Integrating sustainability metrics into analytical decision-making in biopharmaceutical quality control: an industrial case study

Integrating sustainability metrics into analytical decision-making in biopharmaceutical quality control: an industrial case study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report Integrating sustainability metrics into analytical decision-making in biopharmaceutical quality control: an industrial case study Thallis Martins Souza This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8980645/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose : Analytical methods of pharmaceutical quality control (QC) are typically selected for regulatory compliance and analytical performance, while sustainability and operational feasibility are often secondary. This case study aimed to demonstrate how combining greenness and whiteness metrics can support method-related decisions in QC under batch practice. Methods : Compendial non-aqueous potentiometric titrations (PT) for aromatic amino acids used as raw materials were compared with alternatives spectrofluorimetric methods (SFM) developed for assays. The Advanced National Environmental Methods Index (advanced NEMI) was applied during development as an early screening tool, followed by the Analytical Eco-Scale and Green Analytical Procedure Index (GAPI), and Analytical GREEnness metric (AGREE) to quantify and localize environmental burdens under finalized conditions. Finally, White Analytical Chemistry (WAC) was employed to integrate analytical performance, environmental impact, and implementation practicality. Results : Across these tools, PT was penalized by corrosive non-aqueous chemistry and higher hazardous reagent and waste demands, implying greater safety controls and waste-handling effort at scale. Triangulation converged on the same decision signal: SFM remained fit for release testing while reducing hazardous touchpoints and simplifying waste logistics, yielding superior greenness indicators (Eco-Scale: 93 vs . 74; AGREE: 0.61 vs . 0.34) and a higher WAC score (81.3 vs . 65.5). Conclusion : Overall, the metric set shifted the basis from “analytically compliant” to “fit for deployment,” framed by a Six Sigma improvement logic (Define–Measure–Analyze–Improve–Control), thereby strengthening sustainability-informed analytical development and change-control decisions in industrial QC. compendial methods quality control Six Sigma spectrofluorimetry sustainability metrics Figures Figure 1 Introduction Analytical methods are central to pharmaceutical quality control (QC), supporting the assessment of raw materials, active pharmaceutical ingredients, and finished products to ensure regulatory compliance and patient safety. Method development and validation are therefore critical activities, traditionally driven by performance attributes such as accuracy, precision, selectivity, robustness and alignment with pharmacopeial requirements In industrial settings, these expectations are reinforced by regulatory considerations linked to method lifecycle and the adoption of innovative analytical technologies [ 1 , 2 ]. In contemporary laboratories, analytical procedures are embedded in end-to-end operational processes rather than functioning as stand-alone technical steps. Even when methods deliver comparable analytical outcomes, they may differ markedly in solvent consumption, waste generation, procedural complexity, turnaround time, and occupational risk. These differences shape laboratory cost, efficiency, and environmental footprint, yet they are often treated as secondary criteria when methods are updated or replaced [ 3 ]. This performance-centered approach has fueled interest in evaluation strategies that consider sustainability and implementation feasibility alongside analytical quality. Such broader criteria are increasingly relevant as pharmaceutical manufacturing and control evolve under Industry 4.0 and 5.0 paradigms. Industry 4.0 emphasizes digitalization, automation, and data governance to strengthen process control, compliance and reliability. Although sustainability is not its primary driver, efficiency gains (e.g., lower resource use, tighter control, real-time data) can facilitate greener practices. Industry 5.0 extends this trajectory by reinforcing the synergy between advanced technologies and human expertise, and by explicitly integrating ethical and sustainability-oriented priorities—such as environmental responsibility and social considerations—into efficiency-driven systems [ 4 – 6 ]. This broader view is particularly relevant in QC, where the same procedure is repeated daily and small differences in solvent use, safety controls and waste logistics accumulate into measurable operational burdens. In this context, sustainability metrics have gained relevance as structured tools to translate chemical hazards, solvent intensity and waste-handling demands into comparable indicators of analytical greenness [ 7 ]. In routine practice, several compendial procedures still rely on hazardous substances (corrosive and/or toxic) and may require large reagent volumes depending on the technique. This increases exposure risks and can generate substantial waste. In response, sustainability metrics have been proposed as structured tools to quantify the “greenness” of analytical activities and translate these burdens into comparable indicators [ 8 ]. Within this context, the 12 principles of Green Analytical Chemistry (GAC) provide a relevant foundation for pharmaceutical analysis, where routine testing can be resource-intensive due to solvent, reagent, and energy demands. Applying GAC encourages the development of new analytical strategies—or the redesign of existing ones—to reduce dependence on hazardous chemicals while maintaining (or improving) analytical performance. When properly implemented, this approach yields practical benefits, including reduced hazardous waste, improved laboratory safety, better cost efficiency, and strengthened alignment with Good Manufacturing Practices (GMP) [ 9 ]. Many metrics have been developed to operationalize GAC in practice, yet none fully capture the combined tradeoffs that drive method choices in industrial laboratories—namely environmental, health and safety (EHS) burden, workflow complexity, and analytical performance. In routine settings, these dimensions are inseparable: a method may be analytically compliant and still impose disproportionate demands on solvent logistics, safety controls, and waste handling once repeated at scale. For this reason, this case study adopts a multi-metric strategy to screen constraints early, compare methods under finalized operating conditions, visualize workflow hotspots, and integrate greenness with performance and feasibility in a decision-oriented view [ 10 ]. Within this landscape, screening-type tools help reveal early constraints, whereas more structured metrics support comparisons once operating conditions are finalized. The advanced National Environmental Methods Index (advanced NEMI), for instance, offers a straightforward way to flag critical burdens associated with operator risk, energy demand, reagent consumption and waste volume [ 11 ]. In contrast, the Analytical Eco-Scale provides a semi-quantitative perspective by translating reagent hazards and quantities, occupational risk, energy demand, and waste into penalty points, enabling a direct comparison between analytically acceptable alternatives while maintaining interpretability [ 12 ]. Beyond numerical summaries, workflow-based tools are particularly useful when the sustainability burden is concentrated in specific stages rather than uniformly distributed. In this regard, the Green Analytical Procedure Index (GAPI) allows visualizing impacts across sample preparation, reagents/solvents, instrumentation and waste handling, making it easier to identify stages that act as environmental bottlenecks [ 13 ]. Complementarily, the Analytical GREEnness metric (AGREE) aggregates the 12 GAC principles into a normalized score and pictogram, offering an integrated view of greenness while still indicating which principles contribute most to penalties under batch-level operating conditions [ 14 ]. Finally, because industrial method choices rarely depend on “greenness” alone, the White Analytical Chemistry (WAC) concept is relevant to bring sustainability into a decision-oriented perspective. By integrating analytical performance, environmental impact, and practical feasibility into a whiteness assessment, WAC supports comparisons when methods must remain compliant and operationally viable while reducing environmental and safety burdens. Despite the availability of these tools, reports describing their application as a single input to support method-related decisions in an industrial context—beyond regulatory criteria and conventional analytical performance—remain limited [ 15 ]. This case study examines how the combined use of greenness and whiteness metrics can support methodological decisions during the development and validation