DOE Assisted RP-HPLC Method Development and Validation for Estimation of L-Cysteine and Cystine with Dansyl Chloride derivatization in Presence of Amino Acid Mixture

A. Suneetha¹*, B. Chandra Sekhar², K. Sudheer Babu³

^1,2Department of Pharmaceutical Analysis, Hindu College of Pharmacy,

Amaravathi Road, Guntur 522002, Andhra Pradesh, India.

³AVP-Operations, Megsan Labs – Hyderabad, India.

*Corresponding Author E-mail: drasuneetha@gmail.com

ABSTRACT:

The aim of the present study is to apply Design of Experiments (DoE), to develop an assay and optimize the derivatization reaction. DoE study type is Response Surface, Sub type is Split-plot, Design is D-optimal method was used in the study for the separation of L-Cysteine and L-Cystine in presence of amino acids by RP-HPLC. DoE Response Surface Split-plot method was used in this study. DoE allows to interpret the results with better outcome and enhanced understanding. Pre-column derivatizing agent Dansyl Chloride was used for dansylation of amino acids; 10mm Ammonium Acetate buffer P^H 6.3, Acetonitrile was used as mobile phase in Eclipce XDB C18 column (150mm × 4.6mm, 5μm). Derivatizing reagent volume, derivatization reaction heating time, and temperature was evaluated as variables in the DoE. The wavelength for all amino acids is 222nm. Conclusively DoE as an efficient tool for optimization of dansylation reaction for the separation of L-Cysteine and L-Cystine in presence of amino acids. Method development was established and the design is validated. The proposed method has adequate reproducibility, specificity and accurate for the estimation of L-Cystine and L-Cystine in the presence of amino acids in routine analysis.

KEYWORDS: Design of Experiments (DoE), RP-HPLC, Dansyl Chloride, L-Cysteine, L-Cystine.

INTRODUCTION:

Cysteine is one of the “biogenic” amino acids, structurally it belongs sulfur-containing amino acids, the sulfur atom in the side chain is involved in the formation of a reactive sulfhydryl (–SH) group.

The R groups in these amino acids are more hydrophilic than the analogous amino acids bearing a nonpolar side chain. L-cysteine plays a multipurpose role in the biological system, from forming a structural component of both proteins and the precursor of the radical scavenger GHS, to playing a protective role against several diseases.

Cysteine is the building block of about 2% of proteins, and plays a key role in the biosynthesis of lipids and cell membranes. It is also involved in the synthesis of taurine, which is an imperative factor in conducting electrical nerve impulses in the digestive and vascular systems.

L-cysteine is commonly given intravenously to patients with acetaminophen poisoning to prevent kidney and liver damage.

When given in its precursor form as NAC, cysteine has a protective role in several disorders like angina pectoris, chronic bronchitis, COPD, inflammation, asthma, cystic fibrosis, emphysema, and in doing so by boosting the levels of GHS it may also prevent lung damage.^1-4

Introduction to Design of Experiments (DoE):

DOE is an essential piece of the reliability program pie. It plays an important role in Design for Reliability (DFR) programs, allowing the simultaneous investigation of the effects of various factors and thereby facilitating design optimization.

DOE helps in: Identifying relationships between cause and effect. Providing an understanding of interactions among causative factors. Determining the levels at which to set the controllable factors (product dimension, alternative material, alternative designs, etc.) in order to optimize reliability. Minimizing experimental error (noise). Improving the robustness of the design or process to variation.

With modern technological advances, products and processes are becoming exceedingly complicated. As the cost of experimentation rises rapidly, it is becoming impossible for the analyst, who is already constrained by resources and time, to investigate the numerous factors that affect these complex processes using trial and error methods. Instead, a technique is needed that identifies the "vital few" factors in the most efficient manner and then directs the process to its best setting to meet the ever-increasing demand for improved quality and increased productivity. Designed experiments are much more efficient than one-factor-at-a-time experiments, which involve changing a single factor at a time to study the effect of the factor on the product or process. While the one-factor-at-a-time experiments are easy to understand, they do not allow the investigation of how a factor affects a product or process in the presence of other factors. When the effect that a factor has on the product or process is altered due to the presence of one or more other factors, that relationship is called an interaction. Many times, the interaction effects are more important than the effects of individual factors. This is because the application environment of the product or process includes the presence of many of the factors together instead of isolated occurrences of single factors at different times. The methodology of DOE ensures that all factors and their interactions are systematically investigated; thus, information obtained from a DOE analysis is much more reliable and complete than results from one-factor-at-a-time experiments that ignore interactions and may lead to misleading conclusions. ^(5-10)

