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•Multi-objective algorithm applied to the self-optimization of flow reactor.•Algorithm simultaneously targeted reactor productivity and environmental objectives.•Pareto front shows ...the trade-off between these target objectives.•Gaussian process models provide knowledge about the nature of interactions.
Automated development of chemical processes requires access to sophisticated algorithms for multi-objective optimization, since single-objective optimization fails to identify the trade-offs between conflicting performance criteria. Herein we report the implementation of a new multi-objective machine learning optimization algorithm for self-optimization, and demonstrate it in two exemplar chemical reactions performed in continuous flow. The algorithm successfully identified a set of optimal conditions corresponding to the trade-off curve (Pareto front) between environmental and economic objectives in both cases. Thus, it reveals the complete underlying trade-off and is not limited to one compromise as is the case in many other studies. The machine learning algorithm proved to be extremely data efficient, identifying the optimal conditions for the objectives in a lower number of experiments compared to single-objective optimizations. The complete underlying trade-off between multiple objectives is identified without arbitrary weighting factors, but via true multi-objective optimization.
Coronavirus disease 2019 (COVID-19) exhibits variable symptom severity ranging from asymptomatic to life-threatening, yet the relationship between severity and the humoral immune response is poorly ...understood. We examined antibody responses in 113 COVID-19 patients and found that severe cases resulting in intubation or death exhibited increased inflammatory markers, lymphopenia, pro-inflammatory cytokines, and high anti-receptor binding domain (RBD) antibody levels. Although anti-RBD immunoglobulin G (IgG) levels generally correlated with neutralization titer, quantitation of neutralization potency revealed that high potency was a predictor of survival. In addition to neutralization of wild-type SARS-CoV-2, patient sera were also able to neutralize the recently emerged SARS-CoV-2 mutant D614G, suggesting cross-protection from reinfection by either strain. However, SARS-CoV-2 sera generally lacked cross-neutralization to a highly homologous pre-emergent bat coronavirus, WIV1-CoV, which has not yet crossed the species barrier. These results highlight the importance of neutralizing humoral immunity on disease progression and the need to develop broadly protective interventions to prevent future coronavirus pandemics.
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•Severe COVID-19 associates with higher antibody production and neutralization titers•Neutralization potency of anti-RBD antibodies predicts disease severity and survival•Immunomodulatory COVID-19-directed therapies modulate antibody responses•COVID-19 sera neutralize D614 and G614 variants, but not pre-emergent WIV1-CoV
Garcia-Beltran et al. show that the development of more potent neutralizing antibodies during SARS-CoV-2 infection predicts COVID-19 survival. Protective antibody responses exhibit potent neutralization against the currently circulating SARS-CoV-2 D614G spike variant but lack significant activity against pre-emergent WIV1-CoV spike, suggesting that convalescent patients are likely to remain susceptible to future pandemics.
Reactor automation is revolutionising the way new chemical processes are discovered and developed. Assigning repetitive aspects of chemical synthesis to machines, such as experimental execution and ...data collection, provides more time for researchers to focus on critical interpretation and creative problem solving. The ability to autonomously prepare late‐stage intermediates and complex products, rather than just simple starting materials, will play a central role in applications such as the efficient exploration of chemical space and responsive manufacturing. However, translating automated technologies from specific single‐step tasks to more general multistep syntheses remains a significant challenge, owing to high structural diversity and chemical/physical interdependencies between the steps. Robotic batch and continuous flow platforms are gradually becoming more universal, providing access to a wider range of chemistries required to achieve autonomous multistep synthesis. Advances in process analytical technologies have enhanced our ability to monitor interconnected reactions in real‐time, thus accelerating data collection and giving greater process control for ensuring a high standard of safety and product quality. Integration of these tools with control software creates a feedback loop, which can be harnessed for adaptive and flexible multistep screening or holistic self‐optimisation. This review presents recent developments in the application of automated reactor technologies for multistep chemical synthesis, including batch and continuous flow platforms. Specifically, this review highlights how the integration of control software with advanced process analytical technologies and machine learning algorithms are accelerating the synthesis of complex molecules.
Automation in multistep synthesis involves the use of robotics, advanced software, and machine learning algorithms to streamline the complex process of creating molecules. This review highlights recent advances in automated batch and continuous flow platforms, and presents how these technologies can be applied in the development and optimisation of multistep processes.
Progress reaction profiles are affected by both catalyst activation and deactivation processes occurring alongside the main reaction. These processes complicate the kinetic analysis of reactions, ...often directing researchers toward incorrect conclusions. We report the application of two kinetic treatments, based on variable time normalization analysis, to reactions involving catalyst activation and deactivation processes. The first kinetic treatment allows the removal of induction periods or the effect of rate perturbations associated with catalyst deactivation from kinetic profiles when the quantity of active catalyst can be measured. The second treatment allows the estimation of the activation or deactivation profile of the catalyst when the order of the reactants for the main reaction is known. Both treatments facilitate kinetic analysis of reactions suffering catalyst activation or deactivation processes.
