Modeling reactivity to biological macromolecules with a deep multitask network

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural networkthe XenoSite reactivity modelusing literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive 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 derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity 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 standard reactivity screening experiments. 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. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity

Kinetic modelling of acyl glucuronide and glucoside reactivity and development of structure-property relationships.

Acyl glucuronide metabolites have been implicated in the toxicity of several carboxylic acid-containing drugs, and the rate of their degradation via intramolecular transacylation and hydrolysis has been associated with the degree of protein adduct formation. Although not yet proven, the formation of protein adducts in vivo - and subsequent downstream effects - has been proposed as a mechanism of toxicity for carboxylic acid-containing xenobiotics capable of forming acyl glucuronides. A structurally-related series of metabolites, the acyl glucosides, have also been shown to undergo similar degradation reactions and consequently the potential to display a similar mode of toxicity. Here we report detailed kinetic models of each transacylation and hydrolysis reaction for a series of phenylacetic acid acyl glucuronides and their analogous acyl glucosides. Differences in reactivity were observed for the individual transacylation steps between the compound series; our findings suggest that the charged carboxylate ion and neutral hydroxyl group in the glucuronide and glucoside conjugates, respectively, are responsible for these differences. The transacylation reaction was modelled using density functional theory and the calculated activation energy for this reaction showed a close correlation with the degradation rate of the 1-β anomer. Comparison of optimised geometries between the two series of conjugates revealed differences in hydrogen bonding which may further explain the differences in reactivity observed. Together, these models may find application in drug discovery for prediction of acyl glucuronide and glucoside metabolite behaviour

Role of Biotransformation Studies in Minimizing Metabolism-Related Liabilities in Drug Discovery

Metabolism-related liabilities continue to be a major cause of attrition for drug candidates in clinical development. Such problems may arise from the bioactivation of the parent compound to a reactive metabolite capable of modifying biological materials covalently or engaging in redox-cycling reactions leading to the formation of other toxicants. Alternatively, they may result from the formation of a major metabolite with systemic exposure and adverse pharmacological activity. To avert such problems, biotransformation studies are becoming increasingly important in guiding the refinement of a lead series during drug discovery and in characterizing lead candidates prior to clinical evaluation. This article provides an overview of the methods that are used to uncover metabolism-related liabilities in a pre-clinical setting and offers suggestions for reducing such liabilities via the modification of structural features that are used commonly in drug-like molecules

Prediction Of Carboxylic Acid Toxicity Using Machine Learning Model

Carboxylic acids are organic compounds characterized by the presence of a carboxyl functional group capable of donating a proton and forming carboxylate ions in aqueous solutions. The carboxylic acid has widely been used in in manufacturing and medical applications. The rapid growth in carboxylic acid has established a need to predict its toxicity. The purpose of this paper to build predictive toxicity of carboxylic acid models by using five molecular descriptors (refractive index, The octanol/water partition coefficient (log P), acid dissociation constant (pKa), density, and dipole moment) through Machine Learning algorithms. The accuracy of the Machine Learning algorithm was determined by using three different types of models which are Decision Tree, Random Forest and k-Nearest Neighbour (k-NN). Among the machine learning algorithms used, we have determined that the decision tree is the best model for predicting the toxicity of carboxylic acid. This finding demonstrates that the decision tree model exhibits an acceptable level of performance in predicting toxicity within the field of toxicology

In vitro metabolism of tetrazole aminoquinolines and derivatives of metergoline and fusidic acid

Includes bibliographical references.Drug metabolism is recognised as a key component of the drug discovery and development process. It exerts an influence on the action, duration of action and toxicity of a drug in vivo. The integration of drug metabolism studies is therefore crucial to compound progression through the various stages of the development process. This work details the in vitro metabolism work conducted during the early development of aminoquinoline tetrazoles, and derivatives of metergoline and fusidic acid as potential antiplasmodial and/or antimycobacterial agents

Renal drug metabolism in humans: the potential for drug–endobiotic interactions involving cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT)

This item is under embargo for a period of 12 months from the date of publication, in accordance with the publisher's policy. ‘This is the peer reviewed version of the following article: Knights, K. M., Rowland, A. and Miners, J. O. (2013), Renal drug metabolism in humans: the potential for drug– endobiotic interactions involving cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT). British Journal of Clinical Pharmacology, 76: 587–602, which has been published in final form at doi:10.1111/bcp.12086. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for selfarchiving'.Although knowledge of human renal cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT) enzymes and their role in xenobiotic and endobiotic metabolism is limited compared with hepatic drug and chemical metabolism, accumulating evidence indicates that human kidney has significant metabolic capacity. Of the drug metabolizing P450s in families 1 to 3, there is definitive evidence for only CYP 2B6 and 3A5 expression in human kidney. CYP 1A1, 1A2, 1B1, 2A6, 2C19, 2D6 and 2E1 are not expressed in human kidney, while data for CYP 2C8, 2C9 and 3A4 expression are equivocal. It is further known that several P450 enzymes involved in the metabolism of arachidonic acid and eicosanoids are expressed in human kidney, CYP 4A11, 4F2, 4F8, 4F11 and 4F12. With the current limited evidence of drug substrates for human renal P450s drug–endobiotic interactions arising from inhibition of renal P450s, particularly effects on arachidonic acid metabolism, appear unlikely. With respect to the UGTs, 1A5, 1A6, 1A7, 1A9, 2B4, 2B7 and 2B17 are expressed in human kidney, whereas UGT 1A1, 1A3, 1A4, 1A8, 1A10, 2B10, 2B11 and 2B15 are not. The most abundantly expressed renal UGTs are 1A9 and 2B7, which play a significant role in the glucuronidation of drugs, arachidonic acid, prostaglandins, leukotrienes and P450 derived arachidonic acid metabolites. Modulation by drug substrates (e.g. NSAIDs) of the intrarenal activity of UGT1A9 and UGT2B7 has the potential to perturb the metabolism of renal mediators including aldosterone, prostaglandins and 20-hydroxyeicosatetraenoic acid, thus disrupting renal homeostasis

