Quality Control Methods
Table of Contents:
The first step in ensuring the quality of ayurvedic medicine is ascertaining the identity of the starting herbal raw materials. This is achieved by studying the macromorphology and micromorphology of the herbs. Shape, size, color, texture, surface characteristics, fracture characteristics, appearance of the cut surface, odor and taste of the sample of the herb are studied. Herbarium specimens and descriptions in pharmacopeial monographs are compared with the material (Anonymous 2008a; Carranza-Rojas et al. 2017) (Figure 4.1).
The incoming raw materials should be segregated into one of a group, like powders, woods, barks, leaves, flowers, seeds, fruits, whole plants, rhizomes or roots, and unorganized drugs such as beeswax, guggul gum, olibanum, vegetable oils, agar and opium, which have a generally uniform
FIGURE 4.1 Herbarium sheet image of Datura stramonium L. Reproduced with permission from Carranza- Rojas et al. 2017.
structure but are not composed of cells. By studying the pharmacognostical characteristics of the samples, a clear distinction can be made between the powders of minerals, starches and herbs (D’Amelio 1999).
Microscopy techniques are invariably used to study the finer aspects of the herbs. Characteristics such as crystals, trichomes, palisade ratios, vessels, vein-islet number, starch granules, fibers and sclerenchyma are relied upon for effective identification of the herb (Figure 4.2). Chemical solutions like chloral hydrate solution, potassium hydroxide solution, ether-alcohol mixture, and a solution of chlorinated soda are used for dissolving the chemical inclusions in cells and isolating the tissue elements so that the botanical identity of the herbal material can be confirmed (D’Amelio 1999). Microscopic analysis is also valuable as an initial tool to detect adulterants and contaminants in crude herbal drugs.
Several analytical tests are usually carried out to assess the quality of crude herbs and to detect adulterants and contaminants. They include moisture content, foreign matter, total ash, acid-insoluble ash, alcohol-soluble extractive and water-soluble extractive and T.L.C. profile (Anonymous 2008a). Physicochemical tests are also routinely carried out to evaluate the quality of finished ayurvedic medicines. The tests conducted on various traditional ayurvedic dosage forms are presented in Table 4.3. Descriptions of these test methods are available in Lohar (2008).
Chromatographic techniques are the most versatile tools for the analysis of herbs. They can be utilized for identification and authentication, as well as for the determination of various adulterants and for standardization of the product. Unlike macroscopic, microscopic and many molecular biological methods, they are not restricted to the crude herb. They can also be applied to finished ayurvedic preparations (Wenzig and Bauer 2009).
Chromatographic methods can be used for quality control of ayurvedic medicines, based on marker compounds and chromatographic fingerprint analysis. A marker compound can be any characteristic compound of a plant, whereas biomarkers are bioactive plant constituents, representing a plant’s pharmacological activity (Techen et al. 2004). Generally, the quality of herbal medicines is judged on the basis of the analysis of one or more bioactive or characteristic compounds (Bauer 1998). This approach is widely used in the herbal medicine industry and also by many pharmacopoeias (Wenzig and Bauer 2009). To make chromatographic fingerprint analysis more meaningful, the suggestion has been made to match the fingerprint profile of an herbal preparation to the results of biological assays or clinical studies (Yuan and Lin 2000). The same approach can be adapted for ayurvedic medicines as well.
A chromatographic fingerprint of herbal medicine is a pattern representing the chemical characteristics of its ingredients. This pattern is obtained by analyzing chromatographically the pharmacologically active or characteristic chemical constituents in the preparation (Xie 2001; Ong 2002). The chromatographic profile should represent the herbal medicine being studied and should facilitate
Tests Conducted on Major Traditional Ayurvedic Dosage Forms*
* Lohar 2008.
the identification of similarity or differences between several such profiles. Thus, fingerprints can successfully reveal both “sameness” and “difference” between various samples. Herbal medicines can be accurately authenticated and identified even if the number and/or concentration of chemically characteristic constituents are not very similar in different samples of the medicine. Therefore, fingerprints can be handy tools to evaluate the quality of herbal medicines globally, considering the totality of the known and unknown constituents occurring in them (Montoro et al. 2012). T.L.C., high-performance thin-layer chromatography (H.P.T.L.C.), gas chromatography (G.C.) and high- performance liquid chromatography (H.P.L.C.) methods are commonly used for the development of chromatographic fingerprints of herbal medicines (Fan et al. 2006).
