Complex phenomena challenging drug formulation are elucidated by molecular dynamics (MD) simulations in this work. For the active pharmaceutical ingredients studied, including ibuprofen, diclofenac, and carbamazepine, MD simulations have provided atomistic insights that complement and expand experimental results, offering a mechanistic basis for the rational design of precursor solutions, precipitation, polymorph control, and the dissolution of the obtained crystallites. In the domain of (pre-)nucleation, MD simulations have revealed that nucleation of the APIs ibuprofen and diclofenac does not proceed solely through classical solute-by-solute addition. Instead, nucleation is preceded by the formation of static or dynamic (liquid-like) prenucleation clusters (PNCs), driven by hydrophobic segregation. Shifting from the initial stages to later phases of precipitation, an evaluation of precipitate formation energy as a function of solute size N, as envisioned by CNT, is presented; as well as a discussion of relative size-dependent solubility. In this regard, predictive modelling of polymorph ranking and stability across various sizes and polar versus apolar solvents has been demonstrated and is expanded into investigating the stabilising effect of solvents or polymers on precipitates. With the question of how the acid-induced dissolution process of an API crystallite proceeds, a nuanced, multi-stage mechanism was uncovered. These findings were observed in MD simulations conducted with an advanced protonation scheme employing the instantaneous pK method. Findings on crystallites undergoing substantial surface reorganisation and developing persistent, protonated core-shell structures extend the classical view of dissolution as merely the reverse of crystallisation, namely solute-by-solute dissociation. Released drug species involve both individual molecules and oligo aggregates, forming a “dense-solutes” interfacial domain. This domain acts as a dynamic buffer, regulating solute concentrations at the crystal-solution interface and providing a molecular rationalisation for the classic diffusion layer model described in empirical models like Nernst-Brunner’s. Crucially, the application of MD in drug formulation is not confined to retrospective explanations. Instead, MD supports hypothesis-driven exploration and mechanistic prediction, enabling researchers to evaluate the impact of specific molecular and environmental factors in silico before conducting experimental tests. The combination of simulation and experimental work, as illustrated in these studies, connects molecular processes to macroscopic pharmaceutical outcomes. The mechanistic models and predictive frameworks developed in this work enable the rational design, optimisation, and control of drug formulation processes throughout the development. Hence, MD simulations are a vital tool for enhancing API formulation stability, solubility, migration and ultimately therapeutic efficacy, thus helping to shape the future of pharmaceutical drug formulation and delivery.
Moritz Macht (Thu,) studied this question.