Rapid Response to Selection,Competitive Release and Increased Transmission Potential of Artesunate-Selected Plasmodium chabaudi Malaria Parasites

The evolution of drug resistance, a key challenge for our ability to treat and control infections, depends on two processes: de-novo resistance mutations, and the selection for and spread of resistant mutants within a population. Understanding the factors influencing the rates of these two processes is essential for maximizing the useful lifespan of drugs and, therefore, effective disease control. For malaria parasites, artemisinin-based drugs are the frontline weapons in the fight against disease, but reports from the field of slower parasite clearance rates during drug treatment are generating concern that the useful lifespan of these drugs may be limited. Whether slower clearance rates represent true resistance, and how this provides a selective advantage for parasites is uncertain. Here, we show that Plasmodium chabaudi malaria parasites selected for resistance to artesunate (an artemisinin derivative) through a step-wise increase in drug dose evolved slower clearance rates extremely rapidly. In single infections, these slower clearance rates, similar to those seen in the field, provided fitness advantages to the parasite through increased overall density, recrudescence after treatment and increased transmission potential. In mixed infections, removal of susceptible parasites by drug treatment led to substantial increases in the densities and transmission potential of resistant parasites (competitive release). Our results demonstrate the double-edged sword for resistance management: in our initial selection experiments, no parasites survived aggressive chemotherapy, but after selection, the fitness advantage for resistant parasites was greatest at high drug doses. Aggressive treatment of mixed infections resulted in resistant parasites dominating the pool of gametocytes, without providing additional health benefits to hosts. Slower clearance rates can evolve rapidly and can provide a strong fitness advantage during drug treatment in both single and mixed strain infections.     

The evolutionary epidemiology of multilocus drug resistance

The evolution of resistance to drugs is a major public health concern as it erodes the efficacy of our therapeutic arsenal against bacterial, viral, and fungal pathogens. Increasingly, it is recognized that the evolution of resistance involves genetic changes at more than one locus, both in cases where multiple changes are required to obtain high-level resistance, and where compensatory changes at secondary loci ameliorate the costs of resistance. Similarly, multiple loci are often involved in the evolution of multidrug resistance. There has been widespread interest recently in understanding the evolutionary consequences of multilocus resistance, with many empirical studies documenting extensive patterns of genetic interactions (i.e., epistasis) among the loci involved. Currently, however, there are few general theoretical results available that bridge the gap between classical multilocus population genetics and mathematical epidemiology. Here, such theory is developed to shed new light on these previous studies, and to provide further guidance on the type of data required to predict the evolution of pathogens in response to drug pressure. Our results reveal the importance of feedbacks between the epidemiological and evolutionary dynamics, and illustrate how these feedbacks can be exploited to control resistance. In particular, we show how interventions such as social distancing and isolation can influence rates of recombination, and how this then can slow the spread of multilocus resistance and increase the likelihood of reversion to drug sensitivity once drug therapy has ceased.     

Compensatory mutations, antibiotic resistance and the population genetics of adaptive evolution in bacteria

In the absence of the selecting drugs, chromosomal mutations for resistance to antibiotics and other chemotheraputic agents commonly engender a cost in the fitness of microorganisms. Recent in vivo and in vitro experimental studies of the adaptation to these "costs of resistance" in Escherichia coli, HIV, and Salmonella typhimurium found that evolution in the absence of these drugs commonly results in the ascent of mutations that ameliorate these costs, rather than higher-fitness, drug-sensitive revertants. To ascertain the conditions under which this compensatory evolution, rather than reversion, will occur, we did computer simulations, in vitro experiments, and DNA sequencing studies with low-fitness rpsL (streptomycin-resistant) mutants of E. coli with and without mutations that compensate for the fitness costs of these ribosomal protein mutations. The results of our investigation support the hypothesis that in these experiments, the ascent of intermediate-fitness compensatory mutants, rather than high-fitness revertants, can be attributed to higher rates of compensatory mutations relative to that of reversion and to the numerical bottlenecks associated with serial passage. We argue that these bottlenecks are intrinsic to the population dynamics of parasitic and commensal microbes and discuss the implications of these results to the problem of drug resistance and adaptive evolution in parasitic and commmensal microorganisms in general.     

