Mebendazole

Ethical permission for the study was granted by the Gambian Government and Medical Research Council Ethics Committee, and Gambian National DNA Collection Guidelines were followed regarding the handling of genetic material and information. Parental written informed consent was obtained for all study participants.
A cohort of 780 children aged from 2 to 6 y was recruited from ten rural villages in the West Kiang region of The Gambia at the start of the malaria season, July 2001, with follow-up to December 2001/January 2002. All children were eligible except those with serious chronic illness or those enrolled in another study.
Figure 1 provides an overview of the study population. Ethnic groups were Mandinka (nine villages, 700 children) and Fulani (one village, 80 children). Children had anthropometric measurements taken and were examined by the study clinician. All children received a 3-d course of mebendazole at the start of the study for possible hookworm infection. A blood sample was collected for full blood count, malaria slide, iron status assays, haptoglobin concentration, α-1-antichymotrypsin (a marker of inflammation), and DNA extraction. Children with a temperature over 37.5 °C had a malaria blood film, appropriate clinical treatment, and a blood sample 2 wk later after recovery from illness. Children with malaria parasites on blood film were treated with chloroquine and pyrimethamine-sulfadoxine (Fansidar) according to Gambian Government guidelines. This procedure was repeated at the end of the malaria season for each child.
Malaria incidence is highly seasonal in The Gambia with the majority of malaria cases occurring between September and December [
24 (link)]. Haemoglobin levels in children from the study area were previously found to be highest in July and lowest in November [
25 (link)]. We thus sampled at the start and end of the malaria season to assess the effect of haptoglobin genotype on haemoglobin levels in the malaria season compared to baseline levels. Children were followed across the malaria season to control for multiple individual factors that may influence haemoglobin levels.

The mathematical model used describes the evolution of the parasite distribution in different host age groups and the impact of periodic chemotherapy on host burdens, incorporating the key epidemiological and biological processes influencing transmission. Building on past research
[15 (link),16 ], it includes the observed features of sexual reproduction by the dioecious helminths, heterogeneity in exposure to infection by host age, variation in the intensity of transmission in different human communities, aggregated distributions of worm numbers per host and a decline in fecundity as a function of worm burden (density dependence)
[16 -18 (link)]. The dynamics of transmission under repeated rounds of treatment is examined for the three main intestinal nematodes, Ascaris lumbricoides, Trichuris trichuria and hookworms (Necator americanus and Ancylostoma duodenale). The model is described in detail in the Additional file
1 available online.
Although a full age distribution is embedded in the model, we employ the key age groupings described above to define intervention coverage levels and illustrate their effect. These are infants (0–1 years of age) who cannot be treated under current licensure of the main anthelmintic drugs in wide use (e.g. albendazole and mebendazole), pre-school aged children (pre-SAC, 2–4 years of age), school aged children (SAC, 5–14 years of age) and adults (15+ years of age). Varying combinations of the fraction treated in each age grouping, treatment frequency and duration of treatment are explored. The fraction in each grouping effectively treated is a product of the fraction given treatment and drug efficacy (defined as the proportion of worms expelled). Within the current model, these two aspects of treatment are inseparable, and coverage of the population is represented as a proportion of worms treated. Drug efficacy is typically in the region of 90% or more for Ascaris and hookworms, but somewhat less for Trichuris[19 (link)-22 (link)]. It should be noted that the fraction treated is effectively chosen at random from the subpopulation. This model does not address systematic non-compliance.
The life cycles of these parasites involve free living stages that are passed in the faeces of the human host and mature to infective stages in the external habitat (eggs for Ascaris and Trichuris and larvae for hookworms). The infective stages of the parasite in the environment are represented in the model by a common pool of infectious material. The life span of these stages is typically weeks to months under favourable environmental conditions, and they are excreted in very large numbers
[23 -26 (link)]. Although this duration is short by comparison with adult worm life expectancies in the human host, infectious material in the environment acts as a reservoir which is unaffected by chemotherapy and can play a significant role in the dynamics of treatment. Dynamics of a range of parasites within the host population can be represented by the same model, with distinct parameter ranges for different species (See Additional file
1: Table S1).
Different age groups are thought to both contribute to, and be exposed to, this infective pool to varying degrees. An indication of this is provided by the changes in the intensity of infection by age; the patterns are typically convex for Ascaris and Trichuris, but continue to rise for hookworms as individuals age
[27 (link)-29 (link)] (Figure 
2). The respective roles of age related exposure to infection versus acquired immunity remains uncertain, but rapid re-infection by all three parasites post treatment points to the former as the main driver of age-intensity of infection profiles. On this basis, MCMC methods
[30 ] are employed to fit the model to these age related patterns of infection, to estimate both transmission intensity (measured by the basic reproductive number R₀ - the average number of offspring produced by one female worm that survive to reproductive maturity) and age related exposure. We have endeavoured to choose typical or characteristic infection profiles for the parasite species investigated in the hope that our results will be broadly applicable.

