Health Research BC - Mitacs Industry-Based Opportunity Archives

Develop plant-based microcarrier compositions for expanding DSCs and fibroblasts in dynamic culture

Dermal Sheath Cells (DSc) hold significant promise in regenerative medicine, particularly for hair follicle regeneration and wound healing. However, scalable production remains a challenge. Microcarrier-based culture systems present a viable solution, with the type of microcarrier and culture conditions being critical for optimizing cell yield, viability, and functionality.

This project aims to develop digestable microcarriers made from plant-based resources to facilitate large-scale cultivation of DSc.

The intern, currently a visiting PhD student at the University of Victoria, has extensive experience in cell culture and biomanufacturing. In this project, the intern will gain valuable experience in:

Biomaterial synthesis and extraction from plant-based resources
Microfluidic technology for producing microcarriers
Dynamic culture of therapeutic cells using microcarriers
Problem-solving and adapting protocols for industrial-scale production

The project will benefit the company by:

Advancing research and development of novel regenerative medicine therapies.
Enhancing production scalability and cost-efficiency through innovative culture technologies.
Strengthening the company’s position as a leader in biotechnological advancements for therapeutic applications.

Overall, this internship project aims to develop optimized protocols that leverage the therapeutic potential of DSc, contributing to both scientific progress and practical applications in regenerative medicine. This effort is expected to improve cell culture conditions and overall product quality.

High throughput fabrication and characterization of a novel lipid nanoparticle library

My research in optimizing lipid nanoparticle formulations and developing next-generation drug delivery platforms is advancing translational health innovation in British Columbia. By integrating polymer and peptide-functionalized nanoparticles for extrahepatic delivery, and by establishing automated, scalable formulation platforms, my work accelerates the development of more effective and commercially relevant therapies. Through collaborations with both academic and industry partners, these efforts strengthen BC’s capacity for cutting-edge nanomedicine research, foster interdisciplinary training, and contribute to a vibrant ecosystem for health innovation in the province.

My research in lipid nanoparticle optimization and functionalized drug delivery systems directly addresses pressing health priorities in British Columbia by enabling safer, more effective, and targeted therapies for conditions that are difficult to treat, including cancer and systemic diseases requiring extrahepatic delivery. By integrating automated formulation platforms and fostering academic–industry collaboration, my work accelerates translational research, enhances BC’s capacity for innovative therapeutics, and strengthens the province’s leadership in developing scalable, next-generation health technologies.

A super-resolution platform for in situ molecular analysis of receptor co-localization mediated by co-stimulatory trispecific antibodies

Trispecific T cell-engager (TriTCE) antibodies are an emerging class of therapeutics designed to enhance the body’s ability to fight cancer by targeting three distinct sites. Bispecific TCE antibodies, which target two binding sites, have been developed and clinically approved for treating various types of cancer. These antibodies typically work by binding to both a tumor antigen and the T cell receptor (CD3), thus activating T cells and redirecting cytotoxicity towards cancer cells. However, their effectiveness relies on having a high baseline of T cell infiltration in the tumor microenvironment. This poses a challenge for treating solid tumors that are poorly infiltrated by T cells and rapidly growing, as the bispecific antibodies may not adequately inhibit their growth.

To overcome this limitation, co-stimulatory TriTCE antibodies have been developed. These antibodies not only target a tumor antigen and CD3, but also stimulate a second co-stimulatory T cell signal, known as CD28, which has been shown to enhance T cell activation. This trispecific design has demonstrated enhanced cytokine production and sustained T cell proliferation, leading to improved cytotoxicity against tumor cells. However, the precise mechanism by which TriTCE antibodies bring together the tumor cell antigen, CD3, and CD28 to enhance co-stimulation remains poorly understood.

