Physicochemical and Pharmacokinetic Analysis of Anacardic Acid Derivatives.

Anacardic acid (AA) and its derivatives are well-known for their therapeutic applications ranging from antitumor, antibacterial, antioxidant, anticancer, and so forth. However, their poor pharmacokinetic and safety properties create significant hurdles in the formulation of the final drug molecule. As a part of our endeavor to enhance the potential and exploration of the anticancer activities, a detailed study on the properties of selected AA derivatives was performed in this work. A comprehensive analysis of the drug-like properties of 100 naturally occurring AA derivatives was performed, and the results were compared with certain marketed anticancer drugs. The work focused on the understanding of the interplay among eight physicochemical properties. The relationships between the physicochemical properties, absorption, distribution, metabolism, and excretion attributes, and the in silico toxicity profile for the set of AA derivatives were established. The ligand efficacy of the finally scrutinized 17 AA derivatives on the basis of pharmacokinetic properties and toxicity parameters was further subjected to dock against the potential anticancer target cyclin-dependent kinase 2 (PDB ID: 1W98). In the docked complex, the ligand molecules (AA derivatives) selectively bind with the target residues, and a high binding affinity of the ligand molecules was ensured by the full fitness score using the SwissDock Web server. The BOILED-Egg model shows that out of 17 scrutinized molecules, 3 molecules exhibit gastrointestinal absorption capability and 14 molecules exhibit permeability through the blood-brain barrier penetration. The analysis can also provide some useful insights to chemists to modify the existing natural scaffolds in designing new anacardic anticancer drugs. The increased probability of success may lead to the identification of drug-like candidates with favorable safety profiles after further clinical evaluation.

Introduction

Cancer is one of the major concerns in the medical field as it causes increasing number of deaths day by day.[1] In the past decades, there have been numerous anticancer drugs synthesized. However, tumor recurrence and the adverse effects of the drugs because of toxic intolerance are the key problems in the treatment of cancer. Thus, there is an urgent and desperate need of finding anticancerous drug molecules with negligible side effects and high efficacy. The naturally occurring products have received special attention toward the preparation of novel cancer preventives and therapeutic agents.[2−4] Particularly, more natural products were identified with potential anticancer activity.[5,6] In the present work, virtual screening and suitability of anacardic acid (AA) derivatives that inhibit the anticancer target cyclin-dependent kinase 2 (PDB ID: 1W98) were studied. AA originates from the nutshells of Anacardium occidentale (cashew)[7] and found to act as an antitumor, antibiotic, antioxidant, and gastroprotective agent.[8] It is a combination of numerous closely related organic compounds, each consisting of salicylic acid substituted with an alkyl chain. In addition, it is used as a synthon for the production of a variety of biologically active compounds and a capping agent for the development of nanomaterials.[9] For the treatment of the most serious pathophysiological disorders like cancer, oxidative damage, inflammation, and obesity, AA and its derivatives have been strongly supported as therapeutic agents.[7] Recent studies show that AA derivatives exert their action in treating ovarian cancer, prostate cancer, breast carcinoma, and lung carcinoma through various mechanisms.[10−13] AA acts as a potential inhibitor of histone acetyltransferase activity and sensitizes tumor cells to ionizing radiation.[13] With the anticipation that AA can be eventually applied in the medicinal industry after successful preclinical trials, Xiu et al. have investigated the role of AA in cancer.[14]

However, during the clearance of the molecule under clinical trials, wastage of the drugs and human efforts is unavoidable. In this regard, computation methods were developed to predict the human pharmacokinetic properties. With different levels of complexity for the screening of large data set compounds, a variety of useful in silico ADME (absorption, distribution, metabolism, excretion) models have been established. Nowadays, the in silico tools are faster, simpler, and more cost-effective than transitional investigational procedures.[15] At present, because of the toxicity or lag of optimal pharmacokinetics properties, large attrition rates of preclinical and clinical candidates were observed in pharmaceutical industry. It is possible for medicinal chemists to control the pharmacokinetic and toxicity properties of a molecule through the structural modifications.[16] Hence, in the present work, physicochemical, ADME attributes and in silico toxicity[17] of 100 AA derivatives were evaluated, and the results were compared with well-known anticancer drugs. The study would definitely help to identify the suitable oral AA-based drug molecules based on the abovementioned properties and their binding activities with the target cyclin-dependent kinase 2. Figure shows the schematic diagram of the work done in which the selection of compounds and screening methods were illustrated.

