Mechanistic Assessment of Extrahepatic Contributions to Glucuronidation of Integrase Strand Transfer Inhibitors

Integrase strand transfer inhibitor (INSTI)-based regimens dominate initial human immunodeficiency virus treatment. Most INSTIs are metabolized predominantly via UDP-glucuronosyltransferases (UGTs). For drugs predominantly metabolized by UGTs, including INSTIs, in vitro data recovered from human liver microsomes (HLMs) alone often underpredict human oral clearance. While several factors may contribute, extrahepatic glucuronidation may contribute to this underprediction. Thus, we comprehensively characterized the kinetics for the glucuronidation of INSTIs (cabotegravir, dolutegravir, and raltegravir) using pooled human microsomal preparations from liver (HLMs), intestine (HIMs), and kidney (HKMs) tissues; human embryonic kidney 293 cells expressing individual UGTs; and recombinant UGTs. In vitro glucuronidation of cabotegravir (HLMs≈HKMs>>>HIMs), dolutegravir (HLMs>HIMs>>HKMs), and raltegravir (HLMs>HKMs>> HIMs) occurred in hepatic and extrahepatic tissues. The kinetic data from expression systems suggested the major enzymes in each tissue: hepatic UGT1A9 > UGT1A1 (dolutegravir and raltegravir) and UGT1A1 (cabotegravir), intestinal UGT1A3 > UGT1A8 > UGT1A1 (dolutegravir) and UGT1A8 > UGT1A1 (raltegravir), and renal UGT1A9 (dolutegravir and raltegravir). Enzymes catalyzing cabotegravir glucuronidation in the kidney and intestine could not be identified unequivocally. Using data from dolutegravir glucuronidation as a prototype, a "bottom-up" physiologically based pharmacokinetic model was developed in a stepwise approach and predicted dolutegravir oral clearance within 4.5-fold (hepatic data only), 2-fold (hepatic and intestinal data), and 32% (hepatic, intestinal, and renal data). These results suggest clinically meaningful glucuronidation of dolutegravir in tissues other than the liver. Incorporation of additional novel mechanistic and physiologic underpinnings of dolutegravir metabolism along with in silico approaches appears to be a powerful tool to accurately predict the clearance of dolutegravir from in vitro data

Dynamic regulation of AtDAO1 and GH3 modulates auxin homeostasis

The hormone auxin is a key regulator of plant growth and development, and great progress has been made understanding auxin transport and signaling. Here we show that auxin metabolism and homeostasis are also regulated in a complex manner. The principal auxin degradation pathways in Arabidopsis include oxidation by AtDAO1/2 and conjugation by GH3s. Metabolic profiling of dao1-1 root tissues revealed a 50% decrease in the oxidation product oxIAA, an increase in the conjugated forms IAA-Asp and IAA-Glu of 438-fold and 240-fold respectively, while auxin remains close to wild type. By fitting parameter values to a mathematical model of these metabolic pathways we show that, in addition to reduced oxidation, both auxin biosynthesis and conjugation are increased in dao1-1. We then quantified gene expression in plantae, and found that transcripts of AtDAO1 and GH3 genes are increased in response to auxin, over different time scales and concentration ranges. Including this regulation of AtDAO1 and GH3 in an extended model reveals that auxin oxidation is more important for auxin homoeostasis at lower hormone concentrations, while auxin conjugation is most significant at high auxin levels. Finally, embedding our homeostasis model in a multicellular simulation to assess the spatial effect of the dao1-1 mutant shows that auxin increases in outer root tissues, in agreement with the dao1-1 mutant root hair phenotype. We conclude that auxin homeostasis is dependent on AtDAO1, acting in concert with GH3, to maintain auxin at optimal levels for plant growth and development

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.publishedVersio

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

Modeling Small Molecule Metabolism in Human Liver Microsome to Better Predict Toxicity Risk

