An Autonomous Self-Optimizing Flow Reactor for the Synthesis of Natural Product Carpanone

Paddy: an evolutionary optimization algorithm for chemical systems and spaces

Optimization of chemical systems and processes have been enhanced and enabled by the development of new algorithms and analytical approaches. While several methods systematically investigate how underlying variables correlate with a given outcome, there is often a substantial number of experiments needed to accurately model such relationships. As chemical systems increase in complexity, algorithms are needed to propose experiments that efficiently optimize the underlying objective, while effectively sampling parameter space to avoid convergence on local minima. We have developed the Paddy software package based on the Paddy field algorithm, a biologically inspired evolutionary optimization algorithm that propagates parameters without direct inference of the underlying objective function. We benchmarked Paddy against several optimization approaches: the Tree of Parzen Estimator through the Hyperopt software library, Bayesian optimization with a Gaussian process via Meta's Ax framework, and two population-based methods from EvoTorch-an evolutionary algorithm with Gaussian mutation, and a genetic algorithm using both a Gaussian mutation and single-point crossover-all representing diverse approaches to optimization. Paddy's performance is benchmarked for mathematical and chemical optimization tasks including global optimization of a two-dimensional bimodal distribution, interpolation of an irregular sinusoidal function, hyperparameter optimization of an artificial neural network tasked with classification of solvent for reaction components, targeted molecule generation by optimizing input vectors for a decoder network, and sampling discrete experimental space for optimal experimental planning. Paddy demonstrates robust versatility by maintaining strong performance across all optimization benchmarks, compared to other algorithms with varying performance. Additionally, Paddy avoids early convergence with its ability to bypass local optima in search of global solutions. We anticipate that the facile, versatile, robust and open-source nature of Paddy will serve as a toolkit in chemical problem-solving tasks towards automated experimentation with high priority for exploratory sampling and innate resistance to early convergence to identify optimal solutions.

Benzoxanthenone Lignans Related to Carpanone, Polemanone, and Sauchinone: Natural Origin, Chemical Syntheses, and Pharmacological Properties

Medicinal plants from the genus Saururus are commonly used to treat inflammatory pathologies. They contain numerous bioactive compounds, notably the polycyclic lignan sauchinone from the species Saururus chinensis. An in-depth analysis of benzoxanthenone lignans related to sauchinone, and the analogous products carpanone and polemannones, has been carried out. The review reports the product's isolation, biosynthetic pathway, and chemical strategies to synthesize benzoxanthenones via liquid- and solid-phase syntheses. The metabolic and pharmacokinetic properties of sauchinone are discussed. At the pharmacological level, sauchinone is a potent blocker of the production of pro-inflammatory mediators, such as nitric oxide and prostaglandin E2, and an efficient antioxidant agent. The properties of sauchinone can be exploited to combat multiple pathologies, such as liver injuries, renal dysfunction, osteoarthritis, inflammatory bowel disease, ulcerative colitis, and cancers. The capacity of the natural product to inhibit tumor cell proliferation and to reduce migration/invasion of cancer cells and the development of metastases is underlined, together with the regulation of the epithelial-mesenchymal transition and immune checkpoints. Altogether, the review offers a complete survey of the chemical and biochemical properties of sauchinone-type benzoxanthenones.

Emerging trends in the optimization of organic synthesis through high-throughput tools and machine learning

The discovery of the optimal conditions for chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically, the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and through the design of experiments where reaction variables are modified one at a time to find the optimal conditions for a specific reaction outcome. Recently, a paradigm change in chemical reaction optimization has been enabled by advances in lab automation and the introduction of machine learning algorithms. Therein, multiple reaction variables can be synchronously optimized to obtain the optimal reaction conditions, requiring a shorter experimentation time and minimal human intervention. Herein, we review the currently used state-of-the-art high-throughput automated chemical reaction platforms and machine learning algorithms that drive the optimization of chemical reactions, highlighting the limitations and future opportunities of this new field of research.

