Should Deep-Sequenced Amplicons Become the New Gold Standard for Analyzing Malaria Drug Clinical Trials?

Regulatory clinical trials are required to ensure the continued supply and deployment of effective antimalarial drugs. Patient follow-up in such trials typically lasts several weeks, as the drugs have long half-lives and new infections often occur during this period. "Molecular correction" is therefore used to distinguish drug failures from new infections. The current WHO-recommended method for molecular correction uses length-polymorphic alleles at highly diverse loci but is inherently poor at detecting low-density clones in polyclonal infections. This likely leads to substantial underestimates of failure rates, delaying the replacement of failing drugs with potentially lethal consequences. Deep-sequenced amplicons (AmpSeq) substantially increase the detectability of low-density clones and may offer a new "gold standard" for molecular correction. Pharmacological simulation of clinical trials was used to evaluate the suitability of AmpSeq for molecular correction. We investigated the impact of factors such as the number of amplicon loci analyzed, the informatics criteria used to distinguish genotyping "noise" from real low-density signals, the local epidemiology of malaria transmission, and the potential impact of genetic signals from gametocytes. AmpSeq greatly improved molecular correction and provided accurate drug failure rate estimates. The use of 3 to 5 amplicons was sufficient, and simple, nonstatistical criteria could be used to classify recurrent infections as drug failures or new infections. These results suggest AmpSeq is strongly placed to become the new standard for molecular correction in regulatory trials, with potential extension into routine surveillance once the requisite technical support becomes established.

A Computer Modelling Approach To Evaluate the Accuracy of Microsatellite Markers for Classification of Recurrent Infections during Routine Monitoring of Antimalarial Drug Efficacy.

Antimalarial drugs have long half-lives, so clinical trials to monitor their efficacy require long periods of follow-up to capture drug failure that may become patent only weeks after treatment. Reinfections often occur during follow-up, so robust methods of distinguishing drug failures (recrudescence) from emerging new infections are needed to produce accurate failure rate estimates. Molecular correction aims to achieve this by comparing the genotype of a patient's pretreatment (initial) blood sample with that of any infection that occurs during follow-up, with matching genotypes indicating drug failure. We use an in silico approach to show that the widely used match-counting method of molecular correction with microsatellite markers is likely to be highly unreliable and may lead to gross under- or overestimates of the true failure rates, depending on the choice of matching criterion. A Bayesian algorithm for molecular correction was previously developed and utilized for analysis of in vivo efficacy trials. We validated this algorithm using in silico data and showed it had high specificity and generated accurate failure rate estimates. This conclusion was robust for multiple drugs, different levels of drug failure rates, different levels of transmission intensity in the study sites, and microsatellite genetic diversity. The Bayesian algorithm was inherently unable to accurately identify low-density recrudescence that occurred in a small number of patients, but this did not appear to compromise its utility as a highly effective molecular correction method for analyzing microsatellite genotypes. Strong consideration should be given to using Bayesian methodology to obtain accurate failure rate estimates during routine monitoring trials of antimalarial efficacy that use microsatellite markers.

Optimal Treatments for Severe Malaria and the Threat Posed by Artemisinin Resistance.

BACKGROUND: Standard treatment for severe malaria is with artesunate; patient survival in the 24 hours immediately posttreatment is the key objective. Clinical trials use clearance rates of circulating parasites as their clinical outcome, but the pathology of severe malaria is attributed primarily to noncirculating, sequestered, parasites, so there is a disconnect between existing clinical metrics and objectives. METHODS: We extend existing pharmacokinetic/pharmacodynamic modeling methods to simulate the treatment of 10000 patients with severe malaria and track the pathology caused by sequestered parasites. RESULTS: Our model recovered the clinical outcomes of existing studies (based on circulating parasites) and showed a "simplified" artesunate regimen was noninferior to the existing World Health Organization regimen across the patient population but resulted in worse outcomes in a subgroup of patients with infections clustered in early stages of the parasite life cycle. This same group of patients were extremely vulnerable to resistance emerging in parasite early ring stages. CONCLUSIONS: We quantify patient outcomes in a manner appropriate for severe malaria with a flexible framework that allows future researchers to implement their beliefs about underlying pathology. We highlight with some urgency the threat posed to treatment of severe malaria by artemisinin resistance in parasite early ring stages.

