Discovery of Anticancer Hybrid Molecules by Supervised Machine Learning Models and in Vitro Validation in Drug Resistant Chronic Myeloid Leukemia Cells.

The concept of hybrid drugs for targeting multiple aberrant pathways of cancer, by combining the key pharmacophores of clinically approved single-targeted drugs, has emerged as a promising approach for overcoming drug-resistance. Here, we report the design of unique hybrid molecules by combining the two pharmacophores of clinically approved BCR-ABL inhibitor (ponatinib) and HDAC inhibitor (vorinostat) and results of in vitro studies in drug-resistant CML cells. Robust 2D-QSAR and 3D-pharmacophore machine learning supervised models were developed for virtual screening of the hybrid molecules based on their predicted BCR-ABL and HDAC inhibitory activity. The developed 2D-QSAR model showed five information rich molecular descriptors while the 3D-pharmacophore model of BCR-ABL showed five different chemical features (hydrogen bond acceptor, donor, hydrophobic group, positive ion group, and aromatic rings) and the HDAC model showed four different chemical features (hydrogen bond acceptor, donor, positive ion group, and aromatic rings) for potent BCR-ABL and HDAC inhibition. Virtual screening of the 16 designed hybrid molecules identified FP7 and FP10 with better potential of inhibitory activity. FP7 was the most effective molecule with predicted IC50 using the BCR-ABL based 2D-QSAR model of 0.005 µM and that of the HDAC model of 0.153 µM, and that using the BCR-ABL based 3D-pharmacophore model was 0.02 µM and that with HDAC model was 0.014 µM. In vitro study (dose-response relationship) of FP7 in wild type and imatinib-resistant CML cell lines harboring Thr315Ile or Tyr253His mutations showed growth inhibitory IC50 values of 0.000 16, 0.0039, and 0.01 µM, respectively. This molecule also showed better biocompatibility when tested in whole blood and in PBMCs as compared to ponatinib or vorinostat.

Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer's disease.

Multi-target directed ligand-based 2D-QSAR models were developed using different N-benzyl piperidine derivatives showing inhibitory activity toward acetylcholinesterase (AChE) and ß-Site amyloid precursor protein cleaving enzyme (BACE1). Five different classes of molecular descriptors belonging to spatial, structural, thermodynamics, electro-topological and E-state indices were used for machine learning by linear method, genetic function approximation (GFA) and nonlinear method, support vector machine (SVM) and artificial neural network (ANN). Dataset used for QSAR model development includes 57 AChE and 53 BACE1 inhibitors. Statistically significant models were developed for AChE (R2 = 0.8688, q2 = 0.8600) and BACE1 (R2 = 0.8177, q2 = 0.7888) enzyme inhibitors. Each model was generated with an optimum five significant molecular descriptors such as electro-topological (ES_Count_aaCH and ES_Count_dssC), structural (QED_HBD, Num_TerminalRotomers), spatial (JURS_FNSA_1) for AChE and structural (Cl_Count, Num_Terminal Rotomers), electro-topological (ES_Count_dO), electronic (Dipole_Z) and spatial (Shadow_nu) for BACE1 enzyme, determining the key role in its enzyme inhibitory activity. The predictive ability of the generated machine learning models was validated using the leave-one-out, Fischer (F) statistics and predictions based on the test set of 11 AChE (r2 = 0.8469, r2pred = 0.8138) and BACE1 (r2 = 0.7805, r2pred = 0.7128) inhibitors. Further, nonlinear machine learning methods such as ANN and SVM predicted better than the linear method GFA. These molecular descriptors are very important in describing the inhibitory activity of AChE and BACE1 enzymes and should be used further for the rational design of multi-targeted anti-Alzheimer's lead molecules.

Mechanistic understanding of Phenyllactic acid mediated inhibition of quorum sensing and biofilm development in Pseudomonas aeruginosa.

