An overview of pharmacodynamic modelling, ligand-binding approach and its application in clinical practice

Abstract

The study of the magnitude and variation of drug response is defined as pharmacodynamics (PDs). PD models examine plasma concentration and effect relationship. It can predict the archetypal effect ([Formula: see text]) of a drug as a function of the drug concentration ([Formula: see text]) and estimate an unknown PD parameter ([Formula: see text]). The PD models have been described as fixed, linear, log-linear, [Formula: see text], sigmoid [Formula: see text], and indirect PD response. Ligand binding model is an example of a PD model that works on the underpinning PD principle of a drug, eliciting its pharmacological effect at the receptor site. The pharmacological effect is produced by the drug binding to the receptor to either activate or antagonise the receptor. Ligand binding models describe a system of interacting components, i.e. the interaction of one or more ligands with one or more binding sites. The [Formula: see text] model is the central method that provides an empirical justification for the concentration/dose-effect relationship. However, for ligand binding models justification is provided by theory of receptor occupancy. In essence, for ligand binding models, the term [Formula: see text] is best used to describe the fraction of receptors occupied at a particular ligand concentration. It is stated that the [Formula: see text], which means the effect of a drug should depend on the fraction of receptors that are occupied. In the future, network-based systems pharmacology models using ligand binding principles could be an effective way of understanding drug-related adverse effects. This will facilitate and strengthen the development of rational drug therapy in clinical practice.

Sensitive Detection of Dengue Virus Type 2 E-Proteins Signals Using Self-Assembled Monolayers/Reduced Graphene Oxide-PAMAM Dendrimer Thin Film-SPR Optical Sensor

“…The K D for the assessment of the DENV-2 E-proteins towards Au/IgM and Au/DSU/ NH 2 rGO-PAMAM/IgM were then calculated and found to be 1.1306 pM; R 2 = −0.5 and 0.1496 pM; R 2 = 0.99, respectively. The obtained K D values are found to be consistent with the standard K D value for protein interaction (K D < 10 nM) 92,93 . The smaller K D value revealed that the Au/DSU/NH 2 rGO-PAMAM/IgM sensor film has higher affinity (K A ) interaction towards DENV E-proteins compared with Au/IgM sensor film.…”

Uric Acid Has Direct Proinflammatory Effects on Human Macrophages by Increasing Proinflammatory Mediators and Bacterial Phagocytosis Probably via URAT1

“…Besides studying the apparently proinflammatory effect of uric acid on macrophages, we also wanted to explore the molecular mechanism that is potentially involved. In our study, the production of TNF-alpha, TLR4, and CD11c in response to uric acid exhibited a typical dose-response relationship, where a maximum effect is found and, beyond this point, a plateau can be seen, which indicates saturation and the abolishment of the observed effect [47]. The dose-response relationship has been attributed to the interaction between ligand and its receptor [47,48], which suggested to us the possible involvement of a molecule able to transport uric acid inside the macrophage, as is the case of URAT1.…”

Evaluating the Effectiveness of Snyder's Theory-Based Group Hope Therapy to Improve Self-Efficacy of University Students in Finance

“…As previous study has shown that self-efficacy belief may have a positive effect on academic achievement, Snyder's theory-based group hope therapy is hypothesized to improve academic self-efficacy beliefs. In the present study, the effectiveness of Snyder's theory-based group hope therapy was tested over time and the association between hope, confidence in ability to do homework in classroom, confidence in ability to do homework in outside classroom, confidence in ability to interact with others in university, and confidence in ability to manage work, family and university was investigated (Aliverti et al, 2017;Bich et al, 2017;Elsadek et al, 2017;Oskay et al, 2017;Salahudeen and Nishtala, 2017;Ujwala and Babu, 2017).…”