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Updated: Mar 29, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
Published on: December 9, 2015
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.
Abstract:
Background: Point mutations to the androgen receptor (AR) ligand-binding domain (LBD) are becoming increasingly recognized as a mechanism of therapeutic resistance in castration resistant prostate cancer (CRPC). The present review explores how point mutations induce molecular changes that contribute to the eventual treatment failure of androgen receptor pathway inhibitors (ARPIs) in CRPC. Methods: The PubMed database was searched for structural studies on the AR LBD. Eligible articles included molecular docking analysis and emphasized changes in ligand-receptor interactions after point mutation. Structural data were obtained from the Protein Data Bank (PDB) using the search parameters "Androgen receptor ligand binding domain", "Homo sapiens", and "X-ray diffraction". PDB files of wild-type and point mutant AR LBDs were accumulated for analysis. Results: A functional shift from inhibiting to activating AR has been documented for multiple ARPIs. Crystallography data and in silico evaluation have deciphered how changes in steric hindrance of the AF-2 domain contribute to ARPI loss of function. To combat therapeutic resistance, discovery efforts have begun to consider combination approaches of orthosteric and allosteric inhibitors, as well as compounds that target other AR domains. Although lead compounds have been identified, none have progressed into the clinic. Conclusions: Questions remain regarding the best approach for rationally designing new AR targeting therapeutics. Understanding how structural changes to the AR LBD lead to the failure of clinical therapeutics is a necessary step that should precede drug discovery campaigns. Moreover, computational modeling is a powerful tool that should be leveraged to streamline therapeutic development.
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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