Figure 1

Flowchart illustrating the selection of AA derivatives, screening process, and their interactions with the target (PDB ID: 1W98).

Materials and Methods

One of the main causes of drug development termination is poor pharmacokinetic properties. Less or no toxicity, good oral bioavailability, and optimum values of physicochemical properties are the key parameters for the discovery of anticancer drugs. In the present work, 100 AA derivatives were selected from the previous literatures[8−14,18,19] for the evaluation of their pharmacokinetic properties. Among these, the derivatives AA1–AA25 were obtained by alkyl chain modifications, whereas AA26–AA50 were obtained by modifying both the alkyl chain and functional groups (Figures S1 and S2). AA51–AA100 derivatives were obtained by introducing functional groups like hydroxyl (−OH) and carboxylic (−COOH) groups (Figures S3 and S4). A complete list of AA derivatives used in this study is depicted in Table S1, whereas the name and structures of the finally scrutinized molecules are depicted in Table and the physicochemical properties of these derivatives are listed in Tables –6.

Table 1

Names and Structures of the Screened Out Derivatives

Table 2

Important Computed Physicochemical Properties of the Screened Out AA Derivativesa

AA derivatives	MW	nRot	HBA	HBD	TPSA	M log P	Ali log S	MR
AA11	328.4	9	4	2	66.76	3.66	–6.79	95.4
AA12	302.32	7	5	2	75.99	2.41	–5.19	82.51
AA16	264.36	9	3	2	57.53	3.55	–6.62	78.85
AA20	274.27	4	5	4	97.99	1.91	–4.88	73.73
AA21	242.27	4	3	2	57.53	3.06	–4.76	69.68
AA22	290.27	4	6	5	118.22	1.37	–4.94	75.75
AA23	220.26	3	3	2	57.53	2.39	–5.01	62.31
AA24	238.28	7	4	3	77.76	1.93	–4.32	65.59
AA25	264.32	9	4	2	74.6	2.37	–5.12	74.24
AA33	266.33	9	4	2	66.76	2.45	–4.8	74.71
AA34	294.39	11	4	2	66.76	2.95	–5.92	84.33
AA72	426.59	5	3	1	34.15	4.21	–5.3	127.19
AA75	388.51	3	4	0	52.83	4.4	–5.08	109.46
AA95	262.34	1	3	0	35.53	3.16	–5.13	75.91
AA96	260.33	1	3	0	35.53	3.08	–4.83	76.23
AA97	276.33	1	4	0	48.06	2.31	–3.69	74.88
AA98	276.33	1	4	0	52.6	2.23	–3.54	76.11

Optimal range: molecular weight (MW) ≤ 600, lipophilicity log or Moriguchi octane–water partition coefficient (M log P) ≤ 5, aqueous solubility descriptor (Ali log S) ≤ 0, hydrogen-bonded acceptor (HBA) ≤ 10, hydrogen-bonded donor (HBD) ≤ 5, topological polar surface area (TPSA) ≤ 150 Å2, number of rotatable bonds (nRot) ≤ 10, and molar refractivity (MR) ≤ 155.

Table 6

Computed Safety End Points for AA Derivatives

Der.	CYP2D6 inhibitor	CYP3A4 inhibitor	total clearance	renal OCT2 substrate	AMES toxicity	hERG I inhibitor	oral rat acute toxicity (LD50)	oral rat chronic toxicity (LOAEL)	hepatotoxicity	skin sensitization
AA11	no	no	0.658	no	no	no	2.901	2.237	no	no
AA12	no	no	0.544	no	no	no	2.919	2.074	no	no
AA16	no	no	1.312	no	no	no	2.53	2.759	no	no
AA20	no	no	0.505	no	no	no	2.353	1.774	no	no
AA21	no	no	0.656	no	no	no	2.673	2.888	no	no
AA22	no	no	0.333	no	no	no	2.317	3.039	no	no
AA23	no	no	0.551	no	no	no	2.432	2.207	no	no
AA24	no	no	0.672	no	no	no	2.324	2.445	no	no
AA25	no	no	1.265	no	no	no	2.554	2.247	no	no
AA33	no	no	0.828	no	no	1.987	2.141	no	no	no
AA34	no	no	1.488	no	no	no	2.097	2.192	no	no
AA72	yes	no	0.725	yes	no	no	2.054	1.463	yes	no
AA75	no	no	0.67	no	no	no	1.829	2.357	yes	no
AA95	no	no	1.351	no	no	no	2.012	2.044	no	no
AA96	no	no	0.619	no	no	no	2.228	1.871	no	no
AA97	no	no	1.174	no	no	no	2.15	1.701	no	no
AA98	no	no	0.645	no	no	no	2.231	1.756	no	no

