Drug Design Approach Research Articles

Virtual screening and structure–activity relationship study of novel BTK inhibitors in Traditional Chinese Medicine for the treatment of rheumatoid arthritis

Bruton tyrosine kinase (BTK) is a known drug target for the treatment of autoimmune diseases, including rheumatoid arthritis (RA). In this study, a series of 1-amino-1H-imidazole-5-carboxamide derivatives with good inhibitory activity against BTK were selected to explore the structure-activity relationships of these BTK inhibitors (BTKIs). Furthermore, we concentrated on 182 prescriptions of Traditional Chinese Medicine with therapeutic effects on RA. 54 herbs with a frequency of ≥10 were counted to establish a database containing 4027 ingredients for virtual screening. Five compounds with relatively higher docking scores and better absorption, distribution, metabolism, elimination and toxicity (ADMET) parameters were then selected for higher precision docking. The results demonstrated that the potentially active molecules form hydrogen bond interactions with the hinge region residues Met477, Glu475, glycine-rich P-loop residue Val416, Lys430 and DFG motif Asp539. In particular, they also interact with the key residues Thr474 and Cys481 of BTK. The molecular dynamics (MD) results demonstrated that all five compounds above could bind with BTK stably as its cognate ligand in dynamic conditions. This work identified several potential BTKIs using a computer-aided drug design approach and may provide crucial information for developing novel BTKIs. Communicated by Ramaswamy H. Sarma

Anti-Tuberculosis Mur Inhibitors: Structural Insights and the Way Ahead for Development of Novel Agents.

Mur enzymes serve as critical molecular devices for the synthesis of UDP-MurNAc-pentapeptide, the main building block of bacterial peptidoglycan polymer. These enzymes have been extensively studied for bacterial pathogens such as Escherichia coli and Staphylococcus aureus. Various selective and mixed Mur inhibitors have been designed and synthesized in the past few years. However, this class of enzymes remains relatively unexplored for Mycobacterium tuberculosis (Mtb), and thus offers a promising approach for drug design to overcome the challenges of battling this global pandemic. This review aims to explore the potential of Mur enzymes of Mtb by systematically scrutinizing the structural aspects of various reported bacterial inhibitors and implications concerning their activity. Diverse chemical scaffolds such as thiazolidinones, pyrazole, thiazole, etc., as well as natural compounds and repurposed compounds, have been reviewed to understand their in silico interactions with the receptor or their enzyme inhibition potential. The structural diversity and wide array of substituents indicate the scope of the research into developing varied analogs and providing valuable information for the purpose of modifying reported inhibitors of other multidrug-resistant microorganisms. Therefore, this provides an opportunity to expand the arsenal against Mtb and overcome multidrug-resistant tuberculosis.

Computational Modeling on Drugs Effects for Left Ventricle in Cardiomyopathy Disease.

Cardiomyopathy is associated with structural and functional abnormalities of the ventricular myocardium and can be classified in two major groups: hypertrophic (HCM) and dilated (DCM) cardiomyopathy. Computational modeling and drug design approaches can speed up the drug discovery and significantly reduce expenses aiming to improve the treatment of cardiomyopathy. In the SILICOFCM project, a multiscale platform is developed using coupled macro- and microsimulation through finite element (FE) modeling of fluid-structure interactions (FSI) and molecular drug interactions with the cardiac cells. FSI was used for modeling the left ventricle (LV) with a nonlinear material model of the heart wall. Simulations of the drugs' influence on the electro-mechanics LV coupling were separated in two scenarios, defined by the principal action of specific drugs. We examined the effects of Disopyramide and Dygoxin which modulate Ca2+ transients (first scenario), and Mavacamten and 2-deoxy adenosine triphosphate (dATP) which affect changes of kinetic parameters (second scenario). Changes of pressures, displacements, and velocity distributions, as well as pressure-volume (P-V) loops in the LV models of HCM and DCM patients were presented. Additionally, the results obtained from the SILICOFCM Risk Stratification Tool and PAK software for high-risk HCM patients closely followed the clinical observations. This approach can give much more information on risk prediction of cardiac disease to specific patients and better insight into estimated effects of drug therapy, leading to improved patient monitoring and treatment.

