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Article

Dipeptidyl Peptidase 4 Inhibitors in Type 2 Diabetes Mellitus Management: Pharmacophore Virtual Screening, Molecular Docking, Pharmacokinetic Evaluations, and Conceptual DFT Analysis

by
Daniela Istrate
and
Luminita Crisan
*
“Coriolan Dragulescu” Institute of Chemistry, 24 M. Viteazu Avenue, 300223 Timisoara, Romania
*
Author to whom correspondence should be addressed.
Processes 2023, 11(11), 3100; https://doi.org/10.3390/pr11113100
Submission received: 26 September 2023 / Revised: 23 October 2023 / Accepted: 27 October 2023 / Published: 28 October 2023
(This article belongs to the Special Issue Natural Compounds Applications in Drug Discovery and Development)
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Abstract

:
Dipeptidyl Peptidase 4 (DPP-4) expressed on the surface of many different cells is a promising target to develop new candidates for Type 2 diabetes mellitus (T2DM) management. In this light, we performed a computer-aided simulation involving 3-D pharmacophore screening, molecular docking, and drug-likeness assessment to identify novel potential DPP-4 inhibitors with an improved physicochemical profile to treat T2DM. In addition, global reactivity descriptors, including HOMO and LUMO energies, HOMO-LUMO gaps, and Fukui indices, were computed to confirm the essential structural features to achieve DPP-4 activity. The gathered outcomes recommend that eight out of 240 million compounds collected from eight pre-built databases (Molport, Chembl30, ChemDiv, ChemSpace, Mcule, Mcule-ultimate, LabNetwork, and ZINC) are drug-like and nontoxic, and may serve as starting points for designing novel, selective, and potent DPP-4 inhibitors. Furthermore, the success of the current workflow to identify DPP-4-potential inhibitors strengthens its potential efficiency to also predict natural compounds as novel adjutants or main therapy for T2DM or discover hit compounds of other targets.

1. Introduction

Type 2 diabetes (T2D), known as insulin resistance, is one of the most complex chronic metabolic disorders and is considered a major healthcare burden worldwide [1]. In general, T2D is marked by high blood sugar levels and in combination with other factors leads to chronic vascular complications [2], myocardial infarction [3], stroke (ischemic stroke, hemorrhagic stroke, transient ischemic attack) [4,5], atherosclerosis [6,7], microangiopathy [8], gangrene of the lower limbs [9,10], dental diseases [11], kidney diseases [12], etc. The most common oral medication for T2D (https://go.drugbank.com/drugs, accessed on 8 August 2023) is considered metformin [13] as an ingredient combined with other medications (DB00331) such as metformin-alogliptin, metformin-canagliflozin, metformin-dapagliflozin, metformin-empagliflozin, metformin-ertugliflozin, metformin-glyburide, metformin-linagliptin, metformin-pioglitazone, metformin-repaglinide, metformin-rosiglitazone, metformin-saxagliptin, metformin-sitagliptin, etc. Other oral drugs also prescribed in the treatment of T2DM that help the human body better manage insulin or remove extra glucose from the blood are (i) dopamine-2 agonist [14] (Bromocriptine, DB01200); (ii) dipeptidyl peptidase-4 (DPP-4) inhibitors [15,16] (alogliptin (DB06203), linagliptin (DB08882), linagliptin-empagliflozin, saxagliptin (DB06335), sitagliptin (DB01261), sitagliptin-simvastatin; (iii) Glucagon-like peptide-1 (GLP-1) receptor agonists [17,18] (dulaglutide (DB09045), exenatide (DB01276), liraglutide (DB06655), lixisenatide (DB09265), semaglutide (DB13928), tirzepatide (DB15171); (iv) sodium-glucose cotransporter-2 (SGLT2) inhibitors [19,20] (canagliflozin (DB08907), dapagliflozin (DB06292), dapagliflozin-metformin, dapagliflozin-saxagliptin, empagliflozin (DB09038), empagliflozin-linagliptin, ertugliflozin (DB11827); and (v) peroxisome proliferator-activated receptor (PPARγ) agonists [21,22] (rosiglitazone (DB00412), pioglitazone (DB01132), pioglitazone-alogliptin, pioglitazone-glimepiride, pioglitazone-metformin, etc. Additionally, diabetic patients (both type 1 and type 2 DM) have their immune responses disrupted and are more susceptible to many kinds of infections [23,24]. However, the global morbidity and mortality rates, which affect patients with T2D, ares on a continuous rise. In 2021, the International Diabetes Federation (IDF) Diabetes Atlas data (https://diabetesatlas.org/atlas/tenth-edition/, accessed on 8 August 2023), informed that 10.5% of the adult population between 20 and 79 years old have diabetes. Guariguata et al. [25] predict that more than 590 million people will be diagnosed with this disease by 2035. An alarming statistic shows that by 2045, approximately 46% of the population will be living with diabetes. Over 90% of these will have type 2 diabetes [26,27] and almost half of them do no know that they are living with the sickness. That is why essential projects (240 national diabetes associations across 160 countries (https://idf.org/our-network/regions-and-members/, accessed on 8 August 2023) are underway in the scientific community to prevent and control this disease. Thus, the scientific results play a significant role if they are made public and can be used as starting points for additional research. A total of 1,149,497 published papers were assembled in the Web of Science Core Collection (WoSCC) until June 2023 on the topics of “diabetes”, 930,263 of which have been published after the year 2000. The scientific community’s interest in the management of this disease has grown considerably (about 40%), observing a significant increase in scientific publications from approximately 17,000 in the year 2000 to approximately 70,000 in the year 2022 (Figure 1). So, in the early stage of drug design, in silico requirements have been managed by using various computational approaches, such as pharmacophore modeling [28,29,30,31], quantitative structure–activity relationships (QSAR) [32,33,34,35], molecular docking [36,37,38,39], molecular dynamics simulation [40,41,42], DFT simulation [35,43,44,45,46], etc. These techniques have generated notable interest by reducing the time required for experimental trials, as well as human and resource costs.
DPP-4, also known as the T-cell activation antigen cluster of differentiation CD26, is an extensively investigated aminopeptidase, a member of the serine peptidase/prolyl oligopeptidase gene family. This aminopeptidase inactivates the incretin hormones glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) and its inhibition is believed to be a result of glucose-lowering therapy in T2D [35,47,48]. Following rapid development in the 2000s, DPP-4 inhibitors known as gliptins, approved by the Food and Drug Administration (FDA), by the European Medicines Agency (EMA), and by the Japanese Pharmaceuticals and Medical Devices Agency (PMDA), have been widely used in the management of T2D [15]. The gliptins as DPP-4 inhibitors appear to be very well tolerated, but some have been associated with different side effects, including severe joint pain (sitagliptin, vildagliptin, saxagliptin) [49], serious allergic and hypersensitivity reactions (sitagliptin) [50], dermal side effects/pruritus (aloglitin) [51], etc. Although gliptins have been associated with few short-term contraindications, the FDA conducts extensive follow-up evaluations of monitoring and reporting of adverse effects. Also, the scientific community pays special attention to finding inhibitors with fewer adverse effects in the fight against T2D disease.
Investigating the binding interactions of DPP-4 inhibitors at the binding site is essential for gaining insights into their effectiveness and for providing guidance in the exploration of new drug candidates. The crystal structure of DPP-4 displays a homodimeric configuration, with two chains, chain A and chain B, and it consists of four domains (a cytoplasmic domain (1–6), a transmembrane domain (TMD) (7–28), a flexible stalk segment (29–39), and the extracellular domain (40–766) with five subsites: S1 (SER630, VAL656, TRP659, TYR662, TYR666, ASN710, VAL711), S2 (ARG125, GLU205, GLU206, PHE357, ARG358, ARG669), S1′ (PHE357, TYR547, PRO550, SER630, TYR631, TYR666), S2′ (TYR547, TRP629, SER630, HIS740), and S2 extensive (VAL207, SER209, PHE357, ARG358) [52,53]. The mandatory ligand’s interactions for DPP-4 inhibition with S1 and S2 subunits were observed both for alogliptin within the 3GB0 binding site, linagliptin within the 2RGU binding site, and sitagliptin within the 1 × 70 binding site. Moreover, alogliptin exhibited additional interactions with the S1’ subunit, while linagliptin revealed additional interactions with both S1’ and S2’ subunits, and in the case of sitagliptin, the additional interactions were observed with the S2 extensive subunit [52,54,55,56]. The additional interactions between the ligand and DPP-4 subsites indicate an enhanced bioavailability potency and, consequently, encourage the exploration of diverse scaffold structures that can play a pivotal role in facilitating these interactions. This approach opens up avenues for the development of novel and potentially more effective DPP-4 inhibitors. Based on these observations, in the present work, we report a virtual screening experiment involving pharmacophore generation, drug-likeness evaluation, and molecular docking simulations to identify potential Dipeptidyl Peptidase 4 (DPP-4) inhibitors with an improved physicochemical profile for the management of type 2 diabetes mellitus (T2DM). To reach our goal, the virtual screening of eight (CHEMBL/ChemDiv/ChemSpace/MCULE/MCULE-ULTIMATE/MolPort/LabNetwork/ZINC) large compound databases (Table 1) using ligand- and structure/receptor-based protocols were engaged (Figure 2).

