Greenness assessment of microextraction techniques in therapeutic drug monitoring

Abstract

Aim: In this study, we evaluated the greenness and whiteness scores for microextraction techniques used in therapeutic drug monitoring. Additionally, the cons and pros of each evaluated method and their impacts on the provided scores are also discussed. Materials & methods: The Analytical Greenness Sample Preparation metric tool and white analytical chemistry principles are used for related published works (2007–2023). Results & conclusion: This study provided valuable insights for developing methods based on microextraction techniques with a balance in greenness and whiteness areas. Some methods based on a specific technique recorded higher scores, making them suitable candidates as green analytical approaches, and some others achieved high scores both in green and white areas with a satisfactory balance between principles.

Plain language summary

Summary points.

• According to our most recent data, this is the first report on the assessment of the greenness and whiteness of a set of microextraction techniques for a particular purpose like therapeutic drug monitoring.

• The greenness and whiteness evaluations of microextraction techniques applied in therapeutic drug monitoring were assessed using the Analytical Greenness Sample Preparation (AGREEprep) metric tool and 12-point white analytical chemistry (WAC) principles.

• The AGREE metric tool was used for assessing specific criteria related to WAC.

• Some microextraction techniques demonstrated high green scores by the AGREEprep tool.

• Most of the evaluated techniques with WAC principles provided high scores in red principles (analytical performance).

• Some microextraction techniques with high greenness scores achieved high whiteness scores to provide a balance between all principles.

Therapeutic drug monitoring (TDM) is the established method for clinically applying personalized drug administration and supervision. It consists of analyzing the levels of drugs and their byproducts in biological samples like blood, urine and saliva by using advanced analytical techniques and computer technology. Collecting blood samples from patients in real time is the most straightforward approach to obtaining drug concentration information for clinical use, promoting rational drug administration [1]. In addition, TDM is especially important for types of drugs that have specific effective concentration ranges and can lead to toxic side effects at the same dose like antibiotics, antiepileptic drugs, antifungal drugs, immunosuppressive drugs, anticancer drugs and cardiovascular system drugs [2]. Furthermore, it has been established that the concentration of drugs in biofluids is a more effective approach than just considering dosage to assess the efficiency or toxicity of drugs in the body. This attribute necessitates offering techniques for on-site use to be sensitive, easy to use and require less time for a complete analysis [3]. In this regard, results obtained from development processes on the application of microextraction techniques, especially in the bioanalysis of drugs, are promising.

Bioanalysis is a kind of analytical study that involves the quantitative measurement of special analytes in various biological samples such as drugs, hormones and proteins [4]. This is considered a significant advancement in the field of medicine, with widespread applications including the development of pharmaceuticals, doping control and monitoring, and forensic medicine [5]. In bioanalytical methods, sample preparation is an important part due to the intricate nature of the matrix of biological samples. Overcoming this complexity is a determining factor in obtaining satisfactory results in the sensitivity and accuracy of methods. Microextraction is a widely used and effective technique for bioanalysis. Several published articles have detailed significant advancements using these techniques for the bioanalysis of drugs [6] to gain special advantages over conventional methods, like providing greater sensitivity (by the combination of efficient extraction, reduced matrix effects, enhanced preconcentration and reduced solvent usage), selectivity and lower detection limits. They are also less time-consuming and require small sample volumes compared with traditional extraction approaches [7]. Microextraction techniques can be classified into two main categories, solid-phase microextraction (SPME) and liquid-phase microextraction (LPME). Their subtypes including in-tube SPME, pipette-tip SPE, μ-SPE, fiber-SPME, microextraction by packed sorbent (MEPS), stir bar sorptive extraction (SBSE), fabric-phase sorptive extraction (FPSE), thin-film microextraction (TFME) and magnetic SPE (MSPE) for SPME, and single-drop microextraction, hollow fiber LPME and dispersive liquid–liquid microextraction for LPME class. Due to the special aforementioned characteristics of these techniques, many modifications and developments have been reported on the use of such microextraction techniques to TDM [8,9]. In most cases, these modifications resulted in green microextraction techniques, although in some studies, this may not have been the intention.

Today, scientists are trying to make analytical and bioanalytical chemistry laboratories green. The first concept of green chemistry was introduced in 1999 by Anastas and Warner [10]. Since then, several researchers have assessed and refined these principles for green analytical chemistry (GAC) [11,12]. This was introduced to provide advancements in analytical techniques reaching greener and more cost-effective methods. This consists of reduced usage of solvents, toxic substances and energy, and also minimizing steps and the time required. Assessing the greenness of an analytical method can be difficult due to various influencing factors [13]. Therefore, specific criteria and factors are necessary to determine the method's greenness area. To do this, several methods for GAC metrics have been suggested, such as the National Environmental Methods Index [14], Analytical Eco-Scale [15], RGB additive color model [16], Green Analytical Procedure Index [17], AGREE [18] and AGREEprep [19].

The Analytical Greenness Sample Preparation (AGREEprep) tool was first proposed in 2022 by Psillakis, Tobiszewski and their coworkers for evaluation of the greenness of sample preparation methods [19]. This metric is developed based on the ten principles of green sample preparation [20] with a 0–1 scale subscore and provides an opportunity to assign different weights for each criterion and finally generate a greenness result by considering all these variables. This user-friendly metric tool can be used by the free-access AGREEprep software which provides a simple platform for inputting data and generating results with an easy-to-read pictogram. The assessment criteria based on the ten principles of green sample preparation [20] consist of 1) favoring in situ sample preparation; 2) using safer solvents and reagents; 3) targeting sustainable, reusable and renewable materials; 4) minimizing waste; 5) minimizing sample, chemical and material amounts; 6) maximizing sample throughput; 7) integrating steps and promoting automation; 8) minimizing energy consumption; 9) choosing the greenest possible post-sample preparation configuration for analysis; and 10) ensuring safe procedures for the operator. For all techniques in this study, we have used the assigned default weight of each criterion in the software. To access the results of all greenness assessments via the AGREEprep tool, please refer to the Supplementary Files.

One of the main important issues in TDM and biological analysis is providing methods to achieve high sensitivity, selectivity and accuracy as well as being cost-effective, simple and fast. In some cases, improving green principles may contradict these requirements and decrease their functionality. In this regard, we need to utilize an algorithm to consider all of these requirements through greening a method. In this context, the white analytical chemistry (WAC) tool may be a suitable choice for this purpose. WAC examines the quality of analytical methods, their applicability and cost–effectiveness, and at the same time covers the greenness of the method. This term was proposed to address the drawbacks of GAC by Nowak et al. in 2021 [21]. The principle of 12-WAC is the extension of 12-GAC. This tool provides the conditions that we can, in addition to focusing on the greening of the analytical method, ensure that the method still produces reliable analytical results. So, by considering this threat while trying to make a method green, the practicality and analytical performance of the method may be negatively affected, making the application of WAC more important specifically in TDM. It is imperative to bear in mind that the main purpose of WAC is to provide a balance across all required criteria of greenness, analytical applicability and economical principles. Considering the therapeutic implications and thus due to the priority of sensitivity, precision and accuracy over GAC in TDM, providing this balance becomes significantly important. However, while concentrating on these critical parameters, promoting the greenness of methods should not be at risk. While in some cases such items may contradict each other, in most cases, we could find a good balance across all required criteria.

In practice, we have used a modified work in the area of 12-WAC principles reported by Nowak et al. [21] which are based on the RGB algorithm [16] that consists of three main parts: red, green and blue, which demonstrate the reality of mixing colors red, blue and green to provide light with white color. In WAC, the red principles are related to analytical performance whereas the green principles are associated with green chemistry criteria based on the AGREE metric, and the blue ones relate to the practical considerations. In this regard, we have to look for a good balance with high scores in these three colors through WAC analysis to achieve a good whiteness in a method. Each principle consists of four criteria. Details are provided below [22], and to find out about the scoring rule, please refer to the article reported by Nowak et al. [21]:

Red principles:

• R1.Scope of application. Analytical methods should have the widest possible range of applicability expressed in the number of simultaneously determined analytes, range of linearity of the determinations, compatibility with various types of samples and resistance to the presence of potential interferences.
• R2. Limit of detection (LOD) and limit of quantification (LOQ). Analytical methods should have the lowest possible LODs and LOQs.
• R3.Precision. Analytical methods should be characterized by the best possible precision expressed in the repeatability and reproducibility of the results.
• R4.Accuracy. Analytical methods should be as accurate as possible (minimal relative error of determinations and recovery as close to 100% as possible).

Green principles:

• G1.Toxicity of reagents. Analytical methods should be characterized by the lowest possible toxicity of reagents used and the maximum share of biodegradable/renewable reagents and materials.
• G2.Number and amount of reagents and waste. Analytical methods should be characterized by the lowest possible consumption of reagents and production of waste (regardless of how toxic they are).
• G3.Energy and other media. Analytical methods should be characterized by the lowest possible consumption of electricity and other utilities. On-site, automated and high-throughput methods are preferred for saving energy.
• G4.Direct impacts. The use of analytical methods should not directly affect humans, animals and genetic naturalness. Exposure of humans (personnel) to harmful factors and the use of animals and/or genetic modifications should be avoided.

Blue principles:

• B1.Cost-efficiency. Analytical methods should be as cost-efficient as possible (where the total cost of analysis should take into account instruments, materials, media and personnel).
• B2.Time-efficiency. Analytical methods should be characterized by the highest possible time-efficiency (the smallest total time of analysis, including method development and all stages of the analytical workflow).
• B3.Requirements. Analytical methods should be characterized by the minimal practical requirements including the amount of sample used, access to advanced equipment, personnel qualifications and laboratory infrastructure.
• B4.Operational simplicity. Analytical methods should be characterized by the highest possible level of miniaturization, integration, automation (online methods, artificial intelligence technologies) and portability (on-site measurements).

