# Multiparametric LC-MS/MS method for simultaneous determination of eleven antifungal drugs and metabolites in human plasma

Jean-Joseph Bendjilali-Sabiani, Céline Constans, Olivier Mathieu, Yoann Cazaubon

► To cite this version:

Jean-Joseph Bendjilali-Sabiani, Céline Constans, Olivier Mathieu, Yoann Cazaubon. Multiparametric LC-MS/MS method for simultaneous determination of eleven antifungal drugs and metabolites in human plasma. Journal of Pharmaceutical and Biomedical Analysis, 2025, 253, pp.116557. 10.1016/j.jpba.2024.116557. hal-05085654

HAL Id: hal-05085654

https://hal.science/hal-05085654v1

Submitted on 26 May 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

Check for
updates

# Multiparametric LC-MS/MS method for simultaneous determination of eleven antifungal drugs and metabolites in human plasma

Jean-Joseph Bendjilali-Sabiani<sup>a</sup>, Céline Constans<sup>a</sup>, Olivier Mathieu<sup>a,b</sup>, Yoann Cazaubon<sup>a,c,*</sup>

## ARTICLE INFO

**Keywords:**

Antifungal

Therapeutic drug monitoring

Mass spectrometry

Multiparametric analysis

Assay method

## ABSTRACT

A multiparametric liquid chromatography-tandem mass spectrometry method has been developed for the simultaneous quantification of 11 antifungal drugs and their metabolites in human plasma. This method addresses the critical need for therapeutic drug monitoring in the treatment of invasive fungal infections, which are increasingly prevalent among immunocompromised patients and those in intensive care units. The method quantifies flucytosin, fluconazole, itraconazole, hydroxy-itraconazole, posaconazole, isavuconazole, voriconazole, voriconazole-N-oxide, anidulafungin, caspofungin, and micafungin. Key challenges in method development included optimising mass spectrometer settings, chromatographic conditions, and sample preparation techniques to ensure accurate, sensitive, and specific detection. Validation of this method was conducted in accordance with the guidelines set by the USA Food and drug administration and the European Medicines Agency covering linearity, precision, accuracy, selectivity, matrix effect, and stability. The method exhibited robust performance with intra- and inter-assay precision under 10 % and average accuracy for intra- and inter-assay comparison of -2.35 % and 0.80 %, respectively. Limits of detection (0.002 to 0.110 mg/L) and a quantification range between 0.005 and 200 mg/L make this method suitable for clinical TDM applications. The ability to simultaneously analyse eleven antifungals and their metabolites within a single 5-minute run enhances its utility in clinical settings, particularly for critically ill patients who may experience significant pharmacokinetic variations. The method requires only 100 µL of plasma, demonstrating good analytical performances rendering it a valuable tool for optimising antifungal therapy and improving patient outcomes in ICU management.

## 1. Introduction

Invasive fungal infections (IFIs) are increasingly becoming a serious healthcare problem as the immunocompromised population grows. Some commonly encountered species causing IFIs include *Candida* spp., *Aspergillus* spp., *Cryptococcus* spp. and agents of mucormycosis. IFIs are rare in the general healthy population but are more frequently found in individuals with weakened immune units or those hospitalised for extended duration. In intensive care units (ICUs), a study conducted in France between 2001 and 2010 estimated the lethality of IFIs at nearly 30 %. Over the same period, the incidence of candidemia increased by 7.8 %, invasive aspergillosis by 4.4 % and mucormycosis by 7.3 % [1]. The French National Reference Center for Invasive Mycoses and Antifungals led an active and sustained nationwide surveillance program on

probable and proven invasive fungal diseases. Between 2013 and 2018, mortality of IFIs at 3 months was 38.8 %, this mortality rate was stable over the period [2]. More recent data estimates the annual global incidence at 6.5 million invasive fungal infections and 2.5 million deaths directly attributable to them between 2019 and 2021. So, despite the increasing in the antifungal (ATF) arsenal with voriconazole (VRC) (2002), posaconazole (POS) (2005), micafungin (MICA), anidulafungin (ADF) (2007) and isavuconazole (ISA) (2016), the mortality has not significantly decreased [3]. Worse, in the same time, ATF resistance for *Candida* spp. was observed, not only for azoles but also for echinocandins [4]. Emerging species as *Candida auris*, a new multi-drug resistant fungus was discovered with an increasing worldwide detection over the last decade. Its intrinsic resistance to fluconazole (FLU) and variable susceptibility to other azole ATF drugs, 5-flucytosine, and

<footnote><sup>a</sup> Department of Pharmacology, Montpellier University Hospital, Avenue du Doyen Gaston Giraud, Montpellier 34090, France</footnote>

<footnote><sup>b</sup> HydroSciences Montpellier, UM-CNRS-IRD, University Montpellier, Montpellier 34090, France</footnote>

<footnote><sup>c</sup> Pathogenesis and Control of Chronic and Emerging Infections (PCCEI), INSERM, University Montpellier, Montpellier 34090, France</footnote>

<footnote>* Corresponding author at: Department of Pharmacology, Montpellier University Hospital, Avenue du Doyen Gaston Giraud, Montpellier 34090, France.
E-mail address: yoann.cazaubon@chu-montpellier.fr (Y. Cazaubon).</footnote>
echinocandins make it a particularly feared pathogen [5]. Faced with these challenges, optimal therapeutic strategy based on a balance between the pathogen (local epidemiology of resistance), the host (the patient's risk factors and underlying diseases, infection site, previous ATF treatments) and the drug (pharmacokinetic characteristics) is needed.

The pharmacokinetic (PK) characteristics of ATFs play an important role. Indeed, most of ATF are lipophilic, explaining a diffusion ratio (organs/plasma) over 50 % in liver, kidneys, lungs, intestine but less than 10 % in brain [6]. Because of their small molecular weight, lowest protein plasma binding and logP, flucytosin (5FC), FLU and VRC are well distributed in the cerebral nervous system, explaining their use in the treatment of fungal neuroinfections. Concerning the metabolism phase, VRC, itraconazole (ITR), ISA are substrate of cytochrome P450 3A4/3A5 or 2C19 and may be subject to drug-drug interaction leading to exposure modifications or genetic polymorphism [7]. Moreover, an increase of voriconazole trough levels was observed with inflammation state, and more particularly with C-reactive protein concentration. In the same way, an hypermetabolic state is observed in patients with burns affection > 20 % of the total body surface area affecting the pharmacokinetics (PK) parameters leading to underexposition [7]. In ICU, patients experience septic conditions where endothelial dysfunction caused by inflammation in sepsis and septic shock results in capillary leakage. This leakage increases the interstitial space, thereby expanding the volume of distribution for drugs, particularly hydrosoluble compounds [8]. In critically ill patients, hypoalbuminemia can also contribute to an increased volume of distribution (Vd), significantly impacting agents with high protein-binding capacity (e.g., POS, ISA, echinocandins) [7]. This condition leads to underexpose the host to ATF drugs including echinocandins and azoles depending the site of infection [7]. ICU patients could also experience extracorporeal membrane oxygenation (ECMO), which can alter PK characteristics of ATF by increasing Vd and modifying clearance, especially for caspofungin (CSP), MICA and FLU [7].

Most of ATF are eliminated by the liver as echinocandins, POS, VRC, and ITR, so hepatic impairment could increase their blood concentration. The same applies to 5FC, ISA and FLU, which are mainly eliminated by the kidneys and whose dysfunction leads to drug accumulation in the blood. Conversely, the use of renal replacement therapy is an identified cofactor altering the drug clearance, especially for ISA and FLU [7,9]. Finally, total body weight, including obesity is a major covariate of PK variability, as most of ATFs are lipophilic, explaining underexposition for MICA, CSP, VRC, and POS, necessitating un nuanced dose adjustment based on identified covariates as total body weight or SOFA score [10,11].

Therapeutic drug monitoring (TDM) is an effective tool for optimising ATF dosage. The recommended doses according to the Summary of Product Characteristics do not account for significant PK variations in critically ill patients, there by supporting the use of TDM to select an effective dosing regimen while minimising toxicity. Monitoring ATF levels ensures that therapeutic targets are met, impacting fungal growth and improving clinical outcomes [7]. TDM is widely used and encouraged for VRC and for azoles and echinocandins in cases of drug-frug interactions, renal replacement therapy, hepatic impairment, or ICU hospitalization [7,12,13] (Table S1). 5FC exhibits significant inter-patient PK variation making TDM mandatory to prevent serious renal and marrow toxicity, as serum concentration correlates with toxicity.

