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Alidoost M, Wilson JL. Preclinical side effect prediction through pathway engineering of protein interaction network models. CPT Pharmacometrics Syst Pharmacol 2024; 13:1180-1200. [PMID: 38736280 PMCID: PMC11247120 DOI: 10.1002/psp4.13150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 03/01/2024] [Accepted: 04/08/2024] [Indexed: 05/14/2024] Open
Abstract
Modeling tools aim to predict potential drug side effects, although they suffer from imperfect performance. Specifically, protein-protein interaction models predict drug effects from proteins surrounding drug targets, but they tend to overpredict drug phenotypes and require well-defined pathway phenotypes. In this study, we used PathFX, a protein-protein interaction tool, to predict side effects for active ingredient-side effect pairs extracted from drug labels. We observed limited performance and defined new pathway phenotypes using pathway engineering strategies. We defined new pathway phenotypes using a network-based and gene expression-based approach. Overall, we discovered a trade-off between sensitivity and specificity values and demonstrated a way to limit overprediction for side effects with sufficient true positive examples. We compared our predictions to animal models and demonstrated similar performance metrics, suggesting that protein-protein interaction models do not need perfect evaluation metrics to be useful. Pathway engineering, through the inclusion of true positive examples and omics measurements, emerges as a promising approach to enhance the utility of protein interaction network models for drug effect prediction.
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Affiliation(s)
- Mohammadali Alidoost
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Jennifer L Wilson
- Department of Bioengineering, University of California, Los Angeles, California, USA
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2
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Bowen ER, DiGiacomo P, Fraser HP, Guttenplan K, Smith BAH, Heberling ML, Vidano L, Shah N, Shamloo M, Wilson JL, Grimes KV. Beta-2 adrenergic receptor agonism alters astrocyte phagocytic activity and has potential applications to psychiatric disease. DISCOVER MENTAL HEALTH 2023; 3:27. [PMID: 38036718 PMCID: PMC10689618 DOI: 10.1007/s44192-023-00050-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 10/26/2023] [Indexed: 12/02/2023]
Abstract
Schizophrenia is a debilitating condition necessitating more efficacious therapies. Previous studies suggested that schizophrenia development is associated with aberrant synaptic pruning by glial cells. We pursued an interdisciplinary approach to understand whether therapeutic reduction in glial cell-specifically astrocytic-phagocytosis might benefit neuropsychiatric patients. We discovered that beta-2 adrenergic receptor (ADRB2) agonists reduced phagocytosis using a high-throughput, phenotypic screen of over 3200 compounds in primary human fetal astrocytes. We used protein interaction pathways analysis to associate ADRB2, to schizophrenia and endocytosis. We demonstrated that patients with a pediatric exposure to salmeterol, an ADRB2 agonist, had reduced in-patient psychiatry visits using a novel observational study in the electronic health record. We used a mouse model of inflammatory neurodegenerative disease and measured changes in proteins associated with endocytosis and vesicle-mediated transport after ADRB2 agonism. These results provide substantial rationale for clinical consideration of ADRB2 agonists as possible therapies for patients with schizophrenia.
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Affiliation(s)
- Ellen R Bowen
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Weill Cornell Medicine, New York, NY, USA
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Phillip DiGiacomo
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Hannah P Fraser
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kevin Guttenplan
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Vollum Institute, Oregon Health & Science University, Portland, OR, USA
| | - Benjamin A H Smith
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Marlene L Heberling
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Laura Vidano
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Nigam Shah
- Center for Biomedical Informatics Research, Stanford School of Medicine, Stanford, CA, USA
| | - Mehrdad Shamloo
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Jennifer L Wilson
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA.
| | - Kevin V Grimes
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA.
