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The construction of novel derivatives has been made possible because of a nucleophilic substitution at a methylene attached to the bridgehead (1-) position of the 2-azabicyclo[2.1.1]hexane ring system. This provides access to epibatidine analogues, which contains heterocyclic substituents, and to homologation at the 1-position.

Since the advent of high-throughput screening (HTS), there has been an urgent need for methods that facilitate the interrogation of large-scale chemical biology data to build a mode of action (MoA) hypothesis. This can be done either prior to the HTS by subset design of compounds with known MoA or post HTS by data annotation and mining. To enable this process, we developed a tool that compares compounds solely on the basis of their bioactivity: the chemical biological descriptor "high-throughput screening fingerprint" (HTS-FP). In the current embodiment, data are aggregated from 195 biochemical and cell-based assays developed at Novartis and can be used to identify bioactivity relationships among the in-house collection comprising ~1.5 million compounds. We demonstrate the value of the HTS-FP for virtual screening and in particular scaffold hopping. HTS-FP outperforms state of the art methods in several aspects, retrieving bioactive compounds with remarkable chemical dissimilarity to a probe structure. We also apply HTS-FP for the design of screening subsets in HTS. Using retrospective data, we show that a biodiverse selection of plates performs significantly better than a chemically diverse selection of plates, both in terms of number of hits and diversity of chemotypes retrieved. This is also true in the case of hit expansion predictions using HTS-FP similarity. Sets of compounds clustered with HTS-FP are biologically meaningful, in the sense that these clusters enrich for genes and gene ontology (GO) terms, showing that compounds that are bioactively similar also tend to target proteins that operate together in the cell. HTS-FP are valuable not only because of their predictive power but mainly because they relate compounds solely on the basis of bioactivity, harnessing the accumulated knowledge of a high-throughput screening facility toward the understanding of how compounds interact with the proteome.

We present a novel method to better investigate adverse drug reactions in chemical space. By integrating data sources about adverse drug reactions of drugs with an established cheminformatics modeling method, we generate a data set that is then visualized with a systems biology tool. Thereby new insights into undesired drug effects are gained. In this work, we present a global analysis linking chemical features to adverse drug reactions.

The elucidation of drug targets is important both to optimize desired compound action and to understand drug side-effects. In this study, we created statistical models which link chemical substructures of ligands to protein domains in a probabilistic manner and employ the model to triage the results of affinity chromatography experiments. By annotating targets with their InterPro domains, general rules of ligand-protein domain associations were derived and successfully employed to predict protein targets outside the scope of the training set. This methodology was then tested on a proteomics affinity chromatography data set containing 699 compounds. The domain prediction model correctly detected 31.6% of the experimental targets at a specificity of 46.8%. This is striking since 86% of the predicted targets are not part of them (but share InterPro domains with them), and thus could not have been predicted by conventional target prediction approaches. Target predictions improve drastically when significance (FDR) scores for target pulldowns are employed, emphasizing their importance for eliminating artifacts. Filament proteins (such as actin and tubulin) are detected to be 'frequent hitters' in proteomics experiments and their presence in pulldowns is not supported by the target predictions. On the other hand, membrane-bound receptors such as serotonin and dopamine receptors are noticeably absent in the affinity chromatography sets, although their presence would be expected from the predicted targets of compounds. While this can partly be explained by the experimental setup, we suggest the computational methods employed here as a complementary step of identifying protein targets of small molecules. Affinity chromatography results for gefitinib are discussed in detail and while two out of the three kinases with the highest affinity to gefitinib in biochemical assays are detected by affinity chromatography, also the possible involvement of NSF as a target for modulating cancer progressions via beta-arrestin can be proposed by this method.

