In Silico Evaluation of Phytoconstituents of Azadirachta indica as Potential Anti-malarial Agents Targeting Adenosine Deaminase Receptor of Plasmodium vivax

1.1. DRUG DISCOVERY

Drug discovery is a process, which aims at identifying a compound therapeutically useful in treating and curing a disease. Typically a drug discovery effort addresses a biological target that has been shown to play a role in the development of the disease or starts from a molecule with interesting biological activities. The process of drug discovery involves the identification of candidates, synthesis, characterization, screening, and assays for therapeutic efficacy. Once a compound has shown its value in these tests, it will begin the process of drug development prior to clinical trials. Drug discovery and development is an expensive process due to the high costs of R&D and human clinical tests. The average total cost per drug development varies from US$ 897 million to US$ 1.9 billion. The typical development time is 10-15 years. The developing world suffers the major burden of infectious disease, yet the range of drugs available for the treatment of many infectious diseases is limited. In the past most drugs have been discovered either by identifying the active ingredient from traditional remedies or by serendipitous discovery. At present a new approach is being tried to understand how disease and infection are controlled at the molecular and physiological level and to target specific entities based on this knowledge. ^[1]

Figure 1: Drug Discovery and Development

1.2. SCREENING AND DESIGN

1. Structure-based drug design (SBDD)

Structure-based drug design must be performed with available structural models of the target proteins, which are provided by X-ray diffraction, nuclear magnetic resonance (NMR) or molecular simulation (homologous protein modeling, etc.) ^[2-6]. Keeping in mind the complexity of cancers which show diverse phenotypes and multiple etiologies, a one-size-fits-all drug design strategy for the development of cancer chemotherapeutics does not yield successful results. Lately, Arjmand et al. adopted a series of methods, such as the combination of X-ray crystal structures and molecular docking, to design, synthesize, and characterize novel chromone based-copper(II) antitumor inhibitors. In general, after obtaining the structure of the receptor macromolecule by x-crystal single-crystal diffraction technique or multi-dimensional NMR, molecular modeling software can be used to analyze the physicochemical properties of drug binding sites on the receptor, especially including electrostatic field, hydrophobic field, hydrogen bond, and key residues. Then, the small molecule database is searched, or the drug design technique is used to identify the suitable molecules whose molecular shapes match the binding sites of the receptor and binding affinity is high. Then, these molecules are synthesized and their biological activities will be tested for further drug development. In short, structure-based drug design plays an extremely important role in drug design.^[7]

Figure 2: Structure based Drug Design

2. Ligand-Based Drug Design (LBDD)

Unlike structure-based drug design, ligand-based drug design doesn’t search small molecule libraries. Instead, it relies on knowledge of known molecules binding to the target macromolecule of interest. Using these known molecules, a pharmacophore model that defines the minimum necessary structural characteristics a molecule must possess in order to bind to the target can be derived ^[8,9]. Then, this model can be further used to design new molecular entities that interact with the target. On the other hand, ligand-based drug design can also use quantitative structure–activity relationships (QSAR) in which a correlation between calculated properties of molecules and their experimentally determined biological activity is derived, to predict the activity of new analogs. This approach is known as "ligand-based drug design".^[10,11]

Figure 3: Ligand based Drug Design

Figure 4: Molecular docking

3. Molecular Docking

Molecular docking, which predicts interaction patterns between proteins and small molecules as wel as proteins and proteins, to evaluate the binding between two molecules ^[12], is widely used in the field of drug screening and design. The theoretical basis is that the process of ligand and receptor recognition relies on spatial shape matching and energy matching, which is the theory of “inducing fit”. Determining the correct binding conformation of small molecule ligands and protein receptors in the formation of complex structures is the basis for drug design and studying its action mechanism. Molecular docking can be roughly divided into rigid docking, semi-flexible docking and flexible docking. In rigid docking, the structure of molecules does not change. The calculation method is relatively simple, and mainly studies the degree of conformation matching, so it is more suitable for studying macromolecular systems, such as protein–protein, protein–nucleic acid systems. In semi-flexible docking, the conformation of molecules can be varied within a certain range, so it is more suitable to deal with the interaction between proteins and small molecules ^[13]. In general, the structure of small molecules can be freely changed, while macromolecules remain rigid or retain some of the rotatable amino acid residues to ensure computational efficiency. In flexible docking, the simulated system conformation is free to change, thus consuming more computing resources while improving accuracy. What’s more, the establishment of binding sites in molecular docking methods is very important. For the first time, Collins successfully determined the binding sites on the surface of proteins using a multi-scale algorithm and performed flexible docking of molecules, which greatly promoted the development of molecular docking.^[14]

