Modeling the modified drug release from curved shape drug delivery systems – Dome Matrix®

Summary of main observation and conclusion Physical encapsulation of drugs into polymer micelles is a common method of loading hydrophobic drugs. Methoxy polyethylene glycol‐poly(D,L‐lactide) (mPEG‐PDLLA) is one of the most commonly used drug carrier. At present, whether a carrier is suitable for the loading of a certain drug is determined by drug loading experiments. This process costs a lot of time. Therefore, an efficient predicting method to avoid time‐consuming tests is critical. In this study, we prepared mPEG5k‐PDLLA5k and used it to load a series of drugs. Three parameters were used to test the miscibility of mPEG5k‐PDLLA5k with drugs, including absolute difference in Hildebrand solubility parameters (|Δδ|), Flory–Huggins interaction parameter (χ) and the distance (D value) calculated from the two‐dimensional solubility parameters. We found the two‐dimensional solubility parameters obtained from JB2013 group contribution (GC) method was useful. By comparing the drug loading content (DLC) with the D value, we found that when the D value was less than 5.0 (MJ/m3)1/2, the miscibility of drug and mPEG5k‐PDLLA5k was good and drug loading capability was high; when the D value was more than 8.0 (MJ/m3)1/2, the drug was barely loaded. Thus, this work provided a rationale to qualitatively predict the loading capability of mPEG5k‐PDLLA5k for hydrophobic drugs.

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“…Many computational studies provided insights into the molecular interactions between drugs and carriers . For example, Wu et al .…”

Section: Background and Originality Contentmentioning

confidence: 99%

Predicting the Loading Capability of mPEG‐PDLLA to Hydrophobic Drugs Using Solubility Parameters^†

Sun

Shen

et al. 2020

Chin. J. Chem.

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“…Considering the better R 2 values recorded for MEL and MZ when applying the Higuchi model, known as a particular situation of the Korsmeyer–Peppas model [ 106 ], it could be concluded that the release of the active formulations is not due to the HA hydrogel swelling. It would be expected to have a releasing mechanism predominantly through diffusion.…”

Section: Resultsmentioning

confidence: 99%

Considerations on the Controlled Delivery of Bioactive Compounds through Hyaluronic Acid Membrane

et al. 2022

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(1) Background: The standard treatment for periodontal disease, a chronic inflammatory state caused by the interaction between biofilms generated by organized oral bacteria and the local host defense response, consists of calculus and biofilm removal through mechanical debridement, associated with antimicrobial therapy that could be delivered either systemically or locally. The present study aimed to determine the effectiveness of a hyaluronic acid membrane matrix as a carrier for the controlled release of the active compounds of a formulation proposed as a topical treatment for periodontal disease, and the influence of pH on the complex system’s stability. (2) Methods: The obtained hyaluronic acid (HA) hydrogel membrane with dispersed melatonin (MEL), metronidazole (MZ), and tetracycline (T) was completely characterized through FTIR, XRD, thermal analysis, UV-Vis and fluorescence spectroscopy, fluorescence microscopy, zeta potential and dielectric analysis. The MTT viability test was applied to check the cytotoxicity of the obtained membranes, while the microbiological assessment was performed against strains of Staphylococcus spp. and Streptococcus spp. The spectrophotometric investigations allowed to follow up the release profile from the HA matrix for MEL, MZ, and T present in the topical treatment considered. We studied the behavior of the active compounds against the pH of the generated environment, and the release profile of the bioactive formulation based on the specific comportment towards pH variation. The controlled delivery of the bioactive compounds using HA as a supportive matrix was modeled applying Korsmeyer–Peppas, Higuchi, first-order kinetic models, and a newly proposed pseudo-first-order kinetic model. (3) Results: It was observed that MZ and T were released at higher active concentrations than MEL when the pH was increased from 6.75, specific for patients with periodontitis, to a pH of 7.10, characterizing the healthy patients. Additionally, it was shown that for MZ, there is a burst delivery up to 2.40 × 10−5 mol/L followed by a release decrease, while for MEL and T a short release plateau was recorded up to a concentration of 1.80 × 10−5 mol/L for MEL and 0.90 × 10−5 mol/L for T, followed by a continuous release; (4) Conclusions: The results are encouraging for the usage of the HA membrane matrix as releasing vehicle for the active components of the proposed topical treatment at a physiological pH.

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“…Mechanistic, physics-based modeling and simulation is increasingly used in healthcare for device engineering and design ( FDA, 2016 ; Caccavo et al, 2017 ; Shirazi et al, 2019 ). A typical example is the transdermal drug delivery systems (TDDS).…”

Section: Introductionmentioning

confidence: 99%

Inverse Mechanistic Modeling of Transdermal Drug Delivery for Fast Identification of Optimal Model Parameters

2021

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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 additional 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.

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Modeling the modified drug release from curved shape drug delivery systems – Dome Matrix®

Cited by 21 publications

References 26 publications

Predicting the Loading Capability of mPEG‐PDLLA to Hydrophobic Drugs Using Solubility Parameters^†

Predicting the Loading Capability of mPEG‐PDLLA to Hydrophobic Drugs Using Solubility Parameters^†

Considerations on the Controlled Delivery of Bioactive Compounds through Hyaluronic Acid Membrane

Inverse Mechanistic Modeling of Transdermal Drug Delivery for Fast Identification of Optimal Model Parameters

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Modeling the modified drug release from curved shape drug delivery systems – Dome Matrix®

Cited by 21 publications

References 26 publications

Predicting the Loading Capability of mPEG‐PDLLA to Hydrophobic Drugs Using Solubility Parameters†

Predicting the Loading Capability of mPEG‐PDLLA to Hydrophobic Drugs Using Solubility Parameters†

Considerations on the Controlled Delivery of Bioactive Compounds through Hyaluronic Acid Membrane

Inverse Mechanistic Modeling of Transdermal Drug Delivery for Fast Identification of Optimal Model Parameters

Contact Info

Product

Resources

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Predicting the Loading Capability of mPEG‐PDLLA to Hydrophobic Drugs Using Solubility Parameters^†

Predicting the Loading Capability of mPEG‐PDLLA to Hydrophobic Drugs Using Solubility Parameters^†