A perspective on multi-target drug discovery and design for complex diseases

Multitarget 1,4-Dioxane Compounds Combining Favorable D2-like and 5-HT1A Receptor Interactions with Potential for the Treatment of Parkinson's Disease or Schizophrenia

The effect of methoxy and hydroxy substitutions in different positions of the phenoxy moiety of the N-((6,6-diphenyl-1,4-dioxan-2-yl)methyl)-2-phenoxyethan-1-amine scaffold on the affinity/activity for D₂-like, 5-HT_1A, and α₁-adrenoceptor subtypes was evaluated. Multitarget compounds with suitable combinations of dopaminergic and serotoninergic profiles were discovered. In particular, the 2-methoxy derivative 3 showed a multitarget combination of 5-HT_1A/D₄ agonism and D₂/D₃/5-HT_2A antagonism, which may be a favorable profile for the treatment of schizophrenia. Interestingly, the 3-hydroxy derivative 8 behaved as a partial agonist at D₂ and as a potent full agonist at D₃ and D₄ subtypes. In addition to its potent 5-HT_1A receptor agonism, such a dopaminergic profile makes 8 a potential multitarget compound for the treatment of Parkinson's disease (PD). Indeed, the activation of 5-HT_1A receptors might be helpful in reducing dyskinetic side effects associated with dopaminergic stimulation.

Quantitative structure-activity relationship models for compounds with anticonvulsant activity

Introduction: Third-generation antiepileptic drugs have seemingly failed to improve the global figures of seizure control and can still be regarded as symptomatic treatments. Quantitative structure-activity relationships (QSAR) can be used to guide hit-to-lead and lead optimization projects and applied to the large-scale virtual screening of chemical libraries. Areas covered: In this review, the authors cover reports on QSAR models related to antiepileptic drugs and drug targets in epilepsy, analyzing whether they refer to classic or non-classic QSAR and if they apply QSAR as a descriptive or predictive approach, among other considerations. The article finally focuses on a more detailed discussion of those predictive studies which include some sort of experimental validation, i.e. papers in which the reported models have been used to identify novel active compounds which have been tested in vitro and/or in vivo. Expert opinion: There are significant opportunities to apply the QSAR methodology to assist the discovery of more efficacious antiepileptic drugs. Considering the intrinsic complexity of the disorder, such applications should focus on state-of-the-art approximations (e.g. systemic, multi-target and multi-scale QSAR as well as ensemble and deep learning) and modeling the effects on novel drug targets and modern screening tools.

Salinomycin and its derivatives - A new class of multiple-targeted "magic bullets"

The history of drug development clearly shows the scale of painstaking effort leading to a finished product - a highly biologically active agent that would be at the same time no or little toxic to human organism. Moreover, the aim of modern drug discovery can move from "one-molecule one-target" concept to more promising "one-molecule multiple-targets" one, particularly in the context of effective fight against cancer and other complex diseases. Gratifyingly, natural compounds are excellent source of potential drug leads. One of such promising naturally-occurring drug candidates is a polyether ionophore - salinomycin (SAL). This compound should be identified as multi-target agent for two reasons. Firstly, SAL combines a broad spectrum of bioactivity, including antibacterial, antifungal, antiviral, antiparasitic and anticancer activity, with high selectivity of action, proving its significant therapeutic potential. Secondly, the multimodal mechanism of action of SAL has been shown to be related to its interactions with multiple molecular targets and signalling pathways that are synergistic for achieving a therapeutic anticancer effect. On the other hand, according to the Paul Ehrlich's "magic bullet" concept, invariably inspiring the scientists working on design of novel target-selective molecules, a very interesting direction of research is rational chemical modification of SAL. Importantly, many of SAL derivatives have been found to be more promising as chemotherapeutics than the native structure. This concise review article is focused both on the possible role of SAL and its selected analogues in future antimicrobial and/or cancer therapy, and on the potential use of SAL as a new class of multiple-targeted "magic bullet" because of its multimodal mechanism of action.

Histone Deacetylase Inhibitors as Multitarget Ligands: New Players in Alzheimer's Disease Drug Discovery?

