Insights into pharmaceutical nanocrystal dissolution: a molecular dynamics simulation study on aspirin

Crumbling crystals: on the dissolution mechanism of NaCl in water

Dissolution of ionic salts in water is ubiquitous, particularly for NaCl. However, an atomistic scale understanding of the process remains elusive. Simulations lend themselves conveniently to studying dissolution since they provide the spatio-temporal resolution that can be difficult to obtain experimentally. Nevertheless, the complexity of various inter- and intra-molecular interactions require careful treatment and long time scale simulations, both of which are typically hindered by computational expense. Here, we use advances in machine learning potential methodology to resolve at an ab initio level of theory the dissolution mechanism of NaCl in water. The picture that emerges is that of a steady ion-wise unwrapping of the crystal preceding its rapid disintegration, reminiscent of crumbling. The onset of crumbling can be explained by a strong increase in the ratio of the surface area to volume of the crystal. Overall, dissolution comprises a series of highly dynamical microscopic sub-processes, resulting in an inherently stochastic mechanism. These atomistic level insights contribute to the general understanding of dissolution mechanisms in other crystals, and the methodology is primed for more complex systems of recent interest such as water/salt interfaces under flow and salt crystals under confinement.

Predicting Surface pH in Unbuffered Conditions for Acids, Bases, and Their Salts - A Review of Modeling Approaches and Their Performance

Dissolution of ionizable drugs and their salts is a function of drug surface solubility driven by the surface pH, i.e., the microenvironmental pH at the solid/liquid interface, which will deviate from bulk pH when there is an acid-base reaction occurring at the solid/liquid interface. In this work, we first present a brief overview of the modeling approaches available in the literature, classified according to the rate-determining step assumed in the dissolution process. In the second part, we present and evaluate the prediction performance of two different modeling approaches for surface pH. The first method relies only on thermodynamic equilibria, while the second method accounts for transport phenomena of charged compounds through the diffusional boundary layer using the Nernst - Planck equation. Model outcomes are compared with experimental data taken from the literature and obtained during this work. In terms of surface pH predictions, the models provide identical values for weak acids or weak bases. The models' outcomes for bases are in good agreement with experimental data in acidic conditions (bulk pH 1-4), while overpredictions are observed in the 5-7 bulk pH range in a system-dependent manner. Deviations can be related to the effect of surface dissolution (also referred to as surface reaction), which may become a controlling mechanism and slow the replenishment of the unionized drug at the surface of the crystal. Surface pH predictions for acids are generally in good agreement with experiments, with a slight underestimation for some drug examples, which could be related to errors in intrinsic solubility determination or to the assumption of thermodynamic equilibrium at the surface of the drug. A good agreement is also observed for salts with the thermodynamic model except for mesylate salts, suggesting that other phenomena, not currently included in the thermodynamic equilibrium model, may determine the surface pH.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

Recent advances in versatile inverse lyotropic liquid crystals

Owing to the rapid and significant progress in advanced materials and life sciences, nanotechnology is increasingly gaining in popularity. Among numerous bio-mimicking carriers, inverse lyotropic liquid crystals are known for their unique properties. These carriers make accommodation of molecules with varied characteristics achievable due to their complicated topologies. Besides, versatile symmetries of inverse LCNPs (lyotropic crystalline nanoparticles) and their aggregating bulk phases allow them to be applied in a wide range of fields including drug delivery, food, cosmetics, material sciences etc. In this review, in-depth summary, discussion and outlook for inverse lyotropic liquid crystals are provided.

Biorelevant dissolution testing and physiologically based absorption modeling to predict in vivo performance of supersaturating drug delivery systems

Supersaturating drug delivery systems (SDDS) enhance the oral absorption of poorly water-soluble drugs by achieving a supersaturated state in the gastrointestinal tract. The maintenance of a supersaturated state is decided by the complex interplay among inherent properties of drug, excipients and physiological conditions of gastrointestinal tract. The biopharmaceutical advantage through SDDS can be mechanistically investigated by coupling biopredictive dissolution testing with physiologically based absorption modeling (PBAM). However, the development of biopredictive dissolution methods possess challenges due to concurrent dissolution, supersaturation, precipitation, and possible redissolution of precipitates during gastrointestinal transit of SDDS. In this comprehensive review, our effort is to critically assess the current state-of-knowledge and provide future directions for PBAM of SDDS. The review outlines various methods used to retrieve physiologically relevant values for input parameters like solubility, dissolution, precipitation, lipid-digestion and permeability of SDDS. SDDS-specific parameterization includes solubility values corresponding to apparent physical form, dissolution in physiologically relevant volumes with biorelevant media, and transfer experiments to incorporate precipitation kinetics. Interestingly, the lack of experimental permeability values and modification of absorption flux through SDDS possess the additional challenge for its PBAM. Supersaturation triggered permeability modifications are reported to fit the observed plasma concentration-time profile. Hence, the experimental insights on good fitting with modified permeability can be potential area of future research for the development of in vitro methods to reliably predict oral absorption of SDDS.