The ADME-related physicochemical properties of 100 AA derivatives including the marketed anticancer drugs were predicted by the Swiss ADME online Web server.[20] Two-dimensional structures were drawn with the help of Cambridge software, that is, ChemDraw Pro, version 12.0. A simplified molecular input line entry system of all AA derivatives was generated with the help of an online tool, that is, Swiss ADME. The physicochemical properties, lipophilicity and solubility, of the derivatives were considered for the analysis.[21]

In addition, the BOILED-Egg model of the molecules was predicted to reveal the capability of gastrointestinal (GI) absorption and permeability of the blood–brain penetration barrier.[22] The cutoff values of the physicochemical properties were set by certain rules, that is, Lipinski’s rule of five (ROF), bioavailability score, Ghose’s, and Veber’s rules.[23−25] For drug-likeliness, the molecular parameters such as MW (molecular weight), HBD (hydrogen bond donor), HBA (hydrogen bond acceptor), log P (lipophilicity log), log S (aqueous solubility), TPSA (topological polar surface area), MW, nRot (number of rotatable bonds), and MR (molar refractivity) were evaluated. For predicting the aforesaid properties, Swiss vector machine algorithm[26] is used.

There are two more important parameters, that is, plasma proteins (Fu) and volume of distribution (VDss), which help in the determination of the distribution of derivatives. The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of all AA derivatives were analyzed through the online available tool pkSCM (http://bleoberis.bioc.cam.ac.uk/pkcsm/).[27]

In silico data corresponding to the major human cytochrome P450 (CYP) isoforms involved in drug metabolism, such as CYP2C9, CYP2D6, and CYP3A4, were also generated. To determine the excretion routes of AA derivatives, the total clearance and renal OCT2 substrate were predicted quantitatively. In drug attrition, the safety profile of the derivatives comes under major factors.[28] As per pharmacokinetic analysis, we assess some of the major toxicity end points of all derivatives. Moreover, certain safety parameters such as LD50, hepatotoxicity, skin sensitization, cellular toxicity, and hERG liability (inhibition of dofetilide binding) were also evaluated.

After the pharmacokinetic evaluation of all the AA derivatives, 17 AA molecules were chosen based on the cutoff values set by Lipinski’s ROF, bioavailability score, Ghose’s, and Veber’s rules. Finally, the chosen molecules were docked with cyclin-dependent kinase 2 to evaluate their binding affinities and active binding residues. For this process, the SwissDock docking server (http://www.swissdock.ch) is used. The preparation of the target for docking was done by UCSF chimera. Various docking parameters such as full fitness (FF, kcal/mol), total number of clusters, cluster rank, Gibbs free energy (ΔG), hydrogen bonding, deltaGligsolvpol, and so forth were analyzed.

Results and Discussion

Structural and Physicochemical Properties

The main emphasis has been on defining the physicochemical property rules for the derivatives to reduce attrition and increase the likelihood of them at various stages of anticancer drug development. Based on the gold standards and earlier published research on various oral bioavailability rules, 100 AA derivatives were evaluated together with 10 marketed anticancer drugs against the key parameters of physicochemical properties, that is, lipophilicity, log P, MW, TPSA, HBD, HBA, nRot, and aqueous solubility (log S).[23−25] All physicochemical properties are depicted in Table S1.