Adverse drug reactions (ADRs) are a serious problem with increasing morbidity, mortality, and health care costs worldwide. In the U.S., ADRs are responsible for more than 50% of acute liver failure cases and are the fourth most common cause of death, costing 100,000 lives annually.Idiosyncratic adverse drug reactions (IADRs) are immune-mediated hypersensitivity ADRs that are difficult to foresee during drug development. IADRs are often caused by reactive metabolites produced during drug metabolism. These reactive metabolites covalently attach to cellular components, and the resulting conjugates may provoke toxic immune response. Because reactive metabolites are short-lived, they can be difficult to detect. Tools to reliably predict whether a compound forms reactive metabolites would enable us to avoid drug candidates prone to causing IADRs and make new medicines safer. Unfortunately, due to inadequate modeling of metabolism, current experimental and computational approaches do not reliably identify drug candidates that form reactive metabolites. Bioactivation pathways leading to reactive metabolite formations often are composed of multiple steps. To accurately predict reactive metabolite formation, we must explicitly model metabolic steps of bioactivation pathways. Therefore, we built models to predict specific metabolic transformations such as hydroxylation, epoxidation, dehydrogenation, quinonation, hydrolysis, reduction, glucuronidation, sulfuration, acetylation, and methylation. Using machine learning and literature-derived data, we trained models that can predict both the likelihood that a molecules undergoes a certain chemical transformation and the specific site(s) within the molecule where this transformation happens. Together, our metabolism models cover ∼ 95% of enzymatically-driven chemical reactions in human. Our models achieve high area under the receiver operating characteristic curve scores (AUCs) of ∼ 90% in cross-validated tests. Our mechanistic approach outperformed structural alerts—a common tool used to screen out candidate compounds during drug development. Structural alerts are chemical moieties that were frequently observed to give rise to reactive metabolite upon bioactivation. However, many safe drugs also contain structural alerts which are not bioactivated and, conversely, many toxic drugs contain no structural alert. We combined models of metabolism, metabolite structure prediction, and reactivity to offer a better prediction of reactive metabolite formation in the context of structural alerts. Based on the known bioactivation pathway(s) of each structural alert, appropriate metabolism models were applied to evaluate whether drugs containing the structural alert actually form reactive metabolites. Our study focused on the furan, phenol, nitroaromatic, and thiophene alerts. Specifically, we used models of epoxidation, quinone formation, reduction, and sulfur-oxidation to predict the bioactivation of furan-, phenol-, nitroaromatic-, and thiophene-containing drugs. Our models separated bioactivated and not-bioactivated furan-, phenol-, nitroaromatic-, and thiophene-containing drugs with AUC performances of 100%, 73%, 93%, and 88%, respectively. In addition, we used our models to uncover bioactivation mechanisms that were previously under-appreciated. For example, N-dealkylation is the oxidation of an alkylated amine at the nitrogen-carbon bond, cleaving the parent compound into an amine and an aldehyde. Even though aldehydes can be toxic, metabolic studies usually neglect to report or investigate them because they are assumed to be efficiently detoxified into carboxylic acids and alcohols. Applying the N-dealkylation model to approved and withdrawn medicines, we found that aldehyde metabolites produced from N-dealkylation may explain the hepatotoxicity of several drugs: indinavir, piperacillin, verapamil, and ziprasidone. These results demonstrated the utility of comprehensive bioactivation models that systematically consider constituent metabolic steps in gauging toxicity risks