Combination of Two but Not Three Current Targeted Drugs Can Improve Therapy of Chronic Myeloid Leukemia

Chronic myeloid leukemia (CML) is a cancer of the hematopoietic system and has been treated with the drug Imatinib relatively successfully. Drug resistance, acquired by mutations, is an obstacle to success. Two additional drugs are now considered and could be combined with Imatinib to prevent resistance, Dasatinib and Nilotinib. While most mutations conferring resistance to one drug do not confer resistance to the other drugs, there is one mutation (T315I) that induces resistance against all three drugs. Using computational methods, the combination of two drugs is found to increase the probability of treatment success despite this cross-resistance. Combining more than two drugs, however, does not provide further advantages. We also explore possible combination therapies using drugs currently under development. We conclude that among the targeted drugs currently available for the treamtent of CML, only the two most effective ones should be used in combination for the prevention of drug resistance.     

Modeling multi-drug chemotherapy: tailoring treatment to individuals

BACKGROUND: Predicting and tailoring optimal cancer treatments presents a major challenge. METHODS: A computational model (kinetically tailored treatment, or KITT model) is developed to predict drug combinations, doses, and schedules likely to be effective in reducing tumor size and prolonging patient life. Treatment strategies may be tailored to individuals based on tumor cell kinetics. The model incorporates intra-tumor heterogeneity and evolution of drug resistance, apoptotic rates, and cell division rates. Tumor growth may follow an exponential or a Gompertzian trajectory. Drug pharmacodynamic and pharmacokinetic models are used. Toxicity is modeled in several ways. RESULTS: A key prediction of KITT is that including cytostatic drugs like tamoxifen and herceptin during treatment with cytotoxic drugs substantially increases the probability of cure and prolongs patient life. Results also suggest that altering drug scheduling may be more effective but not more toxic than dose escalation. CAF chemotherapy (cyclophosphamide, adriamycin, and 5-fluorouracil) is predicted to be more effective than CMF (cyclophosphamide, methotrexate, and 5-fluorouracil). KITT also suggests that tumors with a high proliferative index (PI) may respond better to drug combinations incorporating two cell-cycle phase-specific drugs than do tumors with a low PI. Tumors with a low PI, in contrast, are predicted to respond better to regimens involving two cell-cycle phase-non-specific drugs than do tumors with a high PI. These predictions are borne out by clinical trial results published in the literature, which are discussed.Simulated predictions of the model match well with results from a clinical trial by Silvestrini et al. (2000. Int. J. Cancer 87, 405). The results of simulating the growth of 26896 tumors are used to construct a decision tree for prognosis to identify the key tumor and treatment variables. CONCLUSION: Additional tests of the model are needed in which physicians collect information on apoptotic and proliferative indices, cell-cycle times, and drug resistance from biopsies of each individual's tumor. Computational models may become important tools to help optimize and tailor cancer treatments.     

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets (‘polypharmacology’). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.     

Outwitting evolution: fighting drug-resistant TB, malaria, and HIV

Although caused by vastly different pathogens, the world's three most serious infectious diseases, tuberculosis, malaria, and HIV-1 infection, share the common problem of drug resistance. The pace of drug development has been very slow for tuberculosis and malaria and rapid for HIV-1. But for each disease, resistance to most drugs has appeared quickly after the introduction of the drug. Learning how to manage and prevent resistance is a major medical challenge that requires an understanding of the evolutionary dynamics of each pathogen. This Review summarizes the similarities and differences in the evolution of drug resistance for these three pathogens.     

A mathematical model for the evolutions of anthelmintic resistance in a direct life cycle nematode parasite

Some of the elements required of a mathematical model for the evolution of anthelmintic resistance in strongylid nematodes are described. The model comprises a series of coupled first order differential equations and assumes the parasite has a direct life cycle with overlapping generations. The parasite-host system involved only a single host. In all the cases considered, drug resistance was assumed to be determined by two alleles at a single autosomal locus. The pretreatment allelic frequencies were maintained by heterozygote advantage involving the mortality of the free-living stages of the parasite. The model suggests that alternating anthelmintic with different modes of action may be a less effective resistance management strategy than administering the same drugs simultaneously.     

