Publications by authors named "Francois P Combes"

Asciminib is a first-in-class allosteric inhibitor of the kinase activity of BCR::ABL1, specifically targeting the ABL myristoyl pocket (STAMP). This review focuses on the pharmacokinetic (PK) and pharmacodynamic data of asciminib, which is approved at a total daily dose of 80 mg for the treatment of adult patients with chronic myeloid leukemia in chronic phase who are either resistant or intolerant to ≥ 2 tyrosine kinase inhibitors or those harboring the T315I mutation (at a dose of 200 mg twice daily). Asciminib is predicted to be almost completely absorbed from the gut, with an absolute bioavailability (F) of approximately 73%.

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Background And Objective: Asciminib is approved in patients with Philadelphia chromosome-positive chronic myeloid leukemia in chronic phase (Ph+ CML-CP) treated with ≥ 2 prior tyrosine kinase inhibitors. Here, we aimed to demonstrate similarity in efficacy/safety of asciminib 80 mg once daily (q.d.

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Asciminib (Scemblix) is a first-in-class BCR::ABL1 inhibitor that works by specifically targeting the ABL myristoyl pocket (STAMP) and has potent activity against the T315I mutation. This study aimed to characterize the effect of asciminib exposure on disease progression and to elucidate factors influencing efficacy. Our analysis included 303 patients with chronic myeloid leukemia in chronic phase recruited in a phase I study with dose ranging from 10 to 200 mg twice a day (b.

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Background: Asciminib, a first-in-class, highly potent and specific ABL/BCR-ABL1 inhibitor, has shown superior efficacy compared to bosutinib in patients with Philadelphia chromosome-positive chronic myeloid leukemia in chronic phase, treated with two or more tyrosine kinase inhibitors. This study aimed to describe pharmacokinetic (PK) properties of asciminib and to identify clinically relevant covariates impacting its exposure.

Methods: A population PK (PopPK) model was developed using a two-compartment model with delayed first-order absorption and elimination.

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Everolimus is currently approved in Europe as an adjunctive therapy for patients aged ≥ 2 years with tuberous sclerosis complex (TSC)-associated treatment-refractory partial-onset seizures, based on the EXIST-3 study (NCT01713946) results. As TSC-associated seizures can also affect children aged between 6 months and 2 years, a modeling and simulation (M&S) approach was undertaken to extrapolate exposure (trough plasma concentration (C )) after a dose of 6 mg/m and reduction in seizure frequency (RSF). A physiologically based pharmacokinetic model using Simcyp was developed to predict C in adult and pediatric patients, which was then used by a population pharmacodynamic model and a linear mixed effect model to predict short-term and long-term efficacy in adults (for validation) and in children, respectively.

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In adult patients, nilotinib is indicated for chronic myeloid leukemia at an approved oral dose of 300 or 400 mg BID. Physiologically based pharmacokinetic (PBPK) model was developed to describe and supplement limited PK data in the pediatric population ranging from 2 to less than 6 years of age and ultimately inform dosing regimen. An adult Simcyp PBPK model was established and verified with clinical pharmacokinetic data after a single or multiple oral doses of 400 mg nilotinib (230 mg/m).

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Everolimus is approved in Europe and in the USA for the adjunctive treatment of patients aged 2 years and older whose refractory partial-onset seizures, with or without secondary generalization, are associated with tuberous sclerosis complex. The objective of this analysis was to establish a population pharmacokinetic (PK)/pharmacodynamic model describing the relationship between seizure frequency and everolimus exposure to confirm the recommended target concentration range of 5-15 ng/mL. The PK model was a two-compartment model with first order absorption and clearance.

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Development of antiobesity drugs is continuously challenged by high dropout rates during clinical trials. The objective was to develop a population pharmacodynamic model that describes the temporal changes in body weight, considering disease progression, lifestyle intervention, and drug effects. Markov modeling (MM) was applied for quantification and characterization of responder and nonresponder as key drivers of dropout rates, to ultimately support the clinical trial simulations and the outcome in terms of trial adherence.

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Purpose: When information is sparse, individual parameters derived from a non-linear mixed effects model analysis can shrink to the mean. The objective of this work was to predict individual parameter shrinkage from the Bayesian information matrix (M BF ). We 1) Propose and evaluate an approximation of M BF by First-Order linearization (FO), 2) Explore by simulations the relationship between shrinkage and precision of estimates and 3) Evaluate prediction of shrinkage and individual parameter precision.

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