{"id":50603,"date":"2023-09-30T10:44:03","date_gmt":"2023-09-30T10:44:03","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=50603"},"modified":"2023-10-07T11:21:28","modified_gmt":"2023-10-07T11:21:28","slug":"quality-by-design-approach-progress-in-pharmaceutical-method-development-and-validation","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol16no3\/quality-by-design-approach-progress-in-pharmaceutical-method-development-and-validation\/","title":{"rendered":"Quality by Design Approach: Progress in Pharmaceutical Method Development and Validation"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

Pharmaceutical industrial production is\none of the most carefully regulated and governed sectors by traditional\nregulatory agencies, as the quality of pharmaceuticals is directly tied to\npublic health. Consequently, it is necessary to control the quality of\nmedications. The pharmaceutical industry aims to provide products and\nproduction processes that reliably meet established requirements. The ability\nto meet the demands and expectations of the client in terms of service,\nproduct, and process is what is meant by quality [1]. All regulatory\norganizations for pharmaceutical products place a high value on quality.\nCustomer happiness equates to quality [2].<\/p>\n\n\n\n

To prove that new medications are safe and\neffective, the pharmaceutical industries put lot of efforts into developing,\nproducing, and bringing them to market [3]. They also work hard to comply with\nregulatory regulations. Every year, more medications are released onto the\nmarket. These medications could either be entirely new or structurally modified\nversions of already existing ones. From the moment a drug is released onto the\nmarket to the time, it is included in Pharmacopoeias; there is frequently a lag\nin time. This is brought on by potential risks associated with long-term and\nwidespread use of these medications, reports of novel toxicities (leading to\ntheir removal from the market), the emergence of patient resistance, and rival\ncompanies’ launch of superior medicines. Standards and analytical techniques\nfor certain medications may not be included in the pharmacopoeias under these\ncircumstances. Thus, the need to create newer analytical methods for such\nmedications arises [4]. A new strategy for medication development might boost\nproductivity, offer regulatory clearance and flexibility, and bring about\nsignificant economic gains throughout the product’s life cycle [3].<\/p>\n\n\n\n

Quality assurance and quality control of\npharmaceutical formulations and bulk pharmaceuticals rely heavily on\npharmaceutical analysis. The demand for novel analytical techniques in the\npharmaceutical industries has increased due to the pharmaceutical industries’\nrapid expansion and the manufacture of drugs in different parts of the world.\nThe biopharmaceutical and vaccine industries use analytical techniques for\nresearch and development as well as to manage the inputs and outputs of\nmanufacturing [5]. Establishing the identification, purity, physical\nproperties, and potency of pharmaceuticals, as well as the drug’s\nbioavailability and stability, is the goal of analytical technique development.\nThe improvement of analytical tools has led to recent developments in\nanalytical methodologies. The development of better analytical methods and\ntools has resulted in shorter analysis times, greater precision and accuracy,\nand lower analysis costs. As a result, the majority of pharmaceutical companies\nare spending enormous sums of money to create cutting-edge analytical\nlaboratories [4].<\/p>\n\n\n\n

Product development heavily relies on the\ndevelopment and validation of analytical methods. In addition to guaranteeing\nthat a drug’s quality is reached as per its intended therapeutic use, each\nstage of the product development life cycle includes a purity check that is\nperformed using a trustworthy analytical approach. The analytical method\nutilized for the production of commercial products must be quick, dependable,\nand accurate since the ultimate quality check results of the finished piece and\nother batch data influence when the product can be released to the market.\nAnalytical techniques frequently include estimating the target substance’s\nphysical, chemical, physicochemical, and\/or biological properties. Because they\nhave many advantages over other non-chromatographic methods, chromatographic\nanalytical techniques like High-performance liquid chromatography (HPLC), Gas\nchromatography (GC), and High-performance thin-layer chromatography (HPTLC),\nand supercritical fluid chromatography (SFC) are widely used. They need fewer\nsamples and are sturdy and adaptable. These methods reduce the likelihood of\nhuman error by using automation.<\/p>\n\n\n\n

The development of an analytical procedure\nthat precisely serves the intended function is the analytical chemist’s top\npriority. There are currently two methods used for developing analytical\nmethods in analytical chemistry. The former relies on trial and error and\nanalyses one factor at a time (OFAT), in which a single parameter is optimized\nfor the anticipated response while all other parameters are held constant. This\nprocedure consistently results in the method’s narrow robust behaviour for the\ninstrumental variables used throughout the method development phase. Because of\nthis, developing analytical methods with the OFAT approach involves a\nsignificant chance of method failure and constantly necessitates the development\nof alternate methods or revalidation protocols, which drives up the cost of the\ntechnique [1].<\/p>\n\n\n\n