Super-resolution microscopy offers a powerful tool to visualize and understand protein interactions within cells. Fluorescent imaging enables the direct visualization of the three key targets: the tumor cell antigen, CD3, and CD28. However, traditional fluorescence imaging does not have the resolution to visualize these interactions. With the ability to achieve up to 20nm resolution, super-resolution microscopy can directly image and distinguish the organization of the immune synapse by co-engagement of the tumor cell antigen, CD3, and CD28. Super-resolution microscopy will enable us to observe how TriTCEs organize their targets in the intact cell.

Our project aims to utilize super-resolution microscopy to gain insights into the mechanism of action of TriTCEs. The intern will play a crucial role in integrating super-resolution microscopy with research on TriTCEs. The intern will learn to apply super-resolution microscopy for therapeutic applications and learn to work in an industrial setting. The intern will utilize STED, dSTORM super resolution microscopes, as well as conventional fluorescence microscopy. The intern will optimize labeling parameters, such as antibody concentrations and microscope settings, to identify optimal imaging conditions. Additionally, the intern will present qualitative visualizations and quantitative measures for the interaction of TriTCEs and develop new analysis paradigms for assessing and quantifying receptor-target interactions.

Through this work, we aim to better understand how these antibodies organize their targets, ultimately contributing to our understanding of how co-stimulatory TriTCEs function. Implementing super resolution imaging technology will lead to the development of an analysis platform to provide in situ molecular analysis of the efficacy of current and future trispecific antibody therapeutics under development at Zymeworks Inc.

Identifying and functionally characterizing transcriptional drivers of esophageal cancer progression for nucleic acid-based therapeutic development

Esophageal cancer, a cancer of poor prognosis, arises as either squamous or adenomatous forms, bearing many hallmarks of their tissue of origin. Esophageal squamous cell carcinoma (ESCC) originates from squamous epithelial cells, whereas esophageal adenocarcinoma (EAC) arises from the columnar cells of a precancerous lesion known as Barrett’s Esophagus (BE) following the development of low grade dysplasia (LGD) and subsequent high grade dysplasia (HGD). Furthering our understanding of the biological processes that drive the emergence of these distinct variants will be critical to develop novel targeted therapies and improve outcomes for esophageal cancer patients.

One important biological aspect of cancer progression is the role of nuclear proteins known as transcription factors (TFs). TFs act as master regulators of cellular function and communication by determining which genes a given cell should utilize, activate, or deactivate. In ESCC and EAC, squamous cell (in ESCC) or columnar cell (in EAC) -associated TFs often become aberrantly activated, indicating that divergent programs of gene activation occur in these cancer variants. Furthermore, the overarching cancer-promoting programs engaged by these TFs and their associates, as well as their mechanisms of cancer facilitation through downstream target activation, remain to be full elucidated. Identifying the divergent TF-driven programs activated in these cancers and their consequential impacts on oncogenesis and metastasis could lead to the development of new therapeutic interventions for esophageal cancer patients.

In this project, we aim to identify and characterize the functional impact of the transcriptional programs that drive esophageal cancer progression, with the goal of improving our understanding of the disease’s biology and providing critical insights that could guide innovative therapeutic targeting of oncoproteins.

The proposed project has two primary aims:

Aim 1: Identify functionally relevant TFs driving progression of esophageal cancer variants
To identify functionally relevant TFs that drive ESCC and EAC progression, we will develop and employ a functional DNA barcode-based high-throughput reporter assay to identify TFs that are differentially activated across various esophageal cancer cells and conditions. We will complement this with comparative bioinformatics analysis of ESCC and EAC genomic datasets to identify TFs with differential DNA-binding and regulatory activity between the two cancer types. This integrative approach will enable us to pinpoint key transcriptional regulators specific to ESCC and EAC and shed light on their distinct molecular drivers.

Aim 2: Investigate the downstream targetable gene expression programs regulated by candidate TFs identified in aim 1
To understand how the transcription factors identified in Aim 1 contribute to either ESCC or EAC progression including through the stages of LGD and HGD, it is crucial to elucidate the downstream gene expression programs that they regulate. This aim will focus on mapping the transcriptional and genomic targets of these TFs to determine their functional relevance to esophageal cancer. To do this, we will perform CRISPR gene knockouts of candidate TFs and perform subsequent RNA-sequencing on the wildtype cells compared to knockout cells, to examine genes differentially expressed. By characterizing and contrasting these downstream pathways between esophageal cancer variants, we can identify new molecular mechanisms that drive EAC or ESCC and discover additional therapeutic targets.