The MW range of AA1–AA25 derivatives varies from 220 to 376, AA26–AA50 derivatives varies from 264 to 518, and AA51–AA80 derivatives varies from 388 to 598, whereas the MW range for AA81–AA100 is much broader and varies from 260 to 597. The M log P value of AA1–AA25 derivatives varies from 1.4 to 6, AA26–AA50 derivatives varies from 2.4 to 5.3, and AA51–AA80 derivatives varies from 3.1 to 7.5, whereas AA81–AA100 derivatives show values varying from 2.2 to 6.4. There were no significant differences in the log P values among the four groups, although the alkyl-modified derivative set AA1–AA25 had a lower span of log P values; meanwhile, each set shows predicted values of some derivatives which are higher than the cutoff value set by the Lipinski rule, RO5 ≤ 4.15. This reflects the fact that molecules with out-of-range values of log P are poorly soluble in fats, oil, lipids, and nonpolar solvents. The predicted polarity (TPSA) ranges from 56 to 157 Å2 for AA1–AA25 derivatives, 20 to 120 Å2 for AA26–AA50 derivatives, 14 to 77 Å2 for AA51–AA80 derivatives, and 35 to 115 Å2 for AA81–AA100 derivatives. According to the cutoff values set for TPSA (≤140 Å2) by Veber et al.,[24] 95% of the AA derivatives reported in this study are likely to have high probability of oral bioavailability. A minimal number of HBDs ranging from 0 to 5 was found in the AA derivatives.

All the 100 derivatives of AA in this study satisfy the rule of Lipinski, HBA < 10. One important parameter for the oral bioavailability is “MR” described by the Ghose filter.[25] According to Lipinski’s rule, the value of MR should lie between 40 and 130[23] for drug-likeness. The derivatives AA1–AA50 show that all the predicted values fall within the range as described by the Ghose filter rule, whereas the derivative set AA50–AA100 shows 30% violation.

In order to accurately access drug-likeness, Lipinski’s RO5, Ghose filter, Vebers’s, Egan’s, and Muegge’s rules are compared with certain marketed drugs. The detailed data set of all AA derivatives with the violation of rules is shown in Figure .

Figure 2

Results of AA derivatives against different violations.

The graph shown in Figure a reveals that most of the derivatives show one violation of each rule, and few structures viz. AA11, AA12, AA16, and AA20–AA25 did not show any violation. Hence, they can be provisionally approved for preclinical trials. The structures that show three violations of the Muegge and Ghose filters must be excluded from further preclinical processes. In Figure b, it is clear that the structures AA33 and AA34 did not show any violation, and most of the derivatives violate more than one rule for each filter. The Lipinski RO5 rule graph as shown in Figure c,d suggests that the compounds AA72, AA75, and AA95–AA98 can be subjected for further preclinical trials. Out of 100 investigated compounds, only 17 compounds fall within the allowed range of physicochemical properties and satisfy all the key parameters of physicochemical properties.

Comparison of Physicochemical Properties of AA Derivatives with Marketed Anticancer Drugs

Our analysis shows that the optimal property ranges (covering 100 AA derivatives) are 200 < MW ≤ 600, 1 < M log P ≤ 5, −6 ≤ Ali log S ≤ 0, 2 ≤ HBA ≤ 10, 1 ≤ HBD ≤ 5, 50 ≤ TPSA ≤ 150 Å2, 0 ≤ nRot ≤ 10, and 40 ≤ MR ≤ 155. They may be very helpful in the prospective design of anticancer molecules. According to the RO5 rule, the key parameters of physicochemical properties showed better results as compared to the marketed anticancer drugs. The poor biopharmaceutical properties of the molecules, viz. poor aqueous solubility and slow dissolution rate resulted in poor oral bioavailability. In general, poor aqueous solubility is connected to high lipophilicity, and hydrophilic derivatives are connected to poor permeability, which means low absorption. Hence, ionization constants, solubility, and lipophilicity measurements are helpful in the high-throughput drug discovery paradigm. The relationship between lipophilicity and pharmacokinetic properties is explained in in silico studies.[29,30] In the present work, quantitative analysis shows that the finally scrutinized AA derivatives were predicted to have a higher optimum range of 13.18 for M log P, whereas the predicted values of M log P for the marketed anticancer drugs are in the range 2.12–5.6. Tables S1 and S2 depict the physicochemical and ADMET parameters of all the selected molecules.

BOILED-Egg for Prediction of GI Absorption and Brain Penetration

In the drug development process, GI absorption and blood–brain barrier (BBB) penetration play an important role. The BOILED-Egg model helps in the computation of polarity and lipophilicity of derivatives as it gives datasets with accuracy, speed, and clear graphical outputs.[22] This model helps in drug development by filtering the chemical libraries.

