Pharmacodynamic functions: a multiparameter approach to the design of antibiotic treatment regimens

Invariant set theory for predicting potential failure of antibiotic cycling

Collateral sensitivity, where resistance to one drug confers heightened sensitivity to another, offers a promising strategy for combating antimicrobial resistance, yet predicting resultant evolutionary dynamics remains a significant challenge. We propose here a mathematical model that integrates fitness trade-offs and adaptive landscapes to predict the evolution of collateral sensitivity pathways, providing insights into optimizing sequential drug therapies. Our approach embeds collateral information into a network of switched systems, allowing us to abstract the effects of sequential antibiotic exposure on antimicrobial resistance. We analyze the system stability at disease-free equilibrium and employ set-control theory to tailor therapeutic windows. Consequently, we propose a computational algorithm to identify effective sequential therapies to counter antibiotic resistance. By leveraging our theory with data on collateral sensivity interactions, we predict scenarios that may prevent bacterial escape for chronic Pseudomonas aeruginosa infections.

Lineage-dependent variations in single-cell antibiotic susceptibility reveal the selective inheritance of phenotypic resistance in bacteria

Genetically identical bacterial cells often exhibit heterogeneous responses to antibiotics - some survive, others die. Here, we show that this heterogeneity propagates across generations to give rise to phenotypic resistance. Using real-time single-cell tracking, we exposed Escherichia coli to the β-lactam cefsulodin at its clinical breakpoint concentration and analyzed cell fate within genealogical trees statistically. Cell survival was strongly correlated among family members, driving the selective enrichment of robust lineages within an otherwise susceptible population. Our genealogical population model identified heritable phenotypic resistance as a key factor underlying this enrichment, which was validated experimentally. Comparing enrichment dynamics between the wild-type and a tolC knock-out strain, deficient in multidrug efflux, uncovered nuanced changes that increased the intergenerational memory of phenotypic resistance. Our findings provide evidence for heritable phenotypic resistance and demonstrate how its propagation through cell-to-cell heterogeneity enables the survival of minority cells within isogenic populations.

Fasting-induced ketogenesis sensitizes bacteria to antibiotic treatment

Fasting metabolism is a commonly observed motivational response to acute infections and is conceptualized as being beneficial for host survival. Here, we show that fasting potentiates antibiotic treatment for murine sepsis caused by Salmonella Typhimurium, Klebsiella pneumoniae, and Enterobacter cloacae, resulting in increased bacterial clearance and improved host immune responses and survival. This effect is mediated by fasting-induced ketogenesis and could be alternatively implemented by combination therapy with antibiotics and ketone bodies. We show that the ketone body acetoacetate is an effector that sensitizes bacteria to antibiotic treatment by increasing antibiotic lethality and outer and inner membrane permeability. Our results demonstrate that acetoacetate depletes bacterial amino acids, particularly positively charged amino acids and putrescine, leading to cell membrane malfunctions and redox-related lethality. This study reveals an unrecognized role of ketogenesis in antibiotic treatment and a potential ketone body-based treatment strategy for bacterial sepsis.

Epistasis-mediated compensatory evolution in a fitness landscape with adaptational tradeoffs

The evolutionary adaptation of an organism to a stressful environment often comes at the cost of reduced fitness. For example, resistance to antimicrobial drugs frequently reduces growth rate in the drug-free environment. This cost can be compensated without loss in resistance by mutations at secondary sites when the organism evolves again in the stress-free environment. Here, we analytically and numerically study evolution on a simple model fitness landscape to show that compensatory evolution can occur even in the presence of the stress and without the need for mutations at secondary sites. Fitness in the model depends on two phenotypes-the null-fitness defined as the fitness in the absence of stress, and the resistance level to the stress. Mutations universally exhibit antagonistic pleiotropy between the two phenotypes, that is they increase resistance while decreasing the null-fitness. Initial adaptation in this model occurs in a smooth region of the landscape with a rapid accumulation of stress resistance mutations and a concurrent decrease in the null-fitness. This is followed by a second, slower phase exhibiting partial recovery of the null-fitness. The second phase occurs on the rugged part of the landscape and involves the exchange of high-cost resistance mutations for low-cost ones. This process, which we call exchange compensation, is the result of changing epistatic interactions in the genotype as evolution progresses. The model provides general lessons about the tempo and mode of evolution under universal antagonistic pleiotropy with specific implications for drug resistance evolution.

How Missed Doses of Antibiotics Affect Bacteria Growth Dynamics

What should you do if you miss a dose of antibiotics? Despite the prevalence of missed antibiotic doses, there is vague or little guidance on what to do when a dose is forgotten. In this paper, we consider the effects of different patient responses after missing a dose using a mathematical model that links antibiotic concentration with bacteria dynamics. We show using simulations that, in some circumstances, (a) missing just a few doses can cause treatment failure, and (b) this failure can be remedied by simply taking a double dose after a missed dose. We then develop an approximate model that is analytically tractable and use it to understand when it might be advisable to take a double dose after a missed dose.

A theoretical exploration of protocols for treating prosthetic joint infections with combinations of antibiotics and bacteriophage

With the increase in the placement of prosthetic joints and other hardware in the body has come an increase in associated infections. These infections are particularly difficult to treat due to the underlying bacteria generating matrices which resist clearance by immune system effectors or antibiotics. These matrices, biofilms, have two primary ways of being eradicated, either by physical removal by debridement or by killing the underlying bacteria. The viruses which kill bacteria, bacteriophage, are readily capable of entering into biofilms and eradicating the bacteria therein. Therefore, bacteriophage have therapeutic potential as a supplement to antibiotics for the treatment of prosthetic joint infections. In this investigation, we generate a mathematical-computer simulation model to explore the contributions of the innate immune system with antibiotics, bacteriophage, and the joint action thereof in the control of biofilm-associated infections. Our results question the proposition that bacteriophage are an effective addition in the treatment of prosthetic joint infections.

A mechanistic approach to optimize combination antibiotic therapy

Antimicrobial resistance is one of the most significant healthcare challenges of our times. Multidrug or combination therapies are sometimes required to treat severe infections; for example, the current protocols to treat pulmonary tuberculosis combine several antibiotics. However, combination therapy is usually based on lengthy empirical trials, and it is difficult to predict its efficacy. We propose a new tool to identify antibiotic synergy or antagonism and optimize combination therapies. Our model explicitly incorporates the mechanisms of individual drug action and estimates their combined effect using a mechanistic approach. By quantifying the impact on growth and death of a bacterial population, we can identify optimal combinations of multiple drugs. Our approach also allows for the investigation of the drugs' actions and the testing of theoretical hypotheses. We demonstrate the utility of this tool with in vitro Escherichia coli data using a combination of ampicillin and ciprofloxacin. In contrast to previous interpretations, our model finds a slight synergy between the antibiotics. Our mechanistic model allows investigating possible causes of the synergy.

Adaptive evolutionary trajectories in complexity: Transitions between unicellularity and facultative differentiated multicellularity

Multicellularity spans a wide gamut in terms of complexity, from simple clonal clusters of cells to large-scale organisms composed of differentiated cells and tissues. While recent experiments have demonstrated that simple forms of multicellularity can readily evolve in response to different selective pressures, it is unknown if continued exposure to those same selective pressures will result in the evolution of increased multicellular complexity. We use mathematical models to consider the adaptive trajectories of unicellular organisms exposed to periodic bouts of abiotic stress, such as drought or antibiotics. Populations can improve survival in response to the stress by evolving multicellularity or cell differentiation-or both; however, these responses have associated costs when the stress is absent. We define a parameter space of fitness-relevant traits and identify where multicellularity, differentiation, or their combination is fittest. We then study the effects of adaptation by allowing populations to fix mutations that improve their fitness. We find that while the same mutation can be beneficial to populations of different complexity, e.g., strict unicellularity or life cycles with stages of differentiated multicellularity, the magnitudes of their effects can differ and alter which is fittest. As a result, we observe adaptive trajectories that gain and lose complexity. We also show that the order of mutations, historical contingency, can cause some transitions to be permanent in the absence of neutral evolution. Ultimately, we find that continued exposure to a selective driver for multicellularity can either lead to increasing complexity or a return to unicellularity.

