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• W2022785679 abstract "Objective Antimalarial drugs kill the asexual parasites responsible for causing disease and some, notably chloroquine and the artemisinins, also kill the sexual transmission stages known as gametocytes. It is invariably argued by malariologists that gametocytocidal activity is beneficial because it reduces the rate at which resistance evolves by ‘reducing the transmission of resistant parasites’. This seems dubious from a population genetics perspective, where intuition would lead to the opposite conclusion. The objective was to reconcile these differing views. Methods The effect of gametocytocidal drug activity was quantified mathematically and calibrated using field data. Results It appears to be a robust result that gametocytocidal activity actually promotes the spread of resistance through a population; the underlying reason is that gametocytocidal activity reduces transmission of drug-sensitive forms to a greater extent than the drug resistant, thereby increasing the spread of the latter. The increased rate of spread of resistance is quantified and appears to be small providing drug coverage is moderate or low. Conclusions Citing reduced spread of resistance as a justification for deploying gametocytocidal antimalarials is unjustified; the deliberate use of a gametocytocidal antimalarial at high coverage to reduce transmission may ultimately be counterproductive through its rapid promotion of drug resistance. Keywords Plasmodium falciparum malaria drug resistance combination therapy gametocytocidal Objectifs Tous les médicaments antimalariques tuent les parasites asexués qui causent la maladie. Certains, notamment la chloroquine et l'artemisinine tuent également les stades de transmissions sexuelles qui sont les gamétocytes. Il est invariablement soutenu par les malarialogistes que l'activité gamétocytocide est bénéfique car elle réduit la fréquence avec laquelle la résistance évolue ‘en réduisant la transmission des parasites résistants’. Cela semble un argument douteux pour une vision de génétique de populations ou l'intuition conduirait plutôt vers une conclusion opposée. Notre objectif a été de concilier les différentes visions. Méthodes L'effet de l'activité des médicaments gamétocytocides a été mathématiquement quantifié et calibrée en utilisant des données de terrain. Résultats Il apparaît plutôt consistant qu'en fait, l'activité gamétocytocide favorise la dissémination de résistance dans la population, la raison étant que l'activité gamétocytocide réduit de façon beaucoup plus considérablement la transmission des formes sensibles que celle des formes résistantes au médicament, augmentant ainsi la transmission de ces dernières. L'augmentation du taux de dissémination de résistance a été quantifiée et semble faible lorsque la couverture médicamenteuse est modérée ou faible. Conclusion L'argument de la réduction de la dissémination de la résistance comme justificatif au déploiement de médicaments gamétocytocides n'est pas fondé. L'utilisation délibérée d'antimalariques gamétocytocides sur de vastes couvertures afin de réduire la transmission peut en fin de compte s'avérer antagoniste par la favorisation rapide de la résistance aux médicaments. Mots clés P. falciparum malaria résistance aux médicaments thérapie de combinaison gamétocytocide Objetivos Todos los fármacos antimaláricos terapéuticos matan los estadíos asexuales del parásito, causantes de la enfermedad, y algunos – principalmente la cloroquina y las artemisinas – también matan los estadíos sexuales de transmisión, conocidos como gametocitos. Desde siempre, los malariólogos han argumentado que la actividad gametocida es beneficiosa, puesto que reduce la tasa de evolución de resistencia al ‘‘reducir la transmisión de parásitos resistentes’’. Esto parecería ser dudoso desde la perspectiva de la genética de poblaciones, en donde la intuición lleva a una conclusión opuesta. El objetivo fue reconciliar estos puntos de vista divergentes. Métodos El efecto de la actividad gametocida del fármaco fue cuantificado de forma matemática y calibrado utilizando datos de campo. Resultados Parece ser un resultado robusto el hecho de que la actividad gametocida promueve la dispersión de resistencias en una población. La razón subyacente está en que la actividad gametocida reduce mayoritariamente la transmisión de las formas sensibles al fármaco y no tanto la de las formas resistentes, con lo cual incrementa la dispersión de estas últimas. El aumento en la tasa de dispersión de resistencia es cuantificable y parece ser pequeño, siempre y cuando la cobertura del fármaco sea moderada o baja. Conclusiones El citar la reducción en la dispersión de resistencias como una justificación para la utilización de antimaláricos gametocidas no es justificable; el uso deliberado de antimaláricos gametocidas con una alta cobertura para reducir la transmisión puede en últimas ser contraproducente por su rápida promoción de resistencia a los