沉作为早期口服药物吸收预测的关键参数,第二部分:开发预测模型
Sara Mayer1, Katharina Krollik2, Christian Wagner2
1Institute of Pharmacy, Department of Biopharmaceutics and Pharmaceutical Technology, University of Greifswald, Felix-Hausdorff-Straße 3, 17489 Greifswald, Germany; Global Drug Product Development, Merck KGaA, Frankfurter Straße 250, 64289 Darmstadt, Germany.
概括
预测新药的口服吸收是非常重要的. 一个新的工具使用体外沉和透率数据,超过溶解度,以准确预测人类的药物吸收.
科学领域:
- 药理学 药理学是指药理学的学科.
- 药物发现 药物发现 药物发现
- 生物制药生物制药公司
背景情况:
- 对新化学实体 (NCE) 的口服吸收的准确预测对于早期药物发现至关重要.
- 生物制药分类系统 (BCS) 和生理基础生物制药建模 (PBBM) 等现有方法在定量洞察力或数据要求方面存在局限性.
- 当前的工具往往忽略了药物沉对人类吸收的影响,特别是对于弱基化合物.
研究的目的:
- 开发一种用于口服药物吸收的新型预测工具,使用随时可用的体外测定结果.
- 为了研究体外沉和体内吸收之间的关系.
- 根据口服吸收提供一个直观的工具来排名和选择候选药物.
主要方法:
- 编制了17种基本化合物的数据集,其中包括人体体体内吸收,体外透性,溶解性和沉率的数据.
- 进行了统计分析,包括回归分析和使用分类和回归树 (CART) 算法构建决策树.
- 该研究系统地分析了体外沉与溶解度对体内吸收的影响.
主要成果:
- 发现回归分析不足以预测口服吸收.
- 成功构建了一个结合实验室沉和透性的决策树模型.
- 在体外沉被确定为预测口服吸收时比溶解度更为关键的因素.
结论:
- 开发的决策树模型通过整合体外沉和透性数据,有效地预测口服吸收.
- 在体外沉试验为吸收预测提供了相对于溶解度测量的显著附加值.
- 这种新型工具非常适合药物发现,为适应和未来的进步提供灵活性.
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