Automated Optimization of a Multistep, Multiphase Continuous Flow Process for Pharmaceutical Synthesis

Flow synthesis is becoming increasingly relevant as a sustainable and safe alternative to traditional batch processes, as reaction conditions that are not usually achievable in batch chemistry can be exploited (for example, higher temperatures and pressures). Telescoped continuous reactions have the potential to reduce waste by decreasing the number of separate unit operations (e.g., crystallization, filtration, washing, and drying), increase safety due to limiting operator interaction with potentially harmful materials that can be reacted in subsequent steps, minimize supply chain disruption, and reduce the need to store large inventories of intermediates as they can be synthesized on demand. Optimization of these flow processes leads to further efficiency when exploring new reactions, as with a higher yield comes higher purity, reduced waste, and a greener synthesis. This project explored a two-step process consisting of a three-phase heterogeneously catalyzed hydrogenation followed by a homogeneous amidation reaction. The steps were optimized individually and as a multistep telescoped process for yield using remote automated control via a Bayesian optimization algorithm and HPLC analysis to assess the performance of a reaction for a given set of experimental conditions. 2-MeTHF was selected as a green solvent throughout the process, and the heterogeneous step provided good atom economy due to the use of pure hydrogen gas as a reagent. This research highlights the benefits of using multistage automated optimization in the development of pharmaceutical syntheses. The combination of telescoping and optimization with automation allows for swift investigation of synthetic processes in a minimum number of experiments, leading to a reduction in the number of experiments performed and a large reduction in process mass intensity values.

Self-Driving Laboratories for Chemistry and Materials Science

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning

We report a human-in-the-loop implementation of the multi-objective experimental design via a Bayesian optimization platform (EDBO+) towards the optimization of butylpyridinium bromide synthesis under continuous flow conditions. The algorithm simultaneously optimized reaction yield and production rate (or space-time yield) and generated a well defined Pareto front. The versatility of EDBO+ was demonstrated by expanding the reaction space mid-campaign by increasing the upper temperature limit. Incorporation of continuous flow techniques enabled improved control over reaction parameters compared to common batch chemistry processes, while providing a route towards future automated syntheses and improved scalability. To that end, we applied the open-source Python module, nmrglue, for semi-automated nuclear magnetic resonance (NMR) spectroscopy analysis, and compared the acquired outputs against those obtained through manual processing methods from spectra collected on both low-field (60 MHz) and high-field (400 MHz) NMR spectrometers. The EDBO+ based model was retrained with these four different datasets and the resulting Pareto front predictions provided insight into the effect of data analysis on model predictions. Finally, quaternization of poly(4-vinylpyridine) with bromobutane illustrated the extension of continuous flow chemistry to synthesize functional materials.

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.

Quid Pro Flow

How do you get into flow? We trained in flow chemistry during postdoctoral research and are now applying it in new areas: materials chemistry, crystallization, and supramolecular synthesis. Typically, when researchers think of "flow", they are considering predominantly liquid-based organic synthesis; application to other disciplines comes with its own challenges. In this Perspective, we highlight why we use and champion flow technologies in our fields, summarize some of the questions we encounter when discussing entry into flow research, and suggest steps to make the transition into the field, emphasizing that communication and collaboration between disciplines is key.

Visible-Light-Induced Oxidative Coupling of Phenols and Alkenylphenols with a Recyclable, Solid Photocatalyst

A photocatalytic method for phenol and alkenylphenol oxidative coupling is reported using an inexpensive heterogeneous titanium dioxide photocatalyst with air and visible light. During the coupling process, the Ti-substrate complex is activated under visible light through a ligand to metal charge transfer effect, and the diphenol adduct is proposed to form through a radical cation. The heterogeneous TiO2 catalyst remains stable throughout the reaction and can be easily removed and reused multiple times.

Re-envisioning the design of nanomedicines: harnessing automation and artificial intelligence

INTRODUCTION

Interest in nanomedicines has surged in recent years due to the critical role they have played in the COVID-19 pandemic. Nanoformulations can turn promising therapeutic cargo into viable products through improvements in drug safety and efficacy profiles. However, the developmental pathway for such formulations is non-trivial and largely reliant on trial-and-error. Beyond the costly demands on time and resources, this traditional approach may stunt innovation. The emergence of automation, artificial intelligence (AI) and machine learning (ML) tools, which are currently underutilized in pharmaceutical formulation development, offers a promising direction for an improved path in the design of nanomedicines.

