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  • QbD-Based RP-HPLC Method Development and Validation for Quantification of Antifungal Drugs in Pharmaceutical Dosage Forms

  • Photo Manisha Budhawale* Photo Disha Fartade

  • Shree Warana Vibhag Shikshan Mandal's Tatyasaheb Kore College of Pharmacy, Warananagar, Panhala, Kolhapur, 416 113, Maharashtra, India.

Abstract

Quality by Design (QbD) is a methodical, scientific method of drug development that guarantees the quality of the product by proactively understanding the processes, as opposed to testing them after the product is in-development. This review provides an extensive discussion of the use of QbD concepts in the design and validation of Reverse Phase High Performance Liquid Chromatography (RP-HPLC) quantitative analysis of antifungal drugs in pharmaceutical dosage forms. The article explains the basic principles of QbD such as Quality Target Method Profile (QTMP), Critical Method Attributes (CMAs), Critical Method Parameters (CMPs), and design of experiments (DoE) as the tools of optimization of methods. The review includes the chromatographic principles of RP-HPLC, the most important parameters of analysis, including mobile phase composition, column choice, flow rate, pH, and temperature, and their impact on the performance of methods. Well-known antifungal agents such as fluconazole, itraconazole, voriconazole, ketoconazole, clotrimazole, amphotericin B and terbinafine are discussed in terms of their physicochemical characteristics and analysis problems. Method validation Q2(R1) and Q14 principles are the rules of method validation (linearity, accuracy, precision, specificity, LOD, LOQ, robustness, and system suitability) and are critically examined. The tools like Ishikawa fishbone diagram and failure mode effects analysis (FMEA) are mentioned as the risk assessment tools. Additional method lifecycle management is achieved through the incorporation of Design Space and control strategy in QbD. This review is intended to become a comprehensive source of information to the analysts, formulation scientists and regulatory professionals involved in developing antifungal pharmaceuticals using quality-assured methods.

Keywords

QbD; RP-HPLC; Antifungal drugs; Method validation; Design of experiments; ICH Q2(R1); Design Space; QTMP; Risk assessment; Pharmaceutical analysis

Introduction

Fungal infection is a major health problem in the world especially in immunocompromised individuals, like HIV/AIDS patients, organ transplant patients, patients on chemotherapy, and cancer patients who are undergoing corticosteroid therapy. Fungal diseases have been recognized as a forgotten health issue by the public by World Health Organization (WHO), and accurate, quick, and repeatable analytical techniques are necessary to guarantee the quality, security, and effectiveness of antifungal drugs.

High Performance Liquid Chromatography (HPLC) remains the most widely employed separation technique in pharmaceutical analysis owing to its superior resolving power, sensitivity, versatility, and compatibility with a broad range of detection systems.[1] Among HPLC variants, Reverse Phase HPLC (RP-HPLC) is particularly favored for pharmaceutical quantification as it accommodates analytes of diverse polarities using water-miscible mobile phases and octadecylsilane (ODS/C18) stationary phases, offering excellent reproducibility and robustness.[2Historical development of methodologies has been based on one-factor-at-a-time (OFAT) experiments that are inherently inefficient, time-consuming, and cannot provide any information of the interaction of variables. Introduction Applied to analytical chemistry The International Conference on Harmonisation (ICH) Q8(R2) guideline reflected a paradigm shift to QbD, which is a science-based, risk-informed, approach to method development, which is more aligned with the newly introduced ICH Q14 guideline on analytical procedure development. (4)This review comprehensively discusses the integration of QbD tools—including the Quality Target Method Profile (QTMP), risk assessment using Ishikawa diagrams and Failure Mode and Effects Analysis (FMEA), Design of Experiments (DoE), Design Space, and control strategy—into RP-HPLC method development for antifungal drugs. The article also critically examines method validation per ICH Q2(R1) requirements and reviews key analytical literature pertaining to major antifungal agents including azoles, polyenes, allylamines, and echinocandins.

2. Overview of Antifungal Drugs: Classification and Analytical Significance

Antifungal agents can be classified in general according to their mode of action and chemical structure. (5) every class has its own physicochemical properties and these have a direct impact on the design of the analysis method especially the stationary phase, which is the composition of the mobile phase, the detection wavelength, and the sample preparation plan(6).

