WWARN Explorer Methodology

Data sources

All data contributions to WWARN are displayed on the WWARN Explorer (unless the data contributor opts out). Data from each module has been transformed, cleaned and analysed by the relevant WWARN Scientific Group(s), according to their Data Management and Statistical Analysis Plans. Further details on the methodologies are provided below by module Scientific Group: ClinicalPharmacologyIn vitro and Molecular.

The data currently featured in WWARN Explorer are only a small portion of the large body of research that actually exists on antimalarial resistance. If you're interested in contributing data, please visit our Contributing Data section to learn more about the benefits of contribution and our commitment to data contributors or contact us at info [at] wwarn [dot] org.

Clinical methodology

In any individual study, the analysis of clinical drug efficacy is chosen based on the rationale of the study (for instance standalone efficacy or comparative analysis in randomised clinical trials).1 In order to compare these diverse studies, WWARN Explorer uses a uniform approach to the statistical analysis of clinical drug efficacy data, as outlined in the Clinical Data Management and Statistical Analysis Plan2 As such, the derived parameters presented in WWARN Explorer will often differ slightly from the data presented in the original publications and reports of these studies. 

Outcome classification

 All patient observations are classified according to World Health Organization (WHO) definitions.3 The WWARN Explorer homepage displays the Day 28 cure rate because it is highly informative of drug efficacy and the vast majority of studies follow patients for at least 28 days. The detailed display for each individual study presents day 28, day 42 and day 63 results as relevant to each study.

Statistical methods

WWARN estimates of clinical drug efficacy are presented both before and after adjusting for reinfections. The latter are determined according to local study protocols by PCR analysis of the parasite isolates on admission and at the time of recurrence.

Estimates of efficacy are calculated using a modified Intention-To-Treat analysis (mITT) as described previously2. The evaluable population includes all patients enrolled in the study with parasitemia on day 0, except that late clinical failure (LCF) and late parasitological failure (LPF) outcomes are excluded from the adjusted calculations if genotyping was unsuccessful. The unadjusted and adjusted cumulative risks of recurrence are calculated by survival analysis, using the Kaplan-Meier product limit formula.4 Patients lost to follow-up before the end of the study period or patients with more than 18 days between consecutive blood smear results, are censored on the last day of observation. For the unadjusted calculations, patients with any recurrent parasitemia are censored on the last day of recurrence. For the adjusted calculations, patients with new P. falciparum infections are censored on the day the new infection occurs.

  Outcome Classification Unadjusted by genotyping Adjusted by genotyping

LCF and LTF

Incomplete follow up Censored Censored
ACPR Success Success
ETF Failure Failure
Recrudescence Failure Failure
P. falciparum new infection Failure Censored
Genotyping unsuccessful Failure Excluded
Non-falciparum new infection Censored Censored

Pharmacology methodology

Pharmacology data has been curated to a standard, predefined format. Standard outputs and key variables required for data analysis have been derived from the submitted data according to the Pharmacology Data Management and Statistical Analysis Plan.

Pharmacokinetic results describe measured drug concentrations over time and are currently reported as a median and range, stratified by treatment, age category, study site and treatment response. Future versions of WWARN Explorer will include the effect of other patient factors — such as pregnancy, concomitant medication or comorbid disease — on antimalarial pharmacokinetics.

Concentrations below level of quantification will be replaced by half of the limit of quantification.

WWARN outcomes are based on weekly recordings of parasitemia between day 7 and the last day of follow-up and PCR results, as outlined in the Clinical Data Management and Analysis Plan. Gaps of 18 days or longer in parasitemia recordings are considered a deviation, and patients with with more than 18 days between consecutive blood smear results are treated as if they were lost to follow-up at the last visit before the gap.   

Subjects that do not complete at least 28 days of follow up will be excluded from the summary tables as they do not have a treatment outcome.

The results presented on WWARN Explorer may differ from published results due to different inclusion and exclusion criteria, as well as methods of analysis.

In vitro methodology

Data sources

Data on in vitro susceptibility of P. falciparum parasites to antimalarials currently displayed in WWARN Explorer was contributed by the French Centre National de Référence du Paludisme (CNRP) which is composed of three laboratories:

  • CNR-palu Hôpital Bichat -Claude Bernard (Paris),
  • CNR-palu IMTSSA (Marseille),
  • CNR-palu Hôpital La Pitié Salpetrière (Paris).

Malaria patients presenting at French hospitals, immigrants to France and returning travellers from malaria-endemic regions were referred to CNRP, where samples were collected for in vitro susceptibility testing.

P. falciparum isolates were incubated with 5% CO2 and 10-20% O2. Susceptibility was measured as the concentration of antimalarial which inhibited parasite growth by 50% (IC50). IC50 values were obtained by either isotopic methods5 or ELISA pLDH6.

In the near future, in vitro data from multiple investigators will be displayed in WWARN Explorer.

