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 Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States.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

Quantum Probabilistic Subroutines and Problems in Number Theory

We present a quantum version of the classical probabilistic algorithms $\grave{a}$ la Rabin. The quantum algorithm is based on the essential use of Grover's operator for the quantum search of a database and of Shor's Fourier transform for extracting the periodicity of a function, and their combined use in the counting algorithm originally introduced by Brassard et al. One of the main features of our quantum probabilistic algorithm is its full unitarity and reversibility, which would make its use possible as part of larger and more complicated networks in quantum computers. As an example of this we describe polynomial time algorithms for studying some important problems in number theory, such as the test of the primality of an integer, the so called 'prime number theorem' and Hardy and Littlewood's conjecture about the asymptotic number of representations of an even integer as a sum of two primes.Comment: 9 pages, RevTex, revised version, accepted for publication on PRA: improvement in use of memory space for quantum primality test algorithm further clarified and typos in the notation correcte

Computational Docking Simulations of Nitroanisole and Nitrophenol with CYP2E1

Studies have shown that CYP2E1, a cytochrome P450 enzyme containing two primary binding sites, plays a substantial role in the oxidative metabolism of many foreign substances, including the detoxification reaction of 4-nitroanisole to 4-nitrophenol. Through the advancements of the computational docking software Tripos Sybyl7.2, it has been possible to investigate the mechanism of oxidation of 4-nitroanisole. Docking modules of Sybyl7.2 software, including Surflext and molecular dynamics, have created the ability to ascertain the likely binding configurations of 4-nitranisole and its constitutional isomers in either the distal or proximal binding sites of CYP2E1. Knowing the relative binding relationships of 4-nitroanisole to the heme of the CYP2E1 plays in human oxidative metabolism of xenobiotic substances. The goal in this research was to observe the configuration of nitroanisole and its derivatives in relation to the heme of the enzyme and possibly determine a cause for the unconventional reaction kinetics of CYP2E1

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)

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

Entanglement production in Quantized Chaotic Systems

Quantum chaos is a subject whose major goal is to identify and to investigate different quantum signatures of classical chaos. Here we study entanglement production in coupled chaotic systems as a possible quantum indicator of classical chaos. We use coupled kicked tops as a model for our extensive numerical studies. We find that, in general, presence of chaos in the system produces more entanglement. However, coupling strength between two subsystems is also very important parameter for the entanglement production. Here we show how chaos can lead to large entanglement which is universal and describable by random matrix theory (RMT). We also explain entanglement production in coupled strongly chaotic systems by deriving a formula based on RMT. This formula is valid for arbitrary coupling strengths, as well as for sufficiently long time. Here we investigate also the effect of chaos on the entanglement production for the mixed initial state. We find that many properties of the mixed state entanglement production are qualitatively similar to the pure state entanglement production. We however still lack an analytical understanding of the mixed state entanglement production in chaotic systems.Comment: 16 pages, 5 figures. To appear in Pramana:Journal of Physic

A feasible route for the design and manufacture of customised respiratory protection through digital facial capture

The World Health Organisation has called for a 40% increase in personal protective equipment manufacturing worldwide, recognising that frontline workers need effective protection during the COVID-19 pandemic. Current devices suffer from high fit-failure rates leaving significant proportions of users exposed to risk of viral infection. Driven by non-contact, portable, and widely available 3D scanning technologies, a workflow is presented whereby a user’s face is rapidly categorised using relevant facial parameters. Device design is then directed down either a semi-customised or fully-customised route. Semi-customised designs use the extracted eye-to-chin distance to categorise users in to pre-determined size brackets established via a cohort of 200 participants encompassing 87.5% of the cohort. The user’s nasal profile is approximated to a Gaussian curve to further refine the selection in to one of three subsets. Flexible silicone provides the facial interface accommodating minor mismatches between true nasal profile and the approximation, maintaining a good seal in this challenging region. Critically, users with outlying facial parameters are flagged for the fully-customised route whereby the silicone interface is mapped to 3D scan data. These two approaches allow for large scale manufacture of a limited number of design variations, currently nine through the semi-customised approach, whilst ensuring effective device fit. Furthermore, labour-intensive fully-customised designs are targeted as those users who will most greatly benefit. By encompassing both approaches, the presented workflow balances manufacturing scale-up feasibility with the diverse range of users to provide well-fitting devices as widely as possible. Novel flow visualisation on a model face is presented alongside qualitative fit-testing of prototype devices to support the workflow methodology

Pattern and Outcome of Chest Injuries at Bugando Medical Centre in Northwestern Tanzania.

