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

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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.
Is it more important to match post-treatment activity profiles rather than crude drug half-lives? (A) Two drugs in a CT have broadly similar half-lives. (B) The two drugs in the CT have very different PD profiles. (C) Multiplying concentration profiles post-treatment (shown in (A)) by the dose-effect relationships (shown in (B)) gives a drug activity profile post-treatment; as can be seen these profiles are very different leading one drug to persist as a vulnerable monotherapy. (D) A practical example of this effect: the drugs appear to be perfectly ‘matched’ with similar half-lives (as in (A)) and identical kill rates (both assumed to have the ‘blue’ profile shown in (B)), but toxicity concerns means the ‘blue’ drug must be given at 2.5-fold lower dosages, leading to a severe mismatch in drug activity profiles. Note the similarities between the results shown in (C) and (D). [Figure  2 was constructed using simple PK/PD models and their corresponding equations [26,29]. Parameter values for the two drugs are as follows: Dose is 11 mg/kg for both in (A), (B) and (C) and 11 mg/kg for ‘blue’ and 27.5 mg/kg for ‘red’ in (D); volume of distribution is 150 L/kg; elimination rate per day is 0.03 for ‘blue’ and 0.032 for ‘red’ (equivalent to half-lives of 23.1 and 21.7 days, respectively); maximal drug-killing rate per day (Vmax) is 3.45; IC50 is 0.044 mg/L for ‘blue’ and 0.0176 mg/L for ‘red’ in (A),(B) and (C) and 0.044 mg/L for both in (D); slope of dose-response curve (n) is 6].
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How natural variation in PK/PD may undermine matched post-treatment drug activity profiles. The two drugs have, on average, the same PK/PD parameters so are perfectly matched on average, c.f. Figure  3C. In these examples, natural variation around these mean PK/PD values results in one drug in the CT being exposed as a monotherapy for a significant period post-treatment. These illustrative differences reflect variation in single parameters: mismatches may become much larger once simultaneously variation in all PK/PD parameters is included. (A) An example of the impacts of differences in human PK, elimination rate; the red drug is eliminated by this patient 50% faster than the average while the blue drug is eliminated 50% slower than the average. (B) How variation in parasites PD parameters affect these profiles: the patient has the same PK for each drug (so concentration profiles post-treatment for both drugs are identical) but the parasites inoculated into the patient differ in their sensitivity to the drugs: their 10-fold higher resistance (IC50) to the blue drug means the red one is effectively a monotherapy for a significant period of time post-treatment. [Figure  4 was constructed using simple PK/PD models and their corresponding equations [22,25]. Parameter values for the two drugs are as follows: Dose is 75 mg/kg; volume of distribution is 150 L/kg; elimination rate per day is 0.05 for ‘blue’ and 0.15 for ‘red’ (equivalent to half-lives of 13.8 and 4.6 days, respectively); maximal drug-killing rate per day (Vmax) is 3.45; IC50 is 0.088 mg/L for ‘blue’ and 0.0088 mg/L for ‘red’; slope of dose-response curve (n) is 6].
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R E V I E W Open Access
Pharmacological considerations in the design of
anti-malarial drug combination therapies is
matching half-lives enough?
Ian M Hastings
*
and Eva Maria Hodel
Abstract
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
resistanceis 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 compensateforthisreduceddosage;thisisadangerous
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.
Keywords: Antimalarials, Combination therapy, Pharmacokinetics, Pharmacodynamics, Drug resistance
Background
The benefits of using a combination of drugs to treat in-
fectious diseases has been recognized since the days of
Laveran [1] and Ehrlich [2] who used combination therapy
(CT) with dyestuffs to treat trypanosomiasis. Drug CTs
are now standard policy for treating human immunodefi-
ciency virus (HIV) infections, tuberculosis (TB) and mal-
aria. In the case of TB and HIV, the use of CT was driven
by clinical necessity as patients routinely failed treatment
with monotherapies. The use of CTs to treat falciparum
malaria, currently in the form of artemisinin-based com-
bination therapy (ACT), rests on a different justification.
Most of the partner drugs in ACT are clinically effective
as monotherapies, especially when first deployed, so the
use of anti-malarial CTs is primarily justified as a public
health strategy to delay or even prevent the onset of resist-
ance (e.g. [3], recently reviewed in [4]). There is consider-
able concern about the possible onset of artemisinin
resistance, particularly in South-East Asia [5-9] and its
long-term threat to the future of ACT (e.g. [10-14]). Sev-
eral new anti-malarial drugs are in the developmental
pipeline [15,16] so new forms of CT will have to be con-
sidered. Herein, the design principles underlying anti-
malarial CTs are discussed. Clearly identifying desirable
properties of constituent drugs in an anti-malarial CT
may well help guide the developmental pathway and stop/
go decision-making process of anti-malarials currently
under development [17].
Previous models demonstrating the benefits of CT in
delaying the onset of resistance largely assumed that
* Correspondence: hastings@liv.ac.uk
Liverpool School of Tropical Medicine, Pembroke Place, Liverpool L3 5QA, UK
© 2014 Hastings and Hodel; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public
Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this
article, unless otherwise stated.
Hastings and Hodel Malaria Journal 2014, 13:62
http://www.malariajournal.com/content/13/1/62
parasites are either completely sensitive to the drug, or
else completely resistant (discussed further in [4]). The
first type of model, the basic rationale[4] considers
how new mutations enter the malaria population. As-
suming the mutation rate to resistance to each drug is
10
-9
, then the chance of any individual parasites being
spontaneously resistant to both drugs in a CT is vanish-
ingly small (i.e. 10
-18
). Thus, new mutations should enter
the parasite population extremely rarely (note, however,
the implicit assumption that resistant mutations only
enter the population through drug treatment of the
asexual biomass of typically about 10
11
to 10
12
parasites
per patient [18]; discussed in more detail in [4]). The
second type of model investigates the dynamics of resist-
ance once it has entered the malaria population. For ex-
ample, models of a two-drug CT typically assume that
resistance to each drug is encoded by a different, single
locus with alleles classified as resistant (R) and sensitive
(S). This gives rise to four 2-locus haplotypes that can be
designated S
A
S
B
,R
A
S
B
,S
A
R
B
and R
A
R
B
, where subscript
denotes resistance status to drug Aor Brespectively.
Consequently, infections of types S
A
S
B
,S
A
R
B
and R
A
S
B
are always killed by CT treatment while only R
A
R
B
sur-
vives. This strict dichotomy of resistantversus sensi-
tiveforms is useful for elucidating the general principles
underlying CT design but is not a particularly realistic
representation of treatment outcome which is known to
depend not just on parasites genotype, but on other fac-
tors such as immunity and drug dosage [19]. In addition,
these types of models can only demonstrate the broad
benefits of CT but they cannot provide information
about the optimal designs of these CTs. The subtleties of
how resistance evolves may be complex (for example,
artemisinin resistance may be restricted to specific parts
of the parasite life cycle [20]) and are discussed elsewhere
[4,21]. This manuscript will ignore how resistance actually
arises, will simply assume that it is inevitable, and will
focus on considering how pharmacological modelling can
help design CTs that are robust to increasing levels of
parasite resistance entering the parasite population.
A more nuanced approach to resistance/sensitivity can
be achieved using pharmacokinetic/pharmacodynamic
mechanism-based modelling of drug treatment (recently
reviewed in [22]). In this context of the pharmacology of
malaria treatment, pharmacokinetics (PK) describes how
drugs are processed by the human body, e.g. the drug half-
life, while pharmacodynamics (PD) describes how the drug
affects the parasite e.g. the drug concentration producing
half the desired effect (IC50). There are potentially several
different mechanisms and models of PD depending on
how the drug acts: some drug actions may best be
described by the maximum concentration reached, the
times above a certain concentration, or the extent to
which a drug accumulates at the target site. A recent
review and access to the literature is provided in [23]
but here, for simplicity, the discussion assumes anti-
malarial drugs can be best described by standard mod-
elling [22] as previously applied to malaria drug action
(see below). The technique can be summarized as fol-
lows. The drug concentration profile after treatment is
tracked using PK modelling while the sensitivity of the
parasites to the drugs is defined by its PD parameters.
The drug concentration at any time post-treatment
can, therefore, be translated into a parasite kill rate en-
abling change in parasites numbers post-treatment to
be tracked in order to find whether the parasites are
eventually eliminated, or whether they survive treat-
ment. Critically, this strategy allows researchers to de-
fine resistantparasites in mechanistic, PD, terms such
as increased IC50 rather than simply assuming they are
completely insensitive to the drug. The fate of the resist-
antparasites can then be investigated in the context of all
the other factors known to affect patient outcome; typical
examples are the pharmacological environment parasites
encounter during treatment (derived from varying patient
PK parameters, e.g. drug elimination rate), the patients
adherence to treatment and so on. This approach has
already been applied to malaria [24-27] and recent work
[28-30] has focussed on developing the technical meth-
odology to allow for multiple drug doses, combination
therapies and drug conversion processes. A parallel
data-driven agenda has investigated the nature and extent
of variation in the PK/PD parameters and extension to
real-life deployment such as age- or weight-based dosing
bands and the impact of poor adherence to the recom-
mended regimens [30]. Unlike models based on sensitive/
resistance dichotomy, these PK/PD models do throw light
on what properties define a good CT and herein it is ar-
gued that PK/PD considerations can usefully contribute to
therationaldesignofCTs.
Key pharmacological considerations for potential
combination therapies
There are six distinct considerations that must be addressed
in the design of CT which will each be addressed in turn.
