Design principles of collateral sensitivity-based dosing strategies.
Anti-Bacterial Agents
/ administration & dosage
Bacterial Infections
/ drug therapy
Computer Simulation
Drug Administration Schedule
Drug Collateral Sensitivity
Drug Resistance, Bacterial
/ drug effects
Drug Therapy, Combination
/ methods
Humans
Microbial Sensitivity Tests
Models, Biological
Mutation
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
28 09 2021
28 09 2021
Historique:
received:
19
04
2021
accepted:
10
09
2021
entrez:
29
9
2021
pubmed:
30
9
2021
medline:
24
10
2021
Statut:
epublish
Résumé
Collateral sensitivity (CS)-based antibiotic treatments, where increased resistance to one antibiotic leads to increased sensitivity to a second antibiotic, may have the potential to limit the emergence of antimicrobial resistance. However, it remains unclear how to best design CS-based treatment schedules. To address this problem, we use mathematical modelling to study the effects of pathogen- and drug-specific characteristics for different treatment designs on bacterial population dynamics and resistance evolution. We confirm that simultaneous and one-day cycling treatments could supress resistance in the presence of CS. We show that the efficacy of CS-based cycling therapies depends critically on the order of drug administration. Finally, we find that reciprocal CS is not essential to suppress resistance, a result that significantly broadens treatment options given the ubiquity of one-way CS in pathogens. Overall, our analyses identify key design principles of CS-based treatment strategies and provide guidance to develop treatment schedules to suppress resistance.
Identifiants
pubmed: 34584086
doi: 10.1038/s41467-021-25927-3
pii: 10.1038/s41467-021-25927-3
pmc: PMC8479078
doi:
Substances chimiques
Anti-Bacterial Agents
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
5691Informations de copyright
© 2021. The Author(s).
Références
Luepke, K. H. et al. Past, present, and future of antibacterial economics: increasing bacterial resistance, limited antibiotic pipeline, and societal implications. Pharmacotherapy https://doi.org/10.1002/phar.1868 (2017).
Mcgrath, D. M. et al. Genetic basis for in vivo daptomycin resistance in enterococci. N. Engl. J. Med. 365, 892–900 (2011).
pubmed: 21899450
pmcid: 3205971
doi: 10.1056/NEJMoa1011138
Mwangi, M. M. et al. Tracking the in vivo evolution of multidrug resistance in Staphylococcus aureus by whole-genome sequencing. Proc. Natl Acad. Sci. USA 104, 9451–9456 (2007).
pubmed: 17517606
pmcid: 1890515
doi: 10.1073/pnas.0609839104
Nielsen, E. I. & Friberg, L. E. Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs. Pharmacol. Rev. 65, 1053–1090 (2013).
pubmed: 23803529
doi: 10.1124/pr.111.005769
Bonhoeffer, S., Lipsitch, M. & Levin, B. R. Evaluating treatment protocols to prevent antibiotic resistance. Proc. Natl Acad. Sci. USA 94, 12106–12111 (1997).
pubmed: 9342370
pmcid: 23718
doi: 10.1073/pnas.94.22.12106
Baym, M., Stone, L. K. & Kishony, R. Multidrug evolutionary strategies to reverse antibiotic resistance. Science 351, aad3292–aad3292 (2016).
pubmed: 26722002
pmcid: 5496981
doi: 10.1126/science.aad3292
Imamovic, L. & Sommer, M. O. A. Use of collateral sensitivity networks to design drug cycling protocols that avoid resistance development. Sci. Transl. Med. 5, 204ra132 (2013).
pubmed: 24068739
doi: 10.1126/scitranslmed.3006609
Lejla Imamovic, A. et al. Drug-driven phenotypic convergence supports rational treatment strategies of chronic infections. Cell 172, 121–134 (2018).
pubmed: 29307490
pmcid: 5766827
doi: 10.1016/j.cell.2017.12.012
Podnecky, N. L. et al. Conserved collateral antibiotic susceptibility networks in diverse clinical strains of Escherichia coli. Nat. Commun. 9, 3673 (2018).
pubmed: 30202004
pmcid: 6131505
doi: 10.1038/s41467-018-06143-y
Barbosa, C., Römhild, R., Rosenstiel, P. & Schulenburg, H. Evolutionary stability of collateral sensitivity to antibiotics in the model pathogen Pseudomonas aeruginosa. Elife 8, 1–22 (2019).
doi: 10.7554/eLife.51481
Gonzales, P. R. et al. Synergistic, collaterally sensitive β-lactam combinations suppress resistance in MRSA. Nat. Chem. Biol. 11, 855–861 (2015).
pubmed: 26368589
pmcid: 4618095
doi: 10.1038/nchembio.1911
Barbosa, C. et al. Alternative evolutionary paths to bacterial antibiotic resistance cause distinct collateral effects. Mol. Biol. Evol. 34, 2229–2244 (2017).
pubmed: 28541480
pmcid: 5850482
doi: 10.1093/molbev/msx158
Maltas, J. & Wood, K. B. Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance. PLoS Biol. 17, e3000515 (2019).
pubmed: 31652256
pmcid: 6834293
doi: 10.1371/journal.pbio.3000515
Liakopoulos, A., Aulin, L. B. S., Buffoni, M., van Hasselt, J. G. C. & Rozen, D. E. Allele-specific collateral and fitness effects determine the dynamics of fluoroquinolone-resistance evolution. Preprint at bioRxiv https://doi.org/10.1101/2020.10.19.345058 (2020).
