Current trends in lead discovery: are we looking for the appropriate properties?

Design of the Global Health chemical diversity library v2 for screening against infectious diseases

There is a need for novel chemical matter for phenotypic and target-based screens to find starting points for drug discovery programmes in neglected infectious diseases and non-hormonal contraceptives that disproportionately affect Low- and Middle-Income Countries (LMICs). In some disease areas multiple screens of corporate and other libraries have been carried out, giving rise to some valuable starting points and leading to preclinical candidates. Whilst in other disease areas, little screening has been carried out. Much screening against pathogens has been conducted phenotypically as there are few robustly validated protein targets. However, many of the active compound series identified share the same molecular targets. To address the need for new chemical material, in this article we describe the design of a new library, designed for screening in drug discovery programmes for neglected infectious diseases. The compounds have been selected from the Enamine REAL (REadily AccessibLe) library, a virtual library which contains approximately 4.5 billion molecules. The molecules theoretically can be synthesized quickly using commercially available intermediates and building blocks. The vast majority of these have not been prepared before, so this is a source of novel compounds. In this paper we describe the design of a diverse library of 30,000 compounds from this collection (graphical abstract). The new library will be made available to laboratories working in neglected infectious diseases, subject to a review process. The project has been supported by the Bill & Melinda Gates Foundation and the Wellcome Trust (Wellcome).

Triglyceride-filled albumin-based nanocapsules: A promising new system to avoid discarding poorly water-soluble drug candidates

Even though current drug discovery provides a variety of potential drug candidates, many of those substances are difficult to formulate due to their poor water-solubility. To overcome this obstacle a technological formulation is crucial. Albumin-based nanocarriers are a possible intravenous delivery system which is already approved and commercially available. However, no universal carrier for poorly water-soluble substances is found yet. In the present study, new preparation processes for nanocapsules consisting of a medium-chain triglyceride (MCT) core and a human serum albumin (HSA) shell were developed. The nanocarrier system exhibits desirable physicochemical properties with a hydrodynamic diameter of 150 nm and a polydispersity index of 0.1. Furthermore, the nanocapsules were stable towards the addition of electrolytes and also in basic to neutral pH range. The nanocapsules were storage stable for at least 7 months at 4 °C and could also be lyophilized to reach an even longer shelf life of at least 21 months. In addition, the nanocapsule system showed no cytotoxicity in cell culture. The developed system represents a suitable carrier for a variety of different poorly water-soluble drug substances (e.g., fenofibrate, naproxen, indomethacin) showing a high potential for a universal formulation platform for further lipophilic active pharmaceutical ingredients (APIs).

Identification of lead inhibitors of TMPRSS2 isoform 1 of SARS-CoV-2 target using neural network, random forest, and molecular docking

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was responsible for over 4 million confirmed cases of severe acute respiratory syndrome, of which more than 300,000 cases were confirmed to be dead as of May 2020. The virulent endocytotic activities of SARS-CoV-2 have been associated with angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine 2 (TMPRSS2). Previous studies on the viral activation of TMPRSS2 focused most often than not on the isoform 2 of TMPRSS2, but the isoform 1 (529 residues) has also been shown to be expressed in target cells and contribute to viral activation in host. The inhibition of TMPRSS2 has been reported to grossly reduce the pathogenic effects of SARS-CoV-2 endocytotic activities.

In this study therefore, we developed two machine learning models using random forest classifier (RFC) and neural networks (NNs) based on 2251 serine protease inhibitors to screen a database of 21,000,000 virtual compounds. We screened the hit compounds using absorption, distribution, metabolism, and excretion (ADME) properties and finally docked the filtered compounds into the predicted binding site of TMPRSS2 isoform 1 homology model to determine their corresponding binding affinity and plausible molecular interactions.

One (ASONN) and four (ASOIRFC1–4) lead compounds were obtained from the ADME-NN and RFC filtered hits, respectively, having better binding affinity and lead-likeness properties than those of camostat; this could be due to extensive hydrogen and hydrophobic interactions.

Ultrahigh Throughput Protein-Ligand Docking with Deep Learning

Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models' goals and successes. We present different AI accelerated workflows for uHTVS, mainly through surrogate docking models. We showcase a novel feature representation technique, molecular depictions (images), as a surrogate model for docking. Along with a discussion on analyzing screens using regression enrichment surfaces at the tens of billion scale, we outline a future for uHTVS screening pipelines with deep learning.

Analysis of the uncharted, druglike property space by self-organizing maps

Physicochemical properties are fundamental to predict the pharmacokinetic and pharmacodynamic behavior of drug candidates. Easily calculated descriptors such as molecular weight and logP have been found to correlate with the success rate of clinical trials. These properties have been previously shown to highlight a sweet-spot in the chemical space associated with favorable pharmacokinetics, which is superior against other regions during hit identification and optimization. In this study, we applied self-organizing maps (SOMs) trained on sixteen calculated properties of a subset of known drugs for the analysis of commercially available compound databases, as well as public biological and chemical databases frequently used for drug discovery. Interestingly, several regions of the property space have been identified that are highly overrepresented by commercially available chemical libraries, while we found almost completely unoccupied regions of the maps (commercially neglected chemical space resembling the properties of known drugs). Moreover, these underrepresented portions of the chemical space are compatible with most rigorous property filters applied by the pharma industry in medicinal chemistry optimization programs. Our results suggest that SOMs may be directly utilized in the strategy of library design for drug discovery to sample previously unexplored parts of the chemical space to aim at yet-undruggable targets.