During the internship, the MITACS fellow will develop a diverse set of skills and competencies that will support them as a researcher and enhance their professional development. The fellow will further develop their technical proficiencies in advanced molecular and cellular biology techniques, such as RNA sequencing, high-throughput screening methods such as massively parallel reporter assays, and ultimately CRISPR gene editing. Through this internship, the fellow will also advance their project management skills, as they learn to coordinate their research activities between academic and industry settings, set timelines, and manage deliverables. They will also enhance their critical thinking and problem-solving abilities through troubleshooting experiments and interpreting complex data sets. Furthermore, they will have the opportunity to build their professional network within both the academic and industry circles, as well as improve their communication skills through presentations and collaborative discussions with diverse groups. Overall, this opportunity will enable the fellow to develop a comprehensive skill set that integrates academic research with real-world applications.

This project will support Promirin Therapeutics Ltd’s mission of developing targeted cancer therapeutics. By identifying TFs that drive the progression of esophageal cancer, as well as their downstream targets, the research will lead to a better understanding of the disease at a molecular level. This project will help establish the knowledge based required for Promirin to apply their targeted nucleic-acid based inhibitory technology in the context of this cancer type. Lastly, the project will support the Promirin’s broader goals of promoting innovative research and improving patient outcomes.

Integrating Cognitive Architecture into ADAS for Enhanced Decision-Making in Autonomous and Semi-Autonomous Vehicles

This project, “Integrating Cognitive Architecture into ADAS for Enhanced Decision-Making in Autonomous and Semi-Autonomous Vehicles,” focuses on improving vehicle safety systems through advanced cognitive models. Our objectives include developing a decision-making framework that mimics human cognitive processes, enhancing the performance of driver assistance systems. The intern will gain expertise in cognitive architectures, machine learning, and system integration, contributing to safer automotive technologies. This initiative will support our health organization by advancing research into technologies that prevent accidents and improve road safety, aligning with broader health and safety goals.

Improving the Robustness and Consistency of EMG using High-Density Electrode Arrays – Electrode Shift Analysis

As the future of rehabilitation-related professions depend increasingly on quantitative evidence supporting their efficacy, this has prompted a push to further integrate wearable health monitoring systems into consumer and medical fields. This would enable more robust and accessible remote recovery tracking opportunities, reducing the need for in-person clinician visits for patients managing neurological impairments like stroke. Additionally, this can help bridge the gap in quality-of-care between rural and urban areas while boosting patient motivation and providing a stronger foundation for clinician recommendations given the quantitative recovery metrics these systems would provide.

In response, we propose a novel wireless, wearable electromyography-based (EMG) system using fabric surface EMG (sEMG) sensors. This technology offers a non-invasive means of monitoring muscle activity, facilitating the extraction of measures such as muscle strength, time-to-fatigue, activation timings, and muscle co-contractions, causing sEMG to show promise in tracking the recovery from chronic movement disorders arising from stroke. Furthermore, with smart fabrics as a viable alternative to non-reusable electrodes, and with the proposed data acquisition and analysis system being small and lightweight, this will provide a smart clothing platform for health tracking that is convenient, accessible, and comprehensive in the data that it provides.

To achieve our goals, we must first address existing barriers to sEMG’s clinical adoption, particularly inter-session placement variations that limit longitudinal monitoring conclusions. Through this application, we will use pre-collected data from a healthy population to confirm the potential of our muscle mapping methodology to account for placement variations, determine the required spatial resolution of the wearable system that would employ the technology, validate the fabric sensors that would be used in the system, and extend the mapping algorithm to real-time shift tracking to support the analysis of dynamic exercises before future projects explore the work’s generalizability to a stroke population.

