2. Academic Validation
  3. Mechanistic Language Modeling and Oxygenated 3D Screening Reveal Berberine and Enzalutamide Synergy in Resistant Prostate Cancer

Mechanistic Language Modeling and Oxygenated 3D Screening Reveal Berberine and Enzalutamide Synergy in Resistant Prostate Cancer

  • bioRxiv. 2026 Jan 26:2026.01.24.701539. doi: 10.64898/2026.01.24.701539.
Chih-Hui Lo 1 2,  Katie Shi 1,  Lina Kafadarian 1,  Alexandra Bermudez 1 3,  Johnny Diaz 4,  Liam Edwards 3,  Yunqi Hong 5,  Ziyi Chen 6,  Hyeonji Hwang 6,  Weihong Yan 7,  Alan Levinson 1,  Robert Damoiseaux 1 6 8 9 10,  Cho-Jui Hsieh 5,  Tanya Stoyanova 6 10 11,  Andrew S Goldstein 4 9 10 11,  Neil Y C Lin 1 3 8 9 10 11 12
Affiliations

Affiliations

  • 1 Bioengineering Department, University of California Los Angeles, Los Angeles, CA, USA.
  • 2 College of Pharmacy, National Defense Medical University, Taipei, Taiwan.
  • 3 Mechanical and Aerospace Engineering Department, University of California Los Angeles, Los Angeles, CA, USA.
  • 4 Department of Molecular, Cell, and Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA.
  • 5 Computer Science Department, University of California Los Angeles, Los Angeles, CA, USA.
  • 6 Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, USA.
  • 7 Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA.
  • 8 California NanoSystems Institute, University of California Los Angeles, Los Angeles, CA, USA.
  • 9 Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles, Los Angeles, CA, USA.
  • 10 Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA.
  • 11 Department of Urology, University of California Los Angeles, Los Angeles, CA, USA.
  • 12 Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA, USA.
PMID: 41659618 DOI: 10.64898/2026.01.24.701539
Abstract

Resistance to Androgen Receptor inhibitors remains a primary challenge in prostate Cancer treatment, yet identifying synergistic co-therapies is hindered by immense combinatorial search spaces and the limited interpretability of predictive computation models. Here, we developed an integrated discovery-validation axis coupling knowledge-augmented large language models with oxygen-supplemented 3D spheroid assays. By leveraging inherent model stochasticity, our framework measures the degree of consensus across independent predictions to establish a formal metric for predictive accuracy. This principle enables high-throughput assessment of complex signaling crosstalk, yielding mechanistic rationales for all predictions and defining a high-confidence zone that minimizes experimental attrition. Utilizing this approach to screen 3,592 natural products, we identified a previously unrecognized synergy between berberine and enzalutamide that re-sensitizes resistant cells. Validation confirms that berberine perturbs the PI3K/Akt/mTOR and AMPK axes, a finding consistent with the mechanistic rationales computationally derived by the framework. Integrating interpretable AI with physiologically relevant 3D screening provides a scalable methodology for the rational discovery of synergistic therapies.

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