Nature-derived hit, lead, and drug-like small molecules: Current status and future aspects against key target proteins of Coronaviruses

BACKGROUND

COVID-19 pandemic, the most unprecedented event of the year 2020, has brought millions of scientists worldwide in a single platform to fight against it. Though several drugs are now in the clinical trial, few vaccines available on the market already but the lack of an effect of those is making the situation worse.

AIM OF THE STUDY

In this review, we demonstrated comprehensive data of natural antiviral products showing activities against different proteins of Human Coronaviruses (HCoV) that are responsible for its pathogenesis. Furthermore, we categorized the compounds into the hit, lead, and drug based on the IC50/EC50 value, drug-likeness, and lead-likeness test to portray their potentiality to be a drug. We also demonstrated the present status of our screened antiviral compounds with respect to clinical trials and reported the lead compounds that can be promoted to clinical trial against COVID-19.

METHODS

A systematic search strategy was employed focusing on Natural Products (NPs) with proven activity (in vitro, in vivo, or in silico) against human coronaviruses, in general, and data were gathered from databases like PubMed, Web of Science, Google Scholar, SciVerse, and Scopus. Information regarding clinical trials retrieved from the Clinical Trial database.

RESULTS

Total "245" natural compounds were identified initially from the literature study. Among them, Glycyrrhizin, Caffeic acid, Curcumin is in phase 3, and Tetrandrine, Cyclosporine, Tacrolimus, Everolimus are in phase 4 clinical trial. Except for Glycyrrhizin, all compounds showed activity against COVID-19.

CONCLUSIONS

In summary, our demonstrated specific small molecules with lead and drug-like capabilities clarified their position in the drug discovery pipeline and proposed their future research against COVID-19.

Exploring new targets and chemical space with affinity selection-mass spectrometry

Affinity selection-mass spectrometry (AS-MS) is a high-throughput screening (HTS) technique for drug discovery that enables rapid screening of large collections of compounds to identify ligands for a specific biomolecular target. AS-MS is a binding assay that is insensitive to the functional effects a ligand might have, which is important because it lets us identify novel ligands irrespective of their binding site. This approach is gaining popularity, notably due to its role in the emergence of useful agents for targeted protein degradation. This Perspective highlights the use of AS-MS techniques to explore broad chemical space and identify small-molecule ligands for biological targets that have proven challenging to address with other screening paradigms. We present chemical structures of reported AS-MS hits to illustrate the potential of this screening approach to deliver high-quality hits for further optimization. AS-MS has, thus, evolved from being an infrequent alternative to traditional HTS or DNA-encoded library strategies to now firmly establishing itself as a HTS approach for drug discovery.

A Venomics Approach Coupled to High-Throughput Toxin Production Strategies Identifies the First Venom-Derived Melanocortin Receptor Agonists

Animal venoms are rich in hundreds of toxins with extraordinary biological activities. Their exploitation is difficult due to their complexity and the small quantities of venom available from most venomous species. We developed a Venomics approach combining transcriptomic and proteomic characterization of 191 species and identified 20,206 venom toxin sequences. Two complementary production strategies based on solid-phase synthesis and recombinant expression in Escherichia coli generated a physical bank of 3597 toxins. Screened on hMC4R, this bank gave an incredible hit rate of 8%. Here, we focus on two novel toxins: N-TRTX-Preg1a, exhibiting an inhibitory cystine knot (ICK) motif, and N-BUTX-Ptr1a, a short scorpion-CSαβ structure. Neither N-TRTX-Preg1a nor N-BUTX-Ptr1a affects ion channels, the known targets of their toxin scaffolds, but binds to four melanocortin receptors with low micromolar affinities and activates the hMC1R/Gs pathway. Phylogenetically, these two toxins form new groups within their respective families and represent novel hMC1R agonists, structurally unrelated to the natural agonists.

Organogel Nanoparticles as a New Way to Improve Oral Bioavailability of Poorly Soluble Compounds

PURPOSE

The aim of the study was to evaluate organogel nanoparticles as a lipophilic vehicle to increase the oral bioavailability of poorly soluble compounds. Efavirenz (EFV), a Biopharmaceutical Classification System (BCS) Class II, was used as drug model.

METHODS

Organogel nanoparticles loaded with EFV were formulated with sunflower oil, 12-hydroxystearic acid (HSA) and polyvinyl alcohol (PVA). Various parameters have been investigated in the current study such as (i) the release profile of organogel assessed by USP 4 cell flow dialysis, (ii) the impact of organogel on intestinal absorption, using Caco-2 cells as in vitro model and jejunum segments as ex vivo assay and (iii) the bioavailability of organogel following oral pharmacokinetic study.

RESULTS

250-300 nm spherical particles with a final concentration of 4.75 mg/mL drug loading were obtained, corresponding to a thousand fold increase in EFV solubility, combined to a very high encapsulation efficiency (>99.8%). Due to rapid diffusion, drug was immediately released from the nanoparticles. The biopharmaceutical evaluation on ex vivo jejunum segments demonstrated an increased absorption of EFV from organogel nanoparticles compare to a native EFV suspension. In vitro assays combining Caco-2 cell cultures with TEM and confocal microscopy demonstrated passive diffusion, while paracellular integrity and endocytosis activity remain expelled. Oral pharmacokinetics of EFV organogel nanoparticles improve oral bioavailability (F_r: 249%) and quick absorption compared to EFV suspension.

CONCLUSION

Organogel nanoparticles increase the bioavailability of BCS Class II drugs. The main phenomena is simply oil transfer from the gelled particles through the cell membrane.