Statistical methods

From 1984 to 2006, we calculated the IC50 with d HN-NonLin polynomial7, a regression model using the log transformation of drug concentration. This model requires the user to fix the degree of smoothness.

Since 2006, IC50 values and their 95% Confidence Intervals (CIs) were calculated with an Emax model based on the linear least squared function of R software8, also available at http://www.antimalarial-icestimator.net.

In vitro data are grouped by country of origin, as determined by patient interview. Data are presented in two different ways for each country. The very large data sets in WWARN Explorer were gathered by three laboratories using common protocols and reagents. This allowed the group to define threshold resistance values for a set of drugs analysed by each laboratory. In addition, an extensive review of the literature demonstrates that resistance to many antimalarials is not observed as a discrete change from susceptible to resistant. However, chloroquine resistance can be defined as a threshold, at an accepted concentration of 80-110 nM in the isotopic assay9,10,11,12,13. Based on this rationale, we have used this metric to present most of the data in the WWARN Explorer.

 In vitro data are presented in WWARN Explorer in two ways:

  • Median IC50 value for isolates represented by a box plot.
  • Chloroquine IC50 values are also presented as the ratio of the isolate mean IC50 values divided by the geometric mean of a control 3D7 strain from the same time period, to reduce variability due to differences in time, location and methodology.

 In future versions of WWARN Explorer, data will be analysed according to the In Vitro Data Management and Statistical Analysis Plan and IC50 parameters will be calculated using the In Vitro Analysis and Report Tool (IVART). The tool was developed by WWARN for standardised, high-throughput processing and analysis of in vitro data sets.

Molecular marker methodology

WWARN Explorer presents a uniform analysis of marker prevalence as detailed in the Molecular Data Management and Statiscal Analysis Plan. Therefore, summary results presented in WWARN Explorer may differ slightly from those in the original publications and reports of these studies.

Statistical methods

Prevalence of each marker is calculated by dividing the number of samples bearing the resistant allele by the total number of samples genotyped at that locus. Samples used for prevalence estimates include those taken on day 0 of clinical trials (pre-treatment) and those taken during cross-sectional or population surveys.

For multi-locus genotypes such as the dhfr triple mutant (108N + 51I + 59R), pure resistance genotypes are defined as the absence of wild-type at all codons. Mixed resistant/sensitive genotypes include all resistant genotype samples with up to one resistant/sensitive mixed codon. Samples with more than one resistant/sensitive mixed codon are not included in the numerator, as the presence of a true multi-locus resistant genotype cannot be verified.

Genotype Classification
dhfr 108N +51I + 59R Pure triple mutant
dhfr 108N +51I + 59C/R Mixed triple mutant
dhfr 108N +51N/I + 59C/R Not a triple mutant

Confidence intervals for prevalence estimates are calculated assuming a binomial distribution:
95% CI=p±1.96 √((p(1-p))/n)

References

  1. Verret et al. The effect of varying analytical methods on estimates of anti-malarial clinical efficacy.Mal J 2009; 8:77
  2. Price R et al. World Antimalarial Resistance Network I: clinical efficacy of antimalarial drugs. Mal J 2007; 6:119.
  3. World Health Organization. Methods for surveillance of antimalarial drug efficacy. 2009.
  4. STATA, 2009. www.stata.com.
  5. Desjardins et al. Quantitative assessment of antimalarial activity in vitro by a semiautomated microdilution techniqueAntimicrob Agents Chemother 1979 Dec; 16(6):710-718.
  6. Makler et al. Parasite lactate dehydrogenase as an assay for Plasmodium falciparum drug sensitivityAm J Trop Med Hyg 1993;48(6):739-741.
  7. Copyright H. Noedl, Armed Forces Research Institute for Medical Sciences (USAMC-AFRIMS, Bangkok, Thailand). www.meduniwien.ac.at/user/harald.noedl/malaria/.
  8. The R Project for Statistical Computing www.r-project.org.
  9. Le Bras et al. Plasmodium falciparum: drug sensitivity in vitro of isolates before and after adaptation to continuous cultureExp Parasitol 1983 Aug; 56(1):9-14.
  10. Le Bras et al. Dichlorquinazine (a 4-aminoquinoline) effective in vitro against chloroquine-resistantP. falciparum. Lancet, 1983, i, 73-74 (letter).
  11. Basco and Le Bras. In vitro activity of chloroquine and quinine in combination with desferrioxamine against Plasmodium falciparumAm J Hematol 1993 Apr; 42(4):389-391.
  12. Pradines et al. In vitro sensitivity of Plasmodium falciparum isolates from Gabon to chloroquine and cycloguanil Bull Soc Pathol Exot 1999 May; 92(2):91-94.
  13. Ringwald and Basco. Comparison of in vivo and in vitro tests of resistance in patients treated with chloroquine in Yaoundé, CameroonBull World Health Organ 1999;77(1):34-33.