Research Article

Risk based approach for design and optimization of novel tablet for type-II diabetes

Vaishali Thakkar1, Hardeep Mahida1, Lalji Baldaniya1, Mukesh Gohel1, Tejal Gandhi1, Hitesh Raval1, Nirav Patel2*

1Anand Pharmacy College, Anand 360 001, Gujarat, India 2Dept. of Pharmaceutical Sciences, Saurashtra University, Rajkot 360 005, Gujarat, India

 

*For correspondence

Dr. Nirav Patel,

DST INSPIRE Faculty Dept. of Pharmaceutical science Saurashtra University, Rajkot, Gujarat, India.

Email: nirav2564@gmail.com

Received: 20 November 2015

Revised: 10 December 2015

Accepted: 13 December 2015

ABSTRACT

Objective: The main focus of current investigation was to develop and optimize the manufacturing process of novel tablet containing sustained release Metformin HCl (MET) in core and immediate release Glimepiride (GLIMP) in coating for type II diabetes, by risk assessment approach using Failure Mode Effect Analysis (FMEA) tool.

Methods: Quality risk management (QRM) studies were conducted for each critical process parameters of active coated tablet of MET and GLIMP. A 32 full factorial design was employed for optimization of core tablet of MET to investigate effect of amount of HPMC K100M (A) and HPMC K15M (B) on percent drug release up to 10 hr. GLIMP coating was done on optimize core tablet. A 23 factorial design was used for optimization of coating to investigate the effect of spray rate, inlet air temp., and pan speed on coating parameter. Main effects and interaction plots were generated to study effects of variables. Amount of HPMC K100M and 15M have risk priority number (RPN) 50 and require through investigation and optimization.

Results: The selection of optimized formulation was done on the basis of overlay contour plots and desirability function. The optimized formulation exhibited percent drug release of 30.61, 54.13 and 96.86 at 1, 3 and 10 hr. respectively. Using design of experiment, the optimized conditions selected for coating are spray rate, temperature, pan speed was 3.34 ml/min, 60.60C and 13.2 RPM respectively. The critical quality attributes of unit operations like blending, compression and coating were optimized. The optimized formulations were stable for three months at accelerated and long term stability conditions.

Conclusions: The developed formulation may provide prudently a better substitute for conventional tablet in circumventing its hiccups; improve biopharmaceutical properties, providing sustained and immediate release and may anticipate a better bioavailability and patient compliance.

Keywords: Risk based approach, Metformine hydrochloride, Glimepiride, Type II diabetes, Quality target product profile, Critical quality attributes, Risk assessment

Introduction

Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot effectively use the insulin it produces. 347 million people worldwide have diabetes. In 2014 the global prevalence of diabetes was estimated to be 9% among adults. More than 80% of diabetes deaths occur in low- and middle-income countries.1 WHO projects that diabetes will be the 7th leading cause of death in 2030. More than 80% of diabetes deaths occur in low- and middle-income countries. Type II diabetes is characterized by "insulin resistance" as body cells do not respond appropriately when insulin is present.2

Type II diabetes mellitus is a chronic, progressive disease. As glycemic control deteriorates over time, treatment intensification with the addition of multiple oral antihyperglycemic agents is often required in patients inadequately controlled with monotherapy. The use of a fixed-dose combination of agents with complementary mechanisms of action is associated with improved patient compliance and adherence to treatment, as well as better glycemic control.3,4 The use of a fixed-dose combination of agents with complementary mechanisms of action is associated with improved patient compliance and adherence to treatment, as well as better glycemic control.5,6 MET, the only available biguanide, remains the first-line drug therapy for patients with Type II diabetes mellitus, acting by decreasing the hepatic glucose output and peripheral insulin resistance. It belongs to Biopharmaceutical Class III – Low Permeability, High Solubility compound, oral availability is 50-60%. It is an oral anti-hyperglycemic agent, shows incomplete absorption from the gastrointestinal tract and the absolute bioavailability is 50–60% with a relatively short plasma half-life of 1.5–4.5 h.6,7 An obstacle to more successful use of MET therapy is the high incidence of concomitant gastrointestinal symptoms such as abdominal discomfort, nausea and diarrhea that especially occur during the initial weeks of treatment.8 MET has short plasma elimination half-life about 2 to 6 hours and higher doses at less frequent intervals results in high peak concentrations with the possibility of toxicity, so MET is a suitable drug for sustained release to maintain the concentration within the therapeutic range. A SR formulation that would maintain plasma levels of the drug for 10–16 hours might be sufficient for once-daily dosing of MET to improve patient compliance. GLIMP, a class of oral hypoglycemic agents that exert hypoglycemic activity by stimulating the pancreas to secrete more insulin, active in type II diabetes only. GLIMP belongs to the class of sulfonylurea, melting point 205°C - 207°C. It belongs to Biopharmaceutical Class II – High Permeability, Low Solubility compound, dose 1-4mg once daily, oral availability 100%. So GLIMP is a suitable drug for immediate release which gives loading dose to the provide concentration within therapeutic window. The present research works describe the formulation of novel tablet using combination of fixed dose for dual releases- the immediate release of GLIMP by active coating and sustained release of MET by core tablet for the treatment of type II diabetes. The logic of different mechanism action of this combination9,10 provides effective glycemic control over diabetic patients. GLIMP stimulates the insulin release from pancreatic beta cells and MET increases insulin action in peripheral tissues and reduces hepatic glucose output.

The objective of development of dosage form using QbD- systematic approach is to ensure that industry has identified the critical material attributes and critical process parameters through prior knowledge, experimentation and risk assessment. It is FDA's expectation that sponsors will determine the functional relationships that link critical material attributes and critical process parameters to the product's critical quality attributes. The goal is for sponsors to envision commercialization of drug product at the start of development and continue to keep that objective in mind as they move through the development process. This is also referred to as Quality Target Product Profile (QTPP). The QTPP forms the basis of design for the development of the drug product. A sub-set of the QTPP is the Critical Quality Attributes (CQAs) which form the basis for the product specification.11-13 After succinct amount of literature search of novel tablet form for type II diabetes, no report has been found in the area of MET sustained release core tablet and GLIMP immediate release active coating using stepwise systemic quality by designs (QbD) approach.

The major aims of this study were: (i) step wise systemic formulation development and optimization Novel tablet of GLIMP IR and MET SR using QbD approach (ii) applying principles of risk assessment and Failure Mode and Effects Analysis (FMEA) for formulation and process development (iii) to implement full factorial design as optimization technique for establishment of mathematical equations and graphical results, thus depicting a complete picture of variation of the product/process response(s) as a function of the input variables and (iv) to perform capability analysis to investigate spread and control of process on reproducibility.