The intern, Fraser Douglas is a Biomedical Engineering PhD candidate at the University of British Columbia, specializing in high-density EMG wearables for stroke rehabilitation and prosthesis control. He is expected to gain several key competencies and skills through his work on this project.

Firstly, Fraser will develop research skill with the works focusing on characterizing the effect of distance on sEMG features indicative of neuromuscular recovery. This involves defining the maximum resolvable shift required for the shift detection algorithm. He will also enhance his skills in algorithm development by creating an HDsEMG shift detection algorithm based on underlying muscle activity maps, and adapting this algorithm to track muscle motion during dynamic exercise.
Throughout these tasks, Fraser will develop strong analytical skills and gain experience in using advanced EMG systems. He will also have multiple opportunities to produce academic outputs, including peer-reviewed journal articles and conference presentations. Specifically, these may arise from:
Detailed analyses of the impact of distance on feature values and sensor reattachment effects.
Descriptions of the inter-session shift detection algorithm and its application to continuous muscle tracking during dynamic exercises.
A technical report on the signal quality of Focal Lines’ dry electrodes compared to standard wet electrodes.
Fraser will also have the chance to write a thesis summarizing his work, contributing to his academic development.
Focal Lines, a start-up company developing unique dry surface electrodes designed to be integrated into textiles for use in a Muscular Skeleton Monitoring platform, enabling convenient at-home use, will benefit significantly from the project and Fraser’s work. His contributions will enhance the robustness and consistency of muscle activity measurements, supporting the viability of Focal Lines’ technology for continuous health monitoring. Fraser’s skills and the project outcomes will align closely, fostering his growth in the biomedical engineering field while advancing Focal Lines’ innovative health tech solutions.

Design and screen novel cell-penetrating peptides for intracellular delivery of proteins

Aim: Our industry partner Sinedore Bioscience has licensed our cell-penetrating peptide technology from UBC, aiming to improve intracellular delivery of protein drugs. Based on the lead peptide sequence, Sinedore aims to develop novel and enhanced CPPs to further improve the drug delivery efficiency. Sinedore will use the UBC data to train their computing model, which will suggest new CPP sequences that are anticipated to outperform the lead. The UBC team will synthesize the new peptides, perform cell-based assays, and provide the data to refine the model, aiming to develop an enhanced CPP.

Skills expected to develop: peptide synthesis using a peptide synthesizer; cell-based assays; computer modeling; oral presentation; manuscript writing

Impact: The project will lead to the development of novel and enhanced CPP to improve intracellular delivery of protein drugs. An intern will receive cutting-edge training on computational modeling and delivery of proteins, eventually contributing to the sustainability and growth of the health sector of BC.

High throughput tissue distribution study of polymer-lipid nanoparticle formulations using DNA barcode technology

Aim: Our industry partner GeneStar Bioscience has synthesized a few novel pH-sensitive polymers that can be incorporated into lipid nanoparticles (LNPs) to enhance gene delivery. The preliminary data suggest that their polymers can change the structure of LNPs and redirect their tissue distribution after intravenous delivery. We hypothesize that polymer-lipid nanoparticles (pLNP) prepared with different pH-sensitive polymers will exhibit different tissue distribution profiles, and this project will be focused on testing this hypothesis.

Skills expected to develop: LNP and pLNP fabrication using microfluidics; LNP and pLNP characterization using dynamic light scattering, electrophoresis, and fluorescence spectrometry; animal study; DNA sequencing; oral presentation; manuscript writing

Impact: The project will provide proof-of-concept results on whether and how the novel pH-sensitive polymers change biodistribution of LNP. This will lead to the development of unique formulations for targeting different tissues to perform targeted gene therapy. An intern will receive cutting-edge training on non-viral gene delivery technology and gene therapy, eventually contributing to the sustainability and growth of the health sector of BC.

