# Hollow Fiber Infection Model to Delineate Combination Drug Interaction for Different MTB Metabolic Populations and using Different Pharmacokinetic Profiles > **NIH NIH P01** · UNIVERSITY OF FLORIDA · 2020 · $462,859 ## Abstract Project Summary/Abstract In Project #1, our Goal is to employ the flexible and powerful Hollow Fiber Infection Model (HFIM) to help identify optimal combination chemotherapy regimens that will provide maximal rates of bacterial cell kill and prevent resistance emergence. In so doing, we hope to markedly foreshorten the duration of chemotherapy for patients infected with Mycobacterium tuberculosis (MTB). Part of the power of the HFIM is its ability to study MTB in Log-Phase growth as well as in Acid-Phase and Non-Replicative Persister- (NRP) Phase. In this Project, all of these phases will be examined. There are a large number of possible two drug combinations. Indeed, there are too many combinations to be evaluated in the time frame of this proposal. Consequently, we will employ the fully parametric Greco drug interaction model as a method to rank order the priority with which combinations will be tested. As with the HFIM, we will test all metabolic populations in these checkerboard evaluations. The metric for ranking will consist of evaluation of the α (drug interaction parameter) and its estimated 95% confidence interval. Larger α values and narrower confidence intervals will be given greater weight. We will also look at the actual observed depth of cell kill and the effect parameter from the Greco model. Each of the metabolic populations will contribute 1 set of these parameter values, confidence intervals, etc. It is straightforward to calculate a metric for determining the overall rank order of combinations to be evaluated in the HFIM. We will first use human pharmacokinetic (PK) drug profiles to evaluate the drug interaction for effect (synergy, additivity [Loewe Additivity], antagonism) for each metabolic population. Here, in contradistinction to the screening assay in plates, we will also determine amplification or suppression of less-susceptible subpopulations for the agents in the combination. The PK profile has been demonstrated to have a major impact on cell kill and resistance amplification/ suppression. The use of animal systems may possibly give a misleading conclusion. We will also test these combinations in the HFIM for all metabolic populations using both murine and cynomolgus macaque PK profiles. These findings will allow direct comparison to findings in Projects #2 & #3, where these combinations will be examined in these systems, respectively. Use of mathematical simulation from the animal and from the HFIM outcomes will identify the most reliable information to be gleaned from each model. Finally, we will test what we feel to be the approach which will yield the highest likelihood of achieving major shortening in MTB therapy duration: the evaluation of two regimens that are independent by resistance mechanisms where there is a transition after maximal response has been obtained by the first set of drugs. This Project is informed by all Cores and Projects and informs all other Projects, making this... ## Key facts - **NIH application ID:** 9993196 - **Project number:** 5P01AI123036-05 - **Recipient organization:** UNIVERSITY OF FLORIDA - **Principal Investigator:** George Louis Drusano - **Activity code:** P01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $462,859 - **Award type:** 5 - **Project period:** 2016-08-20 → 2022-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9993196 ## Citation > US National Institutes of Health, RePORTER application 9993196, Hollow Fiber Infection Model to Delineate Combination Drug Interaction for Different MTB Metabolic Populations and using Different Pharmacokinetic Profiles (5P01AI123036-05). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9993196. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*