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  • Implementation of Various QbD Designs in Pharmaceutical Product Development: A Critical Review

  • Dr. L. H. Hiranandani College of Pharmacy, Ulhasnagar

Abstract

Design of experiments (DOE) is an efficient procedure for organizing research so that the resulting data can be analyzed to provide objective and meaningful findings. This systematic and organized strategy is used to determine the relationships between elements that influence a process and its eventual outcome. During these experiments, process variables or factors are deliberately adjusted to observe their impact on one or more response variables. As a structured approach, DOE is essential for evaluating how various components affect method outputs. Within the framework of mathematical modeling, DOE is widely utilized for implementing Quality by Design (QbD) in both industrial and academic research environments. Because it is considered the primary choice for rational pharmaceutical development, this review provides a detailed illustration of DOE methodologies.

Keywords

QbD, Design of Experiment, Factorial Design, Central composite design, Box-Behnken Design, Plackett-Burman

Introduction

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Quality by Design  

"Quality by Design" (QbD) is defined as a systematic developmental methodology that starts with established objectives. This framework prioritizes a deep comprehension of products and manufacturing workflows, alongside rigorous process control, anchored in scientific principles and quality risk management. [1]

Pharmaceutical organizations increasingly acknowledge the critical nature of product quality, safety, and efficacy. The implementation of QbD scientific methods has led to significant improvements in product quality. These methods offer clear, essential insights spanning from initial product design to final production, while QbD-based tools mitigate risks by enhancing both quality and operational productivity. Ultimately, the goal of pharmaceutical development is to create superior products and manufacturing processes that reliably yield intended performance outcomes.[2]

While the pharmaceutical sector has consistently prioritized quality, it has lagged behind other industries regarding manufacturing productivity and efficiency. Adopting a modern approach to drug development can enhance efficiency, offer greater regulatory flexibility and relief, and deliver substantial business advantages across the entire life cycle of a product. According to the ICH Q8 guidelines, the core principle of Quality by Design (QbD) is that quality should be inherent to the design process rather than merely tested into the final product.[3]

Element Of Quality By Design  [4]

1. Quality Target Product Profile (QTPP)  

2. Critical Quality Attributes (CQA’s)  

3. Quality Risk Management  

4. Design Space Development  

5. Control Strategy  

6. Product Lifecycle Management & Continual Improvement  

This Article focuses on development of design space by implementation of design of experiments

Design Of Experiment (DOE):  

Design of Experiments (DoE) is a systematic, organized methodology used to determine how various input factors (independent variables, xi) relate to one or more output responses (dependent variables, y) by establishing mathematical models, typically expressed as y = f(xi). This approach involves the deliberate and systematic variation of controlled input factors to observe their influence on outputs. Consequently, DoE facilitates the identification of the most critical factors, determines the optimal settings for achieving desired responses, and clarifies the interactions between different input variables. [5]

Choosing the most appropriate experimental design requires evaluating several criteria, including the specific research objectives, the total number of factors and interactions under investigation, and the statistical effectiveness and validity of the chosen design. To better understand their practical applications, these designs are generally categorized into two primary types[6]:

a) Screening Designs:

These are typically employed during the initial phase of a DoE study to pinpoint the most significant input factors while filtering out those that are negligible. Common examples include:

? Plackett-Burman

? Fractional Factorial

? Two-level Full Factorial

b) Optimization Designs:

These designs utilize three to five levels for each input factor, enabling the modeling of second-order (quadratic) response surfaces. Because they necessitate a larger number of individual experiments, they are generally reserved for studies involving a limited set of input factors. Key optimization designs include:

? Box-Behnken

? Central Composite Design 

? 3-level Factorial 

? Mixture designs

Application of Design of Experiment in Pharmaceutical Product Development

Factorial Design

Factorial design is a statistical research methodology designed to identify the interactive effects of every possible combination of variables within an experimental set. A full factorial experiment, also known as a fully crossed design, involves two or more factors where each factor is assigned discrete values or "levels". The experimental units in this design encompass all potential combinations of these levels across every included factor. [7]

By using this approach, researchers can evaluate how both individual components and their mutual interactions influence the response variable. In the majority of factorial experiments, factors are limited to two levels. A common example is a 2x2 factorial design, which involves two factors that each have two levels, resulting in four distinct treatment combinations. [8]

Table 1: Application Of Factorial Design in Pharmaceuticals

Type Of Formulation

Independent Variables (Xs)

Dependent Variables (Ys)

Reference

Methotrexate Loaded Chitosan Nanocarriers

Levels Of Chitosan, Tripolyphosphate, Methotrexate

Nanocarrier Formation, Particle Size, And Statistical Analysis

[9].Teja, S. P. S., & Damodharan, N. et a.l(2018)

Fluvastatin Loaded Solid Lipid Nanoparticles

Lipid And Surfactant Concentrations

Entrapment Efficiency  and In-Vitro Drug Release

[10]. Asif, A. H  et. al. (2022)

Hydrodynamically Balanced System Of Ketorolac Tromethamine

 

Amount Of Hydroxypropyl Methylcellulose K4M  And Sodium Bicarbonate
(Nahco3)

(Hardness),  (FLT), Total
Floating Time (TFT), (Swelling Index [SI] In Ph1.2),  (T80%) By Plotting Response Surface Graph And Contour Plots.

