New Targets for Drug Discovery against Malaria.

PLoS ONE 8(3): e59968. doi:10.1371/journal.pone.0059968

Guido Santos, Néstor V. Torres



Malaria affects more than 500 million people worldwide, killing between 1 and 2.5 million people annually, most of whom are children under the age of five. It is caused by Plasmodium genus parasites (Plasmodium vivax, P. ovale, P. malariae, P. knowlesi and P. falciparum), P. falciparum being the most lethal. The parasites multiply inside human erythrocytes, killing the cells in the process, and are transmitted by female Anopheles mosquitoes. The area most affected by malaria is sub-Saharan Africa.

There is currently no effective vaccine against malaria. Some promising preliminary results have been seen, but no solution to this issue is expected over the next few years. To make the situation even worse, the efficacy of transmission control by means of insecticide-treated nets and indoor residual spraying is dropping, because resistance to insecticides is increasing among mosquitoes in Africa. Because of that malaria control is becoming totally dependent on pharmacological treatments.

There are several classes of drugs used to treat malaria. All share the feature of targeting the merozoites, while some target gametocytes as well. Resistance to all these antimalarial drugs has been widely reported. This poses a potentially dangerous and severe scenario, if the resistance spreads to endemic areas in Africa since, to our knowledge; no other effective antimalarial treatments are in sight.

A new line of approach has been proposed to direct drug discovery [1]. This strategy, known as quantitative and systems pharmacology, aims to understand how drugs influence cellular networks in space and time and determine how they affect human pathophysiology. This approach follows the tenets of systems biology, and is based on the development of mathematical models that incorporate data at several temporal and spatial scales and are able to predict the dynamic behavior of the main variables involved in the parasite infection and the therapeutic effects of drugs. The principles of system biology thus provide the methodological framework and perspective needed for modeling system behavior in vivo, establishing the basis for a genuinely rational target identification and drug design. In this context, the defining feature of system biology is the combined use of mathematical and computable models and quantitative experimental data as a means to unravel the network-based (“emergent”) properties which cannot be deduced in any way from the knowledge of their components [2].

Such models, which lead to an educated and informed hypothesis, can then be used to identify the most sensitive processes of the system with a view to reducing the parasitemia (see Länger et al, 2012; [3]). The present work is in line with this approach, and with the current view that in order to identify new and better targets for antimalarial drug-based treatments, a model-based approach is needed.

Results & discussion

The objective of this study was to identify new targets with potential for drug discovery against malaria. The proposed model focuses on the modeling of the underlying processes within the host-parasite dynamics of plasmodium invasion of red blood cells. Its main objective is to allow for the systematic search, by means of an optimization approach, for the most interesting targets for drug research. Given the high number of processes involved and the model’s inherent complexity, we opted for a phenomenological representation that is simple enough to permit the proposed search but at the same time rich enough to provide valuable insight into suitable targets. Based on the results of this search, we would set up the necessary conditions to find therapeutic strategies, including some which are counter-intuitive. These strategies will not necessarily impede infection but will reduce parasitemia and the risk of severe symptoms as well as diminish the risk of drug resistance and selective pressure for resistant plasmodium strains.

The model considered a set of relevant components, processes and interactions. Variables selected for the model were the two phases of the parasite inside the host erythrocytes (mRBC, merozoites, and gRBC, gametocytes), the healthy erythrocytes of the host, hRBC, and the immune activity of the host against the parasite, IS (see Figure 1). All the variables of the model were measured in terms of their concentration inside the bloodstream. The values of the variable IS are given by the concentration of IgG1, which serves as the representative component of the mean immune response against the parasite.

Using the model presented below we find out a set of targets (single or combined), which are presented in the Figure 2. A first target would consist on decreasing the invasion rate of the erythrocytes by the merozoites; such an intervention would lead to the full recovery of the host (Figure 3A). Decreasing this rate means that free merozoites are less effective in invading red blood cells; thus they will be more exposed to the immune system. Although this strategy has already been partially evaluated as a vaccination strategy, our results suggest that it could also be effective as a pharmacological treatment.

The second single target proposed by the model works by increasing the death rate of the gametocytes. This intervention produces a decrease in gametocyte load but an increase in merozoite load, although the maximum peak will be attenuated (Figure 3B). A treatment along these lines will reduce the transmission rate during the initial stages of the disease and can be combined with traditional treatments targeting the asexual phase. Such a strategy has been already used (artemisinin combined treatments), with the predicted effects of a lower transmission rate for the disease.

A third target was to decrease the ability of the gametocytes to activate the immune system. Its effect is to reduce the maximum peaks of both parasite species (Figure 3C), and so it is expected that it would alleviate the symptoms of non-complicated malaria and reduce the transmission rate during the initial stages of the infection. This target also has additional convenient characteristics. Since it does not directly affect the parasites, there is no selective pressure on them and thus the emergence of drug resistance is avoided. Also, if combined with traditional treatments, the doses could be reduced, thereby also reducing the emergence of drug resistance. Since this target refers to a parasite-host interaction, the only chance to observe its effects would be in in vivo conditions. Of equal importance is the fact that it is a very counterintuitive solution. All this could explain why this strategy has never been explored.

