1
|
Jenner AL, Kelly W, Dallaston M, Araujo R, Parfitt I, Steinitz D, Pooladvand P, Kim PS, Wade SJ, Vine KL. Examining the efficacy of localised gemcitabine therapy for the treatment of pancreatic cancer using a hybrid agent-based model. PLoS Comput Biol 2023; 19:e1010104. [PMID: 36649330 PMCID: PMC9891514 DOI: 10.1371/journal.pcbi.1010104] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 02/01/2023] [Accepted: 12/21/2022] [Indexed: 01/18/2023] Open
Abstract
The prognosis for pancreatic ductal adenocarcinoma (PDAC) patients has not significantly improved in the past 3 decades, highlighting the need for more effective treatment approaches. Poor patient outcomes and lack of response to therapy can be attributed, in part, to a lack of uptake of perfusion of systemically administered chemotherapeutic drugs into the tumour. Wet-spun alginate fibres loaded with the chemotherapeutic agent gemcitabine have been developed as a potential tool for overcoming the barriers in delivery of systemically administrated drugs to the PDAC tumour microenvironment by delivering high concentrations of drug to the tumour directly over an extended period. While exciting, the practicality, safety, and effectiveness of these devices in a clinical setting requires further investigation. Furthermore, an in-depth assessment of the drug-release rate from these devices needs to be undertaken to determine whether an optimal release profile exists. Using a hybrid computational model (agent-based model and partial differential equation system), we developed a simulation of pancreatic tumour growth and response to treatment with gemcitabine loaded alginate fibres. The model was calibrated using in vitro and in vivo data and simulated using a finite volume method discretisation. We then used the model to compare different intratumoural implantation protocols and gemcitabine-release rates. In our model, the primary driver of pancreatic tumour growth was the rate of tumour cell division. We were able to demonstrate that intratumoural placement of gemcitabine loaded fibres was more effective than peritumoural placement. Additionally, we quantified the efficacy of different release profiles from the implanted fibres that have not yet been tested experimentally. Altogether, the model developed here is a tool that can be used to investigate other drug delivery devices to improve the arsenal of treatments available for PDAC and other difficult-to-treat cancers in the future.
Collapse
Affiliation(s)
- Adrianne L. Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- * E-mail:
| | - Wayne Kelly
- School of Computer Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Michael Dallaston
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Robyn Araujo
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Isobelle Parfitt
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Dominic Steinitz
- Tweag Software Innovation Lab, London, United Kingdom
- Kingston University, Kingston, United Kingdom
| | - Pantea Pooladvand
- School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia
| | - Peter S. Kim
- School of Mathematics and Statistics, University of Sydney, Sydney, New South Wales, Australia
| | - Samantha J. Wade
- Illawarra Health and Medical Research Institute, Wollongong, New South Wales, Australia
- School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, New South Wales, Australia
| | - Kara L. Vine
- Illawarra Health and Medical Research Institute, Wollongong, New South Wales, Australia
- School of Chemistry and Molecular Bioscience, University of Wollongong, Wollongong, New South Wales, Australia
| |
Collapse
|
2
|
Ferraro R, Ascione F, Dogra P, Cristini V, Guido S, Caserta S. Diffusion‐induced anisotropic cancer invasion: a novel experimental method based on tumour spheroids. AIChE J 2022. [DOI: 10.1002/aic.17678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Rosalia Ferraro
- Università degli Studi di Napoli Federico II Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale Naples Italy
- CEINGE Advanced Biotechnologies Naples Italy
| | - Flora Ascione
- Università degli Studi di Napoli Federico II Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale Naples Italy
| | - Prashant Dogra
- Mathematics in Medicine Program Houston Methodist Research Institute Houston Texas USA
- Department of Physiology and Biophysics Weill Cornell Medical College New York New York USA
| | - Vittorio Cristini
- Mathematics in Medicine Program Houston Methodist Research Institute Houston Texas USA
- Department of Imaging Physics University of Texas MD Anderson Cancer Center Houston Texas USA
- Physiology, Biophysics, and Systems Biology Program, Graduate School of Medical Sciences Weill Cornell Medicine New York New York USA
| | - Stefano Guido
- Università degli Studi di Napoli Federico II Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale Naples Italy
- CEINGE Advanced Biotechnologies Naples Italy
| | - Sergio Caserta
- Università degli Studi di Napoli Federico II Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale Naples Italy
- CEINGE Advanced Biotechnologies Naples Italy
| |
Collapse
|
3
|
