FORMULATION AND IN VITRO CHARACTERIZATION OF METOCLOPRAMIDE LOZENGES BY USING THE QUALITY BY DESIGN (QBD) APPROACH

 Muhammad Abid Mustafa1*image, Aiman Mahmood3,4image, Seerat Shahzad2image, Nayyab Kanwal2image, Namra Rasheed1image, Mahgull Mahmood1image,Umair Amjad1image, Muhammad Ahmed Saeed1image, Muhammad Shahmir Ahsan1image, Shehzada Muhammad Afaq Raza1image, Muhammad Haris Pervaiz1image, Raza Hanif1image, Rania Ahmad1image

1Department of Pharmaceutics, Faculty of Pharmaceutical Sciences, Lahore University of Biological & Applied Sciences, Lahore.

2Department of Pharmacy Practice, Faculty of Pharmaceutical Sciences, Lahore University of Biological & Applied Sciences, Lahore, Pakistan.

3Punjab University College of Pharmacy, University of the Punjab, Lahore, Pakistan. 4Minhaj University, Lahore, Pakistan.

 

Abstract

Objective: Conventional dosage forms may not be suitable for all patients, especially those facing difficulty with swallowing, which results in reduced adherence to medication, impacting treatment outcomes. This study aimed to formulate and characterize metoclopramide lozenges using the quality-by-design approach to provide a patient-friendly alternative.

Materials and Method: Formulations were optimized using response surface methodology and a Design Expert, focusing on metoclopramide, citric acid, and carboxymethyl cellulose, with hardness and dissolution as primary response variables. The experimental design involved 20 runs, six replications of the central point, and a quadratic model for improved model fitting, with adjustments made through transformation and numerical hill-climbing algorithms.

Results and Discussion: Formulations of metoclopramide lozenges were obtained through a systematic quality-by-design approach. Using numerical optimization, the formulation with the highest desirability score of 1.00 was selected, yielding a hardness of 14.55 kg and a dissolution time of 1.25 minutes. The optimal coded values were 1.53223 grams for Metoclopramide (A), 4.84302 grams for Citric acid (B), and 18.5048 grams for carboxymethyl cellulose (C). The optimized formulation was subjected to in vitro testing to validate its performance, yielding a hardness ranging from 13.98 to 15.00 kg/cm2 and a dissolution time ranging from 1.11 to 2.23 minutes. The effectiveness of the QbD approach was confirmed as outcomes closely aligned with the targeted responses. 

Conclusions: The Quality by Design approach was found effective in optimizing the formulation of metoclopramide lozenges. Using the Design of Expert and Response Surface Methodology, an optimized formulation was obtained with the desired hardness and dissolution rate.

Keywords: Design expert, metoclopramide lozenges, optimized formulation, QbD approach, response surface methodology.

 

 

INTRODUCTION

 

Formulations are designed for easy patient administration through oral tablets, capsules, solutions, and injections, catering to specific patient needs and preferences. These delivery systems offer flexibility in treatment options based on individual patient conditions or requirements1. Formulation design is a systematic process that improves drug characteristics through strategies like dissolution, stability, and absorption. It significantly impacts treatment results and drug delivery effectiveness, reducing side effects and improving patient compliance. The oral route is preferred for patient compliance but has disadvantages for the elderly, pediatric, tremors, intellectual disabilities, and impaired swallowing ability2,3 are oral dosage forms for patients who cannot swallow solid dosages or slow-release drugs. They contain vitamins and antibiotics and come in various shapes. They provide local or systematically administered drug levels through the oral cavity, dissolve gently in the mouth, and are used to administer analgesics, sedatives, cleaning agents, antitussives, antimicrobials, antihistamines, and aromatics4. 

The Quality by Design (QbD) approach, replacing the Quality by Testing (QbT) method, focuses on chemistry, control, manufacturing, and safety in drug product formulation design. The QAD approach ensures product safety, effectiveness, and quality by scientifically understanding formulation and process factors influencing product quality5,6.

