Structure and dynamics of molecular networks: A novel paradigm of drug discovery. A comprehensive review (1210.0330v3)

Abstract: Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The central hit strategy selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The network influence strategy works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes or edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing more than 1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level haLLMarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these haLLMarks by a cohesive, global approach.

• The paper introduces a systems-oriented approach by analyzing molecular network structures to optimize drug targeting and reduce late-stage trial failures.
• It details two strategies—Central Hit and Network Influence—that selectively target central or adjacent nodes to disrupt pathological networks.
• The review highlights emerging computational tools and multi-target drug designs that enhance safety and efficacy in complex disease treatments.

Structure and Dynamics of Molecular Networks: A Paradigm of Drug Discovery

The review by Csermely et al., published in Pharmacology & Therapeutics, presents an extensive evaluation of the intersection between network science and pharmaceutical research. The paper explores how the structures and dynamics of molecular networks can be leveraged to enhance drug discovery processes and address the escalating costs and complexities associated with drug development.

Overview of Molecular Network Principles

The primary thrust of the paper is the argument that the analysis of molecular networks—encompassing chemical similarity networks, protein structures, protein-protein interactions, genetic networks, and metabolic networks—provides a systems-oriented approach essential for comprehending drug action and disease complexity. The review examines contemporary methodologies for network topology and dynamics and identifies significant analytical tools used across various types of molecular networks.

Strategies for Network Targeting

Csermely et al. postulate that network-based drug targeting can follow two fundamental strategies:

1. Central Hit Strategy: This approach involves selectively targeting highly central nodes or interactions (edges) within the flexible networks of pathogens or cancer cells. The primary objective is to dismantle these networks effectively, resulting in the elimination of infectious agents or cancerous cells.
2. Network Influence Strategy: This strategy is suited for other diseases where effective reconfiguration of rigid molecular networks is needed. It involves targeting nodes adjacent to central nodes or critical edges to achieve a therapeutic effect without necessarily causing a complete network collapse.

Network-Based Drug Development

The review emphasizes an integrated, multi-layered approach to drug discovery that encompasses:

• Single-Target Drugs: These target individual nodes within molecular networks.
• Edgetic Drugs: These modulate specific protein-protein interactions within the network.
• Multi-Target Drugs: These target multiple nodes or interactions, potentially across different pathways or networks.
• Allo-Network Drugs: These target allosteric sites on proteins, inducing changes in network behavior via conformational shifts.

Impact on Drug Discovery and Development

The authors highlight several key areas where network-based strategies could significantly impact drug discovery:

• Hit Identification and Lead Selection: Network methods can streamline these stages by optimizing drug efficacy and reducing undesirable side-effects and toxicity.
• Minimizing Attrition Rates: By understanding network dynamics and the interplay of multi-target drugs, the industry can reduce the high failure rates in late-phase trials.
• Addressing Complex Diseases: The review discusses successful examples of network-based drug development in infections, cancer, metabolic diseases, neurodegenerative diseases, and aging. The systems-level understanding provided by network analysis aids in crafting more effective therapeutic strategies.

Practical and Theoretical Implications

The paper suggests that by harnessing the power of network analysis, researchers can elucidate the interconnected nature of cellular processes, thereby enhancing the precision and efficacy of drug development. The implications extend beyond practical applications, offering theoretical insights into the modular organization and emergent properties of complex biological systems.

Future Directions

The review points to emerging trends in network methodologies, including improved data integration, predictive modeling, and computational tools tailored to vast and dynamic molecular datasets. It stresses the need for a cohesive, global approach to achieve the systems-level haLLMarks of drug quality, integrating various network-related trends within a unified framework.

Conclusion

In summary, the paper by Csermely et al. provides a comprehensive and detailed review of how network analysis can revolutionize drug discovery. It affirms that understanding and targeting the intricate web of molecular interactions and network dynamics offers a robust paradigm for developing novel therapeutics in an increasingly complex biomedical landscape. The integration of network-based strategies promises to enhance drug efficacy, reduce adverse effects, and streamline the drug discovery process, marking an essential shift towards a more holistic and systems-oriented approach in pharmacology.