Derivatization:

Precolumn derivatization was done to make the amino acids UV active, dansyl chloride reacts with the sulfur compound present in L-Cysteine and Cystine and makes them UV active, analysis of amino acids through LC-MS is more complicated and expensive compared to the analysis of amino acids through HPLC in regular commercial analysis, and precolumn derivatization can be done with different derivatizing agents like o-phthalaldehyde, Fluorenylmethyloxycarbonyl chloride, 6-Aminoquinolyl-N-hydroxysuccinimidyl, but after reviewing the literature and practical results Dansyl Chloride is selected for pre column derivatizing agent.^(11-17)

MATERIALS:

TABLE NUMBER 01:

S. No.	Amino acid	Batch No.	Make
1	L-Cysteine	WS/LCY/21/061	99% Potency
2	L-Cystine	WXBC1604V	Sigma Aldrich
3	L-Asparagine	SLBZ0221	Sigma Aldrich
4	L-Glutamine	N1300739	Avra
5	L-Tyrosine	0000062386	Sigma Aldrich
6	Tryptophan	N1410135	Avra
7	L-Arginine	SLBZ0221	Sigma Aldrich
8	Leucine	N1401675	Avra

TABLE NUMBER 02:

S. No.	SOLVENT	Batch No.	Grade	MAKE
1	Acetone	F19M062	L N Grade	Advent
2	Ammonium Acetate	QD5Q650719	EMPARTA	Merck
3	Acetonitrile	HIR05210132	HPLC Grade	Honey Well
4	Hydrochloric Acid	SR10010521010	HPLC Grade	STANDARD Reagents Pvt.Ltd
5	Orthophosphoric Acid	C19C690559	HPLC Grade	Merck
6	Dansyl Chloride	BCCD2739	Bio reagent	SIGMA ALDRICH
7	Water	NA	HPLC/Milli-Q Grade	Milli-Q
8	Glacial Acetic acid	CAOCF70001	EMPARTA	MERCK

INSTRUMENTATION:

S.NO	Instrument	Model No.	Make
1	HPLC	Waters e 2695	Waters
		L2030 C 3D Plus I Series	Shimadzu
2	Micro Balance	BM-20	AND Company Limited
3	Analytical Balance	225D-101N	Sartorius
4	pH Meter	LP139SA	Polomon
5	Sonicator	Na	Soltec

METHODOLOGY

Analytical Work:

Preparation of Standard solutions:

L-Cysteine 100 PPM standard solution preparation:

10 mg of L-Cysteine working standard was weighed and transferred in 100ml volumetric flask and made up to the mark with 0.1 N HCL.

Cystine 100 PPM standard solution preparation:

10 mg of Cysteine standard was weighed and transferred in 100ml volumetric flask and made up to the mark with 0.1 N HCL.

Sampling solution preparation and Dansylation:

1 ml of 100 PPM L-Cysteine and 1 ml of 100 PPM L-Cystine were taken in amber colour vial to that 1 ml of 1000 PPM DNSCL reagent was added and 1 ml of 0.4 M Sodium Carbonate solution was added to the above solution 5 ml of milli Q water was added to maintain alkaline conditions to avoid oxidation of L-Cysteine and heated at 100°C for 44.6 minutes and 10µl of solution was injected.

Mobile Phase A: 10mm Ammonium Acetate: ACN (95:5) P^H 6.3

Dissolve 0.770 grams of Ammonium Acetate in 1000mL of Milli-Q water and adjust the pH to 6.3 with Dilute Glacial acetic acid, from the solution 50 ml was removed and 50ml of Acetonitrile was added.

