A revised crystallization process for TAK-117, a selective PI3Kα inhibitor currently in Phase 1b clinical trials, was developed that greatly improved the overall purity, recovery, and physiochemical and bulk powder properties of the isolated product. The original process afforded material that was prone to agglomeration during drying, resulting in significant product losses during sieving as well as issues with drug product manufacturability. Opportunities to explore a wide array of possible crystallization routes and solvent options were limited because TAK-117 is only sparingly soluble in most commonly used organic solvents apart from dimethyl sulfoxide (DMSO) and acidic systems. However, reasonable productivities were achieved using DMSO at elevated temperatures (100 °C), and the optimized process leveraged thermal cycling to improve the aspect ratio of the isolated crystals, minimize agglomeration during drying, and improve the powder’s bulk properties. The revised process was found to produce material of acceptable quality across a total of six batches at 15 and 30 kg scales.

Alisertib sodium, an investigational oral oncology drug, posed some challenges toward developing a robust and scalable drying process employing an agitated filter dryer that manifested themselves during the technical transfer to a new manufacturing site. The API studied was a monohydrate that was found to readily dehydrate and agglomerate, impacting both drug product (DP) manufacture and in vitro dissolution. A scale down agitated filter dryer was designed that was used to study the drying unit operation and identify key process parameters. Through a combination of lab-and pilot plant-scale experiments, suitable drying conditions were developed that minimized agglomeration, eliminated dehydration, and produced API that behaved acceptably in downstream DP manufacture.

A revised Miyaura borylation process has been developed using tetrahydroxydiboron that avoids the use of bis(pinacolato) diboron and hence the need to hydrolyze the resulting boronic ester to its corresponding acid. The process was greatly simplified and successfully scaled-up in the pilot plant on a 65 kg scale, reducing plant cycle time and resulting in a 47% overall cost reduction. Furthermore, methodology for the study of the oxygen sensitivity of the process is reported that allowed for optimization of the amount of tetrahydroxydiboron and catalyst used. These studies also demonstrated an oxygen-induced decomposition of tetrahydroxydiboron.

Agitated drying of pharmaceuticals remains a challenging manufacturing step due to the simultaneous heat transfer, mass transfer, and physicochemical changes occurring during the process. This work focuses on the heat transfer component by implementing the discrete element method to model dry particles in a heated bladed mixer. Simulations varying material conductivities and impeller agitation rates were conducted to evaluate the influence on the mean bed temperature and distribution. The results indicated that increasing the agitation rate generally improved heat transfer up until a critical agitation rate where the rate of heat transfer plateaued. The magnitude of this improvement in heat transfer depended on the material's thermal properties. We observed three regimes: a conduction‐dominated regime where particles heated quickly but with an annular temperature gradient, a granular convection‐dominated regime where particles heated slowly but uniformly, and an intermediate regime. The results were nondimensionalized to enable predictions and help inform drying protocols.

An understanding of heat transfer in a bladed mixer is important for drying of pharmaceutical drug crystals. This study presents thermal imaging experiments of the particle bed surface in a bladed mixer to investigate how the impeller speed influences the rate and the uniformity of heat transfer. Next, the process is simulated using the discrete element method. The bed thermal properties are lumped into an effective thermal conductivity, that is calibrated for one impeller speed. The experiments and the simulations show the same trends and generally agree well for all agitated beds. However, to obtain good agreement of the rate of heat transfer between the simulations and experiments in a static bed, we need to adopt a higher thermal conductivity than for the agitated beds. Finally, we discuss the implications of these results for the design of operating protocols.

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.

A modeling-based strategy is disclosed for identifying reaction conditions for the safe and effective scale-up of highly energetic hydrogenation reactions. The model was developed within Scale-up Systems’s DynoChem 2011 and takes under consideration the kinetics of the reaction, the reactor heat transfer capabilities, and the degree of mass transfer. Fourier transform infrared spectroscopy (FT-IR), heat flow, and H2 uptake data were used to determine the reaction kinetics that were found to be most accurately described by a Langmuir–Hinshelwood type model. The scale-up model was validated within our kilo-laboratory using a 5 L reactor.

A material-sparing screening methodology has been developed for assessing the risk of particle size attrition of active pharmaceutical ingredients (APIs) during agitated drying using a single-ball mill process assisted by resonant acoustic mixing. This method requires only gram quantities of material and provides a critical particle fragility assessment that can be used to identify suitable agitation protocols that minimize attrition during at-scale manufacturing. The impact of initial particle size, as well as physical properties such as hardness, aspect ratio, and thickness, on particle breakage was assessed for both Takeda APIs and a range of commercially available compounds. Two models were developed and validated using both laboratory-scale and plant-scale agitated filter dryers, and a suitable workflow for the application of the developed methodology is proposed.