Fractional Factorial Research Articles

Optimizing processes and products: The role of DOE

The Design of Experiment (DOE) methodology is a fundamental tool for systematic inquiry and optimization in both scientific and industrial applications. The DOE’s statistical framework is designed to enhance the efficiency and reliability of experimental investigations by systematically planning, conducting, and evaluating controlled experiments. To support well-informed decision-making, process optimization, and quality improvement, the main goal of DOE is to discover and quantify the effects of various variables on a response variable. Various methods such as full factorial, fractional factorial, Taguchi, and response surface methodologies provide powerful tools for optimising processes and enhancing quality. The study covers the application of DOE in several industries, including engineering, manufacturing, agriculture, and medicine. Defect minimization, process optimization, and quality improvement are all aided by DOE in manufacturing. By determining the best dosages and formulations, it helps in drug development in the pharmaceutical industry. In the field of agriculture, DOE facilitates the identification of optimal growing conditions and techniques. It helps in the engineering and assessing new systems and products. To achieve consistency and accuracy in data collection, the experiment must be carried out with strict adherence to the experimental strategy. Analysing data involves using statistical tools to evaluate the findings and make conclusions, such as ANOVA, regression analysis, and graphical approaches. By pointing out important variables and their interactions, these studies aid in process optimization and product quality enhancement.

Efficient integration of analytical quality-by-design and tri-hued HPLC-DAD approach for synchronized assessment of gemigliptin and rosuvastatin in their fixed-dose pills: Application to stability study

Life-threatening atherosclerotic cardiovascular disorder is a well-recognized risk among diabetics, which is clinically ascribed to their unbalanced plasma lipoproteins. Gemigliptin-Rosuvastatin is a recently introduced polytherapy prescribed for the management of diabetic dyslipidemia. Currently, the main focus of several analysts is to design more ecologically friendly sustainable approaches for pharmaceutical analysis. In this context, an innovative tri-hued (green, blue, and white) stability-indicating HPLC-DAD approach was developed to concurrently quantify Gemigliptin and Rosuvastatin polypills. Consequently, a strategic integration between analytical quality-by-design and green/white analytical chemistry principles was specifically designed to optimize efficiency and practicality while minimizing ecological burden. Two experimental designs, namely two-level fractional factorial and Box-Behnken designs, were employed for screening and optimization procedures, respectively. Optimal separation was attained utilizing an Agilent HC-C18 column in conjunction with a mobile phase consisting of acetonitrile and 10 mM sodium dihydrogen phosphate buffer, pH 3 (60:40 v/v), eluted in an isocratic manner at a 1 mL/min flow rate. Both analytes exhibited good linearities within the concentration range of 0.5–100 μg/mL. Furthermore, stability studies were conducted on both drugs in response to variable stress degradation scenarios. Fortunately, the adopted approach was well-suited to resolve the studied drugs from their respective degradants, thereby verifying the method's stability-indicating power. Tri-hued evaluation for the implemented approach was performed via up-to-date metrics, specifically, the Analytical Eco-scale, AGREE, Blue Applicability Grade Index, and RGB 12. Ultimately, our adopted stability-indicating HPLC approach exhibited great sensitivity, specificity, applicability, and rapidity as well as being ecologically sound. It is worth noting that this is the first stability-indicating HPLC approach for simultaneous assessment of the studied drugs.

Evaluation of orthogonal composite designs for second‐order model in presence of missing observation

AbstractOrthogonal‐array composite designs (OACDs) and orthogonal‐uniform composite designs (OUCDs) are orthogonal composite designs that combine two‐level full or fractional factorial and three‐level orthogonal‐array/uniform designs for estimation of the linear, bilinear, and quadratic effects in a second‐order response surface model. In this study, the effects of missing one observation in the various design portions (factorial (f) axial (a) and center (c)), on the precision of parameter estimates, prediction variance and design efficiency of OACDs and OUCDs for 5 ≤ k ≤ 9 factors at different values of α (the distance of a non‐zero co‐ordinate in an additional design point from the center) are evaluated. The results showed that missing a factorial and an axial point have adverse effect on the precision of parameter estimates of OACDs and OUCDs, while missing a center point has little effect. Missing an axial point caused the highest effect on the prediction variance and design efficiencies. The FDS plots showed OACDs to be better designs for k ≤ 7 and OUCDs for k = 8 and 9 factors.

