Science - USA (2021-07-09)

SCIENCE sciencemag.org 9 JULY 2021 • VOL 373 ISSUE 6551 173-B

GRAPHIC: ADAPTED FROM M. ZIMMERMANN-KOGADEEVA BY N. CARY/

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contribution to drug metabolism even if the

metabolizing species remain unknown by

using data from germ-free mice and mice

harboring a complex microbial community.

We also showed that microbial contribu-

tion to the drug metabolite far exceeds the

host for sorivudine, an antiviral drug with

different host and microbiome metabolism

rates, and for clonazepam, an anxiolytic and

anticonvulsant drug converted to multiple

metabolites ( 12 ).

Quantifying the metabolic host-microbi-

ome interactions is not the only purpose of

our model. Having a robust model of host-

microbiome interaction allows us to study,

explain, and predict the system’s behavior

in different conditions. By analyzing how

drug and metabolite profiles change when

model parameters are varied, we found

that the similarity of drug serum profiles

between germ-free and colonized mice can

be explained by the fast and microbiota-

independent drug absorption from the

small intestine. Our model further suggests

that even for rapidly absorbed drugs, micro-

biome contributions to a host’s metabolism

can be substantial under certain conditions

(e.g., a high microbiome to host ratio of

drug metabolism or extensive enterohepatic

circulation of the drug and its metabolites)

( 13 ). Such computational models enable us

to investigate host-microbiota interactions

in silico, guide experimental design, and

help reduce the number of experiments

needed to confirm model predictions. To

systematically investigate microbial capac-

ity to metabolize drugs, we next conducted

a high-throughput in vitro screen. We found

that microbiota contribution to drug me-

tabolism might even be more widespread

than we anticipated—two-thirds (176 out of

271) of the human-targeted drugs we exam-

ined were metabolized by at least one of the

76 tested bacteria ( 14 ).

Although follow-up studies are required

to test these microbiota-drug interactions

in vivo, our findings emphasize that the mi-

crobiota should be considered when devel-

oping new drugs, stratifying patients, and

choosing the most efficient treatment strat-

egies. In the future, I believe that compu-

tational models combined with quantitative

experimental data will allow us to measure

host-microbiome interactions beyond drug

metabolism and to better understand, pre-

dict, and control the effect of the microbi-

ome on our health in everyday life. j

REFERENCES AND NOTES

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4. J. Durack, S. V. Lynch, J. Exp. Med. 216 , 20 (2019).


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7. I. D. Wilson, J. K. Nicholson, Tr a n s l. R e s. 179 , 204 (2017).


8. T. S. B. Schmidt, J. Raes, P. Bork, Cell 172 , 1198 (2018).


9. M. Alexander, P. J. Turnbaugh, Immunity 53 , 264 (2020).


10. C. Tropini, K. A. Earle, K. C. Huang, J. L. Sonnenburg, Cell
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11. H. Machida et al., Biochem. Pharmacol. 49 , 763 (1995).


12. M. Zimmermann, M. Zimmermann-Kogadeeva,
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    (2019).


13. M. Zimmermann-Kogadeeva, M. Zimmermann,
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14. M. Zimmermann, M. Zimmermann-Kogadeeva, R.
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    10.1126/science.abi9357

Metabolite

Metabolite

Drug (brivudine)

Drug Metabolite

Serum

Cecum

Amount Amount

Amount

Amount Amount

Time (hours) Time (hours) Time (hours)

Bacterial

metabolism

Host

metabolism

Metabolite

absorption

Elimination

Model ft Model prediction:

Bacterial metabolite

Host metabolite

Total metabolite

0.2

0

0 357 9

0.2

0

0 3579

0.2

0

0 35 7 9

0.6

0

0 3579 0 357

0

0.15

9

Host process

Microbial process

Drug

Jejunum

Ileum

Propagation

Propagation

GNMUT GNWT

Colon

Elimination

DuodenumAbsorption

Experimental and computational approaches that quantify host

and microbial contributions to drug metabolism

Oral drugs are administered to gnotobiotic mice that differ in a single microbial drug-metabolizing enzyme

(GNMUT, mutant; GNWT, wild type); drug and drug metabolite kinetics are then quantified across tissues. A

microbiome-host pharmacokinetic model developed from these measurements accurately predicts serum

metabolite exposure and untangles host and microbiome contributions to drug metabolism.

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