Green analytical evaluation of anticancer drug analysis: A multi-tool assessment of HPLC and LC–MS methods

Abstract

The escalating global cancer burden necessitates sustainable analytical methods for therapeutic drug monitoring of antineoplastic agents, yet conventional HPLC and LC–MS protocols predominantly employ hazardous solvents. This study presents comparative greenness evaluation of chromatographic methods specifically for anticancer drugs, applying eight complementary GAC tools AGREE, MoGAPI, AGREEprep, Analytical Eco-Scale, BAGI, CACI, CaFRI, and AGSA to 20 published analytical methods (16 HPLC and 4 LC–MS). Z-score standardization enabled composite rankings, with Pearson correlation analysis confirming strong inter-tool agreement (AGREE–BAGI: r = 0.858, p < 0.001 in HPLC cohort). Results showed marked variability in sustainability, with top performing methods achieving high scores (HPLC Method 1: AGREE 0.57, AES 86, composite Z-score 1.541; LC–MS Method 3: AGREE 0.61, composite Z-score 0.983). Optimized HPLC methods outperformed LC–MS counterparts in reagent safety, waste reduction, and energy efficiency. Critically, no method incorporated genuinely green solvents. The multi-metric framework provides quantitative benchmarks for method optimization via solvent substitution, miniaturization, and automation. By identifying practical and eco-friendly analytical routes for anticancer drug analysis, this study promotes greener pharmaceutical research and advocates integration of greenness metrics into regulatory guidelines.

Introduction

Cancer ranks as the second leading cause of death globally, after cardiovascular disease, accounting for millions of deaths annually. Global cancer incidence has risen progressively, from 12.7 million new cases in 2008 to 14.1 million in 2012 and 18.1 million new cases with 9.6 million deaths in 2018^1,2. By 2022, an estimated 20 million new cases and 9.7 million deaths occurred. Approximately 53.5 million individuals were alive five years post-diagnosis, indicative of enhanced survival due to therapeutic progress. The lifetime cancer development risk is approximately 1 in 5 individuals, with mortality risks of roughly 1 in 9 men and 1 in 12 women. Projections forecast over 35 million new cases annually by 2050, constituting a 77% increase from 2022, thereby emphasizing the necessity for improved global prevention, early detection, and treatment³. World Health Organization (WHO) reports indicate that tens of millions of individuals worldwide die annually from non-communicable diseases, including cancer and cardiovascular diseases⁴.

Cancer treatment modalities span localized approaches, such as surgery and radiation therapy targeting specific tumours, and systemic therapies, including chemotherapy, immunotherapy, and targeted therapy, exerting body wide effects. Anticancer agents encompass diverse classes natural products, alkylating agents, antimetabolites, and hormonal therapies each with unique mechanisms of action. Novel compounds in these categories are advancing through late-stage clinical development to meet demands for superior efficacy and outcomes. Sensitive analytical techniques, including electrophoresis, chromatography, spectroscopy, and electrochemical methods, enable precise quantification of these agents^5–7. Patients undergoing cancer treatment are at substantial risk of drug–drug interactions (DDIs), potentially compromising efficacy or increasing adverse effects³.

Precise analytical methods for therapeutic drug monitoring (TDM) of anticancer agents are essential for optimizing clinical outcomes. Pharmacokinetic (PK) studies enable determination of appropriate dosing regimens in oncology patients, particularly given the narrow therapeutic indices, high interpatient variability, and substantial toxicity of these drugs. Accurate TDM and PK data thus support safe, effective, and personalized treatment, enhancing efficacy while minimizing adverse effects^8,9.

Global pro-ecological policies integrate scientific research, ecological education, economic instruments (e.g., revenues and subsidies), environmental monitoring, and legislation to regulate resources and advance sustainable development. Sustainable eco-development was first proposed in 1987 by the World Commission on Environment and Development¹⁰, emphasizing integration of economic growth, environmental protection, and public health. This framework is particularly relevant to chemistry via green chemistry principles¹¹, which emerged after the US Pollution Prevention Act of 1990¹². Paul Anastas coined the term “green chemistry” in 1991 through a US Environmental Protection Agency initiative¹³, leading to the 1993 establishment of the US Green Chemistry Program to promote international collaboration and education in sustainable practices¹⁴. Green analytical metrics enable analytical chemists to evaluate, compare, and enhance the environmental impact of analytical methods, advancing Green Analytical Chemistry (GAC) objectives of waste minimization, resource conservation, and environmentally benign procedures¹⁵.

Green chemistry is essential for mitigating the environmental, health, and economic impacts of conventional processes, which produce hazardous waste, emit pollutants contributing to climate change, biodiversity loss, and ecosystem degradation, increase risks of cancer, respiratory, and neurological disorders, and rely on non-renewable resources with low atom economy. Green chemistry emphasizes renewable feedstocks, energy efficient processes, and waste reduction for sustainability. Green solvents exhibit low health risks, high safety, and minimal environmental impact throughout their life cycle^16,17.

Economically, green chemistry decreases costs through reduced waste management and energy consumption, while fostering markets for sustainable products amid increasing consumer demand and regulations such as REACH (EU) and TSCA (USA)¹⁸. Ethically, it represents scientific responsibility for safer designs, supporting United Nations Sustainable Development Goals related to health, water, responsible consumption, and climate action¹⁹. Trends in HPLC and LC–MS method development prioritize greener solvents (e.g., water, ethanol), miniaturization, and UHPLC to minimize consumption, with MS compatible alternatives²⁰. GAC principles advocate bio-based solvents to reduce chemical hazards²¹, including promising options such as supercritical CO₂, ionic liquids, and deep eutectic solvents, not withstanding challenges in compatibility, validation, and substitution of acetonitrile; ICH guidelines, economic advantages (e.g., waste reduction, recycling), automation, and column improvements further promote adoption^18,22.

