Preparation of novel shell-ionotropically crosslinked micelles based on hexadecylamine and tripolyphosphate for cancer drug delivery.

AIMS: Micellar systems have the advantage of being easily prepared, cheap, and readily loadable with bioactive molecular cargo. However, their fundamental pitfall is poor stability, particularly under dilution conditions. We propose to use simple quaternary ammonium surfactants, namely, hexadecylamine (HDA) and hexadecylpyridinium (HDAP), together with tripolyphosphate (TPP) anion, to generate ionotropically stabilized micelles capable of drug delivery into cancer cells. METHODS: optimized mixed HDA/HDAP micelles were prepared and stabilized with TPP. Curcumin was used as a loaded model drug. The prepared nanoparticles were characterized by dynamic light scattering, infrared spectroscopy, transmission electron microscopy, and differential scanning calorimetry. Moreover, their cellular uptake was assessed using flow cytometry and confocal fluorescence microscopy. RESULTS: The prepared nanoparticles were found to be stable under dilution and at high temperatures and to have a size range from 139 nm to 580 nm, depending on pH (4.6-7.4), dilution (up to 100 times), and temperature (25 - 80 °C). They were effective at delivering their load into cancer cells. Additionally, flow cytometry indicated the resulting stabilized micellar nanoparticles to be non-cytotoxic. CONCLUSIONS: The described novel stabilized micelles are simple to prepare and viable for cancer delivery.

Synthesis and antitumor activity of model cyclopentene-[g]annelated isoindigos.

A set of cyclopenten-[g]annelated isoindigos (5a-g) has been prepared and tested for their in vitro antiproliferative activities against MCF-7 and HL60 cells. Among, the N-1-methyl-5'-nitro derivative (5g) displayed the highest activity against HL60 cells (IC50 = 67 nM) and acted as the most potent Flt3 inhibitor. Compounds 5d-g exhibited moderate activity against MCF-7 (IC50 = 50-80 µM).

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.

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors.

Background: The unprecedented global spread of coronavirus disease 2019 (COVID-19) has imposed huge challenges on the healthcare facilities, and impacted every aspect of life. This has led to the development of several vaccines against COVID-19 within one year. This study aimed to assess the attitudes and the side effects among Arab communities after receiving a COVID-19 vaccine and use of machine learning (ML) tools to predict post-vaccination side effects based on predisposing factors. Methods: An online-based multinational survey was carried out via social media platforms from 14 June to 31 August 2021, targeting individuals who received at least one dose of a COVID-19 vaccine from 22 Arab countries. Descriptive statistics, correlation, and chi-square tests were used to analyze the data. Moreover, extensive ML tools were utilized to predict 30 post vaccination adverse effects and their severity based on 15 predisposing factors. The importance of distinct predisposing factors in predicting particular side effects was determined using global feature importance employing gradient boost as AutoML. Results: A total of 10,064 participants from 19 Arab countries were included in this study. Around 56% were female and 59% were aged from 20 to 39 years old. A high rate of vaccine hesitancy (51%) was reported among participants. Almost 88% of the participants were vaccinated with one of three COVID-19 vaccines, including Pfizer-BioNTech (52.8%), AstraZeneca (20.7%), and Sinopharm (14.2%). About 72% of participants experienced post-vaccination side effects. This study reports statistically significant associations (p < 0.01) between various predisposing factors and post-vaccinations side effects. In terms of predicting post-vaccination side effects, gradient boost, random forest, and XGBoost outperformed other ML methods. The most important predisposing factors for predicting certain side effects (i.e., tiredness, fever, headache, injection site pain and swelling, myalgia, and sleepiness and laziness) were revealed to be the number of doses, gender, type of vaccine, age, and hesitancy to receive a COVID-19 vaccine. Conclusions: The reported side effects following COVID-19 vaccination among Arab populations are usually non-life-threatening; flu-like symptoms and injection site pain. Certain predisposing factors have greater weight and importance as input data in predicting post-vaccination side effects. Based on the most significant input data, ML can also be used to predict these side effects; people with certain predicted side effects may require additional medical attention, or possibly hospitalization.

Inhibition of aggregation of amyloid-ß through covalent modification with benzylpenicillin; potential relevance to Alzheimer's disease.

