Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER), Sector 67, S.A.S. Nagar 160 062, Punjab, India.
Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research (NIPER), Sector 67, S.A.S. Nagar 160 062, Punjab, India.
Eur J Pharm Biopharm. 2019 Sep;142:165-178. doi: 10.1016/j.ejpb.2019.06.018. Epub 2019 Jun 19.
The present study focused upon the forced degradation behaviour of fosamprenavir (FPV), an antiretroviral drug. A total of six degradation products (DPs) were separated on a non-polar stationary phase by high performance liquid chromatography (HPLC). For the characterization, comprehensive mass fragmentation pathway of the drug was initially established using high resolution mass spectrometry (HRMS) and multi-stage tandem mass spectrometry (MS) data. Subsequently, LC-HRMS and LC-MS studies were carried out on the forced degraded samples containing the DPs. Five DPs were isolated and subjected to extensive 1D (H, C, and DEPT-135 (distortionless enhancement by polarization)) and 2D (COSY (correlation spectroscopy), TOCSY (total correlation spectroscopy), HSQC (heteronuclear single quantum coherence) and HMBC (heteronuclear multiple bond correlation)) nuclear magnetic resonance (NMR) studies to ascertain their structures, while one degradation product was subjected to LC-NMR studies, as it could not be isolated. The collated information was helpful in characterization of all the DPs, and to delineate the degradation pathway of the drug. Additionally, physicochemical, as well as absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of the drug and its DPs were evaluated in silico by ADMET Predictor™ software.
本研究集中于抗逆转录病毒药物福沙那韦(FPV)的强制降解行为。通过高效液相色谱(HPLC),在非极性固定相上分离出总共六种降解产物(DP)。为了进行表征,最初使用高分辨率质谱(HRMS)和多级串联质谱(MS)数据确定了药物的全面质谱碎裂途径。随后,对含有 DP 的强制降解样品进行了 LC-HRMS 和 LC-MS 研究。分离出五个 DP,并进行了广泛的一维(H、C 和 DEPT-135(极化无失真增强))和二维(COSY(相关光谱)、TOCSY(总相关光谱)、HSQC(异核单量子相干)和 HMBC(异核多键相关))核磁共振(NMR)研究,以确定其结构,而一个降解产物则进行了 LC-NMR 研究,因为无法对其进行分离。汇集的信息有助于对所有 DP 进行表征,并阐明药物的降解途径。此外,还通过 ADMET Predictor™软件对药物及其 DP 的物理化学、吸收、分布、代谢、排泄和毒性(ADMET)性质进行了计算机预测。