Exploring compound promiscuity patterns and multi-target activity spaces

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

Analgesic Activity of 5-Acetamido-2-Hydroxy Benzoic Acid Derivatives and an In-Vivo and In-Silico Analysis of Their Target Interactions

The design, synthesis, and evaluation of novel non-steroidal anti-inflammatory drugs (NSAIDs) with better activity and lower side effects are big challenges today. In this work, two 5-acetamido-2-hydroxy benzoic acid derivatives were proposed, increasing the alkyl position (methyl) in an acetamide moiety, and synthesized, and their structural elucidation was performed using 1H NMR and 13C NMR. The changes in methyl in larger groups such as phenyl and benzyl aim to increase their selectivity over cyclooxygenase 2 (COX-2). These 5-acetamido-2-hydroxy benzoic acid derivatives were prepared using classic methods of acylation reactions with anhydride or acyl chloride. Pharmacokinetics and toxicological properties were predicted using computational tools, and their binding affinity (kcal/mol) with COX-2 receptors (Mus musculus and Homo sapiens) was analyzed using docking studies (PDB ID 4PH9, 5KIR, 1PXX and 5F1A). An in-silico study showed that 5-acetamido-2-hydroxy benzoic acid derivates have a better bioavailability and binding affinity with the COX-2 receptor, and in-vivo anti-nociceptive activity was investigated by means of a writhing test induced by acetic acid and a hot plate. PS3, at doses of 20 and 50 mg/kg, reduced painful activity by 74% and 75%, respectively, when compared to the control group (20 mg/kg). Regarding the anti-nociceptive activity, the benzyl showed reductions in painful activity when compared to acetaminophen and 5-acetamido-2-hydroxy benzoic acid. However, the proposed derivatives are potentially more active than 5-acetamido-2-hydroxy benzoic acid and they support the design of novel and safer derivative candidates. Consequently, more studies need to be conducted to evaluate the different pharmacological actions, the toxicity of possible metabolites that can be generated, and their potential use in inflammation and pain therapy.

Cheminformatics analysis of molecular datasets of transcription factors associated with quorum sensing in Pseudomonas aeruginosa

There are molecular structural features that are key to defining the agonist or antagonist activity on LasR, RhlR and PqsR transcription factors, associated with quorum sensing in Pseudomonas aeruginosa.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".

Chemical activation of Arabidopsis SnRK2.6 by pladienolide B

Abscisic acid (ABA) is an important phytohormone mediating osmotic stress responses. SUCROSE NONFERMENTING 1 (SNF1)-RELATED PROTEIN KINASE 2.6 (SnRK2.6, also named OPEN STOMATA1 and SNF1-RELATED KINASE 2E) is central in the ABA signaling pathway; therefore, manipulating its activity may be useful to confer stress tolerance in plants. Pladienolide B (PB) is an mRNA splicing inhibitor and enhances ABA responses. Here, we analyzed the effect of PB on Arabidopsis SnRK2.6. PB enhanced the activity of recombinant SnRK2.6 in vitro through direct physical interaction as predicted by molecular docking simulations followed by mutation experiments and isothermal titration calorimetry. Structural modeling predicted probable interaction sites between PB and SnRK2.6, and experiments with mutated SnRK2.6 revealed that Leu-46 was the most essential amino acid residue for SnRK2.6 activation by PB. This study demonstrates the feasibility of SnRK2.6 chemical manipulation and paves the way for the modification of plant osmotic stress responses.

Toward an Understanding of Pan-Assay Interference Compounds and Promiscuity: A Structural Perspective on Binding Modes

Pan-assay interference compounds (PAINS) are promiscuous compound classes that produce false positive hits in high-throughput screenings. Yet, the mechanisms of PAINS activity are poorly understood. Although PAINS are often associated with protein reactivity, several recent studies have shown that they also mediate noncovalent interactions. Aiming at a deep understanding of PAINS promiscuity, we performed an analysis of the Protein Data Bank to characterize the binding modes of PAINS. We explored the binding mode conservation of 34 PAINS classes present in 871 ligands and among 517 protein targets. The two major findings of this work are the following: First, different PAINS classes exhibit different levels of binding mode conservation. Our novel classification of PAINS based on binding mode similarity enables a rational assessment of PAINS from a structural perspective. Second, PAINS classes with variable binding modes can bind with high affinity. The evaluation of noncovalent binding modes of PAINS-like compounds sheds light on the mechanisms of promiscuous binding. Our findings could facilitate the decisions on how to deal with PAINS and help scientists to understand why PAINS produce hits in their screenings.