of spectrofluorimetric assays for aromatic amino acids (AAA), using a compendial potentiometric titration (PT) procedure as the benchmark in a biopharmaceutical QC setting, under a sustainability- and efficiency-oriented perspective aligned with Lean principles. Methodology Case study description This case study is based on the analytical methods reported by Souza et al . (2024) [ 16 ] and examines how sustainability-oriented metrics can complement conventional regulatory and performance-based criteria in method-related decisions. The evaluated procedures were designed for routine use, where robustness, compliance and operational feasibility are mandatory. Under such conditions, pharmacopeial acceptance and analytical performance typically guide method development and validation, while factors such as reagent hazards, waste generation, workflow complexity and resource demand may receive less emphasis. Two approaches were considered: (i) a SFM developed for individual determination of AAA (phenylalanine – PHE, tryptophan – TRP, and tyrosine – TYR) used as raw materials in biopharmaceutical formulations; and (ii) a compendial PT applied to these analytes using glacial acetic acid as non-aqueous medium. Although both approaches are suitable for routine application, they differ in solvent profile, reagent hazard, waste stream characteristics, selectivity/robustness and operational requirements. These contrasts make the PT–SFM comparison a practical test case for exploring how sustainability-oriented tools influence method-related decisions. To do so, a set of greenness and whiteness metrics was applied to translate GAC/WAC principles into structured indicators of environmental impact, safety, resource use, and feasibility of implementation. Together, these tools support a multi-angle assessment aligned with sustainability-driven decision trends associated with Industry 5.0. Sustainability and whiteness metrics A set of greenness and whiteness metrics was applied to the analytical methods evaluated in this case study to capture environmental, operational and methodological attributes under a biopharmaceutical laboratory scenario. For all metrics, reagent consumption, waste generation and operational parameters were estimated considering batch-level practice (blank plus triplicate assays), as required for GMP-based release testing. At the development stage, advanced NEMI was used as an initial screening tool. Sustainability-related parameters were classified across four criteria—operator risk, energy consumption, reagent consumption, and waste volume—and were reported using a semi-quantitative color scale (green/yellow/red), according to the metric definition and subsequent refinements. After method development and validation, additional metrics were used to characterize the finalized procedures under routine-operating conditions. Environmental friendliness was assessed using the Analytical Eco-Scale, a semi-quantitative approach based on penalty points assigned to reagent characteristics (amount and hazard classification), energy requirements (instrumental demand), occupational risk (operator exposure), and waste generation (volume and type requiring treatment) (Table S1 ) [ 12 ]. The overall score was calculated using Eq. (1): Higher values indicate greener procedures, whereas lower values reflect an increasing environmental burden. A workflow-oriented evaluation was then performed using GAPI, which considers environmental impact across the analytical process (sample preparation, reagents/solvents, instrumentation, energy consumption, and waste generation) under finalized conditions. Results are presented as five pentagonal diagrams: two related to sample preparation, one addressing reagents/solvents, one associated with instrumentation, and one related to quantification. Each field is color-coded to indicate low (green), moderate (yellow), or high (red) environmental impact according to predefined criteria [ 13 ]. AGREE was used to generate an integrative numerical indicator by mapping the 12 GAC principles into a normalized score. The evaluation was performed using the AGREE software, which assigns weighted scores to each principle based on method characteristics and combines them through an algorithm to produce a value from 0 (non-green) to 1 (ideal green) [ 14 ]. Results are expressed as a circular pictogram with one segment per GAC principle, color-coded from red (low compliance) through yellow (moderate) to green (high compliance). Finally, WAC was used to integrate analytical performance, environmental sustainability and practical applicability into a single assessment, supporting a decision-oriented perspective beyond greenness alone. Scores were assigned to predefined criteria grouped into three dimensions: (i) analytical performance (accuracy, precision, sensitivity, selectivity); (ii) environmental impact (reagent hazard, solvent consumption, energy demand, waste generation); and (iii) practical aspects of implementation (simplicity, analysis time, cost, suitability for routine operation). Criteria were scored according to the WAC methodology, generating normalized dimension-specific values [ 15 ]. These three dimensions were subsequently integrated into an overall whiteness index, represented using a color-based graphical output, where higher values indicate a better balance of analytical performance, environmental sustainability and practicality, consistent with the WAC concept [ 13 ]. Lean-oriented perspective for methodological interpretation In addition to greenness and whiteness metrics, a Lean-oriented perspective was incorporated as a qualitative interpretive layer to support how metric outputs were read in the context of laboratory operation. Lean was not implemented as a standalone quantitative method; rather, it served as a set of guiding criteria to interpret analytical practices in terms of process efficiency, waste reduction and operational value. Metric outputs were examined through Lean concepts such as minimizing non-value-adding activities, reducing material and solvent use, simplifying workflows, improving time efficiency, and supporting robust routine operation. Emphasis was placed on attributes commonly linked to inefficiency in pharmaceutical laboratory practice, including excessive reagent demand, high chemical waste generation, workflow complexity, and analyst exposure to hazardous substances. This Lean-oriented interpretation complemented the sustainability metrics by adding an efficiency-based lens consistent with the human-centered and sustainability-driven operational principles associated with Industry 5.0. Results and Discussion 3.1. Initial sustainability screening during method development For all metrics, reagent use, waste generation and operational parameters were calculated to reflect a batch-release practice (blank plus triplicate assays of each AAA), as required under GMP. This choice avoids single-run bias and better represents environmental impact, EHS implications, and resource demand in an industrial setting. Because sustainability constraints often emerge during optimization, an early screening tool was needed before applying more detailed greenness/whiteness metrics. The advanced NEMI fulfilled this role by comparing SFM and PT across operator risk, energy demand, reagent consumption, and waste volume (Fig. 1 a). PT showed the least favorable classifications mainly for reagent use and waste volume, consistent with its solvent-intensive chemistry: formic acid for solubilization, glacial acetic acid as the non-aqueous titration medium, and 0.1 M perchloric acid prepared in glacial acetic acid. Even at the development stage, these choices translated into higher consumption of hazardous reagents and a more demanding waste stream (Table S2). Table S2 highlights the practical consequence: for each analyzed product batch, PT requires substantially more non-aqueous hazardous reagents than SFM. When scaled to release testing, stability studies, and routine verification, this profile amplifies solvent handling, storage, segregation and disposal needs – adding an operational load that is not captured by analytical performance alone. Operator risk for PT was rated medium due to frequent handling of corrosive reagents during preparation and titration. From an EHS perspective, this requires tighter engineering controls, appropriate personal protective equipment, and controlled waste handling – manageable under a regulated quality system, but adding operational burdens to day-to-day laboratory work. In contrast, SFM showed consistently favorable classifications across the advanced NEMI criteria. Aqueous media, lower hazardous reagent demand, and fewer handling steps supported a safety profile and simpler waste management, which reduced handling burden and supported routine GMP testing. Energy demand was favorable for both methods, indicating that instrumentation was not the main discriminator in this comparison. Overall, advanced NEMI worked as intended: not yielding a definitive ranking, but acting as a an early-warning lens that flags safety-, resource-, and waste-related constraints likely to matter once a method is deployed at scale. 