This study represents a pioneering effort in the comprehensive evaluation of the greenness, applicability and analytical performance of microextraction techniques in TDM. The results of this study fill a gap in a comparative study of microextraction techniques with a focus on greenness and whiteness which is a new insight into the evaluation of these techniques, with potential implications for shaping future research and practices in TDM or any other purposes. In this regard, we have applied specialized metric tools such as AGREEprep and WAC principles to assess the potential greenness and whiteness of each technique. In other words, we have provided a new pathway to overview the ecofriendliness and practicality of microextraction methods by utilizing these established metric tools in a comparative context. In addition, the Grubbs test with a confidence level of 95% for each technique set with more than 10 data of greenness scores was conducted to find if there are any outliers. While there are numerous reports on the assessment of greenness for individual analytical methods, to the best of our knowledge, this study represents the first application of such metric tools to evaluate the greenness and whiteness of these microextraction techniques in comparison with each other. The use of the AGREEprep metric tool with its ten important criteria and the WAC concept with 12 criteria in red, blue and green principles together provide a robust evaluation of the ecofriendliness, practicality and analytical performance of microextraction methods. The whiteness evaluation is an essential step for microextraction in TDM due to the importance of analytical results which cannot be disregarded for other purposes. For WAC analysis, we have selected just one method in each technique set, those with high green scores, and that provided the required data, to evaluate correctly the existence of any contradiction or balance between the increased greenness and whiteness criteria.

Materials & methods

The greenness assessment of each microextraction technique was conducted using AGREEprep free-access software obtained from http://mostwiedzy.pl/AGREEprep. For WAC analysis, the whiteness assessment was conducted by using the prepared Excel file, as a Supplementary File, provided by Nowak et al. [21]. As the green principles of this modified Excel file are based on the AGREE metric tool, in this regard, we have used the AGREE free-access software from https://mostwiedzy.pl/AGREE to assess the scores of required criteria. To evaluate the presence of any outliers in each technique set with more than 10 data, we have used the Grubbs test with a confidence level of 95% by XLSTAT 2022. To do this analysis, relevant literature was systematically searched using the Scopus database. The search keywords were ‘therapeutic drug monitoring’ or ‘TDM’ integrated with the different types of SPME like ‘microextraction by packed sorbent’ or ‘MEPS’, ‘solid-phase microextraction’ or ‘SPME’, ‘in tube solid-phase microextraction’ or ‘in-tube SPME’, ‘pipette tip solid-phase extraction’ or ‘pipette tip SPE’, ‘micro solid-phase extraction’ or ‘micro SPE’, ‘magnetic solid-phase extraction’ or ‘MSPE’, ‘thin film microextraction’ or ‘TFME’, ‘stir bar sorptive extraction’ or ‘SBSE’, ‘fabric phase sorptive extraction’ or ‘FPSE’, and ‘liquid phase microextraction’ or ‘LPME’. WAC and greenness analysis were limited to papers that specifically focused on the application of microextraction techniques to real samples. Finally, articles with provided necessary information for calculating the greenness score were selected.

Results & discussion

AGREEprep analysis

To assess the greenness of microextraction techniques, we have considered a tutorial article that provided a guide on how to collect and use data for each criterion to assess the level of greenness [31]. To simply categorize the results in tables and figures, and to direct reference to the results for each studied work, we have named the articles using the technique name-article number format like MEPS-001 or SPME-003. In some cases, we have made exceptions and rules in certain criteria to compensate for the lack of information in the articles and to ensure comparable results. For instance, when estimating energy consumption for each technique, we assumed that the power level of each device was consistent for all techniques. Similarly, when calculating sample throughput, we assumed that only one sample was prepared during the preparation process, unless the article specifically stated otherwise, as in the case of using a 96-well plate system. In such cases, any mention of simultaneous use of multiple samples with the same device has also been considered in the energy calculation.

Microextraction by packed sorbent

MEPS is a novel approach in comparison with traditional SPE. This is specifically designed for extremely small sample volumes, even less than 0.1 g, for example, MEPS-009, MEPS-011, MEPS-017, MEPS-021 and MEPS-023, suitable for green bioanalytical purposes. This novel format of SPME offers significant improvements in the field of green microextraction techniques. The sorbent bed is built into a liquid handling syringe, enabling accurate sample manipulations with minimal void volume. Moreover, this new technique is quick and simple in use which minimizes the total sample preparation time considerably. This capability will provide the situation of enhancing the sample throughput with a weight of 3 out of 5 in AGREEprep analysis which demonstrates it as one of the important and effective criteria in the greenness score of a work. This potential has been partially demonstrated by MEPS-008, MEPS-009, MEPS-017, MEPS-022 and MEPS-023, with sample throughput of 8–12 and greenness scores ranging from 0.45 to 0.58 in this aspect. Additionally, the nature of this technique allows for potential automation through easy connection to LC–MS/MS and GC–MS as reported by MEPS-002, MEPS-016, MEPS-017, MEPS-021 and MEPS-023. By making the process automated, we can provide the situation of minimal human intervention which not only enhances the precision of the work but also provides safer conditions for personnel. Additionally, one of the most obvious advantages of this technique is the high potentiality of low energy consumption per sample with a high impact weight of 4 out of 5 based on the AGREEprep default analysis. Except for works with high energy consumption (like MEPS-005, MEPS-006 and MEPS-007), Table 1 & Supplementary Figures S1–S26 in the supplementary file represent green scores of more than 0.5 in this criterion or even recorded the greenest score in the works MEPS-001, MEPS-003, MEPS-004, MEPS-008, MEPS-009, MEPS-011, MEPS-014, MEPS-015, MEPS-017, MEPS-021, MEPS-022, MEPS-023 and MEPS-024. All of these advantages provide the opportunity to create a useful, applicable and especially green microextraction for bioanalytical purposes (pharmaceutical, clinical, forensic toxicology, etc.) [32–34].

Table 1.

Microextraction by packed sorbent, stir bar sorptive extraction, fabric-phase sorptive extraction and magnetic SPE techniques, their criteria and greenness scores based on Analytical Greenness Sample Preparation tool analysis.