Quality\nby design approach<\/strong><\/p>\n\n\n\n

Dr. Joseph M. Juran, a quality pioneer, is credited\nwith creating the idea of quality by design (QbD). According to Dr. Juran,\nquality should be built into a product from the start, and most quality crises\nand issues originate from poor product design [6]. The QBD is described as “A systematic\napproach to development that begins with established objectives and stresses\nproduct and process understanding and process control, based on strong science\nand quality risk management”[7] in accordance with the International Conference on Harmonization\nQuality guideline 8 (ICH Q8) criteria. To improve robust production\nprocesses, facilitate product quality, and create products in accordance with\n“six sigma,” the concept of “quality by design” (QbD) has\nbeen developed in the pharmaceutical business [8]. <\/p>\n\n\n\n

According to several experts, the\nprospects for applying QbD to analytical methods are comparable to those for\nproduction processes [9]. The\nAQbD (analytical QBD) assists in the development of a trustworthy and\nreasonably priced analytical method that is relevant across the lifecycle of\nthe product in order to enhance the regulatory leeway in the analytical\napproach. It refers to the ability to modify a method’s parameters anywhere in\nits design space, also known as the method operable design region (MODR) [10,11].<\/p>\n\n\n\n

Objectives\nof Quality by design<\/strong><\/p>\n\n\n\n

Pharmaceutical QbD is a methodical approach to development that emphasizes both process and product comprehension and control based on solid science and quality risk management [6,12]. The following objectives of pharmaceutical QbD may be present:<\/p>\n\n\n\n

Establishing clinical performance-based meaningful product quality specifications<\/p>\n\n\n\n

To increase process capability and minimize product variability and faults by improving product \/ process design, understanding, and control.<\/p>\n\n\n\n

To boost productivity in product development and production<\/p>\n\n\n\n

To improve post approval change management and root cause analysis<\/p>\n\n\n\n

Robustly developed products and processes are\nnecessary for achieving this goal. The identification and management of issues\naffecting the quality of the drug product can also be facilitated by increased\nproduct and process expertise. The procedure should be improved after receiving\nregulatory approval to decrease product variability, flaws, rejections, and\nrecalls.<\/p>\n\n\n\n

Product design and development are done systematically\nwith QbD. As a result, it improves formulation design, development speed, and\ncapabilities. Additionally, it moves resources from an upstream proactive mode\nto a downstream corrective way. It enhances the manufacturer’s capacity to\npinpoint the underlying reasons behind manufacturing failures. Therefore,\nimproving manufacturing and product development efficiency is pharmaceutical\nQbD’s third goal.<\/p>\n\n\n\n

Key\nElements of Quality by\ndesign<\/strong><\/p>\n\n\n\n

In a pharmaceutical QbD strategy for product\ninnovation, a claimant identifies qualities that are crucial to quality, changes\nthem into the critical quality attributes (CQAs) of the drug product, and identifies\nthe association between formulation\/manufacturing factors and CQAs to ensure\nthe patient will receive a medication product with these CQAs. The elements\nthat make up QbD are as follows:<\/p>\n\n\n\n

A quality target product profile (QTPP) that lists the drug product’s crucial quality attributes (CQAs) [6,13].<\/p>\n\n\n\n

To support drug labelling and drug development\nefforts, TPP describes the necessary profile or attributes of a drug product.\nTPP lists the intended application, target, pace of administration, and other\nkey product characteristics, together with quality designing for a drug product\n[14].<\/p>\n\n\n\n

The phrase “TQPP” for product\nquality may be a logical extension of “TPP.” The QTPP is a crucial\ndocument that enables the rationalization and evolution of the data that is not\ninheritable throughout the drug’s lifespan. To affirm the targeted quality, a\nprospective outline of the attributes of quality for a drug product that will\nbe reached while taking into account the safety and effectiveness of the\ntargeted product is provided. Indefinite-quantity, type, strength,\ninstrumentation closure system, identity, indefinite-quantity type, purity, and\nstability are all included in TQPP [15].<\/p>\n\n\n\n

The QTPP is a possible list of the\ncharacteristics of a drug product that should be met in order to ensure that it\nis of the desired quality, in addition to the safety and efficacy of the\nmedicinal product. The QTPP serves as the design framework for creating the product\n[6,12].<\/p>\n\n\n\n

To ensure the required product quality, a CQA should be within appropriate limits. Clinical safety and efficacy, manufacturing attribute, and parameter boundaries approach edge of failure are examples of quality attributes [16]. <\/p>\n\n\n\n

