Monte Carlo Parametric Expectation Maximization (MC-PEM) Method for Analyzing Population Pharmacokinetic/Pharmacodynamic Data

Oxycodone is frequently used for treating cancer-related pain, while not much is known about the factors that influence treatment outcomes in these patients. We aim to unravel these factors by developing a population-pharmacokinetic model to assess the pharmacokinetics of oxycodone and its metabolites in cancer patients, and to associate this with pain scores, and adverse events. Hospitalized patients with cancer-related pain, who were treated with oral oxycodone, could participate. Pharmacokinetic samples and patient-reported pain scores and occurrence and severity of nine adverse events were taken every 12 h. In 28 patients, 302 pharmacokinetic samples were collected. A one-compartment model for oxycodone and each metabolite best described oxycodone, nor-oxycodone, and nor-oxymorphone pharmacokinetics. Furthermore, oxycodone exposure was not associated with average and maximal pain scores, and oxycodone, nor-oxycodone, and nor-oxymorphone exposure were not associated with adverse events (all p > 0.05). This is the first model to describe the pharmacokinetics of oxycodone including the metabolites nor-oxycodone and nor-oxymorphone in hospitalized patients with cancer pain. Additional research, including more patients and a more timely collection of pharmacodynamic data, is needed to further elucidate oxycodone (metabolite) pharmacokinetic/pharmacodynamic relationships. This model is an important starting point for further studies to optimize oxycodone dosing regiments in patients with cancer-related pain.

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Is the subcutaneous route an alternative for administering ertapenem to older patients? PHACINERTA study

Journal of Antimicrobial Chemotherapy ◽

10.1093/jac/dkz385 ◽

2019 ◽

Vol 74 (12) ◽

pp. 3546-3554 ◽

Cited By ~ 2

Author(s):

Claire Roubaud Baudron ◽

Rachel Legeron ◽

Julien Ollivier ◽

Fabrice Bonnet ◽

Carine Greib ◽

...

Keyword(s):

Older Patients ◽

Older Persons ◽

Population Pharmacokinetic ◽

Pharmacokinetic Data ◽

Antibiotic Administration ◽

Subcutaneous Route ◽

Final Model ◽

Once Daily ◽

The Mean ◽

Pharmacodynamic Data

Abstract Background Antibiotic administration by subcutaneous (SC) injection is common practice in French geriatric wards as an alternative to the intravenous (IV) route, but few pharmacokinetic/pharmacodynamic data are available. Ertapenem is useful for the treatment of infections with ESBL-producing enterobacteria. Objectives To report and compare ertapenem pharmacokinetic data between IV and SC routes in older persons. Methods Patients >65 years of age receiving ertapenem (1 g once daily) for at least 48 h (IV or SC, steady-state) were prospectively enrolled. Total ertapenem concentrations [residual (C0), IV peak (C0.5) and SC peak (C2.5)] were determined by UV HPLC. Individual-predicted AUC0–24 values were calculated and population pharmacokinetic analyses were performed. Using the final model, a Monte Carlo simulation involving 10 000 patients evaluated the influence of SC or IV administration on the PTA. Tolerance to ertapenem and recovery were also monitored. ClinicalTrials.gov identifier: NCT02505386. Results Ten (mean ± SD age=87±7 years) and 16 (age=88±5 years) patients were included in the IV and SC groups, respectively. The mean C0 and C2.5 values were not significantly different between the IV and SC groups (C0=12±5.9 versus 12±7.4 mg/L, P=0.97; C2.5=97±42 versus 67±41 mg/L, P=0.99). The mean C0.5 was higher in the IV group compared with the SC group (C0.5=184±90 versus 51±66 mg/L, P=0.001). The mean individual AUCs (1126.92±334.99 mg·h/L for IV versus 1005.3±266.0 mg·h/L for SC, P=0.38) and PTAs were not significantly different between groups. No severe antibiotic-related adverse effects were noted. Conclusions SC administration of ertapenem is an alternative to IV administration in older patients.

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Population Pharmacokinetics of Continuous-Infusion Meropenem in Febrile Neutropenic Patients with Hematologic Malignancies: Dosing Strategies for Optimizing Empirical Treatment against Enterobacterales and P. aeruginosa

Pharmaceutics ◽

10.3390/pharmaceutics12090785 ◽

2020 ◽

Vol 12 (9) ◽

pp. 785

Author(s):

Pier Giorgio Cojutti ◽

Anna Candoni ◽

Davide Lazzarotto ◽

Carla Filì ◽

Maria Zannier ◽

...

