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In silico ADME, Bioactivity and Toxicity Analysis of Some Selected Antimalarial Agents


Neeraj Kumar*1, Shashank Shekhar Mishra2, Chandra Shekhar Sharma2, Hamendra Pratap Singh2
1. Department of Pharmaceutical Chemistry, Geetanjali Institute of Pharmacy, Udaipur 313001, India
2. Department of Pharmaceutical Chemistry, Bhupal Nobles’ College of Pharmacy, Udaipur 313001, India

ABSTRACT

Malaria, one of the most widespread diseases, is caused by a plasmodium parasite and it infects several hundred million people each year, results in several million deaths annually. Because there are four different species of protozoa that cause malaria, no one antimalarial drug is effective against all four species. In this computational research investigation, we performed In-silico pharmacokinetic, bioactivity and toxicity study of some selected antimalarial agents. To design a new molecule having good pharmacological profile, this study will provide the lead information.

Key-words: TPSA, GPCR, ADMETox, nON.

Introduction

Malaria is one of the major life-threatening diseases caused by parasites transmitted through the bites of infected female Anopheles mosquitoes. In 2015, most malaria cases occur in African countries and nearly 3.2 billion people were at risk of malaria. Malaria incidence among population increases day by day. This has become major public health burden due to high mortality rate1.  Malaria in human is caused by six different species of the protozoan parasites Plasmodium, which are P. falciparum, P. vivax, P. malariae, P. ovale, P. knowlesi and P. brasilianum 2. Its original treatment was quinine that became the prototypical molecule for the first generation of synthetic antimalarial agents.

There are four possible sites for drug therapy to treat the malaria-

  1. Kill the sporozoites injected by the mosquito and/ or prevent the sporozoites from entering the liver.
  2. Kill the schizonts residing in hepatocytes and/ or prevent them from becoming merozoites.
  3. Kill the merozoites in the blood and/ or prevent them from developing into gametocytes.
  4. Kill the gametocytes before they can enter the mosquito and reproduce into zygotes. Some argued that the focus at this stage should be block on the male gametocytes. This would block the female gametocytes from mating 3.

New antimalarial drugs must be developed constantly because the protozoa develop resistance by various mechanisms, and there are a wide variety of adverse reactions 3. The combination of cost of drugs and their adverse reactions can make patient compliance difficult. Because there are four different species of protozoa that cause malaria, no one antimalarial drug is effective against all four species. There is a tremendous need for effective antimalarial drug.

This research investigation involves the search of pharmacokinetic, drug-likeness, toxicity and bioactivity profile of available antimalarial drugs on the basis of several physico-chemical parameters by computational methods. To design a new molecule having good pharmacological profile, this study will provide the lead information.

Materials and Methods

In silico ADME analysis-

By applying computational methods, there are various physicochemical features and pharmacokinetic descriptors were calculated for some selected antimalarial agents through the online tool Molinspiration Cheminformatics server (http://www.molinspiration.com). Molinspiration Cheminformatics offers broad range of tools supporting molecule manipulation and processing, including SMILES and SDfile conversion, normalization of molecules, generation of tautomers, molecule fragmentation, calculation of various molecular properties needed in quantitative structure activity relationship (QSAR) study, molecular modeling and drug design, high quality molecule depiction, molecular database tools supporting substructure and similarity searches. This software also provides fragment-based virtual screening, bioactivity prediction and data visualization. Molinspiration tools are written in Java, therefore can be used practically on any computer platform 4.

Drug-likeness is qualitative concept used for drug like property that described as a complex balance of various molecular properties and structural features which determine whether particular molecule is similar to the known drugs. These molecular properties are mainly hydrophobicity, electronic distribution, hydrogen bonding characteristics, molecule size and flexibility and of course presence of various pharmacophoric features that influence the behaviour of molecule in a living organism, including bioavailability, transport properties, affinity to proteins, reactivity, toxicity, metabolic stability and many others. Drug-likeness evaluated by the Lipinski rule of five that deals four simple physicochemical parameter ranges (MWT ≤ 500, log P ≤ 5, Hbond donors ≤ 5, H-bond acceptors ≤ 10) associated with 90% of orally active drugs that have passed phase II clinical status 5. Other calculation methods such as ligand efficiency and lipophilic efficiency can also be used to express drug-likeness as parameters of potency. These physicochemical parameters having acceptable range associated with aqueous solubility and intestinal permeability. Physicochemical parameters take small part of the whole chemical information about the real molecule and became popular as variables in molecular modelling studies.