Materials and Methods

Materials

Metformin hydrochloride and Glimepiride were obtained as a gift sample from Yarrow Chem product Mumbai, India. HPMC K100M, HPMC K15M, Opadry and Starch 200 were obtained as gift sample from Colorcon Asia Pvt. Limited and DFE Pharma respectively. All other solvents and excipients were used in the analysis was of analytical grade and purchased from Sigma Aldrich.

Methods

Quality by design approach for development of novel formulation by active coating technology

QbD is a holistic approach where product specifications, manufacturing process and critical parameters are included in order to ease the final approval and ongoing quality control of new drug. In this research work, novel formulation (Chart 1) containing sustained release metformin HCl and immediate release glimepiride for type II diabetes was developed using two objectives. First was optimization of Metformin core tablet using DOE approach and second was active coating of Glimepiride.

Figure 1. Process flow of novel formulation by active coating technology.

Determination of drug content using HPLC

The tablets were crushed in glass mortar and pestle. An accurately measured amount of powder equivalent to, 2 mg of GLIMP and 250 mg of MET was transferred into 25 ml volumetric flask and diluted with 20 ml mobile phase. The mixture was sonicated for 15 min and then filtered through Whatman filter paper no.42 wetted with mobile phase. The volume was finally made up to the mark with mobile phase. Various aliquots of diluted sample were withdrawn and further diluted with mobile phase to obtain a required concentration of MET (500 µg/ml) and GLIMP (2 µg/ml). The method described above was then applied for determination of peak area and triplicate analysis was performed using the same procedure.

Optimization of MET core tablet using Design of Experiment (DOE) approach

Quality Target Product Profile (QTPP) of MET core tablet

The quality target product profile (QTPP) is "a prospective summary of the quality characteristics of a drug product that ideally will be achieved to ensure the desired quality, taking into account safety and efficacy of the drug product." The QTPP is an essential element of a QbD approach and forms the basis of design of the generic product.14 For Abbreviated new drug approval (ANDAs), the target should be defined early in development based on the properties of the drug substance (DS), characterization of the Reference listed drug (RLD) product and consideration of the RLD label and intended patient population. The International conference of harmonization (ICH) Q8 (R2) recapitulates them as QTPP.15 The QTPP includes all product attributes that are needed to ensure equivalent safety and efficacy to the RLD. By beginning with the end in mind, the result of development is a robust formulation and manufacturing process with a control strategy that ensures the performance of the drug product. QTPP for MET sustained release core tablet is depicted in Table 1.

Preparation and optimization of sustained release MET core tablet

Tablets containing 500 mg of MET were prepared by direct compression method. The ingredients MET, HPMC K-100 M and/or HPMC K-15 M, PVP K-30 and Starch-200 were passed through sieve # 60 and co-ground properly to mix together in motor pestle for 5 mins. Talc and magnesium stearate were passed through sieve # 80, mixed, and blended with initial mixture in a poly-bag. The powder blend was compressed into tablets using oval shaped biconvex punches to get tablets of 900 mg weight on a 12-station rotary tablet machine (Rimek Mini Press). The formulated tablets were stored in a tightly closed glass container and evaluated for various characteristics. A 32 factorial design with two factors, three levels, and nine runs was selected for the optimization study independent and dependent variables with their constraints are listed in Table 2. The data of 9 experimental runs were interpreted using Design-Expert ® Software Version 9 Trial Version and the final batch was optimized on the basis of above result.

Table 2: Formulation variables and their levels for 32 full factorial design.

Factors

Coded levels

Actual levels

X1 : Amount of HPMC K100M in mg.

-1

45

 

0

90

 

1

135

X2 : Amount of HPMC K15M in mg.

-1

45

 

0

90

 

1

135

Responses

Constraints

R1: Percent drug released in 1 h

20 % ≤ R1 ≤ 40%

R2: Percent drug released in 3 h

45 % ≤ R2 ≤ 65%

R3: Percent drug released in 10 h

NLT 85%

Identification of critical quality attributes (CQA)

It is stated that the ICH working definition of CQA was: "A CQA is a quality attribute (a physical, chemical, biological or microbiological property or characteristic) that must be controlled (directly or indirectly) to ensure that the product meets its intended safety, efficacy, stability and performance." CQAs are generally associated with the drug substance, drug product, intermediates. Potential drug product CQAs derived from the QTPP and prior knowledge was used to guide the product and process development. It is necessary to identify the quality attributes that are critical, i.e. those defining purity, potency and surrogate for bioavailability criticality etc. It is based on the impact of quality attribute/parameter on the safety, efficacy and quality (manufacturability) of the product.16,17

Case 1- Influence of blending speed and blending time on content uniformity18

Critical process parameters are identified with their levels and those are discussed with different case study. CQA that affect the final quality of tablet is content uniformity. Blending speed was varied from 10 to 30 rpm and blending time is from 10 to 20 min. different batches were prepared using design space approach and response was observed. Assessment was done on result and ranking were given as per the results obtained. The relative impact of blend time and blending speed on content uniformity is obtained. A screening design was not employed because prior experience with this type of formulation gave a reasonable likelihood that all three factors would be significant to some extent.

Case 2- Influence of compression speed and force on in-vitro release, hardness and appearance of core tablet 19, 20

Based on prior knowledge and process experience, the variables most likely to influence the quality of the drug product were identified.

A risk assessment was then performed using FMEA, to establish those variables that are likely to pose the greatest risk to the quality of the product and be associated with a drug product CQA. In compression unit operation, compression speed and compression force were identified as critical parameters those affect CQAs like dissolution and hardness. Different levels were taken and DOE was used to get experimental runs.

