QbD- a novel approach for design, optimization and evaluation of Linagliptin mucoadhesive microspheres

 Corresponding author: Y. Anand Kumar. Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0. QbDa novel approach for design, optimization and evaluation of Linagliptin mucoadhesive microspheres G. Ragini, Bhumika.C and Y. Anand Kumar * Department of Pharmaceutics, V. L. College of Pharmacy, Raichur, Karnataka, India. GSC Advanced Research and Reviews, 2025, 25(01), 051-066 Publication history: Received on 25 August 2025; revised on 05 October 2025; accepted on 07 October 2025 Article DOI: https://doi.org/10.30574/gscarr.2025.25.1.0300 Abstract The quality by design (QbD) approach was applied for optimizing the formulation of model antidiabetic drug Linagliptin (LIN) mucoadhesive microspheres (MS) through design of experiment (DoE). To Optimize LIN-MS a quality target product profile (QTPP) was established in which critical quality attributes (CQAs) such as MVD, % encapsulation efficiency (% EE) and t50 drug release were quantified. As critical material attributes (CMA) viz., Keltone (KE), Carbopol (CA) and Pectin (PC) were chosen and evaluated their effect on CQAs. Response surface design (RSM) viz., Central Composite Design (CCD) was studied to evaluate effects of CMA on stated CQAs within the design space. The main effect was observed with varied levels of KE, CA and PC on CQAs. Numerical optimization by point prediction method was applied to generate optimized formula with predicted response, later the experimental responses were validated and ratified within the design space. The results suggest QbD through DoE appears to be a useful approach for the rational design, optimization and characterization of LIN-MS. Keywords: Linagliptin; QbD; QTPP; CQAs; CMA; DoE 1. Introduction Pharmaceutical industry is constantly searching the ways to ensure and enhance product safety, quality and efficacy. However, drug recalls, manufacturing failure cost, scale up issues and regulatory burden in recent past produce huge challenge for industry. In traditional, the product quality and performance are predominantly ensured by end product testing, with limited understanding of the process and critical process parameters. Regulatory bodies are therefore focusing on implementing quality by design (QbD), a science based approach that improves process understanding by reducing process variation and the enabling process control strategies. QbD is a systematic approach to optimize pharmaceutical preparations and to improve the control over and the quality of the production process. The QbD approach consistently yields a product with desired characteristics and built in quality1-4. The preferred tool for strategic drug development using the QbD approach is the establishment of a quality target product profile (QTPP)5,6. A QTPP starts with defining the critical quality attributes (CQAs) for the final product. A CQAs can be defined as, physical, chemical, biological or microbiological property or characteristic that should be within an appropriate limit, range or distribution to ensure the desired product quality and thereby adequate performance and safety of the drug product when used7. A subsequent step of the QTTP is the identification of the critical material attributes (CMA) that influence the CQAs. By combining the CQAs and CMA a design space can be created. As long as the formulation and process variables remain within the design space, a product will be obtained that meets the quality requirements. Delivery of a medication for an acute or chronic disease is carried out via conventional pharmaceutical dosage forms such as matrix tablets, capsules, suspensions etc. Therapy with the conventional dosage forms shows variation in the GSC Advanced Research and Reviews, 2025, 25(01), 051-066 52 concentration of the drug in plasma. After administering first dose, the drug concentration declines due to the effect of metabolism. In order to avoid the frequency of administration and to maintain the steady state concentration of drug in plasma the controlled release formulations were used during which the concentration is maintained constant within therapeutic range for prolonged period of time with minimum