Difference between revisions of "Team:UC San Diego/Applied Design"

Product Design

Our Thought Process

As our team endeavored to take our idea of the wetlab and tailor it to real world parameters, we realized that it would be important to gain a fundamental understanding of the problem that we were trying to solve, examine some of the clear flaws with the status quo and existing solutions, and then develop a solution that addresses these pain points. Along the way, we have to revise our designs to incorporate our stakeholders’ needs and address them in a comprehensive manner. In addition, considerations of the broader implementation and lifecycle use also helped optimize our design decisions. As a result of our many interactions, we were able to create Epinoma, a modular beginning-to-end workflow for non-invasive cancer detection that uses machine learning to aid biomarker discovery, a functional assay that uses engineered proteins and principles of synthetic biology to detect specific epigenetic determinants, and a digital health platform that helps streamline doctor-patient communication. This journey would have been impossible without the input of many domain experts immersed in the diagnostic pathway, digital health experts, graduate students in the BLUE LINC incubator, industry professionals (researchers and department heads at Genentech and Roche), social entrepreneurs and innovators at the TATA Institute of Genetics in Society.

Framing the problem and examining existing solutions

Talking to clinical researchers and diagnostic experts throughout the UC San Diego Health System helped us identify some of the fundamental problems with tissue specimen analysis. One of the primary concerns that healthcare professionals have is that tissue specimen analysis is unable to capture the inherent molecular heterogeneity of tumors and the ability of cancer genomes to evolve. From a diagnostician’s perspective, this decreases the method’s predictive value which makes it suboptimal. In addition, doctors must often make a decision regarding a patient’s biopsy, even though it carries the inherent risk of spreading the tumor and further complicating the issue. From a patient’s perspective, tissue biopsy is often very invasive and painful, and can also pose a serious economic burden in the current healthcare system.

As a result, our team turned to research in the liquid biopsy space, which focuses in non-invasive cancer detection techniques. After examining some of the main players and methods in the liquid biopsy space, we realized that although the liquid biopsy space addressed the pain point of physical pain, it still did not fix the issue of diagnostic accuracy. Our team identified several bottlenecks in the commercialization of liquid biopsy tests including: (1) Analysis of cell-free DNA in urine, blood, and saliva was often inaccurate because the low concentrations of DNA could not be properly analyzed given existing technologies, (2) Analysis of the methylation signal often relied on DNA sample treatment with sodium bisulfite, which can induce random breaks in DNA fragments and lead to incomplete deamination and inaccurate results. Talking to several medical institutions led us to realize that our methodology should eliminate the chemical treatment method while still retaining a quantitative output.

With regards to the disease that we were were working with, hepatocellular carcinoma, we also wanted to quantify the potential impact that our solution would have. With 700,000 existing cases of HCC with an additional 43,000 cases in America predicted in the upcoming year, we felt that it would be important . Current healthcare economics and reimbursement strategies mean that it costs almost almost $500 for a simple needle biopsy and nearly $4000 for surgical biopsies; with our predicted price point of $250, we would be able to prevent a significant economic burden for many individuals as well as prevent a preventative measure on various healthcare systems around the world.

Determining crucial criteria and incorporating synthetic biology into our product design

After reflecting on our initial stakeholder interactions, our team started thinking about how to address the issues at hand. After brainstorming several solutions, we determined that the first crucial pivot that we would have to make is to identify and develop a diagnostic metric that would serve as a more consistent, useful assessment of an individual’s health. Thus, we decided that our solution would need to eliminate the invasiveness of tissue biopsy, be cost-effective, have clinically accurate levels of diagnostic accuracy, provide a quantitative output, and pose no threat in case of environmental release.

As we learned more about hepatocellular carcinoma, we realized that our tool would be extremely applicable in local and international low-resource communities. To understand the specifics of what it would take to develop a clinically-viable test in a variety of environments, we talked to Mr. Manoj Kumar, head at the TATA Institute of Genetics and Society, a NGO and philanthropic social incubator that seeks to bring better healthcare tools to low-resource communities in India. From this, we learned about the ASSURED criteria that are crucial to deploying point-of-care devices in low-resource communities; although we were not necessarily planning on a POC device, we felt that many of these guidelines could guide our design criteria. As such, we realized our device had to be affordable, sensitive (low rates of false positives), specific (low rates of false negatives), user-friendly, robust (rigorously tested in different settings), and . Because a large number of these cases are brought to centralized government hospitals and associated clinical labs, we felt that the equipment-free requirement was not as crucial for the deployment of our project. In fact, given that the desired readout was a quantitative signal, it would be almost impossible to meet that requirement.

Our team decided that incorporating synthetic biology would be essential. Engineering a methyl-binding domain protein with a fluorescent reporter gene would be the core of our biosensor as it allowed for a easy, safe transduction element that did not pose issues that existing alternatives had, including invasiveness and inaccurate readout.