2.1 Classification of Antifungal Agents

The major classes of antifungal agents used clinically include:

  • Azoles (Imidazoles and Triazoles): Mechanism involves inhibition of ergosterol biosynthesis by blocking the cytochrome P450 enzyme lanosterol 14α-demethylase. Representatives include fluconazole, itraconazole, voriconazole, posaconazole, ketoconazole, clotrimazole, and miconazole.[7]
  • Polyenes: These work by binding to the ergosterol within the fungi cell membrane resulting in the formation of pores through which the cell contents leak out. The prototype is amphotericin B, nystatin and natamycin are also employed.[8]
  • Allylamines: Inhibit squalene epoxidase, an earlier step in ergosterol synthesis. Terbinafine is the primary representative[9].
  • Echinocandins: block the synthesis of 1,3- 8 -D-glucan interfering with the synthesis of fungal cell walls. Caspofungin, micafungin and anidulafungin are classified in this category. [10].
  • Pyrimidine analogues: Flucytosine acts as an antimetabolite. It is converted intracellularly to 5-fluorouracil, inhibiting nucleic acid synthesis[11].

2.2 Physicochemical Properties and Analytical Implications

An understanding of the physicochemical properties of antifungal drugs is essential for rational RP-HPLC method design. Table 1 summarizes key properties relevant to analytical method development.

Table 1: Physicochemical Properties of Major Antifungal Drugs and Analytical Implications

Drug

Class

Mol. Formula

MW (g/mol)

Key Properties

Fluconazole

Triazole

C13H12F2N6O

306.27

Polar/hydrophilic; well-soluble

Itraconazole

Triazole

C35H38Cl2N8O4

705.64

Lipophilic; BCS Class II

Voriconazole

Triazole

C16H14F3N5O

349.31

Moderate polarity; UV active

Ketoconazole

Imidazole

C26H28Cl2N4O4

531.43

Basic drug; pH-dependent solubility

Clotrimazole

Imidazole

C22H17ClN2

344.84

Highly lipophilic; topical use

Amphotericin B

Polyene

C47H73NO17

924.08

Amphipathic; self-aggregating

Terbinafine

Allylamine

C21H25N

291.43

Lipophilic; fluorescent active

Caspofungin

Echinocandin

C52H88N10O15

1093.23

Large MW; complex matrix challenges

Lipophilicity (log P) and pKa values are critical determinants of chromatographic retention and peak shape. Highly lipophilic drugs such as itraconazole (log P ≈ 5.66) require high organic modifier content in the mobile phase, whereas hydrophilic agents like fluconazole (log P ≈ 0.5) elute early with conventional C18 systems and may require modified stationary phases or ion-pairing strategies.[12] Basic drugs (ketoconazole, clotrimazole) necessitate careful pH adjustment to suppress ionization and avoid peak tailing.[13]

3. Quality by Design (QbD) Framework of Analytical Method Development.

QbD in ICH Q8(R2) refers to a pharmaceutical development strategy, which starts with established goals and focuses on understanding of products and processes and control of processes, founded on good science and quality risk management. The interconnected elements of the stepwise implementation of QbD to the methods of analysis include the following ones.

3.1 Quality Target Method Profile (QTMP)

Analogous to the Quality Target Product Profile (QTPP) in drug product development, the QTMP defines the desired performance characteristics of the analytical method. The QTMP serves as a reference document that guides all subsequent development decisions. Typical QTMP elements for an RP-HPLC assay of an antifungal drug include:

  • Method purpose: Quantitative determination of antifungal drug in pharmaceutical dosage form (tablets, capsules, cream, injection)
  • Analyte concentration range: Commensurate with therapeutic dose and formulation strength
  • Required precision: %RSD ≤ 2.0% for repeatability; ≤ 2.0% for intermediate precision[15]
  • Required accuracy: 98.0–102.0% recovery[16]
  • Specificity: No interference from excipients, degradation products, or related substances[17]
  • Detection: UV spectrophotometric; wavelength selected at analyte λmax
  • Run time: ≤ 15 minutes preferred for routine QC application
  • LOQ: Sufficient for quantification at 0.05–0.1% of nominal concentration[18]
  • Robustness: Capable of functioning reliably across minor variations in chromatographic conditions