A drug product’s quality may or may not be essential. The severity of the patient’s harm determines how critical of an attribute it is. The criticality of an attribute is unaffected by the probability of occurrence, detectability, or controllability [6,12].<\/p>\n\n\n\n

Product design and understanding, including the identification of critical material attributes (CMAs)<\/p>\n\n\n\n

Clinical research confirms that the\nproduct’s design impacts whether it can satisfy patients’ needs. Stability\nstudies, which corroborate this, show that product design also affects whether\na product can retain its performance throughout its shelf life. This kind of\nproduct insight might have avoided some historical stability breakdowns.<\/p>\n\n\n\n

Creating a high-quality product that can deliver the necessary QTPP over the duration of its shelf life is the primary goal of product design and understanding. Designing a product can take many different directions because it is so open-ended. The following are crucial components of product design and comprehension:<\/p>\n\n\n\n

The drug substance’s physical, chemical, and biological characterization (s))<\/p>\n\n\n\n

Determining and selecting the excipient kind and grade, as well as being aware of the inherent excipient variability<\/p>\n\n\n\n

Connections between drugs and excipients<\/p>\n\n\n\n

Formulation optimization and CMA identification for both excipients and the medicinal substance<\/p>\n\n\n\n

CMAs differ from CQAs in that they are\nused for input materials such excipients and drug ingredients. CQAs, on the\nother hand, apply to output materials like completed drug products and product\nintermediates [17].<\/p>\n\n\n\n

3. Process design and\ncomprehension include identifying critical process parameters (CPPs) and having\na solid grasp of scale-up principles that connect CMAs and CPPs to CQAs.<\/p>\n\n\n\n

When all significant causes of variation are\nrecognized and explained, variability is controlled by the process, and product\nquality attributes can be predicted with reasonable accuracy, a process is\noften regarded as being well understood. The input operating parameters like\nmixing time, stirring speed, etc., of unit operation are called process\nparameters. A process parameter should be monitored or managed to guarantee\nthat the process yields the desired quality when variability affects a crucial\nquality feature.<\/s><\/p>\n\n\n\n

The formation of a control plan with three\ntiers of controls, as follows, is the result of the knowledge gathered from\nproperly structured development studies: <\/p>\n\n\n\n

The CQAs of the output materials are\ncontinuously monitored at Level 1 using automatic engineering control. The most\nadaptable level of control is this one. Pharmaceutical control at level 2\nincludes adjustable material attributes, process parameters within the defined\ndesign area, and fewer end-product tests. The typical level of control employed\nin the pharmaceutical sector is Level 3.This control method is based on\nrigorous end-product testing, closely controlled material properties, and\nprocess-related parameters. Any significant change in these necessitates\nregulatory control due to the incomplete categorization of the causes of\nvariability and the lack of knowledge regarding CMAs and CPPs’ effect on the\nCQAs for medicinal products. The formulation of acceptance criteria, the\nnecessity for further controls, and the debate about acceptable variability\nconsume a significant amount of industry and regulatory resources. A hybrid\nstrategy that combines levels 1 and 2 can be applied. A control strategy is\ndescribed by ICH Q8 (R2) as a planned set of controls that are drawn from\ncurrent product and process knowledge and ensure process efficiency and product\nquality.<\/p>\n\n\n\n

4. Process capability and ongoing development<\/p>\n\n\n\n

Process capacity measures the intrinsic variability of\na stable process under statistical control with regard to the established\nacceptance criteria. Through continuous improvement programmes that focus on\nremoving sources of significant variance from the process operation conditions\nand raw material quality, process capability can be used to measure process\nimprovement. When significant deviations are detected, remedial and preventive\nactions must be implemented; this can be done by regularly checking process\ndata for Cpk and other statistical process control metrics.<\/p>\n\n\n\n

Regulatory\nPerspective of QbD<\/strong><\/p>\n\n\n\n

Regulatory bodies now strongly emphasize QbD alone\nrather than only “Quality by Testing” or “Quality by Chance”.\nAnalytical procedures are a crucial component of the control plan concerning\nthe pharmaceutical quality system (ICH Q 10 recommendations). Analytical QbD\nwill be implemented in the manufacturing process to guarantee predetermined\nperformance and product quality as a control technique [7]. <\/p>\n\n\n\n

ICH\nguidelines and QBD<\/strong><\/p>\n\n\n\n

The ICH guidelines provide clear definitions\nof QbD principles: Q8 (R1): pharmaceutical development, Q9: quality risk\nmanagement, and Q10: pharmaceutical quality system.<\/p>\n\n\n\n

Stages\nin QBD vs AQBD<\/strong><\/p>\n\n\n\n

Analytical QbD implementation follows a similar method\nto that of product QbD. Initially, the target measurement for implementing AQbD\nis dependent on the product file in the form of the ATP (analytical target\nprofile) and CQA (ATP is the analogue of QTPP in product design).A comparison\nbetween QBD and AQBD is given in Table 1.<\/p>\n\n\n\n