QbD, or “quality by planning”, is a systematic approach that focuses on target product design, offering benefits like reduced side effects, faster launches, and regulatory flexibility. It uses scientific ideologies and quality risk management to enhance product understanding, reduce manufacturing costs, improve FDA interaction, and enable continuous improvement in drug product manufacturing operations. The FDA endorses the Design of Experiments (DoE) method for its systematic approach, superior findings, and cost-effective description of input factors' impacts in industrial and research settings. Justin-Besancon and colleagues developed metoclopramide in 1964. This drug prevents vomiting by blocking dopamine effects, enhancing acetylcholine release, and sensitizing muscarinic receptors. Metoclopramide, a drug with a short half-life of 4 hours in humans and 60-90 minutes in rats and dogs, is easily absorbed and quickly eliminated. It works quickly after intravenous and intramuscular injections and is soluble in ethanol and water. Its metabolism involves N-deethylation, which is either eliminated in urine or converted to sulfate or glucuronide in bile. Impaired renal function prolongs its half-life, necessitating larger dialysis doses7,8. 

The study aims to improve patient compliance by eliminating first pass metabolism and preventing nausea and vomiting. A new antiemetic tablet has been developed, bypassing the liver for increased bioavailability and effectiveness. Metoclopramide's oral formulation has a bioavailability of around 40.7%. However, it can vary between 30 and 100%. Metoclopramide lozenges are a versatile and easy-to-use option for managing symptoms, particularly beneficial for those experiencing nausea or difficulty keeping pills down. The study focuses on formulation studies using the QbD approach, which improves product quality and optimized formulation, lowers development risks, and increases regulatory compliance9. 

 

MATERIALS AND METHODS

 

The ingredients, carboxymethylcellulose (CMC), citric acid, metoclopramide, sorbitol, fructose, mannitol, natural flavour, sodium bicarbonate, magnesium stearate, and French vanilla flavour,were purchased from Sigma Aldrich, Germany. We used each ingredient exactly as it was provided to us without any purification.

The method used in design expert:

RSM response surface methodology is utilized for process optimization experiments. It provides statistical tools for designing and analyzing experiments, producing predictive models, and specialized software for response surface maps, aiming for high yield and cost-effectiveness10. 

Central composite design:

We will study the Formulation process using a standard RSM design called a central composite design (CCD). It’s well suited for fitting a quadratic surface, which usually works well for process optimization11.

Response surface method for process optimization:

After completing the initial discovery steps and advancing through the breakthrough phase in our experimentation strategy, we strategically identified three variables with their quantities Metoclopramide (A) 1.8 g, Citric Acid (B) 5 ml, and CMC (C) 20 mg—as the most promising candidates for optimization for 100 lozenges. Hardness and Dissolution were measured as the primary response variables for metoclopramide lozenges. The results indicate that y1 corresponds to hardness (N/mm²) of 10–20 kg/N/mm² and y2 to dissolution (min) of 1–1.5 min. The central composite design (CCD) emerged as our preferred choice in RSM design, owing to its flexibility and suitability for our study12. 

The centre point in this CCD is replicated six times—not simply resampled or retested, but entirely rerun. This approach ensures a genuine measure of error, capturing natural variations from start to finish. The number of replicates is selected to encompass a broad region where the standard error of prediction remains relatively stable12. The results presented in Table 1 from the 20 run design originate from an authentic experiment. This experiment serves as the foundation for showcasing the effectiveness of this contemporary RSM in generating valuable predictive models12.

Analysis

Model Selection

A quadratic model was applied to the experimental results, supported by software tailored for RSM. However, upon diagnosing the residuals, which represent the disparities between actual and predicted Hardness and Dissolution, it became evident that a notable enhancement could be achieved by initially taking the inverse of the responses (1/y). Such transformations, frequently involving logarithms, are commonly utilized to enhance statistical properties, thereby validating a model within the factorial region of the RSM experiment13. 

Statisticians use coded factors to avoid round-off problems when using actual units of measure. Maintaining model hierarchy prevents misleading impressions about factors' significance, as they may make a difference at a higher order or in concurrence with other factors. However, some statisticians advocate for maintaining the entire quadratic polynomial as standard practice, as it helps avoid unforeseen inaccuracies in predictive modeling13. 

Optimization

Pharmaceutical experimenters used numerical hill-climbing algorithms to optimize hardness and dissolution in metoclopramide lozenges. These optimized values were then delivered to a second group for formulation, ensuring maximum desirability in evaluation parameters. This approach helps optimize diagnostic models and achieve desired outcomes14. 

