Evidence map›Paper›PMID 42835572›Full record

ArticleBioengineering & translational medicine2026

Adaptive feedback control for the high-precision management of in-tissue drug concentrations.

Murat Kaan Erdal, Julian Gerson, Tod E Kippin, Kevin W Plaxco, Joao P Hespanha

Abstract read
In one paragraph

Article in Bioengineering & translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Murat Kaan ErdalDepartment of Electrical and Computer Engineering University of California, Santa Barbara Santa Barbara California USA.ORCID https://orcid.org/0000-0002-6073-2380
Julian GersonDepartment of Psychological & Brain Sciences University of California, Santa Barbara Santa Barbara California USA.
Tod E KippinDepartment of Psychological & Brain Sciences University of California, Santa Barbara Santa Barbara California USA.
Kevin W PlaxcoDepartment of Chemistry and Biochemistry University of California, Santa Barbara Santa Barbara California USA.ORCID https://orcid.org/0000-0003-4772-8771
Joao P HespanhaDepartment of Electrical and Computer Engineering University of California, Santa Barbara Santa Barbara California USA.

Funding

Feedback controlled, ultra-high-precision drug deliveryR01AI145206 · NIAID · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI PLAXCO, KEVIN W · 2019 to 2023
$2.0M
NIAID NIH HHS R01 AI145206
6 · The paper itself

Abstract

The recent availability of a technology providing real-time, seconds-resolved in vivo drug concentration measurements has opened the door to performing fully autonomous, closed-loop feedback control over drug dosing. The controllers employed in prior demonstrations of such dosing, however, were designed and optimized using population-based pharmacokinetic models. In the face of individual pharmacokinetic variation (between subjects or even within a single subject over time as their physiology varies), these controllers must be set rather conservatively so as to avoid potentially dangerous overshooting. This, however, slows the speed with which they achieve the desired set point and opening the possibility of their nevertheless still overshooting if the response of a subject differs too much from that of the "average" subject. To address these issues, here we have developed an adaptive feedback-control system that, rather than employing population-pharmacokinetic information, instead uses real-time drug concentration measurements to individualize drug delivery to the specific subject, "on the fly." To achieve this, the system estimates the pharmacokinetics of the individual subject during the initial stages of the infusion, and then continuously updates this subject-specific pharmacokinetic model to maintain effective controller performance even in the face of physiological variations brought on, for example, by changing health status. Using this approach, we then demonstrate a feedback controller that rapidly (20-30 min) achieves and accurately (5%-12% root-mean-squared deviations, though this also includes sensor noise) maintains pre-defined concentrations of the anesthetic procaine in the ventricles of live rats, an application that, due to the delays associated with intracranial drug transport, represents a particular challenge for feedback-controlled intravenous drug delivery. Given the precision and accuracy it achieves in our rat animal model, we believe that the use of adaptive feedback control will ultimately enable safer, more precise drug dosing in humans.

Indexed as

biosensorelectrochemical aptamer‐based (EAB) sensorsfeedback‐controlled drug deliverypersonalized medicinetherapeutic drug monitoring

Identifiers

PMID42835572
PMCPMC13636143

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.