Evidence map›Paper›PMID 36225597›Full record

ReviewFrontiers in bioengineering and biotechnology2022

Biomarker-driven feedback control of synthetic biology systems for next-generation personalized medicine.

Bozhidar-Adrian Stefanov, Martin Fussenegger

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.8field-weighted citation impact, top 14% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

9 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. Traceless Regulation of Genetic Circuitry.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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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

2 authors at 2 institutions in 1 country.

Bozhidar-Adrian StefanovETH Zürich, Department of Biosystems Science and Engineering, Basel, Switzerland.
Martin FusseneggerETH Zürich, Department of Biosystems Science and Engineering, Basel, Switzerland.
ETH Zurich · CHUniversity of Basel · CH

Funding

European Research Council 785800
6 · The paper itself

Abstract

Many current clinical therapies for chronic diseases involve administration of drugs using dosage and bioavailability parameters estimated for a generalized population. This standard approach carries the risk of under dosing, which may result in ineffective treatment, or overdosing, which may cause undesirable side effects. Consequently, maintaining a drug concentration in the therapeutic window often requires frequent monitoring, adversely affecting the patient's quality of life. In contrast, endogenous biosystems have evolved finely tuned feedback control loops that govern the physiological functions of the body based on multiple input parameters. To provide personalized treatment for chronic diseases, therefore, we require synthetic systems that can similarly generate a calibrated therapeutic response. Such engineered autonomous closed-loop devices should incorporate a sensor that actively tracks and evaluates the disease severity based on one or more biomarkers, as well as components that utilize these molecular inputs to bio compute and deliver the appropriate level of therapeutic output. Here, we review recent advances in applications of the closed-loop design principle in biomedical implants for treating severe and chronic diseases, highlighting translational studies of cellular therapies. We describe the engineering principles and components of closed-loop therapeutic devices, and discuss their potential to become a key pillar of personalized medicine.

Indexed as

cell-based therapiesgene regulation and expressiongene switchespersonalised medicinesynthetic biology

Identifiers

PMID36225597
PMCPMC9548536
OpenAlexW4297235843

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.