Flow chemistry for process optimisation using design of experiments

Iterative design of training data to control intricate enzymatic reaction networks

Kinetic modeling of in vitro enzymatic reaction networks is vital to understand and control the complex behaviors emerging from the nonlinear interactions inside. However, modeling is severely hampered by the lack of training data. Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designs a sequence of out-of-equilibrium perturbations to maximize the information about the reaction kinetics, yielding a descriptive model that allows control of the output of the network towards any cost function. We experimentally validate the model by forcing the network to produce different product ratios while maintaining a minimum level of overall conversion efficiency. Our workflow scales with the complexity of the system and enables the optimization of previously unobtainable network outputs.

Advanced 3D Electrospinning "Xspin" System: Fabrication of Bifiber Floating Oral Pharmaceutical Scaffolds for Controlled Drug Delivery

Electrospinning has become a widely used and efficient method for manufacturing nanofibers from diverse polymers. This study introduces an advanced electrospinning technique, Xspin - a multi-functional 3D printing platform coupled with electrospinning system, integrating a customised 3D printhead, MaGIC - Multi-channeled and Guided Inner Controlling printheads. The Xspin system represents a cutting-edge fusion of electrospinning and 3D printing technologies within the realm of pharmaceutical sciences and biomaterials. This innovative platform excels in the production of novel fiber with various materials and allows for the creation of highly customized fiber structures, a capability hitherto unattainable through conventional electrospinning methodologies. By integrating the benefits of electrospinning with the precision of 3D printing, the Xspin system offers enhanced control over the scaffold morphology and drug release kinetics. Herein, we fabricated a model floating pharmaceutical dosage for the dual delivery of curcumin and ritonavir and thoroughly characterized the product. Fourier transform infrared (FTIR) spectroscopy demonstrated that curcumin chemically reacted with the polymer during the Xspin process. Thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) confirmed the solid-state properties of the active pharmaceutical ingredient after Xspin processing. Scanning electron microscopy (SEM) revealed the surface morphology of the Xspin-produced fibers, confirming the presence of the bifiber structure. To optimize the quality and diameter control of the electrospun fibers, a design of experiment (DoE) approach based on quality by design (QbD) principles was utilized. The bifibers expanded to approximately 10-11 times their original size after freeze-drying and effectively entrapped 87% curcumin and 84% ritonavir. In vitro release studies demonstrated that the Xspin system released 35% more ritonavir than traditional pharmaceutical pills in 2 h, with curcumin showing complete release in pH 1.2 in 5 min, simulating stomach media. Furthermore, the absorption rate of curcumin was controlled by the characteristics of the linked polymer, which enables both drugs to be absorbed at the desired time. Additionally, multivariate statistical analyses (ANOVA, pareto chart, etc.) were conducted to gain better insights and understanding of the results such as discern statistical differences among the studied groups. Overall, the Xspin system shows significant potential for manufacturing nanofiber pharmaceutical dosages with precise drug release capabilities, offering new opportunities for controlled drug delivery applications.

Design of Experiment for Optimizing Microencapsulation by the Solvent Evaporation Technique

We employed microemulsion combined with the solvent evaporation technique to produce biodegradable polycaprolactone (PCL) MCs, containing encapsulated isophorone diisocyanate (IPDI), to act as crosslinkers in high-performance adhesive formulations. The MC production process was optimized by applying a design of experiment (DoE) statistical approach, aimed at decreasing the MCs' average size. For that, three different factors were considered, namely the concentration of two emulsifiers, polyvinyl alcohol (PVA) and gum arabic (GA); and the oil-to-water phase ratio of the emulsion. The significance of each factor was evaluated, and a predictive model was developed. We were able to decrease the average MC size from 326 μm to 70 µm, maintaining a high encapsulation yield of approximately 60% of the MCs' weight, and a very satisfactory shelf life. The MCs' average size optimization enabled us to obtain an improved distributive and dispersive mixture of isocyanate-loaded MCs at the adhesive bond. The MCs' suitability as crosslinkers for footwear adhesives was assessed following industry standards. Peel tests revealed peel strength values above the minimum required for casual footwear, while the creep test results indicated an effective crosslinking of the adhesive. These results confirm the ability of the MCs to release IPDI during the adhesion process and act as crosslinkers for new adhesive formulations.

Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis

The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experiments (DoE) and machine learning are an effective and more efficient way to predictably control materials synthesis. Here, we present a Viewpoint on recent progress in leveraging such techniques for predicting and controlling the outcomes of inorganic materials synthesis. We first compare how the design choice (statistical DoE vs machine learning) affects the type of control it can offer over the resulting product attributes, information elucidated, and experimental cost. These attributes are supported by discussing select case studies from the recent literature that highlight the power of these techniques for materials synthesis. The influence of experimental bias is next discussed, followed finally by our perspectives on the major challenges in the widespread implementation of predictable and controllable materials synthesis using data-driven techniques.

