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Dynamic Boolean modeling of molecular and cellular interactions in psoriasis predicts drug target candidates

Eirini Tsirvouli1,2, Vincent Noël3,4,5, Åsmund Flobak2,6,7

  • 1Department of Biology, Norwegian University of Science and Technology, 7034 Trondheim, Norway.

Iscience
|February 2, 2024
PubMed

Insights

This study introduces a computational model for psoriasis, revealing that targeting neutrophil activation alongside prostaglandin E2 (PGE2) or STAT3 may be as effective as interleukin-17 (IL-17) inhibition for psoriasis treatment.

Area of Science:

  • Immunology
  • Computational Biology
  • Dermatology

Background:

  • Psoriasis involves complex interactions between keratinocytes and immune cells, causing inflammation and abnormal skin cell cycles.
  • Current psoriasis treatments vary in effectiveness and do not offer a cure, necessitating deeper understanding of disease mechanisms.

Purpose of the Study:

  • To develop a computational model for simulating psoriasis dynamics.
  • To identify potential new therapeutic targets by analyzing intercellular and intracellular signaling pathways.

Main Methods:

  • A Boolean multiscale population model was created, integrating discrete logical formalism with population dynamics.
  • Simulations and network analysis were performed to predict the impact of targeting specific cellular pathways.

Main Results:

  • Model predictions indicate that inhibiting neutrophil activation combined with prostaglandin E2 (PGE2) or STAT3 inhibition shows therapeutic promise.
  • These combined strategies appear comparable in efficacy to interleukin-17 (IL-17) inhibition, a current effective treatment.

Conclusions:

  • Complex intercellular and intracellular interactions are crucial in psoriasis pathogenesis.
  • Computational modeling is a valuable tool for identifying novel drug targets in complex diseases like psoriasis.
Keywords:
DiseaseDrugsMedicine

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