Off-target binding of an anti-amyloid beta monoclonal antibody to platelet factor 4 causes acute and chronic toxicity in cynomolgus monkeys.

ABT-736 is a humanized monoclonal antibody generated to target a specific conformation of the amyloid-beta (Aß) protein oligomer. Development of ABT-736 for Alzheimer's disease was discontinued due to severe adverse effects (AEs) observed in cynomolgus monkey toxicity studies. The acute nature of AEs observed only at the highest doses suggested potential binding of ABT-736 to an abundant plasma protein. Follow-up investigations indicated polyspecificity of ABT-736, including unintended high-affinity binding to monkey and human plasma protein platelet factor 4 (PF-4), known to be involved in heparin-induced thrombocytopenia (HIT) in humans. The chronic AEs observed at the lower doses after repeat administration in monkeys were consistent with HIT pathology. Screening for a backup antibody revealed that ABT-736 possessed additional unintended binding characteristics to other, unknown factors. A subsequently implemented screening funnel focused on nonspecific binding led to the identification of h4D10, a high-affinity Aß oligomer binding antibody that did not bind PF-4 or other unintended targets and had no AEs in vivo. This strengthened the hypothesis that ABT-736 toxicity was not Aß target-related, but instead was the consequence of polyspecificity including PF-4 binding, which likely mediated the acute and chronic AEs and the HIT-like pathology. In conclusion, thorough screening of antibody candidates for nonspecific interactions with unrelated molecules at early stages of discovery can eliminate candidates with polyspecificity and reduce potential for toxicity caused by off-target binding.

Combining Machine Learning and Molecular Dynamics to Predict P-Glycoprotein Substrates.

The efflux transporter P-glycoprotein (P-gp) is responsible for the extrusion of a wide variety of molecules, including drug molecules, from the cell. Therefore, P-gp-mediated efflux transport limits the bioavailability of drugs. To identify potential P-gp substrates early in the drug discovery process, in silico models have been developed based on structural and physicochemical descriptors. In this study, we investigate the use of molecular dynamics fingerprints (MDFPs) as an orthogonal descriptor for the training of machine learning (ML) models to classify small molecules into substrates and nonsubstrates of P-gp. MDFPs encode the information from short MD simulations of the molecules in different environments (water, membrane, or protein pocket). The performance of the MDFPs, evaluated on both an in-house dataset (3930 compounds) and a public dataset from ChEMBL (1114 compounds), is compared to that of commonly used 2D molecular descriptors, including structure-based and property-based descriptors. We find that all tested classifiers interpolate well, achieving high accuracy on chemically diverse subsets. However, by challenging the models with external validation and prospective analysis, we show that only tree-based ML models trained on MDFPs or property-based descriptors generalize well to regions of the chemical space not covered by the training set.

Enzymatic Late-Stage Oxidation of Lead Compounds with Solubilizing Biomimetic Docking/Protecting groups.

Late-stage functionalization of lead compounds is of high interest in drug discovery since it offers an easy access to metabolites and derivatives of a lead compound without the need to redesign an often long multistep synthesis. Owing to their high degree of chemoselectivity, biocatalytic transformations, enzymatic oxidations in particular, are potentially very powerful because they could allow the synthesis of less lipophilic derivatives of a lead compound. In the majority of cases, enzymatic oxidations have been used in an empirical way as their regioselectivity is difficult to predict. In this publication, the concept of using docking/protecting groups in a biomimetic fashion was investigated, which could help steer the regioselectivity of a P450BM3 -mediated oxidation. A novel set of docking/protecting groups was designed that can be cleaved under very mild conditions and address the often problematic aqueous solubility of the substrates. Vabicaserin was used as tool compound containing typical groups such as basic, aliphatic, and aromatic moieties. The results were rationalized with the help of in silico docking and molecular dynamic studies.

Trypanothione reductase: a target protein for a combined in vitro and in silico screening approach.

With the goal to identify novel trypanothione reductase (TR) inhibitors, we performed a combination of in vitro and in silico screening approaches. Starting from a highly diverse compound set of 2,816 compounds, 21 novel TR inhibiting compounds could be identified in the initial in vitro screening campaign against T. cruzi TR. All 21 in vitro hits were used in a subsequent similarity search-based in silico screening on a database containing 200,000 physically available compounds. The similarity search resulted in a data set containing 1,204 potential TR inhibitors, which was subjected to a second in vitro screening campaign leading to 61 additional active compounds. This corresponds to an approximately 10-fold enrichment compared to the initial pure in vitro screening. In total, 82 novel TR inhibitors with activities down to the nM range could be identified proving the validity of our combined in vitro/in silico approach. Moreover, the four most active compounds, showing IC50 values of <1 µM, were selected for determining the inhibitor constant. In first on parasites assays, three compounds inhibited the proliferation of bloodstream T. brucei cell line 449 with EC50 values down to 2 µM.

Docking-based virtual screening of covalently binding ligands: an orthogonal lead discovery approach.

In pharmaceutical industry, lead discovery strategies and screening collections have been predominantly tailored to discover compounds that modulate target proteins through noncovalent interactions. Conversely, covalent linkage formation is an important mechanism for a quantity of successful drugs in the market, which are discovered in most cases by hindsight instead of systematical design. In this article, the implementation of a docking-based virtual screening workflow for the retrieval of covalent binders is presented considering human cathepsin K as a test case. By use of the docking conditions that led to the best enrichment of known actives, 44 candidate compounds with unknown activity on cathepsin K were finally selected for experimental evaluation. The most potent inhibitor, 4-(N-phenylanilino)-6-pyrrolidin-1-yl-1,3,5-triazine-2-carbonitrile (CP243522), showed a K(i) of 21 nM and was confirmed to have a covalent reversible mechanism of inhibition. The presented approach will have great potential in cases where covalent inhibition is the desired drug discovery strategy.

