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Article ; Online: A cohort study on blood coagulation in childhood cancer survivors.

Meyer, Andrew D / Hughes, Tyler B / Rishmawi, Anjana R / Heard, Patty / Shah, Shafqat / Aune, Gregory J

2023 Volume 226, Page(s) 100–106

Abstract: Cancer survivors are at an increased risk of thromboembolism compared to the general pediatric population. Anticoagulant therapy decreases the risk of thromboembolism in cancer patients. We hypothesized that pediatric cancer survivors are in a ... ...

Abstract	Cancer survivors are at an increased risk of thromboembolism compared to the general pediatric population. Anticoagulant therapy decreases the risk of thromboembolism in cancer patients. We hypothesized that pediatric cancer survivors are in a chronically hypercoagulable state compared to healthy controls. Children who survived for more than five years from cancer diagnosis at the UT Health Science Center at San Antonio Cancer Survivorship Clinic were compared to healthy controls. The exclusion criteria were recent NSAID use or a history of coagulopathy. Coagulation analysis included platelet count, thrombin-antithrombin complexes (TAT), plasminogen activator inhibitor (PAI), routine coagulation assays, and thrombin generation with and without thrombomodulin. We enrolled 47 pediatric cancer survivors and 37 healthy controls. Platelet count was significantly lower in cancer survivors at a mean of 254 × 10
MeSH term(s)	Child ; Humans ; Cancer Survivors ; Thrombin ; Cohort Studies ; Neoplasms/complications ; Blood Coagulation ; Blood Coagulation Disorders ; Biomarkers ; Thromboembolism
Chemical Substances	Thrombin (EC 3.4.21.5) ; Biomarkers
Language	English
Publishing date	2023-05-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	121852-9
ISSN	1879-2472 ; 0049-3848
ISSN (online)	1879-2472
ISSN	0049-3848
DOI	10.1016/j.thromres.2023.04.025
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Article ; Online: Modeling the Bioactivation and Subsequent Reactivity of Drugs.

Hughes, Tyler B / Flynn, Noah / Dang, Na Le / Swamidass, S Joshua

Chemical research in toxicology

2021 Volume 34, Issue 2, Page(s) 584–600

Abstract: Electrophilically reactive drug metabolites are implicated in many adverse drug reactions. In this mechanism-termed bioactivation-metabolic enzymes convert drugs into reactive metabolites that often conjugate to nucleophilic sites within biological ... ...

Abstract	Electrophilically reactive drug metabolites are implicated in many adverse drug reactions. In this mechanism-termed bioactivation-metabolic enzymes convert drugs into reactive metabolites that often conjugate to nucleophilic sites within biological macromolecules like proteins. Toxic metabolite-product adducts induce severe immune responses that can cause sometimes fatal disorders, most commonly in the form of liver injury, blood dyscrasia, or the dermatologic conditions toxic epidermal necrolysis and Stevens-Johnson syndrome. This study models four of the most common metabolic transformations that result in bioactivation: quinone formation, epoxidation, thiophene sulfur-oxidation, and nitroaromatic reduction, by synthesizing models of metabolism and reactivity. First, the metabolism models predict the formation probabilities of all possible metabolites among the pathways studied. Second, the exact structures of these metabolites are enumerated. Third, using these structures, the reactivity model predicts the reactivity of each metabolite. Finally, a feedfoward neural network converts the metabolism and reactivity predictions to a bioactivation prediction for each possible metabolite. These bioactivation predictions represent the joint probability that a metabolite forms and that this metabolite subsequently conjugates to protein or glutathione. Among molecules bioactivated by these pathways, we predicted the correct pathway with an AUC accuracy of 89.98%. Furthermore, the model predicts whether molecules will be bioactivated, distinguishing bioactivated and nonbioactivated molecules with 81.06% AUC. We applied this algorithm to withdrawn drugs. The known bioactivation pathways of alclofenac and benzbromarone were identified by the algorithm, and high probability bioactivation pathways not yet confirmed were identified for safrazine, zimelidine, and astemizole. This bioactivation model-the first of its kind that jointly considers both metabolism and reactivity-enables drug candidates to be quickly evaluated for a toxicity risk that often evades detection during preclinical trials. The XenoSite bioactivation model is available at http://swami.wustl.edu/xenosite/p/bioactivation.
MeSH term(s)	Epoxy Compounds/chemistry ; Epoxy Compounds/metabolism ; Humans ; Models, Biological ; Molecular Structure ; Nitrobenzenes/chemistry ; Nitrobenzenes/metabolism ; Oxidation-Reduction ; Quinones/chemistry ; Quinones/metabolism ; Sulfur/chemistry ; Sulfur/metabolism ; Thiophenes/chemistry ; Thiophenes/metabolism
Chemical Substances	Epoxy Compounds ; Nitrobenzenes ; Quinones ; Thiophenes ; Sulfur (70FD1KFU70)
Language	English
Publishing date	2021-01-26
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	639353-6
ISSN	1520-5010 ; 0893-228X
ISSN (online)	1520-5010
ISSN	0893-228X
DOI	10.1021/acs.chemrestox.0c00417
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Article ; Online: The Metabolic Rainbow: Deep Learning Phase I Metabolism in Five Colors.

