Presentation Title
Monte-Carlo Simulations of Drug Concentrations with Measurement and Process Noise
Faculty Mentor
Alona Kryshchenko
Start Date
18-11-2017 10:00 AM
End Date
18-11-2017 11:00 AM
Location
BSC-Ursa Minor 126
Session
Poster 1
Type of Presentation
Poster
Subject Area
physical_mathematical_sciences
Abstract
Pharmacokinetic models describe how a drug behaves once inside the human body. The use of these models is to achieve individualized therapy goals, or how much and how often a drug should be given, for a patient. This is done via the doctor’s assessment of a patient, clinical trials done previously with said drug, and careful monitoring of a patient during the treatment process. This is especially important when dealing with high-risk drugs that have narrow margins of safety. However, errors are inherent in these therapeutic processes and even in the models themselves. The two common sources of error associated with drug treatment and therapeutic processes are measurement and process error. The current widely used models only account for measurement noise, or measurement error specific to a Laboratory where samples are tested, but they do not account for process noise, the process errors such as errors in dosage amount, administration times and model misspecification. Our goal is to show that all errors need to be taken into account when designing therapeutic goals for a patient.
Using a Monte-Carlo simulation model, we design a system of linear stochastic differential equations centered around measuring drug concentration in the body and the rate it changes over time relative to dosage. We then implement white noise into our equations to account for process noise. We will discuss running the simulation across different scenarios where each of the relevant clinical factors is subject to its own error. The data given by each scenario will be analyzed using statistical inference methods and the results will be discussed.
Monte-Carlo Simulations of Drug Concentrations with Measurement and Process Noise
BSC-Ursa Minor 126
Pharmacokinetic models describe how a drug behaves once inside the human body. The use of these models is to achieve individualized therapy goals, or how much and how often a drug should be given, for a patient. This is done via the doctor’s assessment of a patient, clinical trials done previously with said drug, and careful monitoring of a patient during the treatment process. This is especially important when dealing with high-risk drugs that have narrow margins of safety. However, errors are inherent in these therapeutic processes and even in the models themselves. The two common sources of error associated with drug treatment and therapeutic processes are measurement and process error. The current widely used models only account for measurement noise, or measurement error specific to a Laboratory where samples are tested, but they do not account for process noise, the process errors such as errors in dosage amount, administration times and model misspecification. Our goal is to show that all errors need to be taken into account when designing therapeutic goals for a patient.
Using a Monte-Carlo simulation model, we design a system of linear stochastic differential equations centered around measuring drug concentration in the body and the rate it changes over time relative to dosage. We then implement white noise into our equations to account for process noise. We will discuss running the simulation across different scenarios where each of the relevant clinical factors is subject to its own error. The data given by each scenario will be analyzed using statistical inference methods and the results will be discussed.