PROCESSING PARAMETERS OPTIMIZATION OF VACUUM-BAGGING PREFORMING IN OVEN CURE FOR INTER-LAMINAR SHEAR STRENGTH (ILSS) IN GLASS/EPOXY COMPOSITE LAMINATE

Autoclave had been restricted to immense production expenses and overabundant residual stress. These disadvantages had prompted development on the alternative of out-of-autoclave (OoA) processing. The optimization on vacuum-bagging-only (VBO) pre-forming process in producing high inter-laminar shear strength (ILSS) of composite laminate was proposed. The effects of individual and combined pre-forming parameters of VBO-oven cure processing for the conventional low-cost glass/epoxy composite material towards ILSS of cured laminates were quantified. 20 composite panels were manufactured following the designated parameter combinations based on central composite design in fractional factorial for response surface model. Three factors of vacuum debulk duration, edge breather number of sides and intensifier weight were investigated. For validation, two laminates without additional processing parameters were produced via oven (baseline) and autoclave. ILSS test was conducted based on ASTM D 2344. The interaction between combined parameters was analyzed using analysis of variance (ANOVA). Lowest ILSS was found in laminates where intensifier was absent, while highest ILSS was measured with edge breather, debulk and intensifier at different levels. An optimum combination of 30 minutes debulk, each sides of edge breather and 1kg intensifier were validated to produce laminate with highest ILSS of 38.96 MPa, which was 23.53% higher than baseline laminate.

Productivity and nutritional aspects in Moringa oleifera plants fertilized with NPK in south-eastern Brazil

Moringa oleifera is an Asian tree species cultivated for its nutritional and medicinal properties. Present study was carried out during 2021 and 2022 at Federal Institute of Minas Gerais, São João Evangelista-MG, Doce River Valley, South-Eastern Brazil to determine NPK doses for Moringa and critical NPK levels in the shoot. The experimental design was a fractional factorial of the type (1/2)43, with 32 treatments set up in four blocks. The treatments represented combinations of doses of N (0, 40, 80, 160 kg/ha); P2O5 (0, 45, 90, 180 kg/ha); and K2O (0, 20, 40, 80 kg/ha). Each experimental unit was composed of four seedlings arranged in a row. Plant shoot harvests were carried out every 60 days (January, March, May, July, September and November) for two consecutive years. The data were analyzed using regression analysis, and the adjusted response surface model was: Y = b0 + b1 N + b2 N2 + b3 P + b4 P2 + b5 K + b6 K2 + b7 NP + b8 NK + b9 PK. The productivity of fresh and dry matter of the shoot of M. oleifera plants was influenced only by N doses in the first year of cultivation, when the supply of 88.7 and 85.8 kg/ha of N led to the maximum estimated productivity for fresh (10,360.3 kg/ha) and dry matter (2,84.0 kg/ha). The critical contents in the shoot were 3.23 dag/kg (N), 0.41 dag/kg (P), and 2.18 dag/kg (K), while the annual extraction by plants was 78.44 kg/ ha (N), 8.99 kg/ha (P), and 48.81 kg/ha (K).

