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[Image: see text] The selective inhibition of RET kinase as a treatment for relevant cancer types including lung adenocarcinoma has garnered considerable interest in recent years and prompted a variety of efforts toward the discovery of small-molecule therapeutics. Hits uncovered via the analysis of archival kinase data ultimately led to the identification of a promising pyrrolo[2,3-d]pyrimidine scaffold. The optimization of this pyrrolo[2,3-d]pyrimidine core resulted in compound 1, which demonstrated potent in vitro RET kinase inhibition and robust in vivo efficacy in RET-driven tumor xenografts upon multiday dosing in mice. The administration of 1 was well-tolerated at established efficacious doses (10 and 30 mg/kg, po, qd), and plasma exposure levels indicated a minimal risk of KDR or hERG inhibition in vivo, as evaluated by Miles assay and free plasma concentrations, respectively.

Purpose of review We review the new systemic treatment strategies for differentiated thyroid carcinoma, as well as the acquaintance of its molecular biology. Recent findings Multiple kinase inhibitor drugs have become the standard therapy for thyroid cancer, albeit several adverse effects. In the last few years, new molecules have raised with an overall safety profile. Most of them, are considered targeted therapies directed toward driven-molecules alterations, such as neurotrophic tyrosine kinase receptor (NTRK) inhibitors for NTRK-fusion thyroid cancer and rearranged during transfection (RET) inhibitors for RET-fusion thyroid cancer. Recently, promising outcomes and safety data have been presented. Furthermore, other novel strategies for advanced thyroid carcinoma are currently investigated in clinical trials.The ability to provide precision medicine to patients in routine clinical settings depends on the availability of molecular profiling test at their cancer centers. The impossibility to perform molecular characterization could turn out to be a diagnostic and treatment limitation for some patients. Summary The treatment of advanced differentiated thyroid carcinoma has undergone rapid evolution in the last decade. An emerging treatment era is coming. From now to then, we will need to face the different types of diagnostic tools for molecular characterization, their interpretation and, finally the access to targeted therapies.

Interaction signatures of drug candidates are characteristic to off-target (neutral) and antitarget (negative) effects, inferring reduced efficiency, side-effects and high attrition rate. Today's retroactive scaled-down virtual screening (VS) experiments relying on benchmarking datasets are extensively involved to assess ligand enrichment in the real-world problem. In recent years, unbiased benchmarking sets turned into a tremendous need to assist virtual screening methodologies for emerging drug targets. To date, the benchmarking datasets are quite limited, whereas glycogen synthase kinase-3 (GSK-3) is not included into directories of benchmarking datasets such as DUD-e, MUV, etc. Herein we introduced our in-house algorithm to build an unbiased benchmarking dataset, including highly selective, moderately selective and nonselective inhibitors for a significant therapeutic target - GSK-3, suitable for both ligand-based and structure-based VS approaches. These datasets are unbiased in terms of physico-chemical properties and topological descriptors, as resulted from mean(ROC-AUC) leave-one-out cross-validation (LOO CV). and additional 2 D similarity search. Moreover, we investigated the gradual selectivity dataset by application of multiple 2 D similarity coefficients and distances, 3 D similarity and docking. Besides the resulted links between the enrichment of selective GSK-3 inhibitors and their chemical structures, a database of compounds and their 3 D similarity signatures including cut-off thresholds for enhanced selectivity was generated. 2 D similarity space analysis revealed that selectivity problem cannot be evaluated appropriately with 2 D similarity searching alone. The current analysis provided useful, comprehensive insights, which may facilitate the knowledge-based identification of novel selective GSK-3 inhibitors.Communicated by Ramaswamy H. Sarma.

1,3-Dipolar cycloaddition reactions of a sugar-based trifluoromethylated nitrone-to-maleimide and allyl bromide afforded a series of cycloadducts in good yield. The same nitrone reacts with propargyl acetate lead, after rearrangement of 4-isoxazoline, to aziridine with good yield. The obtained compounds were evaluated for their in vitro antimicrobial potency. Additionally, we are interested in predicting their physicochemical parameters such as lipophilicity and bioactivity score as well as their pharmacokinetic properties such as absorption, distribution, metabolism, and excretion (ADME) such as plasma protein binding (PPB) penetration of the blood–brain barrier (BBB), human intestinal absorption (HIA), cellular permeability (PCaco-2), cell permeability of Madin–Darby canine kidney (PMDCK), P-glycoprotein (P-gp) efflux, CYP inducers, substrates and inhibitors’ skin and permeability (PS), and their toxicological behavior [mutagenicity, carcinogenicity, acute, environmental, cardiotoxicity (hERG inhibition)] using in silico computational methods. Also, we aimed to validate QSAR models for the elucidation of their antitarget using 32 sets of end-points (IC50, Ki and Kact). The obtained result provides good information about the pharmacotherapy potential and toxicity of the examined molecules with good compliance between in vitro antimicrobial and the predicted properties. Findings indicated and encouraged the use of these compounds and their derivatives for further in vivo evaluations in the design and the elucidation of the intrinsic mechanisms as well as the efficacy of the selected powerful drug.

