Dose–Response Analysis When There Is a Correlation Between Affinity and Efficacy
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Using Toxicity Tests in Ecological Risk Assessment Toxicity Tests Are Used to Expose Test Organisms to a 2
United States Office of Publication 9345.0-05I Environmental Solid Waste and March 1994 Protection Emergency Response Agency ECO Update Office of Emergency Remedial Response Intermittent Bulletin Hazardous Site Evaluation Division (5204G) Volume 2 Number1 Using Toxicity Tests in Ecological Risk Assessment Toxicity tests are used to expose test organisms to a 2. Toxicity tests can evaluate the aggregate toxic effects of medium—water, sediment, or soil—and evaluate the effects of all contaminants in a medium. Many Superfund sites present contamination on the survival, growth, reproduction, behavior a complex array of contaminants, with a mixture of potentially and/or other attributes of these organisms. These tests may harmful substances present in the media. At such sites, help to determine whether the contaminant concentrations in a chemical data alone cannot accurately predict the toxicity of site’s media are high enough to cause adverse effects in the contaminants. Rather, toxicity tests measure the aggregate organisms. Generally, toxicity tests involve collecting effects of contaminated media on organisms. These effects samples of media from a site and sending them to a toxicity result from characteristics of the medium itself (such as laboratory, where the tests are performed. On occasion hardness and pH, in the case of water), interactions among investigators1 measure toxicity by exposing test organisms to contaminants, and interactions between contaminants and soil or water on site—these are known as in situ tests. media. Consequently, observed toxicity test results may often vary from those predicted by chemical data alone. As the general guidelines at the end of this Bulletin indicate, not all sites require toxicity tests. -
Gene-Signature-Derived Ic50s/Ec50s Reflect the Potency of Causative
www.nature.com/scientificreports OPEN Gene-signature-derived IC50s/EC50s refect the potency of causative upstream targets and downstream phenotypes Stefen Renner1 ✉ , Christian Bergsdorf1, Rochdi Bouhelal1, Magdalena Koziczak-Holbro2, Andrea Marco Amati1,6, Valerie Techer-Etienne1, Ludivine Flotte2, Nicole Reymann1, Karen Kapur3, Sebastian Hoersch3, Edward James Oakeley4, Ansgar Schufenhauer1, Hanspeter Gubler3, Eugen Lounkine5,7 & Pierre Farmer1 ✉ Multiplexed gene-signature-based phenotypic assays are increasingly used for the identifcation and profling of small molecule-tool compounds and drugs. Here we introduce a method (provided as R-package) for the quantifcation of the dose-response potency of a gene-signature as EC50 and IC50 values. Two signaling pathways were used as models to validate our methods: beta-adrenergic agonistic activity on cAMP generation (dedicated dataset generated for this study) and EGFR inhibitory efect on cancer cell viability. In both cases, potencies derived from multi-gene expression data were highly correlated with orthogonal potencies derived from cAMP and cell growth readouts, and superior to potencies derived from single individual genes. Based on our results we propose gene-signature potencies as a novel valid alternative for the quantitative prioritization, optimization and development of novel drugs. Gene expression signatures are widely used in the feld of translational medicine to defne disease sub-types1, severity2 and predict treatment outcome3. Bridging this technology to early drug discovery was previously pro- posed years ago4,5 but its prohibitive costs limited this approach. Te recent advancement of massively parallel gene expression technologies such as RASL-seq.6, DRUG-seq.7, QIAseq.8,9, PLATE-seq.10, or LINCS L100011 are now transforming the feld of compound profling, enabling larger scale profling and screening experiments at a more afordable cost12–17. -
Clinical Pharmacokinetics 38
