Detection of drug-drug interactions by modeling interaction profile fingerprints

Detection of Drug-Drug Interactions by Modeling Interaction Profile Fingerprints Santiago Vilar 1,2 *, Eugenio Uriarte 2 , Lourdes Santana 2 , Nicholas P. Tatonetti 1 , Carol Friedman 1 1Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, United States of America, 2Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Santiago de Compostela, Spain Abstract 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. Citation: Vilar S, Uriarte E, Santana L, Tatonetti NP, Friedman C (2013) Detection of Drug-Drug Interactions by Modeling Interaction Profile Fingerprints. PLoS ONE 8(3): e58321. doi:10.1371/journal.pone.0058321 Editor: Paul Taylor, University of Edinburgh, United Kingdom Received October 25, 2012; Accepted February 1, 2013; Published March 8, 2013 Copyright: ß2013 Vilar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by grants R01 LM010016 (CF), R01 LM010016-0S1 (CF), R01 LM010016-0S2 (CF), R01 LM008635 (CF), ‘‘Plan Galego de Investigacio ´n, Innovacio ´n e Crece-mento 2011–2015 (I2C)’’, European Social Fund (ESF) and Angeles Alvarin ˜o program from Xunta de Galicia (Spain). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected]olumbia.edu Introduction Drug-drug interactions (DDIs) are a major cause of morbidity worldwide and a leading source of treatment inefficacy. For this reason, DDIs cause great concern in patient safety and pharmacovigilance. Adverse drug events (ADEs) may occur when drug combinations target shared metabolical and pharmacological pathways altering the efficacy and safety profile of the drugs. Potential DDIs are evaluated for experimental drugs pre-clinically during development and then monitored by drug safety surveillance programs after they enter the marketplace. The development of predictive tools to help study possible DDIs is of great interest to pharmaceutical companies and regulatory authorities, such as the United States Food and Drug Administration (FDA) [1]. These organizations are interested in better methods to detect and assess drug interactions [2]. Depending on the seriousness of the DDI, different measures are carried out ranging from the introduction of warnings in drug labels to the withdrawal of drugs from the market. As an example, in August 2008 the FDA [1] issued a warning about the possibility of developing rhabdomyolysis, a condition related to severe muscle injury, through combination treatment with simvastatin and amiodarone. In contrast, mibefradil, a calcium channel blocker approved by the FDA [1] in June 1997, was withdrawn from the market shortly after due to potential harmful interactions with drugs that prolong the QT interval [3]. In previous work, we proposed a method that used the DDI DrugBank database along with molecular similarity for detecting DDIs [4]. Medicinal chemistry researchers have exploited the concept of molecular similarity for years [5–12], where the basic idea is that ‘structurally similar molecules are likely to have similar biological properties’. Molecular fingerprints, digital representation of chemical features, are useful representations for comparing the structural similarity between compounds [10– 13]. The basic idea in the development of a molecular fingerprint is to represent molecules through a vector that codifies in different positions the presence/absence of structural features. However, fingerprints could be designed to codify not only molecular structure information but also different biological properties. Following the concept of predictive models based on adverse drug event profiles [14–15] and comparing drug pairs through molecular fingerprints [12], we developed a model to predict DDIs based on the comparison of, what we call, an interaction profile fingerprint (IPF). The IPF codifies the known interaction partners of a given drug as a binary vector of 19s and 09s. Two different interaction fingerprints can be compared using the Tanimoto coefficient (TC), a general method for comparing the similarity of two sets [16]. Our motivating hypothesis is as PLOS ONE | www.plosone.org 1 March 2013 | Volume 8 | Issue 3 | e58321 follows: if drug iand drug jare similar according to their interaction fingerprints, then drug iwill interact with the same drugs as drug jwith a probability related to the similarity of their fingerprints and vice versa. Figure 1 shows how the interactions of two drugs, oxybutynin and dicyclomine, are transformed into vectors, which are fingerprints, and then compared using the TC. The drugs associated with the nonintersecting interactions are predicted