Kim, S., Lieberman, T. D. & Kishony, R. Alternating antibiotic treatments constrain evolutionary paths to multidrug resistance. Proc. Natl Acad. Sci. USA 111, 14494–14499 (2014).
pubmed: 25246554
pmcid: 4210010
doi: 10.1073/pnas.1409800111
Regoes, R. R. et al. Pharmacodynamic functions: a multiparameter approach to the design of antibiotic treatment regimens. Antimicrob. Agents Chemother. 48, 3670–3676 (2004).
pubmed: 15388418
pmcid: 521919
doi: 10.1128/AAC.48.10.3670-3676.2004
Coen Van Hasselt, J. G. & Iyengar, R. Systems pharmacology: defining the interactions of drug combinations. Annu. Rev. Pharmacol. Toxicol. 59, 21–40 (2019).
pubmed: 30260737
doi: 10.1146/annurev-pharmtox-010818-021511
Yoshida, M. et al. Time-programmable drug dosing allows the manipulation, suppression and reversal of antibiotic drug resistance in vitro. Nat. Commun. 8, 15589 (2017).
pubmed: 28593940
pmcid: 5472167
doi: 10.1038/ncomms15589
Maltas, J. & Wood, K. B. Pervasive and diverse collateral sensitivity profiles inform optimal strategies to limit antibiotic resistance. PLoS Biol. 17, e3000515 (2019).
pubmed: 31652256
pmcid: 6834293
doi: 10.1371/journal.pbio.3000515
Nichol, D. et al. Antibiotic collateral sensitivity is contingent on the repeatability of evolution. Nat. Commun. 10, 334 (2019).
pubmed: 30659188
pmcid: 6338734
doi: 10.1038/s41467-018-08098-6
Udekwu, K. I. & Weiss, H. Pharmacodynamic considerations of collateral sensitivity in design of antibiotic treatment regimen. Drug Des. Devel. Ther. ume 12, 2249–2257 (2018).
doi: 10.2147/DDDT.S164316
Falagas, M. E. & Kasiakou, S. K. Toxicity of polymyxins: a systematic review of the evidence from old and recent studies. Crit. Care 10, R27 (2006).
Mattie, H., Craig, W. A. & Pechere, J. C. Determinants of efficacy and toxicity of aminoglycosides. J. Antibiot. 24, 281–293 (1989).
Zasowski, E. J. et al. Identification of vancomycin exposure-toxicity thresholds in hospitalized patients receiving intravenous vancomycin. Antimicrob. Agents Chemother. 62, e01684–17 (2018).
pubmed: 29084753
doi: 10.1128/AAC.01684-17
Andersson, D. I. & Hughes, D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat. Rev. Microbiol. 8, 260–271 (2010).
Band, V. I. et al. Antibiotic failure mediated by a resistant subpopulation in Enterobacter cloacae. Nat. Microbiol. 1, 16053 (2016).
pubmed: 27572838
pmcid: 5154748
doi: 10.1038/nmicrobiol.2016.53
Long, H. et al. Antibiotic treatment enhances the genome-wide mutation rate of target cells. Proc. Natl Acad. Sci. USA 113, E2498–E2505 (2016).
pubmed: 27091991
pmcid: 4983809
doi: 10.1073/pnas.1601208113
Nichol, D. et al. Steering evolution with sequential therapy to prevent the emergence of bacterial antibiotic resistance. PLOS Comput. Biol. 11, e1004493 (2015).
pubmed: 26360300
pmcid: 4567305
doi: 10.1371/journal.pcbi.1004493
Roemhild, R. et al. Cellular hysteresis as a principle to maximize the efficacy of antibiotic therapy. Proc. Natl Acad. Sci. USA 115, 9767–9772 (2018).
pubmed: 30209218
pmcid: 6166819
doi: 10.1073/pnas.1810004115
Chopra, I., O’Neill, A. J. & Miller, K. The role of mutators in the emergence of antibiotic-resistant bacteria. Drug Resist. Updat. 6, 137–145 (2003).
pubmed: 12860461
doi: 10.1016/S1368-7646(03)00041-4
Dai, L., Sahin, O., Tang, Y. & Zhang, Q. A mutator phenotype promoting the emergence of spontaneous oxidative stressresistant mutants in Campylobacter jejun. Appl. Environ. Microbiol. 83, 1–13 (2017).
doi: 10.1128/AEM.01685-17
Looft, C. et al. High frequency of hypermutable Pseudomonas aeruginosa in cystic fibrosis lung infection. Science 288, 1251–1254 (2000).
