Application Of QBD Approach to Analytical Method Development and Validation of Ethosuximide in Bulk and Pharmaceutical Dosage Form

Analytical chemistry:[1]

Chemical analysis is the examination of samples to ascertain the makeup and arrangement of the Constituents found within them. It is the study of geometric features such molecular morphology and The distribution, composition, and identification of species. The modern, sophisticated pharmaceutical Sector is significantly impacted by analytical chemistry. Its broad use and breadth had a major impact On the creation of innovative, robust, trustworthy, and more accurate drug analyses. Highly specific And sensitive analytical techniques play significant roles in the design, development, quality control, And standardization of drug products. They are also routinely employed to assess the safety and Efficacy of medications.

Chromatography: [2,3]

The word “chromatography” describes a widely used analytical technique that disassembles a Combination of chemicals into their component elements so that each can be thoroughly investigated. One of the methods most commonly used to examine API and dosage forms for pharmaceuticals.

• A chromatograph is a technological device that physically separates a mixture of substances Into their constituent parts
•  A mobile phase is a material, such as a solvent, that moves across the column.
• A stationary phase, like silica, is a kind of solid that stays inside the column.
• Eluent is the liquid that flows into the column.
•  The term “elute” refers to the liquid that either drips from the column or gathers in the container.
• The term “analyte” or “sample” refers to the combination of components that must be properly Separated using chromatography

Types of chromatography

1. Column chromatography
2. Thin layer chromatography (TLC)
3. Paper chromatography

Column chromatography: [1]

Another name for this is liquid column chromatography. In the chromatography described above, silica Or alumina can be employed as the stationary phase, while an appropriate solvent is used as the mobile Phase. The column is filled with the silica or alumina sludge. All of the substances in the mixture or Sample will be separated here, regardless of their polarity. The most popular and accurate kind of Chromatography is called high-performance liquid chromatography.

Introduction to HPLC:[3]

High-resolution, high-pressure, high-speed liquid chromatography is known as HPLC. It is a Significantly faster way of separating components and evaluating the substance since it has a lot More resolution power than one-column chromatography. It is applied to biological, inorganic, and Organic material separation and analysis.

Fig. No.1: Instrumentation of HPLC

• Speed: Due to its quick speeds, analysis can be finished in 20 minutes or less.
• Sample preparation: The sample and mobile phase are simple to produce.
• Extremely Perceptive
• Simple sample recovery and maintenance
• The minimum mistakes changes due to high automation and automatic qualification
• Less Labor Needed Because of Automation
• The time and expense involved are minimal.
• Fit for locations with high levels of productivity.

Quality by Design (QbD): [8, 9]

QbD is based on science and a risk-based approach to analytical method development with the goal of Achieving enhanced method efficiency, higher stability, ruggedness, and flexibility for continual Improvement. Understanding the previously set objectives for managing the Critical Method Variables (CMVs) affecting the Critical Method Attributes (CMAs) is its main objective.

5. Analytical Quality by Design :

In order to provide regulatory flexibility in the analytical process, QBD helps in the development of a Dependable and economical analytical method that can be utilized for the duration of the product.Establishing a strong “Method Operable Design Region (MODR),” sometimes referred to as “Design Space,” under important system suitability standards and in progress life cycle management has been The primary objective of QbD. Analytical researchers now lack information or familiarity with the QbD Approach for techniques of analysis. Despite these obstacles, the pharmaceutical industry continues to Debate the use of QbD and how it relates to other parts of pharmaceutical quality systems.

5. Objective of QbD: [10, 11]

1. The primary goal of quality-by-design (QbD) is to guarantee the quality of the goods, which means That the aspects of the product and the process that are critical to the anticipated performance must Come from a combination of historical data and updated estimates made throughout the Development process.
2. Measurements and desirable attributes may be constructed from these data and information.
3. Assures the integration of information gathered throughout the process and product development.
4. An empirical study would be considered a fruitful performance evaluation of the model’s Capabilities throughout Design space.