Melatonin controversies, an update

Melatonin was discovered more than 60 years ago. Since then, several seminal discoveries have allowed us to define its function as a neuroendocrine hormone and its molecular targets in mammals and many other species. However, many fundamental issues have not yet been solved such as the subcellular localization of melatonin synthesis and the full spectrum of its molecular targets. In addition, a considerable number of controversies persist in the field, mainly concerning how many functions melatonin has. Altogether, this illustrates how "immature" the field still is. The intention of this opinion article is to note the controversies and limitations in the field, to initiate a discussion and to make proposals/guidelines to overcome them and move the field forward.

Selective toxicity of antibacterial agents-still a valid concept or do we miss chances and ignore risks?

BACKGROUND

Selective toxicity antibacteribiotics is considered to be due to interactions with targets either being unique to bacteria or being characterized by a dichotomy between pro- and eukaryotic pathways with high affinities of agents to bacterial- rather than eukaryotic targets. However, the theory of selective toxicity oversimplifies the complex modes of action of antibiotics in pro- and eukaryotes.

METHODS AND OBJECTIVE

This review summarizes data describing multiple modes of action of antibiotics in eukaryotes.

RESULTS

Aminoglycosides, macrolides, oxazolidinones, chloramphenicol, clindamycin, tetracyclines, glycylcyclines, fluoroquinolones, rifampicin, bedaquillin, ß-lactams inhibited mitochondrial translation either due to binding to mitosomes, inhibition of mitochondrial RNA-polymerase-, topoisomerase 2ß-, ATP-synthesis, transporter activities. Oxazolidinones, tetracyclines, vancomycin, ß-lactams, bacitracin, isoniazid, nitroxoline inhibited matrix-metalloproteinases (MMP) due to chelation with zinc and calcium, whereas fluoroquinols fluoroquinolones and chloramphenicol chelated with these cations, too, but increased MMP activities. MMP-inhibition supported clinical efficacies of ß-lactams and daptomycin in skin-infections, and of macrolides, tetracyclines in respiratory-diseases. Chelation may have contributed to neuroprotection by ß-lactams and fluoroquinolones. Aminoglycosides, macrolides, chloramphenicol, oxazolidins oxazolidinones, tetracyclines caused read-through of premature stop codons. Several additional targets for antibiotics in human cells have been identified like interaction of fluoroquinolones with DNA damage repair in eukaryotes, or inhibition of mucin overproduction by oxazolidinones.

CONCLUSION

The effects of antibiotics on eukaryotes are due to identical mechanisms as their antibacterial activities because of structural and functional homologies of pro- and eukaryotic targets, so that the effects of antibiotics on mammals are integral parts of their overall mechanisms of action.

Repositioned Drugs for Chagas Disease Unveiled via Structure-Based Drug Repositioning

Chagas disease, caused by the parasite Trypanosoma cruzi, affects millions of people in South America. The current treatments are limited, have severe side effects, and are only partially effective. Drug repositioning, defined as finding new indications for already approved drugs, has the potential to provide new therapeutic options for Chagas. In this work, we conducted a structure-based drug repositioning approach with over 130,000 3D protein structures to identify drugs that bind therapeutic Chagas targets and thus represent potential new Chagas treatments. The screening yielded over 500 molecules as hits, out of which 38 drugs were prioritized following a rigorous filtering process. About half of the latter were already known to have trypanocidal activity, while the others are novel to Chagas disease. Three of the new drug candidates—ciprofloxacin, naproxen, and folic acid—showed a growth inhibitory activity in the micromolar range when tested ex vivo on T. cruzi trypomastigotes, validating the prediction. We show that our drug repositioning approach is able to pinpoint relevant drug candidates at a fraction of the time and cost of a conventional screening. Furthermore, our results demonstrate the power and potential of structure-based drug repositioning in the context of neglected tropical diseases where the pharmaceutical industry has little financial interest in the development of new drugs.