Verification of experiment:

Another group verified the Experiment by formulating lozenges and evaluating their parameters using QbT14. 

Expert design software 

Design-Expert simplifies the use of powerful multifactor testing tools, providing a smooth method for advancing your study and guaranteeing its outstanding success15. 

It is a statistical software program designed especially for experiment design (DoE). Design experts provide comparative testing, screening, characterization, optimization, resilient parameter design, mixture designs, and integrated designs15. 

Design of experiments 

Design-Expert software enhances products and processes by efficiently screening essential factors and components. It allows users to set flags, explore contours on interactive 2D graphs, and visualize response surfaces with rotatable 3D plots. It also enables users to maximize desirability for all responses, identifying the' sweet spot' meeting all specifications. Design-Expert simplifies the application of powerful multifactor testing tools, making the process user friendly16. 

Physicochemical Characterization

Friability 

To determine the friability of the sample tablets, they were weighed and placed in the tester. Once the device was turned on and ran for four minutes at 25 rpm, it was removed and weighed again17. 

Disintegration time

A disintegration test involves placing a tablet in a device with a liquid medium, such as water or simulated gastric fluid. The device agitates the liquid at a constant temperature to simulate bodily conditions. The time taken for the tablet to dissolve fully is recorded18.

Weight variation

Twenty tablets were taken in, and a digital weighing scale was used to calculate each tablet's individual and total weight. The total weight was used to calculate the average weight of one tablet19.

Determination of drug content

Twenty lozenges were finely powdered to ensure the drug's complete solubility. A 100 mL volumetric flask containing 50 ml of 6.8 phosphate buffer was then added to the powder, which was weighed precisely and allowed to stand for 30 minutes with intermittent sonication. Distilled water was added until the mixture reached its full volume. A UV-visible spectrophotometer was used to measure the absorbance at λmax 234 nm after the solution had been appropriately diluted.19

Hardness

The USP method was used to determine the hardness of 20 core and coated tablets. The tablets were tested using a Monsanto hardness tablet tester, with a load applied and the point of fracture calculated. Ten tablets were used for the test, and the hardness was measured20. 

Dissolution time 

For each formulation, three in vitro release tests (n=3) were carried out using a paddle-based USP type II dissolution test device. A 900 ml solution of 0.1N HCl was used as the dissolving medium, and the device was run at 50 rpm at 37±2°C21,22. 

In vitro drug release

Scientists simulate digestion by placing lozenges in a vessel containing a liquid simulating stomach fluid. A rotating paddle keeps the fluid warm (37°C) and mixed (50 rpm). They periodically withdraw small samples and dilute them tenfold for analysis with a UV-visible instrument at 234 nm. This allows them to track how much drug is released from the tablet over time. By calculating the percentage of drugs released at each sampling point, they create a “release profile” that reveals how quickly the medication leaves the tablet23. 

Release kinetics

The DD-Solver add-in program is utilized to apply various kinetic models, such as zero-order, first-order, Korsmeyer, and Peppas, to calculate a drug's release kinetics from its dosage form. The drug release behaviour and pattern are then evaluated24. 

 

RESULTS AND DISCUSSION

 

Formulation and process development

Better process understanding and control are vital to minimizing product waste due to manufacturing failure and producing products of desired quality with reduced end-product testing. These objectives were accomplished by identifying process variables for preparing robust Metoclopramide Lozenges. Subsequent process development studies confirmed the criticality of the process parameters. Critical process parameters are the process inputs that, when varied beyond the proven acceptable ranges (PAR), significantly affect the CQA; therefore, they must be controlled within predetermined limits. We named our critical process factors as A=Metoclopramide, B= Citric Acid, and C=CMC, having units as gram, milliliter, and milligram, respectively. The Coded value for metoclopramide quantity was 1.20 as low and 1.80 as high, the Coded value for Citric Acid was 3.00 as low and 4.00 as high, and the Coded value for CMC was 15.00 as low and 20.00 as high, having mean value as 1.50, 4.00, and 17.50 respectively (Table 2) and having standard deviation as 0.2543, 0.847, and 2.12 respectively.