Clin Pharmacokinet 2000 Jun; 38 (6): 505-518 ORIGINAL RESEARCH ARTICLE 0312-5963/00/0006-0505/$20.00/0 © Adis International Limited. All rights reserved. Pharmacokinetic-Pharmacodynamic Modelling of the Antipyretic Effect of Two Oral Formulations of Ibuprofen Iñaki F. Trocóniz,1 Santos Armenteros,2 María V. Planelles,3 Julio Benítez,4 Rosario Calvo5 and Rosa Domínguez2 1 Department of Pharmacy and Pharmaceutical Technology, Faculty of Pharmacy, University of Navarra, Pamplona, Spain 2 Medical Department, Laboratorios Knoll S.A., Madrid, Spain 3 Department of Paediatrics, Clinic Hospital, Valencia, Spain 4 Department of Pharmacology, Faculty of Medicine, University of Extremadura, Badajoz, Spain 5 Department of Pharmacology, Faculty of Medicine, University of the Basque Country, Lejona, Spain Abstract Objective: To analyse the population pharmacokinetic-pharmacodynamic relation- ships of racemic ibuprofen administered in suspension or as effervescent granules with the aim of exploring the effect of formulation on the relevant pharmaco- dynamic parameters. Design: The pharmacokinetic model was developed from a randomised, cross- over bioequivalence study of the 2 formulations in healthy adults. The pharmaco- dynamic model was developed from a randomised, multicentre, single dose efficacy and safety study of the 2 formulations in febrile children. Patients and participants: Pharmacokinetics were studied in 18 healthy volun- teers aged 18 to 45 years, and pharmacodynamics were studied in 103 febrile children aged between 4 and 16 years with bodyweight ≥25kg. Methods: The pharmacokinetic study consisted of two 1-day study occasions, each separated by a 1-week washout period. On each occasion ibuprofen 400mg was administered orally as suspension or granules. The time course of the anti- pyretic effect was evaluated in febrile children receiving a single oral dose of 7 mg/kg in suspension or 200 or 400mg as effervescent granules. -
Principle of Pharmacodynamics
Principle of pharmacodynamics Dr. M. Emamghoreishi Full Professor Department of Pharmacology Medical School Shiraz University of Medical Sciences Email:[email protected] Reference: Basic & Clinical Pharmacology: Bertrum G. Katzung and Anthony J. Treveror, 13th edition, 2015, chapter 20, p. 336-351 Learning Objectives: At the end of sessions, students should be able to: 1. Define pharmacology and explain its importance for a clinician. 2. Define ―drug receptor‖. 3. Explain the nature of drug receptors. 4. Describe other sites of drug actions. 5. Explain the drug-receptor interaction. 6. Define the terms ―affinity‖, ―intrinsic activity‖ and ―Kd‖. 7. Explain the terms ―agonist‖ and ―antagonist‖ and their different types. 8. Explain chemical and physiological antagonists. 9. Explain the differences in drug responsiveness. 10. Explain tolerance, tachyphylaxis, and overshoot. 11. Define different dose-response curves. 12. Explain the information that can be obtained from a graded dose-response curve. 13. Describe the potency and efficacy of drugs. 14. Explain shift of dose-response curves in the presence of competitive and irreversible antagonists and its importance in clinical application of antagonists. 15. Explain the information that can be obtained from a quantal dose-response curve. 16. Define the terms ED50, TD50, LD50, therapeutic index and certain safety factor. What is Pharmacology?It is defined as the study of drugs (substances used to prevent, diagnose, and treat disease). Pharmacology is the science that deals with the interactions betweena drug and the bodyor living systems. The interactions between a drug and the body are conveniently divided into two classes. The actions of the drug on the body are termed pharmacodynamicprocesses.These properties determine the group in which the drug is classified, and they play the major role in deciding whether that group is appropriate therapy for a particular symptom or disease. -
Pharmacodynamics Drug Receptor Interactions Part-2
Pharmacodynamics: (Drug Receptor Interactions, Part 2) ………………………………………………………………………………………………………………………………………………………………………………………………………………… VPT: Unit I; Lecture-22 (Dated 03.12.2020) Dr. Nirbhay Kumar Asstt. Professor & Head Deptt. of Veterinary Pharmacology & Toxicology Bihar Veterinary College, Bihar Animal Sciences University, Patna Drug Receptor Interactions Agonist It is a drug that possesses affinity for a particular receptor and causes a change in the receptor that result in an observable effect. Full agonist: Produces a maximal response by occupying all or a fraction of receptors. (Affinity=1, Efficacy=1) Partial agonist: Produces less than a maximal response even when the drug occupies