A Chemometric Analysis of Deep-Sea Natural Products

Deep-sea natural products have been created by unique marine organisms that thrive in a challenging environment of extreme conditions for its inhabitants. In this study, 179 deep-sea natural products isolated from 2009 to 2013 were investigated by analysing their physicochemical properties that are important indicators of the ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) profile of a compound. The study and analysis of these molecular descriptors and characteristics enabled the defining of these compounds in various chemical spaces, particularly as an indication of their drug-likeness and position in chemical space and is the first to be conducted to analyse deep-sea derived natural products. It was found that ~40% of all deep-sea natural products were drug-like and 2/3 were within Known Drug Space (KDS), highlighting the high drug-likeness of a significant proportion of deep-sea natural products, most of which have already been shown to have notable biological activities, that should be further investigated as potential therapeutics. Furthermore, this study was able to reveal the general structural differences between compounds from Animalia, Bacteria and Fungi organisms where it was observed that natural products from members of the Animalia kingdom are structurally more varied than compounds from bacteria and fungi. It was also noted that, in general, fungi-derived compounds occupy a more favourable position in drug-like chemical space and are a rich and promising source of biologically-active natural products for the purposes of drug development and therapeutic application.

A Chemometric Analysis of Compounds from Native New Zealand Medicinal Flora

Several hundred (396) compounds from New Zealand flora with medicinal properties were analyzed for their physicochemical properties. It was found that approximately 10 % fulfilled all the requirements to be considered to be lead-like, over half of the compounds were deemed to be in the drug-like space and ≈75 % were in the known drug space. These results indicate the presence of a significant proportion of compounds that are of particular interest to pursue as potential lead compounds or therapeutics. Additionally, compound classes were analyzed separately-most carbonyl-containing compounds (aldehydes, ketones, esters and lactones), along with phenols were the most lead-like compounds, which also displayed very good proportions in the drug-like and known drug space. The information presented herein can be harnessed and utilized in future work, through focussing on the compounds and compound classes that exhibit high-levels of lead-likeness for further development.

Acrylamide Functional Group Incorporation Improves Drug-like Properties: An Example with EGFR Inhibitors

We demonstrate that the acrylamide group can be used to improve the drug-like properties of potential drug candidates. In the EGFR inhibitor development, both the solubility and membrane permeability properties of compounds 6a and 7, each containing an acrylamide group, were substantially better than those of gefitinib (1) and AZD3759 (2), respectively. We demonstrated that incorporation of an acrylamide moiety could serve as a good strategy for improving drug-like properties.

Lignans: A Chemometric Analysis

The physicochemical properties of classical lignans, neolignans, flavonolignans and carbohydrate-lignan conjugates (CLCs) were analysed to assess their ADMET profiles and establish if these compounds are lead-like/drug-like and thus have potential to be or act as leads in the development of future therapeutics. It was found that while no studied compounds were lead-like, a very large proportion (>75%) fulfilled all the requirements to be deemed as present in drug-like space and almost all compounds studied were in the known drug space. Principal component analysis was an effective technique that enabled the investigation of the relationship between the studied molecular descriptors and was able to separate the lignans from their sugar derivatives and flavonolignans, primarily according to the parameters that are considered when defining chemical space (i.e., number of hydrogen bond donors, acceptors, rotatable bonds, polar surface area and molecular weight). These results indicate that while CLCs and flavonolignans are less drug-like, lignans show a particularly high level of drug-likeness, an observation that coupled with their potent biological activities, demands future pursuit into their potential for use as therapeutics.

Methods for Virtual Screening of GPCR Targets: Approaches and Challenges

Virtual screening (VS) has become an integral part of the drug discovery process and is a valuable tool for finding novel chemical starting points for GPCR targets. Ligand-based VS makes use of biochemical data for known, active compounds and has been applied successfully to many diverse GPCRs. Recent progress in GPCR X-ray crystallography has made it possible to incorporate detailed structural information into the VS process. This chapter outlines the latest VS techniques along with examples that highlight successful applications of these methods. Best practices for increasing the likelihood of VS success, as well as ongoing challenges, are also discussed.

Design, synthesis and biological evaluation of renin inhibitors guided by simulated annealing of chemical potential simulations

We have applied simulated annealing of chemical potential (SACP) to a diverse set of ∼150 very small molecules to provide insights into new interactions in the binding pocket of human renin, a historically difficult target for which to find low molecular weight (MW) inhibitors with good bioavailability. In one of its many uses in drug discovery, SACP provides an efficient, thermodynamically principled method of ranking chemotype replacements for scaffold hopping and manipulating physicochemical characteristics for drug development. We introduce the use of Constrained Fragment Analysis (CFA) to construct and analyze ligands composed of linking those fragments with predicted high affinity. This technique addresses the issue of effectively linking fragments together and provides a predictive mechanism to rank order prospective inhibitors for synthesis. The application of these techniques to the identification of novel inhibitors of human renin is described. Synthesis of a limited set of designed compounds provided potent, low MW analogs (IC₅₀s<100nM) with good oral bioavailability (F>20-58%).

A reliable computational workflow for the selection of optimal screening libraries

Background

The experimental screening of compound collections is a common starting point in many drug discovery projects. Successes of such screening campaigns critically depend on the quality of the screened library. Many libraries are currently available from different vendors yet the selection of the optimal screening library for a specific project is challenging. We have devised a novel workflow for the rational selection of project-specific screening libraries.