to participate in interactions with a probability proportional to the TC score (see Figure 1). For example, we predict carbamazepine interactions with dicyclomine with a probability proportional to 0.78 (Figure 1). The model we developed combines the interaction profile similarity information using the DDIs specified in DrugBank to obtain new DDIs, but data from other sources could also be used. The model results were validated using Drugs.com [17] and Drugdex [18] databases as reference standards. We provided in the Table S1 of the Supporting Information a database with 17,230 DDI candidates predicted by the model along with the possible biological effects. Methods Generation of the Established Drug-drug Interaction (DDI) Database (Matrix M 1 ) We collected the database from DrugBank [19] in a previous publication [4]. Only small approved drugs, not including proteins and peptides, were introduced in the previous model resulting in DDI information for 928 drugs and a set of 9,454 unique DDIs. Although we used the same dataset in the current article, improvements through future updates in the DrugBank database or the use of other important sources of DDIs, such as Drugs.com database, could be beneficial. This step would require an overall recalculation of the interaction profiles. We transformed the set of collected DDIs into a 9286928 binary matrix M 1 with value of 1 representing an interaction between two drugs and value of 0 representing no interaction. The model included information about the pharmacological effect of the interaction associated with pairs of drugs as part of the process (e.g, the entry in DrugBank for the DDI between oxybutynin and triprolidine is: two anticholinergics may cause additive anticholinergic effects and enhance their adverse/toxic effects). Figure 1. Examples of interaction profile fingerprints (IPFs) calculated for the drugs oxybutynin and dicyclomine. The similarity of both fingerprints is measured through the TC coefficient. The drugs corresponding to the non-intersecting interactions for the pair are assigned the TC score and form part of the prediction of the model. The effect associated by the interaction is the same as the original interaction source that generated the prediction. doi:10.1371/journal.pone.0058321.g001 Drug-Drug Interactions Using Interaction Profile PLOS ONE | www.plosone.org 2 March 2013 | Volume 8 | Issue 3 | e58321 Generation of the Interaction Profile Similarity Matrix M 2 The interaction profile similarity matrix M 2 is calculated in three steps: Interaction profile fingerprints (IPFs) calculation. We represented all the drugs included in the study by IPFs. The concept of IPFs is similar to molecular structure fingerprints [16,20]. The basic idea in IPFs is to represent the drug interactions for a particular drug as a vector codifying the presence of interactions in specific positions. As an example, in Figure 1 the interactions between oxybutynin and all other drugs are codified as different vector positions (33, 46, 103, 202, 223, 414, 645, 725). Only the positions whose value is 1 are stored in vector-position notations. This is a very efficient way to represent a sparse binary matrix. The same process is carried out for the drug dicyclomine that shares 7 out of 9 unique interactions with oxybutynin (46, 103, 150, 202, 223, 414, 645, 725). The transformation of the molecules into IPFs facilitates comparison. Computation of similarity between fingerprints. We used the Tanimoto coefficient [16], also known as the Jaccard index, to compute similarities between all the IPFs. The TC between two fingerprints A and B is defined as the ratio between the number of features/interactions in the intersection to the union of both fingerprints: TC(A,B)~DA\BD=DA|BD Construction of the matrix M 2 .We created a matrix so that the rows and columns represent drugs and each cell represents the interaction profile similarity based on the TC between the corresponding pair of drugs. We computed this matrix using the MOE software [21]. Prediction of New DDIs (Matrix M 3 ) To calculate the matrix M 3 with new predicted interactions, we multiplied the matrix M 1 (Established DDI database matrix) by the matrix M 2 (Interaction profile similarity matrix) (see Figure 2). It is worth noting that the values in the diagonal of the matrices M 2 and M 3 are 0 since the interaction of a drug with itself is not taken into account. Although the model could generate multiple scores for the same interaction based on similarities from different pairs, we only considered the predicted interaction with the highest TC value. For