IN SILICO ДОСЛІДЖЕННЯ МОЖЛИВИХ ШЛЯХІВ МЕТАБОЛІЗМУ АТРИСТАМІНУ В ОРГАНІЗМІ ЛЮДИНИ

Introduction. The object of the present study is atristamine (2-methyl-3-(phenylaminomethyl)-1H-quinolin-4-one), which is being studied as a promising antidepressant with cerebroprotective, nootropic, analgesic, antihypoxic and actoprotective properties. A prerequisite for its further introduction as a candidate for drugs is the study of the pharmacokinetic characteristics of the molecule. This is impossible without a holistic understanding of the biotransformation processes that the molecule undergoes in the human body. The aim of the study – in silico study of the possible metaboliс pathways of the promising antidepressant atristamine using freely available online resources. Research Methods. For the purpose of in silico research of possible directions of biotransformation of atristamine in the human body, the following online web services were used: Xenosite P450 Metabolism 1.0; Xenosite UGT 2.0; Way2Drug SOMP and Way2Drug RA. Taking into account that the structural feature of quinolin-4(1H)-ones is the possibility of prototropic tautomerism in the heterocycle, computations were performed for both theoretically possible tautomeric forms of the atristamine molecule – 2-methyl-3-(phenylaminomethyl)-1H-quinolin-4-one and 4-hydroxy-2-methyl-3-(phenylaminomethyl)-quinoline. Results and Discussion. Due to the presence of a secondary amino group in the molecule of 2-methyl-3-(phenylaminomethyl)-1H-quinolin-4-one and 4-hydroxy group in the structure of another tautomer (4-hydroxy-2-methyl-3-(phenylaminomethyl)-quinoline) glucuronidation is highly probable with the formation of N- and O-glu­curonides, respectively. For 2-methyl-3-(phenylaminomethyl)-1H-quinolin-4-one as a more stable form, it was shown that aromatic hydroxylation, aliphatic hydroxylation, oxidative deamination, N-hydroxylation and epoxidation can be the main metabolic pathways. The direction of aliphatic hydroxylation deserves the most attention, since, unlike all other pathways, the formation of metabolites with new pharmacological properties (kynurenic acid derivatives) was predicted as a result of this. Conclusions. The results of in silico research of possible pathways of atristamine metabolism in the human body support the fact that this molecule with high probability can be intensively metabolized via cytochrome P450 enzyme systems. This must be taken into account when planning in vivo experiments in the future.Вступление. Объектом представленного исследования является атристамин (2-метил-3-(фенил­аминометил)-1Н-хинолин-4-он), который изучают как перспективный антидепрессант с церебропротекторными, ноотропными, анальгетическими, антигипоксическими и актопротекторными свойствами. Обязательным условием дальнейшего внедрения его в качестве кандидата в лекарственные препараты является исследование фармакокинетических характеристик молекулы. Это невозможно осуществить без целостного понимания процессов биотрансформации, которым подвергается исследуемое соединение в организме человека. Цель исследования – провести in silico исследование возможных путей метаболизма перспективного антидепрессанта атристамина с помощью онлайн-ресурсов, находящихся в свободном доступе. Методы исследования. С целью in silico исследования возможных направлений биотрансформации атристамина в организме человека использовали он-лайн следующие веб-сервисы: “Xenosite P450 Metabo­lism 1.0”; “Xenosite UGT 2.0”; “Way2Drug SOMP” и “Way2Drug RA”. Учитывая то, что структурной особенностью хинолин-4(1Н)-онов является возможность существования прототропной таутомерии в гетероцикле, вычисления проводили для обеих теоретически возможных таутомерных форм молекулы атристамина – 2-метил-3-(фениламинометил)-1Н-хинолин-4-она и 4-гидрокси-2-метил-3-(фениламинометил)-хинолина. Результаты и обсуждение. Наличие вторичной аминогруппы в молекуле 2-метил-3-(фениламинометил)-1Н-хинолин-4-она и 4-гидроксигруппы в молекуле другого таутомера (4-гидрокси-2-метил-3- (фениламинометил)-хинолина) обусловливает высокую вероятность глюкуронирования с образованием, соответственно, N- и О-глюкуронидов. Для 2-метил-3-(фениламинометил)-1Н-хинолин-4-она как более устойчивой формы показано, что основными путями метаболизма могут быть ароматическое гидроксилирование, алифатическое гидроксилирование, окислительное дезаминирование, N-гидроксилирование и эпоксидирование. Наибольшего внимания заслуживает направление алифатического гидроксилирования, поскольку, в отличие от всех других путей, в результате этого прогнозируется образование генерации метаболитов с новыми фармакологическими свойствами (производные кинуреновой кислоты). Вывод. Результаты in silico исследования возможных путей метаболизма атристамина в организме человека свидетельствуют о том, что исследуемое соединение с высокой вероятностью интенсивно метаболизируется при участии энзимных систем цитохрома P450, что обязательно необходимо учесть в дальнейшем при планировании экспериментов in vivo.Вступ. Об’єктом представленого дослідження є атристамін (2-метил-3-(феніламінометил)-1Н-хінолін-4-он), який вивчають як перспективний антидепресант із церебропротекторними, ноотропними, аналгетичними, антигіпоксичними та актопротекторними властивостями. Обов’язковою умовою подальшого впровадження його як кандидата в ліки є дослідження фармакокінетичних характеристик молекули. Це неможливо здійснити без цілісного розуміння процесів біотрансформації, яким піддається досліджувана сполука в організмі людини. Мета дослідження – провести in silico дослідження можливих шляхів метаболізму перспективного антидепресанта атристаміну за допомогою онлайн-ресурсів, що перебувають у вільному доступі. Методи дослідження. З метою in silico дослідження можливих напрямків біотрансформації атристаміну в організмі людини використовували он-лайн такі веб-сервіси: “Xenosite P450 Metabolism 1.0”; “Xenosite UGT 2.0”; “Way2Drug SOMP” та “Way2Drug RA”. З огляду на те, що структурною особливістю хінолін-4(1Н)-онів є можливість існування прототропної таутомерії в гетероциклі, обчислення проводили для обох теоретично можливих таутомерних форм молекули атристаміну – 2-метил-3-(феніламіноме­тил)-1Н-хінолін-4-ону та 4-гідрокси-2-метил-3-(феніламінометил)-хіноліну. Результати й обговорення. Наявність вторинної аміногрупи в молекулі 2-метил-3-(феніламінометил)-1Н-хінолін-4-ону і 4-гідроксигрупи в молекулі іншого таутомера (4-гідрокси-2-метил-3-(феніламінометил)-хіноліну) зумовлює високу ймовірність глюкуронування з утворенням, відповідно, N- та О-глюкуронідів. Для 2-метил-3-(феніламінометил)-1Н-хінолін-4-ону як більш стійкої форми показано, що основними шляхами метаболізму можуть бути ароматичне гідроксилювання, аліфатичне гідроксилювання, окиснювальне дезамінування, N-гідроксилювання та епоксидування. Найбільшої уваги заслуговує напрямок аліфатичного гідроксилювання, оскільки, на відміну від усіх інших шляхів, у результаті цього прогнозується утворення генерації метаболітів з новими фармакологічними властивостями (похідні кінуренової кислоти). Висновок. Результати in silico дослідження можливих шляхів метаболізму атристаміну в організмі людини свідчать на користь того факту, що досліджувана сполука з високою ймовірністю інтенсивно метаболізується з участю ензимних систем цитохрому P450, що обов’язково необхідно врахувати в подальшому при плануванні експериментів in vivo