Systems pharmacology of adverse event mitigation by drug combinations

Shan Zhao, Tomohiro Nishimura, Yibang Chen, Evren U. Azeloglu, Omri Gottesman, Chiara Giannarelli, Mohammad U. Zafar, Ludovic Benard, Juan J. Badimon, Roger J. Hajjar, Joseph Goldfarb, Ravi Iyengar

Research output: Contribution to journalArticle

60 Citations (Scopus)

Abstract

Drugs are designed for therapy, but medication-related adverse events are common, and risk/benefit analysis is critical for determining clinical use. Rosiglitazone, an efficacious antidiabetic drug, is associated with increased myocardial infarctions (MIs), thus limiting its usage. Because diabetic patients are often prescribed multiple drugs, we searched for usage of a second drug ("drug B") in the Food and Drug Administration's Adverse Event Reporting System (FAERS) that could mitigate the risk of rosiglitazone ("drug A")-associated MI. In FAERS, rosiglitazone usage is associated with increased occurrence of MI, but its combination with exenatide significantly reduces rosiglitazone-associated MI. Clinical data from the Mount Sinai Data Warehouse support the observations from FAERS. Analysis for confounding factors using logistic regression showed that they were not responsible for the observed effect. Using cell biological networks, we predicted that the mitigating effect of exenatide on rosiglitazone-associated MI could occur through clotting regulation. Data we obtained from the db/db mouse model agreed with the network prediction. To determine whether polypharmacology could generally be a basis for adverse event mitigation, we analyzed the FAERS database for other drug combinations wherein drug B reduced serious adverse events reported with drug A usage such as anaphylactic shock and suicidality. This analysis revealed 19,133 combinations that could be further studied. We conclude that this type of crowdsourced approach of using databases like FAERS can help to identify drugs that could potentially be repurposed for mitigation of serious adverse events.

Original languageEnglish
Article number206ra140
JournalScience Translational Medicine
Volume5
Issue number206
DOIs
Publication statusPublished - 2013 Oct 9

Fingerprint

rosiglitazone
Drug Combinations
United States Food and Drug Administration
Pharmacology
Myocardial Infarction
Pharmaceutical Preparations
Polypharmacology
Databases
Anaphylaxis
Hypoglycemic Agents
Statistical Factor Analysis
Logistic Models
Drug Therapy

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Zhao, S., Nishimura, T., Chen, Y., Azeloglu, E. U., Gottesman, O., Giannarelli, C., ... Iyengar, R. (2013). Systems pharmacology of adverse event mitigation by drug combinations. Science Translational Medicine, 5(206), [206ra140]. https://doi.org/10.1126/scitranslmed.3006548

Systems pharmacology of adverse event mitigation by drug combinations. / Zhao, Shan; Nishimura, Tomohiro; Chen, Yibang; Azeloglu, Evren U.; Gottesman, Omri; Giannarelli, Chiara; Zafar, Mohammad U.; Benard, Ludovic; Badimon, Juan J.; Hajjar, Roger J.; Goldfarb, Joseph; Iyengar, Ravi.

In: Science Translational Medicine, Vol. 5, No. 206, 206ra140, 09.10.2013.

Research output: Contribution to journalArticle

Zhao, S, Nishimura, T, Chen, Y, Azeloglu, EU, Gottesman, O, Giannarelli, C, Zafar, MU, Benard, L, Badimon, JJ, Hajjar, RJ, Goldfarb, J & Iyengar, R 2013, 'Systems pharmacology of adverse event mitigation by drug combinations', Science Translational Medicine, vol. 5, no. 206, 206ra140. https://doi.org/10.1126/scitranslmed.3006548
Zhao, Shan ; Nishimura, Tomohiro ; Chen, Yibang ; Azeloglu, Evren U. ; Gottesman, Omri ; Giannarelli, Chiara ; Zafar, Mohammad U. ; Benard, Ludovic ; Badimon, Juan J. ; Hajjar, Roger J. ; Goldfarb, Joseph ; Iyengar, Ravi. / Systems pharmacology of adverse event mitigation by drug combinations. In: Science Translational Medicine. 2013 ; Vol. 5, No. 206.
@article{ed4b6e292ec748aba5096569fd185e2b,
title = "Systems pharmacology of adverse event mitigation by drug combinations",
abstract = "Drugs are designed for therapy, but medication-related adverse events are common, and risk/benefit analysis is critical for determining clinical use. Rosiglitazone, an efficacious antidiabetic drug, is associated with increased myocardial infarctions (MIs), thus limiting its usage. Because diabetic patients are often prescribed multiple drugs, we searched for usage of a second drug ({"}drug B{"}) in the Food and Drug Administration's Adverse Event Reporting System (FAERS) that could mitigate the risk of rosiglitazone ({"}drug A{"})-associated MI. In FAERS, rosiglitazone usage is associated with increased occurrence of MI, but its combination with exenatide significantly reduces rosiglitazone-associated MI. Clinical data from the Mount Sinai Data Warehouse support the observations from FAERS. Analysis for confounding factors using logistic regression showed that they were not responsible for the observed effect. Using cell biological networks, we predicted that the mitigating effect of exenatide on rosiglitazone-associated MI could occur through clotting regulation. Data we obtained from the db/db mouse model agreed with the network prediction. To determine whether polypharmacology could generally be a basis for adverse event mitigation, we analyzed the FAERS database for other drug combinations wherein drug B reduced serious adverse events reported with drug A usage such as anaphylactic shock and suicidality. This analysis revealed 19,133 combinations that could be further studied. We conclude that this type of crowdsourced approach of using databases like FAERS can help to identify drugs that could potentially be repurposed for mitigation of serious adverse events.",
author = "Shan Zhao and Tomohiro Nishimura and Yibang Chen and Azeloglu, {Evren U.} and Omri Gottesman and Chiara Giannarelli and Zafar, {Mohammad U.} and Ludovic Benard and Badimon, {Juan J.} and Hajjar, {Roger J.} and Joseph Goldfarb and Ravi Iyengar",
year = "2013",
month = "10",
day = "9",
doi = "10.1126/scitranslmed.3006548",
language = "English",
volume = "5",
journal = "Science Translational Medicine",
issn = "1946-6234",
publisher = "American Association for the Advancement of Science",
number = "206",