The solution was filtered through 0.45µ filter using filtration apparatus and set for D-gas.

Mobile Phase B: (100% ACN)

1000 ml of Acetonitrile was taken in a 1000mL Mobile phase bottle.

OFAT Approach:

Different experiments with OFAT approach by varying different method conditions like Sample concentration, Reagent concentration, Reagent Volume, Heating temperature, heating time were conducted. Initial trails with Methanol: THF:50 mm phosphoric acid (20:20:960) P^H7.5 and methanol water (65:35) the Amino acids were not detected in the column, so pre column derivatizing agent is used and there is no proper identification of analyte peaks due to the lack of stability of derivatized samples. So, reagent was changed to Dansyl Chloride and the 10 mm Ammonium Acetate P^H 6.3 and 100% Acetonitrile was used.

Reagent peak is interfering with L-Cysteine peak so the gradient mode was modified so resolution for all the analytes were achieved.^(18-23)

Optimized Chromatographic Conditions:

	SHIMADZU with PDA Detector
Column	Eclipce XDB C18 column (150 mm × 4.6 mm, 5 μm).
Flow	1mL/min
Column Temperature	30^0C
Sample Temperature	5^0C
Detection	222nm

Gradient programme table:

S. No.	Time	Flow	%A	%B
1	0	1	90	10
2	15	1	75	25
3	30	1	10	90
4	40	1	10	90
5	42	1	90	10
6	50	1	90	10

DoE Approach:

The method development trials are executed in different reagent volumes, different reaction times and temperatures with varying parameters of test conditions as described in the design experiments table. The observed experimental results were fed into the software as against each type of response and data is collated as below. The DOE model is subjected to further statistical evaluation and modelling with the experiment values.

Table number 03: Number of runs suggested by DoE and responses obtained by performing DoE.

		Factor 1	Factor 2	Factor 3	Response 1	Response 2
Group	Run	A: Volume of Reagent	B: Temperature	C:Reaction Time	L-Cysteine Response	Cystine Response
		mL	°C	Minutes	Area at 222nm	Area at 222nm
1	1	0.6	50	45	2182898	165233
1	2	0.6	50	45	2182898	165233
2	3	1	40	30	2195594	244112
2	4	0.6	40	45	2285859	165609
2	5	0.2	40	60	1512111	66475
3	6	0.2	60	45	1767054	0
3	7	1	60	60	2165468	241093
3	8	1	60	30	2059552	237786
4	9	0.6	50	45	2182898	165233
4	10	0.6	50	45	2182898	165233
5	11	0.6	50	45	2182898	165233
5	12	0.6	50	45	2182898	165233
6	13	0.624	40	30	1979407	186168
6	14	1	40	60	2078205	249081
6	15	0.2	40	46.95	1832905	76318
7	16	0.2	60	60	1709741	0
7	17	1	60	45.15	2251701	269513
7	18	0.2	60	30	1672548	0
8	19	0.2	40	30	1776652	75560
8	20	0.58	40	60	2114445	184476
8	21	1	40	43.1025	1991292	248058

Response Evaluation from Model Graphs:

Response-1 (L-Cysteine Area):

Figure 1

Observation-1: Minimal reaction temperatures and minimal reagent volumes gives lower area of L-Cysteine.

Figure 2

Observation-2: For optimal response of L-Cysteine the volume of reagent should be 0.6 to 1 ml, and optimal reaction time should be within 42 to 48 minutes.

Figure 3

Observation-3: L-cysteine response is unaffected within the specified reaction temperature and time.

Response-2 (Cystine Area):

Figure 4

Observation-1: L-Cystine response is near to zero or undetectable if volume of dansyl chloride reagent is less than 0.6 ml.

Figure 5

Observation-2: Reaction time does not affect the l-cystine area but volume of reagent added for dansylation reaction should be more than 0.6 ml to get desired response.

DOE Predicted Solutions:

With the given target ranges and constraints for each of the responses, the software is able to suggest about 15 different solutions with a combination variable factor. Below table illustrates different solutions predicted and suggested by the software to achieve the target discriminatory Separation profile. As it can be observed that the solutions are also having the factors at different ranges other than those tested in the experimental runs.

