Pharmacophore Hypothesis Research Articles

A Combination of Pharmacophore Generation, Ligand-based Virtual Screening, Atom-based 3D-QSAR, and Molecular Docking Studies on Febuxostat-based Amides Analogues as Anti-inflammatory Agents.

A defence mechanism of the body includes inflammation. It is a process through which the immune system identifies, rejects, and starts to repair foreign and damaging stimuli. In the world, chronic inflammatory disorders are the leading cause of death. To obtain optimized pharmacophore, previously reported febuxostat- based anti-inflammatory amide derivatives series were subjected to pharmacophore hypothesis, ligand-based virtual screening, and 3D-QSAR studies in the present work using Schrodinger suite 2022-4. QuikProp module of Schrodinger was used for ADMET prediction, and HTVS, SP, and XP protocols of GLIDE modules were used for molecular docking on target protein (PDB ID:3LN1). Utilising 29 compounds, a five-point model of common pharmacophore hypotheses was created, having pIC50 ranging between 5.34 and 4.871. The top pharmacophore hypothesis AHHRR_ 1 model consists of one hydrogen bond acceptor, two hydrophobic groups and two ring substitution features. The hypothesis model AHHRR_1 underwent ligand-based virtual screening using the molecules from Asinex. Additionally, a 3D-QSAR study based on individual atoms was performed to assess their contributions to model development. The top QSAR model was chosen based on the values of R2 (0.9531) and Q2 (0.9424). Finally, four potential hits were obtained by molecular docking based on virtual screening. The virtual screen compounds have shown similar docking interaction with amino acid residues as shown by standard diclofenac sodium drugs. Therefore, the findings in the present study can be explored in the development of potent anti-inflammatory agents.

In silico exploration of CB2 receptor agonist in the management of neuroinflammatory conditions by pharmacophore modeling

Endocannabinoid system plays a pivotal role in controlling neuroinflammation, and modulating this system may not only aid in managing symptoms of neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, Multiple sclerosis, Epilepsy, Central and Peripheral neuropathic pain, but also, have the potential to target these diseases at an early-stage. In the present study, six different pharmacophore hypotheses were generated from Cannabidiol (CBD)-Cannabinoid Receptor subtype-2 (CB2) and then Zinc database was screened for identification of hit molecules. Identified 215 hit molecules were subjected to preliminary screening with ADMET and drug likeness properties, and about 48 molecules were found with no violations and toxicity properties. In molecular docking studies, six compounds showed better binding energy than CBD and β-caryophyllene (known inhibitor of CB2). These six molecules were designated as leads and subjected to re-docking with glide tool and Lead1 (ZINC000078815430) showed docking score of −9.877 kcal/mol, whereas CBD and β-caryophyllene showed score of −9.664 and −8.499 kcal/mol, respectively. Lead1 and CBD were evaluated for stability studies with Desmond tool by molecular dynamic simulation studies. Lead1 showed better stability than CBD in all studied parameters such as RMSD, RMSF, SSE, Rg, SASA, etc. In MM-GBSA free energy calculations, ΔGbinding energy of CB2-CBD complex and CB2-Lead1 were found to be −103.13±11.19 and −107.94±5.42 kcal/mol, respectively. Six lead molecules stated in the study hold promise with respect to CBD agonistic activity for treating and/or managing chronic conditions and can be explored as an alternative for early-stage cure, which has not yet been experimentally explored.

Identification of Potent Acetylcholinesterase Inhibitors as New Candidates for Alzheimer Disease via Virtual Screening, Molecular Docking, Dynamic Simulation, and Molecular Mechanics-Poisson-Boltzmann Surface Area Calculations.

