Phagocytes, Antibiotics, and Self-Limiting Bacterial Infections.

Most antibiotic use in humans is to reduce the magnitude and term of morbidity of acute, community-acquired infections in immune competent patients, rather than to save lives. Thanks to phagocytic leucocytes and other host defenses, the vast majority of these infections are self-limiting. Nevertheless, there has been a negligible amount of consideration of the contribution of phagocytosis and other host defenses in the research for, and the design of, antibiotic treatment regimens, which hyper-emphasizes antibiotics as if they were the sole mechanism responsible for the clearance of infections. Here, we critically review this approach and its limitations. With the aid of a heuristic mathematical model, we postulate that if the rate of phagocytosis is great enough, for acute, normally self-limiting infections, then (i) antibiotics with different pharmacodynamic properties would be similarly effective, (ii) low doses of antibiotics can be as effective as high doses, and (iii) neither phenotypic nor inherited antibiotic resistance generated during therapy are likely to lead to treatment failure.

The path of least resistance: aggressive or moderate treatment?

The evolution of resistance to antimicrobial chemotherapy is a major and growing cause of human mortality and morbidity. Comparatively little attention has been paid to how different patient treatment strategies shape the evolution of resistance. In particular, it is not clear whether treating individual patients aggressively with high drug dosages and long treatment durations, or moderately with low dosages and short durations can better prevent the evolution and spread of drug resistance. Here, we summarize the very limited available empirical evidence across different pathogens and provide a conceptual framework describing the information required to effectively manage drug pressure to minimize resistance evolution.

Exploring the collaboration between antibiotics and the immune response in the treatment of acute, self-limiting infections.

The successful treatment of bacterial infections is the product of a collaboration between antibiotics and the host's immune defenses. Nevertheless, in the design of antibiotic treatment regimens, few studies have explored the combined action of antibiotics and the immune response to clearing infections. Here, we use mathematical models to examine the collective contribution of antibiotics and the immune response to the treatment of acute, self-limiting bacterial infections. Our models incorporate the pharmacokinetics and pharmacodynamics of the antibiotics, the innate and adaptive immune responses, and the population and evolutionary dynamics of the target bacteria. We consider two extremes for the antibiotic-immune relationship: one in which the efficacy of the immune response in clearing infections is directly proportional to the density of the pathogen; the other in which its action is largely independent of this density. We explore the effect of antibiotic dose, dosing frequency, and term of treatment on the time before clearance of the infection and the likelihood of antibiotic-resistant bacteria emerging and ascending. Our results suggest that, under most conditions, high dose, full-term therapy is more effective than more moderate dosing in promoting the clearance of the infection and decreasing the likelihood of emergence of antibiotic resistance. Our results also indicate that the clinical and evolutionary benefits of increasing antibiotic dose are not indefinite. We discuss the current status of data in support of and in opposition to the predictions of this study, consider those elements that require additional testing, and suggest how they can be tested.

The pharmaco -, population and evolutionary dynamics of multi-drug therapy: experiments with S. aureus and E. coli and computer simulations.

There are both pharmacodynamic and evolutionary reasons to use multiple rather than single antibiotics to treat bacterial infections; in combination antibiotics can be more effective in killing target bacteria as well as in preventing the emergence of resistance. Nevertheless, with few exceptions like tuberculosis, combination therapy is rarely used for bacterial infections. One reason for this is a relative dearth of the pharmaco-, population- and evolutionary dynamic information needed for the rational design of multi-drug treatment protocols. Here, we use in vitro pharmacodynamic experiments, mathematical models and computer simulations to explore the relative efficacies of different two-drug regimens in clearing bacterial infections and the conditions under which multi-drug therapy will prevent the ascent of resistance. We estimate the parameters and explore the fit of Hill functions to compare the pharmacodynamics of antibiotics of four different classes individually and in pairs during cidal experiments with pathogenic strains of Staphylococcus aureus and Escherichia coli. We also consider the relative efficacy of these antibiotics and antibiotic pairs in reducing the level of phenotypically resistant but genetically susceptible, persister, subpopulations. Our results provide compelling support for the proposition that the nature and form of the interactions between drugs of different classes, synergy, antagonism, suppression and additivity, has to be determined empirically and cannot be inferred from what is known about the pharmacodynamics or mode of action of these drugs individually. Monte Carlo simulations of within-host treatment incorporating these pharmacodynamic results and clinically relevant refuge subpopulations of bacteria indicate that: (i) the form of drug-drug interactions can profoundly affect the rate at which infections are cleared, (ii) two-drug therapy can prevent treatment failure even when bacteria resistant to single drugs are present at the onset of therapy, and (iii) this evolutionary virtue of two-drug therapy is manifest even when the antibiotics suppress each other's activity.

