INTRODUCTION

Pharmaceutical consultation is an integral part of the retail sale of drugs to the public [1]. Pharmaceutical services are a critical component of primary health care [2]. The liposomal formulations are targeted to deliver the important drug combinations to the body [3]. Quality of life is the general well-being of individuals and communities [4]. In search of new drugs, (R)-Sarkomycin (1) was isolated for the first time in the year 1953 by Umezawa from the Streptomyces erythrochromogenes, a soil microorganism [5]. Its structure was established in 1955 as 2-methylene-3-oxocyclopentanecarboxylic acid [6]. This cyclopentenone had swiftly captured relevance for its antibiotic as well as strong anti-tumor activity [7-9]. It is well known and explored for its strong inhibitory effect on several human tumors and carcinoma cell lines [10-13]. Since it is chemically unstable [14], various stable sarkomycin esters (2) and the so-called cyclosarkomycin, which is a cyclic lactone (3), have been developed [14-19]. (Figure 1)

Figure 1: Some sarkomycin structures.

Mere synthesis of a chemical compound does not automatically make it an appealing candidate for drug development but certain attributes differentiate a drug from a synthesized compound. Several pharmacokinetic and pharmacodynamic properties are involved in deciding the drug candidature of a synthesized compound. In this regards, Computer-aided drug designing (CADD) or in silico notion is considered as it assists and expedites hit identification, facilitate assortment of hit-to-lead, sieve out a huge library of compounds into mini clusters of predicted bio-active compounds which in turn optimizes the ADME (absorption, distribution, metabolism, and excretion) profile leading avoiding issues related to safety and failure. Thus, assessment of ADME traits at the premature stages of drug discovery not only saves time but also money.

Phosphoinositide 3-kinases (PI3Ks), a member of lipid kinases family is interpreted to perform a pivotal role in nurturing survival and cellular growth of various sorts of cancer. In cancer cells, these PI3Ks are the essential regulators of apoptosis. The phosphorylation of phosphatidylinositol of the inositol ring at the 3′-OH position is catalyzed by the PI3Ks, thus obtaining secondary messenger lipid named phosphatidylinositol-3,4,5-trisphosphate. This trisphosphate triggers the serine/threonine kinase AKT which in return adjusts various signaling pathways thus, regulating apoptosis, survival of cells, proliferation of cells, and motility [20, 21]. PI3KCA gene encoding p110a is the most usually mutated gene in human tumors, thus instilling to be the most salient focus for tumor therapy [22-24]. Molecular docking studies were conducted to get an additional impression of the binding style of the synthesized compounds with PI3K p110α domain (PDB ID: 2RD0).

Our major intention is to evaluate our previously reported sarkomycin ester derivatives [25] for their pharmacokinetics, drug-likeness, and docking scores based on various physico-chemical parameters via in-silico computational tools.

EXPERIMENTAL

Ligand identification

The explored sarkomycin ester derivatives have been synthesized and characterized previously [25]. They are utilized as drug molecules for the present study.

Figure 1: Structures of the selected compounds 1-7 for in-silico studies.

In-silico predictive studies

MolSoft renders several software and service tools for grasping the spatial framework of drug candidates, their biological substrates, facilitate understanding their interactions with each other

at the atomic level by applying a set of rules and algorithms to peculiar biomedical issues. Drug-likeness is one of the qualitative ideas employed for predicting drug-like property. It is designated as an intricate balance of diverse molecular and structural features which plays a pivotal task in establishing whether the specific drug candidate is alike the known drugs or not.

The targeted molecules 1-7 were appraised for predicting the drug-likeness based on 5 separate filters namely Egan [26], Ghose [27], Muegge [28], Veber [29] and Lipinski [30] rules accompanying bioavailability and drug-likeness scores using the Molsoft software and SwissADME program. Besides, SwissADME was also utilized to predict the physico-chemical, pharmacokinetic, and ADME properties.

In-silico molecular docking approach was used to do drug designing based on structure designing to obtain the more powerful inhibitors of PI3K from the synthesized drug candidates. 3D atomic coordinates of the ligands were drawn utilizing the ACD/Labs-Chemsketch program. The PDB ID, 2RD0 was considered to be the most relevant pure 3D crystal structure of p110α subunit of PI3K which is obtained from the protein data bank (PDB) (Source: www.rcsb.org/pdb/). Minimization of the energy of the molecules was done using the Dundee PRODRG2 server [31]. Autodock4 from “Auto-Dock Tools (ADT, 1.5.6)” which is a graphical user interface program was utilized for the docking studies [32]. The Lamarckian Genetic Algorithm (LGA) was chosen over the Monte Carlo method (present in previous versions of AutoDock). Kollman united-atom charges were allocated along with the incorporation of only polar hydrogens to the protein receptor. Gasteiger charge was allotted to and torsions were set. The grids were constructed and tuned in X, Y, Z-axis so that the whole active site of the targeted protein is covered. The docking statements were positioned to the software's default values and a standard etiquette was preserved and escorted throughout the docking evaluations. The results were annotated based on .pdf file created by the software by rating the various ligands concerning the foreseen binding energy. A cluster investigation that is based on values of root means square deviation (RMSD) was administered. The cluster acquiring the lowest energy value was reviewed as the most authentic solution. 3D visualization of the ligand-protein association was done by employing UCSF Chimera 1.11.2 and mcule, a web interface which utilizes the WebGL/Javascript based molecule viewer of GLmol.

