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ABSTRACT: The purpose of this analysis is to describe how levodopa pharmacokinetic and pharmacodynamic parameters change over the first
4years of long-term levodopa treatment in patients with Parkinson’s disease. Twenty previously untreated Parkinsonian patients
were admitted to the general clinical research center (GCRC) for 4days at the beginning of long-term levodopa therapy and
6, 12, 24 and 48months later. On each GCRC admission, patients received a 2hr IV infusion of levodopa on day 1 and day 4
with no oral levodopa between the infusions. After the first GCRC admission patients were treated with oral levodopa dosed
for optimal control of Parkinsonism. Motor function was measured by finger tapping rate. A pharmacokinetic–pharmacodynamic
model incorporating 3 effect compartments was used to fit the individual plasma levodopa concentrations and tapping rates.
Motor function before the first levodopa infusion (E01) improved over the first 20months and subsequently returned to the initial baseline at the start of the study. A similar
pattern was seen in motor function before the second infusion (E02) after the 3days levodopa withdrawal, with a decline predicted to fall below the initial baseline at the start of the study
by 6years. Eight patients showed an increase in maximum tapping rate with levodopa (Emax) approaching a steady state after 16months. Ten patients showed an increase in Emax with a peak at 31months. One patient showed a linear decrease and another patient did not change over the 48months. Longitudinal
progress models were used to describe the time course of pharmacokinetic and pharmacodynamic parameters over 4years. Peak
treatment benefit, defined as the difference between Emax and E01 or E02 (Dmax1 or Dmax2), increased with time particularly after the 3-day levodopa withdrawal. Deterioration of pre-dose motor function (E0) as disease progresses coupled with a greater amplitude of response due to levodopa (Dmax) could be a key factor contributing to motor fluctuations associated with long-term levodopa treatment.
Journal of Pharmacokinetics and Pharmacodynamics 04/2012; 32(3):459-484. · 2.06 Impact Factor
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ABSTRACT: PurposeTo externally validate the model predictions of a DATATOP cohort analysis through application of clinical trial simulation
with the study design of the ELLDOPA trial.
MethodsThe stochastic pharmacokinetic-pharmacodynamic and disease progress model was developed from the large DATATOP cohort of patients
followed for 8years. ELLDOPA was designed to detect a difference between placebo and levodopa treated arms in the total Unified
Parkinson’s Disease Rating Scale (UPDRS) taken at baseline and following 2weeks levodopa washout after 40weeks of treatment.
The total UPDRS response was simulated with different assumptions on levodopa effect (symptomatic with/without disease modifying
capability) and washout speed of symptomatic effect.
ResultsThe observed results of ELLDOPA were similar to the model predictions assuming levodopa slows disease progression and has
a slow washout of symptomatic effect.
ConclusionsThis simulation work confirmed the conclusion of the DATATOP analysis finding that levodopa slows disease progression. The
simulation results also showed that a dose-related increased rate of progression in Parkinson’s disease, obscured by symptomatic
benefit, is very unlikely. Finally, the simulation results also shown that 2weeks washout period was not adequate to completely
eliminate the symptomatic benefits of levodopa.
Pharmaceutical Research 04/2012; 24(4):791-802. · 4.09 Impact Factor
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ABSTRACT: The purpose of the study was to describe the population pharmacokinetics of levodopa in patients with Parkinson’s disease
studied in 5 trials (10 occasions) over 4years. Twenty previously untreated Parkinsonian patients were investigated. Each
trial consisted of a 2-hr IV infusion of levodopa (1mg/kg/h) with concomitant oral carbidopa given on two occasions separated
by 72hr with no levodopa in between. This trial design was repeated at 6, 12, 24 and 48months. A two-compartment pharmacokinetic
model with central volume (V1), peripheral volume (V2), clearance (CL) and inter-compartmental clearance (CLic) was used to fit plasma levodopa concentrations. The model accounted for levodopa dosing prior to each trial and endogenous
levodopa synthesis. Population parameter estimates (geometric mean) and population parameter variability (PPV; SD of normal
distribution) were V1 11.4 l/70kg (0.44), CL 30.9 l/h/70kg (0.25), V2 27.3 l/70kg (0.27), and CLic 34.6 l/h/70kg (0.48). PPV was partitioned into between subject variability (BSV) which was 0.12 V1, 0.13 CL, 0.15 V2, 0.28 CLic, within trial variability (WTV) which was 0.16 V1, 0.13 CL, 0.08 V2, 0.18 CLic and between trial variability (BTV) which was 0.40 V1, 0.17 CL, 0.21 V2, 0.34 CLic. Neither structural nor random levodopa pharmacokinetic parameters were associated with the time course of development of
fluctuation in motor response. Variability in levodopa pharmacokinetic parameters (particularly V1) may result in variability in plasma levodopa concentrations that could contribute to fluctuations in motor response.
