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How competition governs whether moderate or aggressive treatment minimizes antibiotic resistance.

Colijn C, Cohen T - Elife (2015)

Bottom Line: In this study, we demonstrate how one can understand and resolve these apparently contradictory conclusions.We show that a key determinant of which treatment strategy will perform best at the individual level is the extent of effective competition between resistant and sensitive pathogens within a host.We extend our analysis to the community level, exploring the spectrum between strict inter-strain competition and strain independence.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Imperial College London, London, United Kingdom.

ABSTRACT
Understanding how our use of antimicrobial drugs shapes future levels of drug resistance is crucial. Recently, there has been debate over whether an aggressive (i.e., high dose) or more moderate (i.e., lower dose) treatment of individuals will most limit the emergence and spread of resistant bacteria. In this study, we demonstrate how one can understand and resolve these apparently contradictory conclusions. We show that a key determinant of which treatment strategy will perform best at the individual level is the extent of effective competition between resistant and sensitive pathogens within a host. We extend our analysis to the community level, exploring the spectrum between strict inter-strain competition and strain independence. From this perspective as well, we find that the magnitude of effective competition between resistant and sensitive strains determines whether an aggressive approach or moderate approach minimizes the burden of resistance in the population.

No MeSH data available.


Related in: MedlinePlus

Frequency of best policies over key parameters.An aggressive policy (dark blue) is deemed best if the Spearman correlation S between treatment and resistance is S < −0.7, moderate (light blue) is deemed best if S > 0.7 and the classification is neutral (medium blue) otherwise. When the DR strain has a lower growth rate (LamR), an aggressive policy is more likely best because more of the DR strain's population arises through resistance acquisition from the DS population. In this case, reducing the DS strain also reduces DR. Conversely, when ΛR (LamR) is high the DR strain is a more robust competitor and a moderate policy is more frequently best. Similarly, when the DR strain has a low MIC (mR), it is a less robust competitor. In this case, an aggressive policy is more frequently best than when mR is high (second panel). The third panel shows that when the immune system is strong (high kp), an aggressive policy is more frequently best, because again more of the DR population increases are driven by acquisition from DS, due to immune suppression of DR growth. A plot with η on the horizontal axis is very similar to this one. Finally, the right plot shows that when the DS growth rate (LamS) is low, an aggressive strategy is more often best to minimize resistance; this depends on the ability of therapy to prevent the emergence of resistance.DOI:http://dx.doi.org/10.7554/eLife.10559.004
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fig3: Frequency of best policies over key parameters.An aggressive policy (dark blue) is deemed best if the Spearman correlation S between treatment and resistance is S < −0.7, moderate (light blue) is deemed best if S > 0.7 and the classification is neutral (medium blue) otherwise. When the DR strain has a lower growth rate (LamR), an aggressive policy is more likely best because more of the DR strain's population arises through resistance acquisition from the DS population. In this case, reducing the DS strain also reduces DR. Conversely, when ΛR (LamR) is high the DR strain is a more robust competitor and a moderate policy is more frequently best. Similarly, when the DR strain has a low MIC (mR), it is a less robust competitor. In this case, an aggressive policy is more frequently best than when mR is high (second panel). The third panel shows that when the immune system is strong (high kp), an aggressive policy is more frequently best, because again more of the DR population increases are driven by acquisition from DS, due to immune suppression of DR growth. A plot with η on the horizontal axis is very similar to this one. Finally, the right plot shows that when the DS growth rate (LamS) is low, an aggressive strategy is more often best to minimize resistance; this depends on the ability of therapy to prevent the emergence of resistance.DOI:http://dx.doi.org/10.7554/eLife.10559.004

Mentions: We took several approaches to understand how the parameters of each model relate to whether aggressive or moderate treatment minimizes resistance. The most direct approach is simply to choose a set of parameters, vary the dosage, and examine how resistance changes (Figure 2). Naturally, the result depends strongly on the parameter choice. We also vary one parameter at a time, keeping others fixed, and examine the trajectories (Appendix figures 2, 3). The next approach is to examine, over all simulations simultaneously, how the outcome depends on each parameter by stratifying the outcomes (Figure 3). Using heatmaps or scatter plots, it is also possible to explore how pairs of parameters determine an outcome (Figure 4). We take the same approach in the between-host model, with Figure 5 showing demonstrative trajectories under varying treatment strength, Appendix figure 4 showing a sensitivity analysis varying one parameter at a time, and Figures 6, 7 showing the stratified dependence of the outcome on single and paired parameters while other parameters are allowed to vary.10.7554/eLife.10559.003Figure 2.How treatment changes the trajectory of the in-host model.


