Detection Of Drug-drug Interactions Research Articles

Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature.

BackgroundInformation about drug–drug interactions (DDIs) supported by scientific evidence is crucial for establishing computational knowledge bases for applications like pharmacovigilance. Since new reports of DDIs are rapidly accumulating in the scientific literature, text-mining techniques for automatic DDI extraction are critical. We propose a novel approach for automated pharmacokinetic (PK) DDI detection that incorporates syntactic and semantic information into graph kernels, to address the problem of sparseness associated with syntactic-structural approaches. First, we used a novel all-path graph kernel using shallow semantic representation of sentences. Next, we statistically integrated fine-granular semantic classes into the dependency and shallow semantic graphs.ResultsWhen evaluated on the PK DDI corpus, our approach significantly outperformed the original all-path graph kernel that is based on dependency structure. Our system that combined dependency graph kernel with semantic classes achieved the best F-scores of 81.94 % for in vivo PK DDIs and 69.34 % for in vitro PK DDIs, respectively. Further, combining shallow semantic graph kernel with semantic classes achieved the highest precisions of 84.88 % for in vivo PK DDIs and 74.83 % for in vitro PK DDIs, respectively.ConclusionsWe presented a graph kernel based approach to combine syntactic and semantic information for extracting pharmacokinetic DDIs from Biomedical Literature. Experimental results showed that our proposed approach could extract PK DDIs from literature effectively, which significantly enhanced the performance of the original all-path graph kernel based on dependency structure.

Genotype 1 hepatitis C virus and the pharmacist's role in treatment.

The treatment of hepatitis C virus (HCV) genotype 1 has changed rapidly with recently approved direct-acting antiviral (DAA) regimens. The role of the pharmacist in the management of HCV therapy has increased. Chronic HCV infection is the main cause of end-stage liver disease and the primary reason for liver transplantation, liver-related death, and hepatocellular carcinoma in the United States. The recent approval of several DAAs has led to improved tolerability, sustained virological response (SVR) rates, and shorter treatment durations compared with treatment with pegylated interferon and ribavirin. Most HCV cases can be treated with the currently available regimens, and expected SVR rates exceed 90%. Several fixed-dose and pangenotypic antiviral regimens are currently in various phases of clinical trials. Pharmacists are well equipped to assist the medical team and patients with comprehensive management of HCV treatment. Pharmacists in various settings can play an instrumental role in access to HCV medications, selection of HCV treatment, detection of drug-drug interactions, and education of patients about potential adverse effects and the importance of adherence and laboratory test monitoring during HCV treatment. However, the high cost of HCV treatment poses challenges for ubiquitous treatment. Available DAA regimens have improved HCV treatment outcomes and are selected based on efficacy, potential drug interactions, and the patient's ability to obtain medication coverage. Each of these factors provides an opportunity for pharmacist involvement in HCV management.

A graph kernel based on context vectors for extracting drug–drug interactions

The clinical recognition of drug–drug interactions (DDIs) is a crucial issue for both patient safety and health care cost control. Thus there is an urgent need that DDIs be extracted automatically from biomedical literature by text-mining techniques. Although the top-ranking DDIs systems explore various features of texts, these features can’t yet adequately express long and complicated sentences. In this paper, we present an effective graph kernel which makes full use of different types of contexts to identify DDIs from biomedical literature. In our approach, the relations among long-range words, in addition to close-range words, are obtained by the graph representation of a parsed sentence. Context vectors of a vertex, an iterative vectorial representation of all labeled nodes adjacent and nonadjacent to it, adequately capture the direct and indirect substructures’ information. Furthermore, the graph kernel considering the distance between context vectors is used to detect DDIs. Experimental results on the DDIExtraction 2013 corpus show that our system achieves the best detection and classification performance (F-score) of DDIs (81.8 and 68.4, respectively). Especially for the Medline-2013 dataset, our system outperforms the top-ranking DDIs systems by F-scores of 10.7 and 12.2 in detection and classification, respectively.

A comparison of five common drug-drug interaction software programs regarding accuracy and comprehensiveness.