Table I: Parallels between QbD for Process and Product, and AQbD<\/strong>.<\/p>\n\n\n\n\n\n\n\n\n\n
\n

QbD<\/strong><\/p>\n<\/td>\n

\n

AQbD<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Involves quality target product profile (QTPP)<\/p>\n<\/td>\n

\n

Involves analytical target profile (ATP)<\/p>\n<\/td>\n<\/tr>\n

\n

CQAs related to patients requirement or product development<\/p>\n<\/td>\n

\n

CQAs related to analytical method development<\/p>\n<\/td>\n<\/tr>\n

\n

Consider design space<\/p>\n<\/td>\n

\n

Consider method operable design region<\/p>\n

(MODR)<\/p>\n<\/td>\n<\/tr>\n

\n

Consist of process<\/p>\n

performance qualification (PPQ)<\/p>\n<\/td>\n

\n

Method validation<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Implementation\nAQBD<\/strong><\/p>\n\n\n\n

Analytical Target Profile (ATP)<\/strong><\/p>\n\n\n\n

 ATP specifies\nthe objective of the development of analytical methods. The definition of ATP,\nrecently offered by PhRMA and EFPIA, is as follows: “ATP is a declaration\nthat describes the method’s goal and is used to guide method selection, design,\nand development activities.” Following regulatory authorities’ approval of\nthe ATP statement, ATP is a crucial AQbD characteristic that enables increased\nimprovement of analytical techniques and their selection. While the examples above\nare mostly focused on directly measurably and changeable technique parameters,\nthe ATP should ideally cover all important aspects of method performance. [20,21].<\/p>\n\n\n\n

Analytical Method Performance Characteristics<\/strong><\/p>\n\n\n\n

These are specified to satisfy the requirements of the\nanalytical target profile. For chromatographic separations, USP and ICH have\npublished numerous validation factors and are regarded as method performance\ncharacteristics. Accuracy, specificity, linearity, precision, detection limit,\nand quantification limit are these parameters. Robustness and range.<\/p>\n\n\n\n

Selection of Analytical Techniques<\/strong><\/p>\n\n\n\n

The chosen analytical methodology must meet the\nvalidation requirements of ICH [8] as well as the required method performance\nspecified in Adenosine\ntriphosphate (ATP).<\/p>\n\n\n\n

Risk Assessment<\/strong><\/p>\n\n\n\n

The parameters that affect the ATP are identified by\nrisk assessment as the essential method variables. Following the identification\nof the technique, AQbD concentrates on developing the method and includes a\nthorough evaluation of the risks related to variability, such as analytical\ntechniques, instrument settings, measurement and methodology parameters, sample\nproperties, sample preparation, and ambient factors. The ICH Q9 guideline must\nbe followed in the risk assessment strategy: Risks to the quality over the\nproduct lifecycle are assessed, controlled, communicated, and reviewed using a\nsystematic approach [22].<\/p>\n\n\n\n

Design of Experiments<\/strong><\/p>\n\n\n\n

Method operable design region (MODR) can be formed in\nthe method development phase, which could serve as a source for reliable and\naffordable methods, in compliance with the requirement of ICH Q8\nrecommendations, regarding “design space” in product development. DoE\nimplementation during the method development phase necessitates a deep\ncomprehension of input variable selection and output reaction. The following are\nthe components of  DoE in the AQbD\ntechnique.<\/p>\n\n\n\n

Screening<\/strong><\/p>\n\n\n\n

Screening allows for the exclusion of qualitative\ninput characteristics. It lists the different critical method parameters (CMP)\nthat should be considered during the optimization studies. The CMP that has to\nbe regulated or subjected to DOE approaches in MODR optimization should be\nseparated as a result of the screening studies.<\/p>\n\n\n\n

Optimization<\/strong><\/p>\n\n\n\n

Quantitative metrics for critical methods in variables\n(i.e., CMP) can be introduced at this point either directly from risk\nassessment or through screening. It provides a basis for comprehending the\nscientific connection between the quantities of input variables (CMP) and\nresponses at the output, which will significantly impact the approach’s\neffectiveness and ATP.<\/p>\n\n\n\n

Selection of DOE Tools <\/strong><\/p>\n\n\n\n

Numerous methods can be employed throughout the\noptimization to derive a statistical correlation (model). The quantity of input\nvariables, acquaintance with regulated parameters, and scientific knowledge of\nthe relationship between outcome and variable (if any) must all be taken into\naccount while selecting the tool for DoE.<\/p>\n\n\n\n

