Integrated in silico formulation design of self-emulsifying drug delivery systems
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TLDR
This research combined machine learning, central composite design, molecular modeling and experimental approaches for rational SEDDS formulation design, which revealed the diffusion behavior in water and the role of cosurfactants.About:
This article is published in Acta Pharmaceutica Sinica B.The article was published on 2021-05-05 and is currently open access. It has received 22 citations till now.read more
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Computational pharmaceutics - A new paradigm of drug delivery
TL;DR: A comprehensive and detailed review in all areas of computational pharmaceutics and "Pharma 4.0", including artificial intelligence and machine learning algorithms, molecular modeling, mathematical modeling, process simulation, and physiologically based pharmacokinetic (PBPK) modeling is provided in this article.
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Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm
TL;DR: In this paper , a machine learning predictive model for LNP-based mRNA vaccines was developed, validated by experiments, and further integrated with molecular modeling, which can be used for virtual screening of LNP formulations in the future.
Journal ArticleDOI
Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm
TL;DR: In this paper, a machine learning predictive model for LNP-based mRNA vaccines was developed, validated by experiments, and further integrated with molecular modeling, which can be used for virtual screening of LNP formulations in the future.
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Fundamental Aspects of Lipid-Based Excipients in Lipid-Based Product Development
Deepa Nakmode,Valamla Bhavana,Pradip Thakor,Jitender Madan,PankajKumar Singh,Shashi Bala Singh,Jessica M. Rosenholm,Kuldeep K. Bansal,Neelesh Kumar Mehra +8 more
TL;DR: In this review, this review exhaustively summarize the lipids excipients in relation to their classification, absorption mechanisms, and lipid-based product development.
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Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts
Prakash Muthudoss,Ishan Tewari,Rayce Lim Rui Chi,Kwok Jia Young,Eddy Yii Chung Ann,Doreen Ng Sean Hui,Ooi Yee Khai,Ravikiran Allada,Manohar Rao,Saurabh Shahane,Samir Das,Irfan Babla,Sandeep Mhetre,Amrit Paudel +13 more
TL;DR: A workflow integrating machine learning to NIR spectral analysis was established and implemented, and NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established.
References
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Journal ArticleDOI
Development and optimization of a self-microemulsifying drug delivery system for atorvastatin calcium by using D-optimal mixture design.
Dong Woo Yeom,Ye Seul Song,Sung Rae Kim,Sang Gon Lee,Min Hyung Kang,Sangkil Lee,Young Wook Choi +6 more
TL;DR: An optimized ATV-loaded SMEDDS formulation is successfully developed by using the d-optimal mixture design, that could potentially be used for improving the oral absorption of poorly water-soluble drugs.
Journal ArticleDOI
Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques.
TL;DR: In the specific ketoprofen–CD systems, machine learning model showed better predictive performance than molecular modeling calculation, while molecular simulation could provide structural, dynamic and energetic information that could produce synergistic effect for interpreting and predicting pharmaceutical formulations.
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Metronidazole/Hydroxypropyl-β-Cyclodextrin inclusion complex nanofibrous webs as fast-dissolving oral drug delivery system.
Asli Celebioglu,Tamer Uyar +1 more
TL;DR: The phase solubility and dissolution tests revealed that the water-solubility of Metronidazole was significantly enhanced by HP-β-CyD inclusion complexation, suggesting that such Metronsidazoles nanofibrous webs can be suitable for fast-dissolving oral drug delivery.
Journal ArticleDOI
Digital Pharmaceutical Sciences.
TL;DR: The machine learning methods commonly used in pharmaceutical sciences are discussed, with a specific emphasis on artificial neural networks due to their capability to model the nonlinear relationships that are commonly encountered in pharmaceutical research.
Journal ArticleDOI
Mechanistic Understanding From Molecular Dynamics Simulation in Pharmaceutical Research 1: Drug Delivery.
Alex Bunker,Tomasz Róg +1 more
TL;DR: This review outlines the growing role that molecular dynamics simulation is able to play as a design tool in drug delivery and focuses on nanomedicine: the development of nanoscale drug delivery vehicles.