Extending a Systems Model of the APP Pathway: Separation of β- and γ-Secretase Sequential Cleavage Steps of APP

Computational neurosciences and quantitative systems pharmacology: a powerful combination for supporting drug development in neurodegenerative diseases

Successful clinical development of new therapeutic interventions is notoriously difficult, especially in neurodegenerative diseases, where predictive biomarkers are scarce and functional improvement is often based on patient's perception, captured by structured interviews. As a consequence, mechanistic modeling of the processes relevant to therapeutic interventions in CNS disorders has been lagging behind other disease indications, probably because of the perceived complexity of the brain. However in this report, we develop the argument that a combination of Computational Neurosciences and Quantitative Systems Pharmacology (QSP) modeling of molecular pathways is a powerful simulation tool to enhance the probability of successful drug development for neurodegenerative diseases. Computational Neurosciences aims to predict action potential dynamics and neuronal circuit activation that are ultimately linked to behavioral changes and clinically relevant functional outcomes. These processes can not only be affected by the disease state, but also by common genotype variants on neurotransmitter-related proteins and the psycho-active medications often prescribed in these patient populations. Quantitative Systems Pharmacology (QSP) modeling of molecular pathways allows to simulate key pathological drivers of dementia, such as protein aggregation and neuroinflammatory responses. They often impact neurotransmitter homeostasis and voltage-gated ion-channels or lead to mitochondrial dysfunction, ultimately leading to changes in action potential dynamics and clinical readouts. Combining these two modeling approaches can lead to better actionable understanding of the many non-linear pharmacodynamic processes active in the human diseased brain. Practical applications include a rational selection of the optimal doses in combination therapies, identification of subjects more likely to respond to treatment, a more balanced stratification of treatment arms in terms of comedications, disease status and common genotype variants and re-analysis of small clinical trials to uncover a possible clinical signal. Ultimately this will lead to a higher success rate of bringing new therapeutics to the right patient populations.

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

Antibody-mediated removal of aggregated β-amyloid (Aβ) is the current, most clinically advanced potential disease-modifying treatment approach for Alzheimer's disease. We describe a quantitative systems pharmacology (QSP) approach of the dynamics of Aβ monomers, oligomers, protofibrils, and plaque using a detailed microscopic model of Aβ₄₀ and Aβ₄₂ aggregation and clearance of aggregated Aβ by activated microglia cells, which is enhanced by the interaction of antibody-bound Aβ. The model allows for the prediction of Aβ positron emission tomography (PET) imaging load as measured by a standardized uptake value ratio. A physiology-based pharmacokinetic model is seamlessly integrated to describe target exposure of monoclonal antibodies and simulate dynamics of cerebrospinal fluid (CSF) and plasma biomarkers, including CSF Aβ₄₂ and plasma Aβ₄₂ /Aβ₄₀ ratio biomarkers. Apolipoprotein E genotype is implemented as a difference in microglia clearance. By incorporating antibody-bound, plaque-mediated macrophage activation in the perivascular compartment, the model also predicts the incidence of amyloid-related imaging abnormalities with edema (ARIA-E). The QSP platform is calibrated with pharmacological and clinical information on aducanumab, bapineuzumab, crenezumab, gantenerumab, lecanemab, and solanezumab, predicting adequately the change in PET imaging measured amyloid load and the changes in the plasma Aβ₄₂ /Aβ₄₀ ratio while slightly overestimating the change in CSF Aβ₄₂ . ARIA-E is well predicted for all antibodies except bapineuzumab. This QSP model could support the clinical trial design of different amyloid-modulating interventions, define optimal titration and maintenance schedules, and provide a first step to understand the variability of biomarker response in clinical practice.

Hallmarks of neurodegenerative disease: A systems pharmacology perspective

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.

Brain Aβ load association and sexual dimorphism of plasma BACE1 concentrations in cognitively normal individuals at risk for AD

INTRODUCTION

Successful development of effective β-site amyloid precursor protein cleaving enzyme 1 (BACE1)-targeted therapies for early stages of Alzheimer's disease requires biomarker-guided intervention strategies.

METHODS

We investigated whether key biological factors such as sex, apolipoprotein E (APOE ε4) allele, and age affect longitudinal plasma BACE1 concentrations in a large monocenter cohort of individuals at risk for Alzheimer's disease. We explored the relationship between plasma BACE1 concentrations and levels of brain amyloid-β (Aβ) deposition, using positron emission tomography global standard uptake value ratios.

