Androgen Receptor Point Mutations: A Mechanism of Therapeutic Resistance and a Framework for Rational Drug Design

Avan Colah1, Sára Ferková2, Han Zhang1

  • 1Division of Pharmaceutical Sciences, School of Pharmacy, University of Wisconsin-Madison, Madison, WI 53705, USA.

Cancers
|March 28, 2026
PubMed

Insights

Point mutations in the androgen receptor (AR) ligand-binding domain (LBD) drive resistance to AR pathway inhibitors (ARPIs) in prostate cancer. Understanding these structural changes is key to developing new ARPIs for castration-resistant prostate cancer (CRPC).

Area of Science:

  • Oncology
  • Molecular Biology
  • Structural Biology

Background:

  • Therapeutic resistance in castration-resistant prostate cancer (CRPC) is increasingly linked to point mutations in the androgen receptor (AR) ligand-binding domain (LBD).
  • These mutations alter AR structure and function, leading to treatment failure of AR pathway inhibitors (ARPIs).

Purpose of the Study:

  • To review how AR LBD point mutations induce molecular changes that cause ARPI treatment failure in CRPC.
  • To explore structural insights into AR mutations and their impact on ARPI efficacy.

Main Methods:

  • Searched PubMed for structural studies on the AR LBD, focusing on molecular docking and ligand-receptor interactions.
  • Utilized Protein Data Bank (PDB) for structural data of wild-type and mutant AR LBDs obtained via X-ray diffraction.
  • Analyzed crystallography and in silico data to understand structural changes and their functional consequences.

Main Results:

  • AR LBD mutations can cause a functional shift, converting AR inhibitors into activators.
  • Changes in steric hindrance within the AF-2 domain of the AR LBD are implicated in ARPI loss of function.
  • New therapeutic strategies involve combination approaches and targeting other AR domains, but clinical progression is limited.

Conclusions:

  • Understanding AR LBD structural changes is crucial for rationally designing next-generation AR-targeting therapeutics.
  • Computational modeling should be employed to streamline drug discovery and development for overcoming therapeutic resistance.
  • Further research is needed to identify optimal strategies for new AR-targeted therapies in CRPC.

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