Crystal size, shape, and conformational changes drive both the disappearance and reappearance of ritonavir polymorphs in the mill.

Organic compounds can crystallize in different forms known as polymorphs. Discovery and control of polymorphism is crucial to the pharmaceutical industry since different polymorphs can have significantly different physical properties which impacts their utilization in drug delivery. Certain polymorphs have been reported to 'disappear' from the physical world, irreversibly converting to new ones. These unwanted polymorph conversions, initially prevented by slow nucleation kinetics, are eventually observed driven by significant gains in thermodynamic stabilities. The most infamous of these cases is that of the HIV drug ritonavir (RVR): Once its reluctant form was unwillingly nucleated for the first time, its desired form could no longer be produced with the same manufacturing process. Here we show that RVR's extraordinary disappearing polymorph as well as its reluctant form can be consistently produced by ball-milling under different environmental conditions. We demonstrate that the significant difference in stability between its polymorphs can be changed and reversed in the mill-a process we show is driven by crystal size as well as crystal shape and conformational effects. We also show that those effects can be controlled through careful design of milling conditions since they dictate the kinetics of crystal breakage, dissolution, and growth processes that eventually lead to steady-state crystal sizes and shapes in the mill. This work highlights the huge potential of mechanochemistry in polymorph discovery of forms initially difficult to nucleate, recovery of disappearing polymorphs, and polymorph control of complex flexible drug compounds such as RVR.

Online 3D Characterization of Micrometer-Sized Cuboidal Particles in Suspension.

Characterization of particle size and shape is central to the study of particulate matter in its broadest sense. Whilst 1D characterization defines the state of the art, the development of 2D and 3D characterization methods has attracted increasing attention, due to a common need to measure particle shape alongside size. Herein, ensembles of micrometer-sized cuboidal particles are studied, for which reliable sizing techniques are currently missing. Such particles must be characterized using three orthogonal dimensions to completely describe their size and shape. To this end, the utility of an online and in-flow multiprojection imaging tool coupled with machine learning is experimentally assessed. Central to this activity, a methodology is outlined to produce micrometer-sized, non-spherical analytical standards. Such analytical standards are fabricated using photolithography, and consist of monodisperse micro-cuboidal particles of user-defined size and shape. The aforementioned activities are addressed through an experimental framework that fabricates analytical standards and subsequently uses them to validate the performance of our multiprojection imaging tool. Significantly, it is shown that the same set of data collected for particle sizing can also be used to estimate particle orientation in flow, thus defining a rapid and robust protocol to investigate the behavior of dilute particle-laden flows.

Simultaneous Estimation of Gas Adsorption Equilibria and Kinetics of Individual Shaped Adsorbents.

Shaped adsorbents (e.g., pellets, extrudates) are typically employed in several gas separation and sensing applications. The performance of these adsorbents is dictated by two key factors, their adsorption equilibrium capacity and kinetics. Often, adsorption equilibrium and textural properties are reported for materials. Adsorption kinetics are seldom presented due to the challenges associated with measuring them. The overarching goal of this work is to develop an approach to characterize the adsorption properties of individual shaped adsorbents with less than 100 mg of material. To this aim, we have developed an experimental dynamic sorption setup and complemented it with mathematical models, to describe the mass transport in the system. We embed these models into a derivative-free optimizer to predict model parameters for adsorption equilibrium and kinetics. We evaluate and independently validate the performance of our approach on three adsorbents that exhibit differences in their chemistry, synthesis, formulation, and textural properties. Further, we test the robustness of our mathematical framework using a digital twin. We show that the framework can rapidly (i.e., in a few hours) and quantitatively characterize adsorption properties at a milligram scale, making it suitable for the screening of novel porous materials.

Solubility of Organic Salts in Solvent-Antisolvent Mixtures: A Combined Experimental and Molecular Dynamics Simulations Approach.

We combine molecular dynamics simulations with experiments to estimate solubilities of an organic salt in complex growth environments. We predict the solubility by simulations of the growth and dissolution of ions at the crystal surface kink sites at different solution concentrations. Thereby, the solubility is identified as the solution's salt concentration, where the energy of the ion pair dissolved in solution equals the energy of the ion pair crystallized at the kink sites. The simulation methodology is demonstrated for the case of anhydrous sodium acetate crystallized from various solvent-antisolvent mixtures. To validate the predicted solubilities, we have measured the solubilities of sodium acetate in-house, using an experimental setup and measurement protocol that guarantees moisture-free conditions, which is key for a hygroscopic compound like sodium acetate. We observe excellent agreement between the experimental and the computationally evaluated solubilities for sodium acetate in different solvent-antisolvent mixtures. Given the agreement and the rich data the simulations produce, we can use them to complement experimental tasks, which in turn will reduce time and capital in the design of complicated industrial crystallization processes of organic salts.

Material Screening for Gas Sensing Using an Electronic Nose: Gas Sorption Thermodynamic and Kinetic Considerations.

To detect multiple gases in a mixture, one must employ an electronic nose or sensor array, composed of several materials, as a single material cannot resolve all the gases in a mixture accurately. Given the many candidate materials, choosing the right combination of materials to be used in an array is a challenging task. In a sensor whose sensing mechanism depends on a change in mass upon gas adsorption, both the equilibrium and kinetic characteristics of the gas-material system dictate the performance of the array. The overarching goal of this work is twofold. First, we aim to highlight the impact of thermodynamic characteristics of gas-material combination on array performance and to develop a graphical approach to rapidly screen materials. Second, we aim to highlight the need to incorporate the gas sorption kinetic characteristics to provide an accurate picture of the performance of a sensor array. To address these goals, we have developed a computational test bench that incorporates a sensor model and a gas composition estimator. To provide a generic study, we have chosen, as candidate materials, hypothetical materials that exhibit equilibrium characteristics similar to those of metal-organic frameworks. Our computational studies led to key learnings, namely, (1) exploit the shape of the sensor response as a function of gas composition for material screening purposes for gravimetric arrays; (2) incorporate both equilibrium and kinetics for gas composition estimation in a dynamic system; and (3) engineer the array by accounting for the kinetics of the materials, the feed gas flow rate, and the size of the device.

An Alternative Approach to Estimate Solute Concentration: Exploiting the Information Embedded in the Solid Phase.

The solute concentration in crystallization processes is generally estimated by observing properties of the liquid phase. Here, a novel method for online estimation of the change in the solute concentration caused by seeded batch crystallization or dissolution of a population of crystals in suspension is presented. The method is based on multiprojection imaging to track variations in the total solid volume of the population, thus enabling inference of the solute concentration through the mass conservation constraint. The solute concentration estimates obtained in this way are validated by using them to measure the solubilities of ß l-glutamic acid and vanillin in water within certain temperature ranges and comparing them to literature data. The presented method shows promise in estimating the solute concentration reliably under circumstances where employing conventional techniques is challenging.