Methods are available for measuring these drugs include bioassay such as liquid chromatography coupled with diode array detector (LC-DAD) or tandem mass spectrometry (LC-MS/MS). The LC-MS/MS approach is preferred due to its superior analytic sensitivity and specificity in polymedicated patients and to shorten run times in multi-parametric methods. Analytical methods have been reported for the quantification of ATF in biological fluids, including FLU, POS, CSF, MICA, ADF, ITR, VRC, and their metabolites, hydroxyitraconazole (OH-

ITR) and voriconazole-N-oxide (VNO) respectively. LC-MS/MS assays were recently reported for the simultaneous quantification of POS, VRC, ISA, CSF, ADF, and MICA in different peripheral blood compartments and of ITR, VRC, and POS in plasma [14]. However, most LC-MS/MS methods simultaneously detect a small number of molecules or high number with a narrow dynamic range. Thus, the aim of this study was to develop and validate a simple sensitive and quantitative method with a large dynamic range for the analysis of eleven ATF drugs. We selected ATF drugs that are widely used in the critical care unit and for which the benefit of achieving PK/Pharmacodynamic (PD) targets has been studied except for Amphotericin B, where the PK/PD target remains controversial. Moreover, total Amphotericin B measured in blood after liposomal amphotericin B administration may not indicate the real exposure [12]. Ultra-performance liquid chromatography coupled with a tandem mass spectrometer (LC-MS/MS) was to optimise and validate to routinely quantify 5FC, FLU, ITR, OH-ITR, POS, ISA, VCZ, VNO, ADF, CSF and MICA in human plasma.

## 2. Materials and methods

### 2.1. Chemicals

CSF, MICA, ADF, VCZ, VNO, POS, ISA, ITR, OH-ITR, FLU, 5FC, were purchased from LGC TRC (Toronto, Canada). Micafungin was supplied from Alsachim (Strasbourg, France). Internal standards (Caspofungin acetate-D4, isavuconazole-D4, itraconazole-D4, hydroxy-itraconazole-D4, voriconazole-D3, 5-Fluorocytosine-13C) were purchased from TRC (Toronto, Canada). Micafungin-13C, were supplied from Alsachim (Strasbourg, France). Acetonitrile (ACN), methanol (MeOH), formic acid (FA) for mobile phase were obtained from Sigma-Aldrich (Darmstadt, Germany). Water for mobile phase was obtained from the Milli-Q water purification system (Millipore, Canada). Plasma was purchased from the French Blood Bank ("Etablissement Français du Sang", Montpellier, France).

### 2.2. Preparation of stock and working solutions

Stock solution concentrations were 1 mg/mL for CSF, ADF, VCZ, POS, ISA, ITR, MICA, 0,1 mg/mL for OH-ITR and VNO and 2 mg/mL for FLU and 5FC. Internal standards (IS) stock solutions were at 100 µg/L. These solutions were prepared in MeOH, pooled and stored at -80°C for six-months. All molecules and IS were pooled in a volumetric flask and diluted with methanol to a final volume of 20 mL and 10 mL for IS. (Figure S1).

### 2.3. Sample preparation

Calibration samples were prepared by adding pooled solutions to plasma free of the analyte prior to extraction. Samples were prepared as follows: 10 µL microliters of the IS solution were added to 100 µL of the plasma sample. Protein precipitation was performed with 250 µL of ACN +0.1 % FA and vortexed and centrifugated at 10,000 rpm for 10 min. A volume of 200 µL of supernatant was diluted with 200 µL of a solution of ACN +0.1 % FA and MeOH +0.1 % FA 50/50. Finally, 200 µL of that mixture was transferred to vials, and 5 µL were injected into the LC-MS/MS system for analysis.

### 2.4. Chromatographic and mass spectrometric conditions

The chromatographic system was coupled to a triple quadrupole TSQ Quantis® mass spectrometer (ThermoFisher scientific, San Jose, USA). Compounds separation was performed with a raptor biphenyl 2.7 µm, 2.1×50 mm column (Restek, Bellefonte, PA, USA). Mobile phases consisted of Water + 0,1 % FA (Phase A) and ACN + 0,1 % FA (Phase B). A programmed mobile-phase gradient was used at a different flow rate. The time of analysis was 5 minutes, including a 1-minute re-
equilibration phase (Table 1). Heated electro-spray ionisation in positive mode was performed with the following settings: sheat gas, 80 arbitrary units (AU); auxiliary gas, 8 AU; sweep gas, 0 AU; spray voltage, static, 5.5 kV positive ion; ion transfer capillary temperature, 350°C; and vaporiser temperature, 350°C. Mass spectrometry was performed using selected reaction monitoring (SRM). Conditions were as follows: Q1 resolution (FWHM), 0.7; Q3 resolution (FWHM), 1.2; CID gas (mTorr), 1.5. Dwell time and RF Lens were optimised for each compound. Energies of the collision were optimised for each transition.

## 2.5. FA, formic acid; ACN, acetonitrile

The optimisation of chromatographic separation was conducted in two steps: first, the column selection, and then the gradient and mobile phase composition using either MeOH or ammonium acetate.

A mass calibration check was carried out every three months, and mass calibration was realised every six months in a positive and negative mode according to the manufacturer's recommendations using an external calibration solution (ThermoFisher scientific, San Jose, USA). Chromeleon 7.3.1 was used for LC-MS, acquisition and processing.

## 2.6. Validation procedure

### 2.6.1. Calibration and validation

Linearity, precision, accuracy, selectivity, matrix effect, and stability assays were meticulously performed. Six-to-eight points calibration curves were constructed within the concentration range 0.5–200 mg/L for FLU and 5FC, 0.1–50 mg/L for MICA, 0.5–20 mg/L for CSP and 0.05–20 mg/L for the rest of analytes. Regarding the calibration curve, the model that provided the best regression coefficient (r²) and the lowest absolute sum of square calculated with Chromeleon 7.3.1 was selected for each compound.

### 2.6.2. Linearity

The limit of detection (LOD), defined as the lowest signal that the system could detect without being confused with noise, was determined as the mean concentration of 10 blank matrix samples plus three standard deviations or the value above the 95 % confidence interval of the noise. The lower limit of quantification (LLOQ) and upper limit of quantification (ULOQ), representing the lowest and highest concentrations the system could accurately quantify within an 80–120 % range and with a precision relative standard deviation (RSD) of less than 20 %, were estimated based on six samples.

### 2.6.3. Precision and accuracy

Precision and accuracy were evaluated using at least two quality control (QC) samples. Home-made QCs were combined to QCs purchased from Chromsystems (Germany) to ensure coverage of the entire measurement range or when the specific molecule was missing from Chromsystems's QC (Table S2). Home-made QC were prepared by adding standard solutions to plasma free of the analyte prior to extraction. Precision was defined as the RSD and should not exceed ± 15 %.

<table><caption>**Table 1**
Gradient Table.</caption><thead><tr><th>Time (minute)</th><th>Flow (mL/min)</th><th>Water+0.1 % FA (%)</th><th>ACN+0.1 %FA (%)</th><th>Curve</th></tr></thead><tbody><tr><td>0</td><td>0.5</td><td>95 %</td><td>5 %</td><td>5</td></tr><tr><td>0.5</td><td>0.5</td><td>95 %</td><td>5 %</td><td>8</td></tr><tr><td>1</td><td>0.5</td><td>90 %</td><td>10 %</td><td>8</td></tr><tr><td>1</td><td>0.7</td><td>90 %</td><td>10 %</td><td>1</td></tr><tr><td>3.6</td><td>0.7</td><td>100 %</td><td>0 %</td><td>1</td></tr><tr><td>4–5<br>(Re-equilibration phase)</td><td>0.5</td><td>95 %</td><td>5 %</td><td>5</td></tr></tbody></table>

For accuracy, the mean concentration needed to be within ± 15 % of the target concentration at each tested level.

### 2.6.4. Selectivity

To investigate interferences, ten blank plasma samples from different plasma batches from "Etablissement Français du Sang" were prepared and analysed individually. As recommended, the absence of interfering components was accepted when the blanks responses were lower than 20 % of the LLOQ for the compounds and 5 % for the corresponding IS.