unwanted effects and with more patient compliance8. A number of approaches have been developed to increase the residence time of the drug formulation. One of the approaches is the formulation of mucoadhesive microspheres (MS)9-11. These class of microspheres have the potential to be used for targeted and controlled drug delivery, but coupling of mucoadhesive properties as additional advantages such as effective absorption and enhanced bioavailability of the drugs, a much more intimate contact with the mucus layer, specific targeting of the drug to the absorption site12-14. Microspheres fabricated using mucoadhesive polymer with a diameter of 1-1000 μm are well known as MS. These microspheres can be tailored to adhere any mucosal tissue including those found in eye, nasal cavity, urinary and gastrointestinal tract, thus offering the possibilities of localized as well as systemic controlled release of drugs. Microspheres prepared with mucoadhesive and biodegradable polymers undergo selective uptake by the M cells of peyer patches in gastrointestinal (GI) mucosa. This uptake mechanism has been used for the delivery of protein and peptide drugs, antigens for vaccination and plasmid DNA for gene therapy1517. MS offer more attention because of their advantages such as targeting the drugs to the specific sites, greater physical and chemical stability during sterilization and storage, entrap both hydrophilic and hydrophobic drugs, ease of transfer, distribution and dosing. QbD regulatory initiative represents a highly systematic approach implementing the Design of Experiments (DoE) for finding the optimal product and process characteristics18,19. DoE, affords the remarkable and even more quantity of instructions as of the slightest number of experimental runs by methodical distinction of the factors and simultaneous evaluation of the effects of multiple variables20. Quality assurance (QA) has altered from the demand to elucidate that the ultimate product gets the predefined requirements and specifications to a novel circumstance where it needs to be confirmed that the product is controlled within a significant and organized design space21. The design space stated as a renowned technique enclosing multidimensional series of input variables and process parameters, which detonated in order to insist typical quality assurance22. Linagliptin (LIN)23, a potent and selective dipeptidyl peptidase (DPP) IV inhibitor mainly used for the treatment of type 2 diabetes. Typically, it improves the glyceamic control by increasing the active level of incretin peptides particularly glucagon like peptide-1 (GLP-1) and glucose-dependent insulin tropic peptide. The rationale of selecting LIN as a model drug for designing MS formulation is to administer once daily to avoid repetitive administration would be further enviable. The aim of the present research was adapting QbD approach to design, optimize and characterize Linagliptin loaded mucoadhesive microspheres using novel mucoadhesive polymer blends viz., Keltone, Carbopol and Pectin by DoE studies. 2. Materials and methods Materials: Linagliptin (LIN) was obtained as gift sample Magnus Pharma Ltd, Birgunj, Nepal. Keltone (KE), Carbopol 934(CA), Pectin (PC), Calcium chloride (CaCl2) were purchased S.D Fine Chemicals Mumbai, Maharashtra, India. All other reagents employed in the study were of pharmaceutical and analytical purity. Choice of design and experimental layout: The design space was calculated using Design Expert Software V13 trial, Stat Ease (DES). The CMA (KE, CA and PC) at varied levels were used for the study and Central Composite Design (CCD) was made for the RSM and the number of runs needed were calculated. The CMA were varied over two levels (-1 low, +1 High) resulting in a setup of 17 runs which were performed randomly to prevent bias. Table 1 shows the ranges of CMA applied and trial run generated and keeping other excipients kept constant. To each run different variables were assigned by the program resulting in different data visualization plots24. For each run a different percentage of KE (X1), CA (X2) and PC (X3) were applied to assess the CQAs viz., Mean vesicle diameter (MVD-Y1) % EE (Y2), and t50 (Y3). The best fitted models were