[11].Dr. G. S. Asane et. al..(2023)

Multi-Layer Tablet Containing Ticagrelor And Aspirin,

Type Of Disintegrant Used In Ticagrelor Layer and The Type Used In Aspirin Layer

Tablet Hardness, Disintegration Time, And In Vitro Drug Release

[12].Mahmoud Mostafa, et. al. (2025)

In Situ Gel Of Sumatriptan

 

Combination Of
Poloxamer 407 And 188,

Xyloglucan

Gel Strength
Gelation Temperature

Percentage Drug Release (After 8 H)

[13].Kassab, et. al..(2023).

Loratadine-Loaded Nanosponge

 

Concentration Of Lor:Ec Ratio And Stirring Rate

Particle Size (Ps), In Vitro Release, Zeta Potential (Zp) And Entrapment Efficiency (Ee).

[14].Sivadasan, D., et. al. (2024).

Ibuprofen Fast-Dissolving Tablets

Effects Of Ocimum Gratisimum Mucilage, Sodium Starch Glycolate, And Croscarmellose Sodium

In Vitro Method, In Water Absorption, And Percent Drug Release At 5 Minutes.

[15].Naik DCS, et. al. 2022.

Emulsion

Span 60 : Sodium Lauryl Sulfate Ratio, Organic : Aqueous Phase Volume Ratio, And Polymer Concentration

Emulsion Phase Stability, Viscosity, And Conductivity

[16.]Badawi MA, et. al. 2014

Lipid Based Nanoemulsifying Cilostazol

Amount Of Oil (Capmul MCM), Amount Of Surfactant (Tween 80), And Amount Of Cosolvent (Transcutol HP)

Globule Size, Span, Equilibrium Solubility Of Cilostazol, Zeta Potential, And Dissolution  Efficiency,  T30 Of Lipid Based Nanoemulsifying Cilostazol

[17].Pund S et.al2014

Pellets For Oral Lysozyme Delivery

Kneading Temperature, Impeller Speed, Liquid Addition, Extrusion Speed, Spheronizer Speed, And Spheronization Time

Activity, Hardness, And Roundness Of Pellets For Oral Lysozyme Delivery

[18].Sovàny T et.al. 2016

Nanostructured Lipid Carries

Surfactant Concentration, Solid/Liquid Lipid Ration, And Ultrasonication Time

Particle Size And Particle Size Distribution Of Nanostructured Lipid Carriers Containing Salicylic Acid For Dermal Use

[19].Kovács et. al.., 2017)

 

Multifunctional Sunscreens

Concentrations Of Ethylhexyl Triazone, Bemotrizinol, And Ferulic Acid In Multifunctional Sunscreens

Antioxidant Activity, And Uva And Uvb Radiation

[20].Peres et. al.., 2017)

 

Of Efavirenz Loaded Solid Lipid Nanoparticles

Poloxamer 188, And Acetone To Methanol Ratio

Particle Size, And Entrapment Efficiency

[21]Raina, Kaur et. al. Jindal, 2017)

.

Gelling Microemulsion Of Lorazepam

Oil To Surfactant/Cosurfactant Ratio And Concentration Of Gellan Gum

In Vitro Drug Release And Viscosity At Physiological Ph Of A Microemulsion Of Lorazepam Via Intranasal Route

[22].Shah et. al.., 2017)

 

Miltefosine-Loaed Polymeric Micelles

Hydration Temperature, Stirring Speed, And Stirring Time

Polydispersity Index Of Miltefosine-Loaded Polymeric Micelles

[23].Valenzuela-Oses et. al.., 2017)

 

Fractional Factorial Design

Due to limitations involving time and  availability of resources, Complete Factorial designs have not always been possible to be conducted. In such cases, Fractional Factorial (FF)  designs  have  been  used [24] 

A Fractional Factorial Design is characterized by the use of a specifically chosen subset of experimental conditions derived from a Full Factorial Design. Essentially, only a portion of the possible Full Factorial conditions are utilized. This methodology proves more cost-effective as it minimizes the total number of required experiments. However, this reduction in trials can lead to aliasing, where certain factor effects cannot be easily distinguished from one another. Consequently, the choice of fractionation level is dictated by the desired resolution: low-resolution designs typically focus on identifying primary effects while ignoring interactions, whereas higher-resolution designs are capable of detailing both main effects and their subsequent interactions. [25,26]

Table2:Application Of Fractional Factorial Design In Pharmaceuticals

Type of Formulation

Independent Variable

Dependent Variable

Reference

Pitavastatin SNEDDS

The Types Of Oil, Surfactant, Co-Surfactant, And Their Concentrations

Transmittance, Emulsification Time, And Drug Load, Were Selected As Responses Followed By Particle Size Along With Zeta Potential

[27].Kuncahyo, I. et. al. (2019).

Gold Nanoparticles

Reducing Agent Type (Chitosan Or Trisodium Citrate), Concentration Of Reducing Agent (10 To 40 Mg), Ph (3.5 To 8.5), Temperature (60 To 100 °C), Agitation Time (5 To 15 Min), And Agitation Speed (400 To 1200 Rpm),

Particle Size And Polydispersity Index

[28].Jani, H et. al. (2025).

Ranolazine Extended-Release Tablets

(Fluid Uptake And Kneading Time)

Dissolution

[29].Jonna, S et. al.. (2023).

