A Comprehensive QBD Strategy for Nanotherapeutics Development: A Review

M. M. Chogale, B. M. N. Shaikh, A. R. Gupta, S. P. Udeg, V. B. Patil, A. S. Jagtap,

doi:10.5281/zenodo.14605197

Review Paper | Open Access
Volume 02 | Issue 01 | Article Id IJPS/240212461

A Comprehensive QBD Strategy for Nanotherapeutics Development: A Review
M. M. Chogale* B. M. N. Shaikh A. R. Gupta S. P. Udeg V. B. Patil A. S. Jagtap
Department of Pharmaceutics, Saraswathi Vidya Bhawan’s College of Pharmacy, Sankara Nagar, Dombivli (East) - 421204, Dist.: Thane, State: Maharashtra, India.

Abstract

Quality by Design (QBD) is a systematic, statistical, structured, product development module commonly employed for the formulation development of drug products in pharmaceutical industry. The inherent purpose of QbD is to ensure consistent drug formulation quality, minimize risks, and enhance patient safety. This approach has now become almost indispensable to dosage form design and formulation optimization. The ability of QBD to delve into comprehensive understanding of drug product and its manufacturing process can make it a robust tool for the development of nanoformulations, whose scale-up is often limited by the numerous complexities involved in their development and optimization. Quality-by-Design can offer a structured framework facilitating swift optimization and simplistic scale-up. This review article offers technical insights into the significance of Quality-by-Design to ensure hassle-free development of innovative and effective nanotherapeutic products. Development of different types of nanoformulations aligned with the multi-step approach of QBD as outlined in the regulatory guidelines has been elaborated in the review. The article also briefly discusses Process Analytical Technology (PAT); another statistical-analytical based tool to facilitate better process control ensuring development of a robust product

Keywords

quality by design, critical factors, nanoformulation, nanotherapeutics, PAT, product design.

Introduction

Pharmaceutical product development not only implies development of a safe, efficacious, stable product that meets the needs of patients and delivers the expected performance; but also establishes a manufacturing process robust enough to consistently deliver a product of desired quality. Traditionally, a methodology known as ‘Quality by Testing’ was employed to ensure product quality. This approach heavily relied on intensive and repeated testing of raw materials, packaging materials, in-process quality control testing, and evaluation of final products. However, while this approach was singularly based on meeting product specifications, it seldom achieved complete product and process understanding and hence often failed to achieve its intended purpose¹. Furthermore, an insufficient understanding of the product led to difficulties in identifying the root cause of formulation failures. Similarly, a lack of process comprehension prevented manufacturers from altering operating parameters without submitting change controls. Hence, a more progressive, risk-based approach supported by experimental design i.e. Quality by Design (QbD) evolved in the 90s decade that assured a quality product while also offering plenty of drug product and production process understanding^2,3. The pharmaceutical arena has swiftly applied this strategy for the optimization and development of drug delivery systems, biotechnological products, and medical devices. While QbD has been successfully and consistently used in the preparation of conventional systems, the more complex web of design and development of nanoformulations can also be simplified by implementing QbD principles^4,5. This review discusses the principles of QbD and their applications in the optimization and development of nanotherapeutic dosage forms. The initial sections of the review delve into the origins and implications of QbD in the realm of pharmaceutical product development. This is followed by an exploration of the potential for applying QbD principles to the creation of nano-dosage forms. The latter part of the review demonstrates with suitable case-studies the applicability of QbD concepts to the preparation and optimization of a few prototype nanosystems, supported by examples from recent publications.

