Hierarchy of knowledge translation: from health problems to ad-hoc drug design

TL;DR

This paper presents a hierarchical model of knowledge translation in drug discovery, emphasizing the importance of organized interdisciplinary research and the topological structure of scientific knowledge networks.

Contribution

It introduces a novel framework for understanding knowledge translation in drug discovery, integrating network analysis and hierarchical organization of biomedical knowledge.

Findings

Hierarchical and modular organization of knowledge bodies is crucial for effective translation.

Interdisciplinary research emergence is essential for successful knowledge translation.

Network analysis reveals the topological structure of scientific knowledge in drug development.

Abstract

An innovative approach to analyze the complexity of translating novel molecular entities and nanomaterials into pharmaceutical alternatives (i.e., knowledge translation, KT) is discussed. First, some key concepts on the organization and translation of the biomedical knowledge (paradigms, homophily, power law distributions, hierarchy, modularity, and research fronts) are reviewed. Then, we propose a model for the knowledge translation (KT) in Drug Discovery that considers the complexity of interdisciplinary communication. Specifically, we address two highly relevant aspects: 1) A successful KT requires the emergence of organized bodies of inter-and transdisciplinary research, and 2) The hierarchical and modular topological organization of these bodies of knowledge. We focused on a set of previously-published studies on KT which rely on a combination of network analysis and computer-assisted analysis of the contents of scientific literature and patents. The selected studies provide a duo of complementary perspectives: the demand of knowledge (cervical cancer and Ebola hemorrhagic fever) and the supply of knowledge (liposomes and nanoparticles to treat cancer and the paradigmatic Doxil, the first nanodrug to be approved).