Histone deacetylase inhibitors (HDACIs) are responsible for controlling gene expression by modulating the acetylation status of histone proteins. Furthermore, they modulate the activity of cytoplasmic non-histone proteins. Due to the involvement of HDACs in neurodevelopment, memory formation, and cognitive processes, HDACIs have been suggested as innovative agents for the treatment of neurodegenerative disorders such as Alzheimer's disease (AD). Given their mechanisms of action and the complex nature of AD, HDACIs have been proposed for the design of novel multitarget ligands (MTLs). To this aim, the fragment responsible for HDAC inhibition has been coupled with other structures that are able to provide additional biological actions, such as antioxidant activity or the inhibition of phosphodiesterase 5, transglutaminase 2, and glycogen synthase kinase 3β. Herein we discuss recent efforts to design HDACI-based MTLs as potential disease-modifying entities.

Exploring the Potential of Spherical Harmonics and PCVM for Compounds Activity Prediction

Biologically active chemical compounds may provide remedies for several diseases. Meanwhile, Machine Learning techniques applied to Drug Discovery, which are cheaper and faster than wet-lab experiments, have the capability to more effectively identify molecules with the expected pharmacological activity. Therefore, it is urgent and essential to develop more representative descriptors and reliable classification methods to accurately predict molecular activity. In this paper, we investigate the potential of a novel representation based on Spherical Harmonics fed into Probabilistic Classification Vector Machines classifier, namely SHPCVM, to compound the activity prediction task. We make use of representation learning to acquire the features which describe the molecules as precise as possible. To verify the performance of SHPCVM ten-fold cross-validation tests are performed on twenty-one G protein-coupled receptors (GPCRs). Experimental outcomes (accuracy of 0.86) assessed by the classification accuracy, precision, recall, Matthews’ Correlation Coefficient and Cohen’s kappa reveal that using our Spherical Harmonics-based representation which is relatively short and Probabilistic Classification Vector Machines can achieve very satisfactory performance results for GPCRs.

Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

The discovery of new pharmaceutical drugs is one of the preeminent tasks-scientifically, economically, and socially-in biomedical research. Advances in informatics and computational biology have increased productivity at many stages of the drug discovery pipeline. Nevertheless, drug discovery has slowed, largely due to the reliance on small molecules as the primary source of novel hypotheses. Natural products (such as plant metabolites, animal toxins, and immunological components) comprise a vast and diverse source of bioactive compounds, some of which are supported by thousands of years of traditional medicine, and are largely disjoint from the set of small molecules used commonly for discovery. However, natural products possess unique characteristics that distinguish them from traditional small molecule drug candidates, requiring new methods and approaches for assessing their therapeutic potential. In this review, we investigate a number of state-of-the-art techniques in bioinformatics, cheminformatics, and knowledge engineering for data-driven drug discovery from natural products. We focus on methods that aim to bridge the gap between traditional small-molecule drug candidates and different classes of natural products. We also explore the current informatics knowledge gaps and other barriers that need to be overcome to fully leverage these compounds for drug discovery. Finally, we conclude with a "road map" of research priorities that seeks to realize this goal.

Facile synthesis of carbon-11-labeled sEH/PDE4 dual inhibitors as new potential PET agents for imaging of sEH/PDE4 enzymes in neuroinflammation

To develop PET tracers for imaging of neuroinflammation, new carbon-11-labeled sEH/PDE4 dual inhibitors have been synthesized. The reference standard N-(4-methoxy-2-(trifluoromethyl)benzyl)benzamide (1) and its corresponding desmethylated precursor N-(4-hydroxy-2-(trifluoromethyl)benzyl)benzamide (2) were synthesized from (4-methoxy-2-(trifluoromethyl)phenyl)methanamine and benzoic acid in one and two steps with 84% and 49% overall chemical yield, respectively. The standard N-(4-methoxy-2-(trifluoromethyl)benzyl)-1-propionylpiperidine-4-carboxamide (MPPA, 4) and its precursor N-(4-hydroxy-2-(trifluoromethyl)benzyl)-1-propionylpiperidine-4-carboxamide (5) were synthesized from methyl 4-piperidinecarboxylate, propionyl chloride and (4-methoxy-2-(trifluoromethyl)phenyl)methanamine in two and three steps with 62% and 34% overall chemical yield, respectively. The target tracers N-(4-[¹¹C]methoxy-2-(trifluoromethyl)benzyl)benzamide ([¹¹C]1) and N-(4-[¹¹C]methoxy-2-(trifluoromethyl)benzyl)-1-propionylpiperidine-4-carboxamide ([¹¹C]MPPA, [¹¹C]4) were prepared from their corresponding precursors 2 and 5 with [¹¹C]CH₃OTf through O-[¹¹C]methylation and isolated by HPLC combined with SPE in 25-35% radiochemical yield, based on [¹¹C]CO₂ and decay corrected to end of bombardment (EOB). The radiochemical purity was >99%, and the molar activity (A_M) at EOB was 370-740 GBq/μmol with a total synthesis time of 35-40-minutes from EOB.

