Computational Approaches for Screening Drugs for Bioactivation, Reactive Metabolite Formation, and Toxicity

Cytochrome P450 enzymes aid in the elimination of a preponderance of small molecule drugs, but can generate reactive metabolites that may adversely conjugate to protein and DNA, in a process known as bioactivation, and prompt adverse reaction, drug candidate attrition, or market withdrawal. Experimental assays are low-throughput and expensive to perform, so they are often reserved until later stages of the drug development pipeline when the drug candidate pools are already significantly narrowed. Reactive metabolites also elude in vivo detection, as they are transitory and generally do not circulate. In contrast, computational methods are high-throughput and cheap to screen millions of potentially toxic molecules during early stages of the drug development pipeline. This work computationally models sequences of metabolic transformations, i.e., pathways, between an input molecule and a corresponding, optional reactive metabolite(s). Additionally, an accurate graph neural network model was developed to assess importance of intermediate metabolites and extract connected subnetworks of relevance to bioactivation. Connecting multiple site of metabolism and structure inference models, we developed an integrated model of metabolism and reactivity to evaluate bioactivation risk driven by epoxidation, quinone formation, thiophene sulfur-oxidation, and nitroaromatic reduction. We applied this framework to an understudied substructure, the isoxazole ring, that is gaining traction in a class of drugs known as bromodomain inhibitors that may potentially drive quinone formation. Finally, we attend to toxicity associated with drug-drug interactions, particularly with NSAID usage reported in electronic health records

Estimating the Operational Impact of Container Inspections at International Ports

A U.S. law mandating nonintrusive imaging and radiation detection for 100% of U.S.-bound containers at international ports has provoked widespread concern that the resulting congestion would hinder trade significantly. Using detailed data on container movements, gathered from two large international terminals, we simulate the impact of the two most important inspection policies that are being considered. We find that the current inspection regime being advanced by the U.S. Department of Homeland Security can only handle a small percentage of the total load. An alternate inspection protocol that emphasizes screening—a rapid primary scan of all containers, followed by a more careful secondary scan of only a few containers that fail the primary test—holds promise as a feasible solution for meeting the 100% scanning requirement

Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort

Drug-drug interactions account for up to 30% of adverse drug reactions. Increasing prevalence of electronic health records (EHRs) offers a unique opportunity to build machine learning algorithms to identify drug-drug interactions that drive adverse events. In this study, we investigated hospitalizations\u27 data to study drug interactions with non-steroidal anti-inflammatory drugs (NSAIDS) that result in drug-induced liver injury (DILI). We propose a logistic regression based machine learning algorithm that unearths several known interactions from an EHR dataset of about 400,000 hospitalization. Our proposed modeling framework is successful in detecting 87.5% of the positive controls, which are defined by drugs known to interact with diclofenac causing an increased risk of DILI, and correctly ranks aggregate risk of DILI for eight commonly prescribed NSAIDs. We found that our modeling framework is particularly successful in inferring associations of drug-drug interactions from relatively small EHR datasets. Furthermore, we have identified a novel and potentially hepatotoxic interaction that might occur during concomitant use of meloxicam and esomeprazole, which are commonly prescribed together to allay NSAID-induced gastrointestinal (GI) bleeding. Empirically, we validate our approach against prior methods for signal detection on EHR datasets, in which our proposed approach outperforms all the compared methods across most metrics, such as area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC)

The Fully Automated and Self-Contained Operations Paradigm of the CSIM Mission

The Compact Spectral Irradiance Monitor (CSIM) CubeSat Mission has been collecting solar spectral irradiance (SSI) data for over two years, contributing to 40+ years of multi-mission SSI data collection. CSIM utilizes a fully automated and self-contained operations paradigm developed at the Laboratory for Atmospheric and Space Physics (LASP). LASP efficiently performs the entire operations workflow for CSIM, from planning through data processing, which nominally requires only 15 minutes of staffed operations support per week. Mission operations students at LASP are responsible for the entire planning process. They query for ground station contacts and solar observation times which are input into a suite of software tools to create the onboard stored command table and the weekly uplink plan. An automated ground station script then configures for the upcoming CSIM contacts by querying Space-Track for overflights. Within 2 minutes from the start of a pass, the script commands the UHF or S-Band antenna to point at the spacecraft, brings up the command-and-control software, and performs an initial health-and-safety check upon AOS (acquisition of signal). Automated command scripts then configure the spacecraft and upload the plan using command success logic checks. This ensures that all commands are sent and accepted by the spacecraft in-order, and without overwriting any non-expired scheduling slots. The week\u27s worth of commands is loaded within a few passes, and science collection typically starts soon after. Ground automation will detect major anomalies and notify the flight control team in real-time, allowing the operators to recover the spacecraft on the next contact and prepare a new activity plan for autonomous upload. Additionally, ground automation queries CSIM health and safety data and sends telemetry trends to the operations team for daily, weekly, and monthly health and safety checks. CSIM science data is downlinked during 1 or 2 passes per day via the S-band antenna. This data is processed twice per day via an automated data processing pipeline which requires no regular human intervention. The self-contained and automated nature of the data processing pipeline ensures that LASP scientists can access CSIM data within a few hours of being received on the ground. We discuss how this efficient single-mission, self-contained operations paradigm will be expanded to support multiple missions and external customers in the future