The in vitro and in silico biological properties of two manganese complexes with the non-steroidal anti-inflammatory drug mefenamic acid (Hmef) in the presence or absence of salicylaldoxime (Η2sao), i.e. [Μn6(O)2(mef)2(sao)6(CH3OH)4] 1, and [Μn(mef)2(CH3OH)4] 2, respectively, are presented in the present contribution. More specifically, the in vitro biological activity of the complexes was investigated by studying their affinity to calf-thymus DNA (by diverse spectroscopic and physicochemical techniques) and their binding towards bovine (BSA) or human serum albumin (HSA) (by fluorescence emission spectroscopy). Molecular docking simulations on the crystal structures of HSA and DNA, exploring in silico the ability of the complexes to bind to these macromolecules, were also employed in order to explain the described in vitro activity of the compounds. Furthermore, in silico predictive tools have been employed to study the properties of the most active complex 2 to act as anticancer agent, in continuation of the previously reported cytotoxic activity. It is adopted in silico studies on a multitude of proteins involved in cancer growth, as well as prediction of drug-induced changes of gene expression profile, protein- and mRNA-based prediction results, prediction of sites of metabolism, quantitative prediction of antitarget interaction profiles etc.

Background: In the absence of SARS-CoV-2 specific antiviral treatments, various repurposed pharmaceutical approaches are under investigation for the treatment of COVID-19. Antiviral drugs considered for this condition include atazanavir, remdesivir, lopinavir-ritonavir, and favipiravir. Whilst the combination of lopinavir and ritonavir has been previously linked to prolongation of the QTc interval on the ECG and risk of torsades de pointes arrhythmia, less is known in this regard about atazanavir, remdesivir, and favipiravir. Unwanted abnormalities of drug-induced QTc prolongation by diverse drugs are commonly mediated by a single cardiac anti-target, the hERG potassium channel. This computational modeling study was undertaken in order to explore the ability of these five drugs to interact with known determinants of drug binding to the hERG channel pore.Methods: Atazanavir, remdesivir, ritonavir, lopinavir and favipiravir were docked to in silico models of the pore domain of hERG, derived from cryo-EM structures of hERG and the closely related EAG channel.Results: Atazanavir was readily accommodated in the open hERG channel pore in proximity to the S6 Y652 and F656 residues, consistent with published experimental data implicating these aromatic residues in atazanavir binding to the channel. Lopinavir, ritonavir, and remdesivir were also accommodated in the open channel, making contacts in a model-dependent fashion with S6 aromatic residues and with residues at the base of the selectivity filter/pore helix. The ability of remdesivir (at 30 μM) to inhibit the channel was confirmed using patch-clamp recording. None of these four drugs could be accommodated in the closed channel structure. Favipiravir, a much smaller molecule, was able to fit within the closed channel and could adopt multiple binding poses in the open channel, but with few simultaneous interactions with key binding residues. Only favipiravir and remdesivir showed the potential to interact with lateral pockets below the selectivity filter of the channel.Conclusions: All the antiviral drugs studied here can, in principle, interact with components of the hERG potassium channel canonical binding site, but are likely to differ in their ability to access lateral binding pockets. Favipiravir's small size and relatively paucity of simultaneous interactions may confer reduced hERG liability compared to the other drugs. Experimental structure-function studies are now warranted to validate these observations.

Glioblastoma (GBM) is known to be the most common and lethal primary malignant brain tumor. Therapies against this neoplasia have a high percentage of failure, associated with the survival of self-renewing glioblastoma stem cells (GSCs), which repopulate treated tumors. In addition, despite new radical surgery protocols and the introduction of new anticancer drugs, protocols for treatment, and technical advances in radiotherapy, no significant improvement in the survival rate for GBMs has been realized. Thus, novel antitarget therapies could be used in conjunction with standard radiochemotherapy approaches. Targeted therapy, indeed, may address specific targets that play an essential role in the proliferation, survival, and invasiveness of GBM cells, including numerous molecules involved in signal transduction pathways. Significant cellular heterogeneity and the hierarchy with GSCs showing a therapy-resistant phenotype could explain tumor recurrence and local invasiveness and, therefore, may be a target for new therapies. Therefore, the forced differentiation of GSCs may be a promising new approach in GBM treatment. This article provides an updated review of the current standard and experimental therapies for GBM, as well as an overview of the molecular characteristics of GSCs, the mechanisms that activate resistance to current treatments, and a new antitumor strategy for treating GSCs for use as therapy.

Safety assessment of pharmaceuticals is a rapidly developing area of pharmacy and medicine. The new advanced guidelines for testing the toxicity of compounds require specialized tools that provide information on the tested drug in a quick and reliable way. Ion channels represent the third-largest target. As mentioned in the literature, ion channels are an indispensable part of the heart’s work. In this paper the most important information concerning the guidelines for cardiotoxicity testing and the way the tests are conducted has been collected. Attention has been focused on the role of selected ion channels in this process.

Muscarinic M3 receptor antagonists and inverse agonists displaying high affinity and subtype selectivity over the antitarget M2 are valuable pharmacological tools and may enable improved treatment of chronic obstructive pulmonary disease (COPD), asthma, or urinary incontinence. On the basis of known M3 antagonists comprising a piperidine or quinuclidine unit attached to a biphenyl carbamate, 5-fluoro substitution was responsible for M3 subtype selectivity over M2, while 3'-chloro substitution substantially increased affinity through a σ-hole interaction. Resultantly, two piperidinyl- and two quinuclidinium-substituted biphenyl carbamates OFH243 (13n), OFH244 (13m), OFH3911 (14n), and OFH3912 (14m) were discovered, which display two-digit picomolar affinities with Ki values from 0.069 to 0.084 nM, as well as high selectivity over the M2 subtype (46- to 68-fold). While weak inverse agonistic properties were determined for the biphenyl carbamates 13m and 13n, neutral antagonism was observed for 14m and 14n and tiotropium under identical assay conditions.