Dang, Na Le / Matlock, Matthew K / Hughes, Tyler B / Swamidass, S Joshua

Journal of chemical information and modeling

2020 Volume 60, Issue 3, Page(s) 1146–1164

Abstract: Metabolism of drugs affects their absorption, distribution, efficacy, excretion, and toxicity profiles. Metabolism is routinely assessed experimentally using recombinant enzymes, human liver microsome, and animal models. Unfortunately, these experiments ... ...

Abstract	Metabolism of drugs affects their absorption, distribution, efficacy, excretion, and toxicity profiles. Metabolism is routinely assessed experimentally using recombinant enzymes, human liver microsome, and animal models. Unfortunately, these experiments are expensive, time-consuming, and often extrapolate poorly to humans because they fail to capture the full breadth of metabolic reactions observed
MeSH term(s)	Animals ; Color ; Deep Learning ; Humans ; Metabolic Networks and Pathways ; Microsomes, Liver ; Neural Networks, Computer
Language	English
Publishing date	2020-02-24
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.9b00836
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Article ; Online: Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism.

Hughes, Tyler B / Swamidass, S Joshua

Chemical research in toxicology

2017 Volume 30, Issue 2, Page(s) 642–656

Abstract: Many adverse drug reactions are thought to be caused by electrophilically reactive drug metabolites that conjugate to nucleophilic sites within DNA and proteins, causing cancer or toxic immune responses. Quinone species, including quinone-imines, quinone- ...

Abstract	Many adverse drug reactions are thought to be caused by electrophilically reactive drug metabolites that conjugate to nucleophilic sites within DNA and proteins, 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 and comprise over 40% of all known reactive metabolites. Quinone metabolites are created by cytochromes P450 and peroxidases. For example, cytochromes P450 oxidize acetaminophen to N-acetyl-p-benzoquinone imine, which is electrophilically reactive and covalently binds to nucleophilic sites within proteins. This reactive quinone metabolite elicits a toxic immune response when acetaminophen exceeds a safe dose. Using a deep learning approach, this study reports the first published method for predicting quinone formation: the formation of a quinone species by metabolic oxidation. We model both one- and two-step quinone formation, enabling accurate quinone formation predictions in nonobvious cases. We predict atom pairs that form quinones with an AUC accuracy of 97.6%, and we identify molecules that form quinones with 88.2% AUC. By modeling the formation of quinones, one of the most common types of reactive metabolites, our method provides a rapid screening tool for a key drug toxicity risk. The XenoSite quinone formation model is available at http://swami.wustl.edu/xenosite/p/quinone .
MeSH term(s)	Area Under Curve ; Drug-Related Side Effects and Adverse Reactions ; Oxidation-Reduction ; Quantum Theory ; Quinones/chemistry ; Quinones/metabolism
Chemical Substances	Quinones
Language	English
Publishing date	2017-02-02
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	639353-6
ISSN	1520-5010 ; 0893-228X
ISSN (online)	1520-5010
ISSN	0893-228X
DOI	10.1021/acs.chemrestox.6b00385
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Article ; Online: Experimentally realized in situ backpropagation for deep learning in photonic neural networks.