Piroxicam Self-Nanoemulsifying Drug Delivery System (Snedds)

 

Type And Concentration Of Oil (Triacetin And Oleic Acid), Surfactant (Kolliphor El And Tween 60), And Co-Surfactants (Transcutol And Peg 400)

Emulsification Time, %Transmittance, Droplet Size, And Drug Loading.

[30].Adi Nugroho et. al..(2023)

Fluid Bed Granulation

Inlet Air Temperature, Air Flow Rate And Binder Spray Rate During The Sprying Phase

Moisture Of Granules And Flow Through An Orifice Of The Granules Obtained By Fluid Bed Granulation

[31].Lourenço et. al.., (2012)

 

Nanosuspension

Indomethacin Concentration, Stabilizer Type, Stabilizer Concentration, Processing Temperature, And Homogenization Pressure

Particle Size Distribution, Zeta Potential, And Physical Form (Xrd) Of Nanosuspensions

[32].Verma et. al.., (2009)

 

Acetaminophen Immediate Release Tablets

Time Of Dissolution, Volume Of Dissolution Media, Ph Of Dissolution Media, And Rotation Speed

Amount Of Acetaminophen Dissolved During

[33].Romero, Lourenço, et. al. (2017)

 

Gold Nanoparticles

Reducing Agent Concentration, Stabilizer Type, And Temperature, Agitation Time (5 To 15 Min), And Agitation Speed

Particle Size And Polydispersity Index

[34].Jani, et. al..(2025)

Central Composite Design

By incorporating both linear and quadratic variables, the Central Composite Design (CCD) mathematical framework creates a response surface model that accurately aligns with practical experimental results. Calculating variable coefficients within this model assists investigators in clarifying the specific connections between influencing factors and their resulting responses. This detailed integration of linear and quadratic components improves response prediction and optimization within the designated experimental boundaries, offering a comprehensive view of the various elements affecting the outcome. [35]

The primary objective of the CCD methodology is to efficiently examine how categorical and continuous variables impact a response while minimizing the total experimental trials required. This efficiency is attained by combining axial, center, and factorial points. Data generated through CCD yields high-precision predictions, and the technique has demonstrated significant success across numerous projects focused on the development and enhancement of pharmaceutical formulations. [36]

Table3:Application Of Central Composite Design In Pharmaceuticals

Type of Formulation

Independent Variables (Xs)

Dependent Variables (Ys)

Reference

Bosutinib Monohydrate Loaded Lipid Nanoparticles

Particle Size (Ps) In Nm  And % Drug Entrapment Efficiency

Precirol Concentration (Ml) And Poloxamer 188 (Mg)

37. Panigrahi D et. al..(2025)

Novel Micelles Of Harmine

 

Hm Amount And Hydration Volume

Encapsulation Efficiency (Ee), Drug-Loading Amount (Ld), Particle Size, And Polydispersity Index (Pdi)

[38]. Bei, Y. et. al.  (2013)

Varenicline Tartrate Dispersible Tablet

Percentage Of Crospovidone And Croscarmellose Sodium

Disintegration Time And Wetting Time

[39].Bhavani B et. al. (2024)

 

Sustained Release Tablets Of Gliclazide

Conc. Of Karaya Gum

Conc. Of Guar Gum

Drug Release In 8hr,

Drug Release In 14hr,

Drug Release In 20hr,

[40].  Danga Neelima et. al. (2025)

Solid Dispersion Of Fluvastatin Sodium

Polymer (W/W) And Surfactant Concentration (% W/V)

T50% (Minutes) , Q90(%) And Percentage Drug Content

[41]. Neelam Sharma Et.Al.(2022)

Oro Dispersible Films

Percentage Of HPMC, Percentage Of Glycerol, And Drying Temperature

Thickness, Weight, Tensile Strength, Elongation At Break, Young’s Modulus, And Disintegration Time Of Oro Dispersible Films

([42].Visser et. al.., (2015)

 

Enoxaparin Sodium Loaded Polymeric Microspheres

Combination Ratio Of Eudragit®Fs-30d / Eudragit® Rs-Po, Pva Concentration In External Phase, And Nacl Concentration On External Phase

Size Of Microspheres, Encapsulation Efficiency Of Enoxaparin Sodium, Percentages Released Over 24h In Gastric, Duodenal And Colonic Media

[43].Hales et. al..,( 2017)

 

Box–Behnken designs (BBDs)

Box-Behnken designs (BBDs) represent a highly efficient class of second-order response surface methodologies specifically engineered to gather extensive data on experimental error and variable influences while minimizing the required sequence of runs. When compared to the frequently utilized Central Composite Design (CCD), BBDs demonstrate superior rotatability and symmetry, often providing exhaustive insights through fewer experimental iterations. These designs are structured to operate across three distinct levels—coded as -1, 0, and +1—making them particularly suitable for complex investigations that involve between 3 and 21 independent factors. A notable feature of BBD is its ability to integrate both numerical and categorical factors during the optimization process; however, investigators should be aware that the inclusion of categorical variables generally leads to a proportional increase in the total number of experimental runs needed to maintain statistical validity. Furthermore, because BBDs do not contain combinations where all factors are at their highest or lowest levels simultaneously, they are often preferred for experiments where extreme physical conditions might lead to failed trials or unsafe processing environments. [44]

Table4: Application Of Box–Behnken designs In Pharmaceuticals

Type Of Formulation

Independent Variables (Xs)

Dependent Variables (Ys)

Reference

Hexatriacontane-Loaded Transethosomal Gel

Lipoid (Mg), Ethanol (%), And Sodium Cholate (Mg)

Particle Size (Nm), Polydispersity Index (Pdi), And Entrapment Efficiency

[45]. Aodah A et.al (2023)

Nanoemulgel For Azithromycin

Tto Concentration, Surfactant Concentration,

Globule Size, Polydispersity Index (Pdi), And Viscosity.