Objective:Drug–drug interactions (DDIs) can cause failure in treatment and adverse events. DDIs screening software is an important tool to aid clinicians in the detection and management of DDIs. However, clinicians should be aware of the advantages and limitations of these programs. We compared the ability of five common DDI programs to detect clinically important DDIs.Methods:Lexi-Interact, Micromedex Drug Interactions, iFacts, Medscape, and Epocrates were evaluated. The programs' sensitivity, specificity, and positive and negative predictive values were determined to assess their accuracy in detecting DDIs. The accuracy of each program was identified using 360 unknown pair interactions, taken randomly from prescriptions, and forty pairs of clinically important ones. The major reference was a clinical pharmacist alongside the Stockley's Drug Interaction and databases including PubMed, Scopus, and Google Scholar. Comprehensiveness of each program was determined by the number of components in the drug interaction monograph. The aggregate score for accuracy and comprehensiveness was calculated.Findings:Scoring 250 out of possible 400 points, Lexi-Interact and Epocrates, provided the most accurate software programs. Micromedex, Medscape, and iFacts ranked third, fourth, and fifth, scoring 236, 202, and 191, respectively. In comprehensiveness test, iFacts showed the highest score, 134 out of possible 134 points, whereas Lexi-Interact rated second, with a score of 120. Scoring 370 and 330 out of possible 534 points, Lexi-Interact and Micromedex, respectively, provided the most competent, complete, and user-friendly applications.Conclusion:Lexi-Interact and Micromedex showed the best performances. An increase in sensitivity is possible by the combination of more than one programs and expert pharmacist intervention.

Text mining for pharmacovigilance: Using machine learning for drug name recognition and drug–drug interaction extraction and classification

Pharmacovigilance (PV) is defined by the World Health Organization as the science and activities related to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problem. An essential aspect in PV is to acquire knowledge about Drug–Drug Interactions (DDIs). The shared tasks on DDI-Extraction organized in 2011 and 2013 have pointed out the importance of this issue and provided benchmarks for: Drug Name Recognition, DDI extraction and DDI classification. In this paper, we present our text mining systems for these tasks and evaluate their results on the DDI-Extraction benchmarks. Our systems rely on machine learning techniques using both feature-based and kernel-based methods. The obtained results for drug name recognition are encouraging. For DDI-Extraction, our hybrid system combining a feature-based method and a kernel-based method was ranked second in the DDI-Extraction-2011 challenge, and our two-step system for DDI detection and classification was ranked first in the DDI-Extraction-2013 task at SemEval. We discuss our methods and results and give pointers to future work.

High-Throughput Cytochrome P450 Cocktail Inhibition Assay for Assessing Drug-Drug and Drug-Botanical Interactions.

Detection of drug-drug interactions is essential during the early stages of drug discovery and development, and the understanding of drug-botanical interactions is important for the safe use of botanical dietary supplements. Among the different forms of drug interactions that are known, inhibition of cytochrome P450 (P450) enzymes is the most common cause of drug-drug or drug-botanical interactions. Therefore, a rapid and comprehensive mass spectrometry-based in vitro high-throughput P450 cocktail inhibition assay was developed that uses 10 substrates simultaneously against nine CYP isoforms. Including probe substrates for CYP1A2, CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and two probes targeting different binding sites of CYP3A4/5, this cocktail simultaneously assesses at least as many P450 enzymes as previous assays while remaining among the fastest due to short incubation times and rapid analysis using ultrahigh pressure liquid chromatography-tandem mass spectrometry. The method was validated using known inhibitors of each P450 enzyme and then shown to be useful not only for single-compound testing but also for the evaluation of potential drug-botanical interactions using the botanical dietary supplement licorice (Glycyrrhiza glabra) as an example.

DINTO: Using OWL Ontologies and SWRL Rules to Infer Drug-Drug Interactions and Their Mechanisms.

The early detection of drug-drug interactions (DDIs) is limited by the diffuse spread of DDI information in heterogeneous sources. Computational methods promise to play a key role in the identification and explanation of DDIs on a large scale. However, such methods rely on the availability of computable representations describing the relevant domain knowledge. Current modeling efforts have focused on partial and shallow representations of the DDI domain, failing to adequately support computational inference and discovery applications. In this paper, we describe a comprehensive ontology for DDI knowledge (DINTO), which is the first formal representation of different types of DDIs and their mechanisms and its application in the prediction of DDIs. This project has been developed using currently available semantic web technologies, standards, and tools, and we have demonstrated that the combination of drug-related facts in DINTO and Semantic Web Rule Language (SWRL) rules can be used to infer DDIs and their different mechanisms on a large scale. The ontology is available from https://code.google.com/p/dinto/.

