Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Applications of Support Vector Machines as a Robust tool in High Throughput Virtual Screening

Chemical space is enormously huge but not all of it is pertinent for the drug designing. Virtual screening methods act as knowledge-based filters to discover the coveted novel lead molecules possessing desired pharmacological properties. Support Vector Machines (SVM) is a reliable virtual screening tool for prioritizing molecules with the required biological activity and minimum toxicity. It has to its credit inherent advantages such as support for noisy data mainly coming from varied high-throughput biological assays, high sensitivity, specificity, prediction accuracy and reduction in false positives. SVM-based classification methods can efficiently discriminate inhibitors from non-inhibitors, actives from inactives, toxic from non-toxic and promiscuous from non-promiscuous molecules. As the principles of drug design are also applicable for agrochemicals, SVM methods are being applied for virtual screening for pesticides too. The current review discusses the basic kernels and models used for binary discrimination and also features used for developing SVM-based scoring functions, which will enhance our understanding of molecular interactions. SVM modeling has also been compared by many researchers with other statistical methods such as Artificial Neural Networks, k-nearest neighbour (kNN), decision trees, partial least squares, etc. Such studies have also been discussed in this review. Moreover, a case study involving the use of SVM method for screening molecules for cancer therapy has been carried out and the preliminary results presented here indicate that the SVM is an excellent classifier for screening the molecules

Network Pharmacology and Traditional Chinese Medicine

Gynaecology & obstetric

Predicting drug metabolism: experiment and/or computation?

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.This is the accepted manuscript of a paper published in Nature Reviews Drug Discovery (Kirchmair J, Göller AH, Lang D, Kunze J, Testa B, Wilson ID, Glen RC, Schneider G, Nature Reviews Drug Discovery, 2015, 14, 387–404, doi:10.1038/nrd4581). The final version is available at http://dx.doi.org/10.1038/nrd458

Development, validation and application of in-silico methods to predict the macromolecular targets of small organic compounds

Computational methods to predict the macromolecular targets of small organic drugs and drug-like compounds play a key role in early drug discovery and drug repurposing efforts. These methods are developed by building predictive models that aim to learn the relationships between compounds and their targets in order to predict the bioactivity of the compounds. In this thesis, we analyzed the strategies used to validate target prediction approaches and how current strategies leave crucial questions about performance unanswered. Namely, how does an approach perform on a compound of interest, with its structural specificities, as opposed to the average query compound in the test data? We constructed and present new guidelines on validation strategies to address these short-comings. We then present the development and validation of two ligand-based target prediction approaches: a similarity-based approach and a binary relevance random forest (machine learning) based approach, which have a wide coverage of the target space. Importantly, we applied a new validation protocol to benchmark the performance of these approaches. The approaches were tested under three scenarios: a standard testing scenario with external data, a standard time-split scenario, and a close-to-real-world test scenario. We disaggregated the performance based on the distance of the testing data to the reference knowledge base, giving a more nuanced view of the performance of the approaches. We showed that, surprisingly, the similarity-based approach generally performed better than the machine learning based approach under all testing scenarios, while also having a target coverage which was twice as large. After validating two target prediction approaches, we present our work on a large-scale application of computational target prediction to curate optimized compound libraries. While screening large collections of compounds against biological targets is key to identifying new bioactivities, it is resource intensive and challenging. Small to medium-sized libraries, that have been optimized to have a higher chance of producing a true hit on an arbitrary target of interest are therefore valuable. We curated libraries of readily purchasable compounds by: i. utilizing property filters to ensure that the compounds have key physicochemical properties and are not overly reactive, ii. applying a similaritybased target prediction method, with a wide target scope, to predict the bioactivities of compounds, and iii. employing a genetic algorithm to select compounds for the library to maximize the biological diversity in the predicted bioactivities. These enriched small to medium-sized compound libraries provide valuable tool compounds to support early drug development and target identification efforts, and have been made available to the community. The distinctive contributions of this thesis include the development and benchmarking of two ligand-based target prediction approaches under novel validation scenarios, and the application of target prediction to enrich screening libraries with biologically diverse bioactive compounds. We hope that the insights presented in this thesis will help push data driven drug discovery forward.Doktorgradsavhandlin

Development and Application of Virtual Screening Methods for G Protein-Coupled Receptors