2. Materials and Methods

Step 1—Pharmacophore generation. Pharmit [57] online program (https://pharmit.csb.pitt.edu/ accessed on 6 December 2022) was engaged to select potential inhibitors for DPP-4 taking into account the interactions between each ligand (alogliptin, sitagliptin, linagliptin) and their binding site (3G0B, 1X70, 2RGU), the structure similarity shape with query molecules, and the Lipinski’s rule of five (molecular weight, MW < 500 g/mol; octanol-water partition coefficient, LogP < 5; rotatable bonds, RB < 10; polar surface area, PSA < 140 Å; hydrogen bond acceptor, nHA < 10; hydrogen bond donor, nHB < 5). The input for Pharmit was automatically generated using reference complex (protein–ligand): 3G0B—alogliptin, 1X70—sitagliptin, 2RGU—linagliptin). Using the RX protein structure, the energy minimization for each pose was available, and the root mean squared deviation (RMSD) between the query drug and the minimized selected compounds was analyzed. The value of shape tolerance was set to 1 for each simulation. The prioritized compounds by the pharmacophore model, were used in the docking procedure.
Step 2—Docking methodology. The co-crystal structures of DPP-4 with approved diabetes drugs: alogliptin, linagliptin, and sitagliptin were retrieved from the Protein Data Bank (https://www.rcsb.org/accessed on 11 January 2023). (PDB ID: 3G0B: Resolution: 2.25 Å; R-value free: 0.242; and R-value work: 0.207 [58], PDB ID: 1X70: Resolution: 2.1 Å; R-value free: 0.228; and R-value work: 0.193 [59], and PDB ID: 2RGU: Resolution: 2.6 Å; R-value free: 0.276; and R-value work: 0.217 [60]). The receptors were prepared for docking using the MakeReceptor (v.3.5.0.4) module from the OpenEye package [61]. The Chain A for each protein was selected, and the outer contour and box volume of 604 Å and 6226 Å for 3G0B, of 686 Å and 5029 Å for 1X70, and of 921 Å and 7114 Å for 2RGU [62] were generated. Additionally, one water molecule (HOH:1) for 3G0B structure, six water molecules (HOH:1551, HOH:1582, HOH:1605, HOH:1935, HOH:1957, HOH:1986) for 1X70, and two water molecules (HOH:1020, HOH:1041) were preserved for docking simulation. The prioritized compounds from step 1 were prepared for docking using the LigPrep module [63] for adding the hydrogen atoms and generating the ionization states at a pH range of 7.2 ± 0.2, and the Omega module (v.4.0.0.4) [64,65] for conformer generation. The docking protocol was validated in two steps. The first step debuted with the redocking of the approved drugs extracted from the crystal complex into the same binding site (3G0B, 1X70, 2RGU), followed by the second step with the RMSD calculating between the best-docked pose for alogliptin, sitagliptin, linagliptin, and corresponding X-ray structure. For this, the FRED program (v.3.5.0.4) [66,67,68,69] and the Superposition option of the Maestro module (v.13.4.134) of Schrödinger [70] were engaged.
Step 3—Quantitative Estimate of Drug-likeness (QED) score was calculated for all the compounds resulting from step 2 in order to quantify and categorize the chemical structures to properties of oral drugs (Equation (1)) [71].
Q E D = exp ⁡ ( 1 n ∑ i = 1 n l n d i )
where di denotes the dth desirability function and n = 8 is the number of drug-likeness-related properties (molecular weight, MW; lipophilicity, logP; the number of aromatic rings, NAr; the number of hydrogen bond donors, nHD; the number of hydrogen bond acceptors, nHA; number of rotatable bonds, Nrotb; topological polar surface area, TPSA; and number of structural alerts, ALERTS).
Step 4—In silico predicted ADMETox Profile. The ADMETlab2.0 [72] online server (https://admetmesh.scbdd.com/, accessed on 25 May 2023) was used to predict the physicochemical (e.g., molecular weight, van der Waals volume, number of hydrogen bond acceptors, number of hydrogen bond donors, topological polar surface area, the n-octanol/water distribution coefficient, the aqueous solubility, etc.), medicinal chemistry (e.g., Lipinski Rule, Pfizer Rule, Pan Assay Interference Compounds (PAINS), etc.), and pharmacokinetic properties (absorption, distribution, metabolism, excretion and toxicity parameters) essential for all the compounds selected in the previous steps. These parameters are based on predictive models and are a viable alternative to experimental determinations of them. Additionally, the bioavailability radar plot outlines the physicochemical quality of the selected compounds, based on following parameters: molecular weight (MW), the logarithm of the n-octanol/water distribution coefficient (logP), the logarithm of aqueous solubility value (logS), the logarithm of the n-octanol/water distribution coefficients at pH = 7.4 (logD), number of hydrogen bond acceptors (nHA), number of hydrogen bond donors (nHD), topological polar surface area (TPSA), number of rotatable bonds (nRot), number of rings (nRings), number of atoms in the biggest ring (MaxRing), number of heteroatoms (nHet), formal charge (fChar), and number of rigid bonds (nRig) [72,73].
Step 5—Molecular Lipophilicity Potential (MLP). The Galaxy Visualizer (v.2022.11 beta, https://www.molinspiration.com/cgi-bin/galaxy, accessed on 17 August 2023) was employed to visualize the three-dimensional representation of molecular lipophilicity potential (MLP), which gives us information about the hydrophobic surface of compounds (violet and blue), and the hydrophilic surface of compounds (orange and red). Investigation of the 3D distribution of hydrophobicity on the molecular surface is very advantageous when presenting differences in predicted ADME parameters of compounds with the same/similar octanol-water partition coefficient values (logP). The 3D parameters offer considerably more information than logP parameter, which is represented by a single value [73,74,75]. For MLP prediction, the milogP parameter developed in-house by Molinspiration is used (https://www.molinspiration.com/services/logp.html, accessed on 17 August 2023). This parameter, named milogP2.2-2005, is calculated as a sum of fragment-based contributions and correction factors by including predicted and experimental logP values for a set of approximately 12,000 compounds, predominantly drug-like molecules [73,74,75].
Step 6—Electronic parameters. The final selected compounds from previous steps (1 to 4) were used as input for DFT studies. The Jaguar module (Schrödinger) [76,77] was engaged for compound optimization with the Becke three-parameter exchange potential and Lee–Yang–Parr correlation functional (B3LYP) [78,79] and 6-31G** basis set [80]. The highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), the HOMO-LUMO gap energy (ΔE), and Fukui indices were explored to add new information about the reactive sites of compounds. The f_NN_HOMO parameter is associated with the Fukui function, f−, and it measures the atomic sites that are accessible for electrophilic attacks. Conversely, f_NN_LUMO is linked to the Fukui function, f+, it identifies the regions that are prone to nucleophilic attacks [81,82].