A Two-Locus Model of the Evolution of Insecticide Resistance to Inform and Optimise Public Health Insecticide Deployment Strategies.

We develop a flexible, two-locus model for the spread of insecticide resistance applicable to mosquito species that transmit human diseases such as malaria. The model allows differential exposure of males and females, allows them to encounter high or low concentrations of insecticide, and allows selection pressures and dominance values to differ depending on the concentration of insecticide encountered. We demonstrate its application by investigating the relative merits of sequential use of insecticides versus their deployment as a mixture to minimise the spread of resistance. We recover previously published results as subsets of this model and conduct a sensitivity analysis over an extensive parameter space to identify what circumstances favour mixtures over sequences. Both strategies lasted more than 500 mosquito generations (or about 40 years) in 24% of runs, while in those runs where resistance had spread to high levels by 500 generations, 56% favoured sequential use and 44% favoured mixtures. Mixtures are favoured when insecticide effectiveness (their ability to kill homozygous susceptible mosquitoes) is high and exposure (the proportion of mosquitoes that encounter the insecticide) is low. If insecticides do not reliably kill homozygous sensitive genotypes, it is likely that sequential deployment will be a more robust strategy. Resistance to an insecticide always spreads slower if that insecticide is used in a mixture although this may be insufficient to outperform sequential use: for example, a mixture may last 5 years while the two insecticides deployed individually may last 3 and 4 years giving an overall 'lifespan' of 7 years for sequential use. We emphasise that this paper is primarily about designing and implementing a flexible modelling strategy to investigate the spread of insecticide resistance in vector populations and demonstrate how our model can identify vector control strategies most likely to minimise the spread of insecticide resistance.

Insecticide resistance evolution with mixtures and sequences: a model-based explanation.

BACKGROUND: Insecticide resistance threatens effective vector control, especially for mosquitoes and malaria. To manage resistance, recommended insecticide use strategies include mixtures, sequences and rotations. New insecticides are being developed and there is an opportunity to develop use strategies that limit the evolution of further resistance in the short term. A 2013 review of modelling and empirical studies of resistance points to the advantages of mixtures. However, there is limited recent, accessible modelling work addressing the evolution of resistance under different operational strategies. There is an opportunity to improve the level of mechanistic understanding within the operational community of how insecticide resistance can be expected to evolve in response to different strategies. This paper provides a concise, accessible description of a flexible model of the evolution of insecticide resistance. The model is used to develop a mechanistic picture of the evolution of insecticide resistance and how it is likely to respond to potential insecticide use strategies. The aim is to reach an audience unlikely to read a more detailed modelling paper. The model itself, as described here, represents two independent genes coding for resistance to two insecticides. This allows the representation of the use of insecticides in isolation, sequence and mixtures. RESULTS: The model is used to demonstrate the evolution of resistance under different scenarios and how this fits with intuitive reasoning about selection pressure. Using an insecticide in a mixture, relative to alone, always prompts slower evolution of resistance to that insecticide. However, when resistance to both insecticides is considered, resistance thresholds may be reached later for a sequence relative to a mixture. Increasing the ability of insecticides to kill susceptible mosquitoes (effectiveness), has the most influence on favouring a mixture over a sequence because one highly effective insecticide provides more protection to another in a mixture. CONCLUSIONS: The model offers an accessible description of the process of insecticide resistance evolution and how it is likely to respond to insecticide use. A simple online user-interface allowing further exploration is also provided. These tools can contribute to an improved discussion about operational decisions in insecticide resistance management.

