Combined QSAR-based virtual screening and fluorescence binding assay to identify natural product mediators of Interferon Regulatory Factor 7 (IRF-7) in pulmonary infection

Abstract

Interferon regulatory factor-7 (IRF-7) is involved in pulmonary infection and pneumonia. Here, a synthetic strategy that combined quantitative structure–activity relationship (QSAR)-based virtual screening and in vitro binding assay was described to identify new and potent mediator ligands of IRF-7 from natural products. In the procedure, a QSAR scoring function was developed and validated using Gaussian process (GP) regression and a structure-based set of protein–ligand affinity data. By integrating hotspot pocket prediction, pharmacokinetics profile analysis and molecular docking calculations, the scoring function was successfully applied to virtual screening against a large library of structurally diverse, drug-like natural products. With the method we were able to identify a number of potential hits, from which several compounds were found to have moderate or high affinity to IRF-7 using fluorescence binding assays, with dissociation constants K_d at micromolar level. We have also examined the structural basis and noncovalent interactions of computationally modelled IRF-7 complex with its potent ligands. It is revealed that hydrophobic forces and van der Waals contacts play a central role in stabilization of the complex architecture, while few hydrogen bonds confer additional specificity for the protein–ligand recognition.     

Synthesis of heteroannulated cyclopent[b]indoles: Exploration of in vitro cytotoxicity and molecular docking studies

A series of novel cyclopent[b]indole analogues that hold isoxazolo-, pyrido-templates were designed and synthesized in good yields. The in vitro cytotoxicity was concerned for all the newly synthesized compounds by MTT assay against HeLa (cervix adeno carcinoma) and MCF-7 (breast cancer). These synthesized compounds were further compared with the standard drug ellipticine, 5-fluorouracil, cisplatin, and methotrexate. The synthesized heteroannulated cyclopent[b]indole compounds were found to show better cytotoxic activity against HeLa and MCF-7 with primary structure activity relationship studies. To identify with the nature of interactions of these molecules, we performed molecular docking studies using the protein kinase CK2 inhibitors. The docking results afforded some valuable information for the future design of more potent inhibitors.     

The selectivity and promiscuity of brain-neuroregenerative inhibitors between ROCK1 and ROCK2 isoforms: An integration of SB-QSSR modelling,QM/MM analysis and in vitro kinase assay

The Rho-associated kinases (ROCKs) have long been recognized as an attractive therapeutic target for various neurological diseases; selective inhibition of ROCK1 and ROCK2 isoforms would result in distinct biological effects on neurogenesis, neuroplasticity and neuroregeneration after brain surgery and traumatic brain injury. However, the discovery and design of isoform-selective inhibitors remain a great challenge due to the high conservation and similarity between the kinase domains of ROCK1 and ROCK2. Here, a structure-based quantitative structure–selectivity relationship (SB-QSSR) approach was used to correlate experimentally measured selectivity with the difference in inhibitor binding to the two kinase isoforms. The resulting regression models were examined rigorously through both internal cross-validation and external blind validation; a nonlinear predictor was found to have high fitting stability and strong generalization ability, which was then employed to perform virtual screening against a structurally diverse, drug-like compound library. Consequently, five and seven hits were identified as promising candidates of 1-o-2 and 2-o-1 selective inhibitors, respectively, from which seven purchasable compounds were tested in vitro using a standard kinase assay protocol to determine their inhibitory activity against and selectivity between ROCK1 and ROCK2. The structural basis, energetic property and biological implication underlying inhibitor selectivity and promiscuity were also investigated systematically using a hybrid quantum mechanics/molecular mechanics (QM/MM) scheme.     