Results

The workflow accepts as input a set of virtual candidate libraries and applies the following steps to each library: (1) data curation; (2) assessment of ADME/T profile; (3) assessment of the number of promiscuous binders/frequent HTS hitters; (4) assessment of internal diversity; (5) assessment of similarity to known active compound(s) (optional); (6) assessment of similarity to in-house or otherwise accessible compound collections (optional). For ADME/T profiling, Lipinski’s and Veber’s rule-based filters were implemented and a new blood brain barrier permeation model was developed and validated (85 and 74 % success rate for training set and test set, respectively). Diversity and similarity descriptors which demonstrated best performances in terms of their ability to select either diverse or focused sets of compounds from three databases (Drug Bank, CMC and CHEMBL) were identified and used for diversity and similarity assessments. The workflow was used to analyze nine common screening libraries available from six vendors. The results of this analysis are reported for each library providing an assessment of its quality. Furthermore, a consensus approach was developed to combine the results of these analyses into a single score for selecting the optimal library under different scenarios.

Conclusions

We have devised and tested a new workflow for the rational selection of screening libraries under different scenarios. The current workflow was implemented using the Pipeline Pilot software yet due to the usage of generic components, it can be easily adapted and reproduced by computational groups interested in rational selection of screening libraries. Furthermore, the workflow could be readily modified to include additional components. This workflow has been routinely used in our laboratory for the selection of libraries in multiple projects and consistently selects libraries which are well balanced across multiple parameters.Graphical abstract

Designing an orally available nontoxic p38 inhibitor with a fragment-based strategy

The MAPK p38 became a focal point of inflammatory research when it was recognized that it played a key role in the production of the pro-inflammatory molecules TNF-alpha, IL-beta, and cyclooxygenase-2 (COX-2). The pharmaceutical industry devoted enormous efforts to creating p38 inhibitors, because blocking p38 had the potential of downregulating a group of pro-inflammatory mediators, and thus, one drug could have a cocktail effect. The market potential seemed to be clearly established (Bonafede et al., Clinicoecon Outcomes Res 6:381-388, 2014) with a multiplicity of TNF-alpha antibodies and a soluble receptor (Mewar and Wilson, Br J Pharmacol 162:785-791, 2011) already on the market, although the relationship between TNF-alpha production and p38 activation is a complicated two-way (Sabio and Davis, Semin Immunol 26:237-245, 2014) signal transduction process. With the discovery that activated p38 stabilizes (Mancini and Di Battista, Inflamm Res 60:1083-1092, 2011) COX-2 mRNA and upregulates expression of IL-beta (Bachstetter and Van Eldik, Aging Dis 1:199-211, 2010) probably in a similar manner, inhibiting p38 appeared to be a way of blocking TNF-alpha, COX-2, and IL-beta simultaneously. At Locus Pharmaceuticals we jumped on this opportunity, because we believed that our fragment-based drug discovery approach was ideally suited for making a potent small molecule p38 inhibitor that did not bind in the ATP site, but also had the solubility, lack of planarity, and low molecular weight required of a clinical candidate. Just to be clear, in our experience highly planar compounds often result in poor pharmacokinetics, because they tend to bind strongly to plasma proteins. At Locus we typically repeated assays by adding increasing amounts of plasma to check for potency degradation in the presence of blood. We found this to be a useful early indicator of pharmacokinetics and in vivo behavior. It became clear from our work and the work of others that binding to the ATP site resulted in nonspecific isoform toxicities, but binding in the adjacent allosteric DFG-site resulted in molecules that were too planar and too hydrophobic. Applying the computational method of Simulated Annealing of Chemical Potential (SACP) to this problem, we at Locus were able to come up with surprising fragment substitution patterns that led to potent non-ATP p38 inhibitors with the solubility and lack of planarity that resulted in potent in vivo efficacy in rodents with 33 % oral bioavailability. By using the simulations, we made only a small number of molecules and created a high quality clinical candidate. We also did extensive co-crystallography work, which demonstrated that the compounds bound in the mode predicted by the simulations. Unfortunately, all p38 programs ultimately shut down, because compelling evidence emerged that inhibiting p38 had no long-term clinical (Genovese, Arthritis Rheum 60:317-320, 2009) benefit. Devoting a large amount of limited resources to a target that ultimately turns out to be a mistake because it was not properly validated is a fatal error for a small company, and this is one of the reasons that Locus ultimately failed.

Panel docking of small-molecule libraries - Prospects to improve efficiency of lead compound discovery

Computational docking as a means to prioritise small molecules in drug discovery projects remains a highly popular in silico screening approach. Contemporary docking approaches without experimental parametrisation can reliably differentiate active and inactive chemotypes in a protein binding site, but the absence of a correlation between the score of a predicted binding pose and the biological activity of the molecule presents a clear limitation. Several novel or improved computational approaches have been developed in the recent past to aid in screening and profiling of small-molecule ligands for drug discovery, but also more broadly in developing conceptual relationships between different protein targets by chemical probing. Among those new methodologies is a strategy known as inverse virtual screening, which involves the docking of a compound into different protein structures. In the present article, we review the different computational screening methodologies that employ docking of atomic models, and, by means of a case study, present an approach that expands the inverse virtual screening concept. By computationally screening a reasonably sized library of 1235 compounds against a panel of 48 mostly human kinases, we have been able to identify five groups of putative lead compounds with substantial diversity when compared to each other. One representative of each of the five groups was synthesised, and tested in kinase inhibition assays, yielding two compounds with micro-molar inhibition in five human kinases. This highly economic and cost-effective methodology holds great promise for drug discovery projects, especially in cases where a group of target proteins share high structural similarity in their binding sites.

