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相关概念视频

Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

Biopharmaceutical Factors Influencing Drug Product Design: Overview

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Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though...
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Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

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Solid dosage forms such as tablets and capsules undergo rigorous manufacturing processes to ensure stability and effectiveness. Their dissolution and absorption properties are influenced significantly by the choice of excipients (inactive ingredients that serve various roles in the formulation), and the methodology applied during production. The manufacturing parameters, such as compression force and granulation techniques, significantly affect dissolution rates. Elevated compression forces...
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Changes in polymorphic forms can significantly influence the bioavailability of poorly soluble drugs. Although the FDA defines pharmaceutical equivalence based on having the same active ingredient, dosage form, and route of administration, it does not automatically disqualify products with different polymorphic forms. This means two products with different polymorphs can still be deemed pharmaceutically equivalent. However, polymorphic differences can affect properties like wettability,...
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Biopharmaceutics and Pharmacokinetics: Overview01:28

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Understanding drugs, drug products, and their performance in pharmaceutical science is pivotal. Drugs, whether simple molecules or complex compounds, are designed to interact with the body's biological systems to diagnose, treat, or prevent diseases. Drug products include various delivery systems such as tablets, capsules, injections, and inhalers. The performance of these drug products is gauged by their ability to deliver the active ingredient to the desired site of action at the...
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Drug Dissolution: Requirements and Profile Comparison01:14

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The acceptance criteria for dissolution profile data are anchored in Q values, representing the percentage of drug dissolved within a specified period. This assessment unfolds in three stages:First Stage: The test passes if all six drug dosage units are equal to or greater than Q plus 5%; otherwise, the sample proceeds to the second stage.Second Stage: The average of twelve units must be equal to or greater than Q, with no unit falling below Q - 15% to pass; if not, it progresses to the final...
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In Vitro Drug Dissolution: Compendial Testing Models I01:13

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Compendial dissolution methods are standardized procedures defined by pharmacopeias to evaluate the rate at which a drug dissolves in a specific medium. These methods ensure batch-to-batch consistency, enable quality control, and support the prediction of drug bioavailability. They are critical for both immediate and modified-release drug products.The apparatuses used for dissolution testing differ in their design and mechanical function, but all aim to simulate the physiological environment of...
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机器学习驱动的生物制药配方开发加速使用辅助剂预测软件 (ExPreSo).

Estefania Vidal-Henriquez1, Thomas Holder1, Nicholas Franciss Lee1

  • 1LEUKOCARE AG, Am Klopferspitz 19a, 82152 Martinsried, Munich, Germany.

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概括

辅助剂预测软件 (ExPreSo) 是一款新的机器学习工具,用于建议蛋白质生物制药的最佳辅助剂. 这种计算方法通过基于蛋白质特性预测辅助剂来帮助配方开发,减少实验选.

关键词:
生物制药产品 生物制药产品辅助物质 辅助物质 辅助物质配方开发 配方开发不活性成分 不活性成分机器学习是机器学习.单克隆抗体是一种单克隆抗体.

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科学领域:

  • 生物制药配方 生物制药配方
  • 计算化学的计算化学
  • 机器学习在药物开发中的作用

背景情况:

  • 蛋白质生物制药配方是复杂的,面临着新药模式和高度的挑战.
  • 有限的药物成分和广泛的分析需求阻碍了实证辅助剂查.
  • 在实验室工作之前,急需使用in silico工具来指导辅助剂的选择.

研究的目的:

  • 引入辅助剂预测软件 (ExPreSo),这是一个用于预测蛋白质生物制药中的辅助剂的机器学习算法.
  • 为了利用蛋白质特性和目标产品概况进行in silico辅助剂预选.
  • 为了减少与传统辅助剂选方法相关的时间,成本和风险.

主要方法:

  • 开发了ExPreSo,这是一个受监督的机器学习算法,在335个监管部门批准的和蛋白质药物产品上进行了训练.
  • 利用预测特征,包括蛋白质结构性质,语言模型嵌入和药物产品特征.
  • 评估了各种ExPreSo变体的性能,包括基于序列和仅蛋白质的特征集.

主要成果:

  • 对于9种常见的辅助剂,ExPreSo表现出了良好的预测性能,并具有最小的过拟合.
  • 一个快速的,基于序列的ExPreSo变体实现了与较慢的,基于分子建模的版本相比的预测能力.
  • 仅使用蛋白质特征的ExPreSo变体显示出强大的性能,不受平台配方影响.

结论:

  • ExPreSo是第一个机器学习算法,可以使用已批准的药物产品数据集来建议生物制药辅助剂.
  • 该软件显示出在生物制药配方开发中简化辅助剂选的巨大潜力.
  • ExPreSo可以减少基于蛋白质的治疗方法的实验工作量,成本和开发时间表.