Drug repurposing for aging research using model organisms

Abstract

SummaryMany increasingly prevalent diseases share a common risk factor: age. However, little is known about pharmaceutical interventions against aging, despite many genes and pathways shown to be important in the aging process and numerous studies demonstrating that genetic interventions can lead to a healthier aging phenotype. An important challenge is to assess the potential to repurpose existing drugs for initial testing on model organisms, where such experiments are possible. To this end, we present a new approach to rank drug‐like compounds with known mammalian targets according to their likelihood to modulate aging in the invertebrates Caenorhabditis elegans and Drosophila. Our approach combines information on genetic effects on aging, orthology relationships and sequence conservation, 3D protein structures, drug binding and bioavailability. Overall, we rank 743 different drug‐like compounds for their likelihood to modulate aging. We provide various lines of evidence for the successful enrichment of our ranking for compounds modulating aging, despite sparse public data suitable for validation. The top ranked compounds are thus prime candidates for in vivo testing of their effects on lifespan in C. elegans or Drosophila. As such, these compounds are promising as research tools and ultimately a step towards identifying drugs for a healthier human aging.

Gene expression‐based drug repurposing to target aging

“…For instance, the Connectivity Map (CMap), a database of drug‐induced gene expression profiles, has been used to identify DR mimetics and found 11 drugs that induced expression profiles significantly similar to those induced by DR in rats and rhesus monkeys (Calvert et al, 2016). Another study generated a combined score reflecting both the aging relevance of drugs based on the GenAge database and GO annotations as well as the likely efficacy of the drugs in model organisms, using structural analyses and other criteria such as solubility (Ziehm et al, 2017). A machine learning approach has been used to identify prolongevity drugs based on the chemical descriptors of the drugs in DrugAge database and GO annotations of their targets (Barardo et al, 2017).…”

High throughput small molecule screening reveals NRF2-dependent and - independent pathways of cellular stress resistance

“…Other screens have revealed the ability of anticonvulsants 16 , angiotensin converting enzyme antagonists 17 , and modulators of other signaling pathways to increase worm lifespan 18, 19 . Recent studies have employed in silico strategies to prioritize compounds for direct lifespan testing in nematodes and other organisms [20][21][22] .…”

“…Cells were then aliquoted into 384-well plates (Greiner cat# 781080) and incubated overnight at 37 o C in a 10% humified CO 2 incubator. On day 2, 0.2 μ l of test compounds (plate columns[3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] or DMSO (plate columns 1-2 and 23-24) were added, for an initial compoundconcentration of 16 μ M and initial DMSO concentration of 0.8%. Plates were then incubated overnight.…”

Gene Expression-Based Drug Repurposing to Target Ageing

“…For instance, the Connectivity Map, a database of drug-induced gene expression profiles, has been used to identify DR mimetics, and found 11 drugs that induced expression profiles significantly similar to those induced by DR in rats and rhesus monkeys (Calvert et al, 2016). Another study generated a combined score reflecting both the ageing relevance of drugs based on the GenAge database and GO annotations as well as the likely efficacy of the drugs in model organisms, using structural analyses and other criteria such as solubility (Ziehm et al, 2017). A machine learning approach has been used to identify pro-longevity drugs based on the chemical descriptors of the drugs in DrugAge database and GO annotations of their targets .…”