Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE).

Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon propagation, resulting in complex speckle patterns. One specific application is the drying of wet powders in the pharmaceutical industry, where quantifying the particle size distribution (PSD) is of particular interest. A non-invasive and real-time monitoring probe in the drying process is required, but there is no suitable candidate for this purpose. In this report, we develop a theoretical relationship from the PSD to the speckle image and describe a physics-enhanced autocorrelation-based estimator (PEACE) machine learning algorithm for speckle analysis to measure the PSD of a powder surface. This method solves both the forward and inverse problems together and enjoys increased interpretability, since the machine learning approximator is regularized by the physical law.

Machine Learning-Derived Correlations for Scale-Up and Technology Transfer of Primary Nucleation Kinetics.

Scaling up and technology transfer of crystallization processes have been and continue to be a challenge. This is often due to the stochastic nature of primary nucleation, various scale dependencies of nucleation mechanisms, and the multitude of scale-up approaches. To better understand these dependencies, a series of isothermal induction time studies were performed across a range of vessel volumes, impeller types, and impeller speeds. From these measurements, the nucleation rate and growth time were estimated as parameters of an induction time distribution model. Then using machine learning techniques, correlations between the vessel hydrodynamic features, calculated from computational flow dynamic simulations, and nucleation kinetic parameters were analyzed. Of the 18 machine learning models trained, two models for the nucleation rate were found to have the best performance (in terms of % of predictions within experimental variance): a nonlinear random Forest model and a nonlinear gradient boosting model. For growth time, a nonlinear gradient boosting model was found to outperform the other models tested. These models were then ensembled to directly predict the probability of nucleation, at a given time, solely from hydrodynamic features with an overall root mean square error of 0.16. This work shows how machine learning approaches can be used to analyze limited datasets of induction times to provide insights into what hydrodynamic parameters should be considered in the scale-up of an unseeded crystallization process.

Use of Wet Milling Combined with Temperature Cycling to Minimize Crystal Agglomeration in a Sequential Antisolvent-Cooling Crystallization.

The objective of the research was to improve the process design of a combined antisolvent-cooling crystallization to reduce the degree of agglomeration of a real active pharmaceutical ingredient product, which was manufactured using a crystallization stage employing a methanol/water solvent system. Knowledge was gained from the use of process analytical technology (PAT) tools to monitor the process variables, allowing particle size, degree of agglomeration, solute concentration, and supersaturation to be tracked throughout the process. Based on knowledge of the solubility behavior and interpretation of the PAT histories, changes were made to the sequences of antisolvent addition and cooling within the crystallization process to reduce agglomeration in the final product. Different seed loadings and seeding addition points were also investigated to maintain operation within lower supersaturation regions of the phase diagram to limit agglomeration and avoid an undesired polymorphic transformation to an unstable form. The improved sequences of operations and seeding conditions did not provide sufficient improvement in the product quality and so were augmented by applying wet milling for further deagglomeration followed by temperature cycling to remove fine particles generated during milling. Open-loop heating and cooling cycles produced some limited improvements, whereas closed-loop direct nucleation control methods using FBRM as a feedback sensor for particle counts per second were much more successful at producing high-quality crystals of the desired polymorphic form. The work shows that understanding the trajectory of the process through the phase diagram to follow appropriate supersaturation profiles gives improved control of the various kinetic mechanisms and can be used to improve the quality of the final product.

Total syntheses of 2,2'-epi-cytoskyrin A, rugulosin, and the alleged structure of rugulin.

The total syntheses of 2,2'-epi-cytoskyrin A, rugulosin, and the alleged structure of rugulin are described. These naturally occurring bisanthraquinones and their relatives are characterized by novel molecular architectures at the core, at which lies a more or less complete, cage-like structural motif termed "skyrane". The strategies developed for their total synthesis feature a cascade sequence called the "cytoskyrin cascade" and deliver these molecules in short order and in a stereoselective manner.

Sanglifehrin-cyclophilin interaction: degradation work, synthetic macrocyclic analogues, X-ray crystal structure, and binding data.

Sanglifehrin A (SFA) is a novel immunosuppressive natural product isolated from Streptomyces sp. A92-308110. SFA has a very strong affinity for cyclophilin A (IC(50) = 6.9 +/- 0.9 nM) but is structurally different from cyclosporin A (CsA) and exerts its immunosuppressive activity via a novel mechanism. SFA has a complex molecular structure consisting of a 22-membered macrocycle, bearing in position 23 a nine-carbon tether terminated by a highly substituted spirobicyclic moiety. Selective oxidative cleavage of the C(26)=C(27) exocyclic double bond affords the spirolactam containing fragment 1 and macrolide 2. The affinity of 2 for cyclophilin (IC(50) = 29 +/- 2.1 nM) is essentially identical to SFA, which indicates that the interaction between SFA and cyclophilin A is mediated exclusively by the macrocyclic portion of the molecule. This observation was confirmed by the X-ray crystal structure resolved at 2.1 A of cyclophilin A complexed to macrolide 16, a close analogue of 2. The X-ray crystal structure showed that macrolide 16 binds to the same deep hydrophobic pocket of cyclophilin A as CsA. Additional valuable details of the structure-activity relationship were obtained by two different chemical approaches: (1) degradation work on macrolide 2 or (2) synthesis of a library of macrolide analogues using the ring-closing metathesis reaction as the key step. Altogether, it appears that the complex macrocyclic fragment of SFA is a highly optimized combination of multiple functionalities including an (E,E)-diene, a short polypropionate fragment, and an unusual tripeptide unit, which together provide an extremely strong affinity for cyclophilin A.