Prioritizing drug targets by perturbing biological network response functions.

Area of Science:

• Systems Biology
• Computational Biology
• Drug Discovery

Background:

• Many critical gene-regulatory drivers and downstream effectors in biological systems are undruggable with conventional small molecule drugs.
• Identifying druggable signaling and regulatory intermediates is crucial for expanding therapeutic target options.

Purpose of the Study:

• To develop and validate a network perturbation theory (NetPert) for identifying and prioritizing druggable intermediates.
• To enhance the ability to target currently undruggable proteins in complex biological networks.

Main Methods:

• Developed NetPert, a mathematical framework based on network perturbation theory for biological networks.
• Applied NetPert to organoid models of metastatic breast cancer to identify and prioritize druggable intermediates.
• Compared NetPert's performance against betweenness centrality and graph diffusion approaches.

Main Results:

• NetPert effectively identifies and prioritizes druggable intermediates that interfere with signaling from driver to response genes.
• NetPert rankings show stronger correlation with wet-lab assays and are more robust to noisy network data compared to other methods.
• Validated drugs targeting NetPert-identified intermediates suppressed additional metastatic phenotypes in wet-lab assays.

Conclusions:

• NetPert provides a superior method for identifying druggable intermediates, expanding the scope of targeted therapies.
• This computational framework holds significant potential for advancing drug discovery for diseases with undruggable targets.
• The approach is effective even for targets not directly differentially expressed, offering new therapeutic avenues.