3.2. Greenness metrics: comparative environmental performance of PT and SFM Once the methods were considered under finalized conditions, greenness metrics made the practical differences between PT and SFM more explicit – at both the single-score and workflow levels. Eco-Scale . PT accumulated higher penalty points for reagent hazards, occupational exposure, energy demand and waste requirements, leading to a lower Eco-Scale score (74) (Fig. 1 b). The main drivers were the use of glacial acetic acid and the larger reagent volumes inherent to titrimetric practice (Table S2). Although both approaches meet performance and regulatory expectations, Eco-Scale shows that analytical adequacy does not imply environmental equivalence once sustainability parameters are scored systematically (Table S3). Operationally, Eco-Scale also captures what matters beyond the bench: corrosive non-aqueous solvents increase the need for controls for handling, storage and disposal. In contrast, SFM achieved a higher score (93) because it relies on less hazardous reagents and yields a more manageable waste stream (Fig. 1 b). Although SFM was still penalized for waste volume – since total waste is tied to batch-level solution preparation – its residues are predominantly aqueous, with minor contributions from diluted hydroxide (0.1 M) and low-concentration phosphate buffer solutions (25 mM) [ 16 ]. In practice, this profile is more compatible with standard pH-neutralization and aqueous waste-handling procedures used in regulated laboratory settings. Consequently, at scale, the sustainability signal is defined not only by “how much waste” is generated, but by its hazard profile and the operational effort required to manage it. GAPI . GAPI complemented Eco-Scale by showing where burdens concentrate across the workflow (Fig. 1 c). For PT, reagent/solvent-related fields were the dominant hotspots. Based on Table S2, unfavorable classifications were assigned to parameters related to reagents used (7), reagent amount (9), health hazard (10), and operator safety (11). Waste amount and treatment (14–15) were also downgraded, reflecting the presence of non-aqueous corrosive residues requiring controlled handling and disposal. In practice, these hotspots translate into extra steps (segregation, documentation, controls) that increase workload without improving analytical output. The results of each parameter assessed are shown in Table S4. SFM showed a more balanced pictogram, with mostly green/yellow fields and no single dominant hotspot. Its aqueous buffers, diluted inorganic reagents, and simpler preparation supported better classifications for reagent-related and waste management parameters. Even when waste volume persists, treatment is straightforward and aligned with routine wastewater handling. AGREE . AGREE provided a single integrative indicator that summarizes these sustainability characteristics across the 12 GAC principles. It favored SFM over PT (0.61 vs 0.34; Fig. 1 d), supporting a decision-oriented interpretation of overall greenness. For SFM, penalties were concentrated rather than widespread. Despite aqueous media and low-toxicity reagents, Principle 7 (waste generation) was penalized because AGREE considers absolute batch-related waste volume. Moderate scores for Principles 3 and 5 reflected its at-line configuration and conventional setup rather than intrinsic limitations. AGREE evaluates waste volume, toxicity, reagent origin and operator safety as separate criteria; however, it does not operationally distinguish easily treatable aqueous waste streams from hazardous non-aqueous residues. This explains why SFM received penalties in specific parameters despite its favorable environmental and operational profile. In contrast, AGREE assigned widespread cumulative penalties to PT, reflecting sustainability limitations intrinsic to the compendial workflow. Penalties were driven by non-aqueous, corrosives compounds (Principles 9–12) and by the solvent-intensive routine that increases waste generation (Principle 7). Additional deductions reflected the conventional at-line/off-line setup and limited simplification/automation (Principles 3–5). Overall, compared with SFM, PT imposes a compounded burden in terms of safety controls and waste handling. WAC . WAC integrated analytical performance, environmental impact, and practical feasibility into a single decision view (Table S5). In the red dimension, both PT and SFM were fit for assay of AAA. PT benefited from compendial acceptance and established routine use, whereas SFM scored higher in selectivity/sensitivity, consistent with fluorescence-based detection. LOD/LOQ values were outside the validation scope adopted for the compendial batch-release assay. Accordingly, PT received a lower score than SFM in this criterion, consistent with the higher sensitivity expected for fluorescence-based detection and with the observed analytical behavior of both approaches. In the green dimension, differentiation was driven primarily by absolute reagent and waste demand per product batch (G2), which was intentionally scored using real batch-level consumption values to reflect operational impact. Under this criterion, PT was penalized due to its higher consumption of non-aqueous, corrosive reagents, whereas SFM benefited from a lower hazardous burden and predominantly aqueous residues. It should be noted that, consistent with trends observed for other sustainability tools applied in this case study, WAC – particularly batch-level criteria such as G2 – tended to favor miniaturization strategies. This tendency is informative for decision-making, but it should be interpreted cautiously when comparing methods that were not designed to be miniaturized, as is the case for both PT and SFM. Toxicity (G1) and user safety (G4) were treated as distinct. The presence of diluted NaOH justifies a minor toxicity penalty for SFM, but it does not materially compromise operational safety when handled under standard controls. In contrast, PT requires frequent handling of volatile/corrosive non-aqueous acids, typically demanding tighter engineering controls and more restrictive waste handling practices. In the blue dimension, practical implementation favored SFM by reducing handling intensity, waste logistics, and associated control steps, supporting better time and cost efficiency over sustained routine use. PT remained viable and compliant, but its higher sample/reagent demand and more complex waste/safety controls translated into greater operational load. On the whole, WAC “closed” the metric set by showing that the better profile of SFM was not driven by a single attribute, but by a more balanced outcome across performance, sustainability, and implementation constraints (Table S5). Taken together, the applied metrics shifted method selection from “analytically compliant” to “fit for deployment” in a GMP-regulated laboratory. Advanced NEMI flagged early constraints; Eco-Scale and GAPI quantified and localized the main environmental burdens; AGREE summarized overall greenness across GAC principles; and WAC consolidated performance, sustainability, and practicality into an implementation-focused view (Table 1 ). Table 1 Overview of the metrics applied in this case study, their decision role, and comparative outputs for non-aqueous potentiometric titrations (PT) and spectrofluorimetric methods (SFM). Metric Dimension assessed Output type PT SFM Advanced NEMI* Environmental + safety + resource screening Color scale red/yellow yellow/green Eco-Scale Environmental friendliness Score (0–100) 74 93 GAPI* Full analytical workflow Pictogram red/yellow predominant yellow/green predominant AGREE GAC compliance Score (0–1) 0.34 0.61 WAC Analytical performance + environmental impact + practical feasibility Whiteness score (0-100) 65.5 81.3 *Color scale: red: unfavorable; yellow: moderate; green: favorable. This combined reading anticipates how each method will behave in batch-release practice, beyond validation results alone. In practical terms, it helps explain why PT carries a higher safety and waste-management burden than SFM once deployed at scale, and it motivates the Lean and quality-system discussion in the next section. 