Mapping ‘partially resistant’, ‘fullyresistant’, and ‘super resistant’ malariaInbarani Naidoo1,2 and Cally Roper1

1 Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK2 Malaria Research Unit, Medical Research Council, PO Box 70380 Overport 4067, South Africa

Review

Sulfadoxine–pyrimethamine (SP) is used throughoutAfrica for intermittent preventive treatment (IPT) of ma-laria, but resistance threatens its efficacy. We foundmarked regional differences in the genotypes responsi-ble for SP resistance when mapping recent surveys ofdihydrofolate reductase (dhfr) and dihydropteroatesynthase (dhps) mutations. In West Africa, a ‘partiallyresistant’ combination of dhfr N51I, N59R, and S108Nwith dhps A437G predominates, whereas in East Africathe ‘fully resistant’ combination of dhfr N51I, N59R, andS108N with dhps A437G + K540E is found. There arethree East African foci where ‘fully resistant’ populationshave additionally acquired dhps 581G and/or dhfr 164Lto become ‘super resistant’. SP-IPT in infants and preg-nant women is reported to have failed in super resistantareas prompting review of SP-IPT use in affected areas.

Current use of SP and the need for resistance mapsSP is no longer used for treatment of clinical malaria inAfrica because of the emergence of resistance; however, itcontinues to be used as prophylaxis and is administeredroutinely as an IPT for malaria. Intermittent preventivetreatment in pregnancy (IPTp) with SP is currently recom-mended throughout sub-Saharan Africa. SP is also recom-mended for intermittent preventive treatment in infants(IPTi) in areas where drug resistance does not compromiseprotective efficacy [1], and in combination with amodia-quine (SP + AQ) as preventative treatment against sea-sonal malaria in children (SMC, formerly known as IPTc)[2]. Although levels of SP resistance are known to varywithin Africa, in vivo measures of resistance levelsthrough clinical efficacy studies have necessarily beendiscontinued. To inform current policy on continuinguse of SP, we rely on molecular markers of resistance todifferentiate high and low grade resistance and describeits geographical distribution. We conducted a literaturereview, focusing on those studies which tested Africanmalaria parasites for the presence of SP resistance muta-tions. By creating a geographical database of molecular

1471-4922/$ – see front matter

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Corresponding author: Roper, C. (Cally.Roper@lshtm.ac.uk).Keywords: malaria; drug resistance; intermittent preventive treatment (IPT);sulfadoxine–pyrimethamine (SP); super resistance.

surveillance data, we can map major trends in geographi-cal distribution of SP resistance mutations across Africaand identify the immediate priorities in surveillance toinform the continuing use of SP in IPT.

The molecular basis of SP resistanceSP resistance occurs via substitutions in the target enzymesdihydrofolate reductase (DHFR) and dihydropteroate syn-thetase (DHPS) as a result of point mutations in the dhfr anddhps genes [3–6]. Mutant dhfr alleles are varied and code fora range of tolerance to pyrimethamine from intermediate tohigh, depending upon the number of mutations present. Atriple mutant dhfr allele combining N51I, C59R, and S108Nmutations is a significant contributor to SP treatment fail-ure. It originated in Asia [7] and has been present in Africasince the 1980s [8,9]. Mutant dhps emerged during the1990s conferring resistance to the sulfadoxine componentof SP. The combination of a dhps double mutant(A437G + K540E) with the dhfr triple mutant in a ‘quintuplemutant’ genotype is associated with clinical SP treatmentfailure [10–12], and the emergence of this genotype heraldedthe arrival of ‘fully SP resistant’ infections.

SP use in IPT in the context of resistanceGuidelines on the use of SP in IPT must take account ofresistance, and molecular markers came into use in policyfor the first time in 2010, when the World Health Organi-zation (WHO) technical consultation on IPTi [1] recom-mended that the prevalence of the dhps K540E mutation(indicating presence of the ‘quintuple mutant’ or ‘fullyresistant’ genotype) be used as the basis for deciding whereto implement SP-IPTi. The WHO recommendation wasthat where prevalence of dhps K540E exceeds 50%, SP-IPT in infants should not be implemented.

SP is also used as IPTp, and the potential effects ofresistance in undermining protective efficacy is of con-cern for the 33 of 37 malaria endemic countries of sub-Saharan Africa (SSA) where SP-IPTp is currently imple-mented. An early study demonstrated significant protec-tion by SP in pregnancy in areas where 25% of in vivo SPtreatments in children fail by day 14 [13]. Current WHOguidelines state that IPTp remains effective in areaswhere the SP treatment failure rate in children withsymptomatic malaria is as high as 50% by day 14 [14].Yet, SP treatment failure rates have continued to rise,and because use of SP for treatment of symptomaticinfection is discontinued, molecular markers must nowbe used to define resistance thresholds while rates of

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Review Trends in Parasitology October 2013, Vol. 29, No. 10

parasite clearance in women and children on IPT contin-ue to be monitored.