Journal of Pharmacokinetics and Pharmacodynamics 04/2012; 32(3):307-331. · 2.06 Impact Factor
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ABSTRACT: Using simulated viral load data for a given maraviroc monotherapy study design, the feasibility of different algorithms to perform parameter estimation for a pharmacokinetic-pharmacodynamic-viral dynamics (PKPD-VD) model was assessed. The assessed algorithms are the first-order conditional estimation method with interaction (FOCEI) implemented in NONMEM VI and the SAEM algorithm implemented in MONOLIX version 2.4. Simulated data were also used to test if an effect compartment and/or a lag time could be distinguished to describe an observed delay in onset of viral inhibition using SAEM. The preferred model was then used to describe the observed maraviroc monotherapy plasma concentration and viral load data using SAEM. In this last step, three modelling approaches were compared; (i) sequential PKPD-VD with fixed individual Empirical Bayesian Estimates (EBE) for PK, (ii) sequential PKPD-VD with fixed population PK parameters and including concentrations, and (iii) simultaneous PKPD-VD. Using FOCEI, many convergence problems (56%) were experienced with fitting the sequential PKPD-VD model to the simulated data. For the sequential modelling approach, SAEM (with default settings) took less time to generate population and individual estimates including diagnostics than with FOCEI without diagnostics. For the given maraviroc monotherapy sampling design, it was difficult to separate the viral dynamics system delay from a pharmacokinetic distributional delay or delay due to receptor binding and subsequent cellular signalling. The preferred model included a viral load lag time without inter-individual variability. Parameter estimates from the SAEM analysis of observed data were comparable among the three modelling approaches. For the sequential methods, computation time is approximately 25% less when fixing individual EBE of PK parameters with omission of the concentration data compared with fixed population PK parameters and retention of concentration data in the PD-VD estimation step. Computation times were similar for the sequential method with fixed population PK parameters and the simultaneous PKPD-VD modelling approach. The current analysis demonstrated that the SAEM algorithm in MONOLIX is useful for fitting complex mechanistic models requiring multiple differential equations. The SAEM algorithm allowed simultaneous estimation of PKPD and viral dynamics parameters, as well as investigation of different model sub-components during the model building process. This was not possible with the FOCEI method (NONMEM version VI or below). SAEM provides a more feasible alternative to FOCEI when facing lengthy computation times and convergence problems with complex models.
Journal of Pharmacokinetics and Biopharmaceutics 11/2010; 38(1):41-61. · 2.06 Impact Factor
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ABSTRACT: To develop a population pharmacokinetic model for maraviroc, a noncompetitive CCR5 antagonist, after oral administration of tablets to healthy volunteers and asymptomatic HIV-infected subjects and to quantify the inherent variability and influence of covariates on the parameters of the model.
Rich pharmacokinetic data available from 15 studies in healthy volunteers (n = 365) and two studies in asymptomatic HIV-infected subjects (n = 48) were analysed using NONMEM. Maraviroc was administered as single or multiple oral tablet doses under fasted and fed conditions. Doses ranged from 100 to 1800 mg day(-1).
A two-compartment model parameterized to separate out absorption and clearance components on bioavailability was used. Absorption was described by a lagged first-order process. A sigmoid E(max) model described the effect of dose on absorption. A visual predictive check and nonparametric bootstrap evaluation confirmed that the model was a good description of the data. Typical CL, V(c) and V(p) values for a 30-year-old non-Asian are 51.5 l h(-1), 132 l and 277 l, respectively.
