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Updated: Jun 28, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
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A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

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RPEM:随机的蒙特卡洛参数期望最大化算法

Rong Chen1,2, Alan Schumitzky2,3, Alona Kryshchenko4

  • 1Certara, Inc., Princeton, New Jersey, USA.

CPT: pharmacometrics & systems pharmacology
|April 16, 2024
PubMed
概括

一个新的随机参数预期最大化 (RPEM) 算法,灵感来自量子蒙特卡洛方法,提供快速和准确的参数估计. 它的性能与复杂的药理动力学模型的现有方法相提并论.

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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