Some places in particular have seen a remarkable esca-lation in levels of SP resistance; one example is in northernTanzania where the last measure of clinical efficacy insymptomatic children was 86% failure by day 14 [15]. Thevery high levels of SP failure in northern Tanzania arelinked to the emergence of a ‘super resistant’ genotype,which contains all five mutations of the ‘fully resistant’genotype plus an additional Pfdhps 581G mutation [15,16].The emergence of the super resistant genotype from 12% to56% during 2003–2007 has been documented [17], and itseffect was loss of all protective efficacy of SP-IPTi in infants[18] or pregnant women [16] in that area. It is furthersuggested that its presence exacerbated placental malariain women receiving SP-IPTp [16].

Concerns about diminishing protective efficacy of SP inIPT in the presence of fully resistant and super resistantparasites mean that surveillance and mapping of theircurrent distribution should underpin the continuing useof these important interventions. In this review of pub-lished molecular surveillance data for dhfr and dhps, welook at trends in the data and identify gaps in surveillancecoverage. Finally, we ask how the current system of mo-lecular surveillance might be improved in order to serveNational Malaria Control Programs (NMCPs) in policydecision making.

Review of the published data on dhfr and dhps

mutationsWe conducted online literature searches between October2005 and February 2011 (see interactive maps atwww.drugresistancemaps.org). Search criteria were re-stricted to published studies of African Plasmodium falci-parum and excluded studies of malaria imported fromAfrica into non-African countries, and studies for whichno sampling dates were given. Surveys of patients takenafter SP treatment was administered were excluded. Wereviewed the full text of these studies to extract the preva-lence of Pfdhfr and Pfdhps mutations (prevalence is de-fined as the proportion of samples tested for a particularmutation that were positive for that mutation) and created

Table 1. Surveys for dhfr and dhps mutations in Africa

dhfr

All surveys 51N 59R 108N

Number of surveys 251 258 279

Samples testeda 23 754 26 513 26 483

Samples positiveb 15 706 16 609 19 344

Unique sites surveyedc 154 163 172

Countries surveyed 36 37 36

Recent surveys (2004 or later) 51N 59R 108N

Number of surveys 70 72 71

Samples testeda 9228 9401 9463

Samples positiveb 6967 6348 7385

Unique sites surveyedc 59 61 62

Countries surveyed 24 25 24

aThe samples were taken from malaria infected individuals prior to treatment.

bPositive samples were those found to contain the mutation.

cUnique geographical sites where surveys were carried out (smaller than total survey

506

a database of georeferenced Pfdhfr and Pfdhps molecularsurveillance data.

The literature review identified 159 publications describ-ing surveillance of one or more dhfr or dhps mutations inalmost 300 surveys in 37 African countries. We created adatabase of georeferenced dhfr and dhps molecular surveil-lance data. Interactive maps at www.drugresistancemap-s.org and www.wwarn.org/surveyor/ identify each study sitetogether with a link to the appropriate publication.

Surveys span a 20-year time interval from 1987 to 2008.Seven studies that did not report the year of survey wereexcluded [19–25]. There is necessarily a time lag betweenthe date when the surveillance survey was conducted andthe date of publication, and that interval was 5 years(median 4–5 years) on average, with a range between 1year and 21 years; hence, the most recent surveys identi-fied were carried out in 2008 and published in 2011.Molecular surveillance studies commonly report the prev-alence of each mutation separately. Details of the numberof surveys in the total dataset, which looked at the preva-lence of each mutation, are given in Table 1. Mutationsdhfr N51I, C59N, and S108N, and dhps A437G and K540Ewere most commonly surveyed, whereas mutationsdhfr164, dhps581, and dhps631 were the least well cov-ered by surveillance. The geographical coverage of thetime-restricted dataset (2004–2008) was less than forthe dataset spanning the entire period (1987–2008), anddetails of the number of surveys, the number of uniquesurvey sites (some surveys were repeated at the same site),and the number of countries covered are compared inTable 1. The countries for which there were no availabledata are listed in Table 2, which shows gaps in coverage forboth intervals (2004–2008 and 1987–2008). There wereconsistent data gaps between 1987 and 2008 in Botswana,Burundi, Cape Verde, Chad, Eritrea, Somalia, SierraLeone, Togo, and Mauritius.

Recent measures of the prevalence of dhfr N51I

We identified 70 surveys for the N51I mutation at 59unique sites in 24 countries from 2004 to 2008. Ofthe 9228 samples tested, 75% contained the N51I muta-tion (Table 1). In Figure 1A, survey sites where the

dhps

164L 437G 540E 581G 613S 613T

184 232 261 121 91 88

19 923 28 851 30 291 15 398 10 312 10 152

130 14 705 10 346 782 134 11

114 154 171 79 67 62

29 28 38 28 27 25

164L 437G 540E 581G 613S 613T

46 86 99 36 23 24

7675 12 759 13 396 6562 3315 3608

110 7172 4816 578 24 3

35 70 80 28 21 21

18 29 29 16 12 12

s because some studies report repeated sampling at the same location).

