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. 2024 Dec 12;17(12):1679.
doi: 10.3390/ph17121679.

The Application of the Design of Experiments and Artificial Neural Networks in the Development of a Fast and Straightforward HPLC-UV Method for Fluconazole Determination in Hemato-Oncologic Pediatric Patients and Its Adaptation to Therapeutic Drug Monitoring

Arkadiusz Adamiszak  1   2 , Andrzej Czyrski  3 , Bartosz Sznek  3 , Edmund Grześkowiak  1 , Agnieszka Bienert  1
Affiliations

The Application of the Design of Experiments and Artificial Neural Networks in the Development of a Fast and Straightforward HPLC-UV Method for Fluconazole Determination in Hemato-Oncologic Pediatric Patients and Its Adaptation to Therapeutic Drug Monitoring

Arkadiusz Adamiszak et al. Pharmaceuticals (Basel). .

Abstract

Objectives: This study aimed to develop an optimized and wide concentration range HPLC-UV method for fluconazole (FLU) determination and its adaptation for pharmacokinetics (PK) studies in the pediatric population. Methods: The following parameters of chromatographic separation were optimized: retention time, tailing factor, peak height, and the sample preconditioning parameter, such as recovery. The optimization process involved the use of a central composite design (CCD) and Box-Behnken design (BBD) in the design of experiments (DoE) approach and a multilayer perceptron (MLP) for artificial neural network (ANN) application. Statistical and PK analyses were performed using Statistica and PKanalix. Results: The acetonitrile (ACN) concentration revealed the most significant factor influencing the retention time, tailing factor, and peak height of FLU and the internal standard. For recovery, the extracting agent's volume was the most significant factor. In most cases, the analysis conducted with the DoE and ANN indicated the same factors in a similar order regarding their impact on the analyzed variables. The optimization process allowed for achieving a wide range of determined concentrations (0.5-100 mg/L) and ~100% recovery. The developed method enabled PK analysis of 12 samples from three pediatric patients, proving its clinical usability. The estimated median clearance (CL) and volume of distribution (Vd) were 1.01 L/h and 18.64 L, respectively. Conclusions: DoE and ANNs are promising and useful tools in the optimization of sample preconditioning as well as the HPLC separation procedure. The investigated fluconazole determination method meets the European Medicines Agency (EMA) validation objectives and might be used in pediatric and adult PK studies.

Keywords: Box–Behnken design; HPLC-UV; central composite design; human plasma; machine learning; method optimization; pharmacokinetics.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Learning curve for retention time analysis for (a) FLU and (b) IS.
Figure 2
Figure 2
Learning curve for tailing factor analysis for (a) FLU and (b) IS.
Figure 3
Figure 3
Learning curve for peak height analysis for (a) FLU and (b) IS.
Figure 4
Figure 4
Learning curve for the FLU recovery analysis.
Figure 5
Figure 5
Patient PK profiles, showing (a) ID1, (b) ID 2, (c) ID3, and (d) observations vs. predicted concentrations plot. The purple lines represent individual PK profiles and the blue dots measured FLU concentrations.
Figure 6
Figure 6
The Pareto chart for the retention times for (a) FLU and (b) the IS.
Figure 7
Figure 7
The RSM diagram for analysis of the retention time when analyzing ACN and the pH for (a) FLU and (b) the IS.
Figure 8
Figure 8
The RSM diagram for analysis of the retention time when analyzing ACN and phosphates for (a) FLU and (b) the IS.
Figure 9
Figure 9
The RSM diagram for analysis of the retention time when analyzing pH level and phosphates for (a) FLU and (b) the IS.
Figure 10
Figure 10
The Pareto chart for tailing factors of (a) FLU and (b) the IS.
Figure 11
Figure 11
The RSM diagram for analysis of the tailing factors when analyzing ACN and the pH level for: (a) FLU and (b) the IS.
Figure 12
Figure 12
The RSM diagram for analysis of the tailing factors when analyzing ACN and phosphates for (a) FLU and (b) the IS.
Figure 13
Figure 13
The RSM diagram for analysis of the tailing factors when analyzing the pH level and phosphates for (a) FLU and (b) the IS.
Figure 14
Figure 14
The Pareto chart for peak height for (a) FLU and (b) the IS.
Figure 15
Figure 15
The RSM diagram for analysis of the peak height when analyzing ACN and pH level for (a) FLU and (b) the IS.
Figure 16
Figure 16
The RSM diagram for analysis of the peak height when analyzing ACN and phosphates for (a) FLU and (b) IS.
Figure 17
Figure 17
The RSM diagram for analysis of the peak height when analyzing pH level and phosphates for (a) FLU and (b) the IS.
Figure 18
Figure 18
The chromatograms of the (a) blank sample, (b) zero sample, (c) 0.5 mg/L (LLOQ) FLU sample, and (d) 75.0 mg/L FLU sample.
Figure 19
Figure 19
(a) The Pareto chart for recovery of FLU analysis and the RSM diagram for recovery of FLU when analyzed: (b) pH and VDCM, (c) VDCM and time, and (d) pH and time.
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