A Pose-Correction Method for Intraoperative Measurements in Total Hip Surgery

Background & Motivation of the Project:
Precise positioning and alignment of implants and surgical instruments are essential for successful outcomes in orthopedic surgery. This is especially true for minimally invasive procedures, which typically rely on mobile C-arm fluoroscopy for continuous, low-dose real-time assessment throughout the operation. While this imaging technique is popular for its versatility and real-time visualization, it has inherent limitations. These include a small field of view, difficulty in using 2D X-rays to assess complex 3D geometries, parallax and image distortion, and the absence of local scaling parameters—all of which can lead to inaccuracies in fluoroscopy-based assessments. Additionally, these techniques often require capturing the operative anatomy from specific angles through a trial-and-error process, which is highly sensitive to small changes in the position and orientation of the image intensifier, as well as the patient’s positioning and joint angles on the surgical table. Many of these variables are challenging, if not impossible, to control in the fast-paced surgical environment, leading to assessment inaccuracies. Ultimately, these challenges can compromise surgical outcomes, increase the risk of complications or disabilities, and may even result in costly repeat surgeries.

To address these challenges, Torus Biomedical is pioneering an innovative solution called the ConfirMap system. This system integrates seamlessly with the existing mobile fluoroscopy equipment in the operating room and uses advanced algorithms to generate bi-planar long radiographic views and measurements of the operative anatomy. By reconciling bi-planar views of the operative anatomy, the system can estimate the 3D pose and orientation of the anatomies in an approach that is least sensitive to non-idealities of the conventional fluoroscopy-based assessments. In collaboration with a leading spine company in the U.S., a version of the ConfirMap technology tailored for spinal surgery has recently been introduced to the market. Currently, the company is focused on incorporating an AI-enabled automated measurement layer to meet the requirements for use during orthopedic trauma and joint arthroplasty procedures. To support the development of this technology and benchmark its performance against alternative techniques, it’s essential to quantify the impact of intraoperative parameters on specific radiographic outcomes.

Project Objectives:
The proposed research will use computer simulations to study how different imaging and patient positioning factors affect measurements during various stages of minimally invasive hip replacement surgery. We will simulate hip replacement surgery on a group of virtual patients, modeled from CT scans, in a virtual environment. This will allow us to recreate surgical scenarios and generate digitally reconstructed radiographs (DRRs), while accounting for expected variations in the positioning of the imaging equipment, patient posture, and joint angles. As the first objective of the the project, we will use simulated radiographs and ground truth data to assess the accuracy and precision of conventional 2D radiographic measurements for key surgical parameters in total hip arthroplasty, such as cup alignment, leg length, and offset. As the second objective, we will perform the same analysis to evaluate the performance of the ConfirMap system, comparing it to ground truth data from CT scans. We hypothesize that various intraoperative factors can significantly reduce the accuracy and precision of conventional radiographic methods, making them unreliable during hip replacement surgery. In contrast, measuring the performance of the ConfirMap system could demonstrate its potential as a more precise and reliable alternative.

Opportunities for Skill/Career Development:
Through this project, the intern will develop a comprehensive skill set in medical imaging techniques, orthopedic biomechanics, and computer-assisted radiology and surgery. Working closely with the company’s engineering team, clinical collaborators, and academic co-applicants with subject matter expertise in orthopedic medical devices, medical image analysis, and computer vision, the intern will receive valuable mentorship and guidance throughout the project. The intern will gain hands-on experience, from conducting literature reviews to simulating various stages of hip surgery using computer tools. This immersive experience will enhance critical thinking, problem-solving, and collaboration skills as the intern addresses complex technical challenges. Additionally, participating in academic publications resulting from the project will boost the intern’s career development and academic credibility in the field. Overall, this internship offers a unique opportunity to expand knowledge, skills, and professional connections in a dynamic and impactful research environment.

Value for the Company:
This project will assist the company in advancing its core technology by testing, improving, and validating its performance in direct comparison with state-of-the-art methods. The results of the research will significantly aid the company in highlighting the shortcomings of existing techniques while demonstrating the value of its system to clinical users, strategic partners, and stakeholders. Furthermore, the outcomes of the research will be submitted to peer-reviewed scientific and clinical journals for broader dissemination and validation within the research community.