AREAS COVERED

the potential of harnessing experimental automation and AI/ML to drive innovation in nanomedicine development. The discussion centers on the current challenges in drug formulation research and development, and the major advantages afforded through the application of data-driven methods.

EXPERT OPINION

The development of integrated workflows based on automated experimentation and AI/ML may accelerate nanomedicine development. A crucial step in achieving this is the generation of high-quality, accessible datasets. Future efforts to make full use of these tools can ultimately contribute to the development of more innovative nanomedicines and improved clinical translation of formulations that rely on advanced drug delivery systems.

Bayesian Self-Optimization for Telescoped Continuous Flow Synthesis

The optimization of multistep chemical syntheses is critical for the rapid development of new pharmaceuticals. However, concatenating individually optimized reactions can lead to inefficient multistep syntheses, owing to chemical interdependencies between the steps. Herein, we develop an automated continuous flow platform for the simultaneous optimization of telescoped reactions. Our approach is applied to a Heck cyclization-deprotection reaction sequence, used in the synthesis of a precursor for 1-methyltetrahydroisoquinoline C5 functionalization. A simple method for multipoint sampling with a single online HPLC instrument was designed, enabling accurate quantification of each reaction, and an in-depth understanding of the reaction pathways. Notably, integration of Bayesian optimization techniques identified an 81 % overall yield in just 14 h, and revealed a favorable competing pathway for formation of the desired product.

A semi-automated material exploration scheme to predict the solubilities of tetraphenylporphyrin derivatives

Acceleration of material discovery has been tackled by informatics and laboratory automation. Here we show a semi-automated material exploration scheme to modelize the solubility of tetraphenylporphyrin derivatives. The scheme involved the following steps: definition of a practical chemical search space, prioritization of molecules in the space using an extended algorithm for submodular function maximization without requiring biased variable selection or pre-existing data, synthesis & automated measurement, and machine-learning model estimation. The optimal evaluation order selected using the algorithm covered several similar molecules (32% of all targeted molecules, whereas that obtained by random sampling and uncertainty sampling was ~7% and ~4%, respectively) with a small number of evaluations (10 molecules: 0.13% of all targeted molecules). The derived binary classification models predicted 'good solvents' with an accuracy >0.8. Overall, we confirmed the effectivity of the proposed semi-automated scheme in early-stage material search projects for accelerating a wider range of material research.

Photochemical Synthesis of the Bioactive Fragment of Salbutamol and Derivatives in a Self-Optimizing Flow Chemistry Platform

The implementation of self-optimizing flow reactors has been mostly limited to model reactions or known synthesis routes. In this work, a self-optimizing flow photochemistry platform is used to develop an original synthesis of the bioactive fragment of Salbutamol and derivatives. The key photochemical steps for the construction of the aryl vicinyl amino alcohol moiety consist of a C-C bond forming reaction followed by an unprecedented, high yielding (>80 %), benzylic oxidative cyclization.

Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform

Computer-aided synthesis planning (CASP) tools can propose retrosynthetic pathways and forward reaction conditions for the synthesis of organic compounds, but the limited availability of context-specific data currently necessitates experimental development to fully specify process details. We plan and optimize a CASP-proposed and human-refined multistep synthesis route toward an exemplary small molecule, sonidegib, on a modular, robotic flow synthesis platform with integrated process analytical technology (PAT) for data-rich experimentation. Human insights address catalyst deactivation and improve yield by strategic choices of order of addition. Multi-objective Bayesian optimization identifies optimal values for categorical and continuous process variables in the multistep route involving 3 reactions (including heterogeneous hydrogenation) and 1 separation. The platform's modularity, robotic reconfigurability, and flexibility for convergent synthesis are shown to be essential for allowing variation of downstream residence time in multistep flow processes and controlling the order of addition to minimize undesired reactivity. Overall, the work demonstrates how automation, machine learning, and robotics enhance manual experimentation through assistance with idea generation, experimental design, execution, and optimization.

