基于脂质的配方在分散和消化过程中生物相关可溶性变化的预测
Lotte Ejskjær1, René Holm2, Martin Kuentz3
1University College Cork, College Road, Cork, Ireland.
概括
计算制药学预测低溶性药物的脂基配方 (LBF) 的溶性变化. 模型确定了关键的分子描述符,以指导LBF的发展,并改善药物吸收预测.
科学领域:
- 制药科学 制药科学
- 计算化学计算化学
- 药物运输 药物运输 药物运输
背景情况:
- 基于脂质的配方 (LBF) 对于改善难溶药物的口服吸收至关重要.
- 药物溶解性和吸收的早期评估在药物产品开发中至关重要.
- 计算方法为预测配方性能提供了一个有希望的途径.
研究的目的:
- 在分散和消化过程中研究计算药学,以预测LBF中溶解性差的药物的溶解性变化.
- 识别影响生物相关介质中药物溶解性的分子描述物.
- 开发用于指导LBF战略和配方选的预测模型.
主要方法:
- 使用MicroDISS ProfilerTM与内线紫外线探针来确定30种水溶性较差的药物的消化前和消化后药物度.
- 采用部分最小平方 (PLS) 建模来识别预测分子描述符和药物特性.
- 开发了多重线性回归 (MLR) 方程,使用关键描述符 (点,LogD,芳香环) 来预测溶解率.
主要成果:
- 药物消化后的药物度因药物离子化状态而异:阴离子药物度较高,而离子和非离子药物没有一致的趋势.
- PLS模型表现出良好的校准和验证,可以预测溶解率变化在消化前 (Q2=0.64) 和消化后 (Q2=0.72).
- 使用点,LogD和芳香环的MLR模型可以很好地预测溶解率 (R2=0.70在消化前,R2=0.68在消化后).
结论:
- 计算模型可以准确地预测在分散和消化过程中,LBF中溶解不良药物的生物相关溶解性变化.
- 开发的模型支持以计算为指导的LBF策略,使得能够基于数据的可开发性决策.
- 这种方法有助于更有效地利用新兴药物的配方选资源.
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