Next Article in Journal
Contribution of Enzyme Catalysis to the Achievement of the United Nations’ Sustainable Development Goals
Next Article in Special Issue
cMolGPT: A Conditional Generative Pre-Trained Transformer for Target-Specific De Novo Molecular Generation
Previous Article in Journal
Influence of a Composite Polylysine-Polydopamine-Quaternary Ammonium Salt Coating on Titanium on Its Ostogenic and Antibacterial Performance
Previous Article in Special Issue
Pharmacophore-Based Virtual Screening and In-Silico Explorations of Biomolecules (Curcumin Derivatives) of Curcuma longa as Potential Lead Inhibitors of ERBB and VEGFR-2 for the Treatment of Colorectal Cancer
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Identification of Some Glutamic Acid Derivatives with Biological Potential by Computational Methods

Octavia-Laura Moldovan
Alexandra Sandulea
Ioana-Andreea Lungu
Șerban Andrei Gâz
3 and
Aura Rusu
Medicine and Pharmacy Doctoral School, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540142 Targu Mures, Romania
Pharmaceutical and Therapeutic Chemistry Department, Faculty of Pharmacy, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540142 Targu Mures, Romania
Organic Chemistry Department, Faculty of Pharmacy, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, 540142 Targu Mures, Romania
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Molecules 2023, 28(10), 4123;
Submission received: 18 March 2023 / Revised: 7 May 2023 / Accepted: 10 May 2023 / Published: 16 May 2023
(This article belongs to the Special Issue Computational Drug Discovery: Methods and Applications)


Glutamic acid is a non-essential amino acid involved in multiple metabolic pathways. Of high importance is its relationship with glutamine, an essential fuel for cancer cell development. Compounds that can modify glutamine or glutamic acid behaviour in cancer cells have resulted in attractive anticancer therapeutic alternatives. Based on this idea, we theoretically formulated 123 glutamic acid derivatives using Biovia Draw. Suitable candidates for our research were selected among them. For this, online platforms and programs were used to describe specific properties and their behaviour in the human organism. Nine compounds proved to have suitable or easy to optimise properties. The selected compounds showed cytotoxicity against breast adenocarcinoma, lung cancer cell lines, colon carcinoma, and T cells from acute leukaemia. Compound 2Ba5 exhibited the lowest toxicity, and derivative 4Db6 exhibited the most intense bioactivity. Molecular docking studies were also performed. The binding site of the 4Db6 compound in the glutamine synthetase structure was determined, with the D subunit and cluster 1 being the most promising. In conclusion, glutamic acid is an amino acid that can be manipulated very easily. Therefore, molecules derived from its structure have great potential to become innovative drugs, and further research on these will be conducted.

Graphical Abstract

1. Introduction

Throughout history, cancer has been a major health problem. It has been shown that there is a positive correlation between cancer incidence and age [1,2,3]. The individual risk of cancer is also influenced by family history, genetic susceptibility or behaviour, and exposure to carcinogenic factors [4]. Furthermore, the Krebs cycle and amino acids are proven to significantly affect cancer metabolism. Thus, interfering with amino acid metabolic pathways is an active area of study in cancer metabolism [5].
Amino acids are essential for cancer development because they can function as opportunistic fuel sources for cells [5]. Cancer cells use multiple strategies to obtain amino acids [6]. Higher-grade cancer cells must be able to supply additional metabolites for bioenergy and synthesise the necessary biosynthetic precursors of proteins, nucleic acids, and membrane lipids to grow substantially [7]. In cancer cells, glutamine is the major amino acid that serves as an anaplerosis metabolite and drives the tricarboxylic acid (TCA) cycle to sustain mitochondrial ATP for energy production [5]. Glutamine is the most abundant amino acid in plasma. The majority of circulating glutamine is produced in muscles and, additionally, in the lungs [8]. However, it has been observed that a reduced exogenous supply of glutamine can impair malignant cells’ survival or tumorigenic potential [6].
Glutamine is a versatile biosynthetic substrate for carbon and nitrogen atoms to generate important precursors for macromolecule biosynthesis [9]. It is the nitrogen donor for the biosynthesis of purines, pyrimidines, nicotinamide adenine dinucleotide, asparagine, and hexosamines via its terminal amide group. A higher expression of enzymes that mediate nucleotide synthesis from glutamine positively correlates with increased proliferation in tumours [10]. Glutamine also drives the uptake of essential amino acids, helps recycle excessive ammonia and glutamate, and activates the mammalian target of rapamycin (mTOR) that is involved in gene transcription and intracellular signalling [8,9,10]. In this regard, compounds that interfere with glutamine metabolism have shown therapeutic potential in preclinical studies by disrupting these growth-promoting processes [9]. In addition to providing building blocks for cell growth, glutamine metabolism plays a critical role in maintaining cellular redox homeostasis, as glutamate is a precursor for glutathione (GSH) [8,11]. GSH is used to maintain redox homeostasis within the cell and to protect it from oxidative damage [12]. Because excessive free radicals lead to DNA damage, lipid peroxidation, and protein denaturation, tumour cells mitigate the excess of free radicals and maintain redox homeostasis principally by GSH synthesis [10]. In this regard, a process called glutaminolysis, catalysed by mitochondrial glutaminase, plays an essential role in the glutamine conversion to glutamate. Furthermore, it regulates reactive oxygen species homeostasis by providing the precursors glutamate and cysteine for GSH synthesis [13,14].
Cancer cells rely on glutaminase activity to maintain a high ratio of glutamate to α-ketoglutarate, which is essential for producing non-essential amino acids. This aspect explains glutamine’s anaplerotic function [10]. Glutamate generates α-ketoglutarate and fuels the TCA cycle through a transamination reaction. In the same way, transaminases, such as aspartate aminotransferase, facilitate the interconversion of aspartic acid. All these biochemical reactions maintain normal metabolism, allowing glutamate to be converted to other amino acids if necessary. Thus, this enzyme is considered to play an essential role in some types of cancer metabolism, such as in pancreatic cancer [5].
Glutamine synthetase (GS) is another critical enzyme involved in glutamine metabolism is because it converts glutamate to glutamine. This biochemical reaction is essential as glutamine is the body’s non-toxic form of ammonia transport. It has also been found that GS activity is important for the proangiogenic, immunosuppressive, and pro-metastatic function of M2-like macrophages [8]. The term “glutamine addiction” has been used to describe the enhanced usage of glutamine in cancer in an anaplerotic sense [15]. However, the inherent properties of tumour cells differ, as the specific mechanism that a tumour cell chooses is dictated by tumour type, oncogene/tumour suppressor status, tumour site, and stage of tumour development [9]. Some cancer types mainly depend on glutamine metabolism for tumour cell survival and proliferation. For example, pancreas cancer, lung cancer, colon cancer, glioblastoma, acute myeloid leukaemia, ovarian cancer or triple-negative breast cancer, which do not express oestrogen, progesterone receptors or human epidermal growth factor receptor 2, mainly depend on glutamine, in contrast with other types of cancer [8,13,16]. Human liver cancer has also been found to be dependent on extracellular glutamine [13]. Therefore, glutamine uptake and glutaminase activity have been actively investigated as oncological targets [5]. Among the therapeutic strategies, one is targeting glutamine metabolism in tumours [13,17,18]. To date, the best-developed molecule is CB-839 (telaglenastat), which interferes with glutamine metabolism. This molecule is a potent, non-competitive allosteric inhibitor of the mitochondrial enzyme glutaminase and the only one that is currently being used in Phase I clinical trials in cancer patients [9,10,13]. CB-839 shows antiproliferative properties in triple-negative breast cancer by reducing glutamine consumption, glutamate production, and levels of TCA intermediates [13,15]. In addition, it exhibits significant efficacy in lung adenocarcinoma, chondrosarcoma and lymphoma cancer, but many liver cancer cell lines fail to respond to CB-839 treatment [13]. Besides CB-839, the compounds 968 (5-[3-bromo-4-(dimethylamino)phenyl]-2,2-dimethyl-1,3,5,6-tetrahydrobenzo[a]phenanthridin-4-one) and BPTES (Bis-2-(5-phenylacetamido-1,2,4-thiadiazol-2-yl)ethyl sulphide) are other glutaminase inhibitors used in preclinical studies [10,15,19] (Figure 1). BPTES led to GSH depletion, making some lung cancer cells more sensitive to radiation treatment. At the same time, compound 968 blocked oncogenic transformation in fibroblasts and reduced the growth of cancer cells [11,15].
Other potential therapeutic alternatives are glutamine mimics such as DON (6-diazo-5-oxo-L-norleucine), JHU-083 (ethyl 2-(2-amino-4-methylpentanamido)-DON), azaserine, and acivicin which are limited by their toxicity [10,15,20]. Similarly, the AOA (aminooxyacetic acid) compound, an aminotransferase inhibitor, and L-asparaginase produce glutamine depletion [10]. EGCG (Epigallocatechin gallate) and R162 (2-allyl-1-hydroxy-9,10-anthraquinone) are glutamate dehydrogenase inhibitors that block the transformation of glutamic acid into α-ketoglutarate. For the moment, both of these are considered preclinical compounds [5,10] (Figure 2).
Additional pathways involving amino acid transport suggest effective therapies. Tumour cells achieve high intracellular concentrations of glutamine primarily through the upregulation of glutamine transporters, including ASCT2 (alanine, serine, cysteine transporter 2 or SLC1A5) [5]. Pharmacological blockade of SLC1A5 can be a successful alternative in some types of cancer. V-9302, an SLC1A5 antagonist (Figure 3), elicited a marked anti-tumour response in preclinical tumour models [10,11]. It has blocked glutamine uptake in a broad spectrum of solid tumours (such as colorectal cancer cell lines) and several xenograft tumour models. This blocked glutamine uptake resulted in a profound alteration of tumour cell growth and survival [9,21]. It has been observed that V-9302 was more productive in inducing triple-negative breast cancer cell death in several human and mouse cell culture models [16]. The combination of CB-839 and V-9302 was also successful because of the dual inhibition of glutamine metabolism, resulting in a decrease in GSH levels and a lethal increase in the levels of free radicals. This resulted in severe DNA damage, especially in liver cancer cells [13].
Another therapeutic strategy could be inhibiting glutamate carboxypeptidase II (GCPII). This enzyme hydrolyses N-acetyl-aspartyl-glutamate (NAAG) to glutamate and N-acetyl aspartate. NAAG is a neurotransmitter in the brain and a glutamate provider to GCPII-positive cancers if other sources do not produce enough glutamate. Therefore, inhibitors of GCPII can lead to cancer cell growth suppression by reducing glutamate concentrations [7]. Antagonists of metabotropic glutamate receptors are also promising anti-cancer alternatives without significant side effects. Metabotropic glutamate receptors (mGluRs) are G-protein coupled receptors (GPCRs) categorised into three groups based on their signal transduction pathways and pharmacological profiles. They seem to be more attractive therapeutic targets since they are not directly involved in excitotoxicity but intervene in modulating glutamate activity [22,23].
This article aims to identify new structural analogues of glutamic acid as potential candidates for anti-cancer therapy by computational methods. Several stages were followed: (1) analysis of recently published scientific data regarding the role of glutamate and its derivatives in the development of tumour cells; (2) identification of some new molecules with biological potential, starting with the structure of glutamic acid and the creation of a compound library; (3) conjugation of molecules of natural origin with glutamic acid residues to reduce glutamic acid toxicity and/or potentiate the anti-cancer effect; (4) selection of compounds with biological action and minimal toxicity according to the structural, physicochemical, pharmacokinetic, and pharmaco-toxicological properties determined by in silico methods; (5) evaluation of anti-tumour potential of selected molecules and the identification of possible mechanisms of action; (6) molecular dynamics simulation and molecular docking study to identify the binding site of a ligand molecule (with biological potential) on a known target.

2. Results and Discussion

The designed glutamic acid derivatives were classified by classes, groups, and subgroups (Table 1). Each one of the compounds received an ID code composed of the following elements: first digit—class; capital letter—group; small letter—subgroup; last digit—the compound’s number in the subgroup; small letter at the end (if applicable)—a derivative of the lead-compound. The online software and test parameters that were used to obtain and characterise the compounds are mentioned in Table S1 (Supplementary Materials). The structures of all obtained compounds and their computational descriptors are given in Table S2 (Supplementary Materials).

