Drug Repurposing for Rare Diseases

Roessler, Helen I.; Knoers, Nine V A M; Haelst, Mieke M. van; Haaften, Gijs van

doi:10.1016/j.tips.2021.01.003

Cited by 161 publications

(94 citation statements)

References 109 publications

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“…However, first human studies of the treatment of the Angelman syndrome have started in 2020 (https://clinicaltrials.gov/search/term=Angelman%20Syndrome). Repositioning of drugs is among strategies to find therapies for these rare diseases as it provides a faster and cheaper option to obtain active drugs than the development of novel compounds from scratch [132][133][134]. Several strategies have been proposed for the efficient search of drug repurposing candidates, of which those using a genomics-based approach are summarized in [116].…”

Section: Discussionmentioning

confidence: 99%

Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis

Kringel

Malkusch

Lötsch

2021

IJMS

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Interactions of drugs with the classical epigenetic mechanism of DNA methylation or histone modification are increasingly being elucidated mechanistically and used to develop novel classes of epigenetic therapeutics. A data science approach is used to synthesize current knowledge on the pharmacological implications of epigenetic regulation of gene expression. Computer-aided knowledge discovery for epigenetic implications of current approved or investigational drugs was performed by querying information from multiple publicly available gold-standard sources to (i) identify enzymes involved in classical epigenetic processes, (ii) screen original biomedical scientific publications including bibliometric analyses, (iii) identify drugs that interact with epigenetic enzymes, including their additional non-epigenetic targets, and (iv) analyze computational functional genomics of drugs with epigenetic interactions. PubMed database search yielded 3051 hits on epigenetics and drugs, starting in 1992 and peaking in 2016. Annual citations increased to a plateau in 2000 and show a downward trend since 2008. Approved and investigational drugs in the DrugBank database included 122 compounds that interacted with 68 unique epigenetic enzymes. Additional molecular functions modulated by these drugs included other enzyme interactions, whereas modulation of ion channels or G-protein-coupled receptors were underrepresented. Epigenetic interactions included (i) drug-induced modulation of DNA methylation, (ii) drug-induced modulation of histone conformations, and (iii) epigenetic modulation of drug effects by interference with pharmacokinetics or pharmacodynamics. Interactions of epigenetic molecular functions and drugs are mutual. Recent research activities on the discovery and development of novel epigenetic therapeutics have passed successfully, whereas epigenetic effects of non-epigenetic drugs or epigenetically induced changes in the targets of common drugs have not yet received the necessary systematic attention in the context of pharmacological plasticity.

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Section: Discussionmentioning

confidence: 99%

Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis

Kringel

Malkusch

Lötsch

2021

IJMS

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“…The pace of defining new diseases based on genome sequencing is rapidly accelerating 1 . The cost and time required to develop novel therapeutics has also increased dramatically 2 , creating huge unmet need.…”

Section: Introductionmentioning

confidence: 99%

Virtual screening for small molecule pathway regulators by image profile matching

Rohban

Fuller

Tan

et al. 2021

Preprint

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Identifying chemical regulators of biological pathways is currently a time-consuming bottleneck in developing therapeutics and small-molecule research tools. Typically, thousands to millions of candidate small molecules are tested in target-based biochemical screens or phenotypic cell-based screens, both expensive experiments customized to a disease of interest. Here, we instead use a broad, virtual screening approach that matches compounds to pathways based on phenotypic information in public data. Our computational strategy efficiently uncovered small molecule regulators of three pathways, containing p38ɑ (MAPK14), YAP1, or PPARGC1A (PGC-1α). We first selected genes whose overexpression yielded distinct image-based profiles in the Cell Painting assay, a microscopy assay involving six stains that label eight cellular organelles/components. To identify small molecule regulators of pathways involving those genes, we used publicly available Cell Painting profiles of 30,616 small molecules to identify compounds that yield morphological effects either positively or negatively correlated with image-based profiles for specific genes. Subsequent assays validated compounds that impacted the predicted pathway activities. This image profile-based drug discovery approach could transform both basic research and drug discovery by identifying useful compounds that modify pathways of biological and therapeutic interest, thus using a computational query to replace certain customized labor- and resource-intensive screens.

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“…Repurposing of existing therapeutic agents has generated significant interest in the realm of rare diseases as an innovative strategy for finding new opportunities to manage these complex conditions ( Delavan et al, 2018 ; Valencic et al, 2018 ; Scherman and Fetro, 2020 ; Roessler et al, 2021 ). As data resources that integrate longitudinal phenotype information with genetic data and patient demographics continue to expand and evolve, so does their utility for identifying new therapeutic insights for an array of rare diseases.…”

Section: Introductionmentioning

confidence: 99%

Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases

et al. 2021

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Repurposing is an increasingly attractive method within the field of drug development for its efficiency at identifying new therapeutic opportunities among approved drugs at greatly reduced cost and time of more traditional methods. Repurposing has generated significant interest in the realm of rare disease treatment as an innovative strategy for finding ways to manage these complex conditions. The selection of which agents should be tested in which conditions is currently informed by both human and machine discovery, yet the appropriate balance between these approaches, including the role of artificial intelligence (AI), remains a significant topic of discussion in drug discovery for rare diseases and other conditions. Our drug repurposing team at Vanderbilt University Medical Center synergizes machine learning techniques like phenome-wide association study—a powerful regression method for generating hypotheses about new indications for an approved drug—with the knowledge and creativity of scientific, legal, and clinical domain experts. While our computational approaches generate drug repurposing hits with a high probability of success in a clinical trial, human knowledge remains essential for the hypothesis creation, interpretation, “go-no go” decisions with which machines continue to struggle. Here, we reflect on our experience synergizing AI and human knowledge toward realizable patient outcomes, providing case studies from our portfolio that inform how we balance human knowledge and machine intelligence for drug repurposing in rare disease.

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Drug Repurposing for Rare Diseases

Cited by 161 publications

References 109 publications

Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis

Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis

Virtual screening for small molecule pathway regulators by image profile matching

Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases

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