Quality by Design: Origins, Overview and Structure

Since its inception by Dr. Joseph Juran as a part of his quality trilogy in 1991, the concept of QbD has been extensively researched and adapted by numerous manufacturing units such as automobiles, electronics, and mass manufacturing general products⁶. Dr. Juran’s fundamental philosophy, which culminated into the conceptualization of QbD, was that ‘quality should be a deliberate choice and it should be incorporated within the product, rather than the notion of inspecting quality within the products’^7,8. The QbD principle has since been implemented in numerous manufacturing companies to not only elevate their product quality, but also to simultaneously reduce associated costs ^9,10. Quality by Design (QbD) has become a prerequisite for the pharmaceutical development industry, aiding in the creation of a diverse range of pharmaceutical, biotechnological products, and medical devices^11,12. Being healthcare products, the quality of these preparations is of utmost significance. QbD is a reliable method to achieve the desired quality, eliminating the need for extended product review processes, preventing delays in market introductions, avoiding inconsistencies in product quality, and enhancing patient compliance^13,14. QbD is integral to enhancing the optimization process for complex pharmaceuticals, as it systematically considers and evaluates various factors of variability that may influence the ultimate quality of the end product¹⁵. The development of QbD was backed by a number of quality guidelines published by the FDA and ICH; i.e. ICH Q8 (2004), ICH Q9 (2005) and ICH Q10 (2008) ^16,17. ICH Q8 guideline outlines principles for pharmaceutical development to ensure that products meet predetermined quality criteria. The ICH Q9 guideline on the other hand offers directions on quality risk management principles and strategies relevant to pharmaceutical development, manufacturing, and quality control processes^18,19. The ICH Q10 guideline provides a thorough framework for pharmaceutical quality systems, emphasizing a holistic approach to ensure consistent product quality and continual improvement throughout the product lifecycle²⁰. QbD is thus a new ray of hope for formulators to accelerate the formulation and optimization of drug products with lower instances of rejected drug products and lower development costs^21–23.

Nanoformulations: Types and Characteristics

Nanomedicine have achieved burgeoning importance in the area of drug delivery systems and clinical therapy; due to its ability to counter the demerits of conventional dosage forms and older drug delivery systems such as frequent dosage regimen, requirement of high doses, lack of target specificity, and side effects^24–27. Nanoformulations consist of drug molecules encapsulated within a lipidic or polymeric coat or drug dispersed within a matrix of the lipid or polymer with their size in the range of 10-1000 nm²⁸. Nanomedicines offer numerous advantages to drug release like increased surface area, higher solubility, targeted drug release, better permeation, higher bioavailability, and amenability to the attachment of numerous ligands thereby facilitating active targeting²⁹. A few commonly researched nanosystems include liposomes, niosomes, dendrimers, polymeric nanoparticles, lipidic nanoparticles, metallic nanoparticles, nanocrystals, etc³⁰. Some recently discovered nanosystems such as bilosomes, silica-based systems, fullerenes are also attracting interest from researchers. More than 1,20,000 scientific articles have been published in the last few years exploring applications of nanosystems to various conditions such as infectious diseases, tumor therapy, and CNS delivery. Several marketed products such as Emend (Aprepitant), TriCor (Fenofibrate), Myocet (Doxorubicin), Abraxane (Paclitaxel), Rapamune (Sirolimus), Ambisome (Amphotericin B), etc. developed as a nanosystem have been commercialized recently^31–33.