Acceptability of Four Intervention Components Supporting Medication Adherence in Women with Breast Cancer: a Process Evaluation of a Fractional Factorial Pilot Optimization Trial

Adjuvant endocrine therapy (AET) reduces mortality in early-stage breast cancer, but adherence is low. We developed a multicomponent intervention to support AET adherence comprising: text messages, information leaflet, acceptance and commitment therapy (ACT), and side-effect website. Guided by the multiphase optimization strategy, the intervention components were tested in the ROSETA pilot optimization trial. Our mixed-methods process evaluation investigated component acceptability. The pilot optimization trial used a 24–1 fractional factorial design. Fifty-two women prescribed AET were randomized to one of eight experimental conditions, containing unique component combinations. An acceptability questionnaire was administered 4 months post-randomization, and semi-structured interviews with 20 participants further explored acceptability. Assessments were guided by four constructs of the theoretical framework of acceptability: affective attitude, burden, perceived effectiveness, and coherence. Quantitative and qualitative findings were triangulated to identify agreements/disagreements. There were high overall acceptability scores (median = 14–15/20, range = 11–20). There was agreement between the qualitative and quantitative findings when triangulated. Most participants “liked” or “strongly liked” all components and reported they required low effort to engage in. Between 50% (leaflet) and 65% (SMS) “agreed” or “strongly agreed,” it was clear how each component would help adherence. Perceived effectiveness was mixed, with 35.0% (text messages) to 55.6% (ACT) of participants “agreeing” or “strongly agreeing” that each component would improve their adherence. Interview data provided suggestions for improvements. The four components were acceptable to women with breast cancer and will be refined. Mixed-methods and triangulation were useful methodological approaches and could be applied in other optimization trial process evaluations.

Application of essential oils as natural antimicrobials in lactic acid bacteria contaminating fermentation for the production of organic cachaça

Sugarcane-based fermentation is an essential process for different sectors of economic importance, such as the food industry with fermented and distilled beverages. However, this process can suffer from high contamination by wild yeasts and bacteria, especially lactic acid bacteria (LAB). This makes it necessary to use decontamination strategies and search for new methods that have a low environmental impact and contribute to the production of organic products. Among the options, oregano and thyme essential oils stand out for their antibacterial compounds. The aim of this study was to use oregano and thyme essential oils as natural antimicrobials in the alcoholic fermentation of sugar cane juice. Initially, the minimum inhibitory concentration of the essential oils in the fermentation was assessed through turbidity in the sensitivity test, which allowed us to determine which concentrations of essential oils would inhibit the contaminants, 3 morphologically selected LAB strains, as well as assessing the viability of CA-11. For LAB, 3 concentrations of each essential oil were tested, ranging from 0.1 to 0.4 μl/mL, while for CA-11 it was 0.06, to 0.1 μl/mL. The results indicated a maximum total value of essential oils per ml of 0.06 μl. Based on this result, a 24-1 fractional factorial was established, with 8 conditions +3 central points, with 4 variables, oregano essential oil (0, 0.03 and 0.06 μl/ml), thyme (0, 0.03 and 0.06 μl/ml), initial soluble solids (14, 16 and 18°Brix) and initial yeast concentration (2.5, 3 and 3.5 g/l), with the temperature set at 32 °C for a period of 12 h. The results showed that the center point condition with 0.03 μl/ml of oregano EO, 0.03 μl/ml of thyme controlled the proliferation of contaminating bacteria compared to the control condition. In the experimental validation, the treatment with essential oils had a lower final population of LAB (5.95 log) than the final population of the control treatment (6.53 log), and it was also observed that the treatment with EOs had an alcohol production around 3 % higher than the treatment without antimicrobials. The experimental validation phase confirmed the synergistic action of oregano and thyme essential oils in controlling the proliferation of contaminating bacteria. In conclusion, it was possible to determine the synergistic antimicrobial action of essential oils against LAB during alcoholic fermentation based on organic sugar cane.