Anti-neuraminidase activity of chemical constituents of Balanophora involucrata

Balanophora involucrata J. D. Hooker has been known to possess potential anti-inflammatory and antibacterial activities; however, its antiviral activity has not been evaluated so far. In order to find new neuraminidase inhibitors (NAIs), the neuraminidase (NA) inhibition activity of different B. involucrata extracts was evaluated. In this study, an in vitro NA inhibition assay was performed to identify which extract of B. involucrata exhibits (maximal) inhibitory activity against NA. Ultra high performance liquid chromatography/quadrupole time-of-flight–tandem mass spectroscopy (MS/MS) and molecular docking techniques were used to identify the specific compounds responsible for the anti-influenza activity of the extract, and to explore the potential natural NAIs. The ethyl acetate extract of B. involucrata exhibited significant inhibitory activity against NA with 50% inhibitory concentration (IC₅₀) value of 159.5 μg/mL. Twenty compounds were identified according to the MS/MS spectra; among them two compounds (quercitrin and phloridzin) showed obvious inhibitory activity against NA, with IC₅₀ of 311.76 and 347.32 μmol/L, respectively. This study suggested that B. involucrata can be a potential natural source of NAIs and may be useful in the fight against ferocious influenza viruses.     

Computational discovery of putative quorum sensing inhibitors against LasR and RhlR receptor proteins of Pseudomonas aeruginosa

Drugs have been discovered in the past mainly either by identification of active components from traditional remedies or by unpredicted discovery. A key motivation for the study of structure based virtual screening is the exploitation of such information to design targeted drugs. In this study, structure based virtual screening was used in search for putative quorum sensing inhibitors (QSI) of Pseudomonas aeruginosa. The virtual screening programme Glide version 5.5 was applied to screen 1,920 natural compounds/drugs against LasR and RhlR receptor proteins of P. aeruginosa. Based on the results of in silico docking analysis, five top ranking compounds namely rosmarinic acid, naringin, chlorogenic acid, morin and mangiferin were subjected to in vitro bioassays against laboratory strain PAO1 and two more antibiotic resistant clinical isolates, P. aeruginosa AS1 (GU447237) and P. aeruginosa AS2 (GU447238). Among the five compounds studied, except mangiferin other four compounds showed significant inhibition in the production of protease, elastase and hemolysin. Further, all the five compounds potentially inhibited the biofilm related behaviours. This interaction study provided promising ligands to inhibit the quorum sensing (QS) mediated virulence factors production in P. aeruginosa.     

Virtual screening of B-Raf kinase inhibitors: A combination of pharmacophore modelling,molecular docking, 3D-QSAR model and binding free energy calculation studies

B-Raf kinase has been identified as an important target in recent cancer treatment. In order to discover structurally diverse and novel B-Raf inhibitors (BRIs), a virtual screening of BRIs against ZINC database was performed by using a combination of pharmacophore modelling, molecular docking, 3D-QSAR model and binding free energy (ΔG_bind) calculation studies in this work. After the virtual screening, six promising hit compounds were obtained, which were then tested for inhibitory activities of A375 cell lines. In the result, five hit compounds show good biological activities (IC₅₀ < 50 μM). The present method of virtual screening can be applied to find structurally diverse inhibitors, and the obtained five structurally diverse compounds are expected to develop novel BRIs.     

Structure-based modelling,scoring, screening,and in vitro kinase assay of anesthetic pkc inhibitors against a natural medicine library

Protein kinase C (PKC) is an intracellular effector of the inositol phosphate-mediated signal transduction pathway. Evidence is emerging that certain general anaesthetics can influence the activity of PKC by interacting with the regulatory domain of the enzyme, and targeting PKC kinase domain is considered as a strategy to modulate the anaesthetic effects. Here, an integrated method was used to perform virtual screening against a large library of natural compounds for the discovery of new and potent PKC modulators. A number of hits were identified and their inhibitory activity against PKC kinase domain was measured by using a standard kinase assay protocol. Three and five compounds were determined to have high and moderate activities with IC₅₀ values at nanomolar and micromolar levels, respectively. These compounds can be considered as promising lead molecular entities to develop efficacious anaesthetic modulators. Structural examination revealed a variety of nonbonded interactions such as hydrogen bonds, cation-π contacts, and hydrophobic forces across the complex interface of PKC with the identified compounds. This study helps to establish an integrative approach to rational kinase inhibitor discovery by efficiently exploiting various existing natural products.     