Pseudomonas aeruginosa depends on its quorum sensing (QS) system for its virulence factors' production and biofilm formation. Biofilms of P. aeruginosa on the surface of indwelling catheters are often resistant to antibiotic therapy. Alternative approaches that employ QS inhibitors alone or in combination with antibiotics are being developed to tackle P. aeruginosa infections. Here, we have studied the mechanism of action of 3-Phenyllactic acid (PLA), a QS inhibitory compound produced by Lactobacillus species, against P. aeruginosa PAO1. Our study revealed that PLA inhibited the expression of virulence factors such as pyocyanin, protease, and rhamnolipids that are involved in the biofilm formation of P. aeruginosa PAO1. Swarming motility, another important criterion for biofilm formation of P. aeruginosa PAO1, was also inhibited by PLA. Gene expression, mass spectrometric, functional complementation assays, and in silico data indicated that the quorum quenching and biofilm inhibitory activities of PLA are attributed to its ability to interact with P. aeruginosa QS receptors. PLA antagonistically binds to QS receptors RhlR and PqsR with a higher affinity than its cognate ligands N-butyryl-L-homoserine lactone (C4-HSL) and 2-heptyl-3,4-dihydroxyquinoline (PQS; Pseudomonas quinolone signal). Using an in vivo intraperitoneal catheter-associated medaka fish infection model, we proved that PLA inhibited the initial attachment of P. aeruginosa PAO1 on implanted catheter tubes. Our in vitro and in vivo results revealed the potential of PLA as anti-biofilm compound against P. aeruginosa.

Understanding the Structure-Function Relationship of Lysozyme Resistance in Staphylococcus aureus by Peptidoglycan O-Acetylation Using Molecular Docking, Dynamics, and Lysis Assay.

Lysozyme is an important component of the host innate defense system. It cleaves the ß-1,4 glycosidic bonds between N-acetylmuramic acid and N-acetylglucosamine of bacterial peptidoglycan and induce bacterial lysis. Staphylococcus aureus (S. aureus), an opportunistic commensal pathogen, is highly resistant to lysozyme, because of the O-acetylation of peptidoglycan by O-acetyl transferase (oatA). To understand the structure-function relationship of lysozyme resistance in S. aureus by peptidoglycan O-acetylation, we adapted an integrated approach to (i) understand the effect of lysozyme on the growth of S. aureus parental and the oatA mutant strain, (ii) study the lysozyme induced lysis of exponentially grown and stationary phase of both the S. aureus parental and oatA mutant strain, (iii) investigate the dynamic interaction mechanism between normal (de-O-acetylated) and O-acetylated peptidoglycan substrate in complex with lysozyme using molecular docking and molecular dynamics simulations, and (iv) quantify lysozyme resistance of S. aureus parental and the oatA mutant in different human biological fluids. The results indicated for the first time that the active site cleft of lysozyme binding with O-acetylated peptidoglycan in S. aureus was sterically hindered and the structural stability was higher for the lysozyme in complex with normal peptidoglycan. This could have conferred reduced survival of the S. aureus oatA mutant in different human biological fluids. Consistent with this computational analysis, the experimental data confirmed decrease in the growth, lysozyme induced lysis, and lysozyme resistance, due to peptidoglycan O-acetylation in S. aureus.

Synthesis, biological evaluation and molecular modeling study of pyrazole derivatives as selective COX-2 inhibitors and anti-inflammatory agents.

A novel series of pyrazole derivatives were synthesized and evaluated in vivo for their anti-inflammatory activity in carrageenan-induced rat paw edema model. Among all compounds, 5a, and 5b showed comparable anti-inflammatory activity to Nimesulide, the standard drug taken for the studies. In silico (docking) studies were carried out to investigate the theoretical binding mode of the compounds to target the cyclooxygenase (COX-2) using Autodock 4.2.

Identification of a Staphylococcus aureus amidase catalytic domain inhibitor to prevent biofilm formation by sequential virtual screening, molecular dynamics simulation and biological evaluation.