Incorporating Stage-Specific Drug Action into Pharmacological Modeling of Antimalarial Drug Treatment.

Pharmacological modeling of antiparasitic treatment based on a drug's pharmacokinetic and pharmacodynamic properties plays an increasingly important role in identifying optimal drug dosing regimens and predicting their potential impact on control and elimination programs. Conventional modeling of treatment relies on methods that do not distinguish between parasites at different developmental stages. This is problematic for malaria parasites, as their sensitivity to drugs varies substantially during their 48-h developmental cycle. We investigated four drug types (short or long half-lives with or without stage-specific killing) to quantify the accuracy of the standard methodology. The treatment dynamics of three drug types were well characterized with standard modeling. The exception were short-half-life drugs with stage-specific killing (i.e., artemisinins) because, depending on time of treatment, parasites might be in highly drug-sensitive stages or in much less sensitive stages. We describe how to bring such drugs into pharmacological modeling by including additional variation into the drug's maximal killing rate. Finally, we show that artemisinin kill rates may have been substantially overestimated in previous modeling studies because (i) the parasite reduction ratio (PRR) (generally estimated to be 10(4)) is based on observed changes in circulating parasite numbers, which generally overestimate the "true" PRR, which should include both circulating and sequestered parasites, and (ii) the third dose of artemisinin at 48 h targets exactly those stages initially hit at time zero, so it is incorrect to extrapolate the PRR measured over 48 h to predict the impact of doses at 48 h and later.

Markov chain Monte Carlo and expectation maximization approaches for estimation of haplotype frequencies for multiply infected human blood samples.

BACKGROUND: Haplotypes are important in anti-malarial drug resistance because genes encoding drug resistance may accumulate mutations at several codons in the same gene, each mutation increasing the level of drug resistance and, possibly, reducing the metabolic costs of previous mutation. Patients often have two or more haplotypes in their blood sample which may make it impossible to identify exactly which haplotypes they carry, and hence to measure the type and frequency of resistant haplotypes in the malaria population. RESULTS: This study presents two novel statistical methods expectation-maximization (EM) and Markov chain Monte Carlo (MCMC) algorithms to investigate this issue. The performance of the algorithms is evaluated on simulated datasets consisting of patient blood characterized by their multiplicity of infection (MOI) and malaria genotype. The datasets are generated using different resistance allele frequencies (RAF) at each single nucleotide polymorphisms (SNPs) and different limit of detection (LoD) of the SNPs and the MOI. The EM and the MCMC algorithm are validated and appear more accurate, faster and slightly less affected by LoD of the SNPs and the MOI compared to previous related statistical approaches. CONCLUSIONS: The EM and the MCMC algorithms perform well when analysing malaria genetic data obtained from infected human blood samples. The results are robust to genotyping errors caused by LoDs and function well even in the absence of MOI data on individual patients.

How Robust Are Malaria Parasite Clearance Rates as Indicators of Drug Effectiveness and Resistance?

Artemisinin-based combination therapies (ACTs) are currently the first-line drugs for treating uncomplicated falciparum malaria, the most deadly of the human malarias. Malaria parasite clearance rates estimated from patients' blood following ACT treatment have been widely adopted as a measure of drug effectiveness and as surveillance tools for detecting the presence of potential artemisinin resistance. This metric has not been investigated in detail, nor have its properties or potential shortcomings been identified. Herein, the pharmacology of drug treatment, parasite biology, and human immunity are combined to investigate the dynamics of parasite clearance following ACT. This approach parsimoniously recovers the principal clinical features and dynamics of clearance. Human immunity is the primary determinant of clearance rates, unless or until artemisinin killing has fallen to near-ineffective levels. Clearance rates are therefore highly insensitive metrics for surveillance that may lead to overconfidence, as even quite substantial reductions in drug sensitivity may not be detected as lower clearance rates. Equally serious is the use of clearance rates to quantify the impact of ACT regimen changes, as this strategy will plausibly miss even very substantial increases in drug effectiveness. In particular, the malaria community may be missing the opportunity to dramatically increase ACT effectiveness through regimen changes, particularly through a switch to twice-daily regimens and/or increases in artemisinin dosing levels. The malaria community therefore appears overreliant on a single metric of drug effectiveness, the parasite clearance rate, that has significant and serious shortcomings.

