Predicting drug metabolism: experiment and/or computation?

Design, Synthesis and Pharmacological Evaluation of Gastro- Protective Anti-inflammatory Analgesic Agents based on Dual Oxidative Stress / Cyclooxygenase Inhibition

BACKGROUND

Non-steroidal anti-inflammatory drugs (NSAIDs) derived local generation of reactive oxygen species (ROS) plays a crucial role in the formation of gastric ulceration.

OBJECTIVE

Therefore, anti-inflammatory analgesics with potent antioxidant activity could be a potential therapeutic strategy for the treatment of pain and inflammatory disorders without gastrointestinal (GI) side effects.

METHODS

In an effort to develop gastroprotective analgesic and anti-inflammatory agents, a series of 2-methylamino-substituted-1H-benzo[d] imidazol-1-yl) (phenyl) methanone derivatives were synthesized and evaluated in vitro for cyclooxygenase (COX) inhibition as well as anti-oxidant potential by the FRAP assay. The compounds with significant in vitro COX-1/COX-2 inhibitory activity and antioxidant activity were further screened in vivo for their anti-inflammatory and analgesic activities. Moreover, the ulcerogenic potential of test compounds was also studied. To gain insight into the plausible mode of interaction of compounds within the active sites of COX-1 and COX-2, molecular docking simulations were performed.

RESULTS

Among the various synthesized molecules, most of the compounds showed good cyclooxygenase inhibitory activity and efficient antioxidant activity in FRAP assay. After preliminary and indicative in vitro assays, three compounds exhibited most significant antiinflammatory and analgesic activity with better gastric tolerability during their in vivo evaluation. Ligand interaction studies indicated highest dock score -43.05 of 1,2- disubstituted benzimidazole derivatives in comparison to the reference ligand -30.70. Overall studies provided us (2-((4-methoxyphenylamino) methyl) -1h-benzo [d] imidazol- 1-yl) (phenyl) methanone as a lead with potent gastro-protective anti-inflammatory and analgesic activities that can be used for future research.

CONCLUSION

From the above results, it can be concluded that designing of multifunctional molecules with COX-1/COX-2 inhibitory and anti-oxidant activities could hold a great promise for further development of GI-safer NSAIDs.

Advances in In-Silico based Predictive In-Vivo Profiling of Novel Potent β-Glucuronidase Inhibitors

BACKGROUND

Intestinal β-glucuronidase enzyme has a significant importance in colorectal carcinogenesis. Specific inhibition of the enzyme helps prevent immune reactivation of the glucuronide- carcinogens, thus protecting the intestine from ROS (Reactive Oxidative Species) mediatedcarcinogenesis.

OBJECTIVES

Advancement in In-silico based techniques has provided a broad range of studies to carry out the drug design and development process smoothly using SwissADME and BOILED-Egg tools.

METHODS

In our designed case study, we used SwissADME and BOILED-Egg predictive computational tools to estimate the physicochemical, human pharmacokinetics, drug-likeness, medicinal chemistry properties and membrane permeability characteristics of our recently In-vitro evaluated novel β-Glucuronidase inhibitors.

RESULTS

Out of the eleven screened potent inhibitors, compound (8) exhibited excellent bioavailability radar against the six molecular descriptors, good (ADME) Absorption, Distribution, Metabolism and Excretion along with P-glycoprotein, CYP450 isozymes and membranes permeability profile. On the basis of these factual observations, it is to be predicted that compound (8) can achieve in-vivo experimental clearance efficiently, Therefore, in the future, it can be a drug in the market to treat various disorders associated with the overexpression of β-Glucuronidase enzyme such as various types of cancer, particularly hormone-dependent cancer such as (breast, prostate, and colon cancer). Moreover, other compounds (1-7, & 9-11), have also shown good predictive pharmacokinetics, medicinal chemistry, BBB and HIA membranes permeability profiles with slight lead optimization to obtain improved results.

CONCLUSION

In consequence, in-silico based studies are considered to provide robustness for a rational drug design and development approach to avoid the possibility of failures of drug candidates in the later stages of drug development phases. The results of this study effectively reveal the possible attributes of potent β-Glucuronidase inhibitors, for further experimental evaluation.

Acmella oleracea (L) R. K. Jansen Reproductive Toxicity in Zebrafish: An In Vivo and In Silico Assessment

The plant species Acmella oleracea L. is used in the north of Brazil for the treatment of a range of illnesses, such as tuberculosis, flu, cough, and rheumatism and as an anti-inflammatory agent; besides, hydroethanolic formulations with this species are popularly used as a female aphrodisiac agent. However, currently, there are no studies performed evaluating its effect on embryonic development. Hence, this research aimed to evaluate the effects of the hydroethanolic extract of A. oleracea (EHFAo) on the reproductive performance (parental) and embryonic development (F1 generation) of zebrafish, at concentrations of 50, 100, and 200 μg/L. Histopathology of parental gonads after 21 days of exposure to EHFAo reveals few alterations in the ovaries and testes, not impairing the reproduction; an increase of eggs deposition was observed in animals treated with EHFAo at the highest concentrations. Nevertheless, concerning the embryonic development of F1, teratogenic effects were observed including tail deformation, cardiac and yolk edema, scoliosis, and growth retardation; these alterations were more prominent in the groups born from progenitors exposed to the highest concentrations (100 and 200 μg/L.); but only the occurrence of yolk and cardiac edema had a statistically significant difference when compared to the control group. The chromatographic analysis shows that spilanthol (affinin) was the primary compound found in the EHFAo. Hence, in silico assessment was performed to evaluate the pharmacokinetic and toxicological properties of this molecule and 37 metabolites derived from it. Overall, our data show that the treatment caused no detrimental changes in progenitors regarding their gonads or fertility but caused some potentially teratogenic activity in embryos, which may be due to the action of spilanthol's metabolites M3, M6, M7, M8, M16, M28, and M31.

Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making

The fields of toxicology and chemical risk assessment seek to reduce, and eventually replace, the use of animals for the prediction of toxicity in humans. In this context, physiologically based kinetic (PBK) modelling based on in vitro and in silico kinetic data has the potential to a play significant role in reducing animal testing, by providing a methodology capable of incorporating in vitro human data to facilitate the development of in vitro to in vivo extrapolation of hazard information. In the present article, we discuss the challenges in: 1) applying PBK modelling to support regulatory decision making under the toxicology and risk-assessment paradigm shift towards animal replacement; 2) constructing PBK models without in vivo animal kinetic data, while relying solely on in vitro or in silico methods for model parameterization; and 3) assessing the validity and credibility of PBK models built largely using non-animal data. The strengths, uncertainties, and limitations of PBK models developed using in vitro or in silico data are discussed in an effort to establish a higher degree of confidence in the application of such models in a regulatory context. The article summarises the outcome of an expert workshop hosted by the European Commission Joint Research Centre (EC-JRC) - European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM), on "Physiologically-Based Kinetic modelling in risk assessment - reaching a whole new level in regulatory decision-making" held in Ispra, Italy, in November 2016, along with results from an international survey conducted in 2017 and recently reported activities occurring within the PBK modelling field. The discussions presented herein highlight the potential applications of next generation (NG)-PBK modelling, based on new data streams.

Identification of Potential Inhibitors from Pyriproxyfen with Insecticidal Activity by Virtual Screening

Aedes aegypti is the main vector of dengue fever transmission, yellow fever, Zika, and chikungunya in tropical and subtropical regions and it is considered to cause health risks to millions of people in the world. In this study, we search to obtain new molecules with insecticidal potential against Ae. aegypti via virtual screening. Pyriproxyfen was chosen as a template compound to search molecules in the database Zinc_Natural_Stock (ZNSt) with structural similarity using ROCS (rapid overlay of chemical structures) and EON (electrostatic similarity) software, and in the final search, the top 100 were selected. Subsequently, in silico pharmacokinetic and toxicological properties were determined resulting in a total of 14 molecules, and these were submitted to the PASS online server for the prediction of biological insecticide and acetylcholinesterase activities, and only two selected molecules followed for the molecular docking study to evaluate the binding free energy and interaction mode. After these procedures were performed, toxicity risk assessment such as LD50 values in mg/kg and toxicity class using the PROTOX online server, were undertaken. Molecule ZINC00001624 presented potential for inhibition for the acetylcholinesterase enzyme (insect and human) with a binding affinity value of -10.5 and -10.3 kcal/mol, respectively. The interaction with the juvenile hormone was -11.4 kcal/mol for the molecule ZINC00001021. Molecules ZINC00001021 and ZINC00001624 had excellent predictions in all the steps of the study and may be indicated as the most promising molecules resulting from the virtual screening of new insecticidal agents.

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.

Studies towards the development of a PET radiotracer for imaging of the P2Y1 receptors in the brain: synthesis, 18F-labeling and preliminary biological evaluation

Purine nucleotides such as ATP and ADP are important extracellular signaling molecules in almost all tissues activating various subtypes of purinoreceptors. In the brain, the P2Y₁ receptor (P2Y₁R) subtype mediates trophic functions like differentiation and proliferation, and modulates fast synaptic transmission, both suggested to be affected in diseases of the central nervous system. Research on P2Y₁R is limited because suitable brain-penetrating P2Y₁R-selective tracers are not yet available. Here, we describe the first efforts to develop an ¹⁸F-labeled PET tracer based on the structure of the highly affine and selective, non-nucleotidic P2Y₁R allosteric modulator 1-(2-[2-(tert-butyl)phenoxy]pyridin-3-yl)-3-[4-(trifluoromethoxy)phenyl]urea (7). A small series of fluorinated compounds was developed by systematic modification of the p-(trifluoromethoxy)phenyl, the urea and the 2-pyridyl subunits of the lead compound 7. Additionally, the p-(trifluoromethoxy)phenyl subunit was substituted by carborane, a boron-rich cluster with potential applicability in boron neutron capture therapy (BNCT). By functional assays, the new fluorinated derivative 1-{2-[2-(tert-butyl)phenoxy]pyridin-3-yl}-3-[4-(2-fluoroethyl)phenyl]urea (18) was identified with a high P2Y₁R antagonistic potency (IC₅₀ = 10 nM). Compound [¹⁸F]18 was radiosynthesized by using tetra-n-butyl ammonium [¹⁸F]fluoride with high radiochemical purity, radiochemical yield and molar activities. Investigation of brain homogenates using hydrophilic interaction chromatography (HILIC) revealed [¹⁸F]fluoride as major radiometabolite. Although [¹⁸F]18 showed fast in vivo metabolization, the high potency and unique allosteric binding mode makes this class of compounds interesting for further optimizations and investigation of the theranostic potential as PET tracer and BNCT agent.

BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification

Background

A number of computational tools for metabolism prediction have been developed over the last 20 years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance.