Pai, Sunil / Sun, Zhanghao / Hughes, Tyler W / Park, Taewon / Bartlett, Ben / Williamson, Ian A D / Minkov, Momchil / Milanizadeh, Maziyar / Abebe, Nathnael / Morichetti, Francesco / Melloni, Andrea / Fan, Shanhui / Solgaard, Olav / Miller, David A B

Science (New York, N.Y.)

2023 Volume 380, Issue 6643, Page(s) 398–404

Abstract: Integrated photonic neural networks provide a promising platform for energy-efficient, high-throughput machine learning with extensive scientific and commercial applications. Photonic neural networks efficiently transform optically encoded inputs using ... ...

Abstract	Integrated photonic neural networks provide a promising platform for energy-efficient, high-throughput machine learning with extensive scientific and commercial applications. Photonic neural networks efficiently transform optically encoded inputs using Mach-Zehnder interferometer mesh networks interleaved with nonlinearities. We experimentally trained a three-layer, four-port silicon photonic neural network with programmable phase shifters and optical power monitoring to solve classification tasks using "in situ backpropagation," a photonic analog of the most popular method to train conventional neural networks. We measured backpropagated gradients for phase-shifter voltages by interfering forward- and backward-propagating light and simulated in situ backpropagation for 64-port photonic neural networks trained on MNIST image recognition given errors. All experiments performed comparably to digital simulations ([Formula: see text]94% test accuracy), and energy scaling analysis indicated a route to scalable machine learning.
Language	English
Publishing date	2023-04-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	128410-1
ISSN	1095-9203 ; 0036-8075
ISSN (online)	1095-9203
ISSN	0036-8075
DOI	10.1126/science.ade8450
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Article ; Online: Mapping the self: A network approach for understanding psychological and neural representations of self-concept structure.

Elder, Jacob / Cheung, Bernice / Davis, Tyler / Hughes, Brent

Journal of personality and social psychology

2022

Abstract: How people self-reflect and maintain a coherent sense of self is an important question that spans from early philosophy to modern psychology and neuroscience. Research on the self-concept has not yet developed and tested a formal model of how beliefs ... ...

Abstract	How people self-reflect and maintain a coherent sense of self is an important question that spans from early philosophy to modern psychology and neuroscience. Research on the self-concept has not yet developed and tested a formal model of how beliefs about dependency relations amongst traits may influence self-concept coherence. We first develop a network-based approach, which suggests that people's beliefs about trait relationships contribute to how the self-concept is structured (Study 1). This model describes how people maintain positivity and coherence in self-evaluations, and how trait interrelations relate to activation in brain regions involved in self-referential processing and concept representation (Study 2 and Study 3). Results reveal that a network-based property theorized to be important for coherence (i.e., outdegree centrality) is associated with more favorable and consistent self-evaluations and decreased ventral medial prefrontal cortex (vmPFC) activation. Further, participants higher in self-esteem and lower in depressive symptoms differentiate between higher and lower centrality positive traits more in self-evaluations, reflecting associations between mental health and how people process perceived trait dependencies during self-reflection. Together, our model and findings join individual differences, brain activation, and behavior to present a computational theory of how beliefs about trait relationships contribute to a coherent, interconnected self-concept. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Language	English
Publishing date	2022-07-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	3103-3
ISSN	1939-1315 ; 0022-3514
ISSN (online)	1939-1315
ISSN	0022-3514
DOI	10.1037/pspa0000315
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Article ; Online: Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites.

Hughes, Tyler B / Dang, Na Le / Kumar, Ayush / Flynn, Noah R / Swamidass, S Joshua

Journal of chemical information and modeling

2020 Volume 60, Issue 10, Page(s) 4702–4716

Abstract: Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across ... ...

