Novel 2-Aminobenzothiazole Derivatives: Docking, Synthesis, and Biological Evaluation as Anticancer Agents.

Sixteen novel 2-aminobenzothiazole compounds with different amines or substituted piperazine moieties were designed, synthesized, and tested using various methods. Potential interactions were assessed by docking new compounds in the adenosine triphosphate (ATP) binding domain of the PI3Kγ enzyme (PDB code: 7JWE) by nucleophilic substitution or solvent-free/neat fusion for docked compound synthesis. Final 2-aminobenzothiazole compounds were characterized by direct probe gas chromatography-mass spectrometry (GC-MS), proton (1H-NMR), carbon-13 (13C-NMR), and attenuated total reflectance-infrared Fourier transform infrared (ATR FT-IR). The synthesized compounds were investigated for anticancer activities on lung cancer (A549) and breast cancer (MCF-7) cell lines. The compounds' PI3Kγ inhibition was evaluated at a 100 µM concentration. 4-Nitroaniline and piperazine-4-nitroaniline combination in OMS5 and OMS14 reduced lung and breast cancer cell line growth. IC50 values for OMS5 and OMS14, the strongest compounds, ranged from 22.13 to 61.03 µM. OMS1 and OMS2 inhibited PI3Kγ at the highest rates (47 and 48%, respectively) at a 100 µM concentration. Results show that the PI3Kγ enzyme suppression is not the main mechanism behind these OMS5 and OMS14 anticancer effects. CDK2, Akt, mTOR, and p42/44 MAPK are affected. EGF receptor suppression matters. AKT1, AKT3, CDK1/cyclin B, PDK1 direct, PIK3CA E542 K/PIK3R1 (p110 α/p85 α), PIK3CD/PIK3R1 (p110 δ/p85 α), and PKN inhibition were measured to evaluate the possible mechanism of compound OMS14. PIK3CD/PIK3R1 (p110 δ/p85 α) is the most, with 65% inhibition, suggesting a possible mechanism of anticancer properties. Furthermore, the NCI 60-cell line inhibition demonstrates promising broad anticancer inhibition against numerous cancer cell lines of OMS5 and OMS14, which could be good lead compounds for future development.

Chemical Synthesis, Biological Evaluation, and Cheminformatics Analysis of a Group of Chlorinated Diaryl Sulfonamides: Promising Inhibitors of Cholesteryl Ester Transfer Protein.

BACKGROUND: Hyperlipidemia is characterized by an abnormally elevated serum cholesterol, triglycerides, or both. The relationship between an elevated level of LDL and cardiovascular diseases is well-established. Cholesteryl ester transfer protein (CETP) is an enzyme that moves cholesterol esters and triglycerides between LDL, VLDL, and HDL. CETP inhibition leads to a reduction in cardiovascular disease by raising HDL and minimizing LDL. OBJECTIVE: This study synthesized ten meta-chlorinated benzene sulfonamides 6a-6j and explored their structure-activity relationship. METHODS: The synthesized molecules were characterized using 1H-NMR, 13C-NMR, IR, and HR-MS. Moreover, cheminformatics analyses included pharmacophore mapping, LibDock studies, and cheminformatics characterization using 2-dimensional (2D) molecular descriptors and principal component analysis. RESULTS: Based on in vitro functional CETP assays, compounds 6e, 6i, and 6j demonstrated the strongest inhibitory activities against CETP, reaching 100% inhibition. The inhibitory activity of compounds 6a-6d and 6f-6h ranged from 47.5% to 96.5% at 10 µM concentration. Pharmacophore mapping results suggested CETP inhibitory action, while the docking scores and calculated binding energies predicted favoring binding at the CETP active site. Best-scoring docking poses predicted critical hydrophobic features corresponding to key interactions with His232 and Cys13. Cheminformatics analysis using 2D molecular descriptors indicated that the synthesized compounds span various physicochemical properties and drug-likeness. CONCLUSION: It was found that a chloro moiety at the ortho-position, or a nitro group at the meta and para-positions, improves the CETP inhibitory activity of synthesized analogs. Computational studies suggest the formation of stable ligand-protein complexes between compounds 6a- 6j and CETP.

In silico Evaluation of Ferulic Acid Based Multifunctional Conjugates as Potential Drug Candidates.