Huperzine A (HUP) plays a crucial role in Alzheimer's therapy by enhancing cognitive function through increased cholinergic activity as a reversible acetylcholinesterase (AChE) inhibitor. Despite some limitations being seen in AChE inhibitors, ongoing research remains dedicated to finding innovative and more effective treatments for Alzheimer's disease. To achieve the goal of the discovery of potential HUP analogues with improved physicochemical properties, less toxic properties, and high biological activity, many in silico methods were applied. Based on the acetylcholinesterase-ligand complex, an e-pharmacophore model was developed. Subsequently, a virtual screening involving a collection of 1762 natural compounds, sourced from the PubChem database, was performed. This screening yielded 131 compounds that exhibited compatibility with the established pharmacophoric hypothesis. These selected ligands were then subjected to molecular docking within the active site of the 4EY5 receptor. As a result, we identified four compounds that displayed remarkable docking scores and exhibited low free binding energy to the target. These top four compounds, CID_162895946, CID_44461278, CID_44285285, and CID_81108419, were submitted to ADMET prediction and molecular dynamic simulations, yielding encouraging findings in terms of their pharmacokinetic characteristics and stability. Finally, the molecular dynamic simulation, cross-dynamic correlation matrix, free energy landscape, and MM-PBSA calculations demonstrated that two ligands from the selected ligands formed very resilient complexes with the enzyme acetylcholinesterase, with significant binding affinity. Therefore, these two compounds are recommended for further experimental research as possible (AChE) inhibitors.

Unveiling potential repurposed drug candidates for Plasmodium falciparum through in silico evaluation: A synergy of structure-based approaches, structure prediction, and molecular dynamics simulations

The rise of drug resistance in Plasmodium falciparum, rendering current treatments ineffective, has hindered efforts to eliminate malaria. To address this issue, the study employed a combination of Systems Biology approach and a structure-based pharmacophore method to identify a target against P. falciparum. Through text mining, 448 genes were extracted, and it was discovered that plasmepsins, found in the Plasmodium genus, play a crucial role in the parasite's survival. The metabolic pathways of these proteins were determined using the PlasmoDB genomic database and recreated using CellDesigner 4.4.2. To identify a potent target, Plasmepsin V (PF13_0133) was selected and examined for protein-protein interactions (PPIs) using the STRING Database. Topological analysis and global-based methods identified PF13_0133 as having the highest centrality. Moreover, the static protein knockout PPIs demonstrated the essentiality of PF13_0133 in the modeled network. Due to the unavailability of the protein’s crystal structure, it was modeled and subjected to a molecular dynamics simulation study. The structure-based pharmacophore modeling utilized the modeled PF13_0133 (PfPMV), generating 10 pharmacophore hypotheses with a library of active and inactive compounds against PfPMV. Through virtual screening, two potential candidates, hesperidin and rutin, were identified as potential drugs which may be repurposed as potential anti-malarial agents.

A structure-based drug design approach for the identification of antiviral compounds targeting the chikungunya virus RdRp protein

There are an estimated 3 million Chikungunya virus (CHIKV) infections per year, but no vaccines or specific pharmaceutical treatments are available. RNA-dependent RNA polymerase (RdRp) of CHIKV, encoded by nonstructural protein 4 (nsP4), was targeted to identify potential antiviral compounds in this study. A 3D model of the nsP4 was generated by homology modeling technique and Pockdrug-Server was used to predict ligand binding sites. The pharmacophore hypothesis was developed followed by virtual screening. 300 most potential ligands were selected from a library of 3000 compounds, after filtering for the molecules satisfying Lipinski's Rule of Five. Using molecular docking followed by ADMET analysis, 27 leads were then selected. We used MD simulations of the three best compounds that bind to the catalytic, palm, and thumb domains to find out how the ligand-protein interactions change over time. RMSD, RMSF, radius of gyration, free energy landscapes, and principal component analyses revealed that complexes nsP4-ZINC-12820763 and nsP4-ZINC-33280972 were more stable. Simulated trajectories were further used to compute MMGBSA binding energies of PubChem-135638918 (-22.33 kcal/mol), ZINC-12820763 (-50.19 kcal/mol) and ZINC-33280972 (-32.69 kcal/mol) bound to catalytic, palm and thumb domains, respectively. HOMO and LUMO of potential molecules were also investigated using density functional theory computations. With the support of computational simulations, three compounds were shown to have the potential to inhibit RdRp of CHIKV and need further evaluation using in-vitro and in-vivo analysis.