Table number 04: DoE Predicted Solutions

Solution Numbers	Volume of Reagent	Temperature	Reaction Time	L Cysteine Response	Cystine Response	Desirability
1	1.000	100.000	44.642	2187963.648	262250.920	0.922
2	1.000	100.000	44.590	2187963.414	262250.833	0.922
3	1.000	99.999	44.691	2187963.158	262249.718	0.922
4	1.000	99.998	44.551	2187962.099	262250.006	0.922
5	1.000	99.997	44.733	2187959.356	262249.905	0.922
6	1.000	99.999	44.471	2187955.247	262249.365	0.922
7	1.000	99.978	44.615	2187963.932	262245.318	0.922
8	1.000	99.999	44.848	2187943.250	262250.206	0.922
9	1.000	99.997	44.953	2187917.957	262249.889	0.922
10	1.000	99.929	44.585	2187968.210	262229.056	0.922
11	1.000	99.999	44.209	2187892.077	262247.882	0.922
12	1.000	99.860	44.742	2187960.279	262213.771	0.922
13	1.000	99.865	44.460	2187955.726	262213.789	0.922
14	1.000	99.996	44.109	2187848.733	262249.601	0.922
15	1.000	99.998	44.073	2187831.490	262250.456	0.922

Desirability Graph observation:

Figure 6

Observation-1: Higher temperatures and higher volume of reagent gives higher detection of both L-Cysteine and L-Cystine.

Figure 7

Observation-2: The reaction time should be between 28 minutes to 60 minutes and volume of Dansyl Chloride reagent should be above 0.4 ml to get higher response of both L-Cysteine and Cystine.

Overlay plot:

Observation: Volume of reagent should be minimum 0.6 ml and reaction time should be between 42 to 48 minutes to obtain optimal response of L-Cystine.

Figure 8

RESULTS:

The model has given predicted solutions of 15 different combinations of selected factors along with the predicted results. Desirability for the given solution has a significant role. Desirability factor 1 solution will give better resolution than the other with less than 1 Out of the predicted solution, solution-1 and solution-15 was selected and experimented to derive practical results with the given combination of factors. The practical result for the selected solution 1 has more area given by the DoE and solution-15 has less area in response 2.

Table number 05: Obtained result for Solution 1 and Solution 15

					Response 1	Response 2
	Solution	Factor 1	Factor 2	Factor 3	L-Cysteine Response	Cystine Response
		A:Volume of Reagent	B:Temperature	C:Reaction Time	Area	Area
Predicted	1	1.000	100.000	44.64	2187963.648	262250.920
Obtained	1	1.000	100.000	44.64	2187963.600	262250.900
Predicted	15	1.000	99.998	44.00	2187831.490	262250.456
Obtained	15	1.000	99	44.00	2187830.110	262249.850

Model Validation:

SPECIFICITY:

L-cysteine and cystine peaks were separated with good resolution in presence of some essential and non-essential amino acids by following conditions of predicted solution 1 of DoE.

Standard solution preparation and Dansylation:

0.1 N HCL is used as diluent in Preparation of amino acid solutions,1 ml of each amino acid solution of 500 ppm concentration of Leucine, Glutamine, Tyrosine, Tryptophan, L-Arginine, L-Asparagine were added individually to amber colour vial to that 1ml of 1000 PPM DNSCL was added and to maintain alkaline environment 1ml of 0.4M Sodium carbonate solution was added and to the above solution 5 ml of milli Q water was added and heated at 100°C for 44.6 minutes and 10µl of the solution was injected.

Figure number 09: Chromatograph of Specificity of Amino acids

Observation:

All the amino acids are separated with good resolution and L-Cysteine and Cystine were separated in the presence of amino acids

Table number 06: Retention Time (RT) of amino acids in Standard Solution

S. No	Amino acid	Retention time (Minutes)
1	L-Cysteine HCL 100 PPM	9.909
2	Cystine 100 PPM	12.542
3	Leucine 500 PPM	19.899
4	L-Glutamine 500 PPM	12.774
5	Tyrosine 500 PPM	21.089
6	Tryptophan 500 PPM	20.2
7	L-Arginine 500 PPM	14.254
8	L-Asparagine 500PPM	11.917

LINEARITY:

L-Cysteine:

Linearity of L-Cysteine was performed by DoE predicted Solution 1 parameters at different concentrations i.e from 5.0 PPM to 100 PPM and Correlation Coefficient (r) is 0.999.