BACKGROUND: Recent research has shown that ferulic acid (FA, trans-4-hydroxy-3- methoxycinnamic acid) has remarkable antioxidant properties and a wide range of biological activities. Conjugation of two or more biologically active compounds to produce a novel molecular scaffold is justified by the need to enhance biological activity against a single target or obtain a conjugate that behaves as a multi-target-directed ligand. In addition, the conjugation strategy decreases dose-dependent side effects by promoting the use of smaller doses of conjugated components to treat the disease. Moreover, the patient's compliance is positively affected when conjugating two active compounds into a single more active compound as this reduces the number of pills to be taken daily. OBJECTIVE: This study aims to shed light on studies that design and synthesize FA-based hybrid compounds with enhanced biological activities and to in silico assess these compounds as potential drug candidates. METHODS: The conjugate compounds were found by searching the literature using the keywords (ferulic acid-based hybrid or ferulic acid-based conjugate). To study conjugate pharmacokinetic parameters and toxicity (ADMET), software suites from Biovia Inc. (San Diego, California) were integrated into Discovery Studio 4.5. The structures were created using ChemDraw Ultra 7.0. RESULTS: 14 conjugates exhibiting variable biological activities were collected and three of them (compounds 3,5, and 6) in addition to the cis FA (compound 12) are the best-predicted compounds with low Daphnia toxicity and hepatotoxicity with acceptable pharmacokinetic properties. CONCLUSION: Cis FA, FA conjugates 3,5, and 6 act as good drug candidates that can be used to modify new hits.

Novel Sulfonamide-Triazine Hybrid Derivatives: Docking, Synthesis, and Biological Evaluation as Anticancer Agents.

The biological benefits of trisubstituted 1,3,5-triazine derivatives include their ability to reduce inflammation and fight cancer. A unique series of sulfonamide-triazine hybrid molecules were produced chemically by synthesizing triazine derivatives utilizing the usual nucleophilic aromatic substitution of cyanuric chloride via the solvent-free/neat fusion method. Fourier-transform infrared spectroscopy (FTIR), 1H NMR, and 13C NMR spectroscopic analyses were used to identify novel trisubstituted synthetic compounds. The synthesized compounds have a moderate inhibition percentage when tested at 100 µM against the phosphoinositol 3-kinases (PI3Kα) enzyme; compounds 20 and 34 showed 46 and 68% anti-PI3Kα activity, respectively. To comprehend the anticipated interactions, the most successful compounds were subsequently docked into a PI3Kα protein's binding site (PDB code: 6OAC, resolution: 3.15 Å). The final synthetic compounds' anticancer activity was tested on the breast (MCF-7) and lung (A549) cancer cell lines at doses of 100 and 50 µM for additional evaluation of anticancer characteristics. The IC50 values for the sulfaguanidine-triazine derivatives 27, 28, 29, 31, and 35 ranged from 14.8 to 33.2 µM, showing that compounds containing sulfaguanidine and diethylamine in their structures significantly inhibited the activity. Compound 34 could be a promising lead compound for developing new target-selected anticancer compounds with low toxicity and high selectivity.

Discovery of new PKN2 inhibitory chemotypes via QSAR-guided selection of docking-based pharmacophores.

Serine/threonine-protein kinase N2 (PKN2) plays an important role in cell cycle progression, cell migration, cell adhesion and transcription activation signaling processes. In cancer, however, it plays important roles in tumor cell migration, invasion and apoptosis. PKN2 inhibitors have been shown to be promising in treating cancer. This prompted us to model this interesting target using our QSAR-guided selection of docking-based pharmacophores approach where numerous pharmacophores are extracted from docked ligand poses and allowed to compete within the context of QSAR. The optimal pharmacophore was sterically-refined, validated by receiver operating characteristic (ROC) curve analysis and used as virtual search query to screen the National Cancer Institute (NCI) database for new promising anti-PKN2 leads of novel chemotypes. Three low micromolar hits were identified with IC50 values ranging between 9.9 and 18.6 µM. Pharmacological assays showed promising cytotoxic properties for active hits in MTT and wound healing assays against MCF-7 and PANC-1 cancer cells.

Pharmacophore Modeling of Targets Infested with Activity Cliffs via Molecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study.

Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically-driven conformational modifications of the target that unveil additional binding interactions, while their weakly-potent counterparts are enthalpically-driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs-infested target(s) from molecular dynamics (MD) frames of purely "enthalpic" potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand-based and structure-based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signalling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD-based, pharmacophores were of comparable performances to ligand-based and structure-based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti-KDR IC50 of 2.76 µM.

Identification of the First "Two Digit Nano-molar" Inhibitors of the Human Glyoxalase-I Enzyme as Potential Anticancer Agents.

BACKGROUND: Glyoxalase-I (Glo-I) enzyme is recognized as an indispensable druggable target in cancer treatment. Its inhibition will lead to the accumulation of toxic aldehyde metabolites and cell death. Paramount efforts were spent to discover potential competitive inhibitors with the aim to eradicate cancer. OBJECTIVE: Based on our previous work on this target for discovering potent inhibitors of this enzyme, herein, we address the discovery of the most potent Glo-I inhibitors reported in the literature with two digits nano-molar activity. METHODS: Molecular docking and in vitro assay were performed to discover these inhibitors and explore the binding pattern within the active site. A detailed SAR scheme was generated, which identifies the major functionalities responsible for the observed activity. RESULTS: Compound 1 with an IC50 of 16.5 nM exhibited the highest activity, which possess catechol moiety as an essential zinc chelating functionality. It has been shown by using molecular modeling techniques that the catechol moiety is responsible for chelation zinc atom at the active site; an essential feature for enzyme inhibition. CONCLUSION: Catechol derivatives are successful zinc chelators in Glo-I enzyme while showing exceptional activity against the enzyme to nanomolar level.

Elaboration of Novel TTK1 Inhibitory Leads via QSAR-Guided Selection of Crystallographic Pharmacophores Followed By In Vitro Assay.

INTRODUCTION: Tyrosine threonine kinase (TTK1) is a key regulator of chromosome segregation. Recently, TTK targeting came into focus for the enhancement of possible anticancer therapies. OBJECTIVE: In this regard, we employed our well-known method of QSAR-guided selection of the best crystallographic pharmacophore(s) to discover considerable binding interactions that transfer inhibitors into TTK1 binding site. METHODS: Sixty-one TTK1 crystallographic complexes were used to extract 315 pharmacophore hypotheses. QSAR modeling was subsequently used to choose a single crystallographic pharmacophore that, when combined with other physicochemical descriptors, elucidates discrepancy in bioactivity of 55 miscellaneous inhibitors. RESULTS: The best QSAR model was robust and predictive (r2(55) = 0.75, r2LOO = 0.72 , r2press against external testing list of 12 compounds = 0.67), Standard error of estimate (training set) (S)= 0.63 , Standard error of estimate (testing set)(Stest) = 0.62. The resulting pharmacophore and QSAR models were used to scan the National Cancer Institute (NCI) database for new TTK1 inhibitors. CONCLUSION: Five hits confirmed significant TTK1 inhibitory profiles with IC50 values ranging between 11.7 and 76.6 mM.

Ligand Based Pharmacophore Modeling Followed by Biological Screening Lead to Discovery of Novel PDK1 Inhibitors as Anticancer Agents.

BACKGROUND: Phosphoinositide-Dependent Kinase-1 (PDK1) is a serine/threonine kinase, which belongs to AGC kinase family required by cancer cells. METHODS: Pharmacophoric space of 86 PDK1 inhibitors using six diverse sets of inhibitors was explored to identify high-quality pharmacophores. The best combination of pharmacophoric models and physicochemical descriptors was selected by genetic algorithm-based QSAR analysis that can elucidate the variation of bioactivity within the training inhibitors. Two successful orthogonal pharmacophores emerged in the optimum QSAR equation (r2 69 = 0.90, r2 LOO= 0.86, F= 51.92, r2 PRESS against 17 test inhibitors = 0.79). Receiver Operating Characteristic (ROC) curve analyses were used to estimate the QSAR-selected pharmacophores. RESULTS: 5 out of 11 compounds tested had shown potential intracellular PDK1 inhibition with the highest inhibition percent for compounds 92 and 93 as follows; 90 and 92% PDK1 inhibition, respectively. CONCLUSION: PDK1 inhibitors are potential anticancer agents that can be discovered by combination method of ligand based design with QSAR and ROC analysis.