Results

To address these limitations, we have developed BioTransformer, a freely available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut as well as the environment (soil and water microbiota), via its metabolism prediction tool. A comprehensive evaluation of BioTransformer showed that it was able to outperform two state-of-the-art commercially available tools (Meteor Nexus and ADMET Predictor), with precision and recall values up to 7 times better than those obtained for Meteor Nexus or ADMET Predictor on the same sets of pharmaceuticals, pesticides, phytochemicals or endobiotics under similar or identical constraints. Furthermore BioTransformer was able to reproduce 100% of the transformations and metabolites predicted by the EAWAG pathway prediction system. Using mass spectrometry data obtained from a rat experimental study with epicatechin supplementation, BioTransformer was also able to correctly identify 39 previously reported epicatechin metabolites via its metabolism identification tool, and suggest 28 potential metabolites, 17 of which matched nine monoisotopic masses for which no evidence of a previous report could be found.

Conclusion

BioTransformer can be used as an open access command-line tool, or a software library. It is freely available at https://bitbucket.org/djoumbou/biotransformerjar/. Moreover, it is also freely available as an open access RESTful application at www.biotransformer.ca, which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identification data.

Electronic supplementary material

The online version of this article (10.1186/s13321-018-0324-5) contains supplementary material, which is available to authorized users.

Lattice engineering enables definition of molecular features allowing for potent small-molecule inhibition of HIV-1 entry

Diverse entry inhibitors targeting the gp120 subunit of the HIV-1 envelope (Env) trimer have been developed including BMS-626529, also called temsavir, a prodrug version of which is currently in phase III clinical trials. Here we report the characterization of a panel of small-molecule inhibitors including BMS-818251, which we show to be >10-fold more potent than temsavir on a cross-clade panel of 208-HIV-1 strains, as well as the engineering of a crystal lattice to enable structure determination of the interaction between these inhibitors and the HIV-1 Env trimer at higher resolution. By altering crystallization lattice chaperones, we identify a lattice with both improved diffraction and robust co-crystallization of HIV-1 Env trimers from different clades complexed to entry inhibitors with a range of binding affinities. The improved diffraction reveals BMS-818251 to utilize functional groups that interact with gp120 residues from the conserved β20-β21 hairpin to improve potency.

Temsavir, a compound that inhibits HIV entry by binding envelope (Env), is currently in clinical development. Here, Lai et al. identify a more than 10-fold improved compound and, using lattice engineering, obtain crystal structures that give insights into improved inhibition between small molecules and Env.

Identifying Attributes That Influence In Vitro-to-In Vivo Concordance by Comparing In Vitro Tox21 Bioactivity Versus In Vivo DrugMatrix Transcriptomic Responses Across 130 Chemicals

Recent efforts aimed at integrating in vitro high-throughput screening (HTS) data into chemical toxicity assessments are necessitating increased understanding of concordance between chemical-induced responses observed in vitro versus in vivo. This investigation set out to (1) measure concordance between in vitro HTS data and transcriptomic responses observed in vivo, focusing on the liver, and (2) identify attributes that can influence concordance. Signal response profiles from 130 substances were compared between in vitro data produced through Tox21 and liver transcriptomic data through DrugMatrix, collected from rats exposed to a chemical for ≤5 days. A global in vitro-to-in vivo comparative analysis based on pathway-level responses resulted in an overall average percent agreement of 79%, ranging on a per-chemical basis between 41% and 100%. Whereas concordance amongst inactive chemicals was high (89%), concordance amongst chemicals showing in vitro activity was only 13%, suggesting that follow-up in vivo and/or orthogonal in vitro assays would improve interpretations of in vitro activity. Attributes identified to influence concordance included experimental design attributes (eg, cell type), target pathways, and physicochemical properties (eg, logP). The attribute that most consistently increased concordance was dose applicability, evaluated by filtering for experimental doses administered to rats that were within 10-fold of those related to likely bioactivity, derived using Tox21 data and high-throughput toxicokinetic modeling. Together, findings suggest that in vitro screening approaches to predict in vivo toxicity are viable particularly when certain attributes are considered, including whether activity versus inactivity is observed, experimental design, chemical properties, and dose applicability.

A framework for application of quantitative property-property relationships (QPPRs) in physiologically based pharmacokinetic (PBPK) models for high-throughput prediction of internal dose of inhaled organic chemicals

New generation of toxicological tests and assessment strategies require validated toxicokinetic data or models that are lacking for most chemicals. This study aimed at developing a quantitative property-property relationship (QPPR)-based human physiologically based pharmacokinetic (PBPK) modeling framework for high-throughput predictions of inhalation toxicokinetics of organic chemicals. A PBPK model was parameterized with QPPR-derived values for hepatic clearance (CL_h) and partition coefficients (P) [blood:air (P_ba) and tissue:air (P_ta) and tissue:blood (P_tb)]. The model was initially applied to an evaluation dataset of 40 organic chemicals in the applicability domain, and then to an expanded dataset of 249 organic chemicals from diverse chemical classes. 'Batch' analyses were performed for rapid assessments of hundreds of chemicals. The simulations of inhalation toxicokinetics following an 8-h exposure to 1 ppm of each chemical were successful. The mean ratios of their predicted-to-experimental values were within a factor of 1.36-2.36 for P_tb and 1.18 for CL_h, for 80% of the chemicals in the evaluation dataset. The predicted 24-h area under the venous blood concentration-time curve (AUC₂₄) values were within the predicted envelopes obtained while using experimental values of P_ba and considering either no or maximal hepatic extraction. The reliability analysis (based on combined sensitivity and uncertainty analyses) indicated that AUC₂₄ predictions for 55% of the expanded dataset were moderately to highly reliable, with 46% exhibiting highly reliable values. Overall, the modeling framework suggests that molecular structure and chemical properties can together be effectively used to obtain first-cut estimates of the toxicokinetics of data-poor organic chemicals for screening and prioritization purposes.