Background Leishmaniasis, malaria, and fungal diseases are burdens on individuals and populations and can present severe complications. Easily accessible chemical treatments for these diseases are increasingly sought-after. Targeting the parasite N-myristoyl transferase while avoiding the human enzyme and other anti-targets may allow the prospect of compounds with pan-activity against these diseases, which would simplify treatments and costs. Developing chemical libraries, both virtual and physical, that have been filtered and flagged early on in the drug discovery process (before virtual screening) could reduce attrition rates of compounds being developed and failing late in development stages due to problems of side-effects or toxicity. Methods Chemical libraries have been screened against the anti-targets pregnane-X-receptor, sulfotransferase, cytochrome P450 2a6, 2c9, and 3a4 with three different docking programs. Statistically significant differences are observed in their interactions with these enzymes as compared to small molecule drugs and bioactive non-drug datasets. Results and conclusion A series of compounds are proposed with the best predicted profiles for inhibition of all parasite targets while sparing the human form and anti-targets. Some of the topranked compounds have confirmed experimental activity against Leishmania, and highlighted are those compounds with best properties for further development.

Matrix metalloproteinase-2 (MMP-2) is established as one of the most important metalloenzymes for targeting cancer. However, homologous MMP-9 of the gelatinase family is implicated as an antitarget of cancer. Therefore, it is an important and challenging task to achieve MMP-2 selectivity over MMP-9. In this article, robust validated chemico-biological quantitative approaches were conducted on a series of in house glutamate-based selective MMP-2 inhibitors over MMP-9 for further refinement of our MMP-2 inhibitor designing approach. The two-dimensional quantitative structure-activity relationship (2D-QSAR) study suggested that arylsulfonamide moiety was better than arylcarboxamide function, which in turn, supported by the hologram QSAR (HQSAR), 3D-QSAR comparative molecular field analysis (CoMFA), and comparative molecular similarity analysis (CoMSIA) studies. Regarding the MMP-2 selectivity, glutamines were better than isoglutamines as evidenced by the quantitative activity-activity relationship (QAAR) and molecular docking studies. Favorable hydrophobic and steric features of aryl function directed towards the S1′ pocket were also well attributed. Naphthyl and p-bromophenoxyphenyl moieties in place of biphenyl function were found to be unfavorable for MMP-2 inhibition and selectivity over MMP-9. Linear or cyclic aliphatic group directed towards the S2′ pocket was favorable, whereas branching was unfavorable for MMP-2 inhibition and selectivity. The importance of biphenyl and 3,5-bistrifluoromethylbenzyl functions directed towards the S1′ and S2′ pockets, respectively, was well attributed for potent MMP-2 inhibition and selectivity over MMP-9.

Elephantopus scaber Linn and its major bioactive component, deoxyelephantopin are known for their medicinal properties and are often reported to have various cytotoxic and antitumor activities. This plant is widely used as folk medicine for a plethora of indications although its safety profile remains unknown. Human ether-a-go-go-related gene (hERG) encodes the cardiac IKr current which is a determinant of the duration of ventricular action potentials and QT interval. The hERG potassium channel is an important antitarget in cardiotoxicity evaluation. This study investigated the effects of deoxyelephantopin on the current, mRNA and protein expression of hERG channel in hERG-transfected HEK293 cells. The hERG tail currents following depolarization pulses were insignificantly affected by deoxyelephantopin in the transfected cell line. Current reduction was less than 40% as compared with baseline at the highest concentration of 50 μM. The results were consistent with the molecular docking simulation and hERG surface protein expression. Interestingly, it does not affect the hERG expression at both transcriptional and translational level at most concentrations, although higher concentration at 10 μM caused protein accumulation. In conclusion, deoxyelephantopin is unlikely a clinically significant hERG channel and Ikr blocker.

International audience; The presence of several binding sites for both substrates and inhibitors is yet a poorly explored thematic concerning the assessment of the drug-drug interactions risk due to interactions of multiple drugs with the human transport protein P-glycoprotein (P-gp or MDR1, gene ABCB1). In this study we measured the inhibitory behaviour of a set of known drugs towards P-gp by using three different probe substrates (digoxin, Hoechst 33,342 and rhodamine 123). A structure-based model was built to unravel the different substrates binding sites and to rationalize the cases where drugs were not inhibiting all the substrates. A separate set of experiments was used to validate the model and confirmed its suitability to either detect the substrate-dependent P-gp inhibition and to anticipate proper substrates for in vitro experiments case by case. The modelling strategy described can be applied for either design safer drugs (P-gp as antitarget) or to target specific sub-site inhibitors towards other drugs (P-gp as target).