Surface Response Plots<\/strong><\/p>\n\n\n\n

Counter (2D) or Surface response plot (3D) represents\nthe impact of input variables on output variables. Numbers like \u22121, 0, and +1,\nin both axes, represent the coded level of variables used in DOE.<\/p>\n\n\n\n

Model Validation<\/strong><\/p>\n\n\n\n

Before choosing from a contour or graph, the results\nof an actual experimental run must verify the expected values for the desired\ntechnique response. The model must then undergo regression analysis in order to\nbe statistically validated.<\/p>\n\n\n\n

Application\nof AQBD for method development and validation<\/strong><\/p>\n\n\n\n

Numerous papers using the DOE methodology for\ndeveloping analytical methods have already been published. For the testing of\nvarious bulk pharmaceuticals or active pharmaceutical ingredients, methods for\nHPLC, UPLC analysis, etc., are developed with high accuracy and precision.\nThese QBD-steered approaches have also been used to determine the number of\npharmaceuticals in various dosage forms, including tablets, capsules, and\nvesicular drug delivery systems, such as liposomes, cubosomes, exosomes,\nethosomes, etc. Most research teams today use pharmacological models to\nvalidate their in vivo results. As a result, the validity of in vitro results\nis uncertain in the absence of in vivo research. In these situations,\nestimating the drug concentration in plasma samples or any other bodily fluids\nis desirable. This problem requires a suitable analytical technique with a\nhigher sensitivity for detecting the minute-to-minute concentrations in fluids.\nThe AQBD methodology primarily produces the analytical method’s robust\nperformance.<\/p>\n\n\n\n

Testing the stability profile of\npharmacological compounds is an intriguing need of the analytical approach. The\nsafety and effectiveness of the therapeutic product are impacted by the\nchemical stability of pharmaceutical molecules, which is a significant problem.\nA drug product may encounter several situations during storage times that could\ncause the product to degrade over time. In these situations, it is preferred to\nuse analytical techniques to detect the degradation products. Understanding a\nmolecule’s stability facilitates the choice of an appropriate formulation and\npackaging and the provision of proper storage conditions and shelf life, all of\nwhich are necessary for regulatory paperwork. The market withdrawal of\nmedications is prevented by an accurate stability indication assay or\nidentification of the degradation products of drugs or formulations.<\/p>\n\n\n\n

Before submitting a registration dossier, stability tests of novel drug moieties are now required. Long-term (12 months) and accelerated stability investigations are also included in the stability studies (6 months). However, intermediate studies (6 months) can be carried out under more hospitable circumstances than those employed in rapid studies. Therefore, it would take considerably longer to analyze degradation products using separation, identification, and quantification methods. Forced degradation studies help produce degradants in a much shorter time than stability experiments, often a few weeks. To establish a stability-indicating approach that can later be used for examining samples produced by accelerated and long-term stability tests, forced degradation samples can be used [22].Some of the exciting Research works involving the use of the QBD approach for HPLC method development are summarized in Table 2.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

Drug<\/strong><\/td>\n		\n

Analytical technique<\/strong><\/p>\n<\/td>\n

\n

Mobile phase <\/strong><\/p>\n<\/td>\n

\n

Experimental design<\/strong><\/p>\n<\/td>\n

\n

Independent variables<\/strong><\/p>\n<\/td>\n

\n

Dependent variables<\/strong><\/p>\n<\/td>\n

\n

Reference<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Abiraterone acetate<\/p>\n<\/td>\n

\n

RP-HPLC Method<\/p>\n<\/td>\n

\n

CAN\/phosphate buffer (20:80 %v\/v)<\/p>\n<\/td>\n

\n

Box-Behnken<\/p>\n<\/td>\n

\n

Mobile phase composition, pH, and flow rate<\/p>\n<\/td>\n

\n

Retention time and peak area<\/p>\n<\/td>\n

\n[23]\n<\/td>\n<\/tr>\n
\n

Amiodarone hydrochloride<\/p>\n<\/td>\n

\n

HPLC<\/p>\n<\/td>\n

\n

ACN\/MeOH\/buffer ( 4.6\/3.4\/2)<\/p>\n<\/td>\n

\n

QBD approach<\/p>\n

 <\/p>\n<\/td>\n

\n

Mobile phase pH, % organic phase, and column temperature<\/p>\n<\/td>\n

\n

—<\/p>\n<\/td>\n

\n

[24]\n<\/td>\n<\/tr>\n

\n

Zolmitriptan, naratriptan, dihydroergotamine, ketotifen, and pizotifen<\/p>\n<\/td>\n