We present a workflow that leverages data from chemogenomics based target predictions with Systems Biology databases to better understand off-target related toxicities. By analyzing a set of compounds that share a common toxic phenotype and by comparing the pathways they affect with pathways modulated by nontoxic compounds we are able to establish links between pathways and particular adverse effects. We further link these predictive results with literature data in order to explain why a certain pathway is predicted. Specifically, relevant pathways are elucidated for the side effects rhabdomyolysis and hypotension. Prospectively, our approach is valuable not only to better understand toxicities of novel compounds early on but also for drug repurposing exercises to find novel uses for known drugs.

Different molecular descriptors capture different aspects of molecular structures, but this effect has not yet been quantified systematically on a large scale. In this work, we calculate the similarity of 37 descriptors by repeatedly selecting query compounds and ranking the rest of the database. Euclidean distances between the rank-ordering of different descriptors are calculated to determine descriptor (as opposed to compound) similarity, followed by PCA for visualization. Four broad descriptor classes are identified, which are circular fingerprints; circular fingerprints considering counts; path-based and keyed fingerprints; and pharmacophoric descriptors. Descriptor behavior is much more defined by those four classes than the particular parametrization. Using counts instead of the presence/absence of fingerprints significantly changes descriptor behavior, which is crucial for performance of topological autocorrelation vectors, but not circular fingerprints. Four-point pharmacophores (piDAPH4) surprisingly lead to much higher retrieval rates than three-point pharmacophores (28.21% vs 19.15%) but still similar rank-ordering of compounds (retrieval of similar actives). Looking into individual rankings, circular fingerprints seem more appropriate than path-based fingerprints if complex ring systems or branching patterns are present; count-based fingerprints could be more suitable in databases with a large number of repeated subunits (amide bonds, sugar rings, terpenes). Information-based selection of diverse fingerprints for consensus scoring (ECFP4/TGD fingerprints) led only to marginal improvement over single fingerprint results. While it seems to be nontrivial to exploit orthogonal descriptor behavior to improve retrieval rates in consensus virtual screening, those descriptors still each retrieve different actives which corroborates the strategy of employing diverse descriptors individually in prospective virtual screening settings.

In this work we explore the possibilities of using fragment-based screening data to prioritize compounds from a full HTS library, a method we call virtual fragment linking (VFL). The ability of VFL to identify compounds of nanomolar potency based on micromolar fragment binding data was tested on 75 target classes from the WOMBAT database and succeeded in 57 cases. Further, the method was demonstrated for seven drug targets from in-house screening programs that performed both FBS of 8800 fragments and screens of the full library. VFL captured between 28% and 67% of the hits (IC 50 < 10microM) in the top 5% of the ranked library for four of the targets (enrichment between 5-fold and 13-fold). Our findings lead us to conclude that proper coverage of chemical space by the fragment library is crucial for the VFL methodology to be successful in prioritizing HTS libraries from fragment-based screening data.

High throughput screening (HTS) data is often noisy, containing both false positives and negatives. Thus, careful triaging and prioritization of the primary hit list can save time and money by identifying potential false positives before incurring the expense of followup. Of particular concern are cell-based reporter gene assays (RGAs) where the number of hits may be prohibitively high to be scrutinized manually for weeding out erroneous data. Based on statistical models built from chemical structures of 650 000 compounds tested in RGAs, we created "frequent hitter" models that make it possible to prioritize potential false positives. Furthermore, we followed up the frequent hitter evaluation with chemical structure based in silico target predictions to hypothesize a mechanism for the observed "off target" response. It was observed that the predicted cellular targets for the frequent hitters were known to be associated with undesirable effects such as cytotoxicity. More specifically, the most frequently predicted targets relate to apoptosis and cell differentiation, including kinases, topoisomerases, and protein phosphatases. The mechanism-based frequent hitter hypothesis was tested using 160 additional druglike compounds predicted by the model to be nonspecific actives in RGAs. This validation was successful (showing a 50% hit rate compared to a normal hit rate as low as 2%), and it demonstrates the power of computational models toward understanding complex relations between chemical structure and biological function.

