Rapid Characterisation of hERG Kinetics Using Optimised Protocols on a High-Throughput System Chon Lok Lei1, Michael Clerx1, David J. Gavaghan1, Liudimila Polonchuk2, Gary R. Mirams3, Ken Wang2 1. Computational Biology, University of Oxford, UK; 2. Pharma Research & Early Development, Roche, Switzerland; 3. Centre for Mathematical Medicine & Biology, University of Nottingham, UK chon.lei@cs.ox.ac.uk [1] Beattie et al. (2018) J. Physiol. [2] Traynelis (1998) J. Neurosci. Methods [3] Sherman et al. (1999) Biophys. J. 0 2 4 6 8 10 12 14 100 0 Voltage [mV] A Calibration B 0 2 4 6 8 10 12 14 20 0 20 Current [A/F] data model fit to data Time [s] Validation 1 Validation 2 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 50 0 Voltage [mV] Validation 3 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 50 0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 50 0 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0 5 10 Current [A/F] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Time [s] 0 10 20 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 5 10 Voltage [mV] 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 50 0 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 Time [s] 50 0 Voltage [mV] Time [s] Current [A/F] 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 0 10 data model prediction 40 20 0 20 40 0.0 0.5 1.0 Normalised Current Voltage [mV] Validation 4 Validation 5 0 1 2 3 4 p1 0 2 4 1e2 40 60 80 p2 0 1 2 0.0 0.1 0.2 0.3 0.4 0.5 p3 0 1 2 1e3 30 40 50 60 70 p4 0.00 0.25 0.50 0.75 1e1 200 300 400 500 600 p5 0.0 0.5 1.0 1.5 Normalised frequency 1e 1 10 20 30 40 50 p6 0 1 2 3 20 40 60 p7 0.0 0.5 1.0 1.5 20 25 30 35 40 p8 0 1 2 3 0 2000 4000 6000 gKr 0 1 2 1e 1 1 2 3 4 5 6 noise 0.0 0.5 1.0 1.5 1e2 0 2 4 1e 4 0 2 4 6 1e 1 0 1 2 3 1e 2 0.0 0.5 1.0 1e1 0 2 4 1e 2 0 2 4 1e 3 0 2 4 Probability density 1e 2 0 1 2 3 1e 2 0.0 0.5 1.0 1e 1 3.5 Model Parameters Correlation 3.3 Variability Across Experiments 3.2 Model Validation 3.1 Experimental Design / Model Training/Fitting Hierarchical Model Structure 1. Introduction Variability in hERG Kinetics References Fig 2: An optimised protocol that is designed for high throughput machine to recover model parameters. It shows the actual experimental measurement and the fitted model. Fig 3: Independent experimental measurements of the same cell to use as model validation. Here we used both 'traditional' protocols (Validation 4, 5) and new physiologically inspired [1] protocols (Validation 1-3) which are the AP-like, EAD-like and DAD-like waveforms. We repeated the same experiment on the same cell-line and recorded 65 individual cell measurements. Can we capture experiment-to- experiment variability? Fig 5: A hierarchical Bayesian model (HBM) was used to capture experiment- to-experiment variability. Marginal distributions of each experiment are shown, which reveal the variability between experiments. The posterior predictive distributions from the HBM are shown in red. Hodgkin-Huxley formulation 2. Cardiac hERG Channel Model 3.4 Consideration of Experimental Error Model The above analyses have assumed experiments were done 'perfectly'. That is we have assumed our input of the command voltage V was what the cell experienced as the membrane voltage Vm during the experiment. However, this might not be exact [2, 3]. Therefore we have also considered the possible error source in the experiment, as error model, to give: where 'mechanistic model' is our IKr model and 'noise model' is a simple Gaussian/white noise model. Fig 6: The 95% C.I. contour plots of the inferred covariance matrix, together with each parameter values. It shows a compar- ison of the parameter varia- bility (blue) and the effect of the voltage error model (red) on synthe- tic data. Fig 5: A schematic of the full hierarchical Bayesian model, which is a multi-level modelling technique that combines individ- ual measures (lower level) as a group (top level). Prior distributions are specified for the , , . This allows us to combine multiple experiments into one causal structure. Notation: 4. Experimental Methods This is not the end! Acknowledgements Fig 1: A simp