Prediction of Drug Combinations by Integrating Molecular and Pharmacological Data

Zhao, Xing‐Ming; Iskar, Murat; Zeller, Georg; Kuhn, Michael; Noort, Vera van; Bork, Peer

doi:10.1371/journal.pcbi.1002323

Cited by 185 publications

(195 citation statements)

References 34 publications

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“…AstraZeneca-Sanger Drug Combination DREAM Challenge was launched using 85 cancer cell lines and 11,759 drug combination screening for 118 drugs [8]. The predictive models were designed to differentiate synergistic, additive and antagonistic combinations and predict new synergistic combinations in silico [9][10][11][12][13][14][15][16][17].…”

Section: Mathematical Optimization Methodsmentioning

confidence: 99%

See 1 more Smart Citation

In-Silico Methodologies for Cancer Multidrug Optimization

Hasan¹,

Eldin

Khedr

et al. 2018

IJCT

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Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic were found out through experience. The molecular mechanisms underlying these drug combinations are often not clear. It is not easy to suggest new drug combinations. Computational approaches are proposed to reduce the search space for defining the most promising combinations and prioritizing their experimental evaluation. In this paper, we review methods, techniques and hypotheses developed for in silico methodologies for drug combination discovery in cancer and discuss the limitations and challenges of these methods.

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Section: Mathematical Optimization Methodsmentioning

confidence: 99%

“…Zhao et al [17] designed a set of predictive features to predict novel drug combinations. The features include target proteins and corresponding downstream pathways, medical indication areas, therapeutic effects as represented in the Anatomical Therapeutic Chemical (ATC) Classification System and side effects.…”

Section: Supervised Methodsmentioning

confidence: 99%

In-Silico Methodologies for Cancer Multidrug Optimization

Hasan¹,

Eldin

Khedr

et al. 2018

IJCT

View full text Add to dashboard Cite

show abstract

“…Molecular structure similarity is a technique which is used to identify similar drug pairs by considering the structural similarity of the drugs [24] [25]. By using the knowledge of known interactions with structural similarity, it is possible to identify new DDIs [6] [26].…”

Section: F Molecular Structure Similaritymentioning

confidence: 99%

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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“…Models predicting polypharmacology by examining binding site similarity and conserved motifs in protein-ligand binding and protein-protein interfaces have been used [133]. Systems biology efforts have been employed to predict combinational drugs for diabetics by evaluating the effect on genes of individual compounds and estimating the effect on the network when combined [134], and other efforts integrate this genomic information with chemical and pharmacological data such as ATC codes, side effect profiles, and therapeutic areas for combination prediction [121,135]. More advanced models have been constructed to evaluate the synergistic effect of up to three components in combination [136].…”

Section: Examples Of Predictive Models For Medicine Developmentmentioning

confidence: 99%