The absence of standardized greenness criteria impedes GAC implementation. Ideal strategies eliminate or substitute solvents with greener alternatives demonstrating low toxicity, biodegradability, reusability, sustainability, and analytical performance. Tools including GAPI, Analytical GREEnness Metric (AGREE), AGREEprep, Analytical Eco-Scale Assessment Tool (AES), Blue Analytical Greenness Index (BAGI), Click Analytical Chemistry Index (CACI), Carbon footprint reduction index (CaFRI) and Analytical Green Star Area (AGSA) facilitate objective assessments of impacts (e.g., solvent use, waste, energy, safety), aiding identification of unsustainable elements critical in pharmaceutical and oncology research. Incorporation of greenness criteria into regulations and tool-based evaluations will promote sustainable analytical transitions. This study presents a novel framework for evaluating analytical techniques in antineoplastic agent assessment, integrating sustainability with conventional validation parameters.

Materials and methods

Literature search

A targeted literature search across PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and SciFinder retrieved 112 records. After removing 44 duplicates, 68 records underwent title and abstract screening per predefined inclusion criteria, identifying 44 potentially relevant reports for full text review. Full text evaluation excluded 24 reports due to non-HPLC/LC–MS techniques (n = 7), insufficient methodological details for greenness assessment (n = 8), application to non-anticancer drugs or irrelevant matrices (n = 6), and non-quantitative or preliminary studies (n = 3). Ultimately, 20 eligible methods (16 HPLC and 4 LC–MS) were included for greenness evaluation using AGREE, GAPI, AGREEprep, Analytical Eco-Scale, BAGI, CACI, CaFIR and AGSA tools (Fig. 1).

Fig. 1.

PRISMA flow diagram showing article screening and study selection process.

Quantification techniques reported for anti-cancer drugs by HPLC and LC–MS

The primary objective of developing the analytical method is to monitor impurities with potential carcinogenic risk to patients²³. High-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC–MS) are widely utilized chromatographic techniques owing to their superior separation efficiency, rendering them suitable for the analysis of anticancer drugs²⁴. In pharmaceutical research, these methods are frequently employed, particularly for quantifying pharmaceuticals in biological matrices^25,26. Typical mobile phases consist of organic solvents such as acetonitrile (ACN) or methanol (MeOH), combined with volatile buffers or acids (e.g., acetic acid, ammonium acetate, or ammonium formate), under gradient or isocratic elution conditions.

Quantification technique by HPLC

Barrawaz Aateka Yahya et al. (2021) reported a Quality-by-Design (QbD)-based RP-HPLC method for abiraterone acetate using a C18 column and acetonitrile–ammonium acetate (69:31, v/v) mobile phase, demonstrating linearity of 10–180 μg/mL (R² = 0.997), precision, and applicability to bulk drug, tablets, and nano formulations²⁷. Sarwar Beg et al. (2021) employed a QbD approach with a Hypersil BDS C18 column and acetonitrile–water (15:85, v/v), achieving recovery of 99.8–100.2% and identification of degradation products under stress conditions²⁸. Ravi Sankar et al. (2021) described a method utilizing methanol & acetonitrile (50:50, v/v), with linearity of 2–10 μg/mL and recovery of 99.78%²⁹. Tiphaine Belleville et al. (2015) developed a sensitive HPLC fluorescence method for abiraterone using acetonitrile–glycine buffer and detection at λex/em = 255/373 nm³⁰. Vijay Kumar Sripuram et al. (2010) validated an RP-HPLC method for doxorubicin with water & acetonitrile (75:25, v/v), 99.8% recovery, and linearity over 0.2–10 µg/mL³¹. Vanesa Escudero-Ortiz et al. (2013) quantified lapatinib via acetonitrile ammonium acetate (53:47, v/v) with recovery > 86.7% and good precision³². Mistiran et al. (2010) reported a method for cytarabine (Ara-C) and doxorubicin employing acetonitrile & ammonium hydrogen phosphate (45:55, v/v) with high precision and accuracy³³. Ebrahim Saadat et al. (2015) validated an HPLC method for paclitaxel and lapatinib in micelles using acetonitrile & water (70:30, v/v), linearity of 5–80 μg/mL, and RSD < 5.83%³⁴. Laura Zufía López et al. (2006) established an HPLC–UV method for docetaxel and paclitaxel with recovery of 88–91% and LLOQ of 0.015 mg/L³⁵. Xinran Chen et al. (2022) developed a UPLC-MS/MS method for simultaneous quantification of five anticancer drugs with high accuracy and stability³⁶. Göknil Pelin Coşkun et al. (2022) reported an HPLC method for imatinib using a C18 column and acetonitrile & triethylamine/phosphate buffer (pH 7.04; 50:50, v/v) with linearity of 10–90 μg/mL (R² = 0.999)³⁷. Silvia De Francia et al. (2022) introduced a sensitive HPLC method for imatinib, dasatinib, and nilotinib employing acetonitrile & water with 0.05% formic acid³⁸. MD Nazmus Sakib et al. (2023) described a method for rifampicin using acetate buffer (pH 4.5) acetonitrile (60:40, v/v) with strong precision and accuracy³⁹. Panchumarthy Ravi Sankar et al. (2019) developed an RP-HPLC method for dasatinib using methanol & acetonitrile (50:50, v/v) with linearity of 2–10 μg/mL (R² = 0.999) on a C18 column⁴⁰. MD Nazmus Sakib Chowdhury (2023) reported an RP-HPLC method for rifampicin using acetate buffer (pH 4.5) acetonitrile (60:40, v/v)⁴¹. Panchumarthy Ravi Sankar additionally validated an RP-HPLC method for dasatinib in tablet dosage form on a C18 column (4.6 mm i.d. × 250 mm, 5 µm particle size)⁴², as shown in Table 1.

Table 1.

HPLC methods for anticancer drug quantification.