fármacos. Palabras clave P. falciparum malaria resistencia a fármacos terapia de combinación gametocida The life history of a Plasmodium falciparum malarial infection can be broadly categorised into three stages: an expansion phase in the liver, the development of a large asexual parasite load in the blood, and the development of sexual transmission stages known as gametocytes. Only the asexual parasite load causes symptomatic disease, so antimalarial drugs are primarily active against this stage (an activity known as schizontocidal) although some are also active against the developing or mature gametocytes (gametocytocidal), and some may also disrupt the development of the ookinete in the mosquito gut (sporontocidal) (Butcher 1997). Gametocytocidal activity is conventionally regarded as advantageous because it may have a public health benefit in decreasing transmission and, it is hypothesised, because killing the transmission stages will reduce the rate at which resistance spreads. This latter assertion appears to be widely accepted (see, for example, Barnes & White 2005, and references therein) so that, for example, it is one of the properties listed by WHO as having the ‘potential to delay or prevent the development of resistance’ (WHO 2001). It is also one of the mainstays for arguments that antimalarial combination therapy should contain artemisinin drugs. There appear to be no published calculations to substantiate this assertion, and it appears to rest solely on intuition. This intuitive argument is unacceptable from a population genetic perspective, where it is clearly recognised that what drives resistance is not the transmission of resistant forms in isolation, but their transmission relative to the sensitive form. A priori, the impact of gametocytocidal activity is expected to be greater on the sensitive than the resistant forms, so gametocytocidal activity may actually enhance the rate at which resistance spreads. So the intuition of geneticists leads to a completely opposite conclusion to that of malariologists. It is therefore necessary that a detailed quantitative analysis be undertaken to resolve this paradox. The purpose of this manuscript is to examine this effect in more detail, to substantiate the assertion that gametocytocidal activity enhances the spread of resistance, and to quantify the likely size of its impact. The selective advantage of drug-resistant parasites is most easily calculated as the number of secondary inoculations produced during the lifespan of a single resistant infection (LSI; the lifetime number of secondary inoculations) compared with the number produced by a drug-sensitive infection. This is achieved by estimating their infective lifespan and multiplying by a constant ‘k’ (Hastings et al. 2002). Assuming the infective lifespan is measured in days, then k represents the number of secondary contacts made via the mosquito vectors (the vectorial capacity) discounted by all the other factors that affect successful transmission such as failure to infect the mosquito, failure of sporozoites to invade the liver, and so on. These factors determine the absolute value of LSI but can remain unknown because k immediately cancels out of the equations, leaving the selective advantage as simply the ratio of the average infective lifespan of resistant to sensitive parasites. So if the mean infective lifespan of resistant parasites is R days and that of the sensitive parasites is S days, the selective advantage of resistance in drug-treated individuals is simply Rk/Sk or R/S as illustrated in 1, 2 and in the Appendix 1. A model of complete drug resistance. Resistant parasites are assumed to be completely unaffected by the drug. The life history of malarial infections and a plausible time course are shown: the light grey box represents the liver stage, the dark grey box represents the asexual blood phase and the solid black line represents the presence of mature gametocytes. An untreated infection is assumed to spend 10 days developing in the liver and then 90 days as an asexual blood infection, before host immunity eliminates the asexual forms. It takes 14 days of blood infection before gametocytes appear and they persist for a further 10 days after their progenitor asexual forms are eliminated by host immunity (or by a secondary event such as presumptive drug treatment). In reality, people may remain infective for up to 28 days but with a rapidly decreasing number of gametocytes (Smalley & Sinden 1977) and hence decreasing infectivity. This is approximated as equivalent to 10 days infectivity of a non-treated infection. As ever, the general conclusions are not affected by this approximation (Appendix 1). Thus an untreated infection lasts 100 days in total during which it is infectious for 86 days (because of the total 100 days of infection, 10 days are spent in the liver, 14 days as an asexual infection with no sexual stages and gametocytes persist for a further 10 days after the asexual forms disappear and 100 − 10 − 14 + 10 = 