Combined High Throughput Screening with QSAR Analysis Unravelling Potential Glyoxalase-I Inhibitors.

AIMS: Discovery of new Glo-I inhibitors as potential anticancer agents. BACKGROUND: Glyoxalase system is ubiquitous system in human cells which has been examined thoroughly for its role in cancerous diseases. It performs detoxifying endogenous harmful metabolites, mainly methylglyoxal (MG) into non-toxic bystanders. OBJECTIVE: Structure based model Hypo(2ZA0_2_02) combined with 3D-QSAR modeling were applied to predict glyoxalase I inhibition and to explain their activity. METHODS: Currently, high throughput screening approach was used to investigate the activity of inhouse database composed of 205 compounds. RESULTS: 15 compounds were found active as glyoxalase I inhibitors. The 15 candidates showed more than 50% inhibition with low micromolar IC50 ranges between 5.0 to 42.0 µM. CONCLUSION: They have been successfully mapped and fitted the Hypo(2ZA0_2_02) model which explain the presence of anti-glyoxalase I activity. This model could be used in future for further development of new and novel glyoxylase I inhibitors.

Investigation of Binding Characteristics of Phosphoinositide-dependent Kinase-1 (PDK1) Co-crystallized Ligands Through Virtual Pharmacophore Modeling Leading to Novel Anti-PDK1 Hits.

BACKGROUND: 3-Phosphoinositide Dependent Protein Kinase-1 (PDK1) is being lately considered as an attractive and forthcoming anticancer target. A Protein Data Bank (PDB) cocrystallized crystal provides not only rigid theoretical data but also a realistic molecular recognition data that can be explored and used to discover new hits. OBJECTIVE: This incited us to investigate the co-crystallized ligands' contacts inside the PDK1 binding pocket via a structure-based receptor-ligand pharmacophore generation technique in Discovery Studio 4.5 (DS 4.5). METHODS: Accordingly, 35 crystals for PDK1 were collected and studied. Every single receptorligand interaction was validated and the significant ones were converted into their corresponding pharmacophoric features. The generated pharmacophores were scored by the Receiver Operating Characteristic (ROC) curve analysis. RESULTS: Consequently, 169 pharmacophores were generated and sorted, 11 pharmacophores acquired good ROC-AUC results of 0.8 and a selectivity value above 8. Pharmacophore 1UU3_2_01 was used in particular as a searching filter to screen NCI database because of its acceptable validity criteria and its distinctive positive ionizable feature. Several low micromolar PDK1 inhibitors were revealed. The most potent hit illustrated anti-PDK1 IC50 values of 200 nM with 70% inhibition against SW480 cell lines. CONCLUSION: Eventually, the active hits were docked inside the PDK1 binding pocket and the recognition points between the active hits and the receptor were analyzed that led to the discovery of new scaffolds as potential PDK1 inhibitors.

Combination of pharmacophore modeling and 3D-QSAR analysis of potential glyoxalase-I inhibitors as anticancer agents.

Glyoxalase system is an ubiquitous system in human cells which has been examined thoroughly for its role in different diseases. It comprises two enzymes; Glyoxalase I (Glo-I) and Glyoxalase II (Glo-II) which perform detoxifying endogenous harmful metabolites, mainly methylglyoxal (MG) into non-toxic bystanders. In silico computer Aided Drug Design approaches were used and ninety two diverse pharmacophore models were generated from eighteen Glyoxalase I crystallographic complexes. Subsequent QSAR modeling followed by ROC evaluation identified a single pharmacophore model which was able to predict the expected Glyoxalase I inhibition. Screening of the National Cancer Institute (NCI) database using the optimal pharmacophore Hypo(3VW9) identified several promising hits. Thirty eight hits were successfully predicted then ordered and evaluated in vitro. Seven hits out of the thirty eight tested compounds showed more than 50% inhibition with low micromolar IC50.

Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis.

BACKGROUND: PI3Kδ is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3Kδ inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3Kδ binding. METHODS: Seventeen PI3Kδ crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors). RESULTS: The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values. CONCLUSION: Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3Kδ inhibitors.

Ligand-based modeling of Akt3 lead to potent dual Akt1/Akt3 inhibitor.

