An In Silico Transwell Device for the Study of Drug Transport and Drug–Drug Interactions

Extensive clinical evidence has demonstrated the efficacy of statin treatment in the prevention of cardiovascular disease (CVD). However, nearly half of patients taking statins discontinue their use, largely as a result of side effects. Many patients receiving statins are elderly, and/or have comorbid conditions, increasing the potential for drug-drug interactions (DDIs). Statin DDIs are largely the result of altered drug metabolism via cytochrome P450s (CYPs), glucuronidation or altered drug transport via organic anion-transporting polypeptides (OATPs) and P-glycoprotein (P-gp). There is a need for discussion and education about DDIs within the clinical consultation. Statins that are not significantly metabolized via the CYP system have a reduced risk of DDIs. To date, pitavastatin has shown a low rate of DDIs compared to other available statins metabolized by CYP3A4 isozymes. Its potential for CYP-mediated DDIs has been studied in combination with a wide range of drug classes known to be CYP inhibitors and has been clinically evaluated in patients populations where multiple medications are used, including the elderly, those high risk of CVD, and those taking protease inhibitors. It may also have beneficial effects on parameters of glucose metabolism, and has shown improved outcomes in patients with chronic kidney disease. Knowledge of statin pharmacokinetics, their dose limitations and contraindications, and their mechanisms of DDI allows improved therapeutic choices, avoiding adverse interactions without compromising patient care.

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In Silico Predictions and In Vivo Results of Drug-Drug Interactions by Ketoconazole and Verapamil on AZD1305, a Combined Ion Channel Blocker and a Sensitive CYP3A4 Substrate

Clinical Pharmacology in Drug Development ◽

10.1002/cpdd.250 ◽

2016 ◽

Vol 5 (5) ◽

pp. 364-373 ◽

Cited By ~ 3

Author(s):

Susanne Johansson ◽

Boel Löfberg ◽

Maria Aunes ◽

Helen Lunde ◽

Lars Frison ◽

...

Keyword(s):

Drug Interactions ◽

Ion Channel ◽

In Silico ◽

Channel Blocker ◽

Cyp3a4 Substrate ◽

In Silico Predictions

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Computational Modelling and Big Data Analysis of Flow and Drug Transport in Microfluidic Systems: A Spheroid-on-a-Chip Study

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2021.781566 ◽

2021 ◽

Vol 9 ◽

Author(s):

Sina Kheiri ◽

Eugenia Kumacheva ◽

Edmond W.K. Young

Keyword(s):

Parameter Space ◽

Microfluidic Device ◽

High Throughput ◽

In Silico ◽

Design Parameter ◽

Drug Transport ◽

Computational Modelling ◽

Operating Conditions ◽

Computational Time ◽

Multicellular Spheroids

Microfluidic tumour spheroid-on-a-chip platforms enable control of spheroid size and their microenvironment and offer the capability of high-throughput drug screening, but drug supply to spheroids is a complex process that depends on a combination of mechanical, biochemical, and biophysical factors. To account for these coupled effects, many microfluidic device designs and operating conditions must be considered and optimized in a time- and labour-intensive trial-and-error process. Computational modelling facilitates a systematic exploration of a large design parameter space via in silico simulations, but the majority of in silico models apply only a small set of conditions or parametric levels. Novel approaches to computational modelling are needed to explore large parameter spaces and accelerate the optimization of spheroid-on-a-chip and other organ-on-a-chip designs. Here, we report an efficient computational approach for simulating fluid flow and transport of drugs in a high-throughput arrayed cancer spheroid-on-a-chip platform. Our strategy combines four key factors: i) governing physical equations; ii) parametric sweeping; iii) parallel computing; and iv) extensive dataset analysis, thereby enabling a complete “full-factorial” exploration of the design parameter space in combinatorial fashion. The simulations were conducted in a time-efficient manner without requiring massive computational time. As a case study, we simulated >15,000 microfluidic device designs and flow conditions for a representative multicellular spheroids-on-a-chip arrayed device, thus acquiring a single dataset consisting of ∼10 billion datapoints in ∼95 GBs. To validate our computational model, we performed physical experiments in a representative spheroid-on-a-chip device that showed excellent agreement between experimental and simulated data. This study offers a computational strategy to accelerate the optimization of microfluidic device designs and provide insight on the flow and drug transport in spheroid-on-a-chip and other biomicrofluidic platforms.

