A molecular map of CQR in Mali

Abstract

Plasmodium falciparum CQR transporter point mutation (PfCRT76T) is known to be the key determinant of CQR. Molecular detection of PfCRT76T in field samples may be used for the surveillance of CQR in the malaria endemic countries. The genotype-resistance index (GRI) which is obtained as the ratio of the prevalence of PfCRT 76T to the incidence of CQR in a clinical trial was proposed as a simple andpractical molecular-based addition to the tools currently available for monitoring CQR in the field. In order to validate the GRI model across populations, time, and resistance patterns, we compiled data from the literature and generated new data from a dozen of sites across Mali. We found a mean PfCRT76T mutation prevalence of 84.5% (range 60.9%–95.1%) across all sites. CQR rates predicted from the GRI model was extrapolated onto a map of Mali to show the patterns of resistance throughout the participating regions. We present a comprehensive map of CQR in Mali, which strongly supports recent changes in drug policy away from chloroquine.

Introduction

Twelve years after the introduction of chloroquine in the 1940s, Plasmodium falciparum resistance to the drug developed and spread steadily throughout malaria endemic regions. From Asia and South America, then into much of Africa, the once efficacious medication has grown increasingly ineffective [1,2]. Chloroquine is no longer recommended as first line treatment in Africa but, the readily available and inexpensive drug persists throughout the continent [3,4]. Therefore, tracking parasite susceptibility to chloroquine may still have important public policy implications as well as research and clinical utility.

The standards for measuring CQR (CQR)are the WHO’s in vivo drug efficacy tests [5–7]. However, these tests require 2–4 weeks to perform and are difficult to interpret in endemic areas where transmission is high. In addition they require multi-site, representative and repeated studies with sufficient sample size to detect changes. Alternative tests have been a variety of in vitro tests [8–10]. They all involve venous blood to be drawn and some level of parasite cultivation that present high rates of assay failure even in highly equipped laboratories and in expert hands [11]. Therefore neither the in vivo tests nor the currently available in vitro tests are suitable for large-scale epidemiological surveys. Point mutations in the P. falciparum CQR transporter gene, pfcrt, have been shown to be the primary molecular basis of CQR [12]. Molecular methods to detect these mutations in field samples were developed. Previous studies showed that the prevalence of PfCRT K76T mutation was consistently higher than the prevalence of in vivo CQR. However, prospective chloroquine efficacy studies at different sites in Mali found that age -adjusted genotype-resistance indices (GRIs), which are obtained as the ratio of the prevalence of PfCRT 76T to the incidence of CQR in a clinical study CQR were remarkably stable (mean GRI = 2.1). Thus, in different settings in Mali, molecular surveys for PfCRT K76T could potentially be used to predict rates of in vivo CQR [13]. If validated in new settings of Mali and in other countries, this GRI model represents a potentially powerful and novel tool for the detailed mapping of CQR at the country or regional levels. To this end, we set out to confirm the GRI model through molecular and in vivo data from a variety of villages in Mali, and in the West-African sub-region through review of the literature.

Materials and Methods

Model verification

In order to confirm the GRI model across population, time, and resistance patterns, we compiled data from the literature, new data from our team and from other Malian scientists. New blood samples blotted onto filter papers were collected from Kollé and Bandiagara, two of the original sites used in the development of the GRI model. Additional samples were collected in the village of Faladié, a new field site for our team. Faladié (3,000 inhabitants) is a rural village located at 80 km Nord-West from Bamako. Malaria transmission is seasonal with a peak in October. The highest rate of in vivo CQR in Mali (60%) were documented in that site in 2003 and rates of PfCRT 76T were above 80% [14]. These samples were taken from patients presenting with symptoms consistent with uncomplicated falciparum malaria in 2002 and 2003 i.e. five to seven years after the initial report of the GRI model.