RESULTS

Baseline and longitudinal mean concentrations of plasma BACE1 were significantly higher in women than men. We also found a positive significant impact of plasma BACE1 on baseline Aβ-positron emission tomography global standard uptake value ratios.

DISCUSSION

Our results suggest a sexual dimorphism in BACE1-related upstream mechanisms of brain Aβ production and deposition. We argue that plasma BACE1 should be considered in further biomarker validation and qualification studies as well as in BACE1 clinical trials.

Angiopoietin-1 accelerates Alzheimer's disease via FOXA2/PEN2/APP pathway in APP/PS1 mice

Angiopoietin-1 (Ang-1), a regulatory angiogenesis protein and it has been found to be involved in the occurrence and progression of Alzheimer's disease. However, it was still to be addressed the distinctly role and the molecular mechanisms of Ang-1 affects Alzheimer's disease. Our data suggest that Ang-1 aggravated the accumulation of Aβ₄₂ and cognitive decline in APP/PS1 mice. The upregulation of APPβ is essential for Aβ₄₂ production in N2a cells overexpressing the mutational human APP gene (N2a/APP695 cells), while downregulation of PEN2 could reduce APP expression. Silencing of FOXA2 lead to inhibition of APP expression, as well as decrease of Aβ₄₂ contents. In conclusion, Ang-1 has an accelerative effect on Alzheimer's disease by increasing the secretion of Aβ₄₂ via FOXA2/PEN2/APP pathway.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Ginsenoside Compound K Regulates Amyloid β via the Nrf2/Keap1 Signaling Pathway in Mice with Scopolamine Hydrobromide-Induced Memory Impairments

The objective of this study was to investigate the neuroprotective and antioxidant effects of ginsenoside compound K (CK) in a model of scopolamine hydrobromide-induced, memory-impaired mice. The role of CK in the regulation of amyloid β (Aβ) and its capacity to activate the Nrf2/Keap1 signaling pathway were also studied due to their translational relevance to Alzheimer's disease. The Morris water maze was used to assess spatial memory functions. Levels of superoxide dismutase, glutathione peroxidase, and malondialdehyde in brain tissues were tested. Cell morphology was detected by hematoxylin and eosin staining and terminal deoxynucleotidyl transferase deoxyuridine triphosphate nick end labeling assay. Immunohistochemistry and western blotting were used to determine expression levels of Nrf2/Keap1 signaling pathway-related factors and Aβ. Ginsenoside CK was found to enhance memory function, normalize neuronal morphology, decrease neuronal apoptosis, increase superoxide dismutase and glutathione peroxidase levels, reduce malondialdehyde levels, inhibit Aβ expression, and activate the Nrf2/Keap1 signaling pathway in scopolamine-exposed animals. Based on these results, we conclude that CK may improve memory function in scopolamine-injured mice by regulating Aβ aggregation and promoting the transduction of the Nrf2/Keap1 signaling pathway, thereby reducing oxidative damage to neurons and inhibiting neuronal apoptosis. This study suggests that CK may serve as a future preventative agent or treatment for Alzheimer's disease.

Use of quantitative clinical pharmacology to improve early clinical development success in neurodegenerative diseases

INTRODUCTION

The success rate of pharmaceutical Research & Development (R&D) is much lower compared to other industries such as micro-electronics or aeronautics with the probability of a successful clinical development to approval in central nervous system (CNS) disorders hovering in the single digits (7%). Areas covered: Inspired by adjacent engineering-based industries, we argue that quantitative modeling in CNS R&D might improve success rates. We will focus on quantitative techniques in early clinical development, such as PharmacoKinetic-PharmacoDynamic modeling, clinical trial simulation, model-based meta-analysis and the mechanism-based physiology-based pharmacokinetic modeling, and quantitative systems pharmacology. Expert commentary: Mechanism-based computer modeling rely less on existing clinical datasets, therefore can better generalize than Big Data analytics, including prospectively and quantitatively predicting the clinical outcome of new drugs. More specifically, exhaustive post-hoc analysis of failed trials using individual virtual human trial simulation could illuminate underlying causes such as lack of sufficient functional target engagement, negative pharmacodynamic interactions with comedications and genotypes, and mismatched patient population. These insights are beyond the capacity of artificial intelligence (AI) methods as they are many more possible combinations than subjects. Unlike 'black box' approaches in AI, mechanism-based platforms are transparent and based on biologically sound assumptions that can be interrogated.