A comprehensive platform for quality control of botanical drugs (PhytomicsQC): a case study of Huangqin Tang (HQT) and PHY906
© Tilton et al; licensee BioMed Central Ltd. 2010
Received: 9 February 2010
Accepted: 20 August 2010
Published: 20 August 2010
Establishing botanical extracts as globally-accepted polychemical medicines and a new paradigm for disease treatment, requires the development of high-level quality control metrics. Based on comprehensive chemical and biological fingerprints correlated with pharmacology, we propose a general approach called PhytomicsQC to botanical quality control.
Incorporating the state-of-the-art analytical methodologies, PhytomicsQC was employed in this study and included the use of liquid chromatography/mass spectrometry (LC/MS) for chemical characterization and chemical fingerprinting, differential cellular gene expression for bioresponse fingerprinting and animal pharmacology for in vivo validation. A statistical pattern comparison method, Phytomics Similarity Index (PSI), based on intensities and intensity ratios, was used to determine the similarity of the chemical and bioresponse fingerprints among different manufactured batches.
Eighteen batch samples of Huangqin Tang (HQT) and its pharmaceutical grade version (PHY906) were analyzed using the PhytomicsQC platform analysis. Comparative analysis of the batch samples with a clinically tested standardized batch obtained values of PSI similarity between 0.67 and 0.99.
With rigorous quality control using analytically sensitive and comprehensive chemical and biological fingerprinting, botanical formulations manufactured under standardized manufacturing protocols can produce highly consistent batches of products.
Certificate of Analysis
The product is a brown-colored powder possessing a little sweet taste
Identify Rf value and absorb spots of TLC to reference standards
Loss on drying
Not more than 10.0%
Not less than 60.0%
Dilute alcohol-soluble extractive
Not less than 60.0%
Not more than 8.0%
Not more than 2.0%
Heavy metals (total)
Not more than 50 ppm
Not more than 50 ppm
Not more than 5 ppm
Not more than 2 ppm
Not more than 0.5 ppm
Not more than 20 ppm
A. Bacteria count (colonies/g)
A. Not more than 10000/g
B. Samonella species and Escherichia. coli
1) Identify HLPC chromatogram retention time match to reference standards
2) Marker 1 > 50.0 mg/g
Marker 2 > 7.0 mg/g
Marker 3 >5.3 mg/g
Total BHC's: Not more than 0.2 ppm
Total DDT's: Not more than 0.2 ppm
PCNB: Not more than 0.2 ppm
While the current standards for quality controls utilizes absolute quantitation of a few specific chemical marker compounds , there is increasing interest in using complete fingerprint patterns to characterize more completely the multi-chemical species . However, no single analytical chemical method has high enough sensitivity and resolution to detect every potential phytochemical class of molecules.. Thus, an orthogonal biological methodology would be a useful complementary QC metric requirement. A robust bioresponse fingerprint incorporating living cells as the biological 'detector' and the resulting genomic differential display profile [16, 17] after exposure to the botanical extract could provide a sensitive and global biological metric that may help validate batch-to-batch similarity and establish quality standards.
Huangqin Tang (HQT) is a classical Chinese medicine formula for treating gastrointestinal ailments including diarrhea, nausea and abdominal cramps . PHY906 is a modified pharmaceutical preparation of HQT (US Patent No. 7,025,993). PHY906 reduces gastrointestinal toxicity and enhances the anti-tumor efficacy of some anti-cancer drugs in animal models [19–21] and is currently under clinical investigations [22–24].
The present study aims to describe and exemplify the PhytomicsQC approach to the quality control of herbal formulae using the example of HQT and its pharmaceutical derivative PHY906.
