Amit Palande, application specialist under the guidance of Akshay Charegaonkar (managing director), Tukaram Thite (senior lab manager) and Dr. Saikat Mallick (lab manager) work at Anchrom Enterprises Pvt Ltd, Mumbai, India. The company specializes in instrumental planar chromatography, and develops new, quantitative, and regulatory compliant analytical methods for pharmaceutical formulations, APIs, herbal products, food products, organic intermediates, dyes etc. Mr. Palande benefits from HPTLC because it is a fast, simple, economical and flexible, “visible chromatography” technique. HPTLC is risk-free and multiple detections can be made without repeating chromatography. This is especially helpful for dihydroartemisinin, because it is not UV absorbing and needs derivatization for densitometric detection.

Dihydroartemisinin (also known as dihydroqinghaosu, artenimol or DHA) is a drug used to treat malaria. It is globally recognized for its efficacy and safety in the clinical treatment of malaria for decades. DHA is the active metabolite of all artemisinin compounds (artemisinin, artesunate, artemether, etc.) and is also available as a drug by itself. It is a semi-synthetic derivative of artemisinin and is widely used as an intermediate in the preparation of other artemisinin derived antimalarial drugs. DHA is often combined with piperaquine phosphate (PPQ). Like any formulation, these tablets need to be tested for shelf life i.e. stability.

Shelf-life studies are performed by accelerated or forced degradation studies as per ICH guidelines Q 1 A (R2).

Chemical structures of dihydroartemisinin and piperaquine phosphate

HPTLC is well suited for stability studies, because it is inexpensive and time-saving. Accelerated degradation studies need a very large number of samples to be analyzed. By HPTLC, 15-20 samples can be quantified simultaneously on one plate in about 40–80 minutes. The solvent consumption is only about 20 mL for those 20 samples and little waste is produced. It was separately established, that piperaquine phosphate did not degrade in the studies. Hence a method was developed that kept piperaquine phosphate at the base of the chromatogram but selectively moved DHA and its two degradation products. DHA and the degradation products were detected by simple derivatization and then quantitatively evaluated.

HPTLC is a cost-effective and time-saving technique for the pharma industry, which deals with a heavy load of samples, also competition to introduce a new formulation is very intense. The presented method is a green method, which only uses 20 mL of solvent for 15–20 samples and produces almost no waste. Since the degradation products are unknown, the common procedure of establishing the calibration function using the diluted main substance (DHA in this case) was applied. Many times, as in this case, the samples have to be overloaded to detect the small quantities of impurities/degradation products.

A stock solution of 1.0 mg/mL of dihydroartemisinin in acetonitrile is prepared and diluted 1:20 (V/V) for analysis.

Ten tablets containing 40.0 mg of dihydroartemisinin and 320.0 mg of piperaquine phosphate are milled. Powdered tablets equivalent to 100.0 mg of DHA are transferred in a 20.0 mL volumetric flask and dissolved in acetonitrile. The supernatant after centrifugation is used for application.

HPTLC plates silica gel 60 F₂₅₄ (Merck), 20 x 10 cm are used.

Samples and standard solutions are applied as bands with the Automatic TLC Sampler (ATS 4), 15 tracks, band length 8.0 mm, distance from left edge 20.0 mm, distance from lower edge 8.0 mm. 10 μL for sample and standard solutions are applied.

Plates are developed in a 20 x 10 cm Twin Trough Chamber with chamber saturation (with filter paper) for 20min, development with cyclohexane – ethyl acetate – glacial acetic acid 10:5:1 (V/V) to the migration distance of 90 mm (from the lower edge), followed by drying for 5 min.

Plates are sprayed with 3.0 mL of anisaldehydesulfuric acid reagent (to 170 mL of cooled methanol, 20 mL of acetic acid and 10 mL of sulfuric acid are added, after cooling to room temperature 1mL of anisaldehyde is added) using the CAMAG Derivatizer. After spraying, the plates are heated at 110 °C for 5 min using the TLC Plate Heater.

Images of the plate are captured with the TLC Visualizer in UV 366 nm and white light.

Absorbance measurement at 540 nm (tungsten lamp) is performed with CAMAG TLC Scanner 4 and visionCATS, slit dimension 6.00 mm x 0.45 mm, scanning speed 20 mm/s, evaluation via peak area.

The determination of the degradation product with the mobile phase cyclohexane – ethyl acetate – glacial acetic acid 10:5:1 (V/V) is verified by the positions of the individual drugs, where PPQ remains at the application position and DHA moves to hRF 40. Any other zones at different hR_F are reported as degradation products. The placebo shows no zones in the chromatogram. Prior to derivatization, no zones are visible. Quantitative evaluation is performed after derivatization by densitometric absorbance measurement at 540 nm. The data is recorded and used to calculate the degradation products. Total impurities in the samples are found to be within the 1.5% limit as per specification, defined in house. A representative densitogram of a stability batch sample and standard is shown. The area of each impurity is calculated as follows and then summarized.

[1] CAMAG Application Note A-86.1: Determination of artemisinin in Artemisia annua leaf by HPTLC

Contact: Akshay Charegaonkar, A 101-104, Shree Aniket Apartment, Navghar Road, Mulund, Mumbai 400081, India, hptlc@anchrom.in, www.anchrom.in

In 2022, CAMAG laboratory introduced the concept of complementary developing solvents (CDS) based on the combination of three automated analyses using three developing solvents (DS): one low polarity (LPDS), one medium polarity (MPDS), and one high polarity (HPDS) solvent [1]. With these three DS, any compound is characterized by three R_F values instead of one. Introduced into a database, a large dataset can be compiled from these values, which then may be subjected to data mining and machine learning.