Method	Analytical Greenness Sample Preparation criteria											Greenness score	Ref.
	1	2	3	4	5	6	7 (Steps)	7 (Automation)	8	9	10
MEPS-001	Ex situ	0.11	50–75%	2.1	0.1	2.7	3	Semiautomatic	8	LC–MS/MS	1	0.59	[35]
MEPS-002	Ex situ	1.92	<25%	2.6	0.1	1.5	4	Automatic	33	HPLC–UV	2	0.4	[36]
MEPS-002	Ex situ	1.92	<25%	3.5	1	1.5	4	Automatic	33	HPLC–UV	2	0.37	[36]
MEPS-003	Ex situ	0.04	>75%	1.4	0.1	3	3	Manual	0	UHPLC–MS/MS	2	0.61	[37]
MEPS-004	Ex situ	0.5	25–50%	0.9	0.15	3	2	Semiautomatic	0	HPLC–MS/MS	2	0.53	[38]
MEPS-005	Ex situ	1.66	25–50%	1.9	0.1	2	3	Semiautomatic	95	HPLC–diode array	2	0.39	[39]
MEPS-006	Ex situ	3.627	25–50%	5.5	0.5	1.7	4	Semiautomatic	285	UHPLC–FLD	3	0.25	[40]
MEPS-007	Ex situ	3	25–50%	2	0.1	1.8	3	Semiautomatic	95	HPLC–diode array	2	0.37	[41]
MEPS-008	Ex situ	10	25–50%	0.8	0.1	12	2	Semiautomatic	0	LC–MS/MS	2	0.68	[42]
MEPS-009	Ex situ	0.2	25–50%	0.8	0.05	8	3	Semiautomatic	6	UHPLC–MS/MS	2	0.58	[43]
MEPS-010	Ex situ	0.85	50–75%	2	0.2	1.6	3	Semiautomatic	50	(LC–MS/MS)	2	0.43	[44]
MEPS-011	Ex situ	0.85	25–50%	1.3	0.05	6	3	Manual	4	HPLC–FLD	2	0.51	[45]
MEPS-012	Ex situ	0.61	25–50%	1.4	0.1	3.5	3	Manual	25	HPLC–UV	2	0.47	[46]
MEPS-013	Ex situ	3.06	50–75%	2.3	0.1	2	3	Manual	26	HPLC–DAD	2	0.42	[47]
MEPS-014	Ex situ	3.23	50–75%	1.6	0.1	6	3	Manual	5	HPLC–DAD	2	0.49	[48]
MEPS-015	Ex situ	0.45	25–50%	5.5	5	6	2	Semiautomatic	0	UHPLC–MS/MS	1	0.5	[49]
MEPS-016	Ex situ	0.336	25–50%	1.5	1	4	3	Automatic	26	LC–MS/MS LC–MS	1	0.53	[50]
MEPS-017	Ex situ	0.63	<25%	1.5	0.05	10	3	Automatic	5	LC–MS/MS	3	0.52	[51]
MEPS-018	Ex situ	0.33	50–75%	4	3	2.5	3	Manual	12.5	GC–MS	1	0.49	[52]
MEPS-018	Ex situ	0.33	50–75%	11	10	3.5	3	Manual	21	GC–MS	1	0.42	[52]
MEPS-019	Ex situ	0.635	50–75%	4.2	3	4	3	Manual	21	HPLC–coulometric detector	2	0.44	[53]
MEPS-020	Ex situ	0.85	25–50%	1.7	0.1	3	3	Manual	25	HPLC–UV	2	0.42	[54]
MEPS-021	Ex situ	0.52	50–75%	1.8	0.025	5	2	Automatic	0	LC–MS/MS	2	0.59	[55]
MEPS-022	Ex situ	1.067	50–75%	1.2	0.2	10	3	Manual	0	HPLC–UV	1	0.56	[56]
MEPS-023	Ex situ	1.033	>75%	1	0.05	10	3	Automatic	0	LC–MS/MS	1	0.63	[57]
MEPS-024	Ex situ	1.067	50–75%	2	0.4	10	3	Manual	0	Non-Aqueous Capillary Electrophoresis–DAD	1	0.56	[58]
SBSE-001	Ex situ	0	25%	3	0.8	0.8	3	Manual	1762	Non-Aqueous Capillary Electrophoresis–DAD	1	0.42	[58]
SBSE-002	Ex situ	3.587	25%	12	4	0.5	3	Manual	2480	HPLC–FLD	2	0.18	[59]
SBSE-003	Ex situ	0.22	25%	10	2	0.9	2	Manual	62	HPLC–UV	1	0.37	[60]
SBSE-004	Ex situ	1	25%	7	0.1	0.6	3	Manual	1245	HPLC–UV	2	0.26	[61]
SBSE-005	Ex situ	0.03	25%	3.2	0.25	1	2	Manual	925	HPLC–UV	2	0.36	[62]
SBSE-006	Ex situ	0.058	25%	5.5	0.2	0.8	3	Manual	35	HPLC–UV	3	0.4	[63]
SBSE-007	Ex situ	0	25%	10.5	1	0.8	3	Manual	725	HPLC–UV	1	0.37	[64]
FPSE-001	Ex situ	0.6	25–50%	2.5	0.5	1	2	Manual	35	HPLC–UV	1	0.43	[65]
FPSE-001	Ex situ	0.6	25–50%	2.5	0.5	1	2	Manual	15	HPLC–UV		0.46	[65]
FPSE-002	Ex situ	1	25–50%	5.5	0.1	1	2	Manual	660	HPLC–ESI–MS	2	0.27	[66]
FPSE-003	Ex situ	0.5	25–50%	2.5	0.02	1.3	2	Manual	40	HPLC–FLD	2	0.42	[67]
FPSE-004	Ex situ	1.25	50–75%	5	0.05	1.5	2	Manual	10	HPLC	2	0.46	[68]
FPSE-005	Ex situ	1.15	25–50%	3.35	0.18	1	2	Manual	40	HPLC–PDA	2	0.38	[69]
FPSE-005	Ex situ	1.15	25–50%	3.65	0.45	1	2	Manual	40	HPLC–PDA	2	0.37	[69]
FPSE-005	Ex situ	1.15	25–50%	4.15	0.9	1	2	Manual	40	HPLC–PDA	2	0.36	[69]
MSPE-001	Ex situ	15.5	25%	21.1	0.05	0.2	3	Manual	101	HPLC–UV	1	0.26	[70]
MSPE-002	Ex situ	0.2	50–75%	1.9	0.1	96	2	Semiautomatic	0	UPLC–MS/MS	2	0.64	[71]
MSPE-003	Ex situ	0.15	50–75%	2.1	0.1	96	2	Semiautomatic	0	UPLC–MS/MS	2	0.65	[72]
MSPE-004	Ex situ	0.2	50–75%	2.32	0.1	96	2	Semiautomatic	0	UPLC–MS/MS	2	0.64	[73]
MSPE-005	Ex situ	0	25%	2.05	0.2	1	3	Manual	148	LC–MS/MS	1	0.47	[74]
MSPE-005	Ex situ	0	25%	2.2	0.05	1	3	Manual	139	LC–MS/MS	1	0.48	[74]
MSPE-006	Ex situ	0.5	25–50%	1	0.1	4	3	Manual	7	HPLC–UV	1	0.55	[75]
MSPE-007	Ex situ	1	>75%	6.23	0.1	0.75	3	Manual	210	UHPLC–MS/MS	2	0.35	[27]

DAD: Diode Array Detector; FLD: Fluorimetric detection; FPSE: Fabric-phase sorptive extraction; MEPS: Microextraction by packed sorbent; MSPE: Magnetic SPE; PDA: Photo diode array; SBSE: Stir bar sorptive extraction; UHPLC: Ultra-HPLC; UPLC: Ultra performance liquid chromatography.

By considering the above explanations, the greenness evaluation of the MEPS technique by the AGREEprep metric tool demonstrates total greenness results in the range of 0.25–0.68 and most of the works achieved greenness scores of more than 0.4. Moreover, the average score of approximately 0.49 indicates that the greenness status of MEPS approaches for TDM is somewhat acceptable. Considering the highest score of 0.68 shows that this modified technique has the potential for green application. Among the various works evaluated for MEPS, the MEPS-008 obtained the highest score of 0.68 which is mostly due to the low energy consumption per sample. In addition, this special method provided high sample throughput with a small sample size of 0.1 ml, minimized solvent consumption and consequently waste generation throughout the entire procedure. This work consists of a semiautomatic system, which reduces the number of steps, leading to high scores in criterion 7. Additionally, this study did not require any clean-up procedure before the microextraction step, resulting in a reduction in waste production, energy consumption and procedural steps.

On the other hand, the MEPS-006 yielded 0.25. Despite the usage of a semiautomatic system, MEPS-006 lacks environmental compatibility due to high energy consumption (285 Wh/sample), high waste production, increased number of sample preparation steps (four steps) and finally reduced sample throughput to approximately 1.7 samples per hour. As the MEPS technique offers the advantage of providing satisfactory analytical results without requiring any clean-up procedure before the microextraction step, like MEPS-008, we can anticipate a higher green score by eliminating the clean-up step from this special work. In addition, as previously mentioned, MEPS has the advantage of extremely low energy consumption. The high energy consumption of this work is mostly because of using a hot plate for evaporating solvents in the clean-up step.

The main question is how we can achieve higher greenness scores for this technique. In this regard, we considered the works with high scores like MEPS-001, MEPS-003, MEPS-008, MEPS-009, MEPS-021 and MEPS-023 to find any potentiality by excerpting criteria (from 1 to 10) with top scores in Table 1. (Criterion 1: ex situ; criterion 2: 0.04; criterion 3: >75%; criterion 4: 0.8; criterion 5: 0.025; criterion 6: 12; criterion 7: three steps and automatic; criterion 8: 0; criterion 9: LC–MS/MS; and criterion 10: 1.) The AGREEprep analysis of this top data yielded a greenness score of 0.72, which is not far from the highest score that was achieved in this set. It should be considered that this assessment is theoretical and is not necessarily correct. For actual results, we must assess the feasibility of any modified works in the lab.

Stir bar sorptive extraction

SBSE is a new technology in microextraction techniques that uses a stir bar as a substrate for sorbent materials. These sorbents act as an extraction phase and aid in the clean-up procedure by adsorbing the target compounds during the agitation of the bar based on the equilibrium distribution between the analytes in the sample and the coating [76]. It was initially introduced by Baltussen et al. in 1999 [77]. Since then, many important modifications have been reported in this technique and provided opportunities to be applied in analytical and bioanalytical procedures, particularly in the field of TDM [78,79]. The greatest advantages of the SBSE technique are simplicity, being a robust and efficient methodology, and having the possibility of automation [8]. Hence, it seems to be among environmentally friendly techniques. However, the obtained results from AGREEprep analysis for these special works applied in TDM show that many more modifications are required to make this technique green. The obtained greenness scores range from 0.18 to 0.42. These results reveal that this system is placed in a red area and requires significant developments to touch the edge of the green area. With careful attention to the data provided in Figure 1B, this technique is characterized as a time-consuming process with low sample throughput. For instance, the highest sample throughput belongs to SBSE-005 with one sample per hour and the score 0 in its related criteria of AGREEprep analysis. This is mainly because the conditioning step of the sorptive bar requires a significantly long time to be prepared for extraction usage, much more than other microextraction techniques. Furthermore, while most of the SBSE works have a high energy consumption per sample (like SBSE-001, SBSE-002, SBSE-004, SBSE-005 and SBSE-007 with 0 scores), some others demonstrated lower energy consumption due to the special improvements in the microextraction process (like SBSE-003 and SBSE-006 with about 62 and 35 Wh per sample and with scores of 0.53 and 0.68, respectively). These modifications along with other improvements like using smaller sample sizes (SBSE-004: 0.1 ml), the utilization of less toxic and hazardous materials like in SBSE-001 and SBSE-007, and also efforts in the procedure steps reduction might cause higher scores and placing this technique set into the green area.

Figure 1.

Overview of the greenness scores by AGREEprep metric tool.

(A) MEPS, (B) SBSE, (C) FPSE, (D) MSPE and (E) TFME.

Further details in Supplementary Figures S1–S57.

FPSE: Fabric-phase sorptive extraction; MEPS: Microextraction by packed sorbent; MSPE: Magnetic SPE; SBSE: Stir bar sorptive extraction; SPE: Solid-phase extraction; TFME: Thin-film microextraction.

By considering the total greenness scores, the SBSE-001 achieved a superior score of 0.42 while considering its red results in criteria 1, 3, 6, 7 and 8 in the AGREEprep metric tool. This is mostly because of avoiding the utilization of toxic substances, less waste production, using minimum sample size and employing only one hazardous material throughout the procedure. In contrast, the SBSE-002 yielded a score of 0.18, indicating a transition toward a red hue. This is because of its significant energy consumption of 2480 Wh per sample, high waste generation, using a considerable number of toxic materials, a considerable sample size and also the utilization of two hazardous materials.