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3
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Wilson JL, Steinberg E, Racz R, Altman RB, Shah N, Grimes K. A network paradigm predicts drug synergistic effects using downstream protein-protein interactions. CPT Pharmacometrics Syst Pharmacol 2022; 11:1527-1538. [PMID: 36204824 PMCID: PMC9662203 DOI: 10.1002/psp4.12861] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 08/05/2022] [Accepted: 08/11/2022] [Indexed: 11/16/2022] Open
Abstract
In some cases, drug combinations affect adverse outcome phenotypes by binding the same protein; however, drug-binding proteins are associated through protein-protein interaction (PPI) networks within the cell, suggesting that drug phenotypes may result from long-range network effects. We first used PPI network analysis to classify drugs based on proteins downstream of their targets and next predicted drug combination effects where drugs shared network proteins but had distinct binding proteins (e.g., targets, enzymes, or transporters). By classifying drugs using their downstream proteins, we had an 80.7% sensitivity for predicting rare drug combination effects documented in gold-standard datasets. We further measured the effect of predicted drug combinations on adverse outcome phenotypes using novel observational studies in the electronic health record. We tested predictions for 60 network-drug classes on seven adverse outcomes and measured changes in clinical outcomes for predicted combinations. These results demonstrate a novel paradigm for anticipating drug synergistic effects using proteins downstream of drug targets.
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Affiliation(s)
- Jennifer L. Wilson
- Department of BioengineeringUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Ethan Steinberg
- Center for Biomedical Informatics ResearchStanford UniversityPalo AltoCaliforniaUSA
| | - Rebecca Racz
- Division of Applied Regulatory ScienceUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - Russ B. Altman
- Department of BioengineeringStanford UniversityPalo AltoCaliforniaUSA,Department of GeneticsStanford UniversityPalo AltoCaliforniaUSA
| | - Nigam Shah
- Center for Biomedical Informatics ResearchStanford UniversityPalo AltoCaliforniaUSA
| | - Kevin Grimes
- Department of Chemical and Systems BiologyStanford UniversityPalo AltoCaliforniaUSA
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Soldatos TG, Kim S, Schmidt S, Lesko LJ, Jackson DB. Advancing drug safety science by integrating molecular knowledge with post-marketing adverse event reports. CPT Pharmacometrics Syst Pharmacol 2022; 11:540-555. [PMID: 35143713 PMCID: PMC9124355 DOI: 10.1002/psp4.12765] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/20/2021] [Accepted: 01/17/2022] [Indexed: 12/15/2022] Open
Abstract
Promising drug development efforts may frequently fail due to unintended adverse reactions. Several methods have been developed to analyze such data, aiming to improve pharmacovigilance and drug safety. In this work, we provide a brief review of key directions to quantitatively analyzing adverse events and explore the potential of augmenting these methods using additional molecular data descriptors. We argue that molecular expansion of adverse event data may provide a path to improving the insights gained through more traditional pharmacovigilance approaches. Examples include the ability to assess statistical relevance with respect to underlying biomolecular mechanisms, the ability to generate plausible causative hypotheses and/or confirmation where possible, the ability to computationally study potential clinical trial designs and/or results, as well as the further provision of advanced features incorporated in innovative methods, such as machine learning. In summary, molecular data expansion provides an elegant way to extend mechanistic modeling, systems pharmacology, and patient‐centered approaches for the assessment of drug safety. We anticipate that such advances in real‐world data informatics and outcome analytics will help to better inform public health, via the improved ability to prospectively understand and predict various types of drug‐induced molecular perturbations and adverse events.
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Affiliation(s)
| | - Sarah Kim
- Department of PharmaceuticsCenter for Pharmacometrics and Systems PharmacologyUniversity of FloridaOrlandoFloridaUSA
| | - Stephan Schmidt
- Department of PharmaceuticsCenter for Pharmacometrics and Systems PharmacologyUniversity of FloridaOrlandoFloridaUSA
| | - Lawrence J. Lesko
- Department of PharmaceuticsCenter for Pharmacometrics and Systems PharmacologyUniversity of FloridaOrlandoFloridaUSA
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Wilson JL, Gravina A, Grimes K. From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways. Integr Biol (Camb) 2022; 14:13-24. [PMID: 35293584 DOI: 10.1093/intbio/zyac002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 02/01/2022] [Accepted: 02/03/2022] [Indexed: 12/20/2022]
Abstract
With high drug attrition, protein-protein interaction (PPI) network models are attractive as efficient methods for predicting drug outcomes by analyzing proteins downstream of drug targets. Unfortunately, these methods tend to overpredict associations and they have low precision and prediction performance; performance is often no better than random (AUROC ~0.5). Typically, PPI models identify ranked phenotypes associated with downstream proteins, yet methods differ in prioritization of downstream proteins. Most methods apply global approaches for assessing all phenotypes. We hypothesized that a per-phenotype analysis could improve prediction performance. We compared two global approaches-statistical and distance-based-and our novel per-phenotype approach, 'context-specific interaction' (CSI) analysis, on severe side effect prediction. We used a novel dataset of adverse events (or designated medical events, DMEs) and discovered that CSI had a 50% improvement over global approaches (AUROC 0.77 compared to 0.51), and a 76-95% improvement in average precision (0.499 compared to 0.284, 0.256). Our results provide a quantitative rationale for considering downstream proteins on a per-phenotype basis when using PPI network methods to predict drug phenotypes.
