Integrating In Vitro and In Vivo Toxicity Data with Computational Modeling to Achieve a Pathway-based Approach to More Efficient Chemical Risk Assessment
A major challenge in developing efficacious and safe drugs is the ability to understand and effectively predict adverse effects of xenobiotic substances on extremely complex biological systems early in the drug discovery process. An alarming fact is that thirty percent of adverse drug reactions (ADRs) cannot be predicted by current pre-clinical animal testing and existing modeling methodologies. The availability of vast amounts of high quality omics data (eg, genomics, proteomics, transcriptomics, metabolomics) and relationship databases, combined with advanced computational and analytical tools such as high-throughput screening (HTS) methodologies, has spurred a move towards QST modeling to better understand the mechanistic basis of ADRs, and achieve a more predictive and accurate approach to risk assessment.
What is QST?
QST, is a multidisciplinary approach which, at the juncture of Systems Biology with Toxicology and Chemistry, integrates classical toxicology with quantitative analysis of the molecular and functional changes that occur across multiple levels of biological organization. QST aims to characterize ADRs by describing modes of action as adverse outcomes pathways and perturbed networks versus conventional empirical end points and animal-based testing.
Regulatory, biomedical, and public health research agencies, including the US Food and Drug Administration (FDA), US Environmental Protection Agency (EPA), European Medicines Agency, National Institutes of Health (NIH), National Institute of Environmental Health Sciences (NIEHS), and the European Commission of Public Health and Safety, have made concerted efforts into the exploration of systems toxicology to achieve more efficient safety assessments. Consortia, including Trans QST and the collaboration between EU-ToxRisk and Toxicology in the 21st Century (Tox 21), have established initiatives to enhance scientific capabilities and improve safety assessment approaches based on alternatives to animal testing.
Benefits of QST
Traditional approaches to safety assessment, using in vitro and in vivo assays, greatly depend on animal testing and provide limited mechanistic information. QST, as a subset of Systems Biology, takes a computational approach to integrate large amounts of de novo and legacy data to gain new insights into the link between molecular interactions and adverse effects. The potential of QST offers significant promise to drug discovery and development:
- Decrease the cost and time to bring new drugs to market by identifying drug toxicity earlier in the drug discovery/drug development pipeline
- Derive increasingly predictive models that better forecast the impact of xenobiotic substances on biological systems
- Increase drug efficacy, reduce ADR risk, and decrease off-target interactions
- Strengthen drug safety assessments and reduce animal testing through the implementation of mechanism-based chemical safety testing strategies
- Gain better understanding of disease progression and drug-induced toxicity to foster the development of safe, targeted, and efficacious network-based drugs
Certara’s Simcyp Quantitative Systems Toxicology Initiative
Our Simcyp division, leaders in physiologically-based pharmacokinetic (PBPK) modeling and simulation, has established a new QST initiative that will focus on understanding the mechanistic determinants of drug toxicity and the development of predictive QST software tools. Leveraging Simcyp’s mechanistic modeling expertise, the combined integrated efforts will provide a holistic, quantitative approach to simultaneously assess drug efficacy, safety, and therapeutic index.