A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports

Tatonetti, Nicholas P.; Fernald, Guy Haskin; Altman, Russ B.

doi:10.1136/amiajnl-2011-000214

Cited by 175 publications

(143 citation statements)

References 22 publications

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“…In the study which was carried out by Nicholas et al, [2], they have used a novel signal detection algorithm to identify hidden DDIs signal from adverse event reports. Fundamental concept of their study is to divide the severe adverse events (SAE) to eight distinct classes based on their clinical significance; cholesterol, renal impairment, diabetes, liver dysfunction, hepatotoxicity, hypertension, depression, and suicide.…”

Section: A Adverse Event Reportsmentioning

confidence: 99%

“…It also retrieves the information from the spontaneous reporting systems such as US Food and Drug Administration's Adverse Event Reporting System (AERS) [2][11] [13] by using the technologies like the semantic web [15] and linked data.…”

Section: Knowledge Based Approachesmentioning

confidence: 99%

“…For example, Terfenadine (Seldane®) in February 1998 and Cisapride (Propulsid®) in January 2000. According to statistics provided by the National Health and Nutrition Examination Survey, over 76% of elderly Americans are on two or more drugs [2]. Unfortunately, adverse drug events (ADEs) are common and account for 770 000 injuries and deaths in each year, and drug interactions account for as much as 30% of these ADEs [2].…”

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confidence: 99%

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Predicting Drug-Drug Interactions for Premarket Drug Development Process: A Network Based Approach

Gunawardena

Weerasinghe

Wijesinghe

2018

Int J on Adv. in ICT for Emerging Countries

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Abstract-Drug-drug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for the safety of the patient but also it is very challenging. In recent years, several drugs have been withdrawn from the market due to interaction related Adverse Events (AEs).This study describes a model which can be used to predict novel DDIs based on the similarity of drug interaction candidates to drugs involved in established DDIs which can be used in a large scale to discover novel DDIs. This model is mainly based on the assumption that if drug A and drug B interact to produce a specific biological effect, then drugs similar to drug A (or drug B) are likely to interact with drug B (or drug A) to produce the same effect. We have created a drug network using the 2011 snapshot of a widely used drug safety database which utilizes 352 distinct drugs and contains 3 700 interactions. Then, it was used to develop the proposed model for predicting future DDIs. The target similarities and side effect similarities (P-score) were calculated for all selected pairs of drugs. Then, it was used to develop the proposed model for predicting future DDIs. The proposed model mainly follows two distinct approaches: 'Which forces the preservation of existing (known) DDIs' and 'Without forced to preserve existing DDIs.' Underneath each of these approaches, three different techniques: target similarity score, side effect similarity (P-score) and resulting score were used to retrieve novel DDIs.The proposed model was evaluated using the Drugbank 2014 snapshot as a gold standard for the same set of drugs which produce novel DDIs with an average accuracy of 95% and 92%, average AUC (Area Under the Curve) of 0.9834 and 0.8651 under each of these two approaches respectively.The results presented in this study demonstrate the usefulness of the proposed network based drug-drug interaction methodology as a promising approach. The method described in this article is very simple, efficient, and biologically sound.

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Section: A Adverse Event Reportsmentioning

confidence: 99%

Section: Knowledge Based Approachesmentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting Drug-Drug Interactions for Premarket Drug Development Process: A Network Based Approach

Gunawardena

Weerasinghe

Wijesinghe

2018

Int J on Adv. in ICT for Emerging Countries

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“…A high potential approach consists in reusing data collected and stored in Electronic Health Records (EHR), either directly or through mining a Clinical Data Warehouse (CDW) derived from an EHR. It becomes, then, possible to identify, confirm, or refute pharmacovigilance signals coming from Adverse Event Reporting System database [4,5] or eventually directly suggest new ADRs. [6][7][8][9] The combination of these methods could finally decrease the time consuming and financial cost of ADR detection for the community.…”

Section: Introductionmentioning

confidence: 99%

“…[10] Methods and tools, known as data mining, have been developed over the years to analyse large sets of data. Data mining methods in pharmacovigilance have been used with several goals, [8,9] (i) in order to automate the search of publications concerning ADR in Medline, [11] (ii) to correlate and predict post-marketing adverse drug effects based on screening data from public databases of chemical structures like Pubchem, [12] (iii) to develop new algorithms to detect new or latent multi-drug adverse events in Adverse Event Reporting System database [5] and (iv) to find out new pharmacovigilance signal by mining EHR data. [13,14] For example, in a recently published work, [13] Tatonetti NP et al…”

Section: Introductionmentioning

confidence: 99%

Detection of Drug–Drug Interactions Inducing Acute Kidney Injury by Electronic Health Records Mining

et al. 2015

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Objective: While risk of Acute Kidney Injury (AKI) is well-documented as adverse effects of some drugs, few studies have assessed relationship between Drug-Drug Interactions (DDI) and AKI. Our objective was to develop an algorithm capable of detecting potential signals on this relationship by mining retrospectively data from an electronic health record. 3 Key Points-The re-use of data from Electronic Health Record (EHR) generated by real clinical activities for pharmacoepidemiological studies is booming, it seemed that DDIs could be identified by mining such databases.-We have developed a new signal detection algorithm using data mining techniques and the RIFLE criteria capable to re-use real care data from EHR in order to identify potential pharmacovigilance signal concerning DDIs and AKI.-This study confirms that real care data from EHRs could be utilized to identify new pharmacovigilance signal concerning DDIs.

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Quantitative Signal Detection and Analysis in Pharmacovigilance

Bate

Pariente

Hauben

et al. 2014

Mann's Pharmacovigilance

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A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports

Abstract: Our method provides an option for detecting hidden interactions in spontaneous reporting systems by using side effect profiles to infer the presence of unreported adverse events.

Cited by 175 publications

References 22 publications

Predicting Drug-Drug Interactions for Premarket Drug Development Process: A Network Based Approach

Predicting Drug-Drug Interactions for Premarket Drug Development Process: A Network Based Approach

Detection of Drug–Drug Interactions Inducing Acute Kidney Injury by Electronic Health Records Mining

Quantitative Signal Detection and Analysis in Pharmacovigilance

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