VAE-Sim: A Novel Molecular Similarity Measure Based on a Variational Autoencoder

Molecular similarity is an elusive but core "unsupervised" cheminformatics concept, yet different "fingerprint" encodings of molecular structures return very different similarity values, even when using the same similarity metric. Each encoding may be of value when applied to other problems with objective or target functions, implying that a priori none are "better" than the others, nor than encoding-free metrics such as maximum common substructure (MCSS). We here introduce a novel approach to molecular similarity, in the form of a variational autoencoder (VAE). This learns the joint distribution p(z|x) where z is a latent vector and x are the (same) input/output data. It takes the form of a "bowtie"-shaped artificial neural network. In the middle is a "bottleneck layer" or latent vector in which inputs are transformed into, and represented as, a vector of numbers (encoding), with a reverse process (decoding) seeking to return the SMILES string that was the input. We train a VAE on over six million druglike molecules and natural products (including over one million in the final holdout set). The VAE vector distances provide a rapid and novel metric for molecular similarity that is both easily and rapidly calculated. We describe the method and its application to a typical similarity problem in cheminformatics.

Unraveling Plant Natural Chemical Diversity for Drug Discovery Purposes

The screening and testing of extracts against a variety of pharmacological targets in order to benefit from the immense natural chemical diversity is a concern in many laboratories worldwide. And several successes have been recorded in finding new actives in natural products, some of which have become new drugs or new sources of inspiration for drugs. But in view of the vast amount of research on the subject, it is surprising that not more drug candidates were found. In our view, it is fundamental to reflect upon the approaches of such drug discovery programs and the technical processes that are used, along with their inherent difficulties and biases. Based on an extensive survey of recent publications, we discuss the origin and the variety of natural chemical diversity as well as the strategies to having the potential to embrace this diversity. It seemed to us that some of the difficulties of the area could be related with the technical approaches that are used, so the present review begins with synthetizing some of the more used discovery strategies, exemplifying some key points, in order to address some of their limitations. It appears that one of the challenges of natural product-based drug discovery programs should be an easier access to renewable sources of plant-derived products. Maximizing the use of the data together with the exploration of chemical diversity while working on reasonable supply of natural product-based entities could be a way to answer this challenge. We suggested alternative ways to access and explore part of this chemical diversity with in vitro cultures. We also reinforced how important it was organizing and making available this worldwide knowledge in an "inventory" of natural products and their sources. And finally, we focused on strategies based on synthetic biology and syntheses that allow reaching industrial scale supply. Approaches based on the opportunities lying in untapped natural plant chemical diversity are also considered.

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

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

Perspective on Antibacterial Lead Identification Challenges and the Role of Hypothesis-Driven Strategies

For the past three decades, the pharmaceutical industry has undertaken many diverse approaches to discover novel antibiotics, with limited success. We have witnessed and personally experienced many mistakes, hurdles, and dead ends that have derailed projects and discouraged scientists and business leaders. Of the many factors that affect the outcomes of screening campaigns, a lack of understanding of the properties that drive efflux and permeability requirements across species has been a major barrier for advancing hits to leads. Hits that possess bacterial spectrum have seldom also possessed druglike properties required for developability and safety. Persistence in solving these two key barriers is necessary for the reinvestment into discovering antibacterial agents. This perspective narrates our experience in antibacterial discovery-our lessons learned about antibacterial challenges as well as best practices for screening strategies. One of the tenets that guides us is that drug discovery is a hypothesis-driven science. Application of this principle, at all steps in the antibacterial discovery process, should improve decision making and possibly the odds of what has become, in recent decades, an increasingly challenging endeavor with dwindling success rates.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