Development of a Fully Continuous-Flow Approach Towards Asymmetric Total Synthesis of Tetrahydroprotoberberine Natural Alkaloids

Continuous flow synthetic technologies had been widely applied in the total synthesis in the past few decades. Fully continuous flow synthesis is still extremely focused on multi-step synthesis of complex natural pharmaceutical molecules. Thus, the development of fully continuous flow total synthesis of natural products is in demand but challenging. Herein, we demonstrated the first fully continuous flow approach towards asymmetric total synthesis of natural tetrahydroprotoberberine alkaloids, (-)-isocanadine, (-)-tetrahydropseudocoptisine, (-)-stylopine and (-)-nandinine. This method features a concise linear sequence involving four chemical transformations and three on-line work-up processing in an integrated flow platform, without any intermediate purification. The overall yield and enantioselectivity of this four-step continuous flow chemistry were up to 50 % and 92 %ee, respectively, in a total residence time of 32.5 min, corresponding to a throughput of 145 mg/h.

The case for data science in experimental chemistry: examples and recommendations

The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to 'co-design' chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.

Automated Flow and Real-Time Analytics Approach for Screening Functional Group Tolerance in Heterogeneous Catalytic Reactions

Heterogeneous hydrogenation reactions are widely used in synthesis, and performing them using continuous flow technologies addresses many of the safety, scalability and sustainability issues. However, one of the main potential...

Developing flow photo-thiol–ene functionalizations of cinchona alkaloids with an autonomous self-optimizing flow reactor

Continuous flow photo-thiol–ene reactions on cinchona alkaloids with a variety of organic thiols have been developed using enabling technologies such as a self-optimizing flow photochemical reactor.

Bayesian based reaction optimization for complex continuous gas–liquid–solid reactions

In recent years, self-optimization strategies have been gradually utilized for the determination of optimal reaction conditions owing to their high convenience and independence from researchers' experience.

Direct low field J-edited diffusional proton NMR spectroscopic measurement of COVID-19 inflammatory biomarkers in human serum

A JEDI NMR pulse experiment incorporating relaxation, diffusion and J-modulation peak editing was implemented at a low field (80 MHz) spectrometer system to quantify two recently discovered plasma markers of SARS-CoV-2 infection and general inflammation.

Accelerated screening of colloidal nanocrystals using artificial neural network-assisted autonomous flow reactor technology

Colloidal semiconductor nanocrystals with tunable optical and electronic properties are opening up exciting opportunities for high-performance optoelectronics, photovoltaics, and bioimaging applications. Identifying the optimal synthesis conditions and screening of synthesis recipes in search of efficient synthesis pathways to obtain nanocrystals with desired optoelectronic properties, however, remains one of the major bottlenecks for accelerated discovery of colloidal nanocrystals. Conventional strategies, often guided by limited understanding of the underlying mechanisms remain expensive in both time and resources, thus significantly impeding the overall discovery process. In response, an autonomous experimentation platform is presented as a viable approach for accelerated synthesis screening and optimization of colloidal nanocrystals. Using a machine-learning-based predictive synthesis approach, integrated with automated flow reactor and inline spectroscopy, indium phosphide nanocrystals are autonomously synthesized. Their polydispersity for different target absorption wavelengths across the visible spectrum is simultaneously optimized during the autonomous experimentation, while utilizing minimal self-driven experiments (less than 50 experiments within 2 days). Starting with no-prior-knowledge of the synthesis, an ensemble neural network is trained through autonomous experiments to accurately predict the reaction outcome across the entire synthesis parameter space. The predicted parameter space map also provides new nucleation-growth kinetic insights to achieve high monodispersity in size of colloidal nanocrystals.

Oxidative dehydrogenative couplings of alkenyl phenols

Alkenyl phenols are utilized by nature in the construction of one of the most important biopolymers, lignin. Using similar building blocks, an array of distinct structures can be formed by selective dimerization of the starting phenols to form lignans, neolignans, oxyneolignans, and norlignans. Given the multitude of possible outcomes, many methods have been reported to affect the desired bond formations and access these biologically relevant scaffolds. The most biomimetic of these methods, discussed here, involve the unprotected phenols undergoing oxidative bond formation that proceeds via dehydrogenative coupling. This review aims to place the known literature in context, highlight the progress made toward the synthesis of these important molecules, and recognize the gaps and limitations that still exist.