Table 2. Survey coverage for each mutationa

Codon Number of countries

(% of SSA malarious

countries n = 48)

Countries for which there are no published data

All surveys 1987–2008

dhfr 51N 11 (22.9) Benin, Botswana, Burundi, Cape Verde, Chad, Eritrea, Mauritius, Namibia, Sierra Leone, Somalia,

Togo

dhfr 59R 10 (20.8) Botswana, Burundi, Cape Verde, Chad, Eritrea, Mauritius, Namibia, Sierra Leone, Somalia, Togo

dhfr 108N 11 (22.9) Benin, Botswana, Burundi, Cape Verde, Chad, Eritrea, Mauritius, Namibia, Sierra Leone, Somalia,

Togo

dhfr 164 18 (37.5) Angola, Benin, Botswana, Burundi, Cape Verde, Chad, Eritrea, Gambia, Guinea, Guinea-Bissau,

Liberia, Mauritius, Namibia, Niger, Sierra Leone, Somalia, Togo, Zimbabwe

dhps 436 11 (22.9) Benin, Botswana, Burundi, Cape Verde, Chad, Eritrea, Liberia, Mauritius, Sierra Leone, Somalia, Togo

dhps 437 9 (18.8) Botswana, Burundi, Cape Verde, Chad, Eritrea, Mauritius, Sierra Leone, Somalia, Togo

dhps 540 9 (18.8) Botswana, Burundi, Cape Verde, Chad, Eritrea, Mauritius, Sierra Leone, Somalia, Togo

dhps 581 19 (39.6) Benin, Botswana, Burkina Faso, Burundi, Cape Verde, Chad, Eritrea, Gambia, Guinea, Guinea-Bissau,

Liberia, Mauritius, Namibia, Nigeria, Sierra Leone, Somalia, Togo, Zambia, Zimbabwe

dhps 613S/T 20 (41.7) Benin, Botswana, Burkina Faso, Burundi, Cape Verde, Chad, Eritrea, Gambia, Guinea, Guinea-Bissau,

Liberia, Mauritius, Namibia, Niger, Nigeria, Sierra Leone, Somalia, Togo, Zambia, Zimbabwe

Recent surveys 2004–2008

dhfr 51N 22 (45.8) Benin, Botswana, Burundi, Cape Verde, Chad, Djibouti, Equatorial Guinea, Eritrea, Gabon, Gambia,

Guinea-Bissau, Liberia, Mauritania, Mauritius, Namibia, Senegal, Sierra Leone, South Africa, North

Sudan, South Sudan, Togo, Zimbabwe

dhfr 59R 23 (47.9) Benin, Botswana, Burundi, Cape Verde, Chad, Djibouti, Equatorial Guinea, Eritrea, Gambia, Guinea-

Bissau, Liberia, Mauritania, Mauritius, Namibia, Senegal, Sierra Leone, Somalia, South Africa, North

Sudan, South Sudan, Togo, Zimbabwe

dhfr 108N 23 (47.9) Benin, Botswana, Burundi, Cape Verde, Chad, Djibouti, Equatorial Guinea, Eritrea, Gabon, Gambia,

Guinea-Bissau, Liberia, Mauritania, Mauritius, Namibia, Senegal, Sierra Leone, Somalia, South Africa,

North Sudan, South Sudan, Togo, Zimbabwe

dhfr 164 29 (60.4) Angola, Benin, Botswana, Burundi, Cameroon, Cape Verde, Chad, Djibouti, Equatorial Guinea, Eritrea,

Gabon, Gambia, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Mauritius, Namibia, Niger, Sao

Tome/Principe, Senegal, Sierra Leone, Somalia, South Africa, North Sudan, South Sudan, Togo,

Zimbabwe

dhps 436 23 (47.9) Benin, Botswana, Burkina Faso, Burundi, Cape Verde, Chad, Djibouti, Equatorial Guinea, Eritrea,

Guinea-Bissau, Liberia, Malawi, Mali, Mauritania, Mauritius, Sao Tome/Principe, Sierra Leone,

Somalia, South Africa, North Sudan, South Sudan, Togo, Zimbabwe

dhps 437 18 (37.5) Botswana, Burundi, Cape Verde, Chad, Djibouti, Equatorial Guinea, Eritrea, Guinea-Bissau, Liberia,

Mauritania, Mauritius, Sierra Leone, Somalia, North Sudan, South Sudan, South Africa, Togo,

Zimbabwe

dhps 540 18 (37.5) Botswana, Burundi, Cape Verde, Chad, Djibouti, Equatorial Guinea, Eritrea, Guinea-Bissau, Liberia,

Mauritania, Mauritius, Sierra Leone, Somalia, South Africa, North Sudan, South Sudan, Togo,

Zimbabwe

dhps 581 31 (64.6) Benin, Botswana, Burkina Faso, Burundi, Cape Verde, Chad, Democratic Republic of Congo, Djibouti,

Equatorial Guinea, Eritrea, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania,

Mauritius, Namibia, Nigeria, Sao Tome/Principe, Senegal, Sierra Leone, Somalia, South Africa, North

Sudan, South Sudan, Togo, Zambia, Zimbabwe

dhps 613S/T 35 (72.9) Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape Verde, Chad, Democratic Republic of

Congo, Djibouti, Equatorial Guinea, Eritrea, Gambia, Gabon, Ghana, Guinea, Guinea-Bissau, Liberia,

Mali, Malawi, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Sao Tome/Principe,

Senegal, Sierra Leone, Somalia, South Africa, North Sudan, South Sudan, Togo, Zambia, Zimbabwe

aAbbreviation: SSA, sub-Saharan Africa.

