PT  - JOURNAL ARTICLE
AU  - Sushmita L. Allam
AU  - Timothy H. Rumbell
AU  - Tuan Hoang Trong
AU  - Jaimit Parikh
AU  - James R. Kozloski
TI  - Neuronal Population Models Reveal Specific Linear Conductance Controllers Sufficient to Rescue Preclinical Disease Phenotypes
AID  - 10.1101/2020.06.01.128033
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.06.01.128033
4099  - http://biorxiv.org/content/early/2020/06/02/2020.06.01.128033.short
4100  - http://biorxiv.org/content/early/2020/06/02/2020.06.01.128033.full
AB  - 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.Competing Interest StatementThe authors have declared no competing interest.