this reason, in each cell of the product of the matrices, only the highest value in the array-multiplication is retained (see Figure 2). We transformed the resulting matrix into the symmetric matrix M 3 considering the highest value (TC) for each pair of drugs. A set of new predicted DDIs are then generated from M 3, and the biological effect provided by the initial DDI source in M 1 is captured and associated to the new DDIs. As an example, Figure 1 shows how we used a known interaction between haloperidol and dicyclomine to predict an interaction between haloperidol and oxybutynin. In addition, we assigned the biological effect of the known interaction ‘‘Increased risk of psychosis and tardive dyskinesia’’ to the predicted interaction. Evaluation Hold-out validation. We divided the database randomly in two sets: training and test sets. We performed two evaluations by moving 15% and 30% of the initial interactions to the test set, and by constructing the model with the remaining interactions in the new matrices M 1 and M 2 . To evaluate the performance in the training and test sets, we plotted the Receiver Operating Characteristic (ROC) curves and used the area under this curve (AUROC) as a summary statistic. Test evaluation. For the assessment of the performance of the model we used four different independent test sets, which do not contain any interactions from the initial DDI database M 1 :A) the top 100 DDIs generated by the model according to the TC value, B) a random set of 100 drug interactions with a TC$0.7, C) a random set of 100 drug interactions with a TC$0.4, and D) the interactions generated by the model with a TC$0.4 for the 50 most frequently sold drugs in 2010 [22]. We used the Interaction Checker from Drugs.com [17] and Drugdex (Micromedex) database [18] as a reference standard to determine the number of interactions that were correctly predicted. The level of documentation in the reference standard ranges from ‘interactions clearly established through controlled studies’ to ‘limited studies but the interactions are recognized through pharmacological knowledge’. We calculated precision and enrichment factor compared to random selection (see formulas in Table 1) for the four sets as measurements of the performance. In addition, in order to provide more information, we plotted a Receiver Operating Characteristic (ROC) curve for test set D. The predicted biological/pharmacological effect associated with the DDIs was also assessed based on the Drugs.com and Drugdex databases. Random results evaluation. The results obtained by the model were compared to random expectations. We created a random system taking into account the list of 50 most frequently sold drugs in 2010. The top 50 list included 50 generic drug names but we only included 41 generic names in the system. Nine of these drugs are not represented in the DrugBank DDI database. We cannot generate interaction predictions for these drugs so we removed them from consideration. These drugs were mometasone, ezetimibe, ferrous fumarate, naloxone, sitagliptin, latanoprost, insulin glargine, insulin aspart, and omega-3-acid ethyl esters. The number of possible interactions for 41 drugs in a matrix of 928 drugs is 37,187 (927 factorial 41 times). We estimated the number of positive cases as 7,068 interactions found in Drugs.com and/or Drugdex and used a one-sided Fisher’s exact test to calculate significance (p-value). Results We combined similarity information by means of interaction profile fingerprint-based modeling with the initial database containing 928 drugs and 9,454 DDIs, as described in the Methods section. The final model generated a matrix of 430,128 DDI scores. Among these interactions are the initial 9,454 DrugBank DDIs used to develop the model. We evaluated the performance of the model through hold-out validation and external test series. Hold-out Validation Model Performance We performed two different evaluations by dividing the initial database into training and testing subsets. In the first we moved 15% of the interactions from the training to the testing set and in the second we moved 30%. Using DrugBank DDIs as true positives we plotted ROC curves and computed the area under the curve (AUROC). We found an AUROC = 0.967 for the 15% hold-out and AUROC = 0.963 for the 30% hold-out (Figure 3). The stability of the model is barely affected even when we removed twice as many interactions. However, high performance in these sets is expected since the similarity matrix was generated using drug interaction profile information where the drugs and Drug-Drug Interactions Using Interaction