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

While risk of acute kidney injury (AKI) is a well documented adverse effect of some drugs, few studies have assessed the relationship between drug-drug interactions (DDIs) and AKI. Our objective was to develop an algorithm capable of detecting potential signals on this relationship by retrospectively mining data from electronic health records. Data were extracted from the clinical data warehouse (CDW) of the Hôpital Européen Georges Pompidou (HEGP). AKI was defined as the first level of the RIFLE criteria, that is, an increase ≥50% of creatinine basis. Algorithm accuracy was tested on 20 single drugs, 10 nephrotoxic and 10 non-nephrotoxic. We then tested 45 pairs of non-nephrotoxic drugs, among the most prescribed at our hospital and representing distinct pharmacological classes for DDIs. Sensitivity and specificity were 50% [95% confidence interval (CI) 23.66-76.34] and 90% (95% CI 59.58-98.21), respectively, for single drugs. Our algorithm confirmed a previously identified signal concerning clarithromycin and calcium-channel blockers (unadjusted odds ratio (ORu) 2.92; 95% CI 1.11-7.69, p=0.04). Among the 45 drug pairs investigated, we identified a signal concerning 55 patients in association with bromazepam and hydroxyzine (ORu 1.66; 95% CI 1.23-2.23). This signal was not confirmed after a chart review. Even so, AKI and co-prescription were confirmed for 96% (95% CI 88-99) and 88% (95% CI 76-94) of these patients, respectively. Data mining techniques on CDW can foster the detection of adverse drug reactions when drugs are used alone or in combination.

State of the art and development of a drug-drug interaction large scale predictor based on 3D pharmacophoric similarity.

Co-administration of drugs is a primary cause of Adverse Drug Reactions (ADRs) and a drain on the health care industry costing billions of dollars and reducing quality of life. Drug-Drug Interactions (DDIs) account for as much as 30% of all ADRs. Unfortunately, DDIs are not systematically explored pre-clinically and are difficult to detect in post-marketing drug surveillance. For this reason, the detection and prediction of DDIs is an important problem in both drug development and pharmacovigilance. The comparison of the 3D drug structures provides a powerful tool for DDI prediction. In this article, we present the first large scale model for predicting DDIs using the drug's 3D molecular structure. In addition to identifying putative drug interactions we can also isolate the pharmacological or clinical effect associated with the predicted interactions. The model has good performance in two different hold-out validations and in external test sets. We found that the top scored drug pairs were significantly enriched for known clinically relevant interactions and that 3D structure data is providing significantly independent information from other approaches, including 2D structure (p=0.003). We demonstrated the usefulness of the proposed methodology to systematically identify pharmacokinetic and pharmacodynamic interactions, provided an exploratory tool that can be used for patient safety and pre-clinical toxicity screening, and reviewed the state of the art methods used to detect DDIs.

Screening of six UGT enzyme activities in human liver microsomes using liquid chromatography/triple quadrupole mass spectrometry.

Uridine 5'-diphosphoglucuronosyltransferase (UGT) enzymes are essential for the clearance of many drugs; however, altered UGT activity is a potential cause of adverse drug-drug interactions (DDI). The early detection of potential DDI is an important aspect of drug discovery that has led to the development of new screening methods for drug interactions. We developed a screening method for the simultaneous evaluation of six human liver UGT enzyme activites using in vitro cocktail incubation and tandem mass spectrometry. The two in vitro cocktail doses were developed to minimize drug interactions among substrates. The method is based on liquid chromatography/tandem mass spectrometry (LC/MS/MS). Electrospray ionization (ESI) in both positive and negative modes was used to quantify the metabolites and the diagnostic loss of the glucuronosyl moiety to form the aglycone product was estimated using the selected reaction monitoring (SRM) mode. The method was validated by comparing inhibition data obtained from the incubation of each individual probe substrate alone with data from the cocktail method. The intra- and inter-day accuracy and precision data for the six UGT metabolites ranged from 92.2 to 100.3% and less than 15.2%, respectively. The IC(50) values showed no significant differences between individual and cocktail incubations. As a screening technique for inhibitory interactions of these six human liver UGT enzymes, this method will be useful for advancing mechanistic understanding of drug interactions.

Similarity-based modeling in large-scale prediction of drug-drug interactions.