First, as the internal standard, anthelmintikum mebendazole (MBZ) was selected [29 (link)] and 2.5 μM stock solution was prepared in DMSO: methanol (1:1). Plasma samples (50 μL) from each mouse/group (n = 4) were lyophilized and dry powder was dissolved in 50 μL of MBZ solution to avoid the excessive dilution of metabolites. Following centrifugation at 9000 RCF for 3 min, the plasma samples were diluted in 50 μL of DMSO: methanol (1:1) and used for analysis. The percentage of ABZ-SO and ABZ-SO₂ recovery in plasma from treated mice was verified via the samples from healthy mice (n = 3). Fifty μL of samples was spiked with one of the metabolites before lyophilization to obtain 1.25 μM of the final concentration, and was further processed as described previously. In order to achieve the selectivity, four blank samples from infected untreated mice were analyzed. Spectral interference of heparin was evaluated since it was used as the anticoagulant in blood samples. Solutions of ABZ and its metabolites were also prepared to measure the retention time. Accuracy of this method was assured with the extraction of five spiked samples of 200 ng/mL of ABZ-SO and 20 ng/mL of ABZ-SO₂.

Mass spectra were obtained using the Shimadzu Prominence system, consisting of a DGU-20A₃ mobile phase degasser, two LC-20AD solvent delivery units, the SIL-20AC cooling auto sampler, a CTO-10AS column oven, an SPD-M20A diode array detector, and an LCMS-2020 mass detector with single quadrupole equipped with an electrospray ion source (Shimadzu, Kyoto, Japan). Binary gradient elution was used: mobile phase A = 5% acetonitrile in water, 0.1% formic acid; mobile phase B = 80% acetonitrile in water, 0.1% formic acid; gradient: 0–3 min 0–40% B, 3–5 min 40–70% B; 5–7 min 70% B, 7–9 min 70–0% B, 9–10 min 0% B. The flow rate was 0.4 mL/min at 25 °C, injection volume was 10 μL, and samples were detected at 285 nm. Retention times (min) were the following: albendazol (6.811), albendazol sulfoxide (5.328), mebendazol (9.945), albendazol sulfoxide (6.206). The MS parameters were as follows: positive mode; ESI interface voltage, 4.5 kV; detector voltage, 1.15 kV; nebulizing gas flow, 1.5 mL.min⁻¹; drying gas flow, 15 mL.min⁻¹; heat block temperature, 200 °C; DL temperature, 250 °C; SCAN mode 200–400 m/z; software LabSolutions ver. 5.75 SP2.

A connectivity map analysis used two reference datasets, (1) Touchstone v1, which has over 8000 well-annotated genetic and small molecule drug perturbagens profiled in a core set of nine cell lines, and (2) the Discover v1 dataset with over 15,000 unannotated small molecular perturbagens tested in a variety of cell lines. The combined databases created the L1000-based compendium CMAP-L1000v1 used to develop the CMap query. We used the top 100 genes that were found using RNA sequencing to be the most differentially downregulated (see Supplementary Table S1) in both MDA-MB-231 and SUM159 cells upon MBZ treatment to perform a CMAP query. Each PCL (perturbagen class) has an assigned mechanism of action (MOA) class with likely targets. Targets with shared MOAs are placed within the same class. The CMAP query provides a connectivity score for each perturbagen. The higher the score, the more likely MBZ shares a MOA with a given perturbagen.
A separate analysis was conducted using the Touchstone v1 dataset, which included mebendazole as a perturbagen. The expression profile of a cell line treated with mebendazole (MBZ) could be compared to other perturbagens used to treat the same cell line. This query compared the transcriptomic profile of the specified perturbagen, MBZ, and identified a similar profile across six cancer cell lines against other compounds that have a similar mechanism of action. In this case, a score of 1 to 100 is provided, with 100 being a perfect match to MBZ.