Design of small molecules for CDK-2 inhibition in colorectal cancer based on substructure search

The global frequency of colorectal cancer motivates extensive drug discovery efforts. CDK2, a key member of the CDK family, has been linked to tumor progression, unregulated cell proliferation, and growth promotion. Water-soluble flavonoids with a fast metabolism called anthocyanins have been shown to have a variety of pharmacological properties, including anti-cancer properties. This study aims to find possible CDK2 inhibitors from Anthocyanin-like molecules. Anthocyanins sourced from PubChem were screened using a virtual screening approach that included a KNIME workflow, QSAR-model, Pharmacophore hypothesis, and a structure-based screening to identify compounds with a better binding affinity and predicted bioactivity compared to the standard, Sorafenib. The top compounds were subjected to a 100 ns MD simulation to confirm their stability at the active site. Compounds 1–5 were shown to have higher binding affinity and bioactivity in this study. These substances interacted with the critical amino acids (LEU 83, ASP 145 and LYS 89) at CDK2's active site. Compared to the reference with a pIC50 value of 6.003 nM, the top compounds listed have superior predicted bioactivity ranging from 6.539 to 6.36 nM. Also, ADMET predictions predicted that Compounds 1–5 were not carcinogenic and not a p-glycoprotein substrate. MD simulation also validated Compound 1's stability at the active site compared to the standard. This study uncovers potential CDK2 inhibitors with good binding affinities, shedding light on their interactions with the target protein. While promising, further in vivo and in vitro investigations are essential to validate the anticancer potential of these compounds. Communicated by Ramaswamy H. Sarma

Identification of potent small-molecule PCSK9 inhibitors based on quantitative structure-activity relationship, pharmacophore modeling, and molecular docking procedure

Abstract Background Proprotein convertase subtilisin/kexin type 9 (PCSK9) attaches to the domain of LDL receptor (LDLR), diminishing LDL-C influx and LDLR cell surface presentation in hepatocytes, resulting in higher circulating LDL-C levels. PCSK9 dysfunction has been linked to lower levels of plasma LDLC and a decreased CHD risk. Purpose To identify a potent small-molecule PCSK9 inhibitor in compounds that are currently being studied in clinical trials. Methods We first performed chemical absorption, distribution, metabolism, excretion, and toxicity filtering of 9800 clinical trial compounds obtained from the ZINC 15 database using Lipinski's rule of five and achieved 3853 compounds. Two-dimensional (2D) Quantitative Structure-Activity Relationship (QSAR) was initiated by computing molecular descriptors and selecting important descriptors of 23 PCSK9 inhibitors. Multivariate calibration was performed with the partial least square regression (PLS) method with 18 compounds for training to design the QSAR model and five compounds for the test set to assess the model. The best latent variables (LV) (LV=6) with the lowest value of Root-Mean-Square Error of Cross-Validation (RMSECV) of 0.48 and leave-one-out cross-validation correlation coefficient (R2CV)=0.83 were obtained for the QSAR model. The low RMSEC (0.21) with high R²cal (0.966) indicates the probability of fit between the experimental data and the calibration model. Results Using QSAR analysis of 3853 compounds, 2635 had a pIC50<1 and were considered for pharmacophore screening. The PHASE module designed the pharmacophore hypothesis through multiple ligands. The top 14 compounds (pIC50>1) were defined as active, whereas nine (pIC50<1) were considered as an inactive set. Three five-point pharmacophore hypotheses achieved the highest score: DHHRR1, DHHRR2, and DHRRR1. The highest and best model with survival scores (5.365) was DHHRR1, comprising one hydrogen donor (D), two hydrophobic groups (H), and two rings of aromatic (R) features. We selected the molecules with a higher 1.5 fitness score (257 compounds) in pharmacophore screening (DHHRR1) for molecular docking screening.Molecular docking indicates that ZINC000051951669, with a binding affinity: of -13.2 kcal/mol and two H-bonds, has the highest binding to the PCSK9 protein. ZINC000011726230 with energy binding: -11.4 kcal/mol and three H-bonds, ZINC000068248147 with binding affinity: -10.7 kcal/mol and one H-bond, ZINC000029134440 with a binding affinity: -10.6 kcal/mol and four H-bonds were ranked next, respectively. Conclusion The archived molecules identified as inhibitory PCSK9 candidates, and especially ZINC000051951669 may therefore significantly inhibit PCSK9 and should be considered in the newly designed trials.Figure 1.The six-top docking compound against PCSK9 with lowest binding affinity. A) ZINC000051951669, B) ZINC000011726230, C) ZINC000068248147, D) ZINC000029134440, E) ZINC000000602086, F) ZINC000034630885.