Risk assessment by failure mode effect analysis (FMEA)

The concept and spurts of quality risk management was introduced in ICH Q9, 2005 guidance.21 The CQAs depend on dosage form designed; type of formulation, manufacturing method, etc. employed and is selected amongst many possible options. Thus based upon feasibility studies, we defined the formulation and manufacturing method as described in flowing sections. An overall risk assessment of the formulation/ process variables was executed using FMEA method. By this method, the failure modes were identified that could have highest impact on product performance and greatest chance of eliciting product failure. FMEA method facilitates in prioritizing failure modes for risk management purposes according to the seriousness of their consequences (effects), how frequently they occur and how easily they can be detected. The relative risk that each formulation/process variable presents was ranked according to risk priority number (RPN).22 Those attributes that could have a major impact on the drug product attributes needed to be studied in detail whereas those attributes that had minor impact on the drug product attributes required no further investigation. Table 3 portrays the FMEA for MET extended release tablet with their respective RPN for each failure mode. The RPN was calculated with Eq. 1 mentioned as below:

………….(1)

Where O is the occurrence probability or the likelihood of an event to occur; we ranked these as 5, frequent; 4, probable; 3, occasional; 2, remote and 1, improbable to occur. The next parameter S is the severity, which is a measure of how severe of an effect a given failure mode would cause; we ranked these as 5, catastrophic; 4, critical; 3, serious; 2, minor and 1, negligible or no effect. The final parameter D is the delectability which means the ease that a failure mode can be detected. Thus the more detectable a failure mode is, the less risk it presents to product quality. For D, we ranked 1, absolute certain or easily detectable; 2, high detectable; 3, moderately detectable; 4, low or remote detectable and 5 as hard to detect or absolute uncertain.

Seal coating

The prepared MET sustained release tablets were seal coated up to 2% weight buildup of total weight of tablets, by using opadry in the suitable solvent. It separates the coating layer and core of the tablet, so that the drug interaction between the coating layer and core of the tablet is prevented. It improves the surface property of the tablet, so tablet surface become very suitable for active coating.

Method of preparation of seal coating solution

Opadry added along with ferric oxide yellow (if required) to the solvent under stirring and stirring continued for 45 minutes. After the complete hydration of the polymer the prepared coating solution is sprayed over the compressed Metformin core tablets using spry dryer.

Table 4: Process variables for Coating and their levels for 23 full factorial design.

Factors

Coded levels

Actual levels

X1 : Spray rate (ml/min)

-1

2

 

1

4

X2 : Inlet air temperature (ºC)

-1

60

 

1

65

X3 : Pan speed (rpm)

-1

10

 

1

15

Responses

Constraints

R1: Assay (%)

As per USP

R2: Coating process

Efficiency (%)

R3: Percentage LOD
Justification for the initial risk assessment of the coating process
Drug Product CQAs

Justification

Assay

Coating weight gain by multiple tablets is decided as per the requirement of dosage of glimepiride in the coating. The Risk is high as the active coating is involved.

Coating process

Efficiency (%)

Coating weight gain is also decided as per the requirement of dosage of glimepiride in the coating. The Risk is high as the active coating is involved.

In-Vitro Dissolution

The solubility of drug in coating solution should be determined and accordingly coating solution should be selected. The film coating should dissolves within the specified period of time. The risk is low.

Drug (GLIMP) coating

GLIMP was dissolved in the mixture of isopropyl alcohol and dichloromethane. Opadry was added in above solution with stirring. Ferric oxide yellow was added along with opadry (which is passed through #100 BSS) under stirring and continued stirring for 45 minutes.

Case 3- Influence of spray rate, Inlet air temperature and Pan Speed on Coating parameter

Based on prior knowledge and process experience, the variables most likely to influence the quality of the drug product were identified. A risk assessment was then performed using FMEA, to establish those variables that are likely to pose the greatest risk to the quality of the product and be associated with a drug product CQA. It is found that the processing factors that most affect coating characteristics were spray rate of solution, speed of coating pan, inlet -air temperature. Since the higher air flow along with the temperature in, provides a higher evaporation rate. One must maintain the drying capacity in the larger unit such that the in-let temperature is similar to the smaller unit's temperature. This can be accomplished either by increased spray rate, increased air temperature, increased air flow or a combination of these variables to obtain suitable results. Coating pan speed also give significant effect on coating uniformity. Process variables for coating and their levels for 23 full factorial design and Justification for the initial risk assessment of the coating process are described in Table 4.

Evaluation of pre-compression parameters and novel active coated tablets

The micromeritic properties of the blend ready for compression is measured from angle of repose, density of the solid dispersion, bulk density, tapped density, % compressibility (Carr's) index and Hausner ratio. Tablets were analysed for Weight variation test, Hardness, Friability etc.

In-vitro drug release study

Drug release studies were conducted using Dissolution Apparatus II USP (Paddle), paddle type (Electrolab, Mumbai, India) at a rotational speed of 50 rpm at 37±0.5ºC. The dissolution media used were 900 mL of 0.1 mol/L HCl for first 2 h followed by pH 6.8 phosphate buffer solutions for the next 8 h and phosphate buffer pH 7.4 for the next 2 hr. Sink condition was maintained for the whole experiment. Samples (10 mL) were withdrawn at regular intervals and the same volume of pre warmed (37±0.5 ºC) fresh dissolution medium was replaced to maintain the volume constant. The samples withdrawn were filtered through a 0.45 μ membrane filter (Nunc, New Delhi, India) and the drug content in each sample was analyzed after suitable dilution for percentage drug releases. The dissolution test was performed in triplicate. Drug dissolved at specified time periods was plotted as cumulative percent release versus time (h) curve.

Coating process efficiency 21

Coating process efficiency (CPE) was measured as the actual percent weight gain relative to the theoretical percent; a theoretical 100% transfer of coating to the tablets would mean no lost coating. Coating process efficiency was determined by the following equation.

CPE = (%Wga /%Wgt) ×100%

Where, Wgt is the theoretical percent weight gain, which in this experiment was 3% in every coating trial, and Wga is the actual percent weight gain, which is computed as

Processing % Wga = [(Wta – Wtb)/ Wtb] ×100%

Where Wtb and Wta are the total batch weights before and after coating, respectively

Percentage loss on drying

Percentage loss on drying (% LOD) is the moisture content of the coated tablet expressed as percent weight. The tablets were weighed, dried at 60 °C for 24 h, and then reweighed. All uncoated tablet cores used in this study had an initial moisture content of 3% and %LOD was determined by the following equation.

 

Where, WtB and WtA are the coated tablet weights before and after drying, respectively.

Gas chromatography (GC)

Gas Chromatography is a natural choice for residual solvents which have relatively low boiling points and are generally thermally stable. The tablet was analysed for determination of total residual content using GC.

Scanning electron microscopy (SEM)22

Surface of tablet was evaluated by scanning electron microscopy to assess the surface morphology, coating uniformity and surface texture of the active coating layer.