Drug-drug interactions (DDIs) are a major cause of adverse drug effects and a public health concern, as they increase hospital care expenses and reduce patients' quality of life. DDI detection is, therefore, an important objective in patient safety, one whose pursuit affects drug development and pharmacovigilance. In this article, we describe a protocol applicable on a large scale to predict novel DDIs based on similarity of drug interaction candidates to drugs involved in established DDIs. The method integrates a reference standard database of known DDIs with drug similarity information extracted from different sources, such as 2D and 3D molecular structure, interaction profile, target and side-effect similarities. The method is interpretable in that it generates drug interaction candidates that are traceable to pharmacological or clinical effects. We describe a protocol with applications in patient safety and preclinical toxicity screening. The time frame to implement this protocol is 5-7 h, with additional time potentially necessary, depending on the complexity of the reference standard DDI database and the similarity measures implemented.

Lessons learnt from the DDIExtraction-2013 Shared Task

The DDIExtraction Shared Task 2013 is the second edition of the DDIExtraction Shared Task series, a community-wide effort to promote the implementation and comparative assessment of natural language processing (NLP) techniques in the field of the pharmacovigilance domain, in particular, to address the extraction of drug–drug interactions (DDI) from biomedical texts. This edition has been the first attempt to compare the performance of Information Extraction (IE) techniques specific for each of the basic steps of the DDI extraction pipeline. To attain this aim, two main tasks were proposed: the recognition and classification of pharmacological substances and the detection and classification of drug–drug interactions. DDIExtraction 2013 was held from January to June 2013 and attracted wide attention with a total of 14 teams (6 of the teams participated in the drug name recognition task, while 8 participated in the DDI extraction task) from 7 different countries. For the task of the recognition and classification of pharmacological names, the best system achieved an F1 of 71.5%, while, for the detection and classification of DDIs, the best result was an F1 of 65.1%. The results show advances in the state of the art and demonstrate that significant challenges remain to be resolved. This paper focuses on the second task (extraction of DDIs) and examines its main challenges, which have yet to be resolved.

Mining clinical text for signals of adverse drug-drug interactions

Electronic health records (EHRs) are increasingly being used to complement the FDA Adverse Event Reporting System (FAERS) and to enable active pharmacovigilance. Over 30% of all adverse drug reactions are caused by drug-drug interactions (DDIs) and result in significant morbidity every year, making their early identification vital. We present an approach for identifying DDI signals directly from the textual portion of EHRs. We recognize mentions of drug and event concepts from over 50 million clinical notes from two sites to create a timeline of concept mentions for each patient. We then use adjusted disproportionality ratios to identify significant drug-drug-event associations among 1165 drugs and 14 adverse events. To validate our results, we evaluate our performance on a gold standard of 1698 DDIs curated from existing knowledge bases, as well as with signaling DDI associations directly from FAERS using established methods. Our method achieves good performance, as measured by our gold standard (area under the receiver operator characteristic (ROC) curve >80%), on two independent EHR datasets and the performance is comparable to that of signaling DDIs from FAERS. We demonstrate the utility of our method for early detection of DDIs and for identifying alternatives for risky drug combinations. Finally, we publish a first of its kind database of population event rates among patients on drug combinations based on an EHR corpus. It is feasible to identify DDI signals and estimate the rate of adverse events among patients on drug combinations, directly from clinical text; this could have utility in prioritizing drug interaction surveillance as well as in clinical decision support.

Text Mining Driven Drug-Drug Interaction Detection.

Identifying drug-drug interactions is an important and challenging problem in computational biology and healthcare research. There are accurate, structured but limited domain knowledge and noisy, unstructured but abundant textual information available for building predictive models. The difficulty lies in mining the true patterns embedded in text data and developing efficient and effective ways to combine heterogenous types of information. We demonstrate a novel approach of leveraging augmented text-mining features to build a logistic regression model with improved prediction performance (in terms of discrimination and calibration). Our model based on synthesized features significantly outperforms the model trained with only structured features (AUC: 96% vs. 91%, Sensitivity: 90% vs. 82% and Specificity: 88% vs. 81%). Along with the quantitative results, we also show learned "latent topics", an intermediary result of our text mining module, and discuss their implications.

