A total of 101 patients were included in this study; these participants were diagnosed with an IFI between January 2023 and June 2024 by the departments of emergency critical care medicine and respiratory critical care medicine of the affiliated hospital of Xuzhou Medical University. The Guidelines for the Diagnosis and Treatment of Invasive Fungal Infection in Critically Ill Patients (Von Lilienfeld-Toal et al., 2019) was used as the foundation for the diagnostic criteria. The patient inclusion requirements are as follows:

(1) Patient should fulfill the necessary diagnostic requirements for an IFI.

Some exclusion criteria were also used on the study group members as follows:

(1) Severe hepatic or renal impairment.

The voriconazole treatment regimen was as follows: a loading dose of 6 mg/kg q12h was administered initially as either intravenous drip or nasogastric administration; then, the maintenance dose of 4 mg/kg q12h was administered starting from the second day (Jiang and Lin, 2024). This study was approved by the ethics committee of the Affiliated Hospital of Xuzhou Medical University (approval number: XYFY2023-KL481-01).

Data were collected from the patients, including their gender, age, BMI, diagnosis at admission, outcome, liver function, renal function, infection index, fungal G/GM tests, albumin level, as well as Acute Physiology and Chronic Health Evaluation II (APACHE-II) score. The adverse reactions of voriconazole include liver function damage, neurotoxicity, visual impairment (e.g., decreased visual acuity and blurred vision), skin reactions, and reactions related to intravenous infusion (e.g., phlebitis and pain at the infusion site). The efficacy of voriconazole treatment was determined according to the Guidelines for the Diagnosis and Treatment of Invasive Fungal Infections in Critically Ill Patients (Von Lilienfeld-Toal et al., 2019).

The tool used for data analysis was SPSS 27.0. The non-normal distribution data were expressed as median and interquartile range (IQR) [M (P25–P75)], and non-parametric tests were used for the intergroup comparisons. The count data were expressed as cases (%), and the normally distributed measurement data were expressed as x ¯ ± s, where t-tests were used for the intergroup comparisons. In the two-tailed test, a p-value of less than 0.05 indicated a statistically significant difference. The chi-squared (χ ² ) test was employed to assess the efficacy of voriconazole treatment and occurrence rate of its adverse reactions in patients with diverse genotypes. Multiple linear regression was utilized to analyze the factors influencing the minimum concentration (C_min) of voriconazole.

A total of 101 patients with IFIs who were treated with voriconazole were enrolled in this study. The demographic, clinical, and pharmacokinetic features of our cohort are summarized in Table 1.

All patients in the cohort were genotyped for ABCB1-rs1045642, which showed that there were 35 CC types (wild type, where C represents the wild allele), 48 CT types (heterozygous mutation, where C represents the wild allele and T represents the mutant allele), and 18 TT types (homozygous mutation, where T represents the mutant allele). The sequencing results are shown in Figure 1. The genotypes of the study samples are in line with the Hardy–Weinberg equilibrium (χ ² = 0.048, p > 0.05), which suggests that the patients chosen are representative and suitable for further research (Figure 1).

The average C_min of the study cohort was 4.5 (3.10, 6.90) mg/L, and the linear range was 0.5–16 mg/L. The voriconazole plasma trough concentrations (mg/L) in patients with different genotypes of ABCB1-rs1045642 were as follows: CC 4.50 (3.15, 7.40); CT 4.50 (3.00, 6.93); TT 4.00 (2.67, 6.20). The results show that the C_min values in the CC group are higher than those in the CT and TT groups, but there is no significant difference (χ ² = 0.095, p = 0.623). Thus, genotype differences were not found to have significant effects on the plasma trough concentrations (Figure 2).

Based on data processing and graphical analysis, the plasma trough concentrations of voriconazole among the patients with CC, CT, and TT genotypes did not exhibit significant differences in either the degree of dispersion or central tendency (Li et al., 2024). This indicates that the different genotypes may have relatively slight impacts on the plasma drug concentration, which is consistent with the conclusion of this study.

This section presents a comparison of the effectiveness and adverse reaction rates of voriconazole in patients with different ABCB1-rs1045642 genotypes.

According to the individualized medication guide for voriconazole (Pasqualotto et al., 2010), the voriconazole blood concentration was divided into three groups as C_min <1.0 mg/L, 1.0 ≤ C_min ≤ 5.5 mg/L, and C_min >5.5 mg/L. Among the three groups with varying C_min, there were no statistically significant variations in the occurrence of adverse reactions or treatment effectiveness (p > 0.05), as shown in Table 3.

