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A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

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Network-based prediction of drug combinations.

Feixiong Cheng^1,2,3,4,5, István A Kovács^1,2, Albert-László Barabási^6,7,8,9

¹Center for Complex Networks Research and Department of Physics, Northeastern University, Boston, MA, 02115, USA.

Nature Communications

|March 15, 2019

Summary

Identifying effective drug combinations is challenging. A network-based approach reveals that drugs hitting separate disease targets within a module are most effective, aiding combination therapy development.

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Area of Science:

Systems biology
Pharmacology
Computational biology

Background:

Drug combinations enhance efficacy and reduce toxicity for complex diseases.
Identifying optimal drug combinations faces challenges due to combinatorial explosion.

Purpose of the Study:

To develop a network-based methodology for identifying clinically effective drug combinations.
To understand the network properties of efficacious drug-drug-disease interactions.

Main Methods:

Quantified network-based relationships between drug targets and disease proteins in the human interactome.
Classified drug-drug-disease combinations into six distinct network-based categories.
Validated findings using approved drug combinations for hypertension and cancer.

Main Results:

Identified six distinct classes of drug-drug-disease combinations based on network properties.
Found that only one class, where drug targets hit separate neighborhoods within a disease module, correlates with therapeutic effects.
Successfully identified and validated antihypertensive drug combinations using this network methodology.

Conclusions:

A network-based approach can effectively identify efficacious drug combinations.
The specific network topology of drug targets is crucial for therapeutic success.
This methodology offers a powerful tool for drug development and combination therapy discovery.