Virtual and in Vitro Screening Employing a Repurposing Approach Reveal 13-cis-Retinoic Acid is a PTP1B Inhibitor.

Current treatments for type 2 diabetes (T2D) mainly rely on exercise, dietary control, and anti-diabetic drugs to enhance insulin secretion and improve insulin sensitivity. However, there is a need for more therapeutic options, as approved drugs targeting different pharmacological objectives are still unavailable. One potential target that has attracted attention is the protein tyrosine phosphatase 1B (PTP1B), which negatively regulates the insulin signaling pathway. In this work, a comprehensive computational screening was carried out using cheminformatics and molecular docking on PTP1B, employing a rigorous repurposing approach. The screening involved approved drugs and compounds under research as anti-diabetics that bind to targets such as peroxisome proliferator-activated receptor gamma (PPAR-γ) and α-glucosidase. Several computational hits were then meticulously tested in vitro against PTP1B, with 13-cis-retinoic acid (3a) showing an IC50 of 0.044 mM and competitive inhibition. Molecular dynamics studies further confirmed that 3a can bind to the catalytic binding site of PTP1B. Finally, 3a is the first time it has been reported as an inhibitor of PTP1B, making it a potentially valuable candidate for further studies in D2T treatment.

Putative mechanism of a multivitamin treatment against insulin resistance.

Insulin resistance is caused by the abnormal secretion of proinflammatory cytokines in adipose tissue, which is induced by an increase in lipid accumulation in adipocytes, hepatocytes, and myocytes. The inflammatory pathway involves multiple targets such as nuclear factor kappa B, inhibitor of nuclear factor κ-B kinase, and mitogen-activated protein kinase. Vitamins are micronutrients with anti-inflammatory activities that have unclear mechanisms. The present study aimed to describe the putative mechanisms of vitamins involved in the inflammatory pathway of insulin resistance. The strategy to achieve this goal was to integrate data mining and analysis, target prediction, and molecular docking simulation calculations to support our hypotheses. Our results suggest that the multitarget activity of vitamins A, B1, B2, B3, B5, B6, B7, B12, C, D3, and E inhibits nuclear factor kappa B and mitogen-activated protein kinase, in addition to vitamins A and B12 against inhibitor of nuclear factor κ-B kinase. The findings of this study highlight the pharmacological potential of using an anti-inflammatory and multitarget treatment based on vitamins and open new perspectives to evaluate the inhibitory activity of vitamins against nuclear factor kappa B, mitogen-activated protein kinase, and inhibitor of nuclear factor κ-B kinase in an insulin-resistant context.

Nitazoxanide Inhibits the Bifunctional Enzyme GlG6PD::6PGL of Giardia lamblia: Biochemical and In Silico Characterization of a New Druggable Target.

Giardiasis, which is caused by Giardia lamblia infection, is a relevant cause of morbidity and mortality worldwide. Because no vaccines are currently available to treat giardiasis, chemotherapeutic drugs are the main options for controlling infection. Evidence has shown that the nitro drug nitazoxanide (NTZ) is a commonly prescribed treatment for giardiasis; however, the mechanisms underlying NTZ's antigiardial activity are not well-understood. Herein, we identified the glucose-6-phosphate::6-phosphogluconate dehydrogenase (GlG6PD::6PGL) fused enzyme as a nitazoxanide target, as NTZ behaves as a GlG6PD::6PGL catalytic inhibitor. Furthermore, fluorescence assays suggest alterations in the stability of GlG6PD::6PGL protein, whereas the results indicate a loss of catalytic activity due to conformational and folding changes. Molecular docking and dynamic simulation studies suggest a model of NTZ binding on the active site of the G6PD domain and near the structural NADP+ binding site. The findings of this study provide a novel mechanistic basis and strategy for the antigiardial activity of the NTZ drug.

Challenges and Discoveries in Polypharmacology of Neurodegenerative Diseases.