\n

RP-HPLC<\/p>\n<\/td>\n

\n

—<\/p>\n<\/td>\n

\n

24
<\/sup>Factorial design<\/p>\n<\/td>\n

\n

ACN% in the mobile phase, mobile-phase pH, nature of the buffer, and column temperature<\/p>\n<\/td>\n

\n

Resolution and Run time<\/p>\n<\/td>\n

\n

[25]\n<\/td>\n<\/tr>\n

\n

Artesunate and<\/p>\n

Amodiaquine impurities<\/p>\n<\/td>\n

\n

Green HPLC method<\/p>\n<\/td>\n

\n

Ethanol and 10mM acetic acid<\/p>\n<\/td>\n

\n

pH, temperature, and gradient slope<\/p>\n<\/td>\n

\n

3-level full factorial design<\/p>\n<\/td>\n

\n

—<\/p>\n<\/td>\n

\n

[26]\n<\/td>\n<\/tr>\n

\n

Atorvastatin<\/p>\n<\/td>\n

\n

RP-HPLC<\/p>\n<\/td>\n

\n

Acetonitrile: water (50: 50)<\/p>\n<\/td>\n

\n

Box\u2013Behnken statistical design<\/p>\n<\/td>\n

\n

Mobile phase (acetonitrile: water), flow rate (Rt), and UV wavelength<\/p>\n<\/td>\n

\n

 Area of the chromatogram (AUC), retention time (Rt, min), and tailingfactor (%)<\/p>\n<\/td>\n

\n

[27]\n<\/td>\n<\/tr>\n

\n

Ceftazidime<\/p>\n<\/td>\n

\n

RP-HPLC<\/p>\n<\/td>\n

\n

ACN to acetic acid (75:25)<\/p>\n<\/td>\n

\n

Face-centred cubic design<\/p>\n<\/td>\n

\n

Mobile phase ratio(ACN) and flow rate<\/p>\n<\/td>\n

\n

Peak area (PA), retention time (Rt), theoretical plate count (TPC),and tailing factor (TF)<\/p>\n<\/td>\n

\n

[28]\n<\/td>\n<\/tr>\n

\n

Ceftriaxone<\/p>\n

Sodium<\/p>\n<\/td>\n

\n

RP-HPLC<\/p>\n<\/td>\n

\n

Acetonitrile to water (0.01% triethylamine with pH 6.5) (70:30, v\/v),<\/p>\n<\/td>\n

\n

Central composite design<\/p>\n<\/td>\n

\n

Mobile phase composition and pH<\/p>\n<\/td>\n

\n

Retention time, theoretical plate, and peak asymmetry<\/p>\n<\/td>\n

\n

[29]\n<\/td>\n<\/tr>\n

\n

Daclatasvir<\/p>\n<\/td>\n

\n

HPLC, LC-MS\/MS, UPLC<\/p>\n<\/td>\n

\n

55% buffer and 45% ACN<\/p>\n<\/td>\n

\n

Central composite design<\/p>\n<\/td>\n

\n

pH and temperature<\/p>\n<\/td>\n

\n

Resolution of impurity (c-h) and drug<\/p>\n<\/td>\n

\n[30]\n<\/td>\n<\/tr>\n
\n

Efavirenz<\/p>\n<\/td>\n

\n

HPLC<\/p>\n<\/td>\n

\n

Methanol, 10 mM ammonium acetate buffer (70:30 v\/v),<\/p>\n<\/td>\n

\n

32<\/sup> full factorial design<\/p>\n<\/td>\n

\n

Flow rate  and pH of the buffer<\/p>\n<\/td>\n

\n

Retention time (y1) and peak area<\/p>\n<\/td>\n

\n

[31]\n<\/td>\n<\/tr>\n

\n

Eltrombopag olamineand its degradation products<\/p>\n<\/td>\n

\n

Stability-indicatingRP-HPLC\/ RP-UPLC<\/p>\n<\/td>\n

\n

0.1 % trifluoroacetic acid (TFA) and acetonitrile<\/p>\n<\/td>\n

\n

24 <\/sup>factorial design<\/p>\n<\/td>\n

\n

Column temperature, flow rate, the organic ratio in mobile phase, and the concentration of TFA<\/p>\n<\/td>\n