Metabolic Mechanism of Aryl Phosphorus Flame Retardants by Cytochromes P450: A Combined Experimental and Computational Study on Triphenyl Phosphate

Understanding metabolic mechanisms is critical and remains a difficult task in the risk assessment of emerging pollutants. Triphenyl phosphate (TPHP), a widely used aryl phosphorus flame retardant (aryl-PFR), has been frequently detected in the environment, and its major metabolite was considered as diphenyl phosphate (DPHP). However, knowledge of the mechanism for TPHP leading to DPHP and other metabolites is lacking. Our in vitro study shows that TPHP is metabolized into its diester metabolite DPHP and mono- and dihydroxylated metabolites by cytochromes P450 (CYP) in human liver microsomes, while CYP1A2 and CYP2E1 isoforms are mainly involved in such processes. Molecular docking gives the conformation for TPHP binding with the active species Compound I (an iron IV-oxo heme cation radical) in specific CYP isoforms, showing that the aromatic ring of TPHP is likely to undergo metabolism. Quantum chemical calculations have shown that the dominant reaction channel is the O-addition of Compound I onto the aromatic ring of TPHP, followed by a hydrogen-shuttle mechanism leading to ortho-hydroxy-TPHP as the main monohydroxylated metabolite; the subsequent H-abstraction-OH-rebound reaction acting on ortho-hydroxy-TPHP yields the meta- and ipso-position quinol intermediates, while the former of which can be metabolized into dihydroxy-TPHP by fast protonation, and the latter species needs to go through type-I ipso-substitution and fast protonation to be evolved into DPHP. We envision that the identified mechanisms may give inspiration for studying the metabolism of several other aryl-PFRs by CYP.

eDoctor: machine learning and the future of medicine

Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer-aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.

Systems Biology, Systems Medicine, Systems Pharmacology: The What and The Why

Systems biology is today such a widespread discipline that it becomes difficult to propose a clear definition of what it really is. For some, it remains restricted to the genomic field. For many, it designates the integrated approach or the corpus of computational methods employed to handle the vast amount of biological or medical data and investigate the complexity of the living. Although defining systems biology might be difficult, on the other hand its purpose is clear: systems biology, with its emerging subfields systems medicine and systems pharmacology, clearly aims at making sense of complex observations/experimental and clinical datasets to improve our understanding of diseases and their treatments without putting aside the context in which they appear and develop. In this short review, we aim to specifically focus on these new subfields with the new theoretical tools and approaches that were developed in the context of cancer. Systems pharmacology and medicine now give hope for major improvements in cancer therapy, making personalized medicine closer to reality. As we will see, the current challenge is to be able to improve the clinical practice according to the paradigm shift of systems sciences.

An integrative machine learning approach for prediction of toxicity-related drug safety

Recent trends in drug development have been marked by diminishing returns caused by the escalating costs and falling rates of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials and the leading cause of drug withdraws after release to the market. Computational methods capable of predicting these failures can reduce the waste of resources and time devoted to the investigation of compounds that ultimately fail. We propose an original machine learning method that leverages identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity. We demonstrate that our method (TargeTox) can distinguish potentially idiosyncratically toxic drugs from safe drugs and is also suitable for speculative evaluation of different target sets to support the design of optimal low-toxicity combinations.

Best Practices for Foundations in Molecular Simulations [Article v1.0]

This document provides a starting point for approaching molecular simulations, guiding beginning practitioners to what issues they need to know about before and while starting their first simulations, and why those issues are so critical. This document makes no claims to provide an adequate introduction to the subject on its own. Instead, our goal is to help people know what issues are critical before beginning, and to provide references to good resources on those topics. We also provide a checklist of key issues to consider before and while setting up molecular simulations which may serve as a foundation for other best practices documents.

Using virtual reality for drug discovery: a promising new outlet for novel leads

Introduction: Virtual reality (VR) environments are increasingly being used by researchers in various fields in addition to being increasingly integrated into various areas of human life, ranging from videogames to different industrial uses. VR can be used to create interactive and multimodal sensory stimuli and thus offers unique advantages over other computer-based approaches for scientific research and molecular-level applications. Consequently, VR is starting to be used in novel drug development, such as in drug discovery, and rational drug design. Areas covered: In this review, the authors discuss the basic development of VR technology, including the available hardware and software. The latest advances of VR technology in novel drug development are then detailed, and the VR programs that can be applied in relevant studies are highlighted. Expert opinion: VR will lead to a revolution in pharmaceutical development. However, there are still obstacles to the successful and extensive application of VR to drug development, including the demand for further improvements to the available hardware and software and the various limitations described with regard to accuracy and precision. As technology continues to improve, the barriers to the widespread adoption of VR will diminish and VR technologies will play an increasingly important role in novel drug development.

Layer-by-Layer Assembly of Nanosized Membrane Fractions for the Assessment of Cytochrome P450 Xenobiotic Metabolism

Herein, we report the use of sequential layer-by-layer (LbL) assembly to design nanostructured films made of recombinant bacterial membrane fractions (MF), which overexpress cytochrome P450 (CYP) and cytochrome P450 reductase. The ability to incorporate MF in LbL multilayered films is demonstrated by an in situ quartz crystal microbalance with dissipation monitoring using poly-l-lysine or poly-l-ornithine as a polycation. Results show that MF preserve a remarkable CYP1A2 catalytic property in the adsorbed phase. Moreover, atomic force microscopy images reveal that MF mostly adopt a flattened conformation in the adsorbed phase with an extensive tendency to aggregate within the multilayered films, which is more pronounced when increasing the number of bilayers. Interestingly, this behavior seems to enhance the ability of embedded MF to remain active after repeated uses. The proposed strategy constitutes a practical alternative for the immobilization of active CYP enzymes. Besides their fundamental interest, MF-based multilayers are useful nano-objects for the creation of new biomimetic reactors for the assessment of xenobiotic metabolism.