### 2.6.5. Matrix effect

The matrix effect was assessed according to the recommendations of the U.S. Food and Drug Administration (FDA) [15] and the European Medicine Agency (EMA) [16]. The mean matrix effect factor was calculated by comparing the area under the peak derived from the matrix spiked after extraction and the area under the peak of a pure solution at the same concentration for six biological matrices of plasma at 3-fold LLOQ and 80 % of ULOQ, both with IS. The normalised factor of the matrix effect was determined for each matrix and compound by comparing the matrix factor of the compound to the matrix factor of the corresponding IS. The RSD of the normalised factors must be less than 15 % [15,16].

### 2.6.6. Carry-over

Carry-over was assessed by measuring the signal of six blank samples analysed directly after the high-concentration standard. According to recommendations, the signal should not exceed 20 % of the LLOQ and 5 % of the IS [15,16].

### 2.6.7. Stability

The stability of the standard solutions and IS was assessed by comparing a 6-month-old solution stored at – 80°C with a freshly prepared solution. The stability of the IS and analytes in extracted plasma was evaluated by keeping processed samples placed in glass vial in the autosampler at +10 °C for 6 and 72 hours. The areas under the chromatographic peaks were compared as well as the areas normalised to the IS.

# 3. Results and discussion

## 3.1. Method development and optimization

First, we optimised the mass spectrometer parameters such as auxiliary gas, sweep gas, flow rate, spray voltage, positive ion, ion transfer capillary temperature, and vaporiser temperature for every single analyte. Direct infusion mass spectrometry was done with solutions of each compound diluted at 1 µg/mL for all azole ATFs and their ISs and to 50 µg/mL for CSP, MICA and their ISs. Following source optimization, various parameters of the multiple reactions monitoring (MRM) mode of acquisition were optimised for each compound, including RF-lens, energy of collision. Optimization was particularly challenging for CSP and MICA. Indeed, to achieve an intensity equivalent to azole ATFs, direct infusion of more concentrated solutions was necessary. After several tests, we selected the most adapted spray voltage to ensure a minimum relative intensity of 10⁴. Two MRM transitions were selected: the first for the quantification and the second for the confirmation of the analyte (Table 2).

Echinocandins were large enough not only to be ionised in the electrospray ionization source to the single protonated molecular ions ([M+H]⁺, m/z 1270.4 m/z 1093.87 and 1140.51 m/z for MICA, CSP and ADF respectively) but also to the double protonated molecular ions ([M+2H]²⁺; 547 m/z for CSP). The relative intensities of the single and double protonated species were highly dependent on the electrospray ionization (ESI) conditions, including flow rate, temperatures, pH and the composition of the mobile phase at the moment of elution. The double protonated molecular ion was selected for CSP because its
<table><caption>**Table 2**
Multiple Reaction Monitoring parameters.</caption><thead><tr><th>Compounds</th><th>Retention Time (min)</th><th>Precursor</th><th>Precursor (m/z)</th><th>Products (m/z)<br>Quantification and Confirmation</th><th>Collision<br>Energy (eV)</th><th>Dwell Time<br>(ms)</th><th>RF Lens (V)</th></tr></thead><tbody><tr><td>5FC</td><td>0.27</td><td>[M+H]<sup>+</sup></td><td>130.04</td><td>112.92<br>57.92</td><td>20<br>33</td><td>4.312</td><td>73</td></tr><tr><td>ADF</td><td>2.4</td><td>[M+H]<sup>+</sup></td><td>1140.5</td><td>1122.5<br>343.17</td><td>13<br>66</td><td>4.312</td><td>207</td></tr><tr><td>CSF</td><td>2.23</td><td>[M+H]<sup>2+</sup></td><td>547.2</td><td>538<br>86</td><td>16<br>40</td><td>4.312</td><td>92</td></tr><tr><td>Caspofungin-D4</td><td>2.223</td><td>[M+H]<sup>+</sup></td><td>1097.68</td><td>1033.5<br>1079.42</td><td>22<br>66</td><td>4.312</td><td>263</td></tr><tr><td>FLU</td><td>2.17</td><td>[M+H]<sup>+</sup></td><td>307.11</td><td>238<br>168.917</td><td>16<br>22</td><td>4.312</td><td>93</td></tr><tr><td>Fluconazole-D4</td><td>2.175</td><td>[M+H]<sup>+</sup></td><td>311.14</td><td>223.00<br>126.917</td><td>18<br>34</td><td>4.312</td><td>103</td></tr><tr><td>Flucytosin-<sup>13</sup>C</td><td>0.27</td><td>[M+H]<sup>+</sup></td><td>133.00</td><td>114.92<br>58.92</td><td>20<br>35</td><td>4.312</td><td>79</td></tr><tr><td>ISA</td><td>2.509</td><td>[M+H]<sup>+</sup></td><td>438.12</td><td>223.92<br>126.92</td><td>18<br>50</td><td>4.312</td><td>103</td></tr><tr><td>Isavuconazole-D4</td><td>2.509</td><td>[M+H]<sup>+</sup></td><td>442.15</td><td>224.00<br>126.90</td><td>20<br>52</td><td>4.312</td><td>130</td></tr><tr><td>ITR</td><td>2.524</td><td>[M+H]<sup>+</sup></td><td>705.46</td><td>392.08<br>394.25</td><td>36<br>35</td><td>4.312</td><td>263</td></tr><tr><td>ITR-OH</td><td>2.42</td><td>[M+H]<sup>+</sup></td><td>721.12</td><td>408<br>392.17</td><td>35<br>35</td><td>4.312</td><td>299</td></tr><tr><td>Itraconazole-D5</td><td>2.524</td><td>[M+H]<sup>+</sup></td><td>710.66</td><td>397.17<br>409.25</td><td>36<br>34</td><td>4.312</td><td>197</td></tr><tr><td>MICA</td><td>2.33</td><td>[M+H]<sup>+</sup></td><td>1270.45</td><td>1190.42<br>1172.42</td><td>11<br>20</td><td>4.312</td><td>196</td></tr><tr><td>Micafungin-<sup>13</sup>C</td><td>2.33</td><td>[M+H]<sup>+</sup></td><td>1276.00</td><td>1196.42<br>1178.42</td><td>11<br>21</td><td>4.312</td><td>299</td></tr><tr><td>POS</td><td>2.405</td><td>[M+H]<sup>+</sup></td><td>701.34</td><td>683.25<br>614.25</td><td>31<br>35</td><td>4.312</td><td>225</td></tr><tr><td>VCZ</td><td>2.343</td><td>[M+H]<sup>+</sup></td><td>350.12</td><td>281.00<br>126.92</td><td>15<br>34</td><td>4.312</td><td>98</td></tr><tr><td>VNO</td><td>2.26</td><td>[M+H]<sup>+</sup></td><td>366.12</td><td>224.0<br>126.0</td><td>13<br>25</td><td>4.312</td><td>94</td></tr><tr><td>Voriconazole-D3</td><td>2.344</td><td>[M+H]<sup>+</sup></td><td>353.15</td><td>284.00<br>126.92</td><td>15<br>40</td><td>4.312</td><td>106</td></tr></tbody></table>

relative intensity was over $10^4$, unlike its single protonated molecular ion. This is in line with literature, where authors preferred the double protonated molecular ion for the quantification of CSP [17,18]. For MICA and ADF, the relative intensity of singles protonated molecular ions were satisfying. CSP is the molecule that induces a protonated double ion due to its structural characteristics. Unlike other echinocandins, CSP has many amine groups, which are protons acceptors, facilitating the formation of double protonated molecular ions.

Then, chromatographic separation was optimised to achieve efficient elution of all compounds. The aim was to obtain good retention of compounds without loss of sensitivity, along with a symmetric Gaussian profile for all chromatographic peaks. Two different columns were tested: the Raptor Biphenyl 2.7 µm, 2.1 × 50 mm (Restek, Bellefonte, PA, USA) and the Acquity HSS T3 1.8 µm, 2.1 × 50 mm (Waters, Milford, MA, USA) columns. Both columns are compatible with 100 % aqueous mobile phase and exhibit good analytical performances for the separation of polar and non-polar compounds.

