Evidence map›Paper›PMID 39149377›Full record

ArticlebioRxiv : the preprint server for biology2024

Computational modeling of drug response identifies mutant-specific constraints for dosing panRAF and MEK inhibitors in melanoma.

Andrew Goetz, Frances Shanahan, Logan Brooks, Eva Lin, Rana Mroue, Darlene Dela Cruz, Thomas Hunsaker, Bartosz Czech, Purushottam Dixit, Udi Segal and 3 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Andrew GoetzgRED Computational Sciences, Genentech, South San Francisco, CA, US.
Frances ShanahanDepartment of Discovery Oncology, Genentech, South San Francisco, CA, US.
Logan BrooksDepartment of Modeling and Simulation Clinical Pharmacology, Genentech, South San Francisco, CA, US.
Eva LinDepartment of Functional Genomics, Genentech, South San Francisco, CA, US.
Rana MroueDepartment of Discovery Oncology, Genentech, South San Francisco, CA, US.
Darlene Dela CruzDepartment of Translational Oncology, Genentech, South San Francisco, CA, US.
Thomas HunsakerDepartment of Translational Oncology, Genentech, South San Francisco, CA, US.
Bartosz CzechRoche Global IT Solution Centre, Roche, Warsaw, PO.
Purushottam DixitDepartment of Biomedical Engineering, Yale University, New Haven, CT, US.
Udi SegalDepartment of Translational Oncology, Genentech, South San Francisco, CA, US.
Scott MartinDepartment of Functional Genomics, Genentech, South San Francisco, CA, US.
Scott A FosterDepartment of Discovery Oncology, Genentech, South San Francisco, CA, US.
Luca GerosagRED Computational Sciences, Genentech, South San Francisco, CA, US.ORCID 0000-0001-6805-9410

Funding

MANET: Maximum Entropy Neural Networks for Mechanistic Modeling of Single Cell BehaviorR35GM142547 · NIGMS · YALE UNIVERSITY · PI DIXIT, PURUSHOTTAM · 2021 to 2025
$1.9M
NIGMS NIH HHS R35 GM142547
6 · The paper itself

Abstract

Purpose: This study explores the potential of preclinical Results: In a drug combination screen of 43 melanoma cell lines, we identified unique dosage landscapes of panRAF and MEK inhibitors for NRAS vs BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma. Computational modeling and molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated Conclusion: Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range.

Indexed as

adaptive resistancedrug combinationmechanistic modelprecision medicinesignal transductionsystems pharmacologytargeted therapy

Identifiers

PMID39149377
PMCPMC11326189

What OpenQuestion holds

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Registered trials

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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.