Recombinational DSBs-intersected genes converge on specific disease- and adaptability-related pathways

Motivation

The budding yeast Saccharomyces cerevisiae is a model species powerful for studying the recombination of eukaryotes. Although many recombination studies have been performed for this species by experimental methods, the population genomic study based on bioinformatics analyses is urgently needed to greatly increase the range and accuracy of recombination detection. Here, we carry out the population genomic analysis of recombination in S.cerevisiae to reveal the potential rules between recombination and evolution in eukaryotes.

Results

By population genomic analysis, we discover significantly more and longer recombination events in clinical strains, which indicates that adverse environmental conditions create an obviously wider range of genetic combination in response to the selective pressure. Based on the analysis of recombinational double strand breaks (DSBs)-intersected genes (RDIGs), we find that RDIGs significantly converge on specific disease- and adaptability-related pathways, indicating that recombination plays a biologically key role in the repair of DSBs related to diseases and environmental adaptability, especially the human neurological disorders. By evolutionary analysis of RDIGs, we find that the RDIGs highly prevailing in populations of yeast tend to be more evolutionarily conserved, indicating the accurate repair of DSBs in these RDIGs is critical to ensure the eukaryotic survival or fitness.

Supplementary information

Supplementary data are available at Bioinformatics online.

MetScore: Site of Metabolism Prediction Beyond Cytochrome P450 Enzymes

The metabolism of xenobiotics by humans and other organisms is a complex process involving numerous enzymes that catalyze phase I (functionalization) and phase II (conjugation) reactions. Herein we introduce MetScore, a machine learning model that can predict both phase I and phase II reaction sites of drugs in a single prediction run. We developed cheminformatics workflows to filter and process reactions to obtain suitable phase I and phase II data sets for model training. Employing a recently developed molecular representation based on quantum chemical partial charges, we constructed random forest machine learning models for phase I and phase II reactions. After combining these models with our previous cytochrome P450 model and calibrating the combination against Bayer in-house data, we obtained the MetScore model that shows good performance, with Matthews correlation coefficients of 0.61 and 0.76 for diverse phase I and phase II reaction types, respectively. We validated its potential applicability to lead optimization campaigns for a new and independent data set compiled from recent publications. The results of this study demonstrate the usefulness of quantum-chemistry-derived molecular representations for reactivity prediction.

Computational Insight Into Vitamin K1 ω-Hydroxylation by Cytochrome P450 4F2

Vitamin K₁ (VK1) plays an important role in the modulation of bleeding disorders. It has been reported that ω-hydroxylation on the VK1 aliphatic chain is catalyzed by cytochrome P450 4F2 (CYP4F2), an enzyme responsible for the metabolism of eicosanoids. However, the mechanism of VK1 ω-hydroxylation by CYP4F2 has not been disclosed. In this study, we employed a combination of quantum mechanism (QM) calculations, homology modeling, molecular docking, molecular dynamics (MD) simulations, and combined quantum mechanism/molecular mechanism (QM/MM) calculations to investigate the metabolism profile of VK1 ω-hydroxylation. QM calculations based on the truncated VK1 model show that the energy barrier for ω-hydroxylation is about 6-25 kJ/mol higher than those at other potential sites of metabolism. However, results from the MD simulations indicate that hydroxylation at the ω-site is more favorable than at the other potential sites, which is in accordance with the experimental observation. The evaluation of MD simulations was further endorsed by the QM/MM calculation results. Our studies thus suggest that the active site residues of CYP4F2 play a determinant role in the ω-hydroxylation. Our results provide structural insights into the mechanism of VK1 ω-hydroxylation by CYP4F2 at the atomistic level and are helpful not only for characterizing the CYP4F2 functions but also for looking into the ω-hydroxylation mediated by other CYP4 enzymes.

Advances in diarylpyrimidines and related analogues as HIV-1 nonnucleoside reverse transcriptase inhibitors

HIV-1 nonnucleoside reverse transcriptase inhibitors (NNRTIs) have been playing an important role in the fight against acquired immunodeficiency syndrome (AIDS). Diarylpyrimidines (DAPYs) as the second generation NNRTIs, represented by etravirine (TMC125) and rilpivirine (TMC278), have attracted extensive attention due to their extraordinary potency, high specificity and low toxicity. However, the rapid emergence of drug-resistant virus strains and dissatisfactory pharmacokinetics of DAPYs present new challenges. In the past two decades, an increasing number of novel DAPY derivatives have emerged, which significantly enriched the structure-activity relationship of DAPYs. Studies of crystallography and molecular modeling have afforded a lot of useful information on structural requirements of NNRTIs, which contributes greatly to the improvement of their resistance profiles. In this review, we reviewed the discovery history and their evolution of DAPYs including their structural modification, derivatization and scaffold hopping in continuous pursuit of excellent anti-HIV drugs. And also, we discussed the prospect of DAPYs and the directions of future efforts.

