The ASU Biodesign Clinical Testing Laboratory began in March 2020 after the severe acute respiratory syndrome, coronavirus 2, began spreading throughout the world. ASU worked towards implementing its own efficient way of testing for the virus, in order to assist the university but also keep the communities around it safe. By developing its own strategy for COVID-19 testing, ASU was on the forefront of research by developing new ways to test for the virus. This process began when research labs at ASU were quickly converted into clinical testing laboratories, which used saliva testing to develop swift COVID-19 diagnostic tests for the Arizona community. The lab developed more accurate and time efficient results, while also converting Nasopharyngeal tests to saliva tests. Not only did this allow for fewer amounts of resources required, but more individuals were able to get tested at faster rates. The ASU Biodesign Clinical Testing Laboratory (ABCTL) was able to accomplish this through the adaptation of previous machines and personnel to fit the testing needs of the community. In the future, the ABCTL will continue to adapt to the ever-changing needs of the community in regards to the unprecedented COVID-19 pandemic. The research collected throughout the past year following the breakout of the COVID-19 pandemic is a reflection of the impressive strategy ASU has created to keep its communities safe, while continuously working towards improving not only the testing sites and functions, but also the ways in which an institution approaches and manages an unfortunate impact on diverse communities.
This thesis project is part of a larger collaboration documenting the history of the ASU Biodesign Clinical Testing Laboratory (ABCTL). There are many different aspects that need to be considered when transforming to a clinical testing laboratory. This includes the different types of tests performed in the laboratory. In addition to the diagnostic polymerase chain reaction (PCR) test that is performed detecting the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), antibody testing is also performed in clinical laboratories. Antibody testing is used to detect a previous infection. Antibodies are produced as part of the immune response against SARS-CoV-2. There are many different forms of antibody tests and their sensitives and specificities have been examined and reviewed in the literature. Antibody testing can be used to determine the seroprevalence of the disease which can inform policy decisions regarding public health strategies. The results from antibody testing can also be used for creating new therapeutics like vaccines. The ABCTL recognizes the shifting need of the community to begin testing for previous infections of SARS-CoV-2 and is developing new forms of antibody testing that can meet them.
As much as SARS-CoV-2 has altered the way humans live since the beginning of 2020,<br/>this virus's deadly nature has required clinical testing to meet 2020's demands of higher<br/>throughput, higher accuracy and higher efficiency. Information technology has allowed<br/>institutions, like Arizona State University (ASU), to make strategic and operational changes to<br/>combat the SARS-CoV-2 pandemic. At ASU, information technology was one of the six facets<br/>identified in the ongoing review of the ASU Biodesign Clinical Testing Laboratory (ABCTL)<br/>among business, communications, management/training, law, and clinical analysis. The first<br/>chapter of this manuscript covers the background of clinical laboratory automation and details<br/>the automated laboratory workflow to perform ABCTL’s COVID-19 diagnostic testing. The<br/>second chapter discusses the usability and efficiency of key information technology systems of<br/>the ABCTL. The third chapter explains the role of quality control and data management within<br/>ABCTL’s use of information technology. The fourth chapter highlights the importance of data<br/>modeling and 10 best practices when responding to future public health emergencies.
In mid-March of 2020, Arizona State University transformed one of its research labs into ASU Biodesign Clinical Testing Laboratory (ABCTL) to meet the testing needs of the surrounding community during the COVID-19 pandemic. The lab uses RT-qPCR, or reverse transcription polymerase chain reaction, to match the components of a biosample to a portion of the SARS-CoV-2 genome. The ABCTL uses the TaqPath™ COVID-19 Combo Kit, which has undergone many different types of efficacy and efficiency tests and can successfully denote saliva samples as positive even when an individual is infected with various emerging strains of the SARS-CoV-2. Samples are collected by volunteers at testing sites with stringent biosafety precautions and processed in the lab using specific guidelines. As the pandemic eventually becomes less demanding, the ABCTL plans to utilize the Devil’s Drop-off program at various school districts around Arizona to increase testing availability, transfer to the SalivaDirect method, and provide other forms of pathogen testing to distinguish COVID-19 from other types of infections in the ASU community.
In recent years, biological research and clinical healthcare has been disrupted by the ability to retrieve vast amounts of information pertaining to an organism’s health and biological systems. From increasingly accessible wearables collecting realtime biometric data to cutting-edge high throughput biological sequencing methodologies providing snapshots of an organism’s molecular profile, biological data is rapidly increasing in its prevalence. As more biological data continues to be harvested, artificial intelligence and machine learning are well positioned to aid in leveraging this big data for breakthrough scientific outcomes and revolutionized medical care. <br/><br/>The coming decade’s intersection between biology and computational science will be ripe with opportunities to utilize biological big data to advance human health and mitigate disease. Standardization, aggregation and centralization of this biological data will be critical to drawing novel scientific insights that will lead to a more robust understanding of disease etiology and therapeutic avenues. Future development of cheaper, more accessible molecular sensing technology, in conjunction with the emergence of more precise wearables, will pave the road to a truly personalized and preventative healthcare system. However, with these vast opportunities come significant threats. As biological big data advances, privacy and security concerns may hinder society's adoption of these technologies and subsequently dampen the positive impacts this information can have on society. Moreover, the openness of biological data serves as a national security threat given that this data can be used to identify medical vulnerabilities in a population, highlighting the dual-use implications of biological big data. <br/><br/>Additional factors to be considered by academia, private industry, and defense include the ongoing relationship between science and society at-large, as well as the political and social dimensions surrounding the public’s trust in science. Organizations that seek to contribute to the future of biological big data must also remain vigilant to equity, representation and bias in their data sets and data processing techniques. Finally, the positive impacts of biological big data lie on the foundation of responsible innovation, as these emerging technologies do not operate in standalone fashion but rather form a complex ecosystem.