Docking Study, ADMET Profiling of the Designed 3-Chlorobenzo[b]thiophene-2-Carbonyl Chloride Derivatives: Promising Anti-Breast Cancer Activity

Globally, breast cancer accounted for approximately 700٫000 deaths in 2020. Estrogen receptor alpha (ERα) is the primary pathway for breast cancer treatment. Tamoxifen (TAM) is the most extensively used drug for estrogen receptor (+) breast cancer. However, it is implicated in endometrial carcinoma, pulmonary thrombus, stroke, and breast cancer resistance. This research aims to overcome these issues in selectivity and side effects. The study was conducted in a new drug design using Structure Based Drug Design (SBDD) approach, molecular docking with ERα and then predicting their ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) profile. GOLD (Genetic Optimization for Ligand Docking, v.5.7.1), SwissADME and BIOVIA Discovery Studio Visualizer 2020 applications used for the predicting and analyzing of the results. It has shown that all of our compounds (MFA1-8) where a higher PLP (the Piecewise Linear Potential) fitness scores with a range (66.21-77.20) than (60.96) for TAM with different molecular interactions such as H- bondings and non-covalent hydrophobic interactions with the pose. They were all exhibited better conformation to prevent helix-12 (H-12) from repositioning over the opening of the binding pocket. This work generated fresh insight into the significant hydrophobic bonding with LEU428. MFA1-8 showed better pharmacokinetics, drug-likeness, and toxicity profiles than TAM. These findings suggest that these novel 3-chlorobenzo[b]thiophene-2-carbonyl chloride derivatives could serve as the lead compounds to fight breast cancer by inhibiting the ERα pathway.

Diversely N-substituted benzenesulfonamides dissimilarly bind to human carbonic anhydrases: crystallographic investigations of N-nitrosulfonamides

Carbonic anhydrases (CAs) are a zinc metalloenzymes that catalyse the reversible hydration of carbon dioxide to bicarbonate and proton, pivotal for a wide range of biological processes. CAs are involved in numerous pathologies and thus represent valuable drug targets in the treatments of several diseases such as glaucoma, obesity, tumour, neuropathic pain, cerebral ischaemia, or as antiinfectives. In the last two decades, several efforts have been made to achieve selective CA inhibitors (CAIs) employing different drug design approaches. However, N-substitutions on primary sulphonamide groups still remain poorly investigated. Here, we reported for the first time the co-crystallisation of a N-nitro sulphonamide derivative with human (h) CA II pointing out the binding site and mode of inhibition of this class of CAIs. The thorough comprehension of the ligand/target interaction might be valuable for a further CAI optimisation for achieving new potent and selective derivatives.

Dockey: a modern integrated tool for large-scale molecular docking and virtual screening.

Molecular docking is a structure-based and computer-aided drug design approach that plays a pivotal role in drug discovery and pharmaceutical research. AutoDock is the most widely used molecular docking tool for study of protein-ligand interactions and virtual screening. Although many tools have been developed to streamline and automate the AutoDock docking pipeline, some of them still use outdated graphical user interfaces and have not been updated for a long time. Meanwhile, some of them lack cross-platform compatibility and evaluation metrics for screening lead compound candidates. To overcome these limitations, we have developed Dockey, a flexible and intuitive graphical interface tool with seamless integration of several useful tools, which implements a complete docking pipeline covering molecular sanitization, molecular preparation, paralleled docking execution, interaction detection and conformation visualization. Specifically, Dockey can detect the non-covalent interactions between small molecules and proteins and perform cross-docking between multiple receptors and ligands. It has the capacity to automatically dock thousands of ligands to multiple receptors and analyze the corresponding docking results in parallel. All the generated data will be kept in a project file that can be shared between any systems and computers with the pre-installation of Dockey. We anticipate that these unique characteristics will make it attractive for researchers to conduct large-scale molecular docking without complicated operations, particularly for beginners. Dockey is implemented in Python and freely available at https://github.com/lmdu/dockey.