Pharmacodynamics of interspecies interactions in polymicrobial infections

The pharmacodynamic response of bacterial pathogens to antibiotics can be influenced by interactions with other bacterial species in polymicrobial infections (PMIs). Understanding the complex eco-evolutionary dynamics of PMIs and their impact on antimicrobial treatment response represents a step towards developing improved treatment strategies for PMIs. Here, we investigated how interspecies interactions in a multi-species bacterial community affect the pharmacodynamic response to antimicrobial treatment. To this end, we developed an in silico model which combined agent-based modeling with ordinary differential equations. Our analyses suggest that both interspecies interactions, modifying either drug sensitivity or bacterial growth rate, and drug-specific pharmacological properties drive the bacterial pharmacodynamic response. Furthermore, lifestyle of the bacterial population and the range of interactions can influence the impact of species interactions. In conclusion, this study provides a foundation for the design of antimicrobial treatment strategies for PMIs which leverage the effects of interspecies interactions.

Nutrient conditions affect antimicrobial pharmacodynamics in Pseudomonas aeruginosa

The infectious microenvironment in chronic respiratory tract infections is characterized by substantial variability in nutrient conditions, which may impact colonization and treatment response of pathogens. Metabolic adaptation of the cystic fibrosis (CF)-associated pathogen Pseudomonas aeruginosa has been shown to lead to changes in antibiotic sensitivity. The impact of specific nutrients on the response to antibiotics is, however, poorly characterized. Here, we investigated how different carbon sources impact the antimicrobial pharmacodynamic responses in P. aeruginosa. We evaluated the effect of six antibiotics (aztreonam, ceftazidime, ciprofloxacin, colistin, imipenem, and tobramycin) on P. aeruginosa cultured in a basal medium enriched for seven different carbon sources (alanine, arginine, aspartate, glucose, glutamate, lactate, and proline). Pharmacodynamic responses were characterized by measuring time-kill profiles for a bioluminescent P. aeruginosa PAO1 Xen41 strain. We show that single-nutrient modifications minimally affected bacterial growth rate. For specific nutrient-antibiotic combinations, we find relevant alterations in antibiotic sensitivity (i.e., EC50) and the maximum drug effect (Emax), in particular for ciprofloxacin, colistin, imipenem, and tobramycin. The most pronounced effect was observed for tobramycin, where glucose was found to reduce the EC50 (0.5-fold), whereas lactate-enriched conditions led to a 4.3-fold increase in EC50. Using pharmacokinetic-pharmacodynamic simulations, we illustrate that the magnitude of the nutrient-driven pharmacodynamic changes impact treatment for clinical dosing strategies of tobramycin. In summary, this study underscores the impact of nutrient composition on antimicrobial pharmacodynamics, which could potentially contribute to observed variability of antimicrobial treatment responses in CF patients.IMPORTANCEChronic respiratory tract infections in cystic fibrosis patients present significant challenges for antibiotic treatment due to the complexity of the respiratory environment. This study investigated how variations in nutrient levels, altered during chronic infections, affect pathogen response to antibiotics in an experimental setting. By simulating different nutrient conditions, we aimed to uncover interactions between nutrient availability and antibiotic sensitivity. Our findings provide critical insights that could lead to more effective treatment strategies for managing chronic respiratory tract infections in cystic fibrosis patients while also guiding future research in improving treatment methodologies.

Unlocking the potential of experimental evolution to study drug resistance in pathogenic fungi

Exploring the dynamics and molecular mechanisms of antimicrobial drug resistance provides critical insights for developing effective strategies to combat it. This review highlights the potential of experimental evolution methods to study resistance in pathogenic fungi, drawing on insights from bacteriology and innovative approaches in mycology. We emphasize the versatility of experimental evolution in replicating clinical and environmental scenarios and propose that incorporating evolutionary modelling can enhance our understanding of antifungal resistance evolution. We advocate for a broader application of experimental evolution in medical mycology to improve our still limited understanding of drug resistance in fungi.

Acquired amphotericin B resistance leads to fitness trade-offs that can be mitigated by compensatory evolution in Candida auris

Candida auris is a growing concern due to its resistance to antifungal drugs, particularly amphotericin B (AMB), detected in 30 to 60% of clinical isolates. However, the mechanisms of AMB resistance remain poorly understood. Here we investigated 441 in vitro- and in vivo-evolved C. auris lineages from 4 AMB-susceptible clinical strains of different clades. Genetic and sterol analyses revealed four major types of sterol alterations as a result of clinically rare variations in sterol biosynthesis genes ERG6, NCP1, ERG11, ERG3, HMG1, ERG10 and ERG12. In addition, aneuploidies in chromosomes 4 and 6 emerged during resistance evolution. Fitness trade-off phenotyping and mathematical modelling identified diverse strain- and mechanism-dependent fitness trade-offs. Variation in CDC25 rescued fitness trade-offs, thereby increasing the infection capacity. This possibly contributed to therapy-induced acquired AMB resistance in the clinic. Our findings highlight sterol-modulating mechanisms and fitness trade-off compensation as risks for AMB treatment failure in clinical settings.

Spatial and temporal distribution of ribosomes in single cells reveals aging differences between old and new daughters of Escherichia coli

Lineages of rod-shaped bacteria such as Escherichia coli exhibit a temporal decline in elongation rate in a manner comparable to cellular or biological aging. The effect results from the production of asymmetrical daughters, one with a lower elongation rate, by the division of a mother cell. The slower daughter compared to the faster daughter, denoted respectively as the old and new daughters, has more aggregates of damaged proteins and fewer expressed gene products. We have examined further the degree of asymmetry by measuring the density of ribosomes between old and new daughters and between their poles. We found that ribosomes were denser in the new daughter and also in the new pole of the daughters. These ribosome patterns match the ones we previously found for expressed gene products. This outcome suggests that the asymmetry is not likely to result from properties unique to the gene expressed in our previous study, but rather from a more fundamental upstream process affecting the distribution of ribosomal abundance. Because damage aggregates and ribosomes are both more abundant at the poles of E. coli cells, we suggest that competition for space between the two could explain the reduced ribosomal density in old daughters. Using published values for aggregate sizes and the relationship between ribosomal number and elongation rates, we show that the aggregate volumes could in principle displace quantitatively the amount of ribosomes needed to reduce the elongation rate of the old daughters.

Interspecies interactions alter the antibiotic sensitivity of Pseudomonas aeruginosa

Polymicrobial infections are infections that are caused by multiple pathogens and are common in patients with cystic fibrosis (CF). Although polymicrobial infections are associated with poor treatment responses in CF, the effects of the ecological interactions between co-infecting pathogens on antibiotic sensitivity and treatment outcome are poorly characterized. To this end, we systematically quantified the impact of these effects on the antibiotic sensitivity of Pseudomonas aeruginosa for nine antibiotics in medium conditioned by 13 secondary cystic fibrosis-associated bacterial and fungal pathogens through time-kill assays. We fitted pharmacodynamic models to these kill curves for each antibiotic-species combination and found that interspecies interactions changing the antibiotic sensitivity of P. aeruginosa are abundant. Interactions that lower antibiotic sensitivity are more common than those that increase it, with generally more substantial reductions than increases in sensitivity. For a selection of co-infecting species, we performed pharmacokinetic-pharmacodynamic modeling of P. aeruginosa treatment. We predicted that interspecies interactions can either improve or reduce treatment response to the extent that treatment is rendered ineffective from a previously effective antibiotic dosing schedule and vice versa. In summary, we show that quantifying the ecological interaction effects as pharmacodynamic parameters is necessary to determine the abundance and the extent to which these interactions affect antibiotic sensitivity in polymicrobial infections.IMPORTANCEIn cystic fibrosis (CF) patients, chronic respiratory tract infections are often polymicrobial, involving multiple pathogens simultaneously. Polymicrobial infections are difficult to treat as they often respond unexpectedly to antibiotic treatment, which might possibly be explained because co-infecting pathogens can influence each other's antibiotic sensitivity, but it is unknown to what extent such effects occur. To investigate this, we systematically quantified the impact of co-infecting species on antibiotic sensitivity, focusing on P. aeruginosa, a common CF pathogen. We studied for a large set co-infecting species and antibiotics whether changes in antibiotic response occur. Based on these experiments, we used mathematical modeling to simulate P. aeruginosa's response to colistin and tobramycin treatment in the presence of multiple pathogens. This study offers comprehensive data on altered antibiotic sensitivity of P. aeruginosa in polymicrobial infections, serves as a foundation for optimizing treatment of such infections, and consolidates the importance of considering co-infecting pathogens.