Bridging chemical and biological space is the key to drug discovery and development. Typically, cheminformatics methods operate under the assumption that similar chemicals have similar biological activity. Ideally then, one could predict a drug's biological function(s) given only its chemical structure by similarity searching in libraries of compounds with known activities. In practice, effectively choosing a similarity metric is case dependent. This work compares both 2D and 3D chemical descriptors as tools for predicting the biological targets of ligand probes, on the basis of their similarity to reference molecules in a 46,000 compound, biologically annotated chemical database. Overall, we found that the 2D methods employed here outperform the 3D (88% vs 67% success) in correct target prediction. However, the 3D descriptors proved superior in cases of probes with low structural similarity to other compounds in the database (singletons). Additionally, the 3D method (FEPOPS) shows promise for providing pharmacophoric alignment of the small molecules' chemical features consistent with those seen in experimental ligand/ receptor complexes. These results suggest that querying annotated chemical databases with a systematic combination of both 2D and 3D descriptors will prove more effective than employing single methods.

Conventional similarity searching of molecules compares single (or multiple) active query structures to each other in a relative framework, by means of a structural descriptor and a similarity measure. While this often works well, depending on the target, we show here that retrieval rates can be improved considerably by incorporating an external framework describing ligand bioactivity space for comparisons ("Bayes affinity fingerprints"). Structures are described by Bayes scores for a ligand panel comprising about 1000 activity classes extracted from the WOMBAT database. The comparison of structures is performed via the Pearson correlation coefficient of activity classes, that is, the order in which two structures are similar to the panel activity classes. Compound retrieval on a recently published data set could be improved by as much as 24% relative (9% absolute). Knowledge about the shape of the "bioactive chemical universe" is thus beneficial to identifying similar bioactivities. Principal component analysis was employed to further analyze activity space with the objective to define orthogonal ligand bioactive chemical space, leading to nine major (roughly orthogonal) activity axes. Employing only those nine activity classes, retrieval rates are still comparable to original Bayes affinity fingerprints; thus, the concept of orthogonal bioactive ligand chemical space was validated as being an information-rich but low-dimensional representation of bioactivity space. Correlations between activity classes are a major determinant to gauge whether the desired multitarget activity of drugs is (on the basis of current knowledge) a feasible concept because it measures the extent to which activities can be optimized independently, or only by strongly influencing one another.

Target identification is a critical step following the discovery of small molecules that elicit a biological phenotype. The present work seeks to provide an in silico correlate of experimental target fishing technologies in order to rapidly fish out potential targets for compounds on the basis of chemical structure alone. A multiple-category Laplacian-modified naïve Bayesian model was trained on extended-connectivity fingerprints of compounds from 964 target classes in the WOMBAT (World Of Molecular BioAcTivity) chemogenomics database. The model was employed to predict the top three most likely protein targets for all MDDR (MDL Drug Database Report) database compounds. On average, the correct target was found 77% of the time for compounds from 10 MDDR activity classes with known targets. For MDDR compounds annotated with only therapeutic or generic activities such as "antineoplastic", "kinase inhibitor", or "anti-inflammatory", the model was able to systematically deconvolute the generic activities to specific targets associated with the therapeutic effect. Examples of successful deconvolution are given, demonstrating the usefulness of the tool for improving knowledge in chemogenomics databases and for predicting new targets for orphan compounds.

High-throughput screening (HTS) plays a pivotal role in lead discovery for the pharmaceutical industry. In tandem, cheminformatics approaches are employed to increase the probability of the identification of novel biologically active compounds by mining the HTS data. HTS data is notoriously noisy, and therefore, the selection of the optimal data mining method is important for the success of such an analysis. Here, we describe a retrospective analysis of four HTS data sets using three mining approaches: Laplacian-modified naive Bayes, recursive partitioning, and support vector machine (SVM) classifiers with increasing stochastic noise in the form of false positives and false negatives. All three of the data mining methods at hand tolerated increasing levels of false positives even when the ratio of misclassified compounds to true active compounds was 5:1 in the training set. False negatives in the ratio of 1:1 were tolerated as well. SVM outperformed the other two methods in capturing active compounds and scaffolds in the top 1%. A Murcko scaffold analysis could explain the differences in enrichments among the four data sets. This study demonstrates that data mining methods can add a true value to the screen even when the data is contaminated with a high level of stochastic noise.