The last single target identified operates by increasing the transformation rate of merozoites into envisaged gametocytes. The effect of this increase is to produce a significant decrease in merozoite load and also a decrease in gametocyte load (see Figure 3D). Although it is not surprising that increasing the merozoite transformation rate would produce a decrease in merozoite load, what is not so intuitive is the model’s prediction of a decrease in the gametocyte load. This type of intervention has been shown to relax the symptomatology of non-complicated malaria [4] and reduce transmission during the initial stages of the infection [5]. It is interesting to observe that the activation of the transformation of merozoites into gametocytes has the effect of impairing the pathogen load. A higher gametocyte growth rate yields an initial increase in its population, but this is followed by a greater decrease in such a way that, as a whole, more parasites are eliminated earlier. This behavior has been observed in other parasitic diseases [3] and can be explained by stating that a pathogen that proliferates rapidly is more likely to be detected by the immune system. Increasing the gametocytogenesis is a strategy that has never been evaluated by structural drug design methods, and our results propose this target as a promising process where these techniques can be applied for drug design.

Our exploration of the effective combinations of two targets showed that reducing the influence of gametocytes on the activation of the immune system while at the same time decreasing the effect of the immune system on the death rate of the red blood cells (see Figure 2B) would cause a reduction in both of the parasite forms (Figure 3E). The latter action can be interpreted as making the immune system less efficient in removing old erythrocytes, thus enhancing their half-life. This therapeutic strategy is optimal in order to prevent the emergence of drug resistance in the parasite. These two proposed targets have never been evaluated by structural methods. There should be great interest in examining these two processes in order to design new drugs, because affecting these processes does not generate selective pressure on the parasites; thus, effective drugs will have a low probability of leading to the emergence of resistance, thereby extending their life span. Here we can see again how the systemic approach might shed new light on a well-studied process and help propose a novel combination of targets, none of which aims to directly kill the parasites, but which could reduce the parasitemia. In all these explorations, the magnitudes of target-related parameter changes, although related with the corresponding processes, do not have a direct translation into a particular process. Accordingly, they have to be interpreted as an indication of the magnitude and sense of the intervention that would lead to the desired effect.

This model-based strategy used can be of assistance in key phases of the drug discovery process, such as the identification of the right targets, since these targets allow us to guide drug design at the molecular level through the systematic use of other means such as structural methods (molecular docking) or data-mining of inhibitor databases. A best-case illustration is provided by the proposed target consisting of decreasing the invasion of red blood cells by merozoites, which relates to the interruption of the interaction between the Apical Membrane Antigen 1 associations with its receptor, the Rhoptry Neck Protein 2. In fact, the structural details of the specific Apical Membrane Antigen 1 with the Rhoptry Neck Protein 2 interaction involved in the invasion mechanism enables the design of molecules with optimal inhibitory properties to treat malarial infection. However, due to the phenomenological character of the model, the proposed targets do not have a direct translation into a concrete, well-defined process. On the contrary, it should be stressed that the contribution of this work is to propose certain processes of the intraerythrocytic cycle of plasmodium as targets for a more detailed analysis that would eventually lead to the determination of a set of concrete processes where the activity of some drugs will cause the desired global effect. This second iteration would be the natural next step in this project.


  1. Ward, R. (2011). Quantitative and systems pharmacology in the post-genomic era: New approaches to discovering drugs and understanding therapeutic mechanisms. An NIH white paper by the QSP workshop group.
  2. Bhalla, U. S. (1999). Emergent properties of networks of biological signaling pathways. Science, 283(5400), 381.
  3. Langer, B. M. (2012). Modeling of leishmaniasis infection dynamics: Novel application to the design of effective therapies. BMC Systems Biology, 6, 1.
  4. Olliaro, P. (2008). Risk associated with asymptomatic parasitaemia occurring post-antimalarial treatment. Tropical Medicine & International Health, 13(1), 83-90.
  5. Okell, L. C. (2008). Reduction of transmission from malaria patients by artemisinin combination therapies: A pooled analysis of six randomized trials. Malaria Journal, 1-13.


Figure 1. Erythrocyte infection model representation. In the picture, red circles represent the red blood cells, blue drops represent merozoites, blue ovals represent gametocytes and the blue “Y” represents the immune system. The variable acronyms are the healthy red blood cells (hRBC), the merozoite-infected red blood cells (mRBC), the gametocyte-infected red blood cells (gRBC) and the immune system (IS). Black continuous arrows represent processes while blue dashed arrows represent regulatory influences of the variables on processes.



Figure 2. Single and combined targets for antimalarial drugs. A. Single targets (from top to bottom): decreasing invasion of hRBC (thick red arrow); increasing transformation of mRBC (thick green arrow); increasing death of gRBC (thick green arrow) and decreasing activation of IS by gRBC (dashed red arrow). B. Combined target: decreasing activation of IS by gRBC combined with decreasing recycling of hRBC (dashed red arrows).



Figure 3. Model-based time course of the system variables after intervention on the selected target rate process. A. After a decrease of V3. B. After an increase of V6. C. Decrease of the influence of gRBC on V7. D. Increase of V5. E. Simultaneous decrease of the influence of gRBC on V7 and of the influence of IS on V2. Continuous lines represent the prediction of the model in control conditions (no intervention on the target rate processes), while red continuous lines represent the prediction of the model after the corresponding interventions.



Dr. Néstor V. Torres Darias

Dpto. de Bioquímica y Biología Molecular

Grupo de Tecnología Bioquímica

Facultad de Biología

Universidad de La Laguna.

Avda. Asfco. Fco. Sánchez, s.n. 38206 La Laguna. Tenerife. Spain.

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