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モデル低次元化の活用によるQSP・ファーマコメトリクス間の隔たりの解消

20170726
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One of the biggest challenges – and, hence, the biggest opportunity for quantitative systems pharmacology (QSP) – is drug attrition in Phase 2 clinical trials. 患者集団を対象として開発化合物を投与する最初の試験は、通常、第 2 相臨床試験において実施されます。多くの医薬品開発計画において、この開発相での失敗がみられます。実際に、第 2 相試験に移行した新規化合物の約 80% は開発中止に至ります。この失敗の主要な原因として、医薬品の有効性もしくは安全性に問題が発生したことが挙げられます。

QSPとは、医薬品研究開発の生産性を向上させる大きな可能性を秘めた近年盛んに研究が進められている新領域です。It provides a framework for constructing mechanistic, mathematical models of drug action in silico. This integrative discipline incorporates pharmacometric, pharmacokinetic/pharmacodynamic (PK/PD), and physiologically-based PK (PBPK) approaches with systems biology models of biological and biochemical processes. Such models can inform the mechanisms of drug efficacy and safety, as well as confirm the ‘drugability’ of proposed targets.

Describing biological systems at this level of detail invokes the issue of model complexity. QSP models are generally too large to be validated or fit in a traditional sense and they can become intractable to standard methods of analysis or even to the modeler’s own intuition. Model reduction can alleviate these issues of complexity by eliminating portions of a system that have minimal effect upon the outputs or time-scales of interest. Such approaches yield simplified models that still provide accurate predictions. By shrinking a model’s parameter space, increasing computational speed and reducing the number of modeled state-variables, reduction techniques can help bridge the gap between pre-clinical and clinical QSP applications.

In this webinar, Dr. Tom Snowden will demonstrate:

  • Why reduction methods are a potent and necessary tool in the modeler’s arsenal;
  • How reduction methods can be applied to QSP models; and,
  • How model reduction can be used to extract scientific and business insights from complex models.

About Our Speaker

Tom Snowden received his PhD in applied mathematics and quantitative systems pharmacology from the University of Reading in 2015. He was then awarded and completed an EPSRC doctoral prize fellowship at the university, continuing his research at the interface of mathematics and pharmacology. In October 2016, Tom joined Certara QSP as a research scientist working on a range of QSP consultancy projects.

One of the biggest challenges—and, hence, the biggest opportunity for quantitative systems pharmacology (QSP)—is drug attrition in Phase 2 clinical trials. 患者集団を対象として開発化合物を投与する最初の試験は、通常、第 2 相臨床試験において実施されます。多くの医薬品開発計画において、この開発相での失敗がみられます。実際に、第 2 相試験に移行した新規化合物の約 80% は開発中止に至ります。この失敗の主要な原因として、医薬品の有効性もしくは安全性に問題が発生したことが挙げられます。

QSPとは、医薬品研究開発の生産性を向上させる大きな可能性を秘めた近年盛んに研究が進められている新領域です。It provides a framework for constructing mechanistic, mathematical models of drug action in silico. This integrative discipline incorporates pharmacometric, pharmacokinetic/pharmacodynamic (PK/PD), and physiologically-based PK (PBPK) approaches with systems biology models of biological and biochemical processes. Such models can inform the mechanisms of drug efficacy and safety, as well as confirm the ‘drugability’ of proposed targets.

Describing biological systems at this level of detail invokes the issue of model complexity. QSP models are generally too large to be validated or fit in a traditional sense and they can become intractable to standard methods of analysis or even to the modeler’s own intuition. Model reduction can alleviate these issues of complexity by eliminating portions of a system that have minimal effect upon the outputs or time-scales of interest. Such approaches yield simplified models that still provide accurate predictions. By shrinking a model’s parameter space, increasing computational speed and reducing the number of modeled state-variables, reduction techniques can help bridge the gap between pre-clinical and clinical QSP applications.

In this webinar, Dr. Tom Snowden demonstrated: why reduction methods are a potent and necessary tool in the modeler’s arsenal; how reduction methods can be applied to QSP models; and, how model reduction can be used to extract scientific and business insights from complex models.

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