all of the receptors. (Affinity=1, Efficacy= 0 to 1) Inverse agonist: Activates a receptor to produce an effect in the opposite direction to that of the well recognized agonist. (Affinity=1, Efficacy= –1 to 0). Source: Rang & Dale’s Pharmacology, Elsevier Source: Good & Gilman’s The Pharmacological Basis of Therapeutics, 13th Edn. Antagonist An antagonist is a drug that blocks the response produced by an agonist. Antagonists interact with the receptor or other components of the effector mechanism, but antagonists are devoid of intrinsic activity (Affinity=1, Efficacy=0). Antagonist contd… Competitive Antagonism: It is completely reversible; an increase in the concentration of the agonist in the bio-phase will overcome the effect of the antagonist. Example: Atropine (Antimuscarinic agent) Diphenhydramine (H1 receptor blocker) Non-competitive antagonism: The agonist has no influence upon the degree of antagonism or its reversibility. Example: Platelet inhibiting action of aspirin (The thromboxane synthase enzyme of platelets is irreversibly inhibited by aspirin, a process that is reversed only by production of new platelets). -
Synthèse Et Evaluation De Nouveau Agents De Protection Contre Les Rayonnements Ionisants Brice Nadal
Synthèse et Evaluation de nouveau agents de protection contre les rayonnements ionisants Brice Nadal To cite this version: Brice Nadal. Synthèse et Evaluation de nouveau agents de protection contre les rayonnements ion- isants. Chimie. Université Paris Sud - Paris XI, 2009. Français. tel-00447089 HAL Id: tel-00447089 https://tel.archives-ouvertes.fr/tel-00447089 Submitted on 14 Jan 2010 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. N° D’ORDRE : 9602 UNIVERSITÉ PARIS SUD XI FACULTÉ DES SCIENCES D’ORSAY THÈSE DE DOCTORAT Présentée en vue de l’obtention du grade de DOCTEUR EN SCIENCES DE L’UNIVERSITÉ PARIS SUD XI Spécialité Chimie Organique Par Brice NADAL Ingénieur CPE Lyon Synthèse et évaluation de nouveaux agents de protection contre les rayonnements ionisants Soutenue le 29 octobre 2009 devant la commission d’examen : Professeur Cyrille Kouklovsky Président Docteur Paul-Henri Ducrot Rapporteur Professeur Olivier Piva Rapporteur Docteur Claude Lion Examinateur Docteur Pierre Bischoff Examinateur Docteur Thierry Le Gall Directeur de thèse N° D’ORDRE -
Measuring Ligand Efficacy at the Mu- Opioid Receptor Using A
RESEARCH ARTICLE Measuring ligand efficacy at the mu- opioid receptor using a conformational biosensor Kathryn E Livingston1,2, Jacob P Mahoney1,2, Aashish Manglik3, Roger K Sunahara4, John R Traynor1,2* 1Department of Pharmacology, University of Michigan Medical School, Ann Arbor, United States; 2Edward F Domino Research Center, University of Michigan, Ann Arbor, United States; 3Department of Pharmaceutical Chemistry, School of Pharmacy, University of California San Francisco, San Francisco, United States; 4Department of Pharmacology, University of California San Diego School of Medicine, La Jolla, United States Abstract The intrinsic efficacy of orthosteric ligands acting at G-protein-coupled receptors (GPCRs) reflects their ability to stabilize active receptor states (R*) and is a major determinant of their physiological effects. Here, we present a direct way to quantify the efficacy of ligands by measuring the binding of a R*-specific biosensor to purified receptor employing interferometry. As an example, we use the mu-opioid receptor (m-OR), a prototypic class A GPCR, and its active state sensor, nanobody-39 (Nb39). We demonstrate that ligands vary in their ability to recruit Nb39 to m- OR and describe methadone, loperamide, and PZM21 as ligands that support unique R* conformation(s) of m-OR. We further show that positive allosteric modulators of m-OR promote formation of R* in addition to enhancing promotion by orthosteric agonists. Finally, we demonstrate that the technique can be utilized with heterotrimeric G protein. The method is cell- free, signal transduction-independent and is generally applicable to GPCRs. DOI: https://doi.org/10.7554/eLife.32499.001 *For correspondence: [email protected] Competing interests: The authors declare that no Introduction competing interests exist. -
Introduction
