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Anti-malarial drugs targeting the Plasmepsin II protein

Anti-malarial drugs targeting the Plasmepsin II protein

J. Savigar

Introduction

Malaria is one of the most widespread infectious diseases in the world, and is caused by the protozoan parasites of the Plasmodium genus.[1]

The malaria parasite, plasmodium falciparum, invades erythrocytes and consumes almost all of the haemoglobin present. Malaria affects hundreds of millions of people every year,[2] and of those affected, 0.7‒2.7 million die each year, the majority of which are children.[3, 4] There are a number of anti-malarial drugs available on the market, but due to the development of resistance within the malaria causing parasites, there is urgent need for anti-malarial drugs with new mechanisms of action.[5] A new series of potential anti-malarials are being designed to target the Plasmepsin proteins. The plasmepsin proteins are aspartic proteases, and two of these proteases have been identified in the initial degradation processes of haemoglobin.[6–8] Plasmepsin I is able to make a cleavage of the haemoglobin protein, which can cause the protein to unravel, leading to rapid proteolysis. Plasmepsin II is also able to cleave native haemoglobin, but has an increased activity on the denatured globin produced by plasmepsin I.[2] The plasmodium falciparum parasites depend upon the nutrients provided by haemoglobin degradation. Therefore, if drugs are designed to inhibit the haemoglobin degrading plasmepsin proteases, the malaria parasites would be killed, indicating the plasmepsin proteases are valid anti-malarial target.

Plasmepsin II

The genes of the plasmepsin II protease have been characterised,[6, 8] and encode for proteins with sequence homology to mammalian aspartic proteases such as Cathepsin D and renin.[2] The crystal structure of plasmepsin II has been characterised by X-ray diffraction and stored in the protein database (pdb). The structure can be seen in Figure 1.[9] The pdb uses ribbon structures to visualise the amino acid sequence of the protein. Each protein in the pdb is given a 4 character unique identifier. Whilst the identifier has no chemical relevance to the protein, it allows for the reference of specific, unique proteins. The structure of the uncomplexed plasmpesin II protein has the unique identifier 1LF4 in the pdb.[9]

Figure 1[9] Model of plasmepsin II protein structure

Patenting Drugs

The drug discovery process is extremely expensive. Therefore, when a drug is taken to market, the company who produced the drunk need to make huge sales in order to make a profit. In order to do this the company will protect their drug with a

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ARTICLE

patent. This prevents other drug companies from releasing generic copies of the original drug, which would otherwise compete for sales. However, if the patent only covered the single, specific compound in the drug, it would be simple to circumvent the patent by producing a very slightly different molecule (e.g. if a halogen was present in the molecule it could be swapped for a different halogen). Therefore Markush structures are used in patents to cover as many variants of the active drug molecule as possible. Markush structures also have the benefit of hiding the exact structure of the useful molecule by depicting general structures with R groups. For instance if an active drug contains a Cl atom on the 4th carbon in a benzene ring, the markush structure would simply indicate an R group on any carbon of the benzene ring, where R is a halogen.

Molecular Representations

A number of compounds have been proposed as anti-malarials, whose mechanisms of action are inhibition of the plasmepsin II protease. The structure of the compounds, 1–25, can be seen in the paper written by Ersmark et al.[1] In order for the chemical structures to be interpreted by a computer, the structures were translated to codes known as SMILES strings. Simplified Molecular-Input Line-Entry System (SMILES) is a form of line notation that describes the structure of chemical species. The SMILES strings are obtained by following a simple algorithm to construct the linear string. Alternatively, software programmes exist that can convert a drawn chemical structure into a SMILES string. Molecular editors are then able to convert the SMILES strings back into 2D drawings or 3D models. There are other coding systems that can be used to represents chemical structures, such as InChi, but SMILES has the advantage of being more human-readable, and has a wide range of software support. The SMILES strings and CAS (Chemical Abstracts Service) numbers of the 25 possible anti-malarials can be found in the supplementary information on

figshare, alongside InChI codes and 3D structures for a sample of the molecules.