Docking theory: The following docking theory topics are available:

1. CDOCKER : Uses a random preliminary ligand placement and full CHARMm forcefield based docking.    

2. LibDock: Fast docking-based on binding site features (‘hotspots’).

3. LigandFit: Docking-based on an initial shape match to the binding site.

4. MCSS: Uses CHARMm to dock fragments by using a unique computationally efficient Multiple Copy Simultaneous Search algorithm.

Drug-receptor interactions occur on atomic scales. To form a deep understanding of how and why drug compounds bind to protein targets, we must consider the biochemical and biophysical properties of both the drug itself and its target at an atomic level. Swiss PDB (protein data bank) is an excellent tool for doing this.

It can predict key physicochemical properties, such as hydrophobicity and polarity that have a profound influence on how drugs bind to proteins.

Figure 5: Method used for protein ligand docking

Applications and importance of molecular docking:

The uses of docking programs to indicate the nature of the atoms and functional groups present in the 3D (three-dimensional) structures also enable to examine the binding of a drug to its target site.

4. Quantitative Structure Activity Relationship (QSAR)

QSAR is a quantitative study of the interactions between small organic molecules and biological macromolecules. It contains a correlation between calculated properties of molecules (e.g., absorption, distribution, metabolism of small organic molecules in living organisms) and their experimentally determined biological activity ^[15]. In the case of unknown receptor structure, the QSAR method is the most accurate and effective method for drug design. Drug discovery often involves the use of QSAR to identify chemical structures that could have good inhibitory effects on specific targets and have low toxicity (non-specific activity). With the further development of structure–activity relationship theory and statistical methods, in the 1980s, 3D structural information was introduced into the QSAR method, namely 3D-QSAR. Since 1990s, with the improvement of computing power and the accurate determination of 3D structure of many biomacromolecules, structure-based drug design has gradually replaced the dominant position of quantitative structure-activity relationship in the field of drug design, but QSAR with the advantages of small amount of calculation and good predictive ability ^[16] still plays an important role in pharmaceutical researches. Based on 3D structural characteristics of ligands and targets, 3D-QSAR explores the 3D conception of bioactive molecules, accurately reflects the energy changes and patterns of interactions between bioactive molecules and receptors, and reveals the drug-receiving mechanism of body interactions. The physicochemical parameters and 3D structural parameters of a series of drugs are fitted to the quantitative relationship. Then, the structures of new compounds are predicted and optimized. In short, 3D-QSAR is actually a research method combining QSAR with computational chemistry and molecular graphics. It is a powerful tool for studying the interactions between drugs and target macromolecules, speculating the image of simulated targets, establishing the relationship of drug structure activity, and designing drugs.

Figure 6: A hypothetical plot of the activity (Log1/C) of a series of compounds against the logarithm of partition coefficient parameter’s (LogP).

Regression analysis is a group of mathematical methods of QSAR used to obtain mathematical equations relating different sets of data that have been obtained from experimental work or calculated using theoretical study. The data are fed into a suitable computer program, which, on execution, produces an equation that represents the line that is the best fit for those data. Regression analysis would calculate the values of m and c that gave the line of best fit to the data.

1.3. COMPUTER AIDED DRUG DESIGN

The development of new drugs is no longer a process of trial and error or strokes of luck in the field of research, it has become a delicate process depends mainly on the overlapping medical and pharmaceutical science and informatics. The explosive development of computer technology and methodologies to calculate molecular properties have increasingly made it possible to use computer technique to aid the drug discovery process.