Virtual screening of protein kinase C inhibitors from natural product library to modulate general anaesthetic effects

Protein kinase C (PKC) plays a key role in neurotransmission in the central nervous system, and targeting PKC domain is considered as a strategy to modulate the anaesthetic effects. In this study, we described a synthetic pipeline to perform high-throughput virtual screening against a large library of 3D structural natural products released recently in order to discover those potential PKC modulators. A total of 100 natural products with top scores were raised, from which 12 promising candidates were tested to determine their inhibitory potencies against PKC. As might be expected, the promiscuous kinase inhibitor staurosporine showed a high PKC inhibitory activity (IC₅₀ = 64 nM), and other two tested compounds, i.e. fisetin and tetrahydropapaverine, were also highly potent with their activities at nanomolar level (IC₅₀ = 370 and 190, respectively).     

Structure-based grafting and identification of kinase–inhibitors to target mTOR signaling pathway as potential therapeutics for glioblastoma

Mammalian target of rapamycin (mTOR), a key mediator of PI3K/Akt/mTOR signaling pathway, has recently emerged as a compelling molecular target in glioblastoma. The mTOR is a member of serine/threonine protein kinase family that functions as a central controller of growth, proliferation, metabolism and angiogenesis, but its signaling is dysregulated in various human diseases especially in certain solid tumors including the glioblastoma. Here, considering that there are various kinase inhibitors being approved or under clinical or preclinical development, it is expected that some of them can be re-exploited as new potent agents to target mTOR for glioblastoma therapy. To achieve this, a synthetic pipeline that integrated molecular grafting, consensus scoring, virtual screening, kinase assay and structure analysis was described to systematically profile the binding potency of various small-molecule inhibitors deposited in the protein kinase–inhibitor database against the kinase domain of mTOR. Consequently, a number of structurally diverse compounds were successfully identified to exhibit satisfactory inhibition profile against mTOR with IC₅₀ values at nanomolar level. In particular, few sophisticated kinase–inhibitors as well as a flavonoid myricetin showed high inhibitory activities, which could thus be considered as potential lead compounds to develop new potent, selective mTOR–inhibitors. Structural examination revealed diverse nonbonded interactions such as hydrogen bonds, hydrophobic forces and van der Waals contacts across the complex interface of mTOR with myricetin, conferring both stability and specificity for the mTOR–inhibitor binding.     

Computer‐aided Discovery of Potential Inhibitors for Transthyretin‐related Amyloidosis

Transthyretin (TTR) is a homotetrameric plasma protein associated with human amyloid diseases. Although Tafamidis has been recently approved for the treatment of TTR familial amyloid polyneuropathy (FAP), there is still a need for more effective drugs in the treatment of TTR amyloidosis diseases. In this study, a computer‐aided approach combining molecular docking, virtual screening and molecular dynamics (MD) simulations was employed to identify potent TTR amyloidosis inhibitors from National Cancer Institute (NCI), Maybridge and Asdi databases. A receptor‐specific scoring function was also developed using comparative binding energy (COMBINE) method to accurately predict the inhibitory activities for the selected compounds during virtual screening. The developed receptor‐specific scoring function demonstrated good predictive ability by yielding strong correlation coefficients between experimental activities and estimated activities for 32 training set and 9 test set compounds, respectively. Moreover, it was successfully applied to rank the candidate compounds from structure‐based virtual screening. Finally, three compounds (NSC220163, MFCD00276817 and SPB06319) were identified as potential leads, which exhibited higher predicted inhibitory activities and higher binding affinities in comparison to the Tafamidis. Our results further suggest that halogen bonding interaction plays a crucial role in stabilizing the TTR‐inhibitor complex. These results indicate that our computational approach could effectively discover more potent TTR amyloidosis inhibitors, which can be further validate by in vitro and in vivo biological tests.     

Identification of novel PfDHODH inhibitors as antimalarial agents via pharmacophore-based virtual screening followed by molecular docking and in vivo antimalarial activity

Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) catalyses the fourth reaction of de novo pyrimidine biosynthesis in parasites, and represents an important target for the treatment of malaria. In this study, we describe pharmacophore-based virtual screening combined with docking study and biological evaluation as a rational strategy for identification of novel hits as antimalarial agents. Pharmacophore models were established from known PfDHODH inhibitors using the GALAHAD module with IC₅₀ values ranging from 0.033 μM to 142 μM. The best pharmacophore model consisted of three hydrogen bond acceptor, one hydrogen bond donor and one hydrophobic features. The pharmacophore models were validated through receiver operating characteristic and Günere–Henry scoring methods. The best pharmacophore model as a 3D search query was searched against the IBS database. Several compounds with different structures (scaffolds) were retrieved as hit molecules. Among these compounds, those with a Q_FIT value of more than 81 were docked in the PfDHODH enzyme to further explore the binding modes of these compounds. In silico pharmacokinetic and toxicities were predicted for the best docked molecules. Finally, the identified hits were evaluated in vivo for their antimalarial activity in a parasite inhibition assay. The hits reported here showed good potential to become novel antimalarial agents.     