Packaging and stability study

The optimized batch was subjected to short term stability testing according to the ICH guidelines for zones III and IV (ICH Q1A(R2) 2003). Tablets were packed in count of 30 into high density polyethylene bottle with child resistant cap and were further induction sealed. Before induction sealed one silica bag was kept in bottle as desiccant. The sealed bottles were exposed to accelerated (40 ± 20C/75 ± 5 % relative humidity) and long term (25 ± 20C/60 ± 5 % relative humidity) stability study was also conducted for three months. The samples were withdrawn periodically (0, 15, 30, 60 and 90 days) and evaluated for different physicochemical parameters like visual inspection, drug content, gastric resistance and in vitro drug release.

Results and Discussion

Analytical method development and optimization

A high performance liquid chromatography method for the estimation of MET and GLIMP in a novel dosage form has been developed according to the principles of Good Laboratory Practices. Optimization runs were carried out using a Phenomenex C18 column. The mobile phase conditions were optimized so that the two drugs could be separated in short run time. The best results were obtained by using mixture of 40mM potassium dihydrogen orthophosphate of pH 7.0 adjusted with triethylamine, acetonitrile and methanol in a ratio of 20: 65: 15. Under these optimized chromatographic conditions, the retention time of MET, GLIMP were 2.612 and 9.121 min respectively (Figure 2). The optimized mobile phase was acetonitrile: methanol: phosphate buffer 40 mm pH 7 (65: 15: 20) at a flow rate of 1 mL/min. The method was validated over the range 0.025–2.0 𝜇g/mL.

Figure 1: Chromatogram of optimized mobile phase.

Quality target product profile (QTPP) of MET core tablet

Laying down QTPP depends upon formulation type and process chosen.23,24 Based on preliminary trials undertaken, the parameters that will be focused in our study were selected and enlisted as QTPP for MET sustined release (Table 1). Thus, except recitation of our QTPP, the further steps to describe the QTPP are not discussed. The said QTPP will lay down the foundation for determining CQA.

Evaluation of pre-compression parameters of MET – HPMC powder blends

The micrometrics properties of all powder blends were evaluated. Bulk density and tapped density were in the range of 38 to 41 mg/ml and 0.47 to 0.51 mg/ml respectively. The angle of repose value was in the range of 30-39° which indicates fair flow properties. The result of Carr's index shows that good compressibility of powder blend for direct compression. All the results were found to be within the desired criteria.

Risk assessment by FMEA

The factors that were embarked and assessed by FMEA in development of MET sustained delivery are highlighted in Table 3. In the current approach for development, the factors that exhibited RPN ≥40 was considered as high risk, ≥20 to ˂40 was considered as medium risk and ˂20 was considered as low risk.25 It is clearly stipulated that amount of HPMC K100M and 15M have RPN 50 and require through investigation and optimization. Thus, the optimization of this two main factors that affect the core tablet formulation i.e. amount of polymers was done statistically using 32 full factorial design for establishing design space. Hardness and Granule sizes RPN fall under moderate risk category.

Table 5: ANOVA results (p values): Effect of the formulation variables on R1, R2, and R3.

Statistical parameters

Responses (% CDR)

at 1 h

(R1)

at 3 h

(R2)

at 10 h

(R3)

P value

0.0270

0.0235

0.0123

F Value

4.93

5.20

6.53

Sum of Square

978.49

1934.55

439.23

Critical Value

26.56

20.58

4.98

Adjusted R2

0.4957

0.5121

0.5804

Press

1443.23

2498.97

538.02

32 Factorial design for optimization of polymer concentration

32 factorial design is applied and 9 runs were designed. % In vitro drug release at 1 h, 3 h and 10 h for all the batches were taken as response R1, R2 and R3. Sustained drug release is very important aspect for maintaining drug concentration for longer time in the GI tract. Batch F1 to F9 were prepared by changing the different proportion of HPMC K100M and HPMC K15M. From the In-vitro dissolution study, it was found that, initially batches fails to retard drug release for 10 hrs. due to low concentration of viscous polymer HPMC K100M. So as the conc. of polymer increases, retardation of drug increases. It may be due to formation viscous gel layer around the matrix tablets. Among all batches, only F5 batch prepared using high concentration of HPMC K100M met the release specification of SR products at 1 and 10 h as per USP criteria, as they released 25.6% in 1st hr and 90.25% after 10 hrs. The f2 value of F5 batch is 85.59 and found to be highest among all prepared batch. The similarity between the target release profile of SR tablet as per USP and F5 batch is clearly demonstrated via f2 value.

Predicted response models and statistical parameters obtained from ANOVA for Factorial Design

Analysis of variance (ANOVA) is a statistical technique that subdivides the total variation in a set of data into component parts associated with specific sources of variation for the purpose of testing hypotheses on the parameters of model.26 As Table 5 shows the F statistic values for all 3 variables were higher. The large value of F indicates that most of the variation in the response can be explained by regression equation. The associated p values are used to estimate whether F statistic is large enough to indicate statistical significance. A p value, less than 0.05, indicates that the model could explain 95 % of the variability. Percent CDR at 1, 3 and 10 hr. were subjected to ANOVA to find out the significance of the individual and combined effects of the two factors (conc. of HPMC K4M and 100M) in controlling the release profile of MET. The individual as well as combined effects of the two factors involved were highly significant (P<0.05) in sustaining the MET release at 1, 3 and 10 hr. respectively. For all three responses model, p value is less than 0.05 so, both the independent variables are significant, and that data fits to the model properly.

Perturbation graph 27

Perturbation plots bear the effect of self-governing factors on the dependent factors. Perturbation graph (Figure 3) shows the effect of each factor A, B on % Drug Release at 1h., 3 h., and 10 h. These graphs give the idea about how the response changes as each factor moves from its defined reference value, with all other factors held constant. A steep slope or curvature in a factor indicates that the response is sensitive to that factor. Here steep line is obtained for factor A which indicates it is significantly affect the all three responses.

Figure 3: Perturbation graph for R1, R2 and R3.

Contour plot for % drug release at 1h., 3 h., and 10 h.

The main results of this study are presented in counter plots, which represent relationship between dependent and independent variables. The counter plot shows the significant effect of HPMC K100M on % Drug release. As the concentration of HPMC K100 M was raised from -1 to +1, the % CDR decreases. It may be due to formation of viscous gelatinous layer on the outer tablet skin as polymer hydrates. A rapid formation of a gelatinous layer is critical to prevent wetting of the interior and disintegration of the tablet core. Once the original protective gel layer is formed, it controls the penetration of additional water into the tablet. As the outer gel layer fully hydrates and dissolves, a new inner layer must replace it and be cohesive and continuous enough to retard the influx of water and control drug diffusion. Although gel strength is controlled by polymer viscosity and concentration, polymer chemistry also plays a significant role. Evidence suggests that the chemistry of HPMC encourages a strong, tight gel formation compared to other cellulosics. As a result, drug-release rates have been sustained longer with HPMC. For these reasons, HPMC is very often the polymer of choice over other cellulosics. It shows the desired response as shown in Figure 4.