Bacterial outer membrane vesicles increase polymyxin resistance in Pseudomonas aeruginosa while inhibiting its quorum sensing

The persistent emergence of multidrug-resistant bacterial pathogens is leading to a decline in the therapeutic efficacy of antibiotics, with Pseudomonas aeruginosa (P. aeruginosa) emerging as a notable threat. We investigated the antibiotic resistance and quorum sensing (QS) system of P. aeruginosa, with a particular focused on outer membrane vesicles (OMVs) and polymyxin B as the last line of antibiotic defense. Our findings indicate that OMVs increase the resistance of P. aeruginosa to polymyxin B. The overall gene transcription levels within P. aeruginosa also reveal that OMVs can reduce the efficacy of polymyxin B. However, both OMVs and sublethal concentrations of polymyxin B suppressed the transcription levels of genes associated with the QS system. Furthermore, OMVs and polymyxin B acted in concert on the QS system of P. aeruginosa to produce a more potent inhibitory effect. This suppression was evidenced by a decrease in the secretion of virulence factors, impaired bacterial motility, and a notable decline in the ability to form biofilms. These results reveal that OMVs enhance the resistance of P. aeruginosa to polymyxin B, yet they collaborate with polymyxin B to inhibit the QS system. Our research contribute to a deeper understanding of the resistance mechanisms of P. aeruginosa in the environment, and provide new insights into the reduction of bacterial infections caused by P. aeruginosa through the QS system.

The evolution of heteroresistance via small colony variants in Escherichia coli following long term exposure to bacteriostatic antibiotics

Traditionally, bacteriostatic antibiotics are agents able to arrest bacterial growth. Despite being traditionally viewed as unable to kill bacterial cells, when they are used clinically the outcome of these drugs is frequently as effective as when a bactericidal drug is used. We explore the dynamics of Escherichia coli after exposure to two ribosome-targeting bacteriostatic antibiotics, chloramphenicol and azithromycin, for thirty days. The results of our experiments provide evidence that bacteria exposed to these drugs replicate, evolve, and generate a sub-population of small colony variants (SCVs) which are resistant to multiple drugs. These SCVs contribute to the evolution of heteroresistance and rapidly revert to a susceptible state once the antibiotic is removed. Stated another way, exposure to bacteriostatic drugs selects for the evolution of heteroresistance in populations previously lacking this trait. More generally, our results question the definition of bacteriostasis as populations exposed to bacteriostatic drugs are replicating despite the lack of net growth.

The Evolution of Heteroresistance via Small Colony Variants in Escherichia coli Following Long Term Exposure to Bacteriostatic Antibiotics

Traditionally, bacteriostatic antibiotics are agents able to arrest bacterial growth. Despite being traditionally viewed as unable to kill bacterial cells, when they are used clinically the outcome of these drugs is frequently as effective as when a bactericidal drug is used. We explore the dynamics of Escherichia coli after exposure to two ribosome-targeting bacteriostatic antibiotics, chloramphenicol and azithromycin, for thirty days. The results of our experiments provide evidence that bacteria exposed to these drugs replicate, evolve, and generate a sub-population of small colony variants (SCVs) which are resistant to multiple drugs. These SCVs contribute to the evolution of heteroresistance and rapidly revert to a susceptible state once the antibiotic is removed. Stated another way, exposure to bacteriostatic drugs selects for the evolution of heteroresistance in populations previously lacking this trait. More generally, our results question the definition of bacteriostasis as populations exposed to bacteriostatic drugs are replicating despite the lack of net growth.

Semisynthetic guanidino lipoglycopeptides with potent in vitro and in vivo antibacterial activity

Gram-positive bacterial infections present a major clinical challenge, with methicillin- and vancomycin-resistant strains continuing to be a cause for concern. In recent years, semisynthetic vancomycin derivatives have been developed to overcome this problem as exemplified by the clinically used telavancin, which exhibits increased antibacterial potency but has also raised toxicity concerns. Thus, glycopeptide antibiotics with enhanced antibacterial activities and improved safety profiles are still necessary. We describe the development of a class of highly potent semisynthetic glycopeptide antibiotics, the guanidino lipoglycopeptides, which contain a positively charged guanidino moiety bearing a variable lipid group. These glycopeptides exhibited enhanced in vitro activity against a panel of Gram-positive bacteria including clinically relevant methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant strains, showed minimal toxicity toward eukaryotic cells, and had a low propensity for resistance selection. Mechanistically, guanidino lipoglycopeptides engaged with bacterial cell wall precursor lipid II with a higher binding affinity than vancomycin. Binding to both wild-type d-Ala-d-Ala lipid II and the vancomycin-resistant d-Ala-d-Lac variant was confirmed, providing insight into the enhanced activity of guanidino lipoglycopeptides against vancomycin-resistant isolates. The in vivo efficacy of guanidino lipoglycopeptide EVG7 was evaluated in a S. aureus murine thigh infection model and a 7-day sepsis survival study, both of which demonstrated superiority to vancomycin. Moreover, the minimal to mild kidney effects at supratherapeutic doses of EVG7 indicate an improved therapeutic safety profile compared with vancomycin. These findings position guanidino lipoglycopeptides as candidates for further development as antibacterial agents for the treatment of clinically relevant multidrug-resistant Gram-positive infections.

The many dimensions of combination therapy: How to combine antibiotics to limit resistance evolution

In combination therapy, bacteria are challenged with two or more antibiotics simultaneously. Ideally, separate mutations are required to adapt to each of them, which is a priori expected to hinder the evolution of full resistance. Yet, the success of this strategy ultimately depends on how well the combination controls the growth of bacteria with and without resistance mutations. To design a combination treatment, we need to choose drugs and their doses and decide how many drugs get mixed. Which combinations are good? To answer this question, we set up a stochastic pharmacodynamic model and determine the probability to successfully eradicate a bacterial population. We consider bacteriostatic and two types of bactericidal drugs-those that kill independent of replication and those that kill during replication. To establish results for a null model, we consider non-interacting drugs and implement the two most common models for drug independence-Loewe additivity and Bliss independence. Our results show that combination therapy is almost always better in limiting the evolution of resistance than administering just one drug, even though we keep the total drug dose constant for a 'fair' comparison. Yet, exceptions exist for drugs with steep dose-response curves. Combining a bacteriostatic and a bactericidal drug which can kill non-replicating cells is particularly beneficial. Our results suggest that a 50:50 drug ratio-even if not always optimal-is usually a good and safe choice. Applying three or four drugs is beneficial for treatment of strains with large mutation rates but adding more drugs otherwise only provides a marginal benefit or even a disadvantage. By systematically addressing key elements of treatment design, our study provides a basis for future models which take further factors into account. It also highlights conceptual challenges with translating the traditional concepts of drug independence to the single-cell level.

Phenotypic resistant single-cell characteristics under recurring ampicillin antibiotic exposure in Escherichia coli

Non-heritable, phenotypic drug resistance toward antibiotics challenges antibiotic therapies. Characteristics of such phenotypic resistance have implications for the evolution of heritable resistance. Diverse forms of phenotypic resistance have been described, but phenotypic resistance characteristics remain less explored than genetic resistance. Here, we add novel combinations of single-cell characteristics of phenotypic resistant E. coli cells and compare those to characteristics of susceptible cells of the parental population by exposure to different levels of recurrent ampicillin antibiotic. Contrasting expectations, we did not find commonly described characteristics of phenotypic resistant cells that arrest growth or near growth. We find that under ampicillin exposure, phenotypic resistant cells reduced their growth rate by about 50% compared to growth rates prior to antibiotic exposure. The growth reduction is a delayed alteration to antibiotic exposure, suggesting an induced response and not a stochastic switch or caused by a predetermined state as frequently described. Phenotypic resistant cells exhibiting constant slowed growth survived best under ampicillin exposure and, contrary to expectations, not only fast-growing cells suffered high mortality triggered by ampicillin but also growth-arrested cells. Our findings support diverse modes of phenotypic resistance, and we revealed resistant cell characteristics that have been associated with enhanced genetically fixed resistance evolution, which supports claims of an underappreciated role of phenotypic resistant cells toward genetic resistance evolution. A better understanding of phenotypic resistance will benefit combatting genetic resistance by developing and engulfing effective anti-phenotypic resistance strategies.

IMPORTANCE

Antibiotic resistance is a major challenge for modern medicine. Aside from genetic resistance to antibiotics, phenotypic resistance that is not heritable might play a crucial role for the evolution of antibiotic resistance. Using a highly controlled microfluidic system, we characterize single cells under recurrent exposure to antibiotics. Fluctuating antibiotic exposure is likely experienced under common antibiotic therapies. These phenotypic resistant cell characteristics differ from previously described phenotypic resistance, highlighting the diversity of modes of resistance. The phenotypic characteristics of resistant cells we identify also imply that such cells might provide a stepping stone toward genetic resistance, thereby causing treatment failure.