Review Trends in Parasitology October 2013, Vol. 29, No. 10

prevalence of N51I exceeded 50% are distinguished fromthose where prevalence of N51I <50% by colors. Onlysurveys in Madagascar 2006–2008 [26], Ivory Coast 2006[27], Niger 2003–2006 [28], Nigeria 2003–2004 [29], Mali2002–2004 [30], and Burkina Faso 2004 [31] recorded aprevalence of less than 50%. These were 35%, 13.2%,21.2%, 36%, 38%, and 42.5%, respectively. There were 11surveys where prevalence of the N51I mutation reached100%, and these were in Huambo and Cabinda in Angolain 2007 [32], Jimma in Ethiopia in 2004 [33], Iguhu in2006 and Kilifi in 2004 in Kenya [8,34], Mzimba andDedza in Malawi in 2005 [35], Sao Tome and PrincipeIsland in 2004 [36], and in Rukungiri and Kabale inUganda in 2005 [37].

Recent measures of the prevalence of dhfr C59R

Of the 9401 samples tested for C59R from 2004 to 2008,67% were positive for this mutation. Out of 72 surveys from61 sites in 25 countries, 58 recorded a prevalence exceeding50% (Figure 1B). The C59R mutation was at <50% preva-lence in surveys from Madagascar 2006–2008 [26], IvoryCoast 2006 [27], Burkina Faso 2004 [31], and Angola [32].There were four surveys where prevalence reached 100%,and these were in Dzeda and Mzimba, in Malawi in 2005[35], and in Miwani and Iguhu in Kenya in 2006 [34].

Recent measures of the prevalence of dhfr S108N

The S108N mutation was screened in 71 surveys conductedduring or after 2004 from 62 unique sites in 24 countries

507

Senegal

MauritaniaMali

BurkinaFaso

Nigeria

Cameroon

CentralAfrican

Republic

SouthSudan

NorthSudan

Uganda

Rwanda

Tanzania

Angola

Zambia Malawi

Comoros

Mozambique

Madagascar

Zimbabwe

SwazilandSouthAfrica

Kenya

Ethiopia

Djibou�

Niger

Gambia

GuineaBissau

Liberia

Guinea

GhanaIvoryCoast

Sao Tomeand

Principe

EquatorialGuinea

Gabon

Republicof

CongoDemocra�c

Republicof

Congo

Kilometres10005000

N

Sites surveyedKey:

Key:

<50% 51I prevalence

>50% 51I prevalenceNo 51I found

Countries

51I present51I Absent

>50% 51I prevalencerecent surveysNo published 51I data

Sites surveyed<50% 59R prevalence

>50% 59R prevalenceNo 59R found

Countries

59R present59R Absent>50% 59R prevalencerecent surveysNo published 59R data

(A)

(B)

Mauritania

GambiaSenegal

Guinea

Liberia

IvoryCoast

BurkinaFasoGuinea

Bissau

Mali

Ghana Benin

EquatorialGuinea

Sao Tomeand

Principe

Niger

Nigeria

Cameroon

Gabon

Republicof

Congo

CentralAfrican

Republic

Democra�cRepublic

ofCongo Tanzania

ZambiaMalawi

Angola

Rwanda

KenyaUganda

SouthSudan

NorthSudan

Djibou�

Ethiopia

Comoros

Madagascar

MozambiqueZimbabwe

SwazilandSouthAfrica

Kilometres10005000

N

TRENDS in Parasitology

Figure 1. Prevalence of dihydrofolate reductase (dhfr) mutations measured in 2004–2008. (A) Prevalence of the dhfr 51I mutation (70 surveys). (B) Prevalence of the dhfr 59R

mutation (72 surveys). (C) Prevalence of the dhfr 108N mutation (71 surveys). Red pie charts indicate the prevalence of the mutation in surveys where prevalence is 50% or

greater; the countries where this occurred are shaded in green. Black pie charts indicate the prevalence of the mutation in surveys where the prevalence was less than 50%.

Countries shaded in pink indicate that the mutation was detected at some stage, whereas countries shaded in white indicate no published data. Vector maps were created in

Mapinfo ver. 9.0.

Review Trends in Parasitology October 2013, Vol. 29, No. 10

508

Kilometres10005000

N

NorthSudan

Ethiopia

Djibou�

KenyaUganda

Rwanda

Tanzania

MalawiZambia

Angola

Democra�cRepublic

ofCongo

Senegal

MauritaniaMali

BurkinaFaso

Nigeria

Niger

Gambia

GuineaBissau

Liberia

Guinea

IvoryCoast

Ghana

Sao Tomeand

Principe

EquatorialGuinea

Comoros

Madagascar

SouthSudan

ZimbabweMozambique

SwazilandSouthAfrica

Cameroon

Republicof

Congo

CentralAfrican

Republic

(C)

Sites surveyed<50% 108N prevalence>50% 108N prevalence

No 108N found

Countries

108N present108N absent

>50% 108N prevalencerecent surveysNo published 108N data

Key:

TRENDS in Parasitology

Figure 1. (Continued ).