Distinguish between Conventional approach and the QbD approach:

Table No. 1: Distinguish betn Conventional approach and QbD approach

The QbD approach to method development is defined primarily by the following Principles:

• Creating a product and production method that satisfy patients’ needs for safety and efficacy
• Creating a production procedure that reliably yields a product that satisfies Predefined quality requirements.
• It is vital to comprehend how input parameters affect product quality in order to effectively construct Controls at process key points                     

Fig. No. 2: HPLC Method Development Using the QbD

The stages of HPLC method applying a QbD technique were as follows:

1. Stage 1: Quality Target Product Profile (QTPP)
2. Stage 2: Determine Critical Quality Attributes (CQAs)
3.  Stage 3: Establish a design space and experiment.
4. Stage 4: Risk assessment
5. Stage 5: Putting a Control Strategy to Work
6. Stage 6: Continual Improvement and Management of the Product Lifecycle

1. Quality Target Product Profile (QTPP): QTPP is described as a technique for method development in the ICH Q8 R (2) guidelines. QTPP is An important step in creating a plan for identifying the elements that directly affect quality. System Suitability tests, also known as QTPP variables, are frequently used in the development of HPLC Techniques. This particular stage examined possible relevant factors. The HPLC approach was Developed with the aim of analyzing anti-epileptic medications, and the quality goal product profiles Selected were Retention Time and Theoretical plates. Accuracy and precision are the performance Requirements that provide the essential information needed to use the recommended approach to Quantify an unknown quantity of the substance.If a method lacks sufficient peak resolution for proper integration, acceptable specificity, linearity Within a certain range, injection repeatability, etc., it cannot be accurate and precise. Since they offer A sizable data set for determining method controls, those previously mentioned critical elements must Be evaluated during method design in order to produce an appropriate and accurate method.
2. Critical Quality Attributes (CQAs) :

According to ICH Q8, Critical Quality Attributes (CQAs) are any physical, chemical, biological, or microbiological properties that must remain within predefined limits to ensure product quality. In analytical methods, CQAs represent parameters that directly influence accuracy, precision, and robustness.

• For HPLC: column type, organic modifier, elution mode, mobile phase pH and ratio, and diluent.
• For GC: inert gas type, gas flow rate, sample concentration, diluent, and oven/injection temperatures.
• For HPTLC: mobile phase composition, TLC plate type, sample volume, development time, colour reagent, and detection method.

In essence, CQAs are critical operational factors that must be controlled to maintain the desired analytical method performance and ensure consistent product quality.

3. Design of Experiments (DoE) :

Design of Experiments (DoE) is a structured, systematic, and statistical approach used to identify the relationship between input variables and their influence on analytical performance. It defines the design space—a multidimensional combination of variables and process parameters that ensures consistent method quality within acceptable limits.

In this study, DoE was applied to optimize critical method parameters by assessing their effect on analytical responses. The mobile phase composition, aqueous phase pH, and flow rate were selected as dependent variables (critical method parameters) using the Ishikawa diagram. The independent responses evaluated included peak asymmetry, theoretical plates, peak area, and retention time.

4. Risk Assessment:

According to ICH Q9, risk assessment is defined as a structured process for identifying, evaluating, controlling, and reviewing potential quality risks throughout the product lifecycle. In the Analytical Quality by Design (AQbD) framework, risk assessment plays a crucial role in establishing method reliability by identifying variables that may cause analytical variability. These variables include analyst performance, instrument design, column characteristics, sample properties (such as solubility, pH, and polarity), environmental conditions, and sample preparation techniques.The Fishbone (Ishikawa) diagram is often used to perform this evaluation by mapping all possible sources of variation that may influence Critical Quality Attributes (CQAs). These include factors related to the environment, instrumentation, and active pharmaceutical ingredient (API). By identifying high-risk factors early, the analytical method can be designed and controlled to minimize deviations and ensure consistent performance.

