Hermann B. Frieboes, Ph.D.


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:

Associate 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. Goodin DA, Frieboes HB.  Evaluation of innate and adaptive immune system interactions in the tumor microenvironment via a 3D continuum model.  Journal of Theoretical Biology 2023 Feb 21;559:111383.  doi: 10.1016/j.jtbi.2022.111383.  Epub 2022 Dec 17.  PMID: 36539112.
  2. Goodin DA, Chau E, Tiwari A, Godin B, Frieboes HB.  Multiple breast cancer liver metastases response to macrophage-delivered nanotherapy evaluated via a 3D continuum model.  Immunology 2022 Dec 5.  doi: 10.1111/imm.13615.  Epub ahead of print. PMID: 36465031.
  3. Shawn LyBarger K, Miller HA, Frieboes HB.  CA125 as a predictor of endometrial cancer lymphovascular space invasion and lymph node metastasis for risk stratification in the preoperative setting.  Scientific Reports 2022 Nov 17;12(1):19783.  doi: 10.1038/s41598-022-22026-1.  PMID: 36396713.  PMCID: PMC9671890.
  4. Miller HA, Miller DM, van Berkel VH, Frieboes HB.  Evaluation of lung cancer patient response to first-line chemotherapy by integration of tumor core biopsy metabolomics with multiscale modeling.  Annals of Biomedical Engineering 2023 Apr;51(4):820-832.  doi: 10.1007/s10439-022-03096-8.  Epub 2022 Oct 12.  PMID: 36224485.  PMCID: PMC10023290 (available on 2024-04-01).
  5. Miller HA, van Berkel VH, Frieboes HB.  Lung cancer survival prediction and biomarker identification with an ensemble machine learning analysis of tumor core biopsy metabolomic data.  Metabolomics 2022 Jul 20;18(8):57.  doi: 10.1007/s11306-022-01918-3 .  PMID: 35857204.  PMCID: PMC9737952 (available on 2023-07-20).
  6. Mueller AN, Morrisey S, Miller HA, Hu X, Kumar R, Ngo PT, Yan J, Frieboes HB.  Prediction of lung cancer immunotherapy response via machine learning analysis of immune cell lineage and surface markers.  CancerBiomarkers 2022;34(4):681-692.  doi: 10.3233/CBM-210529.  PMID: 35662108.
  7. Miller HA, Rai SN, Yin X, Zhang X, Chesney JA, van Berkel VH, Frieboes HB.  Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival.  Metabolomics 2022 May 14;18(5):31.  doi: 10.1007/s11306-022-01891-x.  PMID: 35567637.  PMCID: PMC9724684 (available on 2023-05-14).
  8. Miller HA, Lowengrub J, Frieboes HB.  Modeling of tumor growth with input from patient-specific metabolomic data.  Annals of Biomedical Engineering 2022 Mar;50(3):314-329.  doi: 10.1007/s10439-022-02904-5.  Epub 2022 Jan 26.  PMID: 35083584.  PMCID: PMC9743982.
  9. Chen J, Lee H, Schmitt P, Choy CJ, Miller DM, Williams BJ, Bearer EL, Frieboes HB. Bioengineered models to study microenvironmental regulation of glioblastoma metabolism.  Journal of Neuropathology & Experimental Neurology 2021 Nov 19;80(11):1012–1023.  doi: 10.1093/jnen/nlab092.  Epub 2021 Sep 15.  PMID: 34524448.  PMCID: PMC9432143 .
  10. Goodin DA, Frieboes HB.  Simulation of 3D centimeter-scale continuum tumor growth at sub-millimeter resolution via distributed computing.  Computers in Biology & Medicine 2021 Jul;134:104507.  doi: 10.1016/j.compbiomed.2021.104507.  Epub 2021 May 21.  PMID: 34157612.  PMCID: PMC8277490.

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