Anaya DA, Dogra P, Wang Z, Haider M, Ehab J, Jeong DK, Ghayouri M, Lauwers GY, Thomas K, Kim R, Butner JD, Nizzero S, Ramírez JR, Plodinec M, Sidman RL, Cavenee WK, Pasqualini R, Arap W, Fleming JB, Cristini V. A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases. Cancers (Basel) 2021; 13:cancers13030444. [PMID: 33503971 PMCID: PMC7866038 DOI: 10.3390/cancers13030444] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 01/21/2021] [Indexed: 12/22/2022] Open
Abstract
Simple Summary It is known that drug transport barriers in the tumor determine drug concentration at the tumor site, causing disparity from the systemic (plasma) drug concentration. However, current clinical standard of care still bases dosage and treatment optimization on the systemic concentration of drugs. Here, we present a proof of concept observational cohort study to accurately estimate drug concentration at the tumor site from mathematical modeling using biologic, clinical, and imaging/perfusion data, and correlate it with outcome in colorectal cancer liver metastases. We demonstrate that drug concentration at the tumor site, not in systemic circulation, can be used as a credible biomarker for predicting chemotherapy outcome, and thus our mathematical modeling approach can be applied prospectively in the clinic to personalize treatment design to optimize outcome. Abstract Chemotherapy remains a primary treatment for metastatic cancer, with tumor response being the benchmark outcome marker. However, therapeutic response in cancer is unpredictable due to heterogeneity in drug delivery from systemic circulation to solid tumors. In this proof-of-concept study, we evaluated chemotherapy concentration at the tumor-site and its association with therapy response by applying a mathematical model. By using pre-treatment imaging, clinical and biologic variables, and chemotherapy regimen to inform the model, we estimated tumor-site chemotherapy concentration in patients with colorectal cancer liver metastases, who received treatment prior to surgical hepatic resection with curative-intent. The differential response to therapy in resected specimens, measured with the gold-standard Tumor Regression Grade (TRG; from 1, complete response to 5, no response) was examined, relative to the model predicted systemic and tumor-site chemotherapy concentrations. We found that the average calculated plasma concentration of the cytotoxic drug was essentially equivalent across patients exhibiting different TRGs, while the estimated tumor-site chemotherapeutic concentration (eTSCC) showed a quadratic decline from TRG = 1 to TRG = 5 (p < 0.001). The eTSCC was significantly lower than the observed plasma concentration and dropped by a factor of ~5 between patients with complete response (TRG = 1) and those with no response (TRG = 5), while the plasma concentration remained stable across TRG groups. TRG variations were driven and predicted by differences in tumor perfusion and eTSCC. If confirmed in carefully planned prospective studies, these findings will form the basis of a paradigm shift in the care of patients with potentially curable colorectal cancer and liver metastases.
Collapse
Affiliation(s)
- Daniel A. Anaya
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
- Correspondence: (D.A.A.); (V.C.); Tel.: +1-813-745-1432 (D.A.A.); +1-505-934-1813 (V.C.); Fax: +1-813-745-7229 (D.A.A.)
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Mintallah Haider
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
| | - Jasmina Ehab
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
| | - Daniel K. Jeong
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (D.K.J.); (M.G.); (G.Y.L.); (K.T.)
| | - Masoumeh Ghayouri
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (D.K.J.); (M.G.); (G.Y.L.); (K.T.)
| | - Gregory Y. Lauwers
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (D.K.J.); (M.G.); (G.Y.L.); (K.T.)
| | - Kerry Thomas
- Department of Diagnostic Imaging and Interventional Radiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (D.K.J.); (M.G.); (G.Y.L.); (K.T.)
| | - Richard Kim
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
| | - Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Sara Nizzero
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
| | - Marija Plodinec
- Biozentrum and the Swiss Nanoscience Institute & ARTIDIS AG, University of Basel, 4056 Basel, Switzerland;
| | - Richard L. Sidman
- Department of Neurology, Harvard Medical School, Boston, MA 02115, USA;
| | - Webster K. Cavenee
- Ludwig Institute for Cancer Research, University of California-San Diego, La Jolla, CA 92093, USA;
| | - Renata Pasqualini
- Rutgers Cancer Institute of New Jersey & Division of Cancer Biology, Department of Radiation Oncology, Rutgers New Jersey Medical School, Newark, NJ 07103, USA;
| | - Wadih Arap
- Rutgers Cancer Institute of New Jersey & Division of Hematology/Oncology, Department of Medicine Rutgers New Jersey Medical School, Newark, NJ 07103, USA;
| | - Jason B. Fleming
- Department of Gastrointestinal Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL 33612, USA; (M.H.); (J.E.); (R.K.); (J.B.F.)