Scope of QbD in the Design and Commercialization of Nanotherapeutics

The preceding section markedly emphasizes the wide array of applications of nanoformulations to accentuate the therapeutic efficacy of drug molecules. However, the pace of commercialization of nanoformulations does not match the extravagant pace of research in this area³⁴. Some of the glaring challenges faced by formulators in this area include: i. nanoformulations involve multiple and varied categories of excipients and multivariate manufacturing processes that vary depending upon the type of nanoformulation; ii. exquisite understanding of the impact of such a diverse range of excipients and manufacturing steps is often unfathomable iii. lack of clarity in understanding the CQAs of such formulations; iv complexity in the manufacturing process and process design; v. low reproducibility and extensive variability in the manufacturing outcomes; vi difficulties in scale-up of the sophisticated manufacturing processes involved; vii concerns about the toxicity potential of the products and by-products; and viii. safety and therapeutic efficacy-related regulatory challenges^35,36. A lack of understanding of the impact of manufacturing steps and other formulation variables drastically hamper product quality. Negligence in monitoring product quality may not only impact the business prospects but also result in severe consequences to the patient culminating in legal consequences^37,38. An uncomplicated approach to sort these challenges would be an exhaustive understanding of the role and functionality of the formulation components and processing steps, and studying their influence individually and in combination with the critical product characteristics at the very beginning of the formulation development step itself. Contrary to the conventional approaches, the systematic QbD approach driven by analytical and risk-management methodologies links the desired attributes in a product with the formulation and manufacturing variables early in development³⁹. Risk assessment strategies, software-assisted statistics, and data analytics enable formulators to fully comprehend if and how these variables affect product characteristics. Critical product characteristics for nanoformulations include particle size and polydispersity index, zeta potential, encapsulation efficiency and drug loading, in vitro drug release, particle morphology, drug disposition profile, stability, and impurity profile to name a few⁴⁰. A robust control strategy in correspondence with PAT sensors and analyzers assures control of the highly significant variables, thereby ensuring consistency in product quality^41,42.

Development of Nanopharmaceuticals based on QbD

ICH Q8 (R2) guideline defines QbD as an approach to pharmaceutical development that emphasizes understanding and controlling the manufacturing process to ensure the final product meets predefined quality standards. QbD is executed via the following five steps:

Drafting a Target Product Profile (TPP)
Critical Quality Attributes (CQAs)
Identifying Critical Material Attributes (CMAs) and Critical Process Parameters (CPPs)
Risk Assessment linking the CMAs and CPPs to CQAs
Design Space and Control Strategy²⁹

The following sections detail the execution of these steps for the development of nanoformulations with emphasis on specific prototype nanosystems such as Liposomes, Niosomes, Polymeric and Lipidic Nanoparticles, Nanocrystals, and Micelles. Liposomes and Niosomes are self-assembling vesicles comprising of phospholipids and non-ionic surfactants respectively⁴³. These vesicles are extensively researched owing to their ability to encapsulate drugs with both hydrophilic and lipophilic functional groups and prolonged circulation time⁴⁴. Solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) represent the two generations of lipid-based nanosystems that have been extensively researched for achieving drug targeting. Lipid-based nanoformulations are appealing due to their biological similarities and a multitude of benefits. These include their capability to carry both water-loving (hydrophilic) and fat-loving (lipophilic) drugs, enhanced solubility of drugs and their permeation through biological barriers, their ability to respond to stimuli, as well as their compatibility and degradability in biological systems. These nanosystems also have the ability to protect their contents, which prolongs the drug’s half-life in the bloodstream and prevents absorption by immune cells, thereby reducing the risk of systemic toxicity^45–47. On similar lines, polymeric nanoparticles are composed of biodegradable polymers that maybe of natural or synthetic origin, used for active and passive targeting of numerous drug molecules⁴⁸. Nanocrystals are a relatively newer category of carrier-free nanosystems which constitute of only nanosized drug crystals stabilized with an optimum surfactant that confers stearic and thermodynamic stability to the system . The average particle size of nanocrystals ranges from 10 – 1000 nm^49,50. Micellar structures are generated when surfactants are dissolved in an aqueous liquid; mostly water and entrap hydrophobic drugs. Often a polymer may also be incorporated in the micelles, where the system is referred to as ‘polymeric micelles’⁵¹.

A. Drafting a Target Product Profile (TPP): A TPP implies the characteristic features desired in the intended product based on the available literature and past experiences. The TPP is a living depiction of the product, which needs to be regularly updated throughout the product’s lifecycle. The Quality Target Product Profile (QTPP) is created based on the TPP as a blueprint, and it outlines the goals for product development. A thorough QTPP should encompass the properties of the active pharmaceutical ingredient, its functional traits, and the clinical ramifications of the final product for the specified patient population⁵². Critical Quality Attributes (CQAs) are crucial properties of a pharmaceutical product that are identified from the QTPPs based on whether they influence the safety, effectiveness of the product, and degree of the harm it may cause to the patient.