Discovery of novel IDO1 inhibitors via structure-based virtual screening and biological assays

Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme that catalyzes the first and rate-limiting step in catabolism of tryptophan via the kynurenine pathway, which plays a pivotal role in the proliferation and differentiation of T cells. IDO1 has been proven to be an attractive target for many diseases, such as breast cancer, lung cancer, colon cancer, prostate cancer, etc. In this study, docking-based virtual screening and bioassays were conducted to identify novel inhibitors of IDO1. The cellular assay demonstrated that 24 compounds exhibited potent inhibitory activity against IDO1 at micromolar level, including 8 compounds with IC₅₀ values below 10 μM and the most potent one (compound 1) with IC₅₀ of 1.18?±?0.04 μM. Further lead optimization based on similarity searching strategy led to the discovery of compound 28 as an excellent inhibitor with IC₅₀ of 0.27?±?0.02 μM. Then, the structure–activity relationship of compounds 1, 2, 8 and 14 analogues is discussed. The interaction modes of two compounds against IDO1 were further explored through a Python Based Metal Center Parameter Builder (MCPB.py) molecular dynamics simulation, binding free energy calculation and electrostatic potential analysis. The novel IDO1 inhibitors of compound 1 and its analogues could be considered as promising scaffold for further development of IDO1 inhibitors.

     

Structure-Based Discovery and Bioactivity Evaluation of Novel Aurora-A Kinase Inhibitors as Anticancer Agents via Docking-Based Comparative Intermolecular Contacts Analysis (dbCICA)

Aurora-A kinase plays a central role in mitosis, where aberrant activation contributes to cancer by promoting cell cycle progression, genomic instability, epithelial-mesenchymal transition, and cancer stemness. Aurora-A kinase inhibitors have shown encouraging results in clinical trials but have not gained Food and Drug Administration (FDA) approval. An innovative computational workflow named Docking-based Comparative Intermolecular Contacts Analysis (dbCICA) was applied—aiming to identify novel Aurora-A kinase inhibitors—using seventy-nine reported Aurora-A kinase inhibitors to specify the best possible docking settings needed to fit into the active-site binding pocket of Aurora-A kinase crystal structure, in a process that only potent ligands contact critical binding-site spots, distinct from those occupied by less-active ligands. Optimal dbCICA models were transformed into two corresponding pharmacophores. The optimal one, in capturing active hits and discarding inactive ones, validated by receiver operating characteristic analysis, was used as a virtual in-silico search query for screening new molecules from the National Cancer Institute database. A fluorescence resonance energy transfer (FRET)-based assay was used to assess the activity of captured molecules and five promising Aurora-A kinase inhibitors were identified. The activity was next validated using a cell culture anti-proliferative assay (MTT) and revealed a most potent lead 85(NCI 14040) molecule after 72 h of incubation, scoring IC₅₀ values of 3.5–11.0 μM against PANC1 (pancreas), PC-3 (prostate), T-47D and MDA-MB-231 (breast)cancer cells, and showing favorable safety profiles (27.5 μM IC₅₀ on fibroblasts). Our results provide new clues for further development of Aurora-A kinase inhibitors as anticancer molecules.     

Natural Products Extracted from Fungal Species as New Potential Anti-Cancer Drugs: A Structure-Based Drug Repurposing Approach Targeting HDAC7

Mushrooms can be considered a valuable source of natural bioactive compounds with potential polypharmacological effects due to their proven antimicrobial, antiviral, antitumor, and antioxidant activities. In order to identify new potential anticancer compounds, an in-house chemical database of molecules extracted from both edible and non-edible fungal species was employed in a virtual screening against the isoform 7 of the Histone deacetylase (HDAC). This target is known to be implicated in different cancer processes, and in particular in both breast and ovarian tumors. In this work, we proposed the ibotenic acid as lead compound for the development of novel HDAC7 inhibitors, due to its antiproliferative activity in human breast cancer cells (MCF-7). These promising results represent the starting point for the discovery and the optimization of new HDAC7 inhibitors and highlight the interesting opportunity to apply the “drug repositioning” paradigm also to natural compounds deriving from mushrooms.     