Keyword(s):

Monte Carlo ◽

Monte Carlo Simulations ◽

Continuous Infusion ◽

Hematologic Malignancies ◽

Population Pharmacokinetic Analysis ◽

Empirical Treatment ◽

Pharmacokinetic Analysis ◽

Population Pharmacokinetic ◽

Dosing Strategies ◽

Neutropenic Patients

A population pharmacokinetic analysis of continuous infusion (CI) meropenem was conducted in a prospective cohort of febrile neutropenic (FN) patients with hematologic malignancies. A non-parametric approach with Pmetrics was used for pharmacokinetic analysis and covariate evaluation. Monte Carlo simulations were performed for identifying the most appropriate dosages for empirical treatment against common Enterobacterales and P. aeruginosa. The probability of target attainment (PTA) of steady-state meropenem concentration (Css)-to-minimum inhibitory concentration (MIC) ratio (Css/MIC) ≥1 and ≥4 at the European Committee on Antimicrobial Susceptibility Testing (EUCAST) clinical breakpoint of 2 mg/L were calculated. Cumulative fraction of response (CFR) against Enterobacterales and P. aeruginosa were assessed as well. PTAs and CFRs ≥ 90% were considered optimal. A total of 61 patients with 178 meropenem Css were included. Creatinine clearance (CLCR) was the only covariate associated with meropenem clearance. Monte Carlo simulations showed that dosages of meropenem ranging between 1 g q8h and 1.25 g q6h by CI may grant optimal PTAs of Css/MIC ≥4 at the EUCAST clinical breakpoint. Optimal CFRs may be granted with these dosages against the Enterobacterales at Css/MIC ≥ 4 and against P. aeruginosa at Css/MIC ≥ 1. When dealing against P. aeruginosa at Css/MIC ≥ 4, only a dosage of 1.5 g q6h by CI may grant quasi-optimal CFR (around 80–87%). In conclusion, our findings suggest that dosages of meropenem ranging between 1 g q8h and 1.25 g q6h by CI may maximize empirical treatment against Enterobacterales and P. aeruginosa among FN patients with hematologic malignancies having different degree of renal function.

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Monte Carlo Expectation Maximization with Hidden Markov Models to Detect Functional Networks in Resting-State fMRI

Machine Learning in Medical Imaging - Lecture Notes in Computer Science ◽

10.1007/978-3-642-24319-6_8 ◽

2011 ◽

pp. 59-66 ◽

Cited By ~ 2

Author(s):

Wei Liu ◽

Suyash P. Awate ◽

Jeffrey S. Anderson ◽

Deborah Yurgelun-Todd ◽

P. Thomas Fletcher

Keyword(s):

Monte Carlo ◽

Hidden Markov Models ◽

Expectation Maximization ◽

Resting State ◽

Markov Models ◽

Hidden Markov ◽

Resting State Fmri ◽

Functional Networks ◽

Monte Carlo Expectation Maximization

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Population Pharmacokinetic Study of the Suitability of Standard Dosing Regimens of Amikacin in Critically Ill Patients with Open-Abdomen and Negative-Pressure Wound Therapy

Antimicrobial Agents and Chemotherapy ◽

10.1128/aac.02098-19 ◽

2020 ◽

Vol 64 (4) ◽

Author(s):

Cédric Carrié ◽

Faustine Delzor ◽

Stéphanie Roure ◽

Vincent Dubuisson ◽

Laurent Petit ◽

...

Keyword(s):