In their paper [2], CAMAG researchers describe how the application of a CDS protocol to a large number of highly diverse individual compounds was used in combination with machine learning to predict the R_F values of individual substances from molecular properties, and to generate proposals for the identity of a zone. Coupled with machine learning, the CDS concept as a very powerful, general, and medium to high throughput technique for routine analysis and sophisticated research, may become the future of HPTLC. It may help replacing common tasks such as visual evaluation and pattern recognition, as well as subjective pass/fail decisions by automated procedures and numerical values generated by suitable algorithms.

Individual standard solutions were prepared at a concentration of 1.0 mg/mL (adjusted when necessary). Methanol was used as solvent for iridoids, coumarins, pharmaceutical drugs, flavonoids, triterpenes, sesquiterpenes, steroids, phospholipids and cannabinoids, 50% aqueous acetonitrile for carbohydrates, 50% aqueous methanol for amino acids, and toluene for monoterpenes. System Suitability Test (SST): the ready to use solution of Universal HPTLC Mix (UHM) was prepared in house according to [3] and applied on each plate.

HPTLC plates silica gel 60 F₂₅₄ (Supelco/Merck), 20 x 10 cm are used.

2.0 μL of sample solutions are applied as bands with the Automatic TLC Sampler (ATS 4), 15 tracks, band length 8.0 mm, distance from left edge 20.0 mm, distance from lower edge 8.0 mm.

Plates were developed with the three developing solvents in the ADC 2 with activation of the plate at 33% relative humidity for 10 min using a saturated solution of magnesium chloride. LPDS (toluene, ethyl acetate 9:1 (V/V)) is used without saturation, whereas MPDS (cyclopentyl methyl ether, tetrahydrofuran, water, formic acid 40:24:1:1 (V/V)) and HPDS (ethanol, dichloromethane, water, formic acid 16:16:4:1 (V/V)) are used with 20 min chamber saturation (with saturation pad). The developing distance for all three methods was 70 mm (from the lower edge). Plates were dried for 5 min.

Derivatization with anisaldehyde sulfuric acid reagent (10.0 mL of sulfuric acid were carefully added to the ice-cold mixture of 170.0 mL of methanol and 20.0 mL of acetic acid. To this solution, 1.0 mL of anisaldehyde was added) by spraying (Derivatizer, blue nozzle, 3.0 mL, spraying level 3) was followed by 3 min of heating at 100°C. Images of plates derivatized with Fast blue salt B reagent (250.0 mg of Fast blue salt B (o-dianisidine bis(diazotized) zinc double salt) were dissolved in 10.0 mL of water and mixed with 25.0 mL of methanol and 15.0 mL of dichloromethane) were captured within 2 min after spraying (Derivatizer, green nozzle, 3.0 mL, spraying level 5).

For the derivatization with NP reagent (1.0 g of diphenylborinic acid aminoethylester was dissolved in 200 mL of ethyl acetate ) / PEG (10 g of polyethylene glycol 400 (macrogol) were dissolved in 200 mL of dichloromethane), the plates were heated at 100 °C for 3 min, cooled to room temperature, then sprayed with the mixture NP/PEG 1:1 (V/V) (Derivatizer, green nozzle, 3.0 mL, spraying level 3), and dried for 2 min. Derivatization by immersion (Immersion Device, speed 5, time 0) with toluene sulfonic acid reagent (10% of p-toluene sulfonic acid in ethanol) was followed by heating at 150°C for 3 min.

TLC Visualizer in UV 254 nm, UV 366 nm, and white light prior to derivatization, and UV 366 nm, and white light after derivatization (as needed).

For the UHM, TLC Scanner 4 and visionCATS, absorbance measurement at 254 nm, slit dimension 5.00 mm x 0.20 mm, scanning speed 50 mm/s, and in fluorescence mode at 366>/400 nm.

The open-source software KNIME (version 4.6) was used. The “RDKit KNIME Integration” was applied for curation of the databases and conversion of the chemical structures. 178 chemicals of the learning set were then used to benchmark various machine learning models.

R_F values obtained from peak profiles from images (PPI) or scanning densitometry (PPSD), were used with the Random Forest regressor algorithms including 100 trees.

For building a powerful model, four steps were taken:

Overview of the machine learning pipeline and its workflow

The first step was the collection of data. For this, a training set consisting of 178 known individual substances was selected from various chemical classes, covering molecular weights (MW) ranging from 75.1 g/mol to 1131.3 g/mol, and computed octanol/water partition coefficients (SlogP) in the range of -7.53 to 13.98. Using the open source software KNIME and its extensions, molecular descriptors (e.g. MW, SlogP, topological polar surface area (TPSA)…) were computed for each substance. In addition, each substance was chromatographed with the CDS, generating 178 x 3 = 534 R_F values. In the second step, the dataset was cleaned by filtering all descriptors for null variance. The third step included the training of the model. For this, the performance of three regressors was evaluated according to their capacity to predict the R_F within the training set. The Random Forest, trained with 100 trees, yielded the best correlation coefficients R² 0.55, 0.72, and 0.64 for the LPDS, the MPDS, and the HPDS, respectively.

For testing of the model, a test set was created with 20 other substances. The suitability of the selected substances was verified by demonstrating that the chemical space of the test set was within the chemical space of the training set.