A patent update on PDK1 inhibitors (2015-present)

INTRODUCTION

3-Phosphoinositide-dependent kinase 1 (PDK1), the 'master kinase of the AGC protein kinase family', plays a key role in cancer development and progression. Although it has been rather overlooked, in the last decades a growing number of molecules have been developed to effectively modulate the PDK1 enzyme.

AREAS COVERED

This review collects different PDK1 inhibitors patented from October 2014 to December 2018. The molecules have been classified on the basis of the chemical structure/type of inhibition, and for each general structure, examples have been discussed in extenso.

EXPERT OPINION

The role of PDK1 in cancer development and progression as well as in metastasis formation and in chemoresistance has been confirmed by many studies. Therefore, the pharmaceutical discovery in both public and private institutions is still ongoing despite the plentiful molecules already published. The majority of the new molecules synthetized interact with binding sites different from the ATP binding site (i.e. PIF pocket or DFG-out conformation). However, many researchers are still looking for innovative PDK1 modulation strategy such as combination of well-known inhibitory agents or multitarget ligands, aiming to block, together with PDK1, other different critical players in the wide panorama of proteins involved in tumor pathways.

Modeling the Kinetics of Lipid-Nanoparticle- Mediated Delivery of Multiple siRNAs to Evaluate the Effect on Competition for Ago2

Drug combinations can improve the control of diseases involving redundant and highly regulated pathways. Validating a multi-target therapy early in drug development remains difficult. Small interfering RNAs (siRNAs) are routinely used to selectively silence a target of interest. Owing to the ease of design and synthesis, siRNAs hold promise for combination therapies. Combining siRNAs against multiple targets remains an attractive approach to interrogating highly regulated pathways. Currently, questions remain regarding how broadly such an approach can be applied, since siRNAs have been shown to compete with one another for binding to Argonaute2 (Ago2), the protein responsible for initiating siRNA-mediated mRNA degradation. Mathematical modeling, coupled with in vitro and in vivo experiments, led us to conclude that endosomal escape kinetics had the highest impact on Ago2 depletion by competing lipid-nanoparticle (LNP)-formulated siRNAs. This, in turn, affected the level of competition observed between them. A future application of this model would be to optimize delivery of desired siRNA combinations in vitro to attenuate competition and maximize the combined therapeutic effect.

Harnessing Polypharmacology with Medicinal Chemistry

Polypharmacology has expanded enormously over the last ten years, with several multitarget drugs (MTDs) already in the market. This Viewpoint provides a basis for a discussion about the critical need to develop MTDs in a more rationale and conscious way. A checklist to maximize success in polypharmacology is proposed.

Advancement of multi-target drug discoveries and promising applications in the field of Alzheimer's disease

Complex diseases (e.g., Alzheimer's disease) or infectious diseases are usually caused by complicated and varied factors, including environmental and genetic factors. Multi-target (polypharmacology) drugs have been suggested and have emerged as powerful and promising alternative paradigms in modern medicinal chemistry for the development of versatile chemotherapeutic agents to solve these medical challenges. The multifunctional agents capable of modulating multiple biological targets simultaneously display great advantages of higher efficacy, improved safety profile, and simpler administration compared to single-targeted agents. Therefore, multifunctional agents would certainly open novel avenues to rationally design the next generation of more effective but less toxic therapeutic agents. Herein, the authors review the recent progress made in the discovery and design processes of selective multi-targeted agents, especially the successful application of multi-target drugs for the treatment of Alzheimer's disease.

New Multitarget Hybrids Bearing Tacrine and Phenylbenzothiazole Motifs as Potential Drug Candidates for Alzheimer's Disease

