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相关概念视频

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Polymorphism refers to the existence of a drug substance in multiple crystalline forms, known as polymorphs. Recently, this term has been expanded to include solvates (forms containing a solvent), amorphous forms (non-crystalline forms), and desolvated solvates (forms from which the solvent has been removed).
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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通过合作计算和实验探索,最大限度地降低多态风险

Christopher R Taylor1, Matthew T Mulvee2, Domonkos S Perenyi2

  • 1Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton SO17 1NX, U.K.

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概括
此摘要是机器生成的。

结合实验的计算晶体结构预测 (CSP) 成功识别了异化和异化的难以捉摸的多态,从而最大限度地降低了与固体药物形式相关的风险.

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科学领域:

  • 制药科学
  • 材料科学
  • 计算化学

背景情况:

  • 药物中的多态性在药物开发和制造方面带来了挑战.
  • 可以在发育晚期出现意想不到的多态,增加风险.
  • 计算式晶体结构预测 (CSP) 为探索固态景观提供了一个潜在的解决方案.

研究的目的:

  • 将最先进的CSP与实验方法相结合,以探索制药晶体结构.
  • 合理化获得特定多态的困难,并最大限度地降低晚出现的形式的风险.
  • 调查异化和异化的固体形态.

主要方法:

  • 使用先进的计算晶体结构预测 (CSP) 技术.
  • 采用了广泛的实验结晶方法,包括高压实验.
  • 应用自由能量计算来合理化实验观测.

主要成果:

  • CSP成功预测了前几十年未解决的异化物形式III的结构.
  • CSP准确地预测了iproniazid多态性的风险.
  • 通过实验获得并描述了第一批三种已知的非溶化晶体形式,所有这些都是CSP预测的.

结论:

  • 协同计算-实验方法有效地消除了药物固体形式的风险.
  • CSP是预测和理解多态的强大工具, 帮助药物开发.
  • 高压实验成功地获得了难以捉摸的晶体形式,