\n

Resolution<\/p>\n<\/td>\n

\n

[32]\n<\/td>\n<\/tr>\n

\n

Etofenamate<\/p>\n<\/td>\n

\n

RP-HPLC<\/p>\n<\/td>\n

\n

Methanol and 0.2% triethylamine in water at 85:15<\/p>\n<\/td>\n

\n

Central composite design<\/p>\n<\/td>\n

\n

pH of aqueous<\/p>\n

phase, percentage of<\/p>\n

the aqueous phase, and<\/p>\n

flow rate<\/p>\n<\/td>\n

\n

Retention time<\/p>\n<\/td>\n

\n

[33]\n<\/td>\n<\/tr>\n

\n

Ferulic acid<\/p>\n<\/td>\n

\n

RP-HPLC<\/p>\n<\/td>\n

\n

ACN: water (47:53 % v\/v<\/p>\n<\/td>\n

\n

27<\/sup> Taguchi design, face-centred composite design<\/p>\n<\/td>\n

\n

Mobile phase ratio (X1) and flow rate<\/p>\n<\/td>\n

\n

Peak area (PA), retention time (RT), tailing factor<\/p>\n<\/td>\n

\n

[34]\n<\/td>\n<\/tr>\n

\n

Ketoprofen<\/p>\n<\/td>\n

\n

Stability-indicating<\/p>\n

RP-HPLC<\/p>\n<\/td>\n

\n

Phosphate buffer\u2013methanol (50: 50v\/v)<\/p>\n<\/td>\n

\n

Central composite design<\/p>\n<\/td>\n

\n

Mobile phase ratio and pH of mobile phase<\/p>\n<\/td>\n

\n

Theoretical plates and peak tailing<\/p>\n<\/td>\n

\n

[35]\n<\/td>\n<\/tr>\n

\n

Nevirapine<\/p>\n<\/td>\n

\n

Reversed-phase HPLC bioanalytical method<\/p>\n<\/td>\n

\n

68:9:23% v\/v elution of methanol, acetonitrile, and water<\/p>\n<\/td>\n

\n

Box\u2013Behnken design<\/p>\n<\/td>\n

\n

Mobile phase ratio, pH, and flow rate<\/p>\n<\/td>\n

\n

Peak area, retention time, theoretical plates, and peak tailing<\/p>\n<\/td>\n

\n

[36]\n<\/td>\n<\/tr>\n

\n

Olmesartan medoxomil<\/p>\n<\/td>\n

\n

Stability-Indicating HPLC<\/p>\n<\/td>\n

\n

Acetonitrile and water<\/p>\n

(40 : 60 v\/v)<\/p>\n<\/td>\n

\n

Face-centred cubic design<\/p>\n<\/td>\n

\n

Mobile phase ratio and flow rate<\/p>\n<\/td>\n

\n

Peak area, retention time, theoretical plates and peak tailing<\/p>\n<\/td>\n

\n

[37]\n<\/td>\n<\/tr>\n

\n

Rufinamide<\/p>\n<\/td>\n

\n

RP \u2013 HPLC bioanalytical method<\/p>\n<\/td>\n

\n

Buffer :ACN at 84.7:15.3% v\/v<\/p>\n<\/td>\n

\n

Box Behnken design<\/p>\n<\/td>\n

\n

pH and proportion of the buffer and wavelength of detection<\/p>\n<\/td>\n

\n

Peak area and theoretical plate number<\/p>\n<\/td>\n

\n

[38]\n<\/td>\n<\/tr>\n

\n

Sorafenib tosylate<\/p>\n<\/td>\n

\n

RP-HPLC<\/p>\n<\/td>\n

\n

ACN and water<\/p>\n

65:35 v\/v<\/p>\n<\/td>\n

\n

Taguchi orthogonal arrays and Face centred cubic design<\/p>\n<\/td>\n

\n

Mobile phase ratio and flow rate<\/p>\n<\/td>\n

\n

Peak area, theoretical plates, retention time(Rt) and peak tailing<\/p>\n<\/td>\n

\n

[39]\n<\/td>\n<\/tr>\n

\n

Tamoxifen<\/p>\n

Citrate<\/p>\n<\/td>\n

\n

—<\/p>\n<\/td>\n

\n

ACN and phosphate buffer (pH 3.5) 52:48 v\/v<\/p>\n<\/td>\n

\n

Taguchi design and Box-Behnken design<\/p>\n<\/td>\n

\n

Mobile phase ratio,<\/p>\n

Buffer pH and oven temp<\/p>\n<\/td>\n

\n

Peak area, retention time, theoretical plates, and peak tailing<\/p>\n<\/td>\n

\n

[40]\n<\/td>\n<\/tr>\n

\n

Telmisartan and Hydrochlorothiazide<\/p>\n<\/td>\n

\n

RP-HPLC<\/p>\n<\/td>\n

\n

Mobile phase-A 0.02 M potassium<\/p>\n

dihydrogen phosphate (pH of 3.5) and mobile phase-B- a mixture of Milli-Q water and<\/p>\n

