Quantitative Systems Pharmacology Model for Alzheimer Disease Indicates Targeting Sphingolipid Dysregulation as Potential Treatment Option

Analysis of Cerebrospinal Fluid, Plasma β-Amyloid Biomarkers, and Cognition from a 2-Year Phase 2 Trial Evaluating Oral ALZ-801/Valiltramiprosate in APOE4 Carriers with Early Alzheimer's Disease Using Quantitative Systems Pharmacology Model

INTRODUCTION

ALZ-801/valiltramiprosate is an oral, small-molecule inhibitor of beta-amyloid (Aβ) aggregation and oligomer formation in late-stage development as a disease-modifying therapy for early Alzheimer's disease (AD). The present investigation provides a quantitative systems pharmacology (QSP) analysis of amyloid fluid biomarkers and cognitive results from a 2-year ALZ-801 Phase 2 trial in APOE4 carriers with early AD.

METHODS

The single-arm, open-label phase 2 study evaluated effects of ALZ-801 265 mg two times daily (BID) on cerebrospinal fluid (CSF) and plasma amyloid fluid biomarkers over 104 weeks in APOE4 carriers with early AD [Mini-Mental State Examination (MMSE) ≥ 22]. Subjects with positive CSF biomarkers for amyloid (Aβ42/Aβ40) and tau pathology (p-tau181) were enrolled, with serial CSF and plasma levels of Aβ42 and Aβ40 measured over 104 weeks. Longitudinal changes of CSF Aβ42, plasma Aβ42/Aβ40 ratio, and cognitive Rey Auditory Verbal Learning Test (RAVLT) were compared with the established natural disease trajectories in AD using a QSP approach. The natural disease trajectory data for amyloid biomarkers and RAVLT were extracted from a QSP model and an Alzheimer's disease neuroimaging initiative population model, respectively. Analyses were stratified by disease severity and sex.

RESULTS

A total of 84 subjects were enrolled. Excluding one subject who withdrew at the early stage of the trial, data from 83 subjects were used for this analysis. The ALZ-801 treatment arrested the progressive decline in CSF Aβ42 level and plasma Aβ42/Aβ40 ratio, and stabilized RAVLT over 104 weeks. Both sexes showed comparable responses to ALZ-801, whereas mild cognitive impairment (MCI) subjects (MMSE ≥ 27) exhibited a larger biomarker response compared with more advanced mild AD subjects (MMSE 22-26).

CONCLUSIONS

In this genetically defined and biomarker-enriched early AD population, the QSP analysis demonstrated a positive therapeutic effect of oral ALZ-801 265 mg BID by arresting the natural decline of monomeric CSF and plasma amyloid biomarkers, consistent with the target engagement to prevent their aggregation into soluble neurotoxic oligomers and subsequently into insoluble fibrils and plaques over 104 weeks. Accompanying the amyloid biomarker changes, ALZ-801 also stabilized the natural trajectory decline of the RAVLT memory test, suggesting that the clinical benefits are consistent with its mechanism of action. This sequential effect arresting the disease progression on biomarkers and cognitive decline was more pronounced in the earlier symptomatic stages of AD. The QSP analysis provides fluid biomarker and clinical evidence for ALZ-801 as a first-in-class, oral small-molecule anti-Aβ oligomer agent with disease modification potential in AD.

TRIAL REGISTRY

https://clinicaltrials.gov/study/NCT04693520.

Assessing the performance of QSP models: biology as the driver for validation

Validation of a quantitative model is a critical step in establishing confidence in the model's suitability for whatever analysis it was designed. While processes for validation are well-established in the statistical sciences, the field of quantitative systems pharmacology (QSP) has taken a more piecemeal approach to defining and demonstrating validation. Although classical statistical methods can be used in a QSP context, proper validation of a mechanistic systems model requires a more nuanced approach to what precisely is being validated, and what role said validation plays in the larger context of the analysis. In this review, we summarize current thoughts of QSP validation in the scientific community, contrast the aims of statistical validation from several contexts (including inference, pharmacometrics analysis, and machine learning) with the challenges faced in QSP analysis, and use examples from published QSP models to define different stages or levels of validation, any of which may be sufficient depending on the context at hand.