[46]Iman S. et.al .(2026),

Paliperidon Solid Dispersion

Spray Rate, Atomization Pressure, And Inlet Temperature

Compressibility Index, Particle Size, Solubility And Dissolution

[47] R.K. Surawase et.al (2024)

Albendazole-Loaded Zein Nanoparticles

Concentrations Of Polyvinyl Alcohol, Acetic Acid, And The Weight Of Zein

Particle Size, Polydispersity Index, And Zeta Potential.

[48]Amina T. et. al., 2024,

Release Modulating Matrix Systems Of Losartan Potassium

Aminated Fenugreek Gum, Aminated Tamarind Gum And Aminated Xanthan Gum

Burst Release In 15 Min, Cumulative Percentage Release Of Drug After 60 Min  And Hardness

[49]Shankar et.al. (2021)

Extended Release Cefpodoxime Proxetil Chitosan-Alginate Beads

Sodium Alginate Percentage, Chitosan Percentage, And Calcium Chloride Percentage

Maximum Drug Encapsulation, Particle Size And Drug Release Of Cefpodoxime Proxetil Chitosan-Alginate Beads

[50]Muftaba, Ali, Kohli, et.al. (2014).

Aceclofenac Loaded-Nano Structured Lipid Carriers (Nlcs)

Lipid, Lipid Oil, And Surfactand Phase

Particle Size, Entrapment, Permeation Flux, And Percentage Release Of Aceclofenac Loaded-Nano Structured Lipid Carriers

[51]Garg et. al.., (2017) Garg NK,

Silymarin Nanoemulsion

Amount Of Surfactant/Cosurfactant Mixture, Processing pressure and No. of Homogenization Cycles

Globule Size, Size Distribution (Pdi), Percentage Transmittance, And Drug Release Of Silymarin Nanoemulsion

[52]Nagi et. al.., (2017)

Plackett–Burman Design

Plackett-Burman designs function as specialized resolution III, two-level fractional factorial frameworks. They enable the investigation of up to N-1 factors using N experimental runs, provided N is a multiple of 4. Typically implemented as an initial phase in Design of Experiments (DoE), these screening designs help identify critical input factors while eliminating those that are statistically insignificant. Because they focus on main effects, they are highly efficient for situations where a large number of variables need to be screened with minimal experimental trials.

While Pareto charts serve as effective instruments for this selection process by highlighting the most influential factors, they do not illustrate how changes in factor levels influence output responses. Instead, such insights into response behavior and the direction of the effect are better captured through the use of interaction and main effects plots. This design is particularly valuable in pharmaceutical development for studying the effect of formulation variables on drug release profiles, such as in hot melt sustained release extrudates.  [53]

Table 5:Application Of Plackett–Burman Design In Pharmaceuticals

Type Of Formulation

Independent Variables (Xs)

Dependent Variables (Ys)

Reference

Dabigatran Etexilate Mesylate Immediate-Release Tablets

 

Pregelatinised Starch, Crospovidone, Microcrystalline Cellulose Ph 101, Talc, Magnesium Stearate, Hydroxy Propyl Methyl Cellulose, Hydroxy Propyl Cellulose And Lactose Monohydrate

Release Of Drugs

[54] Gaeade et. al.. (2021)

 

Betamethasone Suspension For Injection Formulation

 

Macrogol Type  Concentration Of Polysorbate 80 (Mg/Ml)  Concentration Of Carmellose Sodium (Mg/Ml)  Filter Type Homogenization Time (Min) 6 Homogenization Speed (Rpm

Particle Size Distribution , Viscosity , Sedimentation Time, Density , The Assay Of Benzyl Alcohol, The Assay Of Methyl Parahydroxybenzoate, The Assay Of Propyl Parahydroxybenzoate, The Assay Of Betamethasone Sodium Phosphate, And The Assay Of Betamethason

Dipropionate

[55] Yerlikaya, F et.al.(2023)

Ivermectin Loaded Ethosomes

Phospholipid Conc. ,Ethanol Conc. , Cholesterol Conc. , Organic Phase Composition Ethanol+Pg Ethanol+Ipa , Stirring Speed (Rpm)

% Entrapment Efficiency

% CDR

[56] Ria N. (2024)

Mixture Designs

Mixture experiments represent a specialized class of Design of Experiments (DoE) specifically applied in industrial and chemical fields where the relative proportions of ingredients, rather than their absolute quantities, influence a response variable. This is common in pharmaceutical and chemical formulation studies where components must sum to a constant total. In numerous practical scenarios, the experimental outcome is binary or dichotomous, such as a pass/fail result, which leads researchers to seek highly efficient and informative experimental designs to ensure statistical validity.

Relying on design suggestions derived from linear normal-theory models with constant variance can often be a simplistic or "naive" approach in these contexts. This study examines the inherent risks associated with such strategies by comparing them against D-optimal mixture designs specifically tailored for binary responses. Furthermore, it assesses the D-efficiency of various design options across different parameter subspaces to determine their robustness.