Chemical space (2D t-SNE projection) covered by the 178 chemicals belonging to the training set (black dots) and the 20 chemicals belonging to the test set (orange squares)

The model was used to predict the R_F values of the compounds in the test set. Most predicted R_F differ by less than 0.1 units from the measured values. R_F in the MPDS and the HPDS are both predicted within the correct range and with very small errors, leading to R² of 0.87, and 0.71, respectively. The variance for each individual prediction (LPDS, MPDS, and HPDS) remains smaller than 10%, except for a few compounds.

Measured and predicted R_F values of the compounds in the test set

A reverse test was also performed. The query molecule defined by its R_F values in LPDS, MPDS and HPDS was compared to the database for a number of rows (four each) matching the specified similarity. To calculate the similarity, the Euclidean distance was selected and the four nearer neighbors (most similar) were displayed in an additional column.

Use of the database for proposal of potential matches

The examples above illustrate the potential of the CDS and its combination with machine learning. In this study, R_F values can be predicted, emphasizing that this feature is encoded within the chemical structure of the molecules. Moreover, the link between chemical structures and R_F allows to generate a list of four molecules likely to correspond to an unknown zone in complex mixtures. This prediction would be even more useful, if additional data such as mass and UV-VIS spectra were added to the database.

[1] T.K.T. Do, M. Schmid, I. Trettin, M. Hänni, E. Reich, Complementary developing solvents for simpler and more powerful routine analysis by high-performance thin-layer chromatography, JPC – J. Planar Chromatogr. – Mod. TLC. (2022). https://doi.org/10.1007/s00764-022-00185-1.
[2] T.K.T. Do, I. Trettin, M. Hänni, E. Reich, Applying machine learning to the data obtained with the complementary developing solvents protocol, J. Liq. Chromatogr. Relat. Technol. (2023).
[3] T.K.T. Do, M. Schmid, M. Phanse, A. Charegaonkar, H. Sprecher, M. Obkircher, E. Reich, Development of the first universal mixture for use in system suitability tests for High-Performance Thin Layer Chromatography, J. Chromatogr. A. 1638 (2021) 461830. https://doi.org/10.1016/j.chroma.2020.461830.

Contact: Dr. Tiên Do, CAMAG, Sonnenmattstrasse 11, 4132 Muttenz, Switzerland, tien.do@camag.com

Dr. Kashyap Thummar, Assistant Professor at Graduate School of Pharmacy (GSP), Gujarat Technological University (GTU), India, employs chromatographic separation techniques, especially HPTLC, to develop new and improved quantitative analytical methods for determination of drugs, impurities, adulteration and naturally occurring compounds in a variety of sample matrices. He prefers HPTLC because it is flexible, inexpensive, time-saving and does not produce toxic waste.

Phthalates are esters of phthalic acid that are commonly added to plastics to improve their flexibility, transparency, durability, and longevity. These phthalates are easily released into the environment from plastic and can harm all living organisms. Phthalates enter the body by contact to plastics, e.g. through ingestion, inhalation, skin absorption, and intravenous injection.

For the detection of extractable and leachable phthalates in pharmaceutical products, a simple, rapid, precise, and accurate HPTLC method was developed. It simultaneously estimates the presence of four different phthalates in various pharmaceutical products and containers: dimethyl phthalate (DMP), diethyl phthalate (DEP), dibutyl phthalate (DBP) and di(2-ethylhexyl) phthalate (DEHP). The method was successfully applied for determination of extractables and leachables from 12 parenteral products packed in plastic containers.

10.0 mg of each phthalate were individually dissolved in 10.0 mL of methanol and subsequently combined to prepare a working standard solution at a concentration of 0.1 mg/mL of each.

2.0 g of the plastic container (extractable) and 30.0 mL of the product packed in it (leachable) are extracted 3 times with 30.0 mL of n-hexane by 10 min of sonication. The organic layers are collected, evaporate to dryness and reconstituted with 1.0 mL of methanol.

HPTLC plates silica gel 60 F₂₅₄ (Merck), 20 x 10 cm are used.

Samples and standard solutions are applied as bands with the Linomat 5, 20 tracks, band length 5.0 mm, distance from left edge 10.0 mm, distance from lower edge 8.0 mm. 20.0 μL for sample solutions and 1.0–14.0 μL for standard solutions (8 points for calibration) are applied.

Plates are developed in a saturated Twin Trough Chamber (15 min, with filter paper) with n-hexane – ethyl acetate 9:1 (V/V) to a migration distance of 90 mm (from the lower edge), followed by drying with cool air for 5 min.

Images of the plate are captured with the TLC Visualizer in UV 254 nm.

Absorbance measurement at 240 nm is performed with the TLC Scanner 4 and winCATS, slit dimension 4.00 mm x 0.30 mm, scanning speed 20 mm/s.

HPTLC chromatogram in UV 254 nm (left) and densitogram measured at 240 nm (right)

A representative densitogram of samples is shown. During evaluation of the chromatogram, phthalates in simulated and test samples give the same R_F values as the standard and are well separated from matrix components. A simulated sample is a laboratory-made sample that is designed to mimic a real-world product or material.