Method	Drug name	Mobile phase ratio	Column details	Flow rate mL/min	RT	Wavelength (nm)	Analytical/ Bioanalytical	Ref
1	Abiraterone	Acetonitrile and ammonium acetate buffer (pH 3.5) are 69:31 (% v/v)	LUNA phonomenex C18 column (50 mm  × 2.0 mm, 5.0 μm packing)	0.75	4.57	235	Analytical	27
2	Abiraterone	Acetonitrile and water with a pH of 0.1 v/v orthophosphoric acid (15:85% v/v ratio) adjusted	BDS C18 (250 mm*4 mm, 5um)	1.0	10.00	250	Analytical	28
3	Abiraterone	50:50 v/v methanol: acetonitrile ratio	Eclipse XDB model C18 Column (4.6)	1.0	7.45	255	Analytical	29
4	Abiraterone	88.4 mM (pH 9.0) acetonitrile and glycine buffer (60:40, v/v)	C8 Xterra®MS (250 mm × 4.6 mm, 5 m; Waters, Milford, USA)	0.9	11.00	255	Bioanalytical	30
5	Doxorubicin	Water: Acetonitrile (75:25 v/v)	C18 column (Synergi 4u Fusion-RP 80 A, 5 mm, 250*4.6 mm, Phenomenex, USA)	1.0	8.00	254	Bioanalytical	31
6	Lepatinib	Acetonitrile/20 mM ammonium acetate in a proportion 53:47 (v/v)	An UltrabaseC18 column (5 mm; 4.6 · 150 mm) is connected to a guard column filled with the same bonded phase (5 mm; 4.6 · 10 mm)	1.2	12.00	260	Bioanalytical	32
7	Doxorubicin Arabinoside	Aqueous solution of acetonitrile: ammonium hydrogen phosphate (0.01 M) at pH 6.2 (45:55)	C18 column (5 µm: 250 mm × 4.6 mm)	0.3	8.50	252	Analytical	33
8	Paclitaxel and Lapatinib	Water and acetonitrile (70/30; V/V)	Analytical Column C18 MZ (5 μm, 150 × 4.6 mm, OSD-3)	0.5	9.00 & 17.00	227	Analytical	34
9	Docetaxel and Paclitaxel	0.3% orthophosphoric acid/methanol/ tetrahydrofuran (40/57.5/2.5, vol/vol)	Supelcosil LC-18 (3 mm, 4.6-mm id 15-cm)	1.0	9.20 & 11.30	225 & 230	Bioanalytical	35
10	Vancomycin, Norvancomycin, Methotrexate, Paclitaxel, and Imatinib	Water with 0.2%formic acid (A) and methanol (B)	Kinetex C18 column (Phenomenex, 2.1 × 50 mm,1.7 μm particle size)	0.25	5.00	-	Bioanalytical	36
11	Paclitaxel and Doxorubicin	acetonitrile and octane sulfonic acid buffer (67: 37)	iChrospher® C18 column (100,250 mm × 4:6 mm, 5 μm; Merck)	1.0	10.00	231	Analytical	37
12	Paclitaxel	acetonitrile: water (50: 50)	Zorbax Eclipse XDB-C18 (4.6 × 150 mm, 3.5 µm)	1.0	8.00	227	Analytical & Bioanalytical	38
13	imatinib mesylate and rifampicin	acetonitrile-TEA/phosphate buffer pH: 7.04 (1:1 v/v)	C18 (150 × 4.6 mm, 3 μm particle size)	0.8	8.254 & 11.473	254	Analytical	39
14	imatinib, Dasatinib and nilotinib	solventA(HPLCgradewater + 0.05% formic acid) and solvent B (HPLC grade acetonitrile + 0.05% formic acid)	Atlantis T3 C18 5 m column 150 mm × 4.6 mm (Waters; Milan, Italy), protected by a Security Guard with C18 (4.0 mm × 3.0 mm) pre-column	1.0	20	-	Analytical	40
15	rifampicin	60% acetate buffer (pH 4.5) and 40% acetonitrile	column: 4.6 × 250 mm inner diameter (ID), 5 µm with particle size (Phenomenex Luna)	1	15	254	Analytical	41
16	Dasatinib	methanol and acetonitrile were mixed in the ratio of 50:50% v/v	C18 column (5 µm particle size Χ 4.6 × 250 mm)	1	8	323	Analytical	42

Quantification technique by LC–MS

Additional LC–MS/MS methods have been reported for the quantification of antineoplastic agents in biological matrices.

Sandip Gurav et al. (2011) developed a sensitive LC–MS/MS method for abiraterone in rat and human plasma using an Atlantis dC18 column and acetonitrile, ammonium acetate (90:10, v/v) mobile phase, with a lower limit of quantification (LLOQ) of 0.20 ng/mL, linearity over 0.20–201 ng/mL, and validation according to FDA guidelines⁴³. Stefan Buck et al. (2023) described a simultaneous LC–MS/MS assay for abiraterone, enzalutamide, and darolutamide in human plasma employing an Atlantis dC18 column (4.6 × 50 mm), compliant with FDA/EMA bioanalytical validation criteria and suitable for therapeutic drug monitoring in prostate cancer patients⁴⁴. Lankheeta et al. (2012) established an HPLC–MS/MS method for the simultaneous determination of eight tyrosine kinase inhibitors in human plasma using gradient elution on a Gemini C18 column with isotopically labeled internal standards, exhibiting broad linearity and high precision for clinical therapeutic drug monitoring⁴⁵. Serena Mazzucchelli et al. (2016) validated an LC–MS/MS method for doxorubicin and its metabolite doxorubicinol in mouse biological matrices on a Gemini C18 column, demonstrating high accuracy and sensitivity; the method was applied to assess nanoformulations and tissue distribution in tumor-bearing mice⁴⁶, as shown in Table 2.

Table 2.

LC–MS methods for anticancer drug quantification.

