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Open Access Publications from the University of California

Scholarly Works (19 results)

  • Article
  • Peer Reviewed
UCLA Previously Published Works (2018)
UNLABELLED: An engineering perspective views cells as complex circuits that process inputs – drugs, environmental cues – to create complex outcomes – disease, growth, death – and this perspective has immense potential for drug development. Logical rules can describe the features of cells and reductionist approaches have exploited these rules for drug development. In contrast, the reductionist approach serially characterizes cellular components and develops a deep understanding of each component’s specific role. This approach underutilizes the full system of biomolecules relevant to disease pathology and drug effects. An engineering perspective provides the tools to understand and leverage the full extent of biological systems; applying both reverse and forward engineering, a strength of the engineering approach has demonstrated progress in advancing understanding of disease and drug mechanisms. Drug development lacks sufficient engineering specifications, or empirical models, of drug pharmacodynamic effects and future efforts to derive empirical models of drug effects will streamline this development. At this stage of progress, the scientist engineer is uniquely poised to solve problems in therapeutics related to modulating multiple diseases with a single or multiple therapeutic agents and identifying pharmacodynamics biomarkers with knowledge of drug pathways. This article underscores the value of these principles in an age where drug development costs are soaring and finding efficacious therapies is challenging. IMPACT STATEMENT: Many untreated diseases are not monogenic and are instead caused by multiple genetic defects. Because of this complexity, computational, logical, and systems understanding will be essential to discovering novel therapies. The scientist engineer is uniquely disposed to use this type of understanding to advance therapeutic discovery. This work highlights benefits of the scientist engineer perspective and underscores the potential impact of these approaches for future therapeutic development. By framing the scientist engineer’s tool set and increasing awareness about this approach, this article stands to impact future therapeutic development efforts in an age of rising development costs and high drug attrition.
    Cover page: A scientist engineer’s contribution to therapeutic discovery and development
    • Article
    • Peer Reviewed
    UCLA Previously Published Works (2024)
    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.
      Cover page: Preclinical side effect prediction through pathway engineering of protein interaction network modelsCreative Commons 'BY-ND' version 4.0 license
      • Thesis
      • Peer Reviewed
      UCLA Electronic Theses and Dissertations (2023)

      A disease can be caused by multiple mechanisms including genetic mutations, proteomic abnormalities, or abnormal mRNA transcription. While the Human Genome Project aims to understand the genetic basis of complex diseases, not all relevant variants are druggable targets. Therefore, a network approach can be useful for understanding if druggable proteins are close to gene variants. Previous development of PathFX, a network algorithm that identifies relationships between drugs and diseases, has given us more comprehension on genes / proteins that might be responsible for drug effects on disease and side effect phenotypes. While PathFX was constructed using a combination of gene expression, gene mutation, proteomics information, and other resources, the goal of the project is to understand how much a drug-phenotype relationship is driven by genetics within the PathFX network. We quantify the genetic component by finding the overlap between the PathFX network and the GWAS catalog data. We discovered that drugs connected to three disease groups: psychological disorders, autoimmune diseases, and inflammatory bowel diseases have greater enrichment of genetic information than other drug-disease pairs. Our quantitative assessment may uncover considerations for new drug developments, given that a disease is more genetically or less genetically enriched.

      • 3 supplemental files
      Cover page: Measuring Genetic Contribution in a Drug-Disease Network
      • Article
      • Peer Reviewed
      UCLA Previously Published Works (2022)
      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.
        Cover page: From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways.
        • Thesis
        • Peer Reviewed
        UCLA Electronic Theses and Dissertations (2023)