In vitro and in vivo investigations into the interactions between the acyl glucuronide metabolite of diclofenac and serum albumin

Adverse drug reactions represent a major challenge to clinicians, healthcare systems, pharmaceutical companies and academia. With carboxylic acid drugs accounting for the most common class of drugs withdrawn from the market, the carboxylate pharmacophore has received much attention as a potential toxicophore. Direct glucuronidation of the carboxylate group, producing chemically unstable and protein reactive acyl glucuronide (AG) metabolites has received much attention as a bioactivation pathway responsible for generation of these off-target hypersensitivity and hepatotoxicity. It is the chemical instability and protein reactivity of AG metabolites that has led to their hypothesised ability to covalently modify proteins in vivo and subsequently stimulate inappropriate immune responses in susceptible patients. Despite this, whilst the reactivity of AGs has been shown in vitro, their reactivity has never been confirmed in any in vivo system, meaning their association with toxicity may be unjustified. The focus of this thesis was to investigate whether acyl glucuronides could identify covalent adducts to protein in vivo. To address this aim, the thesis first investigates the chemistry of interaction between acyl glucuronides and protein during in vitro investigation. 2mM 1-β diclofenac-AG was found to degrade spontaneously via acyl migration following incubation with 0.1M phosphate buffer pH 7.4 at 37°C with a degradation half-life of 0.78 hours, confirming diclofenac as amongst the most reactive AGs. Further incubations confirmed the action of human serum albumin (HSA) as a mild esterase, and the presence of plasma esterases acting to hydrolyse AGs. The covalent binding of diclofenac-AG to HSA was confirmed using both an alkaline hydrolysis as well as direct mass-spectrometric analyses of modified proteins. Covalent modification of lysine residues was specifically identified, and was found to be concentration and time dependent. Further in vitro incubation experiments revealed for the first time that the 1-β isomer of AGs is responsible for the formation of transacylation adducts, and confirmed previous suggestion that acyl migration is required for the extensive glycation of HSA. Following characterisation of the interaction of diclofenac-AG with HSA, investigations were undertaken in the rat to identify interactions of AGs with circulating rat serum albumin in vivo. In vitro incubations of diclofenac-AG revealed RSA contained fewer binding sites when compared to HSA. Further to this no covalent modification of RSA could be detected in vivo following intravenous administration of 60mg/kg diclofenac-AG. The rapid plasma clearance of diclofenac-AG (67.81 ± 12.83 ml min-1 kg-1) in the rat was shown to be 600 fold faster than that of diclofenac (12.00 ± 2.98 ml min-1 kg-1) following bolus intravenous administration. Use of a continuous intravenous infusion drug delivery system revealed an adaptive change in rats upon continuous infusion of diclofenac, resulting in enhanced plasma elimination of the drug, and induction of the ROS scavenging enzymes catalase and superoxide dismutase-2, without detection of hepatotoxicity. The final experiments in the thesis revealed for the first time the detection of glycation adducts to HSA extracted from volunteer patients receiving chronic diclofenac therapy. These were shown through the detection of glycation adducts in three out of six patients tested. Between 1 and 4 lysine residues were identified in patients, with modifications towards one or all of lysine residues 195, 199, 432 and 525. Transacylation adducts were detected towards lysine residues in all six patient samples analysed. Whilst identification of transacylation adducts reveals bioactivation of the carboxylic acid functional group, it is the identification of glycation adducts to albumin isolated from three of the six patients which reveals, for the first time, definitive evidence for AG reactivity in vivo. This reinforces concerns over the potential of AGs to act as haptens, and re-affirms the carboxylic acid structure as a site of bioactivation forming reactive metabolites

Toxicology and Pharmacology Investigation of 2-Phenylaminophenylacetic Acid Derived NSAIDs: Implication of Chemical Structure on Biological Outcomes

Ph.DDOCTOR OF PHILOSOPH

Design, synthesis and biological evaluation of new derivatives of phenolic metabolites

"Multiple (poly)phenolic compounds, related with the consumption of dietary products have been described to modulate microglial cells, influencing the inflammatory response in the brain and microgliamediated neuronal apoptosis. However, low amounts of information is available about small compounds, present in human blood circulation after the metabolization of (poly)phenols. Previously we have shown some of these small metabolites, capable of crossing the blood brain barrier at physiological concentrations, and be neuroprotective.(...)