Modeling of microplastics degradation in aquatic environments using an experimental plan

The aim of this article is to apply advanced predictive modeling techniques to understand the degradation process of microplastics in aquatic environments. Utilizing a Fractional Factorial Central Composite Experimental Plan, this study seeks to develop precise predictive statistical models that enable forecasting the quantity of pollutants generated during the degradation of microplastics under various environmental conditions. This tool was applied to model changes in DOC (dissolved organic carbon) and DEHP (bis(2-ethylhexyl) phthalate) values during the degradation of microplastics in aquatic ecosystems. The methods were developed using data derived from laboratory tests conducted using the GC-MS technique. The obtained approximating functions, considering factors such as degradation time, water temperature, and particle size, significantly reduced the analysis time. A two-stage verification of the approximating functions was conducted, considering the accuracy of the function form, its adequacy, the statistical significance of input variables, and their correlation with DOC and DEHP. The employed a Fractional Factorial Central Composite Experimental Plan allowed for the simultaneous reduction in the number of experiments and prediction of the influence of variables on the output values. Precise predictive models support understanding of the microplastic degradation process, facilitating the development of effective strategies for managing this pollution.

Optimization of Thermal Conductivity and Latent Heat Capacity Using Fractional Factorial Approach for the Synthesis of Nano‐Enhanced High‐Performance Phase‐Change Material

Optimization of Thermal Conductivity and Latent Heat Capacity Using Fractional Factorial Approach for the Synthesis of Nano‐Enhanced High‐Performance Phase‐Change Material

DEVELOPMENT OF A MULTI-PISTON BINDERLESS BRIQUETTING MACHINE

Cooking and heating fuels needed for everyday survival is experiencing either dwindling supply, fluctuating prices or difficulty in accessing it, with developing countries being the worst hit, so the poor and low income earners rely greatly on fuelwood to meet their everyday energy needs. Agricultural waste residues can be a reliable alternative to fossil fuels and fuelwood when converted into solid fuels called briquettes, whose quality is determined by the production factors. In this research, a multi-piston binderless briquetting machine was designed, fabricated and tested by producing briquettes from selected biomass wastes (corn cob, sugarcane bagasse, groundnut shell, sawdust and rice husk) and polyethylene wastes (sachet water wastes), using the machine. Experimental run was designed via Taguchi fractional factorial using Minitab 17 software, for 27 runs orthogonal array. Input factors; moulding temperature (250, 270 and 290 0C), Moulding pressure (46, 56 and 66 MPa), composition of polyethylene (10, 20 and 30%) and dwell time (60, 180 and 300 seconds) were varied. From the analysis of the materials and briquettes produced, highest bulk density of the mixed material was 250kg/m3, highest compressed density of the briquettes was 587 kg/m3, highest relaxed density was 545 kg/m3 while highest calorific value was 26.3162 MJ/kg. For proximate analysis, lowest moisture content of the briquettes was 0.04%, lowest volatile matter was 71.63%, lowest ash content was 2.77% and highest fixed carbon was 20.40%. Fuels produced from these selected materials were fuels of good qualities that can provide alternative to fossil fuels and fuelwood.

Performance of membrane distillation assisted crystallization and crystal characteristics for resource recovery from desalination brine

Membrane distillation crystallization (MDC) has been widely studied in recent year for its potential for resource recovery from desalination brine with high concentrations. In this study, crystal samples produced from a bench-scale MDC system have been analysed for crystal morphology, crystal purity and crystal size distribution (CSD). In addition, orthogonal fractional factorial (OFF) experimental design has been introduced to evaluate the influence of various operating conditions on the crystal size and CSD. Feed temperature (40 to 60 °C), crystallization time (60 to 180 min) and the feed composition have been used as the control factors. The results show that the MD in the MDC system maintains a consistent flux of 1.32 to 4.2 kg/m2·h across various feed compositions; higher feed temperatures led to the formation of larger crystal clusters, while longer crystallization time yielded more cubic-shaped crystals; an overall NaCl purity exceeding 99.08 % has been achieved throughout all the experiments; and the increased feed temperature and prolonged crystallization time results in a narrower CSD. The result of OFF experimental design identifies feed temperature as the dominant factor affecting crystal size and CSD. However, the presence of impurities in the feed water could pose challenge to the thermal stability and longevity of the membrane.