Review Trends in Parasitology October 2013, Vol. 29, No. 10

(Figure 1C). Out of 9463 samples tested for S108N since2004, 78% carry the S108N mutation. Among 62 surveys,just three reported a prevalence of less than 50% and, likeN51I and C59R mutations, these were in Burkina Faso in2004 [31], Ivory Coast in 2006 [27], and Madagascar in 2006–2008 [26]. There were 24 surveys where prevalence was100%, and these were in Cabinda, Huambo, Kwanza Norte,and Uige, Angola in 2007 [32]; Dilla and Jimma, Ethiopia in2004 [33,38]; nine sites across Kenya in 2004–2006 [8,34,39,40]; Dzeda and Mzimba, Blantyre in Malawi in 2005 [35]; inMashesha, Rwanda in 2005 [41]; Sao Tome and PrincipeIsland in 2004 [36]; Korogwe in Tanzania in 2004 [17]; and inRukungiri and Kabale, Uganda in 2005 [37].

Recent measures of the prevalence of dhps 437G

A total of 86 surveys for A437G were conducted during orafter 2004 (Figure 2A). The A437G mutation was detectedin 56% of 12 759 samples tested, occurring at a prevalenceof >50% in 58 sites. In the Nigerian cities of Ibadan andAbuja, the A437G mutation was 34% in 2004 and 47% in2005, respectively [29,42]. In Kolokani, Mali, the A437Gmutation was <21% in 2007 [43]. In Bangui, CentralAfrican Republic, A437G was 19% in 2004 [44]. A preva-lence of 36% was recorded in Madagascar in 2006 [26].There were four surveys where prevalence reached 100% inEthiopia 2004, Kenya 2006, Sao Tome/Principe 2004, andUganda 2005 [33,34,36,37].

Recent measures of the prevalence of dhps 540E

Since 2004, 99 surveys for the K540E mutation have beencarried out in 80 unique sites in 29 countries (Figure 2B).

Overall, this mutation was less common than A437G, beingpresent in just 36% of the total 13 396 samples surveyed,and its geographical distribution was heterogeneous. TheK540E mutation was completely absent in 26 surveys, andthese were predominantly in West Africa (Burkina Faso,Cameroon, Equatorial Guinea, Gabon, Gambia, Ivory Coast,Niger, Nigeria, Senegal, and Angola), and the remainder inMadagascar and Swaziland. By contrast, the prevalence ofK540E was high in East Africa and exceeded 50% in 40surveys in Kenya (9 of 9), Uganda (3 of 3), Tanzania (7 of 7),Zambia (5 of 5), Malawi (6 of 6), Ethiopia (3 of 3), Rwanda (2of 2), and Mozambique (5 of 5). The survey data within theseeight countries are remarkably consistent, and surveyscarried out prior to 2004 also exceeded the 50% cut-off value.The prevalence reached 100% in surveys in Ethiopia in 2004[33], Uganda in 2005 [37], Malawi in 2005 [35], and >95% inTanzania 2007 [17] and Rwanda in 2004 [41]. There weretwo occasions where K540E exceeded 50% in countries otherthan the eight listed, and these do not appear on the map(Figure 2B) because the surveys were prior to 2003. The first,which is in Rutshuru, DRC, in 2002 [45], is at odds with sixother surveys in DRC [46–48] where K540E is <50%. Theclose proximity of Rutshuru to highly SP resistant areas(15 km from southwestern Uganda and 30 km fromRwanda) might explain this anomaly. The second exampleis in Gedaref in northeastern Sudan (120 km from Ethiopia),where a prevalence of >50% was recorded in 2003 [42,49].

Prevalence of super resistant genotypes

Super resistance is defined as having dhps 581G,dhfr164L, and dhps 613S or 613T in addition to the five

509

(A)

(B)

