XenoSite P450 Metabolism
XenoSite Web provides an implementation of the XenoSite Cytochrome P450 Prediction Models . Cytochrome P450s (CYPs) play a fundamental role in the oxidative metabolism of over 90% of FDA approved small-molecule drugs. As such, predicting the way that small molecule drugs are oxidized by CYPs can aid in the rational design of safe, effective drugs that exhibit high bioavailability. You can try XenoSite P450 Metabolism via the online submission tool. For technical details, please see here.
Please note that XenoSite Cytochrome P450 Prediction Models provide predictions of regio-selectivity (which atoms on a molecule are likely to be oxidized by a given CYP enzyme), but they do not explicity model selectivity (which molecules are substrates of a given CYP enzyme).
If you would like performance measures or technical details about XenoSite, please see our Technical Details
If you would like to use results from XenoSite Cytochrome P450 Metabolism Predictor in your publications, please cite the following paper:
Zaretzki, J., Matlock, M., & Swamidass, S. J. (2013). XenoSite: Accurately predicting CYP-mediated sites of metabolism with neural networks. Journal of chemical information and modeling, 53(12), 3373-3383.
Drug toxicity is often caused by electrophilic reactive metabolites that covalently bind to proteins. Epoxides comprise a large class of three-membered cyclic ethers. These molecules are electrophilic and typically highly reactive due to ring tension and polarized carbon-oxygen bonds. Epoxides are metabolites often formed by cytochromes P450 acting on aromatic or double bonds. The specific location on a molecule that undergoes epoxidation is its site of epoxidation (SOE). Identifying a molecule’s SOE can aid in interpreting adverse events related to reactive metabolites and direct modification to prevent epoxidation for safer drugs. This study utilized a database of 702 epoxidation reactions to build a model that accurately predicted sites of epoxidation. The foundation for this model was an algorithm originally designed to model sites of cytochromes P450 metabolism (called XenoSite) and that was recently applied to model the intrinsic reactivity of diverse molecules with glutathione. This modeling algorithm systematically and quantitatively summarizes the knowledge from hundreds of epoxidation reactions with a deep convolution network. The final epoxidation model constructed with this approach identifies SOEs with 94.9% area under the curve accuracy and separated epoxidized and non-epoxidized molecules with 78.6% accuracy. Moreover, within epoxidized molecules, the model separated aromatic or double bond SOEs from all other aromatic or double bonds with accuracies of 92.5% and 95.1%, respectively. Finally, the model separated SOE from sites of sp2 hydroxylation with 83.8% accuracy. Our model is the first of its kind, and can be used for the development of safer drugs by identifying which drug candidates are likely to form epoxide metabolites.
If you would like to use results from XenoSite Epoxidation Predictor in your publications, please cite the following paper:
Hughes, T. B., Miller, G. P., Swamidass, S. J. (2015). Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. ACS Central Science, 1(4), 168-180.
XenoSite Quinone Formation
Around 65% of adverse drug reactions are caused by electrophilically reactive drug metabolites. About 40% of these reactive metabolites are quinones, and consequently quinones can be estimated to cause about 25% of all drug toxicities. Electrophilic reactive metabolites conju gate to nucleophilic sites within DNA and proteins, sometimes causing cancer or toxic immune responses. Quinone species, including quinone imines, quinone methides, and imine methides, are electrophilic Michael acceptors that are often highly reactive. For example, cytochromes P450 oxidize acetaminophen (Tylenol) to N-acetyl-p-benzoquinone imine, which is electrophilically re- active and covalently binds to nucleophilic sites within proteins. This reactive quinone metabolite causes severe liver damage when acetaminophen exceeds a safe dose.
