Hermann B. Frieboes, Ph.D.

Education:

Ph.D., Biomedical Engineering, University of California, Irvine
Integrated experimental and mathematical modeling of cancer growth, vascularization, and drug treatment

M.S., Computer Engineering, University of California, Irvine
Algorithms, real-time embedded systems, and microprocessor architecture

Current Positions:

Professor, Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville
Associate Faculty, Department of Pharmacology/Toxicology, School of Medicine, University of Louisville
Member, Experimental Therapeutics & Diagnostics Program, Brown Cancer Center
Member, Center for Predictive Medicine, University of Louisville

Contact Information:

Lutz Hall, Room 419
University of Louisville
Louisville, KY 40292, USA
Phone 502-852-3302
Fax 502-852-6806
Email: hbfrie01@louisville.edu  [hbfrie01 (at) louisville (dot) edu]  

Research Description:

Our laboratory pursues an improved understanding of disease progression and response to therapy by applying principles from engineering and the physical sciences, including complex systems analysis, mathematical modeling, computational simulation, machine learning, and artificial intelligence.  The focus is to complement biological and clinical expertise to link disease progression, drug delivery, and treatment response across spatio-temporal scales spanning from the omic to the organ.  By integrating clinical and pre-clinical data, the ultimate goal is to customize treatment to patient-specific conditions.  Major projects in the laboratory have advanced techniques to evaluate chemotherapy, nanotherapy, and immunotherapy in cancer and infectious disease.  We have evaluated drug delivery via novel nanoparticles, nanofibers, and 3D-printed constructs.  Recently, we developed a workflow to analyze metabolomics data from fresh biopsied tissue, and showed that, in principle, the patient response and survival can be predicted.

The work includes scientific contributions to the following:

  • Development and characterization of active agent transport and delivery platforms, including fibers, bioprints, and nanovector devices
  • Clinical data-driven modeling and analysis for disease diagnosis and prognosis
  • Mathematical modeling and computational simulation to characterize disease progression and response to treatment
  • Multiscale linking from omic to tissue-scale phenotype during disease progression
  • Modeling and simulation of cancer nanotherapy and immunotherapy.

Literature Cited: 