As much as SARS-CoV-2 has altered the way humans live since the beginning of 2020, this virus's deadly nature has required clinical testing to meet 2020's demands of higher throughput, higher accuracy and higher efficiency. Information technology has allowed institutions, like Arizona State University (ASU), to make strategic and operational changes to combat the SARS-CoV-2 pandemic. At ASU, information technology was one of the six facets identified in the ongoing review of the ASU Biodesign Clinical Testing Laboratory (ABCTL) among business, communications, management/training, law, and clinical analysis. The first chapter of this manuscript covers the background of clinical laboratory automation and details the automated laboratory workflow to perform ABCTL’s COVID-19 diagnostic testing. The second chapter discusses the usability and efficiency of key information technology systems of the ABCTL. The third chapter explains the role of quality control and data management within ABCTL’s use of information technology. The fourth chapter highlights the importance of data modeling and 10 best practices when responding to future public health emergencies.
This project is designed as part of the multi-student ASU Biodesign Clinical Testing Laboratory (ABCTL) thesis project sponsored and organized by Dr. Carolyn Compton, professor of Life Sciences at ASU and medical director with the ABCTL. This project divides students into teams with Business, Law, Laboratory, IT, and Documentary focused groups, with the goal of providing a comprehensive overview of the operations of the ABCTL as a reference for other institutions and to produce a documentary film about the laboratory. As a member of the IT team, this writeup will focus on quality control throughout the transfer of data in the testing process, security and privacy of data, HIPAA and regulatory compliance, and accessibility of data while maintaining such restrictions.
As much as SARS-CoV-2 has altered the way humans live since the beginning of 2020, this virus's deadly nature has required clinical testing to meet 2020's demands of higher throughput, higher accuracy and higher efficiency. Information technology has allowed institutions, like Arizona State University (ASU), to make strategic and operational changes to combat the SARS-CoV-2 pandemic. At ASU, information technology was one of the six facets identified in the ongoing review of the ASU Biodesign Clinical Testing Laboratory (ABCTL) among business, communications, management/training, law, and clinical analysis. The first chapter of this manuscript covers the background of clinical laboratory automation and details the automated laboratory workflow to perform ABCTL’s COVID-19 diagnostic testing. The second chapter discusses the usability and efficiency of key information technology systems of the ABCTL. The third chapter explains the role of quality control and data management within ABCTL’s use of information technology. The fourth chapter highlights the importance of data modeling and 10 best practices when responding to future public health emergencies.
The traditional drug discovery process often amasses substantial costs, faces high attrition rates, progress at an extremely slow pace, and has no guarantee of receiving FDA approval by the end of the process. On average, the total cost and timeframe of drug discovery are $2.6 billion and at least 10 years (PhRMA, 2015). Alternatively, drug repositioning has become an increasingly attractive approach to pharmaceutical development and drug discovery because it has the potential to circumvent these obstacles by utilizing “de-risked” FDA-approved compounds, employing lower-cost computational research methods, and necessitating shorter development timelines (Pushpakom et al, 2019). Used effectively, drug repositioning can save a lot of money, time, and lives.
One potential application of drug repositioning research is in neurodegenerative diseases, which are diseases that primarily affect neurons in the brain. Many of these diseases manifest themselves through complex mechanisms that can impair memory, cognition, and movement. Huntington’s Disease (HD) is a fatal genetic progressive neurodegenerative disease that causes the progressive breakdown of neurons in the brain. This disease is caused by a trinucleotide repeat disorder known as a CAG repeat. This means that, due to a mutation in a person’s DNA, a set of code in the DNA erroneously repeats itself an excessive number of times. These mutations lead to the production of deformed, highly reactive proteins that can cause neuronal dysfunction, degeneration, and death. The number of repetitions varies from person to person, and longer repeat chains tend to cause the onset of HD to occur earlier in life. Symptoms include loss in motor function, personality and behavioral changes, decline in cognitive function, severe weight loss, and suicidal ideation (Heemskerk and Roos, 2012). One unique facet of the disease is that symptoms generally do not begin to appear until ages 30-50 and worsen over the course of a 10-25-year period. HD is also an autosomal dominant hereditary disease, meaning that any parent who is a carrier of the genetic disorder has a 50% chance or higher of passing the gene to his/her child. The high transmission rate, coupled with the prolonged symptoms of the disease, makes HD a devastating disease for families, as individuals are often unaware of their HD disease until after they have already had offspring. Currently, there are approximately 30,000 symptomatic HD patients and more than 200,000 individuals at risk for developing HD. The disease is also significantly more frequent in Western countries. There is no known cure for the disease, and the only focus of treatment is managing symptoms.
The goal of this Honors Thesis project is to utilize basic drug repositioning methods to develop a disease profile for HD and curate a set of drugs that can be tested and validated for HD treatment in future experiments.