acetonitrile (100: 900 v\/v) respectively<\/p>\n<\/td>\n

\n

Three-Level Factorial design<\/p>\n<\/td>\n

\n

Flow rate, column<\/p>\n

Temperature and buffer pH<\/p>\n<\/td>\n

\n

Resolution between drug and impurity<\/p>\n<\/td>\n

\n

[41]\n<\/td>\n<\/tr>\n

\n

Fusidic acid (FA)<\/p>\n<\/td>\n

\n

RP-HPLC<\/p>\n<\/td>\n

\n

Methanol:<\/p>\n

acetonitrile (5: 95, v\/v)<\/p>\n<\/td>\n

\n

Taguchi designand Central Composite Design<\/p>\n<\/td>\n

\n

The ratio of solvents %w\/w) and (Water %w\/w)<\/p>\n<\/td>\n

\n

Theoretical<\/p>\n

Plates, assay<\/p>\n

(%) and tailing<\/p>\n

factor<\/p>\n<\/td>\n

\n

[42]\n<\/td>\n<\/tr>\n

\n

Valsartan<\/p>\n<\/td>\n

\n

RP-HPLC<\/p>\n<\/td>\n

\n

Methanol, ACN, water, and buffers<\/p>\n<\/td>\n

\n

Box\u2013Behnken design<\/p>\n<\/td>\n

\n

Mobile phase pH, flow rate, and % organic modifier<\/p>\n<\/td>\n

\n

Peak area, retention time, theoretical plate count, and peak tailing (PT)<\/p>\n<\/td>\n

\n

[43]\n<\/td>\n<\/tr>\n

\n

15 fixed-dose combinations (FDCs) of anti-hypertensive drugs<\/p>\n<\/td>\n

\n

RP-HPLC<\/p>\n<\/td>\n

\n

ACN-water (pH 6.2; 42:58 %, v\/v).<\/p>\n<\/td>\n

\n

Box-Behnken design<\/p>\n<\/td>\n

\n

—<\/p>\n<\/td>\n

\n

—<\/p>\n<\/td>\n

\n

[44]\n<\/td>\n<\/tr>\n

\n

Rotigotine<\/p>\n<\/td>\n

\n

RP-HPLC<\/p>\n<\/td>\n

\n

ACN proportion: 54% v\/v<\/p>\n<\/td>\n

\n

Plackett-Burman design and Box-Behnken design<\/p>\n<\/td>\n

\n

ACN proportion, pH of the buffer, and flow rate<\/p>\n<\/td>\n

\n

The number of theoretical plates and retention time<\/p>\n<\/td>\n

\n

[45]\n<\/td>\n<\/tr>\n

\n

Efavirenz<\/p>\n<\/td>\n

\n

RP-HPLC<\/p>\n<\/td>\n

\n

mobile phase: CAN 51.17%v\/v<\/p>\n<\/td>\n

\n

Plackett-Burman design and Box-Behnken design<\/p>\n<\/td>\n

\n

ACN proportion, pH of the phosphate buffer, and mobile phase flow rate<\/p>\n<\/td>\n

\n

Retention time and number of theoretical plates<\/p>\n<\/td>\n

\n

[46]\n<\/td>\n<\/tr>\n

\n

Bosutinib<\/p>\n<\/td>\n

\n

Stability-Indicating RP-HPLC Method<\/p>\n<\/td>\n

\n

ACN-1.0% triethylamine (v\/v) in water<\/p>\n<\/td>\n

\n

Central composite design<\/p>\n<\/td>\n

\n

Critical method attributes<\/p>\n<\/td>\n

\n

Critical analytical attributes<\/p>\n<\/td>\n

\n

[47]\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

ACN:\nAcetonitrile
\n<\/strong><\/p>\n\n\n\n

Conclusion<\/strong><\/p>\n\n\n\n

In\nthe pharmaceutical sector, AQbD\nis crucial for assuring method consistency and non-variability in outcomes. In\norder to improve quality, scientists can quickly identify the threads. The\nperformance of analytical methods for currently available pharmaceuticals must\nbe periodically reviewed to rectify any gaps and risk factors utilizing AQbD.<\/p>\n\n\n\n

Acknowledgement<\/strong><\/p>\n\n\n\n

The authors are thankful to the management of Vels  Institute of Science, Technology and Advanced Studies\n(VISTAS), Pallavaram, Chennai-600 117, Tamil Nadu, India for providing Digital\nLibrary facility for completing this work successfully.<\/p>\n\n\n\n

Conflict of Interest <\/strong><\/p>\n\n\n\n

The authors declare that there are no Conflicts of\nInterests among us.<\/p>\n\n\n\n

 Funding Support <\/strong><\/p>\n\n\n\n

There are no funding Sources<\/p>\n\n\n\n

References<\/strong><\/p>\n\n\n\n

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