Profile PLOS ONE | www.plosone.org 3 March 2013 | Volume 8 | Issue 3 | e58321 Figure 2. The model generates interactions through the multiplication of the matrix M 1 (Established DDI matrix) by the matrix M 2 (Interaction profile similarity matrix. Note that each cell shows the TC between drugs A, B and C but interactions with more drugs are considered to calculate the TC value). The values in the diagonal of the matrices are set 0 since drug interactions with themselves are not taken into account. In the final matrix M 3 only the maximum value in the multiplication-array in each cell is preserved and a symmetry-based transformation is Drug-Drug Interactions Using Interaction Profile PLOS ONE | www.plosone.org 4 March 2013 | Volume 8 | Issue 3 | e58321 interactions in DrugBank have been specified and form a closed system. As an example, the interactions retrieved for two drugs that share all the interactions in the initial database have the maximum score (TC = 1). For this reason, further evaluation of the model using independent test sets with no interactions previously collected in the initial DDI database is a necessary step to prove the prediction power. Test Set Model Performance Test set A. The TOP 100 drug interactions predicted by the model (TC_threshold = 0.92). We found 50 out of 100 interactions in Drugs.com and/or Drugdex (Micromedex) databases. The precision of this external set is 0.50. Random expectations (see Methods section) selecting 100 interactions in a set of 37,187 possible interactions where there are 7,068 positive cases, would detect 19 positive cases (random precision = 0.19). The performance of the model showed a 2.6 fold (p,0.001) enrichment factor (see Table 1 and Table S2). Test set B. Random set of predicted 100 drug interactions with a TC $ 0.7. We found similar results for the second independent test where 43 out of 100 random interactions with a TC$0.7 were in the reference standard. The precision in the second test set is 0.43 and the enrichment factor 2.3 (p,0.001) (see Table 1 and Table S3). Test set C. Random set of predicted 100 drug interactions with a TC $ 0.4. In the evaluation of the third test, we detected in our reference standard 45 out of 100 random interactions with a TC$0.4 (see Table 1 and Table S4 for more details). Test set D. Interactions predicted for the 50 most frequently sold drugs in 2010. 46% of the generated interactions with TC$0.4 were confirmed in the reference standard. The model presents an enrichment factor of 2.4 (p,0.001) (see Table 1 and Table S5 for a detailed description of the evaluation). In addition, we plotted the ROC curve taking into account as true positives all the interactions in the set confirmed in drugs.com/drugdex (see Figure 4a). The area under the curve is 0.69. Figure 5 shows the enrichment factor and precision achieved by the model for each drug. Out of the 50 drugs, we included 41 in the evaluation. Nine drugs were not taken into account because they were not included in our initial DrugBank DDI database and the model could not predict any interaction. Our method outperforms other commonly used approaches. A method recently published by our research group based on molecular structure similarity [4] showed less predictive capacity (AUROC = 0.668) compared to our model (AUROC = 0.687) when applied to the test set D (see Figure 4). In addition we tested if our model could predict pharmacodynamic interactions as well as pharmacokinetic. Using DrugBank annotations, we identified and removed any interactions between drugs with shared metabolism by a cytochrome p450 (CYP) metabolizing enzyme (1A2, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, 3A4, 3A5 and 3A7) [19]. 14,242 interactions in the test set D included in the CYP list were removed. We found that our approach performed nearly as well (AUROC = 0.674), but that the performance of the molecular structure based approach performed was reduced by 3% (AUROC = 0.636) (Figure 4). Pharmacological Effect Prediction A pharmacology expert manually reviewed and compared the pharmacological effect described in the predicted interactions for the test sets to the effect found in Drugs.com and Drugdex databases. The interactions predicted by the model belong to two categories: some are generated comparing interaction profiles of pairs of drugs in the same pharmacological class, whereas the origin of other interactions resides in the comparison of the profile fingerprints of pairs of drugs that are not in the same class. For the test set A with the top 100 interactions, 43 out of 50 true interactions (86%) were confirmed