Identification of semicarbazones, thiosemicarbazones and triazine nitriles as inhibitors of Leishmania mexicana cysteine protease CPB

Cysteine proteases of the papain superfamily are present in nearly all eukaryotes. They play pivotal roles in the biology of parasites and inhibition of cysteine proteases is emerging as an important strategy to combat parasitic diseases such as sleeping sickness, Chagas' disease and leishmaniasis. Homology modeling of the mature Leishmania mexicana cysteine protease CPB2.8 suggested that it differs significantly from bovine cathepsin B and thus could be a good drug target. High throughput screening of a compound library against this enzyme and bovine cathepsin B in a counter assay identified four novel inhibitors, containing the warhead-types semicarbazone, thiosemicarbazone and triazine nitrile, that can be used as leads for antiparasite drug design. Covalent docking experiments confirmed the SARs of these lead compounds in an effort to understand the structural elements required for specific inhibition of CPB2.8. This study has provided starting points for the design of selective and highly potent inhibitors of L. mexicana cysteine protease CPB that may also have useful efficacy against other important cysteine proteases.

Synthesis and mechanism of hypoglycemic activity of benzothiazole derivatives

Adenosine 5'-monophosphate activated protein kinase (AMPK) has emerged as a major potential target for novel antidiabetic drugs. We studied the structure of 2-chloro-5-((Z)-((E)-5-((5-(4,5-dimethyl-2-nitrophenyl)furan-2-yl)methylene)-4-oxothiazolidin-2-ylidene)amino)benzoic acid (PT-1), which attenuates the autoinhibition of the enzyme AMPK, for the design and synthesis of different benzothiazoles with potential antidiabetic activity. We synthesized several structurally related benzothiazole derivatives that increased the rate of glucose uptake in L6 myotubes in an AMPK-dependent manner. One compound, 2-(benzo[d]thiazol-2-ylmethylthio)-6-ethoxybenzo[d]thiazole (34), augmented the rate of glucose uptake up to 2.5-fold compared with vehicle-treated cells and up to 1.1-fold compared to PT-1. Concomitantly, it elevated the abundance of GLUT4 in the plasma membrane of the myotubes and activated AMPK. Subcutaneous administration of 34 to hyperglycemic Kuo Kondo rats carrying the Ay-yellow obese gene (KKAy) mice lowered blood glucose levels toward the normoglycemic range. In accord with its activity, compound 34 showed a high fit value to a pharmacophore model derived from the PT-1.

Wine Compounds as a Source for HTS Screening Collections. A Feasibility Study

High throughput screening (HTS) is extensively used to identify hit and lead compounds in drug discovery programmes. Designing quality screening libraries is a challenge in terms of water solubility, stability and potential oral bioavailability of the compounds. Wines are widely consumed and wine compounds are inherently water soluble, stable and relatively non-toxic. Furthermore, many wine compounds have been proved health-beneficial. To evaluate the feasibility to use wine compounds 3317 were collected from the literature. Their physiochemical properties were evaluated with main stream molecular descriptors. According to the results ∼25 % of the compounds are lead-like; nearly 80 % lie within drug-like chemical space and finally 90 % conform to known drug space (KDS). The rotatable bonds descriptor was the most effective defining lead-like space. The results suggest that many of the wine compounds are interesting and suitable candidates for screening libraries after suitable filtering.

Design and in vitro activities of N-alkyl-N-[(8-R-2,2-dimethyl-2H-chromen-6-yl)methyl]heteroarylsulfonamides, novel, small-molecule hypoxia inducible factor-1 pathway inhibitors and anticancer agents

The hypoxia inducible factor (HIF) pathway is an attractive target for cancer, as it controls tumor adaptation to growth under hypoxia and mediates chemotherapy and radiation resistance. We previously discovered 3,4-dimethoxy-N-[(2,2-dimethyl-2H-chromen-6-yl)methyl]-N-phenylbenzenesulfonamide as a novel, small-molecule HIF-1 pathway inhibitor in a high-throughput cell-based assay, but its in vivo delivery is hampered by poor aqueous solubility (0.009 μM in water; log P(7.4) = 3.7). Here we describe the synthesis of 12 N-alkyl-N-[(8-R-2,2-dimethyl-2H-chromen-6-yl)methyl]heteroarylsulfonamides, which were designed to possess optimal lipophilicities and aqueous solubilities by in silico calculations. Experimental log P(7.4) values of 8 of the 12 new analogs ranged from 1.2-3.1. Aqueous solubilities of three analogs were measured, among which the most soluble N-[(8-methoxy-2,2-dimethyl-2H-chromen-6-yl)methyl]-N-(propan-2-yl)pyridine-2-sulfonamide had an aqueous solubility of 80 μM, e.g., a solubility improvement of ∼9000-fold. The pharmacological optimization had limited impact on drug efficacy as the compounds retained IC(50) values at or below 5 μM in our HIF-dependent reporter assay.

Structure-based drug screening for G-protein-coupled receptors

G-protein-coupled receptors (GPCRs) represent a large family of signaling proteins that includes many therapeutic targets; however, progress in identifying new small molecule drugs has been disappointing. The past 4 years have seen remarkable progress in the structural biology of GPCRs, raising the possibility of applying structure-based approaches to GPCR drug discovery efforts. Of the various structure-based approaches that have been applied to soluble protein targets, such as proteases and kinases, in silico docking is among the most ready applicable to GPCRs. Early studies suggest that GPCR binding pockets are well suited to docking, and docking screens have identified potent and novel compounds for these targets. This review will focus on the current state of in silico docking for GPCRs.

A physicochemical descriptor-based scoring scheme for effective and rapid filtering of kinase-like chemical space

Background

The current chemical space of known small molecules is estimated to exceed 10⁶⁰ structures. Though the largest physical compound repositories contain only a few tens of millions of unique compounds, virtual screening of databases of this size is still difficult. In recent years, the application of physicochemical descriptor-based profiling, such as Lipinski's rule-of-five for drug-likeness and Oprea's criteria of lead-likeness, as early stage filters in drug discovery has gained widespread acceptance. In the current study, we outline a kinase-likeness scoring function based on known kinase inhibitors.

