Computational Methods for the Integration of Biological Activity and Chemical Space

One general aim of medicinal chemistry is the understanding of structure-activity relationships of ligands that bind to biological targets. Advances in combinatorial chemistry and biological screening technologies allow the analysis of ligand-target relationships on a large-scale. However, in order to extract useful information from biological activity data, computational methods are needed that link activity of ligands to their chemical structure. In this thesis, it is investigated how fragment-type descriptors of molecular structure can be used in order to create a link between activity and chemical ligand space. First, an activity class-dependent hierarchical fragmentation scheme is introduced that generates fragmentation pathways that are aligned using established methodologies for multiple alignment of biological sequences. These alignments are then used to extract consensus fragment sequences that serve as a structural signature for individual biological activity classes. It is also investigated how defined, chemically intuitive molecular fragments can be organized based on their topological environment and co-occurrence in compounds active against closely related targets. Therefore, the Topological Fragment Index is introduced that quantifies the topological environment complexity of a fragment in a given molecule, and thus goes beyond fragment frequency analysis. Fragment dependencies have been established on the basis of common topological environments, which facilitates the identification of activity class-characteristic fragment dependency pathways that describe fragment relationships beyond structural resemblance. Because fragments are often dependent on each other in an activity class-specific manner, the importance of defined fragment combinations for similarity searching is further assessed. Therefore, Feature Co-occurrence Networks are introduced that allow the identification of feature cliques characteristic of individual activity classes. Three differently designed molecular fingerprints are compared for their ability to provide such cliques and a clique-based similarity searching strategy is established. For molecule- and activity class-centric fingerprint designs, feature combinations are shown to improve similarity search performance in comparison to standard methods. Moreover, it is demonstrated that individual features can form activity-class specific combinations. Extending the analysis of feature cliques characteristic of individual activity classes, the distribution of defined fragment combinations among several compound classes acting against closely related targets is assessed. Fragment Formal Concept Analysis is introduced for flexible mining of complex structure-activity relationships. It allows the interactive assembly of fragment queries that yield fragment combinations characteristic of defined activity and potency profiles. It is shown that pairs and triplets, rather than individual fragments distinguish between different activity profiles. A classifier is built based on these fragment signatures that distinguishes between ligands of closely related targets. Going beyond activity profiles, compound selectivity is also analyzed. Therefore, Molecular Formal Concept Analysis is introduced for the systematic mining of compound selectivity profiles on a whole-molecule basis. Using this approach, structurally diverse compounds are identified that share a selectivity profile with selected template compounds. Structure-selectivity relationships of obtained compound sets are further analyzed

Design of chemical space networks incorporating compound distance relationships

Networks, in which nodes represent compounds and edges pairwise similarity relationships, are used as coordinate-free representations of chemical space. So-called chemical space networks (CSNs) provide intuitive access to structural relationships within compound data sets and can be annotated with activity information. However, in such similarity-based networks, distances between compounds are typically determined for layout purposes and clarity and have no chemical meaning. By contrast, inter-compound distances as a measure of dissimilarity can be directly obtained from coordinate-based representations of chemical space. Herein, we introduce a CSN variant that incorporates compound distance relationships and thus further increases the information content of compound networks. The design was facilitated by adapting the Kamada-Kawai algorithm. Kamada-Kawai networks are the first CSNs that are based on numerical similarity measures, but do not depend on chosen similarity threshold values