Staphylococcus aureus (S. aureus) is one of the common causes of implant associated biofilm infections and their biofilms are resistant to antibiotics. S. aureus amidase (AM) protein, a cell wall hydrolase that cleaves the amide bond between N-acetylmuramic acid and L-alanine residue of the stem peptide, is several fold over-expressed under biofilm conditions. Previous studies demonstrated an autolysin mutant in S. aureus that lacks the AM protein, is highly impaired in biofilm development. We carried out a structure-based small molecule design using the crystal structure of AM protein catalytic domain to identify inhibitors that can block amidase activity and therefore inhibits S. aureus biofilm formation. Sequential virtual screening followed by pharmacokinetic analysis and bioassay studies filtered 25 small molecules from different databases. Two compounds from the SPECS database, SPECS-1 and SPECS-2, were selected based on the best docking score and minimum biofilm inhibitory concentration towards S. aureus biofilms. SPECS-1 and SPECS-2 were further tested for their structural/energetic stability in complex with the AM protein using molecular dynamics simulation and MM-GBSA techniques. In vitro, biofilm inhibition studies on different surfaces confirmed that treatment with SPECS-1 and SPECS-2 at a concentration of 250 µg/ml exhibited significant prevention and disruption of S. aureus biofilms. Finally, the in vitro anti-biofilm activities of these two compounds were validated against Methicillin-resistant S. aureus clinical isolates. We concluded that the discovered compounds SPECS-1 and SPECS-2 are safe and exhibit biofilm preventive and disruption activity for inhibiting the S. aureus biofilms and hence can be used to treat implant-associated biofilm infections.

Identification of a PD1/PD-L1 inhibitor by structure-based pharmacophore modelling, virtual screening, molecular docking and biological evaluation.

PD-1/PD-L1 is a critical druggable target for immunotherapy against sepsis. Chemoinformatics techniques involved the structure-based 3D pharmacophore model development followed by virtual screening of small molecule databases to identify the small molecules against PD-L1 pathway inhibition. Raltitrexed and Safinamide act as potent repurposed drugs, and three other Specs database compounds using in silico methods. These compounds were screened based on the pharmacophore fit score and binding affinity towards the active site of the PD-L1 protein. In silico pharmacokinetic profiling of these screened compounds was done to test their biological activity. Next, experimental validation of the best four virtually screened hits was done in vitro for its hemocompatibility and cytotoxicity. Among these, Raltitrexed, Safinamide and Specs compound (AK-968/40642641) effectively increased the proliferation of immune cells and IFN-γ production. These compounds can act as potent PDL-1 inhibitors for adjuvant therapy against sepsis.

Doxycycline prevents blood-brain barrier dysfunction and microvascular hyperpermeability after traumatic brain injury.

The main objective of this study was to determine the cellular and molecular effects of doxycycline on the blood-brain barrier (BBB) and protection against secondary injuries following traumatic brain injury (TBI). Microvascular hyperpermeability and cerebral edema resulting from BBB dysfunction after TBI leads to elevation of intracranial pressure, secondary brain ischemia, herniation, and brain death. There are currently no effective therapies to modulate the underlying pathophysiology responsible for TBI-induced BBB dysfunction and hyperpermeability. The loss of BBB integrity by the proteolytic enzyme matrix metalloproteinase-9 (MMP-9) is critical to TBI-induced BBB hyperpermeability, and doxycycline possesses anti-MMP-9 effect. In this study, the effect of doxycycline on BBB hyperpermeability was studied utilizing molecular modeling (using Glide) in silico, cell culture-based models in vitro, and a mouse model of TBI in vivo. Brain microvascular endothelial cell assays of tight junction protein immunofluorescence and barrier permeability were performed. Adult C57BL/6 mice were subjected to sham versus TBI with or without doxycycline treatment and immediate intravital microscopic analysis for evaluating BBB integrity. Postmortem mouse brain tissue was collected to measure MMP-9 enzyme activity. It was found that doxycycline binding to the MMP-9 active sites have binding affinity of -7.07 kcal/mol. Doxycycline treated cell monolayers were protected from microvascular hyperpermeability and retained tight junction integrity (p < 0.05). Doxycycline treatment decreased BBB hyperpermeability following TBI in mice by 25% (p < 0.05). MMP-9 enzyme activity in brain tissue decreased with doxycycline treatment following TBI (p < 0.05). Doxycycline preserves BBB tight junction integrity following TBI via inhibiting MMP-9 activity. When established in human subjects, doxycycline, may provide readily accessible medical treatment after TBI to attenuate secondary injury.