86; examples i and ii). A resistant infection is assumed to be completely unaffected by the drug (but see Figure 2) so follows the same time course irrespective of treatment and has an infective period of 86 days (iii and v). Drug treatment is represented by the arrow and is assumed to occur 30 days after initial infection by which time, allowing for 14 days in the liver and 10 days for gametocytes to appear, it has been infective for 6 days. If the drug is non-gametocytocidal it takes 10 days for the gametocytes to disappear after their progenitor asexuals have disappeared meaning sensitive infections have been infective for a total of 16 days (example iv). If the drug does kill gametocytes (example vi) then the infective period is 6 days. The selection favouring the resistance forms in treated individuals can be calculated as their lifetime secondary inoculations (LSI) relative to that of the sensitive (see main text and Appendix 1). Using this timescale for life-history stages, the selective advantage for resistance in the presence of the drug is 5.4 if the drug is non-gametocytocidal and increases to 14.3 if the drug is gametocytocidal. A model of recrudescent drug resistance. The resistant parasites are badly affected by the drug and reduced to low numbers before recrudescing as a detectable infection. The life history and timescale of the malaria infection is the same as for the complete resistance model illustrated on Figure 1 except that, for the purposes of illustration, it is assumed to take 20 days after treatment for the infection to recrudesce back to an infective form (there may be a lag between the recrudescence of asexual parasites and the subsequent reappearance of sexual stages so the recrudescent period is defined as the time between the infection being drug treated and it becoming re-infective). The loss of infective period for a schizontocidal drug with no gametocytocidal activity is 10 days because, during the period before recrudescence, gametocytes persist for 10 days after treatment and 20 − 10 = 10. The loss is 20 days for gametocytocidal drug because it kills all gametocytes present at the time of treatment. This timescale for life-history stages results in a 4.7-fold selective advantage for resistance if the drug is non-gametocytocidal, rising to 11-fold if the drug is gametocytocidal. This is a population genetic approach that tracks the number of secondary inoculations as a measure of reproductive success and investigates the spread of resistance on a timescale of parasite generations. Resistance is assumed to be encoded by a single gene so the process is equivalent to competition between sensitive and resistant ‘strains’ and, in principle, it is also possible to track the process on a continuous timescale using an epidemiological approach based on differential equations integrated over time (see Anderson & May 1992 for details). The two approaches are analogous and the same qualitative results would arise. The population genetic approach is preferred here because it can serve as the basis for future studies of resistance whose genetic determination is more complex, for example if two or more genes are required to encode resistance, in which case recombination and linkage disequilibrium become important. The method implicitly assumes that the population is constant over time. If it is expanding (e.g. in an epidemic) or contracting (e.g. because of interventions), then the value of k may alter over time. In an epidemic, for example, transmissions that occur earlier from the infection may have greater ‘reproductive value’ than later transmissions (such as occur after drug treatment). This will not affect the qualitative conclusions although it may slightly affect the quantitative ones (mathematically adept readers can find further details in Charlesworth 1994). The basic argument can be made both algebraically (Appendix 1) and intuitively. The latter approach is presented graphically in 1, 2, and explained in the appropriate figure legends. Figure 1 examines the case where the resistant parasites are completely unaffected by the drug and serves as a simple and compelling example. More commonly, ‘resistant’ infections are greatly affected by drug treatment: the vast majority of parasites are killed and the infection becomes sub-patent until the survivors increase in number sufficiently to cause a relapse of ‘drug resistant’ malaria. This is referred to as the ‘recrudescent’ model and is presented on Figure 2. An operational question arises when considering combinations of antimalarial drugs where the first drug targets only asexual stages while the second is gametocytocidal. Intuitively, the clinical efficacy of this second drug must exceed a critical value to overcome the drawback of its gametocytocidal activity. This critical value can be calculated numerically for the models shown on 1, 2 as described in Appendix 2. These models capture the essential elements of malaria infections, and of the drug treatments they encounter. They