Optimization of Responses

Numerical optimization is crucial when analyzing multiple factors with multiple responses, providing valuable insights when combined with graphical analysis. However, subject-matter knowledge is crucial for success. To avoid defining impossible criteria, set broad acceptable ranges and narrow them down as you learn about factor levels' impact on responses.

Therefore, by utilizing the values of these crucial factors (ingredients), we can attain our desired outcome, namely, an optimized response rate of 14.55 kg hardness and 1.25 minutes dissolution.

The responses, along with their optimized values, are shown in Table 3. While the software proposed numerous solutions, we opted for the one with the highest desirability score, which is 1.00.

Ingredients (Factors)

A = Metoclopramide ……………. Grams (g)

B = Citric Acid………………………. Milliliter (ml)

C=CMC………………………………Milligrams (mg)

3D Surface of Responses

When fitting a quadratic model with two or more continuous predictors, it becomes beneficial to provide a graphical representation of the fitted surface. Given the particular significance of surface visualization in response-surface methods, the function “3D Graphs” was devised and integrated into the RSM package in pursuit of this.

Development of optimized formulation

The lozenge formulation, comprising Metoclopramide, citric acid, CMC, and all other components, undergo wet granulation, followed by drying the granules in an oven at 40°C for 6 hours. Subsequently, these granules were directly compressed into tablets using a press, with an average weight of 3000 mg, as detailed in Table 225.  

In vitro analysis on the optimized formulation

The optimized formulation, after development, was subjected to in-vitro laboratory testing to evaluate the optimized responses due to the optimized concentration are as follows: 

Physicochemical Characterization 

Weight variation

The weight variation of the formulations was determined to be within pharmacopeial limits, ranging from 2999.3±1.58 mg to 3000.7±2.6 mg. The results are indicated in Table 4. 

Friability

The friability of all formulations is no more than one, indicating all formulations comply with the official limits. The results are indicated in Table 4. 

Disintegration Time

The disintegration time ranges between 2 and 8 minutes, as described in Table 4, indicating a good disintegration time for drug release.

Drug Content

After an assay, the drug content percentage of each lozenge ranged from 98.13±1.71 to 99.81±1.48, as indicated in Table 4.

Hardness test and Dissolution test

The 10 lozenge sample was subjected to a hardness and dissolution test to evaluate its hardness and dissolution time. 

As shown in Table 5, the hardness of the sampled lozenges ranged from 13.98 to 15.00 kg/cm2, and the dissolution time ranged from 1.11 to 2.23 minutes. These values are close to the optimized hardness responses, indicating precise optimization of formulation concentration through Quality by Design (QBD)22. Additionally, 3D surface modeling of hardness and dissolution was carried out, and the results are displayed in Figure 1. 

In vitro drug release

The drug-release characteristics of metoclopramide were evaluated using in vitro drug-release tests on metoclopramide lozenges. The dissolution rate of the medication increased steadily across all formulations (F1 to F7) over a 30 minute period. Most formulations, except F4, achieved 90% drug release after 30 minutes, with a relatively rapid initial release period followed by a slow-release pattern. 

Notably, formulation F6 demonstrated the highest drug release over the 30 min period, reaching 95.17%, as shown in Figure 3.

Release kinetic 

The in vitro dissolution rate pattern was calculated using various mathematical model coefficients. The value of 𝑟2, which helps in understanding the nature of metoclopramide lozenges, is shown in Table 6. The r2 value for first-order kinetics is 0.9769, and for zero-order kinetics, it is 0.9347. The r2 value was close to 1 for first order kinetics, confirming that the metoclopramide lozenges followed first-order kinetics and showed immediate drug release. In the Korsmeyer-Peppas model, all formulations had an n value between 0.296 and 0.664, demonstrating Fickian release and first-order kinetics.