A total of 18 batches of HQT were included in the present study. Four batches coded as PHY906-6, 7, 8, 10 were manufactured with PhytoCeutica's proprietary SOP. Eight batches of HQT were purchased from Sun Ten Pharmaceutical Co. LTD in Taiwan and designated as HQT-E, F, G, H, I, J, K and L. Six batches of HQT were obtained from various vendors (Chung Song Zong, Ko Da, Min Tong, Sheng Chang, Sheng Foong, Kaiser; Taiwan) who did not provide quality information, and were labeled as HQT-CSZ, KD, MT, SC, SF and KP3. The proprietary standard operating procedures (SOP) by PhytoCeutica for PHY906 used hot water extraction (80°C) of four herbs, namely Scutellaria baicalensis Georgi (S), Paeonia lactiflora Pall. (P), Glycyrrhiza uralensis Fisch. (G) and Ziziphus jujuba Mill. (Z) (ratio 3:2:2:2). The hot water extraction is then spray dried with insoluble dextran into a granulated powder, packaged and stored in foil containers at 4°C.
Chemical standards including baicalin (S), baicalein (S), wogonin (S), scutellarin (S), glycyrrhizin (G), ononin (G), liquiritin (G), liqiritigenin (G), paeoniflorin (P) and albiflorin (P), were obtained from Chromadex (USA). Apigenin and formic acid were obtained from Sigma-Aldrich (USA). Solvents were of LC/MS grade from JT Baker (USA).
Dried PHY906 or HQT powder (100 mg) was dissolved in one mL of 80°C water. The mixture was vortexed for one minute, placed in an 80°C water bath for 30 additional minutes with one minute of vortexing for every ten minutes. The sample was then cooled in a water bath of ambient temperature for five minutes, centrifuged for ten minutes at 10,000 rpm (Eppendorf Model 5810R, USA) and the resulting supernatant was filter (0.2 μm) sterilized. For subsequent LC/MS analysis, a 20 μL aliquot of this light brown extract was diluted with 980 μL of water. The final nominal concentration after extraction and dilution was 2 mg of dry weight PHY906 or HQT powder extract per mL of water. For biological experiments, the 100 mg/mL nominal concentration solution stock was diluted in the appropriate buffer or medium to the required final concentration.
High-performance liquid chromatography (HPLC) was performed with a Waters (USA) CapLC XE Pump equipped with a CapLC autosampler and a Waters (USA) CapLC 2996 Photodiode Array Detector. The eluents were (A) 100% water with 0.1% formic acid and (B) 100% acetonitrile with 0.1% formic acid and the column was a Waters Atlantis dC18 3 μm 0.3 mm × 150 mm NanoEase column (USA). The column was heated to 40°C and was preceded by a 0.5 μm precolumn frit. Gradient elution from 0 to 50% B over 70 minutes at 8 μL/min was used with an initial hold of five minutes. The column was then ramped to 95% B over four minutes, held in place for two minutes and returned to initial conditions over two minutes. Total run time was 120 minutes. Electrospray ionization was performed on a Micromass (UK) Q-Tof-II mass spectrometer. Samples (0.5 μL) were introduced without splitting into the electrospray interface through a 60 μm stainless steel capillary tube. A positive capillary voltage of 3.25 kV was used in positive ion mode and a negative capillary voltage of 3.25 kV was used in negative ion mode. The electrospray source was heated to 80°C and the desolvation gas (N2) was heated to 150°C at a flow rate of 400 L/hr. The Q-Tof was scanned from 50-2000 amu over one second. The resolution of the instrument under these conditions was ~10,000. For exact mass measurements, a reserpine lock mass ([M+H] of 609 amu) was introduced at the electrospray interface allowing mass measurements to be within 0.0002 amu. With external standards, mass accuracy to 0.002 amu was routine with experimental and theoretical mass matching accuracy of 20 ppm or better.