Due to these explanations, it is a significant challenge to answer the question of how we can provide a green SBSE technique. Let us examine the criteria with the highest scores in this set to theoretically anticipate any potential for greenness. For this purpose, we extracted the top-scored criteria from Table 1. (Criterion 1: ex situ; criterion 2: 0; criterion 3: <25%; criterion 4: 3; criterion 5: 0.1; criterion 6: 1; criterion 7: two steps and manual; criterion 8: 35; criterion 9: Non-Aqueous Capillary Electrophoresis–Diode Array Detector and criterion 10: 1.) The AGREEprep analysis of this top data yielded a greenness score of 0.55, placing it surprisingly in the green area. However, to make this anticipation real, special efforts in laboratories are required to verify this potential.

Fabric-phase sorptive extraction

FPSE is a good candidate for biomedical application as a novel sample preparation due to its simplicity and generating great analytical results. As depicted in Figure 2B, this technique usually uses a flexible and permeable fabric substrate which is coated with a sol-gel organic–inorganic sorbent. Since its introduction to the Analytical Chemistry Society in 2014 [23], significant developments [80,81] have been made to increase the scope of determined analytes through this technique. Some of these developments resulted in providing the conditions for extracting analytes with minimal sample pretreatment, as seen in the FPSE-004. Additionally, the processing steps have been decreased to just two, based on the AGREEprep principles. However, the greenness scores for this criterion in all studied works in this set are completely in the red area due to using manual settings. Nevertheless, there are opportunities to fully automate the process and achieve the highest score in this criterion [8]. Furthermore, the volume of solvents used for the back-extraction step is typically quite low, often less than 500 μl. For instance, in the FPSE-003 study, the back-extraction of the analytes from the FPSE membranes was carried out using about 100 μl of methanol. Similarly, in FPSE-005, the applied methanol in this step was 150 μl. We expect that this reduction in solvent usage will assist in minimizing waste generation. However, according to the waste generation criterion, the obtained scores for this criterion are near the edge of the green area, ranging from 0.36 to 0.48. This high waste generation is mostly attributed to the use of solvents in the cleaning, activating and rinsing steps. One of the special features of the FPSE technique is that solvent evaporations and sample reconstitution are not essential steps. This often leads to lower energy consumption by not using hot plates with high energy power. For example, the greenness score for energy consumption was 1 for FPSE-004 and 0.9 for FPSE-001.

Figure 2.

An overview of the techniques.

(A) MEPS, (B) FPSE, (C) magnetic SPE, (D) thin-film microextraction and (E) μ-SPE techniques.

FPSE: Fabric-phase sorptive extraction; MEPS: Microextraction by packed sorbent; NP: Normal phase; SPE: Solid-phase extraction; UHPLC: Ultra-HPLC.

Taken from [23–27].

The AGREEprep analysis of the FPSE technique resulted in greenness scores within the range of 0.27–0.46, indicating a relatively low level of greenness. This is primarily because of the manual configuration of the studied technique in this set; the lack of significant use of sustainable, renewable or reusable materials; and also because of the time-consuming nature of the technique which reduces sample throughput to between 1 and 1.5 samples per hour. However, by considering WAC and AGREE metrics for FPSE-004, the results obtained are promising. It is important to note that these scores provide room for improvement to enhance the usefulness, analytical performance and environmental friendliness of this special technique in TDM. Fortunately, there are encouraging indications that the greenness of the FPSE technique can be further enhanced. For instance, the energy consumption can be optimized by utilizing energy-efficient technologies. Similarly, sample throughput can also be increased without compromising environmental considerations by employing innovative techniques or advanced systems. Furthermore, the utilization of sustainable or renewable materials is another way to enhance the greenness of the FPSE technique. By replacing toxic and hazardous materials or reagents with ecofriendly ones, this technique can reduce its ecological footprint. Additionally, limiting the use of hazardous substances not only safeguards the environment but also protects the health and safety of laboratory personnel. By considering these potential improvements, we can anticipate a slight modification in the greenness scores of the FPSE technique and a movement closer toward the green area.

According to the aforementioned advantages and possibilities for this special technique, we can expect higher greenness scores following certain modifications. In this context, we considered the highest greenness scores for each criterion in this set, as extracted from Table 1, to anticipate theoretically the possibility of a greenness score for the FPSE technique. (Criterion 1: ex situ; criterion 2: 0.5; criterion 3: 50–75%; criterion 4: 2.5; criterion 5: 0.05; criterion 6: 1.5; criterion 7: two steps and fully automated; criterion 8: 10; criterion 9: HPLC; and criterion 10: 1.) In this hypothetical scenario, we considered a fully automated setup according to the previously mentioned possibility. Under this theoretical condition, the AGREEprep assessment of this top data yielded a greenness score of 0.58, placing the technique in the green area.

Magnetic SPE

MSPE is a type of dispersive SPME that uses magnetic materials as a sorbent to be dispersed in the sample solution and provides huge surfaces for adsorption of the analyte of interest. This approach simplifies the process of isolating and preparing samples for analysis by using sorbents with superparamagnetic properties that can be easily removed from a sample solution by using an external magnet. The choice of magnetic sorbents used in this technique significantly impacts the speed and effectiveness of separation and analytical results like sensitivity, selectivity, accuracy, ability to resist interference and reproducibility. In this regard, the ideal magnetic sorbents are highly magnetic; capable of being dispersed effectively; possess a large surface area with appropriate porosity; and are stable, reusable, cost-effective and environmentally friendly [82,83]. MSPE is a simple technique with a short sample preparation time. It can potentially be automated by using special systems such as 96-well plates. These features will increase the sample throughput, leading to a high greenness score for this parameter in the AGREEprep metric tool, carrying a weight of 3 out of 5. In addition, with the implementation of an automation system and decreased procedural steps, we expect a high greenness score in the relevant criterion. For example, MSPE-002, MSPE-003 and MSPE-004 gained the highest greenness score in these two criteria by employing the 96-well plate and a semiautomatic system, with just two process steps. Furthermore, in some cases, a special pretreatment procedure is not required when using this technique. In this regard, a significant reduction in energy consumption might be seen as evidenced in MSPE-002, MSPE-003, MSPE-004 and MSPE-006, with a score of 1 for the relevant criterion in the AGREEprep metric tool. However, studies with sample pretreatment process were completely placed into the deep red area of energy consumption criterion, for example, MEPS-007 and MEPS-005 with the energy consumption of about 210 and 148 Wh per sample and the greenness scores of 0.22 and 0.33, respectively. Another notable aspect of this technique is the considerably low required sample size for the microextraction process. As depicted in Supplementary Figures S42–S49, every greenness score for this criterion in this microextraction set reported 0.9 and 1. Taking all these advantages into account, we expect to modify a technique with a high greenness score easily.

It was found that the MSPE's greenness scores obtained from AGREEprep analysis ranged from 0.26 to 0.65, with a mean score of 0.43. Among the analysis results, MSPE-002, MSPE-003 and MSPE-004 placed as top performers in terms of greenness scores. This is mainly due to their effectiveness in minimizing the utilization of toxic materials, by applying 50–75% of reagents and materials being sustainable or renewable. These techniques also succeeded in reducing the sample size to 0.1 ml, minimizing the process to just two steps and utilizing a 96-well plate system, leading to high sample throughput, semiautomated system, minimal waste generation and reduced energy consumption per sample. In contrast, MSPE-001 received the lowest score because of requiring a time-consuming sample clean-up before the microextraction step which has led to a low sample throughput of 0.2. Additionally, it generated a significantly higher amount of waste compared with similar approaches, with less than 25% of the materials and reagents being sustainable or renewable and on the other hand, a large quantity of toxic materials, approximately 15.5 ml, was utilized in this work.

Although this technique achieved satisfactory scores in the greenness area, there are still opportunities for further improvements in these scores. Like previous paragraphs, we created a hypothetical scenario by extracting the highest scores of each criterion from Table 1, to determine the highest greenness score theoretically. (Criterion 1: ex situ; criterion 2: 0; criterion 3: >75%; criterion 4: 1; criterion 5: 0.05; criterion 6: 96; criterion 7: two steps and semiautomated; criterion 8: 0; criterion 9: HPLC; and criterion 10: 1.) As expected, the result of AGREEprep analysis in this theoretical condition reported the greenness score of 0.78. Given the aforementioned benefits, it might be possible to replicate this condition in the laboratory and confirm this result.

Thin-film microextraction

TFME is an attractive SPME technique due to its simplicity, easy automation during sample preparation and the potential to achieve a high sample throughput with high sensitivity. The high sensitivity of this technique can result from the substantial surface area-to-volume ratio and the expansion of the extraction-phase volume provided by the sheet of flat film [24]. Considering these advantages, TFME-004 and TFME-005 have employed an automated system with greatly reduced process steps to two by using CONCEPT 96-well plate systems. Using such systems not only provides automation but also enhances the sample throughput significantly. Hence, as demonstrated in Supplementary Figures S55 & S56, both TFME-004 and TFME-005 scored 1, the highest greenness score, in both relevant criteria. In addition, TFME-006 achieved a greenness score of 0.91 with a sample throughput of 48 samples per hour, despite not being an automated system. This is mainly because of using a manual 96-well plate device which confirmed that although there are instances with low greenness scores in this criterion, like TFME-001 and TFME-003 with 0 score, achieving a high sample throughput through this technique is easily possible. The sample preparation time using this technique is somewhat lengthy, like in TFME-003. However, using systems like 96-plates can increase the sample throughput, as seen in TFME-004. In Supplementary Figures S50–S57, all papers in this set showed high greenness scores in energy consumption. This is mainly due to not requiring a lengthy evaporation procedure or special high-energy instruments during sample preparation. One other benefit of this technique is the requirement of a small sample size in the microextraction process which in most cases leads to greenness scores higher than 0.5, placing them in the green area. For example, TFME-004 had a sample size of 0.25 and a greenness score of 0.87. However, this technique requires a larger volume of eluent during the desorption process. In this regard, as depicted in Supplementary Figures S50–S57, the generation of a large waste amount in all studies in this set with low green scores of 0.08.