A comprehension on structure guided alignment dependent 3D-QSAR modelling, and molecular dynamics simulation on 2,4-thiazolidinediones as aldose reductase inhibitors for the management of diabetic complications

Aldose reductase is an oxo-reductase enzyme belonging to the aldo-keto reductase class. Compounds having thiazolidine-2,4-dione scaffold are reported as potential aldose reductase inhibitors for diabetic complications. The present work uses structure-guided alignment-dependent Gaussian field- and atom-based 3D-QSAR on a dataset of 84 molecules. 3D-QSAR studies on two sets of dataset alignment have been carried out to understand the favourable and unfavourable structural features influencing the affinity of these inhibitors towards the enzyme. Using common pharmacophore hypotheses, the five-point pharmacophores for aldose reductase favourable features were generated. The molecular dynamics simulations (up to 100 ns) were performed for the potent molecule from each alignment set (compounds 24 and 65) compared to reference standard tolrestat and epalrestat to study target-ligand complexes’ binding energy and stability. Compound 65 was most stable with better interactions in the aldose reductase binding pocket than tolrestat. The MM-PBSA study suggests compound 65 possessed better binding energy than reference standard tolrestat, i.e. −87.437 ± 19.728 and −73.424 ± 12.502 kJ/mol, respectively. The generated 3D-QSAR models provide information about structure–activity relationships and ligand-target binding energy. Target-specific stability data from MD simulation would be helpful for rational compound design with better aldose reductase activity. Communicated by Ramaswamy H. Sarma

A computational investigation of thymidylate synthase inhibitors through a combined approach of 3D-QSAR and pharmacophore modelling

Thymidylate synthase (TS) is a crucial target of cancer drug discovery and is mainly involved in the De novo synthesis of the DNA precursor thymine. In the present study, to generate reliable models and identify a few promising molecules, we combined QSAR modelling with the pharmacophore hypothesis-generating technique. Input molecules were clustered on their similarity, and a cluster of 74 molecules with a pyrimidine moiety was chosen as the set for 3D-QSAR and pharmacophore modelling. Atom-based and field-based 3D-QSAR models were generated and statistically validated with R2 > 0.90 and Q2 > 0.75. The common pharmacophore hypothesis(CPH) generation identified the best six-point model ADHRRR. Using these best models, a library of FDA-approved drugs was screened for activity and filtered via molecular docking, ADME profiling, and molecular dynamics simulations. The top ten promising TS-inhibiting candidates were identified, and their chemical features profitable for TS inhibitors were explored. Communicated by Ramaswamy H. Sarma

Identification of novel curcumin derivatives against pancreatic cancer: a comprehensive approach integrating 3D-QSAR pharmacophore modeling, virtual screening, and molecular dynamics simulations

Pancreatic cancer, known as the “silent killer,” poses a daunting challenge in cancer therapy. The dysregulation of the PI3Kα signaling pathway in pancreatic cancer has attracted considerable interest as a promising target for therapeutic intervention. In this regard, the use of curcumin derivatives as inhibitors of PI3Kα has emerged, providing a novel and promising avenue for developing effective treatments for this devastating disease. Computational approaches were employed to explore this potential and investigate 58 curcumin derivatives with cytotoxic activity against the Panc-1 cell line. Our approach involved ligand-based pharmacophore modeling and atom-based 3D-QSAR analysis. The resulting QSAR model derived from the best-fitted pharmacophore hypothesis (AAHRR_1) demonstrated remarkable performance with high correlation coefficients (R2) of 0.990 for the training set and 0.977 for the test set. The cross-validation coefficient (Q2) of 0.971 also validated the model’s predictive power. Tropsha’s recommended criteria, including the Y-randomization test, were employed to ensure its reliability. Furthermore, an enrichment study was conducted to evaluate the model’s performance in identifying active compounds. AAHRR_1 was used to screen a curated PubChem database of curcumin-related compounds. Two molecules (CID156189304 and CID154728220) exhibited promising pharmacokinetic properties and higher docking scores than Alpelisib, warranting further investigation. Extensive molecular dynamics simulations provided crucial insights into the conformational dynamics within the binding site, validating their stability and behavior. These findings contribute to our understanding of the potential therapeutic effectiveness of these compounds as PI3Kα inhibitors in pancreatic cancer. Communicated by Ramaswamy H. Sarma