        Development of drugs often fails due to toxicity and intolerable side effects. Recent advancements in the scientific community have rendered it possible to leverage machine learning techniques to predict individual side effects with domain knowledge features, such as drug classification. While several factors can be used to anticipate drug effects including their targets, pathways, and drug classes, it is unclear which domain knowledge is most predictive and whether certain domain knowledge is more important than others for different side effects. The goal of this project is to understand the predictive values of drug targets, drug classification (level 2 ATC codes), and protein-protein interaction networks (PathFX targets and network proteins) for the prediction of 30 frequently occurring side effects. We compared the prediction accuracy for individual side effects of trained models across five domain knowledge combinations and discovered that level 2 ATC codes have the highest predictive value across the domain knowledge features. Logistic regression coefficient analyses further suggest that side effects are significantly influenced by drug targets and drug classes, and not PathFX targets and network proteins. Our quantitative assessments may inform the development of safe and effective drugs by understanding the domain knowledge features underlying frequently occurring drug-induced side effects.

          Cover page: Analysis of Domain Knowledge for Machine Learning Prediction of Frequently Occurring Drug Side-Effects
          • Article
          • Peer Reviewed
          UCLA Previously Published Works (2018)
          Biomarkers are the pillars of precision medicine and are delivering on expectations of molecular, quantitative health. These features have made clinical decisions more precise and personalized, but require a high bar for validation. Biomarkers have improved health outcomes in a few areas such as cancer, pharmacogenetics, and safety. Burgeoning big data research infrastructure, the internet of things, and increased patient participation will accelerate discovery in the many areas that have not yet realized the full potential of biomarkers for precision health. Here we review themes of biomarker discovery, current implementations of biomarkers for precision health, and future opportunities and challenges for biomarker discovery. Impact statement Precision medicine evolved because of the understanding that human disease is molecularly driven and is highly variable across patients. This understanding has made biomarkers, a diverse class of biological measurements, more relevant for disease diagnosis, monitoring, and selection of treatment strategy. Biomarkers' impact on precision medicine can be seen in cancer, pharmacogenomics, and safety. The successes in these cases suggest many more applications for biomarkers and a greater impact for precision medicine across the spectrum of human disease. The authors assess the status of biomarker-guided medical practice by analyzing themes for biomarker discovery, reviewing the impact of these markers in the clinic, and highlight future and ongoing challenges for biomarker discovery. This work is timely and relevant, as the molecular, quantitative approach of precision medicine is spreading to many disease indications.
            Cover page: Biomarkers: Delivering on the expectation of molecularly driven, quantitative health
            • Article
            • Peer Reviewed
            UCLA Previously Published Works (2021)
              Cover page: Three questions to address rigour and reproducibility concerns in your grant proposal
              • Article
              • Peer Reviewed
              UCLA Previously Published Works (2013)
              RNA-interference (RNAi) studies hold great promise for functional investigation of the significance of genetic variations and mutations, as well as potential synthetic lethalities, for understanding and treatment of cancer, yet technical and conceptual issues currently diminish the potential power of this approach. While numerous research groups are usefully employing this kind of functional genomic methodology to identify molecular mediators of disease severity, response, and resistance to treatment, findings are generally confounded by "off-target" effects. These effects arise from a variety of issues beyond non-specific reagent behavior, such as biological cross-talk and feedback processes so thus can occur even with specific perturbation. Interpreting RNAi results in a network framework instead of merely as individual "hits" or "targets" leverages contributions from all hit/target contributions to pathways via their relationships with other network nodes. This interpretation can ameliorate dependence upon individual reagent performance and increase confidence in biological validation. Here we provide background on RNAi studies in cancer applications, review key challenges with functional genomics, and motivate the use of network models grounded in pathway analyses.
                Cover page: Integrated network analyses for functional genomic studies in cancer
                • Article
                • Peer Reviewed
                UCLA Previously Published Works (2019)
                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.
                  Cover page: PathFXweb: a web application for identifying drug safety and efficacy phenotypes
                  • Article
                  • Peer Reviewed
                  UCLA Previously Published Works (2022)
                  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.
                    Cover page: A network paradigm predicts drug synergistic effects using downstream protein–protein interactions
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