Cell culture for gene expression studies
Three cell lines, namely Jurkat (ATCC no TIB-152), KB (ATCC no CCL-17) and HepG2 (ATCC no HB-8065), were selected for the experiments. HepG2 was selected for three reasons: (1) the cell line is stable, robust and well characterized; (2) the number of differentially expressed genes in HepG2 is generally observed to be higher than in the other two cell lines and (3) the liver is considered the primary drug-metabolizing organ for oral drugs. The HepG2 hepatocellular carcinoma cell line was cloned and a cell-bank created. A strict set of SOPs were developed to ensure reproducible growth characteristics including passage number and cell density. A HepG2 sub-clone cell was thawed with three passages to 80% confluency in 10% FBS complete MEME media at 37°C with 5% CO2. Computed IC50 values (concentration required to inhibit cell growth by 50%) were based on three independent experiments comparing a 72-hour exposure of the cells to eight concentrations ranging from 0.001 to 10 mg/mL of the PHY906-6 extract with control untreated cells. Cells were stained with 0.5% methylene blue, lysed with 1% sarcosine and cell viability determined by UV/VIS absorbance at A595.
Three independent experiments were performed on the HepG2 cells treated with one IC50 dose of the herbal extract or control buffer for 24 hours. At this time point, 100% of the cells were still viable. RNA was collected for gene profiling. GeneChip hybridization experiments with Affymetrix Human genome chip U133A (USA) were carried out at the Affymetrix Resource Laboratory, Yale University School of Medicine, USA. Data were processed with Microarray Suite 5.0 (Affymetrix, USA) software to generate a list of candidate genes for further investigation.
Quantitative real-time polymerase chain reaction (qRT-PCR) experiments
Selected gene probes were purchased as Assays-on-Demand from Applied Biosystems (USA) to confirm and quantify the candidate genes identified in the GeneChip experiments.
where L is length, W is width.
After 10 to 14 days, mice with tumor sizes of 150-300 mm3 were selected. Treatment groups consisted of five mice each. Tumor size, body weight and mortality of the mice were monitored daily. Mice were sacrificed when the tumor size reached 10% of the body weight.
PHY906 was administered per oral (po) whereas Camptosar® was administered intraperitoneally (ip). PHY906 was given twice daily (bid) at approximately 10 am and 3 pm. On days when Camptosar® was also administered, PHY906 was given 30 minutes earlier. Unless otherwise indicated, dosages were 500 mg/kg for PHY906 and 360 mg/kg for Camptosar®. Mice in the control groups were administered a vehicle of either PBS (ip) or water (po). All animal studies were conducted at the Yale University Animal Facility and approved by the Institutional Animal Care and Use Committee.
Pattern comparison by R value and Phytomics Similarity Index (PSI)
The correlation value R for each column i.e. peak, can be obtained with the standard Pearson coefficient or the Spearman ranked coefficient . The result of this analysis is a vector of R scores, where each vector element corresponds to a data point (e.g. MS peak, or gene) that is common to both datasets. While each data point (i) has its own correlation score, Ri, the average of all of the individual R scores produces a diagnostic single value for similarity defined as the PSI. In this example, the PSI score would range between 0.0 (complete dissimilarity) to 1.0 (complete identity) to -1.0 (perfect anti-correlation). The individual PSI values can be weighted by a variety of factors including intensity, slope or biological importance. A weighting function found to be valuable is the individual peak slope calculated from plotting (n-1) ratios for peak i batch A to the equivalent (n-1) ratios for peak i in batch B. Highly similar batches tend to have PSI values greater than 0.85 with only a few outliers at lower PSI values. Batches that have poor similarity tend to have PSI values less than 0.75 with a greater number of individual outliers at lower PSI values. The PSI algorithm along with tools for filtering and sorting the LC/MS data were implemented in the software package PhytomicsQC™.
Quantitative analysis was performed for six markers from (S), two markers from (G) and two markers from (P). No relevant marker from (Z) was available although one definitive marker peak is identified with mass 159.085 amu. Recovery studies reported a range between 96% and 105%. Standard curves for all markers were linear in the range 0.1 to 20 mg/ml with linear correlation R-values greater than 0.99. The ten marker standards accounted for approximately 20% of the total mass of PHY906, 38% of the total mass of phytochemicals after correction for excipient and residual water content and 58% of the total mass of phytochemicals excluding excipient, residual water content and simple sugars (See Additional file 1).