Discovery of small molecule modulators targeting β-catenin interactions using molecular docking and molecular dynamics strategy

The rapid activation of signaling pathways is the primary factor responsible for the progression of serious malignancies. Particularly, overexpression of β-catenin is one of the preventative oncogenic biomarkers that is widely present in many tumor types. Therefore, we have designed an integrated pharmacophore strategy to discover the potential inhibitor against β-catenin. Primarily, the ligand-, energy-optimized and the receptor cavity-based pharmacophore hypothesis AAHHR, ADR and ANNNRRR were constructed to screen the active molecules from the database. In order to improve the screening efficacy, the resultant molecules were scrutinized by integrating the results obtained through docking and MM/GBSA calculations. Further, the common hit molecules were subjected to a pharmacokinetic and toxicity study which resulted in two potent β-catenin inhibitors: BAS03014832 and BAS01077671. The existence of thiazole and pyrazole moiety is responsible for the anti-neoplastic activity of these molecules. Finally, a deep neural network technique was used to assess the inhibitory action of these compounds across 66 colorectal cancer cell lines. Overall, the results of our study portray a pathway for the experimental biologist in developing selective β-catenin inhibitors.

Identification of natural product as selective PI3Kα inhibitor against NSCLC: multi-ligand pharmacophore modeling, molecular docking, ADME, DFT, and MD simulations.

Non-small cell lung cancer (NSCLC) is a widespread and often aggressive form of cancer affecting people worldwide. PIK3CA missense mutations play a significant role in the progression of growth factor signaling in cancer, making PI3Kα an important biological target for inhibition against NSCLC. Natural product molecules with PI3Kα inhibitory activity are promising therapeutic agents for the treatment of NSCLC, owing to their selectivity and potentially lower toxicity compared to synthetic compounds. To discover new natural product molecules, we integrated ligand-based virtual screening with structure-based virtual screening. We developed a multi-ligand pharmacophore hypothesis, validated it with 3D Field-based QSAR, and screened a Natural-Product-Based Library (ChemDiv) containing 3601 molecules. After initial screening, 137 hit molecules were generated and further screened using the extra precision (XP) Glide docking protocol. The best ten molecules were selected for free binding energy (ΔG) analysis using MMGBSA and ADME predictions. For further optimization, the top four hits were subjected to induced fit docking (IFD), quantum chemical descriptors analysis by Frontier Molecular Orbital (FMO) studies, and a 100ns molecular dynamics (MD) simulation. The compounds-S721-1955, CM4579-5085, S721-1963, and S721-1999-exhibited better results than the PI3Kα selective inhibitor alpelisib. In silico prediction analysis of S721-1955 and alpelisib revealed that the former exhibited superior selectivity theoretically, as evidenced by its higher affinity for the target protein. The selective natural product molecule identified in this study holds promise as a potential anti-cancer drug against NSCLC in the near future, but further in vitro and in vivo studies are necessary to confirm its efficacy.