Identifying mechanism-of-action targets for drugs and probes

Notwithstanding their key roles in therapy and as biological probes, 7% of approved drugs are purported to have no known primary target, and up to 18% lack a well-defined mechanism of action. Using a chemoinformatics approach, we sought to “de-orphanize” drugs that lack primary targets. Surprisingly, targets could be easily predicted for many: Whereas these targets were not known to us nor to the common databases, most could be confirmed by literature search, leaving only 13 Food and Drug Administration—approved drugs with unknown targets; the number of drugs without molecular targets likely is far fewer than reported. The number of worldwide drugs without reasonable molecular targets similarly dropped, from 352 (25%) to 44 (4%). Nevertheless, there remained at least seven drugs for which reasonable mechanism-of-action targets were unknown but could be predicted, including the antitussives clemastine, cloperastine, and nepinalone; the antiemetic benzquinamide; the muscle relaxant cyclobenzaprine; the analgesic nefopam; and the immunomodulator lobenzarit. For each, predicted targets were confirmed experimentally, with affinities within their physiological concentration ranges. Turning this question on its head, we next asked which drugs were specific enough to act as chemical probes. Over 100 drugs met the standard criteria for probes, and 40 did so by more stringent criteria. A chemical information approach to drug-target association can guide therapeutic development and reveal applications to probe biology, a focus of much current interest

HiTSEE KNIME: a visualization tool for hit selection and analysis in high-throughput screening experiments for the KNIME platform

We present HiTSEE (High-Throughput Screening Exploration Environment), a visualization tool for the analysis of large chemical screens used to examine biochemical processes. The tool supports the investigation of structure-activity relationships (SAR analysis) and, through a flexible interaction mechanism, the navigation of large chemical spaces. Our approach is based on the projection of one or a few molecules of interest and the expansion around their neighborhood and allows for the exploration of large chemical libraries without the need to create an all encompassing overview of the whole library. We describe the requirements we collected during our collaboration with biologists and chemists, the design rationale behind the tool, and two case studies on different datasets. The described integration (HiTSEE KNIME) into the KNIME platform allows additional flexibility in adopting our approach to a wide range of different biochemical problems and enables other research groups to use HiTSEE

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.Comment: 49 pages, 2 figures, 2 tables, 10 supplementary figures, 13 supplementary table

Mycobacterial dihydrofolate reductase inhibitors identified using chemogenomic methods and in vitro validation.

The lack of success in target-based screening approaches to the discovery of antibacterial agents has led to reemergence of phenotypic screening as a successful approach of identifying bioactive, antibacterial compounds. A challenge though with this route is then to identify the molecular target(s) and mechanism of action of the hits. This target identification, or deorphanization step, is often essential in further optimization and validation studies. Direct experimental identification of the molecular target of a screening hit is often complex, precisely because the properties and specificity of the hit are not yet optimized against that target, and so many false positives are often obtained. An alternative is to use computational, predictive, approaches to hypothesize a mechanism of action, which can then be validated in a more directed and efficient manner. Specifically here we present experimental validation of an in silico prediction from a large-scale screen performed against Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis. The two potent anti-tubercular compounds studied in this case, belonging to the tetrahydro-1,3,5-triazin-2-amine (THT) family, were predicted and confirmed to be an inhibitor of dihydrofolate reductase (DHFR), a known essential Mtb gene, and already clinically validated as a drug target. Given the large number of similar screening data sets shared amongst the community, this in vitro validation of these target predictions gives weight to computational approaches to establish the mechanism of action (MoA) of novel screening hit

Drug Repurposing: A Systematic Approach to Evaluate Candidate Oral Neuroprotective Interventions for Secondary Progressive Multiple Sclerosis

Objective: To develop and implement an evidence based framework to select, from drugs already licenced, candidate oral neuroprotective drugs to be tested in secondary progressive multiple sclerosis. Design: Systematic review of clinical studies of oral putative neuroprotective therapies in MS and four other neurodegenerative diseases with shared pathological features, followed by systematic review and meta-analyses of the in vivo experimental data for those interventions. We presented summary data to an international multi-disciplinary committee, which assessed each drug in turn using pre-specified criteria including consideration of mechanism of action. Results: We identified a short list of fifty-two candidate interventions. After review of all clinical and pre-clinical evidence we identified ibudilast, riluzole, amiloride, pirfenidone, fluoxetine, oxcarbazepine, and the polyunsaturated fatty-acid class (Linoleic Acid, Lipoic acid; Omega-3 fatty acid, Max EPA oil) as lead candidates for clinical evaluation. Conclusions: We demonstrate a standardised and systematic approach to candidate identification for drug rescue and repurposing trials that can be applied widely to neurodegenerative disorders