Conventional designs intended for normal theory models often prove inadequate for binary responses because they frequently prioritize boundary points of the experimental space. Conversely, D-optimal mixture designs for binary outcomes typically distribute design points in areas where the predicted response probabilities are of a moderate magnitude, providing more useful data for model estimation. It is strongly recommended that investigators carefully consider the known characteristics of underlying process models and the binary nature of the data when choosing the most suitable mixture designs for their specific applications. [57]

Table 6: Application Of Mixture Design In Pharmaceuticals

Type of Formulation

Independent Variables (Xs)

Dependent Variables (Ys)

Reference

SEDDS for Protein Kinase Inhibitor-Pazopanib Hydrochloride

Labrafac WL1349, Labrasol, and Transcutol-P

Solubility, precipitation after 15 min, and particle size

[58] Amit Gupta et al., 2023

Herbal Mixture

P. crispum M., C. sativum L., and A. graveolens L.

DPPH free radical scavenging activity, total antioxidant capacity (TAC), and total phenolic content (TPC)

[59] Nouioura G. et al., 2023

Nutraceutical Hard Candy

Artemisia herba-alba Asso extract, 1.5 mL Glycyrrhiza glabra L. extract, and 1.5 mL Zingiber officinale extract

Color, taste, flavor, texture, aroma, and overall acceptability

[60] Souiy et al., 2023

Fruit Extract Capsule

Extract of M. charantia and A. esculentu

Fasting plasma glucose (FPG)

[61] Peter E. L. et al., 2022

Matrix Tablets

Carbomer (Carbopol® 971P NF) and Hydroxypropyl methylcellulose (Methocel® K100M or Methocel® K4M)

Amount of TP released, release rate, and mechanism varied with carbomer ratio in total matrix and HPMC viscosity

[62] Petrovic A. et al., 2009

Desonide-Loaded Emulgel

Oil, Smix, and water

Particle size, polydispersity index (PDI), zeta potential, % transmittance, and cumulative % drug release (CDR%)

[63] Kaithwar et al., 2026

Erythropoietin in Nanoparticles

Chitosan and pectin concentrations

Particle size, polydispersity index (PDI), zeta potential, and entrapment efficiency

[64] Nuryanti et al., 2026

CONCLUSION

In conclusion, the implementation of Design of Experiment (DOE) within the Quality by Design (QbD) framework provides a systematic and organized approach to pharmaceutical product development. By establishing robust mathematical models, DOE clarifies the intricate relationships between critical input factors and desired product quality attributes. This review highlights that the strategic selection of experimental designs—ranging from screening designs like Plackett-Burman and Fractional Factorial to optimization designs such as Central Composite Design, Box-Behnken, and Mixture designs—is essential for maximizing research efficiency. Ultimately, the application of these methodologies facilitates a science- and risk-based approach, ensuring the development of robust, high-quality, and efficient pharmaceutical products throughout their lifecycle.

