Using What We Already Have: Uncovering New Drug Repurposing Strategies in Existing Omics Data

Pulley, Jill M.; Rhoads, Jillian P.; Jerome, Rebecca N; Challa, Anup P.; Erreger, Kevin; Joly, Meghan M.; Lavieri, Robert R.; Perry, Kelly E.; Zaleski, Nicole M.; Shirey-Rice, Jana K.; Aronoff, David M.

doi:10.1146/annurev-pharmtox-010919-023537

Cited by 44 publications

(38 citation statements)

References 127 publications

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“…Moreover, pancreatic cancer mortality is projected to be second only to lung cancer by 2030 (7). Hence, this malignant cancer will has a greater chance of success and can save both time and money since drug safety has already been validated (11)(12)(13)(14). Given the advantages of repurposing, we have been seeking to identify CSC-targeting drugs from among existing drugs with established safety profiles, irrespective of whether they have proven efficacy against cancer (15)(16)(17).…”

Section: Introductionmentioning

confidence: 99%

Therapeutic targeting of pancreatic cancer stem cells by dexamethasone modulation of the MKP-1–JNK axis

Okada

Sanomachi

Togashi

et al. 2020

Journal of Biological Chemistry

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Postoperative recurrence from microscopic residual disease must be prevented to cure intractable cancers, including pancreatic cancer. Key to this goal is the elimination of cancer stem cells (CSCs) endowed with tumor-initiating capacity and drug resistance. However, current therapeutic strategies capable of accomplishing this are insufficient. Using in vitro models of CSCs and in vivo models of tumor initiation in which CSCs give rise to xenograft tumors, we show that dexamethasone induces expression of MKP-1, a MAPK phosphatase, via glucocorticoid receptor activation, thereby inactivating JNK, which is required for self-renewal and tumor-initiation by pancreatic CSCs as well as for their expression of survivin, an anti-apoptotic protein implicated in multi-drug resistance. We also demonstrate that systemic administration of clinically relevant doses of dexamethasone together with gemcitabine prevents tumor formation by CSCs in a pancreatic cancer xenograft model. Our study thus provides preclinical evidence for the efficacy of dexamethasone as an adjuvant therapy to prevent postoperative recurrence in patients with pancreatic cancer.

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Section: Introductionmentioning

confidence: 99%

Therapeutic targeting of pancreatic cancer stem cells by dexamethasone modulation of the MKP-1–JNK axis

Okada

Sanomachi

Togashi

et al. 2020

Journal of Biological Chemistry

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“…Our team of investigators has formed a first-in-kind research collaboration of engineers, informaticians, and clinicians dedicated to the development of computational tools to predict adverse drug outcomes in pregnancy from existing healthcare data on pregnant populations and in vitro drug exposure models that are more representative of pregnant human physiology than the in vivo animal platforms currently employed in this space. This group—called Modeling Adverse Drug Reactions in Embryos (MADRE) 13, 90, 91 —proposes refinement of the teratogenicity QSAR reported in this manuscript by harnessing a more continuous spectrum of relevant phenotype information. Given that data quality and availability issues with teratogenicity scores restricted the scope of this study, we propose a medication history-wide association study (MedWAS) that can leverage billing-encoded, population-level EHR data as a label set.…”

Section: Discussionmentioning

confidence: 99%

“…A hallmark of the binning within this scale is the absence of definitive human data: at present, teratogenicity scores are established pre-clinically by pharmacologists, who evaluate biomarkers of fetal toxicity in animal models 5,6 . This approach is inherently limited, as common in vivo models are not sufficiently representative of human physiology 13 , and human subjects are not included in the teratogenicity scoring process for ethical reasons 11, 14, 15 . Indeed, the limited human data available for teratology scoring are often derived retrospectively from high-profile cases of fetal malformation resulting from drug exposure 9, 16, 17 .…”

Section: Introductionmentioning

confidence: 99%

“…The inherent contradiction between the limited target rationale for teratogenesis and the extent of uncertainty that guides prescribing behavior for gravid populations speaks to the need for more rigorous predictions of small molecule teratogenicity. Furthermore, computational modeling on healthcare data is the most accurate method of predicting drug safety in pregnant women, given that phase I trials are unethical for expectant populations and animal models are inherently limited for studying human health 12, 13, 24 .…”

Section: Introductionmentioning

confidence: 99%

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Machine learning on drug-specific data to predict small molecule teratogenicity

Challa

Beam

Shen

et al. 2019

Preprint

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1Pregnant women are an especially vulnerable population, given the sensitivity of a developing 2 fetus to chemical exposures. However, prescribing behavior for the gravid patient is guided on 3 limited human data and conflicting cases of adverse outcomes due to the exclusion of pregnant 4 populations from randomized, controlled trials. These factors increase risk for adverse drug 5 outcomes and reduce quality of care for pregnant populations. Herein, we propose the 6 application of artificial intelligence to systematically predict the teratogenicity of a prescriptible 7 small molecule from information inherent to the drug. Using unsupervised and supervised 8 machine learning, our model probes all small molecules with known structure and teratogenicity 9 data published in research-amenable formats to identify patterns among structural, meta-10 structural, and in vitro bioactivity data for each drug and its teratogenicity score. With this 11 workflow, we discovered three chemical functionalities that predispose a drug towards increased 12 teratogenicity and two moieties with potentially protective effects. Our models predict three 13 clinically-relevant classes of teratogenicity with AUC = 0.8 and nearly double the predictive 14 accuracy of a blind control for the same task, suggesting successful modeling. We also present 15 extensive barriers to translational research that restrict data-driven studies in pregnancy and 16 therapeutically "orphan" pregnant populations. Collectively, this work represents a first-in-kind 17 platform for the application of computing to study and predict teratogenicity. 18

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“…Most previous reviews of drug repurposing technologies have focused on methods development [3,[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], with only a few providing cursory analysis of evaluation of those methods [29,35,36]. Brown and Patel have previously reported a review of "validation" strategies for computational drug repurposing [37], broadly categorizing various evaluation metrics into "(1) validation with a single example or case study of a single disease area, (2) sensitivitybased validation only and (3) both sensitivity-and specificity-based validation" [37].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Performance of Drug Repurposing Technologies

Schuler

Falls

Mangione

et al. 2020

Preprint

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Drug repurposing technologies are growing in number and maturing. However, comparison to each other and to reality is hindered due to lack of consensus with respect to performance evaluation. Such comparability is necessary to determine scientific merit and to ensure that only meaningful predictions from repurposing technologies carry through to further validation and eventual patient use. Here, we review and compare performance evaluation measures for these technologies using version 2 of our shotgun repurposing Computational Analysis of Novel Drug Opportunities (CANDO) platform to illustrate their benefits, drawbacks, and limitations. Understanding and using different performance evaluation metrics ensures robust cross platform comparability, enabling us to continuously strive towards optimal repurposing by decreasing time and cost of drug discovery and development.

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Using What We Already Have: Uncovering New Drug Repurposing Strategies in Existing Omics Data

Cited by 44 publications

References 127 publications

Therapeutic targeting of pancreatic cancer stem cells by dexamethasone modulation of the MKP-1–JNK axis

Therapeutic targeting of pancreatic cancer stem cells by dexamethasone modulation of the MKP-1–JNK axis

Machine learning on drug-specific data to predict small molecule teratogenicity

Evaluating Performance of Drug Repurposing Technologies

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