Label-free techniques, in broad terms, are not new to drug discovery. Before the target-directed approaches took the center stage three decades ago, drugs were mostly discovered through phenotypic assessment of isolated tissues and whole animals without labels. With the rise of combinatorial chemistry, molecular biology, genomics and pharmacology in the past decades, labeling techniques have become dominating in early drug discovery process. Compared with traditional approaches, labeling techniques provide clear advantages in sensitivity, through

The human ether-a-go-go-related gene channel is a voltage-activated K + channel involved in cardiac action potential. Its inhibition can lead to QT prolongation, and eventually to potentially fatal arrhythmia. Therefore, it is considered a primary antitarget in safety pharmacology. To assess the risk of human ether-a-go-go-related gene channel inhibition by medicinal plants, 700 extracts from different parts of 142 medicinal plants collected in Southern Africa were screened on Xenopus laevis oocytes. A CH 2 Cl 2 extract from the stems and leaves of Galenia africana (Aizoaceae) reduced the peak tail human ether-a-go-go-related gene current by 50.4 ± 5.5 % (n = 3) at a concentration of 100 µg/mL. By means of high-performance liquid chromatography-based activity profiling, nine flavonoids were identified in the active time windows. However, the human ether-a-go-go-related gene channel inhibition of isolated compounds was less pronounced than that of extract and active microfractions (human ether-a-go-go-related gene inhibition between 10.1 ± 5 and 14.1 ± 1.6 at 100 µM). The two major constituents, 7,8-methylenedioxyflavone ( 1 ) and 7,8-dimethoxyflavone ( 13 ), were quantified (4.3 % and 9.4 %, respectively, in the extract). Further human ether-a-go-go-related gene inhibition tests for compounds 1 and 13 at 300 µM showed a concentration-dependent inhibitory activity (33.2 ± 12.4 and 30.0 ± 7.4, respectively). In a detailed phytochemical profiling of the active extract, a total of 20 phenolic compounds, including six new natural products, were isolated and identified.

Covering: 1996–December 2016 The human Ether-a-go-go Related Gene (hERG) channel is a voltage-gated potassium channel playing an essential role in the normal electrical activity in the heart. It is involved in the repolarization and termination of action potentials in excitable cardiac cells. Mutations in the hERG gene and hERG channel blockage by small molecules are associated with increased risk of fatal arrhythmias. Several drugs have been withdrawn from the market due to hERG channel-related cardiotoxicity. Moreover, as a result of its notorious ligand promiscuity, this ion channel has emerged as an important antitarget in early drug discovery and development. Surprisingly, the hERG channel blocking profile of natural compounds present in frequently consumed botanicals (i.e. dietary supplements, spices, and herbal medicinal products) is not routinely assessed. This comprehensive review will address these issues and provide a critical compilation of hERG channel data for isolated natural products and extracts over the past two decades (1996–2016). In addition, the review will provide (i) a solid basis for the molecular understanding of the physiological functions of the hERG channel, (ii) the translational potential of in vitro/in vivo results to cardiotoxicity in humans, (iii) approaches for the identification of hERG channel blockers from natural sources, (iv) future perspectives for cardiac safety guidelines and their applications within phytopharmaceuticals and dietary supplements, and (v) novel applications of hERG channel modulation (e.g. as a drug target).

Matrix metalloproteinases (MMPs) have long been implicated for roles in cancer initiation, tumor growth, and metastasis. However, pancreatic cancer clinical trials using broad-based MMP inhibitors were discouraging. To better evaluate the use of MMP inhibitors in pancreatic cancer, (a) more precise roles of individual MMPs in pancreatic cancer needed to be determined and (b) animal models that more accurately represented human pancreatic cancer needed to be developed. The last decade has seen substantial progress in both areas. MT1-MMP has been recognized as a critical mediator of several steps in pancreatic cancer progression, while MMP-9 appears to be an antitarget when considering pancreatic cancer therapies.

A delicate balance exists between a drug molecule's toxicity and its activity. Indeed, efficacy, toxicity, and side effect problems are a common cause for the termination of drug candidate compounds and development projects. To address this, an antitarget interaction profile is built and combined with virtual screening and cross docking for new inhibitors of HIV-1 integrase, in order to consider possible off-target interactions as early as possible in a drug or hit discovery program. New ranking techniques using triangular numbers improve ranking information on the compounds and recovery of known inhibitors into the top compounds using different docking programs. This improved ranking arises from using consensus of ranks between docking programs and ligand efficiencies to derive a new rank, instead of using absolute score values, or average of ranks. The triangular number rerank also allowed the objective combination of results from several protein targets or screen conditions and several programs. Triangular number reranking conserves more information than other reranking methods such as average of scores or averages of ranks. In addition, the use of triangular numbers for reranking makes possible the use of thresholds with a justified leeway based on the number of available known inhibitors, so that the majority of the compounds above the threshold in ranks compare to the compounds that have known experimentally determined biological activity. The battery of anti- or off-targets can be tailored to specific molecular or drug design challenges. In silico filters can thus be deployed in successive stages, for prefiltering, activity profiling, and for further analysis and triaging of libraries of compounds.