In the BOILED-Egg model, the high probability of passive absorption of the GI tract is represented by the white region, whereas the yellow region (yolk) represents the high probability of the BBB penetration. In addition, the blue color indicator of the molecule shows that the molecule is actively effluxed by P-glycoprotein, represented as (PGP+), whereas the red color indicator shows the nonsubstrate of P-gp, represented as (PGP–).

The analysis predicts that the molecules AA1–AA25 (Figure S5a) show high GI absorption, with nearly 40% of the molecules also exhibiting BBB penetration, and all of them are predicted as the nonsubstrates of P-gp (PGP–); 90% of the molecules AA26–AA50 (Figure S5b) show GI absorption, with nearly 40% of them also exhibiting BBB penetration. The BOILED-Egg plot of all the AA derivatives is presented in Figure S5. The predicted results of the derivatives AA51–AA80 (Figure S5c) consisting of benzamide and benzyl amine are shown. All the benzamide-based AA molecules and most of the benzyl amine-substituted AA derivatives show low GI absorption, and they do not exhibit significant BBB permeability. Most of the derivatives in the molecule set AA5–AA80 are P-gp substrates. The prediction of the BOILED-Egg results of the molecule set AA81–AA100 (Figure S5d), which includes thiazole, imidazole, oxazole, sildenafil, dihydropyridine, and macrolide AA derivatives, shows low GI absorption, and they do not exhibit BBB permeation. The BOILED-Egg model prediction of GI absorption and BBB permeation of the finally scrutinized 17 AA derivatives is presented in Figure .

Figure 3

BOILED-Egg model of AA derivatives.

Prediction of ADMET and Related Properties

For the development of a drug, the derivatives should have good ADMET profile. With the help of significant advancement in high-throughput in vitro ADME, computational scientists were able to predict the potential liabilities, that is, susceptibility to efflux transporters, low permeability, and so forth, of derivatives, which are associated with new potential derivatives. For the assessment of Caco2 cell permeability, P-gp efflux liability, and human intestinal absorption of anacardic derivatives, we have evaluated the ADME properties (in silico profiling).[32] On the basis of the ADME model, we can evaluate the permeability of a molecule, whether it is low or high. The permeability, that is, log Papp (10–6 cm/s) rate, is considered to be high if log Papp > 0.9 and considered to be low if log Papp < 0.9. The Caco2 cell permeability of AA derivatives is mentioned in Table S2.

Of the AA derivatives, 80% show high log Papp values. Similar results were also analyzed in the assessment of P-gp efflux liabilities of AA derivatives. The assessment of P-gp efflux liabilities was done with the help of preADMET’s [https://preadmet.bmdrc.kr/] P-gp substrate model. Only 25% of anacardic derivatives were found as P-gp efflux substrates. The predicted ADMET parameters of AA derivatives are listed in Table S2. For the achievement of an optimal clinical drug/derivative, a derivative should have high log Papp and low P-gp efflux liability.

In addition, we have calculated the total human intestinal absorption percentage (%). For the GI tract dissolution and stability, a systematic oral dosage should have pH 1–2 in fasted state and 3–7 in fed state in stomach, including a neutral environment of small intestine, that is, pH 4.4–6.6.[32] The human intestinal absorption percentage (%) of the AA derivatives was analyzed with the help of an online server tool, pkCSM.[27] Except two AA molecules (AA99 and AA100), the rest of the other AA derivatives shows good human intestinal absorption. The predicted human intestinal absorption percentage (%) values for all the AA molecules are listed in Table S2.

In plasma, almost all drugs exist in equilibrium between the bound and unbound states with serum proteins at different affinities. Only an unbound drug can show interactions with anticipated molecular targets.[22] Therefore, the efficiency of a drug is affected by the binding efficacy of the drug with whole blood proteins. With the help of the predictive model of pkCSM, we have evaluated the fraction unbound (Fu) and steady-state volume of distribution (VDss) of 100 AA derivatives. VDss is a key parameter, which helps in suggestion for the total dose of a drug. The predicted values of Fu and VDss of all AA derivatives are listed in Table S2.

