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

Cloning and functional analysis of GhDFR1, a key gene of flavonoid synthesis pathway in naturally colored cotton

BACKGROUND

The naturally colored brown cotton fiber is the most widely used environmentally friendly textile material, which primarily contains proanthocyanidins and their derivatives. Many structural genes in the flavonoid synthesis pathway are known to improve the genetic resources of naturally colored cotton. Among them, DFR is a crucial late enzyme to synthesis both anthocyanins and proanthocyanidins in the plant flavonoid pathway.

METHODS

The protein sequences of GhDFRs were analyzed using bioinformatic tools. The expression levels of GhDFRs in various tissues and organs of upland cotton Zongxu1 (ZX1), were analyzed by quantitative real-time PCR, and the expression pattern of GhDFR1 during fiber development of white cotton and brown cotton was analyzed further. The function of GhDFR1 in NCC ZX1 was preliminarily analyzed by virus induced gene silencing (VIGS) technology.

RESULTS

Bioinformatic analysis revealed that GhDFRs sequences in upland cotton genome were extremely conserved. Furthermore, evolutionary tree analysis revealed that the functions of GhDFR1 and GhDFR2, and GhDFR3 and GhDFR4, presented different and shared some similarities. Our study showed GhDFR1 and GhDFR2 were specifically expressed in fibers, while GhDFR3 and GhDFR4 were specifically expressed in petals. GhDFR1 was exclusively expressed in brown cotton fiber at various stages of development and progressively increased with the growth of fiber, but the trend of expression in white cotton was quite the opposite. We silenced GhDFR1 expression in brown cotton fiber using VIGS technology, and observed the VIGS-interference plants. After reducing the expression level of GhDFR1, the period for significant GhDFR1 expression in the developing fibers changed, reducing the content of anthocyanins, and lightening the color of mature cotton fibers.

CONCLUSION

GhDFR1 was preferentially expressed in brown cotton during fiber development. The timing of GhDFR1 expression for flavonoid synthesis altered, resulting in anthocyanin contents reduced and the fiber color of the GhDFR1i lines lightened. These findings showed the role of GhDFR1 in fiber coloration of NCC and provided a new candidate for NCC genetic improvement.

Evidence to the role of interflavan linkages and galloylation of proanthocyanidins at sustaining long-term dentin biomodification

OBJECTIVES

The interactivity of proanthocyanidins (PACs) with collagen modulates dentin matrix biomechanics and biostability. Herein, PAC extracts selected based on structural diversity were investigated to determine key PAC features driving sustained effects on dentin matrices over a period of 18months.

METHODS

The chemical profiles of PAC-rich plant sources, Pinus massoniana (PM), Cinnamomum verum (CV) and Hamamelis virginiana (HV) barks, as well as Vitis vinifera (VV) seeds, were obtained by diol HPLC analysis after partitioning of the extracts between methyl acetate and water. Dentin matrices (n=15) were prepared from human molars to determine the apparent modulus of elasticity over 18months of aging. Susceptibility of the dentin matrix to degradation by endogenous and exogenous proteases was determined by presence of solubilized collagen in supernatant, and resistance to degradation by bacterial collagenase, respectively. Data were analyzed using ANOVA and Games-Howell post hoc tests (α=0.05).

RESULTS

After 18months, dentin matrices modified by PM and CV extracts, containing only non-galloylated PACs, were highly stable mechanically (p<0.05). Dentin matrices treated with CV exhibited the lowest degradation by bacterial collagenase after 1h and 18months of aging (p<0.05), while dentin matrices treated with PM showed the least mass loss and collagen solubilization by endogenous enzymes over time (p<0.05).

SIGNIFICANCE

Resistance against long-term degradation was observed for all experimental groups; however, the most potent and long-lasting dentin biomodification resulted from non-galloylated PACs.

The Botanical Drug Substance Crofelemer as a Model System for Comparative Characterization of Complex Mixture Drugs

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.

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

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.