Organic Isothiocyanates as Hydrogen Sulfide Donors

Significance: Hydrogen sulfide (H₂S), the "new entry" in the series of endogenous gasotransmitters, plays a fundamental role in regulating the biological functions of various organs and systems. Consequently, the lack of adequate levels of H₂S may represent the etiopathogenetic factor of multiple pathological alterations. In these diseases, the use of H₂S donors represents a precious and innovative opportunity. Recent Advances: Natural isothiocyanates (ITCs), sulfur compounds typical of some botanical species, have long been investigated because of their intriguing pharmacological profile. Recently, the ITC moiety has been proposed as a new H₂S-donor chemotype (with a l-cysteine-mediated reaction). Based on this recent discovery, we can clearly observe that almost all the effects of natural ITCs can be explained by the H₂S release. Consistently, the ITC function was also used as an original H₂S-releasing moiety for the design of synthetic H₂S donors and original "pharmacological hybrids." Very recently, the chemical mechanism of H₂S release, resulting from the reaction between l-cysteine and some ITCs, has been elucidated. Critical Issues: Available literature gives convincing demonstration that H₂S is the real player in ITC pharmacology. Further, countless studies have been carried out on natural ITCs, but this versatile moiety has been used only rarely for the design of synthetic H₂S donors with optimal drug-like properties. Future Directions: The development of more ITC-based synthetic H₂S donors with optimal drug-like properties and selectivity toward specific tissues/pathologies seem to represent a stimulating and indispensable prospect of future experimental activities.

Natural Product Target Network Reveals Potential for Cancer Combination Therapies

A body of research demonstrates examples of in vitro and in vivo synergy between natural products and anti-neoplastic drugs for some cancers. However, the underlying biological mechanisms are still elusive. To better understand biological entities targeted by natural products and therefore provide rational evidence for future novel combination therapies for cancer treatment, we assess the targetable space of natural products using public domain compound-target information. When considering pathways from the Reactome database targeted by natural products, we found an increase in coverage of 61% (725 pathways), relative to pathways covered by FDA approved cancer drugs collected in the Cancer Targetome, a resource for evidence-based drug-target interactions. Not only is the coverage of pathways targeted by compounds increased when we include natural products, but coverage of targets within those pathways is also increased. Furthermore, we examined the distribution of cancer driver genes across pathways to assess relevance of natural products to critical cancer therapeutic space. We found 24 pathways enriched for cancer drivers that had no available cancer drug interactions at a potentially clinically relevant binding affinity threshold of < 100nM that had at least one natural product interaction at that same binding threshold. Assessment of network context highlighted the fact that natural products show target family groupings both distinct from and in common with cancer drugs, strengthening the complementary potential for natural products in the cancer therapeutic space. In conclusion, our study provides a foundation for developing novel cancer treatment with the combination of drugs and natural products.

Structure-Activity Relationships in Metal-Binding Pharmacophores for Influenza Endonuclease

Metalloenzymes represent an important target space for drug discovery. A limitation to the early development of metalloenzyme inhibitors has been the lack of established structure-activity relationships (SARs) for molecules that bind the metal ion cofactor(s) of a metalloenzyme. Herein, we employed a bioinorganic perspective to develop an SAR for inhibition of the metalloenzyme influenza RNA polymerase PA_N endonuclease. The identified trends highlight the importance of the electronics of the metal-binding pharmacophore (MBP), in addition to MBP sterics, for achieving improved inhibition and selectivity. By optimization of the MBPs for PA_N endonuclease, a class of highly active and selective fragments was developed that displays IC₅₀ values <50 nM. This SAR led to structurally distinct molecules that also displayed IC₅₀ values of ∼10 nM, illustrating the utility of a metal-centric development campaign in generating highly active and selective metalloenzyme inhibitors.

Inhibitors and chemical probes for molecular chaperone networks

The molecular chaperones are central mediators of protein homeostasis. In that role, they engage in widespread protein-protein interactions (PPIs) with each other and with their "client" proteins. Together, these PPIs form the backbone of a network that ensures proper vigilance over the processes of protein folding, trafficking, quality control, and degradation. The core chaperones, such as the heat shock proteins Hsp60, Hsp70, and Hsp90, are widely expressed in most tissues, yet there is growing evidence that the PPIs among them may be re-wired in disease conditions. This possibility suggests that these PPIs, and perhaps not the individual chaperones themselves, could be compelling drug targets. Indeed, recent efforts have yielded small molecules that inhibit (or promote) a subset of inter-chaperone PPIs. These chemical probes are being used to study chaperone networks in a range of models, and the successes with these approaches have inspired a community-wide objective to produce inhibitors for a broader set of targets. In this Review, we discuss progress toward that goal and point out some of the challenges ahead.