Inhibition of the human ether-a-go-go-related gene channel is the single most important risk factor leading to acquired long QT syndrome. Drug-induced QT prolongation can cause severe cardiac complications, including arrhythmia, and is thus a liability in drug development. Considering the importance of the human ether-a-go-go-related gene channel as an antitarget and the daily intake of plant-derived foods and herbal products, surprisingly few natural products have been tested for channel blocking properties. In an assessment of possible human ether-a-go-go-related gene liabilities, a selection of widely used herbal medicines and edible plants (vegetables, fruits, and spices) was screened by means of a functional two-microelectrode voltage-clamp assay with Xenopus oocytes. The human ether-a-go-go-related gene channel blocking activity of selected extracts was investigated with the aid of a high-performance liquid chromatography-based profiling approach, and attributed to tannins and alkaloids. Major European medicinal plants and frequently consumed food plants were found to have a low risk for human ether-a-go-go-related gene toxicity.

Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDA-required procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure–activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83–0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg).

The pFC plays a key role in flexible, context-specific decision making. One proposal [Machens, C. K., Romo, R., & Brody, C. D. Flexible control of mutual inhibition: A neural model of two-interval discrimination. Science, 307, 1121–1124, 2005] is that prefrontal cells may be dynamically organized into opponent coding circuits, with competitive groups of cells coding opposite behavioral decisions. Here, we show evidence for extensive, temporally evolving opponent organization in the monkey pFC during a cued target detection task. More than a half of all randomly selected cells discriminated stimulus category in this task. The largest set showed target-positive activity, with the strongest responses to the current target, intermediate activity for a nontarget that was a target on other trials, and lowest activity for nontargets never associated with the target category. Second most frequent was a reverse, antitarget pattern. In the ventrolateral frontal cortex, opponent organization was strongly established in phasic responses at stimulus onset; later, such activity was widely spread across dorsolateral and ventrolateral sites. Task-specific organization into opponent cell groups may be a general feature of prefrontal decision making.

The evaluation of possible interactions between chemical compounds and antitarget proteins is an important task of the research and development process. Here, we describe the development and validation of QSAR models for the prediction of antitarget end-points, created on the basis of multilevel and quantitative neighborhoods of atom descriptors and self-consistent regression. Data on 4000 chemical compounds interacting with 18 antitarget proteins (13 receptors, 2 enzymes, and 3 transporters) were used to model 32 sets of end-points (IC(50), K(i), and K(act)). Each set was randomly divided into training and test sets in a ratio of 80% to 20%, respectively. The test sets were used for external validation of QSAR models created on the basis of the training sets. The coverage of prediction for all test sets exceeded 95%, and for half of the test sets, it was 100%. The accuracy of prediction for 29 of the end-points, based on the external test sets, was typically in the range of R(2)(test) = 0.6-0.9; three tests sets had lower R(2)(test) values, specifically 0.55-0.6. The proposed approach showed a reasonable accuracy of prediction for 91% of the antitarget end-points and high coverage for all external test sets. On the basis of the created models, we have developed a freely available online service for in silico prediction of 32 antitarget end-points: http://www.pharmaexpert.ru/GUSAR/antitargets.html.

The pregnane X receptor (PXR), a member of the nuclear hormone superfamily, regulates the expression of several enzymes and transporters involved in metabolically relevant processes. The significant induction of CYP450 enzymes by PXR, in particular CYP3A4, might significantly alter the metabolism of prescribed drugs. In order to early identify molecules in drug discovery with a potential to activate PXR as antitarget, we developed fast and reliable in silico filters by ligand-based QSAR techniques. Two classification models were established on a diverse dataset of 434 drug-like molecules. A second augmented set allowed focusing on interesting regions in chemical space. These classifiers are based on decision trees combined with a genetic algorithm based variable selection to arrive at predictive models. The classifier for the first dataset on 29 descriptors showed good performance on a test set with a correct classification of both 100% for PXR activators and non-activators plus 87% for activators and 83% for non-activators in an external dataset. The second classifier then correctly predicts 97% activators and 91% non-activators in a test set and 94% for activators and 64% non-activators in an external set of 50 molecules, which still qualifies for application as a filter focusing on PXR activators. Finally a quantitative model for PXR activation for a subset of these molecules was derived using a regression-tree approach combined with GA variable selection. This final model shows a predictive r(2) of 0.774 for the test set and 0.452 for an external set of 33 molecules. Thus, the combination of these filters consistently provide guidelines for lowering PXR activation in novel candidate molecules.

Matched molecular pairs analysis (MMPA)1,2 is an inverse quantitative structure activity relationship (QSAR) technique that is rapidly gaining popularity in the retrospective analysis of large experimental datasets.3,4 While much of the recent focus has been on the differences in properties between structurally related groups of existing compounds, attempts to extend this methodology to the de-novo design of novel structures have been limited. To our knowledge the aggregate effect of multiple transformations, all suggesting the same molecular structure, has only ever being considered within a very limited dataset.5 We therefore sought to test this exciting new approach to the design (and absolute property prediction - effectively QSAR-by-MMPA) of novel chemical entities based on a larger, more diverse dataset, and couple these designs to MMPA-based predictions of antitarget activity.