Being similar in functional properties, the QTPPs and CQAs for most of the nanosystems overlap with each other as mentioned in Table I. It is important to note here that the enlisted QTPPs and CQAs are based only on general examples and may deviate depending upon the route of administration. In specific cases i.e. a nanosystem to be delivered by the ocular/ parenteral route will also include sterility, isotonicity, and viscosity as the QTPPs and CQAs. Further, nanosystems delivered by the inhalation route will include aerodynamic diameter as one of the QTPP and CQA^53,54 .

Table I: QTPPs and CQAs for Nanoformulations

Type of Nanosystem	QTPPs	CQAs
Liposomes	Indication, type of dosage form, route of administration, physical and chemical stability, liposomal size and polydispersity, liposomal surface charge and morphology, assay, encapsulation efficacy and drug release^39,55,56	Particle size, surface charge, morphology, physical stability, and drug release, entrapment efficiency^39,55,56
Niosomes	Same as that for liposomes⁵⁷
Polymeric Nanoparticles	Particle size and polydispersity, morphology, surface charge, drug loading capacity, and drug release kinetics. Stability, encapsulation efficiency, biocompatibility, long-term stability^58–60	Particle size, surface charge, morphology, physical stability, and drug release, entrapment efficiency^58–60
Lipidic Nanoparticles	Particle Size and polydispersity, morphology, surface charge, drug loading capacity, and drug release kinetics. Stability, encapsulation efficiency, biocompatibility, long-term stability^61–63	Particle size, surface charge, morphology, physical stability, and drug release, entrapment efficiency^61–63
Nanocrystals	Type of dosage form, route of administration, particle size and polydispersity, physical stability^31,64	Particle size and polydispersity, zeta potential, morphology, solid-state form, and physical stability^31,64
Polymeric Micelles	Indication, type of dosage form, patient population, Administration route, Site of activity, Dosage strength, Dosage form, Viscosity, Osmolality, pH, Mucoadhesive properties (depending upon indication and route of administration), Particle characteristics (Particle Size and polydispersity, morphology, surface charge, drug loading capacity, drug release kinetics. Stability, encapsulation efficiency), Safety, Solubility, Drug release, Drug permeability^65,66	Particle size, surface charge, morphology, physical stability, and drug release, entrapment efficiency^65,66

B. Identifying CMAs and CPPs: CMAs are those properties of the raw materials i.e. API and excipients that have a markedly significant effect on the drug product CQAs. A typical CMA maybe the concentration of an API or excipient or their bulk characteristics like particle size, polymorphic nature, presence of pseudopolymorphs, or molecular weight/ viscosity of the polymer. Likewise, CPPs constitute those parameters of the unit operations performed while preparing a formulation that affects the product quality. This may include parameters like the speed of mixing during a process, processing time and temperature, amount of pressure applied, pH, etc. Due to the varied number of excipients and numerous methods of preparation involved, the CMAs and CPPs for the development of nanosystems distinctly vary from each other⁶⁷. Table II depicts the CMAs for different nanoformulations, while Table III represents the CPPs involved depending upon the method of preparation.

Table II: CMAs to be Studied for Nanoformulations

Type of Nanosystem	CMAs
Liposomes	Phospholipid: cholesterol molar ratio, Drug:lipid ratio, Type and conc. of cryoprotectant, Phase transition temperature of the lipid³⁷
Niosomes	Drug concentration, Surfactant: cholesterol ratio, Type, and conc. of cryoprotectant⁶⁸
Polymeric Nanoparticles	Type and concentration of polymer, Drug: polymer ratio, Polymer: surfactant ratio, Solvent type, and composition, Polymer molecular weight, Salting out agent concentration^69,70
Lipidic Nanoparticles	Drug concentration, Type and concentration of the lipid, Drug: lipid concentration, Lipid: surfactant concentration^71,72
Nanocrystals	Type of surfactant, Drug loading, Drug: surfactant ratio³¹
Polymeric Micelles	Polymer properties such as molecular weight, log P, CMC value, HLB value, and temperature concentration solubility, LD₅₀ value; drug properties such as molecular weight, log P, melting point, solubility, LD₅₀ value, solvent properties including pH, ionic strength, volume, temperature; excipient properties including those of surfactants and cryoprotectants^65,66