A structure-based approach to explore novel COX-2 inhibitors using pharmacophore modelling, 3D-QSAR analysis, virtual screening and dynamics simulation study

Anti-inflammatory medications are one of the most commonly preferred over-the-counter medications because inflammation and pain are closely related and caused by various disease conditions. Extensive research is mandatory to develop an effective anti-inflammatory therapy and good safety profile since both cyclooxygenase (COX-1 and COX-2) inhibition causes severe gastrointestinal irritation and cardiac toxicities. Only 190 molecules published in 2022 had selected for the investigation out of the many chemical architectures reported in the literature as COX-2 inhibitors. Using a four-point pharmacophore hypothesis AAAR2 (A-hydrogen bond acceptor and R- aromatic ring sites) generated from 138 molecules out of 190 with a site score of 0.84, a 3D atom-based QSAR model was built. With an outstanding correlation value of R2 0.92, fisher ratio of F 235.4, strong predictability of Q2 0.73, and Pearson-r 0.87, the 3D-QSAR model displays the highest and lowest efficient ligands reveals the favourable and unfavourable structural domains for the interaction toward COX-2. The selected 3D QSAR model is then utilised to screen compounds from bindingDB like a search engine. The molecules which passed the rule of five were chosen for further analysis after the screened molecules underwent an ADME analysis. Virtual screening (PDB ID-5KIR) was then used to identify 43 hit molecules, with the best docking scores falling between -11.07 and -9.55 kcal/mol, and these results were compared to the standard rofecoxib (docking score-9.479 kcal/mol). To evaluate the stable binding and interaction of the best molecule (Hit1), which is crucial for inhibiting COX-2, the molecular dynamics study was carried out for 100 ns.

Structural and functional characterization of 6-phosphogluconate dehydrogenase in Plasmodium falciparum (3D7) and identification of its potent inhibitors

The malarial parasite Plasmodium falciparum predominantly causes severe malaria and deaths worldwide. Moreover, resistance developed by P. falciparum to frontline drugs in recent years has markedly increased malaria-related deaths in South Asian Countries. Ribulose 5-phosphate and NADPH synthesized by Pentose Phosphate Pathway (PPP) act as a direct precursor for nucleotide synthesis and P. falciparum survival during oxidative challenges in the intra-erythrocytic growth phase . In the present study, we have elucidated the structure and functional characteristics of 6-phosphogluconate dehydrogenase (6PGD) in P. falciparum and have identified potent hits against 6PGD by pharmacophore-based virtual screening with ZINC and ChemBridge databases. Molecular docking and Molecular dynamics simulation, binding free energies (MMGBSA & MMPBSA), and Density Functional Theory (DFT) calculations were integratively employed to validate and prioritize the most potential hits. The 6PGD structure was found to have an open and closed conformation during MD simulation. The apo form of 6PGD was found to be in closed conformation, while a open conformation attributed to facilitating binding of cofactor. It was also inferred from the conformational analysis that the small domain of 6PGD has a high influence in altering the conformation that may aid in open/closed conformation of 6PGD. The top three hits identified using pharmacophore hypotheses were ChemBridge_11084819, ChemBridge_80178394, and ChemBridge_17912340. Though all three hits scored a high glide score, MMGBSA, and favorable ADMET properties, ChemBridge_11084819 and ChemBrdige_17912340 showed higher stability and binding free energy. Moreover, these hits also featured stable H-bond interactions with the active loop of 6PGD with binding free energy comparable to substrate-bound complex. Therefore, the ChemBridge_11084819 and ChemBridge_17912340 moieties demonstrate to have high therapeutic potential against 6PGD in P. falciparum. Communicated by Ramaswamy H. Sarma

High-throughput virtual screening of phenylpyrimidine derivatives as selective JAK3 antagonists using computational methods

In this study, we used phenylpyrimidine derivatives with known biological activity against JAK3, a critical tyrosine kinase enzyme involved in signaling pathways, to find similar compounds as potential treatments for rheumatoid arthritis. These inhibitors inhibited JAK3 activity by forming a covalent bond with the Cys909 residue, which resulted in a strong inhibitory effect. Phenylpyrimidine is considered a promising therapeutic target. For pharmacophore modeling, 39 phenylpyrimidine derivatives with high pIC50 (Exp) values were chosen. The best pharmacophore model produced 28 molecules, and the five-point common pharmacophore hypothesis from P HASE (DHRRR_1) revealed the requirement for a hydrogen bond donor feature, a hydrophobic group feature, and three aromatic ring features for further design. The validation of the pharmacophore model phase was performed through 3D-QSAR using partial least squares (P LS). The 3D-QSAR study produced two successful models, an atom-based model (R2 = 0.95; Q2 = 0.67) and a field-based model (R2 = 0.93; Q2 = 0.76), which were used to predict the biological activity of new compounds. The pharmacophore model successfully distinguished between active and inactive medications, discovered potential JAK3 inhibitors, and demonstrated validity with a ROC of 0. 77. ADME-Tox was used to eliminate compounds that might have adverse effects. The best pharmacokinetics and affinity derivatives were selected for covalent docking. A molecular dynamics simulation of the selected molecules and the protein complex was performed to confirm the stability of the interaction with JAK3, whereas MM/GBSA simulations further confirmed their binding affinity. By using the principle of retrosynthesis, we were able to map out a pathway for synthesizing these potential drug candidates. This study has the potential to offer valuable and practical insights for optimizing novel derivatives of phenylpyrimidine. Communicated by Ramaswamy H. Sarma