BACKGROUND: Neurological disorders are composed of several diseases that affect the central and peripheral nervous system; among these are neurodegenerative diseases, which lead to neuronal death. Many of these diseases have treatment for the disease and symptoms, leading patients to use several drugs that cause side effects. INTRODUCTION: The search for new treatments has led to the investigation of multi-target drugs. METHODS: This review aimed to investigate in the literature the multi-target effect in neurological disorders through an in silico approach. Studies were reviewed on the diseases such as epilepsy, Alzheimer's disease, Amyotrophic Lateral Sclerosis (ALS), Huntington's disease, cerebral ischemia, and Parkinson's disease. RESULTS: As a result, the study emphasize the relevance of research by computational techniques such as quantitative structure-activity relationship (QSAR) prediction models, pharmacokinetic prediction models, molecular docking, and molecular dynamics, besides presenting possible drug candidates with multi-target activity. CONCLUSION: It was possible to identify several targets with pharmacological activities. Some of these targets had diseases in common such as carbonic anhydrase, acetylcholinesterase, NMDA, and MAO being relevant for possible multi-target approaches.

The Use of Zidovudine Pharmacophore in Multi-Target-Directed Ligands for AIDS Therapy.

The concept of polypharmacology embraces multiple drugs combined in a therapeutic regimen (drug combination or cocktail), fixed dose combinations (FDCs), and a single drug that binds to different targets (multi-target drug). A polypharmacology approach is widely applied in the treatment of acquired immunodeficiency syndrome (AIDS), providing life-saving therapies for millions of people living with HIV. Despite the success in viral load suppression and patient survival of combined antiretroviral therapy (cART), the development of new drugs has become imperative, owing to the emergence of resistant strains and poor adherence to cART. 3'-azido-2',3'-dideoxythymidine, also known as azidothymidine or zidovudine (AZT), is a widely applied starting scaffold in the search for new compounds, due to its good antiretroviral activity. Through the medicinal chemistry tool of molecular hybridization, AZT has been included in the structure of several compounds allowing for the development of multi-target-directed ligands (MTDLs) as antiretrovirals. This review aims to systematically explore and critically discuss AZT-based compounds as potential MTDLs for the treatment of AIDS. The review findings allowed us to conclude that: (i) AZT hybrids are still worth exploring, as they may provide highly active compounds targeting different steps of the HIV-1 replication cycle; (ii) AZT is a good starting point for the preparation of co-drugs with enhanced cell permeability.

New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools.

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer's disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.

Network pharmacology reveals multitarget mechanism of action of drugs to be repurposed for COVID-19.

The coronavirus disease 2019 pandemic accelerated drug/vaccine development processes, integrating scientists all over the globe to create therapeutic alternatives against this virus. In this work, we have collected information regarding proteins from SARS-CoV-2 and humans and how these proteins interact. We have also collected information from public databases on protein-drug interactions. We represent this data as networks that allow us to gain insights into protein-protein interactions between both organisms. With the collected data, we have obtained statistical metrics of the networks. This data analysis has allowed us to find relevant information on which proteins and drugs are the most relevant from the network pharmacology perspective. This method not only allows us to focus on viral proteins as the main targets for COVID-19 but also reveals that some human proteins could be also important in drug repurposing campaigns. As a result of the analysis of the SARS-CoV-2-human interactome, we have identified some old drugs, such as disulfiram, auranofin, gefitinib, suloctidil, and bromhexine as potential therapies for the treatment of COVID-19 deciphering their potential complex mechanism of action.

Common Structural Pattern for Flecainide Binding in Atrial-Selective Kv1.5 and Nav1.5 Channels: A Computational Approach.

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Its treatment includes antiarrhythmic drugs (AADs) to modulate the function of cardiac ion channels. However, AADs have been limited by proarrhythmic effects, non-cardiovascular toxicities as well as often modest antiarrhythmic efficacy. Theoretical models showed that a combined blockade of Nav1.5 (and its current, INa) and Kv1.5 (and its current, IKur) ion channels yield a synergistic anti-arrhythmic effect without alterations in ventricles. We focused on Kv1.5 and Nav1.5 to search for structural similarities in their binding site (BS) for flecainide (a common blocker and widely prescribed AAD) as a first step for prospective rational multi-target directed ligand (MTDL) design strategies. We present a computational workflow for a flecainide BS comparison in a flecainide-Kv1.5 docking model and a solved structure of the flecainide-Nav1.5 complex. The workflow includes docking, molecular dynamics, BS characterization and pattern matching. We identified a common structural pattern in flecainide BS for these channels. The latter belongs to the central cavity and consists of a hydrophobic patch and a polar region, involving residues from the S6 helix and P-loop. Since the rational MTDL design for AF is still incipient, our findings could advance multi-target atrial-selective strategies for AF treatment.

