Ocular Fluid Mechanics and Drug Delivery: A Review of Mathematical and Computational Models

2021 ◽  
Author(s):  
Ajay Bhandari
Keyword(s):  
Drug Delivery ◽  
Fluid Mechanics ◽  
Computational Models

scholarly journals Mathematical and computational models of drug transport in tumours

2014 ◽  
Vol 11 (94) ◽  
pp. 20131173 ◽  
Author(s):  
C. M. Groh ◽  
M. E. Hubbard ◽  
P. F. Jones ◽  
P. M. Loadman ◽  
N. Periasamy ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Drug Administration ◽  
Drug Transport ◽  
Computational Models ◽  
Computational Modelling ◽  
Tumour Cells ◽  
Drug Binding ◽  
Binding Model ◽  
Transport Modelling ◽  
Optimal Drug

The ability to predict how far a drug will penetrate into the tumour microenvironment within its pharmacokinetic (PK) lifespan would provide valuable information about therapeutic response. As the PK profile is directly related to the route and schedule of drug administration, an in silico tool that can predict the drug administration schedule that results in optimal drug delivery to tumours would streamline clinical trial design. This paper investigates the application of mathematical and computational modelling techniques to help improve our understanding of the fundamental mechanisms underlying drug delivery, and compares the performance of a simple model with more complex approaches. Three models of drug transport are developed, all based on the same drug binding model and parametrized by bespoke in vitro experiments. Their predictions, compared for a ‘tumour cord’ geometry, are qualitatively and quantitatively similar. We assess the effect of varying the PK profile of the supplied drug, and the binding affinity of the drug to tumour cells, on the concentration of drug reaching cells and the accumulated exposure of cells to drug at arbitrary distances from a supplying blood vessel. This is a contribution towards developing a useful drug transport modelling tool for informing strategies for the treatment of tumour cells which are ‘pharmacokinetically resistant’ to chemotherapeutic strategies.

scholarly journals Enhancing physiologic simulations using supervised learning on coarse mesh solutions

2015 ◽  
Vol 12 (104) ◽  
pp. 20141073 ◽  
Author(s):  
Kumaran Kolandaivelu ◽  
Caroline C. O'Brien ◽  
Tarek Shazly ◽  
Elazer R. Edelman ◽  
Vijaya B. Kolachalama
Keyword(s):  
Drug Delivery ◽  
Supervised Learning ◽  
Computational Models ◽  
Computational Cost ◽  
Computational Modelling ◽  
Supervised Machine Learning ◽  
Coarse Mesh ◽  
Process Data ◽  
Distance Information ◽  
Drug Concentrations

Computational modelling of physical and biochemical processes has emerged as a means of evaluating medical devices, offering new insights that explain current performance, inform future designs and even enable personalized use. Yet resource limitations force one to compromise with reduced order computational models and idealized assumptions that yield either qualitative descriptions or approximate, quantitative solutions to problems of interest. Considering endovascular drug delivery as an exemplary scenario, we used a supervised machine learning framework to process data generated from low fidelity coarse meshes and predict high fidelity solutions on refined mesh configurations. We considered two models simulating drug delivery to the arterial wall: (i) two-dimensional drug-coated balloons and (ii) three-dimensional drug-eluting stents. Simulations were performed on computational mesh configurations of increasing density. Supervised learners based on Gaussian process modelling were constructed from combinations of coarse mesh setting solutions of drug concentrations and nearest neighbourhood distance information as inputs, and higher fidelity mesh solutions as outputs. These learners were then used as computationally inexpensive surrogates to extend predictions using low fidelity information to higher levels of mesh refinement. The cross-validated, supervised learner-based predictions improved fidelity as compared with computational simulations performed at coarse level meshes—a result consistent across all outputs and computational models considered. Supervised learning on coarse mesh solutions can augment traditional physics-based modelling of complex physiologic phenomena. By obtaining efficient solutions at a fraction of the computational cost, this framework has the potential to transform how modelling approaches can be applied in the evaluation of medical technologies and their real-time administration in an increasingly personalized fashion.

scholarly journals Validation of computational models in biomechanics

2009 ◽  
Vol 224 (7) ◽  
pp. 801-812 ◽  
Author(s):  
H B Henninger ◽  
S P Reese ◽  
A E Anderson ◽  
J A Weiss
Keyword(s):  
Fluid Mechanics ◽  
Model Validation ◽  
Patient Outcomes ◽  
Basic Science ◽  
Computational Models ◽  
Peer Acceptance ◽  
Biological Systems ◽  
Verification And Validation ◽  
Validation Process ◽  
Computational Biomechanics

The topics of verification and validation have increasingly been discussed in the field of computational biomechanics, and many recent articles have applied these concepts in an attempt to build credibility for models of complex biological systems. Verification and validation are evolving techniques that, if used improperly, can lead to false conclusions about a system under study. In basic science, these erroneous conclusions may lead to failure of a subsequent hypothesis, but they can have more profound effects if the model is designed to predict patient outcomes. While several authors have reviewed verification and validation as they pertain to traditional solid and fluid mechanics, it is the intent of this paper to present them in the context of computational biomechanics. Specifically, the task of model validation will be discussed, with a focus on current techniques. It is hoped that this review will encourage investigators to engage and adopt the verification and validation process in an effort to increase peer acceptance of computational biomechanics models.