1. The half-lives and activity profiles of constituent drugs
It has long been realized [31] that a mismatch in half-
lives will leave the drug with the longer half-life to per-
sist as a vulnerable monotherapy (Figure 1) because
new infections emerging from the liver (or inoculated
by the mosquito in the case of drugs that are active
against liver stages such as atovaquone) need only be resist-
ant to a single drug to survive. This effect constitutes one
of the three main drivers of anti-malarial drug resistance
[4]. Therefore, a key recommendation in CT design is
that the drug half-lives should be matched (e.g. [32]) but
this argument warrants further examination.
Hastings and Hodel Malaria Journal 2014, 13:62 Page 2 of 15
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This argument is couched in terms of half-life but
matching is more subtle because mutual protection be-
tween the drugs depends on there being active concen-
trations of both drugs simultaneously. It, therefore,
appears more important that drug anti-malarial activity
profiles post-treatment are the critical factors to be
matched, rather than simply their half-lives. The activity
profile post-treatment depends on three factors, the drug
half-lives, the dosage given (and absorbed) and the drugs
endogenous anti-malarial activity. Figure 2 shows how
matched half-lives may be undermined by differences
in anti-malarial drug activity and/or if concerns over
toxicity mean the drugs must be given at different
dosages; either situation will result in a period of time
where only one drug is responsible for killing parasites
and so is in effect a monotherapy.
One way in which matching of drug killing can be
quantified is by their time above the minimal inhibitory
concentration (MIC) a concept originally used in
bacteriology but now being increasingly used in malaria
(e.g. [34] for a recent example). Time above MIC for any
anti-malarial drug is a function of the maximum concen-
tration (Cmax) and drug clearance, which are again
dependent on the dose and the formulation, and the
MIC, which is described by the PD profile. More encour-
agingly, the interaction between factors such as half-life,
dosage and parasite drug sensitivity means that a well-
designed CT could, in principle, allow for mismatch in
any of these variables by altering the relative dosages
(Figure 3) to achieve matched activity profiles post-
treatment. This does raise the interesting, and largely un-
considered, operational question of whether both
drugs should be deployed in a CT at their maximum
dosages,orwhetheronedrugmaybeincludedatare-
duced dosage to match killing activities. The former is
probably more robust. Maximal concentrations of both
drugs optimize clinical effectiveness and help protect
against resistance being driven through drug failures
(the matchingargument only applies to selection for
resistance post-treatment [35]) and natural variation in
PK/PD may, in practice, largely undermine matching
done on average PK/PD values (see below). It is diffi-
cult to bring toxicity arguments into this discussion at
present because this is currently an under-researched area
(see below). The onset of toxicity associated with anti-
malarial drugs is generally not indicated by clinically clear
on/off signals and, moreover, an increased risk of adverse
events does not necessarily follow a direct correlation with
plasma exposure (either area under the curve, AUC, or
Cmax).
In summary, although it is essential to reiterate the
usual assertion that matching half-lives is important we
suggest it should be seen in a more subtle way and that
activity profiles after treatment are of more importance
than crude half-lives.
2. Natural variation in population PK/PD
One key operational question in CT design is the extent
to which parasites vary in their PD parameters such as
IC50. A first step in investigating the likely effectiveness of
CT design is to obtain a value for mean IC50 plus the na-
ture and extent of variation around this mean [28]. The
variation in IC50 values is critical as it determines the ex-
tent to which natural variation in PD will undermine an
idealized matched profile. Balancing half-lives, dosages
and drug sensitivity can only be plausibly done on the
Figure 1 The consensus view that drugs in a CT should have matching half-lives [32,33]. (A) The constituent drugs have very different
half-lives (as in the current generation of ACT) leaving the bluedrug to persist as a vulnerable monotherapy for an extended period of time
post-treatment after the reddrug concentration has decayed to sub-therapeutic concentrations. (B) The constituent drugs have roughly similar
half-lives meaning they should, in principle (but see main text), provide mutual protection post treatment. [Figure 1 was constructed using simple
PK/PD models and their corresponding equations [26,29]. Parameter values for the two drugs were as follows: Dose is 11 mg/kg; volume of distribution
is150 L/kg. Elimination rates per day were 0.03 for blueand 0.07 for red(equivalent to half-lives of 23.1 and 9.9 days, respectively) in (A) changing to
0.032 for redin (B) (equivalent to half-life of 21.7 days)].
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basis of their average values (as used in Figures 2 and
3);inpractice,thestrategywillbehinderedbythe
huge natural variation that occurs in both PK and PD
parameters. Even if it were possible to match drug pro-
files in an idealhuman, it would appear inevitable that
natural variation in PK/PD parameters would cause a mis-
match in individual patientspost-treatment concentration
and activity profiles and leave one drug persisting as a vul-
nerable monotherapy. The variation typically noted in
PK and PD parameters is huge. The between-subject
variability in human PK parameters is typically 3050%
[36] while variation in parasite isolates IC50 values typi-
cally vary 100 to 10,000 fold (see, for example, Figure 3 of
[37]). Figure 4 illustrates how this variation may
undermine matching. The natural variation in PK will
be augmented by human behaviouralvariation e.g. food
intake, age, nutritional status and factors such as preg-
nancy (reviewed in [38]). If these factors differentially
affect the individual drugs in the CT then mismatches
may be widened. In order to avoid amplification of the
already large between-subject variation in PK parame-
ters clear recommendations must be given on covari-
ates that affect PK such as co-administration of food
intake and dose adjustments in children under the age of
five or pregnant women. Implementing these recom-
mendations is not straightforward because providing
such advice must be balanced against the need for clear
instructions on drug use in resource-poor regions where
patients are often treated in the informal sector and may
have only casual access to anti-malarials.
It is, therefore, inevitable that mismatch will arise in
individual treatment and that mutual protection will be
Figure 2 Is it more important to match post-treatment activity profiles rather than crude drug half-lives? (A) Two drugs in a CT have
broadly similar half-lives. (B) The two drugs in the CT have very different PD profiles. (C) Multiplying concentration profiles post-treatment (shown
in (A)) by the dose-effect relationships (shown in (B)) gives a drug activity profile post-treatment; as can be seen these profiles are very different leading
one drug to persist as a vulnerable monotherapy. (D) A practical example of this effect: the drugs appear to be perfectly matchedwith similar
half-lives (as in (A)) and identical kill rates (both assumed to have the blueprofile shown in (B)), but toxicity concerns means the blue
drug must be given at 2.5-fold lower dosages, leading to a severe mismatch in drug activity profiles. Note the similarities between the
results shown in (C) and (D). [Figure 2 was constructed using simple PK/PD models and their corresponding equations [26,29]. Parameter
values for the two drugs are as follows: Dose is 11 mg/kg for both in (A),(B) and (C) and 11 mg/kg for blueand 27.5 mg/kg for redin
(D); volume of distribution is 150 L/kg; elimination rate per day is 0.03 for blueand 0.032 for red(equivalent to half-lives of 23.1 and
21.7 days, respectively); maximal drug-killing rate per day (Vmax) is 3.45; IC50 is 0.044 mg/L for blueand 0.0176 mg/L for redin (A),(B)
and (C) and 0.044 mg/L for both in (D); slope of dose-response curve (n) is 6].
Hastings and Hodel Malaria Journal 2014, 13:62 Page 4 of 15
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much less than anticipated. The extent to which this will
undermine the advantages of CT is uncertain although
recent PK/PD modelling suggests the selection pressures
associated with this mismatch (the windows of selection)
is likely to be much lower that currently thought [39].
Most arguments for matching half-lives are over-
optimistic and simply show a variant of Figure 1B without
considering how factors such as natural variation in
PK/PD can affect this matching. In conclusion, it is only
plausible to make approximate matches on the basis of
half-live. Most drugs outside the artemisinin class have
relatively long half-lives so could be approximately
matched with the caveats listed above.
3. Dosages and toxicity
There are two main types of toxicity associated with
malaria drugs. Those which are dose- or concentration
dependent (also referred to as type A) adverse drug
reactions (ADRs), which are mostly predictable and
consistent between patients because they are explained
by the drugs known pharmacological action. The second is
type BADRs that are generally, or at least partially, dose- or
concentration independent and which are largely unpredict-
able and dependent on an individual patients metabolism,
immune system or genetics. One common example for
anti-malarial drugs is glucose-6-phosphate dehydrogenase
(G6PD) deficiency which mediates toxicity in drugs from at
least two different classes, e.g. primaquine and dapsone
(Table 1). It is relatively easy to cure malaria, but curing
malaria without poisoning the patient is much more prob-
lematic. Guinea-Bissau (G-B) overcame its problem of
chloroquine (CQ) resistance by simply doubling the dos-
age of CQ given to patients. This was an effective strategy,
but raises safety concerns over toxicity (although adverse
events were not observed in practice [40]) and to date no
other country has followed this approach. One feature of
Figure 3 How altering relative dosages can compensate for differences in half-live and/or endogenous anti-malarial activity. (A) The
half-lives of two drugs in this CT differ by a factor of 2, leading to one drug being left as a vulnerable monotherapy; most arguments on design
of CT end here by concluding the drugs are not well matched. (B) The drug kill rates against parasites as a function of drug concentration; they
differ in their IC50 values. (C) Compensating for differing half-lives and IC50s by increasing the dosage of drug illustrated in red2.5 fold: killing is
now matched and drugs provide mutual protection. [Figure 3 was constructed using simple PK/PD models and their corresponding equations
[26,29]. Parameter values for the two drugs are as follows: Dose is 75 mg/kg for blueand 187.5 mg/kg for red; volume of distribution is 150 L/kg;
elimination rate per day is 0.05 for blueand 0.1 for red(equivalent to half-lives of 13.8 and 6.9 days, respectively); maximal drug-killing rate per day
(Vmax) is 3.45; IC50 is 0.088 mg/L for blueand 0.044 mg/L for red; slope of dose-response curve (n) is 6].