doi: 10.1126/science.288.5469.1251
Long, H. et al. Antibiotic treatment enhances the genome-wide mutation rate of target cells. Proc. Natl Acad. Sci. USA 113, E2498 LP–E2505 (2016).
doi: 10.1073/pnas.1601208113
Hansen, E., Woods, R. J. & Read, A. F. How to use a chemotherapeutic agent when resistance to it threatens the patient. PLoS Biol. 15, 1–21 (2017).
doi: 10.1371/journal.pbio.2001110
Aulin, L. B. S. et al. Distinct evolution of colistin resistance associated with experimental resistance evolution models in Klebsiella pneumoniae. J. Antimicrob. Chemother. 76, 533–535 (2021).
pubmed: 33150358
doi: 10.1093/jac/dkaa450
Välitalo, P. A. J. et al. Structure-based prediction of anti-infective drug concentrations in the human lung epithelial lining fluid. Pharm. Res. 33, 856–867 (2016).
pubmed: 26626793
doi: 10.1007/s11095-015-1832-x
Aulin, L. B. S. et al. Validation of a model predicting anti-infective lung penetration in the epithelial lining fluid of humans. Pharm. Res. 35, 26 (2018).
pubmed: 29368211
pmcid: 5783989
doi: 10.1007/s11095-017-2336-7
Boucher, A. N. & Tam, V. H. Mathematical formulation of additivity for antimicrobial agents. Diagn. Microbiol. Infect. Dis. 55, 319–325 (2006).
pubmed: 16626903
doi: 10.1016/j.diagmicrobio.2006.01.024
Loewe, S. Die quantitativen probleme der pharmakologie. Ergebnisse der Physiol. 27, 47–187 (1928).
doi: 10.1007/BF02322290
Bliss, C. I. The toxicity of poisons applied jointly. Ann. Appl. Biol. 26, 585–615 (1939).
doi: 10.1111/j.1744-7348.1939.tb06990.x
Aulin, L. B. S. et al. Biomarker‐guided individualization of antibiotic therapy. Clin. Pharmacol. Ther. 0, cpt.2194 (2021).
Aulin, L. B. S. et al. Population pharmacokinetics of unbound and total teicoplanin in critically ill pediatric patients. Clin. Pharmacokinet. 60, 353–363 (2021).
pubmed: 33030704
doi: 10.1007/s40262-020-00945-4
De Cock, P. A. J. G. et al. Population pharmacokinetics of cefazolin before, during and after cardiopulmonary bypass to optimize dosing regimens for children undergoing cardiac surgery. J. Antimicrob. Chemother. 72, 791–800 (2017).
pubmed: 27999040
Sharma, R. & Sharma, S. Physiology, Blood Volume 6–9 (StatPearls, 2020).
Gerlini, A. et al. The role of host and microbial factors in the pathogenesis of pneumococcal bacteraemia arising from a single bacterial cell bottleneck. PLoS Pathog. 10, e1004026 (2014).
Martínez, J. L. & Baquero, F. Mutation frequencies and antibiotic resistance. Antimicrob. Agents Chemother. 44, 1771–1777 (2000).
pubmed: 10858329
pmcid: 89960
doi: 10.1128/AAC.44.7.1771-1777.2000
Angst, D. C., Tepekule, B., Sun, L., Bogos, B. & Bonhoeffer, S. Comparing treatment strategies to reduce antibiotic resistance in an in vitro epidemiological setting. Proc. Natl Acad. Sci. USA 118, 1–7 (2021).
doi: 10.1073/pnas.2023467118
Tepekule, B., Uecker, H., Derungs, I., Frenoy, A. & Bonhoeffer, S. Modeling antibiotic treatment in hospitals: a systematic approach shows benefits of combination therapy over cycling, mixing, and mono-drug therapies. PLoS Comput. Biol. 13, 1–22 (2017).
doi: 10.1371/journal.pcbi.1005745
van Duijn, P. J. et al. The effects of antibiotic cycling and mixing on antibiotic resistance in intensive care units: a cluster-randomised crossover trial. Lancet Infect. Dis. 18, 401–409 (2018).
pubmed: 29396000
doi: 10.1016/S1473-3099(18)30056-2
Wang, W., Hallow, K. M. & James, D. A. A tutorial on RxODE: simulating differential equation pharmacometric models in R. 3–10, https://doi.org/10.1002/psp4.12052 (2016).
Fidler, M., Hallow, M., Wilkins, J. & Wang, W. RxODE: Facilities for Simulating from ODE-Based Models. R package version 1.0.6. (2021).
Aulin, L. B. S. Design principles of collateral sensitivity-based dosing strategies. https://doi.org/10.5281/zenodo.5410785 (2021).
Pál, C., Papp, B. & Lázár, V. Collateral sensitivity of antibiotic-resistant microbes. Trends Microbiol. 23, 401–407 (2015).
pubmed: 25818802
pmcid: 5958998
doi: 10.1016/j.tim.2015.02.009