The clarifying role of time series data in the population genetics of HIV

HIV can evolve remarkably quickly in response to antiretroviral therapies and the immune system. This evolution stymies treatment effectiveness and prevents the development of an HIV vaccine. Consequently, there has been a great interest in using population genetics to disentangle the forces that govern the HIV adaptive landscape (selection, drift, mutation, and recombination). Traditional population genetics approaches look at the current state of genetic variation and infer the processes that can generate it. However, because HIV evolves rapidly, we can also sample populations repeatedly over time and watch evolution in action. In this paper, we demonstrate how time series data can bound evolutionary parameters in a way that complements and informs traditional population genetic approaches. Specifically, we focus on our recent paper (Feder et al., 2016, eLife), in which we show that, as improved HIV drugs have led to fewer patients failing therapy due to resistance evolution, less genetic diversity has been maintained following the fixation of drug resistance mutations. Because soft sweeps of multiple drug resistance mutations spreading simultaneously have been previously documented in response to the less effective HIV therapies used early in the epidemic, we interpret the maintenance of post-sweep diversity in response to poor therapies as further evidence of soft sweeps and therefore a high population mutation rate (θ) in these intra-patient HIV populations. Because improved drugs resulted in rarer resistance evolution accompanied by lower post-sweep diversity, we suggest that both observations can be explained by decreased population mutation rates and a resultant transition to hard selective sweeps. A recent paper (Harris et al., 2018, PLOS Genetics) proposed an alternative interpretation: Diversity maintenance following drug resistance evolution in response to poor therapies may have been driven by recombination during slow, hard selective sweeps of single mutations. Then, if better drugs have led to faster hard selective sweeps of resistance, recombination will have less time to rescue diversity during the sweep, recapitulating the decrease in post-sweep diversity as drugs have improved. In this paper, we use time series data to show that drug resistance evolution during ineffective treatment is very fast, providing new evidence that soft sweeps drove early HIV treatment failure.     

Fitness landscapes emerging from pharmacodynamic functions in the evolution of multidrug resistance

Adaptive evolution often involves beneficial mutations at more than one locus. In this case, the trajectory and rate of adaptation is determined by the underlying fitness landscape, that is, the fitness values and mutational connectivity of all genotypes under consideration. Drug resistance, especially resistance to multiple drugs simultaneously, is also often conferred by mutations at several loci so that the concept of fitness landscapes becomes important. However, fitness landscapes underlying drug resistance are not static but dependent on drug concentrations, which means they are influenced by the pharmacodynamics of the drugs administered. Here, I present a mathematical framework for fitness landscapes of multidrug resistance based on Hill functions describing how drug concentrations affect fitness. I demonstrate that these ‘pharmacodynamic fitness landscapes’ are characterized by pervasive epistasis that arises through (i) fitness costs of resistance (even when these costs are additive), (ii) nonspecificity of resistance mutations to drugs, in particular cross‐resistance, and (iii) drug interactions (both synergistic and antagonistic). In the latter case, reciprocal drug suppression may even lead to reciprocal sign epistasis, so that the doubly resistant genotype occupies a local fitness peak that may be difficult to access by evolution. Simulations exploring the evolutionary dynamics on some pharmacodynamic fitness landscapes with both constant and changing drug concentrations confirm the crucial role of epistasis in determining the rate of multidrug resistance evolution.     

Structure of the scientific community modelling the evolution of resistance

Faced with the recurrent evolution of resistance to pesticides and drugs, the scientific community has developed theoretical models aimed at identifying the main factors of this evolution and predicting the efficiency of resistance management strategies. The evolutionary forces considered by these models are generally similar for viruses, bacteria, fungi, plants or arthropods facing drugs or pesticides, so interaction between scientists working on different biological organisms would be expected. We tested this by analysing co-authorship and co-citation networks using a database of 187 articles published from 1977 to 2006 concerning models of resistance evolution to all major classes of pesticides and drugs. These analyses identified two main groups. One group, led by ecologists or agronomists, is interested in agricultural crop or stock pests and diseases. It mainly uses a population genetics approach to model the evolution of resistance to insecticidal proteins, insecticides, herbicides, antihelminthic drugs and miticides. By contrast, the other group, led by medical scientists, is interested in human parasites and mostly uses epidemiological models to study the evolution of resistance to antibiotic and antiviral drugs. Our analyses suggested that there is also a small scientific group focusing on resistance to antimalaria drugs, and which is only poorly connected with the two larger groups. The analysis of cited references indicates that each of the two large communities publishes its research in a different set of literature and has its own keystone references: citations with a large impact in one group are almost never cited by the other. We fear the lack of exchange between the two communities might slow progress concerning resistance evolution which is currently a major issue for society.     