Quick identification of xanthine oxidase inhibitor and antioxidant from Erycibe obtusifolia by a drug discovery platform composed of multiple mass spectrometric platforms and thin‐layer chromatography bioautography

As a final step of the purine metabolism process, xanthine oxidase catalyzes the oxidation of hypoxanthine and xanthine into uric acid. Our research has demonstrated that Erycibe obtusifolia has xanthine oxidase inhibitory properties. The purpose of this paper is to describe a new strategy based on a combination of multiple mass spectrometric platforms and thin‐layer chromatography bioautography for effectively screening the xanthine oxidase inhibitory and antioxidant properties of E. obtusifolia. This strategy was accomplished through the following steps. (i) Separate the extract of E. obtusifolia into fractions by an autopurification system controlled by liquid chromatography with mass spectrometry. (ii) Determine the active fractions of E. obtusifolia by thin‐layer chromatography bioautography. (iii) Identify the structure of the main active compounds with the information provided by direct analysis in real time mass spectrometry. (iv) Calculate the IC₅₀ value of each compound against xanthine oxidase using high‐performance liquid chromatography. Using the caulis of E. obtusifolia as the experimental material, seven target peaks were screened out as xanthine oxidase inhibitors or antioxidants. Our screening strategy allows for rapid analysis of small molecules with almost no sample preparation and can be completed within a week, making it a useful assay to identify unstable compounds and provide the empirical foundation for E. obtusifolia as a natural remedy for gout and oxidative‐stress‐related diseases.     

Inverse in silico screening for identification of kinase inhibitor targets

Protein kinases are clinically relevant, attractive drug targets for cancer. One major problem with kinase inhibitors is broad promiscuity, causing off-target actions and side effects. In silico prediction of targets of a compound would immensely facilitate and accelerate drug development. Using a virtual "inverse" screening approach, where single compounds are docked into protein structures from a database, we identify among known targets of indirubin derivatives phosphoinositide-dependent kinase 1 (PDK1) as a target of one derivative (6BIO) in particular. This prediction is functionally supported by an in vitro kinase assay, inhibition of intracellular phosphorylation of PDK1-substrates, and inhibition of endothelial cell migration, which highly depends on PDK1. Virtual inverse screening combined with biological tests, thus, is proposed as a valuable tool for the drug discovery process and re-examination of already established kinase inhibitors.     

Studies on α-glucosidase,aldose reductase and glycation inhibitory properties of sesquiterpenes and flavonoids of Zingiber zerumbet Smith

Eight known phytochemicals, four sesquiterpenes and four flavonoids of Zingiber zerumbet were screened against α-glucosidase enzyme, aldose reductase enzyme and antiglycation property under in vitro conditions. The results established kaempferol-3-O-methylether as a potent inhibitor of α-glucosidase enzyme with an IC₅₀ value of 7.88 μM. In aldose reductase enzyme inhibition assay, all the compounds except zerumbone epoxide showed good to excellent inhibition properties. Among these, the flavonoid compounds were found to be potent aldose reductase inhibitors compared with the four sesquiterpenes. In addition, compounds such as α-humulene, kaempferol, kaempferol-3-O-methylether and 3″,4″-O-diacetylafzelin displayed potent antiglycation properties. From overall results, we found that kaempferol and kaempferol-3-O-methylether are potent inhibitors of α-glucosidase enzyme, aldose reductase enzyme and glycation reaction, the three main targets of drugs for the treatment of diabetes and its complications.     