We gathered additional filter papers from the villages of Mantéourou, and Bancoumana, two sites used by other Malian investigators. In addition to the collection of blood filter papers from cases of fever, field clinicians kept detailed accounts of the level of CQR seen in vivo in the locality using the WHO in vivo protocols [7].

DNA was extracted from each sample and the PfCRT K76T genotype determined by nested PCR as previously described [15]. PCR banding was grouped as no band, wild type, mutant, and mixed. Mixed infections were considered to have the mutant phenotype. Samples giving no band were excluded from further analysis.

To further test the validity and applicability of the model, the published literature was searched for studies that had compared the prevalence of the PfCRT 76 marker with in vivo resistance patterns. Inclusion criteria for the literature reviews were that the data be published in a peer reviewed journal and have both data on in vivo CQR and molecular data on the PfCRT 76T mutation from the same geographic location. Reported PfCRT 76T prevalence had to be less than 100%.

Data Analysis

From the new Mali molecular and in vivo data, we calculated the “predicted in vivo chloroquine resistance” using the published GRI formula as follows: PfCRT 76T prevalence/2.1 = predicted in vivo CQR. This predicted in vivo resistance was then compared to the actual observed day-14 in vivo chloroquine resistance for that same site and year. This process was repeated for the data extracted from the literature.

Using the model to predict in vivo chloroquine resistance

Following the verification of the GRI model, the next step was to field-test the model and develop a map of CQR in Mali. Wanting to test the method in the most practical and real world settings, villages that had yet to be included in research studies were targeted. Importantly, these villages had no prior association with our team. The association of Malian “bush doctors”, Médecins de Campagne, who provide medical care in rural areas throughout Mali, was invited to participate in the study. These medical doctors practice in rural villages where neither electricity, nor running water is available. They perform their rounds in a radius of 5–15km around their headquarters on foot, bicycles or motorcycles. Diagnosis of malaria and other illnesses is solely based on clinical symptoms as no laboratory is available. Eighteen physicians from 18 villages widely dispersed throughout the Sahel region of Mali to Bamako, the capital city of Mali were invited to participate in this pilot implementation of the GRI model. These physicians were asked to participate in the collection of blood spot filter papers for use in molecular testing. They received a day-long training session which included information on the molecular basis of CQR, the importance of accurate drug resistance monitoring, and a detailed description of their role within the study. They were instructed on proper informed consent and sampling techniques. At the end of the training session, each physician was given a “GRI kit” containing adequate gloves, alcohol, filter paper, sterile lancets, pencils, and tape to allow for the collection of 100 samples. They were instructed to offer participation and collect a filter paper for each individual presenting with a suspected case of uncomplicated malaria based on their clinical evaluation. No other data was collected from the study participants.

Filter papers were then sent from these villages to the MRTC laboratory for molecular analysis. Once at the laboratory in Bamako, Mali, the same technique as described in the model verification above was used to detect the PfCRT 76T mutation.

The study protocol received ethical clearance from the Ethics Committee of the Faculty of Medicine, Pharmacy and Odonto-Stomatology, University of Bamako.

Map Development

Once all of the season’s samples were collected and analyzed, the GRI model was used to calculate the predicted level of CQR by village. The data was then extrapolated ontoa map of Mali to show the patterns of resistance throughout the participating regions. In addition to the participating villages, all villages that were used in the confirmation of the model were mapped. Data published in 2004 from the region of Kidal where the GRI model was first field tested in Mali and used to inform policy in the management of an epidemic of malaria was also mapped [16].

Results

From the five Malian villages a total of 951 blood filter paper samples were collected. The rate of mutant PfCRT ranged from a low of 32.4% in Manteourou to a high of 91.5% in Bancoumana. The predicted in vivo resistance and the actual observed in vivo resistance are pictured in Figure 1. The difference between the observed and predicted in vivo resistance ranged from a low of 3.9 to a high of 14.3 percentage points.

Figure 1.

