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OCR for page 237
11
The Evolution of Drug Resistance and
the Curious Orthodoxy of Aggressive
Chemotherapy
ANDREW F. READ,*†§ TROY DAY,‡ AND SILVIE HUIJBEN*
The evolution of drug-resistant pathogens is a major challenge for 21st century
medicine. Drug use practices vigorously advocated as resistance management
tools by professional bodies, public health agencies, and medical schools rep-
resent some of humankind’s largest attempts to manage evolution. It is our
contention that these practices have poor theoretical and empirical justi -
fication for a broad spectrum of diseases. For instance, rapid elimination of
pathogens can reduce the probability that de novo resistance mutations occur.
This idea often motivates the medical orthodoxy that patients should com-
plete drug courses even when they no longer feel sick. Yet “radical pathogen
cure” maximizes the evolutionary advantage of any resistant pathogens that
are present. It could promote the very evolution it is intended to retard. The
guiding principle should be to impose no more selection than is absolutely
necessary. We illustrate these arguments in the context of malaria; they
likely apply to a wide range of infections as well as cancer and public health
insecticides. Intuition is unreliable even in simple evolutionary contexts; in
a social milieu where in-host competition can radically alter the fitness costs
and benefits of resistance, expert opinion will be insufficient. An evidence-
based approach to resistance management is required.
*Center for Infectious Disease Dynamics, Departments of Biology and Entomology,
Pennsylvania State University, University Park, PA 16802; †Fogarty International Center,
National Institutes of Health, Bethesda, MD 20892; and ‡Departments of Mathematics,
Statistics, and Biology, Queen’s University, Kingston, ON, Canada K7L 3N6. §To whom
correspondence should be addressed. E-mail: a.read@psu.edu.
237
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238 / Andrew F. Read et al.
T
he evolution of drug-resistant pathogens significantly affects
human well-being and health budgets. Consequently, existing
and new antimicrobials should be viewed as precious resources
in need of careful stewardship (Owens, 2008; Spellberg et al., 2008). An
important aspiration is to maximize the therapeutically useful life span
of a compound, the time a given antimicrobial yields clinical benefits
before drug efficacy is undermined by resistance evolution. Attempting
to do so is essentially an exercise in evolutionary management.
Various practices are widely thought to be effective resistance man -
agement strategies (American Academy of Microbiology, 2009; World
Health Organization, 2010a; zur Wiesch et al., 2011). For instance, there
is near-universal agreement that combination drug therapy, the coad -
ministration of drugs with unrelated modes of action, prolongs the use -
ful life of the component compounds for diseases as diverse as leprosy,
HIV, malaria, and tuberculosis (TB). Another practice is the restriction
of treatment to those patients who need it on clinical grounds, so as to
reduce unnecessary selection for resistance. This philosophy underpins
restrictions on the use of antibiotics in hospitals and in the community
at large, and it has led to calls for reductions in drug use in animal feed.
A third practice thought to be an effective resistance management
strategy is the use of drugs to clear all target pathogens from a patient as
fast as possible. We hereafter refer to this practice as “radical pathogen
cure.” For a wide variety of infectious diseases, recommended drug
doses, interdose intervals, and treatment durations (which together
constitute “patient treatment regimens”) are designed to achieve com -
plete pathogen elimination as fast as possible. This is often the basis
for physicians exhorting their patients to finish a drug course long after
they feel better (long-course chemotherapy). Our claim is that aggres -
sive chemotherapy cannot be assumed to be an effective resistance
management strategy a priori. This is because radical pathogen cure nec -
essarily confers the strongest possible evolutionary advantage on the
very pathogens that cause drugs to fail.
At one level, our argument is simple. Elementary population genet -
ics shows that, all else being equal, the stronger the strength of selection,
the more rapid is the spread of a favored allele (Maynard Smith, 1989a).
F or drug use, the strength of selection is determined by how many
p eople are being treated and, among the treated people, the treatment
regimen. The more aggressive the regimen, the greater is the selection
pressure in favor of resistance. Because overwhelming chemical force
necessarily confers the strongest possible selective advantage on any
pathogen capable of resisting it, radical pathogen cure can very effectively
drive resistant pathogens through a population. As we will argue, this
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The Evolution of Drug Resistance / 239
problem is especially important when there is genetic diversity among
pathogens within an infected individual.
AIMS OF PATIENT TREATMENT
Ignoring economic considerations, patient treatment should seek to
achieve the following:
( i ) Make the patient healthy.
( ii ) Prevent the patient from infecting others.