For drug designing and screening of new chemical drugs, pharmaceutical industries use in silico studies for early prediction.[33] For evaluating AA derivatives as anticancer drugs, we have evaluated the toxicity end points with the help of the pkCSM tool. The toxicity end points consist of the inhibition of cytochrome P450 (CYPs) monooxygenase enzymes.[34] AMES toxicity, LD50 (lethal rat acute toxicity), hepatotoxicity, skin sensitization, and inhibition of hERG potassium ion channel effects are determined for the evaluation of drug–drug interactions (DDIs).[35] The toxicity data sets of all AA derivatives are listed in Table S3. With the help of pkCSM, inhibitions of CYP2D6 and CYP3A4 were qualitatively predicted and the results are depicted in Table S3. Out of 100 AA derivatives, 80% AA derivatives were evaluated as noninhibitors of CYP3A4, and only two AA derivatives show inhibition against CYP2D6. After the evaluation of these parameters, drug excretion and drug metabolism were evaluated. With the prior knowledge of metabolic pathways of drugs, one can easily predict DDIs, pharmacokinetics, and toxicities.[36] Here, our primary concern is the inhibition of CYP3A4, which helps in finding out the correlation with increasing MW and log P.[37] Inhibition of CYP3A4 is related to issues with DDIs and clearance. The total clearance of 100 AA derivatives was measured, which is a combination of hepatic and renal clearance, and is listed in Table S3.

The major evaluation that emerged from these data was that the derivatives showing clog P < 3 and TPSA > 75 Å2 were 2.5 times more nontoxic, whereas derivatives showing high lipophilicity (clog P > 3) and low polar surface area (TPSA < 75 Å2) are considered to be highly toxic in short-term animal studies. Hence, we have evaluated the lipophilicity of AA derivatives. Lipophilicity with small polar functionalities has high chances of becoming toxic. From the evaluation, it is revealed that toxicity occurs when log P > 3; on the other hand, the value of TPSA shows very little or no influence on the drug toxicity. Hence, it is clear that the early prediction of log P and TPSA is very helpful in the drug designing and development process in order to avoid wastage of chemicals.

Establishment of meaningful correlations between the physicochemical properties and ADMET properties of AA derivatives might be useful for future anticancer drug discovery. Based on our analysis, the results of AA derivatives show good and accepted prediction of physicochemical properties in comparison to Lipinski’s RO5, Ghose filter, Vebers’s, Egan’s, and Muegge’s rules for oral bioavailable drugs and in comparison with the physicochemical properties of marketed anticancer drugs. The correlation of the results of the accepted physicochemical properties with the predicted ADMET results shows that only seven AA derivatives, AA11, AA22, AA24, AA25, AA95, AA97, and AA98, fall within the accepted range of the parameters of ADMET. However, the docking studies of the finally scrutinized 17 derivatives were investigated, as they acquire adequate pharmacokinetic properties for oral bioavailability.

Molecular Docking Studies

Molecular docking studies were analyzed for exploring the interaction mechanism between the receptor sites and inhibitors.[38] In the field of drug discovery, the prediction of interactions between molecules and their targets has a great importance. One can easily find out the mechanisms of selectivity by the docking of molecules with protein targets.[39−41] In this work, the activities of anacardic derivatives were screened out against the target cyclin-dependent kinase 2 (PDB ID: 1w98). Cyclin-dependent kinase 2, also known as cell-division protein kinase 2, is an enzyme usually found in humans. It is encoded as CDK2 gene. It is necessary for cell cycle progression and mostly overexpressed in cancer cells. By using the SwissDock Web server (http://www.swissdock.ch), molecular docking of all the filtered 17 AA derivatives (AA11, AA12, AA16, AA20–AA25, AA33, AA34, AA72, AA75, AA95, AA96, AA97, and AA98) was performed against human cyclins or cell division protein kinase 2 (PDB ID: 1w98).[42] For the cell cycle, cyclins are essential proteins. Perturbation of the cyclin function can cause cancer formation. The structural factors and atomic coordinates of the protein cyclin were deposited.[43] The results pointed out that the filtered AA derivatives form conventional bonds with the receptor sites. Different parameters like FF (kcal/mol), ΔG (Gibb’s free energy), ligand solvation energy, hydrogen bonding (interactions), and so forth of docking were analyzed. On the basis of FF and cluster formation, all suitable binding modes were analyzed.[44] All the output clusters were ranked in accordance with hydrogen bonding (interactions) and the FF score. A cluster rank “0” is considered to be the best FF score. A molecule is said to have more favorable binding modes with a better fit if the FF score shows greater negative value.[45] The docked conformations having the lowest binding energy were selected for discussion.[46] However, for the most favorable interaction, if a molecule has a small docking score, then it is said to have a higher binding affinity of ligands for a particular receptor.[47] The FF score, binding affinity, and active residues of docked conformations are depicted in Table . Out of 17 docked complexes, the derivatives AA20, AA21, and AA22 show three hydrogen-bonding interactions with the target residues, whereas the derivative AA75 shows the maximum FF score (−3054.45) with one hydrogen-bonding interaction with LEU 2.418 Å. The docked conformations of 17 AA derivatives and the target (PDB ID: 1w98) are presented in Figure .