represent the two ends of the spectrum of drug resistance, one where the parasites with the resistant mutation are entirely unaffected by the drug (Figure 1), and the other where they are instantaneously eradicated and recrudesce some time later (Figure 2). They establish the important basic principles and allow approximate quantification of the effects of gametocytocidal activity. The effects of other aspects of malaria biology can be discussed in the context of these calculations, and shown not to affect the general conclusions (Appendix 3). Antimalarial drugs with gametocytocidal activity (here assumed to kill both mature and developing gametocytes) select for resistance more strongly than those without. In the model of complete resistance shown on Figure 1, the selective advantage of resistance if the drug is non-gametocytocidal is 5.4-fold rising to 14.3-fold if the drug is gametocytocidal, an increase by a factor of 2.6. In the recrudescent model shown on Figure 2, the corresponding figures are 4.7-fold in the presence of a non-gametocytocidal drug, rising to 11-fold if the drug is gametocytocidal, an increase by a factor of 2.3. The timescales used in these examples, for example the amount of time spent in the hepatic stage, are arbitrary but the arguments are qualitatively robust and not dependent on their exact values (Appendix 1). The ratio of LSI values gives the selective advantage in the presence of the drug. Many infections are not drug treated so the overall selective advantage for resistance has to be calculated taking this into account [Eqns (A1.1–A1.4) in Appendix 1]. Figure 3a shows the increased rate of evolution of resistance to gametocytocidal drugs over a single generation for various drug treatment rates. As might be expected, the increase was small when drug use was rare, becoming much larger as the proportion of people treated increases. As a specific example, using the lifecycle timeframes shown on 1, 2 and drug treatment rate of 0.3, resistance to a gametocytocidal drug spreads 5% faster than a purely schizontocidal drug in the model of complete resistance and 1% faster in the recrudescence model. Although small, these differences become somewhat larger when compounded over the numerous parasite generations that occur during the evolution of resistance. Figure 3b shows how these differences of 5% and 1% per parasite generation can affect the useful therapeutic lifespan (UTL) of the antimalarial drug. Assuming that UTL finishes once resistance exceeds 20%, under a model of complete resistance the UTL of gametocytocidal drug is 6.2 years against 7.2 years for a non-gametocytocidal drug – a reduction in UTL of nearly 15%. The selective differences in the recrudescence model are much lower and UTL only falls from 8.3 to 8 years, a reduction in UTL of 4%. The increase in resistance to gametocytocidal and non-gametocytocidal antimalarial drugs. (a) The increased rate of evolution of resistance if a drug is gametocytocidal compared with that for a non-gametocytocidal drug that targets only the asexual stages. The upper line represents the increase calculated for a model of complete resistance using the time periods illustrated on Figure 1, while the lower line is calculated from the recrudescent model illustrated on Figure 2. The rates are over a single parasite generation and drug treatment rate is the proportion of infections treated. (b) The evolution of resistance over time (calculated on a timescale of parasite generations and converted to years by assuming five parasite generations per year) based on the models of complete or recrudescent drug resistance and depending on whether the drug is gametocytocidal or non-gametocytocidal. The lifecycle timescales used to illustrate 1, 2 were used, starting frequency of resistance is 10−5 and drug treatment rate is 0.3. The critical values of clinical efficacy that need to be exceeded to overcome gametocytocidal activity in drug combinations are shown in Figure 4. At low to moderate levels of drug use, even a marginally effective gametocytocidal drug retards the evolution of resistance. For example, even if 30% of infections are treated, the second drug only needs to increase cure rates by 16% in the full resistance model, or by 4% in the recrudescence model, to significantly retard the evolution of resistance. The critical threshold of its protective efficacy becomes much higher at high levels of drug coverage. For example, at 70% coverage, the second drug must increase cure rates by 29% or 19% in the complete resistance and recrudescence models respectively. The minimum clinical efficacy of a gametocytocidal partner drug that must be exceeded before its inclusion into a combination therapy slows the spread of resistance. Shown as a function of drug treatment rate for models of Complete and Recrudescent resistance, using the infective lifespan used to illustrate 1, 2 (Further details in Appendix 2). It may be difficult to accept that a factor such as gametocytocidal activity that