 

DISCUSSION

 

Metoclopramide lozenges are a new drug delivery method for treating nausea and vomiting in patients who cannot swallow pills. They work by locally delivering active molecules in the oral cavity, bypassing traditional ingestion and resulting in faster action and improved patient compliance by passing traditional ingestion, resulting in faster action and improved patient compliance. Response Surface Methodology (RSM) was used to develop and evaluate formulations with Generate Expert. Process optimization for formulation processes was done using central composite design (CCD). Metoclopramide, citric acid, and carboxymethylcellulose were the main ingredients focused on, with hardness and dissolution as the primary factors considered for optimization. Increasing the quantity of carboxymethylcellulose enhances the dissolution rate of metoclopramide hydrochloride. The study uses numerical hill-climbing algorithms to optimize hardness and dissolution in metoclopramide lozenges. The results from the 20 run design demonstrate the effectiveness of this contemporary RSM in generating predictive models, ensuring maximum desirability in evaluation parameters.

This approach helps optimize diagnostic models and achieve desired results. The formulation for the lozenges, which includes metoclopramide, citric acid, CMC, and other ingredients chosen for their unique properties, is granulated moist and then dried for six hours at 40°C in an oven. Then, using a press, these granules were immediately crushed into lozenges, each weighing 3000 mg on average, ensuring consistent dosage and ease of administration.

Following development, in-vitro laboratory testing was conducted on the optimized formulation to assess the optimized responses resulting from the optimized concentration. In their work “Response surface methodology (RSM) as a tool for optimization in analytical chemistry”, Bezerra et al., noted similar results of response optimization26. The formulation with the greatest desirability score of 1.00 was chosen by numerical optimization, resulting in a hardness of 14.55 kg and a dissolve time of 1.25 minutes. A hardness and dissolution test were conducted to assess the hardness and dissolving time of the 10 lozenge sample. The hardness of the examined lozenges varied from 13.98 to 15.00 kg/cm2. In their study on the formulation and evaluation of theophylline lozenges, Pravalika L et al., noted similar patterns for hardness27. The dissolve time of the examined lozenges varied from 1.11 to 2.23 minutes. In their development and evaluation of novel lozenges containing marshmallow root extract, Niko Benbassat et al., noted similar results in dissolution studies28. These data demonstrate accurate formulation concentration optimization using Quality by Design (QBD) since they are near the optimal hardness responses.

Numerical optimization chose the formulation with the greatest desirability score of 1.00, resulting in a hardness of 14.55 kg and a dissolve time of 1.25 minutes. The ideal coded values are as follows: 4.84302 grams for citric acid (B), 18.5048 grams for carboxymethyl cellulose (CMC) (C), and 1.53223 grams for metoclopramide (A). Most formulations, except F4, achieved 90% drug release after 30 minutes, with a relatively rapid initial release period followed by a slow-release pattern. Notably, formulation F6 demonstrated the highest drug release over the 30-minute period, reaching 95.17%. The r2 value was close to 1 for first-order kinetics, confirming that the metoclopramide lozenges followed first-order kinetics and showed immediate release effects.

Results showed a strong alignment with the intended responses, confirming the effectiveness of the QbD approach. This suggests that the quality of the product was consistently maintained throughout the development process. It also indicates that the QbD approach can be a reliable method for ensuring product quality and consistency. Furthermore, these findings demonstrate the importance of implementing QbD principles in pharmaceutical development to meet regulatory requirements and customer expectations. Overall, the study highlights the significant impact of utilizing a QbD approach on improving product quality and reducing risks in the manufacturing process. Future research should focus on optimizing formulation parameters and conducting in vivo studies to evaluate the efficacy of metoclopramide lotions. This will provide a better understanding of the QBD approach's potential benefits. Various administration methods and dosage forms could enhance the effectiveness of metoclopramide lozenges.

Additionally, investigating stability and shelf life under different storage conditions is crucial for ensuring quality and efficacy.

Limitations of the study

The study's limitations include the lack of long-term stability testing of the lozenges and potential variability in results due to differences in manufacturing processes. Additionally, the study did not assess the impact of different storage conditions on the performance of the metoclopramide lozenges. Future research could focus on conducting long-term stability testing to assess the durability of the lozenges over time. Furthermore, investigating the effects of various storage conditions on the performance of metoclopramide lozenges could provide valuable insights for optimizing their quality and efficacy. 