( iii ) P revent the spread of resistant pathogens to others.
The first aim concerns the health of the patient being treated. The
second and third aims concern the effects of patient treatment on the
h ealth of others.
A single strategy cannot simultaneously best achieve all three aims.
In the limit, zero treatment will usually be the best resistance management
strategy. It is important to identify and justify compromises because this
makes explicit problems in need of solution and is a prerequisite for
evidence-based resistance management. There may come a time when
resistance management strategies are required that put overall public
health ahead of patient health (Foster and Grundmann, 2006). We do
not think the problems of resistant pathogens are yet so dire as to require
this. In our view, the current scientific challenge is to identify, among
patient treatment regimens that are similarly effective at restoring health
and preventing transmission, those regimens that best effect resistance
management.
The aim of resistance management is to prevent clinical failures
caused by high-level resistance. Resistance is often a continuous trait,
and there can be varying degrees of intermediate resistance. Sometimes
referred to as “tolerance,” intermediate resistance confers the ability
to survive concentrations of drug below those considered therapeutic
(Fig. 11.1). We define high-level resistance as that which undermines
patient health by causing therapeutic failure. It is the rate of spread of
high-level resistance that needs to be managed because this determines
the therapeutically useful life span of a drug.
The useful life span of a drug is determined by two processes. The first
is the rate at which genetic events conferring high-level resistance on an
individual pathogen actually occur. For simplicity, we refer to these events
as de novo mutations, but we use this to include any heritable change
that confers de novo high-level resistance on a pathogen individual. For
example, in bacteria, this event can be the acquisition by lateral transfer
of genetic material from another species. The second process affecting the
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Muta on
A+B+C
Dru concentration
Muta on A+B
Muta on A
ug
wildtype
Time
Window of selection for mutation Window of selection for mutation A
A in a drug with a short half-life in a drug with a long half-life
FIGURE 11.1 Hypothetical path to drug resistance. Solid curves show drug
concentration in a treated patient for two drugs with very different half-lives;
concentrations wane when treatment ceases. In this schematic, wild-type parasites
can survive very low concentrations, with mutations A, B, and C conferring the
ability to survive (“tolerate”) successively higher drug concentrations. High-level
resistance (full clinical resistance) is where treatment has a negligible direct impact
on pathogens with all three mutations. The windows of selection for mutation
A are shown. In those windows, parasites with mutation A have a selective ad-
vantage over wild-type parasites. Note that the duration of the window depends
critically on the drug half-life, which for antimalarial drugs can vary from hours
(e.g., artemisinin), to weeks (e.g., SP), to months (e.g., mefloquine).
rate of evolution is the strength of selection acting on this genetic change.
Because both mutational and selection processes together determine
the useful life span of a drug, resistance evolution can be retarded
by managing mutations, selection, or, ideally, both. Our view is that
conventional wisdom focuses too much on managing mutational events
(genetic origins), often with the consequence that the selection pres -
sures are ignored.
A REAL-WORLD CONTEXT
Our logic likely applies to a very wide range of pathogens, but, as
we discuss further below, there will not be simple generalities. To make
things more concrete, we base our discussion on malaria, a disease
that typifies the clinical and financial problems posed by drug resistance.
Resistance has evolved to all classes of frontline antimalarial drugs
(Hyde, 2005), and several have had to be withdrawn from use in many
countries. The eventual failure of drugs in the face of parasite evolution is
now accepted as inevitable by the World Health Organization (WHO) (Roll
Back Malaria, 2008) and others (American Academy of Microbiology,
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The Evolution of Drug Resistance / 241
2009). A key component of the Global Malaria Action Plan is an explicit
plan for a discovery pipeline to deliver replacement drugs continuously
(Roll Back Malaria, 2008). This pipeline will cost more than U.S. $2.5 bil -
lion in research and development for the coming decade and, once the
currently inadequate drug arsenal is rebuilt, U.S. $1.5 billion thereafter for
every decade until malaria is eradicated (Roll Back Malaria, 2008). Even if
we assume that an unlimited supply of drug classes can be discovered,
more than money is at stake. Drugs can fail more rapidly than the time it
t akes to get them through modern regulatory processes, and the cost
in terms of human suffering is high. National authorities switch their
choice of first-line drug only when forced to by declining patient cure
rates; thus, disease burdens are considerable. WHO currently recom-
mends that a drug be withdrawn once treatment failure rates attribut-
able to resistance reach 10% (World Health Organization, 2010a, p. 8).