Research on neurodegenerative brain disorders, namely the age-dependent Alzheimer's disease (AD), has been intensified in the last decade due to the absence of a cure and the recognized increasing of life expectancy for populations. To address the multifactorial nature and complexity of AD, a multi-target-directed ligand approach was herein employed, by designing a set of six selected hybrids (14⁻19) that combine in the same entity two pharmacophores: tacrine (TAC) and 2-phenylbenzothiazole (PhBTA). The compounds contain a methoxy substituent at the PhBTA moiety and have a variable length linker between that and the TAC moiety. The docking studies showed that all the compounds assure a dual-binding mode of acetylcholinesterase (AChE) inhibition, establishing π-stacking and H-bond interactions with aminoacid residues at both active binding sites of the enzyme (CAS and PAS). The bioassays revealed that the designed compounds display excellent AChE inhibitory activity in the sub-micromolar range (0.06⁻0.27 μM) and moderate inhibition values for amyloid-β (Aβ) self-aggregation (27⁻44.6%), compounds 14 and 15 being the lead compounds. Regarding neuroprotective effects in neuroblastoma cells, compounds 15, 16 and 19 revealed the capacity to prevent Aβ-induced toxicity, but compound 16 showed the highest neuroprotective effect. Overall these hybrid compounds, in particular 15 and 16, with promising multitarget anti-AD ability, encourage further pursuing studies on this type of TAC-PhBTA derivatives for potential AD therapy.

Discovery of the First-in-Class GSK-3β/HDAC Dual Inhibitor as Disease-Modifying Agent To Combat Alzheimer's Disease

Several evidence pointed out the role of epigenetics in Alzheimer's disease (AD) revealing strictly relationships between epigenetic and "classical" AD targets. Based on the reported connection among histone deacetylases (HDACs) and glycogen synthase kinase 3β (GSK-3β), herein we present the discovery and the biochemical characterization of the first-in-class hit compound able to exert promising anti-AD effects by modulating the targeted proteins in the low micromolar range of concentration. Compound 11 induces an increase in histone acetylation and a reduction of tau phosphorylation. It is nontoxic and protective against H₂O₂ and 6-OHDA stimuli in SH-SY5Y and in CGN cell lines, respectively. Moreover, it promotes neurogenesis and displays immunomodulatory effects. Compound 11 shows no lethality in a wt-zebrafish model (<100 μM) and high water solubility.

Development of multitarget agents possessing soluble epoxide hydrolase inhibitory activity

Over the last two decades polypharmacology has emerged as a new paradigm in drug discovery, even though developing drugs with high potency and selectivity toward a single biological target is still a major strategy. Often, targeting only a single enzyme or receptor shows lack of efficacy. High levels of inhibitor of a single target also can lead to adverse side effects. A second target may offer additive or synergistic effects to affecting the first target thereby reducing on- and off-target side effects. Therefore, drugs that inhibit multiple targets may offer a great potential for increased efficacy and reduced the adverse effects. In this review we summarize recent findings of rationally designed multitarget compounds that are aimed to improve efficacy and safety profiles compared to those that target a single enzyme or receptor. We focus on dual inhibitors/modulators that target the soluble epoxide hydrolase (sEH) as a common part of their design to take advantage of the beneficial effects of sEH inhibition.

Potent and highly selective dual-targeting monoamine oxidase-B inhibitors: Fluorinated chalcones of morpholine versus imidazole

Two series of fluorinated chalcones containing morpholine and imidazole-based compounds (f1-f8) were synthesized and evaluated for recombinant human monoamine oxidase (MAO)-A and -B as well as acetylcholinesterase inhibitory activities. Our results indicate that morpholine containing chalcones are highly selective MAO-B inhibitors having reversibility properties. All the imidazole-based fluorinated chalcones showed weak MAO inhibitions in both isoforms. Among the tested compounds, (2E)-3-(3-fluorophenyl)-1-[4-(morpholin-4-yl)phenyl]prop-2-en-1-one (f2) showed potent inhibitory activity for recombinant human MAO-B (IC₅₀  = 0.087 μM) with a high selectivity index (SI) of 517.2. In the recovery experiments using dialysis, the residual activity of MAO-B inhibited by f2 was close to that with the reversible reference inhibitor. Inhibition assays revealed that the K_i values of f1 and f2 for MAO-B were 0.027 and 0.020 μM, respectively, with competitive patterns. All the morpholine-based compounds (f1-f4) showed moderate inhibition toward acetylcholinesterase with IC₅₀ values ranging between 24 and 54 μM. All morpholine-containing compounds exhibit good blood-brain barrier permeation in the PAMPA method. The rational approach regarding the highly selective MAO-B inhibitor f2 was further ascertained by induced fit docking and molecular dynamics simulation studies.

