Abstract
Crofelemer is a botanical polymeric proanthocyanidin that inhibits chloride channel activity and is used clinically for treating HIV-associated secretory diarrhea. Crofelemer lots may exhibit significant physicochemical variation due to the natural source of the raw material. A variety of physical, chemical, and biological assays were used to identify potential critical quality attributes (CQAs) of crofelemer, which may be useful in characterizing differently sourced and processed drug products. Crofelemer drug substance was extracted from tablets of one commercial drug product lot, fractionated, and subjected to accelerated thermal degradation studies to produce derivative lots with variations in chemical and physical composition potentially representative of manufacturing and raw material variation. Liquid chromatography, UV absorbance spectroscopy, mass spectrometry, and nuclear magnetic resonance analysis revealed substantial changes in the composition of derivative lots. A chloride channel inhibition cell-based bioassay suggested that substantial changes in crofelemer composition did not necessarily result in major changes to bioactivity. In 2 companion papers, machine learning and data mining approaches were applied to the analytical and biological data sets presented herein, along with chemical stability data sets derived from forced degradation studies, to develop an integrated mathematical model that can identify CQAs which are most relevant in distinguishing between different populations of crofelemer.