By considering the aforementioned explanations, this technique, which was initially introduced in 2001 [84], is a strong candidate for both routine and on-site applications, given its aforementioned attributes and its appropriateness for incorporating sampling and sample preparation procedures [85]. However, many more modifications to such techniques are required to enhance the scope of drug analytes for TDM purposes.

After careful greenness analysis of the TFME technique by the AGREEprep metric tool, scores fall within the range of 0.30–0.62, with a mean score of 0.44. Among the evaluated studies in this set, TFME-002 achieved the highest green score of 0.62. This is related to several factors including the use of a small sample size, minimal waste generation, the limited use of hazardous materials and finally the utilization of a specialized vortex container with a capacity of 30 samples. The incorporation of this container not only decreased the energy consumption but also improved the sample throughput. It is important to note that without considering this capacity, the greenness score would be much lower. In addition, TFME-004 also gained a satisfactory green score of 0.57, primarily due to the usage of an automatic system, specifically the CONCEPT 96-well plate. This system increased sample throughput to 20 and reduced the energy consumption to just 1 Wh per sample. In contrast, TFME-003 received the lowest green score due to the use of a large amount of sample solution and excessive solvent usage for sample dilution.

To identify additional possibilities for higher greenness scores, criteria with top scores extracted from Table 2 are considered. (Criterion 1: ex situ; criterion 2: 0; criterion 3: 50–75%; criterion 4: 2.4; criterion 5: 0.25; criterion 6: 160; criterion 7: two steps and automated; criterion 8: 0; criterion 9: HPLC; and criterion 10: 1.) By considering this hypothetical condition, the AGREEprep analysis yielded the greenness score of 0.77, demonstrating a substantial potential for achieving higher greenness scores in laboratory situations.

Table 2.

Thin-film microextraction, μ-SPE, pipette-tip SPE, in-tube solid-phase microextraction, solid-phase microextraction and liquid-phase microextraction techniques, their criteria and greenness scores based on Analytical Greenness Sample Preparation tool analysis.

Method	Analytical Greenness Sample Preparation criteria											Greenness score	Ref.
	1	2	3	4	5	6	7 (steps)	7 (automation)	8	9	10
TFME-001	Ex situ	2	50–75%	9	0.5	0.7	4	Manual	50	HPLC–UV	1	0.36	[86]
TFME-002	Ex situ	0	25–50%	2.4	0.7	8	3	Manual	2.7	UHPLC–MS/MS	1	0.62	[87]
TFME-003	Ex situ	0.6	25–50%	30.5	4	1	3	Manual	29	HPLC–UV	3	0.3	[88]
TFME-003	Ex situ	0.5	25%	30.5	10	1.3	3	Manual	22	HPLC–UV	2	0.32	[88]
TFME-003	Ex situ	0.5	25–50%	30.5	2	1.3	3	Manual	22	HPLC–UV	2	0.35	[88]
TFME-004	Ex situ	1	50–75%	5	0.25	20	2	Automatic	1	LC–MS/MS	2	0.57	[89]
TFME-005	Ex situ	1.15	50–75%	4.5	1	160	2	Automatic	0	LC–MS/MS	2	0.59	[90]
TFME-006	Ex situ	0.96	<25%	2.5	1	48	2	Manual	0	LC–MS	2	0.51	[91]
μ-SPE-001	Ex situ	0.495	50–75%	1.51	0.3	20	2	Semiautomatic	5	LC–MS/MS	2	0.58	[92]
μ-SPE-002	Ex situ	0.04	50–75%	0.6	0.2	20	2	Semiautomatic	0	LC–MS/MS	2	0.66	[93]
μ-SPE-003	Ex situ	0.751	25–50%	1.55	0.18	20	2	Semiautomatic	0	HPLC–MS/MS	2	0.56	[94]
Pipette-tip SPE-001	Ex situ	0	25–50%	2.25	0.4	1.5	4	Manual	0	HPLC–MS/MS	1	0.59	[95]
Pipette-tip SPE-002	Ex situ	1	50–75%	5.7	0.4	3	3	Manual	0	HPLC–UV	2	0.46	[96]
Pipette-tip SPE-003	Ex situ	0.5	<25%	1.62	0.5	4	2	Manual	0	HPLC–UV	2	0.48	[97]
Pipette-tip SPE-004	Ex situ	0.3	50–75%	1.15	0.1	2	2	Manual	25	GC–MS	1	0.53	[98]
In-tube SPME-001	Ex situ	0	50–75%	0.65	0.25	3	2	Automatic	0	LC–Fluorescence detection	0	0.74	[29]
In-tube SPME-002	Ex situ	0	25–50%	2.2	0.5	3	2	Automatic	27	LC–UV	1	0.63	[99]
In-tube SPME-003	Ex situ	0	50–75%	0.6	0.25	6	2	Automatic	0	LC–Fluorescence detection	0	0.76	[100]
In-tube SPME-004	Ex situ	0	<25%	0.75	0.2	1	2	Automatic	113	Hydrophilic interaction liquid chromatography (HILIC)–MS/MS	1	0.55	[101]
SPME-001	Ex situ	0.01	>75%	2.6	1.5	3	2	Semiautomatic	3.5	ESI–MS	1	0.63	[102]
SPME-002	Ex situ	0.005	25–50%	4.2	0.2	2	2	Semiautomatic	10	ESI–MS	1	0.59	[103]
SPME-003	Ex situ	0.4	<25%	0.65	0.05	0.7	2	Manual	17	GC–MS	2	0.38	[104]
SPME-004	Ex situ	0.3	>75	6	1	0.7	2	Manual	336	LC–UV	2	0.34	[105]
SPME-005	Ex situ	0	<25	1.23	0.2	13	3	Semiautomatic	30	Microfluidic open interface–MS/MS	1	0.6	[106]
SPME-006	Ex situ	1.2	50–75%	4.6	1	0.3	3	Semiautomatic	22	LC–MS/MS	2	0.41	[107]
SPME-007	Ex situ	1	25–50%	2	0.5	1	2	Manual	71	UPLC–MS/MS	1	0.39	[108]
SPME-007	Ex situ	1	<25%	11.2	10	1	2	Manual	71	UPLC–MS/MS	1	0.3	[108]
SPME-008	Ex situ	1.15	50–75%	4.5	0.25	120	2	Automatic	0	LC–MS/MS	2	0.6	[109]
SPME-009	Ex situ	0	50–75%	0.9	0.3	0.4	2	Semiautomatic	40	UHPLC–MS/MS	1	0.59	[30]
SPME-009	Ex situ	0	50–75%	0.9	0.3	0.8	2	Semiautomatic	40	ESI–MS/MS	1	0.57	[30]
SPME-010	Ex situ	0	25–50%	16	16	0.6	3	Manual	1713	LC–MS/MS	1	0.35	[110]
SPME-011	Ex situ	0.005	50–75%	1.7	0.3	10	2	Semiautomatic	2	Open Port Probe–MS	2	0.64	[111]
SPME-012	Ex situ	1	50–75%	3.5	0.01	0.3	3	Automatic	0	LC–MS/MS	1	0.52	[112]
SPME-013	Ex situ	0	>75%	1.7	0.02	30	3	Semiautomatic	0	ESI–MS/MS	0	0.74	[113]
LPME-01	Ex situ	0.05	50–75%	2.1	0.25	2	2	Manual	40	LC–MS/MS	1	0.53	[107]
LPME-02	Ex situ	0.05	50–75%	2.1	0.25	2	3	Manual	38	LC–MS/MS	1	0.53	[114]
LPME-03	Ex situ	0	>75%	6.5	0.2	1	3	Manual	45	HPLC–UV	1	0.54	[115]
LPME-04	Ex situ	2.1	<25%	5.5	0.05	3	3	Manual	14	UHPLC–PDA UHPLC–MS/MS	3	0.38	[116]
LPME-05	Ex situ	0.01	<25%	3.2	0.5	4	3	Manual	139	UPLC–MS/MS	2	0.46	[117]
LPME-06	Ex situ	0.82	25–50%	11	2	3	3	Manual	146	Digital image colorimetry Smartphone based	4	0.31	[118]
LPME-07	Ex situ	0.1	<25%	2.1	0.1	3	4	Manual	14	LC/ESI–MS/MS	3	0.46	[119]
LPME-08	Ex situ	0	>75%	4.25	0.25	2	4	Manual	39	LC–MS/MS	3	0.51	[120]
LPME-09	Ex situ	3.2	50–75%	110	50	1	3	Manual	72.5	HPLC–UV	1	0.25	[121]
LPME-10	Ex situ	0	<25%	0.5	0.01	4	3	Manual	29	UPLC–MS/MS	2	0.58	[122]
LPME-11	Ex situ	0.1	50–75%	2.3	1	4	2	Manual	17	LC–MS/MS	1	0.55	[123]
LPME-12	Ex situ	0	50–75%	8	0.5	2	3	Manual	33	HPLC	2	0.51	[124]
LPME-13	Ex situ	0.04	50–75%	13	1	4	2	Manual	28	GC–Flame Ionization Detector	3	0.48	[125]
LPME-14	Ex situ	1	50–75%	14	0.7	1	3	Manual	38	Fluorimeter	3	0.36	[126]
LPME-15	Ex situ	0.02	<25%	1.5	1	1	2	Manual	112	HPLC–UV	2	0.43	[127]
LPME-16	Ex situ	0.5	50–75%	2.5	1	0.3	3	Manual	42	HPLC–FLU	1	0.43	[128]
LPME-17	Ex situ	0	50–75%	11.5	10	4	3	Manual	140	HPLC–UV	1	0.46	[129]
LPME-17	Ex situ	0	50–75%	12.5	1	3	3	Manual	140	HPLC–UV	1	0.48	[129]
LPME-18	Ex situ	0.02	>75%	5.5	1	2	3	Manual	58	HPLC–FLU	2	0.49	[130]
LPME-19	Ex situ	0	<25%	3.5	3	3	2	Manual	143	GC–FPD GC–Flame Ionization Detector	1	0.48	[131]
LPME-20	Ex situ	0	>75%	5.5	1	1	3	Manual	53	HPLC–FLU	1	0.52	[132]
LPME-21	Ex situ	0.05	>75%	6	0.5	0.09	3	Manual	13	Reverse Phase–HPLC	3	0.49	[133]
LPME-22	Ex situ	0.17	50–75%	7	0.05	3	3	Manual	15	GC–MS	3	0.47	[134]

DAD: Diode Array Detector; FLD: Fluorimetric detection; LPME: Liquid-phase microextraction; PDA: Photo Diode Array; SPME: Solid-phase microextraction; TFME: Thin-film microextraction; UHPLC: Ultra-HPLC; UPLC: Ultra performance liquid chromatography.