3. Results

The Pharmit tool generated the pharmacophoric points (Figure 3) for alogliptin, which includes three hydrogen acceptor (HA—orange), one hydrogen donor (HD—white), one aromatic (Ar—purple), and three hydrophobic features (Hy—green); for sitagliptin, which contains two hydrogen acceptors (HA—orange), one hydrogen donor (HD—white), one aromatic (Ar—purple), and three hydrophobic features (Hy—green); and for linagliptin, which comprises two hydrogen acceptors (HA—orange), one hydrogen donor (HD—white), two aromatics (Ar—purple), and one hydrophobic feature (Hy—green).
The virtual screening experiment based on pharmacophoric points, RMSD, and shape tolerance for alogliptin (3G0B), sitagliptin (1X70), and linagliptin (2RGU), set as reference molecules, generated 1109 compounds, 1619 compounds, and 24 compounds, respectively (Table 1), from a total number of 244,888,582 compounds. The RMSD range values for alogliptin and the 1109 selected compounds were between 0.160 and 0.935 Å, for sitagliptin and the 1619 selected compounds were between 0.218 and 0.934 Å, and for linagliptin and the 24 selected compounds were between 0.418 and 0.887 Å. All RMSD values are lower than 2 and are in agreement with the accepted threshold [83,84].
In order to improve the accuracy of screening, the high-performance molecular docking procedure was involved, by using Openeye’s FRED (v.3.5.0.4) [66,67,68,69]. Thus, the best molecule “hits” (1109 for 3G0B, 1619 for 1X70, and 24 for 2RGU) were also downloaded and prepared (LigPrep and Omega) for the molecular docking studies. Preliminary to docking of all selected compounds, the native ligands (approved drugs: alogliptin, sitagliptin, and linagliptin) were redocked back into their active site (3GB0, 1X70, and 2RGU). The very low values of RMSD (0.430 Å for alogliptin, 0.618 Å for sitaglitin, and 1.056 Å for linagliptin), indicate the reliability of the docking procedure for the molecule “hits” against the selected targets. Also, the orientation of each RX ligand–receptor complex reproduced with significant accuracy. The docking analysis unveiled the following: (i) in the active binding site of 3GB0, 10 hydrogen bonds: HOH1, ARG125, GLU205, GLU206, SER630, TYR631, TRP629, TYR662, two with TYR547, and five hydrophobic: PHE357, TYR662, TYR666 and two with TYR547 for alogliptin; (ii) in the active binding site of 1X70, 10 hydrogen bonds: HOH1605, ARG125, SER209, ARG358, ASN710, GLU206, and two with GLU205 and TYR662, five halogen bonds: GLU205, GLU206, ASN710, and two with VAL207, six hydrophobic: ARG358, TYR662, TYR666, HIS740, and two with PHE357; and (iii) in the active binding site of 2RGU, 12 hydrogen bonds: HOH1020, GLU205, GLU206, SER630, TYR631, TYR662, TYR666, two with TYR547, and three with HOH1041, and nine hydrophobic: PHE357, VAL656, TRP627, VAL711, two with TYR547, and three with TRP629. These critical amino acid residue interactions of the alogliptin, sitagliptin, and linagliptin revealed comparable chemgauss4 (CG4) docking scores, of CG4 = −10.404, CG4 = −10.500, and CG4 = −9.771 in the active site of the targets 3G0B, 1X70, and 2RGU. Detailed results regarding the bond length, type of interactions, and the atoms implicated in these interactions are present in Figure 4 and Table S1.
After validating the molecular docking procedure, the “hit” molecules resulting from step 1 were docked in the active site of the corresponding protein, following the same methodology. Then, a short analysis of the CG4 values, a search for possible IC50 values determined experimentally in the literature, and a visual analysis of the common fragment structures were performed (Figure 5).
Thus, from the 1109 compounds docked in 3GB0, 23 compounds with CG4 higher than alogliptin were selected. Among them, only 11 present QED values higher than 0.67 (Table 2 and Table S2). The analysis of these 11 compounds revealed six compounds (CHEMBL4162340, CHEMBL227676, CHEMBL1651766, CHEMBL3329689, CHEMBL1650443, CHEMBL1650449) that have IC50 values experimentally determined on DDP4; two compounds that have the same scaffold (PubChem-72845648 with CSC076365308); and three compounds that do not present experimentally determined IC50 values nor the same scaffold (CSC079167462, ZINC95941402, ZINC408512952). Based on the above, the four compounds named CSC076365308, ZINC95941402, ZINC408512952, and CSC079167462, similar to alogliptin, will be analyzed (Figure 6A). The results are shown in Figure 7 and Table 2. Also, the very low RMSD values between these compounds and alogliptin (https://pharmit.csb.pitt.edu/, accessed on 20 December 2022): 0.679 Å (CSC076365308), 0.601 Å (ZINC95941402), 0.857 Å (ZINC408512952), and 0.726 Å (CSC076365308) indicate a very good selection strategy in the search for new inhibitors for DPP-4.
Exploring the binding interactions of potential DPP-4 inhibitors in the binding site is essential for understanding their action and also for further investigations to make them drug candidates. The selected compounds (Figure 6A) give more binding interactions in the active site of 3G0B and better values of CG4 scoring functions than alogliptin, as follows: (i) CSC076365308 arrayed a CG4 value of −11.248 and exhibited two water hydrogen bond interactions with HOH1, six conventional hydrogen bond interactions with SER630, GLU205, TYR662, ARG125 (two bonds), HIS740, and seven hydrophobic interactions with TYR547 (Pi-Sigma), TRP629 (Pi-Pi Stacked, three bonds), TYR547 (Pi-Alkyl), TYR662 (Pi-Alkyl), and TYR666 (Pi-Alkyl); (ii) ZINC95941402 showed a CG4 value of −10.904 and two water hydrogen bond interactions with HOH1, three conventional hydrogen bond interactions with SER630, ARG125 (two bonds), one carbon hydrogen bond interaction with SER630, one Pi-Donor hydrogen bond interaction with TYR547, one electrostatic interaction with ARG125 (Pi-Cation), and seven hydrophobic interactions with TYR666 (Pi-Sigma), TYR547 (Pi-Pi Stacked), TYR666 (Pi-Pi Stacked), TRP629 (Pi-Alkyl, two bonds), TYR631 (Pi-Alkyl), and TYR662 (Pi-Alkyl); (iii) ZINC408512952 led to a CG4 value of −10.783 and formed one water hydrogen bond interaction with HOH1, two conventional hydrogen bond interactions with TYR547, TYR666, carbon hydrogen bond interaction with TYR547, one Pi-Donor hydrogen bond interaction with TYR662 and six hydrophobic interactions with TYR662 (Pi-Pi Stacked), PHE357 (Pi-Pi Stacked), TYR666 (Pi-Pi T-shaped, two bonds), and PHE357 (Pi-Alkyl, two bonds); and (iv) CSC079167462 conducted to a CG4 value of −10.449 and displayed two conventional hydrogen bond interactions with GLU206, ARG669, one carbon hydrogen bond interaction with GLU206, one electrostatic interaction with ARG125 (Pi-Cation), and five hydrophobic interactions with TYR662 (Pi-Pi Stacked), VAL711 (Alkyl), ARG358 (Alkyl, TYR662 (Pi-Alkyl), and HIS740 (Pi-Alkyl). Figure 6 portrays the binding modes of each “hit” molecule into the active of 3GB0. Also, the bond length, type of interactions, and the atoms implicated in interactions are present in detail in Table S3.
From the 1619 compounds docked in 1 × 70, 10 compounds with CG4 higher than sitagliptin were selected. Among them, only nine present QED values higher than the threshold value of 0.67 (Table 2 and Tables S4 and S5). The analysis of these nine compounds revealed five compounds that have the same scaffold (SC078285176, CSC088278900, CSC073335161, CSC091579518 with CSC092194469 and CSC102344798 with ZINC305224681) and four compounds that do not present experimentally determined IC50 values nor the same scaffold (ZINC305224681, CSC092194469, ZINC12327733, ZINC718764852—Figure 6B). In view of this, the four compounds named ZINC305224681, CSC092194469, ZINC12327733, and ZINC71876485 will be analyzed (Figure 6B). The results are shown in Figure 8 and Table 2. Likewise, the very low RMSD values between these compounds and sitagliptin (https://pharmit.csb.pitt.edu/, accessed on 28 December 2022): 0.570 Å (ZINC305224681), 0.597 Å (CSC092194469), 0.856 Å (ZINC12327733), and 0.702 Å (ZINC71876485) demonstrate a very good selection technique in the search for new DPP-4 inhibitors. The selected compounds (Figure 6B and Figure 8) give more binding interactions in the active site of 1 × 70 and better values of CG4 scoring functions than sitagliptin, as follows: (i) ZINC305224681 with a CG4 value of −11.107 displayed three water hydrogen bond interactions with HOH1551, HOH1582, HOH1605, HOH1957, five conventional hydrogen bond interactions with GLU206, ARG125, TYR547, GLU205 (two bonds), one Pi-Donor hydrogen bond interaction with TYR662, one Halogen (Fluorine) with ARG125, and three hydrophobic interactions with TYR662 (Pi-Pi Stacked), PHE357 (Pi-Pi T-shaped), and TYR666 (Pi-Pi T-shaped); (ii) CSC092194469 exhibited a CG4 value of −10.968 and formed three water hydrogen bond interactions with HOH1957, HOH1551, HOH1605, HOH1957, three conventional hydrogen bond interactions with GLU206, GLU205, TYR662, one Pi-Donor hydrogen bond interaction with TYR662, and three hydrophobic interactions with TYR662 (Pi-Pi Stacked), PHE357 (Pi-Pi Stacked), TYR666 (Pi-Pi T-shaped) and HIS740 (Pi-Alkyl); (iii) ZINC12327733 revealed a CG4 value of −10.712 and established one water hydrogen bond interaction with HOH1605, five conventional hydrogen bond interactions with SER209, GLU205 (two bonds), ASN710 (two bonds), one Pi-Donor hydrogen bond interaction with TYR662, one Halogen (Fluorine) with SER630, two electrostatic interactions with GLU206 (Attractive Charge, Salt Bridge), and one hydrophobic interaction with TYR666 (Pi-Pi T-shaped); and (iv) ZINC71876485 showed a CG4 value of −10.540 and displayed one water hydrogen bond interaction with HOH1605, two electrostatic interactions with GLU206 (Attractive Charge), GLU205 (Salt Bridge), two conventional hydrogen bond interactions with GLU205, ASN710, two carbon hydrogen bond interactions with SER209, GLU206, one Halogen (Fluorine) with HIS740, two Pi-Donor hydrogen bond interaction with SER209, TYR662, and three hydrophobic interactions with TYR666 (Pi-Pi T-shaped), ARG358 (Alkyl), and PHE357 (Pi-Alkyl).
The term PAINS (Pan Assay INterference compoundS) is associated with promiscuous bioactivity and assay interference in all virtual high-throughput screening (vHTS) simulations. The selected compounds show zero PAINS alerts and are assumed not to give false positive results and, thus, will not be flagged as suspicious compounds in the screening compound databases. In addition, it was found that all selected compounds meet the Pfizer criteria with one exception, compound CSC092194469, which presents logP = 3.453 and TPSA = 61.360 (Table 2). Regarding Lipinski’s rule (molecular weight (MW) less than 500 Da, no more than 10 hydrogen bond acceptors (nHA), no more than five hydrogen bond donors (nHD), and an octanol–water partition coefficient (logP) not greater than 5), it can be seen that all the criteria to predict drug-like properties for selected compounds are satisfied (Table 2). Therefore, all selected compounds named CSC076365308, ZINC95941402, ZINC408512952, and CSC079167462 are similar to alogliptin, and ZINC305224681, CSC092194469, ZINC12327733, and ZINC71876485 are similar to Sitagliptin and will be further analyzed by molecular docking and the DFT approaches (Figure 6).
The Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) parameters related to the bioavailability of the eight best-selected compounds (Figure 6) are also reported in Tables S6–S10. The green color displays an excellent predicted value, the yellow color indicates a medium predicted value, and the red color portrays a poor predicted value for each parameter. “How much drug is absorbed if administered orally?” “How much is absorbed in the gastrointestinal tract?” “How are distribution and metabolism affected by poor absorption?” and “Which properties lead to toxicity?” are the most important questions to which researchers try to find answers to understand the disposition of a drug within a human body. Thus, this step is one of the most essential parts of in silico drug design because it can predict the applicability of the compounds as a drug. The predicted absorption parameters (Table S6) show that Caco-2 Permeability (the human colon adenocarcinoma cell lines) has an excellent score for all four compounds (CSC076365308, ZINC95941402, ZINC408512952, CSC079167462) versus alogliptin. For this parameter, excellent values are predicted, and for ZINC305224681, CSC09219446, ZINC12327733, ZINC71876485, and sitagliptin. The human intestinal absorption parameter has excellent values for all compounds except CSC076365308. The computed distribution parameters are presented in Table S7. The same trend, from poor for the blood–brain barrier (BBB) permeation to excellent for volume distribution VDss, was observed for all compounds. The metabolism parameters are related to the cytochrome P450, which is a necessary detoxification enzyme in the human body. The five isozymes of CYP450, named 1A2, 3A4, 2C9, 2C19, and 2D6 are responsible for metabolizing approximately two-thirds of known drugs. The values presented in Table S8 show the compound’s probability of being a substrate/inhibitor. The excretion parameters (Table S9) for CL (hepatic and renal clearance of a drug) have excellent values for CSC076365308 and CSC079167462 versus alogliptin. AMES Toxicity parameter, which is related to mutagenicity and with a close relationship to carcinogenicity, has excellent values for ZINC95941402 and ZINC40851295, and medium values for CSC076365308 and CSC079167462 versus aloglitin, which has poor value. Related to the sitagliptin, which displays a poor value for the AMES Toxicity parameter, the excellent value for ZINC305224681, CSC092194469, and ZINC12327733, and medium value for ZINC71876485 were observed. All toxicity-predicted parameters together with toxicity rules (Skin Sensitization Rule, Acute Toxicity Rule, Genotoxic Carcinogenicity Rule, NonGenotoxic Carcinogenicity Rule, and FAF-Drugs4 Rule) are presented in Table S10.
Knowledge of hydrophobicity is very important for evaluating ligand–protein and protein–protein/membrane interactions but also for characterizing molecules. The Molecular Lipophilicity Potential (MLP) of the molecular surface is estimated from atomic hydrophobicity contributions and delivers valuable information about the hydrophobicity distribution for compounds that have even the same/very similar logP values. Thus, the CSC079167462 exhibit very different 3D hydrophobicity distribution versus alogliptin, the CSC092194469 and ZINC12327733, which display very different 3D hydrophobicity distributions versus sitagliptin and are expected to easily penetrate membranes, while ZINC95941402, ZINC408512952 and CSC076365308 show a large hydrophilic region similar to alogliptin and prefer the transcellular route versus intracellular [85,86]. The MLP representation delivers a pathway to understanding the influence of a particular atom or fragment on hydrophobicity. In this way, an accurate representation is very useful to explore the substituent effects of each compound (Figure 9 and Figures S3 and S4). This structural information is completed with the chemical reactivity and kinetic stability data of each selected compound by analysis of HOMO and LUMO orbitals resulting from DFT optimization (Figure 10) [44,45,76,77]. For further investigation, the HOMO and LUMO energy values expressed in hartree were transformed into Electron-volt (Ev).
The density functional theory (DFT) calculations incorporating the B3LYP-D3/6-31G** basis set were performed in the gas phase. The HOMO energy values were found between −5.890 (CSC079167462) and −9.516 (ZINC12327733), while the LUMO energy values were found between −0.241 (CSC079167462) and −4.013 (ZINC95941402). The difference between energy values (energy gap between the HOMO and LUMO, ΔE) was found between 3.754 (ZINC408512952) and 5.945 (ZINC12327733). The large ΔE value for ZINC12327733 indicates that this molecule is stable and less reactive, while the low ΔE value for ZINC408512952 shows a substantial potential for charge transfer interactions within this molecule (Figure 10). The biggest differences in the HOMO (the donor) and the LUMO (the acceptor) distribution are observed for ZINC408512952, CSC079167462, ZINC305224681, ZINC12327733, and ZINC71876485 (Figure 10). For these compounds, the distinct localization of orbitals displays its tendency to bind with the DPP-4 receptor because the HOMO-donor/LUMO-acceptor of the compound and the LUMO-acceptor/HOMO-donor of the receptor’s residues could share orbital interactions during the binding steps. For ZINC408512952, the influence of the HOMO energy on the biological activity can be characterized in terms of Pi-Pi charge transfer with PHE357 and pyridazine rings of it. In a similar way, the influence of the HOMO energy for CSC079167462 is related in terms of the Pi-Cation charge transfer with ARG125 and its phenyl ring, for ZINC305224681, it is related in terms of the Pi-Pi charge transfer with PHE357 and its phenyl ring, for ZINC12327733, it is related in terms of Pi-Donor HB with HOH1605 and the phenyl ring substituted with an F atom, and for ZINC71876485, it is related in terms of Pi-Donor HB with HOH1605 and SER209 and its pyrazole ring. Instead, the LUMO orbitals localization suggests the susceptibility of the selected molecules toward nucleophilic attack. This is in agreement with the molecular docking results (Figure 7, Tables S3 and S5).
In addition, to pinpoint the most reactive sites within each selected molecule for both electrophilic and nucleophilic reactions, the Fukui indices were computed and displayed (Figure 11). The sites favored for electrophilic attacks are indicated by the highest positive values of f−, while the preferred centers for nucleophilic attacks are denoted by the highest positive values of f+ [87,88]. The f_NN indices named f_NN HOMO and f_NN LUMO were assessed, and are generally of interest because they do not require any changes in either the spin density or the spin multiplicity. A high positive value of f_NN HOMO suggests the atom’s ability to donate electrons, and, therefore, acts as a nucleophile, while a high positive value of f_NN LUMO indicates the atom’s ability to accept electrons, and, thus, acts as an electrophile. According to the computations, the most significant positive values of f_NN HOMO indices, which are associated with the f−, and f_NN LUMO indices, which are associated with the f−, for carbon, nitrogen and oxygen atoms, were displayed and highlighted in Figure 11.
The most relevant values are in the atom N18 (0.4325), N19 (0.3654) of the pyridazine ring (interactions with PHE357) of ZINC408512952; C8 (0.3868) of the pyrazole ring (interactions with PHE357, GLN209) of ZINC7187648; C1 (0.3040), C10 (0.2523) of phenyl ring substituted with one fluorine atom (interactions with HOH1605) of ZINC12327733; N22 (0.3003) of the pyrazole ring (interactions with TYR547, TYR666, HOH1) of ZINC95941402; C1 (0.2858), C9 (0.2571) of phenyl ring substituted with one fluorine and methyl (interactions with PHE357, HOH1605) of ZINC305224681; C7 (0.2598) of phenyl ring substituted with fluorine atom (interactions with PHE357, HOH1605), N19 (0.1975) of the urea group (interactions with HOH1551) of CS092194469; C6 (0.2500) of phenyl ring (interactions with ARG125) of CSC079167462; and C3 (0.2098), C4 (0.2080) of quinolone ring (interactions with TRP629) of CSC076365308 for electrophilic attack. The most relevant values are in the atom C13 (0.3505) of urea group of CS092194469; C1 (0.2841) and C11 (0.2750) of phenyl ring (interactions with TYR662) of CSC079167462; C7 (0.2796), C10 (0.2055) of phenyl ring substituted with two fluorine atoms (interactions with TYR666) of ZINC305224681; C2 (0.2557), C7 (0.2327) of phenyl ring substituted with one fluorine at-om (interactions with TYR662, TYR666) of ZINC7187648; C19 (0.2246), C7 (0.2087) of phenyl ring substituted with two fluorine atoms (interactions with TYR662, TYR666) of ZINC12327733; C9 (0.2221) of acetamide group of ZINC95941402; C6 (0.1881), N21 (0.1495) of quinolone ring (interactions with TRP629) of CSC076365308; and O21 (0.1227), O22 (0.1215) of isoindoline-dione ring (interactions with TRP547, TYR666) of ZINC408512952 for nucleophilic attack. These outcomes reinforce those previously mentioned and also demonstrate the involvement of these molecular fragments in the essential ligand–receptor interactions, confirming the connection between the electronic properties and possible potency of these compounds. It is therefore important to explore these eight compounds as potential, non-toxic DPP4 inhibitors in the management of T2DM.

