Selection of dosing regimen with WST11 by Monte Carlo simulations, using PK data collected after single IV administration in healthy subjects and population PK modeling

2007 ◽  
Vol 96 (12) ◽  
pp. 3444-3456 ◽  
Author(s):  
Marc‐Antoine Fabre ◽  
Eliane Fuseau ◽  
Herve Ficheux
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Healthy Subjects ◽  
Dosing Regimen ◽  
Population Pk ◽  
Selection Of

Population pharmacokinetic (PK)/pharmacodynamic (PD) modeling and simulations for exposure‐;tumor response relationships: Motesanib in a phase II thyroid cancer (TC) trial

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e14528-e14528
Author(s):  
J. Lu ◽  
L. Claret ◽  
L. Sutjandra ◽  
M. Kuchimanchi ◽  
D. Stepan ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Thyroid Cancer ◽  
Monte Carlo Simulations ◽  
Tumor Size ◽  
Tumor Response ◽  
Area Under The Curve ◽  
Population Pharmacokinetic ◽  
Phase 2 ◽  
Population Pk ◽  
The Difference

e14528 Background: Motesanib is a highly selective, oral inhibitor of VEGF receptors 1, 2, and 3; PDGFR; and Kit that is being investigated for its antitumor activity. In a phase 2 monotherapy study, a response rate of 14% (per RECIST) was observed in patients (pts) with differentiated thyroid cancer (DTC; NEJM 359:31–42, 2008) compared with 1% in pts with medullary TC (MTC; Endocr Soc Ann Meeting 2007, abstract OR39–3). We evaluated the relationship between motesanib PK and tumor response, investigated whether differences in PK between MTC and DTC pts contributed to the observed difference in response, and simulated tumor response with different dose regimens in pts with TC. Methods: Data from the phase 2 TC trial were used for PK/PD modeling. The study enrolled 93 DTC and 91 MTC pts who received motesanib 125 mg once daily (QD). Motesanib concentrations were fitted to a 2- compartment population PK model. Estimates of pts’ PK parameters were used to calculate concentration and steady-state area under the curve values for motesanib, which were used as the exposure measures in population PK/PD modeling (ie, longitudinal exposure-tumor response modeling of drug effect on tumor growth dynamics). Monte Carlo simulations were used to evaluate the potential effect of doses other than 125 mg QD (75 mg and 100 mg QD) on tumor response in TC pts. Results: Clearance in MTC pts was 40% faster than in DTC pts (74 vs 44 L/h). The fit was significantly improved (P<0.001) when exposure instead of dose was used in the model. The exposure-tumor response model that incorporated the difference in exposure described change in tumor size well in both MTC and DTC populations. Clinical trial simulations using the preliminary model based on week 24 data predicted that DTC pts would achieve 19.7%, 15.7%, and 11.3% reductions in tumor size at week 24 following doses of 125 mg QD, 100 mg QD, and 75 mg QD, respectively. The actual change in median tumor size at week 24 following 125-mg QD dosing in DTC pts included in the PK/PD analysis was 17.9%. Conclusions: The use of 125 mg QD motesanib in DTC pts was supported by PK/PD modeling and Monte Carlo simulations. Differences in PK may explain the difference in tumor response observed in MTC and DTC patient populations. [Table: see text]

scholarly journals Prospective Evaluation of a Model-Based Dosing Regimen for Amikacin in Preterm and Term Neonates in Clinical Practice

2015 ◽  
Vol 59 (10) ◽  
pp. 6344-6351 ◽  
Author(s):  
A. Smits ◽  
R. F. W. De Cock ◽  
K. Allegaert ◽  
S. Vanhaesebrouck ◽  
M. Danhof ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Steady State ◽  
Monte Carlo Simulations ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Prospective Evaluation ◽  
Dosing Regimen ◽  
Model Based ◽  
Almost All ◽  
Trough Levels

ABSTRACTBased on a previously derived population pharmacokinetic model, a novel neonatal amikacin dosing regimen was developed. The aim of the current study was to prospectively evaluate this dosing regimen. First, early (before and after second dose) therapeutic drug monitoring (TDM) observations were evaluated for achieving target trough (<3 mg/liter) and peak (>24 mg/liter) levels. Second, all observed TDM concentrations were compared with model-predicted concentrations, whereby the results of a normalized prediction distribution error (NPDE) were considered. Subsequently, Monte Carlo simulations were performed. Finally, remaining causes limiting amikacin predictability (i.e., prescription errors and disease characteristics of outliers) were explored. In 579 neonates (median birth body weight, 2,285 [range, 420 to 4,850] g; postnatal age 2 days [range, 1 to 30 days]; gestational age, 34 weeks [range, 24 to 41 weeks]), 90.5% of the observed early peak levels reached 24 mg/liter, and 60.2% of the trough levels were <3 mg/liter (93.4% ≤5 mg/liter). Observations were accurately predicted by the model without bias, which was confirmed by the NPDE. Monte Carlo simulations showed that peak concentrations of >24 mg/liter were reached at steady state in almost all patients. Trough values of <3 mg/liter at steady state were documented in 78% to 100% and 45% to 96% of simulated cases with and without ibuprofen coadministration, respectively; suboptimal trough levels were found in patients with postnatal age <14 days and current weight of >2,000 g. Prospective evaluation of a model-based neonatal amikacin dosing regimen resulted in optimized peak and trough concentrations in almost all patients. Slightly adapted dosing for patient subgroups with suboptimal trough levels was proposed. This model-based approach improves neonatal dosing individualization.