Feature optimization in high dimensional chemical space: statistical and data mining solutions

Objectives

The primary goal of this experiment is to prioritize molecular descriptors that control the activity of active molecules that could reduce the dimensionality produced during the virtual screening process. It also aims to: (1) develop a methodology for sampling large datasets and the statistical verification of the sampling process, (2) apply screening filter to detect molecules with polypharmacological or promiscuous activity.

Results

Sampling from large a dataset and its verification were done by applying Z-test. Molecular descriptors were prioritized using principal component analysis (PCA) by eliminating the least influencing ones. The original dimensions were reduced to one-twelfth by the application of PCA. There was a significant improvement in statistical parameter values of virtual screening model which in turn resulted in better screening results. Further improvement of screened results was done by applying Eli Lilly MedChem rules filter that removed molecules with polypharmacological or promiscuous activity. It was also shown that similarities in the activity of compounds were due to the molecular descriptors which were not apparent in prima facie structural studies.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3535-y) contains supplementary material, which is available to authorized users.

How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors

Discovery of new pharmaceutical substances is currently boosted by the possibility of utilization of the Synthetically Accessible Virtual Inventory (SAVI) library, which includes about 283 million molecules, each annotated with a proposed synthetic one-step route from commercially available starting materials. The SAVI database is well-suited for ligand-based methods of virtual screening to select molecules for experimental testing. In this study, we compare the performance of three approaches for the analysis of structure-activity relationships that differ in their criteria for selecting of "active" and "inactive" compounds included in the training sets. PASS (Prediction of Activity Spectra for Substances), which is based on a modified Naïve Bayes algorithm, was applied since it had been shown to be robust and to provide good predictions of many biological activities based on just the structural formula of a compound even if the information in the training set is incomplete. We used different subsets of kinase inhibitors for this case study because many data are currently available on this important class of drug-like molecules. Based on the subsets of kinase inhibitors extracted from the ChEMBL 20 database we performed the PASS training, and then applied the model to ChEMBL 23 compounds not yet present in ChEMBL 20 to identify novel kinase inhibitors. As one may expect, the best prediction accuracy was obtained if only the experimentally confirmed active and inactive compounds for distinct kinases in the training procedure were used. However, for some kinases, reasonable results were obtained even if we used merged training sets, in which we designated as inactives the compounds not tested against the particular kinase. Thus, depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.

Re-emergence of an orphan therapeutic target for the treatment of resistant prostate cancer - a thorough conformational and binding analysis for ROR-γ protein

Recent studies have linked a deadly form of prostate cancer known as metastatic castration-resistant prostate cancer to retinoic acid-related orphan-receptor gamma (ROR-γ). Most of these studies continued to place ROR-γ as orphan because of unidentifiable inhibitor. Recently identified inhibitors of ROR-γ and their therapeutic potential were evaluated, among which inhibitor XY018 was the potent. However, molecular understanding of the conformational features of XY018-ROR-γ complex is still elusive. Herein, molecular dynamics simulations were conducted on HC9-ROR-γ and XY018-ROR-γ complexes to understand their conformational features at molecular level and the influence of XY018 binding on the dynamics of ROR-γ with the aid of post-dynamic analytical tools. These include; principal component analysis, radius of gyration, binding free energy calculation (MM/GBSA), per-residue fluctuation and hydrogen bond occupancy. Findings from this study revealed that (1) hydrophobic packing contributes significantly to binding free energy, (2) Ile136 and Leu60 exhibited high hydrogen-bond occupancy in XY018-ROR-γ and HC9-ROR-γ, respectively, (3) XY018-ROR-γ displayed a relatively high loop region residue fluctuation compared to HC9-ROR-γ, (4) electrostatic interactions are a potential binding force in XY018-ROR-γ complex compared to HC9-ROR-γ, (5) XY018-ROR-γ assumes a rigid conformation which is highlighted by a decrease in residual fluctuation, (6) XY018 could potentially induce pseudoporphyria, nephritis and interstitial nephritis but potentially safe in renal failure. This study could serve as a base line for the design of new potential ROR-γ inhibitors.