2.1. Algorithm for Designing Glutamic Acid Derivatives and Studies Underlying Their Development

The derivatives included in the first two classes were designed based on the specific chemical properties of amino acids resulting from reactions at the carboxyl and amino functional groups. Blocking these essential functional groups in the amino acid’s structure could bring significant changes in terms of its biochemical metabolism; consequently, derivatives with potential pharmaceutical effects are sought [24,25,26,27]. The following classes of compounds comprise structures containing pharmacophores responsible for the anti-cancer effect: thiazole derivatives, 1,3-oxazole derivatives [28,29,30], alkylating agents [28,31,32,33,34,35], inhibitors of histone deacetylase [36,37,38,39], ribonucleotide reductase [40,41,42], glutamate synthetase inhibitors, and mitochondrial transporters of the SLC25A family [43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60]. Compounds belonging to these classes have been intensively studied [7,61].
Based on data published about histone deacetylase, compounds from class 4B were designed [39,42,62,63,64,65]. Class 4Cb compounds are based on the structure of Trimidox, an RR inhibitor [40]. Class 4D compounds, inhibitors of GS, and mitochondrial transporters for glutamate are based on Lukasz Berlicki’s (2008) work [66]. The derivative possessing the 4Dd4.m ID code is a metabolite with potential GS inhibitory effect resulting from the hydrolysis of compounds related to tabtoxin (dipeptide) prodrugs: 4Dd4.1, 4Dd4.2 and 4Dd4.3 [67,68,69,70]. Compounds containing sulphur pharmacophores (4Ad1-3, 4Da1-11) are based on the study conducted by Urlich L. (2019) [71].
Based on the information about plant-derived substances with proven anti-cancer effect, we have structurally created compounds of group 5A-E: colchicine derivatives, neferine derivatives, 7-hydroxynuciferine derivatives, lycorine derivatives, 5,6-dehydroglycorine derivatives, and natural compounds conjugated with glutamic acid residues [28,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89]. In addition, the hypothesis that conjugation with a single molecule of glutamic acid could bring benefits compared with the basic compounds of natural origin is being tested through computational studies.
The in silico determination of (1) physicochemical and structural parameters, which implies the determination of heavy atoms (HA), heavy aromatic atoms (HAA), fraction Csp3, rotatable bonds (RB), H-bond acceptors, H-bond donors, molar refractivity (MR), and total polar surface area (TPSA); (2) protonation (acidic pKa, basic pKa, pKa score, isoelectric point (pI), and microspecies); and (3) electric charge (molar polarisability), is detailed in Table S3 (Supplementary Materials). Water solubility was computed using AquaSol [90], Chemicalize [91], and SwissADME [92], and the results are detailed in Table S4 (Supplementary Materials). Lipophilicity and partition coefficients are presented in Table S5 (Supplementary Materials). Toxicity studies were performed in silico by applying the Cramer rules, and the Kroess and Verhaar scheme (Table S6; Supplementary Materials). Other toxicity parameters were also determined, such as carcinogenicity (genotoxic and non-genotoxic) and mutagenicity, skin and eye irritation/corrosion, effect on the reproductive system, biodegradability, and protein and DNA binding alerts, which were evaluated using Toxtree and OSIRIS [93,94]. Results are listed in Tables S7 and S8 (Supplementary Materials).
Pharmacokinetic properties were evaluated for each compound in terms of permeability (gastrointestinal absorption, blood–brain barrier permeability) and interactions with P-gp. In addition, we assessed the enzyme inhibitory effect on some isoforms of cytochrome P450 (Table S9; Supplementary Materials). Based on the previously calculated properties, we evaluated whether these compounds meet the “drug-likeness” criteria according to the Lipinski, Ghose, Veber, Egan, and Muegge rules. The number of rules violated by each molecule is shown in Table S10 (Supplementary Materials), along with the bioavailability score, drug-likeness score, lead-likeness score, and synthetic accessibility score.
Compounds that were too reactive, toxic, or did not have the suitable properties to become lead compounds were removed. Therefore, the screening was performed in several steps according to the rules of Lipinski [91,95,96,97], Veber [95], Ghose [97,98], Egan [99], and Muegge [100] (Table 2), the “overall drug-likeness” score [94,101,102], lead-likeness [103,104], CNSMPO [105], SA [103,106,107,108,109], and by toxicity criteria [93,94,110,111,112,113,114,115,116,117,118,119,120,121,122,123] and pharmacokinetic properties [103,124,125,126,127,128,129,130].
SA is the synthetic accessibility score, which varies from one to ten. It is a parameter used to estimate the ease of synthesising a drug-like molecule: 1 representing being very easy to synthesise and 10 very difficult. This parameter was considered during the abovementioned stages because the subsequent synthesis of the proposed structures will depend heavily on it [92].

2.2. The Elimination of Reactive and Toxic Compounds

The elimination of reactive and toxic compounds was carried out in several steps, as follows:
  • Step 1. In the first stage, compounds belonging to at least two toxicity classes are eliminated, as the risk of them causing severe adverse reactions is high.
  • Step 2. This step involves the removal of compounds that do not follow Lipinski and Veber’s rules, and which have a CNS MPO score less than 4, as well as compounds with low solubility and/or an inhibitory effect on cytochrome P450 and/or gp-P enzymes.
  • Step 3. Compounds with medium toxicity, which fall into Class III (Cramer rules) and are positive for at least one toxicity criterion, are eliminated if the overall drug-likeness score does not exceed 0.90.
  • Step 4. Compounds that have violated all Ghose’s rule criteria (four out of four) and belong to Cramer class III or II or overlap with the violation of at least one Muegge rule are eliminated.
  • Step 5. Compounds that have violated at least three Ghose criteria and at least two Muegge rules and belong to Cramer class III are eliminated.
  • Step 6. Removal of Cramer Class III compounds that violate at least one Ghose and Muegge rule, having an SA score below 2.
  • Step 7. Elimination of Class III Cramer compounds that violate at least one Ghose and Muegge rule, regardless of the SA score achieved.
  • Step 8. Removal of compounds that violate at least one Ghose and Muegge rule with a low GI absorption value.
  • Step 9. Compounds that violate at least one Ghose and Muegge rule with an SA score below 4, regardless of Cramer toxicity class, are eliminated.
  • Step 10. Elimination of Cramer Class III compounds that violate at least two Muegge criteria and have an SA score below 3 and/or overall drug-likeness score below 0.5.
Only nine compounds proved to have suitable properties or properties that can be easily optimised, representing 7.3% of the total. These selected compounds are presented in Table 3, along with their geometrical and isomer-conformation properties.

2.3. Characterisation of the “Lead” Compounds

The “lead” compounds were characterised by chemical structure, geometric isomers, isomerism, and conformations using the MarvinSketch platform [105] (Table 3). The platform automatically generated the conformations, and their number was limited to ten. The energy was calculated using force field methods, and the conformer with the lowest energy, i.e., having the highest stability, was chosen.
The main pathways of metabolism, bioactivity, action on cancer cells, mechanisms of action and possible adverse effects, and acute toxicity in rodents were further evaluated by in silico methods. For this, we used Toxtree [22] to assess the metabolism of the nine compounds (primary, secondary, tertiary, and quaternary sites of metabolism) and also SmartCyp and SOMP to determine the most reactive atom (involved in interactions with CYP3A4, CYP2D6, and CYP2C9) (Table 4 and Table 5). The algorithm used by the Smartcyp online platform requires a reactivity descriptor (E) and an accessibility descriptor (A). “E” estimates the energy required for a CYP to react at this position, and “A” is the relative topological distance of an atom from the centre of the molecule. The score is calculated for each atom according to the equation Score = E − 8*A − 0.04*SASA (where SASA is the solvent-accessible surface area). A lower score corresponds to an increased probability of being a site of metabolism [131].
The bioactivity of the nine selected compounds was characterised using the following parameters: G protein-coupled receptor ligand, ion channel modulator, kinase inhibitor, nuclear receptor ligand, protease inhibitor, and enzyme inhibitor (Table 6). In addition, the most probable molecular targets and their identification data were determined using the SWISSTarget predictor (Table 7) [133].
Regarding the interpretation of the results from Table 6, a larger score value correlates with a higher probability for the particular molecule to be active. More explicitly, if the bioactivity score is more than 0.0, the compound is considered active; if the score is between −0.5 and 0.0, it exhibits moderate activity; if the bioactivity score is less than −0.5, then it is inactive [134].
The anticarcinogenic effect of the nine compounds was assessed using CLC-Pred software [135], predicting the most probable cell lines for which compounds exhibit cytotoxicity (Table 8).
Possible mechanisms of action and adverse/toxic effects, lethal doses (LD50) in acute toxicity determined in rodents (intraperitoneal, intravenous, oral, and subcutaneous administration), and the classification of chemical compounds according to the OECD Project were also determined by in silico methods (Table 9, Table 10 and Table 11) [135,136,137,138].
Based on the results of the bioactivity assessment by Molinspiration [134] (Table 6), molecular dynamics and docking studies were performed on compound 4Db6 and the bacterial GS enzyme from Salmonella typhimurium (Figure S1; Supplementary Materials) [43,66,139,140,141]. The Protein Data Bank (PDB) code for GS is 1lgr [142,143].
The molecular dynamics simulation study was carried out using the UCSF Chimera 1.15 software [144,145]. Before the actual dynamics simulation, the chemical structure was processed according to the protocol established in the literature: hydrogen atoms were inserted, the protonation status corresponding to glutamic acid was used, and Gasteiger partial charges were assigned. The study was performed in water as solvent (SPCBOX, cube size 3 Å) with a density of 1024 g/cm3 to simulate physiological conditions. In the neutralisation phase, we added Na/Cl counterions. The next step was the minimisation phase, whereby the system’s energy would tend towards 0.
In the equilibration phase, the temperature was set to 310 K (36.85 ⁰C, approximately physiological temperature) with a gradient of 10 K/ps. In the production phase, the following settings were made: Andersen barostat—pressure 1.0132 bar, relaxation time 1.5; Nose thermostat—emperature 310 K, relaxation time: 0.2. The entire simulation time was set to 100 ns. The energy values resulting from the molecular dynamics simulation for compound 4Db6 are included in Table 12.
Geometry optimisation was performed following the Gaussian model, and we used the standard topology for non-protein molecules. Most biological processes involve, at the atomic scale, the recognition of one molecule by another. Estimation of such interactions at the molecular level is performed by docking methods [146]. In the molecular docking study, the interaction of the 4Db6 derivative with the GS enzyme was evaluated in comparison with phosphinothricin ((2S)-2-amino-4-(hydroxy-methyl-phosphoryl)butanoic acid), whose PDB code is PPQ [67,69,70,142,147]. Phosphinothricin, a GS inhibitor, shows the closest similarity (86.9%) to compound 4Db6, as scored by SwissSimilarity (Score = 0.869) [148]. The comparison was made to identify the most probable binding site in the enzyme structure [149].
The study was conducted using SwissDock [150,151,152], PatchDock [136,153,154], and AutoDockVina 1.1.2 [151,155]. In a study evaluating a crystalline structure of GS inhibited by phosphinothricin, the inhibitor molecule preferentially binds to the enzyme in the D subunit’s active site. Phosphinothricin occupies the glutamate pocket and stabilises the Glu327 residue in a position that prevents glutamate from entering the active site [149]. This crystal structure (PDB code: 1FPY) was observed using the Mol* Viewer web app of RCSB PDB [142,156]. The preference for the D subunit was also confirmed by results obtained using the PatchDock app, which estimated the most probable binding site for the 4Db6 compound [136,153,154]. The top 10 best solutions are shown in Table 13. Figure 4 illustrates the first best result generated.
However, the selected derivative does not bind to the active site. Thus, these derivatives will probably not show inhibitory activity towards the enzyme. Molecular docking was performed using SwissDock [134,150,152] and AutoDockVina 1.1.2 [151,155] to increase the accuracy of the study.
For PPQ, SwissDock found 257 conformations. The most probable binding site was chosen according to the conformation with the lowest energy, having ΔG = −10.43 kcal/mol and a FullFitness value of −2192.23 kcal/mol [150,152,157]. The FullFitness parameter for a cluster is calculated using the average of 30% of the most favourable energies of its elements to lower the risk of inhibition of the entire cluster by some complexes. This energy is represented by the sum of the system’s total energy and a solvation term [158]. For example, for compound 4Db6, SwissDock found 160 conformations. By comparing the PPQ binding site with the sites of the 160 conformations, we consider that clusters 1, 6 and 33 could bind to the same site in a relatively similar way (Table 14).
The inhibition constant (Ki) was calculated using the following formula: Ki = e^((ΔG × 1000)/(R × T)), where e = 2.7182, R = 1.98719 cal/(mol × K) (Regnault constant) and T = 298.15 K = 25 °C [159]. It can be seen that cluster 1 shows the lowest energy according to the ΔG value, but Ki and the maximum FullFitness value belong to complex 33. Visualisation and processing of the results obtained in the molecular docking study (Figure 5) were performed using UCSF Chimera 1.15 [144,145]. The grid sizes used in SwissDock for cluster 1 are (x, y, z) = (15.5, 15.5, 20.5) with centre coordinates (x, y, z) = (−98, 13.711, −87.161).
To perform molecular docking using AutoDock Vina (a new version of the Webina online platform), the exhaustiveness of the search was set to 8 and the maximum energy difference to 3 kcal/mol. The space in which the test took place is represented by the volume of a cube (having the following dimensions: width = 20.4346, length = 27.864, height = 18.3759), and whose centre is defined by the coordinates x = −4.86256, y = −15.0503, z = −67.7222) [160]. Preparation for docking involves the insertion of hydrogen atoms on the chemical structure of both the ligand and the receptor molecule and the removal of the solvent. The protonation state corresponding to histidine was used, and Gasteiger partial charges were assigned (Figure 6).
The molecular docking results performed with AutoDock Vina are shown in Table 15, and the corresponding figures are presented in Figure S2 (Supplementary Materials). We chose to work further with model no.1 due to its low free energy (−6.3 kcal/mol) and root-mean-square deviation (RMSD) values that were below 2 Å. The 2 Å limit is often used as a criterion for predicting the correct binding site. The RMSD for two structures, a and b, of an identical molecule can be defined as follows:
RMSDab = max(RMSD′ab, RMSD′ba)
RMSD ab = 1 N i m i n j r ij 2
where rij represents the interatomic distance and the sum is over all N HA in structure a; the minimum is over all atoms in structure b with the same element type as the atom in structure a. RMSD is a measure of the distance between experimental and predicted structures that takes into account symmetry, partial symmetry (e.g., within a rotating branch), and near-symmetry [160,161,162,163,164].
The main residues in the D subunit of the GS enzyme involved in interactions (within 1.49–2.81 Å) with the 4Db6 ligand are THR-223 (2 bonds) and GLU-129. Hydrogen bond connections play a key role in determining protein–ligand interactions [160,165]. In addition, the first conformation shows four active torsions: between C4 and P8, CA6 and C7, P8 and C9, and P8 and O11 [160].