The gradient described in Table 1 was tested on the two columns. For HSS T3 column, the shape of the peaks were less sharp than with the Raptor Biphenyl and not Gaussian for OH-ITRA and ITRA. So, the Raptor biphenyl column was selected as it provided more sharp and Gaussian peaks compared to the HSS T3 column.

We also tried the approach of Jourdil *et al.*, [19] by incorporating ammonium acetate (2 mM) into the mobile phase to enhance peak quality and detect pH-sensitive compounds. However, this addition resulted in a lower signal for MICA compared to results obtained without ammonium acetate, non-Gaussian peaks for VRC and ISA and a double peak for 5FC (Figure S2), Consequently, ammonium acetate was excluded from the mobile phases. A Gradient using MeOH (Figure S3) was also tested, resulting in improvements to the method, especially in

the shape of the chromatographic peaks (Figure S4). However, this adjustment led to longer retention times (Figure S5). This phenomenon can be attributed to the composition of the stationary phase, as its aromatic phenyl cycles can generate π-π interactions with the analytes of interest. Indeed, MeOH, being an organic solvent without π bonds, facilitates more π-π interactions between the stationary phase and the analytes, resulting in increased retention of compounds on the biphenyl column compared to C18 columns. Conversely, ACN possessed a triple bond capable of generating additional π-π interactions with the biphenyl column. This can alter the way analytes interact with the stationary phase, increasing the elution strength of ACN even beyond what is typically observed with C18 column. Consequently, this results in lower retention on the biphenyl column compared to C18 columns [20].

Regarding sample preparation, three procedures were tested: deproteinization with 5 % zinc sulfate (w/v), MeOH and ACN. The use of 5 % zinc sulfate (w/v) did not significantly enhance the peak characteristics compared to results obtained without it. The dilution of the supernatant was very crucial for CSP and MICA, as signals were saturated.

Dilution at a 50:50 ratio with ACN + 0.1 % FA and MeOH + 0.1 % FA was tested, with the latter enhancing peak characteristics without lengthening the retention times. We tried to dry the supernatant, resulting in double peaks, especially for OH-ITR and MICA as previously described in Boonstra *et al.'s* study (Figure S6) [21].

Finally, Raptor Biphenyl column and gradient described in Table 1 was selected regarding optimised separation of compounds with varying polarity and lipophilicity (Fig. 1). The gradient started with polar compounds such as 5FC (retention time: 0.25 min) and ended with least polar compounds such as ITR (retention time: 2.524 min). This was predictable based on the prior, analysis of each compound's polarity
<figure><img src="image_3.png" /><figcaption>Fig. 1. Final chromatogram in plasma of Level I Chromsystems.</figcaption></figure>

(Table S3). It was anticipated that more lipophilic molecules would be more retained longer in the column resulting in longer retention times. Echinocandins, due to their large molecular sizes and aromatic cycles capable of establishing π-π and hydrophobic interactions with the biphenyl column, exhibited increased retention times. Among echino-candins, CSP eluted earlier than ADF and MICA due to having fewer π bonds leading to fewer interactions with the column.

As shown in Fig. 1, all analytes were eluted by 2.7 minutes. To improve the method and protect the mass spectrometer from clogging, we optimised the use of switching valves. At 3 minutes, the valve diverts the flow away from the mass spectrometer and directs it to the waste container.

## 3.2. Method validation

### 3.2.1. Linearity, precision and accuracy

For each ATF drug, four regression models were tested: linear (L) or

quadratic (Q) curve with or without weighting (1/X). The model that provided the lowest bias on quality controls, the best regression co-efficients and the lowest absolute sum of square was selected. For all analytes, the precision and accuracy of the LLOQ and ULOQ (n = 6) were within recommended limits for each compound (Table 3). The RSD of QCs ranged from 2.88 % to 14.88 % for both intra- and inter-assay precision (Table 4) meeting the acceptance criteria of the FDA and EMA [15,16]. The accuracy evaluation of QCs showed a RSD less than ± 20 % (86.47–118.7 %) from the target concentration at each tested level (Table 4). The regression models and coefficients for each compound are summarised in Table 3 and were always greater than 0.9944.

For all analytes, the regression method selected for calibration mode satisfied all predefined criteria. Over the considered concentration range, regression coefficient (r²) of the calibration curves were always greater than 0.997 (n = 10) with less bias after back calculated calibration samples within ±15 % (±20 % at LLOQ) of nominal concentration for each analytical level: LLOQ, ULOQ and QCs. Except for

<caption>**Table 3**</caption>

Limit of detection (LOD), lower limit of quantification (LLOQ), upper limit of quantification (ULOQ), and calibration curve parameters (n = 6). Results are expressed as relative standard deviation (%) for precision and bias (%) for accuracy. IS, internal standard; Q, quadratic without weighting; Q1/X, quadratic with 1/X weighting; L1/X, linear with 1/X weighting; R2, regression coefficient.

<table><thead><tr><th>Molecule</th><th>IS</th><th>LOD (mg/L)</th><th>LLOQ (mg/L)</th><th>LLOQ Precision</th><th>LLOQ Accuracy</th><th>ULOQ (mg/L)</th><th>ULOQ Precision</th><th>ULOQ Accuracy</th><th>Calibration curve and Weighting</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>CSP</td><td>CSP-D4</td><td>0.03</td><td>0.5</td><td>14.84 %</td><td>91.46 %</td><td>20</td><td>4.83 %</td><td>98.74 %</td><td>Q</td><td>0.9983</td></tr><tr><td>VRC</td><td>VRC-D3</td><td>0.01</td><td>0.05</td><td>13.03 %</td><td>118.1 %</td><td>20</td><td>1.71 %</td><td>99.06 %</td><td>Q 1/X</td><td>0.9971</td></tr><tr><td>POS</td><td>ISA-D4</td><td>0.01</td><td>0.05</td><td>7.56 %</td><td>110.86 %</td><td>20</td><td>0.94 %</td><td>99.88 %</td><td>Q 1/X</td><td>0.9982</td></tr><tr><td>MICA</td><td>MICA-13C</td><td>0.02</td><td>0.1</td><td>15.44 %</td><td>117.08 %</td><td>50</td><td>9.11 %</td><td>105.97 %</td><td>Q</td><td>0.9986</td></tr><tr><td>ITR</td><td>ITR-D5</td><td>0.003</td><td>0.05</td><td>17.4 %</td><td>101.16 %</td><td>20</td><td>1.34 %</td><td>99.15 %</td><td>Q 1/X</td><td>0.9944</td></tr><tr><td>OH-ITR</td><td></td><td>0.003</td><td>0.005</td><td>11.68 %</td><td>105.07 %</td><td>2</td><td>0.95 %</td><td>99.97 %</td><td>Q1/X</td><td>0.9956</td></tr><tr><td>ISA</td><td>ISA-D4</td><td>0.005</td><td>0.05</td><td>10.00 %</td><td>10.72 %</td><td>20</td><td>1.59 %</td><td>99.21 %</td><td>Q1/X</td><td>0.9962</td></tr><tr><td>5FC</td><td>5FC-13C</td><td>0.12</td><td>0.5</td><td>10 %</td><td>105.79 %</td><td>200</td><td>1.10 %</td><td>99.86 %</td><td>Q 1/X</td><td>0.9981</td></tr><tr><td>FLU</td><td>FLU-D4</td><td>0.05</td><td>0.5</td><td>10.02 %</td><td>109.4 %</td><td>200</td><td>0.39 %</td><td>99.85 %</td><td>L 1/X</td><td>0.9996</td></tr><tr><td>VNO</td><td>VRC-D3</td><td>0.003</td><td>0.005</td><td>12.15 %</td><td>97 %</td><td>2</td><td>0.45 %</td><td>99.67 %</td><td>Q</td><td>0.9986</td></tr><tr><td>ADF</td><td></td><td>0.002</td><td>0.05</td><td>11.01 %</td><td>110.5 %</td><td>20</td><td>3.51 %</td><td>108.08 %</td><td>Q</td><td>0.9989</td></tr></tbody></table>
<caption>**Table 4**</caption>

Intra and inter-assay precision and accuracy for quality controls. Results are expressed as relative standard deviation (%) for precision and bias (%) for accuracy. QC: Quality control.