Synthesis, biological evaluation and molecular docking studies of stellatin derivatives as cyclooxygenase (COX-1, COX-2) inhibitors and anti-inflammatory agents.

Stellatin (4), isolated from Dysophylla stellata is a cyclooxygenase (COX) inhibitor. The present study reports the synthesis and biological evaluation of new stellatin derivatives for COX-1, COX-2 inhibitory and anti-inflammatory activities. Eight derivatives showed more pronounced COX-2 inhibition than stellatin and, 17 and 21 exhibited the highest COX-2 inhibition. They also exhibited the significant anti-inflammatory activity in TPA-induced mouse ear edema assay and their anti-inflammatory effects were more than that of stellatin and indomethacin at 0.5mg/ear. The derivatives were further evaluated for antioxidant activity wherein 16 and 17 showed potent free radical scavenging activity against DPPH and ABTS radicals. Molecular docking study revealed the binding orientations of stellatin and its derivatives into the active sites of COX-1 and COX-2 and thereby helps to design the potent inhibitors.

Discovery of dual binding site acetylcholinesterase inhibitors identified by pharmacophore modeling and sequential virtual screening techniques.

Dual binding site acetylcholinesterase (AChE) inhibitors are promising for the treatment of Alzheimer's disease (AD). They alleviate the cognitive deficits and AD-modifying agents, by inhibiting the ß-amyloid (Aß) peptide aggregation, through binding to both the catalytic and peripheral anionic sites, the so called dual binding site of the AChE enzyme. In this Letter, chemical features based 3D-pharmacophore models were developed based on the eight potent and structurally diverse AChE inhibitors (I-VIII) obtained from high-throughput in vitro screening technique. The best 3D-pharmacophore model, Hypo1, consists of two hydrogen-bond acceptor lipid, one hydrophobe, and two hydrophobic aliphatic features obtained by Catalyst/HIPHOP algorithm adopted in Discovery studio program. Hypo1 was used as a 3D query in sequential virtual screening study to filter three small compound databases. Further, a total of nine compounds were selected and followed on in vitro analysis. Finally, we identified two leads--Specs1 (IC(50)=3.279 µM) and Spec2 (IC(50)=5.986 µM) dual binding site compounds from Specs database, having good AChE enzyme inhibitory activity.

Predicting the Molecular Mechanism of EGFR Domain II Dimer Binding Interface by Machine Learning to Identify Potent Small Molecule Inhibitor for Treatment of Cancer.

Epidermal growth factor receptor (EGFR) is a transmembrane druggable target controlling cellular differentiation, proliferation, migration, survival and invasion. EGFR activation mainly occurs by its homo/hetro dimerization molecular phenomenon leading to tumor development and invasion. Several tyrosine kinase based inhibitors were discovered as potent anti-cancer drugs. However, mutations in its kinase domain confer resistance to most of these drugs. To overcome this drug resistance, development of small molecule inhibitors disrupting the EGFR Domain II dimer binding by machine learning methods are promising. Based on this insight, a structure-based drug repurposing strategy was adopted to repurpose the existing FDA approved drugs in blocking the EGFR Domain II mediated dimerization. We identified five best repurposed drug molecules showing good binding affinity at its key arm-cavity dimer interface residues by different machine learning methods. The molecular mechanisms of action of these repurposed drugs were computationally validated by molecular electrostatics potential mapping, point mutations at the dimer arm-cavity binding interface, molecular docking and receptor interaction studies. The present machine learning strategy thus forms the basis of identifying potent and putative small molecule drugs for the treatment of different types of cancer.

A phytochemical-based medication search for the SARS-CoV-2 infection by molecular docking models towards spike glycoproteins and main proteases.

