After learning about the long process of bringing drugs from research labs to patients, we recognized the need for a high-throughput drug screening platform for glioma. In order to implement our design and produce an engineering product, we have been considering different implementations to target a wide range of users, including but not limited to, patients, researchers, and pharmaceutical companies. From the innovation phase to the deployment of the product, we are considering the needs of the target users as well as addressing the safety concerns related to real-life implementation of the design through conversations with the key stakeholders in the field. We have developed a reporter system for the purpose of enabling drug discovery researchers to better analyze the impacts of different therapeutics. We have also demonstrated that our co-culture system along with the reporter has the potential to quantify drug effects in vitro. In addition, we are planning on expanding upon current transfection protocols to determine how to optimally introduce our constructs into cell lines that are relevant to our model system, specifically primary glioma cell lines as the lack of such a transfection protocol currently presents a barrier in evaluating new constructs. In order to remove the bottleneck in processing organoid images in terms of image analysis for researchers, we are developing Brain Profiler, a deep learning-based suite for high throughput end-to-end analysis of organoid scans.
Current drug development pipelines rely on animal testing for safety and efficacy screening, but this process is costly, time-consuming, and often disappointing. More than $18 billion is spent on animal drug screening experiments every year, only for many drugs to show poor efficacy in animals (Tonkens et al., 2005). There is currently no way around this dilemma as animal testing is the best way to validate novel treatments.
We designed our reporter system with the goal of creating a model that would allow drug development researchers to gain a better understanding of the exact molecular and metabolic impacts of their compounds on disease samples. After choosing to target IDH1 mutation-specific gliomas, we concentrated our efforts on quantifying the amount of the oncometabolite D-2-HG in the cell. Due to the downstream effects of D-2-HG accumulation, which include genome-wide histone and DNA methylation alterations, monitoring its levels within the system and observing how those levels change in response to novel therapeutics is crucial to find a cure for this lethal disease. We hope that our system can help address the ongoing problem in cancer research of lacking a scalable and physiologically-relevant model system, and that this system can be implemented into multiple research settings to facilitate advancements in this field.
How to test drugs with NODES
We have successfully performed initial drug testing on our co-culture systems, which has followed expected trends. We demonstrated that our co-culture system can be used to easily and effectively determine the effects of applied treatments on tumor invasion and development. This proof-of-concepts suggests that our final co-culture system, with the integrated reporter, has a high likelihood of meaningfully quantifying drug effects in vitro. Moving forward, we hope to offer NODES as a drug screening platform in the preclinical phase to eliminate drugs that are harmful or ineffective before they enter clinical trials.
Optimizing Transfection in Non-Standard Cell Lines
There are several well-established cell lines, like HEK293T, utilized by the synthetic biology field to test plasmid constructs in vitro and assess how they might act in a cellular system. The human body is composed of over 200 cell types, each with a unique physiology and function. Thus, it is crucial to verify engineered plasmid constructs in relevant cell lines to gain a more comprehensive understanding of plasmid expression in specific cell types and their impact on specific areas of the body. However, current transfection protocols are not optimized for nonstandard cell lines, which acts as a barrier for thorough evaluation of new constructs and their function in their relevant setting.
With this in mind, our team wanted to expand upon current transfection protocols to determine how to optimally introduce our constructs into cell lines that are relevant to our model system, specifically primary glioma cell lines. We hope to test several electroporation protocols, tuning it to produce the most efficient method that results in optimal gene expression with high transfection efficiency and low cell death. In this phase, we have successfully developed a protocol to introduce recombinant plasmids via non-viral delivery into patient-derived glioma cells (see Proof of Concept to learn more). Ultimately, we hope to contribute new insight on how to successfully and efficiently transfect primary cells, which will advance new synthetic biology applications in primary glioma cells.
Bioimage segmentation is a common but challenging task, especially in dealing with complex biological properties due to highly dense systems, high variation in shapes and sizes, and complex morphology. Especially when working with organoids, one of the major bottlenecks in the analysis is with processing large amounts of microscopy image data. However, due to the sizes and shapes of organoids varying greatly over different time points, drug treatment types, and cell types, there have been challenges in developing a high-throughput screening workflow that could be generalized for all types of organoids.
Such a need for an accurate and generalizable pipeline to analyze and extract quantitative measurements from organoid scans motivated us to develop Organoid Profiler, a deep learning-based suite for a complete end-to-end organoid analysis. Organoid Profiler is composed of two major components - a trained segmentation model(Brain OS) and a trained classifier model. We have currently developed Brain OS (Organoid Segmentation), a DL-based segmentation model for organoid-based segmentation. In the future, we are planning on developing the classifier model to distinguish between alive and dead cells and therefore completing the Organoid Profiler pipeline. As such a task is challenging even for a trained human, we hope to utilize machine learning to extract subtle phenotypic and distribution characteristics of organoids. With Brain OC (Organoid Classifier), the Organoid Profiler pipeline would be complete, enabling an end-to-end processing of organoid images and allowing the user to extract valuable quantitative measurements.
Our team also considered safety concerns that would need to be addressed during the ultimate real-world implementation of our device. We envision that our products will be mostly applied in a clinical research setting, with an extremely low chance of being introduced into the environment. We acknowledge, however, that there will be a possibility that the final product we deliver will be assessed to pose a potential threat when in contact with the outside environment, and a kill-switch will be implemented on the system upon need. Our guiding principle is that we will limit our products from contacting the environment as much as possible, and if contact is necessary, an intrinsic kill-switch will be installed to prevent escape of recombinant DNA.
Tonkens, Ross. "An Overview of the Drug Development Process." Physician Executive 31.3 (2005): 48-52. ProQuest. 20 Oct. 2021 .