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Assessing the impact of tumor evolution on oncology drug development and commercialization

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dc.contributor.advisor Ernst R. Berndt and Mark Trusheim. en_US Sterk, Joseph P. (Sterk, Joseph Phillip) en_US
dc.contributor.other Harvard University--MIT Division of Health Sciences and Technology. en_US 2011-08-30T15:45:54Z 2011-08-30T15:45:54Z 2011 en_US 2011 en_US
dc.description Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2011. en_US
dc.description Vita. Cataloged from PDF version of thesis. en_US
dc.description Includes bibliographical references (p. 91-97). en_US
dc.description.abstract This thesis investigates the commercial viability of developing and commercializing targeted oncology drugs directed at a specific tumor mutation instead of all forms and mutations of a single target. While oncologic drugs targeted to aberrant or overexpressed pro-proliferative proteins have revolutionized cancer treatment, tumors treated for long periods may mutate over time, gain resistance to these drugs and proliferate rapidly again. I hypothesize that drugs developed to inhibit specific resistant tumor genotypes can be commercially viable from a pharmaceutical manufacturer's perspective. To assess this hypothesis empirically, I construct a patient flow model in order to quantify the treatment of CML, a relatively rare and indolent hematological malignancy with extensive clinical data available and well-delineated disease phases and response criteria. To represent the rate of diagnosis, patients are "added" to the model every month, and thereafter there is a probability that a patient may either 1) become sufficiently intolerant to his drug in order to discontinue treatment, 2) fail to respond to treatment but remain in the same disease phase, 3) fail to respond to treatment and progress to the next phase of disease, or 4) adequately respond to treatment and stay on the same drug in the same phase. Patients that fail to respond (categories 2 and 3 above) have a chance of manifesting a resistance mutation that is adequately controlled by a hypothetical drug (in addition to their current treatment) but is otherwise untreatable. The aim of this analysis is to track the number of patients that accrue the chosen resistance mutation and thus would be good candidates to receive the hypothetical drug. Patient treatment rates are converted to sales figures, and are weighed against clinical development costs, timelines, and probabilities to determine the net present value (NPV) of a project to develop the hypothetical drug. In addition, parameters are varied in order to conduct a sensitivity analysis and determine the "boundary conditions" that make a drug profitable or unprofitable. To supplement the model results and confirm the model dynamics, I interviewed investment analysts, clinical oncology thoughtleaders, academic cancer researchers and clinical, commercial and regulatory personnel from drug manufacturers to gauge their opinions on the CML market and the hurdles particular to developing drugs aimed at resistant genotypes. The conclusion I reach from this analysis is that development of a specific mutation-directed therapy for resistant CML is unlikely to be profitable. Given the significantly smaller patient population, favorable conditions in pricing and clinical development would be required to make the hypothetical candidate even marginally profitable. en_US
dc.description.statementofresponsibility by Joseph P. Sterk. en_US
dc.format.extent 97 p. en_US
dc.language.iso eng en_US
dc.publisher Massachusetts Institute of Technology en_US
dc.rights M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. en_US
dc.rights.uri en_US
dc.subject Harvard University--MIT Division of Health Sciences and Technology. en_US
dc.title Assessing the impact of tumor evolution on oncology drug development and commercialization en_US
dc.type Thesis en_US S.M. en_US
dc.contributor.department Harvard University--MIT Division of Health Sciences and Technology. en_US
dc.identifier.oclc 746799124 en_US

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