The big data challenge – and how polypharmacology supports the translation from pre-clinical research into clinical use against neurodegenerative diseases and beyond

Abstract

The big data challenge -and how polypharmacology supports the translation from pre-clinical research into clinical use against neurodegenerative diseases and beyondIntroductory comments: The identification and validation of disease-modifying proteins are fundamental aspects in drug development.However, the multifactority of neurodegenerative diseases poses a real challenge for targeted therapies.Furthermore, the behavior of individually (over-)expressed target proteins in vitro is likely to differ from their actual functional behavior when embedded in cascades and pathways in vivo.Increased compartmentalization, e.g., in the brain, adds to the complexity.More fundamental problems arise from the use of historical data acquired by others years or even decades before with, back then, different perspectives and assumptions.Researchers from different parts of the world of varying disciplines and educational backgrounds investigate different aspects of the same neurodegenerative disease using different techniques.Despite the unambiguous importance of data diversity, this decentralized and competing research gives rise to numerous obstacles that fundamentally impact the quality and quantity of shared heterogeneous scientific data that we would like to address in this perspective, and how we envision polypharmacology as a solution for many obstacles in the field of neurodegenerative diseases. The data bias: experimental obstacles:The analysis of individual proteins is an important cornerstone of drug development.However, as no standardized procedures or language in any field of biotechnology, molecular pharmacology, or medicinal chemistry exist, experimental setups may differ in many assay parameters (Stefan et al., 2022).Greater complexity occurs in in vivo experiments, which are more commonly applied in neurodegeneration research (Mhle and Stefan et al., 2023;Wu et al., 2022).Here, data depends additionally on the disease model, treatment window, way of application, endpoints, or manner and quality of histological data to support hypotheses and (neuro-) pathological observations [e.g., amyloid-beta in Alzheimer's disease models (Wu et al., 2022) or muHTT in Huntington's disease models (Mhle and Stefam et al., 2023) in organspecific tissues].

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