Despite the large number of studies published on anti-malarial drug efficacy, as reviewed by Myint et al
], approximately 10% have specifically addressed the in vivo
-molecular correlates of resistance with criteria proposed here. In total, 92 met the inclusion criteria, enrolling more than 1,000 patients for each of the major molecular markers of drug resistance. For the drugs presented in this analysis, resistance occurs via two fundamentally different mechanisms. Quinoline resistance is multigenic and epistatic and, at least for chloroquine, affects drug accumulation in the parasite food vacuole [87
]. In contrast the underlying molecular mechanism of antifolate resistance involves accumulation of single mutations of the gene encoding for the respective target enzymes [89
Both pfcrt and pfmdr1 polymorphisms have been associated with chloroquine resistance. The Odds Ratio (OR) of the pfcrt K76T mutation for therapeutic failure after chloroquine exceeded 7.0 at 28 days and 2.0 at day 14. The robustness of this association is confirmed by the high number of null studies (77) required to negate it.
The association between CRT polymorphism and amodiaquine failure has not been adequately addressed. In the analysis presented the pfmdr1 N86Y polymorphism was the most frequently studied mutation and predicted failure to both chloroquine (1.9 (95%CI: 1.3–2.7, p < 0.001)) and amodiaquine (5.4 (95%CI: 2.6 – 11.2, p < 0.001)). However the association of this mutation and clinical response to chloroquine was weak since few null studies would challenge this observation. The predictivity of the combined pfmdr1 + pfcrt was comparable (OR = 3.9 (95%CI: 2.6–5.8)) compared to pfcrt alone and the number needed to nullify this association decreased to 40. However few studies combined both markers.
While the relationship between mutations in the Pfdhfr
genes and parasite resistance to antifolates is well described [90
], the relative role of different mutations in either gene in determining treatment outcome is less clear. Although the degree of in vitro
resistance and treatment failures to antifolates in this meta-analysis was expected to be proportional to accumulating mutations of Pfdhfr
, there was no clear difference in the predictive values of single and triple mutants: OR = 3.5 (95%CI: 1.9–6.3, p < 0.001) and OR = 4.3 (95%CI: 3.0–6.3, p < 0.001) respectively. Most studies failed to analyse the link between mutations at codons 51, 59 and 108. When data were provided on the therapeutic failure rates associated with each of these codons, it was not always possible to carry out a cumulative analysis. The low difference for OR between single and triple mutants suggest that single mutants maybe markers for presence of other point mutations. Due to these limitations, the only predictive value that should be taken into account was the OR for triple mutants. Several other mutant patterns or drug combinations have been studied, but none provided sufficient data to be included in the meta-analysis.
Overall polymorphisms in Pfdhps at positions 437 and 540 were predictive of therapeutic failure (OR = 3.9 (95%CI: 2.6–5.8, p < 0.001), but these data should be considered with caution because of methodological issues with the studies included (different duration of follow-up and different use of genotyping). Pfdhfr + Pfdhps quintuple mutants were analysed from three different studies, providing an OR = 5.2 (95%CI: 3.2–8.8, p < 0.001), with 38 null studies required -suggesting the high predictive value of this composite genotype. It was impossible however to clearly address the question of the predictive role of the increase number of Pfdhfr + Pfdhps mutations since cumulative data from the same patient were rare.
The meta-analysis confirmed and quantified the association of the four genes studied and their underlying associated with the risk of therapeutic failure (Table ). However there are several caveats. Firstly the resistance of the infecting parasite is only one determinant of treatment outcome. Multiple studies have highlighted the importance of host immunity to the underlying therapeutic efficacy in clinical studies. Such immunity is acquired over time with multiple exposures and thus related to the age of the patient and the transmission intensity [91
]. Other contributing factors include the biomass of parasites at the start of treatment, the patient's adherence to treatment, the dose of drug used and its adequate absorption [92
]. There were no enough studies in the present analysis for a subgroup or multivariate analysis incorporating age and other confounding factors, which reduces the power of the analysis to detect independent parasite factors associated with treatment failure.
Odds ratios related to polymorphisms linked to resistance, according to the drug and the duration of the follow-up. Genotyping (gen.) means that analyse was limited to studies that discriminate between reinfection and recrudescence.
Second, most of the studies included were conducted in Africa over the past 10 years, limiting the relevance of the conclusions in space and time [93
]. For instance, during the study period chloroquine resistance was well established, and failure and prevalence of mutations rates were often at saturation, decreasing the power to detect a significant association. In view of the low number of studies meeting inclusion criteria, it was not possible to compare the OR between areas or periods with low mutation rates to areas or periods with mutations close to fixation.
Third, only published studies indexed in PubMed were considered for this meta-analysis, and one cannot exclude a publication bias towards positive studies. However, considering the number of null studies needed to change the data obtained, the effect of unpublished studies is likely to be limited.
Fourth, study methodology varied with respect to inclusion criteria, age of subjects, treatment schedules, PCR methods and reporting, level of transmission at trial site. A frequent reason for excluding a study was insufficient details in the paper to allow coherent data extraction. Moreover, approximately half of the studies included followed patients for only 14 days (Table ), and as such will not identify late treatment failures, often the earliest manifestation of resistance [94
]. Genotyping of recurrent infections to distinguish between re-infection and recrudescence was only available in 53% of studies assessed. When the analyses were restricted to studies where true failures could be determined, the ORs varied significantly and power was lost.
Lastly, the proportion of patients studied for molecular markers represents a fraction of those enrolled or analysed at the end of follow-up. As no explanation is given for patient attrition, a selection bias cannot be excluded.