Joseph D. Butner

Bio: Joseph D. Butner is an academic researcher from Houston Methodist Hospital. The author has contributed to research in topics: Immunotherapy & Population. The author has an hindex of 9, co-authored 32 publications receiving 516 citations. Previous affiliations of Joseph D. Butner include Sandia National Laboratories & University of New Mexico.

Mathematical modeling in cancer nanomedicine: a review

Abstract: Cancer continues to be among the leading healthcare problems worldwide, and efforts continue not just to find better drugs, but also better drug delivery methods The need for delivering cytotoxic agents selectively to cancerous cells, for improved safety and efficacy, has triggered the application of nanotechnology in medicine This effort has provided drug delivery systems that can potentially revolutionize cancer treatment Nanocarriers, due to their capacity for targeted drug delivery, can shift the balance of cytotoxicity from healthy to cancerous cells The field of cancer nanomedicine has made significant progress, but challenges remain that impede its clinical translation Several biophysical barriers to the transport of nanocarriers to the tumor exist, and a much deeper understanding of nano-bio interactions is necessary to change the status quo Mathematical modeling has been instrumental in improving our understanding of the physicochemical and physiological underpinnings of nanomaterial behavior in biological systems Here, we present a comprehensive review of literature on mathematical modeling works that have been and are being employed towards a better understanding of nano-bio interactions for improved tumor delivery efficacy

Mechanistic Modeling Identifies Drug-Uptake History as Predictor of Tumor Drug Resistance and Nano-Carrier-Mediated Response

Abstract: A quantitative understanding of the advantages of nanoparticle-based drug delivery vis-a-vis conventional free drug chemotherapy has yet to be established for cancer or other diseases despite numerous investigations. Here, we employ first-principles cell biophysics, drug pharmaco-kinetics, and drug pharmaco-dynamics to model the delivery of doxorubicin (DOX) to hepatocellular carcinoma (HCC) tumor cells and predict the resultant experimental cytotoxicity data. The fundamental, mechanistic hypothesis of our mathematical model is that the integrated history of drug uptake by the cells over time of exposure, which sets the cell death rate parameter, and the uptake rate are the sole determinants of the dose response relationship. A universal solution of the model equations is capable of predicting the entire, nonlinear dose response of the cells to any drug concentration based on just two separate measurements of these cellular parameters. This analysis reveals that nanocarrier-mediated delivery overcomes res...

Theory and Experimental Validation of a Spatio-temporal Model of Chemotherapy Transport to Enhance Tumor Cell Kill

Abstract: It has been hypothesized that continuously releasing drug molecules into the tumor over an extended period of time may significantly improve the chemotherapeutic efficacy by overcoming physical transport limitations of conventional bolus drug treatment. In this paper, we present a generalized space- and time-dependent mathematical model of drug transport and drug-cell interactions to quantitatively formulate this hypothesis. Model parameters describe: perfusion and tissue architecture (blood volume fraction and blood vessel radius); diffusion penetration distance of drug (i.e., a function of tissue compactness and drug uptake rates by tumor cells); and cell death rates (as function of history of drug uptake). We performed preliminary testing and validation of the mathematical model using in vivo experiments with different drug delivery methods on a breast cancer mouse model. Experimental data demonstrated a 3-fold increase in response using nano-vectored drug vs. free drug delivery, in excellent quantitative agreement with the model predictions. Our model results implicate that therapeutically targeting blood volume fraction, e.g., through vascular normalization, would achieve a better outcome due to enhanced drug delivery. Author Summary Cancer treatment efficacy can be significantly enhanced through the elution of drug from nano-carriers that can temporarily stay in the tumor vasculature. Here we present a relatively simple yet powerful mathematical model that accounts for both spatial and temporal heterogeneities of drug dosing to help explain, examine, and prove this concept. We find that the delivery of systemic chemotherapy through a certain form of nano-carriers would have enhanced tumor kill by a factor of 2 to 4 over the standard therapy that the patients actually received. We also find that targeting blood volume fraction (a parameter of the model) through vascular normalization can achieve more effective drug delivery and tumor kill. More importantly, this model only requires a limited number of parameters which can all be readily assessed from standard clinical diagnostic measurements (e.g., histopathology and CT). This addresses an important challenge in current translational research and justifies further development of the model towards clinical translation.