to have the same effect as the described in our reference standard (see Table S2). We found a similar result for the test set B (100 interactions with TC$0.7) where the effect in 36 out of 43 confirmed interactions (84%) was considered correct (see Table S3 for a detailed description). For these test sets, the model generated the majority of the reviewed interactions through the comparison of pairs of drugs catalogued in the same or similar pharmacological class (48 out of 50 and 38 out of 43 for test A and B respectively). As the TC values decrease so does our confidence in the predicted effect as these predictions result from comparing pairs of drugs with different pharmacological profiles. In test set C with TC$0.4, the pharmacological effect was correct for the 66.7% of the interactions, i.e. 30 out of the 45 interactions found in the reference standard (see Table S4). For the last test set D, we carried out a more challenging evaluation and only the effect of the interactions generated through the comparison of pairs of drugs belonging to different pharmacological classes was evaluated. Out of the 640 correct DDIs predicted by the model for test set D, 215 were from comparing drugs belonging to different pharmacological classes. We reviewed the pharmacological effect for this set of 215 predicted interactions carried out retaining the highest TC value. In the example, the initial interactions A–B and A–C (red color) have a TC score of 0.9 in the matrix M 3 . The system generated a new predicted interaction between B and C with a TC score of 0.8 (green color). doi:10.1371/journal.pone.0058321.g002 Table 1. Model performance in the four independent test sets A, B, C and D along with random results. Test set model performance Set A: TOP 100 predicted interactions according to TC value TP FP Precision EF p-value 50 50 0.50 2.63 ,.001 Set B: 100 predicted interactions randomly selected with TC$0.7 TP FP Precision EF p-value 43 57 0.43 2.26 ,.001 Set C: 100 predicted interactions randomly selected with TC$0.4 TP FP Precision EF p-value 45 55 0.45 2.37 ,.001 Set D: Predicted interactions with TC$0.4 for the TOP 50 drugs sold in 2010 TP FP Precision EF p-value 640 744 0.46 2.43 ,.001 Random system calculated for the TOP 50 drugs sold in 2010 TP FP Precision – – 19 81 0.19 TP = True positives, FP = False positives, Precision = TP/(TP+FP), EF(Enrichment factor)~ Ncorrect interactions in test set N interactions in test set =N correct interactions in random set N interactions in random set doi:10.1371/journal.pone.0058321.t001 Drug-Drug Interactions Using Interaction Profile PLOS ONE | www.plosone.org 5 March 2013 | Volume 8 | Issue 3 | e58321 showing a percentage of correct classification of 59% (the effect was correct in 126 out of 215 cases). Table 2 describes some example predictions from the test dataset D (detailed description is provided in Table S6) for which the model correctly detected interactions comparing drugs of different pharmacological classes as well as the effect produced by these interactions. For instance, the model detected that amoxapine, a tetracyclic antidepressant of the dibenzoxazepine family, has some similarity with the interaction profile of the antibiotic linezolid (TC = 0.40), and for this reason the model predicted the interaction escitalopram-amoxapine with a possible serotoninergic syndrome. The model also predicted that levofloxacin could interact with propafenone, fluconazole, ibutilide, ranolazine, saquinavir and telithromycin with risk of cardiotoxicity and arrhythmias (see Table 2 and S6). The interactions were corroborated in Drugs.com database with a similar effect. We predicted other combinations, such as atazanavir-salmeterol, to cause cardiotoxicity and arrhythmias. The model predicted possible hypertensive crisis with the combination methylphenidate and linezolid. The system generated the interaction because linezolid has a similar interaction profile as the monoamine oxidase inhibitor rasagiline (TC = 0.52) and the interaction methylphenidate-rasagiline was included in the initial database. Among other examples, we also detected that the antidiabetic pioglitazone could interact with the macrolide antibiotic clarithromycin, and with the anti-HIV drugs indinavir and nelfinavir producing and increased effect of pioglitazone (see Table 2 and S6). Although all the new DDIs generated by the model have corresponding predicted biological effects, it is important to take into account that as the TC value associated with the new interaction decreases so does the certainty of the associated effect. Discussion The desirable and