Table III: CPPs to be Studied for the Various Preparation Methods of Nanoformulations

Type of Nanosystem	Method of Preparation	CPPs
Liposomes⁷³	Thin Film Hydration	Speed of rotation, Hydration time, Hydration temperature, pH of the buffer
	Emulsification solvent removal	Speed and time of agitation, Speed of mixing of phases, Temperature of mixing, Injection speed
	Sonication	Sonication time
	Extrusion	Extrusion temperature, No. of passes through the extrusion membrane
	Freeze Thawing	Freezing temperature
Niosomes⁷⁰	Same as that for liposomes
Polymeric Nanoparticles⁷⁴	Solvent evaporation	Mixing speed and time, evaporation rate, temperature and humidity, sonication time and amplitude, drying time, and temperature
	Emulsification/ solvent diffusion	Emulsification parameters (high-pressure homogenization, ultrasonication), solvent diffusion rate, temperature, and cooling rate
	Reverse Salting Out	Mixing time, and temperature, and drying time, and temperature
	Nanoprecipitation	Mixing speed, anti-solvent addition rate, temperature, pH, drying parameters (freeze-drying or spray-drying
Lipidic Nanoparticles⁷⁵	Homogenization	Homogenization speed, Homogenization pressure, No. of passes through the homogenizer
	Sonication	Sonication time, Amplitude of sonication
	Microfluidization	Microfluidization pressure, No. of passes through the microfluidizer
Nanocrystals³¹	Media Milling	Weight of milling media, Milling speed, Milling time
Polymeric Micelles⁷⁶	Direct dissolution	Solubilization of API, solubilization of the polymer, mixing time, rotation speed, sequence of addition of excipients
	Dialysis method	Diameter of the dialysis tube, flow rate and dialysis time, solubility, and contact volume.
	Oil-in-water emulsion method	Time and temperature of mixing and addition, Phase separation
	Freeze drying method	Freezing time and temperature; Time, pressure, and pressure of drying; Vapor pressure
	Thin film method/ vacuum evaporation	Temperature, speed, and duration of rotation, hydration time and properties of hydration media, starting and ending pressure, scale of decompression,

C. Risk Assessment linking the CMAs and CPPs to CQAs: Risk assessment as per either of the quality risk management (QRM) tools enlisted in the guideline ICH Q9 is performed to link the CMAs and CPPs to CQAs¹⁹. Risk estimation matrix (REM) and failure mode effect analysis (FMEA) are among the frequently utilized tools in Quality Risk Management (QRM)⁷⁷. On the other hand, Ishikawa fish-bone diagrams are typically chosen for establishing cause-and-effect connections. Risk assessment allows the identification of extremely crucial CMAs and CPPs which are to be further optimized using statistical-based experimental designs^78,79.

The example given below in Figure 1 relates to the development of PLGA nanoparticles by a modified nanoprecipitation method⁸⁰. The fishbone diagram illustrates how the variables of formulation and process impact the quality of nanoparticle products. The diagram enables the formulator to visualize and identify the CMAs and CPPs that may be deemed as high risk.

Figure 1: Ishikawa Fishbone Diagram for PLGA Nanoparticles Prepared by Modified Nanoprecipitation Method

Table IV represents a Risk Estimation Matrix to perform a risk assessment of a thermosensitive liposome formulation composed of a blend of phospholipids prepared by thin film hydration followed by vacuum drying. The CMAs/ CPPs are designated as High/ Medium/ Low risk depending upon the possibility of the risk occurring and the severity of the consequences. The parameters deemed high risk i.e. the amount of phospholipids incorporated in the formulation were further optimized using statistical-based optimization tools³⁹.