Design of Experiments for Optimizing Ultrasound-Assisted Extraction of Bioactive Compounds from Plant-Based Sources

Plant-based materials are an important source of bioactive compounds (BC) with interesting industrial applications. Therefore, adequate experimental strategies for maximizing their recovery yield are required. Among all procedures for extracting BC (maceration, Soxhlet, hydro-distillation, pulsed-electric field, enzyme, microwave, high hydrostatic pressure, and supercritical fluids), the ultrasound-assisted extraction (UAE) highlighted as an advanced, cost-efficient, eco-friendly, and sustainable alternative for recovering BC (polyphenols, flavonoids, anthocyanins, and carotenoids) from plant sources with higher yields. However, the UAE efficiency is influenced by several factors, including operational variables and extraction process (frequency, amplitude, ultrasonic power, pulse cycle, type of solvent, extraction time, solvent-to-solid ratio, pH, particle size, and temperature) that exert an impact on the molecular structures of targeted molecules, leading to variations in their biological properties. In this context, a diverse design of experiments (DOEs), including full or fractional factorial, Plackett–Burman, Box-Behnken, Central composite, Taguchi, Mixture, D-optimal, and Doehlert have been investigated alone and in combination to optimize the UAE of BC from plant-based materials, using the response surface methodology and mathematical models in a simple or multi-factorial/multi-response approach. The present review summarizes the advantages and limitations of the most common DOEs investigated to optimize the UAE of bioactive compounds from plant-based materials.

Datacentric Similarity Matching of Emergent Stigmergic Clustering to Fractional Factorial Vectoring: A Case for Leaner-and-Greener Wastewater Recycling

Water scarcity is a challenging global risk. Urban wastewater treatment technologies, which utilize processes based on single-stage ultrafiltration (UF) or nanofiltration (NF), have the potential to offer lean-and-green cost-effective solutions. Robustifying the effectiveness of water treatment is a complex multidimensional characteristic problem. In this study, a non-linear Taguchi-type orthogonal-array (OA) sampler is enriched with an emergent stigmergic clustering procedure to conduct the screening/optimization of multiple UF/NF aquametric performance metrics. The stochastic solver employs the Databionic swarm intelligence routine to classify the resulting multi-response dataset. Next, a cluster separation measure, the Davies–Bouldin index, is used to evaluate input and output relationships. The self-organized bionic-classifier data-partition appropriateness is matched for signatures between the emergent stigmergic clustering memberships and the OA factorial vector sequences. To illustrate the proposed methodology, recently-published multi-response multifactorial L9(34) OA-planned experiments from two interesting UF-/NF-membrane processes are examined. In the study, seven UF-membrane process characteristics and six NF-membrane process characteristics are tested (1) in relationship to four controlling factors and (2) to synchronously evaluate individual factorial curvatures. The results are compared with other ordinary clustering methods and their performances are discussed. The unsupervised robust bionic prediction reveals that the permeate flux influences both the UF-/NF-membrane process performances. For the UF process and a three-cluster model, the Davies–Bouldin index was minimized at values of 1.89 and 1.27 for the centroid and medoid centrotypes, respectively. For the NF process and a two-cluster model, the Davies–Bouldin index was minimized for both centrotypes at values close to 0.4, which was fairly close to the self-validation value. The advantage of this proposed data-centric engineering scheme relies on its emergent and self-organized clustering capability, which retraces its appropriateness to the fractional factorial rigid structure and, hence, it may become useful for screening and optimizing small-data wastewater operating conditions.

Design of experiment-oriented development of solvent-free mixed micellar chromatographic method for concomitant determination of metronidazole and ciprofloxacin hydrochloride

A green, fast and robust solvent-free chromatographic method has been developed for concomitant analysis of ciprofloxacin HCl and metronidazole in bulk powder as well as in dosage form using levofloxacin as internal standard (I.S.). Two different designs including fractional factorial (FFD) and Box–Behnken (BBD) designs were implemented for screening and optimization steps, respectively. The optimum chromatographic separation was accomplished using mobile phase composed of 0.13 M sodium dodecyl sulfate and 0.02 M Birij-35 solution adjusted to pH 2.5 using phosphoric acid at a flow rate of 1.3 mL/min and column oven temperature of 40 °C. Chromatographic analysis was performed on X-Bridge (150 mm × 4.6 mm, 5 μm) column with UV detection at 280 nm. A linear response was acquired over the range of 0.4–50 μg/mL for both drugs. The developed method was applied for quantitation of cited drugs in commercially available tablet with mean percent recovery ± SD of 99.45 ± 0.72 and 100.13 ± 0.81 for metronidazole and ciprofloxacin respectively. The method was proven to be green as evaluated by three greenness assessment tools. The run time was 8 min, thus saving time and reagent.