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

Pregnant women are still viewed as therapeutic orphans to the extent that they are avoided as participants in mainstream clinical trials and not considered a priority for targeted drug research despite the fact that many clinical conditions exist during pregnancy for which pharmacotherapy is warranted. Part of the challenge is the uncertain risk potential that pregnant women represent in the absence of timely and costly toxicology and developmental pharmacology studies, which only partly mitigate such risks. Even when clinical trials are conducted in pregnant women, they are often underpowered and absent biomarkers and exclude evaluation across multiple stages of pregnancy where relevant development risk could have been assessed. Quantitative systems pharmacology model development has been proposed as one solution to fill knowledge gaps, make earlier and perhaps more informed risk assessment, and design more informative trials with better recommendations for biomarker and end point selection including design and sample size optimality. Funding for translational research in pregnancy is limited but will fill some of these gaps, especially when joined with ongoing clinical trials in pregnancy that also fill certain knowledge gaps, especially biomarker and end point evaluation across pregnancy states linked to clinical outcomes. Opportunities exist for further advances in quantitative systems pharmacology model development with the inclusion of real-world data sources and complimentary artificial intelligence/machine learning approaches. The successful coordination of the approach reliant on these new data sources will require commitments to share data and a diverse multidisciplinary group that seeks to develop open science models that benefit the entire research community, ensuring that such models can be used with high fidelity. New data opportunities and computational resources are highlighted in an effort to project how these efforts can move forward.

A Phase 1 First-in-Human Single-Ascending-Dose Trial With ESB1609, a Selective Agonist to the Sphingosine-1-Phosphate Receptor 5

ESB1609 is a small-molecule sphingosine-1-phosphate-5 receptor-selective agonist designed to restore lipid homeostasis by promoting cytosolic egress of sphingosine-1-phosphate to reduce abnormal levels of ceramide and cholesterol in disease. A phase 1 study was conducted in healthy volunteers to determine the safety, tolerability, and pharmacokinetics of ESB1609. Following single oral doses, ESB1609 demonstrated linear pharmacokinetics in plasma and cerebrospinal fluid (CSF) for formulations containing sodium laurel sulfate. Plasma and CSF median time to maximum drug concentration (t_max ) were reached by 4-5 hours and 6-10 hours, respectively. The delay in achieving t_max in CSF relative to plasma, likely due to the high protein binding of ESB1609, was also observed in 2 rat studies. Continuous CSF collection via indwelling catheters confirmed that a highly protein-bound compound is measurable and established the kinetics of ESB1609 in human CSF. Mean plasma terminal elimination half-lives ranged from 20.2 to 26.8 hours. The effect of either a high-fat or standard meal increased maximum plasma concentration and area under the concentration-time curve from time 0 to infinity compared to the fasted state by 2.42-4.34-fold higher, but t_max and half-life remained the same irrespective of fed state. ESB1609 crosses the blood-brain barrier with CSF:plasma ratios ranging between 0.04% and 0.07% across dose levels. ESB1609 demonstrated a favorable safety and tolerability profile at exposures expected to be efficacious.

Clinical relevance of animal models in aging-related dementia research

Alzheimer's disease (AD) and other, less prevalent dementias are complex age-related disorders that exhibit multiple etiologies. Over the past decades, animal models have provided pathomechanistic insight and evaluated countless therapeutics; however, their value is increasingly being questioned due to the long history of drug failures. In this Perspective, we dispute this criticism. First, the utility of the models is limited by their design, as neither the etiology of AD nor whether interventions should occur at a cellular or network level is fully understood. Second, we highlight unmet challenges shared between animals and humans, including impeded drug transport across the blood-brain barrier, limiting effective treatment development. Third, alternative human-derived models also suffer from the limitations mentioned above and can only act as complementary resources. Finally, age being the strongest AD risk factor should be better incorporated into the experimental design, with computational modeling expected to enhance the value of animal models.