3.3. Management-system and Lean-oriented interpretation Building on the decision signals provided by the applied metrics, the PT–SFM comparison can be translated into quality-system and operational consequences that become visible once a method is embedded in batch-release practice. In this case study, the key differentiator was not analytical acceptability, but how chemistry and workflow shape EHS controls, waste logistics, deviation/investigation load and execution stability under GMP. From a Lean perspective, PT concentrates non-value-adding work in steps that do not improve the analytical decision but increase process friction: handling and storage of non-aqueous corrosive reagents; segregation and controlled disposal of hazardous residues; and the controls required to keep the workflow compliant and reproducible. In contrast, SFM shifts routine execution toward predominantly aqueous handling and simpler waste streams, reducing hazardous touchpoints and the operational overhead tied to waste management. This difference aligns with Industry 4.0/5.0 expectations for QC, where tighter process control and digital traceability support reliable execution, while sustainability and human-centered operation increasingly influence what is considered fit for deployment beyond analytical compliance. To structure the decision as a controlled improvement rather than an academic comparison, a DMAIC (Define–Measure–Analyze–Improve–Control) logic, commonly used in Six Sigma-driven process improvement, was used as a pragmatic structure to translate metric outputs into a managed method-lifecycle decision under GMP. In this context, DMAIC-style reading helps frame why a change is not solely sustainability-driven. The opportunity is dual: reduce the solvent/EHS burden and mitigate the higher analytical susceptibility observed for PT (e.g., interference sensitivity), while maintaining release suitability. The metrics provide the measurement and diagnosis layer – penalties, workflow hotspots, and the performance–environment–practicality balance – showing structural drivers (non-aqueous corrosive chemistry and solvent-intensive workflow for PT versus predominantly aqueous handling for SFM) and explaining the consistent direction across tools. Operationally, adopting SFM as an alternative can support shorter and more stable execution with simplified safety and waste handling. At scale, this can reduce turnaround time, lessen queueing/lead-time impact, and decrease reanalysis triggers linked to variability and interference susceptibility. In a regulated quality system, the change should be sustained through risk assessment and change control, with defined acceptance criteria, method verification/transfer strategy, analyst training and qualification, and post-implementation monitoring of deviations, OOS/OOT trends, and rework indicators, as well as providing governance-ready traceability. On this basis, the metrics do more than score greenness: they function as a structured input for industrial decision-making by anticipating downstream consequences – safety controls, waste-handling effort, and investigation workload – once the method is executed repeatedly for release and lifecycle activities. Conclusion This case study demonstrates that applying a complementary set of greenness and whiteness metrics provides actionable, governance-ready evidence for method selection in biopharmaceutical QC beyond analytical compliance alone. Under GMP-like batch practice, advanced NEMI efficiently flagged early constraints, while Eco-Scale, GAPI, and AGREE quantified and localized the main sustainability drivers, revealing that the compendial PT concentrates burdens in hazardous non-aqueous reagents and waste management requirements. WAC consolidated these findings with analytical performance and implementation feasibility, translating sustainability signals into a decision-support view suitable for regulated change management. Collectively, the metrics supported the adoption of SFM as a more deployable alternative by reducing EHS touchpoints, simplifying waste logistics, and improving operational efficiency while remaining fit for release testing. Importantly, the key contribution is not a single-score ranking, but the structured triangulation of evidence that connects chemistry, workflow and quality-system consequences, enabling sustainability-informed analytical development decisions aligned with modern industrial expectations. Declarations Supplementary Information. The online version contains supplementary material available for this article (Tables S1–S5). Acknowledgements . The author thanks Bio-Manguinhos/Fiocruz for institutional support. Author Contributions. TMS: Conceptualization; Methodology; Analysis; Writing—original draft; Writing—review and editing. Funding. None. Data Availability. All data supporting the findings of this study are included in the article and its Supplementary Material. Ethics Approval . Not applicable. Consent to Participate. Not applicable. Consent for Publication . Not applicable. Competing Interests . The author declares no competing interests. Human and Animal Rights . Not applicable. 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TrAC Trends Anal Chem. 2021;138:116223. https://doi.org/10.1016/j.trac.2021.116223 . Souza TM, Angelino RS, Ornella LM, Ribeiro ES, Fernandes SFR. Direct spectrofluorimetric methods as alternatives to compendial ones used for the quality control of biopharmaceuticals: development, validation and application. Braz J Pharm Sci. 2024;60:e23126. https://doi.org/10.1590/s2175-97902024e23126 . Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8980645","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":600671798,"identity":"8a9ebb96-e9a2-4299-b289-cafe6b9b490d","order_by":0,"name":"Thallis Martins Souza","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYDACCQbGAw8MYLwKIGZmbsCrg0eCgeFAAlzLGZAWRmK0wHiMbWASvxZ76eYHBxIK6qLl23uMP/ycVxvN3w7U8qNiG25bZI4ZAB12OHfDmTMGhr3bjufOOMzYwNhz5jYehyWAtBzI3SCRY5DAu+1YbgNQCzNjGz4t6R+AWupy58/IMTj4d86x3PmEteSAbGHObbiRY9jM21CTu4Gglhs5BVC/HCtmljl2IHcjUMtBfH5hn5G+8cGHP0CHtTdv/vimpi533vnDBx/8qMCtBR0cBpMHiFYPBHWkKB4Fo2AUjIIRAgAnP2JBeVMrGQAAAABJRU5ErkJggg==","orcid":"","institution":"Oswaldo Cruz Foundation","correspondingAuthor":true,"prefix":"","firstName":"Thallis","middleName":"Martins","lastName":"Souza","suffix":""}],"badges":[],"createdAt":"2026-02-26 18:09:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8980645/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8980645/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104205616,"identity":"1cfe94ea-e73d-4cb8-9f48-b742712f499e","added_by":"auto","created_at":"2026-03-09 06:43:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5572063,"visible":true,"origin":"","legend":"\u003cp\u003eComparative sustainability metrics assessment of non-aqueous potentiometric titrations and spectrofluorimetric methods for aromatic amino acids under batch-level practice: a) advanced NEMI screening (red/yellow/green = low/medium/high greenness); b) Analytical Eco-Scale scores; c) GAPI pictograms (red/yellow/green = low/medium/high greenness); d) AGREE scores and pictograms.\u003c/p\u003e","description":"","filename":"Figure1v.final.png","url":"https://assets-eu.researchsquare.com/files/rs-8980645/v1/c4e46d6f1ab9ebc3b7da3d7a.png"},{"id":104205629,"identity":"6910842b-b74b-4704-86d8-912c8cd29e2d","added_by":"auto","created_at":"2026-03-09 06:43:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5785703,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8980645/v1/4c98e136-6532-4619-9aa7-9241d8eddb1b.pdf"},{"id":104205612,"identity":"a671bcd7-f48b-4ea7-a8dc-044b3bf48bfa","added_by":"auto","created_at":"2026-03-09 06:43:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":69157,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8980645/v1/543d5dcfde075f468adce962.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating sustainability metrics into analytical decision-making in biopharmaceutical quality control: an industrial case study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnalytical methods are central to pharmaceutical quality control (QC), supporting the assessment of raw materials, active pharmaceutical ingredients, and finished products to ensure regulatory compliance and patient safety. Method development and validation are therefore critical activities, traditionally driven by performance attributes such as accuracy, precision, selectivity, robustness and alignment with pharmacopeial requirements In industrial settings, these expectations are reinforced by regulatory considerations linked to method lifecycle and the adoption of innovative analytical technologies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contemporary laboratories, analytical procedures are embedded in end-to-end operational processes rather than functioning as stand-alone technical steps. Even when methods deliver comparable analytical outcomes, they may differ markedly in solvent consumption, waste generation, procedural complexity, turnaround time, and occupational risk. These differences shape laboratory cost, efficiency, and environmental footprint, yet they are often treated as secondary criteria when methods are updated or replaced [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis performance-centered approach has fueled interest in evaluation strategies that consider sustainability and implementation feasibility alongside analytical quality. Such broader criteria are increasingly relevant as pharmaceutical manufacturing and control evolve under Industry 4.0 and 5.0 paradigms. Industry 4.0 emphasizes digitalization, automation, and data governance to strengthen process control, compliance and reliability. Although