Sampling and Analysis in Flow: The Keys to Smarter, More Controllable, and Sustainable Fine-Chemical Manufacturing

Process analytical technology (PAT) is a system designed to help chemists better understand and control manufacturing processes. PAT systems operate through the combination of analytical devices, reactor control elements, and mathematical models to ensure the quality of the final product through a quality by design (QbD) approach. The expansion of continuous manufacturing in the pharmaceutical and fine-chemical industry requires the development of PAT tools suitable for continuous operation in the environment of flow reactors. This requires innovative approaches to sampling and analysis from flowing media to maintain the integrity of the reactor content and the analyte of interest. The following Review discusses examples of PAT tools implemented in flow chemistry for the preparation of small organic molecules, and applications of self-optimization tools.

Integrated “Generate, Make, and Test” for Formulated Products using Knowledge Graphs

In the multi-billion dollar formulated product industry, state of the art continues to rely heavily on experts during the “generate, make and test” steps of formulation design. We propose automation aids to each step with a knowledge graph of relevant information as the central artifact. The generate step usually focuses on coming up with new recipes for intended formulation. We propose to aid the experts who generally carry out this step manually by providing a recommendation system and a templating system on top of the knowledge graph. Using the former, the expert can create a recipe from scratch using historical formulations and related data. With the latter, the expert starts with a recipe template created by our system and substitutes the requisite constituents to form a recipe. In the current state of practice, the three steps mentioned above operate in a fragmented manner wherein observations from one step do not aid other steps in a streamlined manner. Instead of manually operated labs for the make and test steps, we assume automated or robotic labs and in-silico testing, respectively. Using two formulations, namely face cream and an exterior coating, we show how the knowledge graph may help integrate and streamline the communication between the generate, the make, and the test steps. Our initial exploration shows considerable promise.

Data-science driven autonomous process optimization

Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield.

Olympus: a benchmarking framework for noisy optimization and experiment planning

Research challenges encountered across science, engineering, and economics can frequently be formulated as optimization tasks. In chemistry and materials science, recent growth in laboratory digitization and automation has sparked interest in optimization-guided autonomous discovery and closed-loop experimentation. Experiment planning strategies based on off-the-shelf optimization algorithms can be employed in fully autonomous research platforms to achieve desired experimentation goals with the minimum number of trials. However, the experiment planning strategy that is most suitable to a scientific discovery task is a priori unknown while rigorous comparisons of different strategies are highly time and resource demanding. As optimization algorithms are typically benchmarked on low-dimensional synthetic functions, it is unclear how their performance would translate to noisy, higher-dimensional experimental tasks encountered in chemistry and materials science. We introduce Olympus, a software package that provides a consistent and easy-to-use framework for benchmarking optimization algorithms against realistic experiments emulated via probabilistic deep-learning models. Olympus includes a collection of experimentally derived benchmark sets from chemistry and materials science and a suite of experiment planning strategies that can be easily accessed via a user-friendly Python interface. Furthermore, Olympus facilitates the integration, testing, and sharing of custom algorithms and user-defined datasets. In brief, Olympus mitigates the barriers associated with benchmarking optimization algorithms on realistic experimental scenarios, promoting data sharing and the creation of a standard framework for evaluating the performance of experiment planning strategies.

Simultaneous self-optimisation of yield and purity through successive combination of inline FT-IR spectroscopy and online mass spectrometry in flow reactions

