Hierarchical Bayesian inference for ion channel screening dose-response data

Ross H Johnstone (1) , Rémi Bardenet (2, 3) , David J Gavaghan (1) , Gary R Mirams (4, 1)

1 Departement of Computer of Science University of Oxford
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 CNRS - Centre National de la Recherche Scientifique
4 UON - University of Nottingham, UK

Dose-response (or ‘concentration-effect’) relationships commonly occur in biological and pharmacological systems and are well characterised by Hill curves. These curves are described by an equation with two parameters: the inhibitory concentration 50% (IC50); and the Hill coefficient. Typically just the ‘best fit’ parameter values are reported in the literature. Here we introduce a Python-based software tool, PyHillFit, and describe the underlying Bayesian inference methods that it uses, to infer probability distributions for these parameters as well as the level of experimental observation noise. The tool also allows for hierarchical fitting, characterising the effect of inter-experiment variability. We demonstrate the use of the tool on a recently published dataset on multiple ion channel inhibition by multiple drug compounds. We compare the maximum likelihood, Bayesian and hierarchical Bayesian approaches. We then show how uncertainty in dose-response inputs can be characterised and propagated into a cardiac action potential simulation to give a probability distribution on model outputs.

Domaines: Sciences pharmaceutiques; Statistiques [math.ST]

Ross H Johnstone, Rémi Bardenet, David J Gavaghan, Gary R Mirams. Hierarchical Bayesian inference for ion channel screening dose-response data. Wellcome Open Research, 2016, ⟨10.12688/wellcomeopenres.9945.1⟩. ⟨hal-01580351⟩