appears intuitively beneficial could have counter-productive properties. However the underlying logic appears robust and the conclusions seem inescapable. The verbal assertion that gametocytocidal activity slows the spread of resistance is invariably along the lines that gametocytocidal activity ‘reduces the transmission of resistant forms thereby slowing the spread of resistance’ (see Barnes & White 2005 for a recent illustrative example). This is true but is quite literally only half the equation [the numerators of Appendix Eqns (A1.4 and A2.1) to be precise]. The important comparison is the extent to which gametocytocidal activity also reduces the spread of sensitive forms. Since the latters’ infective lifespans are, by definition, terminated by the drug, it stands to reason that the loss of infective lifespan due to gametocytocidal activity will constitute a bigger proportion of the sensitives’ infective lifespan than it will of the resistants’ infective lifespan. This inevitably results in an increased selective advantage for the resistant form. The algebraic equivalent of this argument is to note that subtracting a constant amount from each LSI disproportionately affects the sensitive form, thereby increasing the ratio of resistant to sensitive transmissions (Appendix 1). For example, if resistant forms generate 20 inoculations, and sensitive forms generate 10, then the advantage of resistance is 20/10, i.e. twofold. If gametocytocidal activity prevents two inoculations from each LSI, this results in an advantage of 18/8, i.e. 2.2-fold, preventing five inoculations from each LSI results in an advantage of 15/5, i.e. threefold, and so on. The models described above provide a simple method to establish the basic impact of gametocytocidal activity in antimalarial drugs. Additional impacts of gametocytocidal activity and putative differences in malaria biology and epidemiology can be incorporated and shown not to affect the basic qualitative conclusion; these are discussed separately in Appendix 3 to avoid disrupting the main argument. Gametocytocidal activity gives an extra impetus to the spread of resistance, and it is necessary to quantify the effect. The increased rate of evolution appears to be small if drug treatment rates are low to moderate (Figure 3a). Furthermore, the addition of a gametocytocidal drug to a non-gametocytocidal monotherapy will remain beneficial (Figure 4) in these circumstances. Artemisinin derivatives used in a combination therapy typically reduce drug failure rates to around 20–50% that of the equivalent monotherapy (International Artemisinin Study Group 2004) so adding artesunate to another antimalarial drug remains a beneficial strategy, despite the former's slight gametocytocidal activity. This was one of the key reasons for assuming that the drug is completely gametocytocidal and kills both developing and mature gametocytes. If complete gametocytocidal activity can be shown to have only a minimal impact on the rate of evolution of resistance, it is a highly robust conclusion that the lesser gametocytocidal activity of drugs such as CQ and artesunates (which kill developing, immature gametocytes, not the mature, infective stages (Kumar & Zeng 1990; Butcher 1997; Pukrittayakamee et al. 2004) will have little impact on the rate of evolution of resistance. However, at high levels of drug coverage the effects of gametocytocidal activity may become far more serious. Firstly, the increased rate of resistance to a single drug becomes much higher: for example, if 80% of infections are treated, gametocytocidal activity increases the evolution of resistance by 36% and 22% per generation under assumptions of complete resistance and recrudescence, respectively (Figure 3a). Secondly, gametocytocidal drugs in a combination therapy need to be much more clinically effective to overcome the inherent drawback of their gametocytocidal activity; for example if 80% of infections are treated then the partner drug's additional clinical effect must exceed 26% or 20% under models of complete resistance or recrudescence, respectively (Figure 4). This has operational implications because past attempts at mass drug administrations often employed the gametocytocidal drug primaquine, the justification being that high drug coverage may have a substantial impact on local transmission intensity, with a corresponding decrease in the disease burden (von Seidlein & Greenwood 2003). This may be the case but policy makers need to be aware that this strategy may greatly increase the spread of resistance. There are sound theoretical reasons why antimalarial drugs should be used in combination (see White 1999 and Hastings & D'Alessandro 2000 for access to the literature). Combinations reduce the probability of spontaneous resistance mutations surviving and spreading from the ‘biomass’ of parasites within a drug-treated individual, the spread of resistance is slowed because resistant parasites are less likely to survive treatment with a combination, and sexual