Computer-aided drug design is a pc era that designs a product and files the design's technique. CADD can also facilitate the manufacturing system through shifting unique diagrams of a products materials, methods, tolerances and dimensions with unique conventions for the product in query. It can be used to supply both two-dimensional or 3-dimensional diagrams, that can then when rotated to be considered from any attitude, even from the inside searching out. The channel of drug discovery from concept to marketplace includes seven fundamental steps: ailment choice, target selection, lead compound identification, lead optimization, pre-medical trial trying out, medical trial checking out and pharmacogenomic optimization. In practice, the closing five steps required to skip again and again.

The compounds for trying out may be obtained from herbal supply (Plants, animals, microorganisms) and by using chemical synthesis. These compounds can be rejected as perspectives owing to absence or low hobby, life of toxicity or carcinogenicity, complexity of synthesis, inadequate performance etc. As a result, simplest one of a hundred thousand investigated compounds may be delivered to the market and one average fee of improvement of latest drug rose as much as 800 million bucks. The discount of time-ingesting and value of the last ranges of drug trying out is not likely due to strict kingdom popular on their cognizance. Therefore, essential efforts to increasing performance of development of medicine are directed to levels of discovery and optimization of ligands.

Computer aided drug design CADD can contribute not only the design of potent compounds but too many of the step of going ‘from concept to clinic.' In the development of the new drug, CADD methodologies and technology are used to calculate molecular properties to aid in drug design process ^[17]. CADD methods are aimed at using the information which is in the three-dimensional structure of the unliganded target to design completely new lead compounds de novo, as well as to construct large virtual combinatorial libraries of compounds that then can be screened computationally before going to attempt and expense of actually synthesizing and testing them ^[18].

ADVANTAGES OF CADD:

• Less Time requires
• Accuracy
• Information about the disease
• Increase productivity & higher quality designs.
• Screening is reduced
• Database screening & optimization
• less manpower is required
• CADD gives valuable information about target molecules,

The latest advancements like QSAR, combinatorial chemistry different databases & available new software tools provide a basis for designing of ligands & inhibitors that require specificity.  

APPLICATION OF CADD:

• Determine the lowest free energy structures for the receptor-ligand complex Search database and rank hits for lead generation.
• Calculate the differential binding of a ligand to two different macromolecular receptors. Study the geometry of a particular complex Propose modification of a lead molecules to optimize potency or other properties de novo design for lead generation.
• Library design.
• Design Review and Evaluation. Review and Evaluation is checking whether the designed part has been designed properly.³⁴

BENEFITS OF CADD:

CADD methods and bioinformatics tools offer significant benefits for drug discovery programs.

• Cost Savings: - The Tufts Report suggests that the cost of drug discovery and development has reached $800 million for each drug successfully brought to market. Many biopharmaceutical companies now use computational methods and bioinformatics tools to reduce this cost burden. Virtual screening, lead optimization and predictions of bioavailability and bioactivity can help guide experimental research. Only the most promising experimental lines of inquiry can be followed and experimental dead-ends can be avoided early based on the results of CADD simulations.
• Time-to-Market: - The predictive power of CADD can help drug research programs choose only the most promising drug candidates. By focusing drug research on specific lead candidates and avoiding potential “dead-end” compounds, biopharmaceutical companies can get drugs to market more quickly.
• Drug Development: - Researchers in Germany report an advance toward the much-awaited era in which scientists will discover and design drugs for cancer, arthritis, AIDS and other diseases almost entirely on the computer, instead of relying on the trial-and error methods of the past. In the report, Michael C. Hutter12 and colleagues note that computer-aided drug design already is an important research tool. The method involves using computers to analyze the chemical structures of potential drugs and pinpoint the most promising candidates. Existing computer programs check a wide range of chemical features to help distinguish between drug-like and nondrug materials. These programs usually cannot screen for all features at the same time, an approach that risks overlooking promising drug-like substances. In the new study, researchers describe a more gradual and efficient system. Their new program uses an initial quick screen for drug-like features followed immediately by a second, more detailed screen to identify additional drug -like features. They applied this new classification scheme to a group of about 5,000 molecules that had previously been screened for drug-like activity. The new strategy was more efficient at identifying drug-like molecules “whereby up to 92 percent of the non-drugs can be sorted out without losing considerably more drugs in the succeeding steps,” the researchers say.³⁴

