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Quantifying Age-related Brain Volume Changes to Support Neurodegenerative Disease Drug Development

20181115
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Incomplete understanding of the pathophysiology of neurodegenerative chronic diseases (eg, Alzheimer’s) has hindered the development of effective drug treatments. In particular, considering the impact of aging is important when analyzing clinical data derived from a geriatric population.

Physiological modeling and simulation including brain physiology (and/or pharmacology) have typically assumed that the brain has a constant volume. However, several studies have shown that the volume of the brain varies with age. It increases in volume during development with a peak around age 20 and then shows a slow and steady decrease over time. Quantifying the changes in brain volume with age is important for estimating the concentration of biomarkers in the brain, eg, amyloid beta, cholesterol, etc.

Quantitative systems pharmacology (QSP) models combine computational modeling and experimental data to examine the relationships between a drug, the biological system, and the disease process. この新興分野では、定量的な薬物データが作用機序の知見と統合されます。QSPにより、ゲノミクスおよびプロテオミクスのデータといった、現在利用可能な莫大な情報の活用が可能になると期待されます。

QSPモデルは 薬物が時空間的に細胞ネットワークをどのように変化させ、その変化とヒトの病態生理学が相互にどのように影響を与えあうかを予測します。Additionally, QSP facilitates evaluating complex, heterogeneous diseases such as cancer, immunological, metabolic, and CNS diseases that will probably require combination therapies to fully control them.

In this webinar, Dr. Cesar Pichardo will explain how he:

  • Leveraged QSP to assess the impact of brain volume changes on estimating biomarker concentrations
  • Compared the results of a model with variable brain volume with a model using constant brain volume
  • Evaluated the effect of variability in brain volume changes in a given population on estimates of biomarker concentrations

Attend this webinar to learn why many major pharma organizations are investing in QSP for its potential to improve pharma R&D productivity.

About Our Speaker

Cesar Pichardo is currently a Principal Scientist at Certara, creating quantitative systems pharmacology (QSP) models and solutions for drug discovery. He earned his first degree in Chemical Engineering (MEng) and a MSc in Systems Engineering (Control Theory) from Simon Bolivar University (Venezuela) and got his PhD in Applied Mathematics from Ecole Centrale de Lille (France). He has spent the last 15 years developing biological, physiological and medical models for lifestyle interventions, drug development, mortality risk, and actuarial science.

He has worked as a senior researcher in both academia (eg, University College London, University of Sheffield, INSA de Lyon) and industry (Pfizer, XenologiQ).

Incomplete understanding of the pathophysiology of neurodegenerative chronic diseases (eg, Alzheimer’s) has hindered the development of effective drug treatments. In particular, considering the impact of aging is important when analyzing clinical data derived from a geriatric population.

Physiological modeling and simulation including brain physiology (and/or pharmacology) have typically assumed that the brain has a constant volume. However, several studies have shown that the volume of the brain varies with age. It increases in volume during development with a peak around age 20 and then shows a slow and steady decrease over time. Quantifying the changes in brain volume with age is important for estimating the concentration of biomarkers in the brain, eg, amyloid beta, cholesterol, etc.

Quantitative systems pharmacology (QSP) models combine computational modeling and experimental data to examine the relationships between a drug, the biological system, and the disease process. この新興分野では、定量的な薬物データが作用機序の知見と統合されます。QSPにより、ゲノミクスおよびプロテオミクスのデータといった、現在利用可能な莫大な情報の活用が可能になると期待されます。

QSPモデルは 薬物が時空間的に細胞ネットワークをどのように変化させ、その変化とヒトの病態生理学が相互にどのように影響を与えあうかを予測します。Additionally, QSP facilitates evaluating complex, heterogeneous diseases such as cancer, immunological, metabolic, and CNS diseases that will probably require combination therapies to fully control them.

In this webinar, Dr. Cesar Pichardo explained how he:

  • Leveraged QSP to assess the impact of brain volume changes on estimating biomarker concentrations
  • Compared the results of a model with variable brain volume with a model using constant brain volume
  • Evaluated the effect of variability in brain volume changes in a given population on estimates of biomarker concentrations

Watch webinar to learn why many major pharma organizations are investing in QSP for its potential to improve pharma R&D productivity.

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