μ-SPE

Micro-SPE is a miniaturized technique that employs a small quantity of sorbent materials (<500 mg) within a porous membrane enclosure. This technique endeavors to reduce the size of the device by utilizing micro- or nanomaterials, curtailing the quantity of sorbents and minimizing the utilization of organic solvents. These modifications, in conjunction with its simplicity, high enrichment factor, selectivity and sensitivity, as well as its compatibility with diverse systems, prove the benefits and advantages of the technique, encompassing decreased time and costs [8,25,135,136]. Taking into account these superior advantages of this technique, we expect to achieve scores completely in the green area. For instance, because of the short sample preparation time needed for this technique, all the evaluated techniques in this category scored 0.71 in the sample throughput criterion, processing 20 samples per hour. Moreover, by considering the miniaturized system of the μ-SPE technique, the required samples are extremely small (0.3, 0.5 and 0.18 ml) as demonstrated in Supplementary Figures S58–S60, with the greenness scores of 0.84, 0.77 and 0.92, respectively for the sample size criterion. Under this condition, it is evident that the required organic solvents will be minimized, leading to minimal waste production during the process (e.g., μ-SPE-002: waste production = 0.6 g or ml with a greenness score of 0.71). Furthermore, the clean-up procedure in this technique is considerably small with the capability of utilizing 96-well plate systems and also eliminating the need for evaporation through the use of high-energy power instruments like hot plates. These capabilities led to a semiautomated system with only two steps and also with extremely low energy consumption of about zero. In this regard, the highest possible greenness score was achieved for the energy consumption criterion for all studied works in this category. The obtained score for the integration and automated criterion is 0.5, placing it on the edge of the green area. Furthermore, the technique demonstrates a minimal use of toxic materials, as reported in μ-SPE-002. This aspect is crucial in ensuring environmental safety and minimizing potential harm to both the ecosystem and laboratory personnel. Nevertheless, this technique has its drawbacks, including the delicate nature of the fibers, limited selection of stationary phases and the potential for carryover [8].

The assessment of μ-SPE techniques in this work demonstrated a commendable level of environmental friendliness. The greenness scores resulting from the AGREEprep analysis fall within the range of 0.56–0.66, with a mean score of 0.60. These grades above 0.5 indicate green approaches with a relatively high level of sustainability. Based on the evaluated articles, this is a technique with the capability of utilizing semiautomatic systems like 96-well plates, allowing for the simultaneous preparation of 96 samples. As mentioned in the last paragraph, this feature significantly enhances the sample throughput and reduces energy consumption, enabling the system to process a large number of samples in a short amount of time.

By considering these special advantages of this technique, let us predict the highest greenness score under a hypothetical condition by extracting data related to the criteria with the highest greenness scores from Table 2. (Criterion 1: ex situ; criterion 2: 0.036; criterion 3: 50–75%; criterion 4: 0.6; criterion 5: 0.18; criterion 6: 20; criterion 7: two steps and semiautomated; criterion 8: 0; criterion 9: HPLC–MS/MS; and criterion 10: 2.) The AGREEprep analysis for this theoretical condition resulted the score of 0.67. It indicates that the best condition for achieving the highest green method is already applied in μ-SPE-002 with a score of 0.66. So, further modifications are required to obtain higher scores in this technique, such as providing a fully automated system, using 384-well plates to increase sample throughput and using more sustainable, reusable or renewable, and less hazardous materials. In conclusion, the μ-SPE technique evaluated in this study exhibits a high level of greenness, as evidenced by the majority of scores falling within a favorable range, 0.56–0.66. The utilization of a semiautomatic system, the 96-well plate, the small sample size, and the limited use of energy, solvents and toxic materials all contributed to the method's overall sustainability.

Pipette-tip SPE

Pipette-tip SPE refers to the process of SPE utilizing a pipette tip, as depicted in Figure 3A. This technique is employed to extract and purify analytes from a sample by utilizing a small pipette tip that is filled with a SPE material. Applying pipette-tip SPME for sample preparation provides simple, faster and better recovery compared with using traditional SPE cartridges [137]. Although this technique is introduced as a quick approach, our evaluations indicated that in this special category, sample preparation time is about 30 min which causes low sample throughput, as resulted in pipette-tip SPE-001 with a sample throughput of 1.5 and the achieved score of 0.1 in this criterion. Many modifications for bioanalytical [28,138–143] purposes especially in the TDM area [95–98] are reported. As a result of these modifications and as depicted in Supplementary Figures S61–S64, in most studies, the reduction of sample size, toxic and hazardous materials (pipette-tip SPE-001 and -004), solvents volume (pipette-tip SPE-004), and more utilization of sustainable, renewable or reusable materials (pipette-tip SPE-001) are recorded. The other feature of this technique is a considerable low energy consumption. As this criterion has a high impact weight in AGREEprep analysis, it will provide a suitable condition for this technique to be placed in the green range.

Figure 3.

Overview.

(A) Pipette-tip SPE device, (B) operation mode of in-tube solid-phase microextraction coupled with LC and (C) fiber-SPME coupled to MS.

SPME: Solid-phase microextraction.

Taken from [28–30].

The AGREEprep analysis indicates that the greenness of the pipette-tip SPE technique is in an acceptable range of scores with a mean score of approximately 0.52. Upon reviewing the evaluated articles in this set, it is evident that pipette-tip SPE-001, with the greenness score of 0.59, can get more modifications in some AGREEprep criteria compared with other similar works with lower scores. This indicates that there are potential areas to improve the greenness scores of this technique. For example, the number of steps involved in an approach can be reduced from four to two (like pipette-tip SPE-003 and -004) by simplifying the clean-up process. Additionally, the amount of sustainable and renewable materials used in the method ranges from 25 to 50%. This also can be improved by increasing the utilization of such materials to 75% or more, further enhancing the method's greenness score. For methods with lower greenness scores like pipette-tip SPE-002, the main areas for improvement are typically the use of toxic materials and the generation of excessive waste. Fortunately, these issues can be easily addressed and improved upon. In pipette-tip SPE-001, only one hazardous material was used, and no toxic materials were applied. These aspects contribute to the method's overall sustainability.

To examine the highest possible greenness score based on the top-scored criteria from Table 2, several criteria were considered in AGREEprep analysis. (Criterion 1: ex situ; criterion 2: 0; criterion 3: 50–75%; criterion 4: 1.15; criterion 5: 0.1; criterion 6: 4; criterion 7: two steps and manual; criterion 8: 0; criterion 9: HPLC and criterion 10: 1.) By the greenness assessment of the aforementioned criteria; a score of 0.67 is obtained which strongly indicates that the greenness improvement of this technique in the laboratory condition might be possible.

In-tube SPME

In-tube SPME (see Figure 3B), also called capillary microextraction, utilizes a capillary column made of fused silica as the SPME fiber. The fiber is covered with a stationary phase that interacts specifically with the desired analytes. The sample is inserted into the column and the analytes are extracted onto the fiber via partitioning between the sample matrix and the fiber coating. There are four different types of capillary tubes used for in-tube SPME, which are classified based on the shape of the extraction phase inside the tubes. These types include surface-coated, sorbent-packed, fiber-packed and monolithic capillaries. The production of these capillary tubes is influenced by several factors, including the type of supporting capillary tubes, the synthesis method and the physicochemical properties of the extraction phase [144]. This technique, first introduced in 1997 by Eisert and Pawliszyn [145], allows for online coupling to LC or MS/MS systems and can be fully automated, from sample preparation to the separation and detection of target analytes through online coupling to LC using a column-switching technique. This capability is evidenced by all evaluated studies in this microextraction set, resulting in a greenness score of 1, the highest possible, in this criterion. In addition, in-tube SPME will result in minimal energy consumption, which is one of the most important and impactful criteria in AGREEprep analysis. Except in in-tube SPME-002 with a greenness score of 0.74 in the energy consumption criterion, all other methods in this category scored 1. The microextraction process occurs within the flow of the sample solution, and analytes are extracted onto the inner surface of capillaries, allowing for their concentration. Analytes can be desorbed by introducing a solvent into the capillary or through the mobile phase [146,147]. This functionality can lead to the least usage of organic solvents that can modify several important criteria in AGREEprep analysis. As depicted in Supplementary Figures S65–S68, hazardous materials, waste and operator's safety scored 1, ≥0.68 and ≥0.75, respectively. Despite these significant capabilities, one of the most obvious drawbacks of this technique is its extremely low sample throughput. For instance, as demonstrated in Supplementary Figure S68, the greenness score for the sample throughput criterion in in-tube SPME-004 was recorded as 0, with about one sample per hour. The higher sample throughput in this category belongs to in-tube SPME-003 which was provided the greenness score of 0.42, with about six samples per hour. This is mainly due to its coupling to LC or MS/MS systems. The microextraction process begins with injecting the sample into the analytical instrument. Subsequent extraction rounds begin after each successive injection, necessitating a wait until the run time is completed.