Integrated Computational Approaches for Inhibiting Sex Hormone-Binding Globulin in Male Infertility by Screening Potent Phytochemicals.

Male infertility is significantly influenced by the plasma-protein sex hormone-binding globulin (SHBG). Male infertility, erectile dysfunction, prostate cancer, and several other male reproductive system diseases are all caused by reduced testosterone bioavailability due to its binding to SHBG. In this study, we have identified 345 phytochemicals from 200 literature reviews that potentially inhibit severe acute respiratory syndrome coronavirus 2. Only a few studies have been done using the SARS-CoV-2 inhibitors to identify the SHBG inhibitor, which is thought to be the main protein responsible for male infertility. In virtual-screening and molecular-docking experiments, cryptomisrine, dorsilurin E, and isoiguesterin were identified as potential SHBG inhibitors with binding affinities of -9.2, -9.0, and -8.8 kcal/mol, respectively. They were also found to have higher binding affinities than the control drug anastrozole (-7.0 kcal/mol). In addition to favorable pharmacological properties, these top three phytochemicals showed no adverse effects in pharmacokinetic evaluations. Several molecular dynamics simulation profiles' root-mean-square deviation, radius of gyration, root-mean-square fluctuation, hydrogen bonds, and solvent-accessible surface area supported the top three protein-ligand complexes' better firmness and stability than the control drug throughout the 100 ns simulation period. These combinatorial drug-design approaches indicate that these three phytochemicals could be developed as potential drugs to treat male infertility.

Thiazolidinone-Heterocycle Frameworks: A Concise Review of Their Pharmacological Significance.

Molecular hybridization is deemed an optimistic approach in drug design and the discovery of novel biologically active molecules as it may advance their affinity and potency while concurrently decreasing associated resistance and side effects. Approximately 20 % of approved drugs were developed using this approach in the past few years. Thiazolidinone is one of the privileged pharmacophores in medicinal chemistry and is associated with various biological activities; it forms a functional unit in several FDA-approved drugs. Consequently, this pharmacophore has attracted the attention of many research groups to further explore its pharmacological relevance by coupling it with other pharmacophoric moieties. This review presents a concise account of scholarly research exploits directed at the biological activities of newly synthesized thiazolidinone-tagged molecular hybrids. Focused attention is given to the existing structural activity relationship in each compound library and the toxicity profile of potent compounds including in silico docking studies (where applicable). This work would provide a base on which new pharmaceuticals with improved potency can be modelled.

Applications of hydrogen deuterium exchange ensemble reweighting to model native protein ensembles, macromolecular interactions and protein drug interactions.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is a powerful approach to probe the structural dynamics of a protein in solution. It simultaneously provides time-resolved information on the structural environment of the amide hydrogen of every residue of a protein except Prolines. As such, HDX-MS has been successfully applied to provide insight into the characterization of native state ensembles of proteins, macromolecular interactions, protein-small molecule interactions or protein folding and unfolding studies. However, it remains largely limited to peptide-level resolution information. On the other hand, molecular dynamics (MD) simulations provide a high-resolution view of a protein's structural dynamics by simulating the motion of a protein's atoms over time starting from a high-resolution structure. However, the time scales accessible to MD simulations are limited and can lead to inadequate exploration of a protein's conformational landscape. To bridge this gap, efforts have aimed at developing quantitative and integrative approaches that leverage HDX-MS data to assess and refine computationally generated structural ensembles. HDX ensemble reweighting (HDXer) is one such approach which uses a post hoc maximum entropy approach to adjusts the weights of individual frames of a structural ensemble to conform to HDX-MS data. We had previously applied HDXer to model the native ensemble of the cytoplasmic heme binding protein, PhuS. Here, we have validated the proposed model ensemble by using structure-based drug design approach to successfully target novel, cryptic drug binding sites that would have otherwise not been targetable using HDX-MS or MD simulations alone. Finally, we demonstrate the broader applicability of such HDX-MS guided integrative approach to model macromolecular interactions, missing electron density and protein-small molecule drug interactions

Computational Screening for Finding New Potent COX-2 Inhibitors as Anticancer Agents