The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is severely constrained by random peptide mixtures

The prevalence of antibiotic-resistant pathogens has become a major threat to public health, requiring swift initiatives for discovering new strategies to control bacterial infections. Hence, antibiotic stewardship and rapid diagnostics, but also the development, and prudent use, of novel effective antimicrobial agents are paramount. Ideally, these agents should be less likely to select for resistance in pathogens than currently available conventional antimicrobials. The usage of antimicrobial peptides (AMPs), key components of the innate immune response, and combination therapies, have been proposed as strategies to diminish the emergence of resistance. Herein, we investigated whether newly developed random antimicrobial peptide mixtures (RPMs) can significantly reduce the risk of resistance evolution in vitro to that of single sequence AMPs, using the ESKAPE pathogen Pseudomonas aeruginosa (P. aeruginosa) as a model gram-negative bacterium. Infections of this pathogen are difficult to treat due the inherent resistance to many drug classes, enhanced by the capacity to form biofilms. P. aeruginosa was experimentally evolved in the presence of AMPs or RPMs, subsequentially assessing the extent of resistance evolution and cross-resistance/collateral sensitivity between treatments. Furthermore, the fitness costs of resistance on bacterial growth were studied and whole-genome sequencing used to investigate which mutations could be candidates for causing resistant phenotypes. Lastly, changes in the pharmacodynamics of the evolved bacterial strains were examined. Our findings suggest that using RPMs bears a much lower risk of resistance evolution compared to AMPs and mostly prevents cross-resistance development to other treatments, while maintaining (or even improving) drug sensitivity. This strengthens the case for using random cocktails of AMPs in favour of single AMPs, against which resistance evolved in vitro, providing an alternative to classic antibiotics worth pursuing.

Influence of β-lactam pharmacodynamics on the systems microbiology of gram-positive and gram-negative polymicrobial communities

Objectives

We sought to evaluate the pharmacodynamics of β-lactam antibacterials against polymicrobial communities of clinically relevant gram-positive and gram-negative pathogens.

Methods

Two Enterococcus faecalis isolates, two Staphylococcus aureus isolates, and three Escherichia coli isolates with varying β-lactamase production were evaluated in static time-killing experiments. Each gram-positive isolate was exposed to a concentration array of ampicillin (E. faecalis) or cefazolin (S. aureus) alone and during co-culture with an E. coli isolate that was β-lactamase-deficient, produced TEM-1, or produced KPC-3/TEM-1B. The results of the time-killing experiments were summarized using an integrated pharmacokinetic/pharmacodynamics analysis as well as mathematical modelling to fully characterize the antibacterial pharmacodynamics.

Results

In the integrated analysis, the maximum killing of ampicillin (Emax) against both E. faecalis isolates was ≥ 4.11 during monoculture experiments or co-culture with β-lactamase-deficient E. coli, whereas the Emax was reduced to ≤ 1.54 during co-culture with β-lactamase-producing E. coli. In comparison to monoculture experiments, culturing S. aureus with KPC-producing E. coli resulted in reductions of the cefazolin Emax from 3.25 and 3.71 down to 2.02 and 2.98, respectively. Two mathematical models were created to describe the interactions between E. coli and either E. faecalis or S. aureus. When in co-culture with E. coli, S. aureus experienced a reduction in its cefazolin Kmax by 24.8% (23.1%RSE). Similarly, β-lactamase-producing E. coli preferentially protected the ampicillin-resistant E. faecalis subpopulation, reducing Kmax,r by 90.1% (14%RSE).

Discussion

β-lactamase-producing E. coli were capable of protecting S. aureus and E. faecalis from exposure to β-lactam antibacterials.

Prophage maintenance is determined by environment-dependent selective sweeps rather than mutational availability

Prophages, viral sequences integrated into bacterial genomes, can be beneficial and costly. Despite the risk of prophage activation and subsequent bacterial death, active prophages are present in most bacterial genomes. However, our understanding of the selective forces that maintain prophages in bacterial populations is limited. Combining experimental evolution with stochastic modeling, we show that prophage maintenance and loss are primarily determined by environmental conditions that alter the net fitness effect of a prophage on its bacterial host. When prophages are too costly, they are rapidly lost through environment-specific sequences of selective sweeps. Conflicting selection pressures that select against the prophage but for a prophage-encoded accessory gene can maintain prophages. The dynamics of prophage maintenance additionally depend on the sociality of this accessory gene. Prophage-encoded genes that exclusively benefit the lysogen maintain prophages at higher frequencies compared with genes that benefit the entire population. That is because the latter can protect phage-free "cheaters," reducing the benefit of maintaining the prophage. Our simulations suggest that environmental variation plays a larger role than mutation rates in determining prophage maintenance. These findings highlight the complexity of selection pressures that act on mobile genetic elements and challenge our understanding of the role of environmental factors relative to random chance events in shaping the evolutionary trajectory of bacterial populations. By shedding light on the key factors that shape microbial populations in the face of environmental changes, our study significantly advances our understanding of the complex dynamics of microbial evolution and diversification.

Regulation of Cysteine Homeostasis and Its Effect on Escherichia coli Sensitivity to Ciprofloxacin in LB Medium

Cysteine and its derivatives, including H2S, can influence bacterial virulence and sensitivity to antibiotics. In minimal sulfate media, H2S is generated under stress to prevent excess cysteine and, together with incorporation into glutathione and export into the medium, is a mechanism of cysteine homeostasis. Here, we studied the features of cysteine homeostasis in LB medium, where the main source of sulfur is cystine, whose import can create excess cysteine inside cells. We used mutants in the mechanisms of cysteine homeostasis and a set of microbiological and biochemical methods, including the real-time monitoring of sulfide and oxygen, the determination of cysteine and glutathione (GSH), and the expression of the Fur, OxyR, and SOS regulons genes. During normal growth, the parental strain generated H2S when switching respiration to another substrate. The mutations affected the onset time, the intensity and duration of H2S production, cysteine and glutathione levels, bacterial growth and respiration rates, and the induction of defense systems. Exposure to chloramphenicol and high doses of ciprofloxacin increased cysteine content and GSH synthesis. A high inverse relationship between log CFU/mL and bacterial growth rate before ciprofloxacin addition was revealed. The study points to the important role of maintaining cysteine homeostasis during normal growth and antibiotic exposure in LB medium.

Theoretical considerations and empirical predictions of the pharmaco- and population dynamics of heteroresistance

Antibiotics are considered one of the most important contributions to clinical medicine in the last century. Due to the use and overuse of these drugs, there have been increasing frequencies of infections with resistant pathogens. One form of resistance, heteroresistance, is particularly problematic; pathogens appear sensitive to a drug by common susceptibility tests. However, upon exposure to the antibiotic, resistance rapidly ascends, and treatment fails. To quantitatively explore the processes contributing to the emergence and ascent of resistance during treatment and the waning of resistance following cessation of treatment, we develop two distinct mathematical and computer-simulation models of heteroresistance. In our analysis of the properties of these models, we consider the factors that determine the response to antibiotic-mediated selection. In one model, heteroresistance is progressive, with each resistant state sequentially generating a higher resistance level. In the other model, heteroresistance is non-progressive, with a susceptible population directly generating populations with different resistance levels. The conditions where resistance will ascend in the progressive model are narrower than those of the non-progressive model. The rates of reversion from the resistant to the sensitive states are critically dependent on the transition rates and the fitness cost of resistance. Our results demonstrate that the standard test used to identify heteroresistance is insufficient. The predictions of our models are consistent with empirical results. Our results demand a reevaluation of the definition and criteria employed to identify heteroresistance. We recommend that the definition of heteroresistance should include a consideration of the rate of return to susceptibility.