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA; (P.D.); (Z.W.); (J.D.B.); (S.N.); (J.R.R.)
- Correspondence: (D.A.A.); (V.C.); Tel.: +1-813-745-1432 (D.A.A.); +1-505-934-1813 (V.C.); Fax: +1-813-745-7229 (D.A.A.)
| |
Collapse
|
4
|
Zaid M, Elganainy D, Dogra P, Dai A, Widmann L, Fernandes P, Wang Z, Pelaez MJ, Ramirez JR, Singhi AD, Dasyam AK, Brand RE, Park WG, Rahmanuddin S, Rosenthal MH, Wolpin BM, Khalaf N, Goel A, Von Hoff DD, Tamm EP, Maitra A, Cristini V, Koay EJ. Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma Exhibit Differential Growth and Metabolic Patterns in the Pre-Diagnostic Period: Implications for Early Detection. Front Oncol 2020; 10:596931. [PMID: 33344245 PMCID: PMC7738633 DOI: 10.3389/fonc.2020.596931] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 10/28/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Previously, we characterized subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed-tomography (CT) scans, whereby conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we hypothesized that these imaging-based subtypes would exhibit different growth-rates and distinctive metabolic effects in the period prior to PDAC diagnosis. MATERIALS AND METHODS Retrospectively, we evaluated 55 patients who developed PDAC as a second primary cancer and underwent serial pre-diagnostic (T0) and diagnostic (T1) CT-scans. We scored the PDAC tumors into high and low delta on T1 and, serially, obtained the biaxial measurements of the pancreatic lesions (T0-T1). We used the Gompertz-function to model the growth-kinetics and estimate the tumor growth-rate constant (α) which was used for tumor binary classification, followed by cross-validation of the classifier accuracy. We used maximum-likelihood estimation to estimate initiation-time from a single cell (10-6 mm3) to a 10 mm3 tumor mass. Finally, we serially quantified the subcutaneous-abdominal-fat (SAF), visceral-abdominal-fat (VAF), and muscles volumes (cm3) on CT-scans, and recorded the change in blood glucose (BG) levels. T-test, likelihood-ratio, Cox proportional-hazards, and Kaplan-Meier were used for statistical analysis and p-value <0.05 was considered significant. RESULTS Compared to high delta tumors, low delta tumors had significantly slower average growth-rate constants (0.024 month-1 vs. 0.088 month-1, p<0.0001) and longer average initiation-times (14 years vs. 5 years, p<0.0001). α demonstrated high accuracy (area under the curve (AUC)=0.85) in classifying the tumors into high and low delta, with an optimal cut-off of 0.034 month-1. Leave-one-out-cross-validation showed 80% accuracy in predicting the delta-class (AUC=0.84). High delta tumors exhibited accelerated SAF, VAF, and muscle wasting (p <0.001), and BG disturbance (p<0.01) compared to low delta tumors. Patients with low delta tumors had better PDAC-specific progression-free survival (log-rank, p<0.0001), earlier stage tumors (p=0.005), and higher likelihood to receive resection after PDAC diagnosis (p=0.008), compared to those with high delta tumors. CONCLUSION Imaging-based subtypes of PDAC exhibit distinct growth, metabolic, and clinical profiles during the pre-diagnostic period. Our results suggest that heterogeneous disease biology may be an important consideration in early detection strategies for PDAC.