3. Materials and Methods

Several series of analogous compounds (123 derivatives) have been theoretically designed based on the structure of glutamic acid to build a compound library of glutamic acid derivatives. From simple structure groups to more complex molecules, the chemical structures of the compounds were designed using BIOVIA Draw 21.1. [166]. The number of 123 compounds was reached after analysing the structure of glutamic acid to make as many specific structural modifications as possible. The classes of compounds and the structural changes made to the fundamental molecule were selected following the information found in the scientific literature. Our purpose was initially to design as many structural derivatives as possible because, after characterising and selecting these compounds based on well-established steps, we would be left with as many derivatives with optimal properties as possible to study further.
We also used the same software to generate the computational descriptors. To select suitable candidates for our purpose, we evaluated some properties of the molecules and their behaviour in the human organism. Physico-chemical characterisation of the desired compounds was carried out using SwissADME [92,103] and MarvinSketch [105]. Water solubility was tested using AquaSol [90], Chemicalize [91] and SwissADME [167,168]. Lipophilicity was analysed using SwissADME to determine the partition coefficients [169,170,171,172,173,174]. Toxicity was assessed using Toxtree [93] by applying the Cramer rules and the Kroess and Verhaar scheme, and GUSAR [175] was used to evaluate the acute toxicity in rodents.
Pharmacokinetic properties were analysed in terms of permeability and interactions with P-glycoprotein (P-gp) and some isoforms of cytochrome P450 using the SwissADME program. In addition, we evaluated the “drug-likeness” criteria according to Lipinski, Ghose, Veber, Egan, and Muegge rules using MarvinSketch, Chemicalize and DruLiTo [97].
The metabolism of the compounds was assessed using Toxtree, SmartCyp [131], and SOMP [132] and the bioactivity was evaluated using Molinspiration [134] and SWISSTarget prediction [133] (to predict the most probable molecular targets). The anticarcinogenic effect was assessed with the CLC-Pred software (Version 2.0) [135], which estimates in silico the cytotoxic effect based on the structural formula; the mechanism of action and adverse/toxic effects were tested using PASSonline [137].
Molecular docking was performed using SwissDock [150], PatchDock Beta 1.3. [153,176], AutoDockVina 1.1.2. [155], and UCSF Chimera 1.15 [177]; the similarity between compounds was evaluated using SwissSimilarity [178]. We assessed the irritant/corrosive effect on the skin and eyes, the effect on the reproductive system, biodegradability, and protein and DNA binding alerts using Toxtree and OSIRIS Property Explorer [94].
Considering all the computed properties and their biological potential, “lead” compounds were selected.
We also attempted to validate our experimental procedures using positive and negative controls. Therefore, we chose methionine sulfoximine and phosphinothricin as positive controls for their proven activity of inhibiting glutamine synthetase [66,149]. As a negative control, we initially thought of glutamic acid, being the parent molecule for our derivatives [179]. However, it was interesting to observe that, according to the CLC-Pred software, it can show cytotoxic activity on four cell lines [135]. Therefore, in the end, we chose ampicillin as the negative control, which, according to the software, does not show cytotoxicity in any cancer cell line. All compounds were characterized using the previously described platforms and programs, passing through the same steps as the designed glutamic acid derivatives. Molecular docking was assessed using the ProteinsPlus online platform [180]. The results are presented in Tables S13–S18 and Figure S3 (Supplementary Materials).
To increase the accuracy of the study, molecular docking was carried out using several programs since they provided us with different information. PatchDock/ProteinPlus indicated the most probable binding sites in the protein’s structure, calculated the surface area available for ligand binding, and generated the grid-box coordinates. Autodock Vina used these data and refined them, generating the values of ligand affinity for the target molecule and the distance from the RMSD lower bound and RMSD upper bound. It also showed the active torsions between atoms. Finally, SwissDock generated additional information, such as deltaG values and FullFitness, which were used to calculate the inhibition constant Ki.

4. Conclusions

Glutamic acid is an amino acid that can be manipulated very easily, and molecules derived from its structure have great potential to become innovative drugs. Of the 123 new GLA derivatives, 9 molecules proved to have biological potential, but more studies and optimisation are needed. The selected compounds show cytotoxicity against breast adenocarcinoma, lung cancer cell lines, colon carcinoma, and T cells from acute leukaemia. Compound 2Ba5 exhibited the lowest toxicity, while derivative 4Db6 exhibited the most intense bioactivity and could act like an ion channel modulator, protease inhibitor or enzyme inhibitor. A molecular docking study determined the binding site of the 4Db6 compound in the GS structure, D subunit, and found cluster 1 to be the most promising, having the lowest free energy value. Since compounds 5Aa1–5Ea3 were eliminated due to their increased toxicity, it is most probable that a single glutamic acid residue bound to the parent molecule cannot reduce the side effects or increase its biological activity. The toxicity of these compounds did not change significantly compared with the parent molecules, except for 7-hydroxynuciferine derivatives, which showed a higher risk of irritation, negative effects on the reproductive system, genotoxic carcinogenicity, tumorigenesis, and a higher risk of mutagenicity compared with 7-hydroxynuciferine. On the other hand, GLA-lycorine and GLA-dehydrolycorine complexes were less irritating to the skin than lycorine and dehydrolycorine, according to data provided by Toxtree and OSIRIS (Table S12; Supplementary Material). Further studies can be performed using these plant-derived molecules combined with more glutamic acid residues or poly-L glutamic acid to obtain more favourable results.
Based on the results provided by Molinspiration and CLC-Pred, further studies can be performed on other enzymes, ion channels, or proteases specific to the colon HCT-116 carcinoma cell line to simulate an interaction with the tumour itself. By marking isotopes at carbon 9 (bonded to the phosphorus atom) in the structure of 4Db6, the molecule can be analysed as a radiopharmaceutical compound (radioligand) as a potential candidate for anti-cancer therapy.

Supplementary Materials

The following supporting information can be downloaded at:, Table S1: Programs and tested parameters; Table S2: Chemical structures, ID codes, and computational descriptors of glutamic acid derivatives obtained with Biovia Draw; Table S3: Structural and physicochemical properties: protonation and electric charge; Table S4: Water solubility; Table S5: Lipophilicity—partition coefficients; Table S6: Toxicity—I: Cramer rules, Kroess and Verhaar scheme; Table S7: Toxicity—II. Carcinogenic (genotoxic and non-genotoxic) and mutagenic effects evaluated using two different apps (Toxtree and OSIRIS); Table S8: Toxicity—III. Irritant/corrosive effect on the skin and eyes, effect on the reproductive system, biodegradability, and protein and DNA binding alerts, as assessed using Toxtree and OSIRIS; Table S9: Permeability and interactions with P-gp. Enzyme inhibitory effect on isoforms of cytochrome P450; Table S10: The number of broken rules, according to Lipinski, Ghose, Veber, Egan, and Muegge and the bioavailability score, the drug-likeness score, the lead-likeness score and the synthetic accessibility score; Figure S1: Homododecameric structure of the bacterial GS enzyme and D subunit; Figure S2: Molecular docking results visualised using UCSF Chimera and AutoDock Vina (Webina); Table S11: Chemical structures of colchicine, neferine, 7-hydroxynuciferine, lycorine, and 5,6-dehydrolycorine; Table S12: Toxicity comparison of vegetal compounds and their complexes with glutamic acid; Table S13: Characterization of phosphinothricin, methionine sulfoximine, glutamic acid, and ampicillin; Table S14: Molecular dynamics simulation results for phosphinothricin, methionine sulfoximine, glutamic acid, and ampicillin; Table S15: Molecular docking results for phosphinothricin, methionine sulfoximine, glutamic acid, and ampicillin; Figure S3: Molecular docking results. Interaction with glutamine synthetase of (a) hosphinothricin, (b) methionine sulfoximine, (c) glutamic acid, and (d) ampicillin; Table S16: Grid sizes used in Swissdock and energetic values of the most probable ligand–receptor complexes for phosphinothricin, methionine sulfoximine, glutamic acid, and ampicillin; Table S17: Molecular docking results obtained using AutoDock Vina for phosphinothricin, methionine sulfoximine, glutamic acid, and ampicillin; Table S18: Grid sizes used in AutoDock Vina for phosphinothricin, methionine sulfoximine, glutamic acid and ampicillin.

Author Contributions

Conceptualisation, O.-L.M. and A.R.; methodology, O.-L.M., A.S. and A.R.; writing—original draft preparation, A.S., O.-L.M. and A.R.; writing—review and editing, O.-L.M., A.S. and A.R.; visualisation, O.-L.M., I.-A.L. and Ș.A.G.; supervision, A.R.; funding acquisition, O.-L.M. All authors have read and agreed to the published version of the manuscript.


This work was supported by George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures [grant number 164/21/10.01.2023].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Sample Availability

Not applicable.


ADMEAbsorption, Distribution, Metabolism and Excretion
HAAHeavy aromatic atoms
BBBBlood–Brain Barrier
CLC-PredCell Line Cytotoxicity Predictor
CNS MPOCentral Nervous System Multiparameter Optimisation
ESOLEstimating Aqueous Solubility Directly from Molecular Structure
GSGlutamine synthetase
GPCRG-protein coupled receptor
GPLGeneral Public License
GUSARGeneral Unrestricted Structure–Activity Relationships
HAHeavy atoms
HLBHydrophilic Lipophilic Balance
KiInhibition constant
LD50Lethal dose 50
MRMolar refractivity
PDBProtein Data Bank
pIIsoelectric point
QSARQuantitative Structure–Activity Relationships
QSPRQuantitative Structure–Property Relationships
RBRotatable bonds
SASynthetic accessibility score
SLC25The solute carrier family 25
SN1Nucleophilic substitution type 1
SN2Aliphatic nucleophilic substitution type 2
SOMPSite of Metabolism Prediction
TPSATotal polar surface area
TTCThreshold of Toxicological Concern