Chest injuries constitute a continuing challenge to the trauma or general surgeon practicing in developing countries. This study was conducted to outline the etiological spectrum, injury patterns and short term outcome of these injuries in our setting. This was a prospective study involving chest injury patients admitted to Bugando Medical Centre over a six-month period from November 2009 to April 2010 inclusive. A total of 150 chest injury patients were studied. Males outnumbered females by a ratio of 3.8:1. Their ages ranged from 1 to 80 years (mean = 32.17 years). The majority of patients (72.7%) sustained blunt injuries. Road traffic crush was the most common cause of injuries affecting 50.7% of patients. Chest wall wounds, hemothorax and rib fractures were the most common type of injuries accounting for 30.0%, 21.3% and 20.7% respectively. Associated injuries were noted in 56.0% of patients and head/neck (33.3%) and musculoskeletal regions (26.7%) were commonly affected. The majority of patients (55.3%) were treated successfully with non-operative approach. Underwater seal drainage was performed in 39 patients (19.3%). One patient (0.7%) underwent thoracotomy due to hemopericardium. Thirty nine patients (26.0%) had complications of which wound sepsis (14.7%) and complications of long bone fractures (12.0%) were the most common complications. The mean LOS was 13.17 days and mortality rate was 3.3%. Using multivariate logistic regression analysis, associated injuries, the type of injury, trauma scores (ISS, RTS and PTS) were found to be significant predictors of the LOS (P < 0.001), whereas mortality was significantly associated with pre-morbid illness, associated injuries, trauma scores (ISS, RTS and PTS), the need for ICU admission and the presence of complications (P < 0.001). Chest injuries resulting from RTCs remain a major public health problem in this part of Tanzania. Urgent preventive measures targeting at reducing the occurrence of RTCs is necessary to reduce the incidence of chest injuries in this region

Partial pulmonary embolization disrupts alveolarization in fetal sheep

BACKGROUND: Although bronchopulmonary dysplasia is closely associated with an arrest of alveolar development and pulmonary capillary dysplasia, it is unknown whether these two features are causally related. To investigate the relationship between pulmonary capillaries and alveolar formation, we partially embolized the pulmonary capillary bed. METHODS: Partial pulmonary embolization (PPE) was induced in chronically catheterized fetal sheep by injection of microspheres into the left pulmonary artery for 1 day (1d PPE; 115d gestational age; GA) or 5 days (5d PPE; 110-115d GA). Control fetuses received vehicle injections. Lung morphology, secondary septal crests, elastin, collagen, myofibroblast, PECAM1 and HIF1 alpha abundance and localization were determined histologically. VEGF-A, Flk-1, PDGF-A and PDGF-R alpha mRNA levels were measured using real-time PCR. RESULTS: At 130d GA (term approximately 147d), in embolized regions of the lung the percentage of lung occupied by tissue was increased from 29 +/- 1% in controls to 35 +/- 1% in 1d PPE and 44 +/- 1% in 5d PPE fetuses (p < 0.001). Secondary septal crest density was reduced from 8 +/- 0% in controls to 5 +/- 0% in 1d PPE and 4 +/- 0% in 5d PPE fetuses (p < 0.05), indicating impaired alveolar formation. The deposition of differentiated myofibroblasts (23 +/- 1% vs 28 +/- 1%; p < 0.001) and elastin fibres (3 +/- 0% vs 4 +/- 0%; p < 0.05) were also impaired in embolized lung regions of PPE fetuses compared to controls. PPE did not alter the deposition of collagen or PECAM1. At 116d GA in 5d PPE fetuses, markers of hypoxia indicated that a small and transient hypoxic event had occurred (hypoxia in 6.7 +/- 1.4% of the tissue within embolized regions of 5d PPE fetuses at 116d compared to 0.8 +/- 0.2% of tissue in control regions). There was no change in the proportion of tissue labelled with HIF1 alpha. There was no change in mRNA levels of the angiogenic factors VEGF and Flk-1, although a small increase in PDGF-R alpha expression at 116d GA, from 1.00 +/- 0.12 in control fetuses to 1.61 +/- 0.18 in 5d PPE fetuses may account for impaired differentiation of alveolar myofibroblasts and alveolar development. CONCLUSIONS: PPE impairs alveolarization without adverse systemic effects and is a novel model for investigating the role of pulmonary capillaries and alveolar myofibroblasts in alveolar formation