Results

The method employs a collection of 22,615 known kinase inhibitors from the ChEMBL database. A kinase-likeness score is computed using statistical analysis of nine key physicochemical descriptors for these inhibitors. Based on this score, the kinase-likeness of four publicly and commercially available databases, i.e., National Cancer Institute database (NCI), the Natural Products database (NPD), the National Institute of Health's Molecular Libraries Small Molecule Repository (MLSMR), and the World Drug Index (WDI) database, is analyzed. Three of these databases, i.e., NCI, NPD, and MLSMR are frequently used in the virtual screening of kinase inhibitors, while the fourth WDI database is for comparison since it covers a wide range of known chemical space. Based on the kinase-likeness score, a kinase-focused library is also developed and tested against three different kinase targets selected from three different branches of the human kinome tree.

Conclusions

Our proposed methodology is one of the first that explores how the narrow chemical space of kinase inhibitors and its relevant physicochemical information can be utilized to build kinase-focused libraries and prioritize pre-existing compound databases for screening. We have shown that focused libraries generated by filtering compounds using the kinase-likeness score have, on average, better docking scores than an equivalent number of randomly selected compounds. Beyond library design, our findings also impact the broader efforts to identify kinase inhibitors by screening pre-existing compound libraries. Currently, the NCI library is the most commonly used database for screening kinase inhibitors. Our research suggests that other libraries, such as MLSMR, are more kinase-like and should be given priority in kinase screenings.

Size estimation of chemical space: how big is it?

OBJECTIVES

To estimate the size of organic chemical space and its sub-regions, i.e. drug-like chemical space and known drug space (KDS).

METHODS

Analysis of the growth of organic compounds as a function of their carbon atoms based on a power function (f(x)=A×B, C=x) and an exponential function (f(x)=AeBx). Also, the statistical distribution of KDS and drug-like chemical space (drugs with good oral-bioavailability) based on their carbon atom count was used to deduce their size.

KEY FINDINGS

The power function (f(x)=A×B, C=x) gives a superior fit to the growth of organic compounds leading to an estimate of 3.4×109 populating chemical space. KDS is predicted to be 2.0×106 molecules and drug-like chemical space is calculated to be 1.1×106 compounds.

CONCLUSIONS

The values here are much smaller than previously reported. However, the numbers are large but not astronomical. A clear rationale on how we reach these numbers is given, which hopefully will lead to more refined predictions.

Ionic channels as targets for drug design: a review on computational methods

Ion channels are involved in a broad range of physiological and pathological processes. The implications of ion channels in a variety of diseases, including diabetes, epilepsy, hypertension, cancer and even chronic pain, have signaled them as pivotal drug targets. Thus far, drugs targeting ion channels were developed without detailed knowledge of the molecular interactions between the lead compounds and the target channels. In recent years, however, the emergence of high-resolution structures for a plethora of ion channels paves the way for computer-assisted drug design. Currently, available functional and structural data provide an attractive platform to generate models that combine substrate-based and protein-based approaches. In silico approaches include homology modeling, quantitative structure-activity relationships, virtual ligand screening, similarity and pharmacophore searching, data mining, and data analysis tools. These strategies have been frequently used in the discovery and optimization of novel molecules with enhanced affinity and specificity for the selected therapeutic targets. In this review we summarize recent applications of in silico methods that are being used for the development of ion channel drugs.

Towards Proteome-Wide Interaction Models Using the Proteochemometrics Approach

A proteochemometrics model was induced from all interaction data in the BindingDB database, comprizing in all 7078 protein-ligand complexes with representatives from all major drug target categories. Proteins were represented by alignment-independent sequence descriptors holding information on properties such as hydrophobicity, charge, and secondary structure. Ligands were represented by commonly used QSAR descriptors. The inhibition constant (pKi ) values of protein-ligand complexes were discretized into "high" and "low" interaction activity. Different machine-learning techniques were used to induce models relating protein and ligand properties to the interaction activity. The best was decision trees, which gave an accuracy of 80 % and an area under the ROC curve of 0.81. The tree pointed to the protein and ligand properties, which are relevant for the interaction. As the approach does neither require alignments nor knowledge of protein 3D structures virtually all available protein-ligand interaction data could be utilized, thus opening a way to completely general interaction models that may span entire proteomes.

Comparative virtual screening and novelty detection for NMDA-GlycineB antagonists

Identification of novel compound classes for a drug target is a challenging task for cheminformatics and drug design when considerable research has already been undertaken and many potent lead structures have been identified, which leaves limited unclaimed chemical space for innovation. We validated and successfully applied different state-of-the-art techniques for virtual screening (Bayesian machine learning, automated molecular docking, pharmacophore search, pharmacophore QSAR and shape analysis) of 4.6 million unique and readily available chemical structures to identify promising new and competitive antagonists of the strychnine-insensitive Glycine binding site (Glycine(B) site) of the NMDA receptor. The novelty of the identified virtual hits was assessed by scaffold analysis, putting a strong emphasis on novelty detection. The resulting hits were tested in vitro and several novel, active compounds were identified. While the majority of the computational methods tested were able to partially discriminate actives from structurally similar decoy molecules, the methods differed substantially in their prospective applicability in terms of novelty detection. The results demonstrate that although there is no single best computational method, it is most worthwhile to follow this concept of focused compound library design and screening, as there still can new bioactive compounds be found that possess hitherto unexplored scaffolds and interesting variations of known chemotypes.

