Georg Hempel

Objectives: A high-performance liquid chromatography (HPLC) with mass spectrometry (MS) detection method was developed to quantify several tyrosine kinase inhibitors (TKIs) and their relevant metabolites. This method is suitable for therapeutic drug monitoring (TDM) of alectinib, brigatinib, dabrafenib, lenvatinib, lorlatinib, osimertinib and trametinib in patients with solid tumors using volumetric absorptive microsampling (VAMS^®). Methods: The HPLC-MS system contained four pumps, a Turboflow HTLC CycloneTM 1.0 × 50 mm solid phase extraction column for analyte enrichment, and a Kinetex 2.6 µm C18 100Å, 100 × 3.0 mm column for analyte separation. An acetonitrile–water gradient was used for the separation, and the ions generated by ESI (+)-ionization were detected in single-ion mode. This method was validated according to recent European Medicines Agency (EMA) and Food and Drug Administration (FDA) guidelines. Results: The accuracy and precision shown during method validation were within the acceptable limits for all analytes in plasma and whole blood. All analytes showed acceptable stability in both matrices for at least 28 days when stored at −21 °C. So far, 100 venous plasma and 94 capillary blood samples have been collected and analyzed. Conclusions: We developed a reliable method to quantify several TKIs from plasma and capillary blood, which is intended for TDM purposes in clinical practice.

Tramadol is a widely used opioid, and pharmacogenetics is partially responsible for the variability in its response. The objective was to develop a tramadol population pharmacokinetic (popPK) model including pharmacogenetic information. A single oral dose of 37.5 mg tramadol + 400 mg ibuprofen arginine was administered to 24 European healthy volunteers, and 18 blood samples were obtained from 0.25 h to 24 h after drug intake. Subjects were genotyped for the main pharmacogenes and a popPK model was built using NONMEM^® (version 7.3). The role of CYP2D6, CYP2B6, and CYP3A4 phenotypes was analyzed. The final parameter estimates were compared with results obtained by noncompartmental analysis (NCA) in the same cohort. Simulations depending on CYP2D6 phenotype were performed in single dose and steady state conditions. A two-compartment model, with transit absorption into the depot compartment and first-order elimination, was the best fit to the data. In total, 8 volunteers were CYP2D6 intermediate metabolizers (IMs) and 14 were normal metabolizers (NMs), merged for the analysis with the two ultrarapid metabolizers (UMs), whereas no poor metabolizers (PMs) were present (European frequencies: 38.3%, 49.2%, 2.3%, and 6.5%, respectively). CYP2D6 phenotype affected clearance, which was 20% reduced in IMs compared with NMs + UMs. CYP2B6 (NMs = 11, IMs = 11, and PMs = 2) and CYP3A4 (NMs = 20, IMs = 3) phenotypes did not affect clearance. PopPK and NCA estimates were in close agreement. Simulations indicated a 20% and even 40% higher area under the curve after single dose and steady state conditions, respectively, in CYP2D6 IMs compared with NMs and UMs. A popPK model including CYP2D6 phenotype well described the data. Further research with increased sample size is needed to analyze the clinical impact and effect of CYP2B6 and CYP3A4 phenotype on tramadol pharmacokinetics.

Objectives: A simple method for quantifying fluconazole in small blood volumes has been developed using volumetric absorptive microsampling (VAMS^®) technology and isocratic high-performance liquid chromatography (HPLC) with ultraviolet (UV) detection. Methods: For sample collection, Mitra^® devices are used to keep the sample volume at 10 µL. For the quantitative determination of fluconazole, the Mitra^® samples are extracted using acetonitrile as the extraction agent, containing 2-(4-chlorophenyl)-1,3-bis(1,2,4-triazol-1-yl)propan-2-ol as the internal standard. A Synergi 4 μm Polar-RP 80 Å (150 × 2 mm) column forms the stationary phase, and a mixture of acetonitrile and phosphate buffer is the mobile phase. The UV detection is set at a wavelength of 210 nm. The therapeutic concentration range of 5 to 160 mg/L is covered, and the linear equation with 1/x² weighting is used to determine unknown samples. This method has been validated according to the current EMA and FDA guidelines for bioanalytical methods. Results: The validation data obtained after analysing whole blood samples (EDTA) showed within- and between-run accuracy between 94.4% and 115% and precision between 0.4% and 9.4%, respectively. A lower limit of quantification (LLOQ) of 5 mg/L was sufficient for therapeutic drug monitoring in paediatric patients receiving fluconazole as antifungal prophylaxis after haematopoietic cell transplantation. Conclusions: So far, 211 samples from 49 patients were successfully analysed, and concentrations between 5.84 mg/L and 107 mg/L were determined for whole blood Mitra^® samples. To our knowledge, this is the first application of VAMS^® technology using simple and cheap HPLC-UV quantification.

