Towards the virtual human patient. Quantitative Systems Pharmacology in Alzheimer's disease

Geerts, Hugo; Spiros, Äthan; Roberts, Patrick D.; Carr, Robert

doi:10.1016/j.ejphar.2017.05.062

Cited by 9 publications

(11 citation statements)

References 69 publications

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“…Third, our CCM is a simplified causal model of biomarkers interacting with each other, an abstraction that does not model the actual underlying cellular and molecular processes. Prior CCM efforts in AD have modeled the disease at a molecular and cellular level [11, 12] as well as at a whole brain, systems level using MRI and EEG data [13, 14]. There have been few prior CCM applications that have specifically focused on clinical AD biomarkers [13, 15], and only one has incorporated all three clinically available categories of biomarkers, amyloidopathy, tauopathy, and neurodegeneration [16].…”

Section: Discussionmentioning

confidence: 99%

Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease

Petrella

Hao

Rao

et al. 2019

Computational and Mathematical Methods in Medicine

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Background. Alzheimer’s disease (AD) is a major public health concern, and there is an urgent need to better understand its complex biology and develop effective therapies. AD progression can be tracked in patients through validated imaging and spinal fluid biomarkers of pathology and neuronal loss. We still, however, lack a coherent quantitative model that explains how these biomarkers interact and evolve over time. Such a model could potentially help identify the major drivers of disease in individual patients and simulate response to therapy prior to entry in clinical trials. A current theory of AD biomarker progression, known as the dynamic biomarker cascade model, hypothesizes AD biomarkers evolve in a sequential but temporally overlapping manner. A computational model incorporating assumptions about the underlying biology of this theory and its variations would be useful to test and refine its accuracy with longitudinal biomarker data from clinical trials. Methods. We implemented a causal model to simulate time-dependent biomarker data under the descriptive assumptions of the dynamic biomarker cascade theory. We modeled pathologic biomarkers (beta-amyloid and tau), neuronal loss biomarkers, and cognitive impairment as nonlinear first-order ordinary differential equations (ODEs) to include amyloid-dependent and nondependent neurodegenerative cascades. We tested the feasibility of the model by adjusting its parameters to simulate three specific natural history scenarios in early-onset autosomal dominant AD and late-onset AD and determine whether computed biomarker trajectories agreed with current assumptions of AD biomarker progression. We also simulated the effects of antiamyloid therapy in late-onset AD. Results. The computational model of early-onset AD demonstrated the initial appearance of amyloid, followed by biomarkers of tau and neurodegeneration and the onset of cognitive decline based on cognitive reserve, as predicted by the prior literature. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid or nonamyloid-related tauopathy, depending on the magnitude of comorbid pathology, and also closely matched the biomarker cascades predicted by the prior literature. Forward simulation of antiamyloid therapy in symptomatic late-onset AD failed to demonstrate any slowing in progression of cognitive decline, consistent with prior failed clinical trials in symptomatic patients. Conclusions. We have developed and computationally implemented a mathematical causal model of the dynamic biomarker cascade theory in AD. We demonstrate the feasibility of this model by simulating biomarker evolution and cognitive decline in early- and late-onset natural history scenarios, as well as in a treatment scenario targeted at core AD pathology. Models resulting from this causal approach can be further developed and refined using patient data from longitudinal biomarker studies and may in the future play a key role in personalizing approaches to treatment.

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Section: Discussionmentioning

confidence: 99%

Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease

Petrella

Hao

Rao

et al. 2019

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Third, our CCM is a simplified causal model of biomarkers interacting with each other, an abstraction that does not model the actual underlying cellular and molecular processes. Prior CCM efforts in AD have modeled the disease at a molecular and cellular level (12,13) as well as at a whole-brain, systems level using MRI and EEG data (14,15). There have been few CCM approaches that have specifically focused on clinical AD biomarkers (14,16), and none have incorporated all three clinically available categories of biomarkers, amyloidopathy, tauopathy, and neurodegeneration.…”

Section: Discussionmentioning

confidence: 99%

Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease

Petrella

Hao

Rao

et al. 2018

Preprint

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Background: Alzheimer's disease (AD) is a major public health concern and there is an urgent need to better understand its complex biology and develop effective therapies. AD progression can be tracked in patients though validated imaging and spinal fluid biomarkers of pathology and neuronal loss. We still, however, lack a coherent quantitative model that explains how these biomarkers interact and evolve over time. Such a model could potentially help identify the major drivers of disease in individual patients and simulate response to therapy prior to entry in clinical trials. A current theory of AD biomarker progression, known as the dynamic biomarker cascade model, hypothesizes AD biomarkers evolve in a sequential, but temporally overlapping manner. A computational model incorporating assumptions about the underlying biology of this theory and its variations would be useful to test and refine its accuracy with longitudinal biomarker data from clinical trials. Methods:We implemented a causal model to simulate time-dependent biomarker data under the descriptive assumptions of the dynamic biomarker cascade theory. We modeled pathologic biomarkers (beta-amyloid and tau), neuronal loss biomarkers and cognitive impairment as non-linear first order ordinary differential equations (ODEs) to include amyloid-dependent and non-dependent neurodegenerative cascades. We tested the feasibility of the model by adjusting its parameters to simulate three specific natural history scenarios in early-onset autosomal dominant AD and late-onset AD, and determine whether computed biomarker trajectories agreed with current assumptions of AD biomarker progression. We also simulated the effects of anti-amyloid therapy in late-onset AD.Results: The computational model of early-onset AD demonstrated the initial appearance of amyloid, followed by biomarkers of tau and neurodegeneration, followed by onset of cognitive decline based on cognitive reserve, as predicted by prior literature. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid or non-amyloid-related tauopathy, depending on the magnitude of comorbid pathology, and also closely matched the biomarker cascades predicted by prior literature. Forward simulation of anti-amyloid therapy in symptomatic late-onset AD failed to demonstrate any slowing in progression of cognitive decline, consistent with prior failed clinical trials in symptomatic patients. Conclusions:We have developed and computationally implemented a mathematical causal model of the dynamic biomarker cascade theory in AD. We demonstrate the feasibility of this model by simulating biomarker evolution and cognitive decline in early and late-onset natural history scenarios, as well as in a treatment scenario targeted at core AD pathology. Models resulting from this causal approach can be further developed and refined using patient data from longitudinal biomarker studies, and may in the future play a key role in personalizing approaches to treatment.

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“…QSP, which combines systems biology and PK/PD, offers a mechanism-based computer model of relevant neuronal circuits for CNS diseases. 225,226 It can be used for evaluating drug effects and optimizing the design of clinical trials. Collectively, the computational strategies above show promise in exploiting multi-omics data in pursuit of AD drug repurposing.…”

Section: Network-based Approachesmentioning

confidence: 99%

Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing

Fang

Pieper

Nussinov

et al. 2020

Medicinal Research Reviews

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Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how

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Towards the virtual human patient. Quantitative Systems Pharmacology in Alzheimer's disease

Cited by 9 publications

References 69 publications

Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease

Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease

Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer’s Disease

Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing

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