Antibiotic dose and nutrient availability differentially drive the evolution of antibiotic resistance and persistence

Effective treatment of bacterial infections proves increasingly challenging due to the emergence of bacterial variants that endure antibiotic exposure. Antibiotic resistance and persistence have been identified as two major bacterial survival mechanisms, and several studies have shown a rapid and strong selection of resistance or persistence mutants under repeated drug treatment. Yet, little is known about the impact of the environmental conditions on resistance and persistence evolution and the potential interplay between both phenotypes. Based on the distinct growth and survival characteristics of resistance and persistence mutants, we hypothesized that the antibiotic dose and availability of nutrients during treatment might play a key role in the evolutionary adaptation to antibiotic stress. To test this hypothesis, we combined high-throughput experimental evolution with a mathematical model of bacterial evolution under intermittent antibiotic exposure. We show that high nutrient levels during antibiotic treatment promote selection of high-level resistance, but that resistance mainly emerges independently of persistence when the antibiotic concentration is sufficiently low. At higher doses, resistance evolution is facilitated by the preceding or concurrent selection of persistence mutants, which ensures survival of populations in harsh conditions. Collectively, our experimental data and mathematical model elucidate the evolutionary routes toward increased bacterial survival under different antibiotic treatment schedules, which is key to designing effective antibiotic therapies.

A Pharmacodynamic Study of Aminoglycosides against Pathogenic E. coli through Monte Carlo Simulation

This research focuses on combating the increasing problem of antimicrobial resistance, especially in Escherichia coli (E. coli), by assessing the efficacy of aminoglycosides. The study specifically addresses the challenge of developing new therapeutic approaches by integrating experimental data with mathematical modeling to better understand the action of aminoglycosides. It involves testing various antibiotics like streptomycin (SMN), kanamycin (KMN), gentamicin (GMN), tobramycin (TMN), and amikacin (AKN) against the O157:H7 strain of E. coli. The study employs a pharmacodynamics (PD) model to analyze how different antibiotic concentrations affect bacterial growth, utilizing minimum inhibitory concentration (MIC) to gauge the effective bactericidal levels of the antibiotics. The study's approach involved transforming bacterial growth rates, as obtained from time-kill curve data, into logarithmic values. A model was then developed to correlate these log-transformed values with their respective responses. To generate additional data points, each value was systematically increased by an increment of 0.1. To simulate real-world variability and randomness in the data, a Gaussian scatter model, characterized by parameters like κ and EC50, was employed. The mathematical modeling was pivotal in uncovering the bactericidal properties of these antibiotics, indicating different PD MIC (zMIC) values for each (SMN: 1.22; KMN: 0.89; GMN: 0.21; TMN: 0.32; AKN: 0.13), which aligned with MIC values obtained through microdilution methods. This innovative blend of experimental and mathematical approaches in the study marks a significant advancement in formulating strategies to combat the growing threat of antimicrobial-resistant E. coli, offering a novel pathway to understand and tackle antimicrobial resistance more effectively.

Optimizing Antimicrobial Treatment Schedules: Some Fundamental Analytical Results

This work studies fundamental questions regarding the optimal design of antimicrobial treatment protocols, using pharmacodynamic and pharmacokinetic mathematical models. We consider the problem of designing an antimicrobial treatment schedule to achieve eradication of a microbial infection, while minimizing the area under the time-concentration curve (AUC), which is equivalent to minimizing the cumulative dosage. We first solve this problem under the assumption that an arbitrary antimicrobial concentration profile may be chosen, and prove that the ideal concentration profile consists of a constant concentration over a finite time duration, where explicit expressions for the optimal concentration and the time duration are given in terms of the pharmacodynamic parameters. Since antimicrobial concentration profiles are induced by a dosing schedule and the antimicrobial pharmacokinetics, the 'ideal' concentration profile is not strictly feasible. We therefore also investigate the possibility of achieving outcomes which are close to those provided by the 'ideal' concentration profile, using a bolus+continuous dosing schedule, which consists of a loading dose followed by infusion of the antimicrobial at a constant rate. We explicitly find the optimal bolus+continuous dosing schedule, and show that, for realistic parameter ranges, this schedule achieves results which are nearly as efficient as those attained by the 'ideal' concentration profile. The optimality results obtained here provide a baseline and reference point for comparison and evaluation of antimicrobial treatment plans.

Theoretical Considerations and Empirical Predictions of the Pharmaco- and Population Dynamics of Heteroresistance

Antibiotics are considered one of the most important contributions to clinical medicine in the last 100 years. Due to the use and overuse of these drugs, there have been increasing frequencies of infections with resistant pathogens. One form of resistance, heteroresistance, is particularly problematic; pathogens appear sensitive to a drug by common susceptibility tests. However, upon exposure to the antibiotic, resistance rapidly ascends, and treatment fails. To quantitatively explore the processes contributing to the emergence and ascent of resistance during treatment and the waning of resistance following cessation of treatment, we develop two distinct mathematical and computer-simulations models of heteroresistance. In our analysis of the properties of these models, we consider the factors that determine the response to antibiotic-mediated selection. In one model, heteroresistance is progressive, with each resistant state sequentially generating a higher resistance level. In the other model, heteroresistance is non-progressive, with a susceptible population directly generating populations with different resistance levels. The conditions where resistance will ascend in the progressive model are narrower than those of the non-progressive model. The rates of reversion from the resistant to the sensitive states are critically dependent on the transition rates and the fitness cost of resistance. Our results demonstrate that the standard test used to identify heteroresistance is insufficient. The predictions of our models are consistent with empirical results. Our results demand a reevaluation of the definition and criteria employed to identify heteroresistance. We recommend the definition of heteroresistance should include a consideration of the rate of return to susceptibility.

Mathematical Model for Growth and Rifampicin-Dependent Killing Kinetics of Escherichia coli Cells

Antibiotic resistance is a global health threat. We urgently need better strategies to improve antibiotic use to combat antibiotic resistance. Currently, there are a limited number of antibiotics in the treatment repertoire of existing bacterial infections. Among them, rifampicin is a broad-spectrum antibiotic against various bacterial pathogens. However, during rifampicin exposure, the appearance of persisters or resisters decreases its efficacy. Hence, to benefit more from rifampicin, its current standard dosage might be reconsidered and explored using both computational tools and experimental or clinical studies. In this study, we present the mathematical relationship between the concentration of rifampicin and the growth and killing kinetics of Escherichia coli cells. We generated time-killing curves of E. coli cells in the presence of 4, 16, and 32 μg/mL rifampicin exposures. We specifically focused on the oscillations with decreasing amplitude over time in the growth and killing kinetics of rifampicin-exposed E. coli cells. We propose the solution form of a second-order linear differential equation for a damped oscillator to represent the mathematical relationship. We applied a nonlinear curve fitting solver to time-killing curve data to obtain the model parameters. The results show a high fitting accuracy.

Time-Kill Analysis of Canine Skin Pathogens: A Comparison of Pradofloxacin and Marbofloxacin

Time-kill curves (TKCs) are more informative compared with the use of minimum inhibitory concentration (MIC) as they allow the capture of bacterial growth and the development of drug killing rates over time, which allows to compute key pharmacodynamic (PD) parameters. Our study aimed, using a semi-mechanistic mathematical model, to estimate the best pharmacokinetic/pharmacodynamic (PK/PD) indices (ƒAUC/MIC or %ƒT > MIC) for the prediction of clinical efficacy of veterinary FQs in Staphylococcus pseudintermedius, Staphylococcus aureus, and Escherichia coli collected from canine pyoderma cases with a focus on the comparison between marbofloxacin and pradofloxacin. Eight TCKs for each bacterial species (4 susceptible and 4 resistant) were analysed in duplicate. The best PK/PD index was ƒAUC24h/MIC in both staphylococci and E. coli. For staphylococci, values of 25-40 h were necessary to achieve a bactericidal effect, whereas the calculated values (25-35 h) for E. coli were lower than those predicting a positive clinical outcome (100-120 h) in murine models. Pradofloxacin showed a higher potency (lower EC50) in comparison with marbofloxacin. However, no difference in terms of a maximal possible pharmacological killing rate (Emax) was observed. Taking into account in vivo exposure at the recommended dosage regimen (3 and 2 mg/kg for pradofloxacin and marbofloxacin, respectively), the overall killing rates (Kdrug) computed were also similar in most instances.

Antibiotic-degrading resistance changes bacterial community structure via species-specific responses

Some bacterial resistance mechanisms degrade antibiotics, potentially protecting neighbouring susceptible cells from antibiotic exposure. We do not yet understand how such effects influence bacterial communities of more than two species, which are typical in nature. Here, we used experimental multispecies communities to test the effects of clinically important pOXA-48-plasmid-encoded resistance on community-level responses to antibiotics. We found that resistance in one community member reduced antibiotic inhibition of other species, but some benefitted more than others. Further experiments with supernatants and pure-culture growth assays showed the susceptible species profiting most from detoxification were those that grew best at degraded antibiotic concentrations (greater than zero, but lower than the starting concentration). This pattern was also observed on agar surfaces, and the same species also showed relatively high survival compared to most other species during the initial high-antibiotic phase. By contrast, we found no evidence of a role for higher-order interactions or horizontal plasmid transfer in community-level responses to detoxification in our experimental communities. Our findings suggest carriage of an antibiotic-degrading resistance mechanism by one species can drastically alter community-level responses to antibiotics, and the identities of the species that profit most from antibiotic detoxification are predicted by their intrinsic ability to survive and grow at changing antibiotic concentrations.