Known drug space as a metric in exploring the boundaries of drug-like chemical space

In this work, marketed drug compounds (or known drug space) were used as a metric to test the principles of eliminating parent structures of the nitrenium ion (aryl-amine/nitro compounds) as well as sulphur and halogen containing molecules from screening compound collections. Molecules containing such moieties and/or atoms have biological and physiochemical properties, which possibly make them less attractive as leads in drug development. It was found that precursors to the nitrenium ion were relatively abundant in known drug space at 14%. Thus, their simple elimination from drug-like chemical space is not advisable. Interestingly, the mutagenic potential of the nitrenium ions is linked to their stability and quantum mechanical calculations can be used to estimate it. Furthermore, 24% of drugs investigated contained sulphur atoms and around 28% were halogenated. As some sulphur containing moieties were abundant whilst others were scarce, it was deduced that it would be more effective to eliminate specific molecular scaffolds rather than all sulphur containing molecules. In conclusion, it has been shown that by statistically analysing known drug space a better understanding of the boundaries of drug-like chemical space was established which can help medicinal chemists in finding rewarding regions of chemical space.

Comprehensive mechanistic analysis of hits from high-throughput and docking screens against beta-lactamase

High-throughput screening (HTS) is widely used in drug discovery. Especially for screens of unbiased libraries, false positives can dominate "hit lists"; their origins are much debated. Here we determine the mechanism of every active hit from a screen of 70,563 unbiased molecules against beta-lactamase using quantitative HTS (qHTS). Of the 1,274 initial inhibitors, 95% were detergent-sensitive and were classified as aggregators. Among the 70 remaining were 25 potent, covalent-acting beta-lactams. Mass spectra, counter-screens, and crystallography identified 12 as promiscuous covalent inhibitors. The remaining 33 were either aggregators or irreproducible. No specific reversible inhibitors were found. We turned to molecular docking to prioritize molecules from the same library for testing at higher concentrations. Of 16 tested, 2 were modest inhibitors. Subsequent X-ray structures corresponded to the docking prediction. Analog synthesis improved affinity to 8 microM. These results suggest that it may be the physical behavior of organic molecules, not their reactivity, that accounts for most screening artifacts. Structure-based methods may prioritize weak-but-novel chemotypes in unbiased library screens.

Functionalized fullerenes mediate photodynamic killing of cancer cells: Type I versus Type II photochemical mechanism

Photodynamic therapy (PDT) employs the combination of nontoxic photosensitizers (PS) and harmless visible light to generate reactive oxygen species (ROS) and kill cells. Most clinically studied PS are based on the tetrapyrrole structure of porphyrins, chlorines, and related molecules, but new nontetrapyrrole PS are being sought. Fullerenes are soccer-ball shaped molecules composed of 60 or 70 carbon atoms and have attracted interest in connection with the search for biomedical applications of nanotechnology. Fullerenes are biologically inert unless derivatized with functional groups, whereupon they become soluble and can act as PS. We have compared the photodynamic activity of six functionalized fullerenes with 1, 2, or 3 hydrophilic or 1, 2, or 3 cationic groups. The octanol-water partition coefficients were determined and the relative contributions of Type I photochemistry (photogeneration of superoxide in the presence of NADH) and Type II photochemistry (photogeneration of singlet oxygen) were studied by measurement of oxygen consumption, 1270-nm luminescence and EPR spin trapping of the superoxide product. We studied three mouse cancer cell lines: (J774, LLC, and CT26) incubated for 24 h with fullerenes and illuminated with white light. The order of effectiveness as PS was inversely proportional to the degree of substitution of the fullerene nucleus for both the neutral and the cationic series. The monopyrrolidinium fullerene was the most active PS against all cell lines and induced apoptosis 4-6 h after illumination. It produced diffuse intracellular fluorescence when dichlorodihydrofluorescein was added as an ROS probe, suggesting a Type I mechanism for phototoxicity. We conclude that certain functionalized fullerenes have potential as novel PDT agents and phototoxicity may be mediated both by superoxide and by singlet oxygen.

Modern agrochemical research: a missed opportunity for drug discovery?

The word "agrochemical" has often taken on a pejorative character in the public mind. Some of the negative tone might have coloured the perception of the industry by pharma, together with views on the chemical nature of agrochemicals that seem to be based on older pesticides that date back to the 1950s and 1960s. In this review, we try to address some of these concerns, draw out the similarities between agrochemical and pharmaceutical research and highlight opportunities for drug discovery that are offered by pesticide-related compounds, particularly with regard to herbicides and compounds with leadlike physical properties.

A practical view of ‘druggability’

The introduction of Lipinski's 'Rule of Five' has initiated a profound shift in the thinking paradigm of medicinal chemists. Understanding the difference between biologically active small molecules and drugs became a priority in the drug discovery process, and the importance of addressing pharmacokinetic properties early during lead optimization is a clear result. These concepts of 'drug-likeness' and 'druggability' have been extended to proteins and genes for target identification and selection. How should these concepts be integrated practically into the drug discovery process? This review summarizes the recent advances in the field and examines the usefulness of 'the rules of the game' in practice from a medicinal chemist's standpoint.