4. Workflow Applicability and Future Research Direction

The applicability of the current workflow lies in the successful use of complementary computational methods in the prediction of DPP-4 inhibitors with improved properties compared to approved drugs for T2DM. The current workflow, with the inherent limitations of a purely theoretical simulation, will be effectively used to screen databases of natural compounds to select new NPs as adjuvants or even as primary therapy for T2DM. For this, the first steps were performed, and the Natural product-likeness score (NPscore) [72] for selected compounds was investigated. The NPscore is a helpful measure based on fragments from natural products’ chemical space that can help to guide the design of new compounds with bioactive areas. The best NPscore of −0.482 for CSC079167462 and −0.950 for ZINC12327733 were identified. Thus, these two compounds were involved in a similarity search (Tanimoto coefficient greater than 0.85 [89] in the COCONUT (COlleCtion of Open Natural ProdUcTs) natural products database (https://coconut.naturalproducts.net/, accessed on 30 August 2023) [90] and 22 NPs were selected (Table S11). These NPs will be the subject of a new investigation that continues the topic presented here and may open new avenues to guide the quick design and prediction of NPs from natural resources with improved properties in T2DM management. Also, the selective inhibition of the DPP-4 enzyme in relation to other DPP family members (enzymes with high-sequence homology e.g., DPP-8 and DPP-9 [53,91,92] will be investigated.

5. Conclusions

In summary, in the present work, we developed a trustworthy in silico workflow involving a pharmacophore virtual screening search, molecular docking, ADMETox, and DFT simulations to identify the key structural characteristics responsible for DPP-4 inhibitors’ activity. The study was initiated by conducting virtual screening using pharmacophores, molecular shape, and energy minimization, directly providing the ligand–receptor complex structure from the PDB (alogliptin—3G0B, sitagliptin—1X70, and linagliptin-2RGU) in the online platform Pharmit [57]. The pharmacophore search was performed using eight large pre-built databases (CHEMBL30/ChemDiv/ChemSpace/MCULE/MCULE-ULTIMATE/MolPort/LabNetwork/ZINC). The in silico analysis revealed that eight compounds (CSC076365308, ZINC95941402, ZINC40851295, CSC079167462 similar to alogliptin, and ZINC305224681, CSC092194469, ZINC12327733, ZINC71876485 similar to sitagliptin) fulfilled all the parameters investigated here. The selected molecules have strong hydrogen bonds and hydrophobic interactions with the most important amino acids from the binding site, GLN205, GLN206, TYR547 and SER630, and implicitly superior docking scores to that of the FDA-approved drugs for diabetes, alogliptin and sitagliptin. These findings were supported by the HOMO-LUMO gap energy, which was used to investigate the stability of the molecular interactions for the selected compounds. The present study provided here will be a trustworthy theoretical basis for chemists who are interested in designing, predicting, and synthesizing new potent DPP-4 inhibitors. This methodology will be applied to identify new NPs from extended natural compounds’ libraries both for DPP-4 and for other targets involved in appropriate diseases.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/pr11113100/s1, Figure S1: 2D representation and docking view (3D and 2D representation) of the selected compounds similar to alogliptin; the bioavailability radar for the selected compounds is also depicted; Figure S2: 2D representation and docking view (3D and 2D representation) of the selected compounds similar to sitagliptin; the bioavailability radar for the selected compounds is also depicted; Figure S3: The Molecular Lipophilicity Potential of the molecular surface for selected compounds similar to alogliptin; the hydrophobic surface are pictured in the violet and blue; the hydrophilic surface are portrayed in orange and red; Figure S4: The Molecular Lipophilicity Potential of the molecular surface for selected compounds similar to sitagliptin; the hydrophobic surface are pictured in the violet and blue; the hydrophilic surface are portrayed in orange and red; Table S1: Docked interaction analysis of approved drugs with target proteins alogliptin—3G0B, sitagliptin—1X70, and linagliptin—2RGU; Table S2: Physicochemical parameters, QED and CG4 values for the selected compounds similar to alogliptin; Table S3: Docked interaction analysis of selected compounds similar to alogliptin—3G0B; Table S4: Physicochemical parameters, QED and CG4 values for the selected compounds similar to sitagliptin; Table S5: Docked interaction analysis of selected compounds similar to sitagliptin—1X70; Table S6: Absorption parameters for the selected compounds similar to alogliptin (A) and to sitagliptin (B); Table S7: Distribution parameters for the selected compounds similar to alogliptin (A) and to sitagliptin (B); Table S8: Metabolism parameters for the selected compounds similar to alogliptin (A) and to sitagliptin (B); Table S9: Excretion parameters for the selected compounds similar to alogliptin (A) and to sitagliptin (B); Table S10: Toxicity parameters for the selected compounds similar to alogliptin (A) and to sitagliptin (B); Table S11: Twenty-two NPs were selected from COCONUT (COlleCtion of Open Natural ProdUcTs) natural products database (https://coconut.naturalproducts.net/, accessed on 30 August 2023).

Author Contributions

Conceptualization, L.C. and D.I.; methodology, L.C.; validation, L.C. and D.I.; formal analysis, L.C. and D.I.; investigation, L.C. and D.I.; resources, L.C.; writing—original draft preparation, L.C. and D.I.; writing—review and editing, L.C.; visualization, L.C. and D.I.; supervision, L.C., project administration, L.C.; funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. The APC was funded by L.C.

Data Availability Statement

Not applicable.

Acknowledgments

The authors thank OpenEye Ltd., and BIOVIA software Inc. (Discovery Studio Visualizer) for providing academic license. This work was supported by Project No. 1.2 from the “Coriolan Dragulescu” Institute of Chemistry, Timisoara, Romania.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are mentioned in this manuscript: ADMETox, Absorption, Distribution, Metabolism, Excretion, and Toxicity; DFT, Density Functional Theory; DPP-4, Dipeptidyl Peptidase 4; Hy, hydrophobic; HOMO, highest occupied molecular orbital; LUMO, lowest unoccupied molecular orbital; MLP, Molecular Lipophilicity Potential; nHA, Hydrogen Bond Acceptor; nHD, Hydrogen Bond Donor; NPs, natural products; PAINS, Pan Assay INterference compoundS; PDB, Protein Data Bank; RA, ring aromatic; RMSD, Root Mean Squared Deviation; T2DM, Type 2 diabetes mellitus; QED, Quantitative Estimate of Drug-likeness.