  1. Ageing as an Important Risk Factor for Cancer|Anticancer Research. Available online: (accessed on 25 January 2023).
  2. Solary, E.; Abou-Zeid, N.; Calvo, F. Ageing and Cancer: A Research Gap to Fill. Mol. Oncol. 2022, 16, 3220–3237. [Google Scholar] [CrossRef]
  3. Risk Factors: Age—NCI. Available online: (accessed on 25 January 2023).
  4. American Cancer Society. Global Cancer Facts & Figures, 4th ed.; American Cancer Society: Atlanta, GA, USA, 2018; Available online: (accessed on 13 January 2023).
  5. Lieu, E.L.; Nguyen, T.; Rhyne, S.; Kim, J. Amino Acids in Cancer. Exp. Mol. Med. 2020, 52, 15–30. [Google Scholar] [CrossRef]
  6. Zhang, H.; Wang, Y.; Li, J.; Chen, H.; He, X.; Zhang, H.; Liang, H.; Lu, J. Biosynthetic Energy Cost for Amino Acids Decreases in Cancer Evolution. Nat. Commun. 2018, 9, 4124. [Google Scholar] [CrossRef]
  7. Nguyen, T.; Kirsch, B.J.; Asaka, R.; Nabi, K.; Quinones, A.; Tan, J.; Antonio, M.J.; Camelo, F.; Li, T.; Nguyen, S.; et al. Uncovering the Role of N-Acetyl-Aspartyl-Glutamate as a Glutamate Reservoir in Cancer. Cell Rep. 2019, 27, 491–501.e6. [Google Scholar] [CrossRef]
  8. Palmieri, E.M.; Menga, A.; Martín-Pérez, R.; Quinto, A.; Riera-Domingo, C.; De Tullio, G.; Hooper, D.C.; Lamers, W.H.; Ghesquière, B.; McVicar, D.W.; et al. Pharmacologic or Genetic Targeting of Glutamine Synthetase Skews Macrophages toward an M1-like Phenotype and Inhibits Tumor Metastasis. Cell Rep. 2017, 20, 1654–1666. [Google Scholar] [CrossRef]
  9. Jiang, J.; Srivastava, S.; Zhang, J. Starve Cancer Cells of Glutamine: Break the Spell or Make a Hungry Monster? Cancers 2019, 11, 804. [Google Scholar] [CrossRef]
  10. Natarajan, S.K.; Venneti, S. Glutamine Metabolism in Brain Tumors. Cancers 2019, 11, 1628. [Google Scholar] [CrossRef]
  11. Boysen, G.; Jamshidi-Parsian, A.; Davis, M.A.; Siegel, E.R.; Simecka, C.M.; Kore, R.A.; Dings, R.P.M.; Griffin, R.J. Glutaminase Inhibitor CB-839 Increases Radiation Sensitivity of Lung Tumor Cells and Human Lung Tumor Xenografts in Mice. Int. J. Radiat. Biol. 2019, 95, 436–442. [Google Scholar] [CrossRef]
  12. Walker, M.C.; van der Donk, W.A. The Many Roles of Glutamate in Metabolism. J. Ind. Microbiol. Biotechnol. 2016, 43, 419–430. [Google Scholar] [CrossRef]
  13. Jin, H.; Wang, S.; Zaal, E.A.; Wang, C.; Wu, H.; Bosma, A.; Jochems, F.; Isima, N.; Jin, G.; Lieftink, C.; et al. A Powerful Drug Combination Strategy Targeting Glutamine Addiction for the Treatment of Human Liver Cancer. eLife 2020, 9, e56749. [Google Scholar] [CrossRef]
  14. Bertero, T.; Oldham, W.M.; Grasset, E.M.; Bourget, I.; Boulter, E.; Pisano, S.; Hofman, P.; Bellvert, F.; Meneguzzi, G.; Bulavin, D.V.; et al. Tumor-Stroma Mechanics Coordinate Amino Acid Availability to Sustain Tumor Growth and Malignancy. Cell Metab. 2019, 29, 124–140.e10. [Google Scholar] [CrossRef]
  15. Bott, A.; Maimouni, S.; Zong, W.-X. The Pleiotropic Effects of Glutamine Metabolism in Cancer. Cancers 2019, 11, 770. [Google Scholar] [CrossRef]
  16. Edwards, D.N.; Ngwa, V.M.; Raybuck, A.L.; Wang, S.; Hwang, Y.; Kim, L.C.; Cho, S.H.; Paik, Y.; Wang, Q.; Zhang, S.; et al. Selective Glutamine Metabolism Inhibition in Tumor Cells Improves Antitumor T Lymphocyte Activity in Triple-Negative Breast Cancer. J. Clin. Investig. 2021, 131, e140100. [Google Scholar] [CrossRef]
  17. Wang, Z.; Liu, F.; Fan, N.; Zhou, C.; Li, D.; Macvicar, T.; Dong, Q.; Bruns, C.J.; Zhao, Y. Targeting Glutaminolysis: New Perspectives to Understand Cancer Development and Novel Strategies for Potential Target Therapies. Front. Oncol. 2020, 10, 589508. [Google Scholar] [CrossRef]
  18. Shen, Y.-A.; Chen, C.-L.; Huang, Y.-H.; Evans, E.E.; Cheng, C.-C.; Chuang, Y.-J.; Zhang, C.; Le, A. Inhibition of Glutaminolysis in Combination with Other Therapies to Improve Cancer Treatment. Curr. Opin. Chem. Biol. 2021, 62, 64–81. [Google Scholar] [CrossRef]
  19. Wu, C.; Chen, L.; Jin, S.; Li, H. Glutaminase Inhibitors: A Patent Review. Expert Opin. Ther. Pat. 2018, 28, 823–835. [Google Scholar] [CrossRef]
  20. Hanaford, A.R.; Alt, J.; Rais, R.; Wang, S.Z.; Kaur, H.; Thorek, D.L.J.; Eberhart, C.G.; Slusher, B.S.; Martin, A.M.; Raabe, E.H. Orally Bioavailable Glutamine Antagonist Prodrug JHU-083 Penetrates Mouse Brain and Suppresses the Growth of MYC-Driven Medulloblastoma. Transl. Oncol. 2019, 12, 1314–1322. [Google Scholar] [CrossRef]
  21. Schulte, M.L.; Fu, A.; Zhao, P.; Li, J.; Geng, L.; Smith, S.T.; Kondo, J.; Coffey, R.J.; Johnson, M.O.; Rathmell, J.C.; et al. Pharmacological Blockade of ASCT2-Dependent Glutamine Transport Leads to Antitumor Efficacy in Preclinical Models. Nat. Med. 2018, 24, 194–202. [Google Scholar] [CrossRef]
  22. Iovino, L.; Tremblay, M.E.; Civiero, L. Glutamate-Induced Excitotoxicity in Parkinson’s Disease: The Role of Glial Cells. J. Pharmacol. Sci. 2020, 144, 151–164. [Google Scholar] [CrossRef]
  23. Crupi, R.; Impellizzeri, D.; Cuzzocrea, S. Role of Metabotropic Glutamate Receptors in Neurological Disorders. Front. Mol. Neurosci. 2019, 12, 20. [Google Scholar] [CrossRef]
  24. Montero Domínguez, P.A.; Garduño Juárez, R.; Mares Sámano, S. Computer Simulation Studies of a Kainate (GluK1) Receptor with Two Glutamate Analogues. CyS 2019, 23, 313–324. [Google Scholar] [CrossRef]
  25. Esslinger, C.S.; Cybulski, K.A.; Rhoderick, J.F. Nγ-Aryl Glutamine Analogues as Probes of the ASCT2 Neutral Amino Acid Transporter Binding Site. Bioorg. Med. Chem. 2005, 13, 1111–1118. [Google Scholar] [CrossRef]
  26. Dubey, S.K.; Sharma, A.K.; Narain, U.; Misra, K.; Pati, U. Design, Synthesis and Characterization of Some Bioactive Conjugates of Curcumin with Glycine, Glutamic Acid, Valine and Demethylenated Piperic Acid and Study of Their Antimicrobial and Antiproliferative Properties. Eur. J. Med. Chem. 2008, 43, 1837–1846. [Google Scholar] [CrossRef]
  27. Pagire, S.H.; Lee, E.; Pagire, H.S.; Bae, E.J.; Ryu, S.J.; Lee, D.; Kim, M.H.; Kim, G.R.; Hwang, K.-S.; Ahn, S.; et al. Design, Synthesis and Biological Evaluation of Glutamic Acid Derivatives as Anti-Oxidant and Anti-Inflammatory Agents. Bioorg. Med. Chem. Lett. 2018, 28, 529–532. [Google Scholar] [CrossRef]
  28. Harvey, R.A. Lippincott Ilustrated Reviews-Pharmacology, Ediţia a 5-a, Ed. Medicală Callisto, Bucureşti, 2013, 496, 499–501, 507–508, 5th ed.; Medical Callisto: Bucharest, Romania, 2013. [Google Scholar]
  29. Swinney, D.C. Annual Reports in Medicinal Chemistry, Chapter 18, Molecular Mechanism of Action (MMoA) in Drug Discovery; Academic Press: Cambridge, MA, USA, 2011; Volume 46. [Google Scholar]
  30. Semenyuta, I.; Kovalishyn, V.; Tanchuk, V.; Pilyo, S.; Zyabrev, V.; Blagodatnyy, V.; Trokhimenko, O.; Brovarets, V.; Metelytsia, L. 1,3-Oxazole Derivatives as Potential Anticancer Agents: Computer Modeling and Experimental Study. Comput. Biol. Chem. 2016, 65, 8–15. [Google Scholar] [CrossRef]
  31. Sanderson, B.J.S.; Shield, A.J. Mutagenic Damage to Mammalian Cells by Therapeutic Alkylating Agents. Mutat. Res./Fundam. Mol. Mech. Mutagen. 1996, 355, 41–57. [Google Scholar] [CrossRef]
  32. Puyo, S.; Montaudon, D.; Pourquier, P. From Old Alkylating Agents to New Minor Groove Binders. Crit. Rev. Oncol./Hematol. 2014, 89, 43–61. [Google Scholar] [CrossRef]
  33. Nikolova, T.; Roos, W.P.; Krämer, O.H.; Strik, H.M.; Kaina, B. Chloroethylating Nitrosoureas in Cancer Therapy: DNA Damage, Repair and Cell Death Signaling. Biochim. Biophys. Acta (BBA)—Rev. Cancer 2017, 1868, 29–39. [Google Scholar] [CrossRef] [PubMed]
  34. Kawanishi, S.; Yamamoto, K. Mechanism of Site-Specific DNA Damage Induced by Methylhydrazines in the Presence of Copper(II) or Manganese(III). Biochemistry 1991, 30, 3069–3075. [Google Scholar] [CrossRef]
  35. Moreira-Pais, A.; Ferreira, R.; Gil da Costa, R. Platinum-Induced Muscle Wasting in Cancer Chemotherapy: Mechanisms and Potential Targets for Therapeutic Intervention. Life Sci. 2018, 208, 1–9. [Google Scholar] [CrossRef]
  36. Anh, D.T.; Hai, P.-T.; Huong, L.-T.-T.; Park, E.J.; Jun, H.W.; Kang, J.S.; Kwon, J.-H.; Dung, D.T.M.; Anh, V.T.; Hue, V.T.M.; et al. Exploration of Certain 1,3-Oxazole- and 1,3-Thiazole-Based Hydroxamic Acids as Histone Deacetylase Inhibitors and Antitumor Agents. Bioorg. Chem. 2020, 101, 103988. [Google Scholar] [CrossRef] [PubMed]
  37. de Ruijter, A.J.M.; van Gennip, A.H.; Caron, H.N.; Kemp, S.; van Kuilenburg, A.B.P. Histone Deacetylases (HDACs): Characterization of the Classical HDAC Family. Biochem. J. 2003, 370, 737–749. [Google Scholar] [CrossRef] [PubMed]
  38. Witt, O.; Deubzer, H.E.; Milde, T.; Oehme, I. HDAC Family: What Are the Cancer Relevant Targets? Cancer Lett. 2009, 277, 8–21. [Google Scholar] [CrossRef]
  39. Licciardi, P.; Ververis, K.; Hiong, A.; Karagiannis, T. Histone Deacetylase Inhibitors (HDACIs): Multitargeted Anticancer Agents. Biol. Targets Ther. 2013, 7, 47–60. [Google Scholar] [CrossRef] [PubMed]
  40. Fritzer-Szekeres, M.; Grusch, M.; Luxbacher, C.; Horvath, S.; Krupitza, G.; Elford, H.L.; Szekeres, T. Trimidox, an Inhibitor of Ribonucleotide Reductase, Induces Apoptosis and Activates Caspases in HL-60 Promyelocytic Leukemia Cells. Exp. Hematol. 2000, 28, 924–930. [Google Scholar] [CrossRef]
  41. Aye, Y.; Li, M.; Long, M.J.C.; Weiss, R.S. Ribonucleotide Reductase and Cancer: Biological Mechanisms and Targeted Therapies. Oncogene 2015, 34, 2011–2021. [Google Scholar] [CrossRef] [PubMed]
  42. Shao, J.; Liu, X.; Zhu, L.; Yen, Y. Targeting Ribonucleotide Reductase for Cancer Therapy. Expert Opin. Ther. Targets 2013, 17, 1423–1437. [Google Scholar] [CrossRef] [PubMed]
  43. Suárez, I.; Bodega, G.; Fernández, B. Glutamine Synthetase in Brain: Effect of Ammonia. Neurochem. Int. 2002, 41, 123–142. [Google Scholar] [CrossRef]
  44. Pavlova, N.N.; Thompson, C.B. The Emerging Hallmarks of Cancer Metabolism. Cell Metab. 2016, 23, 27–47. [Google Scholar] [CrossRef]
  45. DeBerardinis, R.J.; Chandel, N.S. Fundamentals of Cancer Metabolism. Sci. Adv. 2016, 2, e1600200. [Google Scholar] [CrossRef] [PubMed]
  46. Marin-Valencia, I.; Yang, C.; Mashimo, T.; Cho, S.; Baek, H.; Yang, X.-L.; Rajagopalan, K.N.; Maddie, M.; Vemireddy, V.; Zhao, Z.; et al. Analysis of Tumor Metabolism Reveals Mitochondrial Glucose Oxidation in Genetically Diverse Human Glioblastomas in the Mouse Brain In Vivo. Cell Metab. 2012, 15, 827–837. [Google Scholar] [CrossRef]
  47. Tardito, S.; Oudin, A.; Ahmed, S.U.; Fack, F.; Keunen, O.; Zheng, L.; Miletic, H.; Sakariassen, P.Ø.; Weinstock, A.; Wagner, A.; et al. Glutamine Synthetase Activity Fuels Nucleotide Biosynthesis and Supports Growth of Glutamine-Restricted Glioblastoma. Nat. Cell Biol. 2015, 17, 1556–1568. [Google Scholar] [CrossRef] [PubMed]
  48. Wasfy, R.E.; Eldeen, A.A.S. Roles of Combined Glypican-3 and Glutamine Synthetase in Differential Diagnosis of Hepatocellular Lesions. Asian Pac. J. Cancer Prev. 2015, 16, 4769–4775. [Google Scholar] [CrossRef] [PubMed]
  49. Kung, H.-N.; Marks, J.R.; Chi, J.-T. Glutamine Synthetase Is a Genetic Determinant of Cell Type–Specific Glutamine Independence in Breast Epithelia. PLoS Genet. 2011, 7, e1002229. [Google Scholar] [CrossRef]