Multi-target strategies for the improved treatment of depressive states: Conceptual foundations and neuronal substrates, drug discovery and therapeutic application

Major depression is a debilitating and recurrent disorder with a substantial lifetime risk and a high social cost. Depressed patients generally display co-morbid symptoms, and depression frequently accompanies other serious disorders. Currently available drugs display limited efficacy and a pronounced delay to onset of action, and all provoke distressing side effects. Cloning of the human genome has fuelled expectations that symptomatic treatment may soon become more rapid and effective, and that depressive states may ultimately be "prevented" or "cured". In pursuing these objectives, in particular for genome-derived, non-monoaminergic targets, "specificity" of drug actions is often emphasized. That is, priority is afforded to agents that interact exclusively with a single site hypothesized as critically involved in the pathogenesis and/or control of depression. Certain highly selective drugs may prove effective, and they remain indispensable in the experimental (and clinical) evaluation of the significance of novel mechanisms. However, by analogy to other multifactorial disorders, "multi-target" agents may be better adapted to the improved treatment of depressive states. Support for this contention is garnered from a broad palette of observations, ranging from mechanisms of action of adjunctive drug combinations and electroconvulsive therapy to "network theory" analysis of the etiology and management of depressive states. The review also outlines opportunities to be exploited, and challenges to be addressed, in the discovery and characterization of drugs recognizing multiple targets. Finally, a diversity of multi-target strategies is proposed for the more efficacious and rapid control of core and co-morbid symptoms of depression, together with improved tolerance relative to currently available agents.

Hierarchical docking of databases of multiple ligand conformations

Ligand flexibility is an important problem in molecular docking and virtual screening. To address this challenge, we investigate a hierarchical pre-organization of multiple conformations of small molecules. Such organization of pre-calculated conformations removes the exploration of ligand conformational space from the docking calculation and allows for concise representation of what can be thousands of conformations. The hierarchy also recognizes and prunes incompatible conformations early in the calculation, eliminating redundant calculations of fit. We investigate the method by docking the MDL Drug Data Report (MDDR), an annotated database of 100,000 molecules, into apo and holo forms of seven unrelated targets. This annotated database allows us to track the ranking of tens to hundreds of annotated ligands in each of the docking systems. The binding sites and database are prepared in an automated fashion in an attempt to remove some human bias from the calculations. Many thousands of explicit and implicit ligand conformations may be docked in calculations not much longer than required for single conformer docking. As long as internal energies are not considered, recombination with the hierarchy is additive as the number of degrees of freedom is increased. Molecules with even millions of conformations can be docked in a few minutes on a single desktop computer.

Structure-based optimization of a non-beta-lactam lead results in inhibitors that do not up-regulate beta-lactamase expression in cell culture

Bacterial expression of beta-lactamases is the most widespread resistance mechanism to beta-lactam antibiotics, such as penicillins and cephalosporins. There is a pressing need for novel, non-beta-lactam inhibitors of these enzymes. One previously discovered novel inhibitor of the beta-lactamase AmpC, compound 1, has several favorable properties: it is chemically dissimilar to beta-lactams and is a noncovalent, competitive inhibitor of the enzyme. However, at 26 microM its activity is modest. Using the X-ray structure of the AmpC/1 complex as a template, 14 analogues were designed and synthesized. The most active of these, compound 10, had a K(i) of 1 microM, 26-fold better than the lead. To understand the origins of this improved activity, the structures of AmpC in complex with compound 10 and an analogue, compound 11, were determined by X-ray crystallography to 1.97 and 1.96 A, respectively. Compound 10 was active in cell culture, reversing resistance to the third generation cephalosporin ceftazidime in bacterial pathogens expressing AmpC. In contrast to beta-lactam-based inhibitors clavulanate and cefoxitin, compound 10 did not up-regulate beta-lactamase expression in cell culture but simply inhibited the enzyme expressed by the resistant bacteria. Its escape from this resistance mechanism derives from its dissimilarity to beta-lactam antibiotics.

Virtual screening of chemical libraries

Virtual screening uses computer-based methods to discover new ligands on the basis of biological structures. Although widely heralded in the 1970s and 1980s, the technique has since struggled to meet its initial promise, and drug discovery remains dominated by empirical screening. Recent successes in predicting new ligands and their receptor-bound structures, and better rates of ligand discovery compared to empirical screening, have re-ignited interest in virtual screening, which is now widely used in drug discovery, albeit on a more limited scale than empirical screening.

Capter 11 Filtering in Drug Discovery

This chapter discusses the concept of filtering in drug discovery. Multiple filters may be incorporated into a definition of drug-likeness and this leads to tradeoffs among compound properties in compounds intended for screening. The optimization of compound properties may require some type of multiparameter optimization scheme in library design. Fingerprint algorithms can be used to guide diversity. Filters also need to be employed in the chemistry synthesis planning process so that good quality compounds are made. Differences in property ranges between oral and injectable drugs are summarized in the chapter. Oral drugs are lower in MWT and have fewer H-bond donors, acceptors, and rotatable bonds. A scheme for separating central nervous system (CNS)- from non-CNS-active drugs in the WDI allowed the discovery of simple parameters relating to passive blood brain barrier (BBB) permeability and the prediction of p-glycoprotein (PGP) affinity. The PGP transporter is a major barrier to the entry of compounds to the CNS. Appropriately determined PGP efflux ratios can be used as a measure of compound affinity to PGP.

Lead- and drug-like compounds: the rule-of-five revolution

Citations in CAS SciFinder to the rule-of-five (RO5) publication will exceed 1000 by year-end 2004. Trends in the RO5 literature explosion that can be discerned are the further definitions of drug-like. This topic is explored in terms of drug-like physicochemical features, drug-like structural features, a comparison of drug-like and non-drug-like in drug discovery and a discussion of how drug-like features relate to clinical success. Physicochemical features of CNS drugs and features related to CNS blood-brain transporter affinity are briefly reviewed. Recent literature on features of non-oral drugs is reviewed and how features of lead-like compounds differ from those of drug-like compounds is discussed. Most recently, partly driven by NIH roadmap initiatives, considerations have arisen as to what tool-like means in the search for chemical tools to probe biology space. All these topics frame the scope of this short review/perspective.: