The path of least resistance: aggressive or moderate treatment?

Adaptive Landscape by Environment Interactions Dictate Evolutionary Dynamics in Models of Drug Resistance

The adaptive landscape analogy has found practical use in recent years, as many have explored how their understanding can inform therapeutic strategies that subvert the evolution of drug resistance. A major barrier to applications of these concepts is a lack of detail concerning how the environment affects adaptive landscape topography, and consequently, the outcome of drug treatment. Here we combine empirical data, evolutionary theory, and computer simulations towards dissecting adaptive landscape by environment interactions for the evolution of drug resistance in two dimensions-drug concentration and drug type. We do so by studying the resistance mediated by Plasmodium falciparum dihydrofolate reductase (DHFR) to two related inhibitors-pyrimethamine and cycloguanil-across a breadth of drug concentrations. We first examine whether the adaptive landscapes for the two drugs are consistent with common definitions of cross-resistance. We then reconstruct all accessible pathways across the landscape, observing how their structure changes with drug environment. We offer a mechanism for non-linearity in the topography of accessible pathways by calculating of the interaction between mutation effects and drug environment, which reveals rampant patterns of epistasis. We then simulate evolution in several different drug environments to observe how these individual mutation effects (and patterns of epistasis) influence paths taken at evolutionary "forks in the road" that dictate adaptive dynamics in silico. In doing so, we reveal how classic metrics like the IC50 and minimal inhibitory concentration (MIC) are dubious proxies for understanding how evolution will occur across drug environments. We also consider how the findings reveal ambiguities in the cross-resistance concept, as subtle differences in adaptive landscape topography between otherwise equivalent drugs can drive drastically different evolutionary outcomes. Summarizing, we discuss the results with regards to their basic contribution to the study of empirical adaptive landscapes, and in terms of how they inform new models for the evolution of drug resistance.

A pivot mutation impedes reverse evolution across an adaptive landscape for drug resistance in Plasmodium vivax

BACKGROUND

The study of reverse evolution from resistant to susceptible phenotypes can reveal constraints on biological evolution, a topic for which evolutionary theory has relatively few general principles. The public health catastrophe of antimicrobial resistance in malaria has brought these constraints on evolution into a practical realm, with one proposed solution: withdrawing anti-malarial medication use in high resistance settings, built on the assumption that reverse evolution occurs readily enough that populations of pathogens may revert to their susceptible states. While past studies have suggested limits to reverse evolution, there have been few attempts to properly dissect its mechanistic constraints.

METHODS

Growth rates were determined from empirical data on the growth and resistance from a set of combinatorially complete set of mutants of a resistance protein (dihydrofolate reductase) in Plasmodium vivax, to construct reverse evolution trajectories. The fitness effects of individual mutations were calculated as a function of drug environment, revealing the magnitude of epistatic interactions between mutations and genetic backgrounds. Evolution across the landscape was simulated in two settings: starting from the population fixed for the quadruple mutant, and from a polymorphic population evenly distributed between double mutants.

RESULTS

A single mutation of large effect (S117N) serves as a pivot point for evolution to high resistance regions of the landscape. Through epistatic interactions with other mutations, this pivot creates an epistatic ratchet against reverse evolution towards the wild type ancestor, even in environments where the wild type is the most fit of all genotypes. This pivot mutation underlies the directional bias in evolution across the landscape, where evolution towards the ancestor is precluded across all examined drug concentrations from various starting points in the landscape.

CONCLUSIONS

The presence of pivot mutations can dictate dynamics of evolution across adaptive landscape through epistatic interactions within a protein, leaving a population trapped on local fitness peaks in an adaptive landscape, unable to locate ancestral genotypes. This irreversibility suggests that the structure of an adaptive landscape for a resistance protein should be understood before considering resistance management strategies. This proposed mechanism for constraints on reverse evolution corroborates evidence from the field indicating that phenotypic reversal often occurs via compensatory mutation at sites independent of those associated with the forward evolution of resistance. Because of this, molecular methods that identify resistance patterns via single SNPs in resistance-associated markers might be missing signals for resistance and compensatory mutation throughout the genome. In these settings, whole genome sequencing efforts should be used to identify resistance patterns, and will likely reveal a more complicated genomic signature for resistance and susceptibility, especially in settings where anti-malarial medications have been used intermittently. Lastly, the findings suggest that, given their role in dictating the dynamics of evolution across the landscape, pivot mutations might serve as future targets for therapy.

Multidrug evolutionary strategies to reverse antibiotic resistance

Antibiotic treatment has two conflicting effects: the desired, immediate effect of inhibiting bacterial growth and the undesired, long-term effect of promoting the evolution of resistance. Although these contrasting outcomes seem inextricably linked, recent work has revealed several ways by which antibiotics can be combined to inhibit bacterial growth while, counterintuitively, selecting against resistant mutants. Decoupling treatment efficacy from the risk of resistance can be achieved by exploiting specific interactions between drugs, and the ways in which resistance mutations to a given drug can modulate these interactions or increase the sensitivity of the bacteria to other compounds. Although their practical application requires much further development and validation, and relies on advances in genomic diagnostics, these discoveries suggest novel paradigms that may restrict or even reverse the evolution of resistance.

Evolutionary Rationale for Phages as Complements of Antibiotics

Antibiotic-resistant bacterial infections are a major concern to public health. Phage therapy has been proposed as a promising alternative to antibiotics, but an increasing number of studies suggest that both of these antimicrobial agents in combination are more effective in controlling pathogenic bacteria than either alone. We advocate the use of phages in combination with antibiotics and present the evolutionary basis for our claim. In addition, we identify compelling challenges for the realistic application of phage-antibiotic combined therapy.

Opportunities and challenges of Integral Projection Models for modelling host-parasite dynamics

Epidemiological dynamics are shaped by and may in turn shape host demography. These feedbacks can result in hard to predict patterns of disease incidence. Mathematical models that integrate infection and demography are consequently a key tool for informing expectations for disease burden and identifying effective measures for control.

A major challenge is capturing the details of infection within individuals and quantifying their downstream impacts to understand population‐scale outcomes. For example, parasite loads and antibody titres may vary over the course of an infection and contribute to differences in transmission at the scale of the population. To date, to capture these subtleties, models have mostly relied on complex mechanistic frameworks, discrete categorization and/or agent‐based approaches.

Integral Projection Models (IPMs) allow variance in individual trajectories of quantitative traits and their population‐level outcomes to be captured in ways that directly reflect statistical models of trait–fate relationships. Given increasing data availability, and advances in modelling, there is considerable potential for extending this framework to traits of relevance for infectious disease dynamics.

Here, we provide an overview of host and parasite natural history contexts where IPMs could strengthen inference of population dynamics, with examples of host species ranging from mice to sheep to humans, and parasites ranging from viruses to worms. We discuss models of both parasite and host traits, provide two case studies and conclude by reviewing potential for both ecological and evolutionary research.

How competition governs whether moderate or aggressive treatment minimizes antibiotic resistance

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

DOI:http://dx.doi.org/10.7554/eLife.10559.001

Antibiotics are chemical compounds used to treat bacterial infections. The discovery of antibiotics, starting with penicillin in 1929, revolutionized medicine, making it possible to cure or prevent life-threatening infections such as tetanus and pneumonia. However, many bacteria have become resistant to one or more antibiotics and so can no longer be killed by these drugs.

The emergence of antibiotic resistance reflects an evolutionary process that occurs during antibiotic treatment. While the antibiotic will kill most bacteria, some bacteria may naturally have a feature or genetic mutation that allows them to survive in the presence of the antibiotic. These bacteria then reproduce and pass on their resistant traits, eventually leading to the emergence of a new antibiotic-resistant strain of bacteria. Once a resistant strain exists it may be able to spread from one person to another.

There is conflicting evidence about how best to prevent antibiotic-resistant bacteria from evolving and spreading. The results of some experiments suggest that treating bacteria with large doses of antibiotics early in an infection is the most effective way to optimize treatment and minimize the risk of an antibiotic-resistant strain developing. However, other studies suggest that exposing bacteria to high levels of antibiotics more efficiently selects for resistance; in this case a more moderate approach should be used when treating bacterial infections.

Here, Colijn and Cohen present a mathematical model that suggests that the natural competition between the antibiotic-resistant and antibiotic-sensitive strains of bacteria influence which treatment strategy should be taken. Strains were modeled both within individual hosts and spreading in a community of individuals. In the models, aggressive antibiotic treatment is most effective (in that it minimizes antibiotic resistance) when the antibiotic-resistant strain either does not experience strong competition from the non-resistant strains of bacteria or is not fit enough to be a good competitor. However, a more moderate treatment is appropriate when the two strains are competing and the antibiotic-resistant strain is a fit competitor. Competition may mean that moderate treatment is best to avoid resistance at the community level, even in situations when aggressive treatment is likely best for individuals.

Two important future challenges are to better understand the diversity of strains in bacterial infections, and to develop tools to measure to what extent strains are effectively competing with each other.

DOI:http://dx.doi.org/10.7554/eLife.10559.002

Dynamics of preventive vs post-diagnostic cancer control using low-impact measures

Cancer poses danger because of its unregulated growth, development of resistance, and metastatic spread to vital organs. We currently lack quantitative theory for how preventive measures and post-diagnostic interventions are predicted to affect risks of a life threatening cancer. Here we evaluate how continuous measures, such as life style changes and traditional treatments, affect both neoplastic growth and the frequency of resistant clones. We then compare and contrast preventive and post-diagnostic interventions assuming that only a single lesion progresses to invasive carcinoma during the life of an individual, and resection either leaves residual cells or metastases are undetected. Whereas prevention generally results in more positive therapeutic outcomes than post-diagnostic interventions, this advantage is substantially lowered should prevention initially fail to arrest tumour growth. We discuss these results and other important mitigating factors that should be taken into consideration in a comparative understanding of preventive and post-diagnostic interventions.

