Characterization of the oligomeric proanthocyanidin crofelemer toward development of an integrated mathematical model for comparison of complex molecules

Regulatory approval of follow-on biologics and other generic versions of complex pharmaceutics requires that a potential biosimilar (test) product demonstrates similarity to an innovator (reference) product through a stepwise, totality of the evidence approach. Although the best statistical approaches for assessing analytical similarity are still under debate, these investigations rely heavily upon comparability of a given pharmaceutical’s critical quality attributes (CQAs) – physicochemical and biological properties that are most relevant to clinical safety and efficacy. Selection of proper CQAs from the large amounts of physical, chemical, and biological data needed for sufficient characterization of these kinds of pharmaceutics can be difficult due to their inherent complexity and heterogeneity. Crofelemer, a botanically sourced polymeric proanthocyanidin, exhibits significant variation in final drug product resulting from processing and purification of the raw material and the botanical nature of the crude source material. From a single lot of crofelemer, various physically and chemically degraded samples were produced in an effort to create artificial lots with varying “similarity” to the reference starting material. Physical, chemical, and biological variability of the original and artificial lots were investigated using a variety of spectroscopic, chromatographic, mass-spectrometry, and biological techniques. The entirety of the analytical data collected for each crofelemer lot was then utilized in a machine learning approach to identify individual and/or combinations of CQAs which can accurately identify and distinguish subtle variations between the complex drug product (crofelemer) and the artificial lots comprised of its adulterated forms

Comparative Characterization of Crofelemer Samples Using Data Mining and Machine Learning Approaches With Analytical Stability Data Sets

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.There is growing interest in generating physicochemical and biological analytical data sets to compare complex mixture drugs, for example, products from different manufacturers. In this work, we compare various crofelemer samples prepared from a single lot by filtration with varying molecular weight cutoffs combined with incubation for different times at different temperatures. The 2 preceding articles describe experimental data sets generated from analytical characterization of fractionated and degraded crofelemer samples. In this work, we use data mining techniques such as principal component analysis and mutual information scores to help visualize the data and determine discriminatory regions within these large data sets. The mutual information score identifies chemical signatures that differentiate crofelemer samples. These signatures, in many cases, would likely be missed by traditional data analysis tools. We also found that supervised learning classifiers robustly discriminate samples with around 99% classification accuracy, indicating that mathematical models of these physicochemical data sets are capable of identifying even subtle differences in crofelemer samples. Data mining and machine learning techniques can thus identify fingerprint-type attributes of complex mixture drugs that may be used for comparative characterization of products

Chemical Stability of the Botanical Drug Substance Crofelemer: A Model System for Comparative Characterization of Complex Mixture Drugs

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.As the second of a 3-part series of articles in this issue concerning the development of a mathematical model for comparative characterization of complex mixture drugs using crofelemer (CF) as a model compound, this work focuses on the evaluation of the chemical stability profile of CF. CF is a biopolymer containing a mixture of proanthocyanidin oligomers which are primarily composed of gallocatechin with a small contribution from catechin. CF extracted from drug product was subjected to molecular weight–based fractionation and thiolysis. Temperature stress and metal-catalyzed oxidation were selected for accelerated and forced degradation studies. Stressed CF samples were size fractionated, thiolyzed, and analyzed with a combination of negative-ion electrospray ionization mass spectrometry (ESI-MS) and reversed-phase-HPLC with UV absorption and fluorescence detection. We further analyzed the chemical stability data sets for various CF samples generated from reversed-phase-HPLC-UV and ESI-MS using data-mining and machine learning approaches. In particular, calculations based on mutual information of over 800,000 data points in the ESI-MS analytical data set revealed specific CF cleavage and degradation products that were differentially generated under specific storage/degradation conditions, which were not initially identified using traditional analysis of the ESI-MS results