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Open accessJournal ArticleDOI: 10.1016/J.JCONREL.2021.03.005

Not only in silico drug discovery: Molecular modeling towards in silico drug delivery formulations.

04 Mar 2021-Journal of Controlled Release (J Control Release)-Vol. 332, pp 390-417
Abstract: The use of methods at molecular scale for the discovery of new potential active ligands, as well as previously unknown binding sites for target proteins, is now an established reality. Literature offers many successful stories of active compounds developed starting from insights obtained in silico and approved by Food and Drug Administration (FDA). One of the most famous examples is raltegravir, a HIV integrase inhibitor, which was developed after the discovery of a previously unknown transient binding area thanks to molecular dynamics simulations. Molecular simulations have the potential to also improve the design and engineering of drug delivery devices, which are still largely based on fundamental conservation equations. Although they can highlight the dominant release mechanism and quantitatively link the release rate to design parameters (size, drug loading, et cetera), their spatial resolution does not allow to fully capture how phenomena at molecular scale influence system behavior. In this scenario, the "computational microscope" offered by simulations at atomic scale can shed light on the impact of molecular interactions on crucial parameters such as release rate and the response of the drug delivery device to external stimuli, providing insights that are difficult or impossible to obtain experimentally. Moreover, the new paradigm brought by nanomedicine further underlined the importance of such computational microscope to study the interactions between nanoparticles and biological components with an unprecedented level of detail. Such knowledge is a fundamental pillar to perform device engineering and to achieve efficient and safe formulations. After a brief theoretical background, this review aims at discussing the potential of molecular simulations for the rational design of drug delivery systems.

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Topics: Drug discovery (51%)
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Open accessJournal ArticleDOI: 10.1002/ADMA.202008635
01 Jun 2021-Advanced Materials
Abstract: The Martini model, a coarse-grained force field initially developed with biomolecular simulations in mind, has found an increasing number of applications in the field of soft materials science. The model's underlying building block principle does not pose restrictions on its application beyond biomolecular systems. Here, the main applications to date of the Martini model in materials science are highlighted, and a perspective for the future developments in this field is given, particularly in light of recent developments such as the new version of the model, Martini 3.

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9 Citations


Journal ArticleDOI: 10.1016/J.ADDR.2021.05.016
Pauric Bannigan1, Matteo Aldeghi1, Zeqing Bao1, Florian Häse1  +2 moreInstitutions (1)
Abstract: Machine learning (ML) has enabled ground-breaking advances in the healthcare and pharmaceutical sectors, from improvements in cancer diagnosis, to the identification of novel drugs and drug targets as well as protein structure prediction. Drug formulation is an essential stage in the discovery and development of new medicines. Through the design of drug formulations, pharmaceutical scientists can engineer important properties of new medicines, such as improved bioavailability and targeted delivery. The traditional approach to drug formulation development relies on iterative trial-and-error, requiring a large number of resource-intensive and time-consuming in vitro and in vivo experiments. This review introduces the basic concepts of ML-directed workflows and discusses how these tools can be used to aid in the development of various types of drug formulations. ML-directed drug formulation development offers unparalleled opportunities to fast-track development efforts, uncover new materials, innovative formulations, and generate new knowledge in drug formulation science. The review also highlights the latest artificial intelligence (AI) technologies, such as generative models, Bayesian deep learning, reinforcement learning, and self-driving laboratories, which have been gaining momentum in drug discovery and chemistry and have potential in drug formulation development.

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Topics: Drug development (65%), Drug delivery (51%)

7 Citations


Open accessPosted ContentDOI: 10.21203/RS.3.RS-732929/V1
21 Jul 2021-
Abstract: Providing an efficient system for drug delivery and chemotherapy has always been an important issue. Modification of the surface of silica nanoparticles (SiO2) provides an opportunity for achieving stimulus-sensitive drug delivery system. Here, we have modified the surface of SiO2 using hydrogen bonding interactions by employing an aqueous two-phase system (ATPS) based on polyethylene glycol and lysine. This novel biocompatible ATPS provides an environment for simultaneous drug encapsulation, SiO2 modification, and drug partitioning in one pot. Addition of SiO2 to ATPS increased the partitioning of doxorubicin (DOX) as an anti-cancer drug from 47.92 in the absence of nanoparticles to 92.33 due to the interactions between drug and nanoparticles. The formation of nanoformulation and its characteristics were investigated applying microscopy, spectroscopy and thermal analysis. Drug release study demonstrated that DOX is loaded on nanoformulations efficiently with an encapsulation efficiency of 63.84% and shows lower release in physiological environment compared to the unmodified nanoparticles. While in acidic conditions of pH 5.5, significant increase was observed in the release profile. MTT assay on MCF-7 cancer cells confirmed that the nanoformulations were non-toxic and DOX-loaded nanocarrier showed anti-cancer behavior. These results indicate that the prepared nanoformulations are promising nanocarriers for controlled drug release purposes.

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Topics: Nanocarriers (58%)

1 Citations


Open accessJournal ArticleDOI: 10.1039/D1CP02113C
Abstract: In this work, the long-time diffusion of a solute in a chemically crosslinked and flexible hydrogel is computed from a bead-spring model of a polymeric network to assess the effect of steric obstruction. The relative diffusivities obtained for a wide variety of systems can be described by an exponential decay depending on a parameter that differs from that employed for rigid gels. The mathematical expression derived here can approximately predict the diffusivity in flexible gels if steric hindrance is the mechanism ruling diffusion.

