(2ih) Data Science Enabled Cell Analysis for Improving Pre-Clinical to Clinical Translation Pipelines | AIChE

(2ih) Data Science Enabled Cell Analysis for Improving Pre-Clinical to Clinical Translation Pipelines

Authors 

Helmbrecht, H. - Presenter, University of Washington
Nance, E., UNIVERSITY OF WASHINGTON
Research Philosophy:

Pre-clinical to clinical translation of neurological drugs is a difficult and expensive process. Of all drugs that make it to clinical trials about 90% fail, mostly during later stages1. Economically, the median cost to bring a drug to market is 19 million USD.2 In addition to the high failure rate and large economic burden, no drug approved for neurological disease is curative. The translation of pre-clinical to clinical treatments needs a new systematic approach for improved success. The overall goal of my research is to use information about failed and successful drug research to enable prediction of pre-clinical experiments that are likely to have higher chances of success for implementation in the clinic. My background in data science and chemical engineering will advance this overall goal through three aims: (1) Develop new software and data science pipelines to increase insight from pre-existing methods; (2) Develop databases to enhance connection between disparate sets of biological data; and (3) Apply text mining and natural language processing to gather data and assess bottlenecks from pre-existing publications. These three aims are supported by sub-goals in creating open-source databases of high impact biological data for general use, developing educational materials to effectively train scientists on new techniques, and setting up automated workflows to enable high-throughput experimentation (Figure 1).

Figure 1. Current and future research goals for data science enabled approaches to improving pre-clinical to clinical pipelines. (rainbow arrow with images below) The pipeline transition from pre-clinical to clinical and clinical to approval of neurological treatments typically begins in in vitro cell models followed by ex vivo brain slice models, rodent - or other small mammal models, and non-human primates before reaching human clinical trials. (overall goals bar) The four overall effects that are likely to occur due to the aims of my research. (outer circles of Venn diagram) The three main aims of my research for improving pre-clinical to clinical translation timelines. (two-circle connectors) The sub-aim that connects each major aim to another. (middle of Venn diagram) The overarching goal of my research. (rainbow bars) Rainbow bars attached to each aim, sub-aim, and final goal represent which part of the pre-clinical to clinical translation timeline that will be affected. (yellow boxes) The related papers that I have published are listed in associated with the related aim.

Research Interests and Skills:

Data Science: Ph.D. Research: Most of my Ph.D. work has focused on developing software from pre-existing methods to increase insight into cellular behavior in the brain using immunofluorescent neural images (Figure 2). Immunofluorescent neural images are one of the most common ways to visualize and quantify all cells of the central nervous system. Confocal microscopy of microglia, the immune cells of the brain, is a particularly common way to semi-quantitatively study the brain’s response to disease and treatment. However, the amount of information that we can gain from every image is severely limited by common methods. An image processing pipeline that can robustly segment cells from an image and quantify their morphological features while connecting those features back to biological relevance is of interest in a wide variety of fields. I developed a cell and disease model agnostic pipeline, and my application of this pipeline was focused primarily the study of microglia. Future Research Goals: In my future research, I hope to be able to develop and apply image processing pipelines in models at different points of the pre-clinical to clinical pipeline such as in animal models other than the small rodent or in images of the human brain.

Other Data Science Skills: Database management, machine learning, data visualization, basic graph theory. Languages: Python, R, SQL, MATLAB

Neuroscience: Ph.D. Research: Microglia, the immune cells of the brain, are of particular interest for imaging analysis since microglia are genetically all derived from the same source and yet naturally heterogeneous in phenotype. Microglial also respond to injury and treatment with both regional and sex-based reactivity. My Ph.D. work focused on applying the data science pipeline I developed to images from multiple species, including rat, ferret, pig, and human tissue; multiple disease models, including hypoxia-ischemia, infection-induced neuroinflammation, and fetal uterine growth restriction; and exposure of microglia to multiple treatments, including erythropoietin, azithromycin, and brain cell derived extra cellular vesicles. The pipeline that I developed is disease and species agnostic which enables flexibility for any disease model using fluorescent imaging and comparison across similar models. My expertise lies in developing image processing-based data science pipelines and then developing interpretable and explainable results with biological meaning. Future Research Goals: The data science pipeline I developed in my Ph.D. work is cell agnostic even though I focused most of my research on microglia. With the expertise from my Ph.D. I hope to apply my work in cell morphology analysis to other cell types and in different diseases such as to cancer applications or aging populations.