Identifying best bioactive phytochemicals from different medicinal plants using molecular docking techniques demonstrates a potential pre-clinical compound discovery against SARS-CoV-2 viral infection. The in silico screening of bioactive phytochemicals with the two druggable targets of SARS-CoV-2 by simple precision/extra precision molecular docking methods was used to compute binding affinity at its active sites. phyllaemblicin and cinnamtannin class of phytocompounds showed a better binding affinity range (-9.0 to -8.0 kcal mol-1) towards both these SARS-CoV-2 targets; the corresponding active site residues in the spike protein were predicted as: Y453, Q496, Q498, N501, Y449, Q493, G496, T500, Y505, L455, Q493, and K417; and Mpro: Q189, H164, H163, P168, H41, L167, Q192, M165, C145, Y54, M49, and Q189. Molecular dynamics simulation further established the structural and energetic stability of protein-phytocompound complexes and their interactions with their key residues supporting the molecular docking analysis. Protein-protein docking using ZDOCK and Prodigy server predicted the binding pose and affinity (-13.8 kcal mol-1) of the spike glycoprotein towards the human ACE2 enzyme and also showed significant structural variations in the ACE2 recognition site upon the binding of phyllaemblicin C compound at their binding interface. The phyllaemblicin and cinnamtannin class of phytochemicals can be potential inhibitors of both the spike and Mpro proteins of SARS-CoV-2; furthermore, its pharmacology and clinical optimization would lead towards novel COVID-19 small-molecule therapy.

Comparative molecular field analysis and comparative molecular similarity indices analysis of hydroxyethylamine derivatives as selective human BACE-1 inhibitor.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed based on comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), on a series of 43 hydroxyethylamine derivatives, acting as potent inhibitors of beta-site amyloid precursor protein (APP) cleavage enzyme (BACE-1). The crystal structure of the BACE-1 enzyme (PDB ID: 2HM1) with one of the most active compound 28 was available, and we assumed it to be the bioactive conformation of the studied series, for 3D-QSAR analysis. Statistically significant 3D-QSAR model was established on a training set of 34 compounds, which were validated by a test set of 9 compounds. For the best CoMFA model, the statistics are, r2 = 0.998, r2 cv =0.810, n = 34 for the training set and r2 pred = 0.934, n = 9 for the test set. For the best CoMSIA model (combined steric, electrostatic, hydrophobic, and hydrogen bond donor fields), the statistics are r2 = 0.978, r2 cv = 0.754, n = 34 for the training set and r2 pred = 0.750, n = 9 for the test set. The resulting contour maps, produced by the best CoMFA and CoMSIA models, were used to identify the structural features relevant to the biological activity in this series of analogs. The data generated from the present study will further help to design novel, potent, and selective BACE-1 inhibitors.

Multiple e-Pharmacophore modeling to identify a single molecule that could target both streptomycin and paromomycin binding sites for 30S ribosomal subunit inhibition.

The bacterial ribosome is an established target for anti-bacterial therapy since decades. Several inhibitors have already been developed targeting both defined subunits (50S and 30S) of the ribosome. Aminoglycosides and tetracyclines are two classes of antibiotics that bind to the 30S ribosomal subunit. These inhibitors can target multiple active sites on ribosome that have a complex structure. To screen putative inhibitors against 30S subunit of the ribosome, the crystal structures in complex with various known inhibitors were analyzed using pharmacophore modeling approach. Multiple active sites were considered for building energy-based three-dimensional (3D) pharmacophore models. The generated models were validated using enrichment factor on decoy data-set. Virtual screening was performed using the developed 3D pharmacophore models and molecular interaction towards the 30S ribosomal unit was analyzed using the hits obtained for each pharmacophore model. The hits that were common to both streptomycin and paromomycin binding sites were identified. Further, to predict the activity of these hits a robust 2D-QSAR model with good predictive ability was developed using 16 streptomycin analogs. Hence, the developed models were able to identify novel inhibitors that are capable of binding to multiple active sites present on 30S ribosomal subunit.

Therapeutic potential of Voltage gated Calcium channels.

Voltage-gated Calcium channels (VGCCs) play important roles in neurotransmitter release, excitation-contraction coupling, hormone secretion, and a variety of other physiological processes. Currently, there exist ion channel therapeutics for anxiety, epilepsy, hypertension, insomnia and pain. There is limited amount of study in this area despite their relevance to human disease and VGCCs remain considerably underexploited. The present review mainly focuses on calcium channel blockers (CCBs), especially for L-type channels and T-type channels, and therein lie some of the opportunities and advantages associated with VGCCs as drug targets.