Diameter and Size Distributions of Poly (Lactide-Co-Glycolide) [PLG] Based Microspheres

2005 ◽  
Author(s):  
Nader Berchane ◽  
Farzaneh F. Jebrail ◽  
Kenneth H. Carson ◽  
Allison C. Rice-Ficht ◽  
Malcolm J. Andrews
Keyword(s):  
Drug Delivery ◽  
Pharmaceutical Industry ◽  
Turbulent Flow ◽  
Fluid Mechanics ◽  
Size Distribution ◽  
Quantitative Study ◽  
Controlled Drug Delivery ◽  
Size Distributions ◽  
Microsphere Size ◽  
Microsphere Preparation

A quantitative study of Poly (lactide-co-glycolide) [PLG] microsphere size distribution is presented. A fluid mechanics based correlation for the average microsphere diameter is formulated based on the theory of emulsification in turbulent flow under non-coalescing conditions. Measured average diameters of PLG microspheres prepared at different stirring speeds are used to construct the correlation formula. This correlation is particularly useful to the pharmaceutical industry for predicting the size of the encapsulated microspheres used in controlled drug delivery prior to microsphere preparation.

Investigation of Coronary Artery Stenosis Hemodynamics Using Experimental and Computational Models: Influence of Flowrate, Size, Symmetry and Wall Roughness

2001 ◽  
Author(s):  
C. J. Egelhoff ◽  
R. S. Budwig ◽  
J. K. Foster ◽  
B. L. Hansen
Keyword(s):  
Myocardial Infarction ◽  
Coronary Artery ◽  
Fluid Mechanics ◽  
Computational Models ◽  
Coronary Artery Stenosis ◽  
Plaque Rupture ◽  
Artery Stenosis ◽  
Wall Roughness ◽  
Sequence Of Events

Abstract The motivation for the present study is to better understand the hemodynamics that may be involved with the onset of Myocardial Infarction (MI). We know the sequence of events is plaque rupture followed by thrombogenesis and then MI. There are several theories about the cause of rupture and fluid mechanics is cited as a possible theory.

scholarly journals Improving the Predictions of Computational Models of Convection-Enhanced Drug Delivery by Accounting for Diffusion Non-gaussianity

2018 ◽  
Vol 9 ◽  
Author(s):  
Eirini Messaritaki ◽  
Suryanarayana Umesh Rudrapatna ◽  
Greg D. Parker ◽  
William P. Gray ◽  
Derek K. Jones
Keyword(s):  
Drug Delivery ◽  
Computational Models

Techniques For The Preparation of Tissue Samples Containing Injectable Polymer Microcapsules

1985 ◽  
Vol 43 ◽  
pp. 710-711
Author(s):  
G.E. Visscher ◽  
R. L. Robison ◽  
G. J. Argentieri
Keyword(s):  
Drug Delivery ◽  
Drug Delivery Systems ◽  
Gastrocnemius Muscle ◽  
Degradation Process ◽  
Delivery Systems ◽  
Tissue Reaction ◽  
Electron Microscopic ◽  
Tissue Samples ◽  
Injectable Polymer ◽  
Microscopic Techniques

The use of various bioerodable polymers as drug delivery systems has gained considerable interest in recent years. Among some of the shapes used as delivery systems are films, rods and microcapsules. The work presented here will deal with the techniques we have utilized for the analysis of the tissue reaction to and actual biodegradation of injectable microcapsules. This work has utilized light microscopic (LM), transmission (TEM) and scanning (SEM) electron microscopic techniques. The design of our studies has utilized methodology that would; 1. best characterize the actual degradation process without artifacts introduced by fixation procedures and 2. allow for reproducible results.In our studies, the gastrocnemius muscle of the rat was chosen as the injection site. Prior to the injection of microcapsules the skin above the sites was shaved and tattooed for later recognition and recovery. 1.0 cc syringes were loaded with the desired quantity of microcapsules and the vehicle (0.5% hydroxypropylmethycellulose) drawn up. The syringes were agitated to suspend the microcapsules in the injection vehicle.

Nanotheranostic agents for neurodegenerative diseases

2020 ◽  
Vol 4 (6) ◽  
pp. 645-675
Author(s):  
Parasuraman Padmanabhan ◽  
Mathangi Palanivel ◽  
Ajay Kumar ◽  
Domokos Máthé ◽  
George K. Radda ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Neurodegenerative Diseases ◽  
Cell Types ◽  
Specific Cell ◽  
Ageing Population ◽  
Target Tissue ◽  
Therapeutic Approaches ◽  
Surface Receptors ◽  
Theranostic Agents ◽  
Preclinical Animal Models

Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), affect the ageing population worldwide and while severely impairing the quality of life of millions, they also cause a massive economic burden to countries with progressively ageing populations. Parallel with the search for biomarkers for early detection and prediction, the pursuit for therapeutic approaches has become growingly intensive in recent years. Various prospective therapeutic approaches have been explored with an emphasis on early prevention and protection, including, but not limited to, gene therapy, stem cell therapy, immunotherapy and radiotherapy. Many pharmacological interventions have proved to be promising novel avenues, but successful applications are often hampered by the poor delivery of the therapeutics across the blood-brain-barrier (BBB). To overcome this challenge, nanoparticle (NP)-mediated drug delivery has been considered as a promising option, as NP-based drug delivery systems can be functionalized to target specific cell surface receptors and to achieve controlled and long-term release of therapeutics to the target tissue. The usefulness of NPs for loading and delivering of drugs has been extensively studied in the context of NDDs, and their biological efficacy has been demonstrated in numerous preclinical animal models. Efforts have also been made towards the development of NPs which can be used for targeting the BBB and various cell types in the brain. The main focus of this review is to briefly discuss the advantages of functionalized NPs as promising theranostic agents for the diagnosis and therapy of NDDs. We also summarize the results of diverse studies that specifically investigated the usage of different NPs for the treatment of NDDs, with a specific emphasis on AD and PD, and the associated pathophysiological changes. Finally, we offer perspectives on the existing challenges of using NPs as theranostic agents and possible futuristic approaches to improve them.

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