The DDI corpus: An annotated corpus with pharmacological substances and drug–drug interactions

The management of drug–drug interactions (DDIs) is a critical issue resulting from the overwhelming amount of information available on them. Natural Language Processing (NLP) techniques can provide an interesting way to reduce the time spent by healthcare professionals on reviewing biomedical literature. However, NLP techniques rely mostly on the availability of the annotated corpora. While there are several annotated corpora with biological entities and their relationships, there is a lack of corpora annotated with pharmacological substances and DDIs. Moreover, other works in this field have focused in pharmacokinetic (PK) DDIs only, but not in pharmacodynamic (PD) DDIs. To address this problem, we have created a manually annotated corpus consisting of 792 texts selected from the DrugBank database and other 233 Medline abstracts. This fined-grained corpus has been annotated with a total of 18,502 pharmacological substances and 5028 DDIs, including both PK as well as PD interactions. The quality and consistency of the annotation process has been ensured through the creation of annotation guidelines and has been evaluated by the measurement of the inter-annotator agreement between two annotators. The agreement was almost perfect (Kappa up to 0.96 and generally over 0.80), except for the DDIs in the MedLine database (0.55–0.72). The DDI corpus has been used in the SemEval 2013 DDIExtraction challenge as a gold standard for the evaluation of information extraction techniques applied to the recognition of pharmacological substances and the detection of DDIs from biomedical texts. DDIExtraction 2013 has attracted wide attention with a total of 14 teams from 7 different countries. For the task of recognition and classification of pharmacological names, the best system achieved an F1 of 71.5%, while, for the detection and classification of DDIs, the best result was F1 of 65.1%. These results show that the corpus has enough quality to be used for training and testing NLP techniques applied to the field of Pharmacovigilance. The DDI corpus and the annotation guidelines are free for use for academic research and are available at http://labda.inf.uc3m.es/ddicorpus.

Abstract 349: Measurement of Platelet Cyclooxygenase-1 Acetylation Quantifies Interaction of Nonsteroidal Anti-Inflammatory Drugs with Aspirin in vivo

Aspirin prevents heart attack and stroke by irreversibly acetylating serine-529 in human platelet cyclooxygenase-1 (COX-1) and thereby inhibiting the biosynthesis of thromboxane (Tx). We hypothesized that precise quantification of COX-1 acetylation would allow the direct detection of drug-drug interactions between reversible COX inhibitors, nonsteroidal anti-inflammatory drugs (NSAIDs), and aspirin, which may abrogate its cardio-protective effect. We developed a stable isotope dilution liquid chromatography multiple reaction monitoring/mass spectrometry (LC-MRM/MS) method for measurement of platelet COX-1 acetylation. Serine-529 acetylation reflected faithfully aspirin pharmacology determined with conventional markers (serum TxB2 formation, urinary excretion of 11-dehydro-TxB2, and arachidonic acid induced platelet aggregation), but showed lower technical (CV% 3.7±2.7) and inter-individual variability (CV% 3.7). Oral administration of 325 mg of plain aspirin resulted in complete acetylation of platelet COX-1 within 2 hours and sustained maximal acetylation (71 ± 3% of total COX-1 amount) for up to two days in health volunteers (n=8). Recovery from acetylation, which reflects platelet turn-over, occurred at a rate of 7.2 ± 0.2 % per day. We compared the potential of five NSAIDs to block aspirin from acetylating platelet COX-1 in healthy volunteers (n=7 for each NSAID). Single doses of naproxen (500 mg), ibuprofen (600 mg), and diclofenac (100 mg) but not celecoxib (200 mg) and acetaminophen (1000 mg) prevented complete COX-1 acetylation by 325 mg aspirin administered 2 hours after the NSAID. The competition of NSAIDs with aspirin was conditioned by NSAID pharmacokinetics, potency, and COX isoform selectivity, suggesting variability in the likelihood of the drug-drug interaction between compounds and between patients. Monitoring platelet COX-1 acetylation may have utility in the identification of drug interactions that may limit the cardio-protective effect of low dose aspirin.