Therapeutic resistance resulting from mutations in Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR signaling pathways

Chemotherapy remains a commonly used therapeutic approach for many cancers. Indeed chemotherapy is relatively effective for treatment of certain cancers and it may be the only therapy (besides radiotherapy) that is appropriate for certain cancers. However, a common problem with chemotherapy is the development of drug resistance. Many studies on the mechanisms of drug resistance concentrated on the expression of membrane transporters and how they could be aberrantly regulated in drug resistant cells. Attempts were made to isolate specific inhibitors which could be used to treat drug resistant patients. Unfortunately most of these drug transporter inhibitors have not proven effective for therapy. Recently the possibilities of more specific, targeted therapies have sparked the interest of clinical and basic researchers as approaches to kill cancer cells. However, there are also problems associated with these targeted therapies. Two key signaling pathways involved in the regulation of cell growth are the Ras/Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR pathways. Dysregulated signaling through these pathways is often the result of genetic alterations in critical components in these pathways as well as mutations in upstream growth factor receptors. Furthermore, these pathways may be activated by chemotherapeutic drugs and ionizing radiation. This review documents how their abnormal expression can contribute to drug resistance as well as resistance to targeted therapy. This review will discuss in detail PTEN regulation as this is a critical tumor suppressor gene frequently dysregulated in human cancer which contributes to therapy resistance. Controlling the expression of these pathways could improve cancer therapy and ameliorate human health.     

THE EFFECT OF NEW DRUGS ON THE RHYTHMIC FUNCTION OF THE HEART

Epinephrine and related drugs (sympathomimetic amines) are the only compounds which effectively increase the rhythmic function of the heart.Effects of two new non-pressor sympathomimetic compounds were observed. One of these compounds, the isopropyl homologue of epinephrine, was found to be about five times more active than epinephrine.From clinical observations on the action of a new cardiac depressant drug, alpha-fagarine, it was concluded that the drug is very effective but that toxic reactions are unpredictable.The potency of alpha-fagarine and related compounds suggests the possible development of chemically related non-toxic drugs having an effective cardiac depressant action.     

Antimalarial drugs - host targets (re)visited

Every year, forty percent of the world population is at risk of contracting malaria. Hopes for the erradication of this disease during the 20th century were dashed by the ability of Plasmodium falciparum, its most deadly causative agent, to develop resistance to available drugs. Efforts to produce an effective vaccine have so far been unsuccessful, enhancing the need to develop novel antimalarial drugs. In this review, we summarize our knowledge concerning existing antimalarials, mechanisms of drug-resistance development, the use of drug combination strategies and the quest for novel anti-plasmodial compounds. We emphasize the potential role of host genes and molecules as novel targets for newly developed drugs. Recent results from our laboratory have shown Hepatocyte Growth Factor/MET signaling to be essential for the establishment of infection in hepatocytes. We discuss the potential use of this pathway in the prophylaxis of malaria infection.     

The evolution of drug-resistant malaria: the role of drug elimination half-life

This paper seeks to define and quantify the influence of drug elimination half-life on the evolution of antimalarial drug resistance. There are assumed to be three general classes of susceptibility of the malaria parasite Plasmodium falciparum to a drug: Res0, the original, susceptible wildtype; Res1, a group of intermediate levels of susceptibility that are more tolerant of the drug but still cleared by treatment; and Res2, which is completely resistant to the drug. Res1 and Res2 resistance both evolve much faster if the antimalarial drug has a long half-life. We show that previous models have significantly underestimated the rate of evolution of Res2 resistance by omitting the effects of drug half-life. The methodology has been extended to investigate (i) the effects of using drugs in combination, particularly when the components have differing half-lives, and (ii) the specific example of the development of resistance to the antimalarial pyrimethamine-sulphadoxine. An important detail of the model is the development of drug resistance in two separate phases. In phase A, Res1 is spreading and replacing the original sensitive forms while Res2 remains at a low level. Phase B starts once parasites are selected that can escape drug action (Res1 genotypes with borderline chemosensitivity, and Res2): these parasites are rapidly selected, a process that leads to widespread clinical failure. Drug treatment is clinically successful during phase A, and health workers may be unaware of the substantial changes in parasite population genetic structure that predicate the onset of phase B. Surveillance programs are essential, following the introduction of a new drug, to monitor effectively changes in treatment efficacy and thus provide advance warning of drug failure. The model is also applicable to the evolution of antibiotic resistance in bacteria: in particular, the need for these models to incorporate drug pharmacokinetics to avoid potentially large errors in their predictions.     