For the typical non-Asian subject, fasted bioavailability increased asymptotically with dose from 24% at 100 mg to 33% at 600 mg. A high-fat meal taken with maraviroc reduced exposure by 43% for a 100-mg dose to approximately 25% at doses of 600 mg. The typical Asian subject had a 26.5% higher AUC than the typical non-Asian subject irrespective of dose, a difference not considered to be clinically relevant. None of the other covariates tested had any clinically relevant effects on exposure.
British Journal of Clinical Pharmacology 05/2008; 65 Suppl 1:76-85. · 2.96 Impact Factor
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[show abstract]
[hide abstract]
ABSTRACT: To externally validate the model predictions of a DATATOP cohort analysis through application of clinical trial simulation with the study design of the ELLDOPA trial.
The stochastic pharmacokinetic-pharmacodynamic and disease progress model was developed from the large DATATOP cohort of patients followed for 8 years. ELLDOPA was designed to detect a difference between placebo and levodopa treated arms in the total Unified Parkinson's Disease Rating Scale (UPDRS) taken at baseline and following 2 weeks levodopa washout after 40 weeks of treatment. The total UPDRS response was simulated with different assumptions on levodopa effect (symptomatic with/without disease modifying capability) and washout speed of symptomatic effect.
The observed results of ELLDOPA were similar to the model predictions assuming levodopa slows disease progression and has a slow washout of symptomatic effect.
This simulation work confirmed the conclusion of the DATATOP analysis finding that levodopa slows disease progression. The simulation results also showed that a dose-related increased rate of progression in Parkinson's disease, obscured by symptomatic benefit, is very unlikely. Finally, the simulation results also shown that 2 weeks washout period was not adequate to completely eliminate the symptomatic benefits of levodopa.
Pharmaceutical Research 05/2007; 24(4):791-802. · 4.09 Impact Factor
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ABSTRACT: We have modelled the Unified Parkinson's Disease Rating Scale (UPDRS) scores collected in 800 subjects followed for 8 years. Newly diagnosed and previously untreated subjects were initially randomized to treatment with placebo, deprenyl, tocopherol or both and, when clinical disability required, received one or more dopaminergic agents (levodopa (carbidopa/levodopa), bromocriptine, or pergolide). Using models for disease progression and pharmacodynamic models for drug effects we have characterized the changes in UPDRS over time to determine the influence of the various drug treatments. We have confirmed and quantitated the relative symptomatic benefits of the dopaminergic agents and provide model-based evidence for slowing of disease progression.
Journal of Pharmacokinetics and Pharmacodynamics 07/2006; 33(3):281-311. · 2.06 Impact Factor
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ABSTRACT: The purpose of this analysis is to describe how levodopa pharmacokinetic and pharmacodynamic parameters change over the first 4 years of long-term levodopa treatment in patients with Parkinson's disease. Twenty previously untreated Parkinsonian patients were admitted to the general clinical research center (GCRC) for 4 days at the beginning of long-term levodopa therapy and 6, 12, 24 and 48 months later. On each GCRC admission, patients received a 2 hr IV infusion of levodopa on day 1 and day 4 with no oral levodopa between the infusions. After the first GCRC admission patients were treated with oral levodopa dosed for optimal control of Parkinsonism. Motor function was measured by finger tapping rate. A pharmacokinetic-pharmacodynamic model incorporating 3 effect compartments was used to fit the individual plasma levodopa concentrations and tapping rates. Motor function before the first levodopa infusion (E0(1)) improved over the first 20 months and subsequently returned to the initial baseline at the start of the study. A similar pattern was seen in motor function before the second infusion (E0(2)) after the 3 days levodopa withdrawal, with a decline predicted to fall below the initial baseline at the start of the study by 6 years. Eight patients showed an increase in maximum tapping rate with levodopa (E(max)) approaching a steady state after 16 months. Ten patients showed an increase in E(max) with a peak at 31 months. One patient showed a linear decrease and another patient did not change over the 48 months. Longitudinal progress models were used to describe the time course of pharmacokinetic and pharmacodynamic parameters over 4 years. Peak treatment benefit, defined as the difference between E(max) and E0(1) or E0(2) (D(max)1 or D(max)2), increased with time particularly after the 3-day levodopa withdrawal. Deterioration of pre-dose motor function (E0) as disease progresses coupled with a greater amplitude of response due to levodopa (D(max)) could be a key factor contributing to motor fluctuations associated with long-term levodopa treatment.
