Drug—drug interaction through molecular structure similarity analysis

Abstract

Background Drugedrug interactions (DDIs) are responsible for many serious adverse events; their detection is crucial for patient safety but is very challenging. Currently, the US Food and Drug Administration and pharmaceutical companies are showing great interest in the development of improved tools for identifying DDIs. Methods We present a new methodology applicable on a large scale that identifies novel DDIs based on molecular structural similarity to drugs involved in established DDIs. The underlying assumption is 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. DrugBank was used as a resource for collecting 9454 established DDIs. The structural similarity of all pairs of drugs in DrugBank was computed to identify DDI candidates. Results The methodology was evaluated using as a gold standard the interactions retrieved from the initial DrugBank database. Results demonstrated an overall sensitivity of 0.68, specificity of 0.96, and precision of 0.26. Additionally, the methodology was also evaluated in an independent test using the Micromedex/Drugdex database. Conclusion The proposed methodology is simple, efficient, allows the investigation of large numbers of drugs, and helps highlight the etiology of DDI. A database of 58 403 predicted DDIs with structural evidence is provided as an open resource for investigators seeking to analyze DDIs.

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

“…By using the knowledge of known interactions with structural similarity, it is possible to identify new DDIs [6] [26]. In simple terms, structural similarity is based on the basic idea that if drug A interacts with drug B, and drug C is structurally similar to A, then C should also interact with B (the argument also follows if A is replaced with B).…”

“…In simple terms, structural similarity is based on the basic idea that if drug A interacts with drug B, and drug C is structurally similar to A, then C should also interact with B (the argument also follows if A is replaced with B). The identification of structural similarity is a three-step process: (1) collecting and processing drug structures, (2) structural representation and similarity measures, computation, and (3) data representation respectively [6]. Then these structural similarity calculations are used along with the established drug interactions to predict novel DDIs.…”

“…Fig. 3 Steps to predicting DDIs [6] Figure 1: Process of predicting DDIs is a combined result of the DrugBank interaction database 2011 snapshot and target similarity score / P-score. In step 1, interaction matrix M1 was created.…”

“…July 2018 information about possible molecular structural similarity [6] and interaction profile fingerprints (IPFs) [5]. The proposed method could also be combined with other methodologies using different types of information, such as the Food and Drug Administration's Adverse Event Reporting System [2][11] [13], which was created to provide post-marketing drug safety information, or the use of clinical data in electronic health records [4].…”

“…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].…”