Background: Breast cancer ranks first in women and the second most common type of cancer overall. It is the most important barrier to the rise of life expectancy, globally affecting disease modalities. Cyclooxygenase-2 (COX-2) has become a prominent hallmark as inhibition target for breast cancer, and this therapeutic target for anti-inflammatory drugs regulates cell proliferation, angiogenesis, tumor growth and apoptosis. There is a need to explore new anti-cancerous drugs for searching the best possible hit candidates for cancer treatment. The computer-aided drug design approach was conducted to discover the new alternative COX-2 inhibitors. Objective: The research framework of this study is to identify new potent inhibitors for the COX-2 using computer-aided drug design. Methods: In the present investigation, an in-silico approach was used to screen with the best established three biological databases (Zinc15, ChemSpider and BindingDB) and docked against the COX-2 protein structure (PDB ID: 5IKR). Molecular docking was carried out using the Schrodinger Maestro suite. The compounds were filtered out based on their physicochemical, ADMET, and other drug-like properties. Several computational approaches such as molecular docking, binding free energy calculation, ADMET analysis, protein-ligand interaction and MD simulation were performed to determine the suitability of correct ligands for selected COX-2 target. Results: The two ligands showed relatively better binding affinities (-10.028 kcal/mol for compound A and -10.007 kcal/mol for ZINC000048442590) than the standard (-9.751 kcal/mol). These compounds followed Lipinski’s rule and drug-likeness index, which exhibited a good predicted therapeutic druggability profile. The interaction of the protein-ligand complex correlates with the COX-2. The MD simulation of the protein-ligand complex showed good stability in the time period of 10ns. Conclusion: It is the first study in which two new compounds ZINC000048442590 and compound A were found to be highly promising and have active potential in inhibiting cyclooxygenase-2 enzyme and could be effective as the potential drug candidates for breast cancer against COX-2 protein. Hopefully, in the future, these compounds as anti-inflammatory drug molecules could be used as new templates for the development of anticancer agents.

Computer-aided drug design approaches applied to screen natural product’s structural analogs targeting arginase in Leishmania spp

Introduction: Leishmaniasis is a disease with high mortality rates and approximately 1.5 million new cases each year. Despite the new approaches and advances to fight the disease, there are no effective therapies. Methods: Hence, this study aims to screen for natural products' structural analogs as new drug candidates against leishmaniasis. We applied Computer-aided drug design (CADD) approaches, such as virtual screening, molecular docking, molecular dynamics simulation, molecular mechanics–generalized Born surface area (MM–GBSA) binding free estimation, and free energy perturbation (FEP) aiming to select structural analogs from natural products that have shown anti-leishmanial and anti-arginase activities and that could bind selectively against the Leishmania arginase enzyme. Results: The compounds 2H-1-benzopyran, 3,4-dihydro-2-(2-methylphenyl)-(9CI), echioidinin, and malvidin showed good results against arginase targets from three parasite species and negative results for potential toxicities. The echioidinin and malvidin ligands generated interactions in the active center at pH 2.0 conditions and hydrogen bonds enhancing enzyme–ligand coupling. Conclusions: This work suggests the potential anti-leishmanial activity of the compounds and thus can be further in vitro and in vivo experimentally validated.

Benzoquinoline Chemical Space: A Helpful Approach in Antibacterial and Anticancer Drug Design.

Benzoquinolines are used in many drug design projects as starting molecules subject to derivatization. This computational study aims to characterize e benzoquinone drug space to ease future drug design processes based on these molecules. The drug space is composed of all benzoquinones, which are active on topoisomerase II and ATP synthase. Topological, chemical, and bioactivity spaces are explored using computational methodologies based on virtual screening and scaffold hopping and molecular docking, respectively. Topological space is a geometrical space in which the elements composing it can be defined as a set of neighbors (which satisfy a particular axiom). In such space, a chemical space can be defined as the property space spanned by all possible molecules and chemical compounds adhering to a given set of construction principles and boundary conditions. In this chemical space, the potentially pharmacologically active molecules form the bioactivity space. Results show a poly-morphological chemical space that suggests distinct characteristics. The chemical space is correlated with properties such as steric energy, the number of hydrogen bonds, the presence of halogen atoms, and membrane permeability-related properties. Lastly, novel chemical compounds (such as oxadiazole methybenzamide and floro methylcyclohexane diene) with drug-like potential, active on TOPO II and ATP synthase have been identified.