Sphingosine 1-phosphate attenuates neuronal dysfunction induced by amyloid-β oligomers through endocytic internalization of NMDA receptors

Soluble oligomers arising from the aggregation of the amyloid beta peptide (Aβ) have been identified as the main pathogenic agents in Alzheimer's disease (AD). Prefibrillar oligomers of the 42-residue form of Aβ (Aβ₄₂ O) show membrane-binding capacity and trigger the disruption of Ca²⁺ homeostasis, a causative event in neuron degeneration. Since bioactive lipids have been recently proposed as potent protective agents against Aβ toxicity, we investigated the involvement of sphingosine 1-phosphate (S1P) signalling pathway in Ca²⁺ homeostasis in living neurons exposed to Aβ₄₂ O. We show that both exogenous and endogenous S1P rescued neuronal Ca²⁺ dyshomeostasis induced by toxic Aβ₄₂ O in primary rat cortical neurons and human neuroblastoma SH-SY5Y cells. Further analysis revealed a strong neuroprotective effect of S1P₁ and S1P₄ receptors, and to a lower extent of S1P₃ and S1P₅ receptors, which activate the G_i -dependent signalling pathways, thus resulting in the endocytic internalization of the extrasynaptic GluN2B-containing N-methyl-D-aspartate receptors (NMDARs). Notably, the S1P beneficial effect can be sustained over time by sphingosine kinase-1 overexpression, thus counteracting the down-regulation of the S1P signalling induced by Aβ₄₂ O. Our findings disclose underlying mechanisms of S1P neuronal protection against harmful Aβ₄₂ O, suggesting that S1P and its signalling axis can be considered promising targets for therapeutic approaches for AD.

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.

New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer’s disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.

Applications of Quantitative System Pharmacology Modeling to Model-Informed Drug Development

Significant advances in analytical technologies have dramatically improved our ability to deconvolute disease biology at molecular, cellular, and tissue levels. Quantitative system pharmacology (QSP) modeling is a computational framework to systematically integrate pharmaceutical properties of a drug candidate with scientific understanding of that deeper disease etiology, target expression, genetic variability, and human physiological processes, thus enabling more insightful drug development decisions related to efficacy and safety. In this chapter, we discuss the key attributes of QSP models in comparison to traditional models. We discuss a recommended four-step process to construct a QSP model to support drug development decisions. A number of illustrative QSP examples related to high-value drug development questions and decisions impacting target identification, lead generation and optimization, first in human studies, and clinical dose and schedule optimization are covered in the chapter. The future perspectives of QSP in the context of potential regulatory acceptance are also discussed.

Quantitative systems pharmacology model for Alzheimer’s disease to predict the effect of aducanumab on brain amyloid

Alzheimer's disease (AD) is an irreversible, progressive brain disorder that impairs memory and cognitive function. Dysregulation of the amyloid‐β (Aβ) pathway and amyloid plaque accumulation in the brain are hallmarks of AD. Aducanumab is a human, immunoglobulin gamma 1 monoclonal antibody targeting aggregated forms of Aβ. In phase Ib and phase III studies, aducanumab reduced Aβ plaques in a dose dependent manner, as measured by standard uptake value ratio of amyloid positron emission tomography imaging. The goal of this work was to develop a quantitative systems pharmacology model describing the production, aggregation, clearance, and transport of Aβ as well as the mechanism of action for the drug to understand the relationship between aducanumab dosing regimens and changes of different Aβ species, particularly plaques in the brain. The model was used to better understand the pharmacodynamic effects observed in the clinical trials of aducanumab and assist in the clinical development of future Aβ therapies.