Hastings and Hodel Malaria Journal 2014, 13:62 Page 5 of 15
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current anti-malarial drugs is that their therapeutic index
(TI; the ratio between a drugsuppertoxicand lower
curativethresholds) is very narrow (between 1.5 and 3
[41]) and only artesunate (AS) and dihydroartemisinin
(DHA) show a TI 5. This feature hinders attempts to
balance doses to obtain a matched elimination profiles
(see Figure 3C) because dosing regimens with doses ex-
ceeding the upper threshold of the target dose range are
likely to increase the number of patients experiencing type
A reactions.
Combining two drugs always increases the risk of tox-
icity, but it is important to quantify this increased risk and
to describe how it can be mitigated. The obvious assump-
tion is that risk of toxicity to the CT is additive (i.e. the
product of the risks of each drug given individually) but,
in principle, toxicity could be synergistic (i.e. the risk is
greater than twice the product) or may be antagonistic if
the presence of a drug decreases the risk of toxicity
caused by the other drug. Predicting synergy or other-
wise in drug toxicity is problematic. Laboratory studies
are vital to estimate this and, for example, animal stud-
ies showed that the teratogenic potential of the combin-
ation sulphadoxine-pyrimethamine (SP) was two-fold
higher than expected from the drugs individually (for
review see e.g. [45]). Synergy may plausibly occur if drugs
share a common toxicity mechanism e.g. in G6PD-
deficient patient, so such drugs should probably not
be combined. Synergy could also arise if drugs share
the same metabolic pathways for elimination because
competition for metabolizing enzymes could extend the
elimination half-life and hence the areas under the drug
concentration curve. Similarly, competition for plasma
protein binding sites could result in higher plasma drug
levels for both drugs. Note that both increased half-
lives and increased concentrations would actually in-
crease the effectiveness of the drugs so drug effective-
ness and toxicity may well be positively correlated.
One operational question in CT design is the extent to
which doses of the constituent drugs can be reduced to
lower the risk of ADRs; the reduced effectiveness of each
drug (caused by its lower dose) would be offset by the
presence of its partner drugs in the CT. For example, in
both animal and human Plasmodium infections, pyri-
methamine and sulphadoxine administered together are
curative at one-eighth the dose of either used alone [45].
An important factor in considering this strategy is that in-
creasing individual drug dosages display a law of diminish-
ing returns in terms of anti-malarial activity (Figure 5)
while increasing doses often increase the risk of toxicity
in an additive manner. Development of the promising
anti-malarial drug combination chlorproguanil-dapsone
(Lapdap) plus AS was discontinued after the drug
showed G6PD-associated toxicity in Phase 3 clinical trials
[46]. It is believed that dapsone was responsible for the
toxicity and it remains a possibility that the CT could
be re-evaluated by reducing dapsone dosages to reduce
toxicity and relying on artesunate to offset the reduced
therapeutic effects of the lower dapsone dose. Such a
Figure 4 How natural variation in PK/PD may undermine matched post-treatment drug activity profiles. The two drugs have, on average,
the same PK/PD parameters so are perfectly matched on average, c.f. Figure 3C. In these examples, natural variation around these mean PK/PD
values results in one drug in the CT being exposed as a monotherapy for a significant period post-treatment. These illustrative differences reflect
variation in single parameters: mismatches may become much larger once simultaneously variation in all PK/PD parameters is included. (A) An
example of the impacts of differences in human PK, elimination rate; the red drug is eliminated by this patient 50% faster than the average while
the blue drug is eliminated 50% slower than the average. (B) How variation in parasites PD parameters affect these profiles: the patient has the
same PK for each drug (so concentration profiles post-treatment for both drugs are identical) but the parasites inoculated into the patient differ
in their sensitivity to the drugs: their 10-fold higher resistance (IC50) to the blue drug means the red one is effectively a monotherapy for a significant
period of time post-treatment. [Figure 4 was constructed using simple PK/PD models and their corresponding equations [22,25]. Parameter values for the
twodrugsareasfollows:Doseis75mg/kg;volumeofdistributionis150L/kg;eliminationrateperdayis0.05forblueand 0.15 for red(equivalent to
half-lives of 13.8 and 4.6 days, respectively); maximal drug-killing rate per day (Vmax) is 3.45; IC50 is 0.088 mg/L for blueand 0.0088 mg/L for red;slope
of dose-response curve (n) is 6].
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proposal would probably have to initially rest on accurate
PK/PD modelling of its efficacy, which could simulta-
neously investigate how the reduced-dose CT would
by threatened by, and possibly drive, the spread of
resistance [28].
4. Do the drugs have independent pharmacodynamics?
Drugs within a CT may act additively, synergistically or
antagonistically in their ability to kill malaria parasites.
Chou [47] noted that the definition of synergy is fraught
with difficulty and misunderstanding and readers should
view this paper for a full discussion. A simple intuitive
approach can be used in this context where synergy is
an action greater than the sum of the two drugs used
separately and antagonism occurs where the two drugs
have less anti-malarial activity than would be expected
from their individual activities. Synergy/antagonism in
PD effects is often detected in vitro through construction
of isobolograms, which quantify how parasite killing or
growth inhibition depends on the concentrations of both
drugs in a combination [48-51].
It would be expected intuitively that drugs with the
same mode of action would be largely antagonistic.
For example, if a drug completely blocks the haem
polymerization pathway then there would appear little
point in combining it with another haem-inhibiting
drugs and blocking it twice. Similarly artemisinins are
converted to their active metabolite DHA in vivo, and
presumably both forms have the same target; it would
be probably wrong to regard both forms as having inde-
pendent PD (see discussion in [28]).
An alternative intuitiveexpectation is that using two
drugs with the same mode of action would be additive
and, crudely, would have double the effect of either one
alone; this is consistent with the fact that all anti-
malarial drugs so far deployed have had their dosages in-
creased to improve effectiveness. In fact, the reverse is
probably true: PK/PD arguments suggest that increasing
dosages will suffer from a law of diminishing returns if
drugs share the same mode of action; see Figure 5. In es-
sence, increasing the dose of the same drug extends the
duration of effect, rather than it magnitude (Figure 5) if
Table 1 Currently available classes of anti-malarial drugs
Drug class Example drugs Comments
Artemisinins
(or artemisinin derivatives)
Artesunate, artemether and
dihydroartemisinin
The most widely used of the anti-malarial drugs with very short
half-lives. These are sub-curative in standard 3 day regimens if
used as monotherapies
Antifolates Pyrimethamine, chlorproguanil,
proguanil, sulphadoxine and dapsone
The combination sulphadoxine-pyrimethamine (SP; also known
by its trade name Fansidar) is widely used for therapy. Both
constituents have long half-lives so it was given as a single-dose
regimen but resistance quickly evolved. Its use is now primarily
restricted to treatment/prophylaxis in intermittent treatment
programmes
4-aminoquinolines Chloroquine, amodiaquine, piperaquine,
pyronaridine and naphthoquine
Chloroquine was used in huge quantities as a monotherapy for
over 30 years. Resistance occurred only infrequently and Africa
never developed its own resistance instead it was aquired by
immigrations from South-East Asia [42].
Arylamino alcohols Quinine, mefloquine, lumefantrine
and halofantrine
Quinine was the first anti-malarial to be identified. A long
treatment duration and its safety profile means it is now mainly
used in early pregnancy or as a (parenteral) second-line treatment
either alone or in combination in uncomplicated or severe malaria.
Lumefantrine with artemether is currently the most widely used
anti-malarial combination therapy; it has low-level antagonistic
resistance with chloroquine [43].
Naphthalenes Atovaquone Atovaquone is active against hepatic and asexual stages but
resistance arises spontaneously at very high rates. Has synergistic
pharmacodynamics when combined with proguanil, resistance
no longer occurs at high rates and the combination therapy
widely used as prophylaxis under the trade-name Malarone.
Can also be used curatively but high cost restricts its deployment
in resource-poor health services.
8-aminoquinolines Primaquine and tafenoquine These drugs affect hepatic and transmission stages but do not
affect the pathogenic asexual stages of the plasmodium cycle
so are not routinely uses to cure acute infections. Both are toxic
in glucose-6-phosphate dehydrogenase deficient patients [44].
Primaquine has a short half-life which reduces its therapeutic
effectiveness but means concentration can be allowed to drop
very rapidly in patients identified with adverse reactions.
Antibiotics Tetracycline These drugs do have activity against the asexual stages but
their slow speed of action precludes their use as therapeutics
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two drugs in a CT share the same PD it is likely that
the drug with the longest post-treatment activity (see
Figure 3) will be the main determinant of therapeutic
outcome and its partners contribution may be small to
negligible. Diminished returns may still be operationally
useful, for example the additional 49% parasite killing asso-
ciated with the example on Figure 5 may still be enough to
restore drug efficacy, but it does not necessarily repre-
sentbestuseofdrugswithinaCT,hencetheusualad-
vice to avoid combining drugs with the same mode of
actions (e.g. [32,52]). The law of diminishing returns
may lead to a practical problem of a potential single dose
cure [17,32]. While a single dose regimen might be a real
game changer from a patient adherence perspective, the
single dose needed to achieve the same extent of killing
might, for example, lead to an inacceptable high Cmax, or
the physical tablet size might make it impossible for pa-
tients to swallow it.
