Abstract
Most monogenic diseases can be viewed as conditions caused by dysregulated protein activity; therefore, drugs can be used to modulate gene expression, and thus protein level, possibly conferring clinical benefit. When considering repurposing drugs for loss of function diseases, there are three classes of genetic disease amenable to an increase of function; haploinsufficient dominant diseases, those secondary to hypomorphic recessive alleles, and conditions with rescuing paralogs. This therapeutic model then brings the questions: how frequently do such clinically useful drug–gene interactions occur and what is the most rapid and efficient route by which to identify them. Here we compare three approaches: (1) mining of pre-existing system-wide transcriptomal datasets such as Connectivity Map; (2) utilization of a proprietary causal reasoning engine knowledge base; and, (3) a targeted drug screen using clinically accepted agents tested against normal human fibroblasts. We have determined the validation rate of these approaches for 76 diseases (i.e., in vitro fibroblast mRNA increase); for the Connectivity Map, approximately 5% of tested putative drug–gene interactions validated, for causal reasoning engine knowledge base the rate was 10%, and for the targeted drug screen 9%. The degree of overlap between these methodologies was low suggesting they are complementary not redundant approaches to identify putative drug-gene interactions. Although the validation rate was low, a number of drug–gene interactions were successfully identified and are now being investigated for protein induction and in vivo effect. This analysis establishes potentially valuable therapeutic leads as well as useful benchmarks for the thousands of currently untreatable rare genetic conditions.
Highlights
The estimated 7000 monogenic diseases individually rare are major contributors to human morbidity and mortality collectively affecting approximately 2% of the global population.[1]
We reviewed the rare disease databases OMIM 3 and Orphanet 13 to identify diseases with a potential mRNA target: (1) autosomal dominant rare diseases caused by haploinsufficiency; was little value in terms of identifying true interactions
An expert clinical group (KMB, DD, and clinical members of the FORGE Canada Consortium, 14) further reduced containing a given drug), of 15,810 interactions tested in our cellbased screen, 61 putative interactions were identified (0.4%; this list by looking for: (1) the existence of a pre-symptomatic Supplementary Table 2, column LG)
Summary
The estimated 7000 monogenic diseases individually rare are major contributors to human morbidity and mortality collectively affecting approximately 2% of the global population.[1] For example, rare diseases account for nearly twice the aggregate number of years that lives are shortened by diabetes and almost four times those due to infections.[2] Rare genetic diseases represent a dramatic unmet diagnostic and therapeutic need. The development of rare disease treatment lags far behind the rate of rare disease diagnosis; approximately 500 therapies have been approved for rare diseases (Europe and USA combined[5]). The rate of drug development for rare diseases is slow, due to factors such as extreme disease rarity and obscure disease pathogenesis
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