Network-based Approaches in Pharmacology

In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example.

A simple mathematical approach to the analysis of polypharmacology and polyspecificity data

There many possible types of drug-target interactions, because there are a surprising number of ways in which drugs and their targets can associate with one another.  These relationships are expressed as polypharmacology and polyspecificity.  Polypharmacology is the capability of a given drug to exhibit activity with respect to multiple drug targets, which are not necessarily in the same activity class. Adverse drug reactions ('side effects') are its principal manifestation, but polypharmacology is also playing a role in the repositioning of existing drugs for new therapeutic indications.  Polyspecificity, on the other hand, is the capability of a given target to exhibit activity with respect to multiple, structurally dissimilar drugs.  That these concepts are closely related to one another is, surprisingly, not well known.  It will be shown in this work that they are, in fact, mathematically related to one another and are in essence 'two sides of the same coin'.  Hence, information on polypharmacology provides equivalent information on polyspecificity, and vice versa. Networks are playing an increasingly important role in biological research. Drug-target networks, in particular, are made up of drug nodes that are linked to specific target nodes if a given drug is active with respect to that target.  Such networks provide a graphic depiction of polypharmacology and polyspecificity.  However, by their very nature they can obscure information that may be useful in their interpretation and analysis.  This work will show how such latent information can be used to determine bounds for the degrees of polypharmacology and polyspecificity, and how to estimate other useful features associated with the lack of completeness of most drug-target datasets.

Molecular Mechanism of Action of Antimalarial Benzoisothiazolones: Species-Selective Inhibitors of the Plasmodium spp. MEP Pathway enzyme, IspD

The methylerythritol phosphate (MEP) pathway is an essential metabolic pathway found in malaria parasites, but absent in mammals, making it a highly attractive target for the discovery of novel and selective antimalarial therapies. Using high-throughput screening, we have identified 2-phenyl benzo[d]isothiazol-3(2H)-ones as species-selective inhibitors of Plasmodium spp. 2-C-methyl-_D-erythritol-4-phosphate cytidyltransferase (IspD), the third catalytic enzyme of the MEP pathway. 2-Phenyl benzo[d]isothiazol-3(2H)-ones display nanomolar inhibitory activity against P. falciparum and P. vivax IspD and prevent the growth of P. falciparum in culture, with EC₅₀ values below 400 nM. In silico modeling, along with enzymatic, genetic and crystallographic studies, have established a mechanism-of-action involving initial non-covalent recognition of inhibitors at the IspD binding site, followed by disulfide bond formation through attack of an active site cysteine residue on the benzo[d]isothiazol-3(2H)-one core. The species-selective inhibitory activity of these small molecules against Plasmodium spp. IspD and cultured parasites suggests they have potential as lead compounds in the pursuit of novel drugs to treat malaria.

Synthetic biology for pharmaceutical drug discovery

Synthetic biology (SB) is an emerging discipline, which is slowly reorienting the field of drug discovery. For thousands of years, living organisms such as plants were the major source of human medicines. The difficulty in resynthesizing natural products, however, often turned pharmaceutical industries away from this rich source for human medicine. More recently, progress on transformation through genetic manipulation of biosynthetic units in microorganisms has opened the possibility of in-depth exploration of the large chemical space of natural products derivatives. Success of SB in drug synthesis culminated with the bioproduction of artemisinin by microorganisms, a tour de force in protein and metabolic engineering. Today, synthetic cells are not only used as biofactories but also used as cell-based screening platforms for both target-based and phenotypic-based approaches. Engineered genetic circuits in synthetic cells are also used to decipher disease mechanisms or drug mechanism of actions and to study cell-cell communication within bacteria consortia. This review presents latest developments of SB in the field of drug discovery, including some challenging issues such as drug resistance and drug toxicity.

