizing Clinical Trials for Neurodegenerative Disease

A Safe, Centralized, Scalable Data Environment

Genedata Profiler provides a secure, GxP-ready environment for data collaboration to advance drug development and accelerate its path to regulatory approval. The platform’s decentralized, robust data governance allows data producers to directly regulate data access and handling activities (e.g. loading data, creating analytic sessions) by granting fine-grained user-specific permissions. These controls ensure security over sensitive patient data.  In addition, the platform’s out-of-the-box functionalities such as data integrity checks, record traceability, controlled workflows, and automated reporting ensure compliance with stringent regulatory requirements needed for patient-derived data. 

The platform’s cloud-based scalability and elastic computational power ensure complex datasets of any size from any number of data sources can be easily ingested, stored, and retrieved by data consumers for advanced compute-intensive data wrangling and analysis approaches. With such a solution, high-volume neuroproteomics data derived from a wide range of experiments or clinical studies can be accessed, analyzed and scaled as needed.

Easily Standardizing and Integrating Data Operations

Genedata Profiler is interoperable with MS processing tools such as Genedata Expressionist as well as Nextflow, Maxquant, and Spectronaut. This allows data scientists to efficiently run community-standard processing workflows within the platform’s high-performance analytics environment. They also benefit from advanced data organization, traceability, and access control. Postprocessing proteomics data transformation, quality control, and harmonization can be automated by the platform’s fit-for-purpose yet flexible workflows, ensuring consistent data outputs. Generated standardized data formats can be further integrated with other types of data, such as genomics, transcriptomics, phenotypic, or imaging data, creating analysis-ready data products for downstream exploration. The ability to easily combine them across studies and experiments, e.g., from pre-clinical and clinical workflows, allows scientists to expand their horizons and generate new hypotheses.

Improved Data Discoverability & Reusability

The platform serves as a unified access point for stored or federated, multi-source data, ensuring instant availability within and across projects. With consistently annotated data, integrated workflows, and analyses, users can easily locate and retrieve information when needed. The platform’s metadata-based, project-centric data organization and inventory system facilitate comprehensive cross-project searches based on criteria such as condition, species, sample, or protein detected.  

Additionally, Genedata Profiler features a built-in analytical layer, complete with an “app store” for self-service analysis and visualization of proteomics and other omics data.  By seamlessly connecting to proteomics databases like UniProt and other public data sources, Genedata Profiler enables functional analyses and molecular pathway explorations implicated in disease mechanisms. It also facilitates the comparison and validation of in-house experimental results.  

Combined, these features and functionalities make Genedata Profiler a high-performance solution for managing and analyzing proteomics data (Figure 3), unlocking its full potential to advance CNS drug discovery.

Conclusion

The use of proteomics data has proven invaluable in developing CNS therapies thus far. However, as the CNS drug development community is coming to realize, further progress towards precision medicine for CNS disorders hinges on the ability to integrate and leverage multidimensional big data^9,14. Genedata Profiler, a cloud-based, scalable data integration and analytics platform, enables different stakeholders in R&D projects to collaboratively work on complex pre-clinical and clinical neuroproteomics data. By providing a single point-of-access, streamlining tedious data operations, and equipping data consumers with self-service analytics, the platform improves data use and reuse for effective knowledge extraction. Ultimately, this facilitates rapid, high-quality research and development of novel therapeutics for CNS.