Self-optimisation constitutes a very helpful tool for chemical process development, both in lab and in industrial applications. However, research on the application of model-free autonomous optimisation strategies (based on experimental investigation) for complex reactions of high industrial significance, which involve considerable intermediate and by-product formation, is still in an early stage. This article describes the development of an enhanced autonomous microfluidic reactor platform for organolithium and epoxide reactions that incorporates a successive combination of inline FT-IR spectrometer and online mass spectrometer. Experimental data is collected in real-time and used as feedback for the optimisation algorithms (modified Simplex algorithm and Design of Experiments) without time delay. An efficient approach to handle intricate optimisation problems is presented, where the inline FT-IR measurements are used to monitor the reaction’s main components, whereas the mass spectrometer’s high sensitivity permits insights into the formation of by-products. To demonstrate the platform’s flexibility, optimal reaction conditions of two organic syntheses are identified. Both pose several challenges, as complex reaction mechanisms are involved, leading to a large number of variable parameters, and a considerable amount of by-products is generated under non-ideal process conditions. Through multidimensional real-time optimisation, the platform supersedes labor- and cost-intensive work-up procedures, while diminishing waste generation, too. Thus, it renders production processes more efficient and contributes to their overall sustainability.

Graphical abstract

Development of a continuous flow synthesis of FGIN-1-27 enabled by in-line 19F NMR analyses and optimization algorithms

A continuous flow synthesis of FGIN-1-27 has been developed using enabling technologies such as real-time in-line benchtop 19F NMR analysis and an optimization algorithm.

Low-field benchtop NMR spectroscopy: status and prospects in natural product analysis†

INTRODUCTION

Since a couple of years, low-field (LF) nuclear magnetic resonance (NMR) spectrometers (40-100 MHz) have re-entered the market. They are used for various purposes including analyses of natural products. Similar to high-field instruments (300-1200 MHz), modern LF instruments can measure multiple nuclei and record two-dimensional (2D) NMR spectra.

OBJECTIVE

To review the commercial availability as well as applications, advantages, limitations, and prospects of LF-NMR spectrometers for the purpose of natural products analysis.

METHOD

Commercial LF instruments were compared. A literature search was performed for articles using and discussing modern LF-NMR. Next, the articles relevant to natural products were read and summarised.

RESULTS

Seventy articles were reviewed. Most appeared in 2018 and 2019. Low costs and ease of operation are most often mentioned as reasons for using LF-NMR.

CONCLUSION

As the spectral resolution of LF instruments is limited, they are not used for structure elucidation of new natural products but rather applied for quality control (QC), forensics, food and health research, process control and teaching. Chemometric data handling is valuable. LF-NMR is a rapidly developing niche and new instruments keep being introduced.

Merging Gradient-Based Methods to Improve Benchtop NMR Spectroscopy: A New Tool for Flow Reaction Optimization

Emerging low cost, compact NMR spectrometers that can be connected in-line to a flow reactor are suited to study reaction mixtures. The main limitation of such spectrometers arises from their lower magnetic field inducing a reduced sensitivity and a weaker spectral resolution. For enhancing the spectral resolution, the merging of Pure-Shift methods recognized for line narrowing with solvent elimination schemes was implemented in the context of mixtures containing protonated solvents. One more step was achieved to further enhance the resolution power on compact systems, thanks to multiple elimination schemes prior to Pure-Shift pulse sequence elements. For the first time, we were able to remove up to 6 protonated solvent signals simultaneously by dividing their intensity by 500 to 1700 with a concomitant spectral resolution enhancement for signals of interest from 9 to 12 as compared to the standard 1D ¹ H. Then, the potential of this new approach was shown on the flow synthesis of a complex benzoxanthenone structure.

Gradient-based pulse sequences for benchtop NMR spectroscopy

Benchtop NMR spectroscopy has been on the rise for the last decade, by bringing high-resolution NMR in environments that are not easily compatible with high-field NMR. Benchtop spectrometers are accessible, low cost and show an impressive performance in terms of sensitivity with respect to the relatively low associated magnetic field (40-100 MHz). However, their application is limited by the strong and ubiquitous peak overlaps arising from the complex mixtures which are often targeted, often characterized by a great diversity of concentrations and by strong signals from non-deuterated solvents. Such limitations can be addressed by pulse sequences making clever use of magnetic field gradient pulses, capable of performing efficient coherence selection or encoding chemical shift or diffusion information. Gradients pulses are well-known ingredients of high-field pulse sequence recipes, but were only recently made available on benchtop spectrometers, thanks to the introduction of gradient coils in 2015. This article reviews the recent methodological advances making use of gradient pulses on benchtop spectrometers and the applications stemming from these developments. Particular focus is made on solvent suppression schemes, diffusion-encoded, and spatially-encoded experiments, while discussing both methodological advances and subsequent applications. We eventually discuss the exciting development and application perspectives that result from such advances.