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Affiliation(s)
- Jennifer L Wilson
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Alessio Gravina
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Kevin Grimes
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
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Wilson JL, Wong M, Stepanov N, Petkovic D, Altman R. PhenClust, a standalone tool for identifying trends within sets of biological phenotypes using semantic similarity and the Unified Medical Language System metathesaurus. JAMIA Open 2021; 4:ooab079. [PMID: 34541463 PMCID: PMC8442701 DOI: 10.1093/jamiaopen/ooab079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 08/12/2021] [Accepted: 09/02/2021] [Indexed: 11/13/2022] Open
Abstract
Objectives We sought to cluster biological phenotypes using semantic similarity and create an easy-to-install, stable, and reproducible tool. Materials and Methods We generated Phenotype Clustering (PhenClust)—a novel application of semantic similarity for interpreting biological phenotype associations—using the Unified Medical Language System (UMLS) metathesaurus, demonstrated the tool’s application, and developed Docker containers with stable installations of two UMLS versions. Results PhenClust identified disease clusters for drug network-associated phenotypes and a meta-analysis of drug target candidates. The Dockerized containers eliminated the requirement that the user install the UMLS metathesaurus. Discussion Clustering phenotypes summarized all phenotypes associated with a drug network and two drug candidates. Docker containers can support dissemination and reproducibility of tools that are otherwise limited due to insufficient software support. Conclusion PhenClust can improve interpretation of high-throughput biological analyses where many phenotypes are associated with a query and the Dockerized PhenClust achieved our objective of decreasing installation complexity.
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Affiliation(s)
- Jennifer L Wilson
- Department of Chemical and Systems Biology, Stanford University, Stanford, California, USA
| | - Mike Wong
- CoSE Computing for Life Science, San Francisco State University, San Francisco, California, USA
| | - Nicholas Stepanov
- Department of Computer Science, San Francisco State University, San Francisco, California, USA
| | - Dragutin Petkovic
- CoSE Computing for Life Science, San Francisco State University, San Francisco, California, USA.,Department of Computer Science, San Francisco State University, San Francisco, California, USA
| | - Russ Altman
- Department of Bioengineering, Stanford University, Stanford, California, USA.,Department of Genetics, Stanford University, Stanford, California, USA
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Jerome RN, Joly MM, Kennedy N, Shirey-Rice JK, Roden DM, Bernard GR, Holroyd KJ, Denny JC, Pulley JM. Leveraging Human Genetics to Identify Safety Signals Prior to Drug Marketing Approval and Clinical Use. Drug Saf 2020; 43:567-582. [PMID: 32112228 PMCID: PMC7398579 DOI: 10.1007/s40264-020-00915-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
INTRODUCTION When a new drug or biologic product enters the market, its full spectrum of side effects is not yet fully understood, as use in the real world often uncovers nuances not suggested within the relatively narrow confines of preapproval preclinical and trial work. OBJECTIVE We describe a new, phenome-wide association study (PheWAS)- and evidence-based approach for detection of potential adverse drug effects. METHODS We leveraged our established platform, which integrates human genetic data with associated phenotypes in electronic health records from 29,722 patients of European ancestry, to identify gene-phenotype associations that may represent known safety issues. We examined PheWAS data and the published literature for 16 genes, each of which encodes a protein targeted by at least one drug or biologic product. RESULTS Initial data demonstrated that our novel approach (safety ascertainment using PheWAS [SA-PheWAS]) can replicate published safety information across multiple drug classes, with validated findings for 13 of 16 gene-drug class pairs. CONCLUSIONS By connecting and integrating in vivo and in silico data, SA-PheWAS offers an opportunity to supplement current methods for predicting or confirming safety signals associated with therapeutic agents.