Simplicity, versatility, high speed, specificity, sensitivity and simple sample preparation make T.L.C. an inexpensive and sensitive tool for quality control of herbal medicines. Coupled with image analysis and digital technologies developed in recent years, the evaluation of similarity between different samples is possible. T.L.C. is widely used for initial screening and semi-quantitative evaluation, very often combined with other chromatographic methods (Wenzig and Bauer 2009).
H.P.T.L.C. is an advanced version of T.L.C. The modern H.P.T.L.C. technique, combined with an automated sample application and densitometric scanning, is sensitive, completely reliable and suitable for qualitative and quantitative analysis. H.P.T.L.C. is a valuable tool for reliable identification, as it can generate chromatographic fingerprints that can be visualized and stored as electronic images. The advantages of H.P.T.L.C. include high sample throughput and low cost per analysis. Multiple samples and standards can be separated simultaneously and sample preparation is easier, as the stationary phase is disposable. In H.P.T.L.C. all steps of the T.L.C. process are computer- controlled (Srivastava 2011; Wagner et al. 2011).
H.P.L.C. is widely applied for the analysis of herbal medicines because of its high separation capacity. It can be utilized to analyze almost all constituents of herbal products, provided an optimized procedure involving mobile and stationary phase is developed (Li et al. 2005). H.P.L.C. can be coupled to various detection techniques like ultraviolet diode array detection (D.A.D.), mass spectrometry (M.S.) and nuclear magnetic resonance (N.M.R.). These hyphenated techniques provide information on the structure of the compounds present in a chromatogram (Wenzig and Bauer 2009).
Application of Fingerprint Data
The establishment of a characteristic fingerprint chromatogram for the herbal medicine in question is a critical factor in the application of fingerprint data forjudging its quality (Wong et al. 2004). Several factors like extraction method, detection instruments and operating conditions influence the generation of a good chromatographic fingerprint with phytoequivalence qualities. Meaningful fingerprints are obtained when these factors are optimized. Only then can the information provided by a chromatographic fingerprint be efficiently evaluated. Chemometric methods are employed to generate and evaluate chromatographic fingerprints (Liang et al. 2004).
The information content of a chromatographic fingerprint can be calculated by means of various approaches. Generally signal intensity, retention time, peak area and or peak height of each independent peak without overlapping are taken into consideration. This necessitates the identification of each single peak and the estimation of the noise and error level of a fingerprint. Calculation of information content becomes very complex when peaks overlap (Gong et al. 2003; Liang et al. 2004).
Shifts in chromatographic retention time interfere with fingerprint analysis. They are caused by successive degradation of the stationary phase, minor changes in the composition of the mobile phase, detector and other instrumental shifts, column overloading or interactions between analytes. To avoid erroneous results, these shifts need to be corrected before the evaluation of similarities and differences between chromatograms (Li et al. 2004a; Liang et al. 2004; Wenzig and Bauer 2009). Peak synchronization is achieved by several methods. A useful method is the addition of an internal standard (Liang et al. 2004). Retention time can be corrected mathematically using local least square analysis or spectral correlative chromatography (Li et al. 2004b; 2004c).
Similarity Evaluation of Fingerprints
A simple method for the evaluation of similarity is the calculation of correlation coefficient or congruence coefficient. This method has been successfully applied to the quality control of Chinese medicines. Yang et al. (2005) used an evaluation software for evaluating the similarity of H.P.L.C.- D.A.D. generated fingerprints of 56 Hypericum japonicum samples from six Chinese provinces. They used the correlation coefficient for similarity calculation, with a reference fingerprint representing the median of all chromatograms. The chromatogram with the highest correlation coefficient was selected as an authentic reference fingerprint (Wenzig and Bauer 2009).
Principal-Component Analysis (P.C.A.)
Principal-component analysis (P.C.A.) is also commonly used for comparison of chromatographic fingerprints and detection of variations. In P.C.A. the original multivariate data are projected on a set of orthogonal axes known as principal components, defining a sub-space of the original multivariate data space that maximally describes the variation contained within that multivariate data. The axes are arranged in descending order of the amount of variation in the original data. Each principal component has an associated loading and scores vector (Johnson et al. 2003). By extraction of useful information from object data, P.C.A. is able to construct a theoretical model that has a limited validity of principal components. When a fingerprint shows unexpected properties deviating from those of good major fingerprints or features matching with the theoretical major good fingerprint model, it is excluded from the model and considered to be different (Li et al. 2004b; Gemperline 2006).