A primary goal of 3D similarity searching is to find compounds with similar bioactivity to a reference ligand but with different chemotypes, i.e., "scaffold hopping". However, an adequate description of chemical structures in 3D conformational space is difficult due to the high-dimensionality of the problem. We present an automated method that simplifies flexible 3D chemical descriptions in which clustering techniques traditionally used in data mining are exploited to create "fuzzy" molecular representations called FEPOPS (feature point pharmacophores). The representations can be used for flexible 3D similarity searching given one or more active compounds without a priori knowledge of bioactive conformations or pharmacophores. We demonstrate that similarity searching with FEPOPS significantly enriches for actives taken from in-house high-throughput screening datasets and from MDDR activity classes COX-2, 5-HT3A, and HIV-RT, while also scaffold or ring-system hopping to new chemical frameworks. Further, inhibitors of target proteins (dopamine 2 and retinoic acid receptor) are recalled by FEPOPS by scaffold hopping from their associated endogenous ligands (dopamine and retinoic acid). Importantly, the method excels in comparison to commonly used 2D similarity methods (DAYLIGHT, MACCS, Pipeline Pilot fingerprints) and a commercial 3D method (Pharmacophore Distance Triplets) at finding novel scaffold classes given a single query molecule.

We have previously reported that the application of a Laplacian-modified naive Bayesian (NB) classifier may be used to improve the ranking of known inhibitors from a random database of compounds after High-Throughput Docking (HTD). The method relies upon the frequency of substructural features among the active and inactive compounds from 2D fingerprint information of the compounds. Here we present an investigation of the role of extended connectivity fingerprints in training the NB classifier against HTD studies on the HIV-1 protease using three docking programs: Glide, FlexX, and GOLD. The results show that the performance of the NB classifier is due to the presence of a large number of features common to the set of known active compounds rather than a single structural or substructural scaffold. We demonstrate that the Laplacian-modified naive Bayesian classifier trained with data from high-throughput docking is superior at identifying active compounds from a target database in comparison to conventional two-dimensional substructure search methods alone.

We have previously shown that a machine learning technique can improve the enrichment of high-throughput docking (HTD) results. In the previous cases studied, however, the application of a naive Bayes classifier failed to improve enrichment for instances where HTD alone was unable to generate an acceptable enrichment. We present here a protocol to rescue poor docking results a priori using a combination of rank-by-median consensus scoring and naive Bayesian categorization.

The technology underpinning high-throughput docking (HTD) has developed over the past few years to where it has become a vital tool in modern drug discovery. Although the performance of various docking algorithms is adequate, the ability to accurately and consistently rank compounds using a scoring function remains problematic. We show that by employing a simple machine learning method (naïve Bayes) it is possible to significantly overcome this deficiency. Compounds from the Available Chemical Directory (ACD), along with known active compounds, were docked into two protein targets using three software packages. In cases where HTD alone was able to show some enrichment, the application of naïve Bayes was able to improve upon the enrichment. The application of this methodology to enrich HTD results can be carried out without a priori knowledge of the activity of compounds and results in superior enrichment of known actives compared to the use of scoring methods alone.