Control Strategy:

A control strategy is a predefined and systematic plan that ensures all method parameters remain within acceptable limits to guarantee consistent and accurate performance. It involves monitoring and managing identified risk factors, with special focus on high-risk variables that significantly affect method robustness.The control strategy defines a control space, which must lie within the design space established during method optimization. Regular monitoring of parameters such as mobile phase composition, pH, flow rate, and temperature helps maintain process stability and prevent deviations. A robust control plan ensures that even during scale-up or process transfer, the analytical method maintains its accuracy, precision, and quality attributes without requiring major revalidation.

Fig. No. 4 – Design space cover control

6. Continuous Improvement:

Continuous improvement is an essential component of the QbD lifecycle, ensuring that analytical methods remain efficient, consistent, and aligned with evolving technological and regulatory standards. Through ongoing data collection, method performance evaluation, and equipment maintenance, opportunities for enhancement are continuously identified. Regular reviews of analytical processes, calibration, and preventive maintenance of instruments such as HPLC and UV spectrophotometers are performed to sustain reliability. Knowledge gained during method development and validation contributes to process optimization and long-term quality assurance. Although the design space remains fixed, improvements within a company’s quality management system can further strengthen method robustness and reduce variability over time.

Benefits of Analytical QbD:

1. Gaining knowledge about process and control is beneficial. It surpasses the conventional ICH Approach of analysis and validation.
2. It surpasses the conventional ICH approach of analysis and validation. The capacity to analyze drugs with flexibility, standard API, dosage form impurities, sample Stability, and metabolic products in biological specimens
3. Qbd reduces variance in analytical properties to improve the robustness and ruggedness of the Approach.
4. It is not close to the Out of specifications (OOS) limit for keeping analysis value ranges in line with monographs from Pharmacopoeias.
5. It is possible to have a smooth technique transfer at the manufacturing level.

Advantages offered by AQBD in product development:

1. It is mostly concerned with how robust pharmaceutical processes and methods are in terms of Understanding and controlling variance.
2. Both the design of the products and the development of processes are involved.
3. The control method may be investigated further while the analysis is being conducted.
4. It permits ongoing enhancements during the last stages of the procedure.
5. Lowering experimental attribute variability to increase the robustness of the procedures. Batch mistakes and failures are reduced.

Analytical Method Validation: [17, 18]

Verifying that performance satisfies the requirements for the intended analytical application is the process of validation. Validation is evidence of the high degree of certainty that our created analysis approach can be relied upon, supported by documentation. The newly developed Qbd method was then verified through a series of validation tests.

In accordance with the ICH Q8 Guideline, analytical methods validation is carried out as follows:

i) Accuracy

ii) Precision

1. Repeatability
2. Intra-Day precision
3. Inter-Day precision

iii) Linearity

iv) Specificity

v) Robustness

vi) Limit of Detection

vii) Limit of quantification

Materials And Equipment’s

5. Material:

• Ethosuximide (API)
• Absenz 250 Mg/5 Ml (Syrup)
• Methanol
• Distilled water

5. Equipment:

• HPLC
• UV Visible Spectrophotometer
• Quartz Cuvette
• FTIR
• Digital Weighing Balance
• Ultra Sonicator

Experimental Work:

1. Authentication of Drug:

• Colour: - White light Powder
• Odor: - Odourless
•  Taste: - Bitter
•  Texture: - White fine powder

2. Determination of Melting Point
3. Fourier Transferred Infra-Red Spectroscopy: Identification of compound was done by using infrared spectroscopy (IR). Additionally, it Is employed for detect the structure as well as Functional Group.

Instrument:  ALPHA II, IR

4. HPLC
   1. Selection of solvent system: On standard TLC plates, experiments were conducted using different combinations of solvents in Different quantities to produce the best results. It was noted that Methanol: Water (5:5) was most Suitable because it gave good peak parameters.
   2. Selection of mobile phase: For method development various trials are done for the selection of the mobile phase and column.