Ten of the 39 peaks were identified and confirmed with external marker standards, high-resolution MS and MS/MS fragmentation. An additional 13 of 39 peaks were tentatively identified with high-resolution MS and/or MS/MS. These 23 peaks comprised 78% of the ion current intensity by all 39 peaks. The majority of these identified compounds were flavonoids (60%), saponins and triterpenoids.
PHY906 gene expression bioresponse in HepG2 cell-line
Aldo-keto reductase family 1 member B10
Carnitine palmitoyltransferase 1A
Epithelial membrane protein 2
Cell growth regulation
Glucose-6-phosphatase catalytic subunit
Glutamate-cystein ligase catalytic subunit
Growth differentiation factor 15
Hepcidin antimicrobial peptide
Insulin-like growth factor binding protein 3
Hormone, Immune response,
Cell growth regulation
Serine/threonine protein kinase PIM1
Signalling transduction and cell proliferation, oncogene
Sterile alpha motifs- and SH3 domain-containing protein 1
Cell growth regulation
Solute carrier family 7 member 11
Membrane transport protein
Son of sevenless homolog 1
Signalling transduction and cell death regulation
Tubulin, alpha 3
Signalling transduction and cell death regulation
Validation of the PSI method
The PSI method was tested and validated with artificial data sets created within the boundary conditions of typical experimental data. Two identical datasets produced a PSI value of 1.0. Random data sets provided low PSI values in the range of 0.0 to 0.1. Data values greater than ten provided a robust and stable score whereas five or fewer data points did not provide reliable results. PSI was accurate when the variations between the two datasets were spread over a majority of the data values. If only one of the data points was variable, both the PSI average and the R-value correlation were high. However, the data point was easily identified in the PSI histogram plot as a low value outlier.
Batch-to-batch comparison-chemical fingerprints
PSI and R-values for the Chemical Fingerprints of Seventeen Batches of PHY906 and HQT
Comparison of PSI and R value
Although a very modest correlation (R2 = 0.81) existed between PSI values and R values, the small range of R values could not be used definitively to discriminate between various batches of HQT. The PSI was apparently more sensitive to variations in the intensity pattern because each of the n peaks had (n-1) ratios used in defining the correlation coefficient with the corresponding peak in a separate batch while in the standard R value each peak intensity only contributed 1/n to the overall correlation coefficient.
Batch-to-batch comparison-bioresponse fingerprints
PSI of 15 Gene Expression Bioresponse Fingerprint of PHY906 and HQT batches
The challenge of assessing the consistency of different batches of a botanical extract is inherent in the phytochemical complexity of botanical extracts. This challenge is made more formidable due to the fact that two batches of a botanical extract with the same chemical spectrum may have different biological activities if the bioactive chemical species is not detectable by the specific chemical analysis methodologies. Similarly, two batches of a botanical extract with different chemical fingerprint compositions may exhibit the same biological activity if the phytochemicals responsible for the difference are biologically inert. This challenge demands comprehensive quality control of polychemical botanical extracts to include multiplexed and orthogonal methods for both chemical and biological characterization.
While the traditional chemical analysis of standard marker compounds provides a useful quantitative mass balance, patterns of information-rich chemical fingerprints provide a complementary, powerful and practical approach to herbal quality control. Well suited for the analysis of the phytochemical-rich extract of PHY906, LC-MS offers excellent sensitivity, molecular resolution and good reproducibility in providing a comprehensive chemical fingerprint pattern. Other information-rich analytical chemical methods such as LC-NMR, UV-VIS and FT-IR are also useful. However, while these methods are well suited for the characterization of low molecular weight, phytochemical-rich botanical extracts, these chemical analysis may not be well suited to fully characterize the complex and heterogeneous protein/carbohydrate profiles often associated with important herbal or fungal extracts. A complementary biological methodology is required.