Figure 7: Flow chart of CADD process

1.4. VIRTUAL SCREENING

In recent years, the rapid development of computational resources and small molecule databases have led to major breakthroughs in the development of lead compounds. As the number of new drug targets increases exponentially, computational methods are increasingly being used to accelerate the drug discovery process. This has led to the increased use of computer-assisted drug design and chemical bioinformatics techniques such as high-throughput docking, homology search and pharmacophore search in databases for virtual screening (VS) technology . Virtual screening is an important part of computer-aided drug design methods. It may be the cheapest way to identify potential lead compounds, and many successful cases have proven successful using this technology.

The primary technique for identifying new lead compounds in drug discovery is to physically screen large chemical libraries for biological targets. In experiments, high-throughput screening identifies active molecules by performing separate biochemical analysis of more than one million compounds. However, this technology involves significant costs and time. Therefore, a cheaper and more efficient calculation method came into being, namely, virtual high-throughput screening. The method has been widely used in the early development of new drug. The main purpose is to determine the novel active small molecule structure from the large compound libraries. It is consistent with the purpose of high-throughput screening. The difference is that virtual screening can save a lot of experimental costs by significantly reducing the number of compounds for the measurement of the pharmacological activity, while high-throughput screening needs to perform experiments with all compounds in the database. Here, we will discuss common methods of virtual screening^[19]

Virtual screening can be used to:

− Select compounds for screening from in-house database.

− Choose compounds to purchase from external suppliers.

− Decide which compounds to synthesize next.

Figure 8: Virtual Screening

1.5. ANTI-MALARIAL DRUG

Antimalarial are drugs used to prevent and treat malaria.  Most antimalarial drugs target the erythrocytic stage of malaria infection, which is the phase of infection that causes symptomatic illness. The extent of pre-erythrocytic (hepatic stage) activity for most antimalarial drugs is not well characterized.

Treatment of the acute blood stage infection is necessary for malaria caused by all malaria species. In addition, for infection due to Plasmodium ovale or Plasmodium vivax, terminal prophylaxis is required with a drug active against hypnozoites (which can remain dormant in the liver for months and, occasionally, years after the initial infection).

The mechanisms of action, resistance, and toxicities of antimalarial drugs will be reviewed here. Use of these agents for prevention and treatment of malaria is discussed in detail separately^[20].

Plant work in anti-malarial drug:

1. Neem Plant

The Azadirachta indica (Neem) has a vital role in various problems associated with human health. The chemical constituents present in the neem plant make it a doctor tree due to its wide scope in biological activities associated with it, and has become a global context today.

Taxonomical Classification

Kingdom: Plantae

Order: Rutales

Family: Meliaceae

Genus: Azadirachta

Species: Indica

Biological Source

Neem consists of the fresh or dried leaves and seed oil of Azadirachta indica J. Juss (Melia Indica or M. azadirachta Linn

Table 1: Chemical Constituents of Neem with their structure

Sr no	Chemical constituents	Structure
1	Nimbin
2	Nimbinin
3	Myricetin
4	Meliantriol
5	Stigmasterol
6	Beta- sistosterol
7	Chloroquine(standard)

Uses

Anti- oxidant activity: Free radical or reactive oxygen speciese are one of the fundamental offenders in the genesis of different illness.

Anti-ulcer activity: It is reported that the gastro protective property of dried bark extract of azadirachta indica in the mercepto-methylimidazole induce ulcer. It act mainly by inhibiting acid secretion and blocking the oxidative damage of gastric mucousa

2. MATERIALS AND METHODS

2.1. DOWNLOADING SOFTWARE PROGRAM :

(i) CHEMSKETCH OPENSOURCE SOFTWARE, is a chemical molecule or molecular modeling program used to create , draw and modify images of chemical structures or compounds

(ii) AVOGADRO SOFTWARE is a molecule editor and visualizer.