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  28. Jani H, Ranch K, Chidrawar VR, Mohite P, Singh S. Process Parameter Screening Through Fractional Factorial Design for the Synthesis of Gold Nanoparticles. Processes. 2025;13(10):3157.
  29. Jonna S, Bapatu HR, Subbappa P, Saravanan K. A QbD with the Fractional Factorial Design was Used to Match the Similarity between Ranolazine Extended-Release Tablets 500 mg and 1000 mg. Int J Appl Pharm. 2023;15(2):98–105.
  30. Nugroho SA, Kuncahyo I, Marlina D. Screening of piroxicam self-nanoemulsifying drug delivery system (SNEDDS) using fractional factorial design. J Kimia Riset. 2023;8(1):69-80.
  31. Lourenço V, Lochmann D, Reich G, Menezes JC, Herdling T, Schewitz J. A quality by design study applied to an industrial pharmaceutical fluid bed granulation. Eur J Pharm Biopharm. 2012;81(2):438-47.
  32. Verma S, Lan Y, Golhale R, Burgess DJ. Quality by design approach to understand the process of nanosuspension preparation. Int J Pharm. 2009;377(1-2):185-98.
  33. Romero DC, Lourenço FR. Measurement uncertainty of dissolution test of acetaminophen immediate release tablets using Monte Carlo simulations. Braz J Pharm Sci. 2017;53(3):e00163.
  34. Jani H, Ranch K, Chidrawar VR, Mohite P, Singh S. Process Parameter Screening Through Fractional Factorial Design for the Synthesis of Gold Nanoparticles. Processes. 2025;13(10):3157.
  35. Savic Gajic I, Savic I, Boskov I, Žerajíc S, Markovic I, Gajic D. Optimization of ultrasound-assisted extraction of phenolic compounds from black locust (Robiniae Pseudoacaciae) flowers and comparison with conventional methods. Antioxidants (Basel). 2019;8(8):248.
  36. Sruthi S, Gopinath S, Athisayaraj MS, Sukumaran S. Central Composite Design: An Optimization Tool for Developing Pharmaceutical Formulations. J Young Pharm. 2024;16:400-9.
  37. Panigrahi D, Swain S, Jena BR, Parida P, Sahu PK. Central composite design enabled formulation development, optimization, and characterization of bosutinib monohydrate loaded lipid nanoparticles: Cytotoxicity studies. J Appl Pharm Sci. 2025;15(6):137–55.
  38. Bei YY, Zhou XF, You BG, Yuan ZQ, Chen WL, Xia P, et. al.. Application of the central composite design to optimize the preparation of novel micelles of harmine. Int J Nanomedicine. 2013;8:1795–808.
  39. Boddeda B, Chandu DB, Tashin SA, Banu MH, James B, Dharani N. Formulation and optimization of varenicline tartrate dispersible tablets: a central composite design approach. Indian J Pharm Sci. 2024;86(4):1488-98.
  40. Neelima D, Laveti V, Sampath KS, Gorinka G. Formulation development and optimization of sustained release tablets of gliclazide using central composite design. Res J Pharm Technol. 2025;18(8):3509-16.
  41. Sharma N, Kanojia N, Singh S, Antil A. Application of Central Composite Design for Formulation and Optimization of Solid Dispersion for Dissolution Rate Enhancement of BCS Class II Drug. Res J Pharm Technol. 2022;15(12):5659-4.
  42. Visser JC, Dohmen WMC, Hinrichs WLJ, Breitkreutz J, Frijlink HW, Woerdenbag HJ. Quality by design approach for optimizing the formulation and physical properties of extemporaneously prepared orodispersible films. Int J Pharm. 2015;485(1-2):70-6.
  43. Hales D, Vlase L, Porav SA, Bodoki A, Barbu-Tudoran L, Achim M, et. al.. A quality by design (QbD) study on enoxaparin sodium loaded polymeric microspheres for colon specific delivery. Eur J Pharm Sci. 2017;100:249-61.
  44. Beg S, Akhter S. Box–Behnken Designs and Their Applications in Pharmaceutical Product Development. In: Beg S, editor. Design of Experiments for Pharmaceutical Product Development. Singapore: Springer; 2021.
  45. Aodah AH, Hashmi S, Akhtar N, Ullah Z, Zafar A, Zaki RM, et. al.. Formulation Development, Optimization by Box-Behnken Design, and In Vitro and Ex Vivo Characterization of Hexatriacontane-Loaded Transethosomal Gel for Antimicrobial Treatment for Skin Infections. Gels (Basel). 2023;9(4):322.
  46. Jafaar IS, Alwan OM, Al-Shohani ADH. Box-Behnken design-based development and optimization of potentiated antibacterial nanoemulgel for azithromycin. OpenNano. 2026;29.
  47. Surawase RK, Baheti KG, Dehghan MH. Research Journal of Pharmacy and Technology. 2024;17(8):3567-4.
  48. Mneimneh AT, Hayar B, Al Hadeethi S, Darwiche N, Mehanna MM. Application of Box-Behnken design in the optimization and development of albendazole-loaded zein nanoparticles as a drug repurposing approach for colorectal cancer management. Int J Biol Macromol. 2024;281(Pt 4).
  49. Kalbhare SB, Redasani VK, Bhandwalkar MJ, Pawar RK, Bhagwat AM. Role of Aminated derivatives of Natural Gum in Release Modulating Matrix Systems of Losartan Potassium: Optimization of Formulation using Box-Behnken Design. Asian J Pharm Res. 2021;11(2):73-4.
  50. Muftaba A, Ali M, Kohli K. Formulation of extended release cefpodoxime proxetil chitosan-alginate beads using quality by design approach. Int J Biol Macromol. 2014;69:420-9.
  51. Sharma G, Singh B, Nirbhavane P, Tyagi RK, Shukla R, Katare OP. Quality by design (QbD)-enabled development of aceclofenac loaded-nano structured lipid carriers (NLCs): an improved dermatokinetic profile for inflammatory disorder(s). Int J Pharm. 2017;517(1-2):413-31.
  52. B, Kumar S, Sharma S, Ali J, Baboota S. Quality by design based silymarin nanoemulsion for enhancement of oral bioavailability. J Drug Del Sci Technol. 2017;40:35-44.
  53. Jain SP, Singh PP, Javeer S, Amin PD. Use of Placket-Burman statistical design to study effect of formulation variables on the release of drug from hot melt sustained release extrudates. AAPS PharmSciTech. 2010;11(2):936-44.
  54. Gaeade et. al.. [Title not specified]. IJPSR. 2021;12(12):6587-92.
  55. Yerlikaya F, Arslan A, Arabac? B, Gencer P, Nemutlu E. Application of Plackett-Burman design for development and evaluation of a betamethasone suspension for injection formulation. J Fac Pharm Ankara Univ. 2023;47(2):477-89.
  56. Patel RN. Screening of factors using Plackett Burman Design in the Formulation and Development of Ivermectin Loaded Ethosomes. Afr J Biol Sci. 2024;6(12).
  57. Mancenido M, Pan R, Montgomery D, Anderson-Cook CM. Comparing D-optimal designs with common mixture experimental designs for logistic regression. Chemom Intell Lab Syst. 2019;187:11-8.
  58. Gupta A, Dahima R. Application of Simplex Lattice Mixture design and desirability function in the development and Optimization of SEDDS for protein kinase inhibitor-Pazopanib Hydrochloride. Res J Pharm Technol. 2023;16(8):3561-8.
  59. Nouioura G, Tourabi M, El Ghouizi A, Kara M, Assouguem A, Saleh A, et. al.. Optimization of a New Antioxidant Formulation Using a Simplex Lattice Mixture Design of Apium graveolens L., Coriandrum sativum L., and Petroselinum crispum M. Grown in Northern Morocco. Plants. 2023;12(5):1175.
  60. Souiy Z, Amri Z, Sharif H, Souiy A, Cheraief I, Hamden K, et. al.. The Use of D-Optimal Mixture Design in Optimizing Formulation of a Nutraceutical Hard Candy. Int J Food Sci. 2023;2023:7510452.
  61. Peter EL, Sesaazi CD. D-optimal mixture design optimized solid formulation containing fruits extracts of Momordica charantia and Abelmoschus esculentus. PLoS One. 2022;17(6):e0270547.
  62. Petrovic A, Cvetkovic N, Ibric S, et. al.. Application of mixture experimental design in the formulation and optimization of matrix tablets containing carbomer and hydroxy-propylmethylcellulose. Arch Pharm Res. 2009;32:1767–74.
  63. Kaithwar K, Soni PK, Soni R, Paswan SK. D-Optimal Mixture Design Assisted Formulation Optimization of Desonide-Loaded Emulgel for Topical Application. Int J Applied Pharm. 2026;18(1):526–41.
  64. Nuryanti, Nugroho AK, Martien R, Julia M. Application of D-Optimal Mixture Design to Optimization and Formulation of Erythropoietin in Nanoparticles Drug Delivery System. Indones J Pharm. 2026;37(2):345–54.