In practice, governments of poor countries do not have this luxury and
often wait longer before drug withdrawal is implemented (World Health
Organization, 2006, p. 15).
Severe (life-threatening) malaria involves the dysfunction of vital
organs; for patients in this state, the sole aim of treatment is to prevent
death. Uncomplicated malaria constitutes the bulk of treated cases and
those that can drive transmission chains, and hence resistance evolution.
The WHO Guidelines for the Treatment of Malaria (World Health Organiza-
tion, 2010a, p. 6) state: “The objective of treating uncomplicated malaria
is to cure the infection as rapidly as possible,” with cure being defined
as “the elimination from the body of the parasites that caused the illness.”
Patient treatment regimens recommended in the WHO guidelines are
those designed to achieve rapid and full elimination.
It is clear that radical pathogen cure can, in the absence of resistance,
achieve the first two aims of patient treatment (restore health and pre -
vent disease transmission). The consensus view is that it can also achieve
the third aim: “Resistance can be prevented, or its onset slowed consid -
erably” by “ensuring very high cure rates through full adherence to
c orrect dosing regimens” (World Health Organization, 2010a, p. 6). This
is the orthodoxy that concerns us.
The strength of selection on resistance is primarily determined by the
fate of resistant parasites in treated and untreated hosts. Resistant strains
gain an advantage in treated hosts but often pay a cost in untreated
hosts. In both types of host, the social milieu of strains within individual
infections plays a very important role in mediating these costs and ben -
efits. To explain why, we need to summarize some within-host ecology.
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Genetic Diversity of Infections
Human malaria infections normally consist of more than one asexually
proliferating parasite lineage (“clone”). Thus, the majority of Plasmodium
falciparum clones in the world share their human hosts with at least
one other lineage (Read and Taylor, 2001). Mixed infections arise from
inoculations of genetically diverse parasites by a single mosquito or
contemporaneous bites by multiple mosquitoes infected with different
parasites. Consequently, the coexistence of drug-sensitive and drug-
resistant parasites is common, and indeed may even be the rule (Day et
al., 1992; Arnot, 1998; Babiker et al., 1999; Smith et al., 1999; Bruce et al.,
2000; Jafari et al., 2004; Juliano et al., 2007, 2010; McCollum et al., 2008;
Zhong et al., 2008; Owusu-Agyei et al., 2009).
A substantial body of epidemiological evidence is consistent with
crowding effects within infections, whereby the population densities of
individual genotypes are suppressed when other genotypes are present
(Daubersies et al., 1996; Mercereau-Puijalon, 1996; Smith et al., 1999;
Bruce et al., 2000; Hastings, 2003; Talisuna et al., 2003, 2006; Färnert,
2008; Harrington et al., 2009; Orjuela-Sánchez et al., 2009; Baliraine et
al., 2010). For example, parasite densities are unrelated to the number of
clones per host, and high turnover rates are observed in mixed-genotype
infections.
Direct experimental evidence of crowding cannot be ethically obtained
from human infections because formally demonstrating competition
requires deliberate infection and/or the withholding of treatment (Read
and Taylor, 2001). However, in a rodent malaria model, P. chabaudi in
laboratory mice, we and others have experimentally demonstrated that
densities of individual clones within an infection are severely suppressed
when coinfecting clones are present (Jarra and Brown, 1985; Taylor et al.,
1997a,b; Taylor and Read, 1998; de Roode et al., 2003, 2004a,b, 2005a,b;
R aberg et al., 2006; Wargo et al., 2007; Huijben et al., 2010; Pollitt et al.,
2011). This competitive suppression substantially reduces the density
of transmission stages (Wargo et al., 2007; Huijben et al., 2010), and
hence transmission of individual clones to mosquitoes (Taylor et al.,
1997a; Taylor and Read, 1998; de Roode et al., 2004a). To date, there is
no evidence of direct interference competition analogous to bacteriocin-
mediated competition in bacteria (Riley and Gordon, 1999). Instead, the
competition between coinfecting malaria parasites probably arises from
competition for resources. Most likely, this competition is for access to
red blood cells (Hellriegel, 1992; Yap and Stevenson, 1994; Hetzel and
Anderson, 1996; Haydon et al., 2003; Antia et al., 2008; Mideo et al.,
2008; Kochin et al., 2010; Miller et al., 2010; Pollitt et al., 2011), although
other resources, such as glucose, may also be involved (de Roode et
al., 2003). Immune-mediated apparent competition, wherein the immune
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The Evolution of Drug Resistance / 243
response provoked by one strain suppresses the population densities of
a coinfecting strain (Read and Taylor, 2001), likely also plays a major role
(Mota et al., 2001; Raberg et al., 2006).