<table><thead><tr><th></th><th>Molecule</th><th>IS</th><th>Homemade QC Precision</th><th>Homemade QC Accuracy</th><th>QC Level I Precision</th><th>QC Level I Accuracy</th><th>QC Level II Precision</th><th>QC Level II Accuracy</th></tr></thead><tbody><tr><td rowspan="10">Intra-assay (n=6)</td><td>CSP</td><td>CSP-D4</td><td>-</td><td>-</td><td>14.88 %</td><td>109.19 %</td><td>10.26 %</td><td>110.17 %</td></tr><tr><td>VRC</td><td>VRC-D3</td><td>12.5 %</td><td>97.53 %</td><td>14.41 %</td><td>90.4 %</td><td>12.49 %</td><td>96.03 %</td></tr><tr><td>POS</td><td>ISA-D4</td><td>5.65 %</td><td>111.32 %</td><td>5.49 %</td><td>90.26 %</td><td>5.91 %</td><td>95.67 %</td></tr><tr><td>MICA</td><td>MICA-13C</td><td>13.18 %</td><td>101.95 %</td><td>13.02 %</td><td>88.81 %</td><td>14.71 %</td><td>101.37 %</td></tr><tr><td>FLU</td><td>FLU-D4</td><td>2.88 %</td><td>101.99 %</td><td>8.18 %</td><td>88.83 %</td><td>9.15 %</td><td>85.31 %</td></tr><tr><td>ITR</td><td>ITR-D5</td><td>-</td><td>-</td><td>7.31 %</td><td>92.42 %</td><td>10.11 %</td><td>89.42 %</td></tr><tr><td>OH-ITR</td><td>-</td><td>-</td><td>-</td><td>13.06 %</td><td>95.52 %</td><td>9.72 %</td><td>96.66 %</td></tr><tr><td>ISA</td><td>ISA-D4</td><td>5.95 %</td><td>112.59 %</td><td>8.15 %</td><td>102.77 %</td><td>7.21 %</td><td>106.88 %</td></tr><tr><td>5FC</td><td>5FC-13C</td><td>10.06 %</td><td>104.24 %</td><td>10.78 %</td><td>85.2 %</td><td>8.41 %</td><td>89.21 %</td></tr><tr><td>VNO</td><td>VRC-D3</td><td>8.69 %</td><td>88.89 %</td><td>9.07 %</td><td>87.87 %</td><td>9.42 %</td><td>98 %</td></tr><tr><td>ADF</td><td>-</td><td>-</td><td>-</td><td>3.56 %</td><td>106.7 %</td><td>9.99 %</td><td>112.41 %</td></tr><tr><td>Mean (sd)</td><td>-</td><td>-</td><td>8.42 % (3.8 %)</td><td>102.64 % (8.09 %)</td><td>9.81 % (3.72 %)</td><td>94.91 % (8.2 %)</td><td>9.76 % (2.38 %)</td><td>99.58 % (8.05 %)</td></tr><tr><td rowspan="10">Inter-assay (n=6)</td><td>CSP</td><td>CSP-D4</td><td>-</td><td>-</td><td>12.14 %</td><td>104.34 %</td><td>5.54 %</td><td>113.76 %</td></tr><tr><td>VRC</td><td>VRC-D3</td><td>3.75 %</td><td>99.68 %</td><td>13.77 %</td><td>103.28 %</td><td>3.75 %</td><td>99.68 %</td></tr><tr><td>POS</td><td>ISA-D4</td><td>10.95 %</td><td>94.16 %</td><td>7.49 %</td><td>86.91 %</td><td>7.73 %</td><td>91.86 %</td></tr><tr><td>MICA</td><td>MICA-13C</td><td>11.78 %</td><td>112.21 %</td><td>10.20 %</td><td>108.08 %</td><td>13.38 %</td><td>97.89 %</td></tr><tr><td>ITR</td><td>ITR-D5</td><td>-</td><td>-</td><td>11.30 %</td><td>96.13 %</td><td>12.76 %</td><td>94.11 %</td></tr><tr><td>OH-ITR</td><td>-</td><td>-</td><td>-</td><td>8.91 %</td><td>114.07 %</td><td>11.45 %</td><td>108.82 %</td></tr><tr><td>ISA</td><td>ISA-D4</td><td>7.48 %</td><td>108.15 %</td><td>6.37 %</td><td>97.79 %</td><td>13.70 %</td><td>98.00 %</td></tr><tr><td>5FC</td><td>5FC-13C</td><td>7.28 %</td><td>93.93 %</td><td>10.27 %</td><td>96.22 %</td><td>7.65 %</td><td>93.34 %</td></tr><tr><td>FLU</td><td>FLU-D4</td><td>7 %</td><td>99.19 %</td><td>11.28 %</td><td>99.52 %</td><td>10.67 %</td><td>87.32 %</td></tr><tr><td>VNO</td><td>VRC-D3</td><td>4.34 %</td><td>92.45 %</td><td>6.47 %</td><td>110.07 %</td><td>6.31 %</td><td>96.93 %</td></tr><tr><td>ADF</td><td>-</td><td>-</td><td>-</td><td>9.72 %</td><td>107.55 %</td><td>8.09 %</td><td>103.72 %</td></tr><tr><td>Mean (sd)</td><td>-</td><td>-</td><td>7.51 % (3 %)</td><td>99.97 % (7.57 %)</td><td>9.81 % (2.35 %)</td><td>103.22 % (6.39 %)</td><td>8.88 % (3.43 %)</td><td>99.21 % (7.85 %)</td></tr></tbody></table>

FLUCO which was linear, the calibration curves across the entire concentration range were best described by quadratic regression. Quadratic regression was also commonly employed in the ATF assay method [19, 22].

### 3.2.2. Selectivity

For each specified transition within the related retention times windows, analysis of six blank plasma samples indicated no interference (< 5 % of LLOQ and IS response). For each sample, the response was less than 5 % for IS and 20 % for LLOQ.

### 3.2.3. Matrix effect

Matrix factor (MF) ranged from 0.26 to 3.89 (Table S4). At 3-fold LLOQ, a matrix effect associated with a loss of signal was observed for 5FC (MF 0.26), ISA (MF 0.72). Conversely, a gain of signal was observed for CSP at both 3-fold LLOQ and 80 % ULOQ (MF 3.89 and 3.81, respectively). The use of the deuterated internal standards (IS) was sufficient to correct the matrix effect, since IS normalised matrix factor ranged from 0.88 to 1.11. As the recommendation states, the RSD of the normalised matrix factor was less than 15 % for each ATF drugs (Table S4). Fig. 2 depicted for each analyte the normalised matrix factor, described by the median and interquartiles.

### 3.2.4. Carry-over

As recommended [15,16], for all ATFs drugs and metabolites, the remaining area was less than 20 % of the LLOQ (ranging from 0.36 % to 12.07 %) and less than 5 % of the signal of the IS. These results confirmed the absence of contamination and thus adequate chromatographic conditions.

### 3.2.5. Stability

All analytes and IS were stable in MeOH for at least 6 months stored at -80 °C. Comparison of the normalised areas to the IS provided 72 h post-analytical stability for VRZ, POS, ISA, 5FC, FLU, VNO and ADF with a standard deviation within ± 15 % of the nominal concentration. CSP, MICA, ITR, and OH-ITR were stable for 6 h at 10 °C with acceptable standard deviation.

### 3.3. Applicability

Therapeutic drug monitoring is recommended for VRC, ITR, OH-ITR, POS due to their high interindividual variability (Table S1) [7]. About FLU, ISA, 5FC and echinocandins, there are no clear recommendations, however, TDM may be necessary in certain cases such as septic state, use of organ support devices, polypharmacy, renal or hepatic impairment

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Normalised matrix factor at 3 fold LLOQ (3xLLOQ) and at 80 % of ULOQ concentrations.</figcaption></figure>
[7]. We report three cases where TDM was crucial to ensure optimal drug exposure.

**Case 1. Therapeutic inefficacy.**

A 56-year-old woman was hospitalised in the ICU for sepsis post liver lobectomy. Empirical treatment with piperacillin-tazobactam (16 g/d) and MICA (100 mg/d) was prescribed. After 48 hours without clinical improvement, MICA concentration in plasma was assayed. Studies have shown that exposure to micafungin is lower in hematological patients, and even lower in critically ill patients than in healthy volunteers, which may impact treatment efficacy [11]. To improve drug treatment outcomes in patient with great inter- and intra-individual variability, Model Informed Precision Dosing (MIPD) approach was performed. This approach leverages clinical data with dedicated pharmacometric software capable of applying mathematical models to recommend a personalised dose.

