High-throughput screening of organic reactions in microdroplets using desorption electrospray ionization mass spectrometry (DESI-MS): hardware and software implementation.

This study describes an automated system used for high throughput screening of reaction conditions based on accelerated reactions occurring in small volumes of reagents. Reaction mixtures are prepared in array format using a fluid handling robot and spotted on a flat polytetrafluoroethylene plate at densities up to 6144 per plate. The reaction and analysis steps are performed simultaneously using desorption electrospray ionization (DESI) to release microdroplets containing the reaction mixture from the plate for reaction prior to arrival at a mass spectrometer. Analysis rates are up to 1 reaction mixture per second and data are recorded in real time using an ion trap mass spectrometer. Beacon compounds are used to triangulate position on the plate and this allows tandem mass spectrometry (MS/MS) to be performed on confirm products of interest. Custom software allows the user to control the system. It is also used to receive data from the DESI mass spectrometer to screen the spectra for compounds of interest, to perform MS/MS and to save data. This custom software also communicates with the software controlling the fluid handling robot (Biomek i7) as well as the Beckman software used to prepare reaction mixtures and also the software that controls the solvent used as the DESI spray. Data were recorded for N-alkylation, N-acylation and N-sulfonylation reactions in three 8 hour experiments on successive days to establish the ruggedness and repeatability of the system. Repeatability is high (94-97%) over this period with false negative 6% (depending on noise threshold chosen). Plates containing 384 reaction mixtures are analyzed in 7 min by moving the DESI sprayer in steps under the sprayer instead of continuously.

Mass spectrometric directed system for the continuous-flow synthesis and purification of diphenhydramine.

A highly integrated approach to the development of a process for the continuous synthesis and purification of diphenhydramine is reported. Mass spectrometry (MS) is utilized throughout the system for on-line reaction monitoring, off-line yield quantitation, and as a reaction screening module that exploits reaction acceleration in charged microdroplets for high throughput route screening. This effort has enabled the discovery and optimization of multiple routes to diphenhydramine in glass microreactors using MS as a process analytical tool (PAT). The ability to rapidly screen conditions in charged microdroplets was used to guide optimization of the process in a microfluidic reactor. A quantitative MS method was developed and used to measure the reaction kinetics. Integration of the continuous-flow reactor/on-line MS methodology with a miniaturized crystallization platform for continuous reaction monitoring and controlled crystallization of diphenhydramine was also achieved. Our findings suggest a robust approach for the continuous manufacture of pharmaceutical drug products, exemplified in the particular case of diphenhydramine, and optimized for efficiency and crystal size, and guided by real-time analytics to produce the agent in a form that is readily adapted to continuous synthesis.

Application of feedback control and in situ milling to improve particle size and shape in the crystallization of a slow growing needle-like active pharmaceutical ingredient.

Control of crystal size and shape is crucially important for crystallization process development in the pharmaceutical industries. In general crystals of large size and low aspect ratio are desired for improved downstream manufacturability. It can be extremely challenging to design crystallization processes that achieve these targets for active pharmaceutical ingredients (APIs) that have very slow growth kinetics and needle-like morphology. In this work, a batch cooling crystallization process for a GlaxoSmithKline patented API, which is characterized by very slow growth rate and needle morphology, was studied and improved using process analytical technology (PAT) based feedback control techniques and in situ immersion milling. Four specific approaches were investigated: Supersaturation control (SSC), direct nucleation control (DNC), sequential milling-DNC, and simultaneous milling-DNC. This is the first time that immersion wet milling is combined with feedback control in a batch crystallization process. All four approaches were found to improve crystal size and/or shape compared to simple unseeded or seeded linear cooling crystallizations. DNC provided higher quality crystals than SSC, and sequential and simultanesou milling-DNC approaches could reduce particle 2D aspect ratio without generating too much fines. In addition, an ultra-performance liquid chromatography (UPLC) system was used online as a novel PAT tool in the crystallization study.

Reaction screening and optimization of continuous-flow atropine synthesis by preparative electrospray mass spectrometry.