Guidance for Assay Development & HTS March 2007 Version 5 Section I: Introduction Introduction Copyright © 2005, Eli Lilly and Company and the National Institutes of Health Chemical Genomics Center. All Rights Reserved. For more information, please review the Privacy Policy and Site Usage and Agreement. Table of Contents A. INTRODUCTION This document is written to provide guidance to investigators that are interested in developing assays useful for the evaluation of compound collections to identify chemical probes that modulate the activity of biological targets. Originally written as a guide for therapeutic projects teams within a major pharmaceutical company, this manual has been adapted to provide guidelines for: a. Identifying potential assay formats compatible with High Throughput Screen (HTS), and Structure Activity Relationship (SAR) b. Developing optimal assay reagents c. Optimizing assay protocol with respect to sensitivity, dynamic range, signal intensity and stability d. Adopting screening assays to automation and scale up in microtiter plate formats e. Statistical validation of the assay performance parameters f. Secondary follow up assays for chemical probe validation and SAR refinement g. Data standards to be followed in reporting screening and SAR assay results. General definition of biological assays This manual is intended to provide guidance in the area of biological assay development, screening and compound evaluation. In this regard an assay is defined by a set of reagents that produce a detectable signal allowing a biological process to be quantified. In general, the quality of an assay is defined by the robustness and reproducibility of this signal in the absence of any test compounds or in the presence of inactive compounds. -
Response Vs. Log [L] - Full Agonist
DavidsonX – D001x – Medicinal Chemistry Chapter 5 – Receptors Part 2 – Ligands Video Clip – Ligand Types Ligands can have different effects on a receptor. Each type of ligand can be readily classified according to its behavior. A type of ligand is the full agonist. The term agonist refers to a compound that binds a receptor and elicits a response (E). Full agonists elicit the same level of full response (E = Emax = 100%) as the endogenous ligand of the receptor. Graphically, a receptor-ligand interaction is plotted as response (E/Emax) vs. log [L]. The relationship is sigmoidal. A full agonist approaches full response (E/Emax = 1.0) as log [L] reaches relatively high levels. response vs. log [L] - full agonist 1 0.9 0.8 0.7 x a 0.6 m E 0.5 / E 0.4 0.3 0.2 0.1 0 log [L] Two ligands can achieve a full response without being equivalent. Ligands can differ with respect to the concentration required to trigger a response. A ligand that affects a response at a lower concentration has a higher potency. Potencies are measured as the effective ligand concentration required to reach a 50% response – EC50 or, in these graphs, log [EC50]. A more potent ligand has a lower EC50 value. full agonist comparison 1 0.9 full agonist 1 0.8 (more potent) full agonist 2 0.7 (less potent) x a 0.6 log EC m 50 E 0.5 / E 0.4 0.3 log EC 0.2 50 0.1 0 log [L] Partial agonists also cause a response, but they cannot reach the same, 100% response level of the endogenous ligand. -
An Online Tool for Calculating and Mining Dose–Response Data Nicholas A
Clark et al. BMC Cancer (2017) 17:698 DOI 10.1186/s12885-017-3689-3 SOFTWARE Open Access GRcalculator: an online tool for calculating and mining dose–response data Nicholas A. Clark1†, Marc Hafner2†, Michal Kouril3, Elizabeth H. Williams2, Jeremy L. Muhlich2, Marcin Pilarczyk1, Mario Niepel2, Peter K. Sorger2 and Mario Medvedovic1* Abstract Background: Quantifying the response of cell lines to drugs or other perturbagens is the cornerstone of pre-clinical drug development and pharmacogenomics as well as a means to study factors that contribute to sensitivity and resistance. In dividing cells, traditional metrics derived from dose–response curves such as IC50, AUC, and Emax, are confounded by the number of cell divisions taking place during the assay, which varies widely for biological and experimental reasons. Hafner et al. (Nat Meth 13:521–627, 2016) recently proposed an alternative way to quantify drug response, normalized growth rate (GR) inhibition, that is robust to such confounders. Adoption of the GR method is expected to improve the reproducibility of dose–response assays and the reliability of pharmacogenomic associations (Hafner