Combining drugs with additive or synergistic action
should also increase parasite clearance post-treatment and
hence may speed the resolution of symptoms. This is obvi-
ously desirable but much less important, certainly from a
resistance standpoint, than whether a patient is actually
cured. Clearance post-treatment is also complicated in
anti-malarials because it is mainly determined by the
fastest acting drug, invariable an artemisinin, in ACT.
Artemisinins have a short half-live and show stage spe-
cific killing so clearance rate is also affected by the malaria
parasite stages that predominate at the time of treatment.
Clearance is also complicated by patient immune status
[53,54], with immune patients clearing parasites more
rapidly.
One potential drawback of synergy is that the inter-
dependency between the drugs means that if resistance
evolves to one individual component then the CT may
start to fail. The best-known example is SP where early
Figure 5 The Law of Diminishing Returns when increasing drug dosages. This example is based on piperaquine using PK/PD parameters
from Table 1 of Winter & Hastings [25]. (A) The drug concentrations post-treatment: the green line is the standard dose of three daily doses of
18 mg/kg given as a single dose of 54 mg/kg (for illustrative purposes), the blue line is a double dose (108 mg/kg), and the red line is a triple
dose (162 mg/kg). (B) The Michaelis-Menton relationship between drug concentration and anti-malarial activity. (C) The activity profiles
post-treatment of the three different doses, obtained by multiplying the drug concentrations by their killing rate. Doubling the dose gave
only an extra 49% area under the drug killing curve while tripling the dose gave only an increase of 19% compared to the double dose. [Figure 5
was constructed using simple PK/PD models and their corresponding equations [26,29]. Parameter values for the three drugs are as follows: Dose is
54 mg/kg for green, 108 mg/kg for blueand 162 mg/kg for red; volume of distribution is150 L/kg; elimination rate per day is 0.03 (equivalent to
half-life of 23.1 days); maximal drug-killing rate per day (Vmax) is 3.45; IC50 is 0.088 mg/L; slope of dose-response curve (n) is 6].
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stages of resistance arise through mutations in the
P. falciparum dihydrofolate reductase gene (pfdhfr)thaten-
codes resistance to pyrimethamine. Sulphadoxine is unable
to clear infections unaided by pyrimethamine and the CT
as a whole started to fail once resistance to pyrimethamine
started to evolve [55,56]. The theoretical basis for CT rests
on the assumption that mutations in two or more genes
are required to encode resistance to the CT. SP fails this
designprincipleandhencethemalariacommunitydoes
not generally regard SP as a trueCT because, oper-
ationally, it behaves as monotherapy with mutations in a
single gene capable of encoding resistance to treatment.
The SP example illustrates a very important and often
overlooked design principle: synergy between drugs in a
CT is obviously beneficial but should not be used a reason
to reduce individual drug dosages in the CT, except as a
strategy to reduce concerns over toxicity (see above).
Ideally each drug should be deployed at dosages that
would be required for it to be effective as monotherapy
so that the CT remains effective even when resistance is
present to one of the components.
5. Do the drugs have independent pharmacokinetics?
Pharmacokinetic processes are often non-independent and/
or saturable so one consideration of CT design is the
extent to which individual drug PK are affected by co-
administration with their partner drugs. For example,
lumefantrine (LF) absorption appears to saturate, so
lower doses given more often is more effective [57];
drugs sharing the same absorption route as LF could
compete for absorption and hence be antagonistic
which would reduce their efficacy within a CT. If drugs
share the same conversion or elimination pathways
post-absorption then their actions could become non-
additive but in unpredictable ways. For example, if con-
version to an active form is impaired by the presence
of a partner drug, and the unconverted form is elimi-
nated while awaiting conversion, then drug PK may be
antagonistic. Conversely, if the same elimination pathway
saturates for both drugs, and both parent forms are active,
then drug half-lives may be extended and anti-malarial
synergy may arise.
There appears to be little literature on the interactions
between PK of anti-malarial drugs. However, the pres-
ence and importance of PK drug interactions is demon-
strated by the much better characterized examples of
interactions between drugs co-administered to treat dif-
ferent diseases. A recent review by Sousa et al. [58]
stated that Rifampicin, a standard component of com-
bination regimens for treating TB, has a great influence
on the bioavailability and the efficacy of several anti-
malarial drugs, not only because of the inhibition of
Phase I and II enzymes of hepatic metabolism, but
also because of its effect on drug absorption and
distribution. It induces almost all cytochrome P450
(CYP)enzymes,itinhibitsN-acetyltransferases and
it alters the expression of membrane transporters.
Malaria and HIV are co-endemic and there are detailed
examples of how co-administration of anti-malarials
and anti-retrovirals affects each othersPK,manyof
which can be explained by the metabolic properties (i.e.
induction or inhibition) of the co-administered drugs (re-
cently reviewed by [59]). One class of anti-HIV drug are
the protease inhibitors (PIs) which tend to increase expos-
ure (defined as area under the plasma concentration
time curve and/or maximum concentration; for details see
Tables 1 and 2 in [59]) of LF and decrease the exposures
of artemether (AM) and DHA. Another class, the non-
nucleoside reverse transcriptase inhibitors (NNRTIs), tend
to decrease the exposures of AM, DHA and LF, when co-
administered with AM-LF [59]. Fewer studies character-
ized the effects of PIs or NNRTIs on AS combinations,
where nevirapine (a NNRTI) increased AS exposure and
ritonavir (a PI) decreased DHA exposure. These interac-
tions may be mutual: AM-LF or AS combinations had lit-
tle effect on the PK of HIV-anti-retrovirals, although AM-
LF resulted in decreased nevirapine exposure and
pyronaridine-AS increased ritonavir exposure [59]. It is
therefore certainly plausible that drugs in an anti-
malarial CT can induce or inhibit each othersmetabol-
ism. Artemisinins drugs are potential inducers of CYP en-
zymes, and the most inducible are CYP2B6 and CYP3A4,
which are believed to be the main enzymes involved in
the auto-induction of artemisinin drugs [60]. In a pooled
PK analysis the onset of auto-induction for artemisinin
was found to be very rapid, e.g. 8 h after the first dose
[61]. This implies that a single dose of artemisinin is
capable of enzyme induction. The analysis suggested auto-
induction has minor effects on the systemic clearance of
artemisinin but results in a 13-fold decrease in its bioavail-
ability. The metabolism of drugs is also affected by regional
differences in the prevalence of anti-malarial drug-
metabolizing enzyme polymorphisms (for a detailed dis-
cussion see [62]) which further complicate the link be-
tween drug dosage and subsequent anti-malarial drug
concentration and treatment outcome.
In essence, the consequences of PK interactions are diffi-
cult to predict and depend on the exact metabolic pathways
and whether or not active metabolites contribute to parasite
killing. One big advantage over the other factors enumerated
here is that PK interactions can be measured in preclinical
studies and clinical (Phase I) studies of CT development in
healthy human volunteers. Pharmacokinetic parameters of
anti-malarial drugs are known to be affected by malaria
infections (e.g. quinine clearance is reduced in acute
malaria, primarily as a result of disease-induced dysfunc-
tion in hepatic mixed-function oxidase activity [63]) so the
Phase I study results are not definitive, but have the huge
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advantage of not requiring infected patients which would
raise a whole series of ethical issues based around the
rights of patients to receive current local best standard of
care.
6. Do the drugs share common mechanisms of cross
resistance?
The benefits of using a CT rest on the parasite popula-
tion having to evolve resistance to both drugs to survive
treatment with the CT (see above). Intuitively, cross-
resistance between the constituent drugs in a CT will
undermine this effect and modelling shows that even
small amounts of cross resistance may significantly re-
duce the expected therapeutic lifespan of both drugs [64].
The clearest example of cross-resistance in anti-malarials
occurs in the antifolates class. The two best-known
antifolate combinations are SP and chlorproguanil-
dapsone. Sulphadoxine and dapsone both inhibit P. fal-
ciparum dihydropteroate synthase (PfDHPS) while
pyrimethamine and chlorcycloguanil, the active me-
tabolite of chlorproguanil, both inhibit P. falciparum dihy-
drofolate reductase (PfDHFR). Parasites carrying single-
point mutations in the pfdhfr gene showed decreased
sensitivity towards pyrimethamine, rising from 10-fold as-
sociated with a single mutations to 1,000-fold associated
with a quadruple-mutant allele [65]. Similarly, parasites
that have accumulated several mutations in the pfdhps
gene display resistance towards sulphadoxine [66].
