Predictive Modeling of Drug Response in Non-Hodgkin's Lymphoma

• Published in: PloS one Vol. 10; no. 6; p. e0129433
• Main Authors: Frieboes, Hermann B, Smith, Bryan R, Wang, Zhihui, Kotsuma, Masakatsu, Ito, Ken, Day, Armin, Cahill, Benjamin, Flinders, Colin, Mumenthaler, Shannon M, Mallick, Parag, Simbawa, Eman, Al-Fhaid, A S, Mahmoud, S R, Gambhir, Sanjiv S, Cristini, Vittorio
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.06.2015; Public Library of Science (PLoS)
• Subjects: Analysis; Angiogenesis; Animal experimentation; Animals; Antibiotics, Antineoplastic - pharmacology; Apoptosis; Bioengineering; Biology; Blood; Blood vessels; Blood volume; Cancer; Cancer therapies; Cell culture; Cell Survival - drug effects; Chemotherapy; Cytotoxicity; Doxorubicin - administration & dosage; Doxorubicin - pharmacology; Drug resistance; Drug Resistance, Neoplasm; Experimentation; Fibroblasts - drug effects; Hypoxia; In vivo methods and tests; Lung cancer; Lymphoma; Lymphoma, Non-Hodgkin - drug therapy; Mathematical models; Mathematics; Medical research; Medicine; Mice; Models, Theoretical; Morphology; Myc protein; Non-Hodgkin's lymphomas; p53 Protein; Parameter sensitivity; Pathology; Physiology; Science; Sensitivity analysis; Tumor cell lines; Tumor Cells, Cultured; Tumors; Xenograft Model Antitumor Assays; Saudi Arabia; Los Angeles California; United States > US; New Mexico
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0129433

We combine mathematical modeling with experiments in living mice to quantify the relative roles of intrinsic cellular vs. tissue-scale physiological contributors to chemotherapy drug resistance, which are difficult to understand solely through experimentation. Experiments in cell culture and in mice with drug-sensitive (Eµ-myc/Arf-/-) and drug-resistant (Eµ-myc/p53-/-) lymphoma cell lines were conducted to calibrate and validate a mechanistic mathematical model. Inputs to inform the model include tumor drug transport characteristics, such as blood volume fraction, average geometric mean blood vessel radius, drug diffusion penetration distance, and drug response in cell culture. Model results show that the drug response in mice, represented by the fraction of dead tumor volume, can be reliably predicted from these inputs. Hence, a proof-of-principle for predictive quantification of lymphoma drug therapy was established based on both cellular and tissue-scale physiological contributions. We further demonstrate that, if the in vitro cytotoxic response of a specific cancer cell line under chemotherapy is known, the model is then able to predict the treatment efficacy in vivo. Lastly, tissue blood volume fraction was determined to be the most sensitive model parameter and a primary contributor to drug resistance.