References

  1. Berbudi, A.; Rahmadika, N.; Tjahjadi, A.I.; Ruslami, R. Type 2 Diabetes and its Impact on the Immune System. Curr. Diabetes Rev. 2020, 16, 442–449. [Google Scholar] [PubMed]
  2. Skyler, J.S.; Bergenstal, R.; Bonow, R.O.; Buse, J.; Deedwania, P.; Gale, E.A.; Howard, B.V.; Kirkman, M.S.; Kosiborod, M.; Reaven, P.; et al. Intensive glycemic control and the prevention of vardiovascular events: Implications of the ACCORD, ADVANCE, and VA Diabetes Trials: A position statement of the American Diabetes Association and a Scientific Statement of the American College of Cardiology Foundation and the American Heart Association. J. Am. Coll. Cardiol. 2009, 53, 298–304. [Google Scholar] [PubMed]
  3. Cui, J.; Liu, Y.; Li, Y.; Xu, F.; Liu, Y. Type 2 Diabetes and Myocardial Infarction: Recent Clinical Evidence and Perspective. Front. Cardiovasc. Med. 2021, 24, 644189. [Google Scholar] [CrossRef] [PubMed]
  4. Putaala, J.; Liebkind, R.; Gordin, D.; Thorn, L.M.; Haapaniemi, E.; Forsblom, C.; Groop, P.-H.; Kaste, M.; Tatlisumak, T. Diabetes mellitus and ischemic stroke in the young: Clinical features and long-term prognosis. Neurology 2011, 76, 1831–1837. [Google Scholar] [CrossRef]
  5. Chen, R.; Ovbiagele, B.; Feng, W. Diabetes and Stroke: Epidemiology, Pathophysiology, Pharmaceuticals and Outcomes. Am. J. Med. Sci. 2016, 351, 380–386. [Google Scholar] [CrossRef]
  6. La Sala, L.; Prattichizzo, F.; Ceriello, A. The Link between Diabetes and Atherosclerosis. Eur. J. Prev. Cardiol. 2019, 26, 15–24. [Google Scholar] [CrossRef]
  7. Ye, J.; Li, L.; Wang, M.; Ma, Q.; Tian, Y.; Zhang, Q.; Liu, J.; Li, B.; Zhang, B.; Liu, H.; et al. Diabetes Mellitus Promotes the Development of Atherosclerosis: The Role of NLRP3. Front. Immunol. 2022, 29, 900254. [Google Scholar] [CrossRef]
  8. Zhang, L.; Jiang, F.; Xie, Y.; Mo, Y.; Zhang, X.; Liu, C. Diabetic endothelial microangiopathy and pulmonary dysfunction. Front. Endocrinol. 2023, 14, 1073878. [Google Scholar] [CrossRef]
  9. Gutman, M.; Kaplan, O.; Skornick, Y.; Klausner, J.M.; Lelcuk, S.; Rozin, R.R. Gangrene of the lower limbs in diabetic patients: A malignant complication. Am. J. Surg. 1987, 154, 305–308. [Google Scholar] [CrossRef]
  10. Gao, L.; Li, T.; Wang, S.; Wang, J. Comprehensive treatment of diabetic hallux gangrene with lower extremity vascular disease: A case report. J. Int. Med. Res. 2019, 47, 6374–6384. [Google Scholar] [CrossRef]
  11. Casanova, L.; Hughes, F.; Preshaw, P. Diabetes and periodontal disease: A two-way relationship. Br. Dent. J. 2014, 217, 433–437. [Google Scholar] [CrossRef] [PubMed]
  12. Yu, S.M.-W.; Bonventre, J.V. Acute Kidney Injury and Progression of Diabetic Kidney Disease. Adv. Chronic Kidney Dis. 2018, 25, 166–180. [Google Scholar] [CrossRef] [PubMed]
  13. Hotta, N. A new perspective on the biguanide, metformin therapy in type 2 diabetes and lactic acidosis. J. Diabetes Investig. 2019, 10, 906–908. [Google Scholar] [CrossRef] [PubMed]
  14. Tavares, G.; Marques, D.; Barra, C.; Rosendo-Silva, D.; Costa, A.; Rodrigues, T.; Gasparini, P.; Melo, B.F.; Sacramento, J.F.; Seiça, R.; et al. Dopamine D2 receptor agonist, bromocriptine, remodels adipose tissue dopaminergic signalling and upregulates catabolic pathways, improving metabolic profile in type 2 diabetes. Mol. Metabol. 2021, 51, 101241. [Google Scholar] [CrossRef] [PubMed]
  15. Saini, K.; Sharma, S.; Khan, Y. DPP-4 inhibitors for treating T2DM-hype or hope? an analysis based on the current literature. Front. Mol. Biosci. 2023, 10, 1130625. [Google Scholar] [CrossRef]
  16. Röhrborn, D.; Wronkowitz, N.; Eckel, J. DPP4 in diabetes. Front. Immunol. 2015, 6, 386. [Google Scholar] [CrossRef]
  17. Hinnen, D. Glucagon-Like Peptide 1 Receptor Agonists for Type 2 Diabetes. Diabetes Spectr. 2017, 30, 202–210. [Google Scholar] [CrossRef]
  18. Zhao, X.; Wang, M.; Wen, Z.; Lu, Z.; Cui, L.; Fu, C.; Xue, H.; Liu, Y.; Zhang, Y. GLP-1 Receptor Agonists: Beyond Their Pancreatic Effects. Front. Endocrinol. 2021, 12, 721135. [Google Scholar] [CrossRef]
  19. Xu, B.; Li, S.; Kang, B.; Zhou, J. The current role of sodium-glucose cotransporter 2 inhibitors in type 2 diabetes mellitus management. Cardiovasc. Diabetol. 2022, 21, 83. [Google Scholar] [CrossRef]
  20. Zhang, S.; Qi, Z.; Wang, Y.; Song, D.; Zhu, D. Effect of sodium-glucose transporter 2 inhibitors on sarcopenia in patients with type 2 diabetes mellitus: A systematic review and meta-analysis. Front. Endocrinol. 2023, 14, 1203666. [Google Scholar] [CrossRef]
  21. Chiarelli, F.; Di Marzio, D. Peroxisome proliferator-activated receptor-gamma agonists and diabetes: Current evidence and future perspectives. Vasc. Health. Risk Manag. 2008, 4, 297–304. [Google Scholar]
  22. Frkic, R.L.; Richter, K.; Bruning, J.B. The therapeutic potential of inhibiting PPARγ phosphorylation to treat type 2 diabetes. J. Biol. Chem. 2021, 297, 101030. [Google Scholar] [CrossRef]
  23. Zhou, K.; Lansang, M.C. Diabetes Mellitus and Infections. In Endotext; MDText.com: South Dartmouth, MA, USA, 2000. Available online: https://www.ncbi.nlm.nih.gov/books/NBK569326/ (accessed on 30 August 2023).
  24. Casqueiro, J.; Janine, C.; Alves, C. Infections in patients with diabetes mellitus: A review of pathogenesis. Indian J. Endocrinol. Metab. 2012, 16, S27–S36. [Google Scholar] [PubMed]
  25. Guariguata, L.; Whiting, D.R.; Hambleton, I.; Beagley, J.; Linnenkamp, U.; Shaw, J.E. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res. Clin. Pract. 2014, 103, 137–149. [Google Scholar] [CrossRef] [PubMed]
  26. Ozougwu, J.C.; Obimba, K.C.; Belonwu, C.D.; Unakalamba, C.B. The pathogenesis and pathophysiology of type 1 and type 2 diabetes mellitus. J. Physiol. Pathophysiol. 2013, 4, 46–57. [Google Scholar] [CrossRef]
  27. Weyer, C.; Bogardus, C.; Mott, D.M.; Pratley, R.E. The natural history of insulin secretory dysfunction and insulin resistance in the pathogenesis of type 2 diabetes mellitus. J. Clin. Investig. 1999, 104, 787–794. [Google Scholar] [CrossRef]
  28. Rahman, S.U.; Ali, H.S.; Jafari, B.; Zaib, S.; Hameed, A.; Al-Kahraman, Y.M.S.A.; Langer, P.; Iqbal, J. Structure-Based Virtual Screening of Dipeptidyl Peptidase 4 Inhibitors and their In vitro Analysis. Comput. Biol. Chem. 2021, 91, 107326–107353. [Google Scholar] [CrossRef]
  29. Meduru, H.; Wang, Y.-T.; Tsai, J.J.P.; Chen, Y.-C. Finding a Potential Dipeptidyl Peptidase-4 (DPP-4) Inhibitor for Type-2 Diabetes Treatment Based on Molecular Docking, Pharmacophore Generation, and Molecular Dynamics Simulation. Int. J. Mol. Sci. 2016, 17, 920. [Google Scholar] [CrossRef]
  30. Crisan, L.; Avram, S.; Pacureanu, L. Pharmacophore-based screening and drug repurposing exemplified on glycogen synthase kinase-3 inhibitors. Mol. Divers. 2017, 21, 385–405. [Google Scholar] [CrossRef]
  31. Pacureanu, L.; Bora, A.; Crisan, L. New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods. Int. J. Mol. Sci. 2023, 24, 9583. [Google Scholar] [CrossRef]
  32. Hermansyah, O.; Bustamam, A.; Yanuar, A. Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure–activity relationship-based artificial intelligence and molecular docking of hit compounds. Comput. Biol. Chem. 2021, 95, 107597–107608. [Google Scholar] [CrossRef] [PubMed]
  33. Ivan, D.; Crisan, L.; Funar-Timofei, S.; Mracec, M. A quantitative structure–activity relationships study for the anti-HIV-1 activities of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine derivatives using the multiple linear regression and partial least squares methodologies. J. Serb. Chem. Soc. 2013, 78, 495–506. [Google Scholar] [CrossRef]
  34. Crisan, L.; Pacureanu, L.; Avram, S.; Bora, A.; Avram, S.; Kurunczi, L. PLS and shape-based similarity analysis of maleimides–GSK-3 inhibitors. J. Enzym. Inhib. Med. Chem. 2014, 29, 599–610. [Google Scholar] [CrossRef] [PubMed]
  35. Syam, Y.M.; Anwar, M.M.; Abd El-Karim, S.S.; Elseginy, S.A.; Essa, B.M.; Sakr, T.M. New quinoxaline compounds as DPP-4 inhibitors and hypoglycemics: Design, synthesis, computational and bio-distribution studies. RSC Adv. 2021, 11, 36989–37011. [Google Scholar] [CrossRef] [PubMed]
  36. Alsamghan, A.S.; Alwabli, A.S.; Abadi, M.; Alsaleem, S.A.; Anbari, D.M.; Alomari, A.S.; Alzahrani, O.; Alam, Q.; Tarique, M. From sequence analysis of DPP-4 to molecular docking based searching of its inhibitors. Bioinformation 2020, 16, 444–451. [Google Scholar] [PubMed]
  37. Crisan, L.; Istrate, D.; Bora, A.; Pacureanu, L. Virtual screening and drug repurposing experiments to identify potential novel selective MAO-B inhibitors for Parkinson’s disease treatment. Mol. Div. 2021, 25, 1775–1794. [Google Scholar] [CrossRef]
  38. Crisan, L.; Bora, A. Small Molecules of Natural Origin as Potential Anti-HIV Agents: A Computational Approach. Life 2021, 11, 722. [Google Scholar] [CrossRef]
  39. Pantaleão, S.Q.; Maltarollo, V.G.; Araujo, S.C.; Gertrudesc, J.C.; Honorio, K.M. Molecular docking studies and 2D analyses of DPP-4 inhibitors as candidates in the treatment of diabetes. Mol. Biosyst. 2015, 11, 3188–3193. [Google Scholar] [CrossRef]
  40. Qi, J.-H.; Chen, P.-y.; Cai, D.-y.; Wang, Y.; Wei, Y.-l.; He, S.-p.; Zhou, W. Exploring novel targets of sitagliptin for type 2 diabetes mellitus: Network pharmacology, molecular docking, molecular dynamics simulation, and SPR approaches. Front. Endocrinol. 2023, 13, 1096655–1096668. [Google Scholar] [CrossRef]
  41. Zhao, L.; Zhang, M.; Pan, F.; Li, J.; Dou, R.; Wang, X.; Wang, Y.; He, Y.; Wang, S.; Cai, S. In silico analysis of novel dipeptidyl peptidase-IV inhibitory peptides released from Macadamia integrifolia antimicrobial protein 2 (MiAMP2) and the possible pathways involved in diabetes protection. Curr. Res. Nutr. Food Sci. 2021, 4, 603–611. [Google Scholar] [CrossRef]
  42. Singh, A.; Mishra, A. Molecular dynamics simulation and free energy calculation studies of Coagulin L as dipeptidyl peptidase-4 inhibitor. J. Biomol. Struct. Dyn. 2022, 40, 1128–1138. [Google Scholar] [CrossRef] [PubMed]
  43. Shoombuatong, W.; Prachayasittikul, V.; Anuwongcharoen, N.; Songtawee, N.; Monnor, T.; Prachayasittikul, S.; Prachayasittikul, V.; Nantasenamat, C. Navigating the chemical space of dipeptidyl peptidase-4 inhibitors. Drug Des. Devel. Ther. 2015, 9, 4515–4549. [Google Scholar] [PubMed]
  44. Visa, A.; Plesu, N.; Maranescu, B.; Ilia, G.; Borota, A.; Crisan, L. Combined Experimental and Theoretical Insights into the Corrosion Inhibition Activity on Carbon Steel Iron of Phosphonic Acids. Molecules 2021, 26, 135. [Google Scholar] [CrossRef] [PubMed]
  45. Crisan, L.; Borota, A.; Bora, A.; Pacureanu, P. Diarylthiazole and Diarylimidazole Selective COX-1 Inhibitors Analysis through Pharmacophore Modeling, Virtual Screening, and DFT-Based Approaches. Struct. Chem. 2019, 30, 2311–2326. [Google Scholar] [CrossRef]