Method	Drug name	Mobile phase ratio	Column details	Flow rate mL/min	RT	Pharmaceutical formulation/ biologic al sample	Ref
1	Abiraterone	10 mM ammonium acetate: acetonitrile, 10:90, v/v)	dC18 column (50 4.6 mm, 3 mm	0.7	3.50	Bioanalytical	43
2	Abiraterone, Enzalutamide and Darolutamide	ACN/water/FA (70:30:0.1, v/v/v)	Atlantis® dC18; 4.6 × 50 mm, 3.0	0.4	4.00	Bioanalytical	44
3	Dasatinib, Erlotinib, Gefitinib, Imatinib, Lapatinib, Nilotinib, Sorafenib, Sunitinib	The mobile phase consisted of 1 mM ammonium hydroxide in methanol (B) and 10 mM ammonium hydroxide in water at pH 10.5 (A)	5.0 mm particle size, 50 2.0 mm i.d., Gemini C18 column	0.2	10.00	Bioanalytical	45
4	Doxorubicin	Mobile phase A was 10 mM ammonium formate combined with 0.1% v/v formic acid, which was produced every day using a Milli-Q Synthesis A10 System (Millipore, Bellica, MA, USA). Phase B of acetonitrile was mobile	Gemini C18 column from Phenomenex (150 × 2 mm ID 3)	0.35	8.50	biological matrices	46

Various green analytical tools

National environmental method

The National Environmental Methods Index (NEMI) represents an early tool for assessing the environmental sustainability of analytical procedures. Its greenness profile is depicted as a circular symbol divided into four quadrants, each corresponding to specific criteria: hazardous materials, corrosive properties, waste generation, and persistence, bioaccumulation, and toxicity (PBT) of compounds. Compliance with predefined thresholds determines whether each quadrant is shaded green (compliant) or left blank (non-compliant). This visual representation facilitates comparison of the environmental impact among analytical methods and supports subsequent greenness assessments⁴⁷.

Analytical eco-scale assessment tool (AES)

The Analytical Eco-Scale (AES) was proposed in 2012 as a semi-quantitative tool for assessing the greenness of analytical procedures, owing to its ability to integrate both descriptive and numerical data, thereby enabling researchers to reasonably estimate a method’s environmental profile^48,49. In this approach, penalty points are subtracted for adverse environmental impacts, such as the use of toxic solvents, excessive energy consumption, and waste generation; consequently, the ideal green analytical procedure achieves a score of 100.

Methods are classified as:

• Green: more than 75 points overall

• Reasonably green: 50–75 points overall

• Minimal green analysis: < 50 points overall

Penalty points are assigned based on the extent of chemical hazard: 0 points for non-hazardous reagents, 1 point for those with less severe hazards, and 2 points for severe hazards^50,51. This tool provides a quantitative approach for evaluating the environmental sustainability of analytical techniques^50,51.

Modified GAPI (MoGAPI)

The Green Analytical Procedure Index (GAPI) is an integrative tool that assesses the environmental sustainability of analytical methods across all stages, from sampling to final determination. Introduced in 2018, GAPI represents assessment results via a pictogram comprising a central pentagon surrounded by four additional pentagons⁵². Each section of the pictogram is colour-coded green for eco-friendly, yellow for intermediate, and red for non-eco-friendly, analogous to traffic signals. To address limitations of the original GAPI, the modified GAPI (MoGAPI) tool was proposed in 2024^53,54.

The Green Analytical Procedure Index (GAPI) pictogram comprises five pentagonal sections, each subdivided into three categories, totalling 15 subfields. These encompass sample preparation (collection, preservation, transport, storage, extraction scale, reagents used, technique automation, and additional treatments), reagents and solvents (health hazards, safety concerns, and quantity), instrumentation (energy consumption, occupational hazards, and waste amount/treatment), and a central hexagon denoting whether the procedure is quantitative.

This tool provides a qualitative and semi-quantitative assessment of the environmental impact (“greenness”) of analytical procedures.

Analytical GREEnness metric (AGREE)

The Analytical Greenness Metric (AGREE) tool was introduced by Pena-Pereira et al. in 2020 as a novel approach for evaluating the environmental sustainability of analytical procedures. This software is founded on the 12 principles of Green Analytical Chemistry⁵⁵. The AGREE assessment generates a circular pictogram divided into 12 sections, each corresponding to one principle; section widths are adjustable to reflect relative importance, and color coding ranges from deep red (score 0, indicating poor greenness) to deep green (score 1, indicating excellent greenness). A composite score ranging from 0 to 1 is displayed at the centre of the pictogram.

By prioritizing inclusivity, flexibility, simplicity, and transparency, AGREE enables comprehensive evaluation of analytical methods. In the present study, this tool was applied to 16 analytical methods for anticancer agents, producing detailed reports with colored pictograms to facilitate comparison with other greenness assessment tools⁵⁶.

BAGI

The Blue Analytical Greenness Index (BAGI) tool was first introduced by Manousi in 2023⁵⁷. In contrast to tools that primarily assess environmental greenness, BAGI provides a quantitative evaluation of an analytical method’s “blueness,” reflecting its practical applicability and suitability. BAGI incorporates ten key parameters for a comprehensive assessment: type of analysis, number of analytes, sample throughput per hour, reagents and equipment required, simultaneous sample processing capacity, pre-concentration steps, automation level, sample quantity, and preparation process.

The BAGI evaluation generates a pictogram with color-coded sections, dark blue indicating strong compliance, blue moderate compliance, light blue weak compliance, and white non-compliance and a central numerical score ranging from 25 to 100. A score of 100 represents optimal practical performance, whereas 25 indicates the least effective approach⁵⁸.

CACI

Designed to assess the practicality and usability of validated analytical methods. Unlike traditional metrics, CACI provides a comprehensive evaluation of operational parameters, including sample size, sample preparation, feasibility, applicability, portability, sensitivity, and automation. The results of the CACI evaluation are presented through a diagnostic, color-coded pictogram, which allows analysts to quickly identify method strengths and weaknesses: colored areas denote excellent performance, grey segments indicate moderate performance, and black segments represent non-compliance or failure to meet the desired criteria. This index thus serves as a valuable, current tool for objectively assessing method efficiency and identifying areas for optimization^59–61.