Potent antibacterial activity in surgical wounds with local administration of D-PLEX100

Despite significant advances in infection control guidelines and practices, surgical site infections remain a substantial cause of morbidity, prolonged hospitalization, and mortality. The most effective component of SSI reduction strategies is the preoperative administration of intravenous antibiotics; however, systemic antibiotics drug exposure diminishes rapidly and may result in insufficient prophylactic activity against susceptible and resistant SSI pathogens at the wound. D-PLEX100 (D-PLEX) is an antibiotic-releasing drug (doxycycline) that is supplied as a sterile powder for paste reconstitution with sterile saline. D-PLEX paste is administered locally into the incision site along the entire length of soft tissue and sternal bone wound surfaces prior to skin closure. A single D-PLEX administration is intended for 30 days of constant antimicrobial prophylaxis in the prevention of incisional SSIs. We evaluated D-PLEX minimal bactericidal concentration (MBC) against a panel of bacteria that is prevalent in the abdominal wall and sternal surgical procedures including doxycycline susceptible and resistant strains. D-PLEX in vivo efficacy was assessed in incisional infection rabbit models (abdominal wall and sternal) challenged with a similar bacterial panel. The D-PLEX drug exposure profile was determined by in vitro release assay, and in vivo by quantitative pharmacokinetic parameters of local and systemic doxycycline concentrations released from D-PLEX after local administration in incisional rabbit models. Analyses of pathogens and variations in antibiotic resistance from wound isolates were determined from patients who participated in a previously reported prospective randomized trial that assessed the SSI rate in D-PLEX plus standard of care (SOC) versus SOC alone in colorectal resection surgery. The D-PLEX MBC values demonstrated >3- Log10 reduction in all the organisms tested relative to untreated controls, including doxycycline-resistant bacteria (i.e., Methicillin-resistant Staphylococcus aureus (MRSA), K. pneumoniae, and P. aeruginosa). In vivo, D-PLEX significantly reduced the bacterial loads in all the bacteria tested in both animal models (p=0.0001) with a marked impact observed in E. Coli (>6.5 Log10 reduction). D-PLEX exhibited a zero-order release kinetics profile in vitro for 30 days (R2 = 0.971) and the matched in vivo release profile indicated a constant local release of protein-unbound doxycycline for 30 days at 3-5 mcg/mL with significantly lower (>3 orders of magnitudes) systemic levels. In colorectal surgery patients, where significant SSI reduction was observed, analysis of the positive cultures in the overall population indicated similar pathogen diversity and antibiotic resistance rates in both treatment arms. However, almost all the patients with positive culture in the SOC arm were adjudicated as SSI (94%) compared to only 28% in the D-PLEX arm. The SSI-adjudicated D-PLEX patients also exhibited lower resistance rates to the SOC antibiotics and to MDRs compared to patients in the SOC arm. Thus, D-PLEX provides safe and effective prophylaxis activity against the most prevalent SSI pathogens including doxycycline-susceptible and resistant bacteria. Our findings suggest that D-PLEX is a promising addition to SSI prophylactic bundles and may address the gaps in current SSI prophylaxis. D-PLEX is now evaluated in Phase 3 clinical trial.

Synergy between Winter Flounder antimicrobial peptides

Some antimicrobial peptides (AMPs) have potent bactericidal activity and are being considered as potential alternatives to classical antibiotics. In response to an infection, such AMPs are often produced in animals alongside other peptides with low or no perceivable antimicrobial activity, whose role is unclear. Here we show that six AMPs from the Winter Flounder (WF) act in synergy against a range of bacterial pathogens and provide mechanistic insights into how this increases the cooperativity of the dose-dependent bactericidal activity and potency that enable therapy. Only two WF AMPs have potent antimicrobial activity when used alone but we find a series of two-way combinations, involving peptides which otherwise have low or no activity, yield potent antimicrobial activity. Weakly active WF AMPs modulate the membrane interactions of the more potent WF AMPs and enable therapy in a model of Acinetobacter baumannii burn wound infection. The observed synergy and emergent behaviour may explain the evolutionary benefits of producing a family of related peptides and are attractive properties to consider when developing AMPs towards clinical applications.

Solving the stochastic dynamics of population growth

Population growth is a fundamental process in ecology and evolution. The population size dynamics during growth are often described by deterministic equations derived from kinetic models. Here, we simulate several population growth models and compare the size averaged over many stochastic realizations with the deterministic predictions. We show that these deterministic equations are generically bad predictors of the average stochastic population dynamics. Specifically, deterministic predictions overestimate the simulated population sizes, especially those of populations starting with a small number of individuals. Describing population growth as a stochastic birth process, we prove that the discrepancy between deterministic predictions and simulated data is due to unclosed-moment dynamics. In other words, the deterministic approach does not consider the variability of birth times, which is particularly important with small population sizes. We show that some moment-closure approximations describe the growth dynamics better than the deterministic prediction. However, they do not reduce the error satisfactorily and only apply to some population growth models. We explicitly solve the stochastic growth dynamics, and our solution applies to any population growth model. We show that our solution exactly quantifies the dynamics of a community composed of different strains and correctly predicts the fixation probability of a strain in a serial dilution experiment. Our work sets the foundations for a more faithful modeling of community and population dynamics. It will allow the development of new tools for a more accurate analysis of experimental and empirical results, including the inference of important growth parameters.

A stochastic analysis of the interplay between antibiotic dose, mode of action, and bacterial competition in the evolution of antibiotic resistance

The use of an antibiotic may lead to the emergence and spread of bacterial strains resistant to this antibiotic. Experimental and theoretical studies have investigated the drug dose that minimizes the risk of resistance evolution over the course of treatment of an individual, showing that the optimal dose will either be the highest or the lowest drug concentration possible to administer; however, no analytical results exist that help decide between these two extremes. To address this gap, we develop a stochastic mathematical model of bacterial dynamics under antibiotic treatment. We explore various scenarios of density regulation (bacterial density affects cell birth or death rates), and antibiotic modes of action (biostatic or biocidal). We derive analytical results for the survival probability of the resistant subpopulation until the end of treatment, the size of the resistant subpopulation at the end of treatment, the carriage time of the resistant subpopulation until it is replaced by a sensitive one after treatment, and we verify these results with stochastic simulations. We find that the scenario of density regulation and the drug mode of action are important determinants of the survival of a resistant subpopulation. Resistant cells survive best when bacterial competition reduces cell birth and under biocidal antibiotics. Compared to an analogous deterministic model, the population size reached by the resistant type is larger and carriage time is slightly reduced by stochastic loss of resistant cells. Moreover, we obtain an analytical prediction of the antibiotic concentration that maximizes the survival of resistant cells, which may help to decide which drug dosage (not) to administer. Our results are amenable to experimental tests and help link the within and between host scales in epidemiological models.

Alternate Antimicrobial Therapies and Their Companion Tests

New antimicrobial approaches are essential to counter antimicrobial resistance. The drug development pipeline is exhausted with the emergence of resistance, resulting in unsuccessful trials. The lack of an effective drug developed from the conventional drug portfolio has mandated the introspection into the list of potentially effective unconventional alternate antimicrobial molecules. Alternate therapies with clinically explicable forms include monoclonal antibodies, antimicrobial peptides, aptamers, and phages. Clinical diagnostics optimize the drug delivery. In the era of diagnostic-based applications, it is logical to draw diagnostic-based treatment for infectious diseases. Selection criteria of alternate therapeutics in infectious diseases include detection, monitoring of response, and resistance mechanism identification. Integrating these diagnostic applications is disruptive to the traditional therapeutic development. The challenges and mitigation methods need to be noted. Applying the goals of clinical pharmacokinetics that include enhancing efficacy and decreasing toxicity of drug therapy, this review analyses the strong correlation of alternate antimicrobial therapeutics in infectious diseases. The relationship between drug concentration and the resulting effect defined by the pharmacodynamic parameters are also analyzed. This review analyzes the perspectives of aligning diagnostic initiatives with the use of alternate therapeutics, with a particular focus on companion diagnostic applications in infectious diseases.