How competition governs whether moderate or aggressive treatment minimizes antibiotic resistance.

Colijn C, Cohen T - Elife (2015)

Frequency of best policies over key parameters.An aggressive policy (dark blue) is deemed best if the Spearman correlation S between treatment and resistance is S < −0.7, moderate (light blue) is deemed best if S > 0.7 and the classification is neutral (medium blue) otherwise. When the DR strain has a lower growth rate (LamR), an aggressive policy is more likely best because more of the DR strain's population arises through resistance acquisition from the DS population. In this case, reducing the DS strain also reduces DR. Conversely, when ΛR (LamR) is high the DR strain is a more robust competitor and a moderate policy is more frequently best. Similarly, when the DR strain has a low MIC (mR), it is a less robust competitor. In this case, an aggressive policy is more frequently best than when mR is high (second panel). The third panel shows that when the immune system is strong (high kp), an aggressive policy is more frequently best, because again more of the DR population increases are driven by acquisition from DS, due to immune suppression of DR growth. A plot with η on the horizontal axis is very similar to this one. Finally, the right plot shows that when the DS growth rate (LamS) is low, an aggressive strategy is more often best to minimize resistance; this depends on the ability of therapy to prevent the emergence of resistance.DOI:http://dx.doi.org/10.7554/eLife.10559.004
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Related In: Results  -  Collection

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fig3: Frequency of best policies over key parameters.An aggressive policy (dark blue) is deemed best if the Spearman correlation S between treatment and resistance is S < −0.7, moderate (light blue) is deemed best if S > 0.7 and the classification is neutral (medium blue) otherwise. When the DR strain has a lower growth rate (LamR), an aggressive policy is more likely best because more of the DR strain's population arises through resistance acquisition from the DS population. In this case, reducing the DS strain also reduces DR. Conversely, when ΛR (LamR) is high the DR strain is a more robust competitor and a moderate policy is more frequently best. Similarly, when the DR strain has a low MIC (mR), it is a less robust competitor. In this case, an aggressive policy is more frequently best than when mR is high (second panel). The third panel shows that when the immune system is strong (high kp), an aggressive policy is more frequently best, because again more of the DR population increases are driven by acquisition from DS, due to immune suppression of DR growth. A plot with η on the horizontal axis is very similar to this one. Finally, the right plot shows that when the DS growth rate (LamS) is low, an aggressive strategy is more often best to minimize resistance; this depends on the ability of therapy to prevent the emergence of resistance.DOI:http://dx.doi.org/10.7554/eLife.10559.004
Mentions: We took several approaches to understand how the parameters of each model relate to whether aggressive or moderate treatment minimizes resistance. The most direct approach is simply to choose a set of parameters, vary the dosage, and examine how resistance changes (Figure 2). Naturally, the result depends strongly on the parameter choice. We also vary one parameter at a time, keeping others fixed, and examine the trajectories (Appendix figures 2, 3). The next approach is to examine, over all simulations simultaneously, how the outcome depends on each parameter by stratifying the outcomes (Figure 3). Using heatmaps or scatter plots, it is also possible to explore how pairs of parameters determine an outcome (Figure 4). We take the same approach in the between-host model, with Figure 5 showing demonstrative trajectories under varying treatment strength, Appendix figure 4 showing a sensitivity analysis varying one parameter at a time, and Figures 6, 7 showing the stratified dependence of the outcome on single and paired parameters while other parameters are allowed to vary.10.7554/eLife.10559.003Figure 2.How treatment changes the trajectory of the in-host model.

Bottom Line: In this study, we demonstrate how one can understand and resolve these apparently contradictory conclusions.We show that a key determinant of which treatment strategy will perform best at the individual level is the extent of effective competition between resistant and sensitive pathogens within a host.We extend our analysis to the community level, exploring the spectrum between strict inter-strain competition and strain independence.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, Imperial College London, London, United Kingdom.

ABSTRACT
Understanding how our use of antimicrobial drugs shapes future levels of drug resistance is crucial. Recently, there has been debate over whether an aggressive (i.e., high dose) or more moderate (i.e., lower dose) treatment of individuals will most limit the emergence and spread of resistant bacteria. In this study, we demonstrate how one can understand and resolve these apparently contradictory conclusions. We show that a key determinant of which treatment strategy will perform best at the individual level is the extent of effective competition between resistant and sensitive pathogens within a host. We extend our analysis to the community level, exploring the spectrum between strict inter-strain competition and strain independence. From this perspective as well, we find that the magnitude of effective competition between resistant and sensitive strains determines whether an aggressive approach or moderate approach minimizes the burden of resistance in the population.

No MeSH data available.


Related in: MedlinePlus