A tutorial in small molecule identification via electrospray ionization-mass spectrometry: The practical art of structural elucidation

The identification of unknown molecules has been one of the cornerstone applications of mass spectrometry for decades. This tutorial reviews the basics of the interpretation of electrospray ionization-based MS and MS/MS spectra in order to identify small-molecule analytes (typically below 2000 Da). Most of what is discussed in this tutorial also applies to other atmospheric pressure ionization methods like atmospheric pressure chemical/photoionization. We focus primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds, rather than describing strategies for large-scale identification in complex samples. We critically discuss topics like the detection of protonated and deprotonated ions ([M + H]+ and [M - H]- ) as well as other adduct ions, the determination of the molecular formula, and provide some basic rules on the interpretation of product ion spectra. Our tutorial focuses primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds (eg, contaminants in chemical production, pharmacological alteration of drugs), rather than describing strategies for large-scale identification in complex samples. This tutorial also discusses strategies to obtain useful orthogonal information (UV/Vis, H/D exchange, chemical derivatization, etc) and offers an overview of the different informatics tools and approaches that can be used for structural elucidation of small molecules. It is primarily intended for beginning mass spectrometrists and researchers from other mass spectrometry sub-disciplines that want to get acquainted with structural elucidation are interested in some practical tips and tricks.

A pathway for every product? Tools to discover and design plant metabolism

The vast diversity of plant natural products is a powerful indication of the biosynthetic capacity of plant metabolism. Synthetic biology seeks to capitalize on this ability by understanding and reconfiguring the biosynthetic pathways that generate this diversity to produce novel products with improved efficiency. Here we review the algorithms and databases that presently support the design and manipulation of metabolic pathways in plants, starting from metabolic models of native biosynthetic pathways, progressing to novel combinations of known reactions, and finally proposing new reactions that may be carried out by existing enzymes. We show how these tools are useful for proposing new pathways as well as identifying side reactions that may affect engineering goals.

The current limits in virtual screening and property prediction

Beyond finding inhibitors that show high binding affinity to the respective target, there is the challenge of optimizing their properties with respect to metabolic and toxicological issues, as well as further off-target effects. To reduce the experimental effort of synthesizing and testing actual substances in corresponding assays, virtual screening has become an indispensable toolbox in preclinical development. The scope of application covers the prediction of molecular properties including solubility, metabolic liability and binding to antitargets, such as the hERG channel. Furthermore, prediction of binding sites and drugable targets are emerging aspects of virtual screening. Issues involved with the currently applied computational models including machine learning algorithms are outlined, such as limitations to the accuracy of prediction and overfitting.

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

Multiclassification Prediction of Enzymatic Reactions for Oxidoreductases and Hydrolases Using Reaction Fingerprints and Machine Learning Methods

Drug metabolism is a complex procedure in the human body, including a series of enzymatically catalyzed reactions. However, it is costly and time consuming to investigate drug metabolism experimentally; computational methods are hence developed to predict drug metabolism and have shown great advantages. As the first step, classification of metabolic reactions and enzymes is highly desirable for drug metabolism prediction. In this study, we developed multiclassification models for prediction of reaction types catalyzed by oxidoreductases and hydrolases, in which three reaction fingerprints were used to describe the reactions and seven machine learnings algorithms were employed for model building. Data retrieved from KEGG containing 1055 hydrolysis and 2510 redox reactions were used to build the models, respectively. The external validation data consisted of 213 hydrolysis and 512 redox reactions extracted from the Rhea database. The best models were built by neural network or logistic regression with a 2048-bit transformation reaction fingerprint. The predictive accuracies of the main class, subclass, and superclass classification models on external validation sets were all above 90%. This study will be very helpful for enzymatic reaction annotation and further study on metabolism prediction.

Predicting mammalian metabolism and toxicity of pesticides in silico

Pesticides must be effective to be commercially viable but they must also be reasonably safe for those who manufacture them, apply them, or consume the food they are used to produce. Animal testing is key to ensuring safety, but it comes late in the agrochemical development process, is expensive, and requires relatively large amounts of material. Surrogate assays used as in vitro models require less material and shift identification of potential mammalian toxicity back to earlier stages in development. Modern in silico methods are cost-effective complements to such in vitro models that make it possible to predict mammalian metabolism, toxicity and exposure for a pesticide, crop residue or other metabolite before it has been synthesized. Their broader use could substantially reduce the amount of time and effort wasted in pesticide development. This contribution reviews the kind of in silico models that are currently available for vetting ideas about what to synthesize and how to focus development efforts; the limitations of those models; and the practical considerations that have slowed development in the area. Detailed discussions are provided of how bacterial mutagenicity, human cytochrome P450 (CYP) metabolism, and bioavailability in humans and rats can be predicted. © 2018 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies

Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated drug attrition occurs despite substantial investments of time and money in developing cellular assays, animal models, and computational models to predict its occurrence in humans. Underperformance in predicting hepatotoxicity associated with drugs and drug candidates has been attributed to existing gaps in our understanding of the mechanisms involved in driving hepatic injury after these compounds perfuse and are metabolized by the liver. Herein we assess in vitro, in vivo (animal), and in silico strategies used to develop predictive DILI models. We address the effectiveness of several two- and three-dimensional in vitro cellular methods that are frequently employed in hepatotoxicity screens and how they can be used to predict DILI in humans. We also explore how humanized animal models can recapitulate human drug metabolic profiles and associated liver injury. Finally, we highlight the maturation of computational methods for predicting hepatotoxicity, the untapped potential of artificial intelligence for improving in silico DILI screens, and how knowledge acquired from these predictions can shape the refinement of experimental methods.