Selected Publications, Proposals, Experiences, and Awards: 668 words

Experiences

2. Certificate from the Advanced Data Science Option, eScience Institute, University of Washington, Summer 2021

1. Pacific Northwest National Laboratories – University of Washington Data Science Traineeship, National Security Initiative, with summer Internship, August 2021 – August 2022

Publications

* Corresponding Author, Co-First Author

Neuroscience

3. Nguyen N. P., Helmbrecht H., Ye Z., Adebayo T., Hashi N., Nance E.* Brain Tissue-Derived Extracellular Vesicle Mediated Therapy in the Neonatal Ischemic Brain. International Journal of Molecular Sciences (2022) January; 620

2. Wood T. R., Hildahl K., Helmbrecht H., Corry K. A., Moralego D., Kolnik S. E., Prater K. E., Juul S. E., Nance E.* A Ferret Ex Vivo Brain Slice Model of Oxygen-Glucose Deprivation Captures Regional Perinatal Injury and Treatment Associated with Specific Microglial Phenotypes. Bioengineering & Translational Medicine (2021) November; 10265

1. Joseph A., Liao R., Zhang M., Helmbrecht H., McKenna M., Filteau J., Nance E.* Nanoparticle-microglial interaction in the ischemic brain is modulated by injury duration and treatment. Bioengineering & Translational Medicine (2020) August; 10175 [Top Cited Article]

Data Science

  1. Helmbrecht H., Xu N., Liao R., Nance E.* Data Management Schema Design for Effective Nanoparticle Formulation for Neurotherapeutics. AIChE Journal (2021) October; 17459 [Cover Article]

Education

  1. Helmbrecht H., Nance E.* Effective Laboratory Education with TEXTILE: Tutorials in EXperimentalisT Interactive Learning. Journal of Chemical Engineering Education. In Press.

Reviews/Perspectives

  1. Helmbrecht H., Joseph A., Zhang M., McKenna M., Nance E.* Governing transport principles for nanomedicine applications in the brain. Current Opinion in Chemical Engineering (2020) 30:112-119

Funded Proposals

2022 NEUROHACK Pilot Grant Application Winner for “An Integrated Data Science Pathway for Informed Drug Discovery in Motor Neuron Disease” (£10,000 from Alan Turing Institute and the Engineering and Physical Sciences Research Council), Principle Investigator

  • The aim of the proposed work is to advance treatment options for Motor Neuron Disease, with a highly connected approach to drug discovery that recognizes new genetic targets with Mendelian Randomization and automatically connects those targets to both (1) federally approved drugs and (2) cutting edge researchers and institutes working on new pre-clinical drugs.

2020 Student Technology Fee Awarded Proposal: Dynamic Light Scattering Autosampler ($23,660), Student Lead Investigator

  • The goal of the proposal was to purchase the Malvern NanoSampler attachment for a previously owned Malvern Zetasizer to enable high-throughput experimentation with autosampling for both research applications and classroom laboratories.

Awards

2022 Author on Top Cited Article in Bioengineering & Translational Medicine Between 1 Jan 2020-31 Dec 2021

2022 Future Leaders Summit (Data Science Consortium), Michigan Institute for Data Science, University of Washington, Selected Participant & eScience Institute Nominee

2022 NEUROHACK Overall Outstanding Contribution (4 individuals selected from over 100 participants)

2022 NEUROHACK Team 1st Prize – Challenge: Exploring genetic variation in motor neuron disease longevity to inform drug discovery

2021 Exceptional University of Washington Womxn Award

2021 Outstanding Service Award – Department of Chemical Engineering, University of Washington

2020 Nance Lab – Green Lab Certification at Silver Level Sustainability Score

2020 McCarthy Award for Excellence in Graduate Student Teaching, Chemical Engineering Department, University of Washington

2020 Chemical Hygiene Officer Lab Safety Award

Selected Talks

Ç‚ Presenting Author

Research

1. Helmbrecht H.Ç‚ , Nance E. Enhancing Insight to Individual and Population-Based Microglial Reactivity with Image Analysis. Oral Presentation. AIChE Annual Conference: Applications of Data Science to High Throughput Experimentation; Boston, Massachusetts; November 2021.

2. Helmbrecht H.Ç‚, Joseph A., Liao R., Xu N., Chen C., Nance E. Data Management Schema Design for Effective Nanoparticle Formulation for Probing and Treating Neurological Disease. Oral Presentation. AIChE Annual Conference; Boston, Massachusetts; November 2021. Carbon Nanomaterials: Graduate Student Award Session.

Education

  1. Helmbrecht H.Ç‚ , Nance E. TEXTILE: Tutorials in EXperimentalisT Interactive LEarning. Oral Presentation. AIChE Annual Conference: Teaching Data Science to Students and Teachers I Session; Boston, Massachusetts; November 2021.

References:

  1. Wood, T.; Nance, E., Disease-directed engineering for physiology-driven treatment interventions in neurological disorders. APL Bioengineering 2019, 3 (4), 040901.
  2. Moore, T. J.; Zhang, H.; Anderson, G.; Alexander, G. C., Estimated Costs of Pivotal Trials for Novel Therapeutic Agents Approved by the US Food and Drug Administration, 2015-2016. JAMA Internal Medicine 2018, 178 (11), 1451.