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Convection-Enhanced Delivery in silico study for personalized brain cancer treatment

10.21203/rs.3.rs-998895/v1 ◽

2021 ◽

Author(s):

Chryso Lambride ◽

Vasileios Vavourakis ◽

Triantafyllos Stylianopoulos

Keyword(s):

Brain Cancer ◽

In Silico ◽

Drug Transport ◽

Drug Distribution ◽

Local Drug Delivery ◽

Patient Specific ◽

Convection Enhanced Delivery ◽

Formidable Challenge ◽

The Impact ◽

The Brain

Abstract Brain cancer therapy remains a formidable challenge in oncology. Convection-enhanced delivery (CED) is an innovative and promising local drug delivery method for the treatment of brain cancer, overcoming the challenges of the systemic delivery of drugs to the brain. To improve our understanding about CED efficacy and drug transport, we present an in silico methodology for brain cancer CED treatment simulation. To achieve this, a three-dimensional finite element biomechanics formulation is utilized which employs patient-specific brain model representation and is used to predict the drug deposition in CED regimes. The model encompasses nonlinear biomechanics and the transport of drugs in the brain parenchyma. Drug distribution was studied under various patho-physiological conditions of the tumor, in terms of tumor vessel wall pore size and tumor tissue hydraulic conductivity as well as for drugs of various sizes, spanning from small molecules to nanoparticles. Our contribution reports for the first time the impact of the size of the vascular wall pores and that of the therapeutic agent on drug distribution during and after CED. The in silico findings provide useful insights of the spatio-temporal distribution and average drug concentration in the tumor towards an effective treatment of brain cancer.

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Studies of intestinal drug transport using an in silico epithelio-mimetic device

Biosystems ◽

10.1016/j.biosystems.2005.06.008 ◽

2005 ◽

Vol 82 (2) ◽

pp. 154-167 ◽

Cited By ~ 18

Author(s):

Yu Liu ◽

C. Anthony Hunt

Keyword(s):

In Silico ◽

Drug Transport ◽

Intestinal Drug Transport

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Mucoadhesive Gelatin Buccal Films with Propranolol Hydrochloride: Evaluation of Mechanical, Mucoadhesive, and Biopharmaceutical Properties

Pharmaceutics ◽

10.3390/pharmaceutics13020273 ◽

2021 ◽

Vol 13 (2) ◽

pp. 273

Author(s):

Marija Jovanović ◽

Nataša Tomić ◽

Sandra Cvijić ◽

Dušica Stojanović ◽

Svetlana Ibrić ◽

...

Keyword(s):

In Silico ◽

Drug Transport ◽

In Vitro Release ◽

Work Of Adhesion ◽

Therapeutic Drug ◽

Propranolol Hydrochloride ◽

Scanning Calorimetry ◽

In Silico Simulation ◽

Buccal Films

This study processes and characterizes propranolol hydrochloride/gelatin mucoadhesive buccal films. Two types of gelatin are used: Gelatin from porcine skin, type A (GA), and gelatin from bovine skin (GB). The influence of gelatin type on mechanical, mucoadhesive, and biopharmaceutical characteristics of buccal films is evaluated. Fourier-Transfer infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) analysis show that GA with propranolol hydrochloride (PRH) in the film (GAP) formed a physical mixture, whereas GB with PRH (GBP) form a compound-complex. Results of mechanical testing (tensile test, hardness) revealed that GAP films exhibit higher elastic modulus, tensile strength, and hardness. A mucoahesion test shows that GBP has higher adhesion strength, while GAP shows higher work of adhesion. Both in vitro release study and in silico simulation indicated that processed films can provide effective drug transport through the buccal mucosa. In silico simulation shows improved bioavailability from buccal films, in comparison to the immediate-release tablets—indicating that the therapeutic drug dose can be markedly reduced.

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