3D profile of scanned sample and standard tracks (measured at 240 nm)

DMP, DEP, DBP and DEHP are separated at R_F values of 0.23, 0.31, 0.44 and 0.60 respectively. The limit of quantification was in the range of 41.7 to 99.8  ng/band for the four phthalates and linearity was established between 100.0 and 1400.0  ng/band. The presence of individual phthalates was found in 12 pharmaceutical products (all parenteral formulations) in significant amounts. Importantly, of the four phthalates, DEHP was found in all tested samples as an extractable and leachable. The HPTLC method for detection of phthalates is cost-effective as compared to available analytical methods such as HPLC, LCMS/ MS, GCMS/MS etc. as these techniques require more solvent consumption, power consumption, analysis time, and involve complex sample preparation methods. The method can be universally applied to other samples apart from pharmaceutical products like water, food items etc. which are sold in plastic containers.

[1] T. H. Broschard et al. (2016) Regulatory Toxicology and Pharmacology 81, 201–211.
[2] K. Thummar and N. Sheth, Publication date: 2022/6/7, Patent office: IN, Patent number: 398670.
[3] K. Thummar et al. (2020) Analytical Chemistry Letters 10 (1), 93–103.

Contact: Dr. Kashyap Thummar, Assistant Professor, Graduate School of Pharmacy, Gujarat Technological University, Gandhinagar, Gujarat, India, ap_kashya@gtu.edu.in

The French company PiLeJe Industrie develops liquid and dry plant-derived ingredients using patented procedures and in-house developed processes. Their products are mainly used in the food supplements industry. The long-term collaboration with Chromacim led to the important development of HPTLC in their laboratory. This article addresses the question, whether it is possible to validate the activity assay of one major compound through its activity only, using the best international current standard.

Ribwort plantain (Plantago lanceolata L.) is a common grassland plant traditionally used for its therapeutic properties. The leaves are used in many European countries for the symptomatic treatment of colds and inflammation of the mouth and throat. Biological activities of P. lanceolata include antihistaminic, anti-spasmodic, anti-nociceptive, neuroprotective, metabolic, and gastro-protective activities. Acteoside is a phenyl-propanoid glycoside, well known for its antioxidant and antiinflammatory properties, which is commonly used as a marker.

Ribwort ipowder® is a plant infusion concentrated on plant totum, a proprietary 100% plant-based product developed by PiLeJe Industrie, made from dried P. lanceolata according to a patented process [1, 2]. Quantification was already shown with standard detection and published in a previous issue of the CBS. Our goal was to prove that this type of activity detection is reliable and transferable to quality assurance with a proper validation package. This needs a level of knowledge that we have developed rather quickly with the help of our partners. The objectives of this work were 1) to develop an HPTLC method using the 2,2-diphenyl 1-picrylhydrazyle (DPPH*) effect-directed chemical reaction for the detection of the antioxidant activity of acteoside for quality control of industrial dry extracts of P. lanceolata and 2) to demonstrate the applicability of the concept of Life Cycle Management of analytical methods to quantitative HPTLC-DPPH* methods.

An acteoside standard is dissolved in methanol at a concentration of 17.40 μg/mL in methanol.

500 mg of Ribwort ipowder® (PiLeJe Industrie) are extracted with 40 mL of ethanol – water 50:50 (V/V) by sonication at 60 °C for 10 min, then filtered and transferred to a 50 mL volumetric flask and filled up to the mark. The solution is diluted 5-fold for application.

HPTLC plates silica gel 60, 20 x 10 cm are used.

4.0 μL of sample and standard solutions are applied as bands with the Automatic TLC Sampler (ATS 4), 20 tracks, band length 8.0 mm, distance from left edge 22.0 mm, distance from lower edge 8.0 mm.

Plates are developed with ethyl acetate – water – acetic acid – formic acid 100:27:11:11 (V/V) to 70 mm (from the lower edge) in the ADC 2 with chamber saturation (20 min, with filter paper) and after activation at 33 % relative humidity for 10 min using a saturated aqueous solution of magnesium chloride.

After drying for 10 min, the plates are immersed into DPPH* reagent (0.5 mM methanolic solution of 2,2-diphenyl-1-picrylhydrazyl, immersion speed 5 cm/s, immersion time 5 s) with the Chromatogram Immersion Device 3. The plates are dried at room temperature in the dark for 90 s and then heated at 60 °C for 30 s (TLC Plate Heater 3).

Images of the plates are captured with the TLC Visualizer in white light after derivatization.

Fluorescence mode is used for measurement at 517 nm (tungsten lamp) with TLC Scanner 4 and visionCATS to obtain a positive response of the peaks of interest.

Analytical methods used for quality control of plants and plant extracts are usually based on the identification and quantification of chemical markers to manage batch reproducibility and efficacy. To measure the concentration of acteoside in Ribwort ipowder®, the HPTLC DDPH* assay was applied. The assay determines the free radical scavenging activity of the plant extract in solution.

Figure 1: Antioxidative properties – DPPH

The first step of this work was the selection of the Analytical Target Profile (ATP) and the determination of the Target Measurement Uncertainty (TMU) taking into account the quality control requirements for such extracts and the applicable range of the detection method. Once the desired range was established, an evaluation of the calibration function was conducted using linear, 1/x and 1/x² weighted linear calibration models and those three models were used to assess accuracy of the method (trueness and precision) by means of accuracy profiles [3]. The 1/x² weighted linear calibration function showed the best performance in the tested range, both in terms of accuracy and uncertainty of measurement.

Figure 2: Accuracy profiles obtained with the selected calibration function, for each function the accuracy limits are given by the vertical doted lines

The method requirement was to assay acteoside amounts around 1.0–2.0% (W/W) in industrial dry extracts of Ribwort plantain with an acceptance criterion of ± 20.0% difference to the true value for the ATP (defined as the combination of the trueness (bias) and precision characteristics), and a TMU of less than 20.0–25.0% relative uncertainty, according to the quality control needs.