Cyclohexane-1,3-dione Derivatives as Future Therapeutic Agents for NSCLC: QSAR Modeling, In Silico ADME-Tox Properties, and Structure-Based Drug Designing Approach.

The abnormal expression of the c-Met tyrosine kinase has been linked to the proliferation of several human cancer cell lines, including non-small-cell lung cancer (NSCLC). In this context, the identification of new c-Met inhibitors based on heterocyclic small molecules could pave the way for the development of a new cancer therapeutic pathway. Using multiple linear regression (MLR)-quantitative structure-activity relationship (QSAR) and artificial neural network (ANN)-QSAR modeling techniques, we look at the quantitative relationship between the biological inhibitory activity of 40 small molecules derived from cyclohexane-1,3-dione and their topological, physicochemical, and electronic properties against NSCLC cells. In this regard, screening methods based on QSAR modeling with density-functional theory (DFT) computations, in silico pharmacokinetic/pharmacodynamic (ADME-Tox) modeling, and molecular docking with molecular electrostatic potential (MEP) and molecular mechanics-generalized Born surface area (MM-GBSA) computations were used. Using physicochemical (stretch-bend, hydrogen bond acceptor, Connolly molecular area, polar surface area, total connectivity) and electronic (total energy, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels) molecular descriptors, compound 6d is identified as the optimal scaffold for drug design based on in silico screening tests. The computer-aided modeling developed in this study allowed us to design, optimize, and screen a new class of 36 small molecules based on cyclohexane-1,3-dione as potential c-Met inhibitors against NSCLC cell growth. The in silico rational drug design approach used in this study led to the identification of nine lead compounds for NSCLC therapy via c-Met protein targeting. Finally, the findings are validated using a 100 ns series of molecular dynamics simulations in an aqueous environment on c-Met free and complexed with samples of the proposed lead compounds and Foretinib drug.

Integrative Ligand-Based Pharmacophore Modeling, Virtual Screening, and Molecular Docking Simulation Approaches Identified Potential Lead Compounds against Pancreatic Cancer by Targeting FAK1

Pancreatic cancer is a very deadly disease with a 5-year survival rate, making it one of the leading causes of cancer-related deaths globally. Focal adhesion kinase 1 (FAK1) is a ubiquitously expressed protein in pancreatic cancer. FAK, a tyrosine kinase that is overexpressed in cancer cells, is crucial for the development of tumors into malignant phenotypes. FAK functions in response to extracellular signals by triggering transmembrane receptor signaling, which enhances focal adhesion turnover, cell adhesion, cell migration, and gene expression. The ligand-based drug design approach was used to identify potential compounds against the target protein, which included molecular docking: ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized born surface area (MM-GBSA). Following the retrieval of twenty hits, four compounds were selected for further evaluation based on a molecular docking approach. Three newly discovered compounds, including PubChem CID24601203, CID1893370, and CID16355541, with binding scores of -10.4, -10.1, and -9.7 kcal/mol, respectively, may serve as lead compounds for the treatment of pancreatic cancer associated with FAK1. The ADME (absorption, distribution, metabolism, and excretion) and toxicity analyses demonstrated that the compounds were effective and nontoxic. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the cancer.

In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations.

Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease Mpro (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop the pandemic from spreading, researchers are working to find more effective Mpro inhibitors against SARS-CoV-2. The 33.8 kDa Mpro protease of SARS-CoV-2, being a nonhuman homologue, has the possibility of being utilized as a therapeutic target against coronaviruses. To develop drug-like compounds capable of preventing the replication of SARS-main CoV-2's protease (Mpro), a computer-aided drug design (CADD) approach is extremely viable. Using MOE, structure-based virtual screening (SBVS) of in-house and commercial databases was carried out using SARS-CoV-2 proteins. The most promising hits obtained during virtual screening (VS) were put through molecular docking with the help of MOE. The virtual screening yielded 3/5 hits (in-house database) and 56/66 hits (commercial databases). Finally, 3/5 hits (in-house database), 3/5 hits (ZINC database), and 2/7 hits (ChemBridge database) were chosen as potent lead compounds using various scaffolds due to their considerable binding affinity with Mpro protein. The outcomes of SBVS were then validated using an analysis based on molecular dynamics simulation (MDS). The complexes' stability was tested using MDS and post-MDS. The most promising candidates were found to exhibit a high capacity for fitting into the protein-binding pocket and interacting with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.

Predictions of the Poses and Affinity of a Ligand over the Entire Surface of a NEET Protein: The Case of Human MitoNEET.

Human NEET proteins contain two [2Fe-2S] iron-sulfur clusters, bound to three Cys residues and one His residue. They exist in two redox states. Recently, these proteins have revealed themselves as attractive drug targets for mitochondrial dysfunction-related diseases, such as type 2 diabetes, Wolfram syndrome 2, and cancers. Unfortunately, the lack of information and mechanistic understanding of ligands binding to the whole functional, cytoplasmatic domain has limited rational drug design approaches. Here, we use an enhanced sampling technique, volume-based metadynamics, recently developed by a team involving some of us, to predict the poses and affinity of the 2-benzamido-4-(1,2,3,4-tetrahydronaphthalen-2-yl)-thiophene-3-carboxylate ligand to the entire surface of the cytoplasmatic domain of the human NEET protein mitoNEET (mNT) in an aqueous solution. The calculations, based on the recently published X-ray structure of the complex, are consistent with the measured affinity. The calculated free energy landscape revealed that the ligand can bind in multiple sites and with poses other than the one found in the X-ray. This difference is likely to be caused by crystal packing effects that allow the ligand to interact with multiple adjacent NEET protein copies. Such extra contacts are of course absent in the solution; therefore, the X-ray pose is only transient in our calculations, where the binding free energy correlates with the number of contacts. We further evaluated how the reduction and protonation of the Fe-bound histidine, as well as temperature, can affect ligand binding. Both such modifications introduce the possibility for the ligand to bind in an area of the protein other than the one observed in the X-ray, with no or little impact on affinity. Overall, our study can provide insights on the molecular recognition mechanisms of ligand binding to mNT in different oxidative conditions, possibly helping rational drug design of NEET ligands.

Carbohydrate-Small Molecule Hybrids as Lead Compounds Targeting IL-6 Signaling.

In the past 25 years, a number of efforts have been made toward the development of small molecule interleukin-6 (IL-6) signaling inhibitors, but none have been approved to date. Monosaccharides are a diverse class of bioactive compounds, but thus far have been unexplored as a scaffold for small molecule IL-6-signaling inhibitor design. Therefore, in this present communication, we combined a structure-based drug design approach with carbohydrate building blocks to design and synthesize novel IL-6-signaling inhibitors targeting glycoprotein 130 (gp130). Of this series of compounds, LS-TG-2P and LS-TF-3P were the top lead compounds, displaying IC50 values of 6.9 and 16 µM against SUM159 cell lines, respectively, while still retaining preferential activity against the IL-6-signaling pathway. The carbohydrate moiety was found to improve activity, as N-unsubstituted triazole analogues of these compounds were found to be less active in vitro compared to the leads themselves. Thus, LS-TG-2P and LS-TF-3P are promising scaffolds for further development and study as IL-6-signaling inhibitors.

Antimalarials Targeting the Malaria Parasite Cation ATPase P. falciparum ATP4 (PfATP4).