sustainability is not its primary driver, efficiency gains (e.g., lower resource use, tighter control, real-time data) can facilitate greener practices. Industry 5.0 extends this trajectory by reinforcing the synergy between advanced technologies and human expertise, and by explicitly integrating ethical and sustainability-oriented priorities\u0026mdash;such as environmental responsibility and social considerations\u0026mdash;into efficiency-driven systems [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis broader view is particularly relevant in QC, where the same procedure is repeated daily and small differences in solvent use, safety controls and waste logistics accumulate into measurable operational burdens. In this context, sustainability metrics have gained relevance as structured tools to translate chemical hazards, solvent intensity and waste-handling demands into comparable indicators of analytical greenness [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn routine practice, several compendial procedures still rely on hazardous substances (corrosive and/or toxic) and may require large reagent volumes depending on the technique. This increases exposure risks and can generate substantial waste. In response, sustainability metrics have been proposed as structured tools to quantify the \u0026ldquo;greenness\u0026rdquo; of analytical activities and translate these burdens into comparable indicators [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin this context, the 12 principles of Green Analytical Chemistry (GAC) provide a relevant foundation for pharmaceutical analysis, where routine testing can be resource-intensive due to solvent, reagent, and energy demands. Applying GAC encourages the development of new analytical strategies\u0026mdash;or the redesign of existing ones\u0026mdash;to reduce dependence on hazardous chemicals while maintaining (or improving) analytical performance. When properly implemented, this approach yields practical benefits, including reduced hazardous waste, improved laboratory safety, better cost efficiency, and strengthened alignment with Good Manufacturing Practices (GMP) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany metrics have been developed to operationalize GAC in practice, yet none fully capture the combined tradeoffs that drive method choices in industrial laboratories\u0026mdash;namely environmental, health and safety (EHS) burden, workflow complexity, and analytical performance. In routine settings, these dimensions are inseparable: a method may be analytically compliant and still impose disproportionate demands on solvent logistics, safety controls, and waste handling once repeated at scale. For this reason, this case study adopts a multi-metric strategy to screen constraints early, compare methods under finalized operating conditions, visualize workflow hotspots, and integrate greenness with performance and feasibility in a decision-oriented view [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWithin this landscape, screening-type tools help reveal early constraints, whereas more structured metrics support comparisons once operating conditions are finalized. The advanced National Environmental Methods Index (advanced NEMI), for instance, offers a straightforward way to flag critical burdens associated with operator risk, energy demand, reagent consumption and waste volume [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In contrast, the Analytical Eco-Scale provides a semi-quantitative perspective by translating reagent hazards and quantities, occupational risk, energy demand, and waste into penalty points, enabling a direct comparison between analytically acceptable alternatives while maintaining interpretability [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond numerical summaries, workflow-based tools are particularly useful when the sustainability burden is concentrated in specific stages rather than uniformly distributed. In this regard, the Green Analytical Procedure Index (GAPI) allows visualizing impacts across sample preparation, reagents/solvents, instrumentation and waste handling, making it easier to identify stages that act as environmental bottlenecks [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Complementarily, the Analytical GREEnness metric (AGREE) aggregates the 12 GAC principles into a normalized score and pictogram, offering an integrated view of greenness while still indicating which principles contribute most to penalties under batch-level operating conditions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinally, because industrial method choices rarely depend on \u0026ldquo;greenness\u0026rdquo; alone, the White Analytical Chemistry (WAC) concept is relevant to bring sustainability into a decision-oriented perspective. By integrating analytical performance, environmental impact, and practical feasibility into a whiteness assessment, WAC supports comparisons when methods must remain compliant and operationally viable while reducing environmental and safety burdens. Despite the availability of these tools, reports describing their application as a single input to support method-related decisions in an industrial context\u0026mdash;beyond regulatory criteria and conventional analytical performance\u0026mdash;remain limited [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis case study examines how the combined use of greenness and whiteness metrics can support methodological decisions during the development and validation of spectrofluorimetric assays for aromatic amino acids (AAA), using a compendial potentiometric titration (PT) procedure as the benchmark in a biopharmaceutical QC setting, under a sustainability- and efficiency-oriented perspective aligned with Lean principles.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCase study description\u003c/h2\u003e \u003cp\u003eThis case study is based on the analytical methods reported by Souza \u003cem\u003eet al\u003c/em\u003e. (2024) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and examines how sustainability-oriented metrics can complement conventional regulatory and performance-based criteria in method-related decisions.\u003c/p\u003e \u003cp\u003eThe evaluated procedures were designed for routine use, where robustness, compliance and operational feasibility are mandatory. Under such conditions, pharmacopeial acceptance and analytical performance typically guide method development and validation, while factors such as reagent hazards, waste generation, workflow complexity and resource demand may receive less emphasis.\u003c/p\u003e \u003cp\u003eTwo approaches were considered: (i) a SFM developed for individual determination of AAA (phenylalanine \u0026ndash; PHE, tryptophan \u0026ndash; TRP, and tyrosine \u0026ndash; TYR) used as raw materials in biopharmaceutical formulations; and (ii) a compendial PT applied to these analytes using glacial acetic acid as non-aqueous medium. Although both approaches are suitable for routine application, they differ in solvent profile, reagent hazard, waste stream characteristics, selectivity/robustness and operational requirements.\u003c/p\u003e \u003cp\u003eThese contrasts make the PT\u0026ndash;SFM comparison a practical test case for exploring how sustainability-oriented tools influence method-related decisions. To do so, a set of greenness and whiteness metrics was applied to translate GAC/WAC principles into structured indicators of environmental impact, safety, resource use, and feasibility of implementation. Together, these tools support a multi-angle assessment aligned with sustainability-driven decision trends associated with Industry 5.0.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSustainability and whiteness metrics\u003c/h3\u003e\n\u003cp\u003eA set of greenness and whiteness metrics was applied to the analytical methods evaluated in this case study to capture environmental, operational and methodological attributes under a biopharmaceutical laboratory scenario. For all metrics, reagent consumption, waste generation and operational parameters were estimated considering batch-level practice (blank plus triplicate assays), as required for GMP-based release testing.\u003c/p\u003e \u003cp\u003eAt the development stage, advanced NEMI was used as an initial screening tool. Sustainability-related parameters were classified across four criteria\u0026mdash;operator risk, energy consumption, reagent consumption, and waste volume\u0026mdash;and were reported using a semi-quantitative color scale (green/yellow/red), according to the metric definition and subsequent refinements.\u003c/p\u003e \u003cp\u003eAfter method development and validation, additional metrics were used to characterize the finalized procedures under routine-operating conditions.\u003c/p\u003e \u003cp\u003eEnvironmental friendliness was assessed using the Analytical Eco-Scale, a semi-quantitative approach based on penalty points assigned to reagent characteristics (amount and hazard classification), energy requirements (instrumental demand), occupational risk (operator exposure), and waste generation (volume and type requiring treatment) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The overall score was calculated using Eq.