Figure Number 10:

L-Cystine:

Linearity of L-Cystine was performed by DoE predicted Solution 1 parameters at different concentrations i.e from 5.0 PPM to 100 PPM and Correlation Coefficient (r) is 0.999.

Figure number 11:

Table number 07: Limit of Detection and Quantitation of L-Cysteine and Cystine

Compound	LOD	LOQ
L-Cysteine	0.06ppm	0.2 ppm
L-Cystine	2.5 ppm	7.5 ppm

DISCUSSION:

To develop an Assay method for multiple Amino acids by OFAT, it requires to conducts multiple experiments to screen all the factors that influence the analytes.

DoE (Design of Experiments) concepts allow application of statistical modelling of few experiments to establish a relation between the individual factors and the predicted responses. Unlike OFAT studies, DoE allows to interpret the results with better outcome and enhanced scientific understanding.

In the present scope of study, Amino acids mixtures are selected as suitable product candidate to evaluate the application of DoE in Assay method development. ^(24-27)

Amino acids mixture separation has very critical factors as they do not retain in the regular column. Hence to achieve the separation, it requires studying multiple factors that influence the separation profiles.

To make the method development more effective DoE model of experimentation is selected for the study.

Different experiments with OFAT approach by varying different method conditions like Sample concentration, Reagent concentration, Reagent Volume, Heating temperature, heating time were conducted. Initial trails with Methanol: THF:50 mm phosphoric acid (20:20:960) P^H7.5 and methanol water (65:35) the Amino acids were not detected in the column, so pre column derivatizing agent is used and there is no proper identification of analyte peaks due to the lack of stability of derivatized samples. So reagent was changed to Dansyl Chloride and the 10 mm Ammonium Acetate P^H 6.3 and 100% Acetonitrile was used. Reagent peak is interfering with L-Cysteine peak so the gradient mode was modified so resolution for all the analytes was achieved.

A DoE experimentation table with possible combination different variables of all selected factors is obtained. The experimentation table is executed to arrive at different Amino acid profiles and the data is fed to the software. The data is then evaluated for model efficacy and to build a statistical design model for predictions of the suitable options that will yield desired Assay method. Once the model is confirmed for accuracy, all the responses are evaluated for subsequent effect of different selected factors. Each response is evaluated for positive and negative effect of each factor.

It was observed that derivatizing reagent volume, heating temperature and reaction time are having significant positive effect on all of the responses.

As a next step the model is applied to predicted possible assay methods that will yield necessary results of the method. For this purpose the software is given certain constraints for each of the factor and response as target ranges and desired outcome of the results.

Point prediction:

As predicted by the model, Solution-1 was selected out of the 15 solutions suggested. These experiments were executed in the lab with suggested combination of factors related substances profile is established. The established related substances profile is compared against the DoE suggested solutions. It was observed that the DoE suggested related substance profiles are closely matching with the experimental results. Hence it can affirm that the DoE model is validated and selected solutions can be finalised as suitable method which can be used for assay and RS to evaluate the quality parameters of L-Cysteine dosage form. With the current scope of study DoE as an effective tool for Assay method development with multiple Amino acids that is having less runtime and can used for assay is employed and validated to prove its efficacy. The developed method can be further utilised for routine analysis. ^(28-31)

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

ACKNOWLEDGMENTS:

I would like to thank aragen life sciences Pvt. Ltd. Hyderabad for their kind support during all lab studies.

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Received on 05.11.2021       Modified on 20.12.2021

Accepted on 25.01.2022 ©Asian Pharma Press All Right Reserved

Asian J. Pharm. Ana. 2022; 12(1):35-42.

DOI: 10.52711/2231-5675.2022.00007