Rapid route design of AZD7594

Carter, Holly L.; Connor, Amand W.; Hart, R. C.; McCabe, James F.; McIntyre, Allyson C.; McMillan, Angus E.; Monks, Natalie; Mullen, A.; Ronson, Thomas O.; Steven, Alan; Tomasi, Simone; Yates, Simon D.

doi:10.1039/c9re00118b

Cited by 14 publications

(16 citation statements)

References 34 publications

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“…Solubility is a critical physical property of organic compounds in drug development, e.g., availability, distribution, metabolism, excretion and toxicity (ADMET) 1 , 2 , protein engineering 3 – 5 , chemical process design 6 , synthetic route prediction 7 , 8 , extraction and crystallisation 9 , 10 . Due to its importance in environmental predictions, biochemistry, and agrochemical and drug design, aqueous solubility prediction has been the subject of intensive research 11 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning with physicochemical relationships: solubility prediction in organic solvents and water

et al. 2020

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Solubility prediction remains a critical challenge in drug development, synthetic route and chemical process design, extraction and crystallisation. Here we report a successful approach to solubility prediction in organic solvents and water using a combination of machine learning (ANN, SVM, RF, ExtraTrees, Bagging and GP) and computational chemistry. Rational interpretation of dissolution process into a numerical problem led to a small set of selected descriptors and subsequent predictions which are independent of the applied machine learning method. These models gave significantly more accurate predictions compared to benchmarked open-access and commercial tools, achieving accuracy close to the expected level of noise in training data (LogS ± 0.7). Finally, they reproduced physicochemical relationship between solubility and molecular properties in different solvents, which led to rational approaches to improve the accuracy of each models.

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Section: Introductionmentioning

confidence: 99%

Machine learning with physicochemical relationships: solubility prediction in organic solvents and water

et al. 2020

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“…The Shibasaki group later expanded the utility of the MWNT-immobilized Nd/Na heterobimetallic complex-catalyzed anti -selective asymmetric nitroaldol reaction for the flow synthesis of a chiral intermediate of AZD7594 ( Scheme 1B ), 47 another potent drug candidate (currently in phase II clinical development) for the treatment of asthma and COPD. 48 The reaction of 1,4-benzodioxane-6-carbaldehyde and nitroethane was investigated in a similar flow setup demonstrated in the earlier study. In this case, the synthesis proved less productive than in the previous example; it afforded 8.9 g (81% yield) of chiral adduct 2 ( anti / syn ratio 20 : 1, ee 95%) in ca.…”

Section: Organometallic Asymmetric Catalysismentioning

confidence: 99%

Continuous flow asymmetric synthesis of chiral active pharmaceutical ingredients and their advanced intermediates

Ötvös

Kappe

2021

Green Chem.

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“…DNA conjugated aryl iodide was tested against 6300 primary amine building blocks in Ullmann type Cu(I) coupling reactions, where Cu(I) was generated in situ using CuSO 4 ⋅ 5H 2 O and sodium ascorbate [41] . Development of a novel synthetic route using HTE to scout conditions was reported by AstraZeneca [42] for the synthesis of AZD7594. The presence of palladium in the Buchwald‐Hartwig amination caused problems downstream for the drug target, leading the authors to investigate Ullmann coupling as an alternative.…”

Section: C−n Bond Forming Reactionsmentioning

confidence: 99%

Advancement in Organic Synthesis Through High Throughput Experimentation

2021

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and cyanoarenes has also been optimized using an HTE approach. [63] Other Photochemical TransformationsNon-activated alkyl bromides were coupled with Michael acceptors in a photoredox Giese-type reaction. [64] Reaction optimization was performed by studying the effect of photocatalyst, solvent, base, and varying amounts of radical initiator (Me 3 Si) 3 SiH on product and by-product profile. Another study of conjugate addition using photocatalytic methodology was shown for the synthesis of unnatural β-alkyl α-amino acid derivatives via decarboxylative photocatalysis. [65] Photocatalysis was performed with oxime and cyanopyridine precursors (Scheme 4g) to form primary amine products. [66] Four different solvents (acetone, DCE, ACN, DMSO), nine different photocatalysts, and two reductants were used to evaluate 45 unique reactions. DMSO with diisopropylamine and Ir[dF(Me) ppy] 2 dtbbpyPF 6 gave the highest yield by UPLC, although many Scheme 4. Photocatalytic transformations in HTE.

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Rapid route design of AZD7594

Abstract: Multidisciplinary collaboration enables the rapid and efficient design and selection of an improved manufacturing route to a new potential medicine for the treatment of asthma.

Cited by 14 publications

References 34 publications

Machine learning with physicochemical relationships: solubility prediction in organic solvents and water

Machine learning with physicochemical relationships: solubility prediction in organic solvents and water

Continuous flow asymmetric synthesis of chiral active pharmaceutical ingredients and their advanced intermediates

Advancement in Organic Synthesis Through High Throughput Experimentation

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