This in-host competition has profound effects on the evolution of
drug resistance because it affects the fitness costs and benefits of resis -
tance. We take these in turn.
Costs of Resistance
It is generally assumed that resistant pathogens are less fit than their
wild-type ancestors in the absence of drug treatment and that this is
the main force slowing the evolution of resistance. In malaria, there is
good evidence of this (Hastings and Donnelly, 2005; Walliker et al., 2005;
Babiker et al., 2009; World Health Organization, 2010a). One consequence
of the social ecology within a host is that it acts as a serious multiplier of
these costs of resistance. Costs of resistance arise from metabolic inef -
ficiencies associated with efflux or detoxification mechanisms, which
can include negative pleiotropic effects on other cellular and biochemi -
cal processes or reduced biochemical efficiencies associated with target
site mutations (Hastings and Donnelly, 2005). These reductions in per-
formance can be quite small (e.g., a few percent), but small differences
can be greatly magnified by competition between clones. For example,
in mice, the social context of the infection can translate modest differ-
ences in performance into differences well in excess of 90% (Fig. 11.2).
The social context within which resistant strains are circulating is thus a
potent determinant of the fitness costs of resistance, the main brake on
the spread of drug resistance.
Benefits of Resistance
The flip side of this ecology is that the fitness advantages resistant
parasites experience in treated hosts are greatly magnified in mixed-clone
infections. Consider the consequences of radical pathogen cure where
competition is occurring. Aggressive chemotherapy will kill all sensitive
or tolerant parasites. This will result in competitive release and enhanced
transmission of any highly resistant strains that are present. In rodent
models, this is precisely what happens (de Roode et al., 2004a; Wargo
et al., 2007; Huijben et al., 2010) (Fig. 11.3). Thus, radical parasitological
cure enhances the transmission of the resistant strains. The impact of this
competitive release on the rate of spread of resistance can be very sub -
stantial, as was first recognized by Hastings and colleagues (Hastings,
1997; Mackinnon and Hastings, 1998; Hastings and D’Alessandro,
2000). Where multiclone infections dominate, this within-host ecology
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FIGURE 11.2 Costs of resistance are greatly affected by competition. Transmis -
sion stage densities of the resistant P. chabaudi clone in laboratory mice in the
absence of drug treatment are shown. Infections were initiated with 106 (Left) or
101 (Right) resistant parasites and either no sensitive parasites (no competition,
solid lines) or 106 sensitive parasites (competition, dashed lines). Performance
of the resistant clone alone includes any physiological costs to resistance. When
the resistant clone shares a host with a sensitive clone, performance is greatly
reduced, and is effectively zero when rare in the inoculum ( Right). Thus, the
costs of resistance depend critically on whether competitors are present and
the frequency of resistant parasites in an infection. PI, post-infection. Plotted
points are the mean (±SEM) densities in peripheral blood from 5 to 10 mice
per group, estimated by quantitative PCR using protocols described elsewhere
(Huijben et al., 2010).
can be the primary determinant of the speed at which resistance spreads,
and a far more important selective force than the simple survival advan-
tage conferred by resistance (Hastings, 1997, 2003, 2006; Mackinnon and
Hastings, 1998; Hastings and D’Alessandro, 2000; Mackinnon, 2005;
Talisuna et al., 2006).
For instance, in an infection composed of two equally represented
clones, aggressive treatment can effectively double the absolute fitness
of the resistant strain if that strain can fully exploit the “infection-space”
created by the removal of its competitor. If the resistant clone was rare
before treatment, the effect can be substantially greater (Fig. 11.3). In
nature, there is wide variation in the number of clones per person. Next-
generation sequencing techniques are already discovering patients
with more than 15 P. falciparum clones (Juliano et al., 2010), some of which
are represented at frequencies significantly less than 1%. Were those rare
clones drug-resistant, aggressive chemotherapy could increase transmis-
sion success of resistant parasites >100-fold.