For us, it was also crucial that we come out with a solution that would be easy to acquire and analyze. The most important factors for that depended on turnaround time, ease of materials acquisition, and overall detection accuracy.

To improve the diagnostic accuracy of our assay (the sensitivity and specificity), we also felt that synthetic biology provided some inherent advantages. Discussions with individuals in academia and clinical labs gave us confidence that our signal amplification strategies, including an exonuclease-driven strategy, would also work ; it also allowed us to design several optimized circuits in addition to our baseline MBD-GFP. Foundational literature suggested that multimerization of the protein would enhance binding ability for our MBD protein which would be crucial in detection at the attomolar levels. Discussions with material scientists also pointed us in the direction of implementation for a graphene oxide platform that could help boost our signal-to-noise ratio in the assay. A follow-up conversation with several doctors associated with the TIGS initiative also allowed us to implement a microfluidic system to enable high-throughput analysis and reduce the overall complexity of our workflow.

Expanding our workflow and developing novel use cases

However, our initial product consisted of a single diagnostic assay; although several technological innovations strengthened the predictive power, our team was dedicated to addressing some of the other key lags in the development of clinically available liquid biopsy tests. Our interactions with the following individuals really brought some fresh perspective on broadening our impact.

Dr. Jian Dai, senior data scientist at Genentech

Part of our needs-finding had uncovered a critical lag in the development of liquid biopsy tests. Researchers and clinicians were unsure of which biomarkers to analyze for different diseases. Our team wanted to take the step of addressing this crucial gap in existing liquid biopsy workflows. Talking to Dr. Dai helped give us additional perspective in the drylab, and gave us awareness of the tools that are needed for data analysis. After talking to Dr. Dai, our team was able to come up with a methodology for an unsupervised machine learning framework that would aid in biomarker discovery. By using techniques such as Random Forest and Lasso-Cox, we would be able to discover the optimal gene panel combinations to detect promoter methylation across a subset of patients based on the disease of interest. This aspect of our workflow can be integrate any existing methylome dataset and will provide disease-specific markers.

Dr. Mikael Eliasson, head of Global Product Development & Strategic Innovation at Genentech

Our conversation with Dr. Eliasson also identified another critical lag in the traditional diagnostics journey. In post-treatment therapy, there is a significant decrease in communications between doctors and patients, and this can hinder one’s ability to determine if the initial treatment was successful. In talking with Dr. Eliasson, we realized we could capitalize on the trends of digital health and implement a digital health platform. In addition, we developed a completely novel use case of using hypermethylation as a continuous variable that could be correlated to tumor burden and assess the effectiveness of a patient’s treatment.

Dr. Matthias Essenpreis, CTO at Roche Diagnostics

We also had an opportunity to meet with the CTO of Roche Diagnostics, Dr. Matthias Essenpreis. By talking to him, we started to understand the value of a platform-based business that could facilitate exchange of information and services between two different stakeholder groups. The ability to analyze patient data (both at an early clinical-screening and post-therapy stage) could be given to healthcare professionals to guide their decision-making, and it could also plug into pharmaceuticals’ companies in order to develop more effective treatments going forward. Dr. Essenpreis explained that a multi-sided platform business model would help address stakeholder needs more effectively, and emphasized that the value is in the insight created by the analysis of the data.

From this, we were able to construct two thorough lifecycle cases and the full clinical protocol.

These interactions helped expand our workflow from a technologically innovative diagnostic assay into a full-scale diagnostics platform that is able to be implemented at multiple points in the patient care journey, and (1) aids in biomarker discovery and addresses an industry-wide lag, and (2) uses digital health data collection and analysis to generate clinical utility that goes beyond the initial interaction.

Feasibility Analysis and Broader Considerations

In addition to having our clinical framework validated by academia and industry professionals, we also were fortunate enough to meet with Dr. Mike Pellini, a venture capitalist who also has a clinical practice. He was able to provide further validation of our entire workflow and found it interesting in its work with post-therapy response via promoter methylation monitoring. The implementation of the cell-free system was an interesting but acceptable way of addressing biosecurity concerns, and he thought that using synthetic biology to advance liquid biopsy and shift cancer diagnosis paradigms was an elegant solution.

Although our project has the potential for broad, positive social impact because of its applications to other researchers and ability for relatively easy implementation in low-resource communities, it was almost important to realize some of the negative consequences of wrongful use of our product, especially in the last component of our workflow.

Medical data and privacy are growing concerns, and discussions with industry professionals have consistently reiterated that point. Our team’s consultations with Harry Gandhi, founder of Medella Health, and his domain knowledge about medical data privacy helped us choose specific security protocols to ensure that data would be properly handled. However, a large concern still remains as there need to be proper monitoring and handling of patient data when being used for drug and treatment development in the future.