(iii) PYRX SOFTWARE is a virtual screening software

(iv) DISCOVERY STUDIO 3.5 was used for molecular interaction and visualization

2.2. PREPARATION OF LIGAND:

1. Using ChemSketch, 2D structures were drawn Software.
2. Then saved in the working folder as .mol file.
3. Accessed in Avogadro Software where the .mol file is converted to . pdb format.

2.3. PREPARATION OF RECEPTOR:

Open the PDB (PROTEIN DATA BANK) site and search the In-silico analysis crystal structure of adenosine deaminase from Plasmodium vivax in complex with MT-coformycin (PDB ID: 3EWC) which is download the structure in .pdb formate.

2.4. VIRTUAL SCREENING:

PYRX software is used for virtual screening protocols.

3. RESULT AND DISCUSSION

3.1. Drug likeliness study: The results of Drug Likeliness study given in table 2.

Table 2: Drug Likeliness Study Results

Discussion Based on Drug Likeness Study:

From a physicochemical standpoint, Meliantriol, Nimbinin, and the standard Chloroquine exhibit favorable properties and comply with Lipinski's rule, suggesting good oral bioavailability.

Beta-Sitosterol, Stigmasterol, and Myricetin show violations that could impact their drug-likeness, especially regarding membrane permeability and solubility. However, such deviations do not necessarily disqualify a compound as a potential drug but highlight the need for further in vivo and formulation studies.

3.2. Toxicity Studies: The results Toxicity study given in Table 3.

Table 3: Toxicity Studies

Graph 1 :Meliantriol

Graph 2:Myricetin

Graph 3:Nimbin

Graph 4: Nimbinin

Graph 5 :Stigmasterol

Graph 6 :Chloroquine

Discussion Based on Toxicity Study:

Among the compounds tested, Meliantriol and Nimbin show favorable toxicity profiles with high LD50 values, indicating lower toxicity. Nimbinin showed the lowest LD50 (55 mg/kg), suggesting higher toxicity. Most compounds were non-hepatotoxic and non-carcinogenic, with Myricetin, Nimbin, and Meliantriol being carcinogenic. Chloroquine, the standard, showed acceptable toxicity, making Meliantriol and Nimbin promising with balanced safety profiles for anti-malarial potential. The image presents toxicity studies of Neem phytoconstituents Most Neem compounds, including Nimbin, Nimbinin, and Stigmasterol, show low to moderate toxicity with inactive hepatotoxicity, making them potentially safe. Meliantriol and Myricitin show some toxic effects like carcinogenicity and nephrotoxicity. Compared to standard Chloroquine, some Neem constituents demonstrate favorable safety profiles and could be considered safer alternatives for antimalarial therapy.

3.3. ADME Study: The results ADME Study given in Table 4.

Table 4: ADME Study Result

Discussion Based on ADME Study:

Among the tested compounds, Meliantriol and Nimbin showed high GI absorption and good ADMET profiles. Beta-sitosterol had the highest binding affinity (-7.5), followed closely by Meliantriol (-7.4), indicating strong potential as anti-malarial agents. Standard drug Chloroquine also performed well but had more CYP enzyme inhibition, which may lead to drug interactions.

3.4. Binding Affinity of different Chemical Constituents.: The results Binding Affinity of different Chemical Constituents given in table 4.

Tabel No. 5: Binding Affinity of different Chemical Constituents.

LIGAND	STRUCTURE	BINDING AFFINITY
betaSitosterol		-7.5
Meliantriol		-7.4
Nimbinin		-7.7
Myricetin		-6.3
Nimbin		-7.4
Stigmasterol		-8.3
Chloroquine  (standard)		-1.5

Discussion Based on Binding Affinity of different Chemical Constituents:

The docking of the receptor 3WEC with chemical constituent of Neem (A. indica) has been done. The table shows the binding energy and inhibition constant of 14 compounds including the standard. In silico studies revealed that all the synthesized molecules show good binding affinity toward the target protein ranging from -8.3

Figure 10. 2D structure of stigmasterol with the receptor 3EWC

Figure 11. 2D Structure of chloroquine with the receptor 3EWC