Evidence map›Paper›PMID 38802422›Full record

ArticleScientific reports2024

Mechanism-based organization of neural networks to emulate systems biology and pharmacology models.

John Mann, Hamed Meshkin, Joel Zirkle, Xiaomei Han, Bradlee Thrasher, Anik Chaturbedi, Ghazal Arabidarrehdor, Zhihua Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Advancing drug development with "Fit-for-Purpose" modeling informed approaches.Journal of pharmacokinetics and pharmacodynamics · 2025
    Review
  2. Challenges and opportunities in uncertainty quantification for healthcare and biological systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025
    Review
  3. Review
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.

John MannDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Hamed MeshkinDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Joel ZirkleDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Xiaomei HanDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Bradlee ThrasherDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Anik ChaturbediDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Ghazal ArabidarrehdorDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA.
Zhihua LiDivision of Applied Regulatory Science, Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, WO Bldg 64 Rm 2084, 10903 New Hampshire Ave, Silver Spring, MD, 20993, USA. Zhihua.li@fda.hhs.gov.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep learning neural networks are often described as black boxes, as it is difficult to trace model outputs back to model inputs due to a lack of clarity over the internal mechanisms. This is even true for those neural networks designed to emulate mechanistic models, which simply learn a mapping between the inputs and outputs of mechanistic models, ignoring the underlying processes. Using a mechanistic model studying the pharmacological interaction between opioids and naloxone as a proof-of-concept example, we demonstrated that by reorganizing the neural networks' layers to mimic the structure of the mechanistic model, it is possible to achieve better training rates and prediction accuracy relative to the previously proposed black-box neural networks, while maintaining the interpretability of the mechanistic simulations. Our framework can be used to emulate mechanistic models in a large parameter space and offers an example on the utility of increasing the interpretability of deep learning networks.

Indexed as

Drug DesignNeural Networks, ComputerPharmacologySystems BiologyAlgorithmsArtificial IntelligenceComputational BiologyDeep LearningMachine LearningPharmacokinetics

Identifiers

PMID38802422
PMCPMC11130269

What OpenQuestion holds

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LicenceCC BY
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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.