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1 Citations


Open accessJournal ArticleDOI: 10.1002/ADBI.202100637
20 Jul 2021-
Abstract: The increasing exploitation of graphene-based materials (GBMs) is driven by their unique properties and structures, which ignite the imagination of scientists and engineers. At the same time, the very properties that make them so useful for applications lead to growing concerns regarding their potential impacts on human health and the environment. Since GBMs are inert to reaction, various attempts of surface functionalization are made to make them reactive. Herein, surface functionalization of GBMs, including those intentionally designed for specific applications, as well as those unintentionally acquired (e.g., protein corona formation) from the environment and biota, are reviewed through the lenses of nanotoxicity and design of safe materials (safe-by-design). Uptake and toxicity of functionalized GBMs and the underlying mechanisms are discussed and linked with the surface functionalization. Computational tools that can predict the interaction of GBMs behavior with their toxicity are discussed. A concise framing of current knowledge and key features of GBMs to be controlled for safe and sustainable applications are provided for the community.

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267 results found


Open accessJournal ArticleDOI: 10.1006/JCPH.1995.1039
Steven J. Plimpton1Institutions (1)
Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

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Topics: Intel Paragon (64%), Intel iPSC (62%), Parallel algorithm (58%) ... show more

26,738 Citations


Open accessJournal ArticleDOI: 10.1002/ANDP.19053220806
01 Jan 1905-Annalen der Physik
Abstract: Este material fue digitalizado en el marco del Proyecto subvencionado por la Fundacion Antorchas y se encuentra en la Biblioteca del Departamento de Fisica de la Facultad de Ciencias Exactas de la Universidad Nacional de La Plata.

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6,377 Citations


Open accessBook
01 Jan 2002-
Abstract: Second and revised edition Understanding Molecular Simulation: From Algorithms to Applications explains the physics behind the "recipes" of molecular simulation for materials science. Computer simulators are continuously confronted with questions concerning the choice of a particular technique for a given application. A wide variety of tools exist, so the choice of technique requires a good understanding of the basic principles. More importantly, such understanding may greatly improve the efficiency of a simulation program. The implementation of simulation methods is illustrated in pseudocodes and their practical use in the case studies used in the text. Since the first edition only five years ago, the simulation world has changed significantly -- current techniques have matured and new ones have appeared. This new edition deals with these new developments; in particular, there are sections on: · Transition path sampling and diffusive barrier crossing to simulaterare events · Dissipative particle dynamic as a course-grained simulation technique · Novel schemes to compute the long-ranged forces · Hamiltonian and non-Hamiltonian dynamics in the context constant-temperature and constant-pressure molecular dynamics simulations · Multiple-time step algorithms as an alternative for constraints · Defects in solids · The pruned-enriched Rosenbluth sampling, recoil-growth, and concerted rotations for complex molecules · Parallel tempering for glassy Hamiltonians Examples are included that highlight current applications and the codes of case studies are available on the World Wide Web. Several new examples have been added since the first edition to illustrate recent applications. Questions are included in this new edition. No prior knowledge of computer simulation is assumed.

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5,824 Citations


Journal ArticleDOI: 10.1038/NMAT2442
Andre E. Nel1, Lutz Mädler2, Darrell Velegol3, Tian Xia1  +5 moreInstitutions (7)
14 Jun 2009-Nature Materials
Abstract: Rapid growth in nanotechnology is increasing the likelihood of engineered nanomaterials coming into contact with humans and the environment. Nanoparticles interacting with proteins, membranes, cells, DNA and organelles establish a series of nanoparticle/biological interfaces that depend on colloidal forces as well as dynamic biophysicochemical interactions. These interactions lead to the formation of protein coronas, particle wrapping, intracellular uptake and biocatalytic processes that could have biocompatible or bioadverse outcomes. For their part, the biomolecules may induce phase transformations, free energy releases, restructuring and dissolution at the nanomaterial surface. Probing these various interfaces allows the development of predictive relationships between structure and activity that are determined by nanomaterial properties such as size, shape, surface chemistry, roughness and surface coatings. This knowledge is important from the perspective of safe use of nanomaterials.

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5,470 Citations


Journal ArticleDOI: 10.1002/JCC.540130812
Abstract: The Weighted Histogram Analysis Method (WHAM), an extension of Ferrenberg and Swendsen's Multiple Histogram Technique, has been applied for the first time on complex biomolecular Hamiltonians. The method is presented here as an extension of the Umbrella Sampling method for free-energy and Potential of Mean Force calculations. This algorithm possesses the following advantages over methods that are currently employed: (1) It provides a built-in estimate of sampling errors thereby yielding objective estimates of the optimal location and length of additional simulations needed to achieve a desired level of precision; (2) it yields the “best” value of free energies by taking into account all the simulations so as to minimize the statistical errors; (3) in addition to optimizing the links between simulations, it also allows multiple overlaps of probability distributions for obtaining better estimates of the free-energy differences. By recasting the Ferrenberg–Swendsen Multiple Histogram equations in a form suitable for molecular mechanics type Hamiltonians, we have demonstrated the feasibility and robustness of this method by applying it to a test problem of the generation of the Potential of Mean Force profile of the pseudorotation phase angle of the sugar ring in deoxyadenosine. © 1992 by John Wiley & Sons, Inc.

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Topics: Histogram (55%), Umbrella sampling (51%)

5,148 Citations


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20219