Predicted and observed in vivo resistance based on the prevalence of the PfCRT 76Tmutation using the GRI model (GRI = 2.1). CQR =CQR; R= resistance

The review of the literature yielded three relevant studies from three West African countries: Mauritania [17] Ghana [18] and Burkina Faso[19] with a total representation of six villages. We compared the prevalence of the PfCRT 76T marker and levels of in vivo resistance. Of the six representative villages, the difference between the predicted and observed CQR values ranged from 0.2–19 percentage points. (Figure 2) The Burkina Faso village of Bama was noted to have a ratio of PfCRT 76T to in vivo resistance that approached 1. These studies reported their own calculated GRI as follows: the Mauritanian study reported a calculated GRI of 1.7, the study in Ghana used the GFI model noting a ratio of 2.8 for clinical failure, the Burkina Faso data from four villages reported a range of GRI from 1.1–3.0. The Ghana authors used genotype-failure index (GFI) which is calculated using the same formula as the GRI but uses rates of clinical failures [6] instead of rates of parasitological resistance [5]. The simple non-weighted average for all sites in our review of the literature review was 2.0.

Figure 2.

Prevalence of the PfCRT 76Tmutation and the predicted and observed rate of in vivo resistance across three West African countries based on the Mali GRI of 2.1. *: Villages from Burkina Faso

Of the 18 invited rural doctors, thirteen made the trip to Bamako. Of the thirteen who attended the training session twelve (92.3%) sent filter paper samples. From these twelve participating Médecins de Campagne, we collected a total of 1260 filter paper samples from 12 villages throughout the Sahel region of Mali. Filter papers from one village failed to yield PCR product and was removed from further analysis. The post PCR results are detailed in the table 1. There was a total ‘no band’ (no amplification despite a nested PCR) rate of 41.1% (range 1.6–73.9%). It should be noted that typical rate of PCR failure in our laboratory is less than 6% (data not shown). From the remaining 645 samples we found a prevalence rate of 84.6% (range 60.9%–95.1%) of the PfCRT 76T mutation across all sites (see table 1). We combined these data with our previous results from other Malian sites used in the model verification to construct a new map of CQR village by village across Mali (Figure 3).

Figure 3.

Map of GRI predicted in vivo resistance to chloroquine in Mali.

Discussion

We present the most comprehensive map of CQR in Mali to date. This map was made in 2005 when the Malian National Malaria Control Program was reviewing antimalarial treatment policy. It demonstrated the extent of CQR in Mali and contributed to the decision to move the first line antimalarial drug from chloroquine to artemisinin-based combination therapies. These data support other studies which showed that in areas with low to moderate transmission patterns, the rate of PfCRT 76T in the population is roughly 2–3 times the observed in vivo resistance [17–20]. We did not have data to test the limits of the GRI as no village had PfCRT 76T prevalence >96%. Other sites with higher PfCRT 76T prevalence such as the Congo, have shown that as PfCRT 76T reaches 100%, the predictive value of the GRI diminishes [21]. However, it is important to note that the WHO advocates for a change in drug policy once in vivo resistant rates to a drug reach 25%. Importantly, the GRI is most accurate in areas where it stands to be of greatest clinical and political utility. Day-14 in vivo CQR data were provided here because the GRI model was developed when the norm was to use day-14 drug efficacy in the field.

We found that the prevalence of the PfCRT 76T mutation varied widely throughout the country from just over 30% to well over 90%. This data strongly supports changes in drug policy away from chloroquine. Indeed, since 2006, the Malian government’s official policy is to use Artemisinin-based Combination Therapy (ACT) as first line treatment for uncomplicated malaria. To this end, the country currently provides free ACTs to children under 5 presenting with uncomplicated malaria.

After a nested PCR amplification of DNA extracted from filter papers obtained from presumptive cases of malaria there was a mean rate of absence of PCR product of 41.1% (range 1.6–73.9%). Although PCR amplification could fail for a number of reasons, the great majority of these samples would contain no malaria parasites. This highlights the problem of over-diagnosis of malaria in rural settings where the treatment decision is solely based on clinical symptoms. This data was used to convince the National Malaria Control Program of Mali to require malaria diagnosis with microscopy or rapid diagnostic tests (RDTs) prior to the prescription of ACTs to children less than 5 years of age. The use of RDTs is now part of the new guidelines for the management of malaria in Mali.