Figure 3: HPTLC chromatogram in white light and densitograms measured in fluorescence mode at 517 nm with a tungsten lamp after DPPH* assay of standards and plantain leaf extract

Due to the lack of reference samples, spiked samples were used to evaluate the accuracy of the method by means of Total Analytical Error (TAE) determination, using prediction intervals calculation for the selected calibration functions. For quality control, the calibration function with the best performance level in accordance with the product specifications was chosen by estimating the Measurement Uncertainty (MU).

As Life Cycle Management of the method also includes its routine use, the MU was checked on spiked and non-spiked extract samples at different dilution levels, in order to verify the accordance of results between those samples, and to prepare a replication strategy for the routine method. Statistical calculations were performed with NeoLiCy® software for analytical methods’ life cycle statistical assessment (NeoLiCy, Marseille- Mâcon, France). The tested dilutions did not show any significant effect on the calculated spiked amount and any significant impact on the extract calculated concentrations. To take care of the measurement dispersion we included repeated sample preparation and measurement in the analytical procedure.

This work demonstrated that the concept of Life Cycle Management of analytical methods can successfully be applied to a HPTLC-DPPH* method, even in the case of complex matrices such as plant extracts, from the definition of the ATP and TMU to the reflection on the replication strategy to be applied in quality control.

The method developed for the quantification of acteoside in Ribwort plantain is applicable in a working range from 75.0 to 225.0 ng of acteoside and fit for purpose for use in quality control laboratories.

This study showed the suitability of HPTLC in this domain. Furthermore, the partnership with Chromacim and NéoLiCy showed its efficiency, within a cumulative working time of less than two weeks only, including statistics and matrix effect evaluation. This method is therefore ready to be transferred to the quality control laboratory of the PiLeJe Group.

This success proves that we were initially right to select this approach and encourages us to continue in this powerful way to develop HPTLC as a relevant technique for our needs.

[1] Dubourdeaux, M. Procédé de Préparation d’extraits Végétaux Permettant l’obtention d’une Nouvelle Forme Galénique. 14 January 2009. Available at https://patents.google.com/patent/EP2080436A2/en, accessed on January 21, 2021)
[2] V. Bardot et al. (2020) Food Funct, https://doi.org/10.1039/c9fo01144g
[3] J.M. Roussel et al. (2021) J Chromtogr B, https://doi.org/10.1016/j.jchromb.2021.122923

Contact: Dr. Valérie Bardot, Naturopôle Nutrition Santé, Les Tiolans, 03800 Saint-Bonnet de Rochefort, France, v.bardot@pileje.com

In quality control with HPTLC, a specific method using optimized developing solvents is generally used for each kind of sample. In order to simplify routine analysis, the lab team at CAMAG has developed the complementary developing solvents (CDS) concept based on one solvent of low polarity (LPDS), one of medium polarity (MPDS), and one of high polarity (HPDS). With these three developing solvents (DS), each on a separate plate and targeting compounds of different polarity, the same complex sample could be spread over up to three times the separation distance on a single plate, making available more information about the sample’s composition. Single substances can be characterized with three R_F values instead of one. Even though this approach triples the analytical workload (3 analyses instead of one), it may be considered, that routine work with multiple and diverse samples can be simplified and maintenance of methods, plates, solvents and standards can be kept to a minimum, particularly if the process is automated. Identification of individual compounds will be more certain. A further advantage of the concept is that all samples can be compared with any other sample that has been previously analyzed with the same CDS and data could be compiled in a database for treatment with advanced algorithms.

In their paper [1] the researchers at CAMAG describe the development, validation and application of a CDS, applicable to a large number of very diverse samples including individual compounds and complex herbal materials. In combination with thorough standardization, the concept could help positioning HPTLC as a very powerful, general, and medium to high throughput technique for routine analysis and sophisticated research.

Visualization of the CDS and its fingerprints

The Universal HPTLC mix (UHM) was prepared in house according to [2]. With the UHM, HPTLC laboratories have a single solution, applicable as system suitability test to a wide range of chromatographic systems.

Powdered herbal drugs and finished products were prepared in methanol using 10 min sonication followed by 5 min centrifugation. Ginkgo biloba, Camellia sinensis, Styphnolobium japonica and Piper nigrum were prepared at 100 mg/mL, Curcuma longa at 66.7 mg/mL, Angelica samples at 200 mg/mL, and the poly-herbal formulation at 50 mg/mL.

HPTLC plates silica gel 60 F₂₅₄ (Merck), 20 x 10 cm are used.

Samples are applied as bands with the Automatic TLC Sampler (ATS 4), 15 tracks, band length 8.0 mm, distance from left edge 20.0 mm, distance from lower edge 8.0 mm.

Plates were developed with the three developing solvents in the ADC 2 with activation of the plate at 33% relative humidity for 10 min using a saturated solution of magnesium chloride. LPDS was used without saturation, whereas MPDS and HPDS were used with 20 min chamber saturation (with filter paper). The developing distance for all three methods was 70 mm (from the lower edge). Plates were dried for 5 min.

