On Solute Recovery and Productivity in Chiral Resolution through Solid-State Deracemization by Temperature Cycling.

Temperature cycling represents an effective means for the deracemization of chiral compounds that crystallize as conglomerates and racemize in solution. In such a process, a suspension enriched in the desired enantiomer is converted into an enantiopure one through periodic cycles of crystal dissolution and crystal growth. We show that performing temperature cycling at higher temperatures leads to faster deracemization and, consequently, higher productivity. However, this comes at the cost of lower recovery, as the solution contains potentially relevant amounts of solute due to the higher solubility at an elevated temperature. In this work, we introduce and compare two process variants that mitigate this issue. The first involves temperature cycling, followed by linear cooling, whereas the second is based on merging the temperature cycles and cooling crystallization. Experiments carried out with the chiral compound N-(2-methylbenzylidene)-phenylglycine amide show that the former variant is faster than the latter, and it is easier to design and implement. In this process, the choice of an appropriate cooling rate is essential to avoid nucleation of the undesired enantiomer.

Monitoring Aqueous Sucrose Solutions Using Droplet Microfluidics: Ice Nucleation, Growth, Glass Transition, and Melting.

Freezing and freeze-drying processes are commonly used to extend the shelf life of drug products and to ensure their safety and efficacy upon use. When designing a freezing process, it is beneficial to characterize multiple physicochemical properties of the formulation, such as nucleation rate, crystal growth rate, temperature and concentration of the maximally freeze-concentrated solution, and melting point. Differential scanning calorimetry has predominantly been used in this context but does have practical limitations and is unable to quantify the kinetics of crystal growth and nucleation. In this work, we introduce a microfluidic technique capable of quantifying the properties of interest and use it to investigate aqueous sucrose solutions of varying concentration. Three freeze-thaw cycles were performed on droplets with 75-µm diameters at cooling and warming rates of 1 °C/min. During each cycle, the visual appearance of the droplets was optically monitored as they experienced nucleation, crystal growth, formation of the maximally freeze-concentrated solution, and melting. Nucleation and crystal growth manifested as increases in droplet brightness during the cooling phase. Heating was associated with a further increase as the temperature associated with the maximally freeze-concentrated solution was approached. Heating beyond the melting point corresponded to a decrease in brightness. Comparison with the literature confirmed the accuracy of the new technique while offering new visual data on the maximally freeze-concentrated solution. Thus, the microfluidic technique presented here may serve as a complement to differential scanning calorimetry in the context of freezing and freeze-drying. In the future, it could be applied to a plethora of mixtures that undergo such processing, whether in pharmaceutics, food production, or beyond.

Exact and Ubiquitous Condition for Solid-State Deracemization in Vitro and in Nature.

Solid-state deracemization is the amplification of an enantiomeric excess in suspensions of conglomerate-forming chiral compounds. Although numerous chemical and biochemical compounds deracemize, its governing mechanism has remained elusive. We introduce a novel formulation of the classical population-based model of deracemization through temperature cycles to prove that suspensions deracemize whenever a simple and ubiquitous condition is met: crystal dissolution must be faster than crystal growth. Such asymmetry is a known principle of crystallization, hence explaining the generality of deracemization. Through both experiments and a theoretical analysis, we demonstrate that this condition applies even for very small temperature cycles and for random temperature fluctuations. These findings establish solid-state deracemization as an attractive route to the manufacture of enantiopure products and as a plausible pathway toward the emergence of homochirality in nature.

Thermodynamics Explains How Solution Composition Affects the Kinetics of Stochastic Ice Nucleation.

The freezing of aqueous solutions is of great relevance to multiple fields, yet the kinetics of ice nucleation, its first step, remains poorly understood. The literature focuses on the freezing of microdroplets, and it is unclear if those findings can be generalized and extended to larger volumes such as those used in the freezing of biopharmaceuticals. To this end, we study ice nucleation from aqueous solutions of ten different compositions in vials at the milliliter scale. The statistical analysis of the approximately 6,000 measured nucleation events reveals that the stochastic ice nucleation kinetics is independent of the nature and concentration of the solute. We demonstrate this by estimating the values of the kinetic parameters in the nucleation rate expression for the selected solution compositions, and we find that a single set of parameters can describe quantitatively the nucleation behavior in all solutions. This holds regardless of whether the nucleation rate is expressed as a function of the chemical potential difference, of the water activity difference, or of the supercooling. While the chemical potential difference is the thermodynamically correct driving force for nucleation and hence is more accurate from a theoretical point of view, the other two expressions allow for an easier implementation in mechanistic freezing models in pharmaceutical manufacturing.

Conceptual Validation of Stochastic and Deterministic Methods To Estimate Crystal Nucleation Rates.