DOI:http://dx.doi.org/10.7554/eLife.06266.001

About one person in every two will get cancer during their lives. Surgery and chemotherapy have long been mainstays of cancer treatment. Both, however, have substantial downsides. Surgery may leave behind undetected cancer cells that can grow into new tumours. Furthermore, in response to chemotherapy drugs, some cancer cells may emerge that resist further treatment. There is therefore interest in whether preventive strategies—including lifestyle changes and medications—could reduce the likelihood of confronting a life-threatening cancer.

Now, Akhmetzhanov and Hochberg have developed a mathematical model to help compare the effectiveness of preventive strategies and traditional cancer treatments. The model—which assumes that a person can only develop a single cancer from a single region of pre-cancerous cells—suggests that long-term cancer prevention strategies reduce the risk of a life-threatening cancer by more than traditional treatment that begins after a tumour is discovered. The preventive measures may be less effective in some cases compared to traditional treatments if they initially fail to stop a tumour growing, although on average they still work better than treating the cancer after detection.

According to Akhmetzhanov and Hochberg's model, surgical removal followed by chemotherapy is less likely to be successful than prevention, and when successful, requires larger impacts on the cancer (and therefore creates more side-effects for the patient) to achieve the same level of control as prevention. The model also suggests that even at very low levels of impact on residual cancer cells, chemotherapies are likely to be counterproductive by boosting the subsequent emergence of treatment-resistant tumours.

Akhmetzhanov and Hochberg's model predicts how effective preventive measures need to be in terms of slowing the growth of cancer cells to result in given reductions in the future risk of a life-threatening cancer. Future work should test this model by measuring the effects on tumour growth of prevention and of traditional therapies.

DOI:http://dx.doi.org/10.7554/eLife.06266.002

Evolutionary rescue can be impeded by temporary environmental amelioration

Rapid evolutionary adaptation has the potential to rescue from extinction populations experiencing environmental changes. Little is known, however, about the impact of short-term environmental fluctuations during long-term environmental deterioration, an intrinsic property of realistic environmental changes. Temporary environmental amelioration arising from such fluctuations could either facilitate evolutionary rescue by allowing population recovery (a positive demographic effect) or impede it by relaxing selection for beneficial mutations required for future survival (a negative population genetic effect). We address this uncertainty in an experiment with populations of a bacteriophage virus that evolved under deteriorating conditions (gradually increasing temperature). Periodic environmental amelioration (short periods of reduced temperature) caused demographic recovery during the early phase of the experiment, but ultimately reduced the frequency of evolutionary rescue. These experimental results suggest that environmental fluctuations could reduce the potential of evolutionary rescue.

Imperfect drug penetration leads to spatial monotherapy and rapid evolution of multidrug resistance

Infections with rapidly evolving pathogens are often treated using combinations of drugs with different mechanisms of action. One of the major goal of combination therapy is to reduce the risk of drug resistance emerging during a patient's treatment. Although this strategy generally has significant benefits over monotherapy, it may also select for multidrug-resistant strains, particularly during long-term treatment for chronic infections. Infections with these strains present an important clinical and public health problem. Complicating this issue, for many antimicrobial treatment regimes, individual drugs have imperfect penetration throughout the body, so there may be regions where only one drug reaches an effective concentration. Here we propose that mismatched drug coverage can greatly speed up the evolution of multidrug resistance by allowing mutations to accumulate in a stepwise fashion. We develop a mathematical model of within-host pathogen evolution under spatially heterogeneous drug coverage and demonstrate that even very small single-drug compartments lead to dramatically higher resistance risk. We find that it is often better to use drug combinations with matched penetration profiles, although there may be a trade-off between preventing eventual treatment failure due to resistance in this way and temporarily reducing pathogen levels systemically. Our results show that drugs with the most extensive distribution are likely to be the most vulnerable to resistance. We conclude that optimal combination treatments should be designed to prevent this spatial effective monotherapy. These results are widely applicable to diverse microbial infections including viruses, bacteria, and parasites.

The potential impact of coinfection on antimicrobial chemotherapy and drug resistance

Across a range of pathogens, resistance to chemotherapy is a growing problem in both public health and animal health. Despite the ubiquity of coinfection, and its potential effects on within-host biology, the role played by coinfecting pathogens on the evolution of resistance and efficacy of antimicrobial chemotherapy is rarely considered. In this review, we provide an overview of the mechanisms of interaction of coinfecting pathogens, ranging from immune modulation and resource modulation, to drug interactions. We discuss their potential implications for the evolution of resistance, providing evidence in the rare cases where it is available. Overall, our review indicates that the impact of coinfection has the potential to be considerable, suggesting that this should be taken into account when designing antimicrobial drug treatments.

Is selection relevant in the evolutionary emergence of drug resistance?

The emergence of drug-resistant pathogens is often considered a canonical case of evolution by natural selection. Here we argue that the strength of selection can be a poor predictor of the rate of resistance emergence. It is possible for a resistant strain to be under negative selection and still emerge in an infection or spread in a population. Measuring the right parameters is a necessary first step toward the development of evidence-based resistance-management strategies. We argue that it is the absolute fitness of the resistant strains that matters most and that a primary determinant of the absolute fitness of a resistant strain is the ecological context in which it finds itself.