Figure 4: Contour Plots for Response R1, R2 and R3.

Figure 5: Overlay plot for optimization.

Constraints and overlay plot for optimization

Concentration of HPMC K100M and HPMC K15M is most significant variables for sustained release of drug from matrix.28 Here concentration of HPMC K100M has maximized effect on the drug release compare to HPMC K15. So HPMC K100M targeted to at optimum level to achieve sustained release of the drug and HPMC K15M is selected in the range in constraint. Because % drug release of MET is very sensitive to matrix forming viscous polymer, the goal was to produce the tablet formula which fulfil % In Vitro drug release as per the USP criteria. The obtained results from the design expert software are 90 mg. of each HPMC k100M and HPMC K15M, which gives % In vitro drug release of 30.61, 54.13 and 96.86 at 1, 3 and 10 hr. respectively (Figure 5).

Case 1- Influence of blending speed and blending time on content uniformity

Insufficient blending results in poor active ingredient mixing with excipients. Excessive blending could adversely impact on the distribution of drug content (content uniformity) in the final product. Companies suffer financial setbacks due to rework of poor quality product, and more severely, legal action being taken because of non-compliance.

Risk analysis by FMEA

32 full factorial design was employed to examine the multidimensional interaction of input variables of the core tablet which were ranked as high risk in the initial risk assessment for establishment of a design space. The acceptable region within which a quality of the product can be constructed is called as design space. The risk mitigation and control strategy is fused outline of how quality is established based on current process and existing product knowledge. Blending speed has risk priority number (RPN) of 4, which shows low risk with containing particle size of drug and excipients. Blending time has RPN of 2, which shows low risk with certain controls. Both the critical parameters have RPN less than 9, which represent low risk level for content uniformity.

Risk evaluation by generation of DOE based design space and multivariate data analysis

For the response surface methodology involving factorial design, a total of 9 experiments were conducted for two factors at three levels each. A suitable polynomial equation involving the individual main effects and interaction factors was selected based on the estimation of several statistical parameters, such as the multiple correlation coefficient (r²), adjusted multiple correlation coefficient (adjusted r²) and the predicted residual sum of squares (PRESS), provided by the Design-Expert software® (9.0.2.0) Trial Version. The polynomial equations comprise the coefficients for intercept, first-order main effects, interaction term, and higher order effects. ANOVA (Table 5) shows multivariate data analysis of two independent variables on response content uniformity. A positive sign of coefficient indicate a synergistic effect while negative term indicates an antagonistic effect upon the response. The significant coefficients (p<0.05) are represented in Table 5. To confirm the omission of non-significant terms, an F statistic was calculated after applying analysis of variance for the full model and the reduced model. The F calculated value is less than the tabled value of F at a 0.05 confidence interval, v1 = 1 and v2 = 3. Hence it is concluded that the omitted terms do not significantly contribute to predicting the content uniformity. For estimation of significance of model, ANOVA was determined as per the provision of design expert using 5 % significance level. A model is considered as significant (p <0.05).

Content Uniformity = +98.89 +5.71*A+0.078* B +0.86* A*B - 4.48 *A2 +1.09 *B2

Final Equation in Terms of Actual Factors:

Content Uniformity = +98.89444 +5.71333 * Blending Time + 0.078333 * Blending Speed +0.85750 * Blending Time * Blending Speed -4.47667 * Blending Time2 +1.08833 *Blending Speed 2

The relationship between the dependent and independent variables was elucidated by constructing response surface plots. Here X1 factor (blending time) and X2 (speed) shows the effect on response Y1 (content uniformity). Graphical optimization displayed the area of feasible response values in the factor space in yellow colour. Regions that did not fit the optimization criteria were grey shaded as represented in plot. Using a computer optimization process and the contour plot, for both X1 and X2, we selected a level of X1 - 0.90 and X2 - 0.90, which gives the maximum content Uniformity of 102%. Below the selected (optimum) levels of X1 and X2, decreases in content uniformity are observed.

Risk reduction by implementation of control strategy

32 full factorial design was employed to examine the multidimensional interaction of input variables of the core tablet which were ranked as high risk in the initial risk assessment for establishment of a design space. The acceptable region within which a quality of the product can be constructed is called as design space.28, 29 The risk mitigation and control strategy is fused outline of how quality is established based on current process and existing product knowledge. Based on process understanding and risk assessment by FMEA tool, it was concluded that the various critical step were expected to occur at each stage of the process, were adequate to reduce the associated risk. Results can be used to identify high vulnerability elements and to guide resource development for best benefits. In order to optimize blending process with lower risk, blending speed was found to be 27 rpm and blending time was found to be 19.2 minutes for optimum content uniformity in finished product.

Case 2- Influence of compression speed and force on in-vitro release, hardness and appearance of core tablet

The variables of the highest potential risks are identified by the prior knowledge. There are variables identified as a result of the initial risk assessment as highest potential risk to quality and requiring further evaluation by DOE design.

Risk analysis by FMEA

Compression speed has RPN of 11 which shows low risk with certain controls. Compression force has RPN of 13 which shows low risk with certain controls. Both the critical parameters have RPN less than 27 which represent low risk level.

Risk evaluation by generation of DOE based design space and multivariate data analysis

For the response surface methodology involving factorial design, a total of 9 experiments were conducted for two factors at three levels each. A suitable polynomial equation involving the individual main effects and interaction factors was selected based on the estimation of several statistical parameters, such as the multiple correlation coefficient (r²), adjusted multiple correlation coefficient (adjusted r²) and the predicted residual sum of squares (PRESS), provided by the Design-Expert software® (9.0.2.0) Trial Version. The polynomial equations comprise the coefficients for intercept, first-order main effects, interaction term, and higher order effects.

ANOVA table shows multivariate data analysis of two independent variables on response content uniformity. A positive sign of coefficient indicate a synergistic effect while negative term indicates an antagonistic effect upon the response. To confirm the omission of non-significant terms, an F statistic was calculated after applying analysis of variance for the full model and the reduced model. The F calculated value is less than the tabled value of F at a 0.05 confidence interval. Hence it is concluded that the omitted terms do not significantly contribute to predicting the Dissolution after 10 Hrs and Hardness. For estimation of significance of model, ANOVA was determined as per the provision of design expert using 5 % significance level. A model is considered as significant (p <0.05).