Factors Controlling Persistent Needle Crystal Growth: The Importance of Dominant One-Dimensional Secondary Bonding, Stacked Structures, and van der Waals Contact

Needle crystals can cause filtering and handling problems in industrial settings, and the factors leading to a needle crystal morphology have been investigated. The crystal growth of the amide and methyl, ethyl, isopropyl, and t-butyl esters of diflunisal have been examined, and needle growth has been observed for all except the t-butyl ester. Their crystal structures show that the t-butyl ester is the only structure that does not contain molecular stacking. A second polymorph of a persistent needle forming phenylsulfonamide with a block like habit has been isolated. The structure analysis has been extended to known needle forming systems from the literature. The intermolecular interactions in needle forming structures have been analyzed using the PIXEL program, and the properties driving needle crystal growth were found to include a 1D motif with interaction energy greater than -30 kJ/mol, at least 50% vdW contact between the motif neighbors, and a filled unit cell which is a monolayer. Crystal structures are classified into persistent and controllable needle formers. Needle growth in the latter class can be controlled by choice of solvent. The factors shown here to be drivers of needle growth will help in the design of processes for the production of less problematic crystal products.

Additives-directed lyotropic liquid crystals architecture: Simulations and experiments

In this study, alkanes and sucrose esters are employed to investigate the influence of additives on lyotropic liquid crystal architecture. After molecular dynamic simulations and experiment characterization, we showed how the additives control the structure of LLCs. By controlling the polarity of additives, the phase behavior of LLCs can be engineered to form the required structure. Dissipative particle dynamics (DPD) is introduced for simulating the self-assembly of phytantriol (PT), providing intuitionistic images and structure information, which shows that additives with low-polarity complicate the internal structure of liquid crystal systems. Then the ternary phase diagrams of additives, PT, and water are constructed to systematically study the effects of additives on the phase behavior of LLCs. Consistent with DPD simulation results, there is a certain regularity in the effects of additives on the structure of liquid crystals. The difference in the structure of LLCs is due to the variability in the critical packing parameter (CPP) obtained by changing the polarity of additives. Our findings demonstrate that additives polarity is a key factor in LLCs structure, and may pave a promising avenue for novel LLCs development and translation, determining the self-assembly process and the resulting phase of LLCs.

Capturing Crystal Shape Evolution from Molecular Simulations

A simple and efficient algorithm for tracking shape evolution of small-molecule organic crystals during molecular simulations is described. It is based on the reconstruction of a crystal surface from molecular coordinates using an alpha-shape triangulation algorithm followed by the DBSCAN clustering of neighboring triangles with similar normal vectors to crystal faces. No information except the unit cell parameters is needed beforehand, enabling the user to automatically detect not only existing but also new forming crystal faces and edges, which is valuable for prediction of growth and dissolution kinetics. The results are demonstrated for aspirin and paracetamol crystals.

Solubility Advantage of Amorphous Ketoprofen. Thermodynamic and Kinetic Aspects by Molecular Dynamics and Free Energy Approaches

Thermodynamic and kinetic aspects of crystalline (c-KTP) and amorphous (a-KTP) ketoprofen dissolution in water have been investigated by molecular dynamics simulation focusing on free energy properties. Absolute free energies of all relevant species and phases have been determined by thermodynamic integration on a novel path, first connecting the harmonic to the anharmonic system Hamiltonian at low T and then extending the result to the temperature of interest. The free energy required to transfer one ketoprofen molecule from the crystal to the solution is in fair agreement with the experimental value. The absolute free energy of the amorphous form is 19.58 kJ/mol higher than for the crystal, greatly enhancing the ketoprofen concentration in water, although as a metastable species in supersaturated solution. The kinetics of the dissolution process has been analyzed by computing the free energy profile along a reaction coordinate bringing one ketoprofen molecule from the crystal or amorphous phase to the solvated state. This computation confirms that, compared to the crystal form, the dissolution rate is nearly 7 orders of magnitude faster for the amorphous form, providing one further advantage to the latter in terms of bioavailability. The problem of drug solubility, of great practical importance, is used here as a test bed for a refined method to compute absolute free energies, which could be of great interest in biophysics and drug discovery in particular.

Remarkable decrease in stiffness of aspirin crystals upon reducing crystal size to nanoscale dimensions via sonochemistry

Nano-dimensional crystals of aspirin generated through sonochemistry exhibit Young's modulus values an order of magnitude softer than macro-dimensional crystals.

Multiscale modeling of aspirin dissolution: from molecular resolution to experimental scales of time and size

Predicted absolute and face-specific rate constants of aspirin dissolution are incorporated in a simulation based on the equations of classical mass transfer to reproduce kinetic dissolution in experiment using a Jamin-type interferometer.