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The Evolution of Drug Resistance / 245
FIGURE 11.3 Competitive release of drug resistance. Infections of P. chabaudi
were initiated in laboratory mice with 106 sensitive parasites (dark lines) and
either 106 (A and C) or 101 (B and D) resistant parasites (gray lines). Panels A and
B, densities of asexual parasites (within-host replicative stages). Panels C and
D, densities of gametocytes (transmission stages). Gray bars indicate period of
drug treatment (four daily doses of 8 mg/kg of pyrimethamine). R, resistant;
S, sensitive; PI, post-infection. Drug treatment rapidly suppresses sensitive
parasites, allowing resistant parasites to dominate post-treatment populations;
the expansion following competitive release is especially marked when the re -
sistant clone is rare. In untreated mice, resistant parasite densities are markedly
lower than sensitive parasite densities throughout the infections, particularly
when they were rare initially (compare with Fig. 11.2, which details the trans -
mission stage densities of resistant parasites in the untreated mice in the same
experiment). Plotted points are the mean (±SEM) densities in peripheral blood
from 5 to 10 mice per group, estimated by quantitative PCR using protocols
described elsewhere (Huijben et al., 2010).
Putting this slightly more formally, highly resistant parasites have
a relative fitness advantage in treated hosts simply because drug treat -
ment reduces the fitness of susceptible parasites. This advantage plays
out even if all infections in a population consist of just a single clone.
When hosts are infected with multiple lineages, however, the removal
of competitors by drug treatment also leads to absolute fitness gains if
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resistant clones are able to capitalize on the newly emptied niche space
in the host. These absolute fitness gains can be very, very large when
resistant parasites are otherwise kept at very low numbers by competi -
tive suppression.
Whence Conventional Wisdom?
Thus, radical parasite cure, by rapidly eliminating sensitive competitor
strains, confers very strong selection in favor of resistance. Despite this,
radical parasite cure is frequently advocated as a resistance management
strategy. This conventional wisdom is based on two arguments. Both
have to do with managing the initial mutational inputs into the system,
essentially trying to prolong the time until high-level resistance appears
in the first place. The first argument is that aggressive chemotherapy
maximally reduces parasite numbers, and thus the probability that resis -
tance mutations will occur in a treated patient [e.g., White (2004) and
World Health Organization (2010a, p. 129)]. This clearly has to be true.
The second argument is essentially a subtle variation of the first. The
idea is that when multiple independent mutations are required to confer
high-level resistance, it is essential to try to minimize positive selection
in favor of any partially resistant mutant because these partially resis -
tant mutants can be important mutational stepping stones toward full
(high-level) resistance (Hastings and Watkins, 2006). Partially resistant
parasites only have an evolutionary advantage at lower drug concen-
trations; thus, from a resistance management perspective, it is important
to minimize the probability that such parasites encounter those lower
concentrations. Low drug concentrations in a patient can arise in several
ways, not least after a course of chemotherapy has finished and the drug
is being metabolized or excreted from the body (Fig. 11.1). During some
of that time, there is a period [the “selection window” (Stepniewska and
White, 2008)] when parasites that are able to survive low drug doses
have a selective advantage. The aim of aggressive chemotherapy is to
ensure that no parasites from the treated infection remain alive during
the selection window, thus reducing the number of parasites in the overall
population experiencing that source of selection for low-level resistance.
DOUBLE-EDGED SWORD
Thus, aggressive chemotherapy is a double-edged sword for resis-
tance management. It can reduce the chances of high-level resistance aris -
ing de novo in an infection. But when an infection does contain resistant
parasites, either from de novo mutation or acquired by transmission from
other hosts, it gives those parasites the greatest possible evolution-
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The Evolution of Drug Resistance / 247
ary advantage both within individual hosts and in the population as a
whole. How do the opposing evolutionary pressures generated by radical
cure combine in different circumstances to determine the useful life
s pan of a drug? There will be circumstances when overwhelming chemi-
cal force retards evolution and other times when it drives things very
rapidly. We contend that for no infectious disease do we have sufficient
theory and empiricism to determine which outcome is more important. It
seems unlikely that any general rule will apply even for a single disease,
let alone across disease systems.
Consider again the case of malaria. There will be many cases where
the resistance management gains of radical pathogen cure (reduced muta-
tional inputs) will not outweigh its costs (maximal selection for high-
level resistance). For instance, where high-level resistance is conferred by
a single point mutation [e.g., atovaquone (White, 2004)], the mutational
stepping stone argument is clearly irrelevant. Moreover, there are about
1012 parasites in an infection at the time radical cure commences (White,
2004), so that every point mutation in the genome can potentially occur
in a single infection. There are at least one-quarter of a billion symp-
tomatic cases of malaria each year (World Health Organization, 2010b),
so that at least 1020 parasites could see a new drug each year. Among
these 1020 parasites, it is quite plausible that there already exists at least
a single parasite completely resistant to most yet-to-be invented drugs.
