INDI: a computational framework for inferring drug interactions and their associated recommendations

Gottlieb, Assaf; Stein, Gideon Y.; Oron, Yoram; Ruppin, Eytan; Sharan, Roded

doi:10.1038/msb.2012.26

Cited by 220 publications

(213 citation statements)

References 62 publications

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“…Another method of detecting DDIs is by using the Inferring Drug Interactions (INDI) [12]. INDI algorithm is designed based on two main objectives (1) predicting both new CYP-related DDIs and non-CYP-related DDIs and (2) developing a general strategy that allows predicting interactions of novel drugs for which no interaction information is currently available.…”

Section: E Inferring Drug Interactionsmentioning

confidence: 99%

“…The similarity based approaches are focused on the pre-market stage and the initial part of the post-market stage is in the drug development process. However, both techniques suffer from several limitations such as the necessity to distinguish drug classes and inability to handle novel drugs for which limited reports exist [12].…”

Section: B Roc Curvesmentioning

confidence: 99%

“…Fig. 2 Steps of INDI Algorithm [12] III. METHODOLOGY The methodology of this study consists of seven major steps: data acquisition, pre-processing, graph construction, identify similar drug pairs, predicting new DDIs, construct resulting matrix and output of final network.…”

Section: F Molecular Structure Similaritymentioning

confidence: 99%

“…Among these measures, the last three measures are gene related. There are various researches which were being carried out to retrieve the drug similarity information such as; utilizing multiple drug-drug similarity measures to predict DDI: Inferring Drug Interactions (INDI) [12], target overlap [16], S-score [3], Pscore [17], target distance, C-score [18], Indication overlap [19] and the molecular structural similarity [6]. Among these approaches, target based methods (target overlap, target distance and S-score) are comparatively better than using indication overlap and the C-score methods [3].…”

Section: Similarity Based Approachesmentioning

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: E Inferring Drug Interactionsmentioning

confidence: 99%

Section: B Roc Curvesmentioning

confidence: 99%

Section: F Molecular Structure Similaritymentioning

confidence: 99%

Section: Similarity Based Approachesmentioning

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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“…With the attention on translational research in recent years, a new set of computational methods are being developed which examine drug-target associations and drug off-target effects through system and network approaches. These new approaches take advantage of the unprecedented large-scale highthroughput measurements, such as drug chemical structures and screens 3,4 , side effect profiles 5,6 , transcriptional responses after drug treatment 7,8 , genome wide association studies 9 , and combined knowledge 10,11 . More importantly there are increasing reports of these findings being validated in experimental models 5,7,12 , thus clarifying the value proposition for computational drug discovery.…”

mentioning

confidence: 99%

Computational Drug Repositioning

Agarwal

Butte

2012

Biocomputing 2013

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Despite increasing investments in pharmaceutical R&D, there is a continuing paucity of new drug approvals. Drug discovery continues to be a lengthy and resource-consuming process in spite of all the advances in genomics, life sciences, and technology. Indeed, it is estimated that about 90% of the drugs fail during development in phase 1 clinical trials 1 and that it takes billions of dollars in investment and an average of 15 years to bring a new drug to the market 2 .Meanwhile, there is an ever-growing effort to apply computational power to improve the effectiveness and efficiency of drug discovery. Traditional computational methods in drug discovery were focused on understanding which proteins could make good drug targets, sequence analysis, modeling drugs binding to proteins, and the analysis of biological data. With the attention on translational research in recent years, a new set of computational methods are being developed which examine drug-target associations and drug off-target effects through system and network approaches. These new approaches take advantage of the unprecedented large-scale highthroughput measurements, such as drug chemical structures and screens 3, 4 , side effect profiles 5,6 , transcriptional responses after drug treatment 7,8 , genome wide association studies 9 , and combined knowledge 10, 11 . More importantly there are increasing reports of these findings being validated in experimental models 5,7,12 , thus clarifying the value proposition for computational drug discovery. As a result, now is an exciting time for computational scientists to gain evidence for reusing an existing drug for a different use or generate testable hypotheses for further screening 13 .Despite the progress, there is clearly room for technical improvement with regard to computational repurposing approaches. Furthermore, to materialize the true potential and impact of these †

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An AI‐based Prediction Model for Drug‐drug Interactions in Osteoporosis and Paget's Diseases from SMILES

Hung

et al. 2022

Molecular Informatics

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The skeleton is one of the most important organs in the human body in assisting our motion and activities; however, bone density attenuates gradually as we age. Among common bone diseases are osteoporosis and Paget's, two of the most frequently found diseases in the elderly. Nowadays, a combination of multiple drugs is the optimal therapy to decelerate osteoporosis and Paget's pathologic process, which comes with various underlying adverse effects due to drug‐drug interactions (DDIs). Artificial intelligence (AI) has the potential to evaluate the interaction, pharmacodynamics, and possible side effects between drugs. In this research, we created an AI‐based machine‐learning model to predict the outcomes of interactions between drugs used for osteoporosis and Paget's treatment, which helps mitigate the cost and time to implement the best combination of medications in clinical practice. In this study, a DDI dataset was collected from the DrugBank database within the osteoporosis and Paget diseases. We then extracted a variety of chemical features from the simplified molecular‐input line‐entry system (SMILES) of defined drug pairs that interact with each other. Finally, machine‐learning algorithms were implemented to learn the extracted features. Our stack ensemble model from Random Forest and XGBoost reached an average accuracy of 74 % in predicting DDIs. It was superior to individual models as well as previous methods in terms of most measurement metrics. This study showed the potential of AI models in predicting DDIs of Osteoporosis‐Paget's disease in particular, and other diseases in general.

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INDI: a computational framework for inferring drug interactions and their associated recommendations

Abstract: INDI is a similarity-based drug–drug interaction prediction method that can infer both pharmacokinetic and pharmacodynamic interactions, as well as their severity levels. Both known and predicted drug interactions are found to be prevalent in clinical practice.

Cited by 220 publications

References 62 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

Computational Drug Repositioning

An AI‐based Prediction Model for Drug‐drug Interactions in Osteoporosis and Paget's Diseases from SMILES

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