3.2 Risk Assessment

Risk assessment is a cornerstone of QbD, enabling prioritization of experimental efforts toward factors most likely to affect method performance. Two primary tools are employed:

3.2.1 Ishikawa (Fishbone) Cause-and-Effect Diagram

The Ishikawa diagram visually maps potential sources of variability in analytical methods, organized under major cause categories such as: Method (mobile phase composition, pH, gradient), Machine (HPLC system, detector, column age), Material (reagent purity, reference standards, sample preparation), Measurement (integration parameters, wavelength accuracy), Environment (temperature, humidity), and Man (analyst training, sample handling). This tool facilitates comprehensive brainstorming of all potential risk factors before experimental design.

3.2.2 Failure Mode and Effects Analysis (FMEA)

 Each possible failure mode is assigned a Risk Priority Number (RPN) by FMEA, which is computed as: RPN = Severity (S) × Occurrence (O) × Detectability (D) with each parameter having a score of 1-10. Those factors that have large RPN values (usually above 100125) are termed Critical Method Parameters (CMPs) and the target of optimization using DoE. [20] FMEA for RP-HPLC typically identifies mobile phase composition, buffer pH, and column temperature as high-risk variables.[21]

3.3 Critical Method Attributes (CMAs) and Critical Method Parameters (CMPs)

CMAs are the measurable outputs of the analytical method whose values must lie within defined limits to ensure method suitability. Common CMAs include: resolution (Rs ≥ 2.0), asymmetry factor/tailing factor (0.8–1.5), theoretical plate count (N ≥ 2000), capacity factor (k' = 1–10), and retention time reproducibility.[22] CMPs are the input variables that exert a significant influence on CMAs. Table 2 presents a risk categorization of key method parameters.

Table 2: Risk Assessment of Critical Method Parameters (CMPs) in RP-HPLC

Method Parameter

Factor

Impact On

Risk Level

Mobile phase composition

Organic modifier ratio

Retention & selectivity

High

Mobile phase pH

Buffer pH

Ionization state of analyte

High

Flow rate

mL/min

Resolution & run time

Moderate

Column temperature

°C

Viscosity & peak shape

Moderate

Wavelength (λ)

nm

Sensitivity & specificity

High

Sample diluent

Solvent composition

Peak shape & recovery

Moderate

Injection volume

µL

Sensitivity & linearity

Low–Moderate

Gradient profile

Time–%B curve

Separation of peaks

High (gradient methods)

3.4 Design of Experiments (DoE)

DoE is a structured, multivariate experimental approach that enables simultaneous evaluation of multiple CMPs and their interactions with high statistical efficiency. Unlike OFAT approaches, DoE maps the entire design space with a minimum number of runs.

3.4.1 Screening Designs

Screening designs are employed first to identify which CMPs have statistically significant effects on CMAs. Commonly used screening designs in RP-HPLC method development include:

  • Plackett-Burman Design (PBD): Evaluates k factors in k+1 runs; highly efficient but cannot estimate interactions. Used to screen 5–11 variables simultaneously.[23]
  • Fractional Factorial Design (FFD): A fraction of a full factorial that allows estimation of main effects and selected interactions with fewer runs.[24]

3.4.2 Optimization Designs

After identifying significant CMPs through screening, optimization designs are used to model the relationship between CMPs and CMAs across the design space. Common designs include:

  • Central Composite Design (CCD): The most widely used RSM design, consisting of a 2k factorial, center points, and axial points. CCD is preferred for RP-HPLC optimization as it efficiently fits a full quadratic model.[25]
  • Box-Behnken Design (BBD): Requires three levels per factor; does not include extreme (corner) points, making it suitable when those conditions are impractical.[26]
  • D-Optimal Design: Computer-generated design customized to the experimental region; useful for irregular or constrained design spaces.[27]

Response Surface Methodology (RSM) is then applied to fit polynomial models (quadratic equations) correlating CMPs with CMAs, enabling prediction of the optimal operating conditions and characterization of the design space.[28]