undesirable drug effects in patients are highly dependent on pharmacokinetic properties, such as absorption, distribution, metabolism and excretion (ADME), and pharmaco- Figure 3. ROC curves in the hold-out validation process: a) training set with the 85% of the DrugBank interactions; b) test set with the 15% of the extracted DrugBank interactions; c) training set with the 70% of the DrugBank interactions; d) test set with the 30% of the extracted DrugBank interactions. doi:10.1371/journal.pone.0058321.g003 Drug-Drug Interactions Using Interaction Profile PLOS ONE | www.plosone.org 6 March 2013 | Volume 8 | Issue 3 | e58321 dynamic properties, such as interaction with pharmacological targets. These important processes can be altered by the coadministration of different drugs at the same time. For this reason, drug interactions are an important problem in the surveillance of approved drugs and in the evaluation and development of new drug candidates. The FDA has shown its concerns to address this issue, and provides guidance to perform in vitro and in vivo drug interactions studies during the developmental stage of new drugs [2,23–24]. A great effort has been made to develop in silico approaches, focused on the integration of in vitro data, to predict in vivo drug interactions [23,25]. These models principally focused on metabolic interactions related to CYP enzymes. Other types of computational models to predict affinity for CYP enzymes based on molecular descriptors have also been developed [26]. Although many interactions are produced by the inhibition of metabolizing enzymes, there are also other possible mechanisms, such as interactions with transporters or pharmacological targets. Systems to further analyze pharmacodynamics interactions in vivo have been also described [27]. Other approaches to predict different types of DDIs have been recently published [28–29]; some of them take into account algorithms to detect interactions in adverse event reports [30], or text mining methods [31]. Our group has also recently described a large-scale DDI predictor based on molecular structure similarity to drug pairs [4]. Gottlieb et al. [32] have recently published a similar interesting large-scale approach to predict pharmacokinetic and pharmacodynamic DDIs. The authors used the concept of similarity to drug pairs, including different measurements, such as chemical structure, drug targets and side effect similarities, to infer new DDIs in a complex system with excellent results. In this article, we developed a novel drug fingerprint based on drug interactions profile with successful application to DDI prediction and pharmacovigilance. Through the inclusion of interaction profile fingerprint-based similarity to the initial wellestablished DDI database, we constructed a large-scale drug Figure 4. ROC curves for test set D: a) ROC curve generated by the IPF model for test set D. Interactions for the top 50 drugs (41 generic names) confirmed in drugs.com/drugdex were considered as true positives within all the possible interactions in a matrix of 416928 drugs. Interactions already in the initial DrugBank DDI database (matrix M 1 ) were not included in the analysis; b) ROC showed by a model applied to test D using MACCS fingerprints; c) ROC curve calculated by the IPF model for test set D but excluding CYP interactions; d) ROC showed by the MACCS fingerprints model applied to the test D without CYP interactions. doi:10.1371/journal.pone.0058321.g004 Drug-Drug Interactions Using Interaction Profile PLOS ONE | www.plosone.org 7 March 2013 | Volume 8 | Issue 3 | e58321 Figure 5. Enrichment factor (a) and precision (b) achieved by the model regarding random results for top drugs sold in 2010 (test set D). The test set of drugs are sorted according to the enrichment factor. doi:10.1371/journal.pone.0058321.g005 Drug-Drug Interactions Using Interaction Profile PLOS ONE | www.plosone.org 8 March 2013 | Volume 8 | Issue 3 | e58321 interaction predictor taking into account different pharmacological effects caused by pharmacokinetic and pharmacodynamic characteristics of the drugs implicated in the interaction. The model generated some predicted interactions comparing the interaction profiles of pairs of drugs in the same pharmacological class, whereas a more challenging task is carried out when the interactions are generated comparing drugs belonging to different classes. The dataset of DDI candidates is available in the Table S1 of the Supporting Information for further study. The aim of the model is to detect interactions when two drugs are implicated and does not account for