Mathematical Modeling Of Gut Microbiota Diversity Under The Effects of Beta-lactam Antibiotics
Carlos Olivares (1), Jean de Gunzburg (2), Etienne Ruppe (1), Tanguy Corbel (2), Stéphanie Ferreira (2), Jeremie Guedj (1), Charles Burdet (1)
(1) Université Paris Cité, IAME, Inserm, F-75018, Paris, France
Introduction/Objectives
Antibiotics treatments shift the composition of the gut microbiota according to their spectrum of activity, posology and intestinal excretion rate. These disruptions in the microbiota generate gut dysbiosis that could lead to chronic diseases. We aimed to build a comprehensive mathematical model to evaluate the ecological consequences of antibiotics exposure on gut microbiota [1,2].
Methods:
We used the data from the DAV-132-CL1006 clinical trial (sponsor Da Volterra), where 144 healthy volunteers (HV) were randomly assigned to receive a 5-day intravenous treatment with ceftriaxone (CRO,1 g once a day), ceftazidime/avibactam (CEF/AVI, 2g /0.5g every 8 hours (q8h)), piperacillin/tazobactam (PIP/TAZ, 4g /0.5g q8h) or to an untreated control group. Some antibiotic-treated subjects were also treated for 7 days with several doses of DAV132, a charcoal-based adsorbent that captures free antibiotics residuals in the late ileum and colon (7.5g or 12g q8h). Plasma concentrations of antibiotics were measured at D1 and D5. Fecal samples were obtained before treatment and up to D37 for measurements of antibiotic concentrations and analysis of the microbiota diversity (Shannon index) by 16S rRNA gene profiling.
Nonlinear mixed effect modeling was used to analyze the antibiotics pharmacokinetics and evaluate the effect of fecal concentrations on bacterial diversity over time. Parameter estimation was performed with the SAEM algorithm implemented in Monolix software (Lixoft, France). Model selection was performed by visual inspection of goodness of fit plots and the corrected Bayesian Information Criteria.
Results:
For all antibiotics, two compartment models with first-order elimination were identified for plasma. Fecal concentrations were modeled using transit compartments between plasma and feces. The effect of antibiotics on the Shannon index was best described by an Emax model describing the effect of the fecal concentration on increasing the loss rate.
The median (min; max) time for antibiotic fecal concentrations to decrease below the limit of quantification after the last administration varied between antibiotic treatment groups: 4.3 [1.3, 11.7] for CRO, 4.6 [1.31, 20.3] for CEF, and 2.3 days [0.28, 17.3] for PIP.
Our model predicted, based on a simulated sample of 1000 subjects without DAV administration, a maximal diversity loss of 0.62[0, 1.78], 1.6[0.04, 2.82], 1.5[0.002, 3.1] reached after 7.5[4.8, 27], 7.6[4, 21], 6.8[3.33, 29.3] days of beginning of treatment for CRO, CZA and PTZ respectively and the corresponding return to 95% of diversity baseline after 15.1[5.2, 71], 26.5[5.6, 76], 25.3[4.4, 83] days of beginning of treatment.
Conclusions:
Our model describes the effect of 3 different beta-lactams on the gut microbiota diversity. This model aims to establish a foundation of the interplay between antibiotics administrations and the gut microbiota for three different antibiotics. Such insight could help to optimize antibiotic treatments to protect gut diversity.
References:
[1] Palleja, A., Mikkelsen, K.H., Forslund, S.K. et al. Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat Microbiol 3, 1255–1265 (2018).
[2] Jean de Gunzburg and others, Protection of the Human Gut Microbiome From Antibiotics, The Journal of Infectious Diseases, Volume 217, Issue 4, 15 February 2018, Pages 628–636,
[3] Burdet, C., Nguyen, T.T., Duval, X., Ferreira, S., Andremont, A., Guedj, J., Mentré, F. and DAV132-CL-1002 Study Group, 2019. Impact of antibiotic gut exposure on the temporal changes in microbiome diversity. Antimicrobial Agents and Chemotherapy, 63(10), pp.10-1128.
[4] Guk, J., Guedj, J., Burdet, C., Andremont, A., de Gunzburg, J., Ducher, A. and Mentré, F. (2021), Modeling the Effect of DAV132, a Novel Colon-Targeted Adsorbent, on Fecal Concentrations of Moxifloxacin and Gut Microbiota Diversity in Healthy Volunteers. Clin. Pharmacol. Ther., 109: 1045-1054.
[5] Guk, J, Bridier-Nahmias, A, Magnan, M, et al. Modeling the bacterial dynamics in the gut microbiota following an antibiotic-induced perturbation. CPT Pharmacometrics Syst Pharmacol. 2022; 11: 906- 918. doi: 10.1002/psp4.12806