The optimization of a lead structure to a development candidate often requires removal of undesirable antitarget activities. To this end, we have developed an approach to extract antitarget activity hotspots from larger databases and to transfer this knowledge onto novel chemical series. These antitarget activity hotspots will be captured as pairs of informative molecules, which are chemically closely related, but differ significantly in biological activity. We illustrate the application of antitarget activity hotspots as informative compound pairs for the optimization of side effects in lead structures for relevant antitargets in pharmaceutical research. The use for prospective design requires establishing a structural link between known antitarget hotspot pairs and a new lead structure: we employ 3D-based similarity comparison for this task. The entire workflow serves as idea generator in early optimization. The feasibility of this approach is demonstrated in several optimization problems related to hERG inhibition, and CYP3A4 inhibition. Several structural examples demonstrate the ability of the 3D-shape searching to identify related scaffolds and the usefulness of the antitarget hotspot information to guide optimization by modulating the undesirable antitarget activity. Such a concept based on the analysis of local similarities and the transfer to 3D-related series is especially promising in those cases, where the construction of antitarget QSAR models fails to detect local SAR trends for guiding the next optimization cycle.

The present work introduces a novel method for drug research based on the sequential building of linked multivariate statistical models, each one introducing a different level of drug description. The use of multivariate methods allows us to overcome the traditional one-target assumption and to link in vivo endpoints with drug binding profiles, involving multiple receptors. The method starts with a set of drugs, for which in vivo or clinical observations and binding affinities for potentially relevant receptors are known, and allows obtaining predictions of the in vivo endpoints highlighting the most influential receptors. Moreover, provided that the structure of the receptor binding sites is known (experimentally or by homology modeling), the proposed method also highlights receptor regions and ligand-receptor interactions that are more likely to be linked to the in vivo endpoints, which is information of high interest for the design of novel compounds. The method is illustrated by a practical application dealing with the study of the metabolic side effects of antipsychotic drugs. Herein, the method detects related receptors confirmed by experimental results. Moreover, the use of structural models of the receptor binding sites allows identifying regions and ligand-receptor interactions that are involved in the discrimination between antipsychotic drugs that show metabolic side effects and those that do not. The structural results suggest that the topology of a hydrophobic sandwich involving residues in transmembrane helices (TM) 3, 5, and 6, as well as the assembling of polar residues in TM5, are important discriminators between target/antitarget receptors. Ultimately, this will provide useful information for the design of safer compounds inducing fewer side effects.

The Ras/Raf/MEK/ERK signal transduction, an oncogenic pathway implicated in a variety of human cancers, is a key target in anticancer drug design. A novel series of pyrimidylpyrrole ERK inhibitors has been identified. Discovery of a conformational change for lead compound 2, when bound to ERK2 relative to antitarget GSK3, enabled structure-guided selectivity optimization, which led to the discovery of 11e, a potent, selective, and orally bioavailable inhibitor of ERK.

Drugs typically exert their desired and undesired biological effects by virtue of binding interactions with protein target(s) and antitarget(s), respectively. Strategies are therefore needed to efficiently manipulate and monitor cross-target binding profiles (e.g., imatinib and isoniazid) as an integrated part of the drug design process. Herein we present such a strategy, which reverses the target --lead rational drug design paradigm. Enabling this approach is a catechol-rhodanine privileged scaffold for dehydrogenases, which is easily tuned for affinity and specificity toward desired targets. This scaffold crosses bacterial (E. coli) cell walls, and proteome-wide studies demonstrate it does indeed bind to and identify NAD(P)(H)-binding proteins that are potential drug targets in Mycobacterium tuberculosis and antitargets (or targets) in human liver. This approach to drug discovery addresses key difficulties earlier in the process by only pursuing targets for which a chemical lead and optimization strategy are available, to permit rapid tuning of target/antitarget binding profiles.

The human ether-à-go-go related gene (hERG) K+ channel is of great interest for both basic researchers and clinicians because its blockade by drugs can lead to QT prolongation, which is a risk factor for torsades de pointes, a potentially life-threatening arrhythmia. A growing list of agents with "QT liability" have been withdrawn from the market or restricted in their use, whereas others did not even receive regulatory approval for this reason. Thus, hERG K+ channels have become a primary antitarget (i.e. an unwanted target) in drug development because their blockade causes potentially serious side effects. On the other hand, the recent identification and functional characterization of hERG K+ channels not only in the heart, but also in several other tissues (e.g. neurons, smooth muscle and cancer cells) may have far reaching implications for drug development for a possible exploitation of hERG as a target, especially in oncology and cardiology.

The FK506-binding protein 51 (FKBP51) is a promising drug target for the treatment of stress-related psychiatric or metabolic disorders. Just recently, the first selective ligands for FKBP51 were reported based on an induced fit mechanism, but they are too large for a further drug development process. We therefore designed and synthesized a novel series of selective ligands to explore the requirements necessary for binding to the induced-fit conformation. All ligands of this series show no binding toward the structurally very similar antitarget FKBP52. With the cocrystal structure of the best ligand in this novel series we confirmed the induced fit mechanism. Furthermore, the structure–affinity relationship provides information about beneficial structural features, which is valuable for the development of improved FKBP51-directed drugs.