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  28. Jani H, Ranch K, Chidrawar VR, Mohite P, Singh S. Process Parameter Screening Through Fractional Factorial Design for the Synthesis of Gold Nanoparticles. Processes. 2025;13(10):3157.
  29. Jonna S, Bapatu HR, Subbappa P, Saravanan K. A QbD with the Fractional Factorial Design was Used to Match the Similarity between Ranolazine Extended-Release Tablets 500 mg and 1000 mg. Int J Appl Pharm. 2023;15(2):98–105.
  30. Nugroho SA, Kuncahyo I, Marlina D. Screening of piroxicam self-nanoemulsifying drug delivery system (SNEDDS) using fractional factorial design. J Kimia Riset. 2023;8(1):69-80.
  31. Lourenço V, Lochmann D, Reich G, Menezes JC, Herdling T, Schewitz J. A quality by design study applied to an industrial pharmaceutical fluid bed granulation. Eur J Pharm Biopharm. 2012;81(2):438-47.
  32. Verma S, Lan Y, Golhale R, Burgess DJ. Quality by design approach to understand the process of nanosuspension preparation. Int J Pharm. 2009;377(1-2):185-98.
  33. Romero DC, Lourenço FR. Measurement uncertainty of dissolution test of acetaminophen immediate release tablets using Monte Carlo simulations. Braz J Pharm Sci. 2017;53(3):e00163.
  34. Jani H, Ranch K, Chidrawar VR, Mohite P, Singh S. Process Parameter Screening Through Fractional Factorial Design for the Synthesis of Gold Nanoparticles. Processes. 2025;13(10):3157.
  35. Savic Gajic I, Savic I, Boskov I, Žerajíc S, Markovic I, Gajic D. Optimization of ultrasound-assisted extraction of phenolic compounds from black locust (Robiniae Pseudoacaciae) flowers and comparison with conventional methods. Antioxidants (Basel). 2019;8(8):248.
  36. Sruthi S, Gopinath S, Athisayaraj MS, Sukumaran S. Central Composite Design: An Optimization Tool for Developing Pharmaceutical Formulations. J Young Pharm. 2024;16:400-9.
  37. Panigrahi D, Swain S, Jena BR, Parida P, Sahu PK. Central composite design enabled formulation development, optimization, and characterization of bosutinib monohydrate loaded lipid nanoparticles: Cytotoxicity studies. J Appl Pharm Sci. 2025;15(6):137–55.
  38. Bei YY, Zhou XF, You BG, Yuan ZQ, Chen WL, Xia P, et. al.. Application of the central composite design to optimize the preparation of novel micelles of harmine. Int J Nanomedicine. 2013;8:1795–808.
  39. Boddeda B, Chandu DB, Tashin SA, Banu MH, James B, Dharani N. Formulation and optimization of varenicline tartrate dispersible tablets: a central composite design approach. Indian J Pharm Sci. 2024;86(4):1488-98.
  40. Neelima D, Laveti V, Sampath KS, Gorinka G. Formulation development and optimization of sustained release tablets of gliclazide using central composite design. Res J Pharm Technol. 2025;18(8):3509-16.
  41. Sharma N, Kanojia N, Singh S, Antil A. Application of Central Composite Design for Formulation and Optimization of Solid Dispersion for Dissolution Rate Enhancement of BCS Class II Drug. Res J Pharm Technol. 2022;15(12):5659-4.
  42. Visser JC, Dohmen WMC, Hinrichs WLJ, Breitkreutz J, Frijlink HW, Woerdenbag HJ. Quality by design approach for optimizing the formulation and physical properties of extemporaneously prepared orodispersible films. Int J Pharm. 2015;485(1-2):70-6.
  43. Hales D, Vlase L, Porav SA, Bodoki A, Barbu-Tudoran L, Achim M, et. al.. A quality by design (QbD) study on enoxaparin sodium loaded polymeric microspheres for colon specific delivery. Eur J Pharm Sci. 2017;100:249-61.
  44. Beg S, Akhter S. Box–Behnken Designs and Their Applications in Pharmaceutical Product Development. In: Beg S, editor. Design of Experiments for Pharmaceutical Product Development. Singapore: Springer; 2021.
  45. Aodah AH, Hashmi S, Akhtar N, Ullah Z, Zafar A, Zaki RM, et. al.. Formulation Development, Optimization by Box-Behnken Design, and In Vitro and Ex Vivo Characterization of Hexatriacontane-Loaded Transethosomal Gel for Antimicrobial Treatment for Skin Infections. Gels (Basel). 2023;9(4):322.
  46. Jafaar IS, Alwan OM, Al-Shohani ADH. Box-Behnken design-based development and optimization of potentiated antibacterial nanoemulgel for azithromycin. OpenNano. 2026;29.
