@article{shukla_integrative_2020,
	title = {Integrative {Omics} for {Informed} {Drug} {Repurposing}: {Targeting} {CNS} {Disorders}},
	copyright = {© 2020, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
	shorttitle = {Integrative {Omics} for {Informed} {Drug} {Repurposing}},
	url = {https://www.biorxiv.org/content/10.1101/2020.04.24.060392v1},
	doi = {10.1101/2020.04.24.060392},
	abstract = {{\textless}h3{\textgreater}Abstract{\textless}/h3{\textgreater} {\textless}p{\textgreater}The treatment of CNS disorders, and in particular psychiatric illnesses, lacks disease-altering therapeutics for many conditions. This is likely due to regulatory challenges involving the high cost and slow-pace of drug development for CNS disorders as well as due to limited understanding of disease causality. Repurposing drugs for new indications have lower cost and shorter development timeline compared to that of de novo drug development. Historically, empirical drug-repurposing is a standard practice in psychiatry; however, recent advances in characterizing molecules with their structural and transcriptomic signatures along with ensemble of data analysis approaches, provides informed and cost-effective repurposing strategies that ameliorate the regulatory challenges. In addition, the potential to incorporate ontological approaches along with signature-based repurposing techniques addresses the various knowledge-based challenges associated with CNS drug development. In this review we primarily discuss signature-based \textit{in silico} approaches to drug repurposing, and its integration with data science platforms for evidence-based drug repurposing. We contrast various \textit{in silico} and empirical approaches and discuss possible avenues to improve the clinical relevance. These concepts provide a promising new translational avenue for developing new therapies for difficult to treat disorders, and offer the possibility of connecting drug discovery platforms and big data analytics with personalized disease signatures.{\textless}/p{\textgreater}},
	language = {en},
	urldate = {2021-03-21},
	journal = {bioRxiv},
	author = {Shukla, Rammohan and Henkel, Nicholas D. and Alganem, Khaled and Hamoud, Abdul-rizaq and Reigle, James and Alnafisah, Rawan S. and Eby, Hunter M. and Imami, Ali S. and Creeden, Justin and Miruzzi, Scott A. and Meller, Jaroslaw and Mccullumsmith, Robert E.},
	month = apr,
	year = {2020},
	note = {ZSCC: NoCitationData[s0] 
Publisher: Cold Spring Harbor Laboratory
Section: New Results},
	pages = {2020.04.24.060392},
}

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The material may not be redistributed, re-used or adapted without the author's permission.","shorttitle":"Integrative Omics for Informed Drug Repurposing","url":"https://www.biorxiv.org/content/10.1101/2020.04.24.060392v1","doi":"10.1101/2020.04.24.060392","abstract":"\\textlessh3\\textgreaterAbstract\\textless/h3\\textgreater \\textlessp\\textgreaterThe treatment of CNS disorders, and in particular psychiatric illnesses, lacks disease-altering therapeutics for many conditions. This is likely due to regulatory challenges involving the high cost and slow-pace of drug development for CNS disorders as well as due to limited understanding of disease causality. Repurposing drugs for new indications have lower cost and shorter development timeline compared to that of de novo drug development. Historically, empirical drug-repurposing is a standard practice in psychiatry; however, recent advances in characterizing molecules with their structural and transcriptomic signatures along with ensemble of data analysis approaches, provides informed and cost-effective repurposing strategies that ameliorate the regulatory challenges. In addition, the potential to incorporate ontological approaches along with signature-based repurposing techniques addresses the various knowledge-based challenges associated with CNS drug development. In this review we primarily discuss signature-based <i>in silico</i> approaches to drug repurposing, and its integration with data science platforms for evidence-based drug repurposing. We contrast various <i>in silico</i> and empirical approaches and discuss possible avenues to improve the clinical relevance. These concepts provide a promising new translational avenue for developing new therapies for difficult to treat disorders, and offer the possibility of connecting drug discovery platforms and big data analytics with personalized disease signatures.\\textless/p\\textgreater","language":"en","urldate":"2021-03-21","journal":"bioRxiv","author":[{"propositions":[],"lastnames":["Shukla"],"firstnames":["Rammohan"],"suffixes":[]},{"propositions":[],"lastnames":["Henkel"],"firstnames":["Nicholas","D."],"suffixes":[]},{"propositions":[],"lastnames":["Alganem"],"firstnames":["Khaled"],"suffixes":[]},{"propositions":[],"lastnames":["Hamoud"],"firstnames":["Abdul-rizaq"],"suffixes":[]},{"propositions":[],"lastnames":["Reigle"],"firstnames":["James"],"suffixes":[]},{"propositions":[],"lastnames":["Alnafisah"],"firstnames":["Rawan","S."],"suffixes":[]},{"propositions":[],"lastnames":["Eby"],"firstnames":["Hunter","M."],"suffixes":[]},{"propositions":[],"lastnames":["Imami"],"firstnames":["Ali","S."],"suffixes":[]},{"propositions":[],"lastnames":["Creeden"],"firstnames":["Justin"],"suffixes":[]},{"propositions":[],"lastnames":["Miruzzi"],"firstnames":["Scott","A."],"suffixes":[]},{"propositions":[],"lastnames":["Meller"],"firstnames":["Jaroslaw"],"suffixes":[]},{"propositions":[],"lastnames":["Mccullumsmith"],"firstnames":["Robert","E."],"suffixes":[]}],"month":"April","year":"2020","note":"ZSCC: NoCitationData[s0] Publisher: Cold Spring Harbor Laboratory Section: New Results","pages":"2020.04.24.060392","bibtex":"@article{shukla_integrative_2020,\n\ttitle = {Integrative {Omics} for {Informed} {Drug} {Repurposing}: {Targeting} {CNS} {Disorders}},\n\tcopyright = {© 2020, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},\n\tshorttitle = {Integrative {Omics} for {Informed} {Drug} {Repurposing}},\n\turl = {https://www.biorxiv.org/content/10.1101/2020.04.24.060392v1},\n\tdoi = {10.1101/2020.04.24.060392},\n\tabstract = {{\\textless}h3{\\textgreater}Abstract{\\textless}/h3{\\textgreater} {\\textless}p{\\textgreater}The treatment of CNS disorders, and in particular psychiatric illnesses, lacks disease-altering therapeutics for many conditions. This is likely due to regulatory challenges involving the high cost and slow-pace of drug development for CNS disorders as well as due to limited understanding of disease causality. Repurposing drugs for new indications have lower cost and shorter development timeline compared to that of de novo drug development. Historically, empirical drug-repurposing is a standard practice in psychiatry; however, recent advances in characterizing molecules with their structural and transcriptomic signatures along with ensemble of data analysis approaches, provides informed and cost-effective repurposing strategies that ameliorate the regulatory challenges. In addition, the potential to incorporate ontological approaches along with signature-based repurposing techniques addresses the various knowledge-based challenges associated with CNS drug development. In this review we primarily discuss signature-based \\textit{in silico} approaches to drug repurposing, and its integration with data science platforms for evidence-based drug repurposing. We contrast various \\textit{in silico} and empirical approaches and discuss possible avenues to improve the clinical relevance. 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