Senegal Mauritania

Gambia

GuineaGuinea

Mali

LiberiaIvoryCoast

BurkinaFaso

Ghana BeninBenin

Niger

Nigeria

CameroonCentralAfricanRepublic

NorthSudan

Djibou�

Ethiopia

KenyaUganda

Democra�cRepublic

ofCongo

Rwanda

Tanzania

Angola

ZambiaMalawi

Comoros

ZimbabweMozambique

Namibia

Sites surveyedKey: <50% 437G prevalence

>50% 437G prevalence

>50% 437G prevalencerecent surveys

No published 437G data

No 437G found

Countries

437G present437G absent

Sites surveyed<50% 540E prevalence>50% 540E prevalence

>50% 540E prevalencerecent surveys

No published 540E data

No 540E found

Countries540E present540E absent

Madagascar

SwazilandSouthAfrica

MauritaniaSenegal

Gambia

Guinea Guinea

Mali

NigerNorthSudan

SouthSudan

Djibou�

Ethiopia

Uganda

KenyaRwanda

Tanzania

ZambiaMalawi

Comoros

Madagascar

Namibia

SouthAfrica

Swaziland

ZimbabweMozambique

Cameroon

Gabon

CentralAfrican

Republic

Republicof

Congo

Angola

Democra�cRepublic

ofCongo

EquatorialGuinea

Nigeria

Sao Tomeand

Principe

BurkinaFaso

Ghana

LiberiaIvoryCoast

Bissau

0 500 1000

N

Kilometres

0 500 1000

N

Kilometres

SouthSudan

Gabon

Republicof

Congo

EquatorialGuinea

Sao Tomeand

Principe

Bissau

Key:

TRENDS in Parasitology

Figure 2. Prevalence of dihydropteroate synthase (dhps) mutations measured in 2004–2008. (A) Prevalence of the dhps 437G mutation (86 surveys). (B) Prevalence of the

dhps 540E mutation (99 surveys). Red pie charts indicate where prevalence of the mutation was 50% or greater, and the 22 countries where this occurred are shaded in

green. Black pie charts indicate surveys where the prevalence was less than 50%. Countries shaded in pink indicate that the mutation was detected at some stage, whereas

countries shaded in white indicate no published data. Vector maps were created in Mapinfo ver. 9.0.

Review Trends in Parasitology October 2013, Vol. 29, No. 10

510

Quadruple muta�on 51/59/108/437

dhfr 164L dhps 581G dhps 613S/T

Samples tested > 10 andprevalence >10% or ≥ 50%

Key:

Quintuple muta�on 51/59/108/437/540 Sub-Saharan countriesMap 3

Map 1

Map 4 Map 5

Map 2

TRENDS in Parasitology

Figure 3. Countries where prevalence cut-off values of major SP resistance mutations are exceeded. Cut-off values were chosen to indicate that the prevalence of resistance

mutations had exceeded a certain threshold in surveys conducted in each country. Cut-off values are shaded in gray. We include only surveys where the sample size was

greater than 10. Map 1: countries where prevalence of the quadruple mutation dhfr51/59/108 and dhps 437 is >50%. Map 2: countries where prevalence of the quintuple

mutation dhfr 51/59/108 and dhps 437/540 is >50%. Map 3: countries where prevalence of dhfr 164L is >10%. Map 4: countries where prevalence of dhps 581 is >10%. Map

5: countries where prevalence of dhps 613S/T is >10%. Abbreviations: dhfr, dihydrofolate reductase; dhps, dihydropteroate synthase; SP, sulfadoxine–pyrimethamine.

Review Trends in Parasitology October 2013, Vol. 29, No. 10

major mutations described above. In Figure 3, Map 1indicates countries where dhfr N51I + C59R + S108N +dhps A437G all exceeded 50% in any survey at any time.Map 2 shows countries where dhfr N51I + C59R + S108Nand dhps A437G + K540 exceeded 50%; North Sudan wasincluded because 540E was consistently over 50% in sur-veys prior to 2004 in addition to Kenya, Uganda, Tanzania,Zambia, Malawi, Ethiopia, Rwanda, and Mozambique.

Maps 3, 4, and 5 (Figure 3) show the countries in whichsurveys have identified dhfr 164L, dhps 581G, and dhps613S/T mutations at a prevalence of 10% or greater. Theoriginal references to these studies and the raw data can allbe viewed on the interactive maps at www.drugresistan-cemaps.org and www.wwarn.org/surveyor/. To identifyareas where super resistance is emerging, we designatedthe arbitrary cut-off value of 10% prevalence – high enoughto have a significant effect on efficacy at the populationlevel. The major foci of super resistance involving dhps581G mutations were in East Africa. Although the 581Gmutation is found in West Africa (Figure 3, Map 4), theabsence of 540E in West Africa precludes these populationsfrom being classified as super resistant.

We observed the 581G super resistance in three majorfoci of east Africa. The first encompasses Rukara, northernRwanda (61% prevalence of 581G and a 12% prevalence of164L [41]), Kabale and Rukungiri in neighboring south-western Uganda (45% prevalence of 581G and 14% preva-lence of 164L [37]), and Rutsuru in eastern DRC, which is30 km from the Rwandan border (30% prevalence of 581G

[45]). The second major area of 581G super resistance is innorthern Tanzania where 581G has been rising steadilyfrom 11% in 2003, 21% in 2004, 38% in 2006, and 55% in2007 [17], although 164L has not been found yet. The thirdis Kisumu in western Kenya where 581G was reported tobe 85% prevalent [50] and 613S/T was 61%. The 164Lmutation has been found in western Kenya but, the prev-alence is below 10% [39,51,52]. In Madagascar, the 164Lmutation has also been found in significant numbers, butthe dhps 540E mutation is absent, thus parasites in thesepopulations are not considered ‘super resistant’.