Mass spectrometric strategies for the investigation of biomarkers of illicit drug use in wastewater

The analysis of illicit drugs in urban wastewater is the basis of wastewater-based epidemiology (WBE), and has received much scientific attention because the concentrations measured can be used as a new non-intrusive tool to provide evidence-based and real-time estimates of community-wide drug consumption. Moreover, WBE allows monitoring patterns and spatial and temporal trends of drug use. Although information and expertise from other disciplines is required to refine and effectively apply WBE, analytical chemistry is the fundamental driver in this field. The use of advanced analytical techniques, commonly based on combined chromatography-mass spectrometry, is mandatory because the very low analyte concentration and the complexity of samples (raw wastewater) make quantification and identification/confirmation of illicit drug biomarkers (IDBs) troublesome. We review the most-recent literature available (mostly from the last 5 years) on the determination of IDBs in wastewater with particular emphasis on the different analytical strategies applied. The predominance of liquid chromatography coupled to tandem mass spectrometry to quantify target IDBs and the essence to produce reliable and comparable results is illustrated. Accordingly, the importance to perform inter-laboratory exercises and the need to analyze appropriate quality controls in each sample sequence is highlighted. Other crucial steps in WBE, such as sample collection and sample pre-treatment, are briefly and carefully discussed. The article further focuses on the potential of high-resolution mass spectrometry. Different approaches for target and non-target analysis are discussed, and the interest to perform experiments under laboratory-controlled conditions, as a complementary tool to investigate related compounds (e.g., minor metabolites and/or transformation products in wastewater) is treated. The article ends up with the trends and future perspectives in this field from the authors' point of view. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 37:258-280, 2018.

A Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening

The new paradigm of toxicity testing approaches involves rapid screening of thousands of chemicals across hundreds of biological targets through use of in vitro assays. Such assays may lead to false negatives when the complex metabolic processes that render a chemical bioactive in a living system are unable to be replicated in an in vitro environment. In the current study, a workflow is presented for complementing in vitro testing results with in silico and in vitro techniques to identify inactive parents that may produce active metabolites. A case study applying this workflow involved investigating the influence of metabolism for over 1,400 chemicals considered inactive across18 in vitro assays related to the estrogen receptor (ER) pathway. Over 7,500 first-generation and second-generation metabolites were generated for these in vitro inactive chemicals using an in silico software program. Next, a consensus model comprised of four individual quantitative structure activity relationship (QSAR) models was used to predict ER-binding activity for each of the metabolites. Binding activity was predicted for ~8-10% of metabolites in each generation, with these metabolites linked to 259 in vitro inactive parent chemicals. Metabolites were enriched in substructures consisting of alcohol, aromatic, and phenol bonds relative to their inactive parent chemicals, suggesting these features are potentially favorable for ER-binding. The workflow presented here can be used to identify parent chemicals that can be potentially bioactive, to aid confidence in high throughput risk screening.

Molecular Mechanism of Alternative P450-Catalyzed Metabolism of Environmental Phenolic Endocrine-Disrupting Chemicals

Understanding the bioactivation mechanisms to predict toxic metabolites is critical for risk assessment of phenolic endocrine-disrupting chemicals (EDCs). One mechanism involves ipso-substitution, which may contribute to the total turnover of phenolic EDCs, yet the detailed mechanism and its relationship with other mechanisms are unknown. We used density functional theory to investigate the P450-catalyzed ipso-substitution mechanism of the prominent xenoestrogen bisphenol A. The ipso-substitution proceeds via H-abstraction from bisphenol A by Compound I, followed by essentially barrierless OH-rebound onto the ipso-position forming a quinol, which can spontaneously decompose into the carbocation and hydroquinone. This carbocation can further evolve into the highly estrogenic hydroxylated and dimer-type metabolites. The H-abstraction/OH-rebound reaction mechanism has been verified as a general reaction mode for many other phenolic EDCs, such as bisphenol analogues, alkylphenols and chlorophenols. The identified mechanism enables us to effectively distinguish between type I (eliminating-substituent as anion) and type II (eliminating-substituent as cation) ipso-substitution in various phenolic EDCs. We envision that the identified pathways will be applicable for prediction of metabolites from phenolic EDCs whose fate are affected by this alternative type of P450 reactivity, and accordingly enable the screening of these metabolites for endocrine-disrupting activity.

WhichP450: a multi-class categorical model to predict the major metabolising CYP450 isoform for a compound

In the development of novel pharmaceuticals, the knowledge of how many, and which, Cytochrome P450 isoforms are involved in the phase I metabolism of a compound is important. Potential problems can arise if a compound is metabolised predominantly by a single isoform in terms of drug-drug interactions or genetic polymorphisms that would lead to variations in exposure in the general population. Combined with models of regioselectivities of metabolism by each isoform, such a model would also aid in the prediction of the metabolites likely to be formed by P450-mediated metabolism. We describe the generation of a multi-class random forest model to predict which, out of a list of the seven leading Cytochrome P450 isoforms, would be the major metabolising isoforms for a novel compound. The model has a 76% success rate with a top-1 criterion and an 88% success rate for a top-2 criterion and shows significant enrichment over randomised models.

Molecular Docking-Based Design and Development of a Highly Selective Probe Substrate for UDP-glucuronosyltransferase 1A10

Intestinal and hepatic glucuronidation by the UDP-glucuronosyltransferases (UGTs) greatly affect the bioavailability of phenolic compounds. UGT1A10 catalyzes glucuronidation reactions in the intestine, but not in the liver. Here, our aim was to develop selective, fluorescent substrates to easily elucidate UGT1A10 function. To this end, homology models were constructed and used to design new substrates, and subsequently, six novel C3-substituted (4-fluorophenyl, 4-hydroxyphenyl, 4-methoxyphenyl, 4-(dimethylamino)phenyl, 4-methylphenyl, or triazole) 7-hydroxycoumarin derivatives were synthesized from inexpensive starting materials. All tested compounds could be glucuronidated to nonfluorescent glucuronides by UGT1A10, four of them highly selectively by this enzyme. A new UGT1A10 mutant, 1A10-H210M, was prepared on the basis of the newly constructed model. Glucuronidation kinetics of the new compounds, in both wild-type and mutant UGT1A10 enzymes, revealed variable effects of the mutation. All six new C3-substituted 7-hydroxycoumarins were glucuronidated faster by human intestine than by liver microsomes, supporting the results obtained with recombinant UGTs. The most selective 4-(dimethylamino)phenyl and triazole C3-substituted 7-hydroxycoumarins could be very useful substrates in studying the function and expression of the human UGT1A10.

