conor parks

Current polymorph prediction methods, known as lattice energy minimization, seek to determine the crystal lattice with the lowest potential energy, rendering it unable to predict solvent dependent metastable form crystallization. Facilitated by embarrassingly parallel, multiple replica, large-scale molecular dynamics simulations, we report on a new method concerned with predicting crystal structures using the kinetics and solubility of the low energy polymorphs predicted by lattice energy minimization. The proposed molecular dynamics simulation methodology provides several new predictions to the field of crystallization. (1) The methodology is shown to correctly predict the kinetic preference for β-glycine nucleation in water relative to α- and γ-glycine. (2) Analysis of nanocrystal melting temperatures show γ- nanocrystals have melting temperatures up to 20 K lower than either α- or β-glycine. This provides a striking explanation of how an energetically unstable classical nucleation theory (CNT) transition state complex leads to kinetic inaccessibility of γ-glycine in water, despite being the thermodynamically preferred polymorph predicted by lattice energy minimization. (3) The methodology also predicts polymorph-specific solubility curves, where the α-glycine solubility curve is reproduced to within 19% error, over a 45 K temperature range, using nothing but atomistic-level information provided from nucleation simulations. (4) Finally, the methodology produces the correct solubility ranking of β- > α-glycine. In this work, we demonstrate how the methodology supplements lattice energy minimization with molecular dynamics nucleation simulations to give the correct polymorph prediction, at different length scales, when lattice energy minimization alone would incorrectly predict the formation of γ-glycine in water from the ranking of lattice energies. Thus, lattice energy minimization optimization algorithms are supplemented with the necessary solvent/solute dependent solubility and nucleation kinetics of polymorphs to predict which structure will come out of solution, and not merely which structure has the most stable lattice energy.

Nanocrystals are receiving increased attention for pharmaceutical applications due to their enhanced solubility relative to their micron-sized counterpart and, in turn, potentially increased bioavailability. In this work, a computational method is proposed to predict the following: (1) polymorph specific dissolution kinetics and (2) the multiplicative increase in the polymorph specific nanocrystal solubility relative to the bulk solubility. The method uses a combination of molecular dynamics and a parametric particle size dependent mass transfer model. The method is demonstrated using a case study of α-, β-, and γ-glycine. It is shown that only the α-glycine form is predicted to have an increasing dissolution rate with decreasing particle size over the range of particle sizes simulated. On the contrary, γ-glycine shows a monotonically increasing dissolution rate with increasing particle size and dissolves at a rate 1.5 to 2 times larger than α- or β-glycine. The accelerated dissolution rate of γ-glycine relative to the other two polymorphs correlates directly with the interfacial energy ranking of γ > β > α obtained from the dissolution simulations, where γ- is predicted to have an interfacial energy roughly four times larger than either α- or β-glycine. From the interfacial energies, α- and β-glycine nanoparticles were predicted to experience modest solubility increases of up to 1.4 and 1.8 times the bulk solubility, where as γ-glycine showed upward of an 8 times amplification in the solubility. These MD simulations represent a first attempt at a computational (pre)screening method for the rational design of experiments for future engineering of nanocrystal API formulations.

Using molecular dynamics simulations, we demonstrate the ability of high intensity, 1.5 V/nm, static electric fields to induce the formation of a new polymorph of paracetamol, one of the most important fever and pain suppressants in the world. In the newly produced polymorphic form, paracetamol molecules adopt a spatial orientation that maximizes the alignment between the electric dipole and the applied electric field vector. As the properties of crystalline materials are ultimately determined by the conformational and packing patterns of molecules in the solid state, it is predicted that electric fields have the potential to spur the creation of never before seen materials with potential novel properties such as increased drug efficacy in vivo. Paracetamol nanocrystal growth and dissolution dynamics are systematically investigated as a function of the applied electric field intensity and temperature. It is shown that the electric field suppresses the growth rate of supersaturated paracetamol nanocrystals, and can both increase and inhibit the dissolution rates of undersaturated paracetamol nanocrystals. This shows that molecular dynamics predicts that electric fields are a useful control variable for the manipulation of crystal size distributions and crystallization dynamics. Analysis of the crystal morphology under the presence of the electric field shows that paracetamol nanocrystals adopt an electric field intensity dependent morphology. Finally, the new polymorph is shown to be metastable in the absence of the electric field with increased aqueous solubility and hence potentially bioavailability relative to form I and II. The new form is stabilized at short times through a temperature quench, but requires longer application of the electric field to maintain the new polymorph during crystallization.