Abstract	Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug development. Most metabolism models focus on predicting sites of metabolism (SOMs): the specific substrate atoms targeted by metabolic enzymes. However, SOMs are merely a proxy for metabolic structures: knowledge of an SOM does not explicitly provide the actual metabolite structure. Without an explicit metabolite structure, computational systems cannot evaluate the new molecule's properties. For example, the metabolite's reactivity cannot be automatically predicted, a crucial limitation because reactive drug metabolites are a key driver of adverse drug reactions (ADRs). Additionally, further metabolic events cannot be forecast, even though the metabolic path of the majority of substrates includes two or more sequential steps. To overcome the myopia of the SOM paradigm, this study constructs a well-defined system-termed the metabolic forest-for generating exact metabolite structures. We validate the metabolic forest with the substrate and product structures from a large, chemically diverse, literature-derived dataset of 20 736 records. The metabolic forest finds a pathway linking each substrate and product for 79.42% of these records. By performing a breadth-first search of depth two or three, we improve performance to 88.43 and 88.77%, respectively. The metabolic forest includes a specialized algorithm for producing accurate quinone structures, the most common type of reactive metabolite. To our knowledge, this quinone structure algorithm is the first of its kind, as the diverse mechanisms of quinone formation are difficult to systematically reproduce. We validate the metabolic forest on a previously published dataset of 576 quinone reactions, predicting their structures with a depth three performance of 91.84%. The metabolic forest accurately enumerates metabolite structures, enabling promising new directions such as joint metabolism and reactivity modeling.
MeSH term(s)	Drug-Related Side Effects and Adverse Reactions ; Forests ; Humans ; Pharmaceutical Preparations
Chemical Substances	Pharmaceutical Preparations
Language	English
Publishing date	2020-09-16
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.0c00360
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Article ; Online: Computationally Assessing the Bioactivation of Drugs by N-Dealkylation.

Dang, Na Le / Hughes, Tyler B / Miller, Grover P / Swamidass, S Joshua

Chemical research in toxicology

2018 Volume 31, Issue 2, Page(s) 68–80

Abstract: Cytochromes P450 (CYPs) oxidize alkylated amines commonly found in drugs and other biologically active molecules, cleaving them into an amine and an aldehyde. Metabolic studies usually neglect to report or investigate aldehydes, even though they can be ... ...

Abstract	Cytochromes P450 (CYPs) oxidize alkylated amines commonly found in drugs and other biologically active molecules, cleaving them into an amine and an aldehyde. Metabolic studies usually neglect to report or investigate aldehydes, even though they can be toxic. It is assumed that they are efficiently detoxified into carboxylic acids and alcohols. Nevertheless, some aldehydes are reactive and escape detoxification pathways to cause adverse events by forming DNA and protein adducts. Herein, we modeled N-dealkylations that produce both amine and aldehyde metabolites and then predicted the reactivity of the aldehyde. This model used a deep learning approach previously developed by our group to predict other types of drug metabolism. In this study, we trained the model to predict N-dealkylation by human liver microsomes (HLM), finding that including isozyme-specific metabolism data alongside HLM data significantly improved results. The final HLM model accurately predicted the site of N-dealkylation within metabolized substrates (97% top-two and 94% area under the ROC curve). Next, we combined the metabolism, metabolite structure prediction, and previously published reactivity models into a bioactivation model. This combined model predicted the structure of the most likely reactive metabolite of a small validation set of drug-like molecules known to be bioactivated by N-dealkylation. Applying this 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. Our results suggest that N-dealkylation may be an under-appreciated bioactivation pathway, especially in clinical contexts where aldehyde detoxification pathways are inhibited. Moreover, this is the first report of a bioactivation model constructed by combining a metabolism and reactivity model. These results raise hope that more comprehensive models of bioactivation are possible. The model developed in this study is available at http://swami.wustl.edu/xenosite/ .
MeSH term(s)	Aldehydes/chemistry ; Aldehydes/metabolism ; Amines/chemistry ; Amines/metabolism ; Dealkylation ; Humans ; Indinavir/metabolism ; Indinavir/pharmacology ; Liver/drug effects ; Liver/metabolism ; Microsomes, Liver/chemistry ; Microsomes, Liver/drug effects ; Microsomes, Liver/metabolism ; Models, Molecular ; Molecular Structure ; Piperacillin/metabolism ; Piperacillin/pharmacology ; Piperazines/metabolism ; Piperazines/pharmacology ; Thiazoles/metabolism ; Thiazoles/pharmacology ; Verapamil/metabolism ; Verapamil/pharmacology
Chemical Substances	Aldehydes ; Amines ; Piperazines ; Thiazoles ; Indinavir (5W6YA9PKKH) ; ziprasidone (6UKA5VEJ6X) ; Verapamil (CJ0O37KU29) ; Piperacillin (X00B0D5O0E)
Language	English
Publishing date	2018-02-06
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	639353-6
ISSN	1520-5010 ; 0893-228X
ISSN (online)	1520-5010
ISSN	0893-228X
DOI	10.1021/acs.chemrestox.7b00191
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Article ; Online: Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds.