Final Equation in Terms of Coded Factors:

In-Vitro Dissolution after 10 Hrs. = +97.69 +1.65* A+5.27 * B

Final Equation in Terms of Actual Factors:

In-Vitro Dissolution after 10 Hrs. = +97.68667+1.64500 *Compression Speed+5.26667 *Compression Force

Final Equation in Terms of Coded Factors:

Hardness = +6.22 +0.85 *A +1.97*B

Final Equation in Terms of Actual Factors:

Hardness = +6.22222+0.85000 * Compression Speed + 1.96667 * Compression Force

Two-dimensional contour plots are presented in Figure 7, which is useful to study the interaction effects of the factors on the responses. The relationship between the dependent and independent variables was elucidated by constructing response surface plots. Here, X1 factor (Compression speed) and X2 (Compression force) shows the effect on response Y1 (In-Vitro Dissolution after 10 hrs.) and Y2 (Hardness). Graphical optimization displayed the area of feasible response values in the factor space in yellow colour. Regions that did not fit the optimization criteria were grey shaded as represented in plot.

Using a computer optimization process, for both X1 and X2, we selected a level of X1 – 1.00 and X2 – 0.13, which gives the maximum In-vitro Dissolution after 10 hrs. of 100% and Hardness of 22.4 KN. Below the selected (optimum) levels of X1 and X2, decreases in responses were observed.

Risk reduction by implementation of control strategy

Innovative approaches such as quality risk management together with design space, continuous improvement programs can be adopted to improve the quality of press coated tablet. Understanding the relationship between critical material and critical process attributes culminates in process control. After overall risk assessment was done, different control strategies have been found that have minimum impact on CQAs. In order to optimize compression process with lower risk, compression speed was found to be 20 rpm and compression force was found to be 22.4 KN for best finished product quality.

Case 3- Influence of spray rate, inlet air temperature and pan speed on coating parameter

23 factorial design to optimize the coating condition

The experiments set up on the basis of factorial design were performed and the resulting tablets were analyzed for following R1 to R3 responses. The experimental conditions and results obtained for all 8 runs, performed as per factorial design are summarized in Table 6.

Pareto chart 30

It is used to estimate the importance of variable, Critical t-value, for α = 0.05 and 3 degrees of freedom (d.f.) was 3.182 for all four responses. All factors whose absolute values of standardized effects are above critical t- value are statistically significant and the ones below this value are statistically insignificant. Pareto charts, of which the length of bars is proportional to the absolute value of standardized effects, are presented in Figure 6 the dashed lines represents critical t- value and the importance of presented variables can be easily noticed. According to obtained results spray rate had important impact on R1, R2 and R3 response. Considering other variables, temperature and pan speed was significant in R2, R3 responses. From Pareto chart we can easily list out possibly significant factors for all responses were determined with their sequence.

Predicted response models and statistical parameters obtained from ANOVA for Factorial Design

Analysis of variance (ANOVA) is a statistical technique that subdivides the total variation in a set of data into component parts associated with specific sources of variation for the purpose of testing hypotheses on the parameters of model. Table 6 represents 23 Factorial design to optimize the coating condition. As Table 7 shows the F statistic values for all 3 variables were higher. The large value of F indicates that most of the variation in the response can be explained by regression equation. The associated p values are used to estimate whether F statistic is large enough to indicate statistical significance. A p value, less than 0.05, indicates that the model could explain 95 % of the variability. % Assay, % Coating efficiency, % LOD at were subjected to ANOVA to find out the significance of the individual and combined effects of the three factors (spray Rate, Inlet air temp., and pan speed) in assay of glimepiride, coating efficiency and amount of moisture present. The individual as well as combined effects of the three factors involved were highly significant (P<0.05) in controlling all the selected responses. For all three responses model, p value is less than 0.05 so, all response are significant, and that data fits to the model properly.

Figure 6: Pareto chart showing effect on R1: A > C > B, in case of R2: A > B > C, in case of R3: B > C > A.

Table 7: ANOVA Table for dependent process variables.

Statistical parameters

Responses

R1

R2

R3

P value

0.008

0.0108

0.0428

F Value

124.74

5037.51

10.24

Sum of Square

70.87

109.19

137.32

CV %

0.37

0.068

2.12

Adj R Squared

0.9888

0.9998

0.8407

Press

0.06

0.23

71.53

(a)(b)

(c)

Figure 7: Perturbation graph showing the effect of Spray Rate (a), Inlet air temp. (b), and pan speed (c) on all R1, R2, R3 responses.

Figure 8: Contour plots showing the effect of each factor X1, X2, and X3 all (A) R1, (B) R2, and (C) R3 responses.

Perturbation graph

Predicted models are presented in the form of perturbation plots for better understanding of results (Figure 7). These graphs give the idea about how the response changes as each factor moves from its defined reference value, with all other factors held constant. A steep slope or curvature in a factor indicates that the response is sensitive to that factor. For all responses steep slope is obtained in case of factor A, so its have significant effect for all the responses variables.

Contour plot

Two-dimensional contour plots are presented in Figure 8A to 8C, which are useful to study the interaction effects of the factors on the responses. The relationship between the dependent and independent variables was elucidated by constructing response surface plots. For R1 response if, X1 from −1 to +1 level increased, coating efficiency was decreased and similar if X2 was increased, the coating efficiency was found to be increased and similar if X3 was increased, the coating efficiency was found to be decreased. Coating efficiency (CE) is a measure of the actual amount of coating applied to the tablets relative to the theoretical quantity of coating applied. It can therefore be another indicator of over wetting or over drying. When over wetting occurs, material can potentially be transferred from the surface of the tablets to the walls of the coating pan, thus reducing CE. Conversely, when over drying occurs, coating solution can dry prematurely in the air stream (commonly called spray drying) and be lost into the exhaust air stream instead of being transferred to the tablets. It has been shown that Inlet air temperature had a negative effect on coating efficiency (i.e. inlet air temperature increased, the coating efficiency decreased). This might be due to at high temperature water evaporation is very fast and particles get dried before reaching to the tablets. For R2 response if, X1 from −1 to +1 level increased drug content of GLIMP was decreased and similar if X2 was increased, the drug content was found to be increased and similar if X3 was increased from −1 to +1 level, the drug content was found to be decreased. It has been shown that, there is no any significant effect of inlet air temp. on the assay of Glimepiride. But spray rate and pan speed increases, drug content decreases due to less time is available for the droplets to be stick on the surface of tablet and it may become dry before attaching on the surface of coated tablets. For R3 response if, X1 from −1 to +1 level increased, %LOD was found to be increased, and similar if X2 was increased, the % LOD was found to be decreased, and similar if X3 was increased from −1 to +1 level, the % LOD was found to be decreased. It has been observed that % LOD was decreased at higher temperature and slow spray rate but as the pan speed decreases, the moisture amount increases due to the more amount of coating solutions is sprayed on the surface of tablet.