The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution

Pharmacokinetic-pharmacodynamic (PKPD) models, which describe how drug concentrations change over time and how that affects pathogen growth, have proven highly valuable in designing optimal drug treatments aimed at bacterial eradication. However, the fast rise of antimicrobial resistance calls for increased focus on an additional treatment optimization criterion: avoidance of resistance evolution. We demonstrate here how coupling PKPD and population genetics models can be used to determine treatment regimens that minimize the potential for antimicrobial resistance evolution. Importantly, the resulting modelling framework enables the assessment of resistance evolution in response to dynamic selection pressures, including changes in antimicrobial concentration and the emergence of adaptive phenotypes. Using antibiotics and antimicrobial peptides as an example, we discuss the empirical evidence and intuition behind individual model parameters. We further suggest several extensions of this framework that allow a more comprehensive and realistic prediction of bacterial escape from antimicrobials through various phenotypic and genetic mechanisms.

What's the Matter with MICs: Bacterial Nutrition, Limiting Resources, and Antibiotic Pharmacodynamics

The MIC of an antibiotic required to prevent replication is used both as a measure of the susceptibility/resistance of bacteria to that drug and as the single pharmacodynamic parameter for the rational design of antibiotic treatment regimes. MICs are experimentally estimated in vitro under conditions optimal for the action of the antibiotic. However, bacteria rarely grow in these optimal conditions. Using a mathematical model of the pharmacodynamics of antibiotics, we make predictions about the nutrient dependency of bacterial growth in the presence of antibiotics. We test these predictions with experiments in broth and a glucose-limited minimal media with Escherichia coli and eight different antibiotics. Our experiments question the sufficiency of using MICs and simple pharmacodynamic functions as measures of the pharmacodynamics of antibiotics under the nutritional conditions of infected tissues. To an extent that varies among drugs: (i) the estimated MICs obtained in rich media are greater than those estimated in minimal media; (ii) exposure to these drugs increases the time before logarithmic growth starts, their lag; and (iii) the stationary-phase density of E. coli populations declines with greater sub-MIC antibiotic concentrations. We postulate a mechanism to account for the relationship between sub-MICs of antibiotics and these growth parameters. This study is limited to a single bacterial strain and two types of culture media with different nutritive content. These limitations aside, the results of our study clearly question the use of MIC as the unique pharmacodynamic parameter to develop therapeutically oriented protocols. IMPORTANCE For studies of antibiotics and how they work, the most-often used measurement of drug efficacy is the MIC. The MIC is the concentration of an antibiotic needed to inhibit bacterial growth. This parameter is critical to the design and implementation of antibiotic therapy. We provide evidence that the use of MIC as the sole measurement for antibiotic efficacy ignores important aspects of bacterial growth dynamics. Before now, there has not been a nexus between bacteria, the conditions in which they grow, and the MIC. Most importantly, few studies have considered sub-MICs of antibiotics, despite their clinical importance. Here, we explore these concentrations in-depth, and we demonstrate MIC to be an incomplete measure of how an infection will interact with a specific antibiotic. Understanding the critiques of MIC is the first of many steps needed to improve infectious disease treatment.

Evolutionary rescue in a fluctuating environment: periodic versus quasi-periodic environmental changes

No environment is constant over time, and environmental fluctuations impact the outcome of evolutionary dynamics. Survival of a population not adapted to some environmental conditions is threatened unless, for example, a mutation rescues it, an eco-evolutionary process termed evolutionary rescue. We here investigate evolutionary rescue in an environment that fluctuates between a favourable state, in which the population grows, and a harsh state, in which the population declines. We develop a stochastic model that includes both population dynamics and genetics. We derive analytical predictions for the mean extinction time of a non-adapted population given that it is not rescued, the probability of rescue by a mutation, and the mean appearance time of a rescue mutant, which we validate using numerical simulations. We find that stochastic environmental fluctuations, resulting in quasi-periodic environmental changes, accelerate extinction and hinder evolutionary rescue compared with deterministic environmental fluctuations, resulting in periodic environmental changes. We demonstrate that high equilibrium population sizes and per capita growth rates maximize the chances of evolutionary rescue. We show that an imperfectly harsh environment, which does not fully prevent births but makes the death rate to birth rate ratio much greater than unity, has almost the same rescue probability as a perfectly harsh environment, which fully prevents births. Finally, we put our results in the context of antimicrobial resistance and conservation biology.

Bolaamphiphile Analogues of 12-bis-THA Cl2 Are Potent Antimicrobial Therapeutics with Distinct Mechanisms of Action against Bacterial, Mycobacterial, and Fungal Pathogens

12-Bis-THA Cl2 [12,12'-(dodecane-1,12-diyl)-bis-(9-amino-1,2,3,4-tetrahydroacridinium) chloride] is a cationic bolalipid adapted from dequalinium chloride (DQC), a bactericidal anti-infective indicated for bacterial vaginosis (BV). Here, we used a structure-activity-relationship study to show that the factors that determine effective killing of bacterial, fungal, and mycobacterial pathogens differ, to generate new analogues with a broader spectrum of activity, and to identify synergistic relationships, most notably with aminoglycosides against Acinetobacter baumannii and Pseudomonas aeruginosa, where the bactericidal killing rate was substantially increased. Like DQC, 12-bis-THA Cl2 and its analogues accumulate within bacteria and fungi. More hydrophobic analogues with larger headgroups show reduced potential for DNA binding but increased and broader spectrum antibacterial activity. In contrast, analogues with less bulky headgroups and stronger DNA binding affinity were more active against Candida spp. Shortening the interconnecting chain, from the most lipophilic twelve-carbon chain to six, improved the selectivity index against Mycobacterium tuberculosis in vitro, but only the longer chain analogue was therapeutic in a Galleria mellonella infection model, with the shorter chain analogue exacerbating the infection. In vivo therapy of Escherichia coli ATCC 25922 and epidemic methicillin-resistant Staphylococcus aureus 15 (EMRSA-15) infections in Galleria mellonella was also achieved with longer-chain analogues, as was therapy for an A. baumannii 17978 burn wound infection with a synergistic combination of bolaamphiphile and gentamicin. The present study shows how this class of bolalipids may be adapted further to enable a wider range of potential applications. IMPORTANCE While we face an acute threat from antibiotic resistant bacteria and a lack of new classes of antibiotic, there are many effective antimicrobials which have limited application due to concerns regarding their toxicity and which could be more useful if such risks are reduced or eliminated. We modified a bolalipid antiseptic used in throat lozenges to see if it could be made more effective against some of the highest-priority bacteria and less toxic. We found that structural modifications that rendered the lipid more toxic against human cells made it less toxic in infection models and we could effectively treat caterpillars infected with either Mycobacterium tuberculosis, methicillin resistant Staphylococcus aureus, or Acinetobacter baumannii. The study provides a rationale for further adaptation toward diversifying the range of indications in which this class of antimicrobial may be used.

Sequential antibiotic therapy in the laboratory and in the patient

Laboratory experiments suggest that rapid cycling of antibiotics during the course of treatment could successfully counter resistance evolution. Drugs involving collateral sensitivity could be particularly suitable for such therapies. However, the environmental conditions in vivo differ from those in vitro. One key difference is that drugs can be switched abruptly in the laboratory, while in the patient, pharmacokinetic processes lead to changing antibiotic concentrations including periods of dose overlaps from consecutive administrations. During such overlap phases, drug-drug interactions may affect the evolutionary dynamics. To address the gap between the laboratory and potential clinical applications, we set up two models for comparison-a 'laboratory model' and a pharmacokinetic-pharmacodynamic 'patient model'. The analysis shows that in the laboratory, the most rapid cycling suppresses the bacterial population always at least as well as other regimens. For patient treatment, however, a little slower cycling can sometimes be preferable if the pharmacodynamic curve is steep or if drugs interact antagonistically. When resistance is absent prior to treatment, collateral sensitivity brings no substantial benefit unless the cell division rate is low and drug cycling slow. By contrast, drug-drug interactions strongly influence the treatment efficiency of rapid regimens, demonstrating their importance for the optimal choice of drug pairs.

Pharmacokinetic study of high-dose oral rifampicin in critically Ill patients with multidrug-resistant Acinetobacter baumannii infection

PURPOSE

Although rifampicin (RIF) is used as a synergistic agent for multidrug-resistant Acinetobacter baumannii (MDR-AB) infection, the optimal pharmacokinetic (PK) indices of this medication have not been studied in the intensive care unit (ICU) settings. This study aimed to evaluate the PK of high dose oral RIF following fasting versus fed conditions in terms of achieving the therapeutic goals in critically ill patients with MDR-AB infections.