Multiple receptor-ligand based pharmacophore modeling and molecular docking to screen the selective inhibitors of matrix metalloproteinase-9 from natural products

Matrix metalloproteinase-9 (MMP-9) is an attractive target for cancer therapy. In this study, the pharmacophore model of MMP-9 inhibitors is built based on the experimental binding structures of multiple receptor-ligand complexes. It is found that the pharmacophore model consists of six chemical features, including two hydrogen bond acceptors, one hydrogen bond donor, one ring aromatic regions, and two hydrophobic (HY) features. Among them, the two HY features are especially important because they can enter the S1′ pocket of MMP-9 which determines the selectivity of MMP-9 inhibitors. The reliability of pharmacophore model is validated based on the two different decoy sets and relevant experimental data. The virtual screening, combining pharmacophore model with molecular docking, is performed to identify the selective MMP-9 inhibitors from a database of natural products. The four novel MMP-9 inhibitors of natural products, NP-000686, NP-001752, NP-014331, and NP-015905, are found; one of them, NP-000686, is used to perform the experiment of in vitro bioassay inhibiting MMP-9, and the IC₅₀ value was estimated to be only 13.4 µM, showing the strongly inhibitory activity of NP-000686 against MMP-9, which suggests that our screening results should be reliable. The binding modes of screened inhibitors with MMP-9 active sites were discussed. In addition, the ADMET properties and physicochemical properties of screened four compounds were assessed. The found MMP-9 inhibitors of natural products could serve as the lead compounds for designing the new MMP-9 inhibitors by carrying out structural modifications in the future.     

Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure–activity relationship-based artificial intelligence and molecular docking of hit compounds

Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side effects might be related to secondary inhibition of DPP-8 and DPP-9. In this study, we identified DPP-4-inhibitor hit compounds selective against DPP-8 and DPP-9. We built a virtual screening workflow using a quantitative structure–activity relationship (QSAR) strategy based on artificial intelligence to allow faster screening of millions of molecules for the DPP-4 target relative to other screening methods. Five regression machine learning algorithms and four classification machine learning algorithms were applied to build virtual screening workflows, with the QSAR model applied using support vector regression (R²_pred 0.78) and the classification QSAR model using the random forest algorithm with 92.2% accuracy. Virtual screening results of > 10 million molecules obtained 2 716 hits compounds with a pIC₅₀ value of > 7.5. Additionally, molecular docking results of several potential hit compounds for DPP-4, DPP-8, and DPP-9 identified CH0002 as showing high inhibitory potential against DPP-4 and low inhibitory potential for DPP-8 and DPP-9 enzymes. These results demonstrated the effectiveness of this technique for identifying DPP-4-inhibitor hit compounds selective for DPP-4 and against DPP-8 and DPP-9 and suggest its potential efficacy for applications to discover hit compounds of other targets.     

Structure-based virtual screening of dipeptidyl peptidase 4 inhibitors and their in vitro analysis

Type 2 diabetes mellitus (T2DM) is one of the most widely prevalent metabolic disorders with no cure to date thus remains the most challenging task in the current drug discovery. Therefore, the only strategy to control diabetes prevalence is to develop novel efficacious therapeutics. Dipeptidyl Peptidase 4 (DPP-4) inhibitors are currently used as anti-diabetic drugs for the inhibition of incretins. This study aims to construct the chemical feature based on pharmacophore models for dipeptidyl peptidase IV. The structure-based pharmacophore modeling has been employed to evaluate new inhibitors of DPP-4. A four-featured pharmacophore model was developed from crystal structure of DPP-4 enzyme with 4-(2-aminoethyl) benzenesulfonyl fluoride in its active site via pharmacophore constructing tool of Molecular Operating Environment (MOE) consisting F1 Hyd (hydrophobic region), F2 Hyd|Cat|Don (hydrophobic cationic and donor region), F3 Acc (acceptor region) and F4 Hyd (hydrophobic region). The generated pharmacophore model was used for virtual screening of in-house compound library (the available compounds which were used for initial screening to get the few compounds for the current studies). The resultant selected compounds, after virtual screening were further validated using in vitro assay. Furthermore, structure-activity relationship was carried out for the compounds possessing significant inhibition potential after docking studies. The binding free energy of analogs was evaluated via molecular mechanics generalized Born surface area (MM-GBSA) and Poisson-Boltzmann surface area (MM-PBSA) methods using AMBER 16 as a molecular dynamics (MD) simulation package. Based on potential findings, we report that selected candidates are more likely to be used as DPP-4 inhibitors or as starting leads for the development of novel and potent DPP-4 inhibitors.