3.5 Design Space

In ICH Q8(R2), Design Space (DS) refers to the combination and interaction of input variables (CMPs) and process parameters in a multidimensional manner which has been shown to give confidence in quality assurance. The DS of RP-HPLC methods is obtained as a result of the RSM models, which are the areas of CMP values that meet the entire specifications of CMA. [29]. The DS is typically visualized as an Overlay Plot (also called a Sweet Spot Plot) generated from superimposing contour plots of all CMAs. Working within the DS is not considered a regulatory change; this provides significant operational flexibility during the method lifecycle.[30]

3.6 Control Strategy and Method Lifecycle Management

The control strategy is the formulation of the planned controls based on the existing understanding of the product and process that promotes the performance of the processes and the quality of products. In the case of analytical methods, the control strategy defines: system suitability requirement at each usage, the normal operating range (NOR) in the DS of each CMP, frequency of standard and calibration inspection, management of reference standards, and requirements of method revalidation.[31]

Method lifecycle management, as emphasized in ICH Q14, envisions continual monitoring of method performance throughout its operational life through periodic trending of system suitability data, application of control charts, and structured change management within the established DS[32].

4. RP-HPLC: Principles and Method Development Considerations

4.1 Chromatographic Principles

RP-HPLC separates analytes based on differential hydrophobic interactions between the analyte and the nonpolar stationary phase [33](e.g., C18, C8, phenyl-hexyl) in the presence of a polar aqueous–organic mobile phase. Analytes with greater hydrophobicity interact more strongly with the stationary phase and elute later. The fundamental chromatographic parameters governing RP-HPLC performance include:

  • Retention Factor (k'): k' = (tR − tM)/tM; optimal range 1–10 ensures adequate retention without excessively long run times.[34]
  • Selectivity (α): α = k'2/k'1; reflects the relative retention of two adjacent peaks; α > 1.2 is generally required.[35]
  • Resolution (Rs): Rs = 2(tR2 − tR1)/(w1 + w2); Rs ≥ 1.5 ensures baseline resolution; Rs ≥ 2.0 provides a sufficient safety margin.[36]
  • Plate Count (N): Measure of column efficiency; N ≥ 2000 for most pharmaceutical methods.[37]
  • Asymmetry/Tailing Factor (As/Tf): As = (b/a) at 10% peak height; ideal range 0.8–1.5.[38]

4.2 Stationary Phase Selection

The stationary phase is a primary determinant of selectivity.[39] For antifungal drug analysis, the following considerations guide column selection:

  • C18 (ODS) columns are the most universal choice, offering high retention for moderately-to-highly lipophilic compounds such as itraconazole, ketoconazole, and terbinafine.[40]
  • C8 columns provide intermediate polarity and are preferred for drugs with intermediate log P values or when C18 retention is excessively strong.[41]
  • Phenyl-hexyl columns offer additional π-π selectivity, beneficial for aromatic antifungals such as fluconazole and voriconazole.[42]
  • Sub-2 µm fully porous or superficially porous (core-shell) particles enable UHPLC operation at higher linear velocities with reduced plate height, significantly improving resolution efficiency and reducing analysis time.[43]
  • Endcapping of silanol groups minimizes unwanted secondary interactions (peak tailing) especially important for basic antifungal drugs such as ketoconazole.[44]

4.3 Mobile Phase Optimization

The mobile phase composition is the most influential CMP in RP-HPLC method development. Key considerations include:

  • Organic modifier selection: Methanol and acetonitrile are the most common. Acetonitrile generally provides superior peak shapes and lower viscosity; methanol may enhance selectivity for certain antifungal pairs[45]. Tetrahydrofuran is occasionally used for highly hydrophobic analytes.[46]
  • Aqueous component: Phosphate buffers (pH 2.5–7.0), acetate buffers (pH 3.5–5.5), and formate buffers (pH 2.5–4.5) are frequently employed[47]. For mass spectrometry-compatible methods, ammonium formate or ammonium acetate buffers are preferred.[48]
  • pH optimization: Critical for ionizable antifungals (basic azoles, flucytosine). The mobile phase pH should ideally be adjusted to suppress ionization (pH = pKa ± 2), ensuring consistent retention and peak symmetry.[49]
  • Gradient vs. isocratic: Isocratic methods are preferred for single-analyte assays; gradient elution is essential for multi-analyte methods or samples with widely varying log P values (e.g., simultaneous determination of fluconazole and itraconazole).[50]