Table 7

Results of Docking of AA Derivatives with 1W98

Der. docked with 1W98	ΔG	deltaGligsolvpol	FF (kcal/mol)	energy	(protein···ligand) sites type/binding residue/H-bonding distance
AA11	–7.2239	–9.9810	–3029.41	7.2787	N–H···O/GLN174/2.345 Å
					O···H/LYS170/2.344 Å
AA12	–7.5062	–11.9606	–3014.64	21.7154	N–H···O/GLN174/2.359 Å
					O···H/LYS170/2.155 Å
AA16	–6.3474	–7.9222	–3040.32	–9.9220	O···H/LEU113/2.618 Å
					N–H···O/LEU113/2.256 Å
AA20	–6.8278	–16.5444	–3045.13	7.2032	O···H/LEU277/1.953 Å
					O···H/TYR112/2.301 Å
					O···H/GLU109/2.646 Å
AA21	–6.5128	–8.9698	–3018.17	16.201	O···H/LYS170/2.013 Å
					O···H/THR171/2.575 Å
					N–H···O/GLN174/2.523 Å
AA22	–7.8004	–19.1000	–3041.91	5.6960	O···H/LEU277/2.19 Å
					O···H/GLU109/2.508 Å
					O···H/GLU278/2.478 Å
AA23	–6.7354	–7.7501	–3035.65	–1.1662	O···H/THR171/2.481 Å
AA24	–6.3980	–10.9386	–3038.37	–3.4394	O···H/GLU109/2.562 Å
					O···H/GLU109/2.310 Å
AA25	–6.9110	–11.2838	–3049.17	–12.0395	O···H/LYS170/2.512 Å
					N–H···O/GLN174/2.393 Å
AA33	–7.1697	–6.6352	–3034.61	–4.6578	O···H/THR171/2.150 Å
AA34	–7.3850	–7.5994	–3031.71	–6.3850	O···H/LYS170/2.586 Å
AA72	–7.8560	–7.7790	–3020.07	–4.9699	O···H/THR202/2.302 Å
AA75	–7.5994	–7.5706	–3054.45	–28.0006	N–H···N/LEU113/2.418 Å
AA95	–6.2015	–4.3915	–3006.90	16.0120	N–H···O/ARG114/2.275 Å
AA96	–6.4796	–4.7592	–3008.22	15.3224	N–H···O/ASN74/2.606 Å
AA97	–6.4855	–6.0909	–2787.35	240.0530	N–H···O/LEU113/2.580 Å
AA98	–6.4602	–7.4286	–3010.43	11.7630	N–H···O/ARG114/2.472 Å

Figure 4

Docked ligands with target-1W98: (a) AA11, (b) AA12, (c) AA16, (d) AA20, (e) AA21, (f) AA22 (g) AA23, (h) AA24, (i) AA25 (j) AA33, (k) AA34, (l) AA72 (m) AA75, (n) AA95, (o) AA96, (p) AA97, and (q) AA98.