Altering Antimalarial Drug Regimens May Dramatically Enhance and Restore Drug Effectiveness.

There is considerable concern that malaria parasites are starting to evolve resistance to the current generation of antimalarial drugs, the artemisinin-based combination therapies (ACTs). We use pharmacological modeling to investigate changes in ACT effectiveness likely to occur if current regimens are extended from 3 to 5 days or, alternatively, given twice daily over 3 days. We show that the pharmacology of artemisinins allows both regimen changes to substantially increase the artemisinin killing rate. Malaria patients rarely contain more than 10(12) parasites, while the standard dosing regimens allow approximately 1 in 10(10) parasites to survive artemisinin treatment. Parasite survival falls dramatically, to around 1 in 10(17) parasites if the dose is extended or split; theoretically, this increase in drug killing appears to be more than sufficient to restore failing ACT efficacy. One of the most widely used dosing regimens, artemether-lumefantrine, already successfully employs a twice-daily dosing regimen, and we argue that twice-daily dosing should be incorporated into all ACT regimen design considerations as a simple and effective way of ensuring the continued long-term effectiveness of ACTs.

Measuring windows of selection for anti-malarial drug treatments.

BACKGROUND: The long half-lives of malaria 'partner' drugs are a potent force selecting for drug resistance. Clinical trials can quantify this effect by estimating a window of selection (WoS), defined as the amount of time post-treatment when drug levels are sufficiently high that resistant parasites can re-establish an infection while preventing drug-sensitive parasites from establishing viable infections. METHODS: The ability of clinical data to accurately estimate the true WoS was investigated using standard pharmacokinetic-pharmacodynamic models for three widely used malaria drugs: artemether-lumefantrine (AR-LF), artesunate-mefloquine (AS-MQ) and dihydroartemisinin-piperaquine (DHA-PPQ). Estimates of the clinical WoS either (1) ignored all new infections occurring after the 63-day follow-up period, as is currently done in clinical trials, or, (2) recognized that all individuals would eventually be re-infected and arbitrarily assigned them a new infection day. RESULTS: The results suggest current methods of estimating the clinical WoS underestimate the true WoS by as much as 9 days for AR-LF, 33 days for AS-MQ and 7 days for DHA-PPQ. The new method of estimating clinical WoS (i.e., retaining all individuals in the analysis) was significantly better at estimating the true WoS for AR-LF and AS-MQ. CONCLUSIONS: Previous studies, based on clinically observed WoS, have probably underestimated the 'true' WoS and hence the role of drugs with long half-lives in driving resistance. This has important policy implications: high levels of drug use are inevitable in mass drug administration programmes and intermittent preventative treatment programmes and the analysis herein suggests these policies will be far more potent drivers of resistance than previously thought.

Improving the role and contribution of pharmacokinetic analyses in antimalarial drug clinical trials.

It is now World Health Organization (WHO) policy that drug concentrations on day 7 be measured as part of routine assessment in antimalarial drug efficacy trials. The rationale is that this single pharmacological measure serves as a simple and practical predictor of treatment outcome for antimalarial drugs with long half-lives. Herein we review theoretical data and field studies and conclude that the day 7 drug concentration (d7c) actually appears to be a poor predictor of therapeutic outcome. This poor predictive capability combined with the fact that many routine antimalarial trials will have few or no failures means that there appears to be little justification for this WHO recommendation. Pharmacological studies have a huge potential to improve antimalarial dosing, and we propose study designs that use more-focused, sophisticated, and cost-effective ways of generating these data than the mass collection of single d7c concentrations.