Figure 9: Overlay plot for coating unit operation.

Table 8: Constraints and solution for coating parameter.

Constraints
Name

Goal

Lower Limit

Upper Limit

A:Spray rate

is in range

-1

1

B:Temperature

is in range

-1

1

C:Pan speed

is in range

-1

1

R1 (%)

Maximize

97.23

98.90

R2 (%)

Maximize

87.56

96.64

R3 (%)

Minimize

0.70

1.26

Solution
Spray rate

-0.33

R1 (%)

97.33

Temperature

-0.99

R2 (%)

92.47

Pan speed

-0.18

R3 (%)

1.005

Constraints and overlay plot

After generating the polynomial equations relating the dependent and independent variables, the process was optimized for the responses. Optimization was performed to obtain the values of X1, X2, and X3, which targeted maximum coating efficiency and assay of glimepiride and % LOD in the range. The optimized independent parameters were used to make the formulation which was also used as the check point of the regression analysis model. The optimized fast dissolving tablet was prepared and evaluated for its physiochemical properties. The goal was to produce solutions in optimization of coating conditions for uniform production of coated tablet. The definition for design space for development of coating condition can be "multidimensional combination and interaction of spray rate, temperature and pan speed to produce active coated tablet. Using Design of Experiment, the Optimized Conditions selected was: Spray rate 3.34 ml/min, temperature 60.6 0C, and Pan speed 13.2 RPM (Table 8 and Figure 9).

Optimization of coating time via weight gain test

Initially weights of tablets were measured and coating was started. It was observed that after 10, 20 and 30 min the weight gain was achieved around 4%, 5% and 6%. The drug content of GLIMP was 2 mg, when 5% weight gain had been done (Figure 10). So coating was done till the 5% weight gain of tablet was achieved.

Figure 10: Drug content (mg) vs. % weight gain.

Risk identification

The initial risk identification of the coating step to impact tablet content uniformity and coating efficiency as high. Process variables that could potentially impact coating uniformity were identified and their associated risk was evaluated. The results seen in Table 9 represented the RPN of coating unit operation. Coating pan speed has RPN of 9 which shows low risk with certain controls. Inlet air Temperature has RPN of 7 which shows low risk with certain controls and spray rate has RPN of 15 which shows low risk with certain controls. All three critical parameters have RPN less than 27 which represent low risk level.

Characterization of coated tablet

Scanning electron microscopy

SEM was carried out to understand the surface morphology of optimized coated tablet. SEM indicates uniform coating on the tablet surface.

Gas Chromatography

GC for standard IPA 500 ppm and GC for active coated tablet were taken which shows Rt at 1.33 min and peak area of IPA in STD chromatogram and in sample were found to be 355179.26 and 5190.26. So, the concentration of IPA was found to be 7.31 ppm in the chromatogram of coated tablet sample. The permitted daily exposure (PDE) limit for IPA is 5000 ppm according to ICH Q3 guideline. Thus the sample was having less residual solvent impurity and passes the test of residual impurity.

Physical evaluation parameter of coated tablet

The formulated coated tablet complied the weight variation (<5 %) and friability (<1%) according to IP 2010, having adequate hardness and thickness. The results of coating of GLIMP showed immediate release within 30 minutes whereas core tablet containing MET showed sustained release over 10 hour.

Figure 11: In vitro drug release study of novel optimized tablet.

In vitro dissolution of novel dosage form

Dissolution of novel dosage form was performed in different media for 10 h. To simulate the conditions that exist in human GI tract transits from stomach to intestine, the release of GLIMP was performed in 0.1 N HCl for 2 h sequential followed by phosphate buffer (pH 6.8) for 10h. Dissolution samples were analyzed by HPLC method. From the in vitro dissolution study of tablet, release of MET was found to be 25 %, 36 %, 61.11 %, and 80 % in 1, 2, 4, 6, and 8 h respectively. Retardation of drug release was observed due to formation of polymeric gel around the tablet which resist to drug diffusion and erosion. The resulting release profiles were observed as per the acceptance criteria for desired release profile (20 % ≤ rel1h ≤ 45%, rel3h ≤ 85% ≤ rel10 h,). The 100 % of glimepiride was released from coating layer within 30 minutes, which provides lag dose and sustain the therapeutic concentration of drug in the body. The release profiles in two dissolution media (0.1N HCl, pH = 1.2 or phosphate buffer, pH = 6.8) for novel dosage form are shown in Figure 11.

Packaging and stability studies

The optimized formulation showed negligible change under the conditions of storage for parameters like appearance, assay, content uniformity and in vitro drug release. The similarity factor (f2)31 was employed for comparison of dissolution profiles on each time point. It was ranged from 87 to 94. Thus the data suggested that the formulation was stable for under the packaging material selected revealing that it risks is under control and low.

Conclusions

With the rising awareness of QbD tools and risk management approaches, the utility of it has now permeated tangibly into research and industry for understanding of process or formulation variable rationally. The manuscript describes the overall QbD approach along with risk assessment using FMEA method, risk analysis and control strategy to mitigate the risk for development of novel dosage form containing sustained release metformin HCl and immediate release glimepiride for type II diabetes. There is no ambiguity that several initiatives are undertaken worldwide to circumvent development hiccups of type II diabetes formulations. The formulation technology used here is simple, easily scalable and adopted in industries. Hence, it endows to be of greater interest especially in under developed or developing countries to epitomize the objectives like cost-effectiveness, feasibility and save resources. In an endeavor to accomplish the objectives of QbD, 32 and 23 factorial design was applied for evaluating the failure modes with high RPN number and defining the relationships between input variables and quality traits desired. Finally, the design space was established and control strategy was developed to mitigate the risk in future. The RPN of updated risk assessment represents that all the failure modes of FMEA analysis were in low risk category, thus the shift in exemplar from traditional approach to QbD approach can provide incisive insight for building quality within the product. Hence, the developed formulation may provide prudently a better substitute for conventional tablet in circumventing its limitations; improve biopharmaceutical properties, providing sustained and immediate release and may anticipate a better bioavailability and patient compliance. The developed formulation has shown promising results in vitro and is potential for assessing in vivo bioavailability. The manufacturing method employed is relatively simple and can easily be adopted in industries.