Unfortunately, current methods of predicting quinone formation are insufficient. Experimental methods are resource intensive and impractical when considering thousands of drug candidates during the initial screening phase of drug development. A commonly used approach of minimizing reactive metabolite formation during drug design is to avoid any motif known to form reactive metabolites in other drugs. However, this “structural alerts" approach is not feasible for quinones, because any phenyl ring—a fundamental component of many drugs—has the potential to form a quinone. Computational methods of predicting metabolism focus only on predicting sites of metabolism, which often does not predict the actual structure of metabolites. Furthermore, quinone formation is often preceded by other metabolic events like aromatic hydroxylation, and neither structural alerts nor computational methods consider sequential metabolism steps, and consequently cannot predict many cases of quinone formation.
The XenoSite Quinone Formation model predicts whether both a molecule and its metabolites will form a quinone. By implicitly modeling successive metabolic steps, the model predicts quinone formation even in non-obvious cases. Predictions are produced both at the atom-level and at the molecule-level. Atom-level predictions reveal where on a molecule a quinone is likely to form, which can direct rational drug design. These predictions accurately identify sites of quinone formation with 96.4% area under the curve accuracy across all pairs of ring carbons. Molecule-level predictions distinguish whether structurally similar molecules will form a quinone, enabling rapid screening for potentially toxic drugs. These predictions accurately identify molecules that form quinones with 88.2% area under the curve accuracy. By modeling the most frequent class of reactive metabolites, this method provides a rapid screening tool for a key drug toxicity risk.
If you would like to use results from XenoSite Quinone Formation Predictor in your publications, please cite the following paper:
Hughes, T. B., Miller, G. P., Swamidass, S. J. (2016). Modeling Quinone Formation with a Deep Machine Learning Network (in preparation).
Despite significant investment of resources, around 40% of drug candidates are discontinued due to toxicity, often arising from reactions between electrophilic drugs or drug metabolites and nucleophilic biological macromolecules, like DNA and proteins. The resulting adducts for DNA may cause gene dysregulation and modifications of the code, while protein adducts can disrupt normal biological functions and induce harmful immune responses. Knowledge of the likelihood of adductive reactions would provide critical insights on their probable role in biological dysfunction, and thus, we modeled the reactivity of molecules to DNA and protein along with common screening traps for electrophiles, i.e. cyanide and glutathione. A deep convolution neural network was trained on literature data to accurately predict both sites and probability of reactivity for molecules. On the site level, cross-validated predictions had AUC performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separates molecules electrophilically reactive with DNA and protein from unreactive molecules with cross- validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model’s performances significantly outperformed reactivity indices from the quantum modeling literature. Moreover, we developed and applied a selec- tivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be “missed” by tra- ditional screening approaches. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy.
If you would like to use results from XenoSite Reactivity Predictor in your publications, please cite the following papers:
Hughes, T. B., Miller, G. P., Swamidass, S. J. (2015). Site of Reactivity Models Predict Molecular Reactivity of Diverse Chemicals with Glutathione. Chemical Research in Toxicology, 28(4), 797-809.
Hughes, T. B., Dang, N. L., Miller, G. P., Swamidass, S. J. (2016). Modeling Reactivity to Biological Macromolecules with a Deep Machine Learning Network. In preparation.
Uridine diphosphate glucunosyltransferases (UGTs) metabolize 15% of FDA approved drugs. Lead optimization efforts benefit from knowing how candidate drugs are metabolized by UGTs. The XenoSite UGT model predicts sites of UGT-mediated metabolism on drug-like molecules. In the training data, the sites of metabolism of 2839 UGT substrates are identified by our method with 86% (Top-1) and 97% (Top-2) accuracy. Both the size of this data set and our results improve over those of previously published UGT-metabolism prediction methods.
If you would like to use results from XenoSite UGT Predictor in your publications, please cite the following paper:
Dang, N. L., Hughes, T. B., Krishnamurthy, V., Swamidass, S. J. (2016). A Simple Formula Predicts UGT-Mediated Metabolism. Bioinformatics (under review).
The authors would like to extend a special thanks to Brook Hauser for her help with web design and graphics.