Commercializing molecular diagnostics in oncology

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 69-81).Historically, the development of molecular companion diagnostics in oncology has underwhelmed the expectation of the medical field since the successful mapping of the human genome a decade ago. There have been several highly successful developments (Her2-neu, Genomic Health, etc.), but not the widespread revolution in clinical practice expected at the turn of the century. The primary goal of this document is to investigate the economics driving molecular diagnostics and its relationship with strategic decisions in developing a diagnostic technology. The document begins with a broad analysis of the funding environment for molecular and esoteric diagnostics in oncology. Following a discussion of funding sources and current trends, the second section reviews the nature of the projects that funding supports. This is accomplished through a set of detailed case studies of Genomic Health and Immunicon to compare and contrast the strategic decisions that led to value creation events for both companies. Finally, the third section abstracts away from specific cases to develop a strategic analytic framework for evaluating the potential risk-adjusted NPV for a new diagnostic technology. Sensitivity analyses are conducted in addition to a discussion of current events that may change the structure of the underlying decision tree. The conclusion revisits the topics discussed in the first three sections, connecting the implications of funding on different strategic decisions and chances of success. Areas for further investigation both for inputs to the current model proposed, as well as for more refined versions of the development model are discussed.by Joshua Copp.M.B.A

Chemistry-Informed Machine Learning Enables Discovery of DNA-Stabilized Silver Nanoclusters with Near-Infrared Fluorescence

DNA can stabilize silver nanoclusters (AgN-DNAs) whose atomic sizes and diverse fluorescence colors are selected by nucleobase sequence. These programmable nanoclusters hold promise for sensing, bioimaging, and nanophononics. However, DNA’s vast sequence space challenges the design and discovery of AgN-DNAs with tailored properties. In particular, AgN-DNAs with bright near-infrared luminescence above 800 nm remain rare, placing limits on their applications for bioimaging in the tissue transparency windows. Here, we present a design method for near-infrared emissive AgN-DNAs. By combining high-throughput experimentation and machine learning with fundamental information from AgN-DNA crystal structures, we distill the salient DNA sequence features that determine AgN-DNA color, for the entire known spectral range of these nanoclusters. A succinct set of nucleobase staple features are predictive of AgN-DNA color. By representing DNA sequences in terms of these motifs, our machine learning models increase the design success for near-infrared emissive AgN-DNAs by 12.3 times as compared to training data, nearly doubling the number of known AgN-DNAs with bright near-infrared luminescence above 800 nm. These results demonstrate how incorporating known structure–property relationships into machine learning models can enhance materials study and design, even for sparse and imbalanced training data

Chemistry-Informed Machine Learning Enables Discovery of DNA-Stabilized Silver Nanoclusters with Near-Infrared Fluorescence

DNA can stabilize silver nanoclusters (AgN-DNAs) whose atomic sizes and diverse fluorescence colors are selected by nucleobase sequence. These programmable nanoclusters hold promise for sensing, bioimaging, and nanophononics. However, DNA’s vast sequence space challenges the design and discovery of AgN-DNAs with tailored properties. In particular, AgN-DNAs with bright near-infrared luminescence above 800 nm remain rare, placing limits on their applications for bioimaging in the tissue transparency windows. Here, we present a design method for near-infrared emissive AgN-DNAs. By combining high-throughput experimentation and machine learning with fundamental information from AgN-DNA crystal structures, we distill the salient DNA sequence features that determine AgN-DNA color, for the entire known spectral range of these nanoclusters. A succinct set of nucleobase staple features are predictive of AgN-DNA color. By representing DNA sequences in terms of these motifs, our machine learning models increase the design success for near-infrared emissive AgN-DNAs by 12.3 times as compared to training data, nearly doubling the number of known AgN-DNAs with bright near-infrared luminescence above 800 nm. These results demonstrate how incorporating known structure–property relationships into machine learning models can enhance materials study and design, even for sparse and imbalanced training data

Chemistry-Informed Machine Learning Enables Discovery of DNA-Stabilized Silver Nanoclusters with Near-Infrared Fluorescence

DNA can stabilize silver nanoclusters (AgN-DNAs) whose atomic sizes and diverse fluorescence colors are selected by nucleobase sequence. These programmable nanoclusters hold promise for sensing, bioimaging, and nanophononics. However, DNA’s vast sequence space challenges the design and discovery of AgN-DNAs with tailored properties. In particular, AgN-DNAs with bright near-infrared luminescence above 800 nm remain rare, placing limits on their applications for bioimaging in the tissue transparency windows. Here, we present a design method for near-infrared emissive AgN-DNAs. By combining high-throughput experimentation and machine learning with fundamental information from AgN-DNA crystal structures, we distill the salient DNA sequence features that determine AgN-DNA color, for the entire known spectral range of these nanoclusters. A succinct set of nucleobase staple features are predictive of AgN-DNA color. By representing DNA sequences in terms of these motifs, our machine learning models increase the design success for near-infrared emissive AgN-DNAs by 12.3 times as compared to training data, nearly doubling the number of known AgN-DNAs with bright near-infrared luminescence above 800 nm. These results demonstrate how incorporating known structure–property relationships into machine learning models can enhance materials study and design, even for sparse and imbalanced training data

Chemistry-Informed Machine Learning Enables Discovery of DNA-Stabilized Silver Nanoclusters with Near-Infrared Fluorescence

DNA can stabilize silver nanoclusters (AgN-DNAs) whose atomic sizes and diverse fluorescence colors are selected by nucleobase sequence. These programmable nanoclusters hold promise for sensing, bioimaging, and nanophononics. However, DNA’s vast sequence space challenges the design and discovery of AgN-DNAs with tailored properties. In particular, AgN-DNAs with bright near-infrared luminescence above 800 nm remain rare, placing limits on their applications for bioimaging in the tissue transparency windows. Here, we present a design method for near-infrared emissive AgN-DNAs. By combining high-throughput experimentation and machine learning with fundamental information from AgN-DNA crystal structures, we distill the salient DNA sequence features that determine AgN-DNA color, for the entire known spectral range of these nanoclusters. A succinct set of nucleobase staple features are predictive of AgN-DNA color. By representing DNA sequences in terms of these motifs, our machine learning models increase the design success for near-infrared emissive AgN-DNAs by 12.3 times as compared to training data, nearly doubling the number of known AgN-DNAs with bright near-infrared luminescence above 800 nm. These results demonstrate how incorporating known structure–property relationships into machine learning models can enhance materials study and design, even for sparse and imbalanced training data