METHODS

29 critically ill patients were included in this study. Under fasting and non-fasting conditions, RIF was given at 1200 mg once daily through a nasogastric tube. Blood samples were obtained at seven time points: exactly before administration of the drug, and at 1, 2, 4, 8, 12, and 24 h after RIF ingestion. To quantify RIF in serum samples, high-performance liquid chromatography (HPLC) was used. The MONOLIX Software and the Monte Carlo simulations were employed to estimate the PK parameters and describe the population PK model.

RESULTS

The mean area under the curve over the last 24-h (AUC0-24) value and accuracy (mean ± standard deviation) in the fasting and fed states were 220.24 ± 119.15 and 290.55 ± 276.20 μg × h/mL, respectively. There was no significant difference among AUCs following fasting and non-fasting conditions (P > 0.05). The probability of reaching the therapeutic goals at the minimum inhibitory concentration (MIC) of 4 mg/L, was only 1.6%.

CONCLUSION

In critically ill patients with MDR-AB infections, neither fasting nor non-fasting administrations of high-dose oral RIF achieve the therapeutic aims. More research is needed in larger populations and with measuring the amount of protein-unbound RIF levels.

Modelling the synergistic effect of bacteriophage and antibiotics on bacteria: Killers and drivers of resistance evolution

Bacteriophage (phage) are bacterial predators that can also spread antimicrobial resistance (AMR) genes between bacteria by generalised transduction. Phage are often present alongside antibiotics in the environment, yet evidence of their joint killing effect on bacteria is conflicted, and the dynamics of transduction in such systems are unknown. Here, we combine in vitro data and mathematical modelling to identify conditions where phage and antibiotics act in synergy to remove bacteria or drive AMR evolution. We adapt a published model of phage-bacteria dynamics, including transduction, to add the pharmacodynamics of erythromycin and tetracycline, parameterised from new in vitro data. We simulate a system where two strains of Staphylococcus aureus are present at stationary phase, each carrying either an erythromycin or tetracycline resistance gene, and where multidrug-resistant bacteria can be generated by transduction only. We determine rates of bacterial clearance and multidrug-resistant bacteria appearance, when either or both antibiotics and phage are present at varying timings and concentrations. Although phage and antibiotics act in synergy to kill bacteria, by reducing bacterial growth antibiotics reduce phage production. A low concentration of phage introduced shortly after antibiotics fails to replicate and exert a strong killing pressure on bacteria, instead generating multidrug-resistant bacteria by transduction which are then selected for by the antibiotics. Multidrug-resistant bacteria numbers were highest when antibiotics and phage were introduced simultaneously. The interaction between phage and antibiotics leads to a trade-off between a slower clearing rate of bacteria (if antibiotics are added before phage), and a higher risk of multidrug-resistance evolution (if phage are added before antibiotics), exacerbated by low concentrations of phage or antibiotics. Our results form hypotheses to guide future experimental and clinical work on the impact of phage on AMR evolution, notably for studies of phage therapy which should investigate varying timings and concentrations of phage and antibiotics.

Assessing the relative importance of bacterial resistance, persistence and hyper-mutation for antibiotic treatment failure

To curb the rising threat of antimicrobial resistance, we need to understand the routes to antimicrobial treatment failure. Bacteria can survive treatment by using both genetic and phenotypic mechanisms to diminish the effect of antimicrobials. We assemble empirical data showing that, for example, Pseudomonas aeruginosa infections frequently contain persisters, transiently non-growing cells unaffected by antibiotics (AB) and hyper-mutators, mutants with elevated mutation rates, and thus higher probability of genetic resistance emergence. Resistance, persistence and hyper-mutation dynamics are difficult to disentangle experimentally. Hence, we use stochastic population modelling and deterministic fitness calculations to investigate the relative importance of genetic and phenotypic mechanisms for immediate treatment failure and establishment of prolonged, chronic infections. We find that persistence causes 'hidden' treatment failure with very low cell numbers if antimicrobial concentrations prevent growth of genetically resistant cells. Persister cells can regrow after treatment is discontinued and allow for resistance evolution in the absence of AB. This leads to different mutational routes during treatment and relapse of an infection. By contrast, hyper-mutation facilitates resistance evolution during treatment, but rarely contributes to treatment failure. Our findings highlight the time and concentration dependence of different bacterial mechanisms to escape AB killing, which should be considered when designing 'failure-proof' treatments.

Editorial: Antibiotics Special Issue on Pharmacokinetic/Pharmacodynamic Models of Antibiotics

Pharmacokinetic/pharmacodynamic (PK/PD) modeling is an essential tool for rational drug development and treatment design [...]

A computational model for microbial colonization of an antifouling surface

Biofouling of marine surfaces such as ship hulls is a major industrial problem. Antifouling (AF) paints delay the onset of biofouling by releasing biocidal chemicals. We present a computational model for microbial colonization of a biocide-releasing AF surface. Our model accounts for random arrival from the ocean of microorganisms with different biocide resistance levels, biocide-dependent proliferation or killing, and a transition to a biofilm state. Our computer simulations support a picture in which biocide-resistant microorganisms initially form a loosely attached layer that eventually transitions to a growing biofilm. Once the growing biofilm is established, immigrating microorganisms are shielded from the biocide, allowing more biocide-susceptible strains to proliferate. In our model, colonization of the AF surface is highly stochastic. The waiting time before the biofilm establishes is exponentially distributed, suggesting a Poisson process. The waiting time depends exponentially on both the concentration of biocide at the surface and the rate of arrival of resistant microorganisms from the ocean. Taken together our results suggest that biofouling of AF surfaces may be intrinsically stochastic and hence unpredictable, but immigration of more biocide-resistant species, as well as the biological transition to biofilm physiology, may be important factors controlling the time to biofilm establishment.

Antimicrobial resistance: new insights and therapeutic implications

Antimicrobial resistance has not been a new phenomenon. Still, the number of resistant organisms, the geographic areas affected by emerging drug resistance, and the magnitude of resistance in a single organism are enormous and mounting. Disease and disease-causing agents formerly thought to be contained by antibiotics are now returning in new forms resistant to existing therapies. Antimicrobial resistance is one of the most severe and complicated health issues globally, driven by interrelated dynamics in humans, animals, and environmental health sectors. Coupled with various epidemiological factors and a limited pipeline for new antimicrobials, all these misappropriations allow the transmission of drug-resistant organisms. The problem is likely to worsen soon. Antimicrobial resistance in general and antibiotic resistance in particular is a shared global problem. Actions taken by any single country can adversely or positively affect the other country. Targeted coordination and prevention strategies are critical in stopping the spread of antibiotic-resistant organisms and hence its overall management. This article has provided in-depth knowledge about various methods that can help mitigate the emergence and spread of antimicrobial resistance globally. KEY POINTS: • Overview of antimicrobial resistance as a global challenge and explain various reasons for its rapid progression. • Brief about the intrinsic and acquired resistance to antimicrobials and development of antibiotic resistance in bacteria. • Systematically organized information is provided on different strategies for tackling antimicrobial resistance for the welfare of human health.

Growth-mediated negative feedback shapes quantitative antibiotic response

Dose-response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole-cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose-response curves. The shape of the dose-response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose-response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose-response curve of the antibiotic trimethoprim is caused by a negative growth-mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose-response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth-mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.

Mechanisms of antibiotic action shape the fitness landscapes of resistance mutations

Antibiotic-resistant pathogens are a major public health threat. A deeper understanding of how an antibiotic's mechanism of action influences the emergence of resistance would aid in the design of new drugs and help to preserve the effectiveness of existing ones. To this end, we developed a model that links bacterial population dynamics with antibiotic-target binding kinetics. Our approach allows us to derive mechanistic insights on drug activity from population-scale experimental data and to quantify the interplay between drug mechanism and resistance selection. We find that both bacteriostatic and bactericidal agents can be equally effective at suppressing the selection of resistant mutants, but that key determinants of resistance selection are the relationships between the number of drug-inactivated targets within a cell and the rates of cellular growth and death. We also show that heterogeneous drug-target binding within a population enables resistant bacteria to evolve fitness-improving secondary mutations even when drug doses remain above the resistant strain's minimum inhibitory concentration. Our work suggests that antibiotic doses beyond this "secondary mutation selection window" could safeguard against the emergence of high-fitness resistant strains during treatment.