  50. Gross, M.I.; Demo, S.D.; Dennison, J.B.; Chen, L.; Chernov-Rogan, T.; Goyal, B.; Janes, J.R.; Laidig, G.J.; Lewis, E.R.; Li, J.; et al. Antitumor Activity of the Glutaminase Inhibitor CB-839 in Triple-Negative Breast Cancer. Mol. Cancer Ther. 2014, 13, 890–901. [Google Scholar] [CrossRef] [PubMed]
  51. Fan, S.; Wang, Y.; Zhang, Z.; Lu, J.; Wu, Z.; Shan, Q.; Sun, C.; Wu, D.; Li, M.; Sheng, N.; et al. High Expression of Glutamate-ammonia Ligase Is Associated with Unfavorable Prognosis in Patients with Ovarian Cancer. J. Cell. Biochem. 2018, 119, 6008–6015. [Google Scholar] [CrossRef]
  52. Furusawa, A.; Miyamoto, M.; Takano, M.; Tsuda, H.; Song, Y.S.; Aoki, D.; Miyasaka, N.; Inazawa, J.; Inoue, J. Ovarian Cancer Therapeutic Potential of Glutamine Depletion Based on GS Expression. Carcinogenesis 2018, 39, 758–766. [Google Scholar] [CrossRef]
  53. Yuneva, M.O.; Fan, T.W.M.; Allen, T.D.; Higashi, R.M.; Ferraris, D.V.; Tsukamoto, T.; Matés, J.M.; Alonso, F.J.; Wang, C.; Seo, Y.; et al. The Metabolic Profile of Tumors Depends on Both the Responsible Genetic Lesion and Tissue Type. Cell Metab. 2012, 15, 157–170. [Google Scholar] [CrossRef]
  54. Bott, A.J.; Shen, J.; Tonelli, C.; Zhan, L.; Sivaram, N.; Jiang, Y.-P.; Yu, X.; Bhatt, V.; Chiles, E.; Zhong, H.; et al. Glutamine Anabolism Plays a Critical Role in Pancreatic Cancer by Coupling Carbon and Nitrogen Metabolism. Cell Rep. 2019, 29, 1287–1298.e6. [Google Scholar] [CrossRef]
  55. Yang, L.; Achreja, A.; Yeung, T.-L.; Mangala, L.S.; Jiang, D.; Han, C.; Baddour, J.; Marini, J.C.; Ni, J.; Nakahara, R.; et al. Targeting Stromal Glutamine Synthetase in Tumors Disrupts Tumor Microenvironment-Regulated Cancer Cell Growth. Cell Metab. 2016, 24, 685–700. [Google Scholar] [CrossRef]
  56. Ruprecht, J.J.; Kunji, E.R.S. The SLC25 Mitochondrial Carrier Family: Structure and Mechanism. Trends Biochem. Sci. 2020, 45, 244–258. [Google Scholar] [CrossRef] [PubMed]
  57. Infantino, V.; Dituri, F.; Convertini, P.; Santarsiero, A.; Palmieri, F.; Todisco, S.; Mancarella, S.; Giannelli, G.; Iacobazzi, V. Epigenetic Upregulation and Functional Role of the Mitochondrial Aspartate/Glutamate Carrier Isoform 1 in Hepatocellular Carcinoma. Biochim. Biophys. Acta (BBA)—Mol. Basis Dis. 2019, 1865, 38–47. [Google Scholar] [CrossRef] [PubMed]
  58. Wong, C.C.; Qian, Y.; Li, X.; Xu, J.; Kang, W.; Tong, J.H.; To, K.-F.; Jin, Y.; Li, W.; Chen, H.; et al. SLC25A22 Promotes Proliferation and Survival of Colorectal Cancer Cells With KRAS Mutations and Xenograft Tumor Progression in Mice via Intracellular Synthesis of Aspartate. Gastroenterology 2016, 151, 945–960.e6. [Google Scholar] [CrossRef]
  59. Chen, M.-W.; Wu, X. SLC25A22 Promotes Proliferation and Metastasis of Osteosarcoma Cells via the PTEN Signaling Pathway. Technol. Cancer Res. Treat. 2018, 17, 153303381881114. [Google Scholar] [CrossRef] [PubMed]
  60. Du, P.; Liang, H.; Fu, X.; Wu, P.; Wang, C.; Chen, H.; Zheng, B.; Zhang, J.; Hu, S.; Zeng, R.; et al. SLC25A22 Promotes Proliferation and Metastasis by Activating MAPK/ERK Pathway in Gallbladder Cancer. Cancer Cell Int. 2019, 19, 33. [Google Scholar] [CrossRef]
  61. Ali, I.; Wani, W.A.; Haque, A.; Saleem, K. Glutamic Acid and Its Derivatives: Candidates for Rational Design of Anticancer Drugs. Future Med. Chem. 2013, 5, 961–978. [Google Scholar] [CrossRef]
  62. Valente, S.; Mai, A. Small-Molecule Inhibitors of Histone Deacetylase for the Treatment of Cancer and Non-Cancer Diseases: A Patent Review (2011–2013). Expert Opin. Ther. Pat. 2014, 24, 401–415. [Google Scholar] [CrossRef]
  63. Nudelman, A.; Levovich, I.; Cutts, S.M.; Phillips, D.R.; Rephaeli, A. The Role of Intracellularly Released Formaldehyde and Butyric Acid in the Anticancer Activity of Acyloxyalkyl Esters. J. Med. Chem. 2005, 48, 1042–1054. [Google Scholar] [CrossRef]
  64. Nudelman, A.; Ruse, M.; Aviram, A.; Rabizadeh, E.; Shaklai, M.; Zimrah, Y.; Rephaeli, A. Novel Anticancer Prodrugs of Butyric Acid. 2. J. Med. Chem. 1992, 35, 687–694. [Google Scholar] [CrossRef]
  65. Zwergel, C.; Valente, S.; Jacob, C.; Mai, A. Emerging Approaches for Histone Deacetylase Inhibitor Drug Discovery. Expert Opin. Drug Discov. 2015, 10, 599–613. [Google Scholar] [CrossRef]
  66. Berlicki, L. Inhibitors of Glutamine Synthetase and Their Potential Application in Medicine. Mini Rev. Med. Chem. 2008, 8, 869–878. [Google Scholar] [CrossRef] [PubMed]
  67. Berlicki, Ł.; Kafarski, P. The Use of Molecular Modelling for Comparison of Three Possible Modes of Action of Herbicidally Active Derivatives of Aminomethylenebisphosphonic Acid. Pestic. Biochem. Physiol. 2002, 73, 94–103. [Google Scholar] [CrossRef]
  68. Sinden, S.L.; Durbin, R.D. Glutamine Synthetase Inhibition: Possible Mode of Action of Wildfire Toxin from Pseudomonas Tabaci. Nature 1968, 219, 379–380. [Google Scholar] [CrossRef] [PubMed]
  69. Durbin, R.D.; Uchytil, T.F. The Role of Zinc in Regulating Tabtoxin Production. Experientia 1983, 41, 136–137. [Google Scholar] [CrossRef]
  70. Langston-Unkefer, P.L.; Macy, P.A.; Durbin, R.D. Inactivation of Glutamine Synthetase by Tabtoxinine-Beta-Lactam: Effects of Substrates and PH. Plant Physiol. 1984, 76, 71–74. [Google Scholar] [CrossRef] [PubMed]
  71. Lücking, U. Neglected Sulfur(vi) Pharmacophores in Drug Discovery: Exploration of Novel Chemical Space by the Interplay of Drug Design and Method Development. Org. Chem. Front. 2019, 6, 1319–1324. [Google Scholar] [CrossRef]
  72. Roy, M.; Liang, L.; Xiao, X.; Feng, P.; Ye, M.; Liu, J. Lycorine: A Prospective Natural Lead for Anticancer Drug Discovery. Biomed. Pharmacother. 2018, 107, 615–624. [Google Scholar] [CrossRef]
  73. Kasai, T.; Nishitoba, T.; Shiroshita, Y.; Sakamura, S. Several 4-Substituted Glutamic Acid Derivatives and Small Peptides in Some Liliaceae Plants. Agric. Biol. Chem. 1984, 48, 2271–2278. [Google Scholar] [CrossRef]
  74. Alkadi, H.; Khubeiz, M.J.; Jbeily, R. Colchicine: A Review on Chemical Structure and Clinical Usage. Infect. Disord.-Drug Targets 2018, 18, 105–121. [Google Scholar] [CrossRef]
  75. Dudkiewicz, I.; Brosh, T.; Perelman, M.; Salai, M. Colchicine Inhibits Fracture Union and Reduces Bone Strength—In Vivo Study. J. Orthop. Res. 2005, 23, 877–881. [Google Scholar] [CrossRef]
  76. Zhang, T.; Chen, W.; Jiang, X.; Liu, L.; Wei, K.; Du, H.; Wang, H.; Li, J. Anticancer Effects and Underlying Mechanism of Colchicine on Human Gastric Cancer Cell Lines in Vitro and in Vivo. Biosci. Rep. 2019, 39, BSR20181802. [Google Scholar] [CrossRef] [PubMed]
  77. Mukherjee, P.K.; Mukherjee, D.; Maji, A.K.; Rai, S.; Heinrich, M. The Sacred Lotus (Nelumbo nucifera)—Phytochemical and Therapeutic Profile. J. Pharm. Pharmacol. 2009, 61, 407–422. [Google Scholar] [CrossRef] [PubMed]
  78. Liu, C.-M.; Kao, C.-L.; Wu, H.-M.; Li, W.-J.; Huang, C.-T.; Li, H.-T.; Chen, C.-Y. Antioxidant and Anticancer Aporphine Alkaloids from the Leaves of Nelumbo nucifera Gaertn. cv. Rosa-Plena. Molecules 2014, 19, 17829–17838. [Google Scholar] [CrossRef] [PubMed]
  79. Chaichompoo, W.; Chokchaisiri, R.; Apiratikul, N.; Chairoungdua, A.; Yingyongnarongkul, B.; Chunglok, W.; Tocharus, C.; Suksamrarn, A. Cytotoxic Alkaloids against Human Colon Adenocarcinoma Cell Line (HT-29) from the Seed Embryos of Nelumbo nucifera. Med. Chem. Res. 2018, 27, 939–943. [Google Scholar] [CrossRef]
  80. Asokan, S.M.; Mariappan, R.; Muthusamy, S.; Velmurugan, B.K. Pharmacological Benefits of Neferine—A Comprehensive Review. Life Sci. 2018, 199, 60–70. [Google Scholar] [CrossRef]
  81. Kadioglu, O.; Law, B.Y.K.; Mok, S.W.F.; Xu, S.-W.; Efferth, T.; Wong, V.K.W. Mode of Action Analyses of Neferine, a Bisbenzylisoquinoline Alkaloid of Lotus (Nelumbo nucifera) against Multidrug-Resistant Tumor Cells. Front. Pharmacol. 2017, 8, 238. [Google Scholar] [CrossRef]
  82. Law, B.Y.K.; Michelangeli, F.; Qu, Y.Q.; Xu, S.-W.; Han, Y.; Mok, S.W.F.; Dias, I.R.D.S.R.; Javed, M.-H.; Chan, W.-K.; Xue, W.-W.; et al. Neferine Induces Autophagy-Dependent Cell Death in Apoptosis-Resistant Cancers via Ryanodine Receptor and Ca2+-Dependent Mechanism. Sci. Rep. 2019, 9, 20034. [Google Scholar] [CrossRef]
  83. Poornima, P.; Kumar, V.B.; Weng, C.F.; Padma, V.V. Doxorubicin Induced Apoptosis Was Potentiated by Neferine in Human Lung Adenocarcima, A549 Cells. Food Chem. Toxicol. 2014, 68, 87–98. [Google Scholar] [CrossRef]
  84. Sivalingam, K.S.; Paramasivan, P.; Weng, C.F.; Viswanadha, V.P. Neferine Potentiates the Antitumor Effect of Cisplatin in Human Lung Adenocarcinoma Cells Via a Mitochondria-Mediated Apoptosis Pathway. J. Cell. Biochem. 2017, 118, 2865–2876. [Google Scholar] [CrossRef]
  85. Manogaran, P.; Beeraka, N.M.; Huang, C.-Y.; Vijaya Padma, V. Neferine and Isoliensinine Enhance ‘Intracellular Uptake of Cisplatin’ and Induce ‘ROS-Mediated Apoptosis’ in Colorectal Cancer Cells—A Comparative Study. Food Chem. Toxicol. 2019, 132, 110652. [Google Scholar] [CrossRef]
  86. Liu, W.; Yi, D.-D.; Guo, J.-L.; Xiang, Z.-X.; Deng, L.-F.; He, L. Nuciferine, Extracted from Nelumbo Nucifera Gaertn, Inhibits Tumor-Promoting Effect of Nicotine Involving Wnt/β-Catenin Signaling in Non-Small Cell Lung Cancer. J. Ethnopharmacol. 2015, 165, 83–93. [Google Scholar] [CrossRef] [PubMed]
  87. Lamoral-Theys, D.; Decaestecker, C.; Mathieu, V.; Dubois, J.; Kornienko, A.; Kiss, R.; Evidente, A.; Pottier, L. Lycorine and Its Derivatives for Anticancer Drug Design. Mini-Rev. Med. Chem. 2010, 10, 41–50. [Google Scholar] [CrossRef] [PubMed]
  88. McNulty, J.; Nair, J.J.; Bastida, J.; Pandey, S.; Griffin, C. Structure-Activity Studies on the Lycorine Pharmacophore: A Potent Inducer of Apoptosis in Human Leukemia Cells. Phytochemistry 2009, 70, 913–919. [Google Scholar] [CrossRef] [PubMed]
  89. Hao, B.; Shen, S.-F.; Zhao, Q.-J. Cytotoxic and Antimalarial Amaryllidaceae Alkaloids from the Bulbs of Lycoris Radiata. Molecules 2013, 18, 2458–2468. [Google Scholar] [CrossRef] [PubMed]
  90. AquaSol: Predict Aqueous Solublity of Small Molecules Using UG-RNN Ensembles (Version 2.0). Available online: (accessed on 21 January 2023).
  91. Chemicalize—Instant Cheminformatics Solutions. Available online: (accessed on 20 January 2023).
  92. SwissADME. Available online: (accessed on 17 January 2023).
  93. Toxtree—Toxtree—Toxic Hazard Estimation by Decision Tree Approach (Version Available online: (accessed on 26 January 2023).
  94. Molecular Properties Prediction—Osiris Property Explorer. Available online: (accessed on 13 February 2023).
  95. Kerns, E.H.; Di, L. Drug-like Properties: Concepts, Structure Design and Methods: From ADME to Toxicity Optimization; Academic Press: Amsterdam, The Netherlands; Boston, MA, USA, 2008; ISBN 978-0-12-369520-8. [Google Scholar]
  96. Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings. Adv. Drug Deliv. Rev. 1997, 23, 3–25. [Google Scholar] [CrossRef]
  97. Drug Likeness Tool (DruLiTo 1) (Version 1.0.). Available online: (accessed on 1 February 2023).
  98. Ghose, A.K.; Viswanadhan, V.N.; Wendoloski, J.J. A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases. J. Comb. Chem. 1999, 1, 55–68. [Google Scholar] [CrossRef]