  46. Mohammad, B.D.; Baig, M.S.; Bhandari, N.; Siddiqui, F.A.; Khan, S.L.; Ahmad, Z.; Khan, F.S.; Tagde, P.; Jeandet, P. Heterocyclic Compounds as Dipeptidyl Peptidase-IV Inhibitors with Special Emphasis on Oxadiazoles as Potent Anti-Diabetic Agents. Molecules 2022, 27, 6001. [Google Scholar] [CrossRef]
  47. Deacon, C.F. Physiology and Pharmacology of DPP-4 in Glucose Homeostasis and the Treatment of Type 2 Diabetes. Front. Endocrinol. 2019, 10, 80. [Google Scholar] [CrossRef]
  48. Makrilakis, K. The Role of DPP-4 Inhibitors in the Treatment Algorithm of Type 2 Diabetes Mellitus: When to Select, What to Expect. Int. J. Environ. Res. Public Health 2019, 16, 2720. [Google Scholar] [CrossRef]
  49. Tarapués, M.; Cereza, G.; Figueras, A. Association of musculoskeletal complaints and gliptin use: Review of spontaneous reports. Pharmacoepidemiol. Drug Saf. 2013, 22, 1115–1118. [Google Scholar] [CrossRef]
  50. Nader, M.A. Inhibition of anaphylaxis like reaction and mast cell activation by Sitagliptin. Int. Immunopharmacol. 2011, 11, 1052–1056. [Google Scholar] [CrossRef]
  51. Rendell, M.; Drincic, A.; Andukuri, R. Alogliptin benzoate for the treatment of type 2 diabetes. Expert Opin. Pharmacother. 2012, 13, 553–563. [Google Scholar] [CrossRef]
  52. Nabeno, M.; Akahoshi, F.; Kishida, H.; Miyaguchi, I.; Tanaka, Y.; Ishii, S.; Kadowaki, T. A comparative study of the binding modes of recently launched dipeptidyl peptidase IV inhibitors in the active site. Biochem. Biophys. Res. Comm. 2013, 434, 191–196. [Google Scholar] [CrossRef]
  53. Arulmozhiraja, S.; Matsuo, N.; Ishitsubo, E.; Okazaki, S.; Shimano, H.; Tokiwa, H. Comparative Binding Analysis of Dipeptidyl Peptidase IV (DPP-4) with Antidiabetic Drugs-An Ab Initio Fragment Molecular Orbital Study. PLoS ONE 2016, 11, e0166275. [Google Scholar] [CrossRef] [PubMed]
  54. Lambeir, A.M.; Durinx, C.; Scharpé, S.; De Meester, I. Dipeptidyl-peptidase IV from bench to bedside: An update on structural properties, functions, and clinical aspects of the enzyme DPP IV. Crit. Rev. Clin. Lab. Sci. 2003, 40, 209–294. [Google Scholar] [CrossRef] [PubMed]
  55. Mathur, V.; Alam, O.; Siddiqui, N.; Jha, M.; Manaithiya, A.; Bawa, S.; Sharma, N.; Alshehri, S.; Alam, P.; Shakeel, F. Insight into Structure Activity Relationship of DPP-4 Inhibitors for Development of Antidiabetic Agents. Molecules 2023, 28, 5860. [Google Scholar] [CrossRef]
  56. Pan, J.; Zhang, Q.; Zhang, C.; Yang, W.; Liu, H.; Lv, Z.; Liu, J.; Jiao, Z. Inhibition of Dipeptidyl Peptidase-4 by Flavonoids: Structure–Activity Relationship, Kinetics and Interaction Mechanism. Front. Nutr. 2022, 9, 892426. [Google Scholar] [CrossRef]
  57. Sunseri, J.; Koes, D.R. Pharmit: Interactive exploration of chemical space. Nucleic Acids Res. 2016, 44, W442–W448. [Google Scholar] [CrossRef]
  58. Zhang, Z.; Wallace, M.B.; Feng, J.; Stafford, J.A.; Skene, R.J.; Shi, L.; Lee, B.; Aertgeerts, K.; Jennings, A.; Xu, R.; et al. Design and Synthesis of Pyrimidinone and Pyrimidinedione Inhibitors of Dipeptidyl Peptidase IV. J. Med. Chem. 2011, 54, 510–524. [Google Scholar] [CrossRef] [PubMed]
  59. Kim, D.; Wang, L.; Beconi, M.; Eiermann, G.J.; Fisher, M.H.; He, H.; Hickey, G.J.; Kowalchick, J.E.; Leiting, B.; Lyons, K.; et al. (2R)-4-Oxo-4-[3-(Trifluoromethyl)-5,6-dihydro[1,2,4]triazolo[4,3-a]pyrazin- 7(8H)-yl]-1-(2,4,5-trifluorophenyl)butan-2-amine:  A Potent, Orally Active Dipeptidyl Peptidase IV Inhibitor for the Treatment of Type 2 Diabetes. J. Med. Chem. 2005, 48, 141–151. [Google Scholar] [CrossRef]
  60. Eckhardt, M.; Langkopf, E.; Mark, M.; Tadayyon, M.; Thomas, L.; Nar, H.; Pfrengle, W.; Guth, B.; Lotz, R.; Sieger, P.; et al. 8-(3-(R)-Aminopiperidin-1-yl)-7-but-2-ynyl-3-methyl-1-(4-methyl-quinazolin-2-ylmethyl)-3,7-dihydropurine-2,6-dione (BI 1356), a Highly Potent, Selective, Long-Acting, and Orally Bioavailable DPP-4 Inhibitor for the Treatment of Type 2 Diabetes. J. Med. Chem. 2007, 50, 6450–6453. [Google Scholar] [CrossRef]
  61. MakeReceptor, v. 3.5.0.4; OpenEye Scientific Software Inc.: Santa Fe, NM, USA, 2020.
  62. Istrate, D.; Crisan, L. Natural Compounds as DPP-4 Inhibitors: 3D-Similarity Search, ADME Toxicity, and Molecular Docking Approaches. Symmetry 2022, 14, 1842. [Google Scholar] [CrossRef]
  63. Schrödinger Release 2022-4: LigPrep; Schrödinger LLC: New York, NY, USA, 2022.
  64. OMEGA, 4.0.0.4; OpenEye Scientific Software: Santa Fe, NM, USA, 2019. Available online: http://www.eyesopen.com(accessed on 30 August 2023).
  65. Hawkins, P.C.D.; Skillman, A.G.; Warren, G.L.; Ellingson, B.A.; Stahl, M.T. Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database. J. Chem. Inf. Model. 2010, 50, 572–584. [Google Scholar] [CrossRef] [PubMed]
  66. FRED, 3.5.0.4; OpenEye Scientific Software: Santa Fe, NM, USA. Available online: http://www.eyesopen.com(accessed on 30 August 2023).
  67. Kelley, B.P.; Brown, S.P.; Warren, G.L.; Muchmore, S.W. POSIT: Flexible Shape-Guided Docking for Pose Prediction. J. Chem. Inf. Model. 2015, 55, 1771–1780. [Google Scholar] [CrossRef]
  68. McGann, M. FRED Pose Prediction and Virtual Screening Accuracy. J. Chem. Inf. Model. 2011, 51, 578–596. [Google Scholar] [CrossRef] [PubMed]
  69. McGann, M. FRED and HYBRID docking performance on standardized datasets. J. Comput. Aided Mol. Des. 2012, 26, 897–906. [Google Scholar] [CrossRef] [PubMed]
  70. Schrödinger Release 2022-4: Maestro, v. 13.4.134; Schrödinger LLC: New York, NY, USA, 2022.
  71. Bickerton, G.R.; Paolini, G.V.; Besnard, J.; Muresan, S.; Hopkins, A.L. Quantifying the chemical beauty of drugs. Nat. Chem. 2012, 4, 90–98. [Google Scholar] [CrossRef] [PubMed]
  72. Xiong, G.; Wu, Z.; Yi, J.; Fu, L.; Yang, Z.; Hsieh, C.; Yin, M.; Zeng, X.; Wu, C.; Lu, A.; et al. ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021, 49, W5–W14. [Google Scholar] [CrossRef]
  73. Veber, D.F.; Johnson, S.R.; Cheng, H.Y.; Smith, B.R.; Ward, K.W.; Kopple, K.D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 2002, 45, 2615–2623. [Google Scholar] [CrossRef]
  74. Ertl, P.; Rohde, B.; Selzer, P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem. 2000, 43, 3714–3717. [Google Scholar] [CrossRef]
  75. 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. Delivery Rev. 1997, 23, 4–25. [Google Scholar] [CrossRef]
  76. Schrödinger Release 2022-4: Jaguar; Schrödinger LLC: New York, NY, USA, 2022.
  77. Bochevarov, A.D.; Harder, E.; Hughes, T.F.; Greenwood, J.R.; Braden, D.A.; Philipp, D.M.; Rinaldo, D.; Halls, M.D.; Zhang, J.; Friesner, R.A. Jaguar: A high-performance quantum chemistry software program with strengths in life and materials sciences. Int. J. Quantum Chem. 2013, 113, 2110–2142. [Google Scholar] [CrossRef]
  78. Gill, P.M.W.; Johnson, B.G.; Pople, J.A.; Frisch, M.J. The performance of the Becke—Lee—Yang—Parr (B—LYP) density functional theory with various basis sets. Chem. Phys. Lett. 1992, 197, 499–505. [Google Scholar] [CrossRef]
  79. Stephens, P.J.; Devlin, F.J.; Chabalowski, C.F.; Frisch, M.J. Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields. J. Phys. Chem. 1994, 98, 11623–11627. [Google Scholar] [CrossRef]
  80. Lee, C.; Yang, W.; Parr, R.G. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. 1988, B37, 785–789. [Google Scholar] [CrossRef] [PubMed]
  81. Fukui, K. Role of Frontier Orbitals in Chemical Reactions. Science 1982, 218, 747–754. [Google Scholar] [CrossRef]
  82. Martínez-Araya, J.I. Why is the dual descriptor a more accurate local reactivity descriptor than Fukui functions? J. Math. Chem. 2015, 53, 451–465. [Google Scholar] [CrossRef]
  83. Alves, M.J.; Froufe, H.J.C.; Costa, A.F.T.; Santos, A.F.; Oliveira, L.G.; Osório, S.R.M.; Abreu, R.M.V.; Pintado, M.; Ferreira, I.C.F. Docking studies in target proteins involved in antibacterial action mechanisms: Extending the knowledge on standard antibiotics to antimicrobial mushroom compounds. Molecules 2014, 19, 1672–1684. [Google Scholar] [CrossRef]
  84. Houston, D.R.; Walkinshaw, M.D. Consensus docking: Improving the reliability of docking in a virtual screening context. J. Chem. Inf. Model. 2013, 53, 384–390. [Google Scholar] [CrossRef]
  85. Barnes, T.M.; Mijaljica, D.; Townley, J.P.; Spada, F.; Harrison, I.P. Vehicles for Drug Delivery and Cosmetic Moisturizers: Review and Comparison. Pharmaceutics 2021, 12, 2012. [Google Scholar] [CrossRef]
  86. Ng, K.W.; Lau, W.M. Skin Deep: The basics of Human Skin Structure and Drug Penetration. In Percutaneous Penetration Enhancers Chemical Methods in Penetration Enhancement, 1st ed.; Dragicevic, N., Maibach, H., Eds.; Springer: Berlin/Heidelberg, Germany, 2015; pp. 3–11. [Google Scholar]
  87. Faver, J.; Merz, K.M., Jr. The Utility of the HSAB Principle via the Fukui Function in Biological Systems. J. Chem. Theory Comput. 2010, 9, 548–559. [Google Scholar] [CrossRef]
  88. Zamora, P.P.; Bieger, K.; Cuchillo, A.; Tello, A.; Muena, J.P. Theoretical determination of a reaction intermediate: Fukui function analysis, dual reactivity descriptor and activation energy. J. Mol. Struct. 2021, 1227, 129369. [Google Scholar] [CrossRef]
  89. Martin, Y.C.; Kofron, J.L.; Traphagen, L.M. Do Structurally Similar Molecules Have Similar Biological Activity? J. Med. Chem. 2002, 45, 4350–4358. [Google Scholar] [CrossRef] [PubMed]
  90. Sorokina, M.; Merseburger, P.; Rajan, K.; Yirik, M.A.; Steinbeck, C. COCONUT online: Collection of Open Natural Products database. J. Cheminform. 2021, 13, 2. [Google Scholar] [CrossRef] [PubMed]
  91. Huan, Y.; Jiang, Q.; Liu, J.; Shen, Z. Establishment of a dipeptidyl peptidases (DPP) 8/9 expressing cell model for evaluating the selectivity of DPP4 inhibitors. J. Pharmacol. Toxicol. Methods 2015, 71, 8–12. [Google Scholar] [CrossRef] [PubMed]
  92. Feng, J.; Zhang, Z.; Wallace, M.B.; Stafford, J.A.; Kaldor, S.W.; Kassel, D.B.; Navre, M.; Shi, L.; Skene, R.J.; Asakawa, T.; et al. Discovery of alogliptin: A potent, selective, bioavailable, and efficacious inhibitor of dipeptidyl peptidase IV. J. Med. Chem. 2007, 50, 2297–2300. [Google Scholar] [CrossRef]
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Figure 1. Number of publications indexed per year in the Web of Science Core Collection (WoSCC); 2023* (indexed before July).
Figure 1. Number of publications indexed per year in the Web of Science Core Collection (WoSCC); 2023* (indexed before July).
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Figure 2. Methodological description of the present study.
Figure 2. Methodological description of the present study.
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Figure 3. Pharmit pharmacophoric points: hydrogen acceptors (HA—orange), hydrogen donors (HD—white), aromatics (Ar—purple), and hydrophobic features (Hy—green); the shape tolerance is displayed in yellow; the carbon atoms are illustrated in cyan for alogliptin, purple in sitagliptin, and blue in linagliptin; the nitrogen atoms are displayed in white; the oxygen atoms are shown in red; the fluorine atoms are portrayed in yellow.