assigned by DES and were chosen based on their significance using an analysis of variance (ANOVA) F-test with appropriate polynomial equations, Yn= b0 + b1X1 + b2X2 + b3X3 + b12X1X2 + b13X1X3 +.............. Where Ynresponses; b0intercept; b1 to b33regression coefficients; X1, X2, X3independent variables GSC Advanced Research and Reviews, 2025, 25(01), 051-066 53 Table 1 Selection of CMA and levels as per CCD Variables (CMA) Levels used, actual (coded) Independent variable Low (-1) High (+1) X1-KE in mg 250 300 X2-CA in mg 75 100 X3-PC in mg 75 100 Dependent variables/Response (CQAs) Y1 - MVD in µm ; Y2 - % EE; Y3 - t50 Preparation of LIN-MS25-27: LIN-MS were prepared by ionotropic gelation method (figure 1). First homogeneous polymer solution was prepared by mixing accurately weighed quantities (table 2) of KE, CA and PC with 10 ml of double distilled water, to this slowly disperse weighed amount of LIN with constant stirring until smooth dispersion was obtained. The smooth dispersion was loaded into syringe and fix needle size 22, further inject slowly the mixture into 10 % w/v CaCl2 solution with constant stirring at 200 rpm for 1 hr on thermostat magnetic stirrer to produce viscous, rigid microspheres. The obtained LIN-MS were washed with distilled water to remove any unreacted calcium ions, and air dried for 24 hr. Dried LIN-MS were stored at room temperature for further evaluation. Table 2 Possible formulation trials as per CCD Trial Runs X1 X2 X3 A:KE (mg) B:CA (mg) C:PC (mg) 1 275 87.5 100 2 275 100 87.5 3 300 75 100 4 250 100 75 5 250 100 100 6 300 75 75 7 275 75 87.5 8 300 100 75 9 250 87.5 87.5 10 275 87.5 87.5 11 250 100 100 12 275 75 75 13 250 75 75 14 300 100 100 15 275 87.5 87.5 16 300 87.5 87.5 17 275 87.5 87.5 GSC Advanced Research and Reviews, 2025, 25(01), 051-066 54 2.1. Evaluation FTIR study: The interaction between LIN and added excipients was studied by comparing FTIR spectrums of LIN and OP-LIN-MS. The FTIR of respective were recorded over the wave number of 4000 cm-1 to 500 cm-1 using BRUKER-FTIR spectrophotometer. During study ground, small amount of solid samples mixed with 100 times its weight of potassium bromide and compressed into a thin transparent pellet using hydraulic press. Transfer these pellets in to FTIR instrument and determine the spectrum. Encapsulation efficiency: The % EE of trial batches of LIN-MS were determined by standard method. In each case transfer 50 mg of powdered microspheres into 25 ml volumetric flask, extract the LIN content with 25 ml of methanol by shaking occasionally for 1hr, followed by sonication for 10 min. Filter the contents, dilute appropriately with 0.1N HCl and measure the absorbance at 293 nm. The percent encapsulation efficiency was determined by using below given formula, % Encapsulation effeciency = Actual amount of drug encapsulated Theoretical drug content ×100 Drug content: The LIN content in LIN-MS trials were determined, in each case powdered microspheres equivalent to 5 mg of LIN was transfered into 25 ml volumetric flask, extract the LIN content with 25 ml of methanol by shaking occasionally for 1h, followed by sonication for 10 min. Filter the contents, dilute appropriately with 0.1N HCl and measure the absorbance at 293 nm. The % drug content was determined by using following equation, % Drug content = Experimenta drug content Theoretical drug content ×100 Mean vesicle diameter (MVD): MVD of trial LIN-MS were determined by sieve analysis method. In each case, weighed amount of microspheres sieved through a set of standard sieves (viz., #16, #22 and #40) arranged in descending order with respect to the aperture size by using mechanical sieve shaker. After shaking period, microspheres which retained on each sieve were weighed and determine the MVD by using following equation, DAvg =∑Xifi fi Where, Xi Mean size range; fi % of microspheres retained on the smaller sieve size range GSC Advanced Research and Reviews, 2025, 25(01), 051-066 55 Figure 1 Scheme for preparation of LIN-MS by inotropic gelation method Surface morphology: Surface morphology studied by scanning electron microscopy (SEM) to check surface topography, texture and to examine the morphology of fractured or sectioned surface of the