Concluding remarks on mapping published dataOur review of published molecular studies of Pfdhfr andPfdhps mutations in African P. falciparum revealed aconsiderable literature, which has accumulated over manyyears. In all, we identified 159 publications describingprevalence in one or more dhfr and dhps codon positionsin almost 300 surveys conducted in 37 African countries.The collation and mapping of molecular resistance data is asubstantial advance because it now allows the spatial andtemporal patterns of resistance to be described and mod-eled in Africa as a whole, and this will allow future sur-veillance to be optimally designed and conducted at theappropriate geographical scale.

The major resistance mutations in dhfr are widespreadand have been thoroughly well established throughoutAfrica. Very few sites found a prevalence of S108N, N51I,and C59R below 50%, and these were Bobo-Dioulasso,

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Burkina Faso in 2004 [31], Yopounon and Adjame districts,Ivory Coast in 2006 [12], and 12 sites in Madagascar in2006–2008 [26]. Elsewhere in Africa, all recent surveysrecord prevalence exceeding 50%, because SP was used asthe first line treatment for clinical malaria for many yearsand exerted strong selection on these mutations [53,54].

There are eight countries in East Africa where all fivemutations comprising the fully resistant genotype or ‘quin-tuple mutant’ have been consistently reported at a preva-lence exceeding 50% [55]; Kenya (nine surveys since 2004),Uganda (three surveys since 2004), Tanzania (seven sur-veys since 2004), Zambia (five surveys since 2004), Malawi(six surveys since 2004), Ethiopia (three surveys since2004), Rwanda (two surveys since 2004), and Mozambique(five surveys since 2004). In addition to those eight coun-tries, we add North Sudan because, at least in the area ofGedaref, 540E was found to exceed the 50% threshold inthree surveys prior to 2003. We also note that a survey in2002 in Rutshuru, DRC [45], 540E was found to exceed the50% threshold, but this is not typical of surveys carried outin the rest of that country. Rutsuru is approximately 15 kmfrom the border with southwest Uganda and approximate-ly 30 km from the Rwandan border. Its proximity to themost highly SP resistant area of East Africa explains itsdivergence from the six other surveys elsewhere in DRC[46–48], which recorded a prevalence of substantially lessthan 50% for 540E.

The Pfdhps A437G mutation is found throughout Africaand in much of West and Central Africa; it is found in theabsence of 540E as a single mutant allele, which hasevolved independently at least three times [42]. In combi-nation with triple mutant dhfr, the dhps single mutant437G has been found to be associated with treatmentfailure [22,56–58], but studies done in vitro indicate thatthe A437G single mutant confers a lesser degree of drugtolerance than A437G + K540E combined [5,59]. Conse-quently, the partially resistant genotype (comprisingS108N, N51I, C59R, and A437G mutations) is consideredto have a less detrimental effect on SP-IPT, and reports ofthe sustained protective efficacy of SP-IPTi in West andCentral Africa [60] are consistent with this.

We introduce the concept of super resistant genotypes,which further raise the threshold of drug tolerance inparasites. Super resistance is emerging through the estab-lishment of additional mutations, in combination withN51I + C59R + S108N and A437G + K540. The additionalmutations are dhfr I164L and dhps A581G, and dhpsA613S and A613T, and although these are comparativelyrare, the initial indications are that their effect on SP-IPTior SP-IPTp efficacy is highly detrimental (see accompa-nying review by Venkatesan et al. [61]). Super resistantgenotypes are found in three east African foci, one span-ning northern Rwanda, southwest Uganda, and easternDRC, the others in northern Tanzania, and western Kenya.These foci may be interconnected, and more detailed sur-veillance is needed in order to determine the extent of theirdistribution. Although 581G, 164L, 613S, and/or 613T alsoexist in significant numbers elsewhere, these populationsare not classified as super resistant because whereN51I + C59R + S108N and A437G occur in the absenceof 540E, its resistance levels are not expected to be as

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high. Measures of efficacy of IPTp from West Africa, anarea where N51I + C59R + S108N and A437G exist incombination with 581G are reassuring [62] and supportthe view that these parasites should be regarded different-ly. However, further research to fully substantiate thisobservation is needed.

Guidelines on SP-IPTp [14] indicate that protectiveefficacy in pregnant women is retained in the presenceof SP resistance. Yet, there are concerns about the associ-ation between super resistant genotypes with loss of pro-tective efficacy of SP-IPTp in northern Tanzania and theoccurrence of increased placental parasite density [16]. InRwanda, where both dhps 581 and dhfr 164L are found athigh frequency [41], the NMCP recently took the step ofwithdrawing SP-IPTp. Early evidence indicates that mo-lecular markers of super resistance may be an importantwarning about failing efficacy of SP-IPTp. For example,Rutshuru in eastern DRC, 30 km from the Rwandan bor-der, where 581G is found at 30% prevalence [45], IPTp isnot effective, unlike other sites elsewhere in the country[63]. Research into the relationship between SP resistancemarkers and the protective efficacy of particular IPTpinterventions is ongoing. A vital part of sustaining effectiveIPTp in the future, either with SP or with a suitablereplacement drug, will be the requirement for ongoingsurveillance of SP resistance levels. Our review hashighlighted gaps in the existing surveillance system,and in the following section we consider what the prioritiesfor a future system of molecular surveillance are.

