Using Parallel Coordinates in Optimization of Nano-Particle Drug Delivery

2021 ◽  
Author(s):  
Timoleon Kipouros ◽  
Ibrahim Chamseddine ◽  
Michael Kokkolaras
Keyword(s):  
Drug Delivery ◽  
Tumor Model ◽  
Mechanistic Modeling ◽  
Analytical Relationship ◽  
Parallel Coordinates ◽  
Modeling And Optimization ◽  
Optimization Framework ◽  
Design Variables ◽  
Nanoparticle Design ◽  
Nanoparticle Sizes

Abstract Nanoparticle drug delivery better targets neoplastic lesions than free drugs and thus has emerged as safer form of cancer therapy. Nanoparticle design variables are important determinants of efficacy as they influence the drug biodistribution and pharmacokinetics. Previously, we determined optimal designs through mechanistic modeling and optimization. However, the numerical nature of the tumor model and numerous candidate nanoparticle designs hinder hypothesis generation and treatment personalization. In this paper, we utilize the parallel coordinates technique to visualize high-dimensional optimal solutions and extract correlations between nanoparticle design and treatment outcomes. We found that at optimality, two major design variables are dependent, and thus the optimization problem can be reduced. In addition, we obtained an analytical relationship between optimal nanoparticle sizes and optimal distribution, which could facilitate the utilization of tumors models in preclincal studies. Our approach has simplified the results of the previously integrated modeling and optimization framework developed for nanotherapy and enhanced the interpretation and utilization of findings. Integrated mathematical frameworks are increasing in the medical field, and our method can be applied outside nanotherapy to facilitate clinical translation of computational methods.

scholarly journals Overcoming physiological barriers by nanoparticles for intravenous drug delivery to the lymph nodes

2021 ◽  
pp. 153537022110107
Author(s):  
Noah Trac ◽  
Eun Ji Chung
Keyword(s):  
Drug Delivery ◽  
Lymph Node ◽  
Lymph Nodes ◽  
Cancer Metastasis ◽  
Subcutaneous Administration ◽  
Mononuclear Phagocyte ◽  
Intravenous Drug ◽  
Rapid Clearance ◽  
Nanoparticle Design ◽  
Lymph Node Targeting

The lymph nodes are major sites of cancer metastasis and immune activity, and thus represent important clinical targets. Although not as well-studied compared to subcutaneous administration, intravenous drug delivery is advantageous for lymph node delivery as it is commonly practiced in the clinic and has the potential to deliver therapeutics systemically to all lymph nodes. However, rapid clearance by the mononuclear phagocyte system, tight junctions of the blood vascular endothelium, and the collagenous matrix of the interstitium can limit the efficiency of lymph node drug delivery, which has prompted research into the design of nanoparticle-based drug delivery systems. In this mini review, we describe the physiological and biological barriers to lymph node targeting, how they inform nanoparticle design, and discuss the future outlook of lymph node targeting.

Anticancer Drug Delivery Capability of Biodegradable PMO in the Chicken Egg Tumor Model

2018 ◽  
pp. 103-116 ◽  
Author(s):  
Kotaro Matsumoto ◽  
Tan Le Hoang Doan ◽  
Ngoc Xuan Dat Mai ◽  
Keigo Nakai ◽  
Aoi Komatsu ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Anticancer Drug ◽  
Tumor Model ◽  
Anticancer Drug Delivery ◽  
Chicken Egg

scholarly journals Inverse mechanistic modeling of transdermal drug delivery for fast identification of optimal model parameters

2020 ◽  
Author(s):  
Thijs Defraeye ◽  
Flora Bahrami ◽  
Rene M Rossi
Keyword(s):  
Drug Delivery ◽  
Inverse Modeling ◽  
Transdermal Drug Delivery ◽  
Drug Transport ◽  
Mechanistic Model ◽  
Mechanistic Modeling ◽  
Drug Uptake ◽  
Added Value ◽  
Model Parameters ◽  
Transdermal Drug Delivery Systems