Table IV: Risk Estimation Matrix for Liposomal Formulation Prepared by Thin Film Hydration Method

Process

Composition

Dissolution of Lipids

Rotary Evaporation

Hydration Phase

Stabilization

Vacuum Drying

CPP/CMA

CQA

Amount of DPPC

Amount of DSCP

Amount of DSCPE-PEG30000

Solvent type

Solvent pH

Rotary evaporator (temperature, pressure)

Time

Temperature

Type of Cryoprotectant

Cryoprotectant concentration

Membrane pore size

Particle size

Particle size distribution

Zeta potential

Morphology

Phase transition temperature

In another example of risk assessment as presented in Table V, FMEA was used for the risk evaluation of venlaflaxine-loaded lipidic nanocarriers by high-pressure homogenization method⁸¹. Risk parameters were evaluated quantitatively, taking into account the severity of the risk, the likelihood of its occurrence, and the simplicity of its detection. Those risk parameters that had a higher ‘Risk Priority Number’ (RPN), which is determined as the product of the three scores, were subjected to further optimization and stringent control.

Table V: Failure Mode Effect Analysis of a Lipidic Nanocarrier Formulation by High Pressure Homogenization Method

Process parameter component	Failure mode	Failure effect	S	Potential cause of root of failure	O	Detectability method or control	D	*RPN (SOD)*
Mutual miscibility of the solid and liquid lipid	Inadequate proportion of solid to liquid lipid	Leaching out of drug, Phase separation	5	Inadequate quantity and formulation composition	2	Visual observation, DSC analysis	2	20
Drug: solid + lipid ratio (D:L)	Insufficient quantity of the lipids	Insufficient drug loading, poor stability	5	Insufficient amount of liquid lipid	5	?	2	50
Conc. of Surfactant	Inadequate amount	Improper size, PDI, and ?	5	Inadequate surfactant concentration	5	Particle size	2	50
Speed and time of high speed homogenizer	Incorrect homogenization process	Incorrect size, and entrapment, Phase separation	4	Incorrect homogenization process	4	Particle size	2	32

D. Statistical-Based Optimization: Following the risk assessment, the screened high-risk parameters are further optimized using software-assisted optimization designs, generically referred to as ‘Design of Experiments (DoE)’. DoE is an efficient tool that eliminates the wastage of time and money due to the conventional trial and error methodologies that involve exhaustive experimentation with no assurance of a quality product. Design of Experiments (DoE) provides an enhanced comprehension of the complex interplay of dependent and independent variables in formulation development⁸². These experimental designs fall into categories known as screening designs (that are used to classify the more important factors from the lesser ones when an extremely large number of factors are to be studied) and optimization designs (used to obtain the design space i.e. an optimized formulation). Screening designs commonly used include Plackett-Burman design (PBD), fractional factorial design (FFD), and Taguchi design (TD which can screen up to 31, 21, and 63 factors respectively. Optimization is performed using response surface designs (RSD) that are capable of quantifying the relationship between multiple variables and one or more responses^75,83. The more popularly used RSD for optimization is the central composite design (CCD) capable of studying 5 levels for each factor and the box-behnken design (BBD) which studies every factor at 3 levels and hence generates a lesser number of trials compared to the CCD. For screening variables, a two-level factorial or fractional factorial design can be employed, whereas an optimization process can utilize a high-resolution factorial design. Both full factorial and fractional design methods allow for the examination of numerous factors by assigning two levels to each - a higher and a lower one^84,85 . Other designs commonly employed include D-optimal design (D-OD) and mixture design (MD)^86,87. Table VI depicts numerous examples of nano-dosage forms with the experimental designs used for optimization.