\u0026nbsp;(1):\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"724\" height=\"106\"\u003e\u003c/p\u003e\u003cp\u003eHigher values indicate greener procedures, whereas lower values reflect an increasing environmental burden.\u003c/p\u003e \u003cp\u003eA workflow-oriented evaluation was then performed using GAPI, which considers environmental impact across the analytical process (sample preparation, reagents/solvents, instrumentation, energy consumption, and waste generation) under finalized conditions. Results are presented as five pentagonal diagrams: two related to sample preparation, one addressing reagents/solvents, one associated with instrumentation, and one related to quantification. Each field is color-coded to indicate low (green), moderate (yellow), or high (red) environmental impact according to predefined criteria [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAGREE was used to generate an integrative numerical indicator by mapping the 12 GAC principles into a normalized score. The evaluation was performed using the AGREE software, which assigns weighted scores to each principle based on method characteristics and combines them through an algorithm to produce a value from 0 (non-green) to 1 (ideal green) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Results are expressed as a circular pictogram with one segment per GAC principle, color-coded from red (low compliance) through yellow (moderate) to green (high compliance).\u003c/p\u003e \u003cp\u003eFinally, WAC was used to integrate analytical performance, environmental sustainability and practical applicability into a single assessment, supporting a decision-oriented perspective beyond greenness alone. Scores were assigned to predefined criteria grouped into three dimensions: (i) analytical performance (accuracy, precision, sensitivity, selectivity); (ii) environmental impact (reagent hazard, solvent consumption, energy demand, waste generation); and (iii) practical aspects of implementation (simplicity, analysis time, cost, suitability for routine operation). Criteria were scored according to the WAC methodology, generating normalized dimension-specific values [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These three dimensions were subsequently integrated into an overall whiteness index, represented using a color-based graphical output, where higher values indicate a better balance of analytical performance, environmental sustainability and practicality, consistent with the WAC concept [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eLean-oriented perspective for methodological interpretation\u003c/h3\u003e\n\u003cp\u003eIn addition to greenness and whiteness metrics, a Lean-oriented perspective was incorporated as a qualitative interpretive layer to support how metric outputs were read in the context of laboratory operation. Lean was not implemented as a standalone quantitative method; rather, it served as a set of guiding criteria to interpret analytical practices in terms of process efficiency, waste reduction and operational value.\u003c/p\u003e \u003cp\u003eMetric outputs were examined through Lean concepts such as minimizing non-value-adding activities, reducing material and solvent use, simplifying workflows, improving time efficiency, and supporting robust routine operation. Emphasis was placed on attributes commonly linked to inefficiency in pharmaceutical laboratory practice, including excessive reagent demand, high chemical waste generation, workflow complexity, and analyst exposure to hazardous substances.\u003c/p\u003e \u003cp\u003eThis Lean-oriented interpretation complemented the sustainability metrics by adding an efficiency-based lens consistent with the human-centered and sustainability-driven operational principles associated with Industry 5.0.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e \u003cb\u003e3.1. Initial sustainability screening during method development\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor all metrics, reagent use, waste generation and operational parameters were calculated to reflect a batch-release practice (blank plus triplicate assays of each AAA), as required under GMP. This choice avoids single-run bias and better represents environmental impact, EHS implications, and resource demand in an industrial setting.\u003c/p\u003e \u003cp\u003eBecause sustainability constraints often emerge during optimization, an early screening tool was needed before applying more detailed greenness/whiteness metrics. The advanced NEMI fulfilled this role by comparing SFM and PT across operator risk, energy demand, reagent consumption, and waste volume (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePT showed the least favorable classifications mainly for reagent use and waste volume, consistent with its solvent-intensive chemistry: formic acid for solubilization, glacial acetic acid as the non-aqueous titration medium, and 0.1 M perchloric acid prepared in glacial acetic acid. Even at the development stage, these choices translated into higher consumption of hazardous reagents and a more demanding waste stream (Table S2).\u003c/p\u003e \u003cp\u003eTable S2 highlights the practical consequence: for each analyzed product batch, PT requires substantially more non-aqueous hazardous reagents than SFM. When scaled to release testing, stability studies, and routine verification, this profile amplifies solvent handling, storage, segregation and disposal needs \u0026ndash; adding an operational load that is not captured by analytical performance alone.\u003c/p\u003e \u003cp\u003eOperator risk for PT was rated medium due to frequent handling of corrosive reagents during preparation and titration. From an EHS perspective, this requires tighter engineering controls, appropriate personal protective equipment, and controlled waste handling \u0026ndash; manageable under a regulated quality system, but adding operational burdens to day-to-day laboratory work.\u003c/p\u003e \u003cp\u003eIn contrast, SFM showed consistently favorable classifications across the advanced NEMI criteria. Aqueous media, lower hazardous reagent demand, and fewer handling steps supported a safety profile and simpler waste management, which reduced handling burden and supported routine GMP testing.\u003c/p\u003e \u003cp\u003eEnergy demand was favorable for both methods, indicating that instrumentation was not the main discriminator in this comparison. Overall, advanced NEMI worked as intended: not yielding a definitive ranking, but acting as a an early-warning lens that flags safety-, resource-, and waste-related constraints likely to matter once a method is deployed at scale.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2. Greenness metrics: comparative environmental performance of PT and SFM\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOnce the methods were considered under finalized conditions, greenness metrics made the practical differences between PT and SFM more explicit \u0026ndash; at both the single-score and workflow levels.\u003c/p\u003e \u003cp\u003e \u003cem\u003eEco-Scale\u003c/em\u003e. PT accumulated higher penalty points for reagent hazards, occupational exposure, energy demand and waste requirements, leading to a lower Eco-Scale score (74) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The main drivers were the use of glacial acetic acid and the larger reagent volumes inherent to titrimetric practice (Table S2). Although both approaches meet performance and regulatory expectations, Eco-Scale shows that analytical adequacy does not imply environmental equivalence once sustainability parameters are scored systematically (Table S3).\u003c/p\u003e \u003cp\u003eOperationally, Eco-Scale also captures what matters beyond the bench: corrosive non-aqueous solvents increase the need for controls for handling, storage and disposal. In contrast, SFM achieved a higher score (93) because it relies on less hazardous reagents and yields a more manageable waste stream (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Although SFM was still penalized for waste volume \u0026ndash; since total waste is tied to batch-level solution preparation \u0026ndash; its residues are predominantly aqueous, with minor contributions from diluted hydroxide (0.1 M) and low-concentration phosphate buffer solutions (25 mM) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In practice, this profile is more compatible with standard pH-neutralization and aqueous waste-handling procedures used in regulated laboratory settings. Consequently, at scale, the sustainability signal is defined not only by \u0026ldquo;how much waste\u0026rdquo; is generated, but by its hazard profile and the operational effort required to manage it.