Augmenting Bioactivity by Docking-Generated Multiple Ligand Poses To Enhance Machine Learning and Pharmacophore Modelling: Discovery of New TTK Inhibitors as Case Study.

Dual specificity protein kinase threonine/Tyrosine kinase (TTK) is one of the mitotic kinases. High levels of TTK are detected in several types of cancer. Hence, TTK inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of TTK inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contacts Fingerprints and docking scoring values were used as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to determine critical descriptors for predicting anti-TTK bioactivity and for pharmacophore generation. Three successful pharmacophores were deduced and subsequently used for in silico screening against the NCI database. A total of 14 hits were evaluated in vitro for their anti-TTK bioactivities. One hit of novel chemotype showed reasonable dose-response curve with experimental IC 50 of 1.0 µM. The presented work indicates the validity of data augmentation using multiple docked poses for building successful machine learning models and pharmacophore hypotheses.

A comparative review on the well-studied GAT1 and the understudied BGT-1 in the brain.

γ-aminobutyric acid (GABA) is the primary inhibitory neurotransmitter in the central nervous system (CNS). Its homeostasis is maintained by neuronal and glial GABA transporters (GATs). The four GATs identified in humans are GAT1 (SLC6A1), GAT2 (SLC6A13), GAT3 (SLC6A11), and betaine/GABA transporter-1 BGT-1 (SLC6A12) which are all members of the solute carrier 6 (SLC6) family of sodium-dependent transporters. While GAT1 has been investigated extensively, the other GABA transporters are less studied and their role in CNS is not clearly defined. Altered GABAergic neurotransmission is involved in different diseases, but the importance of the different transporters remained understudied and limits drug targeting. In this review, the well-studied GABA transporter GAT1 is compared with the less-studied BGT-1 with the aim to leverage the knowledge on GAT1 to shed new light on the open questions concerning BGT-1. The most recent knowledge on transporter structure, functions, expression, and localization is discussed along with their specific role as drug targets for neurological and neurodegenerative disorders. We review and discuss data on the binding sites for Na+, Cl-, substrates, and inhibitors by building on the recent cryo-EM structure of GAT1 to highlight specific molecular determinants of transporter functions. The role of the two proteins in GABA homeostasis is investigated by looking at the transport coupling mechanism, as well as structural and kinetic transport models. Furthermore, we review information on selective inhibitors together with the pharmacophore hypothesis of transporter substrates.

The discovery of potential tumor necrosis factor alpha converting enzyme inhibitors via implementation of K Nearest Neighbor QSAR analysis

Tumor necrosis factor-alpha converting enzyme (TACE) is considered as a pro-inflammatory cytokine which catalyzes the formation of TNF-α from membrane bound TNF-α precursor protein. It has important role in various pathological diseases such as inflammation, anorexia, rheumatoid arthritis, cancer and viral replication. Despite the reporting of a variety of TACE inhibitors of diverse chemical scaffolds whether peptide, peptide-like compounds or others, the bioavailability and pharmacokinetic problems are reflected strongly on its clinical effectiveness. In this effort we employed a novel combination of k-nearest neighbor QSAR modeling to select the best critical ligand-TACE contacts capable of elucidation of TACE inhibitory bioactivity among a long list of inhibitors. The recurrence of one valid pharmacophore hypothesis among the successful models emerged the pharmacophore to be used as 3D search queries to screen the National Cancer Institute’s structural database for new TACE inhibitors. Eight potent TACE inhibitors were discovered with inhibition percentage exceeding 50% at 100 μM inhibitor concentration.

