# Automatic Control of Propanidid and Propofol Anesthesia-Induced Unconsciousness

> **NIH NIH F32** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $68,010

## Abstract

Project Summary/Abstract
Current clinical practice of anesthesiology involves administering anesthetics using standard dosing
guidelines, monitoring physiological responses during administration, and adjusting the dose
depending on the patient responses. Importantly, direct monitoring of the brain is not part of standard
anesthesiology practice, despite the desired effect taking place in the brain. In cases where the brain
is directly monitored, proprietary, drug-independent, non-personalized depth of unconsciousness
indices are commonly used to infer conscious state from electroencephalography (EEG) signals. This
may result in overdosing, as there are significant inter-individual differences in neurological response
to identical doses of anesthetics and signatures of anesthesia vary significantly with age or with choice
of anesthetic. Consequently, older patients are often given more anesthetic than necessary and are at
high risk for developing post-operative cognitive dysfunction (POCD) and delirium, which may last up
to several months. The prevalence of surgical procedures on older patients will rise as the population
ages, necessitating new approaches for ensuring safe, personalized delivery of anesthetics that
ensure unconsciousness, but also safety and return to normal cognitive function following surgery.
This project seeks to develop closed-loop control of general anesthesia in humans as a means of
personalizing anesthesia care. Aim 1 of this project seeks to use machine learning to develop a drug-
specific marker of depth of unconsciousness that reflects the varying signatures of unconsciousness
with age and may be understood clinically in terms of brain function. Aim 2 of this project is to develop
models and nonlinear model predictive control (MPC) algorithms for regulating depth of
unconsciousness during general anesthesia. MPC is a control scheme that may be easily modified to
incorporate clinical safety features and has been used extensively in medical control systems. To
tightly regulate depth of unconsciousness, fast actuation by the anesthetic is critical. Aim 3 of this
project seeks to characterize the effect of propanidid, a fast-acting anesthetic currently used in clinical
practice in Mexico. The culmination of Aim 2 will result in designing clinical trials for the first closed-
loop anesthetic control and the culmination of Aim 3 will result in designing a clinical pharmacokinetic
study of propanidid. The results of this project have the potential to transform clinical practice of
anesthesia and allow individualized anesthesia care. By direct EEG monitoring and control of brain
state rather than secondary markers, the personalized treatment developed by this project should help
reduce overdosing of senior surgical patients and reduce incidence of POCD and delirium.

## Key facts

- **NIH application ID:** 10083163
- **Project number:** 5F32AG064886-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** John Hans Abel
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $68,010
- **Award type:** 5
- **Project period:** 2019-09-01 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10083163

## Citation

> US National Institutes of Health, RePORTER application 10083163, Automatic Control of Propanidid and Propofol Anesthesia-Induced Unconsciousness (5F32AG064886-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10083163. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*