Acknowledgements

The authors would like to thank the Gujarat Council on Science and Technology (GUJCOST), Gandhinagar, Gujarat for providing financial support to conduct research work (GUJCOST Minor Research Project No. GUJCOST/MRP/12-13/60).

Funding: GUJCOST, Gandhinagar, Gujarat, India

Conflict of interest: None declared

References

  1. http://www.who.int/mediacentre/factsheets/fs312/en/
  2. http://www.who.int/topics/diabetes_mellitus /en/
  3. Holman R, Paul S, Bethel M, Matthews D, Neil, H. 10-Year Follow-up of Intensive Glucose Control in Type 2 Diabetes. New Engl J Med. 2008;359:1577-89.
  4. Dunn C, Peters D. Metformin. Drugs. 1995;49:721-49.
  5. Moses R. Combination therapy for patients with Type 2 diabetes: repaglinide in combination with metformin. Expert Rev of Endo & Meta. 2010;5:331-42.
  6. Nathan D. Medical Management of Hyperglycemia in Type 2 Diabetes: A Consensus Algorithm for the Initiation and Adjustment of Therapy: A Consensus Statement of the American Diabetes Association and the European Association for the Study of Diabetes: Response to Woo and Eizirik. Diabetes Care. 2009;32:e37-8.
  7. Defang O, Shufang N, Wei L, Hong G, Hui L, Weisan P. In Vitro and In Vivo Evaluation of Two Extended Release Preparations of Combination Metformin and Glipizide. Drug Dev Ind Pharm. 2005;31:677-85.
  8. Stepensky D, Friedman M, Srour W, Raz I, Hoffman A. Preclinical evaluation of pharmacokinetic and pharmacodynamic rationale for oral CR metformin formulation. J Control Release. 2001;71:107-15.
  9. Pharmacologic Management of Type 2 Diabetes. Can J Diabetes. 2013;37:S312-3.
  10. Leichter S, Thomas S. Combination Medications in Diabetes Care: An Opportunity That Merits More Attention. Clinical Diabetes. 2003;21:175-8.
  11. Draft Guidance for Industry: ANDA Submissions — Prior approval supplements. http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/Manufacturing/QuestionsandAnswersonCurrentGoodManufacturingPracticescGMPforDrugs/UCM176374.pdf
  12. Singh L, Sharma V. Quality by Design (QbD) Approach in Pharmaceuticals: Status, Challenges and Next Steps. Drug Delivery Letters. 2014; (4): 2-8.
  13. Charoo N, Shamsher A, Zidan A, Rahman Z. Quality by design approach for formulation development: A case study of dispersible tablets. Int J Pharm. 2012;423:167-78.
  14. Chileshe N. Quality management concepts, principles, tools and philosophies. J of Eng, Design and Tech. 2007;5:49-67.
  15. Guidance for Industry Q8(R2) Pharmaceutical Development. http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q8_R1/Step4/Q8_R2_Guideline.pdf
  16. Vora C, Patadia R, Mittal K, Mashru R. Risk based approach for design and optimization of stomach specific delivery of rifampicin. Int J Pharm. 2013; (455):169-81.
  17. Quality by Design for ANDAs: An Example for Immediate-Release Dosage Forms. http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/HowDrugsareDevelopedandApproved/ApprovalApplications/AbbreviatedNewDrugApplicationANDAGenerics/UCM304305.pdf
  18. Thwaites P. The effect of mixing time and mixer intensity on the compression properties of tablettose®. Drug Dev Ind Pharm. 1992;18:2001-10.
  19. Perioli L, Ambrogi V, Giovagnoli S, Blasi P, Mancini A, Ricci M. Influence of Compression Force on The Behavior of Mucoadhesive Buccal Tablets. AAPS PharmSciTech. 2008;9:274-81.
  20. Santos J, Batista de, Carvalho L, Pina M. The Influence of the Compression Force on Zidovudine Release from Matrix Tablets. AAPS PharmSciTech. 2010;11:1442-8.
  21. Dubey A, Boukouvala F, Keyvan G, Hsia R, Saranteas K, Brone D. Improvement of Tablet Coating Uniformity Using a Quality by Design Approach. AAPS PharmSciTech. 2012;13:231-46.
  22. Koller D, Hannesschlager G, Leitner M, Khinast J. Non-destructive analysis of tablet coatings with optical coherence tomography. Eur J Pharm Sci. 2011;44:142-8.
  23. Yu L. Pharmaceutical Quality by Design: Product and Process Development, Understanding, and Control. Pharm Res. 2008;25:781-91.
  24. Implementing QbD for biopharmaceutical production processes. Biotech and Bioeng., 2010, 106:845-48.
  25. Bansal S, Beg S, Asthana A, Garg B, Kapil R, Sing B. QbD-enabled systematic development of gastroretentive multiple-unit microballoons of itopride hydrochloride. Drug Deliv. 2014;28:1-15.
  26. Gupta V, Assmus M, Beckert T, Price J. A novel pH- and time-based multi-unit potential colonic drug delivery system. II. Optimization of multiple response variables. Int J Pharm. 2001;213:93-102.
  27. Barmpalexis P, Kanaze F, Georgarakis E. Developing and optimizing a validated isocratic reversed-phase high-performance liquid chromatography separation of nimodipine and impurities in tablets using experimental design methodology. J Pharmaceut Biomed. 2009;49:1192-202.
  28. Perioli L, Ambrogi V, Giovagnoli S, Blasi P, Mancini A, Ricci M, et al. Influence of Compression Force on The Behavior of Mucoadhesive Buccal Tablets. AAPS PharmSciTech. 2008;9:274-81.
  29. Vora C, Patadia R, Mittal K, Mashru R. Risk based approach for design and optimization of site specific delivery of isoniazid. J Pharma Invest. 2014;45:249-64.
  30. Rekhi G, Nellore R, Hussain A, Tillman L, Malinowski H, Augsburger L. Identification of critical formulation and processing variables for metoprolol tartrate extended-release (ER) matrix tablets. J Control Release. 1999;59:327-42.
  31. Costa P, Sousa Lobo J. Modeling and comparison of dissolution profiles. Eur J Pharm Sci. 2001;13:123-33.

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