Prediction of the mutual diffusion coefficient for controlled drug delivery devices

Kale, Swapnil P.; Garg, Sanjeev

doi:10.1016/j.compchemeng.2011.12.001

Cited by 6 publications

(6 citation statements)

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“…GA implemented in MATLAB was linked to the fluid flow model to search for optimal design parameters in the reverse problem. It was shown in a previous study that mutual diffusion coefficient of drug in polymer is a function of various thermodynamic parameters and also on the drug weight fraction (Kale & Garg, 2012). These predicted diffusion-controlled drug delivery devices satisfied the constraint of maintaining the drug concentration in blood within the therapeutic range for desired dosage regimens, for each case, as shown in Table 2.…”

Section: Optimization Resultsmentioning

confidence: 68%

“…The forward problem for simulating the drug release from diffusion-controlled drug delivery devices was reported and discussed in a previous study (Pramanik & Garg, 2018). The mutual diffusion coefficients were calculated with the methodology reported by Kale and Garg (2012). The methodology for calculating partition coefficients was developed and reported by Pramanik and Garg (2018).…”

Section: Mathematical Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…For the diffusion-controlled drug delivery devices, the forward problem involves the use of various prediction models to estimate drug-polymer characteristics including mutual diffusion (Kale & Garg, 2012) and partition coefficients of drug in polymer versus blood (Pramanik & Garg, 2018) which depends on the structure and properties of drug and polymers. The validation of the predicted mutual diffusion coefficient and partition coefficient using reported experimental data was done in previous studies (Kale & Garg, 2012;Pramanik & Garg, 2018). In the reverse problem, for designing diffusion-controlled drug delivery devices, a target release profile is specified as an input to search for the optimal values of the design parameters for the controlled drug delivery devices.…”

Section: Introductionmentioning

confidence: 99%

“…In the current study, previously described fluid flow model (Pramanik & Garg, 2018), along with the predictive quantitative structure-property relationship (QSPR) models for mutual diffusion (Kale & Garg, 2012) and partition coefficients (Pramanik & Garg, 2018), was used to search for the optimal diffusion-controlled drug delivery devices using Paclitaxel as a model drug to establish the viability of the proposed approach. Paclitaxel was chosen as an example drug since it is used to treat solid tumors including ovarian cancer, breast cancer, lung cancer, Kaposi sarcoma, cervical cancer, and pancreatic cancer ("National Center for Biotechnology Information.…”

Section: Introductionmentioning

confidence: 99%

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Design of diffusion‐controlled drug delivery devices for controlled release of Paclitaxel

Pramanik

Garg

2019

Chem Biol Drug Des

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Controlled drug delivery devices were predicted in a reverse engineering framework for the controlled release of Paclitaxel, an anti-cancer drug, widely used in the treatment of solid tumors. Using quantitative structure-property relationship models for mutual diffusion coefficients of the drug in biocompatible and biodegradable polymers and partition coefficients of the drug between polymers and blood, a framework was developed to predict optimal drug delivery devices for desired dosage regimens.The validation of the predicted mutual diffusion and partition coefficients using experimental data was reported in previous studies. Optimal design parameters along with selection of most appropriate polymers suitable for different dosage regimens, selected based on current clinical practice, were predicted for maximum bioavailability of the drug while maintaining the released drug concentration in blood within the therapeutic range. Reservoir and monolithic type of diffusion-controlled drug delivery devices of different shapes and sizes were predicted with different initial drug loadings and bioavailability for different dosage regimens. The effects of the released Paclitaxel from these devices on the tumor growth were also modeled using a previously reported mathematical pharmacokinetic-pharmacodynamic model. The proposed approach can easily be used to design other diffusion-controlled drug delivery devices. K E Y W O R D Scontrolled drug release, in silico modeling, paclitaxel, QSPR, reverse engineering | 1479 PRAMANIK ANd GARG How to cite this article: Pramanik A, Garg S. Design of diffusion-controlled drug delivery devices for controlled release of Paclitaxel. Chem Biol Drug Des.

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Section: Optimization Resultsmentioning

confidence: 68%