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Affiliation(s)
- Rebecca N Jerome
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Meghan Morrison Joly
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nan Kennedy
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jana K Shirey-Rice
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dan M Roden
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Gordon R Bernard
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kenneth J Holroyd
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Technology Transfer and Commercialization, Vanderbilt University, Nashville, TN, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Jill M Pulley
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
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8
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Wilson JL, Wong M, Chalke A, Stepanov N, Petkovic D, Altman RB. PathFXweb: a web application for identifying drug safety and efficacy phenotypes. Bioinformatics 2019; 35:4504-4506. [PMID: 31114840 PMCID: PMC6821302 DOI: 10.1093/bioinformatics/btz419] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/19/2019] [Accepted: 05/15/2019] [Indexed: 11/14/2022] Open
Abstract
Summary Limited efficacy and intolerable safety limit therapeutic development and identification of potential liabilities earlier in development could significantly improve this process. Computational approaches which aggregate data from multiple sources and consider the drug’s pathways effects could add to identification of these liabilities earlier. Such computational methods must be accessible to a variety of users beyond computational scientists, especially regulators and industry scientists, in order to impact the therapeutic development process. We have previously developed and published PathFX, an algorithm for identifying drug networks and phenotypes for understanding drug associations to safety and efficacy. Here we present a streamlined and easy-to-use PathFX web application that allows users to search for drug networks and associated phenotypes. We have also added visualization, and phenotype clustering to improve functionality and interpretability of PathFXweb. Availability and implementation https://www.pathfxweb.net/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jennifer L Wilson
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Mike Wong
- CoSE Computing for Life Sciences, San Francisco State University, San Francisco, CA, USA
| | - Ajinkya Chalke
- Department of Computer Science, San Francisco State University, San Francisco, CA, USA
| | - Nicholas Stepanov
- Department of Computer Science, San Francisco State University, San Francisco, CA, USA
| | - Dragutin Petkovic
- CoSE Computing for Life Sciences, San Francisco State University, San Francisco, CA, USA.,Department of Computer Science, San Francisco State University, San Francisco, CA, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, CA, USA.,Department of Genetics, Stanford University, Stanford, CA, USA
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9
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Pulley JM, Rhoads JP, Jerome RN, Challa AP, Erreger KB, Joly MM, Lavieri RR, Perry KE, Zaleski NM, Shirey-Rice JK, Aronoff DM. Using What We Already Have: Uncovering New Drug Repurposing Strategies in Existing Omics Data. Annu Rev Pharmacol Toxicol 2019; 60:333-352. [PMID: 31337270 DOI: 10.1146/annurev-pharmtox-010919-023537] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The promise of drug repurposing is to accelerate the translation of knowledge to treatment of human disease, bypassing common challenges associated with drug development to be more time- and cost-efficient. Repurposing has an increased chance of success due to the previous validation of drug safety and allows for the incorporation of omics. Hypothesis-generating omics processes inform drug repurposing decision-making methods on drug efficacy and toxicity. This review summarizes drug repurposing strategies and methodologies in the context of the following omics fields: genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, phenomics, pregomics, and personomics. While each omics field has specific strengths and limitations, incorporating omics into the drug repurposing landscape is integral to its success.
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Affiliation(s)
- Jill M Pulley
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Jillian P Rhoads
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Rebecca N Jerome
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Anup P Challa
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Kevin B Erreger
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Meghan M Joly
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Robert R Lavieri
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Kelly E Perry
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Nicole M Zaleski
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - Jana K Shirey-Rice
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| | - David M Aronoff
- Department of Medicine, Division of Infectious Diseases, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.,Departments of Obstetrics and Gynecology, and Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA;
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