Effects of process parameters variations and optimization of biodiesel production from orange seed oil using raw and thermal clay as catalyst

In this study, process parameters for the production of biodiesel from orange seed oil using raw and thermal clay as catalyst were analyzed using randomized optimal design. The design was a response surface method (full fractional factorial) which identified the various design points as being numerical and discrete. The process optimization was performed by varying five factors, each at two different levels. The process parameters: methanol to oil molal ratio (mol/mol), catalyst concentration (weight %), reaction time (minutes), temperature (°C) and agitation speed (revolution per minutes, rpm) were the independent variables (input), while the biodiesel yield (vol/vol) was the dependent variable (response) in the optimization process. The designed matrix, 3D (three dimensional) surface plots, contour plots and analysis of variance (ANOVA), was achieved using the Design Expert Software (version 12.0), the optimum biodiesel yield was predicted and experimented to ascertain the interactive effects of parameters. The correlating regression coefficient indicated the satisfactory performance of the model for the raw and thermal clay. The experimental/actual maximum optimal biodiesel yield for the biodiesel production from the orange seed oil using raw and thermal clay as catalyst was 79.53 and 94.58% v/v while the predicted biodiesel yield was 79.55 and 92.98% v/v. The set of conditions that caused these positive effects were established at Time of 150 minutes, Temperature 65 °C, methanol /sample molal ratio of 12:1, catalyst concentration of 3.0 wt. % and agitation speed at 300 rpm respectively. Thisresults shows agreement between the actual and predicted biodiesel yield. It can be concluded that the best biodiesel yield can be achieved using thermally modified clay as catalyst.

Designing More Informative Multiple-Driver Experiments.

For decades, multiple-driver/stressor research has examined interactions among drivers that will undergo large changes in the future: temperature, pH, nutrients, oxygen, pathogens, and more. However, the most commonly used experimental designs-present-versus-future and ANOVA-fail to contribute to general understanding or predictive power. Linking experimental design to process-based mathematical models would help us predict how ecosystems will behave in novel environmental conditions. We review a range of experimental designs and assess the best experimental path toward a predictive ecology. Full factorial response surface, fractional factorial, quadratic response surface, custom, space-filling, and especially optimal and sequential/adaptive designs can help us achieve more valuable scientific goals. Experiments using these designs are challenging to perform with long-lived organisms or at the community and ecosystem levels. But they remain our most promising path toward linking experiments and theory in multiple-driver research and making accurate, useful predictions.

Quality by design paradigm for optimization of green stability indicating HPLC method for concomitant determination of fluorescein and benoxinate

A green, robust and fast stability indicating chromatographic method has been developed for concomitant analysis of fluorescein sodium and benoxinate hydrochloride in the presence of their degradation products within four minutes. Two different designs including fractional factorial and Box–Behnken designs were implemented for screening and optimization steps, respectively. The optimum chromatographic analysis was achieved using a mixture of isopropanol and 20 mM potassium dihydrogen phosphate solution (pH 3.0) in the ratio 27:73 as mobile phase. The flow rate was 1.5 mL/min and column oven temperature was 40 °C. Chromatographic analysis was performed on Eclipse plus C18 (100 mm × 4.6 mm × 3.5 μm) column with DAD detector set at 220 nm. A linear response was acquired over the range of 2.5–60 μg/mL and 1–50 μg/mL for benoxinate and fluorescein respectively. Stress degradation studies were executed under acidic, basic, and oxidative stress conditions. The method was implemented for quantitation of cited drugs in ophthalmic solution with mean percent recovery ± SD of 99.21 ± 0.74 and 99.88 ± 0.58 for benoxinate and fluorescein respectively. The proposed method is more rapid and eco-friendly compared to the reported chromatographic methods for determination of cited drugs.