Future perspectives: priorities in molecular surveillanceof SP resistanceSP-IPT in pregnancy is by far the most significant use of SPtoday, and molecular surveillance for SP resistance in thefuture will be concerned with informing NMCPs where SPcan still be used to good effect and where it needs replacing.There are weaknesses in the existing ad hoc system ofsurveillance, which need to be addressed if future surveil-lance is to be carried out effectively.

Timely publication

We observed a time lag between the date of the survey andthe date of publication, which was on average 5 years butwith a range between 1 year and 21 years. The timelypublication of recent survey data is essential to its inclu-sion and consideration in IPT policy reviews. Loss of datathrough delay to publication or failure to publish at all is ahuge cost to public health. Data sharing prior to publica-tion can be facilitated through regional resistance surveil-lance networks, and countries using IPT should besupported to generate up-to-date information on the extentof resistance.

Regional coordination

The major categories of resistance described here areregionally distributed: partial resistance in West and Cen-tral Africa, full resistance in East Africa, and super resis-tance in East African foci, one of which spans the borderareas of three separate countries. It follows that surveil-lance will be less fragmentary and most efficient whencoordinated at a regional rather than a national level. The

Mauritania

Mali

BurkinaFasoGuinea

IvoryCoast

Liberia

SierraLeone

GuineaBissau

CapeVerde

Togo

EquatorialGuinea

Sao Tomeand

Principe

Number of people exposed to high malariatransmission (million) where SP used for IPTp

SenegalGambia

60,0150,0 160,0 (1)

(1)(2)(3)

(11)

(12)(9)

60,930,0 39,920,0 29,910,0 19,9

5,0 9,94,91

toto

tototototo

Countries using SP-IPTp

NamibiaBotswana

ZimbabweMozambique

MalawiZambia

Angola

Democra�c Republicof Congo

RwandaBurundi

Tanzania

Uganda

SouthSudan

EthiopiaDjibou�

NorthSudanChad

CentralAfrican

Republic

Republicof

CongoGabon

Cameroon

NigeriaBenin

Niger

Ghana

Kenya

Somalia

Zanzibar

Comoros

Mauri�usMadagascar

SouthAfrica N

500 10000

Key:

TRENDS in Parasitology

Figure 4. Priority countries for dhfr and dhps surveillance. Countries are shaded according to the estimated number of people (million) exposed to high malaria

transmission, and countries using SP-IPTp are indicated. Countries are categorized according to the number of people who are exposed to high malaria transmission [65].

Data for Zanzibar and mainland Tanzania are reported separately. Abbreviations: SP, sulfadoxine–pyrimethamine; IPTp, intermittent preventive treatment in pregnancy;

dhfr, dihydrofolate reductase; dhps, dihydropteroate synthase.

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reinvigoration of regional drug resistance surveillance net-works would assist this [64].

Strategic surveillance

There are currently no guidelines on how often surveil-lance surveys should be carried out, where survey sentinelsites should be situated, how many sites should be sur-veyed, and with what sample size. These questions shouldbe directly addressed with respect to the following SPresistance specific issues. (i) Screening for dhps K540Eis a priority in West and Central Africa where it is stillcomparatively rare. Surveys should be designed to detectemergence of 540E in areas where SP is used both for IPTin pregnancy and SP + AQ for SMC. (ii) Screening for dhpsA581G, dhps A613S/T, and dhfr I164L is a priority in areasof East Africa where fully resistant parasites are estab-lished and the emergence of super resistance threatens thecontinuing efficacy of SP used in IPTp. (iii) Mapping thecurrent extent of dhps A581G super resistance is an urgentpriority in East Africa. The available data do not show howinterconnected the foci of resistance are and crucially isalso out of date by 5 years or more. Strategic surveillance todetermine the present distribution of super resistancegenotypes is essential to maintain effective IPTp in EastAfrica.

Coverage

Although some countries are well served by local researchactivity, there are some with no available data. In Figure 4,the 37 countries in Africa where SP-IPTp is used areindicated and shaded according to the number of peoplein that country who are exposed to high malaria transmis-sion according to WHO data [65]. Among these, 11 coun-tries using SP-IPTp had no recent molecular surveillancedata for dhps 540E, 24 had no recent surveillance data fordhps 581G, and 21 had no recent surveillance data for dhfr164L. Coverage is not proportionate to the size of popula-tion at risk, so for example in Nigeria, where there are 158million people at risk, molecular surveillance data wereavailable from a single site. The populations exposed tohigh malaria transmission where SP is being used for IPTphas been estimated [66], and these estimations of popula-tions at risk should be used to guide appropriate levels ofresistance surveillance.

AcknowledgmentsWe thank the database section of the Malaria Research Program, MedicalResearch Council for their invaluable assistance with data entry. Thedevelopment of the drug resistance database was supported by fundingfrom the Gates Malaria Partnership. The development of the websitehttp://www.drugresistancemaps.org/ipti/ was funded by the IPTi consor-tium.

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