4. 3. Degassing of mobile phase: To remove all air bubbles and avoid disruption produced by dissolved gases, the mobile phase is Degassed using an ultrasonicator.
   4. Filtration of mobile phase: The mobile phase was filtered thorough 0.45μm nylon filters to remove any small particles present in Mobile phase that may block the column.
   5. Preparation of stock solution for standard: A standard stock solution of Ethosuximide was made by the dissolution of 80mg of drug in 100ml of Diluent (Methanol) to get concentration of 800μg/ml.
   6. Preparation of standard solution f or working: Working solution of ETHOSUXIMIDE was formulated by mixing 5 ml of above standard stock Solution up to 10ml sol to give 400μg/ml
   7. Preparation of sample solution: The marketed preparation Absent syrup which contain 250 mg of ethosuximide were taken. Equivalent Weight of 250mg of Ethosuximide was taken and volume was made up to 100 ml with the solvent. Take 4ml above solution and volume was made up to 25ml with the solvent. Sonicated for 15 min. After Then, solution was filtered. And diluted with solvent to obtain the sample solution in the concentration of 400μg/ml.
   8. Loading of mobile phase: The mobile phase were filtered after which it degassed. Filled in reservoir and reservoir is loaded.
   1. Baseline stabilization: The detector was turned on an hour before working. This results into constant UV light are obtained for Detection. Steady baseline is obtained by using proper flow rate and stable detector.
   10. Saturation of column: For saturation of column continuous the mobile phase is traversed into a column with constant flow Rate.
   11. Loading of sample: Prepared solution was filled into vials and put those vials into the loading tray.
   50. Washing of column: After sample analysis, the column was washed by flushing the mobile phase for 25 to 30 minutes.
5. Method Validation: The Linearity, Accuracy, and Precision, Detection Limit (LOD), and Quantification Limit (LOQ) ICH Q2B guidelines were consulted for verification.

1. Linearity: Standard Ethosuximide were prepared in multiple concentrations and they exhibited linearity’s at Absorbance in range of 50-150μg/ml. The graph was plotted with Conc. vs Absorbance.
2. Accuracy: For determining accuracy, standard drug was added to the sample at 3 different levels 50%, 100% And 150%
3. Precision: Precision was determined by taking 5 times determinations of a 100% (400μg/ml) concentration of Standard solution at continuous intervals of time.
4. Limit of Detection: LOD stands for lowest detectable concentration, which may or may not be always qualified to be an extract value.

LOD is calculated by following formula

5. Limit of Quantitation: The smallest number of analysers in the sample that can be precisely and reliably counted is known as The Limit of Quantitation (LOQ). The following formula was used to calculate LOD

6. Design Of Experiment

5. Central Composite Design (CCD) : Central Composite Design (CCD) is one of the most commonly employed experimental designs within the Response Surface Methodology (RSM) framework, used to establish the relationship between independent variables and the measured responses. CCD consists of three major groups of design points: (1) two-level factorial points, (2) axial or star points, and (3) center points. These components enable estimation of second-order (quadratic) effects and interaction terms between variables, providing a comprehensive understanding of the system’s behavior across a multidimensional space. In this study, CCD was utilized to optimize the analytical conditions for the estimation of Ethosuximide. The experimental design incorporated a factorial arrangement with central points to investigate the combined influence of critical parameters, including mobile phase composition and flow rate, on key chromatographic responses such as retention time and peak area. The chromatographic analysis was performed using a Kromasil 100-5 C18 column (250 mm × 4.0 mm, 5 μm), which served as the stationary phase. The mobile phase consisted of methanol and distilled water in the ratio of 60:40 (v/v), and the flow rate was varied between 0.8 to 1.2 mL/min under controlled conditions. The column temperature and injection volume were maintained constant at 32°C and 20 µg/mL, respectively. Application of the CCD yielded nine experimental runs, each representing a unique combination of the chosen independent variables. This design allowed systematic evaluation and optimization of chromatographic conditions to achieve the most desirable analytical response. The optimization aimed to maximize desirable attributes, such as resolution and peak symmetry, while minimizing undesirable outcomes, ensuring robust and reproducible results within the defined design space. The final optimized model achieved a desirability value of 1.00, indicating the best possible performance within the experimental limits.