 

CONCLUSIONS 

 

This study demonstrated the significant benefits of the quality-by-design (QbD) approach. This methodology facilitated the elucidation of various two-way interactions between independent variables, which the conventional one-factor-at-a-time method cannot accomplish. The ability to pinpoint the relationship between critical material and process attributes culminates in process control and continuous improvement. Hence, by implementing the Design of Expert approach, formulation parameters are studied and optimized using the response surface method. This systematic approach allows us to determine optimal parameters and predict desirable outcomes. Optimization helps us save time and reduce resource wastage. The QbD approach offers a promising future for pharmaceutical development. Hence, DOE saved time and hassle by allowing quality testing to be done and provided much better and more sophisticated means for process optimization.

 

ACKNOWLEDGEMENT

 

We would like to extend our sincere gratitude to the Deanship of the Faculty of Pharmaceutical Sciences at Lahore University of Biological & Applied Sciences (UBAS) for providing the necessary resources and support that made this research possible. Their commitment to fostering academic excellence has been invaluable.

 

DATA AVAILABILITY

 

The data will be available to anyone upon request from the corresponding author.

 

AUTHOR’S CONTRIBUTION

 

Mustafa MA: Conceived and designed, manuscript drafting. Mahmood A: methodology. Shahzad S: conceptualization, literature search. Kanwal N: Ideas, experimental studies. Rasheed N: Conceived and designed, manuscript writing. Mahmood M: editing, data analysis. Umair Amjad: collected, analyzed data, wrote manuscript. Saeed MA: data analysis.  Ahsan MS: Methodology, experimental studies. Raza SMA: material collection. Pervaiz MH: data curation, investigation. Hanif R: methodology. Ahmad R: conceptualization. All authors revised the article and approved the final version.

 

CONFLICT OF INTEREST 

 

None to declare. 

 

REFERENCES

 

