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Abstract This study aimed to determine whether published pharmacokinetic (PK) models can adequately predict the PK profile of imatinib in a new indication, such as coronavirus disease 2019 (COVID‐19). Total (bound + unbound) and unbound imatinib plasma concentrations obtained from 134 patients with COVID‐19 participating in the CounterCovid study and from an historical dataset of 20 patients with gastrointestinal stromal tumor (GIST) and 85 patients with chronic myeloid leukemia (CML) were compared. Total imatinib area under the concentration time curve (AUC), maximum concentration (Cmax) and trough concentration (Ctrough) were 2.32‐fold (95% confidence interval [CI] 1.34–3.29), 2.31‐fold (95% CI 1.33–3.29), and 2.32‐fold (95% CI 1.11–3.53) lower, respectively, for patients with CML/GIST compared with patients with COVID‐19, whereas unbound concentrations were comparable among groups. Inclusion of alpha1‐acid glycoprotein (AAG) concentrations measured in patients with COVID‐19 into a previously published model developed to predict free imatinib concentrations in patients with GIST using total imatinib and plasma AAG concentration measurements (AAG‐PK‐Model) gave an estimated mean (SD) prediction error (PE) of −20% (31%) for total and −7.0% (56%) for unbound concentrations. Further covariate modeling with this combined dataset showed that in addition to AAG; age, bodyweight, albumin, CRP, and intensive care unit admission were predictive of total imatinib oral clearance. In conclusion, high total and unaltered unbound concentrations of imatinib in COVID‐19 compared to CML/GIST were a result of variability in acute phase proteins. This is a textbook example of how failure to take into account differences in plasma protein binding and the unbound fraction when interpreting PK of highly protein bound drugs, such as imatinib, could lead to selection of a dose with suboptimal efficacy in patients with COVID‐19.

Pharmacometric methods have hugely benefited from progress in analytical and computer sciences during the past decades, and play nowadays a central role in the clinical development of new medicinal drugs. It is time that these methods translate into patient care through therapeutic drug monitoring (TDM), due to become a mainstay of precision medicine no less than genomic approaches to control variability in drug response and improve the efficacy and safety of treatments. In this review, we make the case for structuring TDM development along five generic questions: 1) Is the concerned drug a candidate to TDM? 2) What is the normal range for the drug's concentration? 3) What is the therapeutic target for the drug's concentration? 4) How to adjust the dosage of the drug to drive concentrations close to target? 5) Does evidence support the usefulness of TDM for this drug? We exemplify this approach through an overview of our development of the TDM of imatinib, the very first targeted anticancer agent. We express our position that a similar story shall apply to other drugs in this class, as well as to a wide range of treatments critical for the control of various life-threatening conditions. Despite hurdles that still jeopardize progress in TDM, there is no doubt that upcoming technological advances will shape and foster many innovative therapeutic monitoring methods.

Abstract A pilot study was conducted aiming at specifying sultiame's pharmacokinetic profile, completed by in vitro assays evaluating the intraerythrocytic transfer of sultiame and by a pharmacokinetic model assessing its distribution. Single oral doses of sultiame (Ospolot® 50, 100, and 200 mg) were administered in open‐label to four healthy volunteers. Serial plasma, whole blood, and urine samples were collected. A spiking experiment was also performed to characterize sultiame's exchanges between plasma and erythrocytes in vitro. Pharmacokinetic parameters were evaluated using standard noncompartmental calculations and nonlinear mixed‐effect modeling. The plasma maximal concentrations (Cmax) showed striking nonlinear disposition of sultiame, with a 10‐fold increase while doses were doubled. Conversely, whole blood Cmax increased less than dose proportionally while staying much higher than in plasma. Quick uptake of sultiame into erythrocytes observed in vivo was confirmed in vitro, with minimal efflux. A two‐compartment model with first‐order absorption, incorporating a saturable ligand to receptor binding, described the data remarkably well, indicating apparent plasma clearance of 10.0 L/h (BSV: 29%) and distribution volume of 64.8 L; saturable uptake into an intracellular compartment of 3.3 L with a maximum binding capacity of 111 mg accounted for nonlinearities observed in plasma and whole blood concentrations. Pharmacokinetic characteristics of sultiame are reported, including estimates of clearance and volume of distribution that were so far unpublished. The noticeable nonlinearity in sultiame disposition should be taken into account for the design of future studies and the interpretation of therapeutic drug monitoring results.