A Novel Huntington’s Disease Assessment Platform to Support Future Drug Discovery and Development

Abstract

Huntington’s disease (HD) is a lethal neurodegenerative disorder without efficient therapeutic options. The inefficient translation from preclinical and clinical research into clinical use is mainly attributed to the lack of (i) understanding of disease initiation, progression, and involved molecular mechanisms; (ii) knowledge of the possible HD target space and general data awareness; (iii) detailed characterizations of available disease models; (iv) better suitable models; and (v) reliable and sensitive biomarkers. To generate robust HD-like symptoms in a mouse model, the neomycin resistance cassette was excised from zQ175 mice, generating a new line: zQ175Δneo. We entirely describe the dynamics of behavioral, neuropathological, and immunohistological changes from 15–57 weeks of age. Specifically, zQ175Δneo mice showed early astrogliosis from 15 weeks; growth retardation, body weight loss, and anxiety-like behaviors from 29 weeks; motor deficits and reduced muscular strength from 36 weeks; and finally slight microgliosis at 57 weeks of age. Additionally, we collected the entire bioactivity network of small-molecule HD modulators in a multitarget dataset (HD_MDS). Hereby, we uncovered 358 unique compounds addressing over 80 different pharmacological targets and pathways. Our data will support future drug discovery approaches and may serve as useful assessment platform for drug discovery and development against HD.

Enrichment analysis of phenotypic data for drug repurposing in rare diseases

“…Small markets and reduced profitability results in low investment in R&D for these diseases. Thus, the ability to screen large libraries of test compounds in disease models for drug repurposing has been recognized as a game changer ( Bellomo et al, 2017 ; Wu et al, 2022 ). Our method extends phenotypic drug-repurposing from cell assays and small animal (worms, flies, fish) screens to whole rodent in silico screen as a first step.…”

“…We chose the heterozygous KI Q175 model, which has been extensively characterized ( Menalled et al, 2012 ; Alexandrov et al, 2016 ). The Q175 models show changes in striatal genes expression starting around 3 months of age ( Menalled et al, 2012 ), and behavioral changes soon thereafter with clear cognitive and motor deficits between 6 and 7 months of age, in both the original and a derived Q175 model ( Curtin et al, 2016 ; Wu et al, 2022 ). Although cognitive aspects of HD are extremely important and can be studied in the lab ( Farrar et al, 2014 ), the present work only focuses on motor function, exploratory behavior, response to aversive stimuli, and simple aspects of learning as adaptation to a novel environment, which can be captured in a high-throughput manner.…”

The contribution of preclinical magnetic resonance imaging and spectroscopy to Huntington’s disease

“…The original zQ175 model has a floxed neomycin resistance cassette (neo cassette) upstream of the Htt gene locus. The neo cassette may reduce Hdh gene expression in the context of the long CAG repeat, which might explain why the phenotype in zQ175 is not markedly more pronounced than in HdhQ140 mice ( Southwell et al, 2016 ; Wu et al, 2022 ).…”

Medicinal polypharmacology: Exploration and exploitation of the polypharmacolome in modern drug development

“…However, the number of compounds annotated with multiple biological information, particularly with biological activity against the targets of interest, is very limited. Most databases store chemical and biological information on a "one target-one compound" basis only, necessitating (i) complementary and redundant biological assessments of new compounds; and (ii) curation of data sets of already published compounds (Mousavian & Masoudi-Nejad, 2014;Stefan et al, 2022;Wu et al, 2022). This is particularly true considering (i) literature pollution with bad actors (Stork & Kirchmair, 2018);…”

“…However, the number of compounds annotated with multiple biological information, particularly with biological activity against the targets of interest, is very limited. Most databases store chemical and biological information on a “one target‐one compound” basis only, necessitating (i) complementary and redundant biological assessments of new compounds; and (ii) curation of data sets of already published compounds (Mousavian & Masoudi‐Nejad, 2014; Stefan et al, 2022; Wu et al, 2022). This is particularly true considering (i) literature pollution with bad actors (Stork & Kirchmair, 2018); (ii) the overall limitation in data comparability and accuracy (Kalliokoski et al, 2013; Kramer et al, 2012; Stefan, 2019), leading to incomplete and/or noisy data (“data barrier”) (Schneider, 2014); and (iii) inaccurately and/or incompletely described assay procedures, limiting reproducibility (Goldmann et al, 2014).…”