4.4 Detection Systems

UV-Visible detection at the analyte's λmax is the standard approach. Most antifungal drugs possess aromatic or conjugated systems with strong UV absorbance:

  • Azoles: 210–270 nm range; fluconazole at 210 nm, itraconazole at 254 nm, voriconazole at 255 nm.[51]
  • Amphotericin B: 405 nm (characteristic heptaene chromophore).[52]
  • Terbinafine: 282 nm.[53]
  • Photodiode Array (PDA) detection enables simultaneous monitoring across multiple wavelengths, providing spectral purity confirmation as an element of specificity.[54]
  • Fluorescence detection provides enhanced sensitivity for fluorescent antifungals (terbinafine: Ex 282/Em 360 nm).[55]
  • LC-MS/MS offers unparalleled sensitivity and specificity; particularly valuable for bioanalytical applications and trace-level impurity profiling.[56]

5. Method Validation per ICH Q2(R1)

Method validation is the establishment of a documented evidence that the method of analysis measures whatever it claims to measure. [57] ICH Q2(R1) (currently under revision as ICH Q2(R2) in conjunction with Q14) defines the following validation characteristics for assay methods:

Table 3: ICH Q2(R1) Validation Parameters, Requirements, and Acceptance Criteria for RP-HPLC Assay Methods

Validation Parameter

Requirement

Acceptance Criteria

Remarks

Linearity

5–6 conc. levels

R² ≥ 0.999

Least squares regression

Accuracy (Recovery)

3 levels × 3 reps

98–102%

Spiked placebo / standard addition

Repeatability (Intra-day)

n = 6

%RSD ≤ 2%

Same day, same analyst

Intermediate Precision (Inter-day)

n = 9 (3 days)

%RSD ≤ 2%

Different days/analysts

Specificity

Qualitative

No interference

Forced degradation studies

LOD

Calculated

S/N ≥ 3

Based on slope & SD

LOQ

Calculated

S/N ≥ 10

Based on slope & SD

Robustness

Deliberate variation

%RSD ≤ 2%

Youden / DoE approach

System Suitability

N, Rs, As, Tf

Per ICH/USP

Reference standard solution

5.1 Linearity

Linearity is assessed by preparing standard solutions at a minimum of five concentration levels spanning 50–150% of the test concentration.[58] The response (peak area or peak height) is plotted against concentration, and linear regression is performed. Correlation coefficient (R 2 ) must be 0.999 and above [59] y- intercept is tested on proportionality; a Student t test can be used. F-test of lack of fit and residual analysis also give further statistical rigor in QbD. [60]

5.2 Accuracy

Accuracy is typically determined by the standard addition method or by analysis of spiked placebo formulations at three concentration levels (80%, 100%, and 120% of the test concentration) in triplicate.[61] Percent recovery is calculated as (measured/theoretical) × 100. The acceptance criterion is typically 98.0–102.0% for assay methods, though dosage form-specific requirements may vary.[62]

5.3 Precision

Repeatability (intra-day precision) is evaluated by six replicate analyses of the test solution at 100% concentration on the same day. Intermediate precision is assessed across different analysts, days, and instruments. The Horwitz equation provides a context-dependent benchmark for acceptable %RSD based on analyte concentration. [63]For pharmaceutical assays, %RSD ≤ 2.0% is the standard requirement.[64]

5.4 Specificity

Specificity is demonstrated through forced degradation studies, where the drug substance is subjected to conditions of acid hydrolysis, alkaline hydrolysis, oxidative stress, photolysis, and thermal stress per ICH Q1A guidelines.[65]  The ability to resolve degradation products from the main peak, confirmed by PDA spectral purity assessment, establishes method specificity.[66] Placebo solutions should demonstrate no interference at the drug retention time.[67]

5.5 Limit of Detection and Limit of Quantification

Pharmaceutical assays LOD and LOQ are determined using the signal-noise method (S/N 3 and S/N 10, respectively) or by taking the standard deviation of the response and the slope of the calibration curve: LOD = 3.3{sigma)/S; LOQ = 10{sigma)/S where sigma is the standard deviation of the y-intercept of the regression line 69]