Physicochemical characteristics of structurally determined metabolite-protein and drug-protein binding events with respect to binding specificity

To better understand and ultimately predict both the metabolic activities as well as the signaling functions of metabolites, a detailed understanding of the physical interactions of metabolites with proteins is highly desirable. Focusing in particular on protein binding specificity vs. promiscuity, we performed a comprehensive analysis of the physicochemical properties of compound-protein binding events as reported in the Protein Data Bank (PDB). We compared the molecular and structural characteristics obtained for metabolites to those of the well-studied interactions of drug compounds with proteins. Promiscuously binding metabolites and drugs are characterized by low molecular weight and high structural flexibility. Unlike reported for drug compounds, low rather than high hydrophobicity appears associated, albeit weakly, with promiscuous binding for the metabolite set investigated in this study. Across several physicochemical properties, drug compounds exhibit characteristic binding propensities that are distinguishable from those associated with metabolites. Prediction of target diversity and compound promiscuity using physicochemical properties was possible at modest accuracy levels only, but was consistently better for drugs than for metabolites. Compound properties capturing structural flexibility and hydrogen-bond formation descriptors proved most informative in PLS-based prediction models. With regard to diversity of enzymatic activities of the respective metabolite target enzymes, the metabolites benzylsuccinate, hypoxanthine, trimethylamine N-oxide, oleoylglycerol, and resorcinol showed very narrow process involvement, while glycine, imidazole, tryptophan, succinate, and glutathione were identified to possess broad enzymatic reaction scopes. Promiscuous metabolites were found to mainly serve as general energy currency compounds, but were identified to also be involved in signaling processes and to appear in diverse organismal systems (digestive and nervous system) suggesting specific molecular and physiological roles of promiscuous metabolites.

Multiple binding sites in the nicotinic acetylcholine receptors: An opportunity for polypharmacolgy

For decades, the development of selective compounds has been the main goal for chemists and biologists involved in drug discovery. However, diverse lines of evidence indicate that polypharmacological agents, i.e. those that act simultaneously at various protein targets, might show better profiles than selective ligands, regarding both efficacy and side effects. On the other hand, the availability of the crystal structure of different receptors allows a detailed analysis of the main interactions between drugs and receptors in a specific binding site. Neuronal nicotinic acetylcholine receptors (nAChRs) constitute a large and diverse family of ligand-gated ion channels (LGICs) that, as a product of its modulation, regulate neurotransmitter release, which in turns produce a global neuromodulation of the central nervous system. nAChRs are pentameric protein complexes in such a way that expression of compatible subunits can lead to various receptor assemblies or subtypes. The agonist binding site, located at the extracellular region, exhibits different properties depending on the subunits that conform the receptor. In the last years, it has been recognized that nAChRs could also contain one or more allosteric sites which could bind non-classical nicotinic ligands including several therapeutically useful drugs. The presence of multiple binding sites in nAChRs offers an interesting possibility for the development of novel polypharmacological agents with a wide spectrum of actions.

Strong nonadditivity as a key structure-activity relationship feature: distinguishing structural changes from assay artifacts

Nonadditivity in protein–ligand affinity data represents highly instructive structure–activity relationship (SAR) features that indicate structural changes and have the potential to guide rational drug design. At the same time, nonadditivity is a challenge for both basic SAR analysis as well as many ligand-based data analysis techniques such as Free-Wilson Analysis and Matched Molecular Pair analysis, since linear substituent contribution models inherently assume additivity and thus do not work in such cases. While structural causes for nonadditivity have been analyzed anecdotally, no systematic approaches to interpret and use nonadditivity prospectively have been developed yet. In this contribution, we lay the statistical framework for systematic analysis of nonadditivity in a SAR series. First, we develop a general metric to quantify nonadditivity. Then, we demonstrate the non-negligible impact of experimental uncertainty that creates apparent nonadditivity, and we introduce techniques to handle experimental uncertainty. Finally, we analyze public SAR data sets for strong nonadditivity and use recourse to the original publications and available X-ray structures to find structural explanations for the nonadditivity observed. We find that all cases of strong nonadditivity (ΔΔpK_i and ΔΔpIC₅₀ > 2.0 log units) with sufficient structural information to generate reasonable hypothesis involve changes in binding mode. With the appropriate statistical basis, nonadditivity analysis offers a variety of new attempts for various areas in computer-aided drug design, including the validation of scoring functions and free energy perturbation approaches, binding pocket classification, and novel features in SAR analysis tools.