Drug discovery and the use of computational approaches for infectious diseases.

For centuries infectious diseases were the scourge of humanity, overcome only by the discovery of vaccination and penicillin. With an armamentarium of effective antibiotics, vaccines and drugs at hand, infectious diseases for many years were considered to be negligible. With the onset of the AIDS pandemic, the return of tuberculosis and influenza (e.g., swine influenza) this notion has changed in recent years. Drug discovery for infectious diseases, therefore, is again gaining increasing interest. This article discusses the drug-discovery process in this area and introduces major computational approaches used to identify suitable drug targets and to discover and optimize chemical lead compounds towards drug candidates using examples from antiparasitic drug discovery.

A comparative chemogenomics strategy to predict potential drug targets in the metazoan pathogen, Schistosoma mansoni.

Schistosomiasis is a prevalent and chronic helmintic disease in tropical regions. Treatment and control relies on chemotherapy with just one drug, praziquantel and this reliance is of concern should clinically relevant drug resistance emerge and spread. Therefore, to identify potential target proteins for new avenues of drug discovery we have taken a comparative chemogenomics approach utilizing the putative proteome of Schistosoma mansoni compared to the proteomes of two model organisms, the nematode, Caenorhabditis elegans and the fruitfly, Drosophila melanogaster. Using the genome comparison software Genlight, two separate in silico workflows were implemented to derive a set of parasite proteins for which gene disruption of the orthologs in both the model organisms yielded deleterious phenotypes (e.g., lethal, impairment of motility), i.e., are essential genes/proteins. Of the 67 and 68 sequences generated for each workflow, 63 were identical in both sets, leading to a final set of 72 parasite proteins. All but one of these were expressed in the relevant developmental stages of the parasite infecting humans. Subsequent in depth manual curation of the combined workflow output revealed 57 candidate proteins. Scrutiny of these for 'druggable' protein homologs in the literature identified 35 S. mansoni sequences, 18 of which were homologous to proteins with 3D structures including co-crystallized ligands that will allow further structure-based drug design studies. The comparative chemogenomics strategy presented generates a tractable set of S. mansoni proteins for experimental validation as drug targets against this insidious human pathogen.

The impact of tautomer forms on pharmacophore-based virtual screening.

In the field of in silico screening, many applications do not automatically consider possible tautomeric states of molecules. However, the detection of new compound candidates might rely on correct structural description, which is important for the perfect fit toward the biologically relevant interactions. In this paper, we present a new exhaustive tautomer enumeration approach implemented by means of the CACTVS software package. The approach contains a set of 21 predefined SMIRKS-based transforms and a powerful transformation engine that is capable of generating most tautomers described comprehensively in the literature or found in databases in the field of medicinal chemistry. User-defined tautomer rules applied to specific structural databases or scientific issues can be implemented easily and used instead of the predefined rules. In addition, we describe the impact of tautomer-enriched databases on pharmacophore screening approaches for human matrix metalloproteinase 8 as an example of a protein-based pharmacophore screening scenario and for human cyclin-dependent kinases as an example of a ligand-based pharmacophore screening approach. In both test cases, as a preprocessing step, we have used our new tautomer enumerator tool for the tautomer enrichment of the screening data sets and have used it as a postprocessing step to remove tautomeric duplicates from the results. We could demonstrate that the tautomer-enriched screening data sets show significant advantages compared to their non-enhanced counterparts. The discrimination between hits and nonhits was significantly better in the case of tautomer-enriched databases. Moreover, it has been proved that tautomer-enhanced databases will lead to a higher number of potential hits.

InfVis--platform-independent visual data mining of multidimensional chemical data sets.

The tremendous increase of chemical data sets, both in size and number, and the simultaneous desire to speed up the drug discovery process has resulted in an increasing need for a new generation of computational tools that assist in the extraction of information from data and allow for rapid and in-depth data mining. During recent years, visual data mining has become an important tool within the life sciences and drug discovery area with the potential to help avoiding data analysis from turning into a bottleneck. In this paper, we present InfVis, a platform-independent visual data mining tool for chemists, who usually only have little experience with classical data mining tools, for the visualization, exploration, and analysis of multivariate data sets. InfVis represents multidimensional data sets by using intuitive 3D glyph information visualization techniques. Interactive and dynamic tools such as dynamic query devices allow real-time, interactive data set manipulations and support the user in the identification of relationships and patterns. InfVis has been implemented in Java and Java3D and can be run on a broad range of platforms and operating systems. It can also be embedded as an applet in Web-based interfaces. We will present in this paper examples detailing the analysis of a reaction database that demonstrate how InfVis assists chemists in identifying and extracting hidden information.

Enhanced CACTVS browser of the Open NCI Database.

A Web-based, graphical user interface has been developed to conduct rapid searches by numerous criteria in the more than 250,000 structures of the Open NCI Database. It is based on the chemistry information toolkit CACTVS. Nearly all structures and anticancer and anti-HIV screening data provided by NCI's Developmental Therapeutics Program have been included. This data set has been augmented by a large amount of additional, mostly computed, data, such as calculated log P values, predicted biological activities, systematically determined names, and others. Complex boolean searches are possible. Flexible substructure searches have been implemented. The user can conduct 3D pharmacophore queries in up to 25 conformations precalculated for each compound. Numerous output formats as well as 2D and 3D visualization options are provided. It is possible to export search results in various forms and with choices for data contents in the exported files, for structure sets ranging in size from a single compound to the entire database. Only a Web browser is needed to use this service, with a few plug-ins being useful but optional.