The rank-sum test was conducted on the C_min levels of each group after the patients were categorized based on factors such as age, gender, BMI, underlying diseases, method of administration, albumin level, and APACHE-II scores. Table 4 indicates that there was a significant difference (p < 0.05) in the voriconazole C_min values between the male and female patient groups; this suggests that gender may be an important factor affecting voriconazole C_min. Further analyses of the other factors and their relationships with voriconazole C_min are also of great significance and will be discussed in the following sections.

Since multiple variables (such as gender and genotype) are involved in this study and included in the model for testing, the Bonferroni correction was adopted to control the error rates of multiple tests before the multiple linear regression analysis. In this step, the overall significance level was set to α = 0.05. The regression analysis involved testing of three variables, namely, gender, C/T, and T/T. After Bonferroni correction, the significance level for each variable was adjusted to α' = 0.05/3 = 0.017.

When interpreting the findings of the regression analysis, α′ is used as the criterion for determining statistical significance. After obtaining the results of the regression analysis, the p-value of the t-test of each variable was compared with α' = 0.017. A variable is considered statistically significant at the adjusted significance level when its p-value is less than 0.017. Conversely, if the p-value is 0.017 or greater, the variable is not statistically significant at this adjusted significance level.

Gender was found to be a statistically significant variable; furthermore, the genotypes of ABCB1-rs1045642 were included in the multiple linear regression analysis model to obtain the following equations: C 1 = 6.09   – 1.33 × Gender  male = 0 , female = 1 − 0.47 × X 1   X 1 : C / T = 1 , non − C / T = 0 ; C 2 = 6.09   – 1.33 × Gender  male = 0 , female = 1 − 0.94 × X 1   X 1 : T / T = 1 , non − T / T = 0 .

After controlling for the impact of gender on voriconazole C_min, as indicated in Table 5, the genotypes of the ABCB1-rs1045642 gene did not significantly affect C_min (R ² = 0.038, p = 0.152).

Several factors may contribute to the gender difference phenomenon:

(1) Physiological differences

Men generally have higher body weights and lean body masses compared to women, which can influence the volume of distribution (Vd) of drugs. Voriconazole is a lipophilic drug and may be distributed differently in males owing to variations in the body composition. Sex-specific differences in hepatic blood flow and enzyme activity, particularly cytochrome P450 (CYP) enzymes such as CYP2C19 that is primarily responsible for voriconazole metabolism, may contribute to the observed disparity. Studies have shown that CYP2C19 activity can vary between the sexes, potentially leading to differences in drug clearance (Allegra et al., 2020).

(2) Hormonal influences

Sex hormones, such as estrogen and testosterone, have been reported to modulate the expressions and activities of drug-metabolizing enzymes and transporters. For instance, estrogen has been shown to downregulate CYP3A4 activity, which could indirectly affect voriconazole metabolism. The interplay between hormonal fluctuations and drug pharmacokinetics is particularly relevant in the case of premenopausal women, who were likely included in our study population.

(3) Drug transporters

Although the ABCB1-rs1045642 polymorphisms did not show significant impacts in our study, sex-based differences in the expressions and functions of drug transporters, including P-gp that is encoded by the ABCB1 gene (Nazir et al., 2020), cannot be ruled out. Previous studies have suggested that males may have higher P-gp activities, which could potentially influence drug distribution and elimination.

The higher voriconazole trough concentrations in men may have important clinical implications. Voriconazole exhibits a narrow therapeutic index (Moriyama et al., 2015), and elevated concentrations of the drug are associated with increased risks of adverse effects, such as hepatotoxicity and neurotoxicity. Therefore, our findings suggest that the sex of the patient should be considered when optimizing their voriconazole dosing regimen. For instance, male patients may require lower initial doses or more frequent therapeutic drug monitoring to avoid toxicity; conversely, female patients may need higher doses to achieve therapeutic concentrations, particularly in cases of treatment failure or suboptimal responses.

Several limitations of this study should be acknowledged. First, the sample size was relatively small, which may have limited the power to detect significant associations, particularly with regard to genetic polymorphisms. Second, we did not account for other potential confounding factors, such as dietary habits, concomitant medications, or hormonal status, which could influence voriconazole pharmacokinetics. Third, the study population was restricted to patients with severe IFIs, which could limit the generalizability of our findings to other patient populations.

Future studies should aim to validate our findings in larger and more diverse cohorts. Additionally, mechanistic studies are needed to elucidate the underlying causes of sex-based differences in voriconazole pharmacokinetics. For example, we recommend investigating the impacts of sex hormones on CYP2C19 and ABCB1 activities as well as exploring the roles of other genetic variants and their interactions with sex-based differences. We also suggest conducting population pharmacokinetic modeling to develop sex-specific dosing algorithms.