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
  • 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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