The pathogenesis of Alzheimer's disease (AD) is correlated with the misfolding and aggregation of amyloid-beta protein (Aß). Here we report that the antibiotic benzylpenicillin (BP) can specifically bind to Aß, modulate the process of aggregation and supress its cytotoxic effect, initially via a reversible binding interaction, followed by covalent bonding between specific functional groups (nucleophiles) within the Aß peptide and the beta-lactam ring. Mass spectrometry and computational docking supported covalent modification of Aß by BP. BP was found to inhibit aggregation of Aß as revealed by the Thioflavin T (ThT) fluorescence assay and atomic force microscopy (AFM). In addition, BP treatment was found to have a cytoprotective activity against Aß-induced cell cytotoxicity as shown by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) cell toxicity assay. The specific interaction of BP with Aß suggests the possibility of structure-based drug design, leading to the identification of new drug candidates against AD. Moreover, good pharmacokinetics of beta-lactam antibiotics and safety on long-time use make them valuable candidates for drug repurposing towards neurological disorders such as AD.

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.

Design and Synthesis of New JAK1 Inhibitors based on Sulfonamide- Triazine Conjugates.

AIMS: Design of sulfonamide-triazine derivatives as JAK1 inhibitors. BACKGROUND: JAK1 is a kinase involved in different autoimmune diseases. JAK1 inhibitors have shown promising results in treating autoimmune diseases. OBJECTIVES: To design new JAK1 inhibitors based on sulfonamides-triazine conjugates capable of binding interactions comparable to observed interactions anchoring potent crystallographic JAK1 inhibitors. METHODS: The crystallographic structures of 4 diverse nanomolar inhibitors complexed within JAK1 were used to guide the synthesis of new diaminotriazine-sulfonamide-based JAK1 inhibitors. RESULTS: Nineteen compounds have been prepared, some of which exhibited low micromolar IC50 values against JAK1. CONCLUSIONS: Crystallographic complexes of diverse JAK1 inhibitors were successfully used to guide the synthesis of novel sulfonamide-triazine-based low micromolar JAK1 inhibitors.

Comprehensive Structural and Molecular Comparison of Spike Proteins of SARS-CoV-2, SARS-CoV and MERS-CoV, and Their Interactions with ACE2.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has recently emerged in China and caused a disease called coronavirus disease 2019 (COVID-19). The virus quickly spread around the world, causing a sustained global outbreak. Although SARS-CoV-2, and other coronaviruses, SARS-CoV and Middle East respiratory syndrome CoV (MERS-CoV) are highly similar genetically and at the protein production level, there are significant differences between them. Research has shown that the structural spike (S) protein plays an important role in the evolution and transmission of SARS-CoV-2. So far, studies have shown that various genes encoding primarily for elements of S protein undergo frequent mutation. We have performed an in-depth review of the literature covering the structural and mutational aspects of S protein in the context of SARS-CoV-2, and compared them with those of SARS-CoV and MERS-CoV. Our analytical approach consisted in an initial genome and transcriptome analysis, followed by primary, secondary and tertiary protein structure analysis. Additionally, we investigated the potential effects of these differences on the S protein binding and interactions to angiotensin-converting enzyme 2 (ACE2), and we established, after extensive analysis of previous research articles, that SARS-CoV-2 and SARS-CoV use different ends/regions in S protein receptor-binding motif (RBM) and different types of interactions for their chief binding with ACE2. These differences may have significant implications on pathogenesis, entry and ability to infect intermediate hosts for these coronaviruses. This review comprehensively addresses in detail the variations in S protein, its receptor-binding characteristics and detailed structural interactions, the process of cleavage involved in priming, as well as other differences between coronaviruses.

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.

Pharmacophore modeling of JAK1: A target infested with activity-cliffs.

Janus kinase 1 (JAK1) is protein kinase involved in autoimmune diseases (AIDs). JAK1 inhibitors have shown promising results in treating AIDs. JAK1 inhibitors are known to exhibit regions of SAR discontinuity or activity cliffs (ACs). ACs represent fundamental challenge to successful QSAR/pharmacophore modeling because QSAR modeling rely on the basic premise that activity is a smooth continuous function of structure. We propose that ACs exist because active ACs members exhibit subtle, albeit critical, enthalpic features absent from their inactive twins. In this context we compared the performances of two computational modeling workflows in extracting valid pharmacophores from 151 diverse JAK1 inhibitors that include ACs: QSAR-guided pharmacophore selection versus docking-based comparative intermolecular contacts analysis (db-CICA). The two methods were judged based on the receiver operating characteristic (ROC) curves of their corresponding pharmacophore models and their abilities to distinguish active members among established JAK1 ACs. db-CICA modeling significantly outperformed ligand-based pharmacophore modeling. The resulting optimal db-CICA pharmacophore was used as virtual search query to scan the National Cancer Institute (NCI) database for novel JAK1 inhibitory leads. The most active hit showed IC50 of 1.04 µM. This study proposes the use of db-CICA modeling as means to extract valid pharmacophores from SAR data infested with ACs.

