Evidence map›Paper›PMID 31494942›Full record

ArticleMedical physics2019

An interventional image-guided microdevice implantation and retrieval method for in-vivo drug response assessment.

Sharath K Bhagavatula, Kunj Upadhyaya, Brendyn J Miller, Patrick Bursch, Alex Lammers, Michael J Cima, Stuart G Silverman, Oliver Jonas

Abstract read
In one paragraph

Article in Medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Review
  6. Spheroids and organoids derived from colorectal cancer as tools forExploration of targeted anti-tumor therapy · 2024
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
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

8 authors.

Sharath K BhagavatulaDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Kunj UpadhyayaDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Brendyn J MillerKoch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main Street, Boston, MA, 02139, USA.
Patrick BurschKoch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main Street, Boston, MA, 02139, USA.
Alex LammersDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Michael J CimaKoch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, 500 Main Street, Boston, MA, 02139, USA.
Stuart G SilvermanDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.
Oliver JonasDepartment of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA.

Funding

Pilot study of an implantable microdevice to test multiple drug responses in prostate cancer patientsR01CA232174 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI HATA, NOBUHIKO, JONAS, OLIVER · 2019 to 2023
$3.4M
Implantable microdevices with integrated optical imaging for high-throughput in situ tumor response and drug sensitivity measurementR37CA224144 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI JONAS, OLIVER · 2018 to 2023
$2.4M
Advanced development and validation of microdevices for high-throughput in situ drug sensitivity testing in tumorsR33CA223904 · NCI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI CIMA, MICHAEL J · 2018 to 2020
$1.2M
Clinical Evaluation of an Implantable Lab-in-a-patient microdevice that measures in-situ response to therapies in advanced ovarian cancerR21CA216796 · NCI · BRIGHAM AND WOMEN'S HOSPITAL · PI JONAS, OLIVER · 2018 to 2020
$399k
NCI NIH HHS R01 CA232174NCI NIH HHS R21 CA216796NCI NIH HHS R33 CA223904NCI NIH HHS R37 CA224144
6 · The paper itself

Abstract

purposeRecently developed implantable microdevices can perform multi-drug response assessment of cancer drugs in-vivo, with potential to develop highly optimized personalized cancer treatment strategies. However, minimally invasive/interventional image-guided methods of in-vivo microdevice implantation, securement, and retrieval are needed for broad clinical translation. Here we demonstrate proof-of-concept of an interventional microdevice implantation and retrieval method for personalized drug response assessment, using ex-vivo phantom, ex-vivo tissue, and in-vivo murine models.

methodsA method for minimally-invasive microdevice implantation and retrieval was developed, by which a custom-prototyped 6 mm retrievable microdevice can be implanted into a live tumor, deliver drugs into 10 discrete regions of adjacent tissue, and retrieved along with the adjacent drug-exposed tissue with a custom-prototyped retrieval needle device to allow in-vivo multi-drug response assessment. Computed tomography (CT) and ultrasound (US)-guided minimally invasive microdevice implantation and retrieval were tested in ex-vivo phantom and tissue models. Successful retrieval was defined as retrieval of the microdevice and adjacent core phantom/tissue sample containing at least 4/10 drug delivery sites. Subsequently, 10 implantation and retrieval trials in phantom models were performed using bi-axial and tri-axial retrieval needles; success rates were calculated and compared using a two-proportion z-test and the number of successfully retrieved drug release sites per microdevice was calculated and compared using a one-tailed independent t-test. Finally, five microdevices, each containing ten reservoirs preloaded with chemotherapy agent Doxorubicin, were implanted into mouse tumors in-vivo, secured for 24-h during drug release, and microdevice/tissue retrieval was performed under ultrasound guidance. Fluorescence microscopy of the retrieved tissue was used to confirm drug delivery and apoptosis staining assessed in-vivo tissue response; correlation of drug release and apoptosis staining were used to assess in-vivo drug efficacy.

resultsImage-guided microdevice implantation and retrieval were successful in ex-vivo phantom and tissue models with both US and CT guidance. Bi-axial retrieval success rate was significantly higher than triaxial retrieval in ex-vivo phantom trials (90% vs 50%, z = 1.95, P = 0.026), and had nonsignificantly higher number of retrieved drug-release sites per microdevice (8.3 vs 7.0, t = 1.37, P = 0.097). Bi-axial retrieval was successful in all five in-vivo mouse tumor models, and allowed in-vivo drug response assessment at up to ten discrete drug delivery sites per microdevice. An average of 6.8/10 discrete tumor sites containing micro-doses of delivered drug were retrieved per in-vivo attempt (min 5, max 10, std 1.93). Tissue regions of drug delivery, as assessed with fluorescent Doxorubicin drug signal, correlated with regions of apoptosis staining in all in-vivo models, indicating drug efficacy. No bleeding, microdevice migration, or other complications were noted during implantation, 24-h observation, or retrieval.

conclusionsThe demonstrated image-guided minimally invasive microdevice implantation and retrieval method is similar to routine outpatient biopsy procedures, obviates the need for surgery, and can be performed at varying depths under CT and/or US guidance. There is potential for this method to enable clinical translation of in-vivo personalized drug response assessment/prediction in a much larger number of patients than currently possible.

Indexed as

Phantoms, ImagingProstheses and ImplantsHumansMicrotechnologyPrecision MedicineSurgery, Computer-AssistedTreatment Outcomeimplantable microdeviceinterventional oncologyin-vivo drug screeningpersonalized medicine

Identifiers

PMID31494942
PMCPMC7412767

What OpenQuestion holds

Textmetadata
LicenceTDM
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.