Monte Carlo ◽

Body Weight ◽

Monte Carlo Simulations ◽

Critically Ill ◽

Negative Pressure ◽

Critically Ill Patients ◽

Open Abdomen ◽

Population Pharmacokinetic ◽

Pressure Therapy ◽

Dosing Regimens

ABSTRACT The aim was to assess the appropriateness of recommended regimens for empirical MIC coverage in critically ill patients with open-abdomen and negative-pressure therapy (OA/NPT). Over a 5-year period, every critically ill patient who received amikacin and who underwent therapeutic drug monitoring (TDM) while being treated by OA/NPT was retrospectively included. A population pharmacokinetic (PK) modeling was performed considering the effect of 10 covariates (age, sex, total body weight [TBW], adapted body weight [ABW], body surface area [BSA], modified sepsis-related organ failure assessment [SOFA] score, vasopressor use, creatinine clearance [CLCR], fluid balance, and amount of fluids collected by the NPT over the sampling day) in patients who underwent continuous renal replacement therapy (CRRT) or did not receive CRRT. Monte Carlo simulations were employed to determine the fractional target attainment (FTA) for the PK/pharmacodynamic [PD] targets (maximum concentration of drug [Cmax]/MIC ratio of ≥8 and a ratio of the area under the concentration-time curve from 0 to 24 h [AUC0–24]/MIC of ≥75). Seventy critically ill patients treated by OA/NPT (contributing 179 concentration values) were included. Amikacin PK concentrations were best described by a two-compartment model with linear elimination and proportional residual error, with CLCR and ABW as significant covariates for volume of distribution (V) and CLCR for CL. The reported V) in non-CRRT and CRRT patients was 35.8 and 40.2 liters, respectively. In Monte Carlo simulations, ABW-adjusted doses between 25 and 35 mg/kg were needed to reach an FTA of >85% for various renal functions. Despite an increased V and a wide interindividual variability, desirable PK/PD targets may be achieved using an ABW-based loading dose of 25 to 30 mg/kg. When less susceptible pathogens are targeted, higher dosing regimens are probably needed in patients with augmented renal clearance (ARC). Further studies are needed to assess the effect of OA/NPT on the PK parameters of antimicrobial agents.

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Monte Carlo Simulation for Polychromatic X-Ray Fluorescence Computed Tomography with Sheet-Beam Geometry

International Journal of Biomedical Imaging ◽

10.1155/2017/7916260 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 6

Author(s):

Shanghai Jiang ◽

Peng He ◽

Luzhen Deng ◽

Mianyi Chen ◽

Biao Wei

Keyword(s):

Computed Tomography ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Expectation Maximization ◽

Huge Amount ◽

X Ray ◽

Beam Geometry ◽

Weight Concentration ◽

Imaging Model ◽

Maximum Likelihood Expectation Maximization

X-ray fluorescence computed tomography (XFCT) based on sheet beam can save a huge amount of time to obtain a whole set of projections using synchrotron. However, it is clearly unpractical for most biomedical research laboratories. In this paper, polychromatic X-ray fluorescence computed tomography with sheet-beam geometry is tested by Monte Carlo simulation. First, two phantoms (A and B) filled with PMMA are used to simulate imaging process through GEANT 4. Phantom A contains several GNP-loaded regions with the same size (10 mm) in height and diameter but different Au weight concentration ranging from 0.3% to 1.8%. Phantom B contains twelve GNP-loaded regions with the same Au weight concentration (1.6%) but different diameter ranging from 1 mm to 9 mm. Second, discretized presentation of imaging model is established to reconstruct more accurate XFCT images. Third, XFCT images of phantoms A and B are reconstructed by filter back-projection (FBP) and maximum likelihood expectation maximization (MLEM) with and without correction, respectively. Contrast-to-noise ratio (CNR) is calculated to evaluate all the reconstructed images. Our results show that it is feasible for sheet-beam XFCT system based on polychromatic X-ray source and the discretized imaging model can be used to reconstruct more accurate images.

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A quantile variant of the expectation–maximization algorithm and its application to parameter estimation with interval data

Journal of Algorithms & Computational Technology ◽

10.1177/1748301818779007 ◽

2018 ◽

Vol 12 (3) ◽

pp. 253-272 ◽

Cited By ~ 1

Author(s):

Chanseok Park

Keyword(s):

Monte Carlo ◽

Expectation Maximization ◽

Likelihood Function ◽

Expectation Maximization Algorithm ◽

Parametric Models ◽

Maximum Likelihood Estimates ◽

Computational Technique ◽

Stable Convergence ◽

Explicit Integration ◽

Monte Carlo Expectation Maximization

The expectation–maximization algorithm is a powerful computational technique for finding the maximum likelihood estimates for parametric models when the data are not fully observed. The expectation–maximization is best suited for situations where the expectation in each E-step and the maximization in each M-step are straightforward. A difficulty with the implementation of the expectation–maximization algorithm is that each E-step requires the integration of the log-likelihood function in closed form. The explicit integration can be avoided by using what is known as the Monte Carlo expectation–maximization algorithm. The Monte Carlo expectation–maximization uses a random sample to estimate the integral at each E-step. But the problem with the Monte Carlo expectation–maximization is that it often converges to the integral quite slowly and the convergence behavior can also be unstable, which causes computational burden. In this paper, we propose what we refer to as the quantile variant of the expectation–maximization algorithm. We prove that the proposed method has an accuracy of [Formula: see text], while the Monte Carlo expectation–maximization method has an accuracy of [Formula: see text]. Thus, the proposed method possesses faster and more stable convergence properties when compared with the Monte Carlo expectation–maximization algorithm. The improved performance is illustrated through the numerical studies. Several practical examples illustrating its use in interval-censored data problems are also provided.

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