Conclusions

For the drug designing and development process, the evaluation of pharmacokinetic and physicochemical properties is the primary task. In the present work, 100 AA derivatives were evaluated with the in silico screening procedure based on ADME parameters. The results show the optimal property range (covering 100 derivatives of AA) used to select oral bioavailable drugs. From the evaluation of in silico computational studies including ADME, the pkCSM data revealed that 80% of the selected AA derivatives in this work have shown high Caco2 permeability of log Papp <0.9 and 25% of the AA derivatives were found as P-gp efflux substrates with low-to-moderate clearance rates. The docking results of the finally scrutinized 17 derivatives which were allowed to dock with the anticancer target cyclin-dependent kinase 2 reveal that the AA derivatives are strongly bound with the targets and inhibit the target through hydrogen-bonding interactions. Better bioactivity score, drug adsorption ability, and drug-likeness of these derivatives pave a new research arena with vegetable feedstocks. Rege et al. have recently explored the dual functionality of certain AA derivatives for efficient paclitaxel delivery in breast cancer therapy.[48,49] The higher binding affinity results of the 17 AA derivatives with the anticancer target (PDB ID: 1W98) and their in silico pharmacokinetic studies would definitely provide motivation to biochemists for performing further wet lab and clinical evaluations.

Table 3

Important Computed ADMET Properties of the Screened Out AA Derivativesa

AA derivatives	Caco2 permeability (log Papp in 10–6 cm/s)	intestinal absorption (human) (% absorbed)	VDss (human) (log L/kg)	fraction unbound (human)	P-gp substrate (yes/no)
AA11	0.684	98.43	–1.596	0.04	no
AA12	1.07	100	–1.485	0.125	yes
AA16	1.25	95.899	–1.496	0.232	no
AA20	0.873	56.592	–0.398	0.199	yes
AA21	1.249	96.995	–1.12	0.089	yes
AA22	0.305	46.001	–0.308	0.153	yes
AA23	1.222	89.976	–0.231	0.206	no
AA24	0.888	95.688	–1.723	0.41	no
AA25	0.92	97.934	–1.641	0.29	no
AA33	1.127	91.257	–0.035	0.166	yes
AA34	1.084	90.567	0.014	0.113	yes
AA72	1.314	97.793	1.026	0.007	yes
AA75	1.568	100	–0.158	0	no
AA95	1.031	96.303	0.107	0.268	no
AA96	1.778	94.729	0.161	0.202	no
AA97	1.386	95.664	0.083	0.225	no
AA98	1.351	97.828	–0.082	0.249	no

Caco-2 cell permeability (log Papp in 10–6 cm/s >0.09); intestinal absorption (human), % absorbed (>30); VDss (human) (log L/kg) (low if <−0.15 and high if >0.45).

Table 4

Important Computed Physicochemical Properties of Some Marketed Anticancer Drugs

Drugs	MW	nRot	HBA	HBD	TPSA	M log P	Ali log S	MR
Abemaciclib	502.63	7	7	1	75	2.87	–5.38	154.18
Ambochlorin	293.37	4	4	0	78.29	1.71	–3.3	83.81
Anastrozole	276.21	5	6	1	74.92	2.03	–4.6	64.19
Capecitabine	317.22	3	7	1	95.23	1.43	–3.63	77.26
Erivedge (Vismodegib)	421.3	5	4	1	84.51	3.24	–5.31	107
Flutamide	499.61	11	5	2	87.55	1.71	–5.25	150.43
Nelarabine	359.35	8	8	3	122.91	0.53	–2.71	85.25
Nilutamide	167.19	0	3	2	119.28	–0.96	–2.31	43.34
Osimertinib	297.27	3	8	4	148.77	–2.12	–1.61	69.17
Purinethol	304.21	9	2	1	40.54	3.29	–2.17	81.01

Table 5

Important Computed ADMET Properties for Some Marketed Anticancer Drugs

Drugs	Caco2 permeability (log Papp in 10–6 cm/s)	intestinal absorption (human) (% absorbed)	VDss (human) (log L/kg)	fraction unbound (human)	P-gp substrate (yes/no)
Abemaciclib	1.38	88.951	0.535	0.285	yes
Ambochlorin	1.069	98.691	–0.03	0.138	no
Anastrazole	0.869	88.757	–0.118	0.029	yes
Capecitabine	1.2	87.504	–0.423	0.097	no
Erivedge (Vismodegib)	1.074	94.883	–0.075	0.127	no
Flutamide	0.811	95.992	1.093	0.145	yes
Nelarabine	0.319	51.344	–0.073	0.424	no
Nilutamide	1.207	84.051	–0.421	0.636	yes
Osimertinib	–0.023	48.895	–0.013	0.841	no
Purinethol	1.439	92.268	–0.165	0.117	no