In silico approaches and tools for the prediction of drug metabolism and fate: A review

The fate of administered drugs is largely influenced by their metabolism. For example, endogenous enzyme-catalyzed conversion of drugs may result in therapeutic inactivation or activation or may transform the drugs into toxic chemical compounds. This highlights the importance of drug metabolism in drug discovery and development, and accounts for the wide variety of experimental technologies that provide insights into the fate of drugs. In view of the high cost of traditional drug development, a number of computational approaches have been developed for predicting the metabolic fate of drug candidates, allowing for screening of large numbers of chemical compounds and then identifying a small number of promising candidates. In this review, we introduce in silico approaches and tools that have been developed to predict drug metabolism and fate, and assess their potential to facilitate the virtual discovery of promising drug candidates. We also provide a brief description of various recent models for predicting different aspects of enzyme-drug reactions and provide a list of recent in silico tools used for drug metabolism prediction.

Toward Multi-Targeted Platinum and Ruthenium Drugs-A New Paradigm in Cancer Drug Treatment Regimens?

While medicinal inorganic chemistry has been practised for over 5000 years, it was not until the late 1800s when Alfred Werner published his ground-breaking research on coordination chemistry that we began to truly understand the nature of the coordination bond and the structures and stereochemistries of metal complexes. We can now readily manipulate and fine-tune their properties. This had led to a multitude of complexes with wide-ranging biomedical applications. This review will focus on the use and potential of metal complexes as important therapeutic agents for the treatment of cancer. With major advances in technologies and a deeper understanding of the human genome, we are now in a strong position to more fully understand carcinogenesis at a molecular level. We can now also rationally design and develop drug molecules that can either selectively enhance or disrupt key biological processes and, in doing so, optimize their therapeutic potential. This has heralded a new era in drug design in which we are moving from a single- toward a multitargeted approach. This approach lies at the very heart of medicinal inorganic chemistry. In this review, we have endeavored to showcase how a "multitargeted" approach to drug design has led to new families of metallodrugs which may not only reduce systemic toxicities associated with modern day chemotherapeutics but also address resistance issues that are plaguing many chemotherapeutic regimens. We have focused our attention on metallodrugs incorporating platinum and ruthenium ions given that complexes containing these metal ions are already in clinical use or have advanced to clinical trials as anticancer agents. The "multitargeted" complexes described herein not only target DNA but also contain either vectors to enable them to target cancer cells selectively and/or moieties that target enzymes, peptides, and intracellular proteins. Multitargeted complexes which have been designed to target the mitochondria or complexes inspired by natural product activity are also described. A summary of advances in this field over the past decade or so will be provided.

α-Chymotrypsin Immobilized on a Low-Density Polyethylene Surface Successfully Weakens Escherichia coli Biofilm Formation

The protease α-chymotrypsin (α-CT) was covalently immobilized on a low-density polyethylene (LDPE) surface, providing a new non-leaching material (LDPE-α-CT) able to preserve surfaces from biofilm growth over a long working timescale. The immobilized enzyme showed a transesterification activity of 1.24 nmol/h, confirming that the immobilization protocol did not negatively affect α-CT activity. Plate count viability assays, as well as confocal laser scanner microscopy (CLSM) analysis, showed that LDPE-α-CT significantly impacts Escherichia coli biofilm formation by (i) reducing the number of adhered cells (-70.7 ± 5.0%); (ii) significantly affecting biofilm thickness (-81.8 ± 16.7%), roughness (-13.8 ± 2.8%), substratum coverage (-63.1 ± 1.8%), and surface to bio-volume ratio (+7.1 ± 0.2-fold); and (iii) decreasing the matrix polysaccharide bio-volume (80.2 ± 23.2%). Additionally, CLSM images showed a destabilized biofilm with many cells dispersing from it. Notably, biofilm stained for live and dead cells confirmed that the reduction in the biomass was achieved by a mechanism that did not affect bacterial viability, reducing the chances for the evolution of resistant strains.

Potential Role of Antipsychotic-Galantamine-Memantine Combination in the Treatment of Positive, Cognitive, and Negative Symptoms of Schizophrenia