Molecular epidemiology of the PfCRT K76T has been also used to guide research efforts. Laufer et al used the diminishing prevalence of the PfCRT 76T marker in Malawi as a basis for a clinical trial that showed that 13 years after the withdrawal of chloroquine from the Malawian market, the drug’s clinical efficacy returned. The authors showed that in absence of the drug’s selection pressure, the PfCRT 76T mutation could disappear in a population, inferring that the resistance trait did not incur a fitness advantage on the parasite. The authors were careful not to advocate for a return to chloroquine to the Malawian market, but rather cautioned that chloroquine may eventually see a return of efficacy on the continent should the drug be wisely removed from the continent’s markets [22,23]. Figure 1 shows that the prevalence of PfCRT 76T in Bandiagara increased from 39% (n= 47) in 2002 to 68.9% (n = 75) in 2003 (P < 0.001) while in Kollé the prevalence of PfCRT 76T decreased from 85% (n = 127) in 2002 to 64.5% (n= 172) in 2003 (P < 0.001). The only drug used in uncomplicated malaria in Bandiagara in 2002–2003 was chloroquine which may have resulted in the increased prevalence of PfCRT 76T in that site. In contrast, in Kollé, a small rural village, there was a randomized trial which compared chloroquine, amodiaquine and sulfadoxine-pyrimethamine [24]. Because both amodiaquine and sulfadoxine-pyrimethamine were highly efficacious on P. falciparum in Kollé, the PfCRT 76T mutant had no fitness advantage, which may explain the observed decrease in the prevalence of that allele in this village. Taken together, our data and the available literature demonstrate the power of drugs in shaping malaria parasite populations.

Today, despite chloroquine’s diminished clinical efficacy, the drug continues to be used as an inexpensive malaria treatment in many parts of the world, notably in sub-Saharan Africa, as often treatment alternatives are not available or are beyond the means of the population [20,21]. Indeed, given the enormous price difference between chloroquine and ACTs, changes in prescribing patterns and pharmacy stocks are likely to take time. Nevertheless, most African countries with high levels of P. falciparum resistance to chloroquine are currently changing policy away from the use of chloroquine [25]. With the change in prescribing patterns from chloroquine to other anti-malarials, tracking resistance patterns to these drugs will become increasingly important. While the GRI model may not fit with all antimalarial drugs or all malarious areas, the molecular approach used in our model may serve as a foundation on which other markers and resistance patterns can be developed and tracked. It remains to be seen if this shift in policy will eventually lead to a decline in the prevalence of mutant PfCRT and in turn in vivo CQR. Certainly data describing Malawi’s transition away from chloroquine support this theory [22,23,26]. Indeed, the GRI may be used to help track how successful the country’s efforts have been in shifting the drug policy away from chloroquine.

Perhaps most importantly this study demonstrated that with very limited resources an efficient, cost-conscious, and scientifically sound drug resistance monitoring project is feasible. From the outset there were numerous obstacles to address, education and training of physicians not familiar with molecular techniques, limited financial resources, project initiation in villages not familiar with medical research, and most importantly the transportation of filter papers back to the laboratory in Bamako. Most of the villages used in the project were far from principle roads. The samples arrived by whatever means possible, including foot, motorcycle courier, bus, plane, and public transport; often by way of a veritable relay of transport from villages along the Sahel belt of southern Mali to as far away as to the North. Despite these obstacles, we show that drug resistance tracking using a molecular biology tool is possible in even the world’s poorest countries. Indeed, by maximizing the resources that were already in place, notably the Médecins de Campagne and the laboratory of MRTC in Bamako, the foundation for a country-wide drug resistance monitoring program was laid.