Transdermal drug delivery systems are a key technology to administer drugs with a high first-pass effect in a non-invasive and controlled way. Physics-based modeling and simulation are on their way to become a cornerstone in the engineering of these healthcare devices since it provides a unique complementarity to experimental data and insights. Simulations enable to virtually probe the drug transport inside the skin at each point in time and space. However, the tedious experimental or numerical determination of material properties currently forms a bottleneck in the modeling workflow. We show that multiparameter inverse modeling to determine the drug diffusion and partition coefficients is a fast and reliable alternative. We demonstrate this strategy for transdermal delivery of fentanyl. We found that inverse modeling reduced the normalized root mean square deviation of the measured drug uptake flux from 26 to 9%, when compared to the experimental measurement of all skin properties. We found that this improved agreement with experiments was only possible if the diffusion in the reservoir holding the drug was smaller than the experimentally-measured diffusion coefficients suggested. For indirect inverse modeling, which systematically explores the entire parametric space, 30 000 simulations were required. By relying on direct inverse modeling, we reduced the number of simulations to be performed to only 300, so a factor 100 difference. The modeling approach's added value is that it can be calibrated once in-silico for all model parameters simultaneously by solely relying on a single measurement of the drug uptake flux evolution over time. We showed that this calibrated model could accurately be used to simulate transdermal patches with other drug doses. We showed that inverse modeling is a fast way to build up an accurate mechanistic model for drug delivery. This strategy opens the door to clinically-ready therapy that is tailored to patients.

scholarly journals Role of mechanical cues and hypoxia on the growth of tumor cells in strong and weak confinement: A dual in vitro–in silico approach

2020 ◽  
Vol 6 (13) ◽  
pp. eaaz7130 ◽  
Author(s):  
V. Le Maout ◽  
K. Alessandri ◽  
B. Gurchenkov ◽  
H. Bertin ◽  
P. Nassoy ◽  
...  
Keyword(s):  
In Silico ◽  
Time Course ◽  
Growth Dynamics ◽  
Tumor Model ◽  
Mechanistic Modeling ◽  
Physical Factors ◽  
Factors Affecting ◽  
Numerical Studies

Characterization of tumor growth dynamics is of major importance for cancer understanding. By contrast with phenomenological approaches, mechanistic modeling can facilitate disclosing underlying tumor mechanisms and lead to identification of physical factors affecting proliferation and invasive behavior. Current mathematical models are often formulated at the tissue or organ scale with the scope of a direct clinical usefulness. Consequently, these approaches remain empirical and do not allow gaining insight into the tumor properties at the scale of small cell aggregates. Here, experimental and numerical studies of the dynamics of tumor aggregates are performed to propose a physics-based mathematical model as a general framework to investigate tumor microenvironment. The quantitative data extracted from the cellular capsule technology microfluidic experiments allow a thorough quantitative comparison with in silico experiments. This dual approach demonstrates the relative impact of oxygen and external mechanical forces during the time course of tumor model progression.

Ultrasound‐mediated nanoparticle drug delivery in an in vivo tumor model

2006 ◽  
Vol 120 (5) ◽  
pp. 3003-3003
Author(s):  
Balasundar I. Raju ◽  
Christopher S. Hall ◽  
Miachael S. Hughes ◽  
Samuel A. Wickline ◽  
Gregory M. Lanza
Keyword(s):  
Drug Delivery ◽  
Tumor Model ◽  
Nanoparticle Drug Delivery

scholarly journals Visible Drug Delivery by Supramolecular Nanocarriers Directing to Single-Platformed Diagnosis and Therapy of Pancreatic Tumor Model

2010 ◽  
Vol 70 (18) ◽  
pp. 7031-7041 ◽  
Author(s):  
Sachiko Kaida ◽  
Horacio Cabral ◽  
Michiaki Kumagai ◽  
Akihiro Kishimura ◽  
Yasuko Terada ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Pancreatic Tumor ◽  
Tumor Model ◽  
Diagnosis And Therapy

Multi-Objective Optimization of a Segmented Lunar Wheel Concept

2011 ◽  
Author(s):  
Michele Faragalli ◽  
Damiano Pasini ◽  
Peter Radzizsewski
Keyword(s):  
Optimization Problem ◽  
Element Model ◽  
Multi Objective Optimization ◽  
Stress Concentrations ◽  
Multi Objective ◽  
Optimization Framework ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Rider Comfort ◽  
Original Concept

The goal of this work is to develop a systematic method for optimizing the structural design of a segmented wheel concept to improve its operating performance. In this study, a wheel concept is parameterized into a set of size and shape design variables, and a finite element model of the wheel component is created. A multi-objective optimization problem is formulated to optimize its directional compliance and reduce stress concentrations, which has a direct affect on the efficiency, traction, rider comfort, maneuverability, and reliability of the wheel. To solve the optimization problem, a Matlab-FE simulation loop is built and a multi-objective genetic algorithm is used to find the Pareto front of optimal solutions. A trade-off design is selected which demonstrates an improvement from the original concept. Finally, recommendations will be made to apply the structural optimization framework to alternative wheel conceptual designs.

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