3D-QSAR CoMFA analysis of C5 substituted pyrrolotriazines as HER2 (ErbB2) inhibitors.

Human cancers are characterized by an up-regulation of some of the RTKs (EGFR and HER2) and have been clinically validated as targets for cancer therapy. C4 and C5 substituted pyrrolotriazines showed dual inhibition of HER2 and EGFR protein tyrosine kinases. To explore the relationship between the structures of the aforementioned classes of molecules and their HER2 inhibition, 3D-QSAR CoMFA analysis have been performed. The developed CoMFA model showed statistically significant results with good predictive ability.

Computational simulations and experimental validation of structure- physicochemical properties of pristine and functionalized graphene: Implications for adverse effects on p53 mediated DNA damage response.

Recent reports indicated DNA damaging potential of few-layer graphene in human cell systems. Here, we used computational technique to understand the interaction of both pristine (pG) or carboxyl functionalized graphene (fG) of different sizes (1, 6, and 10nm) with an important DNA repair protein p53. The molecular docking study revealed strong interaction between pG and DNA binding domains (DBD) of p53 with binding free energies (BE) varying from -12.0 (1nm) to -34 (6nm)kcal/mol, while fG showed relatively less interaction with BE varying from -6.7 (1nm) to -11.1 (6nm)kcal/mol. Most importantly, pG or fG bound p53-DBDs could not bind to DNA. Further, microarray analysis of human primary endothelial cells revealed graphene intervention on DNA damage and its structure-properties effect using comet assay studies. Thus, computational and experimental results revealed the structure-physicochemical property dependent adverse effects of graphene in DNA repair protein p53.

Computer-assisted methods in chemical toxicity prediction.

In Silico predictive ADME/Tox screening of compounds is one of the hottest areas in drug discovery. To provide predictions of compound drug-like characteristics early in modern drug-discovery decision making, computational technologies have been widely accepted to develop rapid high throughput in silico ADMET analysis. It is widely perceived that the early screening of chemical entities can significantly reduce the expensive costs associated with late stage failures of drugs due to poor ADME/Tox properties. Drug toxic effects are broadly defined to include toxicity, mutagenicity, carcinogenicity, teratogenicity, neurotoxicity and immunotoxicity. Toxicity prediction techniques and structure-activity relationships relies on the accurate estimation and representation of physico-chemical and toxicological properties. This review highlights some of the freely and commercially available softwares for toxicity predictions. The information content can be utilized as a guide for the scientists involved in the drug discovery arena.

Preparation, characterization, drug release and computational modelling studies of antibiotics loaded amorphous chitin nanoparticles.

We present a computational investigation of binding affinity of different types of drugs with chitin nanocarriers. Understanding the chitn polymer-drug interaction is important to design and optimize the chitin based drug delivery systems. The binding affinity of three different types of anti-bacterial drugs Ethionamide (ETA) Methacycline (MET) and Rifampicin (RIF) with amorphous chitin nanoparticles (AC-NPs) were studied by integrating computational and experimental techniques. The binding energies (BE) of hydrophobic ETA, hydrophilic MET and hydrophobic RIF were -7.3kcal/mol, -5.1kcal/mol and -8.1kcal/mol respectively, with respect to AC-NPs, using molecular docking studies. This theoretical result was in good correlation with the experimental studies of AC-drug loading and drug entrapment efficiencies of MET (3.5±0.1 and 25± 2%), ETA (5.6±0.02 and 45±4%) and RIF (8.9±0.20 and 53±5%) drugs respectively. Stability studies of the drug encapsulated nanoparticles showed stable values of size, zeta and polydispersity index at 6°C temperature. The correlation between computational BE and experimental drug entrapment efficiencies of RIF, ETA and MET drugs with four AC-NPs strands were 0.999 respectively, while that of the drug loading efficiencies were 0.854 respectively. Further, the molecular docking results predict the atomic level details derived from the electrostatic, hydrogen bonding and hydrophobic interactions of the drug and nanoparticle for its encapsulation and loading in the chitin-based host-guest nanosystems. The present results thus revealed the drug loading and drug delivery insights and has the potential of reducing the time and cost of processing new antibiotic drug delivery nanosystem optimization, development and discovery.