Department of Pharmacy, Faculty of Health and Medical Sciences, University of Copenhagen, Universitetsparken 2, 2100 Copenhagen, Denmark.
University of Eastern Finland, School of Pharmacy, Yliopistonranta 1, P.O. Box 1627, FI-70211 Kuopio, Finland; University Hospital Tübingen, Dept. of Internal Medicine I, Division of Translational Gastrointestinal Oncology, Otfried-Müller-Strasse 10, 72076 Tübingen, Germany.
Eur J Pharm Sci. 2018 Jul 1;119:31-38. doi: 10.1016/j.ejps.2018.04.014. Epub 2018 Apr 10.
Co-amorphous systems consisting of a drug and an amino acid have been investigated extensively for the enhancement of drug solubility and amorphous stability. The purpose of this study is to investigate which molecular descriptors are important for predicting the likelihood of a successful co-amorphisation between amino acid and drug. The predictions are thought to be used in an early screening phase to identify potential drug-amino acid combinations for further studies. A large variety of molecular descriptors was calculated for six drugs (carvedilol, mebendazole, carbamazepine, furosemide, indomethacin and simvastatin) and the twenty naturally occurring amino acids. The descriptor differences for all drug-amino acid combinations were calculated and used as input in the X-matrix of a Partial Least Square Discriminant Analysis (PLS-DA). The Y-matrix of the PLS-DA consisted of the X-ray powder diffraction response ("co-amorphous" or "not co-amorphous") obtained by ball milling all combinations for 60 min. The PLS-DA model showed a clear separation of the not co-amorphous and the co-amorphous samples and was successfully predicting the class membership of 19 out of the 20 completely left out drug-amino acid combinations of mebendazole. The approach seems to be promising for predicting the ability of new drug-amino acids combinations to become co-amorphous.
共无定形系统由药物和氨基酸组成，广泛用于提高药物的溶解度和无定形稳定性。本研究旨在探讨哪些分子描述符对于预测氨基酸和药物之间成功共晶化的可能性很重要。这些预测被认为可以在早期筛选阶段使用，以识别潜在的药物-氨基酸组合，以进行进一步研究。为六种药物（卡维地洛、甲苯咪唑、卡马西平、呋塞米、吲哚美辛和辛伐他汀）和二十种天然存在的氨基酸计算了大量的分子描述符。计算了所有药物-氨基酸组合的描述符差异，并将其用作偏最小二乘判别分析（PLS-DA）的 X 矩阵的输入。PLS-DA 的 Y 矩阵由通过将所有组合球磨 60 分钟获得的 X 射线粉末衍射响应（“共无定形”或“非共无定形”）组成。PLS-DA 模型清楚地区分了非共无定形和共无定形样品，并成功预测了甲苯咪唑的 20 种完全排除在外的药物-氨基酸组合中的 19 种组合的类成员归属。该方法似乎有望预测新的药物-氨基酸组合成为共无定形的能力。