This work presents a generalized framework to assess the accuracy of methods to estimate primary and secondary nucleation rates from experimental data. The crystallization process of a well-studied model compound was simulated by means of a novel stochastic modeling methodology. Nucleation rates were estimated from the simulated data through multiple methods and were compared with the true values. For primary nucleation, no method considered in this work was able to estimate the rates accurately under general conditions. Two deterministic methods that are widely used in the literature were shown to overpredict rates in the presence of secondary nucleation. This behavior is shared by all methods that extract rates from deterministic process attributes, as they are insensitive to primary nucleation if secondary nucleation is sufficiently fast. Two stochastic methods were found to be accurate independent of whether secondary nucleation is present, but they underestimated rates in the case where a large number of primary nuclei are formed. We hence proposed a criterion to probe the accuracy of stochastic methods for arbitrary data sets, thus providing the theoretical foundations required for their rational use. Finally, we showed how both primary and secondary nucleation rates can be inferred from the same set of detection time data by combining deterministic and stochastic considerations.

Stochastic ice nucleation governs the freezing process of biopharmaceuticals in vials.

Biopharmaceuticals commonly require freezing to ensure the stability of the active pharmaceutical ingredients (APIs). At commercial scale, freezing is typically carried out over the course of days in pallets comprising tens of thousands of vials. The selected process conditions have to ensure both complete freezing in all vials and a satisfactory manufacturing throughput. Current process design, however, is mainly experimental, since no mechanistic understanding of pallet freezing and its underlying phenomena has been achieved so far. Within this work, we derive a mechanistic modeling framework and compare the model predictions with engineering run data from the Janssen COVID-19 vaccine. The model qualitatively reproduced all observed trends and reveals that stochastic ice nucleation governs both process duration and batch heterogeneity. Knowledge on the ice nucleation kinetics of the formulation to be frozen thus is required to identify suitable freezing process conditions. The findings of this work pave the way towards a more rational design of pallet freezing, from which a plethora of frozen drug products may benefit. For this reason, we provide open source access to the model in the form of a python package (Deck et al., 2021).

Stochastic shelf-scale modeling framework for the freezing stage in freeze-drying processes.

Freezing and freeze-drying processes are commonly used to improve the stability and thus shelf life of pharmaceutical formulations. Despite strict product quality requirements, batch heterogeneity is widely observed in frozen products, thus potentially causing process failure. Such heterogeneity is the result of the stochasticity of ice nucleation and the variability in heat transfer among vials, which lead to unique freezing histories of individual vials. We present for the first time a modeling framework for large-scale freezing processes of vials on a shelf and publish an open source implementation in the form of a python package on pypi. The model is based on first principles and couples heat transfer with ice nucleation kinetics, thus enabling studies on batch heterogeneity. Ice nucleation is assumed to be an inhomogeneous Poisson process and it is simulated using a Monte Carlo approach. We applied the model to understand the individual pathways leading to batch heterogeneity. Our simulations revealed a novel mechanism how ice nucleation leads to heterogeneity based on thermal interaction among vials. We investigated the effect of various cooling protocols, namely shelf-ramped cooling, holding steps and controlled nucleation, on the nucleation and solidification behavior across the shelf. We found that under rather general conditions holding schemes lead to similar solidification times, as in the case of controlled nucleation, thus identifying a potential pathway for freezing process optimization.

An ultrasensitive non-noble metal colorimetric assay using starch-iodide complexation for Ochratoxin A detection.

Colorimetric sandwich-type biosensors that can both provide sensitivity competitive with fluorescence-based approaches, and leverage reagents that are cost-effective, widely available and as safe as possible, are highly sought after. Herein, we demonstrate an alternative highly-sensitive colorimetric method for paper-based sandwich-type biosensing that uses starch-iodide complexation to simplify practical biosensing using ubiquitous reagents. Targeting the mycotoxin ochratoxin A (OTA), a covalently-immobilised OTA antibody on a cellulose surface captures OTA and forms a sandwich with OTA aptamer-conjugated glucose oxidase. Adding the chromogenic reagents at an optimized concentration, a distinct blue color develops within 30 min, offering excellent contrast with the clear/white of the negative sample. With a sampling volume down to just 5 µL, the assay exhibits concentration limits of detection and quantitation of 20 and 320 pg mL-1, respectively, and a linear range from 10-1 to 105 ng mL-1 (R2 = 0.997). The method displays excellent selectivity against related mycotoxins, excellent %recovery (95-117%) and robust operation in complex matrices (beer, urine and human serum), with no significant difference versus gold-standard liquid chromatography. Along with its excellent analytical performance, this assay benefits from non-toxic and extremely cheap reagents that can be safely disposed of in the field, and presents an attractive alternative to toxic dyes and nanoparticles.