Aggressive chemotherapy can reduce the chances of de novo resistance
mutations occurring in treated patients, but it can make no impact on
the probability that such mutations occurred before treatment. Aggres -
sive use of a new drug will very effectively find these resistant “needles
in the haystack.”
Even when we can be confident that mutational inputs in patients
receiving treatment do limit the rate of evolutionary change (something
that is extremely hard to know, especially for new drugs), there is an
important quantitative argument to be had about the advantage of man -
aging mutational inputs by aggressive chemotherapy. This is because
aggressive treatment regimens increase the probability that any high-level
resistance that has arisen de novo will avoid stochastic loss and reach
transmissible frequencies. It is extremely challenging for a very rare
resistant mutant to replicate to transmissible densities in a host [e.g.,
Mackinnon (2005), Pongtavornpinyo et al. (2009), and Hastings (2011a)],
not least because it will likely compete with the ancestral strain from
which it arose. The performance of the mutant can be especially poor if
de novo resistance is associated with large fitness costs. Large costs can
erode as compensatory mutations accumulate (Levin et al., 2000; zur
Wiesch et al., 2011), but this requires persistence and large population
sizes, both of which are countered by competition. Thus, even when
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aggressive chemotherapy reduces the probability that de novo mutations
occur, it can, by eliminating competitors, increase the population-wide
probability that de novo mutations survive to transmit from hosts, and
hence escape stochastic loss.
Moreover, there are ways to manage mutational inputs that do not
have the unfortunate consequence of simultaneously maximizing selec -
tion for the very mutations they are trying to prevent. Combination ther-
apy is an example. As WHO puts it (World Health Organization, 2010a), if
resistance to one drug has a per parasite probability of 10−12 of spontane-
ously arising, the probability of resistance to two drugs with independent
modes of action arising spontaneously in the same parasite is 10 −24, a van-
ishingly small probability. The duration of the selection window (Fig. 11.1)
depends critically on the half-life of the particular drug. The window can
be weeks long in some cases [sulfadoxine-pyrimethamine (SP)] or just a
few hours in others (artemisinin and its derivatives). Judicious choice
of a drug or drug combination can thus affect the likelihood of stepping
stones to high-level resistance.
EVIDENCE-BASED RESISTANCE MANAGEMENT
The foregoing suggests to us that radical parasite cure is not a
priori the best way to manage resistance and that it could even promote
the very evolution it is intended to retard. The scientific challenge is to
determine how the contrasting evolutionary consequences of aggressive
chemotherapy determine the rate of resistance evolution and whether,
among the vast array of possible regimens, there are other ways of treat-
ing patients that would better delay resistance.
It might be, of course, that the other aims of patient treatment
(restore health and prevent infectiousness) can be achieved only by radical
parasite cure (Hastings, 2011b). If radical parasite cure is indeed critical
for clinical management, an empirical question, we might be stuck with
evolutionary mismanagement as an unavoidable side effect. If so, it is
important to recognize this. Claims that resistance evolution is retarded
by aggressive treatment regimens might be obscuring a serious evolu -
tionary problem in need of solution.
Rational development of treatment regimens that deliver effective
resistance management requires a sound knowledge base (Read and
Huijben, 2009; Goncalves and Paul, 2011; zur Wiesch et al., 2011), and
there is considerable scope for investigating the evolutionary conse -
quences of different treatment regimens for a wide range of diseases.
Ideally, these would involve quantitative comparisons of how contrasting
regimens affect each of the aims of patient treatment: health, infectious -
ness, and resistance management. In principle, such studies can be done
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The Evolution of Drug Resistance / 249
on animal models [e.g., de Roode et al. (2004a), Wargo et al. (2007),
and Huijben et al. (2010)] and, in a more limited way, on humans [e.g.,
Harrington et al. (2009)]. It is possible to measure the evolutionary con -
sequences of competing resistance management strategies in hospitals
(Brown and Nathwani, 2005; Martínez et al., 2006; R. L. Smith et al.,
2008), and it might even be possible in human communities. Penilla et al.
(2007) randomly allocated 24 villages in Mexico to one of four different
m ethods of applying public health insecticides and compared the rate
of rise of resistant mosquitoes over several years. None of the putative
resistance management strategies slowed the spread of phenotypic
resistance. Empirical assessments of evolutionary outcomes are problem -
atic for a drug against which resistance has yet to arise, but once high-level
resistance has arisen, there is an ethical imperative to do such studies.