et al. 500–502, 2017). Results: We describe here an interactive website (www.grcalculator.org) for calculation, analysis, and visualization of dose–response data using the GR approach and for comparison of GR and traditional metrics. Data can be user-supplied or derived from published datasets. The web tools are implemented in the form of three integrated Shiny applications (grcalculator, grbrowser, and grtutorial) deployed through a Shiny server. Intuitive graphical user interfaces (GUIs) allow for interactive analysis and visualization of data. The Shiny applications make use of two R packages (shinyLi and GRmetrics) specifically developed for this purpose. -
Oecd Guideline for the Testing of Chemicals
XXX OECD GUIDELINE FOR THE TESTING OF CHEMICALS Stably Transfected Human Androgen Receptor-α Transcriptional Activation Assay for Detection of Androgenic Agonist Agonist/Antagonist Activity of Chemicals (Version 2010 Nov.25) INTRODUCTION 1. The OECD initiated a high-priority activity in 1998 to revise existing, and to develop new, Test Guidelines for the screening and testing of potential endocrine disrupting chemicals. The OECD conceptual framework for testing and assessment of potential endocrine disrupting chemicals comprises five levels, each level corresponding to a different level of biological complexity (1). The Transcriptional Activation (TA) assay described in this Test Guideline is a level 2 “in vitro assay, providing mechanistic information”. The validation study of the Stably Transfected Transactivation Assay (STTA) by Chemicals Evaluation and Research Institute (CERI) in Japan using the AR-EcoScreenTM cell line to detect Androgenic agonist/antagonist activities mediated through human androgen receptor demonstrated the relevance and reliability of the assay for its intended purpose (2). 2. In vitro TA assays are based upon the production of a reporter gene product induced by a chemical, following binding of the chemical to a specific receptor and subsequent downstream transcriptional activation. TA assays using activation of reporter genes are screening assays that have long been used to evaluate the specific gene expression regulated by specific nuclear receptors, such as the estrogen receptors (ERs) and androgen receptor (AR) (3)(4)(5)(6). They have been proposed for the detection of nuclear receptor mediated transactivation (1)(3)(4). Several in vitro TA and receptor binding assays are currently at validation at national, European and international levels, but are not yet close to completion and full assessment of their validation status. -
Anew Drug Design Strategy in the Liht of Molecular Hybridization Concept
www.ijcrt.org © 2020 IJCRT | Volume 8, Issue 12 December 2020 | ISSN: 2320-2882 “Drug Design strategy and chemical process maximization in the light of Molecular Hybridization Concept.” Subhasis Basu, Ph D Registration No: VB 1198 of 2018-2019. Department Of Chemistry, Visva-Bharati University A Draft Thesis is submitted for the partial fulfilment of PhD in Chemistry Thesis/Degree proceeding. DECLARATION I Certify that a. The Work contained in this thesis is original and has been done by me under the guidance of my supervisor. b. The work has not been submitted to any other Institute for any degree or diploma. c. I have followed the guidelines provided by the Institute in preparing the thesis. d. I have conformed to the norms and guidelines given in the Ethical Code of Conduct of the Institute. e. Whenever I have used materials (data, theoretical analysis, figures and text) from other sources, I have given due credit to them by citing them in the text of the thesis and giving their details in the references. Further, I have taken permission from the copyright owners of the sources, whenever necessary. IJCRT2012039 International Journal of Creative Research Thoughts (IJCRT) www.ijcrt.org 284 www.ijcrt.org © 2020 IJCRT | Volume 8, Issue 12 December 2020 | ISSN: 2320-2882 f. Whenever I have quoted written materials from other sources I have put them under quotation marks and given due credit to the sources by citing them and giving required details in the references. (Subhasis Basu) ACKNOWLEDGEMENT This preface is to extend an appreciation to all those individuals who with their generous co- operation guided us in every aspect to make this design and drawing successful.