While the so-called triple mutantwith point muta-
tions at codons 108, 51 and 59 of the pfdhfr gene is re-
sistant against pyrimethamine and sensitive to
chlorcycloguanil, the additional PfDHFR mutation at
Table 2 An ideal Target Product Profile (TPP) for an anti-malarial combination therapy
Property Attribute Reference
Formulation & dose Single-dose treatment regimen Desirable [17,32]
Stable Critical [32]
Fixed-dose in a single formulation Desirable [32]
Orally, rectally and parentally applicable Desirable [32]
Dose of each drug high enough so that it will remain
effective even if resistance is present to the other drug
Critical This manuscript KPC#4
Mode of action Effective against all stages of parasite development
in the human host
Desirable [32]
Active against hypnozoites and able to prevent relapse Desirable [17]
Transmission-blocking activity Desirable [17]
Robust to the evolution of resistance Critical [32]
Independent, or preferably synergistic, mode of action of drugs Desirable [32]; this manuscript KPC#4
Different metabolic target(s) of drug action Desirable/Critical This manuscript KPC#4
Negative patterns of cross resistance Desirable This manuscript KPC#6
Pharmacokinetics &
pharmacodynamics
(PK/PD)
Elimination half-lives of drugs should be approximately matched Desirable [32,33]
The post-treatment drug activity profiles (based on elimination
half-lives, dosages and drug sensitivity) should be matched
Critical This manuscript KPC#1
(Figures 2&3)
Low levels of inter-individual PK/PD variation to minimise
drug activity profile mismatch in individual infections
Desirable This manuscript KPC#2
(Figure 4)
Extended period of chemoprophylaxis post-treatment Desirable [15,17]
Predictable metabolism via non polymorphic enzymes Desirable This manuscript KPC#5
No pharmacokinetic drug-drug interaction Desirable This manuscript KPC#5
Efficacy & safety Large therapeutic index Desirable This manuscript KPC#3
Toxicity of drugs should be additive or antagonistic Desirable This manuscript KPC#3
Drug conversion and elimination should not share same
metabolic pathway
Desirable This manuscript KPC#3
Dissimilar type B adverse drug reaction profiles Desirable This manuscript KPC#3
Safe and well-tolerated Critical [32]
Efficacious and effective Critical [32]
Cost Affordable/cheap Critical [17,32]
KPC: key pharmacological consideration.
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codon 164 renders chlorproguanil-dapsone ineffective
[67]. The obvious question is the extent to which this
paradigm of cross-resistance in antifolates is likely to ex-
tend to the other classes and it is useful, in this context
to examine the lessons learnt from the evolution of in-
secticide resistance. Researchersinthisfieldhavenoted
a dichotomy in resistance mechanisms (e.g. [68]). Tar g e t
siteresistance arises when mutations in the insecticide
target site disrupt binding of the insecticide to its tar-
get, usually an enzyme. The second mechanism is
metabolic resistancewhere mutations disrupt the ability
of an insecticide to reach, or accumulate in, its target
site; typical mechanisms are metabolic detoxification of
the insecticide or cell pumping mechanism that prevent
insecticide accumulating at its target and these mech-
anism are usually associated with changes in expres-
sion levels of genes in the adenosine triphosphate-
binding cassette (ABC) transporters and CYP gene
families [69]. The main mechanism of resistance to
anti-malarial antifolates appears to be target site resistance
in the pfdhfr and pfdhps genes but known mechanisms of
resistance to CQ, mefloquine (MQ) and LF are meta-
bolic and involve mutations and/or copy number vari-
ationintheP. falciparum chloroquine resistance
transporter (pfcrt) and multiple drug resistance
protein-1 (pfmdr1) genes both of which encode cell mem-
brane transporters. Cross resistance in this case is more
likely to depend on the chemical structure and ionic
charge of the drug than on its eventual target site [70].
This may explain why, for example, mutations in the pfcrt
gene decrease resistance to LF while increasing re-
sistance to CQ. Both drugs are in the same class
(Table 1) but their structure means that they are
recognized differentially by the cell transporter mech-
anism. Similarly Basco & Ringwald [71] demonstrated that
piperaquine (PPQ) remains active against CQ-resistant par-
asites. These drugs are both 4-aminoquinolines (Table 1)
and their close structural similarity suggests they would
have the same mode of action, but these observations sug-
gest there are different mechanisms of resistance, presum-
ably through metabolic resistance, to the different drugs. In
summary, many non-antifolate anti-malarial drugs disrupt
the process of haem crystallization so target a physio-
chemical process rather than having a specific enzyme tar-
get site. The absence of a definite, parasite-encoded target
molecule for such drugs makes it highly likely that meta-
bolic, rather than target-site, resistance is the main
mechanisms of resistance which means the chances of
cross-resistance, even within drugs in the same class, are
greatly diminished.
Cross resistance can be quantified as a correlation be-
tween IC50 observed in field and/or laboratory isolates.
As a recent example, Mu and colleagues [37] measured
the in vitro drug sensitivity (i.e. IC50s) of 185 field isolates
to seven drugs from four different classes. They reported
the correlation between IC50s to different drugs (their
Figure 3). The results were interesting: correlations were
generally weak with correlation coefficients typically
around -0.2 to 0.4, but were not closely dependent on the
class of origin of the drugs. In fact, the two drugs with the
strongest correlation in IC50 came from two separate
classes, the artemisinin derivative DHA, and the arylamino
alcohol MQ (Table 1). In addition, there may be far less di-
versity in resistance mechanisms than might be antici-
pated: Yuan et al. [72] screened a library of 2,811 chemical
compounds for anti-malarial activity, identified 32 highly
active compounds and then tested them in 61 parasite iso-
lates. They found that three resistance loci, pfcrt,pfdhfr,
pfmdr1 were involved in 96% of cases where there was
significant variation in isolate drug sensitivity.
One caveat associated with such data obtained from
field isolates is that the IC50s may reflect the effects of
standing genetic variation at many loci as a kind of
baselinedrug sensitivity before high-level resistance arises
through individual mutations with large effects. There is no
guarantee that the same correlations in IC50s noted from
field isolates will be associated with individual mutations
that have major effects of drug sensitivity. Data are limited
on this and, to date, the only clear example is that of the
pfcrt K76T mutation which increases resistance to CQ but
appears to only slightly increase sensitivity to LF [43]. See
Ecker and colleagues [73] for a more detailed discussion of
how single mutations affect drug sensitivity in general and
in the best known case of pfcrt and CQ resistance [74].
It is often recommended that combining drugs from
same class into a CT should be avoided (e.g. [32,41]) and
the usual reasoning is because a mutation may occur
that encode cross-resistance to members of the class
(and thence the two drugs in the CT). This would be
undesirable but, with the exception of antifolates, such
mutations have not been observed. Far more serious is
the fact that drugs in the same class probably share the
same mechanism of parasites killing and, possibly, mecha-
nisms of human toxicity; these latter two factors are likely
to be of more immediate concern in CT design than the
longer term threat of resistance.
Target product profiles for combination therapies
The development of new products may be informed by
clearly identifying the desired properties of the final
product, the Target Product Profile (TPP). One problem
with TPPs is that they often constitute an idealized, but
often unattainable, wish listof what is required of a
product; with this caveat, such a TPP is presented in
Table 2 which builds on previous work presented by
Kremsner and Krishna [32] and Burrows et al. [17] by
incorporating the pharmacological considerations dis-
cussed above. There is unlikely to be a CT that fulfils all
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these desirable properties so the main operational prob-
lem is to trade-off the different characteristics offered by
different formulations of CTs. This review has attempted
to identify and quantify several key design principles in
order to facilitate choice of CT design in a real world
where no perfect CT is likely to exist in the medium to
long term. The PK/PD arguments discussed above sug-
gest the TPP for an ideal CT should include the follow-
ing properties: the two drugs should act synergistically,
have independent PK, have independent actions on tox-
icity, have negative patterns of cross resistance and
have post-treatment drug activity profiles that can be
matched. Importantly all these criteria are relatively
easy to measure in culture (drug PD synergy, basal
levels of cross resistance) or in preclinical work in
animals (toxicity) and early clinical work in humans
(PK, toxicity).
The problem is that there are only a limited number
of existing forms of anti-malarials and design of new
CTs may not be able to meet this TPP in the near future.
A CT may not be ideal, but may still be useful because,
importantly, even a CT that falls well outside that TPP
can still be highly beneficial as the following success
story illustrates; following Chou [47], the codes A and B
are used for the two drugs. Drug A was failing badly, so
was combined with Drug B. The match to the TPP was
weak: high levels of cross resistance occurred, toxicity
was additive, and drug activity follows a law of diminish-
ing returns although and, more encouragingly, half-lives
were matched. Drug A was CQ and Drug B was also
CQ: as described above, Guinea-Bissau circumvented its
problem of CQ resistance by the simple (although po-
tentially toxic) expedient of doubling the dose of CQ
given to patients to get a highly effective CT[75]. This
perfectly illustrates that it is possible to get a highly ef-
fective CT, capable of overcoming even high levels of
resistance, by combining drugs within the same class
even with 100% levels of cross resistance. The G-B story
thereby illustrates an important design principle: that
desirable and undesirable factors in a TPP can be listed
but the ultimate test must be how a CT performs in the
clinic. The CQ + CQ CTviolates all of the principles
discussed above but its increased efficacy appears suffi-
cient to eliminate parasites which are resistant to stand-
ard doses of CQ. The reason the G-B policy has not
been widely copied is potential toxicity. The TIs of anti-
malarials are relatively small (see above) and there is the
additional operational requirement that drugs be de-
ployed to dose according to weight/age/height bands
within which heavy patients receive relatively low doses
and light people receive higher, potentially toxic doses
[30], and it is easy to see why policy makers are reluctant
to follow the G-B strategy. The primary requirement is
therefore that the two drugs are antagonistic, or at
least not synergistic, in causing toxicity. The biggest
operational decision in CT design is to decide whether
to use the maximum dosages of each drug in a CT for
the largest clinical effect and long-term robustness
against resistance, or whether to reduce the dosages to
reduce the risk of toxicity and hence maximize the
short-term objective of ensuring safety and clinical
approval.