Mathematical models have much to offer, but the challenges are
formidable even in silico. Consider malaria. As we argued above, the
strength and direction of selection are critically affected by the interactions
between competing pathogen lineages within a patient and how drug
treatment affects this ecology. Treatment determines what is transmit -
ted, and changes in the force of infection will, in turn, affect the genetic
diversity within an infection, and hence the ecology. Such feedbacks defy
standard population genetics approaches, which track gene frequencies
without explicit population dynamics (Mackinnon, 2005). Evolutionary-
epidemiological models [e.g., Gandon and Day (2009)] are computation -
ally intensive, and we are unaware of any real-world context in which
resistance evolution is adequately modeled. Unfortunately, the complex -
ity of the situation does not make it go away. Quantitative predictions
of the impact of different treatment regimens on the useful life of a drug
have to involve this social ecology. Such modeling efforts would also
evaluate the resistance management consequences of reductions in dis -
ease transmission by other measures, such as mass drug administration
or transmission-blocking interventions [e.g., World Health Organization
(2011)]. These too will reduce force of infection, and hence alter the in-
host ecology. Reductions in force of infection might reduce the benefits of
resistance by reducing the multiplicity of infection, and hence the levels
of competitive release; however, as argued above, the costs of resistance
will also be lower if there is less competition.
The difficulty of adequately capturing the relevant features in a
m athematical model points to an important bottom line: Intuition
(expert opinion), a very poor guide to evolutionary trajectories at the
b est of times, is really going to struggle in this context.
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HOW TO TREAT PATIENTS?
A corollary of our observation that radical pathogen cure can very
seriously promote the evolution of resistance is that less aggressive drug
treatment could prolong the useful life span of a drug. Because even small
changes in relative fitness can alter the useful therapeutical life span of
a drug by decades (Hastings and Donnelly, 2005), there is a strong case
for investigating the clinical consequences of lighter touch chemotherapy.
Drug treatment is often continued after patient health is restored;
this is a major reason why patients fail to complete prescribed drug
courses. Could there be room to harness the in-host ecology to reduce the
fitness advantages of resistance, in effect retaining some drug-sensitive
pathogens to suppress resistance (Wargo et al., 2007; Read and Huijben,
2009; Huijben et al., 2010)? Critically, patient health does not necessarily
require immediate parasite elimination by drugs. To affect clinical recov -
ery, the immune system often just needs to battle fewer parasites or have
a longer time period over which to ramp up. It may be, for instance, that
only minimal intervention with drugs is required before immunity con -
trols and clears disease-causing pathogens. This could involve a very short
course of treatment with a rapidly clearing drug (or drug combination),
perhaps repeated at well-spaced intervals. Given a bit of help, immunity
can deal very effectively with resistant parasites without imposing any
selection for resistance (Cravo et al., 2001; Rice, 2008a,b; Taubes, 2008).
Some currently heretical rules, such as “stop taking drugs when you feel
better, and take them again if you get sick,” bear examination in such
contexts. Critical questions are how best to combine dose and duration,
how much it is necessary to have an impact on pathogen densities at
first treatment, and how far apart pulses of treatment should be.
A general principle that should guide the rational development of
patient treatment guidelines is to impose no more selection for resistance
than is absolutely necessary. There might be cases where rules like “hit
hard and hit early” (Ehrlich, 1913) or “ensure very high cure rates” (World
Health Organization, 2010a) are consistent with this, but we doubt that
they apply across a wide swath of diseases. For instance, de novo resis -
tance mutants are a major threat to the health of patients infected with
highly mutable pathogens like HIV. In such a case, it probably is wise to
use aggressive chemotherapy to reduce pathogen biomass, and hence the
probability of de novo mutations. For many diseases, however, patients
are at far higher risk of acquiring resistance from other patients. In TB,
for example, up to 99% of cases of drug-resistant infections are acquired
from the community (Luciani et al., 2009). In these circumstances, the
merits of managing de novo mutations with aggressive chemotherapy
are less clear. Chloroquine became ineffective against malaria because
t he highly resistant progeny of a single parasite in Asia spread across
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The Evolution of Drug Resistance / 251
the entire African continent (Wootton et al., 2002; Talisuna et al., 2004).
SP, another inexpensive and initially highly effective antimalarial, was
similarly undermined by vast epidemics derived from very few genetic
events (Roper et al., 2004). Those resistant parasites enjoyed maximum
evolutionary advantage in patients who adhered to regimens effecting
radical cure of susceptible parasites.