G protein-coupled receptors (GPCR) constitute one of the largest family of transmembrane proteins that have been implicated in a multitude of diseases, including cancer and diabetes, and have been an important target in drug deve lopment. While experiment-based high-throughput screening for the unearthing of novel chemical compounds remains the de facto standard for drug discovery, virtual screening has been gaining acceptance as an important complementary method due to its high speed and low cost, which instead employs computers. This dissertation is aimed at the development of virtual screening algorithms as applied to GPCR’s, in addition to the construction of GPCR-related databases (GPCR-EXP, GLASS). MAGELLAN is a ligand-based virtual screening algorithm that makes inferences about what a GPCR would potentially bind based on sequence- and structure-based alignments. Building on top of this work, a sequential virtual screening pipeline combining MAGELLAN with AutoDock Vina was constructed for the discovery of novel, bifunctional opioids with mu opioid receptor (MOR) agonist and delta opioid receptor (DOR) antagonist activity. In the process of developing the virtual screening algorithms, two GPCR-related databases were constructed to provide necessary data for the study. GPCR-EXP is a database of experimentally-validated and predicted GPCR structures. Important features include semi-manual curation of data, weekly updates, a user-friendly web interface, and high-resolution structure models with GPCR-I-TASSER, which many of the other GPCR-related databases lack. Additionally, GLASS database was developed in response to the absence of databases dedicated to GPCR experimental data. As a result, pharmacological data was pooled and integrated into a single source, resulting in over 500,000 unique GPCR-ligand associations; this made it the most comprehensive database of its kind thus far, providing the community with an accessible web interface, freely-available data, and ligands ready for docking. MAGELLAN utilized pharmacological data from GLASS to infer from the ligands of sequence- and structure-based homologues what a target GPCR would bind. It was tested on two public virtual screening databases (DUD-E and GPCR-Bench) and achieved an average EF of 9.75 and 13.70, respectively, which compared favorably with AutoDock Vina (1.48/3.16), DOCK 6 (2.12/3.47), and PoLi (2.2). Lastly, case studies with the mu opioid and motilin receptors demonstrated its applicability to virtual screening in general, as well as GPCR de-orphanization. Subsequently, MAGELLAN was combined with AutoDock Vina into a novel, sequential virtual screen pipeline against both MOR and DOR to compensate for the weaknesses of each algorithm. Retrospective virtual screens against both MAGELLAN and AutoDock Vina were established for both receptors, and both methods were reported to have over-random discrimination between actives and decoys using the GPCR-Bench dataset. In conclusion, structure (GPCR-EXP) and pharmacological data (GLASS) databases were constructed to provide users with a comprehensive source of GPCR data. Moreover, GLASS made it possible for MAGELLAN to be developed, providing it a rich source of experimental data. In return, this resulted in greater performance than competing algorithms. Lastly, a prospective sequential virtual screening pipeline was established for the discovery of novel bifunctional opioids, in which the models for both methods were validated to perform well. In future studies, cAMP and β-arrestin assays will be run on a subset of compounds from a prospective virtual screen in the hopes of discovering a novel opioid with reduced tolerance and withdrawal.PHDBiological ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147623/1/wallakin_1.pd

Sediment toxicity assessment using zebrafish (Danio rerio) as a model system: Historical review, research gaps and trends

Embargo until June 22, 2023Sediment is an important compartment in aquatic environments and acts as a sink for environmental pollutants. Sediment toxicity tests have been suggested as critical components in environmental risk assessment. Since the zebrafish (Danio rerio) has been indicated as an emerging model system in ecotoxicological tests, a scientometric and systematic review was performed to evaluate the use of zebrafish as an experimental model system in sediment toxicity assessment. A total of 97 papers were systematically analyzed and summarized. The historical and geographical distributions were evaluated and the data concerning the experimental design, type of sediment toxicity tests and approach (predictive or retrospective), pollutants and stressors, zebrafish developmental stages and biomarkers responses were summarized and discussed. The use of zebrafish to assess the sediment toxicity started in 1996, using mainly a retrospective approach. After this, research showed an increasing trend, especially after 2014–2015. Zebrafish exposed to pollutant-bound sediments showed bioaccumulation and several toxic effects, such as molecular, biochemical, morphological, physiological and behavioral changes. Zebrafish is a suitable model system to assess the toxicity of freshwater, estuarine and marine sediments, and sediment spiked in the laboratory. The pollutant-bound sediment toxicity in zebrafish seems to be overall dependent on physical and chemical properties of pollutants, experimental design, environmental factor, developmental stages and presence of organic natural matter. Overall, results showed that the zebrafish embryos and larvae are suitable model systems to assess the sediment-associated pollutant toxicity.acceptedVersio