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A new approach to addressing two problems in pharmacokinetics and pharmacodynamics using machine learning

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dc.contributor.authorHabib, Sohaib, author
dc.contributor.authorReisfeld, Brad, advisor
dc.contributor.authorMunsky, Brian, committee member
dc.contributor.authorShipman, Patrick, committee member
dc.date.accessioned2020-09-07T10:08:30Z
dc.date.available2021-09-02T10:08:30Z
dc.date.issued2020
dc.description.abstractIn this work, machine learning was applied to develop solutions for two problems related to drug pharmacokinetics (PK) and pharmacodynamics (PD). The first problem was finding a way to easily predict important pharmacological measures accurately representative of those from simulation results computed via a sophisticated model for drug absorption via oral dosing. This model (OpenCAT: Open source Compartmental And Transit model) comprises a system of differential equations describing the absorption of drugs into the gastrointestinal tract, including such factors as drug dissolution and spatially-distributed absorption, metabolism, and transport. For this problem, a machine learning framework was built to develop a self-contained random forest representation of the model predictions that could be queried for critical PK parameters such as maximum plasma concentration (Cmax), time at which the maximum concentration occurs (tmax), and the area under the concentration-time curve (AUC). The random-forest representation was able to generate predictions for the targeted PK parameters close to the solution of the original OpenCAT model over a wide range of drug characteristics. The second problem involved predicting the pharmacodynamics (cholinesterase reactivation) of antidotes for nerve agents. In this case, a machine learning framework was built to use experimental data and corresponding theoretically-derived chemical descriptors to predict the pharmacodynamics of new candidate antidotes against both tested and untested nerve agents. Overall, this project has demonstrated the utility of machine learning approaches in the fields of drug pharmacokinetics and pharmacodynamics.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierHabiballah_colostate_0053N_16116.pdf
dc.identifier.urihttps://hdl.handle.net/10217/212005
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectnerve agent
dc.subjectpharmacology
dc.subjectXGboost
dc.subjectPBPK models
dc.subjectmachine learning
dc.subjectrandom forest
dc.titleA new approach to addressing two problems in pharmacokinetics and pharmacodynamics using machine learning
dc.typeText
dcterms.embargo.expires2021-09-02
dcterms.embargo.terms2021-09-02
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineChemical and Biological Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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