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.

Recent applications of quantitative systems pharmacology and machine learning models across diseases
AbstractQuantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019 –2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 20, 2021 Category: Drugs & Pharmacology Source Type: research

A longitudinal model for the Mayo Clinical Score and its sub-components in patients with ulcerative colitis
AbstractClinical trials in patients with ulcerative colitis (UC) face the challenge of high and variable placebo response rates. The Mayo Clinical Score (MCS) is used widely as the primary endpoint in clinical trials to describe the clinical status of patients with UC. The MCS is comprised of four subscores, each scored 0, 1, 2 and 3: rectal bleeding (RB), stool frequency (SF), physician ’s global assessment (PGA), and endoscopy (ENDO) subscore. Excluding the PGA subscore gives the modified MCS. Quantitative insight on the placebo response, and its impact on the components of the MCS over time, can better inform clinical...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 16, 2021 Category: Drugs & Pharmacology Source Type: research

Semi-empirical anticoagulation model (SAM): INR monitoring during Warfarin therapy
This study presents a semi-empirical model of INR as a function of time and assigned therapy (Warfarin, k-vitamin). With respect to other methodologies, this model is able to describe the INR using a limited number of parameters and is able to describe the time variation of INR described in the literature. The presented methodology showed great accuracy in model calibration [(trueness (precision)]: 0.2% (0.1%) to 1.2% (0.3%) for coagulation factors, from 5% (9%) to 9.7% (12%) for Warfarin-related parameters and 38% (40%) for K-vitamin-related parameters. The latter value was considered acceptable given the assumptions made...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 15, 2021 Category: Drugs & Pharmacology Source Type: research

Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure
AbstractQuantitative systems pharmacology (QSP) is an important approach in pharmaceutical research and development that facilitates in silico generation of quantitative mechanistic hypotheses and enables in silico trials. As demonstrated by applications from numerous industry groups and interest from regulatory authorities, QSP is becoming an increasingly critical component in clinical drug development. With rapidly evolving computational tools and methods, QSP modeling has achieved important progress in pharmaceutical research and development, including for heart failure (HF). However, various challenges exist in the QSP...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 12, 2021 Category: Drugs & Pharmacology Source Type: research

Exposure-response modeling improves selection of radiation and radiosensitizer combinations
AbstractA central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three ...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 8, 2021 Category: Drugs & Pharmacology Source Type: research

Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progression
AbstractThe incidence of systemic and metabolic co-morbidities increases with aging. The purpose was to investigate a novel paradigm for modeling the orchestrated changes in many disease-related biomarkers that occur during aging. A hybrid strategy that integrates machine learning and stochastic modeling was evaluated for modeling the long-term dynamics of biomarker systems. Bayesian networks (BN) were used to identify quantitative systems pharmacology (QSP)-like models for the inter-dependencies for three disease-related datasets of metabolic (MB), metabolic with leptin (MB-L), and cardiovascular (CVB) biomarkers from the...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 5, 2021 Category: Drugs & Pharmacology Source Type: research

Wide size dispersion and use of body composition and maturation improves the reliability of allometric exponent estimates
AbstractTo evaluate study designs and the influence of dispersion of body size, body composition and maturation of clearance or reliable estimation of allometric exponents. Non-linear mixed effects modeling and parametric bootstrap were employed to assess how the study sample size, number of observations per subject, between subject variability (BSV) and dispersion of size distribution affected estimation bias and uncertainty of allometric exponents. The role of covariate model misspecification was investigated using a large data set ranging from neonates to adults. A decrease in study sample size, number of observations p...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 5, 2021 Category: Drugs & Pharmacology Source Type: research

Development of a genetic algorithm and NONMEM workbench for automating and improving population pharmacokinetic/pharmacodynamic model selection
AbstractThe current approach to selection of a population PK/PD model is inherently flawed as it fails to account for interactions between structural, covariate, and statistical parameters. Further, the current approach requires significant manual and redundant model modifications that heavily lend themselves to automation. Within the discipline of numerical optimization it falls into the “local search” category. Genetic algorithms are a class of algorithms inspired by the mathematics of evolution. GAs are general, powerful, robust algorithms and can be used to find global optimal solutions for difficult problems even ...
Source: Journal of Pharmacokinetics and Pharmacodynamics - October 3, 2021 Category: Drugs & Pharmacology Source Type: research