Improving pharmacokinetic-pharmacodynamic modeling to investigate anti-infective chemotherapy with application to the current generation of antimalarial drugs.

Mechanism-based pharmacokinetic-pharmacodynamic (PK/PD) modelling is the standard computational technique for simulating drug treatment of infectious diseases with the potential to enhance our understanding of drug treatment outcomes, drug deployment strategies, and dosing regimens. Standard methodologies assume only a single drug is used, it acts only in its unconverted form, and that oral drugs are instantaneously absorbed across the gut wall to their site of action. For drugs with short half-lives, this absorption period accounts for a significant period of their time in the body. Treatment of infectious diseases often uses combination therapies, so we refined and substantially extended the PK/PD methodologies to incorporate (i) time lags and drug concentration profiles resulting from absorption across the gut wall and, if required, conversion to another active form; (ii) multiple drugs within a treatment combination; (iii) differing modes of action of drugs in the combination: additive, synergistic, antagonistic; (iv) drugs converted to an active metabolite with a similar mode of action. This methodology was applied to a case study of two first-line malaria treatments based on artemisinin combination therapies (ACTs, artemether-lumefantrine and artesunate-mefloquine) where the likelihood of increased artemisinin tolerance/resistance has led to speculation on their continued long-term effectiveness. We note previous estimates of artemisinin kill rate were underestimated by a factor of seven, both the unconverted and converted form of the artemisinins kill parasites and the extended PK/PD methodology produced results consistent with field observations. The simulations predict that a potentially rapid decline in ACT effectiveness is likely to occur as artemisinin resistance spreads, emphasising the importance of containing the spread of artemisinin resistance before it results in widespread drug failure. We found that PK/PD data is generally very poorly reported in the malaria literature, severely reducing its value for subsequent re-application, and we make specific recommendations to improve this situation.

Pharmacological considerations in the design of anti-malarial drug combination therapies - is matching half-lives enough?

Anti-malarial drugs are now mainly deployed as combination therapy (CT), primarily as a mechanism to prevent or slow the spread of resistance. This strategy is justified by mathematical arguments that generally assume that drug 'resistance' is a binary all-or-nothing genetic trait. Herein, a pharmacological, rather than a purely genetic, approach is used to investigate resistance and it is argued that this provides additional insight into the design principles of anti-malarial CTs. It is usually suggested that half-lives of constituent drugs in a CT be matched: it appears more important that their post-treatment anti-malarial activity profiles be matched and strategies identified that may achieve this. In particular, the considerable variation in pharmacological parameters noted in both human and parasites populations may compromise this matching and it is, therefore, essential to accurately quantify the population pharmacokinetics of the drugs in the CTs. Increasing drug dosages will likely follow a law of diminishing returns in efficacy, i.e. a certain increase in dose will not necessarily lead to the same percent increase in efficacy. This may allow individual drug dosages to be lowered without proportional decrease in efficacy, reducing any potential toxicity, and allowing the other drug(s) in the CT to compensate for this reduced dosage; this is a dangerous strategy which is discussed further. Finally, pharmacokinetic and pharmacodynamic drug interactions and the role of resistance mechanisms are discussed. This approach generated an idealized target product profile (TPP) for anti-malarial CTs. There is a restricted pipeline of anti-malarial drugs but awareness of pharmacological design principles during the development stages could optimize CT design pre-deployment. This may help prevent changes in drug dosages and/or regimen that have previously occurred post-deployment in most current anti-malarial drugs.

Optimizing the programmatic deployment of the anti-malarials artemether-lumefantrine and dihydroartemisinin-piperaquine using pharmacological modelling.

