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      Towards 4th industrial revolution efficient and sustainable continuous flow manufacturing of active pharmaceutical ingredients

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          Abstract

          The convergence of end-to-end continuous flow synthesis with downstream processing, process analytical technology (PAT), artificial intelligence (AI), machine learning and automation in ensuring improved accessibility of quality medicines on demand.

          Abstract

          Continuous flow chemistry has opened a new paradigm in both the laboratory and pharmaceutical industry. This review details the recently reported literature on continuous multistep telescoped synthesis of active pharmaceutical ingredients (APIs), inline flow downstream processing, in-process monitoring by process analytical technology (PAT) in flow, flow automation and artificial intelligence (AI) and robotics. We envisage that the integration of all these techniques can ensure an ‘ideal’ smart and efficient 21st century API chemical process that dramatically improves efficiency, agility, quality and flexibility in the manufacturing of pharmaceuticals.

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          Most cited references85

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          Deep learning.

          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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            Machine learning: Trends, perspectives, and prospects.

            Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing.
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              A mobile robotic chemist

              Technologies such as batteries, biomaterials and heterogeneous catalysts have functions that are defined by mixtures of molecular and mesoscale components. As yet, this multi-length-scale complexity cannot be fully captured by atomistic simulations, and the design of such materials from first principles is still rare1-5. Likewise, experimental complexity scales exponentially with the number of variables, restricting most searches to narrow areas of materials space. Robots can assist in experimental searches6-14 but their widespread adoption in materials research is challenging because of the diversity of sample types, operations, instruments and measurements required. Here we use a mobile robot to search for improved photocatalysts for hydrogen production from water15. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm16-18. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones. Our strategy uses a dexterous19,20 free-roaming robot21-24, automating the researcher rather than the instruments. This modular approach could be deployed in conventional laboratories for a range of research problems beyond photocatalysis.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                RCEEBW
                Reaction Chemistry & Engineering
                React. Chem. Eng.
                Royal Society of Chemistry (RSC)
                2058-9883
                February 01 2022
                2022
                : 7
                : 2
                : 214-244
                Affiliations
                [1 ]Nelson Mandela University, University Way, Port Elizabeth, 6031, South Africa
                Article
                10.1039/D1RE00483B
                0631acd7-aaae-4865-9595-2ac4734cc833
                © 2022

                http://rsc.li/journals-terms-of-use

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