Based on the AGREEprep analysis of the in-tube SPME methods in this set, the greenness scores were obtained from 0.55 to 0.76. The top score of 0.76 (in-tube SPME-003), in conjunction with the mean score of 0.67, serves as confirmation of the technique's exceptional level of sustainability and its appropriateness for green applications. The assessed articles showcase advancements in multiple criteria, including decreased waste generation and smaller sample sizes. It is important to note that all of the evaluated methods were carried out using automated systems with just two procedural steps and reduced energy consumption to near-zero levels. Additionally, the approach avoids the use of toxic and hazardous materials in favor of renewable and sustainable alternatives. Although high green scores have been achieved, there is still room for improvement in areas such as the utilization of sustainable and renewable materials, aiming for a usage rate of over 75%, or a complete usage of such materials, and enhancing the score in the sample throughput criterion.

To predict the possibility of a higher greenness score, we gathered criteria with the highest scores from Table 2. (Criterion 1: ex situ; criterion 2: 0; criterion 3: 50–75%; criterion 4: 0.6; criterion 5: 0.2; criterion 6: 6; criterion 7: two steps and fully automated; criterion 8: 0; criterion 9: HPLC; and criterion 10: 0.) With this theoretical context, we can make an overview, considering the possible reported improvements. By taking into account top criteria, the AGREEprep analysis provided the score of 0.76, which is the exact score of in-tube SPME-003. This analysis indicates that this hypothetical condition has already been observed in laboratory conditions.

Fiber-based SPME, solid-phase dynamic extraction & dried blood spot SPE

Fiber-based SPME was initially introduced in 1989 by Belardi and Pawliszyn [148]. It has become a critical approach to sample preparation, especially in the field of TDM, on which we will focus in the following greenness analysis. As depicted in Figure 3C, fiber-based SPME technique consists of the use of a fiber coated with a sorbent material, which is exposed to the sample to absorb the specific compounds of interest. Sorbent materials play a crucial role in obtaining accurate analytical outcomes. Examples of such materials include nanomaterials, mesoporous materials, carbon nanotubes, graphene, covalent organic frameworks and metal–organic frameworks [149,150].

The AGREEprep scores obtained from this microextraction set range from 0.3 to 0.72. This range demonstrates the potential for positioning the technique within the green area, except for some low scores observed in the results (e.g., SPME-003, SPME-004 and SPME-007 with scores of 0.38, 0.34 and 0.3 respectively). Some of the evaluated articles highlight the SPME methods coupled with advanced analytical instruments such as ESI–MS/MS (e.g., SPME-009 and SPME-013), Open port probe –MS (e.g., SPME-011) and Microfluidic open interface–MS/MS (SPME-005), which contribute to the method's greenness by providing the capabilities of reducing sample preparation time, utilizing smaller sample sizes and leading to less waste production. It should be mentioned that the possibility of using a small sample size by fiber-based SPME technique provides a favorable condition for achieving the highest greenness scores. This is the case in SPME-013, SPME-012 and SPME-003 with the obtained score of 1. Additionally, the implementation of an automated system, such as using the concept of the 96-well plate, can further improve the greenness scores for the integration and automated and in some cases the sample throughput criteria. For example, SPME-008 scored 1 in both mentioned criteria with the analysis of 120 samples per hour (Supplementary Figure S77 & Table 2).

SPME-013 emerged as the top-performing green method among those assessed in this particular set. Furthermore, this distinctive microextraction process has shown the ability to attain superior results by coupling to advanced MS instruments like ESI–MS/MS. While such instruments consume significant energy through the process that might result in lower scores for criterion 9, their usage improves the other criteria with high impact weight. For example, in SPME-013 as well as in SPME-005, SPME-009 and SPME-011 the requirement for the elution step is eliminated which led to minimal solvent usage throughout the whole procedure and thus reduced the waste generation. Furthermore, this technique (SPME-013) requires only a small amount of sample, and the short sample preparation time of approximately 2 min resulted in a high sample throughput. Due to the combination of these positive criteria, along with the use of sustainable and reusable materials, the greenness score of this technique contributed to an attractive greenness score of 0.74.

Despite such positive points for the fiber-based SPME technique, there are also notable negative points to consider. One of the most obvious drawbacks is the fragility of fibers, which might lead to breakage and a short lifespan [8]. Second, the lack of utilization of sustainable, renewable or reusable materials is a concern. While there are possibilities of using such materials, as depicted in Supplementary Figures S72 & S83 for SPME-004 and SPME-013, no significant effort in using such materials has been seen, as indicated in Supplementary Figures S71, 73 & 76 for SPME-006, SPME-005 and SPME-007 with a score of 0 in the relevant criterion. Another issue is the sample size in SPME-007, which has led to escalating waste generation that impacted negatively on the scores of both sample size and waste amount criteria. Moreover, the energy-intensive nature of the pretreatment procedure used in this work greatly reduces the energy-related criterion score, resulting in a total greenness score of 0.3.

Solid-phase dynamic extraction is a technique that uses a covalent organic framework on stainless steel needles as a SPME probe instead of usual fibers, suitable in the preparation of liquid and vapor samples. This type of microextraction has superiority over the fiber-based SPME in that the capillary used is more robust and resistant to physical damage [102,103]. Considering the AGREEprep principles, this technique is among the green approaches achieving total scores of 0.63 and 0.59 in SPME-001 and SPME-002, respectively. Similar to the methods SPME-009 and SPME-013, these two techniques benefit from the utilization of ESI–MS/MS. Given the potential of this technique to provide a high greenness score (detailed in Supplementary Figures S69 & S70), it is expected that further development in TDM will not only result in a higher greenness score but also resolve the technique's disadvantages, such as high carryover due to the retaining of analytes on the inner wall of the needle through the desorption process.

The SPME-012 technique is considered a promising environmentally friendly approach because it combines dried blood spot desorption with online SPE–LC–MS/MS. This technique benefits from point-of-care collection, less invasive microsampling, more economical shipment and convenient storage. Although this special technique received a moderate score of 0.52, its inherent characteristics suggest that it has the potential to become even more environmentally friendly by offering an automated system and less waste generation through the use of small sample sizes and minimal organic solvents needed. These features create opportunities for improving special criteria by reducing the procedural steps, shortening sample preparation time and ultimately increasing the greenness score of the sample throughput criterion.

To obtain the theoretically improved greenness score for fiber-based SPME, we assessed the greenness of the highest-scoring criteria using the AGREEprep metric tool. (Criterion 1: ex situ; criterion 2: 0; criterion 3: >75%; criterion 4: 0.65; criterion 5: 0.02; criterion 6: 120; criterion 7: two steps and fully automated; criterion 8: 0; criterion 9: ESI–MS/MS; and criterion 10: 0.) The assessment of environmental friendliness in this hypothetical scenario yielded a significant score of 0.83, suggesting the potential for achieving a fully sustainable approach.

Liquid-phase microextraction

The LPME technique is a scaled-down form of traditional liquid–liquid extraction that can be classified into three main categories, namely single-drop microextraction, hollow fiber liquid-phase microextraction and dispersive liquid–liquid microextraction. These miniaturization and developments have led to addressing some drawbacks of liquid–liquid extraction techniques by minimizing the sample size, and usage of toxic and hazardous materials as well as shortening the procedure time and steps. These modifications provide a situation to take this approach to green areas [151,152]. For instance, as shown in Table 2, LPME-003, LPME-005, LPME-008, LPME-010, LPME-012, LPME-017, LPME-019 and LPME-020 scored 1 for the hazardous materials criterion, which indicates nonusage of toxic materials. Furthermore, LPME-004, LPME-007, LPME-010 and LPME-022 achieved a score of 1 in the sample size criterion, from 0.01 to 0.1 ml. Various LPME techniques and their advantages and disadvantages were discussed previously in a review by Yamini et al. [153]. While these modifications and simplifications improve the greenness scores, the complexity of some samples has negative effects on the greenness by requiring additional extraction or cleaning steps that lead to increasing the sample preparation time and decreasing sample throughput [154]. As demonstrated in Supplementary Figures S84–S106, all evaluated works in this study acquired extremely low scores in the sample throughput criterion, falling in the deep red valley. Some of them even scored 0 with the analysis of one sample per hour or less, such as LPME-003, LPME-009, LPME-014, LPME-015, LPME-016, LPME-020 and LPME-021.

The greenness results for this microextraction set were obtained in the range of 0.25 to 0.58, with an average score of approximately 0.47. Despite scores being in a moderate greenness range, findings offer room for improvement in achieving higher greenness scores. The highest score of LPME-010 is attributed to the nonusage of toxic materials, high sample throughput, using small sample sizes, low energy consumption and less waste production (Supplementary Figure S93). Based on the data in Table 2 & Figure 4E, and by considering criteria 3, 7, and to some extent 6, achieving desired results is possible by using more sustainable and renewable materials (like LPME-003), integrating the procedures and finally providing fully automated systems.

Figure 4.

Schematic overview of the greenness scores by AGREEprep metric tool.

(Further details in Supplementary Figures S58–S106). (A) μ-SPE, (B) pipette-tip SPE, (C) in-tube SPME, (D) SPME and (E) LPME.

LPME: Liquid-phase microextraction; SPE: Solid-phase extraction; SPME: Solid-phase microextraction.

Based on the Grubbs test with a confidence level of 95%, LPME-009 with the greenness score of 0.25 was detected as an outlier among other scores in this set (provided in the supplementary Excel file). The lowest score of LPME-009 is mainly because of its integration with an SPE technique, which negatively affected its overall score. This combination was developed due to providing a new method with low LODs, good sensitivity, satisfactory recoveries, linearity and enrichment factor for the analysis of trace analytes like drugs in biological samples. The low greenness score is due to the increased sample preparation time and subsequently reducing the sample throughput criterion. Additionally, the technique employs a considerable sample size that has led to higher waste production. Furthermore, while the usage of sustainable and renewable materials is reported, the usage of toxic materials prevents a method to process toward green areas.