Table VI: Optimization of Nanoformulations by Statistical Optimization Methods

Type of Nanoformulation	Drug Incorporated	Screening/ Optimization Design Used	References
Liposomes	Cefoperazone	BBD	⁸⁸
	Simvastatin	CCD	⁵⁶
	Prednisolone	D-OD	³⁷
	Pravastatin	D-OD	⁵⁵
	Azacitidine	BBD	⁷²
Niosomes	Casiopeina	Plackett-Burman, CCD	⁵²
	Lacidipine	BBD	⁴⁴
	Polymyxin	BBD	⁸⁹
	Levosulpiride	BBD	⁷⁰
	Dynorphin-B	BBD	⁵⁷
Polymeric Nanoparticles	Cinacalcet HCl	Taguchi, BBD	⁶⁹
	Flurbiprofen	Plackett-Burman, CCD	⁵⁸
	Pioglitazone	3-level full factorial design	⁵⁹
	Quercetin	FFD with Resolution IV; 6 variables studied at 2 levels	⁹⁰
	Berberine chloride	3²Full Factorial Design	⁶⁰
Lipidic Nanoparticles	Rivastigmine	BBD	⁹¹
	Fluoxetine HCl	3²Full Factorial Design	⁶²
	Phenobarbital	Fractional Factorial, CCD	⁹²
	Atazanavir	CCD	⁶³
	Resveratrol	Plackett-Burman, BBD	⁵⁴
Polymeric Micelles	Posaconazole	3²Full Factorial Design	⁹³
	Resveratrol	CCD	⁶⁵
	Galantamine	Taguchi, CCD	⁶⁶

Executing the batches recommended by the software in experiments, along with analyzing the results through various plots and charts generated by statistical analysis, enhances cognizance of both the product and the process. The visual representation illustrates the influence of individual factors.

E. Design Space and Control Strategy

ICH Q8 (R2) defines design space as the multifaceted combination and interplay of dependent variables i.e. formulation contents and process parameters that have been proven to assure quality¹⁸. Simply put, design space is the mutual effect of CQAs, CMAs, and CPPs within which the product falls within the desired specifications of the QTPP. The design space; a segment-criteria the experimental realm, ensures that the pharmaceutical dosage form meets all the criteria of safety, identity, purity, and strength. So long as alterations made to the manufacturing process fall within the designated design space, they are not deemed as changes. Any adjustments outside the design space necessitate a post-approval change process following regulatory approval⁹⁴. The control strategy is established as a mandatory regulatory requirement at the conclusion of the Quality by Design (QbD) based product development process. It serves as a set of critical factors influencing the variability in Critical Quality Attributes (CQAs) and process controls, ensuring ongoing consistency in the quality of the final product. Essential input parameters identified during the product development phase are documented as quality control strategies and continuously monitored throughout each batch operation^4,17. Narayan et al. formulated nanocrystals of aceclofenac by wet-milling approach by adapting QbD methodology. Statistical optimization was performed using BBD to optimize four CMAs/CPPs i.e. size of milling media (5-15 mm), concentration of stabilizer (0.25-0.75 %), drug loading (200-400 mg), and milling time (1-4 hours). The four factors were evaluated at three levels to optimize the particle size, PDI, and surface charge of the system. Aceclofenac nanocrystals manufactured by loading 200 mg of the drug in 0.25% of the stabilizer solution for 4 hours using zirconium beads of size 5mm yielded nanocrystals as per the desired QTPP. These optimized parameters represent the design space⁶⁴.

PAT for Optimization of Nanotherapeutics

The evolution of the Process Analytical Technology (PAT) concept was driven by the formulator's necessity to institute a science-based methodology for process control. This facilitates the dynamic management of the production and mitigates patient risks in the final dosage form. In direct terms, PAT allows control of CQAs by continuous monitoring of the CPPs and CMAs. In line with this, ICH Q8 emphasizes the adoption of PAT to uphold ensure processes operate within predefined design spaces⁹⁵. PAT involves a range of methods that encompass techniques such as collecting data, analyzing multiple variables, controlling processes, continuously improving, and utilizing existing scientific expertise. This is achieved through at-line, on-line, and in-line monitoring and control of the process ^62,61. Besides enhancing the quality and consistency of the product, PAT can contribute to heightened first-pass outputs, surplus management, lower batch failures, and lower production cycling time. PAT plays a pivotal role in pharmaceutical continuous manufacturing processes, where real-time process control is immensely crucial. Moreover, it accelerates the implementation of real-time release testing^96,97. Table VII presents a list of commonly used spectroscopic techniques as PAT processes with a brief note on their common applications in pharmaceutical process monitoring.