Keep calm and carry on (doing kinetics): Two different kinetic analysis methods, based on variable time normalization analysis (VTNA), are described for studying reactions with catalyst deactivation and activation processes. The cases studied are a hydroformylation reaction catalyzed by a supramolecular rhodium complex and an aminocatalytic Michael reaction.
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.
An autonomous continuous flow platform for the rapid development of multistep synthetic pathways is reported. New multipoint sampling and Bayesian optimization techniques were combined, enabling simultaneous identification of optimum reaction conditions within a pharmaceutical process. The short optimization times achieved are promising for development of telescoped reactions in the future.
A universal multistage cascade CSTR has been developed that is suitable for a wide range of continuous-flow processes. Coined by our group the “Freactor” (free-to-access reactor), the new reactor ...integrates the efficiency of pipe-flow processing with the advanced mixing of a CSTR, delivering a general “plug-and-play” reactor platform which is well-suited to multiphasic continuous-flow chemistry. Importantly, the reactor geometry is easily customized to accommodate reactions requiring long residence times (≥3 h tested).
For the discovery of new candidate molecules in the pharmaceutical industry, library synthesis is a critical step, in which library size, diversity, and time to synthesise are fundamental. In this ...work we propose stopped-flow synthesis as an intermediate alternative to traditional batch and flow chemistry approaches, suited for small molecule pharmaceutical discovery. This method exploits the advantages of both techniques enabling automated experimentation with access to high pressures and temperatures; flexibility of reaction times, with minimal use of reagents (μmol scale per reaction). In this study, we integrate a stopped-flow reactor into a high-throughput continuous platform designed for the synthesis of combinatory libraries with at-line reaction analysis. This approach allowed ∼900 reactions to be conducted in an accelerated timeframe (192 hours). The stopped flow approach used ∼10% of the reactants and solvents compared to a fully continuous approach. This methodology demonstrates a significantly improved synthesis success rate of smaller libraries by simplifying the implementation of cross-reaction optimisation strategies. The experimental datasets were used to train a feed-forward neural network (FFNN) model providing a framework to guide further experiments, which showed good model predictability and success when tested against an external set with fewer experiments. As a result, this work demonstrates that combining experimental automation with machine learning strategies can deliver optimised analyses and enhanced predictions, enabling more efficient drug discovery investigations across the design, make, test and analysis (DMTA) cycle.
Combining experimental stopped flow automation with machine learning strategies can deliver optimised conditions and enhanced predictions, enabling more efficient design, make, test and analysis (DMTA) cycles.
Self-optimising chemical systems have experienced a growing momentum in recent years, with the evolution of self-optimising platforms leading to their application for reaction screening and chemical ...synthesis. With the desire for improved process sustainability, self-optimisation provides a cheaper, faster and greener approach to the chemical development process. The use of such platforms aims to enhance the capabilities of the researcher by removing the need for labor-intensive experimentation, allowing them to focus on more challenging tasks. The establishment of these systems have enabled opportunities for self-optimising platforms to become a key element of a laboratory's repertoire. To enable the wider adoption of self-optimising chemical platforms, this review summarises the history of algorithmic usage in chemical reaction self-optimisation, detailing the functionality of the algorithms and their applications in a way that is accessible for chemists and highlights opportunities for the further exploitation of algorithms in chemical synthesis moving forward.
Self-optimising chemical systems have experienced a growing momentum in recent years. Herein, we review algorithms used for the self-optimisation of chemical reactions in an accessible way for the general chemist.
Genome-wide association studies (GWAS) have been highly informative in discovering disease-associated loci but are not designed to capture all structural variations in the human genome. Using ...long-read sequencing data, we discovered widespread structural variation within SINE-VNTR-
(SVA) elements, a class of great ape-specific transposable elements with gene-regulatory roles, which represents a major source of structural variability in the human population. We highlight the presence of structurally variable SVAs (SV-SVAs) in neurological disease-associated loci, and we further associate SV-SVAs to disease-associated SNPs and differential gene expression using luciferase assays and expression quantitative trait loci data. Finally, we genetically deleted SV-SVAs in the
and
Alzheimer's disease-associated risk loci and in the
Parkinson's disease-associated risk locus and assessed multiple aspects of their gene-regulatory influence in a human neuronal context. Together, this study reveals a novel layer of genetic variation in transposable elements that may contribute to identification of the structural variants that are the actual drivers of disease associations of GWAS loci.
Catalytic reactions play a central role in many industrial processes, owing to their ability to enhance efficiency and sustainability. However, complex interactions between the categorical and ...continuous variables leads to non-smooth response surfaces, which traditional optimisation methods struggle to navigate. Herein, we report the development and benchmarking of a new adaptive latent Bayesian optimiser (ALaBO) algorithm for mixed variable chemical reactions. ALaBO was found to outperform other open-source Bayesian optimisation toolboxes, when applied to a series of test problems based on simulated kinetic data of catalytic reactions. Furthermore, through integration of ALaBO with a continuous flow reactor, we achieved the rapid self-optimisation of an exemplar Suzuki-Miyaura cross-coupling reaction involving six distinct ligands, identifying a 93% yield within a budget of just 25 experiments.
A novel adaptive latent Bayesian optimisation (ALaBO) algorithm accelerates the development of mixed variable catalytic reactions.
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