The hERG channel is an important antitarget in safety pharmacology. Several drugs have been withdrawn from the market or received severe usage restrictions because of hERG-related cardiotoxicity. In a screening of medicinal plants for hERG channel inhibition using a two-microelectrode voltage clamp assay with Xenopus laevis oocytes, a dichloromethane extract of the roots of Gnidia polycephala reduced the peak tail hERG current by 58.8 ± 13.4% (n = 3) at a concentration of 100 μg/mL. By means of HPLC-based activity profiling daphnane-type diterpenoid orthoesters (DDOs) 1, 4, and 5 were identified as the active compounds [55.4 ± 7.0% (n = 4), 42.5 ± 16.0% (n = 3), and 51.3 ± 9.4% (n = 4), respectively, at 100 μM]. In a detailed phytochemical profiling of the active extract, 16 compounds were isolated and characterized, including two 2-phenylpyranones (15 and 16) with an unprecedented tetrahydro-4H-5,8-epoxypyrano[2,3-d]oxepin-4-one skeleton, two new DDOs (3 and 4), two new guaiane sesquiterpenoids (11 and 12), and 10 known compounds (1, 2, 5-10, 13, and 14). Structure elucidation was achieved by extensive spectroscopic analysis (1D and 2D NMR, HRMS, and electronic circular dichroism), computational methods, and X-ray crystallography.

We have developed a Computational Analysis of Novel Drug Opportunities (CANDO) stage subsidized by a 2010 NIH Director's Pioneer Award that investigations compound-proteome connection marks to decide medicate conduct, as opposed to customary single objective methodologies. The stage utilizes similitude of collaboration marks over all proteins as demonstrative of comparable practical conduct and non-comparable marks (or areas of marks) as characteristic of off-and antitarget (symptoms, as a result deducing homology of compound/tranquilize conduct at a proteomic level. We have made a network of anticipated connections between 3,733 human ingestible mixes including FDA endorsed drugs and supplements×48,278 proteins utilizing our progressive chem and bio-informatic section based docking with elements convention (from more than one billion anticipated cooperations absolute). We applied our compound-proteome signature correlation and positioning way to deal with 2030 signs with one affirmed compound and yielded benchmarking correctnesses of 12-25% for 1439 signs with more than endorsed compound. We are tentatively approving "high worth" forecasts in vitro, in vivo and by clinical investigations for in excess of forty signs including dental caries, dengue, tuberculosis, ovarian disease, cholangiocarinomas, among numerous fothers. 58/163 (36%) expectations more than twelve investigations across ten signs show tantamount or better action to existing treatments or micromolar restraint at the cell level and fill in as novel repurposeable treatments. Our methodology is relevant to any compound past those affirmed by the FDA and furthermore incorporate can promptly consider transformations in protein structures to empower personalization dependent on genotype, foretelling another period of quicker, more secure, better and less expensive medication revelation. Medication repurposing is an important device for battling the easing back paces of novel helpful disclosure. The Computational Analysis of Novel Drug Opportunities (CANDO) stage performs shotgun repurposing of 2030 signs/sicknesses utilizing 3733 medications/mixes to foresee collaborations with 46,784 proteins and relating them by means of proteomic connection marks. The exactness is determined by looking at connection similitudes of medications affirmed for similar signs. We played out an exceptional subset examination by separating the full protein library into littler subsets and afterward recombining the best performing subsets into bigger supersets. Up to 14% improvement in exactness is seen after benchmarking the supersets, speaking to a 100–1000-overlap decrease in the quantity of proteins thought about comparative with the full library. Further investigation uncovered that libraries involved proteins with all the more evenhandedly assorted ligand cooperations are significant for depicting compound conduct. Utilizing one of these libraries to produce putative medication up-and-comers against jungle fever, tuberculosis, and enormous cell carcinoma brings about more medications that could be approved in the biomedical writing contrasted with utilizing those proposed by the full protein library. Our work explains the job of specific protein subsets and relating ligand cooperations that assume a job in tranquilize repurposing, with suggestions for sedate structure and AI ways to deal with improve the CANDO stage. Introduction Regular systems in sedate disclosure incorporate forward pharmacology and levelheaded medication structure. In the previous, a library of mixes is screened, regularly in a high all through way, for certain phenotypic impacts in vitro. In the last mentioned, mixes are for all intents and purposes screened against a foreordained natural objective, and high certainty hits are then measured for an ideal adjustment. In the two cases, the hits acquired are then surveyed for adequacy in vivo and continue to clinical preliminaries for possible FDA endorsement if fruitful at each stage. This iterative procedure can cost billions of dollars and as long as 15 years for each medication. These methodologies don't think about the indiscrimination of affirmed drugs with regards to signs/ailments inside living frameworks (confirm by reactions present for all little particle treatments), damning numerous novel therapeutics to fall flat. With the second-driving reason for putative medication whittling down being antagonistic responses [9], there is incredible utility in finding new uses for effectively affirmed drugs, which is officially known as medication repurposing or repositioning. We have built up the Computational Analysis of Novel Drug Opportunities (CANDO) stage to address these medication disclosure challenges. One central principle of CANDO is that medications communicate with various proteins and pathways to correct malady states, and this wanton nature is misused to relate drugs dependent on their proteomic marks. These marks are commonly decided by means of virtual sub-atomic docking reenactments that are applied to anticipate compound–protein collaborations on a proteomic scale. Utilizing an information base of known medication sign endorsements/affiliations, we can recognize putative medication repurposing possibility for a specific sign dependent on the similitude of their proteomic connection marks to every other medication affirmed for (or related with) that sign. At the point when a specific sign doesn't have any endorsed tranquilize, the library of human use mixes present in CANDO is screened against the tertiary structures of all applicable and manageable proteins got by X-beam diffraction or homology displaying from a specific organismal proteome to recommend new medicines that expand official to the illness causing proteins and limit askew impacts. High-certainty putative medication applicants produced by CANDO utilizing the two methodologies have been tentatively approved preclinically for an assortment of signs, including dengue, dental caries, diabetes, hepatitis B, herpes, lupus, intestinal sickness, and tuberculosis, with 58/163 up-and-comers yielding tantamount or preferred remedial movement over standard medicines. Discussion The spliting and ranking convention was initially planned to discover a protein subset that benchmarked as least just as the full set. The improvement of the benchmarking execution is an empowering sign for joining AI in the CANDO stage later on, and finding how increasingly complex weighting and relating of proteins add to sedate repurposing exactness, which is hard to do with straightforward RMSD figurings. The littler estimated protein libraries produced as a major aspect of this investigation, speaking to a 100–1000-crease decrease in size, will be progressively helpful for AI. Highlight decrease through methodologies other than PCA, for example, neural system based auto-encoders, will give a significant differentiation to our proposed technique. The autonomous compound library test showed that enhanced protein sets dependent on a specific library were able to do restoratively describing a totally extraordinary one, demonstrating that these supersets are generalizable. At the end of the day, if another medication/compound is added to the CANDO putative medication library, these decreased size supersets are likely ready to portray its conduct in any event just as utilizing each protein accessible. Notwithstanding encouraging AI, our discoveries propose an incredibly diminished time required to produce new proteomic association vectors, which is especially significant if the program/convention of decision for creating cooperations is computationally costly. Any repurposing up-and-comers recommended from utilizing the supersets are on normal all the more clinically important, as they had the option to recover tranquilize conduct more precisely than utilizing the full protein library in a factually huge way. Conclusion We have developed an integrated pipeline that allows for the elucidation of proteins and their features, which are important for benchmarking in the CANDO platform and therefore important for drug repurposing and design. We were able to reproduce the performance of the complete CANDO protein structure library with orders of magnitude fewer proteins, allowing for more rapid candidate generation when evaluating new putative drug libraries or any other changes to the platform. We discovered that moderately promiscuous proteins, in terms of the structures of ligands with which they are predicted to interact, are important for describing how drugs behave in biological systems, a claim validated by literature evidence supporting putative drug candidates generated by a library composed of a subset of these proteins for the treatment of malaria, tuberculosis, and large cell carcinoma. The implications for drug design are that appreciating the multitarget nature of small molecule therapies and optimizing their interactions with the range of macromolecular targets that they are exposed to in their environments during their absorption, dispersion, metabolism, and excretion may be more fruitful than traditional rational drug design using single targets.

