23 Drug Discovery, Pharmaceuticals & Cannabis Testing Figure 4: Flow chemistry process for reaction optimisation. Flow automation for library generation
Automating the generation of libraries of compounds to explore reactivity and chemical space using fl ow chemistry allows the effi cient synthesis of complex molecules with multiple points of diversifi cation. Structural diversity can be achieved in many ways when building molecules in this fashion, including using different starting materials; varying core structural motifs in multicomponent reactions; combining the synthesis of uncommon, low diversity starting material sub-sets; and creating diversity from convergent synthesis. Realising the full potential of this approach requires a modular and fl exible system that can be confi gured to any fl ow chemistry application and fl uid pathway desired by the chemist.
Automated reagent injection is also vital to allow the rapid exploration of chemistries by introducing a range of chemical ‘building blocks’ at each point in the synthetic route. These injector modules should enable parallel loading of samples using separate, dedicated liquid handing loops – to reduce the time associated with serial loading of samples – and have the ability to pre-pressurise the sample loops prior to injection, to avoid pressure drops and allow the use of smaller aliquots with greater segment tracking. With this in place, chemists can run a series of automated experiments to create diversity in structure. This is typically achieved using the same reaction conditions but can be tailored to the relative reactivities of the starting materials, adding even more control to the synthesis.
Closed loop optimisation and machine learning
The move towards more industry standards for device connectivity – offering the ability to communicate and control a diverse set of laboratory equipment through a central control system – is enabling fl ow chemistry platforms to be integrated with machine learning applications. These approaches can be combined with fl ow synthesis and real-time screening to provide a highly effective approach for optimising reaction methodologies and conditions. So far, these platforms are still mainly found in the academic arena, with several groups – such as Professor Richard Bourne (Univ. of Leeds, IPRD), Professor Steve Ley (Univ. of Cambridge), and Professor Alexie Lapkin (Univ. of Cambridge, iDMT) – actively exploring methods to integrate digital technologies with synthetic processes for the automation, optimisation and development of pharmaceuticals. However, this solution is also being actively pursued by the chemical and pharma industries to enhance their capabilities.
Flow synthesis can be coupled with a variety of in-line analytical techniques to give real-time feedback that can be used to interpret the outcome of a reaction. Machine
learning or artifi cial intelligence algorithms can monitor these reaction conditions, then make decisions on the next set of parameters to select to optimise the workfl ow as part of a ‘closed loop’ system (Figure 5). Open-source industry standards – such as Open Platform Communications – Universal Architecture (OPC UA) – are vital to this approach, allowing users to connect a range of laboratory equipment from a variety of manufacturers to run fully automated, closed loop processes with seamless communication and control.
Figure 5: Closed loop reaction optimisation process for fl ow chemistry. Conclusion
The use of fl ow chemistry to generate compound libraries has helped to accelerate early phase drug discovery. Flow chemistry undoubtedly increases the range and scope of chemistries available through automated processes, allowing greater exploration of the available chemical space. Modern, modular fl ow chemistry platforms allow the development of complex automated experimentation approaches and provide new access to under-used techniques – such as photochemistry and electrochemistry – further expanding the tool kit available to chemists. On top of this, the development of machine learning- or AI-based algorithms that can be used to create closed loop experimentation systems represents a key technology for the future of both fl ow chemistry and drug discovery.
References
1. Cerra, B.; Carotti, A.; Passeri, D.; Sardella, R.; Moroni, G.; Di Michele, A.; Macchiarulo, A.; Pellicciari, R.; Gioiello, A. Exploiting Chemical Toolboxes for the Expedited Generation of Tetracyclic Quinolines as a Novel Class of PXR Agonists. ACS Med. Chem. Lett. 2019, 10(4), 677–681.
https://doi.org/10.1021/acsmedchemlett.8b00459
2. Gioiello, A.; Piccinno, A.; Lozza, A. M.; Cerra, B. The Medicinal Chemistry in the Era of Machines and Automation: Recent Advances in Continuous Flow Technology. J. Med. Chem. 2020, 63(13), 6624–6647.
https://doi.org/10.1021/acs.jmedchem.9b01956
3. Lopez, E.; Lourdes Linares, M.; Alcazar, J. Flow chemistry as a tool to access novel chemical space for drug discovery. Future Med. Chem. Perspectives, 2020, 12(17), 1547–1563.
https://doi.org/10.4155/fmc-2020-0075
Read, Share and Comment on this Article, visit:
www.labmate-online.com
Ground-breaking AI tool to accelerate drug discovery
bioXcelerate, the new health data science division of Optima Partners Ltd, unveils PleioGraph, a ground-breaking AI tool poised to accelerate the process of identifying and developing new drugs. This innovative technology has the capability to analyse intricate medical data at speeds 100 times faster than current state-of-the-art methods used in detecting biological networks crucial for understanding disease risk and facilitating drug discovery.
Powered by bioXcelerate’s proprietary machine learning algorithms, PleioGraph swiftly sifts through vast biological datasets to assist drug developers in pinpointing the genetic colocalization of genes, proteins, and cells implicated in disease progression. By providing rapid and precise insights into disease mechanisms, PleioGraph streamlines the drug development pipeline, expediting the delivery of improved treatments and medications.
Outperforming conventional approaches, PleioGraph offers substantial cost savings and dramatically reduces the time patients must wait for enhanced therapeutic options. Through collaborations with leading pharmaceutical entities, PleioGraph has demonstrated processing speeds between 75 and 100 times faster than industry standards, slashing time-to-insight from months to mere hours.
Founded by academics from leading UK universities such as Cambridge, Edinburgh, Imperial College, and Oxford, bioXcelerate endeavours to bridge the gap between academia and the pharmaceutical sector, fostering a mutually benefi cial relationship aimed at enhancing health outcomes and societal well-being.
More information online:
ilmt.co/PL/4mmk 62382pr@reply-direct.com
Send your Press Releases to:
pr@intlabmate.com
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68