Collapse
Affiliation(s)
- Mohamed Zaid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Dalia Elganainy
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, United States
| | - Annie Dai
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Lauren Widmann
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Pearl Fernandes
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, United States
| | - Maria J. Pelaez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, United States
| | - Javier R. Ramirez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, United States
| | - Aatur D. Singhi
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Anil K. Dasyam
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Randall E. Brand
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Walter G. Park
- Department of Medicine, Stanford University, Stanford, CA, United States
| | - Syed Rahmanuddin
- Department of Radiology, City of Hope, Duarte, CA, United States
| | - Michael H. Rosenthal
- Department of Radiology, Dana Farber Cancer Institute, Boston, MA, United States
| | - Brian M. Wolpin
- Department of Medical Oncology, Dana Farber Cancer Institute, Boston, MA, United States
| | - Natalia Khalaf
- Section of Gastroenterology and Hepatology, Department of Medicine, Baylor College of Medicine, Houston, TX, United States
| | - Ajay Goel
- Department of Molecular Diagnostics and Experimental Therapeutics, City of Hope, Duarte, CA, United States
| | - Daniel D. Von Hoff
- Molecular Medicine, Translational Genomics Research Institute, Phoenix, AZ, United States
| | - Eric P. Tamm
- Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Anirban Maitra
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX, United States
| | - Eugene J. Koay
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States,*Correspondence: Eugene J. Koay,
| |
Collapse
|
5
|
Dogra P, Butner JD, Ramirez JR, Cristini V, Wang Z. Investigating the Effect of Aging on the Pharmacokinetics and Tumor Delivery of Nanomaterials using Mathematical Modeling. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2447-2450. [PMID: 33018501 DOI: 10.1109/embc44109.2020.9175322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The application of nanomedicine for diagnosis and treatment of cancer has immense potential, but has witnessed only limited clinical success, in part due to insufficient understanding of the role of nanomaterial properties and physiological variables in governing nanoparticle (NP) pharmacology. Here, we present a multiscale mathematical model to examine the effects of physiological changes associated with patient age on the pharmacokinetics and tumor delivery efficiency of NPs. We show that physiological changes due to aging prolong the residence of NPs in the systemic circulation, thereby improving passive accumulation of NPs in tumors.Clinical Relevance - Understanding the effect of inter-individual variability on the pharmacological behavior of nanomaterials will improve their clinical translatability.
Collapse
|
6
|
Frieboes HB, Raghavan S, Godin B. Modeling of Nanotherapy Response as a Function of the Tumor Microenvironment: Focus on Liver Metastasis. Front Bioeng Biotechnol 2020; 8:1011. [PMID: 32974325 PMCID: PMC7466654 DOI: 10.3389/fbioe.2020.01011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 08/03/2020] [Indexed: 12/13/2022] Open
Abstract
The tumor microenvironment (TME) presents a challenging barrier for effective nanotherapy-mediated drug delivery to solid tumors. In particular for tumors less vascularized than the surrounding normal tissue, as in liver metastases, the structure of the organ itself conjures with cancer-specific behavior to impair drug transport and uptake by cancer cells. Cells and elements in the TME of hypovascularized tumors play a key role in the process of delivery and retention of anti-cancer therapeutics by nanocarriers. This brief review describes the drug transport challenges and how they are being addressed with advanced in vitro 3D tissue models as well as with in silico mathematical modeling. This modeling complements network-oriented techniques, which seek to interpret intra-cellular relevant pathways and signal transduction within cells and with their surrounding microenvironment. With a concerted effort integrating experimental observations with computational analyses spanning from the molecular- to the tissue-scale, the goal of effective nanotherapy customized to patient tumor-specific conditions may be finally realized.
Collapse
Affiliation(s)
- Hermann B. Frieboes
- Department of Bioengineering, University of Louisville, Louisville, KY, United States
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, United States
- Center for Predictive Medicine, University of Louisville, Louisville, KY, United States
| | - Shreya Raghavan
- Department of Biomedical Engineering, College of Engineering, Texas A&M University, College Station, TX, United States
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, United States
| | - Biana Godin
- Department of Nanomedicine, Houston Methodist Research Institute, Houston, TX, United States
- Department of Obstetrics and Gynecology, Houston Methodist Hospital, Houston, TX, United States
- Developmental Therapeutics Program, Houston Methodist Cancer Center, Houston Methodist Hospital, Houston, TX, United States
| |
Collapse
|
7
|
Dogra P, Butner JD, Nizzero S, Ruiz Ramírez J, Noureddine A, Peláez MJ, Elganainy D, Yang Z, Le AD, Goel S, Leong HS, Koay EJ, Brinker CJ, Cristini V, Wang Z. Image-guided mathematical modeling for pharmacological evaluation of nanomaterials and monoclonal antibodies. WILEY INTERDISCIPLINARY REVIEWS-NANOMEDICINE AND NANOBIOTECHNOLOGY 2020; 12:e1628. [PMID: 32314552 PMCID: PMC7507140 DOI: 10.1002/wnan.1628] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 02/06/2020] [Accepted: 02/15/2020] [Indexed: 12/13/2022]
Abstract
While plasma concentration kinetics has traditionally been the predictor of drug pharmacological effects, it can occasionally fail to represent kinetics at the site of action, particularly for solid tumors. This is especially true in the case of delivery of therapeutic macromolecules (drug-loaded nanomaterials or monoclonal antibodies), which can experience challenges to effective delivery due to particle size-dependent diffusion barriers at the target site. As a result, disparity between therapeutic plasma kinetics and kinetics at the site of action may exist, highlighting the importance of target site concentration kinetics in determining the pharmacodynamic effects of macromolecular therapeutic agents. Assessment of concentration kinetics at the target site has been facilitated by non-invasive in vivo imaging modalities. This allows for visualization and quantification of the whole-body disposition behavior of therapeutics that is essential for a comprehensive understanding of their pharmacokinetics and pharmacodynamics. Quantitative non-invasive imaging can also help guide the development and parameterization of mathematical models for descriptive and predictive purposes. Here, we present a review of the application of state-of-the-art imaging modalities for quantitative pharmacological evaluation of therapeutic nanoparticles and monoclonal antibodies, with a focus on their integration with mathematical models, and identify challenges and opportunities. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Diagnostic Tools > in vivo Nanodiagnostics and Imaging Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.