Journal of Pharmacokinetics and Pharmacodynamics 09/2005; 32(3-4):459-84. · 2.06 Impact Factor
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[show abstract]
[hide abstract]
ABSTRACT: The purpose of the study was to describe the population pharmacokinetics of levodopa in patients with Parkinson's disease studied in 5 trials (10 occasions) over 4 years. Twenty previously untreated Parkinsonian patients were investigated. Each trial consisted of a 2-hr IV infusion of levodopa (1 mg/kg/h) with concomitant oral carbidopa given on two occasions separated by 72 hr with no levodopa in between. This trial design was repeated at 6, 12, 24 and 48 months. A two-compartment pharmacokinetic model with central volume (V1), peripheral volume (V2), clearance (CL) and inter-compartmental clearance (CL(ic)) was used to fit plasma levodopa concentrations. The model accounted for levodopa dosing prior to each trial and endogenous levodopa synthesis. Population parameter estimates (geometric mean) and population parameter variability (PPV; SD of normal distribution) were V1 11.4 l/70 kg (0.44), CL 30.9 l/h/70 kg (0.25), V2 27.3 l/70 kg (0.27), and CL(ic) 34.6 l/h/70 kg (0.48). PPV was partitioned into between subject variability (BSV) which was 0.12 V1, 0.13 CL, 0.15 V(2), 0.28 CL(ic), within trial variability (WTV) which was 0.16 V1, 0.13 CL, 0.08 V2, 0.18 CL(ic) and between trial variability (BTV) which was 0.40 V1, 0.17 CL, 0.21 V2, 0.34 CL(ic.) Neither structural nor random levodopa pharmacokinetic parameters were associated with the time course of development of fluctuation in motor response. Variability in levodopa pharmacokinetic parameters (particularly V1) may result in variability in plasma levodopa concentrations that could contribute to fluctuations in motor response.
Journal of Pharmacokinetics and Pharmacodynamics 09/2005; 32(3-4):307-31. · 2.06 Impact Factor
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ABSTRACT: Clinicians recognize levodopa has a short-duration response (measured in hr) and a long-duration response (measured in days) in Parkinson's disease. In addition there is a diurnal pattern of motor function with better function in the morning. Previous pharmacokinetic-pharmacodynamic modeling has quantified only the short-duration response. We have developed a pharmacokinetic-pharmacodynamic model for the short- and long-duration responses to exogenous levodopa and the effects of residual endogenous levodopa synthesis in patients with Parkinson's disease. Thirteen previously untreated (de novo) patients with Parkinson's disease and twelve patients who had received levodopa orally for 9.7+/-4.0 years (chronic) were investigated. A 2 hr IV infusion of levodopa with concomitant oral carbidopa was given on two occasions separated by 3 days with no levodopa in between. A two compartment pharmacokinetic model was used to fit plasma levodopa concentrations. A sigmoid Emax model was used to relate concentrations from endogenous and exogenous sources to tapping rate (a measure of motor response). A model incorporating three effect compartments (fast equilibration (half life, Teqf). slow equilibration (Teqs) and dopa synthesis (Teqd)), yielded the most descriptive model for levodopa pharmacokinetics and pharmacodynamics. Baseline tapping rate reflected endogenous levodopa synthesis and the long-duration response. Partial loss of the long-duration response during the 3 days without levodopa in the chronic group lowered baseline tapping (36+/-7%, mean+/-SEM) and increased maximum levodopa induced response above baseline (112+/-31%). The maximum levodopa induced response after the drug holiday is a result of lowered baseline tapping due to the loss of long-duration response and not due to a change in levodopa pharmacokinetics or pharmacodynamics.
Journal of Pharmacokinetics and Pharmacodynamics 07/2004; 31(3):243-68. · 2.06 Impact Factor