OP-LIN-MS. The OPLIN - MS was mounted using a double-sided sticking tape and coated with gold (200 Ao) on the SEM sample stab, under reduced pressure (0.001 torr) for 5 min using ion sputtering device (SEM-Jeol JFC-1100E, Tokyo, Japan). The goldcoated samples observed under the SEM and photomicrographs of suitable magnification were obtained. In vitro mucoadhesion test: In vitro mucoadhesion through wash-off test was studied for OP-LIN-MS to analyze extent of mucoadhesion using modified paddle dissolution apparatus as shown in figure 2. During the study freshly cut everted sheep intestine (collected from the slaughter house) 8x3 cm was fixed on to the paddle. Spread about 100 microspheres onto wet and rinsed tissue specimen, and the tissue specimen was given a regular, slow movement in a vessel containing 600 ml of 0.1N HCl at 37°C by rotating the paddle at 50 rpm. Measure the number of microspheres adhering to tissue at different intervals and continued for 6 hr, from the data percentage mucoadhesion was determined by using the formula, GSC Advanced Research and Reviews, 2025, 25(01), 051-066 56 % Mucoadhesion = Number of microsphered adhered Number of microspheres applied × 100 In vitro dissolution: The in vitro drug release was studied for trial LIN-MS and OP-LIN-MS using USP type I basket apparatus. In each case LIN-MS and OP-LIN-MS equivalent to 10 mg of LIN were filled in hard gelatin capsules and studied for drug release. The drug release was studied in two simulated fluids, for first 2 hr studied in 0.1N HCl followed by phosphate buffer pH 6.8 for 12 hr. During the dissolution studies, maintain a speed of 50 rpm and temperature of 37 ± 0.50 C throughout the study period. At different time intervals, 5 ml sample was withdrawn and replaced with fresh dissolution medium to maintain the sink condition. The LIN content at different time intervals was determined by using appropriate analytical method. The drug release data was model fitted with different kinetic models using PCP Disso V3. Figure 2 In vitro wash off test of OP-LIN-MS 3. Results and Discussion The model drug LIN was subjected for preformulation studies such as solubility, melting point partition coefficient. The solubility of LIN complies with the standard values, melting point was found to be 202oC against standard 190-2020C, the partition coefficient is 1.7 against 1.8 and pKa is 8.6 against 7.4 (log P). The results were complies with the standard values indicate the drug sample was stable and pure. The trial LIN-MS appears as slightly pale yellow colour free flowing microspheres, % drug content was in the range of 98.71 ± 0.5590 to 99.11 ± 0.1206, low SD value indicate the drug is uniformly distributed throughout the microspheres. During characterization of trial LIN-MS two important CQAs were generated viz., mean vesicle diameter and % EE. The comparative in vitro dissolution profile was shown in figure 3, the profile suggest the release of drug was less in acidic pH, once it enters into basic pH, the drug release was increased and sustained for 12 hr, from this profile one important CQA viz., t50 was generated under the influence of CMA. GSC Advanced Research and Reviews, 2025, 25(01), 051-066 57 Figure 3 Comparative in vitro dissolution profile of trial LIN-MS as per CCD Table 3 CQAs data of trial batches as per CCD under the influence of CMA Design trials Batch code CMA CQAs X1 X2 X3 Y1 Y2 Y3 A:KE mg B:CA mg C:PC mg MVD µm % EE t50 hr F-1 275 87.5 100 835.52 70.12 5.8 F-2 275 100 87.5 837.23 69.78 5.81 F-3 300 75 100 895.65 78.56 6.52 F-4 250 100 75 800.12 55.65 4.85 F-5 250 100 100 798.56 56.21 4.78 F-6 300 75 75 885.63 79.56 6.61 F-7 275 75 87.5 832.21 68.95 5.79 F-8 300 100 75 891.26 80.12 6.58 F-9 250 87.5 87.5 801.01 58.12 4.81 F-10 275 87.5 87.5 831.24 70.1 5.81 F-11 250 75 100 800.11 56.1 4.79 F-12 275 87.5 75 829.32 71.02 5.77 F-13 250 75 75 799.12 55.62 4.81 F-14 300 100 100 890.21 80.21 6.61 F-15 275 87.5 87.5 830.01 76.56 5.79 F-16 300 87.5 87.5 889.25 79.56 6.64 F-17 275 87.5 87.5 829.98 71.65 5.82 GSC Advanced Research and Reviews, 2025, 25(01), 051-066 58 CQAs studies within the design space: CQAs for LIN-MS set MVD (<1000 µm), % EE (> 50) and t50 (> 4hr) and these limits are based on earlier experiments