co-DDIs or secondary interactions due to primary interactions. The development of a more complex and challenging model would be necessary to address this issue. Information about concentration of the drugs and environmental variables are not included in the model either. However, implicit bioavailability information has been incorporated since our initial DDI database contains examples where two drugs share the same metabolizing enzymes causing a higher bioavailable doses for one of the drugs implicated in the interaction. Targets and drug promiscuity data was not directly introduced although implicit target information is taken into account since pharmacodynamic interactions were included in the system. As an example, Figure 4c has shown that model performance was not affected after eliminating possible CYP-related DDIs. Nevertheless, enhancement in our DDI system could also be achieved through the integration of metabolizing, transporters and pharmacological targets information provided by chemical databases such as PubChem [33]. Pharmacovigilance databases, such as the FDA’s Adverse Event Reporting System (AERS) [34], or the use of clinical data in Electronic Health Records (EHR) [35] could be also combined to further study possible DDI candidates. Other types of models introducing 2D or 3D molecular structure data could be integrated in our system. The information provided by molecular structure can be different or complementary to IPF fingerprint data. We computed the correlation coefficient between the TC for all the pairs of drugs in the study Table 2. Some examples of correct interactions predicted for the 50 most frequently sold drugs in 2010 in which the model generated interactions through the comparison of drugs belonging to different pharmacological classes. Similar drug to A 1 Predicted interaction DrugA-DrugB Similar drug to B 1 TC Predicted effect Aripiprazole-Nelfinavir Itraconazole 0.55 Increased effect of aripiprazole Aripiprazole-Atazanavir Ketoconazole 0.45 Increased effect of aripiprazole Alprazolam Atorvastatin-Digoxin 0.40 Increased effect of digoxin Midazolam Atorvastatin-Omeprazole 0.51 Increased effect of atorvastatin Atorvastatin-Miconazole Imatinib 0.43 Increased effect and toxicity of atorvastatin Buprenorphine-Trospium Triprolidine 0.43 Possible increase adverse/toxic effects due to additivity Buprenorphine-Trimethobenzamide Triprolidine 0.40 Possible increase adverse/toxic effects due to additivity Felodipine Conjugated_Estrogens-Oxcarbazepine 0.51 Decreased levels of estrogens Gefitinib Conjugated_Estrogens-Clarithromycin 0.47 Increased levels/toxicity of estrogens Nifedipine Conjugated_Estrogens-Cimetidine 0.40 Increased the effect of estrogens Duloxetine-Tolterodine Tamsulosin 0.59 Possible decreased metabolism and clearance of Tolterodine. Changes in therapeutic/adverse effects of Tolterodine Duloxetine-Trimethobenzamide Triprolidine 0.40 Possible increase adverse/toxic effects due to additivity Duloxetine-Sibutramine Zolmitriptan 0.53 Increased risk of serotonin syndrome Escitalopram-Amoxapine Linezolid 0.40 Possible serotoninergic syndrome Eszopiclone-Trimethobenzamide Triprolidine 0.40 Possible increased adverse/toxic effects due to additivity Ethinyl_Estradiol-Trimipramine Tacrolimus 0.43 Possible increased blood concentration of Trimipramine Cisapride Levofloxacin-Propafenone 0.44 Increased risk of cardiotoxicity and arrhytmias Methylphenidate-Linezolid Rasagiline 0.52 Possible hypertensive crisis with this combination Gefitinib Norethindrone-Voriconazole 0.48 Possible increased serum concentration of norethindrone. Changes in the therapeutic and adverse effects Oxycodone-Trospium Triprolidine 0.43 Possible increased adverse/toxic effects due to additivity Pioglitazone-Nelfinavir Ketoconazole 0.51 Increased the effect of pioglitazone Dihydro ergotoxine Salmeterol-Delavirdine 0.48 Increase salmeterol toxicity Lidocaine Salmeterol-Atazanavir 0.56 Increased risk of cardiotoxicity and arrhythmias Sildenafil-Clonidine Terazosin 0.47 Increased risk of hypotension Simvastatin-Conivaptan Imatinib 0.43 Increased effect and toxicity of statin Bromazepam Tadalafil-Rifabutin 0.50 Possible decreased serum concentration of Tadalafil. Changes in the therapeutic and adverse effects Tadalafil Zolpidem-Doxazosin 0.55 Risk of significant hypotension with this association TC is the Tanimoto coefficient. 1 The similarity between drugs is based on the drug-drug interaction profile. doi:10.1371/journal.pone.0058321.t002 Drug-Drug Interactions Using Interaction Profile PLOS ONE | www.plosone.org 9 March 2013 | Volume 8 | Issue 3 | e58321