One obvious question is whether triple- or even
quadruple-combinations (as used to treat TB and HIV)
could help meet the criteria of TPP. One obvious benefit
for anti-malarials is that adding another longer half-life
drug would partially remove the currently very large mis-
match in periods of killing between the artemisinins and
their typical partner drugs. In general, the more compo-
nents, the more clinically effective the treatment is likely
to be (for the reasons outlined above), but the obvious
drawbacks are the increased cost, the possibility of more
complicated regimes (which can affect patient adherence)
and the increased risks of toxicity. The rationale design of
such triple- or quadruple combinations can be guided by
the design principles outlined above. One example of a
currently-proposed triple combination anti-malarial is to
add primaquine (PQ) to current ACT [76]. The important
feature of these combinations is their mutually exclu-
sive PD: ACT targets the asexual and early stage ga-
metocytes while PQ targets mature gametocytes. This
means that arguments based on matching PK/killing
and independent/synergistic PD can be ignored and the
combination evaluated on the basis of drug interaction in
toxicity, interference between individual drugsPK and,
possibly, mechanism of cross-resistance. These factors can
then be weighed against the value of PQ in reducing mal-
aria transmission.
Conclusions
Maintaining a pipeline of effective anti-malarial drugs is a
public health priority clearly recognized by the inter-
national community which supports the Medicines for
Malaria Venture (MMV) to co-ordinate the research and
development (R&D) pipeline of such drugs. The pipeline
is relatively healthy [15] but the drug development pro-
cess is slow and inherently unpredictable. MMV have a
strategy for prioritizing development [17,77] but this
unpredictability also places a responsibility on the R&D
community for good stewardship and use of existing and
newly-developed drugs. This is mainly achieved through
their deployment as CTs to minimize the risk of resistance
arising, and to maintain their long-term effectiveness. In
most models of drug resistance, the resistancetrait is
modelled as a purely parasite trait that determines whet-
her or not the infection will be cleared by drug treatment.
A PK/PD mechanism-based approach explicitly recognizes
that drug resistance(usually expressed as an increased
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IC50) is only one of a suite of pharmacological parameters
(12 parameters for two-drug CTs) that determine a pa-
tients therapeutic outcome. In essence, it is necessary to
understand how CTs successfully clear infections before
starting to understand how, why and when individual pa-
tients fail treatment. Placing resistance in this context
therefore reveals the pharmacological principles that de-
termine what makes a goodCT and how the threat of re-
sistance can be minimized in this context. It is argued
herein that adopting a rational and objective method to
simulate CT drug effectiveness using PK/PD principles
can play a valuable role in this process.
Abbreviations
ABC: Adenosine triphosphate-binding cassette; ACT: Artemisinin-based
combination therapy; ADR: Adverse drug reaction; AM: Artemether;
AS: Artesunate; AUC: Area under the curve; Cmax: Maximum concentration;
CT: Combination therapy; CQ: Chloroquine; CYP: Cytochrome P450;
DHA: Dihydroartemisinin; G6PD: Glucose-6-phosphate dehydrogenase; G-
B: Guinea-Bissau; HIV: Human immunodeficiency virus; IC50: Drug
concentration producing half the desired effect; LF: lumefantrine;
MIC: Minimum inhibitory concentration; MMV: Medicines against Malaria
Venture; MQ: Mefloquine; NNRTI: Non-nucleoside reverse transcriptase
inhibitor; n: Slope of dose-response curve; P:Plasmodium;
PD: Pharmacodynamics; pfcrt:Plasmodium falciparum chloroquine resistance
transporter gene; PfDHFR: Plasmodium falciparum dihydrofolate reductase;
pfdhfr:Plasmodium falciparum dihydrofolate reductase gene;
PfDHPS: Plasmodium falciparum dihydropteroate synthase;
pfdhps:Plasmodium falciparum dihydropteroate synthase gene;
pfdmdr1:Plasmodium falciparum multi-drug resistant protein 1 gene;
PI: Protease inhibitor; PK: Pharmacokinetics; PPQ: Piperaquine;
PQ: Primaquine; R: Resistant; R&D: Research and development; S: Sensitive;
SP: Sulphadoxine-pyrimethamine; TB: Tuberculosis; TI: Therapeutic index;
TTP: Target product profile; Vmax: Maximal drug-killing rate per day.
Competing interests
The authors have no competing interests.
Authorscontributions
IMH and EMH have been involved in drafting the manuscript and revising it
critically for important intellectual content. Both have given final approval of
the version to be published.
Acknowledgments
The authors thank Dr Katherine Kay and three anonymous reviewers for
helpful comments on the manuscript. This work was supported by the
Medical Research Council [grant number G1100522] and the Bill and Melinda
Gates Foundation [grant number 37999.01].
Received: 13 December 2013 Accepted: 15 February 2014
Published: 20 February 2014
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doi:10.1186/1475-2875-13-62
Cite this article as: Hastings and Hodel: Pharmacological considerations
in the design of anti-malarial drug combination therapies is matching
half-lives enough? Malaria Journal 2014 13:62.
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... Moreover, different combination of antimalarial formulations can curtail the duration of treatment, hence increasing compliance and minimizing the risk of developing drug resistance as a result of mutations generated during therapy. 4 Artemisinin derivativebased combination therapy (ACT) has remained as the therapy of choice to restrain the widespread resistance in antimalarials especially for the treatment of cerebral malaria induced by Plasmodium falciparum. 3,5 However, artemisinin is facing the risk of resistance development when used widely as monotherapy or in combination resulting in delayed parasite clearance. ...
... 7 Unfortunately, there are reports from South East Asia regarding resistance towards artemisinin and ACT failure which asserts the need to survey the therapeutic-efficacy of the possible newer combination therapies that will control incidences of drug resistance, thereby minimizing poor patient compliance, pharmacokinetic mismatch and overall treatment failure. 8 Although combination drugs with either synergistic or additive actions can increase the clearance rate of the parasite, combination of different antimalarials might pose several undesirable side effects, for example, hepatic toxicity, cardiotoxicity etc. 4 This happens mainly because of the lack of optimal drug dosage which is necessary for better efficacy and reduced toxicity. A controlled or programmed drug release profile needs to be attained for resolving this lingering problem. ...
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The declining effectiveness of the available antimalarial drugs due to drug resistance requires a continued effort to develop new therapeutic approaches. In this context, combination therapies hold a great promise for developing effective first‐line antimalarial treatments for reducing malaria mortality. The present study explores the antimalarial efficacy of nanotized formulation of curcumin in combination with benzothiophene compound 6 (3‐bromo‐N‐(4‐fluorobenzyl)‐benzo[b]thiophene‐2‐carboxamide) with a view to achieve better efficacy at a very low dose in comparison to that accomplished with monotherapy alone. Herein, we formulated nanotized conjugate of curcumin and compound 6 (cur‐compound 6) in the size range of 30–90 nm as observed via TEM, AFM and DLS analysis in the study. The nanotized preparation was found to be readily dispersible in water, physically and chemically stable and exhibited sustained release profile of both curcumin and compound 6 till 48 hr. Treatment of P. falciparum parasites with the nanotized conjugate for 24 hr resulted in rapid clearance of the parasites. Furthermore, P. berghei infected mice treated with nanotized conjugate formulation survived till 90 days with complete eradication of the parasites from RBC. This improved efficacy of the nanotized formulation was possible because of the increased absorption of the compounds via oral administration owing to enhanced dispersibility of the formulation in aqueous medium. Moreover, an improved oral bioavailability of the nanotized formulation lowered the dosage at which the pharmacological effect was achieved while avoiding any observable adverse harmful side effects.
... 5 Chandra et al. (2015) found regulation of glutathione with L-buthionine (S,R)-sulfoximine to increase the efficacy against arteetherresistant Plasmodium vinckei in mice, demonstrating a possible strategy for prolonging the use of artemisinin drugs against resistant strains. 6 Hastings and Hodel (2014) note the use of combination therapy to slow or prevent drug resistance and argue for the importance of a pharmacological rather than a purely genetic approach toward understanding drug resistance as a basis for design of combination therapies and lowering effects of toxicity. 7 Hastings et al. (2016) argue the short halflife of artemisinin drugs allow for their protection against resistance by ACT partner drugs that kill artemisinin-resistant strains but also leave the partner drugs as monotherapies making them more vulnerable to resistance. ...
... 6 Hastings and Hodel (2014) note the use of combination therapy to slow or prevent drug resistance and argue for the importance of a pharmacological rather than a purely genetic approach toward understanding drug resistance as a basis for design of combination therapies and lowering effects of toxicity. 7 Hastings et al. (2016) argue the short halflife of artemisinin drugs allow for their protection against resistance by ACT partner drugs that kill artemisinin-resistant strains but also leave the partner drugs as monotherapies making them more vulnerable to resistance. 8 Hasting et al. (2016) discuss use of their pharmacological model to investigate how ACT design may target developmental stages of the malarial parasite. ...
