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.

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Nov 18th, 10:00 AM Nov 18th, 11:00 AM

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.