Schizophrenia is, in part, a cognitive illness. There are no approved medications for cognitive impairments associated with schizophrenia (CIAS) and primary negative symptoms. Cholinergic and glutamatergic systems, alpha-7 nicotinic acetylcholine (α-7nACh) and N-methyl-D-aspartate (NMDA) receptors, kynurenic acid (KYNA), and mismatch negativity have been implicated in the pathophysiology of CIAS and negative symptoms. Galantamine is an acetylcholinesterase inhibitor that is also a positive allosteric modulator at the α4β2 and α7nACh receptors. Memantine is a noncompetitive NMDA receptor antagonist. Galantamine and memantine alone and in combination were effective for cognition in animals and people with Alzheimer's disease. The objective of this article is to critically dissect the published randomized controlled trials with galantamine and memantine for CIAS to highlight the efficacy signal. These studies may have failed to detect a clinically meaningful efficacy signal due to limitations, methodological issues, and possible medication nonadherence. There is evidence from a small open-label study that the galantamine-memantine combination may be effective for CIAS with kynurenine pathway metabolites as biomarkers to detect the severity of cognitive impairments. Given that there are no available treatments for cognitive impairments and primary negative symptoms in schizophrenia, testing of this "five-pronged strategy" (quintuple hypotheses: dopamine, nicotinic-cholinergic, glutamatergic/NMDA, GABA, and KYNA) is a "low-risk high-gain" approach that could be a major breakthrough in the field. The galantamine-memantine combination has the potential to treat positive, cognitive, and negative symptoms, and targeting the quintuple hypotheses concurrently may lead to a major scientific advancement - from antipsychotic treatment to antischizophrenia treatment.

A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data

Virtual screening has become a widely used technique for helping in drug discovery processes. The key to this success is its ability to aid in the identification of novel bioactive compounds by screening large molecular databases. Several web servers have emerged in the last few years supplying platforms to guide users in screening publicly accessible chemical databases in a reasonable time. In this review, we discuss a representative set of online virtual screening servers and their underlying similarity algorithms. Other related topics, such as molecular representation or freely accessible databases are also treated. The most relevant contributions to this review arise from critical discussions concerning the pros and cons of servers and algorithms, and the challenges that future works must solve in a virtual screening framework.

PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer

Determining the target proteins of new anticancer compounds is a very important task in Medicinal Chemistry. In this sense, chemists carry out preclinical assays with a high number of combinations of experimental conditions (c _j). In fact, ChEMBL database contains outcomes of 65 534 different anticancer activity preclinical assays for 35 565 different chemical compounds (1.84 assays per compound). These assays cover different combinations of c _j formed from >70 different biological activity parameters ( c₀), >300 different drug targets ( c₁), >230 cell lines ( c₂), and 5 organisms of assay ( c₃) or organisms of the target ( c₄). It include a total of 45 833 assays in leukemia, 6227 assays in breast cancer, 2499 assays in ovarian cancer, 3499 in colon cancer, 3159 in lung cancer, 2750 in prostate cancer, 601 in melanoma, etc. This is a very complex data set with multiple Big Data features. This data is hard to be rationalized by researchers to extract useful relationships and predict new compounds. In this context, we propose to combine perturbation theory (PT) ideas and machine learning (ML) modeling to solve this combinatorial-like problem. In this work, we report a PTML (PT + ML) model for ChEMBL data set of preclinical assays of anticancer compounds. This is a simple linear model with only three variables. The model presented values of area under receiver operating curve = AUROC = 0.872, specificity = Sp(%) = 90.2, sensitivity = Sn(%) = 70.6, and overall accuracy = Ac(%) = 87.7 in training series. The model also have Sp(%) = 90.1, Sn(%) = 71.4, and Ac(%) = 87.8 in external validation series. The model use PT operators based on multicondition moving averages to capture all the complexity of the data set. We also compared the model with nonlinear artificial neural network (ANN) models obtaining similar results. This confirms the hypothesis of a linear relationship between the PT operators and the classification as anticancer compounds in different combinations of assay conditions. Last, we compared the model with other PTML models reported in the literature concluding that this is the only one PTML model able to predict activity against multiple types of cancer. This model is a simple but versatile tool for the prediction of the targets of anticancer compounds taking into consideration multiple combinations of experimental conditions in preclinical assays.

Molecular Docking Studies of Phenolic Compounds from Syzygium cumini with Multiple Targets of Type 2 Diabetes

Treatment of type 2 diabetes without any side effects is still a challenge to the medical system. This leads to increasing demand for natural products with antidiabetic activity with fewer side effects. Syzygium cumini is a traditional herbal medicinal plant and is reported to possess a variety of pharmacological actions. It contains various types of chemical constituents including terpenoids, tannins, anthocyanins, flavonoids and other phenolic compounds. Some flavonoids and other phenolic compounds from S. cumini were reported in literature to have type 2 antidiabetic potential. The main objective of the current investigation was in silico screening of some phenolic compounds from S. cumini against multiple targets associated with type 2 diabetes to explore the mechanism of antidiabetic action and prediction of binding mode using molecular docking studies. In silico docking studies were performed for the selected molecules in the binding site of multiple targets associated with type 2 diabetes (α-glucosidas , dipeptidyl peptidase 4, glycogen synthase kinase 3, glucokinase and glucagon receptor). Amongst the compounds tested in silico, rutin showed appreciable binding with multiple targets of type 2 diabetes including α-glucosidase, dipeptidyl peptidase 4, glycogen synthase kinase 3, and glucagon receptor. Catechin was found to inhibit both α-glucosidase, and dipeptidyl peptidase 4. This information can be utilized for the design and development of potent multi-functional candidate drugs with minimal side effects for type 2 diabetes therapeuticsa.