The PK/PD target for MICA associated with clinical success is an area under the curve AUC<sub>24h</sub>/MIC ratio above 3000 and 5000 for *Candida albicans* and *Candida glabrata*, respectively [23]. The clinical breakpoint concentration from the EUCAST was selected as MIC. To estimate the AUC, 2 plasma samples were taken: one at maximal concentration (Cmax) (1 h post-infusion) and one at trough concentration. Based on a population PK model from Boonstra *et al*. [23], the AUC<sub>24h</sub> was estimated using Monolix® (version 2023R1, Lixoft, Antony, France, https://lixoft.com/, accessed on 10 October 2023) by fixing all blocks (θ, ω, ε). The AUC<sub>24h</sub>/MIC was 3267 mg/L*h which was within therapeutic targets for *Candida albicans* but not for *Candida glabrata* (Figure S7). This discrepancy may be explained by the prescribed dose prescribed; according to Kapralos *et al*. [24], the probability of target attainment for *Candida albicans* and *Candida glabrata* at 100 mg per day is 84 % and 17 %, respectively. Given the patient's clinical improvement, no dosage adjustment was proposed.

**Case 2. Management of severe illness**

A 54-year-old man treated for small lymphocytic lymphoma with ibrutinib developed uveitis in January 2022, for which he was treated with corticosteroids. Three months later, he was hospitalised for loss of consciousness and balance, and PCR from lumbar puncture identified *Aspergillus fumigatus*. Cerebral aspergillosis was treated with ISA 200 mg per day and CSP 150 mg per day. Despite this, symptoms persisted. In May 2024, the progression of a parietal temporal lesion necessitated surgical exeresis of the nodule. To avoid any matrix effect, a calibration curve was executed with blank brain tissue (obtained from a previous study): 100 mg of brain was ground with 1 mL of distilled water, pooled solutions and IS were added prior to extraction. QCs, reconstituted in plasma, were diluted 50:50 with ground blank brain solution. For both QC and the patient's sample, IS were added and followed the same procedure. The rest of the protocol remained the same.

ISA concentrations in the brain (2.2 mg/L) and plasma (4 mg/L) were beyond the clinical breakpoint concentration of 1 mg/L from the EUCAST with a diffusion ratio over 50 %, which aligns with the literature [25,26]. For CSP (8.6 mg/L), the concentrations in brain only were higher compared to the literature [27], prompting a proposition for dose reduction.

**Case 3. Bladder Fermentation Syndrome (BFS).**

A 68-year-old man with a history of light chain myeloma was hospitalised due to thrombocytopenia and pain. His medical history included alcohol addiction and heart failure treated with empagliflozin 10 mg per day. Upon admission, the patient received Piperacillin-Tazobactam to manage a septic state and nefopam for pain control. The latter occurred constipation and acute urine retention requiring urinary catherisation. At the same time, they observed macroscopic hematuria requiring bladder irrigation. During a family visit, the nurses discovered a beer can in the waste bin, prompting the measurement of blood and urine ethanol on this alcohol intake necessitated the detection

and measurement of ethanol in the blood and urine Day 0. Then, a cardiocirculatory arrest required transfer to ICU where hazardous repeated assessment of blood and urine ethanol were prescribed, revealing ethanol in urine only, 2 days after the first dosage, leading to a diagnosis of BFS. This condition results from the fermentation of yeasts in high-glucose environment which can be attributable to the mechanism of action of empagliflozin which was observed in our case with measured glycosuria between 62 and 250 mM [28]. Microbiological analysis identified two yeasts: *Candida dubliniensis* and *parapsilosis* with MIC for fluconazole of 0.25 mg/L and 0.5 mg/L, respectively. On Day 4, fluconazole was prescribed with a loading dose of 1200 mg, followed by a daily dose of 600 mg. TDM was performed on plasma and urine samples. Except for Day 9, though concentrations were within the therapeutic range of 10–15 mg/L. In urine, concentrations of fluconazole were more than 70 times higher than MICs of *Candida dubliniensis* and *parapsilosis*. The patient was treated for 10 days with clinical success.

**3.4. Comparison with reported methods**

Our method was developed and validated to simultaneously quantify 11 ATFs drugs, with a chromatographic run time of 5 minutes. Our assay required 100 µL of serum, whereas some published methods used only 10–50 µL [29,30], but these methods included fewer molecules or exhibited lower performances. Similar to our approach, most quantification methods required only 50–100 µL of plasma which is considered a small and acceptable volume, particularly useful for pediatric and intensive care patients. Several other multi-parametric quantification methods (Table S5) in plasma have been published in the literature for ATF5 [14,31–33]. As we did, most authors opted for simple sample pre-treatment with protein precipitation using ACN or MeOH [14,31, 33], whereas others used solid-phase extraction [14,32]. Concerning chromatographic conditions, almost all methods used positive ESI mode [14,31–33]. The choice of the column changed among studies. While we used the Raptor Biphenyl (2.7µm, 2.1 × 50 mm) from Restek (Bellefonte, PA, USA), the most commonly used columns were the Acquity BEH C18 (1.7µm, 2.1 × 50 mm) [22,32] and the Acquity BEH C8 (1.7 µm, 2.1 × 50 mm) from Waters (Milford, MA, USA) [31,33].

Other authors opted for a pentafluorophenyl (PFP) analytical column [19] such as the Kinetex 2.6 µm, 2 mm × 50 mm from Phenomenex (Aschaffenburg, Germany) or a BetaBasic C4 [34] (100 × 3.0 mm) from Thermo Hypersil-Keystone (Dreieich, Germany). ACN/water gradients were often used [19,22,31–33], except for Farowski *et al.* who used a MeOH gradient [34]. Mobile phases were buffered with formic acid [19, 22,31–33], with [22,31,33,34] or without [19,32] ammonium acetate. As a result, chromatographic run time varied significantly between reported methods, ranging from 4 minutes with a pentafluorophenyl column [19] to 10 minutes for the Waters Acquity BEH C8 column [31,33]. Our method offers a 5-minute run time, one minute longer than the Jourdil *et al.*'s method, but quantifying a greater number of compounds (11 versus 4) [19].

Schuster *et al.* [29] proposed a robust and reliable quantification of 11 ATF drugs with short turnaround time of 5 minutes using a high-resolution mass spectrometry (HRMS) coupled with a quaternary RS pump for online solid-phase extraction (SPE) (loading pump) and a binary RS pump for analytical separation (eluting pump) with two six-port switching valves [29]. Our method offers the comparable performances using a simpler analytical procedure. Moreover, short runs and simultaneous quantification are not only saving time and enable rapid delivery of biological results but also conserve materials: less mobile phase consumed, LC-MS/MS preserved from overuse, and laboratory technician time is optimised. This efficiency is crucial in an era where cost control is essential.

Schuster *et al.* [29] demonstrated intra- and inter-assay precision under 10 %, and average accuracy for intra- and inter-assay comparison of −0.85 % and −1.42 %, respectively. In comparison, our method is
equally precise and accurate, with intra- and inter-assay precision under 10 % and average accuracy for intra- and inter-assay comparison of -2.35 % and 0.80 %, respectively. Additionally, our method is sensitive, with LOD ranging from 0.002 to 0.110 mg/L, depending on compound. This sensitivity is equivalent to or even lower than previously published studies [14], particularly for echinocandins.

This is a very satisfying aspect, because a stable and precise mass spectrometric quantification of micafungin and anidulafungin is very difficult to achieve. Indeed, because of their structure and molecular weight, they suffer from some technical difficulties related to the optimal ionization conditions of the molecule and a lack of validation data. Regarding the LLOQ, we decided to fix at 0.05 mg/L for VRZ, VNO, ADF, POS, ISA and ITR, at 0.01 mg/L for MICA, OH-ITR, and at 0.5 mg/L for CSP, 5FC and FLU. These values were selected to be 10–40 times lower than the trough concentrations clinically expected at the recommended doses ( > 0.5 mg/L for ITZ, > 0.5 mg/L for OH-ITZ, > 1 mg/L for POS and VRZ, > 4.7 mg/L for CSP, > 2 mg/L for ISA, > 10 mg/L for FLU, and > 20 mg/L for 5FC, > 1.6 mg/L for MICA) [7,12,13]. Moreover, our method offers a large dynamic range for all analytes, reducing the likelihood of sample dilution. This is particularly useful to assay Cmax, in order to estimate AUC, or in case of delayed plasma elimination with ATF accumulation and toxicity. Thus, our method is suitable for PK studies necessitating a large range, low LODs and LOQs to potentially be able to analyse a third elimination phase, particularly with lipophilic molecules.