\u003c/p\u003e \u003cp\u003e \u003cem\u003eGAPI\u003c/em\u003e. GAPI complemented Eco-Scale by showing where burdens concentrate across the workflow (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). For PT, reagent/solvent-related fields were the dominant hotspots. Based on Table S2, unfavorable classifications were assigned to parameters related to reagents used (7), reagent amount (9), health hazard (10), and operator safety (11). Waste amount and treatment (14\u0026ndash;15) were also downgraded, reflecting the presence of non-aqueous corrosive residues requiring controlled handling and disposal. In practice, these hotspots translate into extra steps (segregation, documentation, controls) that increase workload without improving analytical output. The results of each parameter assessed are shown in Table S4.\u003c/p\u003e \u003cp\u003eSFM showed a more balanced pictogram, with mostly green/yellow fields and no single dominant hotspot. Its aqueous buffers, diluted inorganic reagents, and simpler preparation supported better classifications for reagent-related and waste management parameters. Even when waste volume persists, treatment is straightforward and aligned with routine wastewater handling.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAGREE\u003c/em\u003e. AGREE provided a single integrative indicator that summarizes these sustainability characteristics across the 12 GAC principles. It favored SFM over PT (0.61 \u003cem\u003evs\u003c/em\u003e 0.34; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), supporting a decision-oriented interpretation of overall greenness.\u003c/p\u003e \u003cp\u003eFor SFM, penalties were concentrated rather than widespread. Despite aqueous media and low-toxicity reagents, Principle 7 (waste generation) was penalized because AGREE considers absolute batch-related waste volume. Moderate scores for Principles 3 and 5 reflected its at-line configuration and conventional setup rather than intrinsic limitations. AGREE evaluates waste volume, toxicity, reagent origin and operator safety as separate criteria; however, it does not operationally distinguish easily treatable aqueous waste streams from hazardous non-aqueous residues. This explains why SFM received penalties in specific parameters despite its favorable environmental and operational profile.\u003c/p\u003e \u003cp\u003eIn contrast, AGREE assigned widespread cumulative penalties to PT, reflecting sustainability limitations intrinsic to the compendial workflow. Penalties were driven by non-aqueous, corrosives compounds (Principles 9\u0026ndash;12) and by the solvent-intensive routine that increases waste generation (Principle 7). Additional deductions reflected the conventional at-line/off-line setup and limited simplification/automation (Principles 3\u0026ndash;5). Overall, compared with SFM, PT imposes a compounded burden in terms of safety controls and waste handling.\u003c/p\u003e \u003cp\u003e \u003cem\u003eWAC\u003c/em\u003e. WAC integrated analytical performance, environmental impact, and practical feasibility into a single decision view (Table S5). In the red dimension, both PT and SFM were fit for assay of AAA. PT benefited from compendial acceptance and established routine use, whereas SFM scored higher in selectivity/sensitivity, consistent with fluorescence-based detection. LOD/LOQ values were outside the validation scope adopted for the compendial batch-release assay. Accordingly, PT received a lower score than SFM in this criterion, consistent with the higher sensitivity expected for fluorescence-based detection and with the observed analytical behavior of both approaches.\u003c/p\u003e \u003cp\u003eIn the green dimension, differentiation was driven primarily by absolute reagent and waste demand per product batch (G2), which was intentionally scored using real batch-level consumption values to reflect operational impact. Under this criterion, PT was penalized due to its higher consumption of non-aqueous, corrosive reagents, whereas SFM benefited from a lower hazardous burden and predominantly aqueous residues. It should be noted that, consistent with trends observed for other sustainability tools applied in this case study, WAC \u0026ndash; particularly batch-level criteria such as G2 \u0026ndash; tended to favor miniaturization strategies. This tendency is informative for decision-making, but it should be interpreted cautiously when comparing methods that were not designed to be miniaturized, as is the case for both PT and SFM.\u003c/p\u003e \u003cp\u003eToxicity (G1) and user safety (G4) were treated as distinct. The presence of diluted NaOH justifies a minor toxicity penalty for SFM, but it does not materially compromise operational safety when handled under standard controls. In contrast, PT requires frequent handling of volatile/corrosive non-aqueous acids, typically demanding tighter engineering controls and more restrictive waste handling practices.\u003c/p\u003e \u003cp\u003eIn the blue dimension, practical implementation favored SFM by reducing handling intensity, waste logistics, and associated control steps, supporting better time and cost efficiency over sustained routine use. PT remained viable and compliant, but its higher sample/reagent demand and more complex waste/safety controls translated into greater operational load. On the whole, WAC \u0026ldquo;closed\u0026rdquo; the metric set by showing that the better profile of SFM was not driven by a single attribute, but by a more balanced outcome across performance, sustainability, and implementation constraints (Table S5).\u003c/p\u003e \u003cp\u003eTaken together, the applied metrics shifted method selection from \u0026ldquo;analytically compliant\u0026rdquo; to \u0026ldquo;fit for deployment\u0026rdquo; in a GMP-regulated laboratory. Advanced NEMI flagged early constraints; Eco-Scale and GAPI quantified and localized the main environmental burdens; AGREE summarized overall greenness across GAC principles; and WAC consolidated performance, sustainability, and practicality into an implementation-focused view (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of the metrics applied in this case study, their decision role, and comparative outputs for non-aqueous potentiometric titrations (PT) and spectrofluorimetric methods (SFM).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDimension assessed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOutput type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSFM\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced NEMI*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental +\u003c/p\u003e \u003cp\u003esafety\u0026thinsp;+\u0026thinsp;resource screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eColor scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ered/yellow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyellow/green\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEco-Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental friendliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScore (0\u0026ndash;100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPI*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull analytical workflow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePictogram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ered/yellow\u003c/p\u003e \u003cp\u003epredominant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eyellow/green\u003c/p\u003e \u003cp\u003epredominant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGREE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAC compliance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScore (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnalytical performance\u0026thinsp;+\u0026thinsp;environmental impact\u0026thinsp;+\u0026thinsp;practical feasibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhiteness score\u003c/p\u003e \u003cp\u003e(0-100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Color scale: red: unfavorable; yellow: moderate; green: favorable.\u003c/p\u003e \u003cp\u003eThis combined reading anticipates how each method will behave in batch-release practice, beyond validation results alone. In practical terms, it helps explain why PT carries a higher safety and waste-management burden than SFM once deployed at scale, and it motivates the Lean and quality-system discussion in the next section.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3. Management-system and Lean-oriented interpretation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBuilding on the decision signals provided by the applied metrics, the PT\u0026ndash;SFM comparison can be translated into quality-system and operational consequences that become visible once a method is embedded in batch-release practice. In this case study, the key differentiator was not analytical acceptability, but how chemistry and workflow shape EHS controls, waste logistics, deviation/investigation load and execution stability under GMP.