Matching models to data: a receptor pharmacologist's guide.
In this review we discuss a number of topics related to fitting mechanistic mathematical models to experimental data. These can be divided broadly into issues to consider before beginning an experiment or fitting a model and the advantages of direct fitting of a model over other methods of analysis (e.g. null methods). We have sought to address some commonplace issues of receptor pharmacology with some real-life examples: How should dilutions be distributed along a concentration-response curve? How do problems with dilutions manifest in assay results? What assumptions are made when certain analysis models are applied? What is global data fitting and how does it work? How can it be applied to improve data analysis? How can models such as one-site and two-site fits be compared? What is the principal behind statistical comparison of data fits? It is our hope that after reading this review you will have a greater appreciation of assay planning and subsequent data analysis and interpretation which will improve the quality of the data that you generate.


BACKGROUND
This article is part of a themed section on Analytical Receptor Pharmacology in Drug Discovery. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2010.161.issue-6.
Respiratory CEDD, GlaxoSmithKline, Stevenage, Herts, UK.
David A
DA
Hall
Christopher J
CJ
Langmead
D000818:Animals; D006801:Humans; D017056:Medical Laboratory Personnel; D008956:Models, Chemical; D004364:Pharmaceutical Preparations / Q000378:metabolism; D010600:Pharmacology / Q000379:methods* / Q000639:trends; D011485:Protein Binding / Q000502:physiology; D011955:Receptors, Drug / Q000378:metabolism*; D012107:Research Design
D004364:Pharmaceutical Preparations; D011955:Receptors, Drug