Natural product (NP) reagent (1.0 g of 2-aminoethyl diphenylborinate in 100.0 mL of methanol) is used as derivatization reagent for the identification of Camelia sinensis, Styphnolobium japonicum and Ginkgo biloba. For Ginkgo biloba, the derivatization with NP is followed by anisaldehyde sulfuric acid (AS) reagent [slowly and carefully 170.0 mL of ice-cooled methanol are mixed with 20.0 mL of acetic acid and 10.0 mL of sulfuric acid; mixture is allowed to cool to room temperature, then 1.0 mL of anisaldehyde (p-methoxybenzaldehyde) is added]. Only AS reagent is used for the identification of Curcuma longa and Piper nigrum.

Images are captured with the TLC Visualizer 2 in UV 254 nm, UV 366 nm, and white light prior to derivatization, and UV 366 nm, and white light after derivatization (when needed).

For the system suitability test using the UHM, TLC Scanner 4 and visionCATS are used in absorbance mode at 254 nm and in fluorescence mode at 366>/400 nm, with slit dimension 5.00 x 0.20 mm and scanning speed of 50 mm/s.

The solvents selected for the CDS had to meet the following criteria: be of minimal hazard, stable, and easily available, to cover all selectivity groups according to Snyder, and include a broad range of polarity. The composition and properties of the CDS are shown in the table.

Composition and properties of the CDSs

The power of the CDS concept is illustrated with HPTLC fingerprints for identification of herbal drugs, herbal products, and poly-herbal formulations. Green tea leaves, for example, produce a different fingerprint with each of the DS zooming into specific polarities of the sample composition. The composite fingerprint gives complementary information emulating an extended developing distance.

HPTLC fingerprints of green tea leaves obtained with the CDS

HPTLC can easily detect adulteration of one herbal drug with another, using optimized developing solvents. For example, a method from the HPTLC Association [3] detects adulteration of Ginkgo biloba leaves with fruits of Styphnolobium japonicum. The CDS achieves the same goal, but offers even more certainty based on the data obtained with MPDS and HPDS.

Detection of 20% Styphnolobium japonicum fruit in Ginkgo biloba leaves with method [3] and CDS

Poly-herbal formulations such as products containing Curcuma longa and Piper nigrum are generally identified based on specific markers. The CDS can identify with certainty the presence of curcuminoids for turmeric and piperine for black pepper.

Fingerprints of Curcuma longa and Piper nigrum in comparison to those of two poly-herbal products

The CDS has been qualified using the universal HPTLC mix (UHM). A maximum margin of error of 0.014 was determined for the relevant zones.

Separation of the UHM components with the CDS

The examples above illustrate the potential of the CDS for replacing the established methods for identification of herbal materials. Additional information concerning the chromatographic behavior of representative substances from different chemical classes is presented in the original paper [1]. When combined with fully automated chromatography, the CDS concept may become the basis for new applications of HPTLC in routine analysis and sophisticated research.

[1] T.K.T. Do et al. (2022) JPC, https://doi.org/10.1007/s00764-022-00185-1.
[2] T.K.T. Do et al. (2021) J. Chromatogr. A. 1638, https://doi.org/10.1016/j.chroma.2020.461830.
[3] HPTLC Association, Identification method of Ginkgo biloba, Leaf and leaf extract (flavonoids), (n.d.). https://www.hptlc-association.org/methods/methods_ for_identification_of_herbals.cfm.

Contact: Dr. Tiên Do, CAMAG, Sonnenmattstrasse 11, 4132 Muttenz, Switzerland, tien.do@camag.com

The Jean-Pierre Bourgin Institute (IJPB) is the largest center for plant biology at the National Research Institute for Agriculture, Food and Environment (INRAE). It combines resources and multidisciplinary skills in the areas of biology, chemistry, and mathematics. Located in Versailles, it is a Joint Research Unit between INRAE and AgroParisTech. The DYSCOL (Dynamics and structure of Lipid Droplets) team studies various aspects of lipid accumulation in seeds from oil crops and model plants. The Kennedy pathway allows storage of fatty acids in eukaryotes in the form of triacylglycerols (TAG), through successive acylation of a glycerol backbone by acyltransferases. Hundreds of different fatty acids are found in plants. The different fatty acids incorporated into vegetable oils confer them specific physical, chemical and nutritional properties. DGATs (diacylglycerol acyltransferases) incorporate the final fatty acid in position sn-3 of the glycerol skeleton. They catalyze the rate-limiting step of the whole pathway. Due to their impact on oil yield and quality, DGATs are targets of interest for oil engineering. We aimed to identify candidate proteins as DGATs and to understand their substrate specificity. We used various approaches based on recombinant protein expression in different hosts, and analysis of the products of the reaction by complementary approaches, GC and HPTLC.

Historically, demonstration of DGAT activity used radioactive precursors of substrates [1] followed by tedious extraction of the products (TAGs) [2] and quantification by liquid scintillation counting. Trans-methylation of TAGs produces fatty acid methyl esters, subsequently extracted, then separated and analyzed by GC. Alternatively, extraction of TAGs by improved methods and their separation from other cellular constituents by TLC is a convenient method avoiding radioactivity. Derivatization permits direct identification and quantification of the TAGs by comparison with standards.

HPTLC represents a valuable improvement of TLC. 12–15 samples are routinely separated at the same time on one plate. The method exhibits high sensitivity and reproducibility, and uses low amounts of organic solvents (< 50 mL for one run). Herein, we describe two methods to evidence DGAT activity and specificity using HPTLC.

Method (1) allows the analysis of extracted lipids by derivatization with phosphomolybdic acid reagent. Method (2) is used to study the activity and specificity of a purified DGAT.