Matlock, Matthew K / Hughes, Tyler B / Dahlin, Jayme L / Swamidass, S Joshua

Journal of chemical information and modeling

2018 Volume 58, Issue 8, Page(s) 1483–1500

Abstract: Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that ... ...

Abstract	Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.
MeSH term(s)	Animals ; Computer Simulation ; Databases, Factual ; Drug Discovery/methods ; Enzyme Inhibitors/chemistry ; Enzyme Inhibitors/pharmacology ; High-Throughput Screening Assays/methods ; Histone Acetyltransferases/antagonists & inhibitors ; Histone Acetyltransferases/metabolism ; Humans ; Models, Biological ; Neural Networks, Computer ; Small Molecule Libraries/chemistry ; Small Molecule Libraries/pharmacology
Chemical Substances	Enzyme Inhibitors ; Small Molecule Libraries ; Histone Acetyltransferases (EC 2.3.1.48)
Language	English
Publishing date	2018-07-23
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	190019-5
ISSN	1549-960X ; 0095-2338
ISSN (online)	1549-960X
ISSN	0095-2338
DOI	10.1021/acs.jcim.8b00104
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Article ; Online: Cost of benign versus oncologic colon resection among fee-for-service Medicare enrollees.

Hughes, Byron D / Hancock, Kevin J / Shan, Yong / Thakker, Ravi A / Maharsi, Safa / Tyler, Douglas S / Mehta, Hemalkumar B / Senagore, Anthony J

Journal of surgical oncology

2019 Volume 120, Issue 2, Page(s) 280–286

Abstract: Background and objectives: Reimbursement for colonic pathology by the Centers for Medicare and Medicaid Services (CMS) are grouped in the Medicare Severity-Diagnosis Related Groups (MS-DRG). With limited available data, we sought to compare the relative ...

Abstract	Background and objectives: Reimbursement for colonic pathology by the Centers for Medicare and Medicaid Services (CMS) are grouped in the Medicare Severity-Diagnosis Related Groups (MS-DRG). With limited available data, we sought to compare the relative impact of malignant vs benign colonic pathology on reimbursement under the MS-DRG system. Methods: We used 5% national Medicare data from 2011 to 2014. Patients were classified as having benign disease or malignancy. Descriptive statistics and multivariate regression analysis were used to evaluate the surgical approach and health resource utilization. Results: Of 10 928 patients, most were Non-Hispanic White women. The majority underwent open colectomy in both cohorts (P < .001). Colectomy for benign disease was associated with higher total charges (P < .001) and a longer length of stay (P = .0002). Despite higher charges, payments were not significantly different between the cohorts (P = .434). Both inpatient mortality and discharge to a rehab facility were higher in the oncologic group (P < .001). Conclusion: Payment methodology for colectomy under the CMS MS-DRG system does not appear to accurately reflect the episode cost of care. The data suggest that inpatient costs are not fully compensated. A transition to value-based payments with expanded episode duration will require a better understanding of unique costs before adoption.
MeSH term(s)	Aged ; Aged, 80 and over ; Colectomy/economics ; Colonic Neoplasms/pathology ; Colonic Neoplasms/surgery ; Diagnosis-Related Groups ; Fee-for-Service Plans ; Female ; Health Care Costs ; Humans ; Length of Stay ; Male ; Medicare ; Retrospective Studies ; United States
Language	English
Publishing date	2019-05-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	82063-5
ISSN	1096-9098 ; 0022-4790
ISSN (online)	1096-9098
ISSN	0022-4790
DOI	10.1002/jso.25511
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