  99. Egan, W.J.; Merz, K.M.; Baldwin, J.J. Prediction of Drug Absorption Using Multivariate Statistics. J. Med. Chem. 2000, 43, 3867–3877. [Google Scholar] [CrossRef]
  100. Muegge, I.; Heald, S.L.; Brittelli, D. Simple Selection Criteria for Drug-like Chemical Matter. J. Med. Chem. 2001, 44, 1841–1846. [Google Scholar] [CrossRef]
  101. Khan, T.; Lawrence, A.J.; Azad, I.; Raza, S.; Joshi, S.; Khan, A.R. Computational Drug Designing and Prediction Of Important Parameters Using in Silico Methods- A Review. Curr. Comput.-Aided Drug Des. 2019, 15, 384–397. [Google Scholar] [CrossRef]
  102. Adki, K.M.; Murugesan, S.; Kulkarni, Y.A. In Silico and In Vivo Toxicological Evaluation of Paeonol. Chem. Biodivers. 2020, 17, e2000422. [Google Scholar] [CrossRef]
  103. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A Free Web Tool to Evaluate Pharmacokinetics, Drug-Likeness and Medicinal Chemistry Friendliness of Small Molecules. Sci. Rep. 2017, 7, 42717. [Google Scholar] [CrossRef] [PubMed]
  104. Teague, S.J.; Davis, A.M.; Leeson, P.D.; Oprea, T. The Design of Leadlike Combinatorial Libraries. Angew. Chem. Int. Ed. 1999, 38, 3743–3748. [Google Scholar] [CrossRef]
  105. Marvinsketch (Version 5.0). Available online: (accessed on 19 January 2023).
  106. Baba, Y.; Isomura, T.; Kashima, H. Wisdom of Crowds for Synthetic Accessibility Evaluation. J. Mol. Graph. Model. 2018, 80, 217–223. [Google Scholar] [CrossRef] [PubMed]
  107. Baber, J.; Feher, M. Predicting Synthetic Accessibility: Application in Drug Discovery and Development. Mini-Rev. Med. Chem. 2004, 4, 681–692. [Google Scholar] [CrossRef] [PubMed]
  108. Ertl, P.; Schuffenhauer, A. Estimation of Synthetic Accessibility Score of Drug-like Molecules Based on Molecular Complexity and Fragment Contributions. J. Cheminform. 2009, 1, 8. [Google Scholar] [CrossRef] [PubMed]
  109. Fukunishi, Y.; Kurosawa, T.; Mikami, Y.; Nakamura, H. Prediction of Synthetic Accessibility Based on Commercially Available Compound Databases. J. Chem. Inf. Model. 2014, 54, 3259–3267. [Google Scholar] [CrossRef]
  110. Patlewicz, G.; Jeliazkova, N.; Safford, R.J.; Worth, A.P.; Aleksiev, B. An Evaluation of the Implementation of the Cramer Classification Scheme in the Toxtree Software. SAR QSAR Environ. Res. 2008, 19, 495–524. [Google Scholar] [CrossRef]
  111. Dolan, D.G.; Naumann, B.D.; Sargent, E.V.; Maier, A.; Dourson, M. Application of the Threshold of Toxicological Concern Concept to Pharmaceutical Manufacturing Operations. Regul. Toxicol. Pharmacol. 2005, 43, 1–9. [Google Scholar] [CrossRef]
  112. Renwick, A. Structure-Based Thresholds of Toxicological Concern—Guidance for Application to Substances Present at Low Levels in the Diet. Toxicol. Appl. Pharmacol. 2005, 207, 585–591. [Google Scholar] [CrossRef]
  113. Verhaar, H.J.M.; van Leeuwen, C.J.; Hermens, J.L.M. Classifying Environmental Pollutants. Chemosphere 1992, 25, 471–491. [Google Scholar] [CrossRef]
  114. Verhaar, H.J.M.; Solbé, J.; Speksnijder, J.; van Leeuwen, C.J.; Hermens, J.L.M. Classifying Environmental Pollutants: Part 3. External Validation of the Classification System. Chemosphere 2000, 40, 875–883. [Google Scholar] [CrossRef] [PubMed]
  115. von der Ohe, P.C.; Kühne, R.; Ebert, R.-U.; Altenburger, R.; Liess, M.; Schüürmann, G. Structural AlertsA New Classification Model to Discriminate Excess Toxicity from Narcotic Effect Levels of Organic Compounds in the Acute Daphnid Assay. Chem. Res. Toxicol. 2005, 18, 536–555. [Google Scholar] [CrossRef] [PubMed]
  116. Ellison, C.M.; Madden, J.C.; Cronin, M.T.D.; Enoch, S.J. Investigation of the Verhaar Scheme for Predicting Acute Aquatic Toxicity: Improving Predictions Obtained from Toxtree Ver. 2.6. Chemosphere 2015, 139, 146–154. [Google Scholar] [CrossRef] [PubMed]
  117. Enoch, S.J.; Hewitt, M.; Cronin, M.T.D.; Azam, S.; Madden, J.C. Classification of Chemicals According to Mechanism of Aquatic Toxicity: An Evaluation of the Implementation of the Verhaar Scheme in Toxtree. Chemosphere 2008, 73, 243–248. [Google Scholar] [CrossRef] [PubMed]
  118. Krishna, K.A.; Goel, S.; Krishna, G. SAR Genotoxicity and Tumorigenicity Predictions for 2-MI and 4-MI Using Multiple SAR Software. Toxicol. Mech. Methods 2014, 24, 284–293. [Google Scholar] [CrossRef]
  119. Zeiger, E. The Test That Changed the World: The Ames Test and the Regulation of Chemicals. Mutat. Res./Genet. Toxicol. Environ. Mutagen. 2019, 841, 43–48. [Google Scholar] [CrossRef]
  120. McCann, J.; Ames, B.N. Detection of Carcinogens as Mutagens in the Salmonella/Microsome Test: Assay of 300 Chemicals: Discussion. Proc. Natl. Acad. Sci. USA 1976, 73, 950–954. [Google Scholar] [CrossRef]
  121. Clayden, J.; Greeves, N.; Warren, S. Organic Chemistry; Oxford University Press: Oxford, UK, 2012. [Google Scholar]
  122. Saini, N.; Sterling, J.F.; Sakofsky, C.J.; Giacobone, C.K.; Klimczak, L.J.; Burkholder, A.B.; Malc, E.P.; Mieczkowski, P.A.; Gordenin, D.A. Mutation Signatures Specific to DNA Alkylating Agents in Yeast and Cancers. Nucleic Acids Res. 2020, 48, 3692–3707. [Google Scholar] [CrossRef]
  123. Uddin, N.; Rashid, F.; Ali, S.; Tirmizi, S.A.; Ahmad, I.; Zaib, S.; Zubair, M.; Diaconescu, P.L.; Tahir, M.N.; Iqbal, J.; et al. Synthesis, Characterization, and Anticancer Activity of Schiff Bases. J. Biomol. Struct. Dyn. 2020, 38, 3246–3259. [Google Scholar] [CrossRef]
  124. Daina, A.; Zoete, V. A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules. ChemMedChem 2016, 11, 1117–1121. [Google Scholar] [CrossRef]
  125. Szakács, G.; Váradi, A.; Özvegy-Laczka, C.; Sarkadi, B. The Role of ABC Transporters in Drug Absorption, Distribution, Metabolism, Excretion and Toxicity (ADME–Tox). Drug Discov. Today 2008, 13, 379–393. [Google Scholar] [CrossRef] [PubMed]
  126. Martin, Y.C. A Bioavailability Score. J. Med. Chem. 2005, 48, 3164–3170. [Google Scholar] [CrossRef] [PubMed]
  127. Olsen, L.; Montefiori, M.; Tran, K.P.; Jørgensen, F.S. SMARTCyp 3.0: Enhanced Cytochrome P450 Site-of-Metabolism Prediction Server. Bioinformatics 2019, 35, 3174–3175. [Google Scholar] [CrossRef] [PubMed]
  128. Montanari, F.; Ecker, G.F. Prediction of Drug–ABC-Transporter Interaction—Recent Advances and Future Challenges. Adv. Drug Deliv. Rev. 2015, 86, 17–26. [Google Scholar] [CrossRef] [PubMed]
  129. Kirchmair, J.; Göller, A.H.; Lang, D.; Kunze, J.; Testa, B.; Wilson, I.D.; Glen, R.C.; Schneider, G. Predicting Drug Metabolism: Experiment and/or Computation? Nat. Rev. Drug Discov. 2015, 14, 387–404. [Google Scholar] [CrossRef] [PubMed]
  130. Veith, H.; Southall, N.; Huang, R.; James, T.; Fayne, D.; Artemenko, N.; Shen, M.; Inglese, J.; Austin, C.P.; Lloyd, D.G.; et al. Comprehensive Characterization of Cytochrome P450 Isozyme Selectivity across Chemical Libraries. Nat. Biotechnol. 2009, 27, 1050–1055. [Google Scholar] [CrossRef]
  131. SMARTCyp (Version 3.0). Available online: (accessed on 3 February 2023).
  132. Prediction of Site of Metabolism (Version 2.0). Available online: (accessed on 4 February 2023).
  133. SwissTargetPrediction. Available online: (accessed on 8 February 2023).
  134. Calculation of Molecular Properties and Bioactivity Score. Available online: (accessed on 5 February 2023).
  135. Lagunin, A.A.; Dubovskaja, V.I.; Rudik, A.V.; Pogodin, P.V.; Druzhilovskiy, D.S.; Gloriozova, T.A.; Filimonov, D.A.; Sastry, N.G.; Poroikov, V.V. CLC-Pred: A Freely Available Web-Service for in Silico Prediction of Human Cell Line Cytotoxicity for Drug-like Compounds. PLoS ONE 2018, 13, e0191838. [Google Scholar] [CrossRef]
  136. Schneidman-Duhovny, D.; Inbar, Y.; Nussinov, R.; Wolfson, H.J. PatchDock and SymmDock: Servers for Rigid and Symmetric Docking. Nucleic Acids Res. 2005, 33, W363–W367. [Google Scholar] [CrossRef]
  137. Way2Drug—Main (Version 2.0). Available online: (accessed on 10 February 2023).
  138. Lagunin, A.; Zakharov, A.; Filimonov, D.; Poroikov, V. QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction. Mol. Inf. 2011, 30, 241–250. [Google Scholar] [CrossRef]
  139. Glutamine Synthetase. Available online:’s_College)/Glutamine_Synthetase (accessed on 16 January 2023).
  140. Eisenberg, D.; Gill, H.S.; Pfluegl, G.M.U.; Rotstein, S.H. Structure–Function Relationships of Glutamine Synthetases. Biochim. Biophys. Acta (BBA)—Protein Struct. Mol. Enzymol. 2000, 1477, 122–145. [Google Scholar] [CrossRef]
  141. Liaw, S.-H.; Kuo, I.; Eisenberg, D. Discovery of the Ammonium Substrate Site on Glutamine Synthetase, A Third Cation Binding Site. Protein Sci. 1995, 4, 2358–2365. [Google Scholar] [CrossRef] [PubMed]
  142. Berman, H.M.; Battistuz, T.; Bhat, T.N.; Bluhm, W.F.; Bourne, P.E.; Burkhardt, K.; Feng, Z.; Gilliland, G.L.; Iype, L.; Jain, S.; et al. The Protein Data Bank. Acta Cryst. Sect. D Biol. Cryst. 2002, 58, 899–907. [Google Scholar] [CrossRef] [PubMed]
  143. Liaw, S.-H.; Jun, G.; Eisenberg, D. Interactions of Nucleotides with Fully Unadenylylated Glutamine Synthetase from Salmonella Typhimurium. Biochemistry 1994, 33, 11184–11188. [Google Scholar] [CrossRef] [PubMed]
  144. Pettersen, E.F.; Goddard, T.D.; Huang, C.C.; Couch, G.S.; Greenblatt, D.M.; Meng, E.C.; Ferrin, T.E. UCSF Chimera?A Visualization System for Exploratory Research and Analysis. J. Comput. Chem. 2004, 25, 1605–1612. [Google Scholar] [CrossRef]
  145. Meng, E.C.; Pettersen, E.F.; Couch, G.S.; Huang, C.C.; Ferrin, T.E. Tools for Integrated Sequence-Structure Analysis with UCSF Chimera. BMC Bioinform. 2006, 7, 339. [Google Scholar] [CrossRef]
  146. Grosdidier, A.; Zoete, V.; Michielin, O. SwissDock, a Protein-Small Molecule Docking Web Service Based on EADock DSS. Nucleic Acids Res. 2011, 39, W270–W277. [Google Scholar] [CrossRef]
  147. Bentley, H.R.; McDermott, E.E.; Moran, T.; Pace, J.; Whitehead, J.K. Action of Nitrogen Trichloride on Certain Proteins I. Isolation and Identification of the Toxic Factor. Proc. R. Soc. Lond. B 1950, 137, 402–417. [Google Scholar] [CrossRef]
  148. Zoete, V.; Daina, A.; Bovigny, C.; Michielin, O. SwissSimilarity: A Web Tool for Low to Ultra High Throughput Ligand-Based Virtual Screening. J. Chem. Inf. Model. 2016, 56, 1399–1404. [Google Scholar] [CrossRef]
  149. Gill, H.S.; Eisenberg, D. The Crystal Structure of Phosphinothricin in the Active Site of Glutamine Synthetase Illuminates the Mechanism of Enzymatic Inhibition. Biochemistry 2001, 40, 1903–1912. [Google Scholar] [CrossRef]
  150. SwissDock—The Online Docking Web Server of the Swiss Institute of Bioinformatics—TargetProteins. Available online: (accessed on 11 February 2023).
  151. Grasso, G.; Di Gregorio, A.; Mavkov, B.; Piga, D.; Labate, G.F.D.; Danani, A.; Deriu, M.A. Fragmented Blind Docking: A Novel Protein–Ligand Binding Prediction Protocol. J. Biomol. Struct. Dyn. 2021, 40, 1–10. [Google Scholar] [CrossRef]
  152. Bitencourt-Ferreira, G.; de Azevedo, W.F. Docking with SwissDock. In Docking Screens for Drug Discovery; de Azevedo, W.F., Ed.; Methods in Molecular Biology; Springer: New York, NY, USA, 2019; Volume 2053, pp. 189–202. ISBN 978-1-4939-9751-0. [Google Scholar]