While some studies, such as those by Tanaka *et al.* [32] and Decosterd *et al.* [22] reported lower LLOQs than ours, but they analysed a smaller number of molecules. As the number of simultaneously measured analytes increase, the MS detector must analyse a large number of molecules, affecting the signal with a loss of sensitivity, thereby impacting LLOQs. Compared to published methods, we have the lowest LLOQs with the largest number of analytes. Our method encompasses molecules with a very broad physicochemical properties: basic molecule (CSP), acid molecules (MICA), small molecule (5FC), large molecules (MICA, CSP). Combining all these molecules in a single run was challenging, particularly for MICA and CSP. As an example, Decosterd *et al.* [22] excluded MICA from their UPLC–mass spectrometric multiplex method for the quantification of ADF and CSP along with six ATF drugs and metabolites.

Concerning IS, we used the corresponding Deuterium or isotope labelled molecule for each analyte except for ADF and POS. Micafungin-13C was used because of its similar structure with ADF, isavuconazole-D4 was chosen as IS for POS because of its similar structure and physicochemical properties (Table S3).

Posaconazole-2H5 being very expensive, it was not selected. We also notice that selecting suitable IS, mainly for echinocandins, proved difficult. Either the ISs had no structural similarity to the analyte [14,31,33] or they were custom-synthesized analogs that were not commercially available [17,35]. For an everyday rugged routine analytical method, an easily available IS with similar physicochemical properties would be much more desirable.

## 4. Conclusions

The therapeutic drug monitoring of ATF drugs represents a significant asset in the management of infectious diseases, particularly in ICUs. We have developed and validated a robust, sensitive and rapid LC-MS/MS method with simple sample preparation for the determination of 11 compounds ranging from flucytosin to echinocandins and azoles. To the best of our knowledge, this method offers the largest number of ATF of very different physicochemical nature, the largest dynamic range for each analytes, the shortest run time and the lowest LLOQs, requiring only 100 µL of human plasma using LC-MS/MS compared to those previously published. This method has been successfully used in daily practice for TDM and is especially useful to manage polymedicated or ICU patients.

## Funding

This research received no external funding.

## CRediT authorship contribution statement

**Yoann Cazaubon:** Writing – review & editing, Writing – original draft, Supervision, Methodology, Conceptualization. **Olivier Mathieu:** Writing – review & editing, Writing – original draft, Methodology. **Céline Constans:** Methodology, Investigation, Data curation. **Jean-Joseph Bendjilali-Sabiani:** Writing – review & editing, Writing – original draft, Visualization, Investigation, Data curation.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

This work was supported by Montpellier University Hospital, France.

## Author Agreement

The authors certify that they have seen and approved the final version of the manuscript being submitted. They warrant that the article is the authors’ original work, hasn’t received prior publication and isn’t under consideration for publication elsewhere.

## Institutional review board statement

Not applicable.

## Informed consent statement

Not applicable.

## Appendix A. Supporting information

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jpba.2024.116557.

## Data Availability

Data is contained within the article or supplementary material.

## References

[1] D. Bitar, O. Lortholary, Y. Le Strat, J. Nicolau, B. Coignard, P. Tattevin, D. Che, F. Dromer, Population-based analysis of invasive fungal infections, France, 2001–2010, Emerg. Infect. Dis. 20 (2014) 1149–1155, https://doi.org/10.3201/eid2007.140087.

[2] S. Galmiche, B. Thoreau, S. Bretagne, A. Alanio, A. Paugam, V. Letscher-Bru, S. Cassaing, J.-P. Gangneux, H. Guegan, L. Favennec, A. Minoza, F. Morio, J. Bonhomme, G. Desoubeaux, O. Eloy, L. Hasseine, M. Sasso, L. Millon, A.-P. Bellanger, P. Poirier, M. Moniot, T. Chouaki, A. Huguenin, F. Dalle, B. Bouteille, M. Nicolas, N. Desbois-Nogard, M.-E. Bougnoux, F. Danion, V. Poindron, A. Néel, K. Boukris-Sitbon, F. Lanternier, B. Terrier, French Mycoses Study Group, Invasive fungal diseases in patients with autoimmune diseases: a case series from the French RESSIF network, RMD Open 9 (2023) e003281, https://doi.org/10.1136/rmdopen-2023-003281.

[3] D.W. Denning, Global incidence and mortality of severe fungal disease, e428–e438, Lancet Infect. Dis. 24 (2024), https://doi.org/10.1016/S1473-3099(23)00692-8.

[4] G. Prigent, N. Aît-Ammar, E. Levesque, A. Fekkar, J.-M. Costa, S. El Anbassi, F. Foulet, C. Duvoux, J.-C. Merle, E. Dannaoui, F. Botterel, Echinocandin Resistance in Candida Species Isolates from Liver Transplant Recipients, Antimicrob. Agents Chemother. 61 (2017) e01229-16, https://doi.org/10.1128/AAC.01229-16.

[5] J. Rhodes, M.C. Fisher, Global epidemiology of emerging Candida auris, Curr. Opin. Microbiol 52 (2019) 84–89, https://doi.org/10.1016/j.mib.2019.05.008.
[6] A.H. Groll, S.C. Piscitelli, T.J. Walsh, Clinical pharmacology of systemic antifungal agents: a comprehensive review of agents in clinical use, current investigational compounds, and putative targets for antifungal drug development, Adv. Pharm. 44 (1998) 343–500, https://doi.org/10.1016/s1054-3589(08)60129-5.

[7] B. Kably, M. Launay, A. Derobertmasure, S. Lefeuvre, E. Dannaoui, E.M. Billaud, Antifungal drugs TDM: trends and update, Ther. Drug Monit. 44 (2022) 166, https://doi.org/10.1097/FTD.0000000000000952.

[8] C. Ince, P.R. Mayeux, T. Nguyen, H. Gomez, J.A. Kellum, G.A. Ospina-Tascón, G. Hernandez, P. Murray, D. De Backer, ADQI XIV Workgroup, THE ENDOTHELIUM IN SEPSIS, Shock 45 (2016) 259–270, https://doi.org/10.1097/SHK.0000000000000473.

[9] L. Perez, P. Corne, G. Pasquier, C. Konecki, M. Sadek, C. Le Bihan, K. Klouche, O. Mathieu, J. Reynes, Y. Cazaubon, Population pharmacokinetics of isavuconazole in critical care patients with COVID-19-associated pulmonary aspergillosis and monte carlo simulations of high off-label doses, J. Fungi 9 (2023) 211, https://doi.org/10.3390/jof9020211.

[10] A. Castro-Balado, I. Varela-Rey, B. Mejuto, C. Mondelo-García, I. Zarra-Ferro, T. Rodríguez-Jato, A. Fernández-Ferreiro, Updated antimicrobial dosing recommendations for obese patients, Antimicrob. Agents Chemother. 68 (2024) e01719-23, https://doi.org/10.1128/aac.01719-23.

[11] V. Jullien, E. Azoulay, C. Schwebel, T. Le Saux, P.E. Charles, M. Cornet, B. Souweine, K. Klouche, S. Jaber, J.-L. Trouillet, F. Bruneel, M. Cour, J. Cousson, F. Meziani, D. Gruson, A. Paris, M. Darmon, M. Garroute-Orgeas, J.-C. Navellou, A. Foucrier, B. Allaouchiche, V. Das, J.-P. Gangneux, S. Ruckly, M. Wolff, J.-F. Timsit, EMPIRICUS Trial Study Group, Population pharmacokinetics of micafungin in ICU patients with sepsis and mechanical ventilation, J. Antimicrob. Chemother. 72 (2017) 181–189, https://doi.org/10.1093/jac/dkw352.

[12] A. Gómez-López, Antifungal therapeutic drug monitoring: focus on drugs without a clear recommendation, Clin. Microbiol. Infect. 26 (2020) 1481–1487, https://doi.org/10.1016/j.cmi.2020.05.037.