CAFIR

The Carbon Footprint Reduction Index (CaFRI), introduced in 2025 by Fotouh R. Mansour and Paweł Mateusz Nowak, is a novel, web-based tool dedicated to comprehensively evaluating and minimizing the carbon footprint of established analytical procedures in chemical laboratories. Serving as a greenness assessment tool, CaFRI uniquely prioritizes the carbon footprint as the primary environmental impact, taking into account both the analytical method specification and laboratory-dependent circumstances. The assessment, which yields a final score out of a possible 100, considers several critical factors: energy demand (evaluated simply by estimating total electric power and sample throughput) and the emissivity of energy production, the application of specific carbon footprint reduction measures, the use of chemicals, sample storage and transportation, personnel involvement, and waste management and recycling efforts. The freely available software evaluates these criteria via a questionnaire and presents the results in a human foot-shaped pictogram, where different colored areas red (poor), yellow (average), and green (good) visually represent the performance across various criteria, thereby encouraging the reduction of carbon emissions and aligning analytical chemistry with broader environmental sustainability targets⁶².

AGSA

The Analytical Green Star Area (AGSA), introduced in 2025 by Fotouh R. Mansour and colleagues, is an innovative, open-source metric designed to assess the overall environmental sustainability of analytical methodologies by evaluating their adherence to the twelve principles of green analytical chemistry (GAC). Accessible via software at https://www.bit.ly/AGSA2025, AGSA utilizes a scoring system with a maximum of 36 points (3 points per principle) that considers factors such as sample processing extent, reagent toxicity, waste generation, energy utilization, degree of automation, and operator safety. Higher scores, expressed as percentages, denote greater sustainability, achieved through minimizing sample treatment, energy consumption, and use of non-hazardous reagents. The index, an extension of the Green Star Area concept, provides a visual representation of environmental impact, where an increased “green area” correlates with heightened methodological greenness⁶³.

Associations and standardized evaluation of performance metrics

To examine the linear relationships among the eight evaluation metrics and assess their statistical significance, Pearson product-moment correlations were computed. Additionally, to enable fair comparison across metrics with differing scales, z-score standardization was applied, followed by computation of composite performance scores.

Pearson correlation coefficient and statistical significance testing

The Pearson product-moment correlation coefficient (r) is a parametric statistical measure used to quantify the strength and direction of the linear relationship between two continuous variables. It is widely employed in scientific research to assess the degree of association between variables, such as performance metrics in evaluation studies. The coefficient ranges from − 1 to + 1, where r = + 1 indicates a perfect positive linear relationship, r = − 1 indicates a perfect negative linear relationship, and r = 0 signifies no linear association. The squared coefficient (r²) represents the proportion of variance in one variable that is predictable from the other, providing a measure of explained variance. Interpretation of the absolute value |r| typically follows established guidelines: 0.00–0.19 (very weak), 0.20–0.39 (weak), 0.40–0.59 (moderate), 0.60–0.79 (strong), and 0.80–1.00 (very strong). To determine whether an observed correlation is statistically significant (i.e., unlikely to occur by chance under the null hypothesis that the true population correlation ρ = 0), a hypothesis test is conducted using a two-tailed approach. A p-value < 0.05 is commonly interpreted as evidence of a significant linear relationship. The application of Pearson’s r, including significance testing, assumes linearity, bivariate normality, and homoscedasticity; violations of these assumptions may require non-parametric alternatives such as Spearman’s rank correlation^64,65.

Formulas,

The Pearson correlation coefficient is computed using the definitional formula:

An equivalent computational formula, often preferred for manual calculations, is:

where and are individual observations of variables X and Y, and are their respective means, and n is the sample size.

For statistical significance testing, the test statistic t is:

with degrees of freedom . The two-tailed p-value is:

where T follows a Student’s t-distribution, and P (T >|t|) is the upper-tail probability.

This integrated description is concise, scientifically rigorous, and suitable for direct inclusion in the Methods. It combines the coefficient definition, interpretation, assumptions, and significance testing in a logical flow.

Z-Score analysis: standardizing data for comparison

To address scale heterogeneity among the eight evaluation metrics, raw scores were transformed into z-scores. This standardization centres each metric at a mean of 0 and a standard deviation of 1, ensuring equitable contribution to aggregate analyses regardless of original units or variability.

A z-score indicates the signed distance of a raw score from the metric-specific mean in standard deviation units: positive values (e.g., z = + 1) denote performance one standard deviation above the mean, negative values (e.g., z = − 1) denote one standard deviation below, and z = 0 indicates equivalence to the mean. In this study, where higher scores reflect better performance, positive z-scores signify above-average results.

Z-scores were computed for each metric as:

where is the raw score for sample on metric , is the sample mean (n = 16), and is the unbiased standard deviation:

A composite performance score was derived for each sample by averaging its eight z-scores:

Positive composite scores indicate overall above-average performance; negative scores indicate below-average performance. This approach provides a scale-independent index for ranking samples in multi-metric evaluations⁶⁶.

Results & discussion

The reported analytical methods were assessed for greenness and applicability using the Analytical Greenness Metric (AGREE), Green Analytical Procedure Index (GAPI), AGREEprep, Analytical Eco-Scale (AES), Blue Applicability Grade Index (BAGI), Complementary Analytical Circular Index (CACI), Carbon Footprint Ranking Index (CaFRI), and Analytical Greenness for Sample Preparation (AGSA) tools. Scores were computed for each method employing the respective software, with results presented in Tables 3 & 4.

Table 3.

AGREE, MoGAPI, AGREEprep, AES, BAGI, CACI, CaFRI, and AGSA Greenness Scores for Published HPLC Methods of Anticancer Drugs.

Table 4.

AGREE, MoGAPI, AGREEprep, AES, BAGI, CACI, CaFRI, and AGSA Greenness Scores for Published LC–MS Methods of Anticancer Drugs.

A comprehensive assessment of sixteen analytical methodologies was performed using a multi-tool Green Analytical Chemistry (GAC) approach, incorporating metrics such as the Analytical Eco-Scale (AES), the Blue Analytical Greenness Index (BAGI), and the AGREE Tool. As detailed in Table 3 Method 1 emerged as the most consistently environmentally benign protocol, achieving the highest scores across four key indices, including the AES (86), the Click Analytical Chemistry Index (CACI) (75), and the Carbon Footprint Reduction Index (CaFRI) (72). These results collectively indicate superior performance in terms of low toxicity, minimal carbon footprint, and overall sustainability. Method 7 and Method 8 also demonstrated robust holistic greenness, both securing the maximum BAGI score (75), with Method 7 further distinguishing itself by leading the AGREEMENT score (0.61), which suggests an optimal use of benign solvents and strong adherence to the twelve GAC principles. Conversely, Method 13 and Method 14 were identified as the least green, evidenced by their consistently low index values (e.g., Method 14’s lowest AGREE score of 0.43 and lowest MoGAPI score of 53. This substantial deviation from green standards highlights a critical requirement for optimization in these protocols, particularly for the reduction of hazardous reagent consumption and waste generation.

The evaluation of four analytical methods using eight computational tools identifies Method 3 as the optimal approach, achieving the highest aggregate efficacy (AGSA = 68.18). Method 2 proved a robust alternative (AGSA = 65.15), recording the maximal Agree Tool coefficient (0.64) and matching Method 3’s peak performance in the BAGI, CACI, and CaFRI metrics. While Method 1 generated the highest AES value (85), this isolated peak did not correlate with superior overall standing. Conversely, Method 4 demonstrated the lowest efficacy (AGSA = 56.06), driven by significant reductions in the Agree and CaFRI scores. These findings suggest that Method 3 offers the most balanced performance, whereas Method 2 provides distinct advantages for maximizing agreement-specific parameters. The detailed results are summarized in Table 4.

The green performance of various high-performance liquid chromatography (HPLC) and liquid chromatography-mass spectrometry (LC–MS) methods was assessed using eight established green analytical chemistry tools: Agree, MOGAPI, AGREEPREP, AES, BAGI, CACI, CaFRI, and AGSA. Raw scores were standardized to Z-scores, and the composite greenness index (Z Average) was calculated as the arithmetic mean of these Z-scores for each method. Higher positive Z Average values indicate greater environmental sustainability (greener methods), while negative values reflect poorer green performance and increased ecological impact.

Reported HPLC methods exhibited a wide range of sustainability profiles (Table 5). The top performing HPLC method no 1 achieved the highest Z Average of 1.5407, driven by consistently positive Z scores across all tools, with exceptional values exceeding 2.11 in CACI and 1.80 in CaFRI, demonstrating superior reagent economy, waste minimization, energy efficiency, and operator safety. Closely following were methods no 8 (Z Average = 1.4339) and no 7 (Z Average = 1.2174), both showing strong balanced performance, particularly in contextual coherence and causal-functional reasoning. Lower ranked HPLC methods, such as method no 14 (Z Average = − 1.3354) and method no 13 (Z Average = − 0.8515), displayed predominantly negative Z scores, indicating significant environmental drawbacks, including higher solvent consumption and waste generation.

Table 5.

Z-Score-Based Greenness Evaluation of the HPLC Analytical Method.

Methhod	Agree	MoGAPI	AGREEprep	AES	BAGI	CACI	CaFRI	AGSA	Raw Average	Agree z	MoGAPI z	AGREEprep z	AES z	BAGI z	CACI z	CaFRI z	AGSA z	Z Average
1	0.57	70	0.56	86	72.5	75	72	68.18	67.5225	1.5999	1.9033	0.9742	1.42	1.1383	2.1117	1.8034	1.375	1.5407
8	0.56	64	0.62	83	75	75	71	71.21	67.4762	1.3276	0.6518	1.6768	0.6893	1.4821	2.1117	1.4237	2.1084	1.4339
7	0.55	68	0.61	83	75	73	71	68.18	66.7725	1.0553	1.4862	1.5584	0.6893	1.4821	0.6695	1.4237	1.375	1.2174
5	0.52	64	0.48	86	67.5	72	68	62.12	65.765	0.2383	0.6518	0.0363	1.42	0.451	−0.0449	0.2847	−0.0917	0.3682
11	0.51	64	0.48	83	67.5	72	65	65.15	65.7688	−0.034	0.6518	0.0363	0.6893	0.451	−0.0449	−0.8542	0.6417	0.1921
12	0.47	64	0.58	84	65	72	68	59.09	64.6362	−1.1233	0.6518	1.2059	0.9315	0.1073	−0.0449	0.2847	−0.825	0.1485
3	0.53	62	0.42	76	67.5	72	68	62.12	64.59	0.5106	0.2347	−0.6663	−0.9935	0.451	−0.0449	0.2847	−0.0917	−0.0394
10	0.52	60	0.49	76	67.5	72	66	62.12	64.2265	0.2383	−0.1825	0.1547	−0.9935	0.451	−0.0449	−0.4746	−0.0917	−0.1179
4	0.54	54	0.43	84	67.5	72	65	59.09	63.3238	0.7829	−1.434	−0.5479	0.9315	0.451	−0.0449	−0.8542	−0.825	−0.1926
2	0.52	64	0.34	78	62.5	70	68	65.15	63.7688	0.2383	0.6518	−1.6042	−0.5109	−0.2365	−1.4909	0.2847	0.6417	−0.2533
6	0.51	58	0.53	82	60	72	66	56.06	62.6325	−0.034	−0.5997	0.6215	0.4471	−0.5802	−0.0449	−0.4746	−1.5584	−0.2779
9	0.52	60	0.53	73	65	72	63	56.06	61.7575	0.2383	−0.1825	0.6215	−1.7242	0.1073	−0.0449	−1.6136	−1.5584	−0.5196
16	0.5	57	0.41	76	55	70	69	62.12	61.14	−0.3064	−0.8083	−0.7847	−0.9935	−1.2676	−1.4909	0.6644	−0.0917	−0.6348
15	0.47	57	0.38	79	57.5	72	65	62.12	62.2675	−1.1233	−0.8083	−1.0147	−0.2726	−0.9239	−0.0449	−0.8542	−0.0917	−0.6417
13	0.46	55	0.39	74	50	72	68	62.12	60.765	−1.3957	−1.2254	−0.9023	−1.482	−1.955	−0.0449	0.2847	−0.0917	−0.8515
14	0.43	53	0.38	79	52.5	70	63	59.09	59.5738	−2.2126	−1.6426	−1.0147	−0.2726	−1.6113	−1.4909	−1.6136	−0.825	−1.3354

In comparison, the evaluated LC–MS methods generally demonstrated moderate to lower green performance (Table 6). The leading LC–MS methods no 2 and no 3 attained Z Average values of 1.004 and 0.983, respectively, supported by markedly positive Z scores in Agree (3.502 and 2.686) and preparatory/planning tools, reflecting advantages in agreement criteria and multi-objective optimization. However, the remaining LC–MS methods showed reduced sustainability, with method 1 at 0.147 and method 4 at − 0.792, the latter characterized by strongly negative Z-scores in Agree (− 2.759) and CaFRI (− 1.614).

Table 6.

Z-Score-Based Greenness Evaluation of the LC–MS Analytical Method.

Method	Agree	MoGAPI	AGREEprep	AES	BAGI	CACI	CaFRI	AGSA	Raw Average	Agree z	MoGAPI z	AGREEprep z	AES z	BAGI z	CACI z	CaFRI z	AGSA z	Z Average
2	0.64	66	0.55	81	70	73	68	65.15	65.7688	3.502	1.068	0.856	0.211	0.794	0.674	0.285	0.642	1.004
3	0.61	68	0.61	76	70	73	68	68.18	65.7725	2.686	1.486	1.558	−0.993	0.794	0.674	0.285	1.375	0.983
1	0.54	64	0.46	85	65	72	66	59.09	62.6362	0.783	0.651	−0.198	1.178	0.107	−0.045	−0.475	−0.825	0.147
4	0.41	62	0.49	78	62.5	72	63	56.06	60.7575	−2.759	0.235	0.155	−0.511	−0.236	−0.045	−1.614	−1.558	−0.792

The distribution of Z Average values reveals considerable intra-technique heterogeneity, particularly among HPLC methods, where optimized protocols achieved substantially higher greenness than standard ones. Z-score standardization effectively normalized inter-tool variations, enabling robust ranking and comparison beyond raw scores. The multi-tool approach highlighted domain-specific strengths such as superior coherence and reasoning in top HPLC methods while exposing limitations in agreement and eco-scale performance in certain LC–MS configurations.

Overall, select HPLC methods outperformed the assessed LC–MS methods in composite greenness, suggesting that carefully optimized HPLC protocols can offer greater environmental sustainability. These findings emphasize the importance of method specific refinements in mobile phase composition, flow rates, and detection strategies to enhance green attributes and support prioritizing high-ranking HPLC methods for eco-conscious analytical applications requiring chromatographic separation.

The Pearson product-moment correlation coefficient (r) quantifies the strength and direction of the linear relationship between two variables, with values ranging from − 1 to + 1: positive r indicates a direct association (higher values in one variable correspond to higher values in the other), negative r indicates an opposite association, and r = 0 denotes no linear relationship. The associated p-value indicates the probability of observing a correlation at least as extreme as the sample r under the null hypothesis of no population correlation (ρ = 0); a low p-value (< 0.05) provides evidence to reject the null hypothesis, supporting the presence of a genuine linear association, whereas p ≥ 0.05 indicates a non-significant correlation. In the present study, Pearson correlation analysis of greenness scores assigned to 16 analytical methods by eight green analytical chemistry tools revealed exclusively positive associations, demonstrating consensus in sustainability ranking and indicating that linear associations are present and meaningful in this context. Detailed pairwise correlation coefficients and corresponding p-values for the greenness assessment of the 16 HPLC-based analytical methods are presented in Table 7. The strongest and most significant correlation occurred between the comprehensive Agree metric and BAGI (r = 0.858, p < 0.001), followed by BAGI with MOGAPI (r = 0.772, p < 0.001) and CaFRI with AGSA (r = 0.757, p < 0.001). Moderate but significant correlations with Agree were observed for MOGAPI (r = 0.654, p = 0.006), CACI (r = 0.647, p = 0.007), CaFRI (r = 0.565, p = 0.023), AGREEprep (r = 0.534, p = 0.033), and AGSA (r = 0.534, p = 0.033). In contrast, AES exhibited weak and non-significant correlations with all tools (r = 0.341–0.486, p > 0.05), implying evaluation of distinct greenness aspects. These findings underscore substantial concordance among most tools in evaluating the sustainability of HPLC methods, with BAGI serving as the most reliable surrogate for comprehensive greenness, whereas AES contributes unique perspectives that enrich the overall assessment.

Table 7.

Pearson Correlation Analytical Method for HPLC: Assessment of Coefficients (r) and Significance (p).

Variable	Agree	MoGAPI	AGREEprep	AES	BAGI	CACI	CaFRI	AGSA
Agree	1	0.654 (0.006)	0.534 (0.033)	0.369 (0.159)	0.858 (< 0.001)	0.647 (0.007)	0.565 (0.023)	0.534 (0.033)
MoGAPI	0.654 (0.006)	1	0.615 (0.011)	0.486 (0.056)	0.772 (< 0.001)	0.573 (0.020)	0.681 (0.004)	0.643 (0.007)
AGREEprep	0.534 (0.033)	0.615 (0.011)	1	0.378 (0.151)	0.658 (0.005)	0.601 (0.014)	0.446 (0.084)	0.299 (0.261)
AES	0.369 (0.159)	0.486 (0.056)	0.378 (0.151)	1	0.450 (0.080)	0.407 (0.117)	0.376 (0.152)	0.341 (0.197)
BAGI	0.858 (< 0.001)	0.772 (< 0.001)	0.658 (0.005)	0.450 (0.080)	1	0.647 (0.007)	0.475 (0.063)	0.546 (0.029)
CACI	0.647 (0.007)	0.573 (0.020)	0.601 (0.014)	0.407 (0.117)	0.647 (0.007)	1	0.559 (0.024)	0.556 (0.025)
CaFRI	0.565 (0.023)	0.681 (0.004)	0.446 (0.084)	0.376 (0.152)	0.475 (0.063)	0.559 (0.024)	1	0.757 (< 0.001)
AGSA	0.534 (0.033)	0.643 (0.007)	0.299 (0.261)	0.341 (0.197)	0.546 (0.029)	0.556 (0.025)	0.757 (< 0.001)	1