PROSPECTIVE VALIDATION OF A MODEL-BASED DOSING REGIMEN FOR AMIKACIN IN NEONATES: FEASIBLE AND RELEVANT

2015 ◽  
Vol 101 (1) ◽  
pp. e1.40-e1
Author(s):  
Anne Smits ◽  
Roosmarijn De Cock ◽  
Karel Allegaert ◽  
Sophie Vanhaesebrouck ◽  
Meindert Danhof ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Dosing Regimen ◽  
Model Based ◽  
Prediction Distribution ◽  
Before And After ◽  
Almost All ◽  
Trough Levels

IntroductionA neonatal amikacin dosing regimen was previously developed based on a population pharmacokinetic model. The aim of the current study was to prospectively validate this model-derived dosing regimen.MethodsFirst, early (before and after second dose) therapeutic drug monitoring (TDM) observations were evaluated for achieving target trough (<3 mg/L) and peak (>24 mg/L) levels. Secondly, observed concentrations were compared with model-predicted concentrations, whereby the results of an NPDE (normalized prediction distribution error) were considered as well. Subsequently, Monte Carlo simulations were performed. Finally, remaining causes limiting amikacin predictability (prescription errors and disease characteristics of outliers) were explored.ResultsIn 579 neonates [median (range) birth bodyweight 2285 (420–4850) g, postnatal age 2 (1–30) days, gestational age 34 (24–41) weeks], 90.5% of early peak levels reached 24 mg/L and 60.2% of trough levels was <3 mg/L (93.4% ≤5 mg/L). Observations were accurately predicted by the model without bias, which was confirmed by the NPDE. Monte Carlo simulations showed that peak concentrations >24 mg/L were reached in almost all patients. Trough values <3 mg/L were documented in 78–100% and 45–96% of simulated cases, respectively, when ibuprofen was co-administered or not. Suboptimal trough levels were found in patient subgroups with postnatal age <14 days and current weight >2000g.ConclusionsProspective validation of a model-based neonatal amikacin dosing regimen resulted in optimized peak and trough concentrations in almost all patients. Adapted dosing for patients with suboptimal trough levels was proposed. Besides improving dosing individualization, feasibility and relevance of neonatal prospective validation studies was demonstrated.

scholarly journals Use of Monte Carlo Simulations To Select Therapeutic Doses and Provisional Breakpoints of BAL9141

2004 ◽  
Vol 48 (5) ◽  
pp. 1713-1718 ◽  
Author(s):  
Johan W. Mouton ◽  
Anne Schmitt-Hoffmann ◽  
Stuart Shapiro ◽  
Norman Nashed ◽  
Nieko C. Punt
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Staphylococcus Epidermidis ◽  
Parameter Estimates ◽  
Dosing Regimen ◽  
Target Attainment ◽  
Methicillin Resistant ◽  
Dose Study ◽  
Time Periods ◽  
Therapeutic Doses

ABSTRACT BAL9141, a new antimicrobial agent belonging to the class of parenteral pyrrolidinone-3-ylidenemethyl cephalosporins, is active against most gram-positive microorganisms, including methicillin-resistant variants (methicillin-resistant Staphylococcus aureus [MRSA] and methicillin-resistant Staphylococcus epidermidis [MRSE]), as well as against penicillin-resistant pneumococci (PRP) and many gram-negative microorganisms. BAL9141 is administered as the prodrug BAL5788, which is rapidly converted to BAL9141 by plasma esterases. Pharmacokinetic (PK) data obtained in a previous multiple ascending dose study were used to fit a population PK model to using the NPEM2 program, yielding PK parameter estimates and its covariance matrix for BAL9141. These estimates and matrix were used to perform Monte Carlo simulations (MCSs) and obtain unbiased target attainment rates (TARs) for various time periods during which the concentration remains above the MIC (T >MIC). Assuming a T >MIC of 40%, TARs of 100% were reached with a dose of 500 mg/liter every 12 h for pathogens with MICs of 2 mg/liter and with a dose of 750 mg/liter every 12 h for pathogens with MICs of 4 mg/liter. Because MICs are ≤2 mg/liter for most strains of MRSA, MRSE, and PRP (with some strains showing an MIC of 4 mg/liter), a dosing regimen of 750 mg every 12 h is proposed for clinical studies. The corresponding provisional breakpoint is S (susceptible) ≤ 4 mg/liter.

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