Neuronal Population Models Reveal Specific Linear Conductance Controllers Sufficient to Rescue Preclinical Disease Phenotypes

Abstract

Objective During the preclinical phase of drug development, potential drug candidates are often screened for their ability to alleviate certain in vitro electrophysiological features among neurons. This ability is assessed by measuring treatment outcomes using the population mean, both across different cells and different animals. The go/no-go decision for progressing a drug to a clinical trial is then based on ‘average effects’, yet these measures may not be sufficient to mitigate clinical end point risk. Population-based modeling is widely used to represent the intrinsic variability of electrophysiological features among healthy, disease and drug treated neuronal phenotypes. We pursued a method for optimizing therapeutic target design by identifying a single coherent set of ion channel targets for recovery of the healthy (Wild type) cellular phenotype simultaneously across multiple measures. Specifically, we aimed to determine the set of target modulations that best recover a heterogeneous Huntington’s disease (HD) population of model neurons into a multivariate region of phenotypic measurements corresponding to the healthy excitability profile of a heterogenous Wild type (WT) population of model neurons. Methods Our approach combines mechanistic simulations with populations modeling of striatal neurons using evolutionary algorithms for population optimization to design ‘virtual drugs’. We introduce efficacy metrics to score population of model outcomes and use these to rank our virtual candidates. Results We found that virtual drugs identified using heuristic approaches performed better than single target modulators and those derived from standard classification methods. We compare a real drug to the virtual candidates and demonstrate a novel in silico triaging method.

Degeneracy in epilepsy: Multiple Routes to Hyperexcitable Brain Circuits and their Repair

“…(Modified from Dufour, 2014 andPrescott, 2016. ) provided a successful example for using population neuronal models as a tool for designing new multitarget holistic virtual drugs, which rescue pathologically increased excitability in Huntington's disease (Allam et al, 2021). A similar approach could be adopted in epilepsy research for finding therapeutically efficient sets of perturbations of multiple ion channels that would switch hyperexcitable neuronal phenotypes to control phenotypes with normal excitability.…”

Pareto optimality, economy–effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons

“…Several groups have adopted computational insights and methods of the above-mentioned landmark studies [1,2] to explore ion channel degeneracy in different types of neurons (e.g. [5,14–23]). This approach has been successfully used even outside of neuroscience, for example in heart cell physiology [24].…”

“…This approach has been successfully used even outside of neuroscience, for example in heart cell physiology [24]. It has been called population- [23–25] or database- [16,17] or ensemble-modelling [18]. Population-based computer models have provided a better understanding of cell-to-cell as well as animal-to-animal variability of electrophysiological and ion channel expression data [3,25,26].…”

“…Instead of a ‘one-size-fits-all’ approach in which a computer model simulates average properties of a nerve cell or heart muscle cell, the population-based approach constructs and validates large populations of realistic cellular models that differ in their ion channel configurations and reflect the variability of experimental data [24,27]. Recent work has also shown that such population models may allow pharmacological predictions in silico , thus complementing, and partially replacing animal experiments [23,28].…”