ADMET and Druglikeness Calculations of Sarin, Soman, and Their Hypothetical Derivatives

Absorption, Distribution, Metabolism

Sarin and Soman were hypothetically introduced in new P-O formation with lactic acid. The other reaction was formation of P-N linkage between Sarin or Soman and different amino acids. Both reactions were through fluorine atom with hydroxyl group (P-O formation) and with amine (P-N) ( Figure 1). Sarin, Soman, and their hypothetical derivatives ( Figure 2) were subjected to MarvinSketch program and https://preadmet.bmdrc.kr website to calculate the above mentioned properties (Tables 1 to 5).

Results and Discussion
Different physicochemical characters were calculated by MarvinSketch software (Table 1), i.e. chemical formula, molecular weight, isoelectric point, logP, HLB, and PSA. All calculated compounds were with no isoelectric point. logP data were ranged from -0.58 to 3.02 (Consensus method) and from -0.37 to 3.31 (ChemAxon method). HLB by the three calculation methods were (6.32 -12.65), (4.38 -11.32), and (8.57-15.76) for ChemAxon, Davies, and Griffin methods respectively. The other physicochemical predicator (Polar Surface Area, PSA) was (36. 11 -122.74). ADME data as have been tabulated in Tables (2 & 3 Chemical biological response of any chemical to be a powerful drug needs many steps like ion channel and receptor tests that demand time, money, and prior preparative studies. These descriptive animal studies indicate the penetration of this target to Central Nerve System (CNS) but now these studies can be removed or reduced with the assistance of mathematical models such as ADMET.
One of ADMET calculations is Blood Brain Barrier (BBB) that determines the capability of a chemical to do its action by penetrating this barrier according to its physicochemical properties [14] such as lipophilic character, hydrogen bonding, …etc. toward CNS with easily mechanism and less required energy. CNS is a selective system between liver, intestine, blood, and brain actions of a specific chemical that can be transport by cell diffusion through the membrane, metabolize by enzyme, and pump to the blood by P-glycoprotein -ATP transfer mechanism. Candidate drug needs hydrophilic-lipophilic action to cross bloodmembrane boundary and this action can be performed by the assistance of two effective parameters: first, water tendency to form hydrogen bonding with polarized molecule and second, BBB homogeneity absence as a result of lipid bilayers presence.
Candidate drug can get optimum therapeutic ability by reaching maximum selectivity with the target tissue at the required concentration and to reach this goal ADMET calculations may help scientist to quantify this ability depending upon polar groups and molecular forces that attacked by water or bonded by albumin or α-acidic glycoprotein especially with CNS drug. Blood Brain Barrier (BBB), Buffer solubility character, logP, HLB, HBAs, and HBDs form a gate to understand other predictors which showed a different behavior sequence of Sarin, Soman, and both derivatives.
High presence of polar groups in a compound prevent or obstruct this compound from crossing this BBB and access CNS. BBB character showed that Soman and its derivatives were in high values than Sarin and its derivatives (Tables 2 & 3). Aspartic acid derivatives were with low BBB than glutamic acid (both amino acid are with dicarboxylic groups) and lactic acid derivatives. Glycine derivatives, i.e. less number of atoms and molecular weight amino acid, had less BBB than proline, valine, alanine, and phenylalanine but more than methionine. Methionine derivatives, i.e. sulphide containing amino acid, were higher than glycine, proline, valine, alanine, phenylalanine derivatives. Presence of phenyl ring increased BBB by comparison both phenylalanine and alanine derivatives because of phenyl ring steric effect in spite of HBAs, HBDs, and PSA data look alike when comparison with same parent compound. The impact of phenyl ring was resembling with logP data but in discrepancy with HLB data. So, alanine derivatives may show different diffusion and transfer abilities than phenylalanine derivatives.
The other arrangement of comparison may be explained depending on logP, HLB, HBA, HBD, PSA, and molecular formula or structure. So more number of atoms and phenyl ring presence affected compound crossing BBB to CNS. This might be belonging to the presence of more polar atoms which agreed with the numbers of hydrogen bond acceptors (HBAs) and donors (HBDs) beside PSA data. ADMET data (except BBB) showed that Sarin (or its derivatives) gave high response in number to Buffer solubility, HIA, MDCK, Plasma Protein Binding, Pure water sol., Skin permeability, SK logP, SK logD, SK logS, algae-at, Daphniaacute toxicity, Medakaacute toxicity, and minnow-acute toxicity. A molecule has a good solubility and easy transfer mechanism with less energy has to be more effect than others that may be compared so it toxicity increased.
Daphnia toxicity is related to drug solubility because Daphnia for example are water organism having high speed of growth. So, Daphnia are choosing as aquatic toxicological indicators [15]. Above notifications were with more impact on Sarin (or its derivatives) according to our calculated data than Soman (or its derivatives) (see Tables 2  & 3).
Cancer this deadly disease may be caused by the toxicity of many chemicals as a simple definition of Carcinogenicity. To avoid cost and long-time of rodent in vivo testing, in Silico is the right choice. With the negative predication of Carcino-Mouse or Rat, compound under test causes cancer or it is toxic causing cancer in body. Tables (2 & 3) indicate that all tested compounds in this study were toxic to cause cancer in mouse. Carcino-Rat negative results were only with methionine derivatives (GB-ME & GD-ME).      Cytochrome P450 catalyzes many drug metabolism besides controlling lipid, steroid, or cholesterol synthesis. CYP-2C19 and CYP-2C9 are epoxygenase of unsaturated fatty acid to the corresponding epoxide derivatives. CYP-2C19 inhibition was not found in GD-LA, GB-P, GD-P, GD-AL, GB-PA, and GD-PA but this inhibitor character was found with presence of other 14 calculated compounds (Tables 2 & 3). For CYP-2C9 inhibition, only GB-P and GD-P showed noninhibition character between all 20 calculated compounds. All tested compounds showed noninhibition character with CYP-2D6 and Pgp (Tables 2, 3). As another remarkable note, only GD showed weakly substrate action of two CYP2D6 (Tables 2 & 3).
Positive results toward TA100-10RLI were with GB-PA and GB-ME. Negative results toward TA100-NA were found in all tested compounds except GB as mentioned above (Tables 2 & 3). Positive TA1535-10RLI results characters such as: all 20 tested compounds were with non-inhibition character of Pgp and CYP-2D6; substrate character with CYP-3A4, negative values to skin permeability, negative to Carcino-Mouse, low risk to hERG inhibition. Other calculated predictors were varied in response between all calculated compounds.