7-Aminoalkoxy-Quinazolines from Epigenetic Focused Libraries Are Potent and Selective Inhibitors of DNA Methyltransferase 1.

Inhibitors of epigenetic writers such as DNA methyltransferases (DNMTs) are attractive compounds for epigenetic drug and probe discovery. To advance epigenetic probes and drug discovery, chemical companies are developing focused libraries for epigenetic targets. Based on a knowledge-based approach, herein we report the identification of two quinazoline-based derivatives identified in focused libraries with sub-micromolar inhibition of DNMT1 (30 and 81 nM), more potent than S-adenosylhomocysteine. Also, both compounds had a low micromolar affinity of DNMT3A and did not inhibit DNMT3B. The enzymatic inhibitory activity of DNMT1 and DNMT3A was rationalized with molecular modeling. The quinazolines reported in this work are known to have low cell toxicity and be potent inhibitors of the epigenetic target G9a. Therefore, the quinazoline-based compounds presented are attractive not only as novel potent inhibitors of DNMTs but also as dual and selective epigenetic agents targeting two families of epigenetic writers.

A New Strategy for Multitarget Drug Discovery/Repositioning Through the Identification of Similar 3D Amino Acid Patterns Among Proteins Structures: The Case of Tafluprost and its Effects on Cardiac Ion Channels.

The identification of similar three-dimensional (3D) amino acid patterns among different proteins might be helpful to explain the polypharmacological profile of many currently used drugs. Also, it would be a reasonable first step for the design of novel multitarget compounds. Most of the current computational tools employed for this aim are limited to the comparisons among known binding sites, and do not consider several additional important 3D patterns such as allosteric sites or other conserved motifs. In the present work, we introduce Geomfinder2.0, which is a new and improved version of our previously described algorithm for the deep exploration and discovery of similar and druggable 3D patterns. As compared with the original version, substantial improvements that have been incorporated to our software allow: (i) to compare quaternary structures, (ii) to deal with a list of pairs of structures, (iii) to know how druggable is the zone where similar 3D patterns are detected and (iv) to significantly reduce the execution time. Thus, the new algorithm achieves up to 353x speedup as compared to the previous sequential version, allowing the exploration of a significant number of quaternary structures in a reasonable time. In order to illustrate the potential of the updated Geomfinder version, we show a case of use in which similar 3D patterns were detected in the cardiac ions channels NaV1.5 and TASK-1. These channels are quite different in terms of structure, sequence and function and both have been regarded as important targets for drugs aimed at treating atrial fibrillation. Finally, we describe the in vitro effects of tafluprost (a drug currently used to treat glaucoma, which was identified as a novel putative ligand of NaV1.5 and TASK-1) upon both ion channels' activity and discuss its possible repositioning as a novel antiarrhythmic drug.

In Silico Analysis of Metabolites from Peruvian Native Plants as Potential Therapeutics against Alzheimer's Disease.

BACKGROUND: Despite research on the molecular bases of Alzheimer's disease (AD), effective therapies against its progression are still needed. Recent studies have shown direct links between AD progression and neurovascular dysfunction, highlighting it as a potential target for new therapeutics development. In this work, we screened and evaluated the inhibitory effect of natural compounds from native Peruvian plants against tau protein, amyloid beta, and angiotensin II type 1 receptor (AT1R) pathologic AD markers. METHODS: We applied in silico analysis, such as virtual screening, molecular docking, molecular dynamics simulation (MD), and MM/GBSA estimation, to identify metabolites from Peruvian plants with inhibitory properties, and compared them to nicotinamide, telmisartan, and grapeseed extract drugs in clinical trials. RESULTS: Our results demonstrated the increased bioactivity of three plants' metabolites against tau protein, amyloid beta, and AT1R. The MD simulations indicated the stability of the AT1R:floribundic acid, amyloid beta:rutin, and tau:brassicasterol systems. A polypharmaceutical potential was observed for rutin due to its high affinity to AT1R, amyloid beta, and tau. The metabolite floribundic acid showed bioactivity against the AT1R and tau, and the metabolite brassicasterol showed bioactivity against the amyloid beta and tau. CONCLUSIONS: This study has identified molecules from native Peruvian plants that have the potential to bind three pathologic markers of AD.