5.6 Robustness

Classical method Robustness tests method performance in the presence of known, small changes in method parameters [70]. The Youden ruggedness test (two level, seven factorial design based on a two level, seven-factorial test) and the PlackettBurman design are classical. In QbD, the DS itself offers a rationalized robustness evaluation - any operating point of the DS is a priori robust[71]. The common parameters included mobile phase composition (tolerance of ±5%), pH (tolerance of ±0.2 units), flow rate (tolerance of ±0.1 mL/min), column temperature (tolerance of ±5 o C), and wavelength (tolerance of 2nm). [72]

5.7 System Suitability

System suitability parameters are evaluated using replicate injections (n = 5–6) of the standard solution at 100% concentration. [73] Key parameters and typical acceptance criteria include: number of theoretical plates (N ≥ 2000), tailing factor (0.8–1.5), resolution between adjacent peaks (Rs ≥ 2.0), and %RSD of peak area (≤ 1.0%).[74] System suitability confirms acceptable chromatographic system performance prior to each analytical run.[75]

6. Review of Reported QbD-Based RP-HPLC Methods for Antifungal Drugs

6.1 Fluconazole

Fluconazole is a bis-triazole antifungal widely used for the treatment of Candida and Cryptococcus infections. Its high water solubility (log P ≈ 0.5) and low UV chromophore extinction coefficient at higher wavelengths necessitate detection at 210 nm, creating selectivity challenges due to mobile phase absorbance.[76]  Several QbD-based RP-HPLC methods have been reported utilizing C18 columns with acetonitrile:phosphate buffer (pH 4.5–6.0) mobile phases.[77] Box-Behnken and central composite designs have been employed to optimize mobile phase ratio, flow rate, and pH simultaneously, achieving resolution ≥ 2.5 and run times ≤ 10 minutes.[78] Methods validated per ICH Q2(R1) demonstrated linearity over 10–150 µg/mL, recovery of 99.2–101.3%, and %RSD ≤ 1.5%.[79]

6.2 Itraconazole

Itraconazole is a highly lipophilic triazole (log P ≈ 5.66) belonging to BCS Class II, presenting significant solubility challenges during sample preparation.[80] RP-HPLC methods typically employ high acetonitrile content (70–85%) mobile phases with C18 or C8 columns, detected at 254 nm. [81] QbD-based optimization using CCD and RSM has identified mobile phase acetonitrile content and flow rate as critical CMPs affecting Rs and tailing factor. [82] Gradient methods have been developed for simultaneous determination of itraconazole and its hydroxy metabolite.[83] Validated methods report linearity over 5–100 µg/mL, accuracy 98.5–102.0%, and intermediate precision %RSD ≤ 2.0%.

6.3 Voriconazole

Voriconazole, a second-generation triazole with activity against Aspergillus species, is analyzed at 255 nm. Its moderate lipophilicity (log P ≈ 1.8) and low pKa (1.8) make it amenable to analysis across a range of mobile phase pH values[83]. QbD studies utilizing Plackett-Burman screening followed by CCD optimization have identified acetonitrile:ammonium formate buffer ratio and pH as primary CMPs[84]. Methods achieving baseline resolution from related substances within 12 minutes have been reported, with LOQ as low as 0.05 µg/mL for impurity profiling applications.[85]

6.4 Ketoconazole

Ketoconazole is an imidazole antifungal (pKa ≈ 6.5) that exhibits pH-dependent retention due to its basic character. [86] RP-HPLC methods employ acidic mobile phases (pH 2.5–4.5) to ensure consistent ionization suppression and peak symmetry[87]. A Phenomenex Luna C18 column with acetonitrile:0.025M phosphate buffer (55:45, v/v, pH 3.5) at 1.0 mL/min and UV detection at 225 nm is a typical method configuration that has been shown by QbD-based robustness assessment using Youdens test to be acceptable to deliberate method variations in.[89]

6.5 Amphotericin B

Amphotericin B presents unique analytical challenges due to its macrolide polyene structure, propensity for self-aggregation in solution, photosensitivity, and complex spectral behavior (λmax at 405 nm).[90]  RP-HPLC methods typically employ C18 columns with dimethyl sulfoxide (DMSO)-containing mobile phases or ion-pairing agents to overcome aggregation.[91] A QbD approach for liposomal amphotericin B formulations employed a face-centered CCD to simultaneously optimize organic modifier content, pH, and flow rate, yielding a method with Rs ≥ 3.0 between amphotericin B and its major impurity (amphotericin A) within 15 minutes.[92]