Malaria, caused by parasites of the Plasmodium species and transmitted through the bites of infected female Anopheles mosquitoes, is still a fatal and dangerous disease in mainly tropical and subtropical regions. The widespread resistance of P. falciparum to antimalarial drugs forces the search for new molecules with activity against this parasite. While a large number of compounds can inhibit P. falciparum growth in vitro, unfortunately, only a limited number of targets have been identified so far. One of the most promising approaches has been the identification of effective inhibitors of P-type cation-transporter ATPase 4 (PfATP4) in P. falciparum. PfATP4 is a Na+ efflux pump that maintains a low cytosolic Na+ in the parasite. Thus, upon treatment with PfATP4 inhibitors, the parasites rapidly accumulate Na+, which triggers processes leading to parasite death. PfATP4 is present in the parasite plasma membrane but is absent in mammals; its exclusivity thus makes it a good antimalarial drug target. The current review presents PfATP4 function in the context of the pharmacological influence of its inhibitors. In addition, compounds with inhibitory activities belonging to spiroindolones, dihydroisoquinolones, aminopyrazoles, pyrazoleamides, and 4-cyano-3-methylisoquinolines, are also reviewed. Particular emphasis is placed on the results of preclinical and clinical studies in which their effectiveness was tested. PfATP4-associated antimalarials rapidly cleared parasites in mouse models and preliminary human trials. These findings highlight a fundamental biochemical mechanism sensitive to pharmacological intervention that can form a medicinal chemistry approach for antimalarial drug design to create new molecules with potent PfATP4 inhibitory activity.

Selective modification of diclofenac to reduce the adverse effects; A computer-aided drug design approach

Diclofenac (DCF) is a non-steroidal anti-inflammatory drug (NSAID), commonly used for the treatment of pain. It can inhibit prostaglandin synthesis by blocking cyclooxygenase (COX). It shows some crucial side effects like gastrointestinal, cardiovascular, renal, and liver injury. The gastrointestinal injury occurred due to the presence of a carboxylic (COOH) group at the core of DCF. The hydroxyl (OH) portion of COOH was replaced by inserting some new functional groups (CH3, OCH3, CH2NH2, NH2, NHCOCH3, NHCONH2, Cl, CF3) considering the retrosynthetic strategy which reduces gastrointestinal side effects with improved chemical and biological activity. Herein, we have investigated the physicochemical, spectral, molecular dynamics, biological and pharmacokinetic properties of the mentioned analogues. Density functional theory (DFT) and time-dependent DFT along with the B3LYP/6-31g (d,p) basis set have been utilized to calculate their geometrical, chemical, and spectral properties. Molecular docking and non-bonding interactions have been performed against human prostaglandin synthase protein (PDB ID: 5F1A) to investigate their binding affinities, modes, and stability. ADMET and PASS prediction studies were performed to investigate their microbial inhibition and toxicological profile. Quantum chemical calculations and spectral characterization support the geometry of newly designed analogues. From the molecular docking simulation, most of the analogues exhibited better binding affinity than the parent drug except for DCF-2. ADMET calculations predict the improved pharmacokinetic and non-carcinogenic properties of all DCF analogues. Biological activities and drug-likeness indicate that all the analogues of DCF have been exposed to comparatively lower action of the gastrointestinal hemorrhage than DCF and exhibit antipyretic, analgesic, and anti-inflammatory actions as well as performed to overcome the poor absorption or permeation of drugs. Finally, based on the above investigation, this study can be helpful to design a potential drug candidate with improved medicinal action and reduced selective adverse effects.

Computational Methods for Anticancer Drug Discovery; The MCT4 Paradigm.

Modern anticancer research has employed advanced computational techniques and artificial intelligence methods for drug discovery and development, along with the massive amount of generated clinical and in silico data over the last decades. Diverse computational techniques and state-of-the-art algorithms are being developed to enhance traditional Rational Drug Design pipelines and achieve cost-efficient and successful anticancer candidates to promote human health. Towards this direction, we have developed a pharmacophore- based drug design approach against MCT4, a member of the monocarboxylate transporter family (MCT), which is the main carrier of lactate across the membrane and highly involved in cancer cell metabolism. Specifically, MCT4 is a promising target for therapeutic strategies as it overexpresses in glycolytic tumors, and its inhibition has shown promising anticancer effects. Due to the lack of experimentally determined structure, we have elucidated the key features of the protein through an in silico drug design strategy, including for molecular modelling, molecular dynamics, and pharmacophore elucidation, towards the identification of specific inhibitors as a novel anti-cancer strategy.