Preparative electrospray (ES) exploits the acceleration of reactions in charged microdroplets to perform a small scale chemical synthesis. In combination with on-line mass spectrometric (MS) analysis, it constitutes a rapid screening tool to select reagents to generate specific products. A successful reaction in preparative ES triggers a refined microfluidic reaction screening procedure which includes the optimization for stoichiometry, temperature and residence time. We apply this combined approach for refining a flow synthesis of atropine. A successful preparative ES pathway for the synthesis of the phenylacetyl ester intermediate, using tropine/HCl/phenylacetyl chloride, was optimized for solvent in both the preparative ES and microfluidics flow systems and a base screening was conducted by both methods to increase atropine yield, increase percentage conversion and reduce byproducts. In preparative ES, the first step yielded 55% conversion (judged using MS) to intermediate and the second step yielded 47% conversion to atropine. When combined in two discrete steps in continuous-flow microfluidics, a 44% conversion of the starting material and a 30% actual yield of atropine were achieved. When the reactions were continuously telescoped in a new form of preparative reactive extractive electrospray (EES), atropine was synthesized with a 24% conversion. The corresponding continuous-flow microfluidics experiment gave a 55% conversion with an average of 34% yield in 8 min residence time. This is the first in depth study to utilize telescoped preparative ES and the first use of dual ESI emitters for multistep synthesis.

Nanocrystal Dissolution Kinetics and Solubility Increase Prediction from Molecular Dynamics: The Case of α-, ß-, and γ-Glycine.

Nanocrystals are receiving increased attention for pharmaceutical applications due to their enhanced solubility relative to their micron-sized counterpart and, in turn, potentially increased bioavailability. In this work, a computational method is proposed to predict the following: (1) polymorph specific dissolution kinetics and (2) the multiplicative increase in the polymorph specific nanocrystal solubility relative to the bulk solubility. The method uses a combination of molecular dynamics and a parametric particle size dependent mass transfer model. The method is demonstrated using a case study of α-, ß-, and γ-glycine. It is shown that only the α-glycine form is predicted to have an increasing dissolution rate with decreasing particle size over the range of particle sizes simulated. On the contrary, γ-glycine shows a monotonically increasing dissolution rate with increasing particle size and dissolves at a rate 1.5 to 2 times larger than α- or ß-glycine. The accelerated dissolution rate of γ-glycine relative to the other two polymorphs correlates directly with the interfacial energy ranking of γ > ß > α obtained from the dissolution simulations, where γ- is predicted to have an interfacial energy roughly four times larger than either α- or ß-glycine. From the interfacial energies, α- and ß-glycine nanoparticles were predicted to experience modest solubility increases of up to 1.4 and 1.8 times the bulk solubility, where as γ-glycine showed upward of an 8 times amplification in the solubility. These MD simulations represent a first attempt at a computational (pre)screening method for the rational design of experiments for future engineering of nanocrystal API formulations.

Solubility curves and nucleation rates from molecular dynamics for polymorph prediction - moving beyond lattice energy minimization.

Current polymorph prediction methods, known as lattice energy minimization, seek to determine the crystal lattice with the lowest potential energy, rendering it unable to predict solvent dependent metastable form crystallization. Facilitated by embarrassingly parallel, multiple replica, large-scale molecular dynamics simulations, we report on a new method concerned with predicting crystal structures using the kinetics and solubility of the low energy polymorphs predicted by lattice energy minimization. The proposed molecular dynamics simulation methodology provides several new predictions to the field of crystallization. (1) The methodology is shown to correctly predict the kinetic preference for ß-glycine nucleation in water relative to α- and γ-glycine. (2) Analysis of nanocrystal melting temperatures show γ- nanocrystals have melting temperatures up to 20 K lower than either α- or ß-glycine. This provides a striking explanation of how an energetically unstable classical nucleation theory (CNT) transition state complex leads to kinetic inaccessibility of γ-glycine in water, despite being the thermodynamically preferred polymorph predicted by lattice energy minimization. (3) The methodology also predicts polymorph-specific solubility curves, where the α-glycine solubility curve is reproduced to within 19% error, over a 45 K temperature range, using nothing but atomistic-level information provided from nucleation simulations. (4) Finally, the methodology produces the correct solubility ranking of ß- > α-glycine. In this work, we demonstrate how the methodology supplements lattice energy minimization with molecular dynamics nucleation simulations to give the correct polymorph prediction, at different length scales, when lattice energy minimization alone would incorrectly predict the formation of γ-glycine in water from the ranking of lattice energies. Thus, lattice energy minimization optimization algorithms are supplemented with the necessary solvent/solute dependent solubility and nucleation kinetics of polymorphs to predict which structure will come out of solution, and not merely which structure has the most stable lattice energy.