  47. Surawase RK, Baheti KG, Dehghan MH. Research Journal of Pharmacy and Technology. 2024;17(8):3567-4.
  48. Mneimneh AT, Hayar B, Al Hadeethi S, Darwiche N, Mehanna MM. Application of Box-Behnken design in the optimization and development of albendazole-loaded zein nanoparticles as a drug repurposing approach for colorectal cancer management. Int J Biol Macromol. 2024;281(Pt 4).
  49. Kalbhare SB, Redasani VK, Bhandwalkar MJ, Pawar RK, Bhagwat AM. Role of Aminated derivatives of Natural Gum in Release Modulating Matrix Systems of Losartan Potassium: Optimization of Formulation using Box-Behnken Design. Asian J Pharm Res. 2021;11(2):73-4.
  50. Muftaba A, Ali M, Kohli K. Formulation of extended release cefpodoxime proxetil chitosan-alginate beads using quality by design approach. Int J Biol Macromol. 2014;69:420-9.
  51. Sharma G, Singh B, Nirbhavane P, Tyagi RK, Shukla R, Katare OP. Quality by design (QbD)-enabled development of aceclofenac loaded-nano structured lipid carriers (NLCs): an improved dermatokinetic profile for inflammatory disorder(s). Int J Pharm. 2017;517(1-2):413-31.
  52. B, Kumar S, Sharma S, Ali J, Baboota S. Quality by design based silymarin nanoemulsion for enhancement of oral bioavailability. J Drug Del Sci Technol. 2017;40:35-44.
  53. Jain SP, Singh PP, Javeer S, Amin PD. Use of Placket-Burman statistical design to study effect of formulation variables on the release of drug from hot melt sustained release extrudates. AAPS PharmSciTech. 2010;11(2):936-44.
  54. Gaeade et. al.. [Title not specified]. IJPSR. 2021;12(12):6587-92.
  55. Yerlikaya F, Arslan A, Arabac? B, Gencer P, Nemutlu E. Application of Plackett-Burman design for development and evaluation of a betamethasone suspension for injection formulation. J Fac Pharm Ankara Univ. 2023;47(2):477-89.
  56. Patel RN. Screening of factors using Plackett Burman Design in the Formulation and Development of Ivermectin Loaded Ethosomes. Afr J Biol Sci. 2024;6(12).
  57. Mancenido M, Pan R, Montgomery D, Anderson-Cook CM. Comparing D-optimal designs with common mixture experimental designs for logistic regression. Chemom Intell Lab Syst. 2019;187:11-8.
  58. Gupta A, Dahima R. Application of Simplex Lattice Mixture design and desirability function in the development and Optimization of SEDDS for protein kinase inhibitor-Pazopanib Hydrochloride. Res J Pharm Technol. 2023;16(8):3561-8.
  59. Nouioura G, Tourabi M, El Ghouizi A, Kara M, Assouguem A, Saleh A, et. al.. Optimization of a New Antioxidant Formulation Using a Simplex Lattice Mixture Design of Apium graveolens L., Coriandrum sativum L., and Petroselinum crispum M. Grown in Northern Morocco. Plants. 2023;12(5):1175.
  60. Souiy Z, Amri Z, Sharif H, Souiy A, Cheraief I, Hamden K, et. al.. The Use of D-Optimal Mixture Design in Optimizing Formulation of a Nutraceutical Hard Candy. Int J Food Sci. 2023;2023:7510452.
  61. Peter EL, Sesaazi CD. D-optimal mixture design optimized solid formulation containing fruits extracts of Momordica charantia and Abelmoschus esculentus. PLoS One. 2022;17(6):e0270547.
  62. Petrovic A, Cvetkovic N, Ibric S, et. al.. Application of mixture experimental design in the formulation and optimization of matrix tablets containing carbomer and hydroxy-propylmethylcellulose. Arch Pharm Res. 2009;32:1767–74.
  63. Kaithwar K, Soni PK, Soni R, Paswan SK. D-Optimal Mixture Design Assisted Formulation Optimization of Desonide-Loaded Emulgel for Topical Application. Int J Applied Pharm. 2026;18(1):526–41.
  64. Nuryanti, Nugroho AK, Martien R, Julia M. Application of D-Optimal Mixture Design to Optimization and Formulation of Erythropoietin in Nanoparticles Drug Delivery System. Indones J Pharm. 2026;37(2):345–54.

Photo
Reena Shinde
Corresponding author

Dr. L. H. Hiranandani College of Pharmacy, Ulhasnagar

Photo
Dr. Nilesh Khutle
Co-author

Dr. L. H. Hiranandani College of Pharmacy, Ulhasnagar

Reena Shinde, Dr. Nilesh Khutle, Implementation of Various QbD Designs in Pharmaceutical Product Development: A Critical Review, Int. J. of Pharm. Sci., 2026, Vol 4, Issue 6, 3132-3144. https://doi.org/10.5281/zenodo.20661292

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