1. Solutions of cholesteryl oleate, oleic acid methyl ester, trioleine, oleic acid, cholesterol at 2.7 μg/μL each in CHCl₃ are prepared.
2. NBD-DOG (1-{N-[(7-nitro-2-1,3-benzoxadiazol- 4-yl)-methyl] amino-decanoyl-2-decanoyl-sn-glycerol) stock solution in chloroform – methanol 2:1 (V/V) at 67.5 ng/μL (between 5.5 ng to 2.7 μg are applied to generate a calibration curve).

1. Lipids are extracted from yeast biomass according to Folch [2], dried under nitrogen, and resuspended in 200 μL of chloroform – methanol 2:1 (V/V).
2. DGAT assay to investigate enzyme specificity: NBD-DOG as a fluorescent DAG acceptor and different acyl donors (lauroyl-CoA, palmitoyl-CoA, stearoyl-CoA, oleoyl-CoA, and linoleoyl-CoA. The reaction is carried out at 31°C for one hour under shaking, then stopped by the addition of chloroform – methanol 2:1 (V/V).

HPTLC plates silica gel 60 (Merck), pre-washed with isopropanol, are used.

Between 3.0–6.0 μL for standard solutions and 50.0 μL for sample solutions (corresponding to 400 μg cells, dry weight) are applied as bands with the Automatic TLC Sampler (ATS 3), 15 tracks, band length 5.0 mm, distance from left edge 15.0 mm, distance from lower edge 8.0 mm.

Plates are developed in the Automatic Developing Chamber (ADC 2) with chamber saturation (with filter paper) for 20 min, (1) development with diethyl ether – hexane –methanol – acetic acid 60:40:5:1 (V/V) to the migration distance of 80 mm (from the lower edge), drying for 20 min, (2) development with hexane – diethyl ether – acetic acid 80:20:2 (V/V) to the migration distance of 80 mm (from the lower edge), drying for 20 min.

The plates are immersed in 5% phosphomolybdic acid in ethanol, then incubated for 30 min at 100 °C using an oven.

Images of the plates are captured in white light.

Fluorescence measurement is performed with the TLC Scanner 3 (excitation at 473 nm and emission > 510nm). DGAT activity is expressed as picomoles of TAG formed per minute and per milligram of purified protein, using a calibration curve based on the fluorescent signal of NBD-DOG.

The following figure shows the results of the separation of lipids extracted from three yeast strains (method 1). The control yeast strain was transformed with an empty vector; a second strain was transformed with the AtDGAT1 sequence encoding for Arabidopsis thaliana DGAT1, and the last strain was transformed with the EgDGAT1 sequence encoding for Elaeis guineensis DGAT1-1, a putative DGAT [3]. Both cassettes encoding for plant DGAT1 restored TAG accumulation in the Yarrowia lipolytica mutant strain [4]. Thus, we conclude that EgDGAT1 sequence was encoding for an active E. guineensis DGAT1-1.

Separation of lipids extracted from yeasts strains expressing plant type 1 DGATs (1: standards, 2: empty cassette, 3: AtDGAT1, 4: EgDGAT1-1).

To generate a calibration curve (method 2), the fluorescence of the NBD-DOG standard applied in different amounts is measured. The standard curve is depicting the dependence of the intensity of the fluorescence of NBD-DOG (minus blank value).

Calibration curve of NBD-DOG (scanned at 473 nm in fluorescence mode) depicting the dependence of the intensity of the fluorescence of NBD-DOG (top) as function of the amount separated on HPTLC plate (bottom)

In another experiment, purified recombinant DGA1, a type 2 DGAT from the yeast Yarrowia lipolytica was incubated with NBD-DOG and acyl-CoA with various acyl chain lengths (C12:0, C16:0; C18:0).

Separation of TAG synthetized by purified recombinant yeast type 2 DGAT (DGA1) using acyl-CoA with different chain lengths.

In the absence of an acyl donor, no NBD-TAG was formed (blank track). In the presence of acyl donors (C12:0, C16:0; C18:0–CoA), NBD-TAGs were formed. Noticeably, the migration of the reaction product depended on the length of the acyl chain incorporated. NBD-TAG with C18:0 migrate over a longer distance by comparison to NBD-TAG with C16:0 or C12:0.

HPTLC is a fast (within two hours), reproducible, and robust method to evidence acyltransferases activities. In vivo (1), complementation of microbial strains affected in neutral lipid metabolism by sequences coding for DGAT led to accumulation of TAGs. The products were extracted, separated by HPTLC and identified directly by comparison with standard molecules. In vitro (2), DGATs transferred various acyls to fluorescent DAG acceptors. Fluorescent products of the reaction (TAGs) were quantified using a substrate calibration curve. The method was sensitive enough to distinguish TAGs differing by only two carbons [5]. Both approaches avoided the use of radioactive labeled products.