[13] H.R. Ashbee, R.A. Barnes, E.M. Johnson, M.D. Richardson, R. Gorton, W.W. Hope, Therapeutic drug monitoring (TDM) of antifungal agents: guidelines from the British Society for Medical Mycology, J. Antimicrob. Chemother. 69 (2014) 1162–1176, https://doi.org/10.1093/jac/dkt508.

[14] Y.Z. Zheng, S. Wang, Advances in antifungal drug measurement by liquid chromatography-mass spectrometry, Clin. Chim. Acta 491 (2019) 132–145, https://doi.org/10.1016/j.cca.2019.01.023.

[15] U.S. Food and Drug, Administration, FDA, U.S. Food and Drug Administration C for DE and Analytical Procedures and Methods Validation for Drugs and Biologics (Internet), (2020). <https://www.fda.gov/regulatory-information/searchfda>-guidance-documents/analytical-procedures-and-methods-validation-drugsand-biologics. (Accessed 21 Juil 2022).

[16] European Medicines Agency, EMA, EMA, Bioanalytical Method Validation (Internet), (2022). <https://www.ema.europa.eu/en/bioanalytical-method-validat ion>. (Accessed 21 Juil 2022).

[17] B. Rochat, S. Bolay, A. Pascual, T. Calandra, O. Marchetti, Liquid chromatography-mass spectrometry method for quantification of caspofungin in clinical plasma samples, J. Mass Spectrom. 42 (2007) 440–449, https://doi.org/10.1002/ jms.1171.

[18] X. Cheng, K. Liu, Y. Liu, M. Wang, Y. Ma, Development and validation of a liquid chromatography/tandem mass spectrometry method for determination of caspofungin in dried blood spots, Rapid Commun. Mass Spectrom. 32 (2018) 1068–1074, https://doi.org/10.1002/rcm.8100.

[19] J.-F. Jourdil, J. Tonini, F. Stanke-Labesque, Simultaneous quantitation of azole antifungals, antibiotics, imatinib, and raltegravir in human plasma by two- dimensional high-performance liquid chromatography-tandem mass spectrometry, J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 919–920 (2013) 1–9, https://doi. org/10.1016/j.jchromb.2012.12.028.

[20] M. Yang, S. Fazio, D. Munch, P. Drumm, Impact of methanol and acetonitrile on separations based on π-π interactions with a reversed-phase phenyl column, J. Chromatogr. A 1097 (2005) 124–129, https://doi.org/10.1016/j. chroma.2005.08.028.

[21] J.M. Boonstra, E.M. Jongedijk, R.A. Koster, D.J. Touw, J.-W.C. Alffenaar, Simple and robust LC-MS/MS analysis method for therapeutic drug monitoring of micafungin, Bioanalysis 10 (2018) 877–886, https://doi.org/10.4155/bio-2017- 0275.

[22] L.A. Decosterd, B. Rochat, B. Pesse, T. Mercier, F. Tissot, N. Widmer, J. Bille, T. Calandra, B. Zanolari, O. Marchetti, Multiplex Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry Method for Simultaneous Quantification in Human Plasma of Fluconazole, Itraconazole, Hydroxyitraconazole, Posaconazole, Voriconazole, Voriconazole-N-Oxide, Anidulafungin, and Caspofungin, Antimicrob. Agents Chemother. 54 (2010) 5303–5315, https://doi.org/10.1128/aac.00404-10.

[23] J.M. Boonstra, K.C. van der Elst, J.G. Zijlstra, T.S. van der Werf, J.W.C. Alffenaar, D.J. Touw, Population Pharmacokinetic Model and Optimal Sampling Strategies for Micafungin in Critically Ill Patients Diagnosed with Invasive Candidiasis, Antimicrob Agents Chemother 66 (n.d.) e01113-22. https://doi.org/10.1128/aac .01113-22.

[24] I. Kapralos, E. Mainas, E. Neroutsos, S. Apostolidi, M. Siopi, O. Apostolopoulou, G. Dimopoulos, H. Sambatakou, G. Valsami, J. Meletiadis, A. Dokoumetzidis, Population pharmacokinetics of micafungin over repeated doses in critically ill patients: a need for a loading dose? J. Pharm. Pharm. 72 (2020) 1750–1760, https://doi.org/10.1111/jph.13353.

[25] F. Lamoth, T. Mercier, P. André, J.L. Pagani, O. Pantet, R. Maduri, B. Guery, L. A. Decosterd, Isavuconazole brain penetration in cerebral aspergillosis, J. Antimicrob. Chemother. 74 (2019) 1751–1753, https://doi.org/10.1093/jac/ dkz050.

[26] C. Rouzaud, V. Jullien, A. Herbrecht, B. Palmier, S. Lapusan, M. Morgand, R. Guéry, A. Dureault, F. Danion, S. Puget, L. Goldwirt, F. Lanternier, O. Lortholary, Isavuconazole Diffusion in Infected Human Brain, Antimicrob. Agents Chemother. 63 (2019) e02474-18, https://doi.org/10.1128/AAC.02474- 18.

[27] J. Marx, V. Reinstadler, T. Gasperetti, R. Welte, H. Oberacher, P. Moser, M. Joannidis, R. Bellmann, Human tissue distribution of caspofungin, Int J. Antimicrob. Agents 59 (2022) 106553, https://doi.org/10.1016/j. ijantimicag.2022.106553.

[28] G. Berrut, A.S. Boureau, J.N. Trochu, B. Genet, L. De DecKer, O. Hanon, Empagliflozin in the elderly, Geriatr. Psychol. Neuropsychiatr. Vieil. (2021), https://doi.org/10.1684/pnv.2021.0987.

[29] C. Schuster, M. Paal, J. Lindner, M. Zoller, U. Liebchen, C. Scharf, M. Vogeser, Isotope dilution LC-orbitrap-HRMS with automated sample preparation for the simultaneous quantification of 11 antimycotics in human serum, J. Pharm. Biomed. Anal. 166 (2019) 398–405, https://doi.org/10.1016/j.jpba.2019.01.038.

[30] S.J. Yoon, K. Lee, J. Oh, H.I. Woo, S.-Y. Lee, Experience with therapeutic drug monitoring of three antifungal agents using an LC-MS/MS method in routine clinical practice, Clin. Biochem. 70 (2019) 14–17, https://doi.org/10.1016/j. clinbiochem.2019.06.003.

[31] W. Li, Y. Li, J. Cai, Y. Wang, Y. Liu, H. Hu, L. Liu, Simultaneous Quantification of Seven Antifungal Agents in Human Serum Using Liquid Chromatography-Tandem Mass Spectrometry, Pharm. (Basel) 16 (2023) 1537, https://doi.org/10.3390/ ph16111537.

[32] R. Tanaka, K. Shiraiwa, K. Takano, M. Ogata, S. Honda, N. Yoshida, K. Okuhiro, M. Yoshida, K. Narahara, M. Kai, R. Tatsuta, H. Itoh, High-throughput simultaneous quantification offive azole anti-fungal agents and one active metabolite in human plasma using ultra-high-performance liquid chromatography coupled to tandem mass spectrometry, Clin. Biochem 99 (2022) 87–96, https:// doi.org/10.1016/j.clinbiochem.2021.10.010.

[33] L. Liu, L. Zhang, X. Zheng, X. Liu, W. Liu, J. Wu, LC-MS/MS-based multiplex antibacterial platform for therapeutic drug monitoring in intensive care unit patients, Front Pharm. 14 (2023) 1116071, https://doi.org/10.3389/ fphar.2023.1116071.

[34] F. Farowski, O.A. Cornely, J.J. Vehreschild, P. Hartmann, T. Bauer, A. Steinbach, M.J.G.T. Rüping, C. Müller, Quantitation of azoles and echinocandins in compartments of peripheral blood by liquid chromatography-tandem mass spectrometry, Antimicrob. Agents Chemother. 54 (2010) 1815–1819, https://doi. org/10.1128/AAC.01276-09.

[35] F. Kirchhoff, B. Maier, C. Rieger, H. Ostermann, U. Spöhrer, M. Vogeser, An on-line solid phase extraction procedure for the routine quantification of caspofungin by liquid chromatography-tandem mass spectrometry, Clin. Chem. Lab Med 50 (2011) 521–524, https://doi.org/10.1515/CCLM.2011.802.