Akt1 and Akt3 are important serine/threonine-specific protein kinases involved in G2 phase required by cancer cells to maintain cell cycle and to prevent cell death. Accordingly, inhibitors of these kinases should have potent anti-cancer properties. This prompted us to use pharmacophore/QSAR modeling to identify optimal binding models and physicochemical descriptors that explain bioactivity variation within a set of 74 diverse Akt3 inhibitors. Two successful orthogonal pharmacophores were identified and further validated using receiver operating characteristic (ROC) curve analyses. The pharmacophoric models and associated QSAR equation were applied to screen the national cancer institute (NCI) list of compounds for new Akt3 inhibitors. Six hits showed significant experimental anti-Akt3 IC50 values, out of which one compound exhibited dual low micromolar anti-Akt1 and anti-Akt3 inhibitory profiles.

Discovery of novel potent nuclear factor kappa-B inhibitors (IKK-ß) via extensive ligand-based modeling and virtual screening.

Inhibitor kappa-B kinase-beta (IKK-ß) controls the activation of nuclear transcription factor kappa-B and has been linked to inflammation and cancer. Therefore, inhibitors of this kinase should have potent anti-inflammatory and anticancer properties. Accordingly, we explored the pharmacophoric space of 218 IKK-ß inhibitors to identify high-quality binding models. Subsequently, genetic algorithm-based quantitative structure activity relationship (QSAR) analysis was employed to select the best possible combination of pharmacophoric models and physicochemical descriptors that explain bioactivity variation among training compounds. Three successful pharmacophores emerged in 2 optimal QSAR equations (r12175  = 0.733, r12LOO  = 0.52, F1 = 65.62, r12PRESS against 43 test inhibitors = 0.63 and r22175  = 0.683, r22LOO  = 0.52, F2 = 72.66, r22PRESS against 43 test inhibitors = 0.65). Two pharmacophores were merged in a single binding model. Receiver operating characteristic curve validation proved the excellent qualities of this model. The merged pharmacophore and the associated QSAR equations were applied to screen the National Cancer Institute list of compounds. Ten hits were found to exhibit potent anti-IKK-ß bioactivity, out of which, one illustrates IC50 of 11.0nM.

Discovery of new heat shock protein 90 inhibitors using virtual co-crystallized pharmacophore generation.

The pharmacophoric features of the virtual cocrystallized protein of 178 Hsp90 proteins were obtained from the protein data bank and explored to generate 1260 pharmacophores evaluated using the decoy list composed of 1022 compounds. Accordingly, 51 pharmacophores were selected with high receiver operating characteristic (ROC) value for further processing. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of accessing a self-consistent quantitative structure-activity relationship (QSAR) of optimal predictive potential (R672 = 0.819, F = 43.0, R2LOO = 0.782, R2PRESS against 16 external test inhibitors equal 0.735). Two orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least two binding modes accessible to ligands within the Hsp90 binding pocket. The fifth generated pharmacophoric model from Hsp90 protein 2XJX (2XJX_2_05), and the forth generated cocrystallized pharmacophoric model from Hsp90 protein 4LWF (4LWF_2_04) with area under the curve AUC-ROC values 0.812 and 0.876, respectively were selected to be used as a searching tool sequentially of the National Cancer Institute (NCI) database. The captured hits were mapped based on successful hypotheses and the best predicted hits were selected. Twenty-four hits showed Hsp90 inhibition, 15 hits were measured with low micromolar IC50 ranged from 5.0 µM to 77.1 µM.

Evaluation of novel Akt1 inhibitors as anticancer agents using virtual co-crystallized pharmacophore generation.

The pharmacophoric features of the virtual co-crystallized protein of 17 Akt1 proteins were downloaded from the protein data bank, and explored to end up with 132 generated pharmacophores that had been evaluated using the decoy list composed of 1724 compounds. The areas under the curve of the Receiver-Operating Characteristic (ROC-AUC) were sorted, and the highest ranked pharmacophore 3MV5_2_01 was selected to be used as a searching tool in the National Cancer Institute (NCI) database. The captured hits were mapped based on successful hypotheses and the best fitted compounds were selected. The inhibition of Akt1 was measured and expressed as a percentage of inhibition. 24 out of the 40 compounds showed inhibition of Akt1, out of which 13 compounds showed more than 50% inhibition. Compound 1 showed 93.3% inhibition at 100 µM concentration. To confirm the inhibition of Akt1 phosphorylation, MCF10A cell line was co-treated with 12-O-tetradecanoylphorbol-13-acetate (TPA) and 100 µM of each of the most potent 13 Akt inhibitors (1-13). It was found that compounds 1 exert 91.6% inhibition of Akt1 phosphorylation in MCF10A cell line.

