Evaluating Disparities in the U.S. Technology Transfer Ecosystem to Improve Bench to Business Translation

Background: A large number of highly impactful technologies originated from academic research, and the transfer of inventions from academic institutions to private industry is a major driver of economic growth, and a catalyst for further discovery. However, there are significant inefficiencies in academic technology transfer. In this work, we conducted a data-driven assessment of translational activity across United States (U.S.) institutions to better understand how effective universities are in facilitating the transfer of new technologies into the marketplace. From this analysis, we provide recommendations to guide technology transfer policy making at both the university and national level. Methods: Using data from the Association of University Technology Managers U.S. Licensing Activity Survey, we defined a commercialization pipeline that reflects the typical path intellectual property takes; from initial research funding to startup formation and gross income. We use this pipeline to quantify the performance of academic institutions at each step of the process, as well as overall, and identify the top performing institutions via mean reciprocal rank. The corresponding distributions were visualized and disparities quantified using the Gini coefficient. Results: We found significant discrepancies in commercialization activity between institutions; a small number of institutions contribute to the vast majority of total commercialization activity. By examining select top performing institutions, we suggest improvements universities and technology transfer offices could implement to emulate the environment at these high-performing institutions. Conclusion: Significant disparities in technology transfer performance exist in which a select set of institutions produce a majority share of the total technology transfer activity. This disparity points to missed commercialization opportunities, and thus, further investigation into the distribution of technology transfer effectiveness across institutions and studies of policy changes that would improve the effectiveness of the commercialization pipeline is warranted

Nuclear magnetic resonance sensors and methods for chemical sensing in tissue

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.Cataloged from PDF version of thesis.Includes bibliographical references (pages 100-107).Rapid, sensitive, and minimally invasive sensing of metabolites, chemicals, and biological molecules within tissue is a largely unsolved problem. Sensing molecular oxygen, pH, and water content is of particular interest as they have been shown to be useful for improving disease diagnosis and treatment monitoring in a diverse range of medical fields including trauma, solid tumor cancers, tissue grafts, wound healing, dehydration, athletic performance, and congestive heart failure. Nuclear magnetic resonance (NMR) offers a non-ionizing, rapid, repeatable, and molecularly sensitive measurement technique for chemical sensing. Existing hardware for highly versatile single sided measurement systems is insufficient for clinical use due to constraints on the size and shape of samples that can be measured, inadequate magnetic field performance, and low sensitivity. This thesis describes the development of a portable, single-sided NMR system for research and clinical use. A magnet assembly based on a linear Halbach array was developed to produce a large, remote, and uniform field. Suitable impedance matching circuitry was designed and constructed to efficiently transmit signals between NMR probes and a radiofrequency spectrometer. This system is suitable for use in NMR measurement within a clinical environment.by Ashvin Bashyam.S.M

Portable magnetic resonance sensors and methods for noninvasive disease diagnostics

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 144-154).Many diseases manifest as a shift in fluids between distinct tissue fluid compartments. For example, fluid depletion and fluid overload lead to a deficit or accumulation of fluids within the intramuscular interstitial space. A direct measurement of these fluid shifts could serve as a highly specific diagnostic or prognostic tool to improve clinical management of these disorders. Proton magnetic resonance is exquisitely sensitive to the local physical and chemical environment of water molecules within the body. Therefore, we hypothesized that localized magnetic resonance (MR) measurements could interrogate local tissue fluid distributions and assess systemic fluid volume status. This thesis explored the potential for a portable MR sensor to characterize shifts in tissue fluid distribution and identify the onset and progression of fluid volume status disorders.First, we designed a portable, single sided MR sensor capable of performing remote measurements of the multicomponent T2 signal originating from distinct fluid compartments. Further, we present a design framework to create single sided sensors with magnetic field strength and geometry suitable for a wide range of applications. We then demonstrate that a localized measure of tissue fluid distribution using a portable MR sensor is capable of identifying systemic changes in fluid volume status associated with fluid depletion. We validate these findings via whole animal MR measurements and a standard MRI scanner capable of localizing its measurement towards the muscle tissue. Finally, we explore new strategies to enable the translation of these portable MR sensors towards humans.We demonstrate techniques combining multicomponent T2 relaxometry, depth-resolved measurements, and diffusion-weighted pulse sequences to improve identification of fluid shifts within muscle tissue despite the presence of confounding tissues, such as the subcutaneous tissue. The magnetic resonance sensors and measurement techniques developed here lay the foundations for a non-invasive, portable, and quantitative indicator of tissue fluid distribution. This technology has the potential to serve as a clinical diagnostic for both localized and systemic fluid imbalances. Furthermore, these approaches enabling portable, quantitative MR measurements can be extended to the diagnosis and staging of the progression of other diseases which exhibit shifts in fluid distributions.by Ashvin Bashyam.Ph. D.Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc

Computationally Guided Intracerebral Drug Delivery via Chronically Implanted Microdevices

Treatments for neurologic diseases are often limited in efficacy due to poor spatial and temporal control over their delivery. Intracerebral delivery partially overcomes this by directly infusing therapeutics to the brain. Brain structures, however, are nonuniform and irregularly shaped, precluding complete target coverage by a single bolus without significant off-target effects and possible toxicity. Nearly complete coverage is crucial for effective modulation of these structures. We present a framework with computational mapping algorithms for neural drug delivery (COMMAND) to guide multi-bolus targeting of brain structures that maximizes coverage and minimizes off-target leakage. Custom-fabricated chronic neural implants leverage rational fluidic design to achieve multi-bolus delivery in rodents through a single infusion of radioactive tracer (Cu-64). The resulting spatial distributions replicate computed spatial coverage with 5% error in vivo, as detected by positron emission tomography. COMMAND potentially enables accurate, efficacious targeting of discrete brain regions.National Institute of Biomedical Imaging and Bioengineering (U.S.) (Grant R01 EB016101)National Cancer Institute (U.S.) (Grant P30-CA14051

Machine-learning aided in situ drug sensitivity screening predicts treatment outcomes in ovarian PDX tumors

Long-term treatment outcomes for patients with high grade ovarian cancers have not changed despite innovations in therapies. There is no recommended assay for predicting patient response to second-line therapy, thus clinicians must make treatment decisions based on each individual patient. Patient-derived xenograft (PDX) tumors have been shown to predict drug sensitivity in ovarian cancer patients, but the time frame for intraperitoneal (IP) tumor generation, expansion, and drug screening is beyond that for tumor recurrence and platinum resistance to occur, thus results do not have clinical utility. We describe a drug sensitivity screening assay using a drug delivery microdevice implanted for 24 h in subcutaneous (SQ) ovarian PDX tumors to predict treatment outcomes in matched IP PDX tumors in a clinically relevant time frame. The SQ tumor response to local microdose drug exposure was found to be predictive of the growth of matched IP tumors after multi-week systemic therapy using significantly fewer animals (10 SQ vs 206 IP). Multiplexed immunofluorescence image analysis of phenotypic tumor response combined with a machine learning classifier could predict IP treatment outcomes against three second-line cytotoxic therapies with an average AUC of 0.91