Background/Objectives: Venlafaxine (VEN) is commonly used in young and elderly patients. Bupropion (BUP) is occasionally added to depression treatments with VEN. BUP’s inhibitory potential toward CYP2D6, VEN’s main metabolic pathway, may provoke a higher risk for toxic or adverse drug effects. Therefore, the question arises if a dose reduction in VEN or BUP is needed to avoid clinically relevant changes in exposure to VEN and its metabolite O-desmethylvenlafaxine (ODV). Methods: The literature-based PBPK models of VEN, BUP and their active metabolites under single-dose and steady-state conditions were created by using PK-Sim^®. To evaluate the DDI model‘s predictive performance, trough plasma concentrations (n = 54 and ≥65 years, n = 13) of VEN/ODV were extracted from the TDM database KONBEST. DDI’s clinical extent was assessed by AUC changes in VEN, ODV and active moiety (AM). The prediction was compared to the results of SCHOLZ Databank’s MDDI calculator (MDDIcalc). Results: Models accurately describe VEN’s and BUP’s pharmacokinetics and BUP’s effect on VEN’s metabolism in the age strata. The model predicts higher exposure to VEN (+110% to 132%), lower exposure to ODV (−50.0% to −61.5%) and a negligible change in AM (−1.02% to −2.40%). The AUC changes increase with higher BUP doses but is independent of patients’ age. Because of the missing AUC change in the AM, the DDI is considered clinically irrelevant. The MDDIcalc predicts no relevant effect on the AUC of AM with BUP. Conclusions: Both PBPK and MDDIcalc provide, in their own way, valuable tools to predict the DDI’s extent. Further research is needed regarding elderly patients, renal or hepatic impairment and polymorphisms, especially CYP2D6, CYP2C9, CYP2C19 and UGT.

Background: In neonatal and pediatric intensive care units, Gram-positive infections are a significant cause of morbidity and mortality. The increase in infections caused by methicillin-resistant Staphylococcus aureus and methicillin-resistant coagulase-negative Staphylococci have led to the increased use of glycopeptides, which treat invasive infections caused by Gram-positive organisms, particularly those resistant to beta-lactam antibiotics. Teicoplanin has bacteriostatic activity against Gram-positive bacteria, but its pharmacokinetics in children is highly variable, with most children failing to reach target levels at the recommended dose. This study aimed to develop a cost-effective method for determining concentrations using dried blood spot (DBS). Methods: A method to determine the concentrations of teicoplanin in 20 µL blood or plasma using the Whatman 903 Protein Saver filter was evaluated. High-performance liquid chromatography with ultraviolet detection high-performance liquid chromatography with ultraviolet/vis was used, with internal standard ketoconazole. In addition, a method to quantify teicoplanin using 50 µL of liquid plasma was established to compare the results with the values obtained by DBS and dried plasma methods. Results: The method was successfully developed and validated for 20 µL DBS. Furthermore, 50 µL of plasma was used to quantify teicoplanin with a lower limit of quantification of 10 mg/L. Precision and accuracy ranged from 2.3% to 10.7% and 95%–114.2%, respectively. A consistent factor (1.15) was used to calculate teicoplanin plasma concentrations from whole blood, indicating the reliability of the DBS method for therapeutic drug monitoring of teicoplanin. Conclusions: A simple, reliable, and cost-effective method using high-performance liquid chromatography with ultraviolet/vis was established to determine pediatric teicoplanin concentrations in both small plasma sample volumes and whole blood using DBS, and an accurate correlation factor for estimating teicoplanin plasma concentrations from DBS was identified. This method is suitable for the use in pediatrics.

Summary: Physiologically based pharmacokinetic (PBPK) modelling is an alternative modelling technique that is increasingly used in pharmacokinetics. Due to its nature, it can be complementarily employed to population pharmacokinetics, especially when it comes to small population size. Here, we report the proof of concept of its application to accurately describe the pharmacokinetics of a recombinant L‐asparaginase in paediatric patients with acute lymphoblastic leukaemia. Data from two randomized, double‐blind, phase II/III clinical studies (MC‐ASP.4/ALL; MC‐ASP.5/ALL) were included to setup and evaluate the final model, respectively. Final population values for basic pharmacokinetic parameters were calculated (clearance: 0.0569 L/h/19.5 kg, volume of distribution: 1.251 L, half‐life: 18.5 h, trough concentration: 140.9 IU/L). Pharmacokinetic parameter prediction as well as predictive performance of the model proofed to be comparable to a separately developed population pharmacokinetic model with 13% deviation in predicted median L‐asparaginase trough levels. To the best of our knowledge, this is the first whole‐body PBPK model of a non‐antibody therapeutic protein.

Background: Meropenem is a carbapenem antibiotic often used in pediatric intensive care units due to its broad spectrum of activity. Therapeutic drug monitoring (TDM) is a useful tool to increase the effectiveness of meropenem by adjusting the dose based on plasma levels; however, the relatively large sample volume required for TDM can limit its use in children. Therefore, this study aimed to determine meropenem concentrations and consequently perform TDM effectively using the smallest possible sample volume. Volumetric absorptive microsampling (VAMS) is a sampling technology developed to collect a small, precise volume of blood. For the applicability of VAMS in TDM, plasma concentrations must be reliably calculated from whole blood (WB) collected by VAMS. Methods: VAMS technology using 10 µL of WB was evaluated and compared with EDTA-plasma sampling. High-performance liquid chromatography with UV detection was applied to quantify meropenem in VAMS and plasma samples after the removal of proteins by precipitation. Ertapenem was used as the internal standard. Samples were collected simultaneously from critically ill children receiving meropenem using VAMS and traditional sampling. Results: It was found that no consistent factor could be determined to calculate meropenem plasma concentrations from the WB, indicating that VAMS was not reliable in the TDM of meropenem. Therefore, to reduce the required sample amount in pediatric patients, a method for quantifying meropenem from 50 µL of plasma with a lower limit of quantification of 1 mg/L was developed and successfully validated. Conclusions: A simple, reliable, and low-cost method was established using high-performance liquid chromatography–UV to determine the concentration of meropenem in 50 µL of plasma. VAMS using WB does not seem to be suitable for TDM of meropenem.