Journal of Pharmacokinetics and Pharmacodynamics This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

Improving categorical endpoint longitudinal exposure –response modeling through the joint modeling with a related endpoint
AbstractExposure –response modeling is important to optimize dose and dosing regimens in clinical drug development. While primary clinical trial endpoints often have few categories and thus provide only limited information, sometimes there may be additional, more informative endpoints. Benefits of fully incorporat ing relevant information in longitudinal exposure–response modeling through joint modeling have recently been shown. This manuscript aims to further investigate the benefit of joint modeling of an ordered categorical primary endpoint with a related near-continuous endpoint, through the sharing of model parame...
Source: Journal of Pharmacokinetics and Pharmacodynamics - November 20, 2021 Category: Drugs & Pharmacology Source Type: research

From complex data to biological insight: ‘DEKER’ feature selection and network inference
AbstractNetwork inference is a valuable approach for gaining mechanistic insight from high-dimensional biological data. Existing methods for network inference focus on ranking all possible relations (edges) among all measured quantities such as genes, proteins, metabolites (features) observed, which yields a dense network that is challenging to interpret. Identifying a sparse, interpretable network using these methods thus requires an error-prone thresholding step which compromises their performance. In this article we propose a new method, DEKER-NET, that addresses this limitation by directly identifying a sparse, interpr...
Source: Journal of Pharmacokinetics and Pharmacodynamics - November 17, 2021 Category: Drugs & Pharmacology Source Type: research

Experimental and computational assessment of the synergistic pharmacodynamic drug –drug interactions of a triple combination therapy in refractory HER2-positive breast cancer cells
AbstractThe development of innate and/or acquired resistance to human epidermal growth factor receptor type-2 (HER2)-targeted therapy in HER2-positive breast cancer (HER2  + BC) is a major clinical challenge that needs to be addressed. One of the main mechanisms of resistance includes aberrant activation of the HER2 and phosphatidylinositol 3-kinase/AKT8 virus oncogene cellular homolog/mammalian target of rapamycin (PI3K/Akt/mTOR) pathways. In the present work, w e propose to use a triple combination therapy to combat this resistance phenomenon. Our strategy involves evaluation of two targeted small molecule agents, ev...
Source: Journal of Pharmacokinetics and Pharmacodynamics - November 13, 2021 Category: Drugs & Pharmacology Source Type: research

Generative adversarial networks for construction of virtual populations of mechanistic models: simulations to study Omecamtiv Mecarbil action
In this study, we present a novel approach, which integrates mechanistic modeling and machine learning to analyze in vitro cardiac mechanics data and solve the inverse problem of model parameter inference. We designed a novel generative adversarial network (GAN) and employed it to construct virtual populations of cardiac ventricular myocyte models in order to study the action of Omecamtiv Mecarbil (OM), a positive cardiac inotrope. Populations of models were calibrated from mechanically unloaded myocyte shortening recordings obtained in experiments on rat myocytes in the presence and absence of OM. The GAN was able to infe...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 29, 2021 Category: Drugs & Pharmacology Source Type: research

Statistical analysis of one-compartment pharmacokinetic models with drug adherence
In this study, therefore, considering the random change of dosage at the fixed dosing time interval, we reformulate the classical deterministic one-compartment pharmacokinetic models to the framework of stochastic, and analyze their qualitative properties including the expectation and variance of the drug concentration, existence of limit drug distribution, and the stochastic properties such as transience and recurre nce. In addition, we carry out sensitivity analysis of drug adherence-related parameters to the key values like expectation and variance, especially for the impact on the lowest and highest steady state drug c...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 27, 2021 Category: Drugs & Pharmacology Source Type: research

Population pharmacokinetic model selection assisted by machine learning
AbstractA fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary ...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 27, 2021 Category: Drugs & Pharmacology Source Type: research

Statistical analysis of one-compartment pharmacokinetic models with drug adherence
In this study, therefore, considering the random change of dosage at the fixed dosing time interval, we reformulate the classical deterministic one-compartment pharmacokinetic models to the framework of stochastic, and analyze their qualitative properties including the expectation and variance of the drug concentration, existence of limit drug distribution, and the stochastic properties such as transience and recurre nce. In addition, we carry out sensitivity analysis of drug adherence-related parameters to the key values like expectation and variance, especially for the impact on the lowest and highest steady state drug c...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 27, 2021 Category: Drugs & Pharmacology Source Type: research

Population pharmacokinetic model selection assisted by machine learning
AbstractA fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary ...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 27, 2021 Category: Drugs & Pharmacology Source Type: research

A series acceleration algorithm for the gamma-Pareto (type I) convolution and related functions of interest for pharmacokinetics
AbstractThe gamma-Pareto type I convolution (GPC type I) distribution, which has a power function tail, was recently shown to describe the disposition kinetics of metformin in dogs precisely and better than sums of exponentials. However, this had very long run times and lost precision for its functional values at long times following intravenous injection. An accelerated algorithm and its computer code is now presented comprising two separate routines for short and long times and which, when applied to the dog data, completes in approximately 3 min per case. The new algorithm is a more practical research tool. Potential ph...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 24, 2021 Category: Drugs & Pharmacology Source Type: research