on MS. The results of the CQAs under the study with stated levels of CMA was given in table 3. They were further analyzed with the DES yielding diagnostic and model surface response plots. All the measurements visualized with Normality, Predicted vs actual, Contour and 3D surface plots, which can be used interpret effect, main effect and interaction effects under the influence of varied levels of CMA and provide clear idea about which combination of the CMA give the preferred or unfavorable response for stated CQAs22. The influence of CMA on CQAs were further explained with relative statistical data viz., ANOVA, model fit statistics and polynomial equations generated from the DES, as given in tables 4, 5. Table 4 ANOVA and model fit statistical data of CQAs under the study CQAs MVD % EE t50 Statistical data ANOVA F-Value 371.81 P < 0.0001 68.60 P < 0.0001 860.91 P < 0.0001 MODEL Suggested Quadratic Significant Linear Significant Quadratic Significant Significant model terms AKeltone P < 0.0001 AKeltone P < 0.0001 AKeltone P < 0.0001 A2Keltone2 P < 0.0001 A2Keltone2 P < 0.0039 Actual R2 0.9979 0.9406 0.9991 Predicted R2 0.9794 0.9130 0.9860 Adjusted R2 0.9952 0.9269 0.9979 C.V % 0.3003 3.70 0.5598 Adequate precision 50.029 19.29 74.91 Table 5 Polynomial equations for CQAs MVD Y1 Y1 = + 831.84 + 45.31*A + 0.4660*B + 1.46*C + 0.0925*AB + 1.19*AC -1.70*BC + 12.21*A2 + 1.80*B2 - 0.5011*C2 % EE Y2 Y3 = + 69.30 + 11.62*A + 0.3070*B - 0.0660*C t50 Y3 Y3 = + 5.81 + 0.8920*A + 0.0110*B 0.0120*C + 0.0037*AB + 0.0037*AC + 0.0087*BC - 0.0828*A2 - 0.0078*B20.0228*C2 Effect of factors on CQAs Y1 – MVD: ANOVA suggested Quadratic model with F-value 371.81 implies the model is significant. Here A and A2 were significant model terms, which gives main effect on MVD. The Lack of Fit F-value 16.82 (p= 0.0571) implies non significant Lack of Fit which is good for model to fit. The R2 value was greater than 0.9 (0.9979) explains a perfect positive correlation was existed between the CMA under the study with MVD, justifies with normality plot (figure 4a) where all the MVD values of trial LIN-MS were nearer to the model fit line. This explains linearity among the CMA under the study with CQAs (MVD).The adequate precision was greater than 4 which is desirable and can be used to navigate the relationship within the design space. The actual MVD values for trial LIN-MS was within the desirable range (>1000 µm) and ranges from 800.12 (Trial 4) to 895.65 (Trial 3). The polynomial equation (table 5) suggest second order impact of CMA on MVD with two significant model term viz., A-Keltone and A2-Keltone2, the Keltone and its exponential terms has main effect as indicated by the positive sign, as concentration of Keltone increases the MVD increases significantly, this could be attributed due to the swelling and hydrophilic nature of the Keltone. The actual MVD values were placed within the three ranges of design space and all were placed in and around of the GSC Advanced Research and Reviews, 2025, 25(01), 051-066 59 predicted fitted line as shown in predicted vs actual plot (figure 4b). The Keltone and its exponential terms has significant influence on the MVD as shown in the Contour and 3D surface plot (figure 4c, d), remaining model terms has less or negligible effect on the MVD. Figure 4 Data visualization of MVD under the influence of CMA a) Normality, b) Predicted vs Actual, c) Contour and d) 3D surface Effect of factors on CQAs Y2 – % EE: ANOVA suggested Linear model with F-value 68.60 implies the model is significant. Here A was significant model terms, which gives main effect on % EE. The Lack of Fit F-value was 0.5182 (p= 0.8087) implies non significant Lack of Fit which is good for model to fit. The R2 value was greater than 0.9 (0.9406) explains a perfect positive correlation was existed between the CMA under the study with % EE, justifies with normality plot (figure 5a) where all the % EE values of trial LIN-MS were nearer to the model fit line. This explains linearity among the CMA under the study with CQAs (% EE).The adequate precision was greater than 4 which is desirable and can be used to navigate the relationship within the design space. 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