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in silico Algorithm Advancements for Cancer Chemotherapy Treatment and Coagulopathy Diagnosis

Liparulo, Joseph (2023) in silico Algorithm Advancements for Cancer Chemotherapy Treatment and Coagulopathy Diagnosis. Master's Thesis, University of Pittsburgh. (Unpublished)

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The interdisciplinary field of systems medicine fills a gap in clinical translation with computational models to help inform decisions made by physicians. This approach takes advantage of modern computational methods to examine interactions between components within the models to predict the cause-and-effect relationship between systems and potential treatment strategies. These mathematical models typically incorporate various pharmacokinetic and pharmacodynamic models to help personalize medicine to individual patients, rather than a ‘one-size-fits-all’ mentality. The research herein focuses on improving chemotherapy treatment as well as the prediction of coagulopathies in trauma patients.

The general use of mathematical models for cancer chemotherapy treatment has focused primarily on tumor kill while using constraints on dose magnitude to explicitly mitigate toxicity. By incorporating pharmacodynamic toxicity models in this work into chemotherapy treatment schedule design, the physician is able to specify toxicity explicitly. The pharmacokinetic model of drug distribution throughout the body and pharmacodynamic models of both the antitumor efficacy and drug toxicity are incorporated into optimization of dose scheduling. The performance results of an optimal schedule under clinical constraints are clinically indiscernible from the mathematically-optimal solution since tumor volume and toxicity levels are identical following the treatment cycle. Using nonlinear least squares and clinical measurements, actual patient toxicity/tumor sensitivities can be calculated and the optimal schedule updated. In a clinical setting this algorithm would enable the clinician to prioritize patient quality-of-life through the minimization of individual toxicity while maximizing tumor eradication.

Predicting trauma patient coagulopathies presents a challenge to clinicians; the patient may experience excessive or insufficient clotting and it is imperative that the correct intervention be given as soon as possible. By implementing models of the coagulation cascade with clinical assays such as the thromboelastogram (TEG), it is possible to determine subpopulations of coagulopathies, therefore allowing the clinician to make more informed decisions much faster than using the TEG tracing alone. By decreasing the time required to identify abnormal lysis from over an hour after TEG initialization to mere minutes, the risk of mortality and permanent damage for the patients could be decreased significantly.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Liparulo, Josephjtl19@pitt.edujtl190000-0001-9553-1223
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorParker, Robertrparker@pitt.edurparker
Committee MemberShoemaker, Jasonjason.shoemaker@pitt.edujas518
Committee MemberBanerjee, Ipsitaipb1@pitt.eduipb1
Date: 19 January 2023
Date Type: Publication
Defense Date: 10 November 2022
Approval Date: 19 January 2023
Submission Date: 3 November 2022
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 128
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Chemical Engineering
Swanson School of Engineering > Chemical and Petroleum Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: modeling, cancer, coagulation, machine learning, control theory, ode
Date Deposited: 19 Jan 2023 19:20
Last Modified: 19 Jan 2023 19:20


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