Abstract 3834: Antiproliferative activity of substituted chromone-2-carboxamides on MDA-MB-231 cells and their 3D pharmacophore models

Abstract After lung cancer, breast cancer is the second leading cause of cancer death in women in the United States, accounting for 12.5 % of all new cancer cases globally. Triple-negative breast cancer (TNBC) remains the hardest breast cancer and is difficult to treat. The current treatment option for TNBC is the surgical removal of the tumor, followed by chemotherapy. However, patients with advanced diseases respond poorly to current treatments. Therefore, discovering more effective and less toxic small drug molecules with a unique mechanism of action is one of the top priorities in drug discovery programs. Chromones have a benzo-pyrone skeleton and display interesting pharmacological activities, such as anti-cancer, anti-HIV, antiviral and anti-inflammatory, with low toxicity. In continuation of our current research work, we report the design and synthesis of substituted chromone-2-carboxamides by attaching substituted phenyl, tetrahydropyridine, and tetrahydroisoquinoline groups as anti-breast cancer agents. The starting material, substituted chromone-2-carbonyl chloride, was obtained by the acylation reaction of corresponding carboxylic acids using phosphorous pentachloride in dry cyclohexane. An equimolar amount of acyl chloride was slowly added to a stirred solution of corresponding substituted N-amino salt in dry THF in the presence of triethyl amine and refluxed at 70 °C that led to the formation of the ylides. The ylides were reduced using sodium borohydride to yield the desired substituted chromone-2-carboxamides in moderate to good yields. The synthesized compounds were characterized by NMR and elemental analysis. These compounds were evaluated for their cytotoxic effects on MBA-MB-231 ER-ve breast cancer cell lines using a Synergy HTX multi-mode reader (Bio-Tek, Winooski, VT, USA) with excitation/emission wavelength settings at 550/580. Compound MG-KKR-5-140A showed the most potent cytotoxicity with an IC50 value of 0.82 µg/mL on the MDA-MB-231 cell line. In-silico pharmacophore hypotheses were generated using GALAHAD and PHASE, and the best models with probable bioactive conformation(s) for these compounds were proposed. These confirmations and the alignments of the molecular structures give us an insight into designing compounds with better biological activity. This research was supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number U54 MD007582. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Citation Format: Kinfe Ken Redda, Madhavi Gangapuram, Suresh Eyunni, Elizabeth Mazzio, Karam F. A. Soliman. Antiproliferative activity of substituted chromone-2-carboxamides on MDA-MB-231 cells and their 3D pharmacophore models. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3834.

Hydroxamic acid derivatives as selective HDAC3 inhibitors: computer-aided drug design strategies

Histone deacetylases (HDACs) are critical epigenetic drug targets that have gained significant attention in the scientific community for the treatment of cancer. The currently marketed HDAC inhibitors lack selectivity for the various HDAC isoenzymes. Here, we describe our protocol for the discovery of novel potential hydroxamic acid based HDAC3 inhibitors through pharmacophore modeling, virtual screening, docking, molecular dynamics (MD) simulation and toxicity studies. The ten pharmacophore hypotheses were established, and their reliability was validated by different ROC (receiving operator curve) analysis. Among them, the best model (Hypothesis 9 or RRRA) was employed for searching SCHEMBL, ZINC and MolPort database to screen out hit molecules as selective HDAC3 inhibitors, followed by different docking stages. MD simulation (50 ns) and MMGBSA study were performed to study the stability of ligand binding modes and with the help of trajectory analysis, to calculate the ligand-receptor complex RMSD (root-mean-square deviation), RMSF (root-mean-square fluctuation) and H-bond distance, etc. Finally, in-silico toxicity studies were performed on top screened molecules and compared with reference drug SAHA and established structure-activity relationship (SAR). The results indicated that compound 31, with high inhibitory potency and less toxicity (probability value 0.418), is suitable for further experimental analysis. Communicated by Ramaswamy H. Sarma