Diastereoselective Synthesis of Benzoxanthenones

An oxidative catalytic vanadium(V) system was developed to access the naturally nonabundant diastereomers of carpanone from the corresponding alkenyl phenol monomer in one pot by tandem oxidation, oxidative coupling, and 4+2 cyclization. This system was applied to the synthesis of two other analogues of carpanone. Mild oxidizing silver salts were used as the terminal oxidant to minimize background oxidation which produces the natural diastereomer of carpanone. Further, the first examples of enantioselective oxidative benzoxanthenone formation are reported. Solvent polarity has a strong effect on enantioselectivity, consistent with a mechanism involving binding of vanadium Schiff base catalysts to the alcoholic moiety of the alkenyl phenols during the cyclization step.

Benchtop NMR for Online Reaction Monitoring of the Biocatalytic Synthesis of Aromatic Amino Alcohols

Online analytics provides insights into the progress of an ongoing reaction without the need for extensive sampling and offline analysis. In this study, we investigated benchtop NMR as an online reaction monitoring tool for complex enzyme cascade reactions. Online NMR was used to monitor a two-step cascade beginning with an aromatic aldehyde and leading to an aromatic amino alcohol as the final product, applying two different enzymes and a variety of co-substrates and intermediates. Benchtop NMR enabled the concentration of the reaction components to be detected in buffered systems in the single-digit mM range without using deuterated solvent. The concentrations determined via NMR were correlated with offline samples analyzed via uHPLC and displayed a good correlation between the two methods. In summary, benchtop NMR proved to be a sensitive, selective and reliable method for online reaction monitoring in (multi-step) biosynthesis. In future, online analytic systems such as the benchtop NMR devices described might not only enable direct monitoring of the reaction, but may also form the basis for self-regulation in biocatalytic reactions.

Self-optimising processes and real-time-optimisation of organic syntheses in a microreactor system using Nelder–Mead and design of experiments

Comparing an enhanced simplex algorithm with model-free design of experiments, this work presents a flexible platform for multi-objective, real-time optimisation.

Multivariate analysis of inline benchtop NMR data enables rapid optimization of a complex nitration in flow

Multivariate analysis is applied to inline benchtop NMR data for a complex nitration in flow. This rapid quantification enables reaction optimization using advanced techniques in flow, such as design of experiments and dynamic experimentation.

Reconfigurable Flow Platform for Automated Reagent Screening and Autonomous Optimization for Bioinspired Lignans Synthesis

Naturally occurring benzoxanthenones, which belong to the vast family of lignans, are promising biologically relevant targets. They are biosynthetically produced by the oxidative dimerization of 2-propenyl phenols. In this manuscript, we disclose a powerful automated flow-based strategy for identifying and optimizing a cobalt-catalyzed oxidizing system for the bioinspired dimerization of 2-propenyl phenols. We designed a reconfigurable flow reactor associating online monitoring and process control instrumentation. Our machine was first configured as an automated screening platform to evaluate a matrix of 4 catalysts (plus the blank) and 5 oxidants (plus the blank) at two different temperatures, resulting in an array of 50 reactions. The automated screening was conducted on micromole scale at a rate of one fully characterized reaction every 26 min. After having identified the most promising cobalt-catalyzed oxidizing system, the automated screening platform was straightforwardly reconfigured to an autonomous self-optimizing flow reactor by implementation of an optimization algorithm in the closed-loop system. The optimization campaign allowed the determination of very effective experimental conditions in a limited number of experiments, which allowed us to prepare the natural products carpanone and polemannone B as well as synthetic analogues.

Benchtop NMR for the monitoring of bioprocesses

This mini-review highlights the potential of benchtop nuclear magnetic resonance (NMR) for the monitoring of bioprocesses. It describes recent perspectives opened by the reduced size of devices in relaxometry, magnetic resonance imaging and NMR spectroscopy. In particular, the recent emergence of the benchtop NMR spectroscopy gives access to many applications thanks to the implementation of advanced experiments.