6.6 Terbinafine

Terbinafine (log P ≈ 5.5) is a highly lipophilic allylamine antifungal detected at 282 nm. [93] Its fluorescent properties enable fluorescence detection (Ex/Em: 282/360 nm) for enhanced sensitivity in bioanalytical applications[94] Pharmaceutical QbD-RP-HPLC methods for terbinafine hydrochloride tablets have employed BBD with three CMPs (acetonitrile percentage, pH, and flow rate). The derived design space confirmed stable resolution performance across a defined CMP range, demonstrating operational flexibility without re-validation.[95]

7. Sample Preparation Strategies for Antifungal Dosage Forms

Appropriate sample preparation is essential for accurate quantification of antifungal drugs in complex pharmaceutical matrices. The choice of technique depends on the dosage form, matrix complexity, and analyte physicochemical properties.

7.1 Solid Oral Dosage Forms (Tablets and Capsules)

For tablets and capsules, the standard procedure involves weighing and pulverizing a specified number of dosage units, transferring the powder equivalent to one unit dose into a volumetric flask, adding the diluent (typically the mobile phase or a miscible solvent), sonication (15–30 minutes) to facilitate dissolution, dilution to volume, and filtration through a 0.45 µm PTFE membrane filter.[96] For lipophilic drugs (itraconazole, terbinafine), addition of methanol, DMSO, or dimethylformamide prior to aqueous dilution may be required to ensure complete dissolution.[97]

7.2 Semisolid and Topical Formulations

Antifungal creams, ointments, and gels require dissolution of the base and extraction of the drug prior to HPLC analysis. [98]A weighed quantity of the semisolid is dissolved in a mixture of methanol and mobile phase under ultrasonic agitation.[99] For oil-in-water cream formulations, addition of acetonitrile facilitates phase disruption and drug extraction. [100] The solution is centrifuged and the supernatant filtered before injection.

7.3 Parenteral and Ophthalmic Formulations

Aqueous parenteral solutions (fluconazole injection) require only appropriate dilution with the mobile phase and filtration. Lipid-based parenteral formulations (liposomal amphotericin B) require disruption of the liposomal structure using DMSO or methanol:acetonitrile before analysis. [101] Ophthalmic formulations may require protein precipitation or solid-phase extraction for matrix cleanup.[102]

8. Regulatory Considerations and Evolving ICH Guidelines

The regulatory landscape for analytical method development and validation is undergoing significant evolution, driven by the ICH Q14 guideline on analytical procedure development and the concurrent revision of ICH Q2 to Q2(R2). These guidelines formally introduce the QbD framework into regulatory expectations for analytical procedures, creating alignment between the manufacturing quality system (ICH Q8-Q12) and analytical sciences.

8.1 ICH Q14: Analytical Procedure Development

ICH Q14 provides recommendations on scientific approaches for the development of analytical procedures, including the application of QbD principles, QTMP definition, risk assessment, DoE, design space establishment, and lifecycle management. The guideline encourages submission of enhanced development information in registration dossiers, enabling a more flexible approach to post-approval changes within the established design space, thus reducing the regulatory burden associated with routine method optimization.

8.2 ICH Q2(R2): Analytical Procedure Validation

The revision of ICH Q2(R1) to Q2(R2), developed in parallel with Q14, updates validation requirements to align with modern analytical technologies (including multivariate calibration, NIR spectroscopy, and real-time release testing) and formally integrates lifecycle concepts. The key changes include: expanded guidance on validation of alternative techniques, clarification of the relationship between validation and risk management, and recognition of validation data generated during development (within the design space) as part of the validation package.

