Comparing treatment strategies to reduce antibiotic resistance in an in vitro epidemiological setting

Abstract

The rapid rise of antibiotic resistance, combined with the increasing cost and difficulties to develop new antibiotics, calls for treatment strategies that enable more sustainable antibiotic use. The development of such strategies, however, is impeded by the lack of suitable experimental approaches that allow testing their effects under realistic epidemiological conditions. Here, we present an approach to compare the effect of alternative multidrug treatment strategies in vitro using a robotic liquid-handling platform. We use this framework to study resistance evolution and spread implementing epidemiological population dynamics for treatment, transmission, and patient admission and discharge, as may be observed in hospitals. We perform massively parallel experimental evolution over up to 40 d and complement this with a computational model to infer the underlying population-dynamical parameters. We find that in our study, combination therapy outperforms monotherapies, as well as cycling and mixing, in minimizing resistance evolution and maximizing uninfecteds, as long as there is no influx of double resistance into the focal treated community.

Keywords: antibiotic resistance; combination therapy; experimental epidemiology.

Fig. 1.

(A) Schematic diagram of the dynamical epidemiological model used (Materials and Methods and SI Appendix). Colors correspond to processes in B and phenotypes in C–H. (B) Schematic representation of one treatment arm in the experimental framework. Rounded rectangles represent a hospital (microtiter plate) containing uninfected (wells containing only bacterial growth medium) and infected (growth medium with sensitive or resistant strains) patients. Plates for day n + 1 are prepared with medium and antibiotics of the corresponding treatment arm and are then inoculated from the community (rectangle) and/or from the previous day’s plate according to the chosen scenario (see Results). Infection and superinfection are modeled by additional transfers from the previous day’s (n) plate. After incubation, cultures are used for the next day’s inoculation, and all cultures are spotted on agar plates for the determination of population phenotypes. Colors correspond for processes in A; purple is used for procedures with no direct equivalent in the epidemiological model. (C–H) Population phenotype frequency during experimental evolution in the absence of preexisting resistance (scenario $\varnothing$). Ribbons indicate the observed range in four replicate populations. Lines denote the frequency of population phenotypes after each transfer based on a model fit to all data simultaneously using the mean of the posterior distribution for each parameter. A, resistant to nalidixic acid; B, resistant to streptomycin, A/B, mixed population of single resistants; AB, resistant to both drugs; S, sensitive; U, uninfected patients. (C) No treatment. The gap at transfer 13 is due to missing data resulting from a mechanical malfunction of the setup. (D) Monotherapy A (nalidixic acid). (E) Monotherapy B (streptomycin). (F) Combination therapy. (G) Cycling: Treatment is switched every two transfers between nalidixic acid and streptomycin. (H) Mixing: Treatment with nalidixic acid or streptomycin is randomly assigned to each well for each transfer. In all treatment strategies, each antibiotic is used at 2$\times$ MIC.

Fig. 2.

Posterior distributions (estimated probability distribution for a given parameter [Materials and Methods ]) obtained from fitting the model to different parts of the experimental data. Mono A, mono B, combination, cycling, and mixing refer to fitting the model to data only from these treatment arms, whereas simultaneous refers to the overall fit to the time course of all treatment arms simultaneously. A, B, and C show, respectively, the posterior distributions of the parameters ${\tau }_A$, ${\tau }_B$, and ${\nu }_B$ describing the clearance rate after treatment with antibiotic A, clearance rate after treatment with antibiotic B, and the rate of de novo emergence of B resistance for the sensitive strain. Posterior distributions of the parameters that cannot be identified from a given treatment arm are shown as thin box plots and equal to their prior distribution, which is uniform between zero and one. Posterior distributions, including all model parameters, are presented in SI Appendix, Fig. S5.

Fig. 3.

Frequencies of uninfected and resistant populations for different treatment strategies in different resistance inflow scenarios. Points show population phenotype frequency averaged over transfers 9 to 12 for four biological replicates for scenarios $\varnothing$ (no inflow of resistance), I (inflow of both single resistances), and II (inflow of double resistance). Bars show the median. Samples not sharing a letter are significantly different (generalized linear hypothesis test across treatment strategies, P < 0.05; ANOVA tables and all P values can be found in SI Appendix, Tables S4–S9). R, resistant populations; U, uninfected (sum of phenotypes A, B, A/B, and AB; Fig. 1). The full time series are shown in SI Appendix, Figs. S6–S8.

Fig. 4.

Mean frequencies of double-resistant populations after five transfers at different concentrations of nalidixic acid and streptomycin. The upper left triangle indicates frequency of double resistance to a high concentration of drugs used on plate (10$\times$ MIC), while the lower right triangle indicates double resistance to the drug concentrations used in liquid culture. Full time series of population phenotype frequencies can be found in SI Appendix, Fig. S9.