Transcript Cadwallader, Waterman, Weisstein

Seasonal Malaria and Resistance to Chloroquine: Simulations and Maps
Joyce Cadwallader, Tony Weisstein, and Margaret Waterman
Introduction
Background
Malaria can be seasonal, with precipitation playing a key
role. East Africa has significant wet and dry seasons.
We model chloroquine resistance in Plasmodium as a one-gene, twoallele system. Chloroquine is typically administered during the 75-day
rainy season: during this period, resistant strains have a fitness
advantage during the haploid merozoite stage in human hosts. Other life
stages of Plasmodium experience genetic drift but not
selection. However, chloroquine treatment is seldom used during the dry
season: during this period, resistant strains replicate at a lower rate than
susceptible strains. As a result, resistance typically increases during the
rainy season and subsides during the dry season. In our model, the user
can control the % of human hosts receiving chloroquine treatment, the
drug’s effectiveness against susceptible strains, and the fitness cost of
resistance.
Mosquito populations increase during wet seasons.
Chloroquine was the drug of choice, but resistance
developed. Resistance decreased as other drugs were
used.
Drug treatment and resistance
Chloroquine: The most commonly used drug to prevent and
treat malaria before 1993 was chloroquine. Chloroquine mode
of action was to inhibit the conversion of heme (toxic to
malaria parasite) to hemozoin thus allowing the toxicity to
increase in the cell and lysis to occur releasing sporozoites.
In the late 1970’s resistance to chloroquine was detected and
other drugs were substituted in 1993 or later depending on
country.
A combination of antimalarial agents containing sulfonamide
antibacterial sulfadoxine and the antiparasitic
pyrimethanime begin to be used. Both are antifolate agents
(inhibitors of folic acid synthesis) which work synergistic to
deprive the parasite of this needed nutrient.
Resistance to these drugs is developing.
Tool: HEME (Deme 2.3 Plasmodium)
Quantitative and scientific
reasoning:
1. Modeling and simulation will be used to
give the student engage with quantitative
reasoning. The model will allow the
students to manipulate variables such as
drug treatment, length of rainy season, and
cost of resistance in terms of fitness to
develop hypothesis and design experiments
to investigate selection under different
climate conditions The frequency changes
of the resistant allele (outcome of the
model) will then be interpreted by the
students from graphic displays of the model
simulation.
2. In addition, thinking about the model and its
capabilities and limitation will engage them
in the scientific process and reasoning.
3. Students will use be encourage to use
maps of the climate data and incidence of
malaria and resistance in their hypothesis
formation and interpretation of data.
Bibliography
Chloroquine resistance before and after its withdrawal in Kenya
Leah Mwai1,2†, Edwin Ochong3†, Abdi Abdirahman1, Steven M Kiara1, Steve Ward3,
Gilbert Kokwaro4, Philip Sasi1, Kevin Marsh1,2, Steffen Borrmann1,5, Margaret
Mackinnon1,6 and Alexis Nzila1,2,3,7* Malaria Journal 2009,
8:106 http://www.malariajournal.com/content/8/1/106
MARA “Information on Maps” Mapping Malaria Risk in Africa (MARA)
http://www.mara.org.za/mapsinfo.htm#Risk
Modeling project based on data.
http://iridl.ldeo.columbia.edu/maproom/Health/Regional/Africa/Malaria/MEWS/index.html
Malaria early warning system The International Research Institute for Climate and
Society, Columbia University
http://www.map.ox.ac.uk/explorer/#EntityPlace:Country/q:pfEndemic_eq=true&pvEndemic_
eq=true&continent_eq=Africa Malaria Atlas Project, Oxford University