Calcium channel blockers as drug repurposing candidates for gestational diabetes: Mining large scale genomic and electronic health records data to repurpose medications

Goldstein, Jeffrey A.; Bastarache, Lisa; Denny, Joshua C.; Roden, Dan M.; Pulley, Jill M.; Aronoff, David M.

doi:10.1016/j.phrs.2018.02.013

Cited by 18 publications

(21 citation statements)

References 38 publications

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“…These technologies are successfully employed in cancer [25,26] and viruses [27,28] and leave scope for academic investigators to apply for more diseases. Recently, Goldstein et al (2018) studied calcium channel blockers as repurposing drugs to benefit gestational diabetes [29]. Another study by Oral et al (2017) repurposed an anti-asthma drug to improve glucose levels in patients with diabetes mellitus [30].…”

Section: Introductionmentioning

confidence: 99%

Repurposing of FDA-Approved NSAIDs for DPP-4 Inhibition as an Alternative for Diabetes Mellitus Treatment: Computational and in Vitro Study

et al. 2019

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A drug repurposing strategy could be a potential approach to overcoming the economic costs for diabetes mellitus (DM) treatment incurred by most countries. DM has emerged as a global epidemic, and an increase in the outbreak has led developing countries like Mexico, India, and China to recommend a prevention method as an alternative proposed by their respective healthcare sectors. Incretin-based therapy has been successful in treating diabetes mellitus, and inhibitors like sitagliptin, vildagliptin, saxagliptin, and alogliptin belong to this category. As of now, drug repurposing strategies have not been used to identify existing therapeutics that can become dipeptidyl peptidase-4 (DPP-4) inhibitors. Hence, this work presents the use of bioinformatics tools like the Activity Atlas model, flexible molecular docking simulations, and three-dimensional reference interaction site model (3D-RISM) calculations to assist in repurposing Food and Drug Administration (FDA)-approved drugs into specific nonsteroidal anti-inflammatory medications such as DPP-4 inhibitors. Initially, the Activity Atlas model was constructed based on the top scoring DPP-4 inhibitors, and then the model was used to understand features of nonsteroidal anti-inflammatory drugs (NSAIDs) as a function of electrostatic, hydrophobic, and active shape features of DPP-4 inhibition. The FlexX algorithm was used to infer protein–ligand interacting residues, and binding energy, to predict potential draggability towards the DPP-4 mechanism of action. 3D-RISM calculations on piroxicam-bound DPP-4 were used to understand the stability of water molecules at the active site. Finally, piroxicam was chosen as the repurposing drug to become a new DPP-4 inhibitor and validated experimentally using fluorescence spectroscopy assay. These findings are novel and provide new insights into the role of piroxicam as a new lead to inhibit DPP-4 and, taking into consideration the biological half-life of piroxicam, it can be proposed as a possible therapeutic strategy for treating diabetes mellitus.

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Section: Introductionmentioning

confidence: 99%

Repurposing of FDA-Approved NSAIDs for DPP-4 Inhibition as an Alternative for Diabetes Mellitus Treatment: Computational and in Vitro Study

et al. 2019

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“…The distinction between target and disease-driven strategies is theoretical and should not impair studies which include both arms. For example, in our recent repurposing paper on GDM, we used a target-driven GWAS strategy and a disease driven EHR strategy 31 . For our target driven strategy, we selected drugs and identified targets using the resources mentioned above.…”

Section: ‘Omics Strategiesmentioning

confidence: 99%

“…Because our structure was relatively straightforward with only 2 arms, we suggested the intersection – antinausea serotonin receptor 3 antagonists (e.g. ondansetron) and calcium channel blocking antihypertensives (nifedipine) as targets for future study (Figure 3, 31 ).…”

Section: ‘Omics Strategiesmentioning

confidence: 99%

PregOMICS—Leveraging systems biology and bioinformatics for drug repurposing in maternal‐child health

Goldstein

Bastarache

Denny

et al. 2018

American J Rep Immunol

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Obstetric diseases remain underserved and understudied. Drug repurposing-utilization of a drug whose use is accepted in one condition for a different condition-could represent a rapid and low-cost way to identify new therapies that are known to be safe. In diseases of pregnancy, the known safety profile is a strong additional incentive. We describe the techniques and steps used in the use of 'omics data for drug repurposing. We illustrate these techniques using case studies of published drug repurposing projects. We provide a set of available databases with low barriers to entry which investigators can use to perform their own projects. The promise of 'omics techniques is unbiased screening, either of all drug targets or of all patients using particular drugs to find which are likely to alter disease risk or progression. However, we caution that reproducibility across the underlying studies, and thus the drugs suggested for repurposing, can be poor. We suggest that improved nosology, for example correlating patient clinical conditions with placental pathology, could yield more robust associations. We conclude that 'omics-driven drug repurposing represents a potential fruitful path to discover new, safe treatments of obstetric diseases.

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“…Following this disaster, randomized, controlled trials (RCTs) were modified to exclude pregnant populations, fearing unintended teratogenicity from exposure to unsystematically profiled drugs 10 . This change continues to “orphan” pregnant women, as many diseases in women’s health lack safe and effective drug choices for treatment 8, 11, 12 .…”

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%

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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Calcium channel blockers as drug repurposing candidates for gestational diabetes: Mining large scale genomic and electronic health records data to repurpose medications

Cited by 18 publications

References 38 publications

Repurposing of FDA-Approved NSAIDs for DPP-4 Inhibition as an Alternative for Diabetes Mellitus Treatment: Computational and in Vitro Study

Repurposing of FDA-Approved NSAIDs for DPP-4 Inhibition as an Alternative for Diabetes Mellitus Treatment: Computational and in Vitro Study

PregOMICS—Leveraging systems biology and bioinformatics for drug repurposing in maternal‐child health

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

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