In silico Bioactivity analysis-

The bioactivity score of selected agents were also evaluated using the tool Molinspiration Cheminformatics server (http://www.molinspiration.com). In this computational chemistry technique large chemical databases are analyzed in order to identify possible new drug candidates. Virtual screening techniques range from simple ones, based on the presence or absence of specific substructures, or match in calculated molecular properties, up to sophisticated virtual docking methods aimed at fitting putative ligand molecules into the target receptor site.
Molinspiration bioactivity tool offers very good balance between screening speed, requirements on information needed to start a new virtual screening project and screening performance.

In the Molinspiration tool, the miscreen engine first analyze a training set of active structures (in extreme case even single active molecule is sufficient to built a usable model) and compares it with inactive molecules by using sophisticated Bayesian statistics. Only SMILES or SDfile structures of active molecules are sufficient for the training, no information about the active site or binding mode is necessary. This is particularly useful in projects where structure-based approach cannot be applied because information about 3D receptor structure is not available, for example in screens aiming to find ligands modulating G-protein coupled receptors. Based on this analysis a fragment-based model is developed, where for each substructure fragment a bioactivity contribution is calculated. Once a model is build the bioactivity of screened molecules may be then calculated as a sum of activity contributions of fragments in these molecules. This provides a molecule activity score (a number, typically between -3 and 3). Molecules with the highest activity score have the highest probability to be active. Such in silico screening is very fast, large collections of molecules (more than 100’000 molecules) may be screened in an hour.

Based on the protocol described above, screening models developed for four important drug classes, namely GPCR ligands, ion channel blockers, kinase inhibitors, and nuclear receptor ligands. A virtual screening model for any target may be developed easily by using the miscreen built-in functionality. Another advantage of virtual screening protocol based on Bayesian statistics is, that it is able to generalize, i.e. to learn general structure requirements which are necessary for bioactivity. The identified new bioactive molecules are therefore not limited to molecules similar to the training set, but the protocol is able also to identify new active structure classes (scaffold hopping).

In silico Toxicity analysis-

The toxicity of the selected antiepileptic agents was evaluated by computational method using Pallas version 3.1 ADMETox prediction software Pentium IV processor. This software tool was started by double click on the icon. The molecule to be predicted was drawn by double click on new option, and then molecule was subjected for evaluation of toxicity by selecting ToxAlert options. Various types of toxicities including oncogenicity, neurotoxicity, teratogenicity, immunotoxicity, etc. were generated and toxicity profile of molecule noted.

Result and Discussion

There some antimalarial agents were selected and analyzed to ADME properties and drug likeness (Lipinski’s rule of five) which are given in Table 1. All selected agents have molecular weight in the acceptable range (MWT ≤ 500). Low molecular weight containing molecules are easily absorbed, diffused and transported as compared to high molecular weight compounds. As molecular weight increases except certain limit, the bulkiness of the molecules are also increases comparably 6.

Table 1: ADME Properties of selected antimalarial agents

Name Molecular formula Molecular weight LogP  TPSA nON  nOHNH nrotb volume In silico % absorption
Proguanil C11H16ClN5 253.74 1.79 88.80 5 5 4 229.03 78.36
Sulfadoxine C12H14N4O4S 310.33 0.36 116.44 8 3 5 253.35 68.82
Mefloquine C17H16F6N2O 378.32 4.24 45.15 3 2 4 296.92 93.42
Primaquine C15H21N3O 259.35 2.10 60.18 4 3 6 256.91 88.23
Doxycycline C22H24N2O8 444.44 -0.87 181.61 10 7 2 377.79 46.34
Chloroquine C18H26ClN3 319.88 5.00 28.16 3 1 8 313.12 99.28
Amodiaquine C20H22ClN3O 355.87 5.29 48.38 4 2 6 325.56 92.30
Pyrimethamine C12H13ClN4 248.72 2.84 77.83 4 4 2 216.62 82.14

Among selected antimalarial agents, doxycycline and amodiaquine have one violation according to Lipinski’s rule of five. Doxycycline has 7 hydrogen bong donors and amodiaquine has logP value 5.29 which are higher from acceptable range. The MLogP (octanol / water partition co efficient) of all agents were calculated and found to be within acceptable range according to Lipinski’s rule except amodiaquine. The MLogP value is used to calculate the lipophilic efficiency that measures the potency of drug. Therefore Octanol-water partition coefficient logP value is essential in rational drug design and QSAR studies. In the pharmacokinetic study, hydrophobicity of the molecule is assessed by evaluating logP value because hydrophobicity plays a vital role in the distribution of the drug in the body after absorption7.