Accelerated Chemical Reaction Optimization Using Multi-Task Learning

Functionalization of C-H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C-H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization.

A Brief Introduction to Chemical Reaction Optimization

From the start of a synthetic chemist's training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher's career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

AROPS: A Framework of Automated Reaction Optimization with Parallelized Scheduling

With the development of automated experimental platforms and optimization algorithms, chemists can easily optimize chemical reactions in an automated and high-throughput fashion. However, the modules in existing automated experimental platforms are operated in a linear fashion without orchestrating with the optimization algorithm, thus leaving room for further efficiency improvement. Here, we introduced a framework of automated reaction optimization with parallelized scheduling (AROPS) to realize the integration of the optimization algorithm and module scheduling. AROPS relies on a customized Bayesian optimizer to solve multi-reactor/analyzer reaction optimization problems with three different scheduling modes to arrange tasks for various experimental modules. In addition, a mechanism based on probability of improvement (PI) for discarding unpromising ongoing experiments was developed to facilitate freeing up valuable experimental resources in parallelized optimization. We tested the performance of AROPS using a hardware emulator on three representative benchmark reactions encountered in organic synthesis, illustrating that AROPS can trade off optimization time and cost according to the chemists' preference.

Automated optimization under dynamic flow conditions

The combination of feedback optimization with dynamic operations leads to enhanced data-rich experimentation in flow.

The design of experiments (DoE) in optimization of an aerobic flow Pd-catalyzed oxidation of alcohol towards an important aldehyde precursor in the synthesis of phosphatidylinositide 3-kinase inhibitor (CPL302415)

Herein, we describe the development of a green, scalable flow Pd-catalyzed aerobic oxidation for the key step in the synthesis of CPL302415, which is a new PI3Kδ inhibitor. Applying this environmental-friendly, sustainable catalytic oxidation we significantly increased product yield (up to 84%) and by eliminating of workup step, we improved the waste index and E factor (up to 0.13) in comparison with the stoichiometric synthesis. The process was optimized by using the DoE approach.

Autonomous model-based experimental design for rapid reaction development

To automate and democratize model-based experimental design for flow chemistry applications, we report the development of open-source software, Optipus. Reaction models are built in an iterative and automated fashion, for rapid reaction development.

Forgotten and forbidden chemical reactions revitalised through continuous flow technology

Continuous flow technology has played an undeniable role in enabling modern chemical synthesis, whereby a myriad of reactions can now be performed with greater efficiency, safety and control. As flow chemistry furthermore delivers more sustainable and readily scalable routes to important target structures a growing number of industrial applications are being reported. In this review we highlight the impact of flow chemistry on revitalising important chemical reactions that were either forgotten soon after their initial report as necessary improvements were not realised due to a lack of available technology, or forbidden due to unacceptable safety concerns relating to the experimental procedure. In both cases flow processing in combination with further reaction optimisation has rendered a powerful set of tools that make such transformations not only highly efficient but moreover very desirable due to a more streamlined construction of desired scaffolds. This short review highlights important contributions from academic and industrial laboratories predominantly from the last 5 years allowing the reader to gain an appreciation of the impact of flow chemistry.

Continuous Hydrogenation: Triphasic System Optimization at Kilo Lab Scale Using a Slurry Solution

Despite their widespread use in the chemical industries, hydrogenation reactions remain challenging. Indeed, the nature of reagents and catalysts induce intrinsic safety challenges, in addition to demanding process development involving a 3-phase system. Here, to address common issues, we describe a successful process intensification study using a meso-scale flow reactor applied to a hydrogenation reaction of ethyl cinnamate at kilo lab scale with heterogeneous catalysis. This method relies on the continuous pumping of a catalyst slurry, delivering fresh catalyst through a structured flow reactor in a continuous fashion and a throughput up to 54.7 g/h, complete conversion and yields up to 99%. This article describes the screening of equipment, reactions conditions and uses statistical analysis methods (Monte Carlo/DoE) to improve the system further and to draw conclusions on the key influential parameters (temperature and residence time).

An automated computational approach to kinetic model discrimination and parameter estimation

We herein report experimental applications of a novel, automated computational approach to chemical reaction network (CRN) identification. This report shows the first chemical applications of an autonomous tool to identify the kinetic model and parameters of a process, when considering both catalytic species and various integer and non-integer orders in the model's rate laws. This kinetic analysis methodology requires only the input of the species within the chemical system (starting materials, intermediates, products, etc.) and corresponding time-series concentration data to determine the kinetic information of the chemistry of interest. This is performed with minimal human interaction and several case studies were performed to show the wide scope and applicability of this process development tool. The approach described herein can be employed using experimental data from any source and the code for this methodology is also provided open-source.

We herein report experimental applications of a novel, automated computational approach to chemical reaction network (CRN) identification.