Discovery of novel reductive elimination pathway for 10-hydroxywarfarin

Coumadin (R/S-warfarin) anticoagulant therapy is highly efficacious in preventing the formation of blood clots; however, significant inter-individual variations in response risks over or under dosing resulting in adverse bleeding events or ineffective therapy, respectively. Levels of pharmacologically active forms of the drug and metabolites depend on a diversity of metabolic pathways. Cytochromes P450 play a major role in oxidizing R- and S-warfarin to 6-, 7-, 8-, 10-, and 4\u27-hydroxywarfarin, and warfarin alcohols form through a minor metabolic pathway involving reduction at the C11 position. We hypothesized that due to structural similarities with warfarin, hydroxywarfarins undergo reduction, possibly impacting their pharmacological activity and elimination. We modeled reduction reactions and carried out experimental steady-state reactions with human liver cytosol for conversion o

Bioactivation of isoxazole-containing bromodomain and extra-terminal domain (BET) inhibitors

The 3,5-dimethylisoxazole motif has become a useful and popular acetyl-lysine mimic employed in isoxazole-containing bromodomain and extra-terminal (BET) inhibitors but may introduce the potential for bioactivations into toxic reactive metabolites. As a test, we coupled deep neural models for quinone formation, metabolite structures, and biomolecule reactivity to predict bioactivation pathways for 32 BET inhibitors and validate the bioactivation of select inhibitors experimentally. Based on model predictions, inhibitors were more likely to undergo bioactivation than reported non-bioactivated molecules containing isoxazoles. The model outputs varied with substituents indicating the ability to scale their impact on bioactivation. We selected OXFBD02, OXFBD04, and I-BET151 for more in-depth analysis. OXFBD\u27s bioactivations were evenly split between traditional quinones and novel extended quinone-methides involving the isoxazole yet strongly favored the latter quinones. Subsequent experimental studies confirmed the formation of both types of quinones for OXFBD molecules, yet traditional quinones were the dominant reactive metabolites. Modeled I-BET151 bioactivations led to extended quinone-methides, which were not verified experimentally. The differences in observed and predicted bioactivations reflected the need to improve overall bioactivation scaling. Nevertheless, our coupled modeling approach predicted BET inhibitor bioactivations including novel extended quinone methides, and we experimentally verified those pathways highlighting potential concerns for toxicity in the development of these new drug leads

Effect of mastication on lipid bioaccessibility of almonds in a randomized human study and its implications for digestion kinetics, metabolizable energy, and postprandial lipemia

Background: The particle size and structure of masticated almonds impact significantly on nutrient release (bioaccessibility) and digestion kinetics. Objectives: To quantify the effects of mastication on the bioaccessibility of intracellular lipid of almond tissue and examine microstructural characteristics of masticated almonds. Design: In a randomized, subject-blind, crossover trial, 17 healthy subjects chewed natural (NA) or roasted almonds (RA) on 4 separate mastication sessions. Particle size distributions (PSDs) of the expectorated boluses were measured using mechanical sieving and laser diffraction (primary outcome). The microstructure of masticated almonds, including the structural integrity of the cell walls (i.e. dietary fiber), was examined using microscopy. Lipid bioaccessibility was predicted using a theoretical model, based on almond particle size and cell dimensions, and then compared to empirically-derived release data. Results: Inter-subject variations (n=15, 2 subjects withdrew) in PSDs of both NA and RA samples were small (e.g. laser diffraction, CV = 12% and 9%, respectively). Significant differences in PSDs were found between these two almond forms (P 500 µm) in masticated almonds. Microstructural examination of the almonds indicated that most intracellular lipid remained undisturbed in intact cells post-mastication. No adverse events were recorded. Conclusions: Following mastication, most of the almond cells remained intact with lipid encapsulated by cell walls. Thus, most of the lipid (>88%) in masticated almonds is not immediately bioaccessible and remains unavailable for digestion and absorption. The lipid encapsulation mechanism provides a convincing explanation for why almonds have a low metabolizable energy content and an attenuated impact on postprandial lipemia. Trial registration number; ISRCTN58438021

Long non-coding RNAs: spatial amplifiers that control nuclear structure and gene expression

Over the past decade, it has become clear that mammalian genomes encode thousands of long non-coding RNAs (lncRNAs), many of which are now implicated in diverse biological processes. Recent work studying the molecular mechanisms of several key examples — including Xist, which orchestrates X chromosome inactivation — has provided new insights into how lncRNAs can control cellular functions by acting in the nucleus. Here we discuss emerging mechanistic insights into how lncRNAs can regulate gene expression by coordinating regulatory proteins, localizing to target loci and shaping three-dimensional (3D) nuclear organization. We explore these principles to highlight biological challenges in gene regulation, in which lncRNAs are well-suited to perform roles that cannot be carried out by DNA elements or protein regulators alone, such as acting as spatial amplifiers of regulatory signals in the nucleus