  153. Schneidman-Duhovny, D.; Inbar, Y.; Polak, V.; Shatsky, M.; Halperin, I.; Benyamini, H.; Barzilai, A.; Dror, O.; Haspel, N.; Nussinov, R.; et al. Taking Geometry to Its Edge: Fast Unbound Rigid (and Hinge-Bent) Docking. Proteins 2003, 52, 107–112. [Google Scholar] [CrossRef] [PubMed]
  154. Duhovny, D.; Nussinov, R.; Wolfson, H.J. Efficient Unbound Docking of Rigid Molecules. In Algorithms in Bioinformatics; Guigó, R., Gusfield, D., Eds.; Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2002; Volume 2452, pp. 185–200. ISBN 978-3-540-44211-0. [Google Scholar]
  155. AutoDock Vina (Version 1.1.2). Available online: (accessed on 12 February 2023).
  156. Sehnal, D.; Bittrich, S.; Deshpande, M.; Svobodová, R.; Berka, K.; Bazgier, V.; Velankar, S.; Burley, S.K.; Koča, J.; Rose, A.S. Mol* Viewer: Modern Web App for 3D Visualization and Analysis of Large Biomolecular Structures. Nucleic Acids Res. 2021, 49, W431–W437. [Google Scholar] [CrossRef] [PubMed]
  157. Grosdidier, A.; Zoete, V.; Michielin, O. Blind Docking of 260 Protein-Ligand Complexes with EADock 2.0. J. Comput. Chem. 2009, 30, 2021–2030. [Google Scholar] [CrossRef]
  158. Grosdidier, A.; Zoete, V.; Michielin, O. EADock: Docking of Small Molecules into Protein Active Sites with a Multiobjective Evolutionary Optimization. Proteins 2007, 67, 1010–1025. [Google Scholar] [CrossRef]
  159. Choudhary, D.K.; Kumar, M.; Prasad, R.; Kumar, V. Silico Approach for Sustainable Agriculture; Springer: Singapore, 2018; ISBN 9789811303463. [Google Scholar]
  160. Trott, O.; Olson, A.J. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. J. Comput. Chem. 2009, 31, 455–461. [Google Scholar] [CrossRef] [PubMed]
  161. Wang, R.; Fang, X.; Lu, Y.; Yang, C.-Y.; Wang, S. The PDBbind Database: Methodologies and Updates. J. Med. Chem. 2005, 48, 4111–4119. [Google Scholar] [CrossRef]
  162. Jain, A.N. Scoring Noncovalent Protein-Ligand Interactions: A Continuous Differentiable Function Tuned to Compute Binding Affinities. J. Comput.-Aided Mol. Des. 1996, 10, 427–440. [Google Scholar] [CrossRef]
  163. Bursulaya, B.D.; Totrov, M.; Abagyan, R.; Brooks, C.L., III. Comparative Study of Several Algorithms for Flexible Ligand Docking. J. Comput.-Aided Mol. Des. 2003, 17, 755–763. [Google Scholar] [CrossRef]
  164. López-Camacho, E.; García-Godoy, M.J.; García-Nieto, J.; Nebro, A.J.; Aldana-Montes, J.F. A New Multi-Objective Approach for Molecular Docking Based on RMSD and Binding Energy. In Algorithms for Computational Biology; Botón-Fernández, M., Martín-Vide, C., Santander-Jiménez, S., Vega-Rodríguez, M.A., Eds.; Lecture Notes in Computer Science; Springer International Publishing: Cham, Switzerland, 2016; Volume 9702, pp. 65–77. ISBN 978-3-319-38826-7. [Google Scholar]
  165. Moldovean, S.N.; Timaru, D.-G.; Chiş, V. All-Atom Molecular Dynamics Investigations on the Interactions between D2 Subunit Dopamine Receptors and Three 11C-Labeled Radiopharmaceutical Ligands. Int. J. Mol. Sci. 2022, 23, 2005. [Google Scholar] [CrossRef]
  166. Systèmes, D. BIOVIA Draw for Academics (Version Available online: (accessed on 12 January 2023).
  167. Delaney, J.S. ESOL: Estimating Aqueous Solubility Directly from Molecular Structure. J. Chem. Inf. Comput. Sci. 2004, 44, 1000–1005. [Google Scholar] [CrossRef]
  168. Ali, J.; Camilleri, P.; Brown, M.B.; Hutt, A.J.; Kirton, S.B. Revisiting the General Solubility Equation: In Silico Prediction of Aqueous Solubility Incorporating the Effect of Topographical Polar Surface Area. J. Chem. Inf. Model. 2012, 52, 420–428. [Google Scholar] [CrossRef] [PubMed]
  169. Arnott, J.A.; Planey, S.L. The Influence of Lipophilicity in Drug Discovery and Design. Expert Opin. Drug Discov. 2012, 7, 863–875. [Google Scholar] [CrossRef] [PubMed]
  170. Mannhold, R.; Poda, G.I.; Ostermann, C.; Tetko, I.V. Calculation of Molecular Lipophilicity: State-of-the-Art and Comparison of LogP Methods on More than 96,000 Compounds. J. Pharm. Sci. 2009, 98, 861–893. [Google Scholar] [CrossRef]
  171. Cheng, T.; Zhao, Y.; Li, X.; Lin, F.; Xu, Y.; Zhang, X.; Li, Y.; Wang, R.; Lai, L. Computation of Octanol−Water Partition Coefficients by Guiding an Additive Model with Knowledge. J. Chem. Inf. Model. 2007, 47, 2140–2148. [Google Scholar] [CrossRef] [PubMed]
  172. Wildman, S.A.; Crippen, G.M. Prediction of Physicochemical Parameters by Atomic Contributions. J. Chem. Inf. Comput. Sci. 1999, 39, 868–873. [Google Scholar] [CrossRef]
  173. Moriguchi, I.; Hirono, S.; Liu, Q.; Nakagome, I.; Matsushita, Y. Simple Method of Calculating Octanol/Water Partition Coefficient. Chem. Pharm. Bull. 1992, 40, 127–130. [Google Scholar] [CrossRef]
  174. Moriguchi, I.; Hirono, S.; Nakagome, I.; Hirano, H. Comparison of Reliability of Log P Values for Drugs Calculated by Several Methods. Chem. Pharm. Bull. 1994, 42, 976–978. [Google Scholar] [CrossRef]
  175. Gusar—Create QSAR/QSPR Models on the Basis of the Appropriate Training Sets. Available online: (accessed on 29 January 2023).
  176. PatchDock Server: An Automatic Server for Molecular Docking (Version Beta 1.3). Available online: (accessed on 11 February 2023).
  177. Download UCSF Chimera (Version 1.15). Available online: (accessed on 12 February 2023).
  178. SwissSimilarity. Available online: (accessed on 13 February 2023).
  179. PubChem. Available online: (accessed on 17 January 2023).
  180. ProteinsPlus. Available online: (accessed on 20 April 2023).
Figure 1. The chemical structures of the glutaminase inhibitor compounds (a) CB-839, (b) 968, and (c) BPTES.
Figure 1. The chemical structures of the glutaminase inhibitor compounds (a) CB-839, (b) 968, and (c) BPTES.
Molecules 28 04123 g001
Figure 2. The chemical structures of compounds: (a) DON, (b) JHU-083, (c) azaserine, (d) acivicin, (e) AOA, (f) EGCG, and (g) R162.
Figure 2. The chemical structures of compounds: (a) DON, (b) JHU-083, (c) azaserine, (d) acivicin, (e) AOA, (f) EGCG, and (g) R162.
Molecules 28 04123 g002
Figure 3. The chemical structure of the ASCT2 inhibitor, V-9302 ((2S)-2-amino-4-[bis[[2-[(3- methylphenyl)methoxy]phenyl]methyl]amino]butanoic acid).
Figure 3. The chemical structure of the ASCT2 inhibitor, V-9302 ((2S)-2-amino-4-[bis[[2-[(3- methylphenyl)methoxy]phenyl]methyl]amino]butanoic acid).
Molecules 28 04123 g003
Figure 4. D subunit (in red) and J subunit (blue) of the bacterial GS enzyme and the 4Db6 compound docked at the most probable site estimated by PatchDock [136,153,154]; viewed with UCSF Chimera 1.15 [144,145].
Figure 4. D subunit (in red) and J subunit (blue) of the bacterial GS enzyme and the 4Db6 compound docked at the most probable site estimated by PatchDock [136,153,154]; viewed with UCSF Chimera 1.15 [144,145].
Molecules 28 04123 g004
Figure 5. Ligand (PPQ and 4Db6 compound conformers)–receptor (active subunit of GS enzyme) complexes: GS–PPQ; GS–cluster1; GS–cluster6; GS–cluster33. Visualised with UCSF Chimera 1.15 [144,145].
Figure 5. Ligand (PPQ and 4Db6 compound conformers)–receptor (active subunit of GS enzyme) complexes: GS–PPQ; GS–cluster1; GS–cluster6; GS–cluster33. Visualised with UCSF Chimera 1.15 [144,145].
Molecules 28 04123 g005
Figure 6. Hydrogen bonds made between the ligand molecule (4Db6 compound) and the threonine residue of the receptor molecule. Visualisation of the ligand inserted into the “binding pocket” [144,145,150,151,152,155,157].
Figure 6. Hydrogen bonds made between the ligand molecule (4Db6 compound) and the threonine residue of the receptor molecule. Visualisation of the ligand inserted into the “binding pocket” [144,145,150,151,152,155,157].
Molecules 28 04123 g006
Table 1. Classification of derivatives by classes, groups, and subgroups.
Table 1. Classification of derivatives by classes, groups, and subgroups.
1Compounds resulting from reactions at the carboxyl groupAEstersa-
CAcid chloridesa-
2Compounds resulting from reactions at the amino groupAAmidesa-
BAlkylated glutamic acid derivativesaAzotyperites
CAlcohols resulting from diazotisation a-
3Heterocyclic derivativesAThiazole derivativesaSimple
bWith cyclic anhydride
B1,3 Oxazole derivativesaSimple
bWith cyclic anhydride
4Other derivatives and their potential mechanism of actionAAlkylating agentsaAzotyperites
dAlkyl sulphonates
ePlatinum complexes
BHistone deacetylase inhibitorsa-
CRibonucleotide reductase inhibitorsaHydroxyurea derivatives
bCyclic compounds (based on the structure of Trimidox)
DInhibitors of glutamate synthetase and/or SLC25A mitochondrial transporters aMethionine–sulfoximine analogues
bPhosphinothricin analogues
dVarious inhibitors starting from different structures:
d1. 2-Amino-4-hydroxy aminobutyric acid
d2. Alanosine
d3. Oxetine
d4. Tabtoxin and its metabolite (m)
5Natural substances with proven anti-cancer effects (Table S11; Supplementary Materials) conjugated with glutamic acid molecules AColchicine derivativesaSpindle inhibitors
BNeferine derivativesa-
C7-Hydroxycinuciferine derivativesa-
DLycorine derivativesa-
EDerivatives of 5,6-dehydrolycorinea-
Table 2. The characteristics of Lipinski, Ghose, Veber, Egan, and Muegge drug-likeness rules according to SwissAdme [92].
Table 2. The characteristics of Lipinski, Ghose, Veber, Egan, and Muegge drug-likeness rules according to SwissAdme [92].
Drug-Likeness Rules
MW ≤ 500 Da
MlogP ≤ 4.15
N or O ≤ 10
NH or OH ≤ 5
160 ≤ MW ≤ 480 Da
−0.4 ≤ WlogP ≤ 5.6
40 ≤ MR ≤ 130
20 ≤ atoms ≤ 70
RB ≤ 10
TPSA ≤ 140
WlogP ≤ 5.88
TPSA ≤ 131.6
200 ≤ MW ≤ 600 Da
−2 ≤ XlogP ≤ 5
TPSA ≤ 150
No. of rings ≤ 7
No. of carbon atoms > 4
No. of heteroatoms > 1
No. of RB ≤ 15
H-bond acceptors ≤ 10
H-bond donors ≤ 5
Table 3. Structures of the nine “lead” compounds and their geometrical and isomer-conformation properties [105].
Table 3. Structures of the nine “lead” compounds and their geometrical and isomer-conformation properties [105].
No.ID CodeChemical StructureGeometric IsomersIsomerismConformations
Asymmetric AtomsChiral CentresTautomersStereoisomersEmin (kcal/mol)
11Aa7Molecules 28 04123 i001114210.66
21Aa8Molecules 28 04123 i002112210.79
32Ba2Molecules 28 04123 i0031118212.11
42Ba5Molecules 28 04123 i004114226.45
52Ba6Molecules 28 04123 i005114225.4
63Aa3Molecules 28 04123 i0061116231.59
73Aa5Molecules 28 04123 i0071116231.56
84Da11Molecules 28 04123 i0083346873.63
94Db6Molecules 28 04123 i0092230462.49
Table 4. Compound metabolism assessed using Toxtree [93].
Table 4. Compound metabolism assessed using Toxtree [93].
No.ID CodePrimary Sites of MetabolismSecondary Sites of MetabolismTertiary Sites of MetabolismQuaternary Sites of Metabolism
11Aa7N-dealkylationAmine hydroxylationAliphatic hydroxylationO-dealkylation
21Aa8N-dealkylationAmine hydroxylationAliphatic hydroxylationO-dealkylation
32Ba2N-dealkylationN-oxidationN-dealkylationAliphatic hydroxylation
42Ba5N-dealkylationN-dealkylationN-oxidationAliphatic hydroxylation
63Aa3N-dealkylationAmine hydroxylationAromatic hydroxylationAliphatic hydroxylation
73Aa5N-dealkylationAmine hydroxylationAliphatic hydroxylationAromatic hydroxylation
84Da11N-dealkylationNoneAmine hydroxylationAliphatic hydroxylation
94Db6N-dealkylationNoneAmine hydroxylationAliphatic hydroxylation
Table 5. Compound metabolism assessed using SmartCyp [131] and SOMP [132].