Polymer-directed enzyme prodrug therapy (PDEPT) is a novel two-step antitumor approach that uses a combination of a polymeric prodrug and polymer-enzyme conjugate to generate a cytotoxic drug rapidly and selectively at the tumor site. Previously we have shown that N-(2-hydroxypropyl)methacrylamide (HPMA) copolymer-bound cathepsin B can release doxorubicin intratumorally from an HPMA copolymer conjugate PK1. Here we describe for the first time the synthesis and biological characterization of a PDEPT model combination that uses an HPMA-copolymer-methacryloyl-glycine-glycine-cephalosporin-doxorubicin (HPMA-co-MA-GG-C-Dox) as the macromolecular prodrug and an HPMA copolymer conjugate containing the nonmammalian enzyme beta-lactamase (HPMA-co-MA-GG-beta-L) as the activating component. HPMA-co-MA-GG-C-Dox had a molecular weight of approximately 31 600 Da and a C-Dox content of 5.85 wt %. Whereas free beta-L has a molecular weight of 45 kDa, the HPMA-co-MA-GG-beta-L conjugate had a molecular weight in the range of 75-150 kDa, and following purification no free enzyme was detectable. Against the cephalosporin C or HPMA-co-MA-GG-C-Dox substrates, the HPMA-co-MA-GG-beta-L conjugate retained 70% and 80% of its activity, respectively. In vivo (125)I-labeled HPMA-co-MA-GG-beta-L showed prolonged plasma concentration and greater tumor targeting than (125)I-labeled beta-L due to the enhanced permeability and retention (EPR) effect. Moreover, administration of HPMA-co-MA-GG-C-Dox iv to mice bearing sc B16F10 melanoma followed after 5 h by HPMA-co-MA-GG-beta-L led to release of free Dox. The PDEPT combination caused a significant decrease in tumor growth (T/C = 132%) whereas neither free Dox nor HPMA-co-MA-GG-C-Dox alone displayed activity. The PDEPT combination displayed no toxicity at the doses used, so further evaluation of this approach to establish the maximum tolerated dose (MTD) is recommended.

The 1-(2-nitrophenyl)thiosemicarbazide (TSC) derivative, (S)-1-[4-(4-benzhydrylthiosemicarbazido)-3-nitrobenzenesulfonyl]pyrrolidine-2-carboxylic acid [2-[(2-dimethylaminoethyl)methylamino]ethyl]amide (bradyzide; (S)-4), was recently disclosed as a novel, potent, orally active nonpeptide bradykinin (BK) B2 receptor antagonist. The compound inhibited the specific binding of [3H]BK to NG108-15 cell membrane preparations (rodent neuroblastoma-glioma) expressing B2 receptors with a K(i) of 0.5 +/- 0.2 nM. Compound (S)-4 also demonstrated oral efficacy against Freund's complete adjuvant (FCA)-induced mechanical hyperalgesia in rats with an ED50 value of 0.84 micromol/kg. After we optimized the terminal binding determinants projecting from the TSC framework, we found that it was possible to replace the potentially toxicophoric nitro and divalent sulfur moieties with only a 15-fold loss in binding affinity ((S)-14a). However, bradyzide and its congeners were found to have much lower affinities for cloned human B2 receptors, expressed in Cos-7 cells. The hitherto synthesized TSC series was screened against the human B2 receptor, and the dibenzosuberane (DBS) pharmacophore emerged as the key structural requirement for potency. Incorporation of this group resulted in a series of derivatives ((S)-14d,e and 19b-d) with K(i) ranges of 10.7-176 nM in NG108-15 cells (expressing the rodent B2 receptor) and 0.79-253 nM in Cos-7 cells (expressing the human B2 receptor). There was no evidence of agonist activity with any of the nonpeptides in any of the cell lines tested. In vivo, oral administration of compound 19c reversed FCA-induced and turpentine-induced mechanical hyperalgesia in rodents with ED50 values of 0.027 and 0.32 micromol/kg, respectively. The selectivity profiles of compounds (S)-14f and (S)-14g were also assessed to determine the conformational and/or steric preferences of the double-ring arrangement. The affinity of (S)-14 g for the human B2 receptor suggested that it may be a hydrophobic interaction with the ethane bridge of the DBS moiety that accounts for the increased potency of compounds (S)-14d,e and 19b,c at this receptor, by favoring a binding mode inaccessible to the unsubstituted diphenylmethyl derivative, (S)-4.