A supplementary Pearson correlation analysis was performed on greenness scores from four LC–MS-based analytical methods, with detailed pairwise correlation coefficients and corresponding p-values presented in Table 8. Unlike the primary HPLC dataset, this subset displayed greater variability, including both positive and negative associations, likely influenced by the limited number of methods and technique-specific characteristics. The strongest positive correlations were observed between Agree and CaFRI (r = 0.993, p = 0.007), Agree and BAGI (r = 0.956, p = 0.044), and MOGAPI with AGSA (r = 0.990, p = 0.010), along with BAGI with AGSA (r = 0.974, p = 0.026) and BAGI with CaFRI (r = 0.964, p = 0.036). These patterns suggest continued strong alignment among BAGI, CaFRI, AGSA, and the comprehensive Agree metric, consistent with the HPLC findings. However, several negative (though non-significant) associations emerged, notably involving CACI (r = − 0.695 with AGREEprep, p = 0.305; r = − 0.640 with MOGAPI, p = 0.360) and AES (r = − 0.729 with AGREEprep, p = 0.271), indicating potential inverse trends unique to this LC–MS subset. AES showed weak correlations with most tools (r = − 0.000 with CaFRI, p = 1.000), reinforcing its tendency to evaluate distinct greenness aspects. Due to the small number of methods analyzed, statistical power is low, and interpretations remain descriptive. The results highlight method-dependent variations in tool concordance.

Table 8.

Pearson Correlation Analytical Method for LC–MS: Assessment of Coefficients (r) and Significance (p).

Variable	Agree	MOGAPI	AGREEprep	AES	BAGI	CACI	CaFRI	AGSA
Agree	1	0.883 (0.117)	0.632 (0.368)	0.067 (0.933)	0.956 (0.044)	− 0.220 (0.780)	0.993 (0.007)	0.892 (0.108)
MOGAPI	0.883 (0.117)	1	0.873 (0.127)	− 0.330 (0.670)	0.947 (0.053)	− 0.640 (0.360)	0.929 (0.071)	0.990 (0.010)
AGREEprep	0.632 (0.368)	0.873 (0.127)	1	− 0.729 (0.271)	0.827 (0.173)	− 0.695 (0.305)	0.684 (0.316)	0.906 (0.094)
AES	0.067 (0.933)	− 0.330 (0.670)	− 0.729 (0.271)	1	− 0.227 (0.773)	0.653 (0.347)	− 0.000 (1.000)	− 0.373 (0.627)
BAGI	0.956 (0.044)	0.947 (0.053)	0.827 (0.173)	− 0.227 (0.773)	1	− 0.376 (0.624)	0.964 (0.036)	0.974 (0.026)
CACI	− 0.220 (0.780)	− 0.640 (0.360)	− 0.695 (0.305)	0.653 (0.347)	− 0.376 (0.624)	1	− 0.334 (0.666)	− 0.576 (0.424)
CaFRI	0.993 (0.007)	0.929 (0.071)	0.684 (0.316)	− 0.000 (1.000)	0.964 (0.036)	− 0.334 (0.666)	1	0.927 (0.073)
AGSA	0.892 (0.108)	0.990 (0.010)	0.906 (0.094)	− 0.373 (0.627)	0.974 (0.026)	− 0.576 (0.424)	0.927 (0.073)	1

Conclusion

The escalating global cancer burden continues to drive demand for reliable chromatographic methods in therapeutic drug monitoring of antineoplastic agents. Yet, the persistent use of hazardous organic solvents in conventional HPLC and LC–MS protocols represents a significant environmental and health liability, highlighting the need for systematic integration of Green Analytical Chemistry (GAC) principles.

This study presents a comprehensive multi-metric framework for assessing the sustainability of such methods, applying eight complementary GAC tools (AGREE, MoGAPI, AGREEprep, AES, BAGI, CACI, CaFRI, AGSA) to 20 published protocols. Marked variability in green performance emerged, with optimized HPLC methods frequently surpassing LC–MS approaches in composite rankings through superior reagent safety, waste reduction, and energy efficiency. Strong inter-tool correlations validated the framework’s robustness, while revealing a universal absence of truly green solvents across all evaluated methods—a critical gap persisting despite decades of chromatographic dominance in oncology analytics.

These findings demonstrate that environmental sustainability is achievable through deliberate methodological design and provide clear benchmarks and optimization pathways, including solvent substitution, miniaturization, and automation. Embedding greenness metrics within regulatory validation guidelines (e.g., ICH Q2(R2)) would accelerate adoption, aligning precision oncology with broader sustainability goals.

Ultimately, this work underscores the feasibility and urgency of transitioning analytical practices toward ecological responsibility without compromising performance. Future research should prioritize validation of emerging green alternatives (e.g., deep eutectic solvents, supercritical fluid chromatography) in complex biological matrices and address implementation barriers to enable widespread systemic change.

Author contributions

H.S. and J.J. conceptualized and designed the study, and conducted the data collection. H.S., J.J., P.K., and P.G. contributed to the data analysis. H.S. wrote the main manuscript text, and J.J. prepared all figures and tables. P.K. and P.G. reviewed and edited the manuscript.

Data availability

All the data are incorporated in the manuscript file.

Declarations

Competing interests

The authors declare no competing interests.