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According to the WHO, artemisinin-based combination therapies (ACTs) have been integral to the recent reduction in deaths due to Plasmodium falciparum malaria. ACT-resistant strains are an emerging problem and have evolved altered developmental stages, reducing exposure of the most susceptible stages to artemisinin drugs in popular ACTs. Lipophilicity, log Kow, is a guide in understanding and predicting pharmacokinetic properties such as terminal half-life which alters drug exposure. Consistent log Kow values are not necessarily available for artemisinin derivatives designed to extend terminal half-life, increase bioavailability, and reduce neurotoxicity. For other drugs used in ACTs, an assortment of experimental and computational log Kow values are available in the literature and in some cases, do not account for subtle but important differences between closely related structures such as between diastereomers. Quantum chemical methods such as density functional theory (DFT) used with an implicit solvent model allow for consistent comparison of physical properties including log Kow and distinguish between closely related structures. To this end, DFT, B3LYP/6-31G(d), with an implicit solvent model (SMD) was used to compute ΔGowo and ΔGvowo for 1-octanol–water and olive oil–water partitions, respectively, for 21 antimalarial drugs: 12 artemisinin-based, 4 4-aminoquinolines and structurally similar pyronaridine, and 4 amino alcohols. The computed ΔGowo was close to ΔGowo calculated from experimental log Kow values from the literature where available, with a mean signed error of 2.3 kJ/mol and mean unsigned error of 3.7 kJ/mol. The results allow assignment of log Kow for α-and β-diastereomers of arteether, and prediction of log Kow for β-DHA and five experimental drugs. Linear least square analysis of log Kow and log Kvow versus terminal elimination half-life showed strong linear relationships, once the data points for the 4-aminoquinoline drugs, mefloquine and pyronaridine were found to follow their own linear relationship, which is consistent with their different plasma protein binding. The linear relationship between the computed log Kvow and terminal elimination half-life was particularly strong, R² = 0.99 and F = 467, and can be interpreted in terms of a simple pharmacokinetic model. Terminal elimination half-life for β-DHA and four experimental artemisinin drugs were estimated based on this linear relationship between log Kvow and terminal t1/2. The computed log Kow and log Kvow values for epimers α- and β-DHA and α and β-arteether provide physical data that may be helpful in understanding their different pharmacokinetics and activity based on their different molecular geometries. Relative solubility of quinine and quinidine are found to be sensitive to thermal corrections to enthalpy and to vibrational entropy and do not follow the general trend of longer terminal t1/2 with greater predicted log Kow. Geometric relaxation of α- and β-DHA in solvent and inclusion of thermal correction for enthalpy and entropy results in correct prediction that α-DHA is favored in aqueous environments compared to β-DHA. Predictions made regarding experimental drugs have implications regarding their potential use in response to artemisinin drug-resistant strains.
... Initially characterizing the PK-PD relationship from monotherapy studies is important for guiding dose optimization before being deployed as a combination therapy. Information on contribution of individual drugs and their interactions (i.e., on drug concentration, parasite growth and killing, or both) derived from monotherapy and combination studies is a prerequisite for defining rational dosing regimens of antimalarial combinations (41)(42)(43)(44). ...
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The emergence and spread of parasite resistance to currently available antimalarials has highlighted the importance of developing novel antimalarials. This scoping review provides an overview of antimalarial drug candidates undergoing phase I and II studies between 1 January 2016 and 28 April 2021. PubMed, Web of Science, Embase, clinical trial registries and reference lists were searched for relevant studies. Information regarding antimalarial compound details, clinical trial characteristics, study population, drug pharmacokinetics and pharmacodynamics (PK-PD) were extracted. A total of 50 studies were included of which 24 had published their results and 26 were unpublished. New antimalarial compounds were evaluated as monotherapy (28 studies, 14 drug candidates) and combination therapy (9 studies, 10 candidates). Fourteen active compounds were identified in the current antimalarial drug development pipeline together with 11 compounds that are inactive; six due to insufficient efficacy. PK-PD data were available from 24 studies published as open-access articles. Four unpublished studies have made their results publicly available on clinical trial registries. The terminal elimination half-life of new antimalarial compounds ranged from 14.7 to 483 hours. The log 10 parasite reduction ratio over 48 hours and parasite clearance half-life for P. falciparum following a single dose monotherapy were 1.55–4.1 and 3.4–9.4 hours, respectively. The antimalarial drug development landscape has seen a number of novel compounds, with promising PK-PD properties, evaluated in phase I and II studies over the past 5 years. Timely public disclosure of PK-PD data is crucial for informative decision-making and drug development strategy.
... In the second case, mathematical models can describe the relationship between drug concentration and its killing efficacy. This can be shown as a 'doseresponse curve' showing the efficacy as a function of the measured concentration of the drug in the blood, [38], [41], [63], [71]. ...
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In this paper, we provide a brief survey of mathematical modelling of malaria and how it is used to understand the transmission and progression of the disease and design strategies for its control to support public health interventions and decision-making. We discuss some of the past and present contributions of mathematical modelling of malaria, including the recent development of modelling the transmission-blocking drugs. We also comment on the complexity of the malaria dynamics and, in particular , on its multiscale character with its challenges and opportunities. We illustrate the discussion by presenting a curve fitting using a 95% confidence interval for the South African data for malaria from the years 2001−2018 and provide projections for the number of malaria cases and deaths up to the year 2025.
... Unfortunately, these goals can be at odds when long acting antimalarials prolong efficacy but can also result in long subtherapeutic trials that can select for more resistant parasites. 83,84 Capturing the dynamics of how drug exposure selects for drug resistance with PK/PD models is a valuable contribution to antimalarial regimen selection and dose optimization. Drug resistance in PK/PD models has been influential in simulation studies, guiding selection of the triple ACT regimens, 85 and predicting failure of malaria chemoprevention regimens. ...
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Malaria is an infectious disease which disproportionately effects children and pregnant women. These vulnerable populations are often excluded from clinical trials resulting in one‐size‐fits‐all treatment regimens based on those established for a non‐pregnant adult population. Pharmacokinetic‐pharmacodynamic (PK/PD) models can be used to optimize dose selection as they define the drug exposure‐response relationship. Additionally, these models are able to identify patient characteristics which cause alterations in the expected PK/PD profiles and through simulations can recommend changes to dosing which compensate for the differences. In this review, we examine how PK/PD models have been applied to optimize antimalarial dosing recommendations for young children including those who are malnourished, pregnant women and individuals receiving concomitant therapies such as those for HIV treatment. The malaria field has had great success in utilizing PK/PD models as a foundation to update treatment guidelines and propose the next generation of dosing regimens to investigate in clinical trials. We propose how the malaria field can continue to use modeling to improve therapies by further integrating PK data into clinical studies and including data on drug resistance and host immunity in PK/PD models. Finally, we suggest that other disease areas can achieve similar success in applying pharmacometrics to improve outcomes by implementing three key principals.
... Pyronaridine resistance has previously been selected in Plasmodium berghei and Plasmodium yoelii (Croft et al., 2012;Kimani et al., 2014;Peters and Robinson, 1992); however, the molecular organization of the phenotype was not investigated. Pyramax® (PRD/ASN), remains active against widespread Chloroquine (CQ) and Sufadoxine-Pyrimethamine (SP) drug-resistant P. falciparum clearance of malaria infection, studies indicate that use of drug combination with mismatched pharmacokinetics does not prevent selection of the resistance against longacting drug (Hastings and Hodel, 2014;Li and Hickman, 2015). Artesunate is a short-acting artemisinin derivative with half-life of less than 2 h, while PRD is long-acting with half-life of 16 to 17days (Park and Pradeep, 2010). ...
... Resistance develops rapidly when monotherapy is employed, and thus combination therapy with at least two medications with different mechanisms of action helps mitigate this. Other important considerations to make when determining which partner drugs to use include the following: matching half-lives so that a drug with a long half-life does not persist as monotherapy, pairing drugs with synergistic mechanisms of action and avoiding a combination with antagonistic pharmacokinetics [184,185]. Clinical trials of triple drug regimens with additional partner drugs added to established ACTs are currently underway in Southeast Asia [186,187]. Fast acting compounds such as artemisinin are also less likely to generate resistance rapidly compared to slower-acting compounds like clindamycin or MFQ, as demonstrated by in vitro experiments [188,189] and experience in the field. ...
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Plasmodium falciparum and Plasmodium vivax, the two protozoan parasite species that cause the majority of cases of human malaria, have developed resistance to nearly all known antimalarials. The ability of malaria parasites to develop resistance is primarily due to the high numbers of parasites in the infected person's bloodstream during the asexual blood stage of infection in conjunction with the mutability of their genomes. Identifying the genetic mutations that mediate antimalarial resistance has deepened our understanding of how the parasites evade our treatments and reveals molecular markers that can be used to track the emergence of resistance in clinical samples. In this review, we examine known genetic mutations that lead to resistance to the major classes of antimalarial medications: the 4-aminoquinolines (chloroquine, amodiaquine and piperaquine), antifolate drugs, aryl amino-alcohols (quinine, lumefantrine and mefloquine), artemisinin compounds, antibiotics (clindamycin and doxycycline) and a napthoquinone (atovaquone). We discuss how the evolution of antimalarial resistance informs strategies to design the next generation of antimalarial therapies.
... The ecological and epidemiological models used here are simplified to allow an optimal control framework to be established. Worthwhile extensions to this work would be the addition of a disease incubation period in either the vector or host; use of time delays between egg laying and hatching and pupation and emergence; modelling possible periods of immunity to reinfection conferred by artemisinin treatments (if the prophylactic period is greater than the infectious period; [65]), although the relationship between drug half-life and resistance development is complex [66,67]; a relaxation of the assumptions of random mating between wild and released mosquitoes (unsuccessful mating has been a serious impediment to codling moth control programmes; [68]) and of perfect sterility and lethality; the inclusion of fitness costs for sterile or transgenic mosquitoes. Finally, that other vectors of malaria (e.g. ...