Table 5. Compound metabolism assessed using SmartCyp [131] and SOMP [132].
No.ID Code3A42D62C9
The Most Reactive AtomScoreThe Most Reactive AtomScoreThe Most Reactive AtomScore
Table 6. Bioactivity assessed using Molinspiration [134].
Table 6. Bioactivity assessed using Molinspiration [134].
No.ID CodeGPCR LigandIon Channel Modulator Kinase Inhibitor Nuclear Receptor ligandProtease Inhibitor Enzyme Inhibitor
63Aa3−0.10.23 *−0.38−0.940.27 *0.7 **
73Aa5−0.210−0.28−0.670.33 *0.43 *
84Da11−0.69−0.26−1.36−0.93−0.440.27 *
94Db60.120.83 **−0.65−1.060.67 **0.87 **
* values above 0.2. ** values above 0.5.
Table 7. Bioactivity assessed using the SWISSTarget predictor (most probable molecular targets and their identification data) [133].
Table 7. Bioactivity assessed using the SWISSTarget predictor (most probable molecular targets and their identification data) [133].
No.ID CodeTargetCommon NameUniprot IDTarget ClassProbability
21Aa8Aminopeptidase AENPEPQ07075Protease0.125076
Kynurenine 3-monooxygenaseKMOO15229Oxidoreductase0.125076
Glutamate receptor ionotropic, AMPA 1GRIA1P42261Ligand-gated ion channel0.125076
32Ba2Metabotropic glutamate receptor 3GRM3Q14832Family C G protein-coupled receptor0.150098
Metabotropic glutamate receptor 6GRM6O15303Family C G protein-coupled receptor0.150098
Metabotropic glutamate receptor 2GRM2Q14416Family C G protein-coupled receptor0.150098
42Ba5Glutamate receptor ionotropic kainate 1GRIK1P39086Ligand-gated ion channel0.031227
Glutamate receptor ionotropic AMPA 1GRIA1P42261Ligand-gated ion channel0.031227
Adenosine A3 receptorADORA3P0DMS8Family A G protein-coupled receptor0.031227
52Ba6Glutamate receptor ionotropic kainate 1GRIK1P39086Ligand-gated ion channel0.08057
Glutamate receptor ionotropic AMPA 1GRIA1P42261Ligand-gated ion channel0.08057
Adenosine A3 receptorADORA3P0DMS8Family A G protein-coupled receptor0.08057
63Aa3Kynurenine 3-monooxygenaseKMOO15229Oxidoreductase0.04147
Lysine-specific demethylase 2AKDM2AQ9Y2K7Eraser0.031227
Histone lysine demethylase PHF8PHF8Q9UPP1Eraser0.031227
G protein-coupled receptor 44PTGDR2Q9Y5Y4Family A G protein-coupled receptor0.053518
94Db6Glutamate receptor ionotropic kainate 1GRIK1P39086Ligand-gated ion channel0.08057
Glutamate receptor ionotropic AMPA 1GRIA1P42261Ligand-gated ion channel0.08057
Glutamate receptor ionotropic kainate 5GRIK5Q16478Ligand-gated ion channel0.08057
Table 8. Anticarcinogenic effect: most probable cell lines for which compounds exhibit cytotoxicity. Probability “to be active” (Pa) > Probability “to be inactive” (Pi) [135,136].
Table 8. Anticarcinogenic effect: most probable cell lines for which compounds exhibit cytotoxicity. Probability “to be active” (Pa) > Probability “to be inactive” (Pi) [135,136].
No. ID CodePaPiCell LineCell Line (Full Name)TissueTumour Type
11Aa70.6940.004NCI-H1299Non-small cell lung carcinomaLungCarcinoma
21Aa80.5410.004NCI-H1299Non-small cell lung carcinomaLungCarcinoma
32Ba20.4580.023MDA-MB-453Breast adenocarcinomaBreastAdenocarcinoma
42Ba50.4510.008JurkatAcute leukaemia T-cellsBloodLeukaemia
52Ba60.4380.039MDA-MB-453Breast adenocarcinomaBreastAdenocarcinoma
63Aa30.7170.004DMS-114Lung carcinomaLungCarcinoma
0.5270.005RKOColon carcinomaColonCarcinoma
73Aa50.7280.004DMS-114Lung carcinomaLungCarcinoma
0.5430.005RKOColon carcinomaColonCarcinoma
84Da110.5950.01DMS-114Lung carcinomaLungCarcinoma
94Db60.6570.012HCT-116Colon carcinomaColonCarcinoma
Table 9. Mechanisms of action and adverse/toxic effects (Pa > Pi) [137].
Table 9. Mechanisms of action and adverse/toxic effects (Pa > Pi) [137].
No.ID CodeMechanism of ActionToxic Effects
11Aa70.9650.001Arginine 2-monooxygenase inhibitor0.9820.004 Respiratory toxicity
0.9620.002Protein-disulphide reductase (GSH) inhibitor0.9520.004Euphoria
0.9610.002Methylenetetrahydrofolate reductase (NADPH) inhibitor0.9040.008Weakness
0.9520.001Levanase inhibitor0.8920.007Pure red cell aplasia
0.9510.002Acylcarnitine hydrolase inhibitor0.8850.007Muscle weakness
21Aa80.9690.001Protein-disulphide reductase (GSH) inhibitor0.9760.005Toxic, respiratory failure
0.9610.002Methylenetetrahydrofolate reductase (NADPH) inhibitor0.9320.005Euphoria
0.9560.001Arginine 2-monooxygenase inhibitor0.9000.004Apnoea
0.9530.001Levanase inhibitor0.9000.008Weakness
0.9490.001Aspartate kinase inhibitor0.8710.009Neurotoxic
32Ba20.9560.001Methylamine-glutamate N-methyltransferase inhibitor0.9250.006Euphoria
0.9520.002Acylcarnitine hydrolase inhibitor0.9190.015Toxic, respiratory failure
0.9150.003NADPH peroxidase inhibitor0.8700.011Pure red cell aplasia
0.9060.004Anaphylatoxin receptor antagonist0.8600.003Skin irritation, corrosive
0.9060.006Methylenetetrahydrofolate reductase (NADPH) inhibitor0.8510.019Shivering
42Ba50.9450.002Acylcarnitine hydrolase inhibitor0.9580.009Toxic, respiratory failure
0.9410.001Methylamine-glutamate N-methyltransferase inhibitor0.9350.005Euphoria
0.9200.002Dimethylargininase inhibitor0.9200.004Pure red cell aplasia
0.9090.002Aminoacylase inhibitor0.9010.006Shivering
0.9050.004Gluconate 2-dehydrogenase (acceptor) inhibitor0.8880.003Skin irritation, corrosive
52Ba60.9460.002Acylcarnitine hydrolase inhibitor0.9620.009Toxic, respiratory failure
0.9430.001Methylamine-glutamate N-methyltransferase inhibitor0.9540.004Euphoria
0.9000.001Flavin-containing monooxygenase inhibitor0.9180.002Skin irritation, corrosive
0.8890.007Phobic disorders treatment0.8940.007Pure red cell aplasia
0.8840.003Dimethylargininase inhibitor0.8760.006Postural (orthostatic) hypotension
63Aa30.8660.003Glutamine-phenylpyruvate transaminase inhibitor0.7660.020Respiratory failure
0.8530.005Monodehydroascorbate reductase (NADH) inhibitor0.7310.035Ulcer, aphthous
0.8000.009Arginine 2-monooxygenase inhibitor0.6860.009Anaemia, sideroblastic
0.8030.018Methylenetetrahydrofolate reductase (NADPH) inhibitor0.7070.041Pure red cell aplasia
0.7930.013NADPH peroxidase inhibitor0.6670.033Stomatitis
73Aa50.7970.014Acylcarnitine hydrolase inhibitor0.7640.022Stomatitis
0.7870.005Glutamine-phenylpyruvate transaminase inhibitor0.7190.026Respiratory failure
0.7940.019Methylenetetrahydrofolate reductase (NADPH) inhibitor0.7020.020Asthma
0.7340.002Pyrimidine-deoxynucleoside 2′-dioxygenase inhibitor0.6890.015Respiratory impairment
0.7360.021NADPH peroxidase inhibitor0.6550.020Haematuria
84Da110.9320.004Angiogenesis inhibitor0.4960.074Haematemesis
0.9300.004Anti-inflammatory0.4390.038Thrombocytopoiesis inhibitor
0.9230.004Glutamate-5-semialdehyde dehydrogenase inhibitor0.4360.078Interstitial nephritis
0.8690.001CDK1/cyclin B inhibitor0.4630.109Occult bleeding
0.8650.002Macular degeneration treatment0.4500.105Nephritis
94Db60.9570.002Glutamate-5-semialdehyde dehydrogenase inhibitor0.6510.023Ototoxicity
0.9520.000Sphingosine 1-phosphate receptor 5 antagonist0.5200.069Bronchoconstriction
0.7930.002GABA C receptor antagonist0.3430.158Sneezing
0.7820.003Ornithine cyclodeaminase inhibitor0.2800.097Demyelination
0.7010.003Bone formation stimulant0.3190.159Fibrosis, interstitial
Table 10. Acute toxicity in rodents when administered intraperitoneally, intravenously, orally, and subcutaneously: LD50 in mg/kg [138].
Table 10. Acute toxicity in rodents when administered intraperitoneally, intravenously, orally, and subcutaneously: LD50 in mg/kg [138].
No.ID CodeRat IP LD50 (mg/kg)Rat IV LD50 (mg/kg)Rat Oral LD50 (mg/kg)Rat SC LD50 (mg/kg)
11Aa72593.000 in AD1256.000 in AD5859.000 in AD6254.000 in AD
21Aa83059.000 in AD1268.000 in AD4228.000 in AD4014.000 in AD
32Ba21069.000 in AD1017.000 in AD1978.000 in AD1027.000 in AD
42Ba5436.000 in AD865.000 in AD1861.000 in AD1026.000 out of AD
52Ba6375.200 in AD613.100 in AD1198.000 in AD505.500 in AD
63Aa3418.900 in AD643.600 in AD3172.000 in AD2290.000 in AD
73Aa5585.600 in AD464.800 in AD2623.000 out of AD1923.000 in AD
84Da11551.700 out of AD580.800 in AD3362.000 in AD298.500 in AD
94Db6298.100 out of AD180.400 in AD1456.000 out of AD76.460 in AD
Table 11. Acute toxicity in rodents. Classification of Chemicals according to the OECD Project [138].
Table 11. Acute toxicity in rodents. Classification of Chemicals according to the OECD Project [138].
No.ID CodeRat IP LD50 ClassificationRat IV LD50 ClassificationRat Oral LD50 ClassificationRat SC LD50 Classification
11Aa7Non-Toxic in ADNon-Toxic in ADNon-Toxic in ADNon-Toxic in AD
21Aa8Non-Toxic in ADNon-Toxic in ADClass 5 in ADNon-Toxic in AD
32Ba2Class 5 in ADNon-Toxic in ADClass 4 in ADClass 5 in AD
42Ba5Class 4 in ADNon-Toxic in ADClass 4 in ADClass 5 out of AD
52Ba6Class 4 in ADClass 5 in ADClass 4 in ADClass 4 in AD
63Aa3Class 4 in ADClass 5 in ADClass 5 in ADClass 5 in AD
73Aa5Class 5 in ADClass 5 in ADClass 5 out of ADClass 5 in AD
84Da11Class 5 out of ADClass 5 in ADClass 5 in ADClass 4 in AD
94Db6Class 4 out of ADClass 4 in ADClass 4 out of ADClass 3 in AD
Table 12. Molecular dynamics simulation results for compound 4Db6 [144].
Table 12. Molecular dynamics simulation results for compound 4Db6 [144].
StepTime (fs)Potential Energy (J)Kinetic Energy (J)
Table 13. Molecular docking results for the 4Db6 compound using PatchDock [136,153,154].
Table 13. Molecular docking results for the 4Db6 compound using PatchDock [136,153,154].
No.ScoreInterface AreaCoordinates
12900318.4−1.34; −0.09; 1.38; −23.80; −22.56; −39.82
22858318.31.02; 0.08; 0.88; −48.08; 21.92; −56.70
32834310.4−1.95; 0.16; −1.70; −74.21; −8.23; −46.64
428303061.32; 0.01; −2.83; −44.00; −28.47; −52.58
52814318.5−1.33; −0.27; 1.59; −27.20; −37.47; −60.51
62792316.6−1.14; −0.17; −1.48; −66.81; 35.58; −67.43
72792308.1−1.78; −0.03; 2.84; −41.83; 28.83; −45.53
82790301.5−1.64; 0.44; 1.33; −71.19; −15.76; −81.43
92786310.82.09; −0.02; −1.89; −16.57; 19.50; −51.15
102786296.7−2.08; 0.27; −1.76; −57.26; −20.74; −68.35
Table 14. Energetic values of the most probable ligand (4Db6 compound)–receptor complexes [144,150,152,157].
Table 14. Energetic values of the most probable ligand (4Db6 compound)–receptor complexes [144,150,152,157].
ClusterΔG (kcal/mol)FullFitness (kcal/mol)Ki
1−8.1−2139.911.264 × 10−7
6−7.6−2137.122.904 × 10−7
33−6.8−2126.694.047 × 10−7
Table 15. Molecular docking results for the 4Db6 compound obtained using AutoDock Vina. Run time: 28.3 s.
Table 15. Molecular docking results for the 4Db6 compound obtained using AutoDock Vina. Run time: 28.3 s.
ModeAffinity (kcal/mol)Dist. from RMSD L. BDist. from RMSD U. B
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Moldovan, O.-L.; Sandulea, A.; Lungu, I.-A.; Gâz, Ș.A.; Rusu, A. Identification of Some Glutamic Acid Derivatives with Biological Potential by Computational Methods. Molecules 2023, 28, 4123.

AMA Style

Moldovan O-L, Sandulea A, Lungu I-A, Gâz ȘA, Rusu A. Identification of Some Glutamic Acid Derivatives with Biological Potential by Computational Methods. Molecules. 2023; 28(10):4123.

Chicago/Turabian Style

Moldovan, Octavia-Laura, Alexandra Sandulea, Ioana-Andreea Lungu, Șerban Andrei Gâz, and Aura Rusu. 2023. "Identification of Some Glutamic Acid Derivatives with Biological Potential by Computational Methods" Molecules 28, no. 10: 4123.

Article Metrics

Back to TopTop