Individual Word Length Patterns for Fractional Factorial (Split-Plot) Designs

Individual Word Length Patterns for Fractional Factorial (Split-Plot) Designs

Extraction and Determination of Sildenafil Citrate by Spectrophotometry: Classical Versus Factorial Design Optimization

In this work, the complexation and extraction processes of sildenafil citrate (SC) were evaluated by using classical optimization versus factorial design methodology. The selected factors were based on the reaction of sildenafil with methyl orange (MO) as a chromogenic reagent via the formation of ion-association complex in acidic buffer media, followed by extraction with chloroform and quantitatively measured by visible spectrophotometry at λmax of 427 nm. The optimization step was first carried out by classical one-factor-at-a-time (OFAT) from which the extracted results were exploited in the experimental design optimization by using 24-1 fractional factorial and 32 full factorial designs for the chosen variables such as pH, concentration of reagent, reaction time, extraction time and temperature. Results obtained from fractional factorial design 24-1 showed that only the variables pH, MO concentration and their interaction based on analysis of variance (ANOVA) in term of Pareto chart, were statistically significant at 95% confidence level. Under the optimized conditions, the assay method of SC was validated in term of the analytical figures of merit. SC can be determined in concentration range of 0.5-40 µg mL-1 with correlation coefficient of 0.9991, detection limit (S/N) of 0.15 µg mL-1, RSD (n=10) of 1.43%, accuracy as %Erel of -2.23% and mean percent recovery of 97.77±0.87. The proposed method was applied for the determination of SC in commercial medicaments without fear from excipients interferences with the assay procedure.

A QBD WITH THE FRACTIONAL FACTORIAL DESIGN WAS USED TO MATCH THE SIMILARITY BETWEEN RANOLAZINE EXTENDED-RELEASE TABLETS 500 MG AND 1000 MG

Objective: Formulation and development of Ranolazine extended-release Tablets 500 mg and 1000 mg by using QBD with Fractional factorial design to match the similarity with Branded formulation (RANEXA®). Methods: Ranolazine extended-release tablets were developed by using various polymers, Polyquid PA100 and ETHOCEL TMstandard 7 premium and Hypromellose with a wet granulation process. QBD (Quality by Design) approach was used to identify the formulation and process variables that may affect the CQAs (critical quality attributes). Excipient compatibility studies were conducted to identify the interaction between API (active pharmaceutical ingredient) and selected excipients. Additionally, using a two-level, fractional factorial (22+3) design, the DOE (design of experiments) was employed to confirm the impacts of key process factors (fluid uptake and kneading time) on the formulation's ability to match the dissolution (f2) compared to RLD (reference listed drug) and establish a stable formulation. Results: Initial risk assessment was carried out to identify the various attributes such as API flow properties, solubility, PSD, Hygroscopicity, formulation, and process variables to impact the quality of the drug product. Flow properties of API indicate poor flow. Drug and excipients compatible study results indicate that excipients used in the compatibility study are considered compatible with the active ingredient. As per the saturation solubility studies and sink conditions, dissolution media was selected. Significant differences were found among the drug release profile by examining the various levels of polymers and binders. Using a two-level, fractional factorial (22+3) design, optimum process parameters were identified with selected formulation to match the dissolution (f2) similarity with the reference listed drug (RLD); finally, XRD (X-ray diffraction) studies confirm that the crystalline polymorphic forms (Form 1) peaks in optimum formulation (F07) comparable to the reference listed drug. Conclusion: Optimized formulation and process were established with QbD (quality by design) that provides the consistent drug release to match the f2 similarities with the extended-release tablets of RANEXA®(Ranolazine) 500 mg and 1000 mg.