Systematic in silico analysis of clinically tested drugs for reducing amyloid-beta plaque accumulation in Alzheimer's disease

Introduction

Despite strong evidence linking amyloid beta (Aβ) to Alzheimer's disease, most clinical trials have shown no clinical efficacy for reasons that remain unclear. To understand why, we developed a quantitative systems pharmacology (QSP) model for seven therapeutics: aducanumab, crenezumab, solanezumab, bapineuzumab, elenbecestat, verubecestat, and semagacestat.

Methods

Ordinary differential equations were used to model the production, transport, and aggregation of Aβ; pharmacology of the drugs; and their impact on plaque.

Results

The calibrated model predicts that endogenous plaque turnover is slow, with an estimated half‐life of 2.75 years. This is likely why beta‐secretase inhibitors have a smaller effect on plaque reduction. Of the mechanisms tested, the model predicts binding to plaque and inducing antibody‐dependent cellular phagocytosis is the best approach for plaque reduction.

Discussion

A QSP model can provide novel insights to clinical results. Our model explains the results of clinical trials and provides guidance for future therapeutic development.

A Quantitative Systems Pharmacology Model of Gaucher Disease Type 1 Provides Mechanistic Insight Into the Response to Substrate Reduction Therapy With Eliglustat

Gaucher’s disease type 1 (GD1) leads to significant morbidity and mortality through clinical manifestations, such as splenomegaly, hematological complications, and bone disease. Two types of therapies are currently approved for GD1: enzyme replacement therapy (ERT), and substrate reduction therapy (SRT). In this study, we have developed a quantitative systems pharmacology (QSP) model, which recapitulates the effects of eliglustat, the only first‐line SRT approved for GD1, on treatment‐naïve or patients with ERT‐stabilized adult GD1. This multiscale model represents the mechanism of action of eliglustat that leads toward reduction of spleen volume. Model capabilities were illustrated through the application of the model to predict ERT and eliglustat responses in virtual populations of adult patients with GD1, representing patients across a spectrum of disease severity as defined by genotype‐phenotype relationships. In summary, the QSP model provides a mechanistic computational platform for predicting treatment response via different modalities within the heterogeneous GD1 patient population.

Applications of Quantitative Systems Pharmacology in Model-Informed Drug Discovery: Perspective on Impact and Opportunities

Quantitative systems pharmacology (QSP) approaches have been increasingly applied in the pharmaceutical since the landmark white paper published in 2011 by a National Institutes of Health working group brought attention to the discipline. In this perspective, we discuss QSP in the context of other modeling approaches and highlight the impact of QSP across various stages of drug development and therapeutic areas. We discuss challenges to the field as well as future opportunities.

Beneficial Effects of Fingolimod in Alzheimer's Disease: Molecular Mechanisms and Therapeutic Potential

Alzheimer's disease (AD), the most common cause of dementia remains of unclear etiology with current pharmacological therapies failing to halt disease progression. Several pathophysiological mechanisms have been implicated in AD pathogenesis including amyloid-β protein (Aβ) accumulation, tau hyperphosphorylation, neuroinflammation and alterations in bioactive lipid metabolism. Sphingolipids, such as sphingosine-1-phosphate (S1P) and intracellular ceramide/S1P balance are highly implicated in central nervous system physiology as well as in AD pathogenesis. FTY720/Fingolimod, a structural sphingosine analog and S1P receptor (S1PR) modulator that is currently used in the treatment of relapsing-remitting multiple sclerosis (RRMS) has been shown to exert beneficial effects on AD progression. Recent in vitro and in vivo evidence indicate that fingolimod may suppress Aβ secretion and deposition, inhibit apoptosis and enhance brain-derived neurotrophic factor (BDNF) production. Furthermore, it regulates neuroinflammation, protects against N-methyl-D-aspartate (NMDA)-excitotoxicity and modulates receptor for advanced glycation end products signaling axis that is highly implicated in AD pathogenesis. This review discusses the underlying molecular mechanisms of the emerging neuroprotective role of fingolimod in AD and its therapeutic potential, aiming to shed more light on AD pathogenesis as well as direct future treatment strategies.