arXiv subjects

David Colquhoun

Publications and source records attributed to David Colquhoun.

6 recordsLinked to original sources

arXiv2026

On the interpretation of the kinetics of ligand-receptor binding

When the rates of ligand binding are measured by methods such as surface plasmon resonance, it is common practice to use the observed rate constants for the onset and offset of binding to estimate an equilibrium constant for ligand binding. If this agrees with the equilibrium constant found as the EC50 for binding at equilibrium, this is taken as validation of the measured rates. This is correct only when binding produces no conformation change in the receptor, and ligand binding follows a single exponential time course. Here, we investigate a simple 3 state model in which binding is followed by a conformation change in the receptor. Three special cases of this model in which the time course of onset and offset of ligand binding are close to being single exponentials are analysed. These cases are (1) when binding is much faster than the conformation change, (2) when the conformation change is much faster than binding, and (3) when the rates of ligand dissociation and receptor activation are both fast. It is concluded that the measured rates will often yield an estimate of the equilibrium constant for ligand binding that is close to the effective, or macroscopic, equilibrium constant, the EC50 found by measuring binding at equilibrium, which depends on both of the underlying microscopic equilibrium constants describing ligand binding and the conformation change. The exception to this conclusion is the case when binding is much faster than the subsequent conformation change, though the estimate of the equilibrium constant for ligand binding still depends on both of the underlying microscopic equilibrium constants.

q-bio.QM↗

arXiv2024

The affinity-efficacy problem: an essential part of pharmacology education

A fundamental mistake in receptor theory has led to an enduring misunderstanding of how to estimate the affinity and efficacy of an agonist. These properties are inextricably linked and cannot be easily separated in any case where the binding of a ligand induces a conformation change in its receptor. Consequently, binding curves and concentration-response relationships for receptor agonists have no straightforward interpretation. This problem, the affinity-efficacy problem, remains overlooked and misunderstood despite it being recognised in 1987. To avoid the further propagation of this misunderstanding, we propose that the affinity-efficacy problem should be included in the core curricula for pharmacology undergraduates proposed by the British Pharmacological Society and IUPHAR.

q-bio.OT↗

arXiv2019

A response to critiques of "The reproducibility of research and the misinterpretation of p-values"

I proposed (8, 1, 3) that p values should be supplemented by an estimate of the false positive risk (FPR). FPR was defined as the probability that, if you claim that there is a real effect on the basis of p value from a single unbiased experiment, that you will be mistaken and the result has occurred by chance. This is a Bayesian quantity and that means that there is an infinitude of ways to calculate it. My choice of a way to estimate FPR was, therefore, arbitrary. I maintain that it is a reasonable way, and has the advantage of being mathematically simpler than other proposals and easier to understand than other methods. This might make it more easily accepted by users. As always, not every statistician agrees. This paper is a response to a critique of my 2017 paper (1) by Arandjelovic (2)

stat.OT↗

arXiv2018

The false positive risk: a proposal concerning what to do about p values

It is widely acknowledged that the biomedical literature suffer from a surfeit of false positive results. Part of the reason for this is the persistence of the myth that observation of a p value less than 0.05 is sufficient justification to claim that you've made a discovery. It is hopeless to expect users to change their reliance on p values unless they are offered an alternative way of judging the reliability of their conclusions. If the alternative method is to have a chance of being adopted widely, it will have to be easy to understand and to calculate. One such proposal is based on calculation of false positive risk. It is suggested that p values and confidence intervals should continue to be given, but that they should be supplemented by a single additional number that conveys the strength of the evidence better than the p value. This number could be the minimum false positive risk (that calculated on the assumption of a prior probability of 0.5, the largest value that can be assumed in the absence of hard prior data). Alternatively one could specify the prior probability that it would be necessary to believe in order to achieve a false positive risk of, say, 0.05.

stat.AP↗

arXiv2015

Innovative Science

Sir, We write as senior scientists about a problem vital to the scientific enterprise and prosperity. Nowadays, funding is a lengthy and complex business. First, universities themselves must approve all proposals for submission. Funding agencies then subject those that survive to peer review, a process by which a few researchers, usually acting anonymously, assess a proposal's chances that it will achieve its goals, is the best value for money, is relevant to a national priority and will impact on a socio-economic problem. Only 25% of proposals received by the funding agencies are funded. These protracted processes force researchers to exploit existing knowledge, severely discourage open-ended studies and are hugely time-consuming. They are also new: before 1970, few researchers wrote proposals. Now they are virtually mandatory.

physics.soc-ph↗

arXiv2014

An investigation of the false discovery rate and the misinterpretation of P values

The following proposition is justified from several different points of view. If you use P = 0.05 to suggest that you have made a discovery, you will be wrong at least 30 percent of the time. If, as is often the case, experiments are under-powered, you will be wrong most of the time. It is concluded that if you wish to keep your false discovery rate below 5 percent, you need to use a 3-sigma rule, or to insist on P value below 0.001. And never use the word "significant".

stat.AP↗