recombination in the Plasmodium lifecycle means that parasite genotypes resistant to both drugs will be broken down by genetic recombination. None of these factors are affected by the presence or absence of gametocytocidal activity, and it is important to note that nothing in this manuscript should be interpreted as detracting from the desirability of deploying antimalarial drugs as combination therapies. The only possible exception, considered above, arises if the first drug targets only asexual stages while the second is gametocytocidal because the undesirable gametocytocidal action of the second drug could, in principle, outweigh its advantage in protecting the first drug if deployed at high coverage. However, the results shown on Figure 4 clearly support the policy that antimalarials be deployed as combinations. At low to moderate levels of drug use, even a marginally effective second drug retards the evolution of resistance irrespective of whether or not it is gametocytocidal. In summary, a second drug with gametocytocidal activity is much preferable to no second drug. It appears that gametocytocidal activity is counterproductive and stimulates the spread of drug resistance. However this effect seems to be small, provided drug use is low to moderate (Figure 3). The neutral reader is therefore entitled to wonder why it was deemed necessary to write this manuscript. This can be justified for several reasons. Firstly, the erroneous idea that gametocytocidal activity reduces the evolution and spread of resistance permeates much malaria epidemiological thinking (see the final paragraph of an otherwise excellent paper by Nair et al. 2003 for a recent, but by no means unique, example) and, more importantly, permeates much of the strategic thinking underlying antimalarial deployment policies. It has been used unchallenged to inform and guide regional drug policies over an extended period. There were several proposals in the 1960s (e.g. Rieckmann et al. 1968) to add primaquine to existing antimalarials. Primaquine has no significant effect on asexual forms of P. falciparum, so has no therapeutic benefit, and its inclusion was justified by the assertion that its gametocytocidal effect ‘reduced the spread of resistance to its partner drug’. The added cost, and the possibility of adverse reactions to primaquine, was therefore incurred to achieve an effect that was probably counterproductive. The modern equivalent is the proposal to add primaquine to Artecom (to form a drug called CV8), a policy that has undergone clinical trials over an extended period of time in Vietnam (World Health Organisation 2001); its inclusion may be justified by its action against Plasmodium vivax but it is important to note that it could not be further justified by its gametocytocidal activity against P. falciparum. Finally, gametocytocidal activity has been cited by WHO as one of the factors justifying the selection of artesunate within antimalarial combination therapy. Artesunate has some useful properties making it a suitable partner drug in combination therapies but its slight gametocytocidal activity is unlikely to be one of them. Interestingly, Professor Louis Molineaux raised exactly the same basic point (i.e. that gametocytocidal activity promotes the evolution of resistance) in an unpublished discussion document presented to the WHO Scientific Group on the Chemotherapy of Malaria in 1989 (Louis Molineaux, personal communication), but it was apparently poorly received and disregarded. Herein the effect has been investigated in detail to guide drug policy more objectively. Gametocytocidal activity has been regarded in policy considerations as being unambiguously beneficial with the potential to both reduce transmission and to slow the evolution of resistance. Gametocytocidal activity may have a putative beneficial impact in reducing local transmission intensity but this has to be carefully weighed, and justified, against its impact in increasing the rate at which resistance spreads through these populations. I thank many colleagues for helpful discussions on this topic and/or comments on the manuscript, particularly Bill Watkins, Colin Sutherland, Tim Anderson and three anonymous reviewers. This work was supported by the DFID-funded Malaria Knowledge Programme of the Liverpool School of Tropical Medicine. However, the Department for International Development accepts no responsibility for any information or views expressed. This is the algebraic formulation of the arguments outlined on 1, 2. The life-history timings used in 1, 2 are arbitrary and will vary according to local epidemiology, particularly prevailing levels of human immunity. It is therefore necessary to demonstrate that the conclusions are qualitatively robust by developing an equivalent al" @default.
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• W2022785679 title "Gametocytocidal activity in antimalarial drugs speeds the spread of drug resistance" @default.
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