4-tert-Pentylphenoxyalkyl derivatives - Histamine H3 receptor ligands and monoamine oxidase B inhibitors

The synthesis and biological activity of 4-tert-pentylphenoxypropyl derivatives are described in this manuscript. All compounds (except one) showed human histamine H₃ receptor affinity with K_i values below 760 nM. The inhibitory activity toward human monoamine oxidase B (hMAO B) was evaluated using a fluorometric Amplex-Red assay, and most of the compounds were effective in the submicromolar range. Among them, 1-(3-(4-tert-pPentylphenoxy)propyl)pyrrolidine (5) exhibited hMAO B inhibitory activity with an IC₅₀ value of 4.5 nM. In addition, hMAO B inhibition by 5 was shown to be non-competitive and reversible. Further, recently described potent histamine H₃ receptor ligands - 4-tert-pentylphenoxyalkyl derivatives (with a 4-8 carbon spacer) - were evaluated for hMAO B inhibitory activity, and some of them displayed activity in the submicromolar range. Selected compounds were also tested for human MAO A (hMAO A) inhibitory potencies and exhibited no activity. Moreover, molecular modeling studies were carried out for tested compounds to explain their molecular mechanism of hMAO B inhibition and the selectivity of compounds for hMAO B over hMAO A.

A Perspective on Thiazolidinone Scaffold Development as a New Therapeutic Strategy for Toxoplasmosis

Toxoplasma gondii is one of the most successful parasites due to its ability to infect a wide variety of warm-blooded animals. It is estimated that one-third of the world's population is latently infected. The generic therapy for toxoplasmosis has been a combination of antifolates such as pyrimethamine or trimethoprim with either sulfadiazine or antibiotics such as clindamycin with a combination with leucovorin to prevent hematologic toxicity. This therapy shows limitations such as drug intolerance, low bioavailability or drug resistance by the parasite. There is a need for the development of new molecules with the capacity to block any stage of the parasite's life cycle in humans or in a different type of hosts. Heterocyclic compounds are promissory drugs due to its reported biological activity; for this reason, thiazolidinone and its derivatives are presented as a new alternative not only for its inhibitory activity against the parasite but also for its high selectivity-level with high therapeutic index. Thiazolidinones are an important scaffold known to be associated with anticancer, antibacterial, antifungal, antiviral, antioxidant, and antidiabetic activities. The molecule possesses an imidazole ring that has been described as an antiprotozoal agent with antiparasitic properties and less toxicity. Thiazolidinone derivatives have been reportedly as building blocks in organic chemistry and as scaffolds for drug discovery. Here we present a perspective of how structural modifications of the thiazolidinone core could generate new compounds with high anti-parasitic effect and less toxic results.

Synthesis and Evaluation of Novel Ligustrazine Derivatives as Multi-Targeted Inhibitors for the Treatment of Alzheimer's Disease

A series of novel ligustrazine derivatives 8a–r were designed, synthesized, and evaluated as multi-targeted inhibitors for anti-Alzheimer’s disease (AD) drug discovery. The results showed that most of them exhibited a potent ability to inhibit both ChEs, with a high selectivity towards AChE. In particular, compounds 8q and 8r had the greatest inhibitory abilities for AChE, with IC₅₀ values of 1.39 and 0.25 nM, respectively, and the highest selectivity towards AChE (for 8q, IC₅₀ BuChE/IC₅₀ AChE = 2.91 × 10⁶; for 8r, IC₅₀ BuChE/IC₅₀ AChE = 1.32 × 10⁷). Of note, 8q and 8r also presented potent inhibitory activities against Aβ aggregation, with IC₅₀ values of 17.36 µM and 49.14 µM, respectively. Further cellular experiments demonstrated that the potent compounds 8q and 8r had no obvious cytotoxicity in either HepG2 cells or SH-SY5Y cells, even at a high concentration of 500 μM. Besides, a combined Lineweaver-Burk plot and molecular docking study revealed that these compounds might act as mixed-type inhibitors to exhibit such effects via selectively targeting both the catalytic active site (CAS) and the peripheral anionic site (PAS) of AChEs. Taken together, these results suggested that further development of these compounds should be of great interest.