BACKGROUND: Successful programmatic use of anti-malarials faces challenges that are not covered by standard drug development processes. The development of appropriate pragmatic dosing regimens for low-resource settings or community-based use is not formally regulated, even though these may alter factors which can substantially affect individual patient and population level outcome, such as drug exposure, patient adherence and the spread of drug resistance and can affect a drug's reputation and its eventual therapeutic lifespan. METHODS: An in silico pharmacological model of anti-malarial drug treatment with the pharmacokinetic/pharmacodynamic profiles of artemether-lumefantrine (AM-LF, Coartem®) and dihydroartemisinin-piperaquine (DHA-PPQ, Eurartesim®) was constructed to assess the potential impact of programmatic factors, including regionally optimized, age-based dosing regimens, poor patient adherence, food effects and drug resistance on treatment outcome at population level, and compared both drugs' susceptibility to these factors. RESULTS: Compared with DHA-PPQ, therapeutic effectiveness of AM-LF seems more robust to factors affecting drug exposure, such as age- instead of weight-based dosing or poor adherence. The model highlights the sub-optimally low ratio of DHA:PPQ which, in combination with the narrow therapeutic dose range of PPQ compared to DHA that drives the weight or age cut-offs, leaves DHA at a high risk of under-dosing. CONCLUSION: Pharmacological modelling of real-life scenarios can provide valuable supportive data and highlight modifiable determinants of therapeutic effectiveness that can help optimize the deployment of anti-malarials in control programmes.

Analysing malaria drug trials on a per-individual or per-clone basis: a comparison of methods.

There are a variety of methods used to estimate the effectiveness of antimalarial drugs in clinical trials, invariably on a per-person basis. A person, however, may have more than one malaria infection present at the time of treatment. We evaluate currently used methods for analysing malaria trials on a per-individual basis and introduce a novel method to estimate the cure rate on a per-infection (clone) basis. We used simulated and real data to highlight the differences of the various methods. We give special attention to classifying outcomes as cured, recrudescent (infections that never fully cleared) or ambiguous on the basis of genetic markers at three loci. To estimate cure rates on a per-clone basis, we used the genetic information within an individual before treatment to determine the number of clones present. We used the genetic information obtained at the time of treatment failure to classify clones as recrudescence or new infections. On the per-individual level, we find that the most accurate methods of classification label an individual as newly infected if all alleles are different at the beginning and at the time of failure and as a recrudescence if all or some alleles were the same. The most appropriate analysis method is survival analysis or alternatively for complete data/per-protocol analysis a proportion estimate that treats new infections as successes. We show that the analysis of drug effectiveness on a per-clone basis estimates the cure rate accurately and allows more detailed evaluation of the performance of the treatment.

The impact of Fisher's reproductive compensation on raising equilibrium frequencies of semidominant, nonlethal mutations under mutation/selection balance.

Fisher's reproductive compensation (fRC) occurs when a species' demography means the death of an individual results in increased survival probability of his/her relatives, usually assumed to be full sibs. This likely occurs in many species, including humans. Several important recessive human genetic diseases cause early foetal/infant death allowing fRC to act on these mutations. The impact of fRC on these genetic conditions has been previously calculated and shown to be substantial as quantified by ω, the fold increase in equilibrium frequencies of the mutation under fRC compared with its absence, i.e. ω = 1.22 and ω = 1.33 for autosomal and sex-linked loci, respectively. However, the impact of fRC on the frequency of the much larger class of semidominant, nonlethal mutations is unknown. This is calculated here as ω = 2 - h*s for autosomal loci and ω up to 2 for sex-linked loci where h is dominance (varied between 0.05 and 0.95) and s is selection coefficient (varied between 0.05 and 0.9). These results show that the actions of fRC can almost double the equilibrium frequency of deleterious mutations with low values of h and/or s (noting that "low" is s∼0.05 to 0.1). It is noted that fRC may act differentially across the genome with genes expressed early in life being fully exposed to fRC while those expressed later in life may be unaffected; this could lead to systematic differences in deleterious allele frequency across the genome.

Insecticide resistance management strategies for public health control of mosquitoes exhibiting polygenic resistance: A comparison of sequences, rotations, and mixtures.

