Evidence map›Paper›PMID 30723233›Full record

ArticleScientific reports2019

Prediction of Optimal Drug Schedules for Controlling Autophagy.

Afroza Shirin, Isaac S Klickstein, Song Feng, Yen Ting Lin, William S Hlavacek, Francesco Sorrentino

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Guideline
  2. Article
  3. Review
  4. Review
  5. Review
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Controlling network ensembles.Nature communications · 2021
    Article
  12. Article
  13. Article
  14. Article
  15. 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

6 authors.

Afroza ShirinMechanical Engineering Department, University of New Mexico, Albuquerque, NM, 87131, USA.
Isaac S KlicksteinMechanical Engineering Department, University of New Mexico, Albuquerque, NM, 87131, USA.
Song FengTheoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.ORCID 0000-0003-3983-9009
Yen Ting LinTheoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
William S HlavacekTheoretical Biology and Biophysics Group, Theoretical Division and Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. wish@lanl.gov.ORCID 0000-0003-4383-8711
Francesco SorrentinoMechanical Engineering Department, University of New Mexico, Albuquerque, NM, 87131, USA. fsorrent@unm.edu.

Funding

Computational Model of Autophagy-Mediated Survival in Chemoresistant Lung CancerR01CA197398 · NCI · VAN ANDEL RESEARCH INSTITUTE · PI HLAVACEK, WILLIAM S, MACKEIGAN, JEFFREY PAUL · 2015 to 2019
$2.5M
NCI NIH HHS R01 CA197398
6 · The paper itself

Abstract

The effects of molecularly targeted drug perturbations on cellular activities and fates are difficult to predict using intuition alone because of the complex behaviors of cellular regulatory networks. An approach to overcoming this problem is to develop mathematical models for predicting drug effects. Such an approach beckons for co-development of computational methods for extracting insights useful for guiding therapy selection and optimizing drug scheduling. Here, we present and evaluate a generalizable strategy for identifying drug dosing schedules that minimize the amount of drug needed to achieve sustained suppression or elevation of an important cellular activity/process, the recycling of cytoplasmic contents through (macro)autophagy. Therapeutic targeting of autophagy is currently being evaluated in diverse clinical trials but without the benefit of a control engineering perspective. Using a nonlinear ordinary differential equation (ODE) model that accounts for activating and inhibiting influences among protein and lipid kinases that regulate autophagy (MTORC1, ULK1, AMPK and VPS34) and methods guaranteed to find locally optimal control strategies, we find optimal drug dosing schedules (open-loop controllers) for each of six classes of drugs and drug pairs. Our approach is generalizable to designing monotherapy and multi therapy drug schedules that affect different cell signaling networks of interest.

Indexed as

Models, TheoreticalAllosteric RegulationAMP-Activated Protein KinasesAutophagosomesAutophagyAutophagy-Related Protein-1 HomologClass III Phosphatidylinositol 3-KinasesComputational BiologyDose-Response Relationship, DrugHumansIntracellular Signaling Peptides and ProteinsMechanistic Target of Rapamycin Complex 1Protein Kinase InhibitorsSignal TransductionAMP-Activated Protein KinasesAutophagy-Related Protein-1 HomologClass III Phosphatidylinositol 3-KinasesIntracellular Signaling Peptides and ProteinsMechanistic Target of Rapamycin Complex 1Protein Kinase InhibitorsULK1 protein, human

Identifiers

PMID30723233
PMCPMC6363771

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.