New Multitarget Approaches in the War Against Glioblastoma: A Mini-Perspective

Glioblastoma multiforme (GBM) is the most common tumor of the CNS, and the deadliest form of brain cancer. The rapid progression, the anatomic location in the brain and a deficient knowledge of the pathophysiology, often limit the effectiveness of therapeutic interventions. Current pillars of GBM therapies include surgical resection, radiotherapy and chemotherapy, but the low survival rate and the short life expectation following these treatments strongly underline the urgency to identify innovative and more effective therapeutic tools. Frequently, patients subjected to a mono-target therapy, such as Temozolomide (TMZ), develop drug resistance and undergo relapse, indicating that targeting a single cellular node is not sufficient for eradication of this disease. In this context, a multi-targeted therapeutic approach aimed at using compounds, alone or in combination, capable of inhibiting more than one specific molecular target, offers a promising alternative. Such strategies have already been well integrated into drug discovery campaigns, including in the field of anticancer drugs. In this miniperspective, we will discuss the recent progress in the treatment of GBM focusing on innovative and effective preclinical strategies, which are based on a multi-targeted approach.

Polypharmacology by Design: A Medicinal Chemist's Perspective on Multitargeting Compounds

Multitargeting compounds comprising activity on more than a single biological target have gained remarkable relevance in drug discovery owing to the complexity of multifactorial diseases such as cancer, inflammation, or the metabolic syndrome. Polypharmacological drug profiles can produce additive or synergistic effects while reducing side effects and significantly contribute to the high therapeutic success of indispensable drugs such as aspirin. While their identification has long been the result of serendipity, medicinal chemistry now tends to design polypharmacology. Modern in vitro pharmacological methods and chemical probes allow a systematic search for rational target combinations and recent innovations in computational technologies, crystallography, or fragment-based design equip multitarget compound development with valuable tools. In this Perspective, we analyze the relevance of multiple ligands in drug discovery and the versatile toolbox to design polypharmacology. We conclude that despite some characteristic challenges remaining unresolved, designed polypharmacology holds enormous potential to secure future therapeutic innovation.

Rational application of drug promiscuity in medicinal chemistry

‘Drug promiscuity’ refers to a drug that can act on multiple molecular targets, exhibiting similar or different pharmacological effects. Drugs may interact with unwanted targets, leading to off-target effects (one of the main reasons for side effects). Thus, intervention to prevent off-target effects in the early stages of drug discovery could reduce the risk of failure. The conversion between target and off-target effects is important for drug repurposing. Drug repurposing strategies could reduce research and development costs. This review details the research progress in the rational application of drug promiscuity for the discovery of multi-target drugs, drug repurposing and improving druggability in medicinal chemistry over the last 5 years.

A broad-spectrum antibiotic, DCAP, reduces uropathogenic Escherichia coli infection and enhances vorinostat anticancer activity by modulating autophagy

The cellular recycling pathway of autophagy plays a fundamental role in adaptive responses to nutrient deprivation and other forms of stress under physiological and pathological conditions. However, autophagy can also be a double-edge sword during certain bacterial infections (such as urinary tract infections) and in cancer, where it can be hijacked by the pathogens and cancer cells, respectively, to promote their own survival. Thus, autophagy modulation can potentially have multiple effects in multiple contexts and this property can be leveraged to improve outcomes. In this report, we identify that a broad-spectrum antibiotic, 2-((3-(3, 6-dichloro-9H-carbazol-9-yl)-2-hydroxypropyl) amino)-2-(hydroxymethyl) propane-1, 3-diol (DCAP) modulates autophagy. We employed combined biochemical, fluorescence microscopy and correlative light electron microscopy approaches to demonstrate that DCAP treatment blocks autophagy at the late stages by preventing autophagolysosome maturation and interrupting the autophagic flux. We further show that, DCAP significantly reduces UPEC infection in urinary tract epithelial cells via inhibition of autophagy. Finally, we reveal that DCAP enhances the anticancer activity of the histone acetyltransferase (HDAC) inhibitor, vorinostat, which has been reported to increase susceptibility to bacterial infections as a common adverse effect. Collectively, our data support the concept that DCAP represents a valuable chemical scaffold for the development of an innovative class of bactericidal autophagy inhibitors for treatment of urinary tract infections and/or for adjuvant therapy in cancer treatment.