[1] Erickson, S.K. and Fielfing, P.E. J Lipid Res 27 (1986) 875–883
[2] Folch, J. et al. J Biol Chem 226 (1957) 497–509
[3] Aymé, L., et al. PLoS One (2015) 10: e0143113
[4] Beopoulos, A., et al. (2012) Appl Microbiol Biotechnol 93: 1523–1537
[5] Haili N., et al. (2016) J Biol Chem 277: 6478–6482

Contact: Dr. Laure Aymé and Dr. Thierry Chardot, Institut Jean-Pierre Bourgin, INRAE, AgroParisTech, Université Paris- Saclay, 78000, Versailles, France, laure.ayme[at]recherche.gouv.fr, thierry.chardot@inrae.fr

Didier Rigollet, Amélie Havard and Daniel Dron are working at the R&D department Analytical Innovative Technologies of the Industrial Research Centre at Oril Industrie, in Bolbec, France (Servier group). The use of HPTLC in Oril started even earlier than the foundation of Chromacim, including trainings at CAMAG organised by Pierre Bernard-Savary, where Daniel Dron participated already more than 20 years ago. The R&D team is specialized in purification processes of intermediates and active pharmaceutical ingredients (APIs) for toxicological, galenical or clinical studies. Part of their work is devoted to the isolation of impurities and production of APIs or impurity reference batches. In 2018, the team launched their preparative chromatography service InnoPrepTM, dedicated to small and large-scale purifications. Intermediates, APIs and impurities are characterized by MS and NMR. Quantitative analysis by NMR is also performed on these molecules.

Servier, an independent international pharmaceutical group, is committed to therapeutic progress for the benefit of patients.

Their goal is to speed up the development of new molecules in order to bring a new molecular entity to market every 3 years, particularly in the field of oncology. Preparative chromatography is, therefore, a method of choice in R&D to provide pure products for the first pharmacological, toxicological and clinical studies in a very short time. This technique also makes it possible to isolate impurities present at low levels in active ingredients whose complex structure does not allow rapid synthesis and to provide a batch within the time limits set for toxicological studies.

About 75% of the purifications at Oril Industrie are done with silica gel. The necessary conditions for an efficient purification are determined using TLC. Then, the purification progress by preparative column chromatography is checked by HPTLC. Twenty fractions can be analyzed within one hour. TLC/HPTLC is the method of choice due to its simplicity, rapidness and the successful scale up from TLC to preparative separations. The HPTLC-MS carried out beforehand allows targeting the molecule sought in often very complex mixtures.

Crude product (0.05 g) is dissolved in 5.0 mL of ethyl acetate.

For method development (optimization of purification conditions) TLC plates silica gel 60 F₂₅₄ (Merck), 20 x 5 cm are used, while quantification and coupling to mass spectrometry is done on HPTLC plates silica gel 60 F₂₅₄ s (Merck), 20 x 10 cm.

Samples are applied as bands with the Automatic TLC Sampler (ATS 4), two tracks for TLC and up to 20 tracks for HPTLC, band length 8.0 mm, sample volumes of 1.0 –15.0 μL.

Plates are developed in the Twin Trough Chamber 20 x 20 cm (TLC) or 20 x 10 cm (HPTLC) with chamber saturation (with filter paper) for 20 min with different developing solvents to the separation distance of 100 mm (from the lower edge) for TLC and 50 mm for HPTLC, followed by drying in a stream of cold air for 5 min.

Images of the plates are captured with the TLC Visualizer in UV 254 nm and white light.

Zones are eluted with the TLC-MS Interface (oval elution head) at a flow rate of 0.2 mL/min with methanol – water 1:1 (V/V) into a Q-TOF-MS (Xevo® G2-S QTof, Waters), operating in positive ionization mode (m/z 50 –1200).

The objective of this study was to isolate a sufficient quantity of an impurity present at a content of 0.35% in a batch of an intermediate in a product under development. The aim was to confirm its structure and to carry out toxicological tests. The nature of this impurity was determined beforehand by LC-MS. Its structure being complex, a synthesis would be too time-consuming. The conditions used in RP-HPLC are too complex for direct transfer to preparative chromatography (expensive stationary phase). The mobile phase used was water + 0.1% methane sulfonic acid and acetonitrile +0.1% methane sulfonic acid is too complexed for the isolation after preparative chromatography.

RP-LC-UV Chromatogram of the crude product

TLC was selected for method development to separate the major impurity from the other compounds with a reasonable R_F value allowing an efficient purification.

TLC chromatograms of the crude product in UV 254 nm obtained with different developing solvents

Mass spectra were recorded to characterize the different compounds (main substance at R_F 0.25 and impurity at R_F 0.38).

Instruments used for TLC-MS (left); TLC chromatogram of the main substance and the impurity in white light (middle); mass spectra of the selected zones (a: impurity, b: main substance) measured in positive ionization mode (right)

The purification of the crude product (two injections of 90g dissolved in toluene) on a 20-cm column (packed at 40 bars with 6 kg silica gel 60, 15–40 μm, Merck) at a flow rate of 2.0 L/min with toluene – ethyl acetate 95:5 (V/V), was monitored online with UV 290 nm detection and in parallel offline by HPTLC.

Online monitoring of the purification process by LC-UV (290 nm, left) versus offline by HPTLC-UV (HPTLC chromatogram of the individual fractions at 254 nm, right; C2-C9 are fractions collected during purification and C5 corresponds to the impurity)

The different fractions of the target impurity were collected, and the combined fractions were analyzed by NMR. 670 mg of impurity was obtained (yield: 0.35%) purity > 99%. The quantity obtained after purification on column was also consistent with the estimated content in analytical HPLC. For Oril, the use of HPTLC has a big positive impact on the production costs, with a benefit of thousands of Euros per year. This is due to the use of HPTLC for various optimizations of the manufacturing process and of raw materials external supply.

Contact: Amélie Havard, Daniel Dron, Oril Industrie (Servier), Industrial Research Centre, Department of Analytical Innovative Technologies R&D, 13 rue Auguste Desgenétais, CS 60125, 76210 Bolbec, France, amelie.havard@servier.com, daniel.dron@servier.com