More broadly, resistance management strategies will probably have to
be tailored to particular drug-bug combinations and epidemiological cir-
cumstances. For instance, where single-clone infections dominate (acute
childhood diseases or malaria where force of infection is low), the relative
fitness of resistant and sensitive strains will be quite different from situ-
ations where most infections have a high multiplicity of infection. Where
there is lateral transfer of resistance genes from the environment
(many bacteria), persistent subpopulations [e.g., Escherichia coli (Levin
and Rozen, 2006)], or infection sites that are difficult to treat [e.g., TB (Dye,
2009)], or where treated stages are diploid [e.g., helminths (Prichard and
Tait, 2001)], things could again be different. Where the social interactions
between coinfecting strains differ from those we have described for
malaria [e.g., West et al. (2006)], things could be different again.
It might also be that patient treatment regimens need to be modi-
fied as resistance evolution proceeds. Perhaps, for instance, aggressive
chemotherapy can reduce the probability that mutations to high-level
resistance will occur. If so, it could be worth moving to less aggressive
regimens as soon as high-level resistance is detected in a region. Regimens
involving lower doses or shorter treatments will impose weaker selec -
tion on that new resistance. Such a switch may be difficult in practice.
Health messaging may require constancy, or it may be that by the time
unambiguous evidence of high-level resistance has been obtained and
policy changed, it is already too late.
CODA
Arguments somewhat analogous to ours have also been made for
bacterial diseases (Lipsitch and Samore, 2002; Rice, 2008a,b). Aggressive
chemotherapy could be particularly problematic in the case of many bacte-
rial infections, where exhortations for patients to adhere to long-course
regimens probably generate sustained selection on gut commensals to har-
bor resistance genes. These can be readily passed to any disease-causing
bacteria that subsequently invade. Our discussion also has strong par -
allels with the management of Clostridium difficile in hospitals, where
aggressive use of broad-spectrum antibiotics is responsible for the com -
petitive release of the more virulent C. difficile (Vonberg et al., 2008).
OCR for page 252
252 / Andrew F. Read et al.
An analogous situation also occurs in cancer therapy, where cell
lineages within a tumor compete for access to space and nutrients. There,
the argument has recently been made that less aggressive chemotherapy
might sustain life better than overwhelming drug treatment, which sim -
ply removes the competitively more able susceptible cell lineages, allow -
ing drug-resistant lineages to kill the host (Gatenby, 2009; Gatenby et
al., 2009). Mouse experiments support this: Conventionally treated mice
died of drug-resistant tumors, but less aggressively treated mice survived
(Gatenby et al., 2009). Elsewhere, we and others have also argued that
by concentrating on malaria control rather than vector control, selection
for insecticide-resistant mosquitoes can be managed and even eliminated,
obviating the need for an insecticide discovery pipeline (Koella et al.,
2009; Read et al., 2009; Gourley et al., 2011). In all this, the key issue is to
impose only the selection needed to achieve health gains and no more.
There is widespread agreement that stewardship of antimicrobials
means restricting their use to only those patients who need them. We sug -
gest that a similar default philosophy of sparing use should apply at the
within-host level to patient treatment regimens. Overwhelming chemical
force may at times be required, but we need to be very clear about when
and why that is. Aggressive chemotherapy will, under a wide range of
circumstances, spread resistance.
ACKNOWLEDGMENTS
Our arguments benefited from discussion with V. Barclay, C.
Bergstrom, S. Bonhoeffer, N. Colegrave, M. Ferdig, A. Griffin, J. Hansen, J.
Juliano, M. Laufer, B. Levin, J. Lloyd Smith, M. Mackinnon, S. Meshnick,
N. Mideo, S. Nee, R. Paul, J. de Roode, P. Schneider, F. Taddei, and A. Wargo,
not all of whom agree with our conclusions. We thank J. Antonovics, A.
Bell, K. Foster, M. Greischar, S. Reece, and an anonymous referee for
comments on the manuscript and members of the Research and Policy
in Infectious Disease Dynamics Program of the Science and Technology
Directorate, Department of Homeland Security, and the Fogarty Inter-
national Center, National Institutes of Health, for stimulating discussion.
Award R01GM089932 from the National Institute of General Medical Sci -
ences supported the empirical work reported here; work under Awards
R01AI089819 and U19AI089676 from the National Institute of Allergy and
Infectious Diseases contributed to conceptual development. This work
greatly benefited from a Fellowship to A.F.R. at the Wissenschaftskolleg
zu Berlin.
