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U01HL131072 (start date Sept. 2016) Multi-PI: Dartois , Flynn, Kirschner , Linderman

A Multi-scale Systems Pharmacology Approach to TB Treatment: Model Credibility Plan Jennifer Linderman Dept. Chemical Engineering University of Michigan March 24, 2017. U01HL131072 (start date Sept. 2016) Multi-PI: Dartois , Flynn, Kirschner , Linderman. Grant Overview.

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U01HL131072 (start date Sept. 2016) Multi-PI: Dartois , Flynn, Kirschner , Linderman

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  1. A Multi-scale Systems Pharmacology Approach to TB Treatment: Model Credibility Plan Jennifer Linderman Dept. Chemical Engineering University of Michigan March 24, 2017 U01HL131072(start date Sept. 2016) Multi-PI: Dartois, Flynn, Kirschner, Linderman

  2. Grant Overview TB - pulmonary disease resulting from infection with M. tuberculosis. Pathological hallmarks of TB are granulomas – dense collections of immune cells and bacteria – that form as the immune response plays out over various time and length scales and that present a barrier for antibiotic penetration. Improving antibiotic treatment of TB will require consideration of: existing, new and repurposed antibiotics (huge design space); antibiotic PKPD; drug distributions in granuloma; development of resistance; host-scale readouts.

  3. Grant Overview Improve TB treatment with antibiotics by pairing computational modeling with experimental methods for imaging drug distribution within granulomas from humans, non-human primates (NHP) and rabbits. Specific aims: Determine spatial and temporal distributions of TB antibiotics within granulomas, and predict the development of resistance Identify optimal antibiotic treatment regimens for TB using search algorithms to narrow the combinatorial design space of antibiotics Perform virtual clinical trials to test treatment regimens we identify, and test the optimal regimen in the NHP system against a standard regimen.

  4. Model framework • Granuloma model (at right): • agent-based model, ODEs, PDEs • bacteria are also agents • Paired with • Antibiotic PKPD • Immune cell dynamics in lymph nodes and blood (ODEs) • And also • Uncertainty and sensitivity analyses • Optimization algorithms • Virtual clinical trials • Currently • About 30K lines of C++ code (all options) • Several thousand lines of Bash, Perl, and Python script code • Three open source libraries (Boost, FFTW, Qt) • Use of XSEDE, NERSC, and Univ. Michigan computational resources

  5. Perspective of Committee on Credible Practice of Modeling and Simulation in Healthcare Rule 1 – Define context clearly Rule 2 – Use appropriate data Rule 3 – Evaluate within context Rule 4 – List limitations explicitly Rule 5 – Use version control Rule 6 – Document adequately Rule 7 – Disseminate broadly Rule 8 – Get independent reviews Rule 9 – Test competing implementations Rule 10 – Conform to standards -> will refer to these on next slides

  6. Context of use and audience Context of use (Rule 1) • Identify immune and bacterial factors that influence the course of infection • Understand how antibiotic PKPD properties, host variation, dosing schedules, etc. affect efficacy • Interpret animal data (here, NHP and rabbit) Audience • TB researchers in various setting; modeling community • Model users: our modeling graduate students & postdocs and, via collaboration, experimentalists & other modelers • Dissemination primarily through publication & presentations at this point (Rule 7)

  7. Components of our model credibility plan I. Model validation and calibration (Rules 2-4) • Rabbit (Dartois) and NHP (Flynn) data – PIs on grant • Granuloma composition, size, CFU, structure, knockout and depletion phenotypes; antibiotic PKPD, penetration into granulomas; immune cells in blood; etc. Spatial and temporal data. New and existing data. • Modeling students work directly with experimental students (via email, Skype, in-person meetings) • Uncertainty and sensitivity analyses • Explicit acknowledgement of model limitations

  8. Components of our model credibility plan II. Verification of the computational code (Rules 5,6,9,10) • Multiple users test under a variety of conditions • ODE sub-models: compare in-house and Matlab solutions • PDE sub-models: compare to COMSOL; implement multiple algorithms (FTCS, ADE, spectral methods) within code and compare • Tunable resolution: compare behavior of model versions with coarse/fine graining of particular pathways/mechanisms • Version control • Documentation: heavily commented code, pseudo-code, state transition diagrams, group web pages Cilfone et al. 2014; Kirschner et al. 2014

  9. Components of our model credibility plan III. Independent evaluation (Rule 8) • Require: ABM, ODE, PDE, C++, object-oriented programming • Would like to identify MSM community evaluator

  10. Timeline, milestones, progress • 6 months in: • Continual biological calibration and validation • Building on newly generated experimental data • Continual computational verification • Need to arrange for independent evaluation from MSM community

  11. Challenges, opportunities, uniqueness • Computational and experimental complexity • Multiple animal models and disparate types of data • Stochastic and deterministic model elements • Uncertainty and sensitivity analyses • HPC resources for simulations • Independent evaluator • Best way to identify third-party evaluator? Host at Univ. Michigan?

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