Antiviral resistance and the control of pandemic influenza: the roles of stochasticity, evolution and model details

Antiviral drugs, most notably the neuraminidase inhibitors, are an important component of control strategies aimed to prevent or limit any future influenza pandemic. The potential large-scale use of antiviral drugs brings with it the danger of drug resistance evolution. A number of recent studies have shown that the emergence of drug-resistant influenza could undermine the usefulness of antiviral drugs for the control of an epidemic or pandemic outbreak. While these studies have provided important insights, the inherently stochastic nature of resistance generation and spread, as well as the potential for ongoing evolution of the resistant strain have not been fully addressed. Here, we study a stochastic model of drug resistance emergence and consecutive evolution of the resistant strain in response to antiviral control during an influenza pandemic. We find that taking into consideration the ongoing evolution of the resistant strain does not increase the probability of resistance emergence; however, it increases the total number of infecteds if a resistant outbreak occurs. Our study further shows that taking stochasticity into account leads to results that can differ from deterministic models. Specifically, we find that rapid and strong control cannot only contain a drug sensitive outbreak, it can also prevent a resistant outbreak from occurring. We find that the best control strategy is early intervention heavily based on prophylaxis at a level that leads to outbreak containment. If containment is not possible, mitigation works best at intermediate levels of antiviral control. Finally, we show that the results are not very sensitive to the way resistance generation is modeled.     

Evaluating the efficacy of antimicrobial cycling programmes and patient isolation on dual resistance in hospitals

Antibiotic-resistant bacteria cause a number of infections in hospitals and are considered a threat to public health. A strategy suggested to curb the development of resistant hospital-acquired infections is antimicrobial cycling, in which antibiotic classes are alternated over time. This can be compared with a mixing programme in which, when given two drugs, half of the physicians prescribe one drug over the other. A mathematical model of antimicrobial cycling in a hospital population setting is developed to evaluate the efficacy of a cycling programme with an emphasis on reducing the emergence and significance of dual resistance. The model also considers the effects of physician compliance and isolating patients harbouring dual-resistant bacteria. Simulation results show that the optimal antimicrobial drug usage programme in hospital populations depends upon the type of resistance being targeted for treatment; a cycling programme is more effective against dual resistance compared with mixing. Patient isolation and high compliance to a cycling programme is also shown to dramatically decrease dual resistance in hospitalized populations. Ultimately, the exclusive use of antimicrobials in fighting nosocomial infection does not solve the problem but just slows down what appears to be a losing battle against drug resistance. We hope that this paper serves to instigate discussion on the many dimensions of the complex problem of drug resistance in hospital settings.     

Defeating pathogen drug resistance: guidance from evolutionary theory

Many of the greatest challenges in medicine and public health involve the evolution of drug resistance by pathogens. Recent advances in the theory of natural selection suggest that there are two broad classes of pathogen traits that can be targeted by drugs or vaccines. The first class, consisting of traits that benefit the individual organisms bearing them, causes a strong evolutionary response and the rapid emergence of drug resistance. The second class, consisting of traits that benefit groups of pathogen organisms including the individual provider, causes a weaker evolutionary response and less drug resistance. Although most previous drug development has targeted the first class, it would be advantageous to focus on the second class as targets for drug and vaccine development. Specific examples and test cases are discussed.     

Analysis of the dynamics of adaptation to transgenic corn and crop rotation by western corn rootworm (Coleoptera: Chrysomelidae) using a daily time-step model

Western corn rootworm, Diabrotica virgifera virgifera LeConte, has overcome crop rotation in several areas of the north central United States. The effectiveness of crop rotation for management of corn rootworm has begun to fail in many areas of the midwestern United States, thus new management strategies need to be developed to control rotation-resistant populations. Transgenic corn, Zea mays L., effective against western corn rootworm, may be the most effective new technology for control of this pest in areas with or without populations adapted to crop rotation. We expanded a simulation model of the population dynamics and genetics of the western corn rootworm for a landscape of corn; soybean, Glycine max (L.); and other crops to study the simultaneous development of resistance to both crop rotation and transgenic corn. Results indicate that planting transgenic corn to first-year cornfields is a robust strategy to prevent resistance to both crop rotation and transgenic corn in areas where rotation-resistant populations are currently a problem or may be a problem in the future. In these areas, planting transgenic corn only in continuous cornfields is not an effective strategy to prevent resistance to either trait. In areas without rotation-resistant populations, gene expression of the allele for resistance to transgenic corn, R, is the most important factor affecting the evolution of resistance. If R is recessive, resistance can be delayed longer than 15 yr. If R is dominant, resistance may be difficult to prevent. In a sensitivity analysis, results indicate that density dependence, rotational level in the landscape, and initial allele frequency are the three most important factors affecting the results.