Stable Chitosan-Based Nanoparticles Using Polyphosphoric Acid or Hexametaphosphate for Tandem Ionotropic/Covalent Crosslinking and Subsequent Investigation as Novel Vehicles for Drug Delivery.

Chitosan nanoparticles (NPs) are widely studied as vehicles for drug, protein, and gene delivery. However, lack of sufficient stability, particularly under physiological conditions, render chitosan NPs of limited pharmaceutical utility. The aim of this study is to produce stable chitosan NPs suitable for drug delivery applications. Chitosan was first grafted to phthalic or phenylsuccinic acids. Subsequently, polyphosphoric acid (PPA), hexametaphosphate (HMP), or tripolyphosphate (TPP) were used to achieve tandem ionotropic/covalently crosslinked chitosan NPs in the presence of 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide (EDC). Thermal and infrared traits confirmed phosphoramide bonds formation tying chitosan with the polyphosphate crosslinkers within NPs matrices. DLS and TEM size analysis indicated spherical NPs with size range of 120 to 350 nm. The generated NPs exhibited excellent stabilities under harsh pH, CaCl2, and 10% FBS conditions. Interestingly, DLS, NPs stability and infrared data suggest HMP to reside within NPs cores, while TPP and PPA to act mainly as NPs surface crosslinkers. Drug loading and release studies using methylene blue (MB) and doxorubicin (DOX) drug models showed covalent PPA- and HMP-based NPs to have superior loading capacities compared to NPs based on unmodified chitosan, generated by ionotropic crosslinking only or covalently crosslinked by TPP. Doxorubicin-loaded NPs were of superior cytotoxic properties against MCF-7 cells compared to free doxorubicin. Specifically, DOX-loaded chitosan-phthalate polyphosphoric acid-crosslinked NPs exhibited 10-folds cytotoxicity enhancement compared to free DOX. The use of PPA and HMP to produce covalently-stabilized chitosan NPs is completely novel.

Inhibition of monoglyceride lipase by proton pump inhibitors: investigation using docking and in vitro experiments.

BACKGROUND: Currently, there is overwhelming evidence linking elevated plasma free fatty acids with insulin resistance and inflammation. Monoglyceride lipase (MGL) plays a crucial metabolic role in lipolysis by mediating the release of fatty acids. Therefore, inhibiting MGL should be a promising pharmacological approach for treating type 2 diabetes and inflammatory disorders. Proton pump inhibitors (PPIs) have been reported to improve glycemic control in type 2 diabetes albeit via largely unknown mechanism. METHODS: The anti-MGL bioactivities of three PPIs, namely, lansoprazole, rabeprazole, and pantoprazole, were investigated using docking experiments and in vitro bioassay. RESULTS: The three PPIs inhibited MGL in low micromolar range with rabeprazole exhibiting the best IC50 at 4.2 µM. Docking experiments showed several binding interactions anchoring PPIs within MGL catalytic site. CONCLUSION: Our study provides evidence for a new mechanism by which PPIs improve insulin sensitivity independent of serum gastrin. The three PPIs effectively inhibit MGL and, therefore, serve as promising leads for the development of new clinical MGL 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.

Design and Synthesis of New Sulfonamides-Based Flt3 Inhibitors.

BACKGROUND: Flt3 is an oncogenic kinase involved in different leukemias. It is most prominently associated with acute myeloid leukemia (AML). Flt3-specific inhibitors have shown promising results in interfering with AML. METHODS: The crystallographic structures of two inhibitors complexed within Flt3, namely, quizartinib and F6M, were used to guide the synthesis of new sulfonamide-based Flt3 inhibitors. RESULTS: One of the prepared compounds showed low micromolar anti-Flt3 bioactivity, and interestingly, low micromolar bioactivity against the related oncogenic kinase VEGFR2. CONCLUSION: Sulfonamides were successfully used as privileged scaffolds for the synthesis of novel Flt3 inhibitors of micromolar potencies.

Discovery of new JNK3 inhibitory chemotypes via QSAR-Guided selection of docking-based pharmacophores and comparison with other structure-based pharmacophore modeling methods.

The kinase c-Jun N-terminal Kinase 3 (JNK3) plays an important role in neurodegenerative diseases. JNK3 inhibitors have shown promising results in treating Alzheimer's and Parkinson's diseases. This prompted us to model this interesting target via three established structure-based computational workflows; namely, docking-based Comparative Intermolecular Contacts Analysis (db-CICA), pharmacophore modeling via molecular-dynamics based Ligand-Receptor Contact Analysis (md-LRCA), and QSAR-guided selection of crystallographic pharmacophores. Moreover, we compared the performances of resulting pharmacophores against binding models generated via a newly introduced technique, namely, QSAR-guided selection of docking-based pharmacophores. The resulting pharmacophores were validated by receiver operating characteristic (ROC) curve analysis and used as virtual search queries to screen the National Cancer Institute (NCI) database for promising anti-JNK3 hits of novel chemotypes. Eleven nanomolar and low micromolar hits were identified, three of which were captured by QSAR-guided docking-based pharmacophores.