Integrated PK-PD and agent-based modeling in oncology.

Abstract: Mathematical modeling has become a valuable tool that strives to complement conventional biomedical research modalities in order to predict experimental outcome, generate new medical hypotheses, and optimize clinical therapies Two specific approaches, pharmacokinetic-pharmacodynamic (PK-PD) modeling, and agent-based modeling (ABM), have been widely applied in cancer research While they have made important contributions on their own (eg, PK-PD in examining chemotherapy drug efficacy and resistance, and ABM in describing and predicting tumor growth and metastasis), only a few groups have started to combine both approaches together in an effort to gain more insights into the details of drug dynamics and the resulting impact on tumor growth In this review, we focus our discussion on some of the most recent modeling studies building on a combined PK-PD and ABM approach that have generated experimentally testable hypotheses Some future directions are also discussed

The Essential Physics Of Medical Imaging

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Numerical methods for engineers

Abstract: The fifth edition of "Numerical Methods for Engineers" continues its tradition of excellence. Instructors love this text because it is a comprehensive text that is easy to teach from. Students love it because it is written for them--with great pedagogy and clear explanations and examples throughout. The text features a broad array of applications, including all engineering disciplines. The revision retains the successful pedagogy of the prior editions. Chapra and Canale's unique approach opens each part of the text with sections called Motivation, Mathematical Background, and Orientation, preparing the student for what is to come in a motivating and engaging manner. Each part closes with an Epilogue containing sections called Trade-Offs, Important Relationships and Formulas, and Advanced Methods and Additional References. Much more than a summary, the Epilogue deepens understanding of what has been learned and provides a peek into more advanced methods. Approximately 80% of the end-of-chapter problems are revised or new to this edition. The expanded breadth of engineering disciplines covered is especially evident in the problems, which now cover such areas as biotechnology and biomedical engineering. Users will find use of software packages, specifically MATLAB and Excel with VBA. This includes material on developing MATLAB m-files and VBA macros.

A Nanoparticle Size Series for In Vivo Fluorescence Imaging

Abstract: Any design of nanoparticle vectors for cancer therapy or imaging must take into account the interaction of the nanoparticles with the tumor microenvironment. Size, charge, and shape have been shown to dominate this interaction.[1, 2] In vivo probing of solid tumors with particles of different sizes simultaneously has thus far been challenging due to the limitations of available nano-sized probes.[3–5] Fluorescent dextrans and other macromolecule probes have been used in studies with intravital microscopy, but heterogeneities across samples has prevented their use for the simultaneous imaging of a size series of probes within the same tumour.[5] MRI contrast agents are another attractive set of probes due to the minimally-invasive nature of the technology,[6, 7] but the lower spatial resolution of MRI limits the imaging of heterogeneity within tumors, and the technique does not allow for simultaneous imaging and tracking of a size series of probes within the same tumor.

A Review on Applications of Computational Methods in Drug Screening and Design

Abstract: Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.

T cell-targeting nanoparticles focus delivery of immunotherapy to improve antitumor immunity

Abstract: Targeted delivery of compounds to particular cell subsets can enhance therapeutic index by concentrating their action on the cells of interest. Because attempts to target tumors directly have yielded limited benefit, we instead target endogenous immune cell subsets in the circulation that can migrate actively into tumors. We describe antibody-targeted nanoparticles that bind to CD8+ T cells in the blood, lymphoid tissues, and tumors of mice. PD-1+ T cells are successfully targeted in the circulation and tumor. The delivery of an inhibitor of TGFβ signaling to PD-1-expressing cells extends the survival of tumor-bearing mice, whereas free drugs have no effect at such doses. This modular platform also enables PD-1-targeted delivery of a TLR7/8 agonist to the tumor microenvironment, increasing the proportion of tumor-infiltrating CD8+ T cells and sensitizing tumors to subsequent anti-PD-1. Targeted delivery of immunotherapy to defined subsets of endogenous leukocytes may be superior to administration of free drugs.Targeted delivery of immunomodulatory compounds to defined subsets of endogenous immune cells may improve the efficacy of combination immunotherapies. Here, the authors use PD-1-targeting nanoparticles containing a TGFβ inhibitor or a TLR7/8 agonist to deliver these payloads to T cells or via T cells to the tumor microenvironment, respectively, leading to anti-tumor efficacy in vivo.