Collapse
Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Joseph D Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Sara Nizzero
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Achraf Noureddine
- Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, New Mexico, USA
| | - María J Peláez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA.,Applied Physics Graduate Program, Rice University, Houston, Texas, USA
| | - Dalia Elganainy
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Zhen Yang
- Center for Bioenergetics, Houston Methodist Research Institute, Houston, Texas, USA
| | - Anh-Dung Le
- Nanoscience and Microsystems Engineering, University of New Mexico, Albuquerque, New Mexico, USA
| | - Shreya Goel
- Cancer Systems Imaging, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Hon S Leong
- Biological Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Eugene J Koay
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - C Jeffrey Brinker
- Department of Chemical and Biological Engineering and UNM Comprehensive Cancer Center, University of New Mexico, Albuquerque, New Mexico, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, Texas, USA
| |
Collapse
|
8
|
Rathi B, Kempaiah P. Bioactive Chemical Entities: Pre-Clinical and Clinical Aspects - Part-VIII. Curr Top Med Chem 2020; 20:336. [DOI: 10.2174/156802662005200304123756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Brijesh Rathi
- Laboratory for Translational Chemistry and Drug Discovery Hansraj College, University of Delhi Delhi-110007, India
| | - Prakasha Kempaiah
- Department of Medicine Loyola University Health Sciences Division 2160 South 1st Avenue Chicago, IL 60153, United States
| |
Collapse
|
9
|
Dogra P, Butner JD, Ruiz Ramírez J, Chuang YL, Noureddine A, Jeffrey Brinker C, Cristini V, Wang Z. A mathematical model to predict nanomedicine pharmacokinetics and tumor delivery. Comput Struct Biotechnol J 2020; 18:518-531. [PMID: 32206211 PMCID: PMC7078505 DOI: 10.1016/j.csbj.2020.02.014] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 02/14/2020] [Accepted: 02/22/2020] [Indexed: 02/07/2023] Open
Abstract
Towards clinical translation of cancer nanomedicine, it is important to systematically investigate the various parameters related to nanoparticle (NP) physicochemical properties, tumor characteristics, and inter-individual variability that affect the tumor delivery efficiency of therapeutic nanomaterials. Comprehensive investigation of these parameters using traditional experimental approaches is impractical due to the vast parameter space; mathematical models provide a more tractable approach to navigate through such a multidimensional space. To this end, we have developed a predictive mathematical model of whole-body NP pharmacokinetics and their tumor delivery in vivo, and have conducted local and global sensitivity analyses to identify the factors that result in low tumor delivery efficiency and high off-target accumulation of NPs. Our analyses reveal that NP degradation rate, tumor blood viscosity, NP size, tumor vascular fraction, and tumor vascular porosity are the key parameters in governing NP kinetics in the tumor interstitium. The impact of these parameters on tumor delivery efficiency of NPs is discussed, and optimal values for maximizing NP delivery are presented.
Collapse
Affiliation(s)
- Prashant Dogra
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Joseph D. Butner
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Javier Ruiz Ramírez
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Yao-li Chuang
- Department of Mathematics, California State University, Northridge, CA 91330, USA
| | - Achraf Noureddine
- Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, NM 87106, USA
| | - C. Jeffrey Brinker
- Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque, NM 87106, USA
- UNM Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM 87102, USA
| | - Vittorio Cristini
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Zhihui Wang
- Mathematics in Medicine Program, Houston Methodist Research Institute, Houston, TX 77030, USA
- Corresponding author at: Mathematics in Medicine Program, The Houston Methodist Research Institute, HMRI R8-122, 6670 Bertner Ave, Houston, TX 77030, USA.
| |
Collapse
|