Discovery of nanomolar phosphoinositide 3-kinase gamma (PI3Kγ) inhibitors using ligand-based modeling and virtual screening followed by in vitro analysis.

Phosphoinositide 3-kinase gamma (PI3Kγ) is member of a family of enzymes involved in cancer pathogenesis. Accordingly, considerable efforts have been carried out to develop new PI3Kγ inhibitors. Towards this end we explored the pharmacophoric space of PI3Kγ using three diverse sets of inhibitors. Subsequently, we employed genetic algorithm-based QSAR analysis to select optimal combination of pharmacophoric models and physicochemical descriptors that can explain bioactivity variation within training inhibitors. Interestingly, two successful pharmacophores were selected within two statistically consistent QSAR models. The close similarity among the two binding models prompted us to merge them in a hybrid pharmacophore. The resulting model showed superior receiver operator characteristic curve (ROC) and closely resembled binding interactions seen in crystallographic ligand-PI3Kγ complexes. The resulting model was employed to screen the national cancer institute (NCI) list of compounds to search for new PI3Kγ ligands. After testing captured hits in vitro, 19 compounds showed nanomolar IC50 values against PI3Kγ. The chemical structures and purities of most potent hits were validated using NMR and MS experiments.

Discovery of novel urokinase plasminogen activator (uPA) inhibitors using ligand-based modeling and virtual screening followed by in vitro analysis.

Urokinase plasminogen activator (uPA)-a serine protease-is thought to play a central role in tumor metastasis and angiogenesis and, therefore, inhibition of this enzyme could be beneficial in treating cancer. Toward this end, we explored the pharmacophoric space of 202 uPA inhibitors using seven diverse sets of inhibitors to identify high-quality pharmacophores. Subsequently, we employed genetic algorithm-based quantitative structure-activity relationship (QSAR) analysis as a competition arena to select the best possible combination of pharmacophoric models and physicochemical descriptors that can explain bioactivity variation within the training inhibitors (r (2) 162 = 0.74, F-statistic = 64.30, r (2) LOO = 0.71, r (2) PRESS against 40 test inhibitors = 0.79). Three orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least three binding modes accessible to ligands within the uPA binding pocket. This conclusion was supported by receiver operating characteristic (ROC) curve analyses of the QSAR-selected pharmacophores. Moreover, the three pharmacophores were comparable with binding interactions seen in crystallographic structures of bound ligands within the uPA binding pocket. We employed the resulting pharmacophoric models and associated QSAR equation to screen the national cancer institute (NCI) list of compounds. The captured hits were tested in vitro. Overall, our modeling workflow identified new low micromolar anti-uPA hits.

Elaborate ligand-based modeling reveal new migration inhibitory factor inhibitors.

Recent research suggested the involvement of migration inhibitor factor (MIF) in cancer and inflammatory diseases, which prompted several attempts to develop new MIF inhibitors. Accordingly, we investigated the pharmacophoric space of 79 MIF inhibitors using seven diverse subsets of inhibitors to identify plausible binding hypotheses (pharmacophores). Subsequently, we implemented genetic algorithm and multiple linear regression analysis to select optimal combination of pharmacophores and physicochemical descriptors capable of explaining bioactivity variation within the training compounds (QSAR model, r63=0.62, F=42.8, rLOO(2)=0.721,rPRESS(2) against 16 external test inhibitors=0.58). Two orthogonal pharmacophores appeared in the optimal QSAR model suggestive of at least two binding modes available to ligands inside MIF binding pocket. Subsequent validation using receiver operating characteristic (ROC) curves analysis established the validity of these two pharmacophores. We employed these pharmacophoric models and associated QSAR equation to screen the National Cancer Institute (NCI) list of compounds. Eight compounds gave >50% inhibition at 100µM. Two molecules illustrated >75% inhibition at 10µM.