A Machine Learning Method for Automated Intraoperative Assessments in Total Hip Surgery

Achieving precise positioning and alignment of anatomical structures and surgical instruments is paramount for successful outcomes in orthopedic surgery. The current standard practice heavily relies on intraoperative fluoroscopy (low dose real-time X-ray imaging) to assess the position and alignment of operative anatomy. However, these methods are qualitative and prone to errors and poor repeatability due to variabilities in positioning and orienting the imaging equipment, leading to suboptimal patient outcomes and increased risk of disabilities, often necessitating repeat surgeries. To address these challenges, Torus Biomedical has pioneered a unique solution known as ConfirMap system. This system seamlessly integrates with existing mobile fluoroscopy equipment in operating rooms and employs innovative calibration, tracking, and image processing techniques to provide extended radiographic visualization and precise measurements. Through strategic collaboration with a leading spine company in the US, a specialized version of the ConfirMap technology tailored for spinal surgery is slated to launch in the US market by Q2 2024. Currently, the company is dedicated to enhancing its technology to meet the specific demands of the orthopedic trauma field, particularly in femoral and tibial bone fracture reduction surgeries. This initiative aims to tackle critical technical challenges, thereby enhancing the viability of the ConfirMap system for orthopedic trauma applications, especially for procedures involving long bone fractures.

To ensure the effectiveness of the ConfirMap system in the ortho-trauma market, automating image analysis and measurement reporting with minimal surgical staff intervention is paramount. Our primary focus lies in procedures involving subtrochanteric and midshaft femoral fractures, where assessing the length, alignment, and rotation of the fractured bone poses significant challenges. Our project aims to leverage machine learning and computer vision techniques to develop and train a convolutional neural network (CNN) specifically tailored for the automatic segmentation of key bone landmarks based on the image format generated by the ConfirMap system. The outcome will streamline quick access to critical radiographic measurements during surgery, promising to enhance surgical efficiency and precision in ortho-trauma cases. The post-doctoral intern assigned to this project will play a pivotal role in developing a new machine learning model for the solution. As the initial step, they will establish a pipeline for creating realistic synthetic fluoroscopic images and corresponding labeling metadata from Computed Tomography (CT) images. Subsequently, they will construct, train, and optimize a deep neural network model, drawing inspiration from architecture layers proposed in academic literature. An important advancement of this research in the clinical application of the ConfirMap product will be the incorporation of multiple fluoroscopic images along with their spatial coordinates as input to the model.

Through this project the intern will have the opportunity to cultivate a comprehensive skill set encompassing research and development in medical imaging and machine learning. Collaborating with the company’s engineering team and academic co-applicants, esteemed authorities in medical image analysis and computer vision, the intern will benefit from invaluable mentorship, supervision, and guidance across all project stages. From conducting thorough literature reviews to designing and developing the neural network model from inception, the intern will gain hands-on experience in applying cutting-edge techniques to an innovative surgical imaging solution. This immersive experience will nurture the development of critical thinking, problem-solving, and collaboration skills as the intern tackles complex technical challenges. Furthermore, the intern’s participation in academic publications resulting from the project will not only advance their career development but also enhance their academic credibility in the field. Overall, this internship provides a unique and enriching opportunity for the intern to expand their knowledge, skills, and professional network within a dynamic and impactful research environment.

This project will support the company in developing a crucial technical solution to propel its product into new market opportunities. Introducing a new software feature for automatic radiographic measurements holds the potential to significantly enhance the effectiveness and versatility of the ConfirMap system for ortho-trauma applications. Upon demonstrating that the developed techniques meet the necessary accuracy and performance standards, the research outcomes will be seamlessly integrated into the ConfirMap system. Additionally, the findings of the research will be submitted to peer-reviewed scientific and clinical journals for wider dissemination and validation within the research community.