  1. DA Goodin, E Chau, J Zheng, C O’Connell, A Tiwari, Y Xu, S-H Chen, B Godin*, HB Frieboes*. Characterization of the Breast Cancer Liver Metastasis Microenvironment via Machine Learning Analysis of the Primary Tumor Microenvironment. Cancer Res Comm 2024; 4(10):2846-2857. PMID: 39373616. PMCID: PMC11525956
  2. OS Cooney (1), DA Goodin (1), TJ Mouw, RCG Martin, HB Frieboes. Intra-abdominal temperature variation during hyperthermic intraperitoneal chemotherapy evaluated via computational fluid dynamics modeling. J Gastrointestinal Oncol 2024; 15(4):1847-1860. PMID: 39279970. PMCID: PMC11399869
  3. A Craig, HB Frieboes, PA Videira. Advancing Cancer Immunotherapy: From Innovative Preclinical Models to Clinical Insights. Invited editorial for The Cancer Immunotherapy Collection, Scientific Reports 2024; 14(1):1205. PMID: 38216668. PMCID: PMC10786836
  4. HA Miller, Y. Zhang, BR Smith*, HB Frieboes*. Anti-tumor effect of pH-sensitive drug-loaded nanoparticles optimized via an integrated computational/experimental approach. Nanoscale 2024; 16(4):1999-2011. PMID: 38193595. DOI: 10.1039/d3nr06414j
  5. HA Miller, A Tran, KS LyBarger, HB Frieboes. A clinical marker-based modeling framework to preoperatively predict lymph node and vascular space involvement in endometrial cancer patients. Eur J Surg Oncology 2024; 50(1):107309. PMID: 38056021. DOI: 10.1016/j.ejso.2023.107309
  6. DM. Miller, K Yadanapudi, V Rai, SN Rai, J Chen, HB Frieboes, A Masters, A McCallum, B Williams. Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach. Am J Med Sci 2023; 366(3):185-198. PMID: 37330006. DOI: 10.1016/j.amjms.2023.06.010
  7. ME Baxter (1), HA Miller (1), J Chen, BJ Williams*, HB Frieboes*. Metabolomic differentiation of tumor core vs. edge in glioma. Neurosurgical Focus 2023; 54(6):E4. PMID: 37283447. DOI: 10.3171/2023.3.FOCUS2379
  8. DA Goodin, HB Frieboes. Evaluation of innate and adaptive immune system interactions in the tumor microenvironment via a 3D continuum model J Theor Biol 2023 559:111383. PMID: 36539112. DOI: 10.1016/j.jtbi.2022.111383
  9. HA Miller, DM Miller, V van Berkel, HB Frieboes. Evaluation of lung cancer patient response to first-line chemotherapy by integration of tumor core biopsy metabolomics with multiscale modeling. ABME 2023 51(4):820-832. PMID: 36224485. PMCID: PMC10023290
  10. AN Mueller, S Morrisey, HA Miller, X Hu, R Kumar, PT Ngo, J Yan*, HB Frieboes*. Prediction of lung cancer immunotherapy response via machine learning analysis of immune cell lineage and surface markers.  Cancer Biomarkers 2022; 34(4):681-692. doi: 10.3233/CBM-210529.  PMID: 35662108
  11. DA Goodin, E Chau, A Tiwari, B Godin*, HB Frieboes*. Multiple breast cancer liver metastases response to macrophage-delivered nanotherapy evaluated via a 3D continuum model Immunology 2022; 169(2):132-140. PMID: 36465031. DOI: 10.1111/imm.13615
  12. KS LyBarger, HA Miller, HB Frieboes. CA125 as a predictor of endometrial cancer lymphovascular space invasion and lymph node metastasis for risk stratification in the preoperative setting. Scientific Reports 2022;12(1):19783. PMID: 36396713.  PMCID: PMC9671890
  13. HA Miller, V van Berkel, HB Frieboes. Lung cancer survival prediction and biomarker identification with an ensemble machine-learning analysis of tumor core biopsy metabolomic data. Metabolomics 2022; 18(8):57. PMID: 35857204.  PMCID: PMC9737952
  14. HA Miller, S Rai, X Yin, X Zhang, JA Chesney, V van Berkel, HB Frieboes.  Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival. Metabolomics 2022; 18(5):31 PMID: 35567637.  PMCID: PMC9724684
  15. HA Miller, J Lowengrub, HB Frieboes. Modeling of tumor growth with input from patient-specific metabolomic data.  ABME 2022; 50(3):314-329 PMID: 35083584.  PMCID: PMC9743982.
  16. J Chen, H Lee, P Schmitt, CJ Joy, DM Miller, BJ Williams, EL Bearer, HB Frieboes. Bioengineered Models to Study Microenvironmental Regulation of Glioblastoma Metabolism  J Neuropathol Exp Neurol 2021; 80(11):1012–1023. PMID: 34524448.  PMCID: PMC9432143 
  17. D Goodin, HB Frieboes. Simulation of 3D centimeter-scale tissue via a distributed computing solution of a mixture model of tumor growth. Computers in Biology and Medicine 2021; 134:104507. PMID: 34157612.  PMCID: PMC8277490
  18. HA Miller, Y Xinmin, .SA Smith, X Hu, X Zhang, J Yan, DM Miller, V van Berkel, HB Frieboes. Evaluation of disease staging and chemotherapeutic response in non-small cell lung cancer from patient-tissue derived metabolomics data. Lung Cancer 2021; 156:20-30. PMID: 33882406. PMCID: PMC8138715
  19. HA Miller, R Emman, CM Lynch, S Bockhorst, HB Frieboes. Discrepancies in metabolomic biomarker identification from patient-derived lung cancer revealed by variation in data pre-treatment and imputation methods.  Metabolomics 2021; 17(4):37. PMID: 33772663. PMCID: PMC8138701
  20. Y Wang, E Broding, K Nishii, HB Frieboes, S Mumenthaler, JL Sparks, P Macklin. Impact of tumor-parenchyma biomechanics on liver metastatic progression: a multimodel approach. Scientific Reports 2021; 11(1):1710. PMID: 33462259. PMCID: PMC7813881
  21. LT Curtis, S Sebens, HB Frieboes. Modeling of tumor response to macrophage and T lymphocyte interactions in the liver metastatic microenvironment. Cancer Immunology Immunotherapy 2021; 70(5):1475-1488. PMID: 33180183. PMCID: PMC10992133

PubMed Information