Design and Synthesis of Hybrid Compounds as Epigenetic Modifiers.

Epigenetic modifiers acting through polypharmacology mechanisms are promising compounds with which to treat several infectious diseases. Histone deacetylase (HDAC) enzymes, mainly class I, and extra-terminal bromodomains (BET) are involved in viral replication and the host response. In the present study, 10 compounds were designed, assisted by molecular docking, to act against HDAC class I and bromodomain-4 (BRD4). All the compounds were synthesized and characterized by analytical methods. Enzymatic assays were performed using HDAC-1, -4, and -11 and BRD4. Compounds (2-10) inhibited both HDAC class I, mainly HDAC-1 and -2, and reduced BRD4 activity. For HDAC-1, the inhibitory effect ranged from 8 to 95%, and for HDAC-2, these values ranged from 10 to 91%. Compounds (2-10) decreased the BRD4 activity by up to 25%. The multi-target effects of these compounds show desirable properties that could help to combat viral infections by acting through epigenetic mechanisms.

An in silico pipeline for the discovery of multitarget ligands: A case study for epi-polypharmacology based on DNMT1/HDAC2 inhibition.

The search for novel therapeutic compounds remains an overwhelming task owing to the time-consuming and expensive nature of the drug development process and low success rates. Traditional methodologies that rely on the one drug-one target paradigm have proven insufficient for the treatment of multifactorial diseases, leading to a shift to multitarget approaches. In this emerging paradigm, molecules with off-target and promiscuous interactions may result in preferred therapies. In this study, we developed a general pipeline combining machine learning algorithms and a deep generator network to train a dual inhibitor classifier capable of identifying putative pharmacophoric traits. As a case study, we focused on dual inhibitors targeting DNA methyltransferase 1 (DNMT) and histone deacetylase 2 (HDAC2), two enzymes that play a central role in epigenetic regulation. We used this approach to identify dual inhibitors from a novel large natural product database in the public domain. We used docking and atomistic simulations as complementary approaches to establish the ligand-interaction profiles between the best hits and DNMT1/HDAC2. By using the combined ligand- and structure-based approaches, we discovered two promising novel scaffolds that can be used to simultaneously target both DNMT1 and HDAC2. We conclude that the flexibility and adaptability of the proposed pipeline has predictive capabilities of similar or derivative methods and is readily applicable to the discovery of small molecules targeting many other therapeutically relevant proteins.

Synthesis, Docking, 3-D-Qsar, and Biological Assays of Novel Indole Derivatives Targeting Serotonin Transporter, Dopamine D2 Receptor, and Mao-A Enzyme: In the Pursuit for Potential Multitarget Directed Ligands.

A series of 27 compounds of general structure 2,3-dihydro-benzo[1,4]oxazin-4-yl)-2-{4-[3-(1H-3indolyl)-propyl]-1-piperazinyl}-ethanamides, Series I: 7(a-o) and (2-{4-[3-(1H-3-indolyl)-propyl]-1-piperazinyl}-acetylamine)-N-(2-morfolin-4-yl-ethyl)-fluorinated benzamides Series II: 13(a-l) were synthesized and evaluated as novel multitarget ligands towards dopamine D2 receptor, serotonin transporter (SERT), and monoamine oxidase-A (MAO-A) directed to the management of major depressive disorder (MDD). All the assayed compounds showed affinity for SERT in the nanomolar range, with five of them displaying Ki values from 5 to 10 nM. Compounds 7k, Ki = 5.63 ± 0.82 nM, and 13c, Ki = 6.85 ± 0.19 nM, showed the highest potencies. The affinities for D2 ranged from micro to nanomolar, while MAO-A inhibition was more discrete. Nevertheless, compounds 7m and 7n showed affinities for the D2 receptor in the nanomolar range (7n: Ki = 307 ± 6 nM and 7m: Ki = 593 ± 62 nM). Compound 7n was the only derivative displaying comparable affinities for SERT and D2 receptor (D2/SERT ratio = 3.6) and could be considered as a multitarget lead for further optimization. In addition, docking studies aimed to rationalize the molecular interactions and binding modes of the designed compounds in the most relevant protein targets were carried out. Furthermore, in order to obtain information on the structure-activity relationship of the synthesized series, a 3-D-QSAR CoMFA and CoMSIA study was conducted and validated internally and externally (q2 = 0.625, 0.523 for CoMFA and CoMSIA and r2ncv = 0.967, 0.959 for CoMFA and CoMSIA, respectively).