RESULT AND DISCUSSION

The research work outlined here was conducted with the purpose to forecast the physicochemical attributes, drug-likeness, pharmacokinetic/ADME properties, and molecular docking simulations of our previously reported sarkomycin ester derivatives 1-7 using in-silico tools.

Several physicochemical traits like no. of rotatable bonds, count of specific atom types, molecular refractivity, lipophilicity, and water solubility were listed. TPSA (Topological Polar Surface Area) is a basic physiochemical variable gauged for assessing drug transport features. Calculations were undertaken to predict % Absorption for all the targeted compounds by the reported formula (%ABS = 109-(0.345 X TPSA)) [33]. All the tested compounds demonstrated excellent in-silico % absorption with the highest being 94.04%. These physicochemical properties are given in Table 1.

Table 1: Physicochemical properties of the selected compounds 1-7.

^aThe ratio of sp³ hybridized carbons over the total carbon count of the molecule; ^bnumber of hydrogen bond acceptors; ^cnumber of hydrogen bond donors; ^d lipophilicity; ^eMolar refractivity, ^fWater solubility (SILICOS-IT [S=Soluble]); ^gtopological polar surface area (Ų).

Forecast for the pharmacokinetic/ADME properties of the screened compounds 1-7 is given in Table 2. All of the tested compounds displayed high gastrointestinal (GI) absorption and are P-gp (p-glycoprotein) non-inhibitors. All the tested molecules were able to pass the blood-brain barrier (BBB) except compound 1. The predictions for the passive BBB permeation, HIA (human gastrointestinal absorption), and P-gp substrates are displayed together in the BOILED-Egg diagram as shown in Figure 2. None of the tested compounds inhibit the Cytochrome P450 isomers except compound 7 which inhibits CYP1A2 and CYP2C19. The compounds emerged to be low on skin permeability.

Table 2. Pharmacokinetic/ADME properties of the selected compounds 1-7.

^aGastro Intestinal absorption, ^bBlood Brain Barrier permeant, ^cP-glycoprotein substrate, ^d CYP1A2: Cytochrome P450 family 1 subfamily A member 2 (PDB:2HI4), ^e CYP2C19: Cytochrome P450 family 2 subfamily C member 19 (PDB:4GQS), ^fCYP2C9: Cytochrome P450 family 2 subfamily C member 9 (PDB:1OG2), ^g CYP2D6: Cytochrome P450 family 2 subfamily D member 6 (PDB:5TFT), ^h CYP3A4: Cytochrome P450 family 3 subfamily A member 4 (PDB:4K9T), ⁱSkin permeation in cm/s.

Figure 2: BOILED-Egg diagram of the selected compounds 1-7.

Drug likeness is examined as an integral element that provides the base for the molecules to be a powerful drug aspirant. Various rules viz. Lipinski, Ghose, Veber, Egan, and Muegge were considered to gauge drug-likeness of the candidate compounds (1-7) to find out whether they can be bioactive drug candidates according to some acute criterion like molecular weight, LogP, number of hydrogen bond acceptors and donors. The number of violations to the above-disclosed rules along with bioavailability and drug-likeness scores is given in Table 3. None of the compounds 1-7 violated any rule except for compounds 2,3 and 4 which are discarded with one violation according to Muegge rule. All the screened compounds disclosed bioavailability score around 0.55 and exhibited moderate to good drug-likeness scores ranged from -1.13 to

-0.30. The bioavailability radar of the compounds 1-7 is displayed in figure 3. The prime in-silico conformation was corroborated utilizing molecular docking screenings of the compounds 1-7 on p110α subunit of PI3K (PDB ID: 2RD0) as the target protein. To entertain this objective AutoDock 4.0, a Lamarckian genetic algorithm docking program was involved. Docking of the ligand 6 is demonstrated in Figure 4 and the docking scores are reported in Table 3. Docking scores range from -5.1 to -6.2 where the maximum docking score was obtained by compound 6 i.e -6.2.

Table 3. Drug likeness predictions and docking scores of the selected compounds 1-7.

Figure 3: Bioavailability Radar of the tested compounds (1-7) The pink area represents the optimal range for each property (lipophilicity: XLOGP3 between − 0.7 and + 5.0, size: MW between 150 and 500 g/mol, polarity: TPSA between 20 and 130 (Ų)., solubility: log S not higher than 6, saturation: the fraction of carbons in the sp³ hybridization not less than 0.25, and flexibility: no more than 9 rotatable bonds. In this example, the compound is predicted not orally bioavailable, because too flexible and too polar.

Figure 4: Docking of compound 6 into the active site of PI3K (PDB ID: 2RD0).