“The whole is greater than the sum of its parts” -Aristotle
This quote from the great 4th century BCE Greek philosopher and scientist has become the mantra for many endeavors, sectors, organizations, and disciplines. From biology, chemistry, and physics to agriculture, engineering, and business, it is the foundation for synergy.
What is the connection between Aristotle’s famous quote and quantitative systems toxicology (QST)? QST’s origins lie in systems biology, which asserts that biological systems have properties that emerge from a system as a whole rather than its constituent parts. Systems biology applies a non-linear, integrative, quantitative, and holistic approach using an interdisciplinary mix of biology, computational modeling, engineering, bioinformatics, and other sciences to understand complex biological systems. The underlying basis of “the whole is greater…” in systems biology is to decipher how complex interactions give rise to the function and behavior of biological systems, eg, cell signaling networks. In other words, systems biology can be looked upon as a “network of networks:” how all components inter- and intra-connect and change in response to perturbation. The foundation for this approach lies with the emergence and evolution of “omics” technologies—genomics, proteomics, metabolomics, transcriptomics, and others.
QST sits at the juncture of systems biology with toxicology and chemistry. It integrates classical toxicology with quantitative analysis of large networks of molecular and functional changes occurring across multiple levels of biological organization. Sponsors employ QST to characterize adverse drug reactions (ADRs) and predict toxicity early in the drug discovery process. Current pre-clinical animal tests and modeling technologies fail to predict around 30% of ADRs. These knowledge gaps impede the development of new, efficacious drugs. The availability of omics data and advanced computational and high throughput screening tools has spurred the move towards using QST models to better understand ADR mechanisms to achieve more predictive and accurate risk assessment.
QST integrates in vitro and in vivo toxicity data with a large computational network approach to risk assessment. Keeping with Aristotle’s “whole is the sum of its parts” theory, the “sum” of QST “parts”, eg, reliable models, pathway knowledge, high content technologies, linking perturbations to adverse outcomes, addressing uncertainty, and developing pathway-based test strategies, will result in the “whole”—effective and safer drugs.
Aristotle also stated, “All men by nature desire knowledge.” This quote also applies to QST by providing insights into the link between molecular interactions and adverse effects. There is also substantial potential that QST offers to drug discovery and development: lowering the cost and time to bring new drugs to market, better predictive models for adverse effects, increasing drug efficacy, reducing ADR risk, and reducing animal testing are only a few of the benefits that can be derived from QST. I believe Aristotle would be quite pleased, as a scientist and scholar, with how his philosophies have contributed to the field of quantitative systems modeling.
At Certara, we’re exploring a systems approach to pharmacology and toxicology. Quantitative systems pharmacology (QSP), another subset of systems biology, combines computational modeling and experimental data to examine the relationships between a drug, the biological system, and the disease process. Earlier this year, we launched the Systems Pharmacology Immunogenicity Consortium fashioned after our Simcyp® Consortium. We have also begun looking at how we can lend knowledge and tools to advance the quantitative systems toxicology approach—read more on this topic in our white paper.