In conclusion, while this technique set shows promise, some areas require special attention for improvements to enhance the overall greenness scores. In addition, by addressing issues like sample preparation time, sample size and especially waste generation, this technique will contribute to a greener approach. Considering Table 2 & Supplementary Figures S84–S106, criterion 7 shows that all LPME methods are in the red zone, forwarding attention toward providing automated systems for the LPME technique.

Based on the procedure outlined in the preceding paragraphs, we also examined the potential of achieving the maximum greenness score for this category by considering the top scoring criteria from Table 2. (Criterion 1: ex situ; criterion 2: 0; criterion 3: >75%; criterion 4: 0.5; criterion 5: 0.01; criterion 6: 4; criterion 7: two steps and manual; criterion 8: 13; criterion 9: GC; and criterion 10: 1.) This AGREEprep analysis yielded a score of 0.71, suggesting significant potential for achieving higher greenness scores. However, achieving this score under laboratory conditions appears challenging, due to the variety of LPME types in this category that reduce the accuracy of this prediction.

WAC analysis

In this study, we evaluated the whiteness of ten microextraction techniques [42,64,68,73,87,93,98,100,113,122] with one work selected from each category, MEPS-008, SBSE-007, FPSE-004, MSPE-004, SPME-013, in-tube SPME-003, pipette-tip SPE-004, μ-SPE-002, TFME-002 and LPME-010. The selection of these methods was based on their high green scores from AGREEprep analysis and also providing necessary information for WAC analysis. Results of this assessment are presented in detail in Figures 5 & 6, with the experimental data available in Supplementary Tables S2–S5. Results demonstrate a wide range of whiteness, ranging from 55.3% (SBSE-007) to 90.7% (SPME-013).

Figure 5.

Comparison of the ten microextraction techniques for therapeutic drug monitoring according to the 12 principles of white analytical chemistry, performed using the RGB 12 algorithm.

RGB: Red–green–blue; TDM: Therapeutic drug monitoring; WAC: White analytical chemistry.

Figure 6.

Comparison of the primary assessment outcomes derived from the RGB 12 analysis.

The 100.00 level represents signifying complete suitability for therapeutic drug monitoring. Values surpassing 100% indicate supplementary capabilities that exceed existing requirements.

FPSE: Fabric-phase sorptive extraction; LPME: Liquid-phase microextraction; MEPS: Microextraction by packed sorbent; MSPE: Magnetic SPE; SBSE: Stir bar sorptive extraction; SPME: Solid-phase microextraction; TFME: Thin-film microextraction.

By diving into the details of obtained results, we may face alignments or conflicts of principles. For instance, the LPME-010 method, while having the highest green score (87.7%) based on the AGREE metric, scored lower in the red and blue principles, 82.5 and 56.2%, respectively, and led to a whiteness score of 75.5%. The lower scores in red and blue principles are because of low scores obtained in criteria B2, B3, B4 and R2. In addition, SBSE-007 recorded the lowest scores across all principles, with red = 71.3%, blue = 41.8% and green = 52.9%. While the method provided a satisfactory performance in the red principles, it exhibited unsatisfactory results on criteria related to sensitivity and accuracy. The greenness score of this method is negatively affected by high energy consumption and waste production. The blue principles in this method present an even more challenging situation consisting of time-consuming and high-cost processes, a lack of integration and automation, and a demand for advanced skills and facilities. These collective findings indicate that the SBSE-007 method falls short of meeting the criteria for satisfactory performance in TDM. Deep modifications across all three principles are required to enhance the method's suitability for its intended purposes.

Among all examined methods, SPME-013 achieved the highest whiteness score, reflecting exceptional performance across all three principles, with estimated scores of 105, 83.8 and 83.3% for the red, green and blue principle,s respectively. This remarkable balance of principles underscores this analytical technique's potential to serve as an applicable and environmentally friendly approach, as evidenced by both the AGREE and AGREEprep metrics. Furthermore, the SPME-013 demonstrated outstanding results in terms of LOD and LOQ at 0.1 and 0.2 ppb, respectively, showcasing its suitability for TDM. Its application scope is highlighted by its applicability to various biological matrices and a wide range of drugs, coupled with excellent precision and accuracy. Notably, the method utilizes a semiautomatic and miniaturized microextraction approach, coupled with a triple quadrupole mass spectrometer, leading to near-zero energy consumption and high sample throughput. Additionally, the use of sustainable and renewable materials and the elimination of the use of toxic or hazardous materials protected the method's greenness rate. These exceptional consequences made the SPME-013 a strong candidate as a green-and-white analytical method, aligning and balancing well across all principles.

In the other area, both methods, TFME-002 and μ-SPE-002, achieved exceptional recovery% values, exceeding 100%. This is obtained due to utilizing the LC–MS/MS instrument, demonstrating a broad scope of application, and delivering satisfactory accuracy and precision with very low LOD and LOQ results. Certainly, as can be inferred from Figure 5, these results have significantly increased the whiteness of these methods in the range of 80–85%. However, μ-SPE-002 exhibited a lower greenness score based on the AGREE metric, indicating a relative weakness compared with TFME-002. Notably, the blue principles of both methods demonstrate powerful potential for achieving higher scores through specific modifications, particularly in the B3 and B4 criteria. These modifications will lead to improvements in the overall results of whiteness and will provide a balance among all principles.

As previously mentioned, achieving a high greenness score may not always be the main purpose, and in all cases, the practicality and analytical performance of techniques and methods must be in consideration. However, in some circumstances, modifications in some criteria must be of particular attention. For instance, based on Table 2 & Supplementary Table S5, the in-tube SPME-003 demonstrated high greenness scores based on both AGREE and AGREEprep metrics (77.2% and 0.76 out of 1, respectively). Nevertheless, the practicality score (blue part) of 57.8% indicates that further modifications are necessary to enhance the method's practicality. The limitations of in-tube SPME-003 coupled with LC–Fluorescence detector are attributed to its time-consuming nature and the requirement for advanced skills and facilities. These shortcomings must be addressed to make the method more practical and accessible. Therefore, while greenness is an important consideration, it cannot be the sole focus, and the other important principles like practicality and analytical performance of methods must also be evaluated to ensure methods' suitability for the intended purposes.

Conclusion

In this study, we assessed the greenness and whiteness of each microextraction category by performing AGREEprep and WAC analysis to provide a comparative condition for identifying any relevant drawbacks or privileges. In this regard, we considered a wide range of drugs (Supplementary Table S1) extracted from biological samples using specific microextraction techniques for the TDM. Monitoring these particular drugs with narrow therapeutic ranges is crucial to avoid potential medical interactions or toxic effects. Due to the importance of this issue, several microextraction techniques have been developed with satisfactory analytical performance [8]. Although the main purpose of these efforts was only the improve analytical results, our assessments provided satisfactory conclusions, aligning well with both greenness and whiteness principles.

First, considering the obtained results from the AGREEprep analysis, we could identify potential improvements for higher greenness scores. We examined the top-scored criteria for each microextraction category to determine the maximum possible greenness score for each microextraction technique. Although these results are according to a hypothetical scenario, they can pave the way for implementing these results in laboratory conditions. Notably, the highest greenness scores in a laboratory situation were obtained by in-tube SPME and fiber-based SPME with scores of 0.76 and 0.72, respectively. However, analysis under hypothetical conditions revealed that fiber-based SPME, MSPE, TFME, in-tube SPME and MEPS scored 0.83, 0.78, 0.77, 0.76 and 0.72, respectively (Figure 7). Although there were no significant improvements in the scores of in-tube SPME and fiber-based SPME under these hypothetical conditions, the results confirm that these techniques achieved the highest greenness scores both in the laboratory and under ideal hypothetical conditions.

Figure 7.

An overview of obtained greenness range scores for each microextraction technique.

It must be considered that, due to the small number of articles on some techniques (like μ-SPE, in-tube SPME, pipette-tip SPE, etc.), providing a comparison situation is not statistically correct. Moreover, it is important to note that each of these techniques may have provided different results in the analysis of other analytes of interest or samples. Therefore, this figure just provides an overview of the current status of each microextraction technique for therapeutic drug monitoring.

FPSE: Fabric-phase sorptive extraction; LPME: Liquid-phase microextraction; MEPS: Microextraction by packed sorbent; MSPE: Magnetic SPE; SBSE: Stir bar sorptive extraction; SPE: Solid-phase extraction; SPME: Solid-phase microextraction; TFME: Thin-film microextraction.

Second, considering the results of the WAC analysis (Figures 5 & 6), fiber-based SPME remained one of the top-scoring techniques with a strong balance across all RGB principles. Additionally, in-tube SPME achieved satisfactory whiteness and great analytical performance scores (80 and 105, respectively). Nevertheless, this technique could not record a powerful balance across RGB principles (the obtained applicability [blue] score is 57.8).

These findings offer valuable insights for scientists seeking to develop microextraction approaches that prioritize analytical performance, practicality and environmental responsibility for specific applications. There may be potential modifications for each microextraction technique while applying it for other analytical and bioanalytical purposes. These advancements should also be considered as suitable options in the effort to provide microextraction techniques, in TDM, that are more environmentally friendly.

Author contributions

HP Parastoo: investigation, writing (original draft). MS Foad: investigation, interpretation of data for the work, writing and editing (original draft). D Seyed Mosayeb : design of the work, conceptualization, supervision, writing (review and editing).

Financial disclosure

The authors have no financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, stock ownership or options and expert testimony.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.