TPSA (Topological Polar Surface Area) is a very useful physiochemical parameter of molecule that gives the information about polarity of compounds. This parameter was evaluated for analyzing drug transport properties. Polar surface area is the sum of all polar atoms mainly oxygen and nitrogen including attached hydrogen8. Percent absorption were also evaluated for all selected antiepileptic agents by %ABS = 109- (0.345 * TPSA) 9. Molecular volume assesses the transport properties of the molecule such as blood-brain barrier penetration. The number of rotatable bond was calculated and have found relevant. A molecule which have more number of rotatable bond become more flexible and have good binding affinity with binding pocket.

Bioactivity of all selected antimalarial agents was evaluated against six different protein structures. Biological activity is measured by bioactivity score that are categorized under three different ranges-

  1. If bioactivity score is more than 0.00, having considerable biological activity.
  2. If bioactivity score is 0.5 to 0.00, having moderately activity.
  3. If bioactivity score is less than -0.50, having inactivity 10.

 Table 2: Bioactivity of Antimalarial agents

Name GPCR Ligand Ion channel modulator Kinase inhibitor Nuclear receptor ligand Protease inhibitor Enzyme inhibitor
Proguanil -0.23  -0.23  -0.66  -1.23  -0.46  0.09 
Sulfadoxine 0.26  -0.21  0.16  -0.62  -0.18  0.11 
Mefloquine 0.45  0.21  -0.05  0.30  0.36  0.21 
Primaquine 0.20  0.22  0.22  -0.35  0.04  0.15 
Doxycycline -0.24  -0.32  -0.54  -0.17  0.06  0.51 
Chloroquine 0.32  0.32  0.38  -0.19  0.05  0.11 
Amodiaquine 0.21  0.17  0.52  -0.08  -0.05  0.17 
Pyrimethamine 0.31  0.07  0.38  -0.61  -0.14  0.66 

The result of this study was found that the selected agents are biologically active and have physiological effect. The bioactivity score profile of the all selected agents is given in Table 2. Sulfadoxine, Mefloquine, Primaquine and chloroquine having good bioactivity score against GPCR Ligand which could indicates they could bind more effectively with GPCR. The bioactivity score graph of mefloquine for different protein is given in figure 1.

The bioactivity score provide the information about the binding cascade of the drugs that is used for the development of a new functional drug with increased binding selectivity profile and less undesirable effects. All selected antimalarial agents were evaluated to toxicity profile and given in Table 3. All of the drugs were found to be highly probable to toxicity except amodiaquine and proguanil.

The interesting fact about toxicity is all selected antiepileptic agents were found to be exhibited teratogenecity. These research findings provide the lead for the design and development of new potent antimalarial drugs. Computational study of all selected antimalarial drugs gives the information about the pharmacokinetics of the existing drugs that provide the lead for development of functional drug with more effectiveness and lesser toxicity.

Figure 1: The bioactivity score graph of mefloquine for different proteins

Table 3: Toxicity Profile of Antimalarial agents

Name Toxicity Overall toxicity Oncogenicity Mutagenicity Teratogenicity Irritation Sensitivity Immunotoxicity Neurotoxicity
Proguanil Not Probable 18 0 0 18 0 0 0 0
Sulfadoxine Highly Probable 76 76 51 19 0 29 0 0
Mefloquine Highly Probable 71 53 71 0 0 0 0 0
Primaquine Highly Probable 71 53 71 19 0 0  0 0
Doxycycline Highly Probable 76 76 29 34 53 0 0 29
Chloroquine Highly Probable 71 53 71 18 0 0 0 0
Amodiaquine Probable 53 53 53 18 53 0 0 29
Pyrimethamine Highly Probable 64 64 51 19 0 29 0 0

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