Figure 3. Pharmit pharmacophoric points: hydrogen acceptors (HA—orange), hydrogen donors (HD—white), aromatics (Ar—purple), and hydrophobic features (Hy—green); the shape tolerance is displayed in yellow; the carbon atoms are illustrated in cyan for alogliptin, purple in sitagliptin, and blue in linagliptin; the nitrogen atoms are displayed in white; the oxygen atoms are shown in red; the fluorine atoms are portrayed in yellow.
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Figure 4. The best re-docked pose of the ligand superimposed on the co-crystallized one; docking view (3D and 2D representation) of the X-ray native alogliptin ((A), green), sitagliptin ((B), magenta), and linagliptin ((C), blue) and of the redocked pose in the active site of the target; the bioavailability radar for approved drugs is also depicted. In the bioavailability radar, the red and orange areas represent the lower and the upper limits of the physicochemical property values (MW = 100 ÷ 600, LogP = 0 ÷ 3, LogS = −4 ÷ 0.5, LogD = 1 ÷ 3; nHA = 0 ÷ 12; nHD = 0 ÷ 7; TPSA = 0 ÷ 140; nRot = 0 ÷ 11; NRing = 0 ÷ 6; MaxRing = 0 ÷ 18; nHet = 1 ÷ 15; fChar = −4 ÷ 4; nRig = 0 ÷ 30), while the blue area corresponds to the predicted values of the physicochemical properties of under-study compounds.
Figure 4. The best re-docked pose of the ligand superimposed on the co-crystallized one; docking view (3D and 2D representation) of the X-ray native alogliptin ((A), green), sitagliptin ((B), magenta), and linagliptin ((C), blue) and of the redocked pose in the active site of the target; the bioavailability radar for approved drugs is also depicted. In the bioavailability radar, the red and orange areas represent the lower and the upper limits of the physicochemical property values (MW = 100 ÷ 600, LogP = 0 ÷ 3, LogS = −4 ÷ 0.5, LogD = 1 ÷ 3; nHA = 0 ÷ 12; nHD = 0 ÷ 7; TPSA = 0 ÷ 140; nRot = 0 ÷ 11; NRing = 0 ÷ 6; MaxRing = 0 ÷ 18; nHet = 1 ÷ 15; fChar = −4 ÷ 4; nRig = 0 ÷ 30), while the blue area corresponds to the predicted values of the physicochemical properties of under-study compounds.
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Figure 5. The schematic representation of results from steps 2 and 3; CG4 = Chemgauss4 docking score; QED = Quantitative Estimate of Drug-likeness; SF = structure fragment; IC50 = the concentration of a drug/inhibitor needed to inhibit a biological process or response by 50%.
Figure 5. The schematic representation of results from steps 2 and 3; CG4 = Chemgauss4 docking score; QED = Quantitative Estimate of Drug-likeness; SF = structure fragment; IC50 = the concentration of a drug/inhibitor needed to inhibit a biological process or response by 50%.
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Figure 6. The 2D structure representation of selected compounds similar to alogliptin (A), and sitagliptin (B).
Figure 6. The 2D structure representation of selected compounds similar to alogliptin (A), and sitagliptin (B).
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Figure 7. The electrostatic potential profiles; 3D and 2D representations of docking results and the bioavailability radar for selected compounds similar to alogliptin; CSC076365308 (A); ZINC95941402 (B); ZINC408512952 (C); and CSC079167462 (D); In the bioavailability radar the red and orange areas represent the lower and the upper limits of the physicochemical property values (MW = 100 ÷ 600, LogP = 0 ÷ 3, LogS = −4 ÷ 0.5, LogD = 1v3; nHA = 0 ÷ 12; nHD = 0 ÷ 7; TPSA = 0 ÷ 140; nRot = 0 ÷ 11; NRing = 0 ÷ 6; MaxRing = 0 ÷ 18; nHet = 1 ÷ 15; fChar = −4 ÷ 4; nRig = 0 ÷ 30), while the blue area corresponds to the predicted values of the physicochemical properties of compounds under study.
Figure 7. The electrostatic potential profiles; 3D and 2D representations of docking results and the bioavailability radar for selected compounds similar to alogliptin; CSC076365308 (A); ZINC95941402 (B); ZINC408512952 (C); and CSC079167462 (D); In the bioavailability radar the red and orange areas represent the lower and the upper limits of the physicochemical property values (MW = 100 ÷ 600, LogP = 0 ÷ 3, LogS = −4 ÷ 0.5, LogD = 1v3; nHA = 0 ÷ 12; nHD = 0 ÷ 7; TPSA = 0 ÷ 140; nRot = 0 ÷ 11; NRing = 0 ÷ 6; MaxRing = 0 ÷ 18; nHet = 1 ÷ 15; fChar = −4 ÷ 4; nRig = 0 ÷ 30), while the blue area corresponds to the predicted values of the physicochemical properties of compounds under study.
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Figure 8. The electrostatic potential profiles; 3D and 2D representations of docking results and the bioavailability radar for selected compounds similar to sitagliptin; ZINC305224681 (A), CSC092194469 (B), ZINC12327733 (C), and ZINC7187648 (D); In the bioavailability radar the red and orange areas represent the lower and the upper limits of the physicochemical property values (MW = 100 ÷ 600, LogP = 0 ÷ 3, LogS = −4 ÷ 0.5, LogD = 1v3; nHA = 0 ÷ 12; nHD = 0 ÷ 7; TPSA = 0 ÷ 140; nRot = 0 ÷ 11; NRing = 0 ÷ 6; MaxRing = 0 ÷ 18; nHet = 1 ÷ 15; fChar = −4 ÷ 4; nRig = 0 ÷ 30), while the blue area corresponds to the predicted values of the physicochemical properties of compounds understudy.
Figure 8. The electrostatic potential profiles; 3D and 2D representations of docking results and the bioavailability radar for selected compounds similar to sitagliptin; ZINC305224681 (A), CSC092194469 (B), ZINC12327733 (C), and ZINC7187648 (D); In the bioavailability radar the red and orange areas represent the lower and the upper limits of the physicochemical property values (MW = 100 ÷ 600, LogP = 0 ÷ 3, LogS = −4 ÷ 0.5, LogD = 1v3; nHA = 0 ÷ 12; nHD = 0 ÷ 7; TPSA = 0 ÷ 140; nRot = 0 ÷ 11; NRing = 0 ÷ 6; MaxRing = 0 ÷ 18; nHet = 1 ÷ 15; fChar = −4 ÷ 4; nRig = 0 ÷ 30), while the blue area corresponds to the predicted values of the physicochemical properties of compounds understudy.
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Figure 9. The Molecular Lipophilicity Potential of the molecular surface; the hydrophobic surface is pictured in violet and blue; the hydrophilic surface is portrayed in orange and red; compounds similar to alogliptin (A), and compounds similar to sitagliptin (B).
Figure 9. The Molecular Lipophilicity Potential of the molecular surface; the hydrophobic surface is pictured in violet and blue; the hydrophilic surface is portrayed in orange and red; compounds similar to alogliptin (A), and compounds similar to sitagliptin (B).
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Figure 10. The highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) for selected compounds similar to alogliptin (A); the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) for selected compounds similar to sitagliptin (B); the blue and red color of orbitals denotes a positive and a negative phase distribution in the molecular orbital wave function.
Figure 10. The highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) for selected compounds similar to alogliptin (A); the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) for selected compounds similar to sitagliptin (B); the blue and red color of orbitals denotes a positive and a negative phase distribution in the molecular orbital wave function.
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Figure 11. Plots of the reactivity Fukui indices for selected compounds similar to alogliptin (A) and sitagliptin (B).
Figure 11. Plots of the reactivity Fukui indices for selected compounds similar to alogliptin (A) and sitagliptin (B).
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Table 1. The initial number of compounds from databases and the number of selected molecules as “hits”.
Table 1. The initial number of compounds from databases and the number of selected molecules as “hits”.
DatabaseNo of
Molecules		Alogliptin
(Molecules “Hits”)		Sitagliptin
(Molecules “Hits”)		Linagliptin
(Molecules “Hits”)
MolPot4,807,813107834
CHEMBL301,998,181138558
ChemDiv(2015)1,456,1201681
ChemSpace50,181,6783264131
MCULE45,257,0861907181
MCULE-ULTIMATE126,471,502178142
LabNetwork1,794,28658112
ZINC12,921,9161543285
TOTAL244,888,5821109161924
The bold of number represents the sum (TOTAL) of the number of compounds on each column.
Table 2. Physicochemical parameters, QED, and CG4 values for the selected compounds similar to alogliptin (A) and sitagliptin (B) *.
Table 2. Physicochemical parameters, QED, and CG4 values for the selected compounds similar to alogliptin (A) and sitagliptin (B) *.
(A)
CSC076365308ZINC95941402ZINC408512952CSC079167462Alogliptin
MW357.210385.220327.120343.180339.170
Volume372.349387.613323.978365.066345.687
Density0.9590.9941.0100.9400.981
nHA 69757
nHD24132
nRot76593
nRing33323
MaxRing106966
nHet69757
fChar00000
nRig1820181321
Flexibility0.3890.3000.2780.6920.143
Stereo Centers12121
TPSA74.690131.05092.62078.79097.050
logS−1.511−1.832−2.903−2.535−2.103
logP1.7400.781.632.1281.185
logD1.7141.6191.4972.5081.452
PAINS0 alerts0 alerts0 alerts0 alerts0 alerts
Lipinski RuleAcceptedAcceptedAcceptedAcceptedAccepted
Pfizer RuleAcceptedAcceptedAcceptedAcceptedAccepted
Npscore−1.407 −0.929−1.042−0.482−1.318
QED0.8200.6930.8280.6850.873
CG4−11.248−10.904−10.783−10.470−10.404
(B)
ZINC305224681CSC092194469ZINC12327733ZINC71876485Sitagliptin
MW331.050316.160351.140317.190407.120
Volume291.848329.958342.472328.881343.983
Density1.1340.9581.0250.9641.184
nHA 44346
nHD 23212
nRot57456
nRing22333
MaxRing66669
nHet856512
fChar00000
nRig1413181717
Flexibility0.3570.5380.2220.2940.353
Stereo Centers12111
TPSA66.40061.36043.70041.29077.040
logS−3.063−4.277−3.219−1.797−0.783
logP2.5183.4532.6642.3140.694
logD2.4063.8312.8722.2231.932
PAINS0 alerts0 alerts0 alerts0 alerts0 alerts
Lipinski RuleAcceptedAcceptedAcceptedAcceptedAccepted
Pfizer RuleAcceptedRejectedAcceptedAcceptedAccepted
Npscore−1.854−1.260−0.950−1.884−1.404
QED0.8820.7910.8900.9220.672
CG4−11.107−10.968−10.712−10.540−10.500
* See Table S2 footer for parameter meanings.
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Istrate, D.; Crisan, L. Dipeptidyl Peptidase 4 Inhibitors in Type 2 Diabetes Mellitus Management: Pharmacophore Virtual Screening, Molecular Docking, Pharmacokinetic Evaluations, and Conceptual DFT Analysis. Processes 2023, 11, 3100. https://doi.org/10.3390/pr11113100

AMA Style

Istrate D, Crisan L. Dipeptidyl Peptidase 4 Inhibitors in Type 2 Diabetes Mellitus Management: Pharmacophore Virtual Screening, Molecular Docking, Pharmacokinetic Evaluations, and Conceptual DFT Analysis. Processes. 2023; 11(11):3100. https://doi.org/10.3390/pr11113100

Chicago/Turabian Style

Istrate, Daniela, and Luminita Crisan. 2023. "Dipeptidyl Peptidase 4 Inhibitors in Type 2 Diabetes Mellitus Management: Pharmacophore Virtual Screening, Molecular Docking, Pharmacokinetic Evaluations, and Conceptual DFT Analysis" Processes 11, no. 11: 3100. https://doi.org/10.3390/pr11113100

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