The experimental plan and data analysis were executed using ’esign-Expert® software (Version 13.0, Stat-Ease Inc., USA). The CCD model was fitted to a second-order polynomial equation to describe the relationship between the responses (Y) and the independent variables (A and B), represented as:

Y = β_0 + β_1A + β_2B + β_3AB + β_4A^2 + β_5B^2

Where: Y = Measured response (e.g., retention time, peak area), A and B = Coded levels of independent variables (mobile phase and flow rate) Β? = Intercept, Β?–β? = Regression coefficients corresponding to linear, interaction, and quadratic effects.

CMP: Critical method parameter, CQA: Critical quality attributes

Table No.5 – Optimized chromatographic results

Optimized chromatographic conditions given by software:

Result:

1. Determination of Melting Point:

Table No.6: Melting Point

2. Fourier Transferred Infra-Red Spectroscopy:

Identification of compound was done by using Infra-Red Spectroscopy (IR). It is also used to detect the structure as well as Functional Group

Fig No. 5: Interpretation of IR of Ethosuximide

Interpretation of IR:

Table No.7– Interpretation of IR

3. Solubility:

• Soluble in chloroform.
• Very soluble in Methanol and Water.

4. UV visible spectroscopy:

• The spectra of Ethosuximide in diluents were scanned on a UV- visible Spectrophotometer in the 200nm to 400nm region, with the diluent used as a Blank.
• The graph was plotted absorbance vs. wavelength Ethosuximide was estimated To be at 224 nm.

5. High-performance liquid chromatography:

Results of HPLC are carried out as follows

• Selection of wavelength

Fig No. 6 – UV absorbance of Ethosuximide

1. 1. Mobile phase selection: One the basis of Thin Layer Chromatography results, the Methanol and Distilled Water show best results with no tailing effect.

Methanol: Distilled Water / 60: 40

6. QBD Approach Developed Method: The suggested by software the respons
7. Table No.8 – Factor Responses

Fig No.7 – Chromatogram of Ethosuximide

Table No.9 – Optimized batch results

5. Factor 1 (Retention time):

Effect of independent factors on Retention Time (X)

• The coding of factors is coded.
• The sum of squares is partial (Type III).
• The model is likely significant based on its model F-value of 6.39. The probability that an F-value this great may be the result of noise is merely 3.26%.
•  Model terms are considered significant when P-values are less than 0.0500. A is a Significant model term in this instance. The model terms are not important if the value is Bigger than 0.1000. Model reduction could make your model better if it has a large number of unimportant model terms (apart from those needed to maintain hierarchy).

Fig.8: Three-dimensional plot for retention time as a function of amount of Methanol and Flow Rate.

Effect of independent factors on Area (X)

• A factor code has a code.
• Square sums are of Type III, Partial
• The model is deemed significant based on its F-value of 15.30. This kind of big F-value has a 2.41% probability of being caused by noise.
• Model terms are considered significant when P-values are less than 0.0500. B, AB, and B2 are important model terms in this instance. The model terms are not important if the value is bigger than 0.1000. Model reduction could make your model better if it has a large number of unimportant model terms (apart from those needed to maintain hierarchy).

Fig. 9: Three-dimensional plot for Area as a function of amount of Methanol and Flow Rate

Fig. No. 10: Predicted and actual value for response R1-Retention time, R2- Area.

Fig. No. 11: Predicted and actual value for response R1-Retention time, R2- Area.

Fig. No 12: Overlay plot showing the optical analytical design of experiment

1. Linearity: Graph of linearity was found to be with their concentration vs Area

Fig. No. 13– Linearity Graph of Ethosuximide.

2. Accuracy:

The recovery results of Ethosuximide by RP-HPLC are as follows

Precision: The results of precision for Ethosuximide intraday and Intraday are as Follows

Fig. No 14 – Precision trials overlay

Interday precision:

Table No.16– Results of Precision for Interday

Assay of syrup:

Table No.18- Results of LOD and LOQ of Ethosuximide

Fig no 17 – LOD and LOQ of Ethosuximide