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Bias translation: The final frontier?

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Biased G protein-coupled receptor (GPCR) signaling is common in drug discovery. Translating in vitro bias to in vivo effects remains challenging for predicting therapeutic value.

Keywords:
biased signallingdrug discoveryreceptor agonism

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Area of Science:

  • Pharmacology
  • Biochemistry
  • Drug Discovery

Background:

  • Biased signaling is an inherent property of G protein-coupled receptor (GPCR) allosteric modulation.
  • This phenomenon is expected from both natural and synthetic agonists and is frequently observed in agonist discovery projects.
  • Biased signaling can be a beneficial or detrimental characteristic of potential drug candidates.

Purpose of the Study:

  • To review the challenges in translating in vitro detected biased GPCR signaling to in vivo systems.
  • To discuss the difficulties in predicting the therapeutic value of biased signaling due to translation failures.
  • To explore novel strategies for improving the translation of biased signaling for therapeutic exploitation.

Main Methods:

  • Review of existing literature on GPCR signaling and biased agonism.
  • Analysis of the complexities in translating in vitro findings to in vivo pharmacological outcomes.
  • Discussion of current and emerging methodologies for assessing and predicting in vivo efficacy of biased ligands.

Main Results:

  • Biased signaling is a ubiquitous feature of GPCRs, arising from their allosteric function.
  • The translation of in vitro identified biased signaling to in vivo efficacy is problematic and hinders therapeutic prediction.
  • Current methods face challenges in accurately predicting the in vivo consequences of biased signaling observed in vitro.

Conclusions:

  • Biased signaling is an integral aspect of GPCR pharmacology that must be considered in drug development.
  • Overcoming the in vitro to in vivo translation gap is crucial for harnessing the therapeutic potential of biased GPCR agonists.
  • Further research into novel predictive models and experimental approaches is needed to effectively exploit biased signaling mechanisms.