Two-drug antimicrobial chemotherapy: a mathematical model and experiments with Mycobacterium marinum.

Multi-drug therapy is the standard-of-care treatment for tuberculosis. Despite this, virtually all studies of the pharmacodynamics (PD) of mycobacterial drugs employed for the design of treatment protocols are restricted to single agents. In this report, mathematical models and in vitro experiments with Mycobacterium marinum and five antimycobacterial drugs are used to quantitatively evaluate the pharmaco-, population and evolutionary dynamics of two-drug antimicrobial chemotherapy regimes. Time kill experiments with single and pairs of antibiotics are used to estimate the parameters and evaluate the fit of Hill-function-based PD models. While Hill functions provide excellent fits for the PD of each single antibiotic studied, rifampin, amikacin, clarithromycin, streptomycin and moxifloxacin, two-drug Hill functions with a unique interaction parameter cannot account for the PD of any of the 10 pairs of these drugs. If we assume two antibiotic-concentration dependent functions for the interaction parameter, one for sub-MIC and one for supra-MIC drug concentrations, the modified biphasic Hill function provides a reasonably good fit for the PD of all 10 pairs of antibiotics studied. Monte Carlo simulations of antibiotic treatment based on the experimentally-determined PD functions are used to evaluate the potential microbiological efficacy (rate of clearance) and evolutionary consequences (likelihood of generating multi-drug resistance) of these different drug combinations as well as their sensitivity to different forms of non-adherence to therapy. These two-drug treatment simulations predict varying outcomes for the different pairs of antibiotics with respect to the aforementioned measures of efficacy. In summary, Hill functions with biphasic drug-drug interaction terms provide accurate analogs for the PD of pairs of antibiotics and M. marinum. The models, experimental protocols and computer simulations used in this study can be applied to evaluate the potential microbiological and evolutionary efficacy of two-drug therapy for any bactericidal antibiotics and bacteria that can be cultured in vitro.

Functional relationship between bacterial cell density and the efficacy of antibiotics.

OBJECTIVES: To determine the functional relationship between the density of bacteria and the pharmacodynamics of antibiotics, and the potential consequences of this inoculum effect on the microbiological course of antibiotic treatment of Staphylococcus aureus infections. METHODS: In vitro time-kill, MIC estimation and antibiotic bioassay experiments were performed with S. aureus ATCC 25923 to ascertain the functional relationship between rates of kill and the MICs of six classes of antibiotics and the density of bacteria exposed. The potential consequences of the observed inoculum effects on the microbiological course of antibiotic treatment are explored with a mathematical model. RESULTS: Modest or substantial inoculum effects on efficacy were observed for all six antibiotics studied, such as density-dependent declines in the rate and extent of antibiotic-mediated killing and increases in MIC. Although these measures of antibiotic efficacy declined with inoculum, this density effect did not increase monotonically. At higher densities, the rate of kill of ciprofloxacin and oxacillin declined with the antibiotic concentration. For daptomycin and vancomycin, much of this inoculum effect is due to density-dependent reductions in the effective concentration of the antibiotic. For the other four antibiotics, this density effect is primarily associated with a decrease in per-cell antibiotic concentration. With parameters in the range estimated, our mathematical model predicts that the course of antibiotic treatment can be affected by cell density; treatment protocols based on conventional (density-independent) MICs can fail to clear higher density infections. CONCLUSIONS: The MICs used for pharmacokinetic/pharmacodynamic indices should be functions of the anticipated densities of the infecting population.

Vibrational stark effect probes for nucleic acids.

The vibrational Stark effect (VSE) has proven to be an effective method for the study of electric fields in proteins via the use of infrared probes. To explore the use of VSE in nucleic acids, we investigated the Stark spectroscopy of nine structurally diverse nucleosides. These nucleosides contained nitrile or azide probes in positions that correspond to both the major and minor grooves of DNA. The nitrile probes showed better characteristics and exhibited absorption frequencies over a broad range; that is, from 2253 cm-1 for 2'-O-cyanoethyl ribonucleosides 8 and 9 to 2102 cm(-1) for a 13C-labeled 5-thiocyanatomethyl-2'-deoxyuridine 3c. The largest Stark tuning rate observed was |Deltamu| = 1.1 cm(-1)/(MV/cm) for both 5-cyano-2'-deoxyuridine 1 and N2-nitrile-2'-deoxyguanosine 7. The latter is a particularly attractive probe because of its high extinction coefficient (epsilon = 412 M-1cm-1) and ease of incorporation into oligomers.