A Better, Faster Road From Biological Data to Human Health: A Systems Biology Approach for Engineered Cell Cultures

Abstract

Key Issues and Opportunities• Because of the limitations of animal models and in vitro assays, the pipeline for new therapeutics grows smaller and chemical toxicity screening is failing to meet the growing demand for hazard assessment.• Engineered cellular constructs provide access to physiologically relevant in vitro data of sufficient quality for improved predictive modeling of human responses.• Sophisticated computational tools are needed to translate in vitro biological data to actionable information about health effects of bioactive compounds. Research Brief RTI Press June 2017Traditionally, the interactions of drugs and toxicants with human tissue have been investigated in a reductionist way-for example, by focusing on specific molecular targets and using single-cell-type cultures before testing compounds in whole organisms. More recently, systems biology approaches attempt to enhance the predictive value of in vitro biological data by adopting a comprehensive description of biological systems and using sophisticated computational tools that can deal with the complexity of these systems. However, the utility of computational models resulting from these efforts completely relies on the quality of the data used to construct them. Here, we propose that recent advances in the development of bioengineered, 3D, multicellular constructs provide in vitro data of sufficient complexity and physiological relevance to be used in predictive systems biology models of human responses. Such predictive models are essential to maximally leveraging these emerging bioengineering technologies to improve both therapeutic development and toxicity risk assessment.This brief outlines the opportunities presented by emerging technologies and approaches for the acceleration of drug development and toxicity testing, as well as the challenges lying ahead for the field. What Is Systems Biology?• The data-based, computational, and mathematical modeling of complex biological systems and their dynamic behavior• A paradigm in antithesis to reductionist approaches of biological response focused on "one gene, one receptor, one mechanism"• Driven by the advent of high-throughput bioassays and increased computational power that enable bioinformatics analysis of -omics data• Network-centric view, with focus on mathematical description of signaling, transcriptional, and metabolic networks and their interactions• Aims at discovering network-level properties not evident from the study of individual components (emergent properties)