Malaria control uses insecticides to kill Anopheles mosquitoes. Recent successes in malaria control are threatened by increasing levels of insecticide resistance (IR), requiring insecticide resistance management (IRM) strategies to mitigate this problem. Field trials of IRM strategies are usually prohibitively expensive with long timeframes, and mathematical modeling is often used to evaluate alternative options. Previous IRM models in the context of malaria control assumed IR to have a simple (monogenic) basis, whereas in natural populations, IR will often be a complex polygenic trait determined by multiple genetic variants. A quantitative genetics model was developed to model IR as a polygenic trait. The model allows insecticides to be deployed as sequences (continuous deployment until a defined withdrawal threshold, termed "insecticide lifespan", as indicated by resistance diagnosis in bioassays), rotations (periodic switching of insecticides), or full-dose mixtures (two insecticides in one formulation). These IRM strategies were compared based on their "strategy lifespan" (capped at 500 generations). Partial rank correlation and generalized linear modeling was used to identify and quantify parameters driving the evolution of resistance. Random forest models were used to identify parameters offering predictive value for decision-making. Deploying single insecticides as sequences or rotations usually made little overall difference to their "strategy lifespan", though rotations displayed lower mean and peak resistances. Deploying two insecticides in a full-dose mixture formulation was found to extend the "strategy lifespan" when compared to deploying each in sequence or rotation. This pattern was observed regardless of the level of cross resistance between the insecticides or the starting level of resistance. Statistical analysis highlighted the importance of insecticide coverage, cross resistance, heritability, and fitness costs for selecting an appropriate IRM strategy. Full-dose mixtures appear the most promising of the strategies evaluated, with the longest "strategy lifespans". These conclusions broadly corroborate previous results from monogenic models.

ogaraK: a population genetics simulator for malaria.

MOTIVATION: The evolution of resistance in Plasmodium falciparum malaria against most available treatments is a major global health threat. Population genetics approaches are commonly used to model the spread of drug resistance. Due to uncommon features in malaria biology, existing forward-time population genetics simulators cannot suitably model Plasmodium falciparum malaria. RESULTS: Here we present ogaraK, a population genetics simulator for modelling the spread of drug-resistant malaria. OgaraK is designed to make malaria simulation computationally tractable as it models infections, not individual parasites. OgaraK is also able to model the life cycle of the parasite which includes both haploid and diploid phases and sexual and asexual reproduction. We also allow for the simulation of different inbreeding levels, an important difference between high and low transmission areas and a fundamental factor influencing the outcome of strategies to control or eliminate malaria. AVAILABILITY: OgaraK is available as free software (GPL) from the address http://popgen.eu/soft/ogaraK.

Policy options for deploying anti-malarial drugs in endemic countries: a population genetics approach.

BACKGROUND: Anti-malarial drugs are constantly exposed to the threat of evolving drug resistance so good stewardship of existing therapy is an essential component of public health policy. However, the widespread availability of numerous different drugs through informal providers could undermine official drug deployment policies. A policy of multiple first-line therapy (MFT) is compared with the conventional policy of sequential drug deployment, i.e., where one drug is used until resistance evolves and then replaced by the next drug in the sequence. METHODS: Population genetic models of drug resistance are used to make the comparison; this methodology explicitly tracks the genetics of drug resistance (including, importantly, recombination in the sexual stage, intrahost dynamics, and direction of linkage disequilibrium). RESULTS: A policy of MFT outlasts sequential application providing drug usages are low to moderate, and appears not to drive widespread multi-drug resistance. Inadequate dosing is an even more potent driver of drug resistance than the MFT/sequential policy decision. CONCLUSIONS: The provision of MFT as a deliberate policy can be encouraged provided overall treatment rates are low or moderate (less than around half of malaria infections are treated) and the ad hoc provision of MFT through the private sector may be tolerated. This must be fully supported by education to ensure people take adequate doses of each of the drugs.