Evidence map›Paper›PMID 38169893›Full record

ReviewiScience2024

Designing clinical trials for patients who are not average.

Thomas E Yankeelov, David A Hormuth, Ernesto A B F Lima, Guillermo Lorenzo, Chengyue Wu, Lois C Okereke, Gaiane M Rauch, Aradhana M Venkatesan, Caroline Chung

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

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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

9 authors.

Thomas E YankeelovDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
David A HormuthOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.
Ernesto A B F LimaOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.
Guillermo LorenzoOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.
Chengyue WuOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.
Lois C OkerekeOden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.
Gaiane M RauchDepartment of Abdominal Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
Aradhana M VenkatesanDepartment of Abdominal Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
Caroline ChungDepartment of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.

Funding

Imaging-based tumor forecasting to predict brain tumor progression and response to therapyR01CA260003 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI QUARLES, CHRISTOPHER CHAD, YANKEELOV, THOMAS E · 2022 to 2025
$3.2M
INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCERU24CA226110 · NCI · BAYLOR COLLEGE OF MEDICINE · PI LEWIS, MICHAEL T., RUBIN, DANIEL L · 2019 to 2023
$3.2M
Clinical Development of Rhenium Nanoliposomes (RNL186) for GlioblastomaR01CA235800 · NCI · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI BRENNER, ANDREW JACOB · 2019 to 2023
$3.0M
Using label-free Raman microscopy to predict therapeutic resistance of TNBC cellsU01CA253540 · NCI · UNIVERSITY OF TEXAS AT AUSTIN · PI BROCK, AMY, YANKEELOV, THOMAS E · 2020 to 2024
$2.1M
Mathematical modeling and molecular imaging to maximize response while minimizing toxicities from systemic therapies in preclinical models of breast cancerR01CA276540 · NCI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI SORACE, ANNA C, YANKEELOV, THOMAS E · 2023 to 2025
$1.4M
NCI NIH HHS R01 CA235800NCI NIH HHS R01 CA260003NCI NIH HHS R01 CA276540NCI NIH HHS U01 CA253540NCI NIH HHS U24 CA226110
6 · The paper itself

Abstract

The heterogeneity inherent in cancer means that even a successful clinical trial merely results in a therapeutic regimen that achieves, on average, a positive result only in a subset of patients. The only way to optimize an intervention for an individual patient is to reframe their treatment as their own, personalized trial. Toward this goal, we formulate a computational framework for performing personalized trials that rely on four mathematical techniques. First, mathematical models that can be calibrated with patient-specific data to make accurate predictions of response. Second, digital twins built on these models capable of simulating the effects of interventions. Third, optimal control theory applied to the digital twins to optimize outcomes. Fourth, data assimilation to continually update and refine predictions in response to therapeutic interventions. In this perspective, we describe each of these techniques, quantify their "state of readiness", and identify use cases for personalized clinical trials.

Indexed as

Health sciences

Identifiers

PMID38169893
PMCPMC10758956

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.