Hallmarks of neurodegenerative disease: A systems pharmacology perspective

Abstract

Age‐related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease‐modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.

Opportunities for Systems Biology and Quantitative Systems Pharmacology to Address Knowledge Gaps for Drug Development in Pregnancy

“…Its benefits are many for numerous existing and novel therapeutic areas but also for subpopulations often viewed as outside the mainstream development target population, including pediatrics and people with rare diseases. 19,20,36,48 These benefits inform not only key decision criteria (study population, indication, study design, and sampling scheme) but also the basis for clinical use (e.g., dosing timing and adjustments) and prescribing information (therapeutic window, contraindications, DDI potential, etc.). In pregnant women, for whom targeted clinical investigation is expected to be a continual challenge, we must leverage in silico techniques as much as possible, especially when they are well vetted against RWD sources or data from prospective clinical evaluation.…”

“…QSP is an important component of a model‐based drug development paradigm adopted by many within the pharmaceutical industry. Its benefits are many for numerous existing and novel therapeutic areas but also for subpopulations often viewed as outside the mainstream development target population, including pediatrics and people with rare diseases 19,20,36,48 . These benefits inform not only key decision criteria (study population, indication, study design, and sampling scheme) but also the basis for clinical use (e.g., dosing timing and adjustments) and prescribing information (therapeutic window, contraindications, DDI potential, etc.).…”

“…Examples of how these advances have made a difference include identification of new targets in the field of cancer immunotherapy and identification of targets to overcome resistance against tuberculosis drug combinations. 34 Systems biology platforms are now actively applied in the search for new therapeutics for nonalcoholic steatohepatitis and long COVID (following infection with the COVID-19 virus, some people continue to experience health problems long after having COVID-19) and are expected to play an important role in the advancement of novel medicines for neurodegenerative diseases 35,36 given the neurologic component of both disease trajectories. In a similar way, computational and systems biology has the potential to advance our understanding of the tissue-and cell-specific changes that occur during different stages of pregnancy, for example, providing a broader and more in-depth understanding of lipid and metabolic changes, and predicting how these underlying changes in metabolism can impact drug efficacy or drug metabolism.…”

A combined physiologically‐based pharmacokinetic and quantitative systems pharmacology model for modeling amyloid aggregation in Alzheimer's disease

“…Out of scope for this work is a general QSP model that tries to address the complicated multifactorial nature of neurodegenerative diseases in general and AD in particular, for instance, the interaction between amyloid and tau pathology. A recent publication 14 highlights known commonalities (disrupted proteostasis, oxidative and endoplasmic reticulum stress, metabolic dysfunction, neuroimmune system dysfunction) among pathological mechanisms across different neurodegenerative diseases and provides a summary of published QSP models that address individual pathological pathways.…”

Mechanistic models of α-synuclein homeostasis for Parkinson's disease: A blueprint for therapeutic intervention

“…To this end, the experimental analysis calls for a quantitative systems pharmacology (QSP) approach at the interface between pharmacological research and systems biology [14]. As recently highlighted in Geerts et al [15], Abrams et al [16], Bloomingdale et al [17], and Bloomingdale et al [18], QSP models indeed hold great potential in neuroscience as they provide a systems-level understanding of complex biological processes and insights into their response to drugs. Specifically, modeling efforts in PD research are moving toward multiscale QSP models that describe molecular, cellular, whole-brain, and organism levels to assess how the effects of a molecular perturbation can scale up to influence the clinical outcome [19].…”