Detection of Drug-Drug Interactions by Modeling Interaction Profile Fingerprints

Drug-drug interactions (DDIs) constitute an important problem in postmarketing pharmacovigilance and in the development of new drugs. The effectiveness or toxicity of a medication could be affected by the co-administration of other drugs that share pharmacokinetic or pharmacodynamic pathways. For this reason, a great effort is being made to develop new methodologies to detect and assess DDIs. In this article, we present a novel method based on drug interaction profile fingerprints (IPFs) with successful application to DDI detection. IPFs were generated based on the DrugBank database, which provided 9,454 well-established DDIs as a primary source of interaction data. The model uses IPFs to measure the similarity of pairs of drugs and generates new putative DDIs from the non-intersecting interactions of a pair. We described as part of our analysis the pharmacological and biological effects associated with the putative interactions; for example, the interaction between haloperidol and dicyclomine can cause increased risk of psychosis and tardive dyskinesia. First, we evaluated the method through hold-out validation and then by using four independent test sets that did not overlap with DrugBank. Precision for the test sets ranged from 0.4–0.5 with more than two fold enrichment factor enhancement. In conclusion, we demonstrated the usefulness of the method in pharmacovigilance as a DDI predictor, and created a dataset of potential DDIs, highlighting the etiology or pharmacological effect of the DDI, and providing an exploratory tool to facilitate decision support in DDI detection and patient safety.

Potential Drug-drug Interactions in Post-CCU of a Teaching Hospital.

Drug-drug interactions (DDIs) can lead to increased toxicity or reduction in therapeutic efficacy. This study was designed to assess the incidence of potential drug interactions (PDI) and rank their clinical value in post coronary care unit (Post-CCU) of a teaching hospital in Tehran, Iran. In this prospective study, three pharmacists with supervision of a clinical pharmacist actively gathered necessary information for detection of DDIs. Data were tabulated according to the combinations of drugs in treatment chart. Verification of potential drug interactions was carried out using the online Lexi-Interact™ 2011. A total of 203 patients (113 males and 90 females) were enrolled in the study. The mean age of patients was 61 ± 12.55 years (range = 26-93). A total of 90 drugs were prescribed to 203 patients and most prescribed drugs were atorvastatin, clopidogrel and metoprolol. Mean of drugs was 11.22 per patient. A total of 3166 potential drug interactions have been identified by Lexi- Interact™, 149 (4.71%) and 55 (1.73%) of which were categorized as D and X, respectively. The most serious interactions were clopidogrel+omeprazole and metoprolol+salbutamol. Drug interactions leading to serious adverse effects are to be cautiously watched for when multiple drugs are used simultaneously. In settings with multiple drug use attendance of a pharmacist or clinical pharmacist, taking the responsibility for monitoring drug interactions and notifying the physician about potential problems could decrease the harm in patient and increase the patient safety.

Drug-drug interactions in patients admitted to an infectious diseases unit in a trauma hospital

BackgroundThe current complexity of pharmacotherapy in patients with orthopaedic infections increases the risk of drug-drug interactions (DDI).PurposeThe aim of this study is to identify potential DDI (severe/moderate) and its clinical...

A computerized system for detecting signals due to drug–drug interactions in spontaneous reporting systems

WHAT IS ALREADY KNOWN ABOUT THE SUBJECT: * Concomitant use of different drugs may yield excessive risk for adverse drug reactions and it is a challenging task to do surveillance on the safety profile of the interaction between different drugs. * Currently, several methods are used by pharmacoepidemiologists and statisticians to detect possible drug-drug interactions in spontaneous reporting systems. * However, with the increasing number of reports in the system, there is a growing need for a computerized system that could facilitate the process of data arrangement and detection of drug interaction. * We had already developed a computerized system to detect adverse drug reaction signals due to single drugs. * After the development of this system, interaction between different drugs could also be detected automatically and intelligently. In spontaneous reporting systems (SRS), there is a growing need for the automated detection of adverse drug reactions (ADRs) resulting from drug-drug interactions. In addition, special attention is also needed for systems facilitating automated data preprocessing. In our study, we set up a computerized system to signal possible drug-drug interactions by which data acquisition and signal detection could be carried out automatically and the process of data preprocessing could also be facilitated. This system was developed with Microsoft Visual Basic 6.0 and Microsoft Access was used as the database. Crude ADR reports submitted to Shanghai SRS from January 2007 to December 2008 were included in this study. The logistic regression method, the Omega shrinkage measure method, an additive model and a multiplicative model were used for automatic detection of drug-drug interactions where two drugs were used concomitantly. A total of 33 897 crude ADR reports were acquired from the SRS automatically. The 10 drug combinations most frequently reported were found and the 10 most suspicious drug-drug ADR combinations for each method were detected automatically after the performance of the system. Since the detection of drug-drug interaction depends upon the skills and memory of the professionals involved, is time consuming and the number of reports is increasing, this system might be a promising tool for the automated detection of possible drug-drug interactions in SRS.