8.3 USP General Chapters

USPC General Chapters <1225> (Validation of Compendial Methods), <621> (Chromatography), <1058> (Analytical Instrument Qualification), and <1210> (Statistical Tools for Procedure Validation) provide complementary guidance, particularly for the US regulatory environment. The 2023 harmonization of USP <1225> with ICH Q2(R1) has further aligned US expectations with international standards.[103]

9. Challenges, Limitations, and Future Perspectives

9.1 Challenges in QbD Implementation

Despite its numerous advantages, the implementation of QbD in RP-HPLC method development presents several practical challenges:

  • Resource intensity: Comprehensive risk assessment and DoE require significantly more initial investment in experimental time, statistical expertise, and software resources compared to conventional OFAT approaches.[104]
  • Regulatory novelty: Although ICH Q14 provides guidance, regulatory agencies vary in their expectations for enhanced analytical submissions, creating uncertainty for innovator companies.[105]
  • Complexity of method transfer: While QbD methods are inherently more transferable due to their defined design spaces, communicating the DS concept to receiving laboratories requires training and robust documentation.
  • Generic formulation challenges: For complex antifungal products (liposomal formulations, nanoparticles), method development complexity is amplified by matrix effects and analyte instability.[106]

9.2 Emerging Technologies and Future Directions

Several emerging technologies are expected to further transform RP-HPLC method development for antifungal drugs:

  • UHPLC and superficially porous particles: Sub-2 µm and core-shell particle columns enable high-resolution separations at reduced analysis times, and their integration with QbD design spaces is increasingly reported.[107]
  • Two-dimensional LC (2D-LC): Orthogonal selectivity in the second dimension provides superior resolution for complex antifungal impurity profiles, with QbD optimization of both dimensions being an active research area.[108]
  • Machine learning and artificial intelligence: AI-assisted DoE design, model selection, and design space visualization tools are emerging, offering the potential to automate large portions of the QbD workflow.[109]
  • Green analytical chemistry: The application of the GAPI (Green Analytical Procedure Index) and AGREE (Analytical Greenness) metrics to evaluate and minimize the environmental impact of antifungal RP-HPLC methods is gaining attention, with mobile phase miniaturization and solvent substitution within QbD frameworks.[110]
  • Hyphenated techniques: LC-MS, LC-NMR, and LC-ICP-MS provide structural confirmation and elemental analysis capabilities beyond UV, expanding the analytical toolkit for antifungal characterization.[111]

CONCLUSION

The integration of Quality by Design principles into RP-HPLC method development for antifungal drugs represents a significant paradigm shift from empirical, trial-and-error approaches to a systematic, science-based methodology grounded in mechanistic understanding and statistical rigor. By defining the QTMP, conducting comprehensive risk assessment via Ishikawa diagrams and FMEA, employing DoE for efficient optimization, and establishing a formal Design Space, QbD-based methods deliver superior robustness, transferability, and regulatory acceptability compared to conventional approaches.

The diverse physicochemical properties of antifungal agents—spanning the hydrophilic fluconazole to the highly lipophilic itraconazole, and from the small azole scaffold to the macrolide amphotericin B—demand tailored method development strategies. QbD provides the framework to rationally navigate these challenges while ensuring consistent analytical performance across the method lifecycle.

The evolving regulatory landscape, particularly the imminent implementation of ICH Q14 and Q2(R2), is expected to accelerate adoption of QbD in analytical chemistry globally. Future integration of AI-assisted design tools, green chemistry metrics, and advanced separation technologies within QbD frameworks will further enhance the scientific rigor and environmental sustainability of antifungal pharmaceutical analysis.

This review serves as a comprehensive reference for pharmaceutical scientists, analytical chemists, and regulatory professionals engaged in the development of high-quality, scientifically justified analytical methods for antifungal pharmaceuticals.

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Manisha Budhawale
Corresponding author

Shree Warana Vibhag Shikshan Mandal's Tatyasaheb Kore College of Pharmacy, Warananagar, Panhala, Kolhapur, 416 113, Maharashtra, India.

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Disha Fartade
Co-author

Shree Warana Vibhag Shikshan Mandal's Tatyasaheb Kore College of Pharmacy, Warananagar, Panhala, Kolhapur, 416 113, Maharashtra, India.

Manisha Budhawale, Disha Fartade, QbD-Based RP-HPLC Method Development and Validation for Quantification of Antifungal Drugs in Pharmaceutical Dosage Forms, Int. J. of Pharm. Sci., 2026, Vol 4, Issue 4, 5039-5059. https://doi.org/10.5281/zenodo.19920668

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