Degradability of chitosan micro/nanoparticles for pulmonary drug delivery.

Chitosan, a natural carbohydrate polymer, has long been investigated for drug delivery and medical applications due to its biodegradability, biocompatibility and low toxicity. The micro/nanoparticulate forms of chitosan are reported to enhance the efficiency of drug delivery with better physicochemical properties including improved solubility and bioavailability. This polymer is known to be biodegradable and biocompatible; however, crosslinked chitosan particles may not be biodegradable. Crosslinkers (e.g., tripolyphosphate and glutaraldehyde) are needed for efficient micro/nanoparticle formation, but it is not clear whether the resultant particles are biodegradable or able to release the encapsulated drug fully. To date, no studies have conclusively demonstrated the complete biodegradation or elimination of chitosan nanoparticles in vivo. Herein we review the synthesis and degradation mechanisms of chitosan micro/nanoparticles frequently used in drug delivery especially in pulmonary drug delivery to understand whether these nanoparticles are biodegradable.

Discovery of novel Flt3 inhibitory chemotypes through extensive ligand-based and new structure-based pharmacophore modelling methods.

Flt3 is an oncogenic kinase involved in different types of leukemia. It is most prominently associated with acute myeloid leukemia (AML). Flt3-specific inhibitors have shown promising results in interfering with AML prompting us to model this interesting target. We implemented ligand-based, QSAR-guided, pharmacophore exploration combined with novel structure-based computational workflow based on docking-based comparative intermolecular contacts analysis (db-CICA) combined with homology modelling to explore the pharmacophoric features of 93 diverse cyclic Flt3 inhibitors. The resulting pharmacophore models were used as virtual search queries to scan the National Cancer Institute (NCI) database for novel Flt3 inhibitory leads. Ten hits of novel scaffolds were captured showing anti-Flt3 IC50 values ranging from 1.2 to 14.7 µM. Interestingly, six of them illustrated low micromolar and submicromolar potencies against the mutated active form of Flt3 (Flt3D835Y) and the related vascular endothelial growth factor receptor 2 (VEGFR2).

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.

The herbicide quinclorac as potent lipase inhibitor: Discovery via virtual screening and in vitro/in vivo validation.

Lipolysis is primarily controlled by the stepwise action of hormone-sensitive lipase (HSL) and monoglyceride lipase (MGL) to release free fatty acids and glycerol. A high level of circulating free fatty acids is well-known to mediate insulin resistance. Thus, the need to discover lipase inhibitors against both enzyme systems remains urgent. Agrochemicals are tightly regulated chemicals and therefore are potential source of new medicinal agents. Accordingly, we implemented a computational workflow to search for new lipase inhibitory leads by virtually screening commercial agrochemicals against HSL and MGL employing binding pharmacophores and docking experiments. Ten agrochemicals were identified as potential lipase inhibitors, out of which quinclorac, a safe herbicide, achieved high-ranking score. Subsequent in vitro evaluation against rat epididymal lipase activity showed quinclorac to exhibit nanomolar anti-lipase IC50 . Subsequent in vivo testing showed quinclorac to significantly decrease blood glycerol levels after acute exposure (150 mg/kg) and multiple dosing (50 or 25 mg/kg) (p < 0.05).

Discovery of new Gyrase ß inhibitors via structure based modeling.

Gyrase B is an essential enzyme in the prokaryotes which became an attractive target for antibacterial agents. In our study, we implemented a wide range of docking configurations to dock 120 inhibitors into the in the ATP- binding pocket of Gyrase B enzyme (PDB code: 4GEE). LigandFit docking engines and six scoring functions were utilized in the study. Furthermore, the ligands were docked in their ionized and unionized forms into the hydrous and anhydrous binding pocket. We used docking-based Comparative Intermolecular Contacts Analysis (db-CICA) which is a novel methodology to validate and identify the optimal docking configurations. Three docking configurations were found to achieve self-consistent db-CICA models. The resulting db-CICA models were used to construct corresponding pharmacophoric models that were used to screen the National Cancer Institute (NCI) list of compounds. In-vitro study represents antibacterial activities for twelve hit molecules with the most active having IC50 of 20.9 µM.