}

TY - JOUR

T1 - Systems pharmacology of adverse event mitigation by drug combinations

AU - Zhao, Shan

AU - Nishimura, Tomohiro

AU - Chen, Yibang

AU - Azeloglu, Evren U.

AU - Gottesman, Omri

AU - Giannarelli, Chiara

AU - Zafar, Mohammad U.

AU - Benard, Ludovic

AU - Badimon, Juan J.

AU - Hajjar, Roger J.

AU - Goldfarb, Joseph

AU - Iyengar, Ravi

PY - 2013/10/9

Y1 - 2013/10/9

N2 - Drugs are designed for therapy, but medication-related adverse events are common, and risk/benefit analysis is critical for determining clinical use. Rosiglitazone, an efficacious antidiabetic drug, is associated with increased myocardial infarctions (MIs), thus limiting its usage. Because diabetic patients are often prescribed multiple drugs, we searched for usage of a second drug ("drug B") in the Food and Drug Administration's Adverse Event Reporting System (FAERS) that could mitigate the risk of rosiglitazone ("drug A")-associated MI. In FAERS, rosiglitazone usage is associated with increased occurrence of MI, but its combination with exenatide significantly reduces rosiglitazone-associated MI. Clinical data from the Mount Sinai Data Warehouse support the observations from FAERS. Analysis for confounding factors using logistic regression showed that they were not responsible for the observed effect. Using cell biological networks, we predicted that the mitigating effect of exenatide on rosiglitazone-associated MI could occur through clotting regulation. Data we obtained from the db/db mouse model agreed with the network prediction. To determine whether polypharmacology could generally be a basis for adverse event mitigation, we analyzed the FAERS database for other drug combinations wherein drug B reduced serious adverse events reported with drug A usage such as anaphylactic shock and suicidality. This analysis revealed 19,133 combinations that could be further studied. We conclude that this type of crowdsourced approach of using databases like FAERS can help to identify drugs that could potentially be repurposed for mitigation of serious adverse events.

AB - Drugs are designed for therapy, but medication-related adverse events are common, and risk/benefit analysis is critical for determining clinical use. Rosiglitazone, an efficacious antidiabetic drug, is associated with increased myocardial infarctions (MIs), thus limiting its usage. Because diabetic patients are often prescribed multiple drugs, we searched for usage of a second drug ("drug B") in the Food and Drug Administration's Adverse Event Reporting System (FAERS) that could mitigate the risk of rosiglitazone ("drug A")-associated MI. In FAERS, rosiglitazone usage is associated with increased occurrence of MI, but its combination with exenatide significantly reduces rosiglitazone-associated MI. Clinical data from the Mount Sinai Data Warehouse support the observations from FAERS. Analysis for confounding factors using logistic regression showed that they were not responsible for the observed effect. Using cell biological networks, we predicted that the mitigating effect of exenatide on rosiglitazone-associated MI could occur through clotting regulation. Data we obtained from the db/db mouse model agreed with the network prediction. To determine whether polypharmacology could generally be a basis for adverse event mitigation, we analyzed the FAERS database for other drug combinations wherein drug B reduced serious adverse events reported with drug A usage such as anaphylactic shock and suicidality. This analysis revealed 19,133 combinations that could be further studied. We conclude that this type of crowdsourced approach of using databases like FAERS can help to identify drugs that could potentially be repurposed for mitigation of serious adverse events.

UR - http://www.scopus.com/inward/record.url?scp=84885441247&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84885441247&partnerID=8YFLogxK

U2 - 10.1126/scitranslmed.3006548

DO - 10.1126/scitranslmed.3006548

M3 - Article

C2 - 24107779

AN - SCOPUS:84885441247

VL - 5

JO - Science Translational Medicine

JF - Science Translational Medicine

SN - 1946-6234

IS - 206

M1 - 206ra140

ER -