\u003c/p\u003e \u003cp\u003eFrom a Lean perspective, PT concentrates non-value-adding work in steps that do not improve the analytical decision but increase process friction: handling and storage of non-aqueous corrosive reagents; segregation and controlled disposal of hazardous residues; and the controls required to keep the workflow compliant and reproducible. In contrast, SFM shifts routine execution toward predominantly aqueous handling and simpler waste streams, reducing hazardous touchpoints and the operational overhead tied to waste management. This difference aligns with Industry 4.0/5.0 expectations for QC, where tighter process control and digital traceability support reliable execution, while sustainability and human-centered operation increasingly influence what is considered fit for deployment beyond analytical compliance.\u003c/p\u003e \u003cp\u003eTo structure the decision as a controlled improvement rather than an academic comparison, a DMAIC (Define\u0026ndash;Measure\u0026ndash;Analyze\u0026ndash;Improve\u0026ndash;Control) logic, commonly used in Six Sigma-driven process improvement, was used as a pragmatic structure to translate metric outputs into a managed method-lifecycle decision under GMP.\u003c/p\u003e \u003cp\u003eIn this context, DMAIC-style reading helps frame why a change is not solely sustainability-driven. The opportunity is dual: reduce the solvent/EHS burden and mitigate the higher analytical susceptibility observed for PT (e.g., interference sensitivity), while maintaining release suitability. The metrics provide the measurement and diagnosis layer \u0026ndash; penalties, workflow hotspots, and the performance\u0026ndash;environment\u0026ndash;practicality balance \u0026ndash; showing structural drivers (non-aqueous corrosive chemistry and solvent-intensive workflow for PT versus predominantly aqueous handling for SFM) and explaining the consistent direction across tools.\u003c/p\u003e \u003cp\u003eOperationally, adopting SFM as an alternative can support shorter and more stable execution with simplified safety and waste handling. At scale, this can reduce turnaround time, lessen queueing/lead-time impact, and decrease reanalysis triggers linked to variability and interference susceptibility. In a regulated quality system, the change should be sustained through risk assessment and change control, with defined acceptance criteria, method verification/transfer strategy, analyst training and qualification, and post-implementation monitoring of deviations, OOS/OOT trends, and rework indicators, as well as providing governance-ready traceability.\u003c/p\u003e \u003cp\u003eOn this basis, the metrics do more than score greenness: they function as a structured input for industrial decision-making by anticipating downstream consequences \u0026ndash; safety controls, waste-handling effort, and investigation workload \u0026ndash; once the method is executed repeatedly for release and lifecycle activities.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis case study demonstrates that applying a complementary set of greenness and whiteness metrics provides actionable, governance-ready evidence for method selection in biopharmaceutical QC beyond analytical compliance alone. Under GMP-like batch practice, advanced NEMI efficiently flagged early constraints, while Eco-Scale, GAPI, and AGREE quantified and localized the main sustainability drivers, revealing that the compendial PT concentrates burdens in hazardous non-aqueous reagents and waste management requirements. WAC consolidated these findings with analytical performance and implementation feasibility, translating sustainability signals into a decision-support view suitable for regulated change management. Collectively, the metrics supported the adoption of SFM as a more deployable alternative by reducing EHS touchpoints, simplifying waste logistics, and improving operational efficiency while remaining fit for release testing. Importantly, the key contribution is not a single-score ranking, but the structured triangulation of evidence that connects chemistry, workflow and quality-system consequences, enabling sustainability-informed analytical development decisions aligned with modern industrial expectations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information.\u003c/strong\u003e The online version contains supplementary material available for this article (Tables S1\u0026ndash;S5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e. The author thanks Bio-Manguinhos/Fiocruz for institutional support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions.\u003c/strong\u003e TMS: Conceptualization; Methodology; Analysis; Writing\u0026mdash;original draft; Writing\u0026mdash;review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability.\u003c/strong\u003e All data supporting the findings of this study are included in the article and its Supplementary Material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e. Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate.\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e. Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e. The author declares no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman and Animal Rights\u003c/strong\u003e. Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration.\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). ICH Harmonised Guideline: Validation of Analytical Procedures Q2(R2). 2023. 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White Analytical Chemistry: An approach to reconcile the principles of Green Analytical Chemistry and functionality. TrAC Trends Anal Chem. 2021;138:116223. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.trac.2021.116223\u003c/span\u003e\u003cspan address=\"10.1016/j.trac.2021.116223\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouza TM, Angelino RS, Ornella LM, Ribeiro ES, Fernandes SFR. Direct spectrofluorimetric methods as alternatives to compendial ones used for the quality control of biopharmaceuticals: development, validation and application. Braz J Pharm Sci. 2024;60:e23126. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/s2175-97902024e23126\u003c/span\u003e\u003cspan address=\"10.1590/s2175-97902024e23126\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"compendial methods, quality control, Six Sigma, spectrofluorimetry, sustainability metrics","lastPublishedDoi":"10.21203/rs.3.rs-8980645/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8980645/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: Analytical methods of pharmaceutical quality control (QC) are typically selected for regulatory compliance and analytical performance, while sustainability and operational feasibility are often secondary. This case study aimed to demonstrate how combining greenness and whiteness metrics can support method-related decisions in QC under batch practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Compendial non-aqueous potentiometric titrations (PT) for aromatic amino acids used as raw materials were compared with alternatives spectrofluorimetric methods (SFM) developed for assays. The Advanced National Environmental Methods Index (advanced NEMI) was applied during development as an early screening tool, followed by the Analytical Eco-Scale and Green Analytical Procedure Index (GAPI), and Analytical GREEnness metric (AGREE) to quantify and localize environmental burdens under finalized conditions. Finally, White Analytical Chemistry (WAC) was employed to integrate analytical performance, environmental impact, and implementation practicality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Across these tools, PT was penalized by corrosive non-aqueous chemistry and higher hazardous reagent and waste demands, implying greater safety controls and waste-handling effort at scale. Triangulation converged on the same decision signal: SFM remained fit for release testing while reducing hazardous touchpoints and simplifying waste logistics, yielding superior greenness indicators (Eco-Scale: 93 \u003cem\u003evs\u003c/em\u003e. 74; AGREE: 0.61 \u003cem\u003evs\u003c/em\u003e. 0.34) and a higher WAC score (81.3 \u003cem\u003evs\u003c/em\u003e. 65.5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Overall, the metric set shifted the basis from “analytically compliant” to “fit for deployment,” framed by a Six Sigma improvement logic (Define–Measure–Analyze–Improve–Control), thereby strengthening sustainability-informed analytical development and change-control decisions in industrial QC.\u003c/p\u003e","manuscriptTitle":"Integrating sustainability metrics into analytical decision-making in biopharmaceutical quality control: an industrial case study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 06:42:24","doi":"10.21203/rs.3.rs-8980645/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df360da4-1fc9-45bd-a6a8-89460ba3ffce","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-10T19:23:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 06:42:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8980645","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8980645","identity":"rs-8980645","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}