In silico tools to study molecular targets of neglected diseases: inhibition of TcSir2rp3, an epigenetic enzyme of Trypanosoma cruzi.

There is a growing interest to study and address neglected tropical diseases (NTD). To this end, in silico methods can serve as the bridge that connects academy and industry, encouraging the development of future treatments against these diseases. This chapter discusses current challenges in the development of new therapies, available computational methods and successful cases in computer-aided design with particular focus on human trypanosomiasis. Novel targets are also discussed. As a case study, we identify amentoflavone as a potential inhibitor of TcSir2rp3 (sirtuine) from Trypanosoma cruzi (20.03 µM) with a workflow that integrates chemoinformatic approaches, molecular modeling, and theoretical affinity calculations, as well as in vitro assays.

A Definition of "Multitargeticity": Identifying Potential Multitarget and Selective Ligands Through a Vector Analysis.

The design of multitarget drugs is an essential area of research in Medicinal Chemistry since they have been proposed as potential therapeutics for the management of complex diseases. However, defining a multitarget drug is not an easy task. In this work, we propose a vector analysis for measuring and defining "multitargeticity." We developed terms, such as order and force of a ligand, to finally reach two parameters: multitarget indexes 1 and 2. The combination of these two indexes allows discrimination of multitarget drugs. Several training sets were constructed to test the usefulness of the indexes: an experimental training set, with real affinities, a docking training set, within theoretical values, and an extensive database training set. The indexes proved to be useful, as they were used independently in silico and experimental data, identifying actual multitarget compounds and even selective ligands in most of the training sets. We then applied these indexes to evaluate a virtual library of potential ligands for targets related to multiple sclerosis, identifying 10 compounds that are likely leads for the development of multitarget drugs based on their in silico behavior. With this work, a new milestone is made in the way of defining multitargeticity and in drug design.

The impact of chemoinformatics on drug discovery in the pharmaceutical industry.

Introduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field.Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field.Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.

Dealing with frequent hitters in drug discovery: a multidisciplinary view on the issue of filtering compounds on biological screenings.

Introduction: The timely identification biologically active chemicals, in disease relevant screening assays, is a major endeavor in drug discovery. The existence of frequent hitters (FHs) in non-related assays poses a formidable challenge in terms of whether to consider these molecules as chemical gold or promiscuous non-selective reactive trash (also known as PAINS - pan assay interference compounds).Areas covered: In this review, the authors bring together expertize in synthetic chemistry, cheminformatics and biochemistry, three key areas for dealing with FHs. They discuss synthetic methods facilitating preparation of chemically diverse molecular libraries, while favoring activity in the biological space. They also survey and discuss recent computational advances in the prediction of PAINS from chemical structures. Finally, they review experimental approaches for the validation of the biological activity of screening hits and discuss alternatives for exploiting promiscuity and chemical reactivity.Expert opinion: It's essential to develop more efficient computational methods to reliably recognize PAINS in distinct molecular environments. Accordingly, advances in synthetic chemistry hold the promise to provide a better quality of chemical matter for drug discovery. Medicinal chemists should be more open to screening for hits showing biologically complex mechanisms of action rather than discarding molecules that may prove valuable as innovative disease treatments.

Network-Based Assessment of Adverse Drug Reaction Risk in Polypharmacy Using High-Throughput Screening Data.

The risk of adverse drug reactions increases in a polypharmacology setting. High-throughput drug screening with transcriptomics applied to human cells has shown that drugs have effects on several molecular pathways, and these affected pathways may be predictive proxy for adverse drug reactions. Depending on the way that different drugs may contribute to adverse drug reactions, different options may exist in the clinical setting. Here, we formulate a network framework to integrate the relationships between drugs, biological functions, and adverse drug reactions based on the high-throughput drug perturbation data from the Library of Integrated Network-Based Cellular Signatures (LINCS) project. We present network-based parameters that indicate whether a given reaction may be related to the effect of a single drug or to the combination of several drugs, as well as the relative risk of adverse drug reaction manifestation given a certain drug combination.