Table VII: List of PAT Techniques^98,99

PAT Process	Application
Near Infrared spectroscopy (NIR)	% Drug content or % Water content in powder mixing and drying processes
Tunable Diode Laser Spectroscopy (TDLS)	Headspace analysis, Lyophilisation
UV-Visible spectroscopy (UV Vis)	Used in hot melt extrusion for analysis of residence time. Blend uniformity, presence of amorphous vs. crystalline phases
Raman Spectroscopy	Determination of chemical structure, identification of polymorphs
Focused Beam Reflectance Measurements (FBRM)	Particle size, particle growth, size, and surface area
X Ray Fluorescence	Elemental analysis

A host of newer tools are used monitoring the CQAs involved in the manufacturing of nanoformulations i.e. particle size and polydispersity. Dynamic Light Scattering (DLS) microscopy based on Spectral Domain Optical Coherence Tomography (SDOCT) and Multi-angle static light scattering are used in-line monitoring of particle size. DLS which is used as the basis for analysis of particle size distribution in a flowing sample; can identify and analysing even dense concentrations without dilution. SDOCT on the other hand can collect data for nanoparticle samples at variable depths. Such tools broaden the horizons of PAT application to the process monitoring of nanoformulations by facilitating data regarding particle dimensions and flow characteristics. A groundbreaking non-destructive PAT tool, known as the 'NanoFlowSizer,' is currently in the developmental pipeline. Functioning on the axiom of Spectral-Domain Low-Coherence Interferometry, this tool offers continuous and in-line characterization of particle size and polydispersity for stationary as well flowing samples of nanoformulations. The device offers path length resolution along with dynamic light scattering data which also accounts for the influence of flow and/or multiple scattering¹⁰⁰. Another novel PAT based tool is the Focused beam reflectance measurement for surveilling the appearance, concentration, and structural configuration of particles based on the statistical distribution of chord lengths drawn across the particles within a given dataset or system. This chord length distribution help in characterizing the size and distribution of the particles within the sample. ParticleTrack and EasyViewer probes working on this principle efficiently distinguish between a stable nanodispersion and one containing agglomerates. They have also been applied for the detection of small deviations in the range of pore sizes within a material such as an adsorbent as it interacts with an antigenic adjuvant. The pore size distribution is crucial because it determines if the antigenic adjuvant can penetrate and retain within the adsorbent. To synopsize, PAT tools may exert a significant influence in on-line and/or at-line evaluation of nanoformulations as well, ensuring a quality product as per the principles of QbD. When complemented with digitized approaches and computerized methodologies, their impact would escalate exponentially leading to enhanced digital production and data analytics^101–103.

Closing Remarks:

The pivotal phase in the approval process of a drug product involves ensuring that the formulator adheres to the specifications set by the regulatory agency. An elaborate examination and comprehension of the CMC of the active ingredient, excipient profiles, and multivariate production steps is crucial for the development of a stable, and quality nanoformulation. The presence of numerous factors and variables affecting the final product further introduces complexities in achieving reproducible product quality. Thus, Quality by Design with its principle of achieving consistent product performance with end goals in mind can serve as a fine tool to accelerate the commercialization of nanoformulations while saving crucial resources. Though regulatory requirements do not mandate the adoption of such paradigms, QbD-based development is certainly encouraged as is evident by the recent news articles and white papers, which emphasize that regulatory agencies prefer applications with QbD-based development.

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