The need to early predict the possible failure of a drug candidate is becoming an absolute requirement in the drug discovery process. For this reason, from the initial phases of lead development, great attention is paid to the ADMET characteristics of the compounds. In this context, the recent discovery that hitting some well-identified macromolecular targets can induce undesired side effects has led drug designers to apply some classical in silico technologies to the goal of avoiding the interaction of lead candidates with such antitargets.

The goal of this study was to design, experimentally validate, and apply a virtual screening workflow to identify novel hERG channel blockers. The hERG channel is an important antitarget in drug development since cardiotoxic risks remain as a major cause of attrition. A ligand-based pharmacophore model collection was developed and theoretically validated. The seven most complementary and suitable models were used for virtual screening of in-house and commercially available compound libraries. From the hit lists, 50 compounds were selected for experimental validation through bioactivity assessment using patch clamp techniques. Twenty compounds inhibited hERG channels expressed in HEK 293 cells with IC50 values ranging from 0.13 to 2.77 μM, attesting to the suitability of the models as cardiotoxicity prediction tools in a preclinical stage.

Although the primary function of U1 snRNA is to define the 5' donor site of an intron, it can also block the accumulation of a specific RNA transcript when it binds to a donor sequence within its terminal exon. This work was initiated to investigate if this property of U1 snRNA could be exploited as an effective method for inactivating any target gene. The initial 10-bp segment of U1 snRNA, which is complementary to the 5' donor sequence, was modified to recognize various target mRNAs (chloramphenicol acetyltransferase [CAT], beta-galactosidase, or green fluorescent protein [GFP]). Transient cotransfection of reporter genes and appropriate U1 antitarget vectors resulted in >90% reduction of transgene expression. Numerous sites within the CAT transcript were suitable for targeting. The inhibitory effect of the U1 antitarget vector is directly related to the hybrid formed between the U1 vector and target transcripts and is dependent on an intact 70,000-molecular-weight binding domain within the U1 gene. The effect is long lasting when the target (CAT or GFP) and U1 antitarget construct are inserted into fibroblasts by stable transfection. Clonal cell lines derived from stable transfection with a pOB4GFP target construct and subsequently stably transfected with the U1 anti-GFP construct were selected. The degree to which GFP fluorescence was inhibited by U1 anti-GFP in the various clonal cell lines was assessed by fluorescence-activated cell sorter analysis. RNA analysis demonstrated reduction of the GFP mRNA in the nuclear and cytoplasmic compartment and proper 3' cleavage of the GFP residual transcript. An RNase protection strategy demonstrated that the transfected U1 antitarget RNA level varied between 1 to 8% of the endogenous U1 snRNA level. U1 antitarget vectors were demonstrated to have potential as effective inhibitors of gene expression in intact cells.