From Classical Toxicology to Tox21: Some Critical Conceptual and Technological Advances in the Molecular Understanding of the Toxic Response Beginning From the Last Quarter of the 20th Century

Toxicology has made steady advances over the last 60+ years in understanding the mechanisms of toxicity at an increasingly finer level of cellular organization. Traditionally, toxicological studies have used animal models. However, the general adoption of the principles of 3R (Replace, Reduce, Refine) provided the impetus for the development of in vitro models in toxicity testing. The present commentary is an attempt to briefly discuss the transformation in toxicology that began around 1980. Many genes important in cellular protection and metabolism of toxicants were cloned and characterized in the 80s, and gene expression studies became feasible, too. The development of transgenic and knockout mice provided valuable animal models to investigate the role of specific genes in producing toxic effects of chemicals or protecting the organism from the toxic effects of chemicals. Further developments in toxicology came from the incorporation of the tools of "omics" (genomics, proteomics, metabolomics, interactomics), epigenetics, systems biology, computational biology, and in vitro biology. Collectively, the advances in toxicology made during the last 30-40 years are expected to provide more innovative and efficient approaches to risk assessment. A goal of experimental toxicology going forward is to reduce animal use and yet be able to conduct appropriate risk assessments and make sound regulatory decisions using alternative methods of toxicity testing. In that respect, Tox21 has provided a big picture framework for the future. Currently, regulatory decisions involving drugs, biologics, food additives, and similar compounds still utilize data from animal testing and human clinical trials. In contrast, the prioritization of environmental chemicals for further study can be made using in vitro screening and computational tools.

In vitro metabolism study of a novel P38 kinase inhibitor: in silico predictions, structure elucidation using MS/MS-I

Aim: Metabolism study of PH-797804, a promising newly developed drug for treatment of chronic inflammation which inhibits P38 mitogen-activated protein kinase. Materials & methods: Susceptibility of PH-797804 to metabolism was first investigated using SMARTCyp and Xenosite web servers. Molecular docking of the drug into CYP3A4 crystal structures evaluated binding interactions with active site. The predicted results were confirmed by in vitro incubation with rat S9 fraction. Metabolites of PH-797804 were identified by MS/MS. Results: A hydroxy metabolite and a cysteine/glutathione conjugate were detected. Computational prediction of reactive site of PH-797804 was conducted. Conclusion: The probable cysteine/glutathione adduct is indicative of potential drug chemical reactivity with potential to damage DNA and may provide guidance to the design of analogs with minimum toxicity.

Automating drug discovery

Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.

In silico ligand-based modeling of hBACE-1 inhibitors

Alzheimer's disease is a chronic neurodegenerative disease affecting more than 30 million people worldwide. Development of small molecule inhibitors of human β-secretase 1 (hBACE-1) is being the focus of pharmaceutical industry for the past 15-20 years. Here, we successfully applied multiple ligand-based in silico modeling techniques to understand the inhibitory activities of a diverse set of small molecule hBACE-1 inhibitors reported in the scientific literature. Strikingly, the use of only a small subset of 230 (13%) molecules allowed us to develop quality models that performed reasonably well on the validation set of 1,476 (87%) inhibitors. Varying the descriptor sets and the complexity of the modeling techniques resulted in only minor improvements to the model's performance. The current results demonstrate that predictive models can be built by choosing appropriate modeling techniques in spite of using small datasets consisting of diverse chemical classes, a scenario typical in triaging of high-throughput screening results to identify false negatives. We hope that these encouraging results will help the community to develop more predictive models that would support research efforts for the debilitating Alzheimer's disease. Additionally, the integrated diversity of the techniques employed will stimulate scientists in the field to use in silico statistical modeling techniques like these to derive better models to help advance the drug discovery projects faster.

Pharmacological use of a novel scaffold, anomeric N,N-diarylamino tetrahydropyran: molecular similarity search, chemocentric target profiling, and experimental evidence

Rational drug design against a determined target (disease, pathway, or protein) is the main strategy in drug discovery. However, regardless of the main strategy, chemists really wonder how to maximize the utility of their new compounds by drug repositioning them as clinical drug candidates in drug discovery. In this study, we started our drug discovery "from curiosity in the chemical structure of a drug scaffold itself" rather than "for a specific target". As a new drug scaffold, anomeric diarylamino cyclic aminal scaffold 1, was designed by combining two known drug scaffolds (diphenylamine and the most popular cyclic ether, tetrahydropyran/tetrahydrofuran) and synthesized through conventional Brønsted acid catalysis and metal-free α-C(sp3)-H functionalized oxidative cyclization. To identify the utility of the new scaffold 1, it was investigated through 2D and 3D similarity screening and chemocentric target prediction. The predicted proteins were investigated by an experimental assay. The scaffold 1 was reported to have an antineuroinflammatory agent to reduce NO production, and compound 10 concentration-dependently regulated the expression level of IL-6, PGE-2, TNF-α, ER-β, VDR, CTSD, and iNOS, thus exhibiting neuroprotective activity.