Comprehensive biological methodologies such as a quantifiable and global bioresponse fingerprinting are more advantageous than a few specific single enzyme/receptor assays. The advantages are due primarily to the inherent multi-factorial biological activities of botanical extracts. Even in the absence of a complete understanding of the exact bioactive chemical species and the underlying mechanisms of action, the global fingerprints provide a comprehensive and objective assessment of an herbal extract according to quality control metrics. As illustrated by the example of PHY906, the results indicate that a sensitive cellular detector and a gene expression readout is a useful approach to characterizing an integrated bioresponse of macromolecule-rich extracts found in various fungal extracts. Examination of multiple cell types as potential "detectors" revealed that these complex polychemical mixtures only regulate a few hundreds of genes out of a total of ~18,000 possible genes. This list of a few hundred genes could be filtered down to a smaller subset of genes to form a selective, unique and quantifiable bioresponse signature pattern. Interestingly, we found no obvious similarity in the gene expression bioresponse pattern for any of the individual herbal ingredients used in the manufacture of PHY906 as compared with the complete PHY906 formulation. This finding suggests that the bioresponse of PHY906 mixture, is more complex than the simple summation of the individual bioresponses of the ingredients.
The ability to manufacture consistent batches of herbal extracts is fundamental to evidence-based scientific and clinical study of botanical extracts. The problems of botanical extract consistency [27–29] are mainly due to poor product manufacturing protocols or non-standard manufacturing procedures. The results of this study of eighteen different batches of HQT confirm that significant differences could be observed from samples from different vendors. However, the analysis also strongly indicates that when careful sourcing of botanical ingredients and standardized manufacturing protocols are employed, that multiple batches of a complex botanical formulation, produced in different years and with different harvests of raw herbal ingredients, can also be highly consistent. The present study suggests that herbal batches with chemical fingerprint PSI similarity scores greater than 0.85 are likely to be pharmacologically bioequivalent.
Chemical fingerprints and bioresponse fingerprints corroborated by an in vivo pharmacology model, provide orthogonal and complementary characterization methodologies for determining batch-to-batch similarity. Both LC/MS and qRT-PCR are standardized, highly reproducible and cost-effective for characterizing pharmaceutical botanical extracts. While neither methodology by itself is sufficient to characterize a botanical extract, the combination of chemical and biological characterization does provide information-rich, high resolution metrics for comparing different batches of an herbal extract.
PhytomicsQC will be continually improved. The next generation of the PhytomicsQC platform will include sophisticated data mining tools and multiplexed chemical and biological response fingerprints to identify the biologically active subset of the chemical fingerprints and utilize PSI values that combine chemical and biological information..
PhytomicsQC is a first generation platform for botanical quality control that integrates high resolution, global chemical fingerprints, novel bioresponse genomic expression fingerprints, in vivo validation and a statistical pattern comparison algorithm, to provide an information-rich approach to determining the batch-to-batch similarity of botanical extracts. When this comprehensive methodology was used to analyze HQT and its pharmaceutical derivative PHY906, some significant differences were found between herbal batches from different manufacturers. However, when herbal selection and manufacturing are carefully controlled, batches manufactured years apart could be highly similar in their chemical, cellular response and pharmacological profiles.
Scutellaria baicalensis Georgi
Paeonia lactiflora Pall
Glycyrrhiza uralensis Fisch
Ziziphus jujuba Mill
per oral or by mouth
"bis in die"
Latin for twice a day
Phytomics Similarity Index
- LC/MS (+) (-):
Liquid Chromatography coupled Mass Spectrometry (positive mode) (negative mode)
Total Ion Current
High Pressure Liquid Chromatography
Thin Layer Chromatography
Institutional Animal Care and Use Committee
Phytomics Similarity Index
We gratefully acknowledge the support of National Center for Complimentary and Alternative Medicine (NCCAM) and the Office of Dietary Supplements (ODS) (R44-AT001448) and the National Cancer Institute (NCI) (CA-63477) of the National Institute of Health USA and the National Foundation for Cancer Research. We also acknowledge that a small subset of the data and descriptions within this paper have been published in a recent clinical article, as a strict requirement to demonstrate quality control of PHY906 .
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