Biomathematics / Computational Biology Colloquium

QSP modeling efforts to advance global health: application to TB drugs and vaccines

Speaker: Karim Azer, Bill & Melinda Gates Medical Research Institute

Location: Warren Weaver Hall 1314

Date: Tuesday, December 3, 2019, 12:30 p.m.

Synopsis:

Mathematical biology and pharmacology models are increasingly utilized in monoclonal antibody and drug development, recognizing the need for improving the probability of success or reducing the cost of development. More mechanistic, quantitative systems pharmacology (QSP) models are being leveraged to aid in the identification of novel targets in early research, in the translational medicine activities for bringing molecules into the clinic, achieving proof of mechanism, and understanding variability in response to novel compounds in later clinical development. Many of these modeling efforts are focused on capturing the immunological components of disease biology and corresponding therapeutic perturbation of the underlying immunological state, such as in immuno-oncology, lung diseases like asthma. Other are focused on predicting the immunogenicity response to specific therapies like enzyme replacement. These modeling and experimental efforts to elucidate the state and activity of the immune system can partially pave the way towards the application of mechanistic models for vaccine development. However, more work is needed to understand the activity of a vaccine on the background of an existing immunological state, and the link between vaccine activity and correlates of protection.

To support drug regimen development in tuberculosis (TB), we present a computational framework combining PBPK with QSP. The QSP approach enables representation of relevant granuloma features and heterogeneous bacterial burdens, including host immune markers. Combined, PBPK-QSP enables integration of in vitro and preclinical candidate data for prioritization and down-selection of novel regimens into the clinic, leveraging biomarkers and drug combination efficacy data. However, to extend the model to support vaccine efforts, more work is needed to link biomarker and bioinformatics outcomes, including systems immunology and serology efforts, to inform on vaccine immunogenicity components in the QSP model.

Given the significant body of literature on TB biology and vaccine research, we have developed a TB Knowledgebase, an interactive web-based application that aggregates, organizes, and analyzes publicly available literature and clinical trial documentation. It employs text mining to derive associative and linguistic relationships among host-, bacteria-, and intervention-related terms. This tool provides a more efficient and effective way to explore the TB literature and to identify knowledge, data, and models that can inform and guide model informed drug and vaccine development. Together, the TB QSP model and the TB Knowledgebase are two steps towards realizing an integrated framework for supporting TB drugs and vaccine development pipeline.