Application of Fingerprinting to Quality Control
Detection of Adulterants
The fingerprinting technique is being applied to quality control of ayurvedic herbs. The bark of Asoka (Saraca asoca) is the major ingredient of ayurvedic medicines indicated in dysfunctional bleeding. However, during a survey of major Indian crude drug markets, it was observed that almost all the samples of Asoka were mixtures of Saraca asoca, Saraca declinata and Polyalthia longifo- lia (Khatoon et al. 2009). This practice might have originated as Saraca asoca is now considered to be an endangered species. Khatoon et al. (2009) collected stem bark of Saraca asoca, Saraca declinata and Polyalthia longifolia and subjected them to H.P.T.L.C. analysis. Using p-sitosterol and stigmasterol as marker compounds, they demonstrated that all three species could be differentiated easily.
Authentication of Herbs
Irshad et al. (2016) studied the T.L.C. profiles of commercial samples of Sankhupuspi for which four different plants like Convolvuluspluricaulis, Clitoria ternatea, Evolvulus alsinoides and Tephrosia purpurea are used in different parts of the country. The authors procured commercial samples of Sankhupuspi from eight herb markets of India viz., Lucknow, Delhi, Varanasi, Hisar, Jalandhar, Dehradun, Mumbai and Jaipur. The samples were subjected to T.L.C. fingerprinting. Caffeic acid, ferulic acid, (3-sitosterol and lupeol were used as markers. The results provided interesting information. The Delhi, Jaipur and Hisar market samples comprised only Convolvulus pluricaulis. Nevertheless, samples procured from Lucknow and Varanasi were mixtures of Convolvulus pluricaulis and Evolvulus alsinoides. Sample from Jalandhar seemed to be a mixture of Convolvulus pluricaulis and Tephrosia purpurea. The Dehradun sample resembled Evolvulus alsinoides and Tephrosia purpurea. The Mumbai sample was entirely different from the other seven samples. This study show's that T.L.C. fingerprinting can be successfully applied to the quality control of ayurvedic herbs.
Quality Control of Finished Formulations
Terminalia arjuna is recommended in Ayurveda for the treatment of heart disease (Kumar and Prabhakar 1987). Recent studies report the efficacy of Terminalia arjuna in treating cardiac conditions such as anginal pain, palpitation, hypertension and ischemic heart disease (Dw'ivedi and Agarw'al 1994; Bharani et al. 1995; Dwivedi and Jauhari 1997). On account of the growing interest in this herb and to satisfy the need for a quality control tool, Chitlange et al. (2009) compared the H.P.L.C. chromatograms of authentic Arjuna ciirna and three marketed formulations. The analysis of the retention time values and U.V. data revealed eight characteristic peaks in the chromatograms, which unambiguously confirmed the presence of the authentic crude herb used in the product (Figure 4.3). The fingerprints of six common peaks observed in chromatograms of sapogenins in the standardized formulation and marketed formulations could serve as a quality control parameter for the Arjuna ciirna formulation (Figure 4.4).
Pathyasadangam kvatha is a classical ayurvedic medicine used in the treatment of cluster headache, migraine, upper respiratory diseases, earache and night blindness (Murthy 2017d). As there is a lack of information on the quality control of this medicine, Abraham et al. (2018) attempted H.P.T.L.C. fingerprinting of this medicine. Three batches of the kvatha were prepared according to standard procedures. H.P.T.L.C. analysis revealed that a mobile system consisting of toluene: ethyl acetate: formic acid (2.5: 2.0: 0.5) was suitable for the characterization of the kvatha. The analysis also generated an H.P.T.L.C. fingerprint with similarity in number, Rf, intensity and color of bands, indicating that the bioactives present in all the three batches were similar, thus helping to establish batch-to-batch consistency of the formulation (Figures 4.5 and 4.6). H.P.L.C. analysis of methanol extracts of three batches of Pathyasadangam kvatha and andrographis kvatha was carried out along with andrographolide standard. H.P.L.C. analysis furnished a fingerprint w'ith similar characteristics and showed that andrographolide is a suitable marker for standardization of the kvatha.
Sulaiman and Balachandran (2015) carried out the chemical profiling of Amrtottaram kvatha using liquid chromatography coupled with electrospray ionization mass spectrometry. Amrtottaram kvatha is an important ayurvedic formulation prepared using Tinospora cordifolia (stem), Terminalia chebula (pericarp) and Zingiber officinale (dry rhizome). It is the remedy of choice for fever. Lyophilized Amrtottaram kvatha was dissolved in deionized water and subjected to H.P.L.C. profiling using a H.P.L.C. system equipped with a photodiode array detector. The photodiode array chromatogram showed 11 major peaks. The unique H.P.L.C. fingerprint developed can be used as an analytical tool to ensure the quality of Amrtottaram kvatha.