In-silico was a term that was first used to describe artificial life by Christopher Langton, an American computer scientist, at the Los Almos National Laboratory in 1987 [1]. The term was used to characterize the experiments of biological nature in a simulated environment in a computer. The first proper reference of this term in literature was by a team of French scientists in the year in 1991[2]. The first referenced chapter in a chapter format was in 1990, in a book authored by Hans B. Sieburg. Originally, the term was meant to indicate only computer simulations, but now, is also used to refer to any calculations done by the computer regarding the simulations. The term was also used to indicate the simulation of certain biological systems, but now it also applies to any biological or chemical data that is simulated in a computer. With the advent of the field of genomics, particularly the progress and the completion of the Human Genome Project, and the rise of Bioinformatics as a field of interest, in-silico methods began to see widespread use in multiple fields [3].
In the field of medicinal chemistry, the advent of in-silico studies implied the shortening of the immense time required in the process of drug discover and drug design [4]. Traditionally, the process of drug discovery was tedious and time consuming with each molecule having to synthesized individually and then also screened for activity in-vitro or in-vivo, and then be taken into clinical trials by a sponsor. Even if this process was relatively straight forward, there were still chances of the drug being rejected in clinical trials, not due to a lack of activity, but due to toxicity or some other adverse effect. With the aid of computational tools, the process of drug discovery can be greatly reduced, since a large number of molecules can be screened virtually, without any need for synthesis [5]. Newer tools also have the ability to predict the various parameters of the molecule such as its physicochemical properties, clinically relevant parameters such as its bioavailability, clearance, plasma binding tendency, and even its toxicity parameters. These predictions are not absolute however, since they are all made using algorithms based on existing compounds and their chemical features. So it is entirely possible for a new molecule that has good activity and no toxicity to be completely missed by the software or for a molecule to be wrongly flagged as inactive [6]. However, with the increasing ability of the software, including the latest artificial intelligence powered algorithms and not to mention the growing size of chemical
libraries, the predictions made by each generation of the computational software is getting more accurate [7].
With almost 900 species and subspecies, the enormous genus Eucalyptus (Eucalyptus spp.) belongs to the Myrtaceae family. The second-largest genus after acacia is this tall, evergreen endemic to Australia and Tasmania [8]. It has been effectively introduced into 90 different nations since the 1850s, where it is currently one of the most significant and commonly planted general. In the past, Aboriginal people employed the eucalyptus plant for a variety of uses, including food and medicine. The oil is produced from leaves, fruits, buds, and bark, and because it has antibacterial, antiseptic, antioxidant, anti-inflammatory, and anticancer properties, it is used to treat respiratory conditions like the common cold and influenza as well as sinus congestion. The leaf, stem, and root of E. globulus are rich sources of phytochemicals, including flavonoids, alkaloids, tannins, and propanoids [9]. In order to separate the phytoconstituents from the plant's organs, several studies were carried out: Numerous volatile substances, including 1,8-cineole (eucalyptol), aromadendrene, -gurjunene, globulol, ß-pinene, pipertone, ß-myrcene and terpinen-4-ol, and alloaromadendrene, were discovered in the leaves and shoots of the eucalyptus plant. Eucalyptol is the primary and most significant substance. Asparagine, cysteine, glycine, glutamic acid, ornithine, and threonine were isolated from fruits [10]; borneol, caproic acid, citral, eudesmol, fenchone, p-menthane, myrcene, myrtenol, terpineol, verbinone, and asparagine, cysteine, glycine
Despite the fact that essential oil contains more than 18 different components, eucalyptol accounts for 79.85% of the chemical composition. The essential oil also revealed a high concentration of oxygenated monoterpenes, which differ across each species of Eucalyptus and may have different medicinal characteristics [11]. Seasons and geographic location, for example, have an impact on the composition pattern of essential oils, which has an impact on biological processes. Many nations, including China, India, South Africa, Portugal, Brazil, and Tasmania, use Essential oil extensively in food and beverage preparation, perfumery, cosmetics, aromatherapy, and phytotherapy products [12].
In-silico studies, despite having been introduced in the early 1980s, did not see full acceptance in the pharmaceutical industry until the early 2000s (Agarwal et.al). It was only after the successful use of computational tools in the discovery and design of various new drugs, that there was a widespread adoption of these computational tools [13].
In the field of drug discovery and design, computational tools are of main importance in the context of molecular screening (Rosales et.al). It aimed to reduce the time involved in the finding of a new lead molecule, and could also help in predicting, to a certain extent, the biological activity parameters of the compound and even its toxicity parameters. The quality of these predictions varies from one tool to another, since they use different algorithms and make different assumptions regarding molecular interactions [14].
One of the main tools that is used in screening the various pharmacokinetic properties of compounds, like its absorption, distribution, metabolism, excretion and toxicity, collectively called as ADMET properties, is an online tool called as ADMETLab, and more recently ADMETLab 2.0. This is in contrast to traditional methods, where the molecules ADMET properties would be screened only after verifying its activity on a receptor, which lead to the discovery of many compounds that were active in-vitro, but also highly toxic.
ADMETLab was a software that was developed by a team of Chinese scientists in 2018, (Xiong et.al) to be used as a web tool to quickly analyze the various properties of a molecule. Its predictions are based on experimental data and customized Quantitative Structure Property Relationship (QPSR) models [15].
This webserver tool can be used to calculate a total of 88 ADMET related properties including physicochemical properties, medicinal chemistry, and even toxicity parameters. It also had drawbacks, such as redundant compounds, or incomplete or unfinished relations/endpoints and in the earlier versions; the results were still up for interpretation. In the latest version ADMETLab 2.0, these shortcomings are overcome and it gives better results [16].
One of the properties that is of particular importance is the Lipinski’s Rule of Five, also known
as Pzifer’s Rule of Five. It is a guideline that evaluates drug likeness to determine a compound’s oral activity. It was first proposed by Christopher A. Lipinski in 1997 based on the common characteristic feature of most common orally active compounds, their lipophilicity. The five feature or rules that describe a molecule’s activity are as follows: No more than 5 hydrogen bond donors, not more than 10 hydrogen bond acceptors, a molecular weight of less than 500 Daltons, a partition coefficient that does not exceed 5. These features or factors are known to affect the molecule’s pharmacokinetic parameters. It is known however, that there are multiple exceptions to this rule, with many molecules obeying this rule, yet being orally inactive and others being active despite deviating from this Rule [17].
The most probable activity of the molecules was screened for using PASS Online, (Filimonov et.al) a tool that uses structure descriptors and mathematical relations to predict the activity of a molecule by relating the structural features with a pre-existing library of molecules whose activity is confirmed. The result is displayed in a tabular format, with the values indicating the probability of the molecule having that activity [18].
After the activity of the molecules was identified using PASS Online, the activity was confirmed by carrying out docking studies in Maestro a software provided by Schrodinger. They were docked with the human histamine H1 protein (Protein ID:3RZE) obtained from PDB, an receptor that has been identifies to play a key role in Eczema. Eucalyptus of the tribe Eucalypteae and genus Eucalyptus contains more than 700 species of tall trees and shrubs which is having ecological importance majorly found in Australia countries like Ethiopia, Malaysian, Brazil, Philippines, Indonesia, Tasmania and nearby island [19].