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Background: The sterile insect technique and transgenic equivalents are considered promising tools for controlling vector-borne disease in an age of increasing insecticide and drug-resistance. Combining vector interventions with artemisinin-based therapies may achieve the twin goals of suppressing malaria endemicity while managing artemisinin resistance. While the cost-effectiveness of these controls has been investigated independently, their combined usage has not been dynamically optimized in response to ecological and epidemiological processes. Results: An optimal control framework based on coupled models of mosquito population dynamics and malaria epidemiology is used to investigate the cost-effectiveness of combining vector control with drug therapies in homogeneous environments with and without vector migration. The costs of endemic malaria are weighed against the costs of administering artemisinin therapies and releasing modified mosquitoes using various cost structures. Larval density dependence is shown to reduce the cost-effectiveness of conventional sterile insect releases compared with transgenic mosquitoes with a late-acting lethal gene. Using drug treatments can reduce the critical vector control release ratio necessary to cause disease fadeout. Conclusions: Combining vector control and drug therapies is the most effective and efficient use of resources, and using optimized implementation strategies can substantially reduce costs.
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Leishmaniasis is a tropical infectious disease caused by the protozoan Leishmania parasite. The disease is transmitted by female sand flies and, depending on the infecting parasite species, causes either cutaneous (stigmatizing skin lesions), mucocutaneous (destruction of mucous membranes of nose, mouth and throat) or visceral disease (a potentially fatal infection of liver, spleen and bone marrow). Although more than 1 million new cases occur annually, chemotherapeutic options are limited and their efficacy is jeopardized by increasing treatment failure rates and growing drug resistance. To delay the emergence of resistance to existing and new drugs, elucidating the currently unknown causes of variable drug efficacy (related to parasite susceptibility, host immunity and drug pharmacokinetics) and improved use of genotypic and phenotypic tools to define, measure and monitor resistance in the field are critical. This review highlights recent progress in our understanding of drug action and resistance in Leishmania , ongoing challenges (including setbacks related to the COVID-19 pandemic) and provides an overview of possible strategies to tackle this public health challenge.
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Background Artemisinin derivatives are the leading class of antimalarial drugs due to their rapid onset of action and rapid clearance of circulating parasites. The parasite clearance (PC) half-life measures the rate of loss of parasites from blood after treatment, and this is currently used to assess antimalarial activity of novel agents and to monitor resistance. However, a number of recent studies have challenged the use of PC to measure drug activity, arguing that many circulating parasites may be non-viable. Methods Plasmodium falciparum infected subjects (n=10) in a malaria volunteer infection study were administered a single dose of artesunate (2 mg/kg). Circulating parasite concentration was assessed by quantitative PCR (qPCR). Parasite viability after artesunate administration was estimated by mathematical modelling of the ex vivo growth of parasites collected from subjects. Results We showed that in artemisinin-sensitive infection, viable parasites declined to <0.1% of baseline within 8 h after artesunate administration, while the total number of circulating parasites measured by qPCR remained unchanged. In artemisinin-resistant infections over the same interval, viable parasites declined to 51.4% (SEM: 4.6%) of baseline. Conclusions These results demonstrate that in vivo drug activity of artesunate is faster than is indicated by the PC half-life.
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Investigations into the molecular basis of Plasmodium parasite resistance to antimalarial drugs have made strong progress in defining key determinants. Mutations in the digestive vacuole transmembrane proteins P. falciparum chloroquine resistance transporter (PfCRT) and P. falciparum multidrug resistance protein 1 (PfMDR1) are important drivers of parasite resistance to several quinoline based drugs including chloroquine, amodiaquine, and to a lesser extent quinine. Amplification of pfmdr1 can also mediate resistance to mefloquine and impact lumefantrine efficacy. Parasite resistance to antifolates has been mapped to point mutations in the target enzymes dihydrofolate reductase and dihydropteroate synthase, and mutations in cytochrome b have been found to ablate atovaquone efficacy. Antibiotic resistance has been associated with mutations that preclude drug inhibition of protein translation in the parasite apicoplast. The study of resistance to artemisinin derivatives and several partner drugs used in artemisinin-based combination therapies is an area of active research that has yet to define clearly how in vitro resistance can translate into predictions of treatment failures. Research in this area is important not only for its ability to generate molecular markers of treatment failure but also for the insights it can provide into drug mode of action and the development of chemical and pharmacological strategies to overcome resistance mechanisms.
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The standard drug development process for antimalarials and other drugs uses weight-based dosing (mg/kg) to predict blood concentrations of the drug, and hence their effect. Consequently, the current World Health Organization Guidelines for the treatment of malaria [1] provide target doses and therapeutic dose ranges in mg/ kg/day. However, in resource-poor settings, age-based dosing is often employed instead of weight-based dosing because of the scarcity of correctly functioning weighing scales outside of clinical settings. Due to the wide variation in weight by age this approach inevitably results in over- and under-dosing of a proportion of the population. We have recently developed a modelling method to create statistically robust global and regional malaria-specific weight-for-age references representative of the malaria-endemic countries [2] and employed it to predict optimized age-based regimens for artemisinin-based combination therapies (ACTs) for case management of uncomplicated malaria (unpublished). The presented work now assesses the robustness of these age-based regimens using an in silico model of antimalarial drug treatment to predict treatment outcome based on individual infection parameters such as parasite numbers, variation in patient pharmacokinetics, and parasite variation in their drug sensitivity [3]. This extended pharmacokinetic/pharmakodynamic model for ACTs allowed us to investigate extreme treatment scenarios in a large number of patients over long follow-up periods that for ethical reasons could not be applied in clinical trials: typical examples include poor adherence (e.g. delayed, reduced or missed doses) or administration of doses above or below recommended therapeutic dose ranges and particularly in most vulnerable individuals such as infants and young children. Pharmacological modelling of antimalarial treatment cannot replace the gold standard of clinical trials, but the model outputs can identify patient groups that are at higher risk of treatment failure due to under-dosing or adverse events due to over-dosing. We acknowledge the Medical Research Council for funding of this work.
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Antimalarial drugs have usually been first deployed in areas of malaria endemicity at doses which were too low, particularly for high-risk groups such as young children and pregnant women. This may accelerate the emergence and spread of resistance, thereby shortening the useful life of the drug, but it is an inevitable consequence of the current imprecise method of dose finding. An alternative approach to dose finding is suggested in which phase 2 studies concentrate initially on pharmacokinetic-pharmacodynamic (PK-PD) characterization and in vivo calibration of in vitro susceptibility information. PD assessment is facilitated in malaria because serial parasite densities are readily assessed by microscopy, and at low densities by quantitative PCR, so that initial therapeutic responses can be quantitated accurately. If the in vivo MIC could be characterized early in phase 2 studies, it would provide a sound basis for the choice of dose in all target populations in subsequent combination treatments. Population PK assessments in phase 2b and phase 3 studies which characterize PK differences between different age groups, clinical disease states, and human populations can then be combined with the PK-PD observations to provide a sound evidence base for dose recommendations in different target groups.
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Artemisinin combination therapies are the first-line treatments for uncomplicated Plasmodium falciparum malaria in most malaria-endemic countries. Recently, partial artemisinin-resistant P. falciparum malaria has emerged on the Cambodia-Thailand border. Exposure of the parasite population to artemisinin monotherapies in subtherapeutic doses for over 30 years, and the availability of substandard artemisinins, have probably been the main driving force in the selection of the resistant phenotype in the region. A multifaceted containment programme has recently been launched, including early diagnosis and appropriate treatment, decreasing drug pressure, optimising vector control, targeting the mobile population, strengthening management and surveillance systems, and operational research. Mathematical modelling can be a useful tool to evaluate possible strategies for containment.
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Chemotherapy remains the only practicable tool to control falciparum malaria in sub-Saharan Africa, where >90% of the world's burden of malaria mortality and morbidity occurs. Resistance is rapidly eroding the efficacy of chloroquine, and the combination pyrimethamine–sulfadoxine is the most commonly chosen alternative. Resistant populations of Plasmodium falciparum were selected extremely rapidly in Southeast Asia and South America. If this happens in sub-Saharan Africa, it will be a public health disaster because no inexpensive alternative is currently available. This article reviews the molecular mechanisms of this resistance and discusses how to extend the therapeutic life of antifolate drugs.
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Management of HIV and malaria co-infection is challenging due to potential drug-drug interactions between antimalarial and HIV-antiviral drugs. Little is known of the clinical significance of these drug interactions, and this review provides a comprehensive summary and critical evaluation of the literature. Specifically, drug interactions between WHO-recommended artemisinin combination therapies (ACT) and HIV-antivirals are discussed. An extensive literature search produced eight articles detailing n = 44 individual pharmacokinetic interactions. Only data pertaining to artemether-lumefantrine and two other artesunate combinations are available, but most of the interactions are characterized on at least two occasions by two different groups. Overall, protease inhibitors (PIs) tended to increase the exposure of lumefantrine and decrease the exposures of artemether and dihydroartemisinin, a pharmacologically active metabolite of artemether. Non-nucleoside reverse transcriptase inhibitors (NNRTIs) tended to decrease the exposures of artemether, dihydroartemisinin, and lumefantrine when co-administered with artemether-lumefantrine. Fewer studies characterized the effects of PIs or NNRTIs on artesunate combinations, but nevirapine increased artesunate exposure and ritonavir decreased dihydroartemisinin exposure. On the other hand, artemether-lumefantrine or artesunate combinations had little effect on the pharmacokinetics of HIV-antivirals, with the exception of decreased nevirapine exposure from artemether-lumefantrine or increased ritonavir exposure from pyronaridine/artesunate co-administration. In general, pharmacokinetic interactions can be explained by the metabolic properties of the co-administered drugs. Despite several limitations to the studies, these data do provide valuable insights into the potential pharmacokinetic perturbations, and the consistently marked elevation or reduction in ACT exposure in some cases cannot be overlooked.