Connecting Neuronal Cell Protective Pathways and Drug Combinations in a Huntington’s Disease Model through the Application of Quantitative Systems Pharmacology

Abstract

Quantitative Systems Pharmacology (QSP) is a drug discovery approach that integrates computational and experimental methods in an iterative way to gain a comprehensive, unbiased understanding of disease processes to inform effective therapeutic strategies. We report the implementation of QSP to Huntington’s Disease, with the application of a chemogenomics platform to identify strategies to protect neuronal cells from mutant huntingtin induced death. Using the STHdh Q111 cell model, we investigated the protective effects of small molecule probes having diverse canonical modes-of-action to infer pathways of neuronal cell protection connected to drug mechanism. Several mechanistically diverse protective probes were identified, most of which showed less than 50% efficacy. Specific combinations of these probes were synergistic in enhancing efficacy. Computational analysis of these probes revealed a convergence of pathways indicating activation of PKA. Analysis of phospho-PKA levels showed lower cytoplasmic levels in STHdh Q111 cells compared to wild type STHdh Q7 cells, and these levels were increased by several of the protective compounds. Pharmacological inhibition of PKA activity reduced protection supporting the hypothesis that protection may be working, in part, through activation of the PKA network. The systems-level studies described here can be broadly applied to any discovery strategy involving small molecule modulation of disease phenotype.

Systems pharmacology approach reveals the antiinflammatory effects of Ampelopsis grossedentata on dextran sodium sulfate-induced colitis

“…Considering the complex combination and multitarget interactions of Chinese herbal plants, it is quite difficult to conduct a systematic study of the effects of AMP on diseases using conventional methods. Whereas, systems pharmacology, an emerging systems-oriented approach which has been reported to reveal the mechanism of a disease and link it to the chemical network of a drug[ 18 , 19 ], provides new perspectives to predict the active ingredients and candidate targets through a holistic process of active compound screening, target fishing, network construction and analysis[ 20 , 21 ]. To further investigate the potential mechanisms and effects of AMP on UC, a systems pharmacology analysis and animal experiments were conducted in this study.…”

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

“…The copyright holder for this preprint (which this version posted June 2, 2020. ; https://doi.org/10.1101/2020.06.01.128033 doi: bioRxiv preprint An important consideration not included in this modeling study is a distinction between different classes of MSNs. Previous studies attributed intrinsic excitability differences to dichotomous D1 and D2 type dopamine receptors (D1Rs and D2Rs) that modulate different classes of MSNs and affect how they process synaptic input integration (67) content screen measures and electrophysiological recordings (24,74) with a systems biology model capturing the D1 and D2 receptor modulation of DARPP-32 substrate via PKA and PLC signaling pathways (75), the mapping may provide a way to quantify the effect of DARPP-32 phosphorylation ion channel modulation of membrane activity, and this is therefore a promising direction for future investigation.…”

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

“…A unified signaling network generated through the chemogenomic approach (see Fig. 1a’ and Pei et al 2017, 2019) in investigation of drugs of abuse. Black arrows represent the activation, inhibition, and translocation events during signal transduction.…”

“…In silico chemogenomic approach (Fig. 1a’–f), inferring the molecular mechanisms of a phenotype of interest based on a collection of chemicals identified through phenotypic screening, offers an alternative framework to identify novel therapeutics (Bredel and Jacoby 2004; Brennan et al 2009; Digles et al 2016; Pei et al 2017; Prathipati and Mizuguchi 2016). The collection of chemicals is used therein to mine the DTI or chemical-target interaction databases (Gaulton et al 2017; Kooistra et al 2016; Szklarczyk et al 2016; Wishart et al 2018) and extract ML-based information on associated targets (Cobanoglu et al 2015; Gfeller et al 2014; Nickel et al 2014; Yamanishi et al 2014), which may be further linked to enriched pathways and gene ontology (GO) annotations (Huntley et al 2015; Kanehisa et al 2017; Slenter et al 2018).…”

“…Thus, the cellular pathways and environment and the biological functions and processes affected by the chemicals are systematically explored. Such system-level analyses (Bian et al 2019; Pei et al 2017, 2019; Wei et al 2018; Wu et al 2019; Xu et al 2016) assist in deciphering polypharmacological effects and disease mechanisms (Fig. 2).…”