Modelling Drug Activity

In order to model the drug activity, a number of different descriptors needed to be identified. Descriptors are numerical values that characterise molecular properties, and can be used to model observed data. Descriptors can range from a simple property, such as the molecular weight, to something far more complex that would require quantum mechanical calculations to identify. The usefulness of the descriptor is often inversely correlated with the ease of calculating the descriptor. Lipinski's rule of five indicates some potentially useful descriptors for modelling drug activity. Lipinski's rule of five is a rule of thumb used to evaluate the likelihood of a compound being an orally active drug in humans. The rules are:

No more than five H-bond donors No more than ten H-bond acceptors Molecular weight less than 500 daltons Octanol-water partition coefficient (LogP)

no greater than five.

Therefore, descriptors such as the LogP values of the compounds, their molecular weights, and the number of nitrogen and oxygen atoms may be some useful descriptors to look at. A full set of descriptors for the compound can be found in the supplementary information on figshare.

However, it is important to identify useful descriptors. A useful descriptor is one which provides unique information about the compound. If two descriptors are highly correlated to one another, they are not providing unique information about the compound, and only one of them is required. It is also important to consider the spread of a descriptor. If there is no spread in the values of the descriptor, it's not likely to be

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useful. Different descriptors must also have similar ranges of size and spread, otherwise certain descriptors may dominate over others in the model.

The potential anti-malarials were randomly divided into two sets, the training set, which would be used to build the model, and the test set, which would be used to validate the model. The training set was made up of compounds: 1, 2, 4, 7, 9, 13, 16, 17, 18, 19, 20, 23, 25. The test set was made up of compounds: 3, 5, 6, 8, 10, 11, 12, 14, 15, 21, 22, 24. The compounds who's activities were not well defined (e.g. stated as >5000) were eliminated from the data sets. By refining the model on the training set the following expression for modelling compound activity was suggested:Equation 1 Ki = inhibitory constant (M); nHDon = number of H bond donors; nHAcc = number of H bond acceptors; nRB = number of rotatable bonds.

Log(1/Ki) = - 0.35049(LogP) + 0.83941(nHDon) – 0.85003(nHAcc) + 0.30670(nRB) + 7.2109

This model was validated on the test set. Validation on the test set indicated the presence of a possible outlier. Once the outlier was removed, the model gave a good correlation between predicted and observed values of the test set, indicating a valid model (Figure 3). Leave-one-out analysis also indicated a valid model (See figshare data).

Figure 2 Relationship between observed activity and activity predicted by the model for the training set.

Figure 3 Relationship between observed activity and activity predicted by the model for the test set.

Conclusions

A set of drug activity data was divided into two subsets, the training subset was used to produce a model, whilst the validity of the model was evaluated on the test set. A number of descriptors were modelled against the log of the reciprocal of the active drug concentration. A model was proposed, that when cross-validated on the test set, indicated good predictive ability. Leave-one-out analysis also indicated good predictive ability of the model. Some of the compounds indicate potential as anti-malarial drugs, showing activity at the nM scale. They also act on a protein which is a proven target for anti-malarial drugs.

References

1 Ersmark et al. (2005). J Med Chem. 48(19):6090-106.

2 Silva et al. (1996). Proc Natl Acad Sci U S A. 93(19): 10034–10039.

3 Breman, J. (2001). Am. J. Trop. Med. Hyg. 64 (1, 2)S, 1-11.

4 Sturchler, D. (1989) Parasitol. Today 5, 39-40.5 Wongsrichanalai et al. (2002). Lancet Infect. Dis.

2, 209-218.6 Francis et al. (1994) EMBO J. 13, 306-317.7 Gluzman et al. (1994) J. Clin. Invest. 93, 1602-

1608.8 Dame et al. (1994) Mol. Biochem. Parasitol. 64,

177-1909 Asojo et al. (2003) J. Mol. Biol. 327, 173–181

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