  1. Hwang R-cR, Kowalski DL. Design of experiments for formulation development 2005.
  2. Chanda R, Nallaguntla L. Formulation and evaluation of medicated lozenges for sore throat. Asian J Pharm Clin Res 2020:62-7.
  3. Kesharwani R, Ansari MS, Patel D. Novel technology used in the preformulation study: A review. J Drug Deliv Therap 2017;7(4):20-33.http://dx.doi.org/10.22270/jddt.v7i4.1487
  1. Mallappa DP, Bhat USJAJPHR. Formulation development and evaluation of promethazine as a lozenge 2020; 8(8):18-30.
  2. Awadeen RH, Boughdady MF, Meshali MM. Quality by design approach for preparation of zolmitriptan/chitosan nanostructured lipid carrier particles–formulation and pharmacodynamic assessment. Int J Nanomed 2020:8553-68. http://dx.doi.org/10.2147/IJN.S274352
  3. Jana S, Ali SA, Nayak AK, Sen KK, Basu SK. Development of topical gel containing aceclofenac-crospovidone solid dispersion by “Quality by Design (QbD)” approach. Chem Eng Res Des 2014;92(11):2095-105. https://doi.org/10.1016/j.cherd.2014.01.025
  4. Beg S, Swain S, Rahman M, Hasnain MS, Imam SS. Application of design of experiments (DoE) in pharmaceutical product and process optimization. Pharmaceutical quality by design: Elsevier 2019; 43-64.https://doi.org/10.1016/B978-0-12-815799-2.00003-4
  1. Nadpara NP, Thumar RV, Kalola VN, Patel PB. Quality by design (QBD): A complete review. Int J Pharm Sci Rev Res 2012;17(2):20-8.
  2. Vidyadhara S, Sasidhar R, Deepti B, Wilwin E, Sowjanyalakshmi B. Formulation and evaluation of amoxicillin trihydrate lozenges. Dhaka University J Pharm Sci 2015;14(1):61-70.
  3. Popoola LT. Nano-magnetic walnut shell-rice husk for Cd (II) sorption: Design and optimization using artificial intelligence and design expert. Heliyon 2019;5(8).https://doi.org/10.1016/j.heliyon.2019.e02381
  1. Saghira A, Khanb A, Umerc MF, Khand J, Malike O, Joharf M. Study of influence of formulation variables on drug release: Optimization of sustained release matrix tablets of metoclopramide hcl using central composite experimental design. American Sci Res J Eng Tech Sci 2020;64(1):210-21.
  2. Md S, Dhanalakshmi B, Doddayya H, Khan KAA. Factorial design based optimisation of metoclopramide hydrochloride fast dissolving tablets. RGUHS J Pharm Sci 2020;10(3).
  3. Nithya S. Formulation Development and evaluation of metoclopramide hydrochloride medicated hard candy lozenges: College of Pharmacy, Madras Medical College, Chennai; 2019.
  4. Alanezi AA. Development of an orally disintegrating mini-tablet (ODMTs) containing metoclopramide HCl to enhance patient compliance: The University of Toledo; 2014.
  5. Myers RH, Montgomery DC, Anderson-Cook CM. Response surface methodology: Process and product optimization using designed experiments: John Wiley & Sons; 2016.
  6. Raina H, Kaur S, Jindal AB. Development of efavirenz loaded solid lipid nanoparticles: Risk assessment, quality-by-design (QbD) based optimisation and physicochemical characterisation. J Drug Delivery Sci Tech 2017;39:180-91. https://doi.org/10.1016/j.jddst.2017.02.013
  7. Mustafa FH, ElRab EKMG, Kamel RM, Elshaarawy RF. Cost-effective removal of toxic methylene blue dye from textile effluents by new integrated crosslinked chitosan/aspartic acid hydrogels. Int J Biol Macromol 2023;248:125986. https://doi.org/10.1016/j.ijbiomac.2023.125986
  8. Low A, Kok SL, Khong Y, Chan SY, Gokhale R. Pharmaceutics, drug delivery and pharmaceutical technology: A new test unit for disintegration end-point determination of orodispersible films. J Pharm Sci 2015;104(11):3893-903. https://doi.org/10.1002/jps.24609
  9. Mustafa MA, Khan AM, Ali N, et al. Formulation and in vitro Evaluation of pH Dependent Colon Targeted Controlled Release Tablet of Mesalamine Containing Cyamopsis tetragonoloba Gum and Sodium Alginate. Int. J. Pharm. Investig. 2023 Jul 1;13(3). Doi : http://doi.org/5530/ijpi.13.3.059
  10. Pujari AS, Gaikwad NA, Mane IV, Vambhurkar GB, Honmane PP. In-vitro evaluation of different marketed brands of paracetamol tablets using quality control tests. Asian J Pharm Technol 2018;8(3):119-22.https://doi.org/10.5958/2231-5713.2018.00019.3
  1. Rao M, Gogad VK, Sonar GS, Vanshiv SD, Badhe AS. Preparation and evaluation of immediate release tablet of metoclopramide HCl using simplex centroid mixture design. Int J PharmTech Res 2010;2:1105-11.
  2. Mustafa MA. Design and fabrication of metoclopramide fast dissolving films by using combined natural and synthetic polymers, and in vitro Medical Sci 2024;28:e19ms3303.
  3. Mustafa MA, Khan AM, et al. Fabrication and in-vitro characterization of mucoadhesive tablet using a natural biocompatible polymer containing metformin HCl. Medical Sci 2024;28:e38ms3335.https://doi.org/10.54905/disssi.v28i147.e38ms3335
  1. Mustafa MA, Khan AM, Arif M, et al. A Green approach to synthesize ondansetron oral mucoadhesive tablets by using natural polymers and in vitro Int J Pharm Invest 2024; 14(2):462-471.
  2. Budiman A, Sofian FF, Santi NMW, Aulifa DL. The formulation of lozenge using black mulberries (Morus nigra) leaf extract as an α-glucosidase inhibitor. J Pharm Bioall Sci 2020;12(2):171-6.https://doi.org/10.4103/jpbs.JPBS_219_19
  1. Bezerra MA, Santelli RE, Oliveira EP, Villar LS, Escaleira LA. Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta 2008;76(5):965-77. https://doi.org/10.1016/j.talanta.2008.05.019
  2. Pravalika L, Kumar SY. Formulation and evaluation of theophylline lozenges. Res J Pharm Tech 2021;14(3):1601-6.https://doi.org/10.5958/0974-360X.2021.00284.5
  1. Benbassat N, Kostova B, Nikolova I, Rachev D. Development and evaluation of novel lozenges containing marshmallow root extract. Pak J Pharm Sci 2013;26(6):1103-7.