Objective: The seeds of Cornus officinalis Sieb. et Zucc., a by-product of the Chinese medicine industry, have been firstly reported in our previous work to contain abundant 1,2,3,6-tetragalloylglucose (TgG). TgG is a polyol ester formed by a β - D -glucose that constitutes its core and four gallic acids. It is known to possess several pharmaceutical activities. The objective of this work was to establish a method effectively preparing high-purity TgG from C. officinalis seeds. Methods: TgG is unstable and degrades because of internal redox reactions and polymerizations under high temperatures. And there is a lack of effective methods to produce TgG, which limits its applications. Ultrasound-assisted extraction (UAE) can be performed at low temperatures and can be used to extract TgG effectively, thereby preventing its degradation. Therefore, in this study a UAE coupled with resin enrichment and reverse-phase silica-gel purification to achieve high-purity TgG from C. officinalis seeds was developed for the exploration of its potential value. Moreover, water was selected as the extracting solvent because of the excellent solubility of the seed extract. It provides a clean and green extracting method for producing TgG from C. officinalis seeds in a sustainable manner. Results: It was indicated that the purity of the obtained TgG was 96.2%, and the total TgG recovery of the established procedure was 73.5%. Conclusion: The established method demonstrates good precision and accuracy, making it suitable for the preparation of high-purity TgG from the C. officinalis seeds. It not only provided a novel and alliterative method to obtain TgG for its wider application in the pharmaceutical industry but also added potential value to the herbal medicine industry using C. officinalis via the use of by-product seeds.

Herein we describe a green-by-design approach to route selection and development, assisted by predictive analytics and historical data. In order to aid the selection of more efficient strategies, we created a user-friendly web application, the “PMI Prediction Calculator,” to foretell the probable efficiencies of proposed synthetic routes, prior to their evaluation in the laboratory. This tool can also be used to benchmark the outcome performance of a developed process. We expect that use of this app will bring greater awareness of sustainability during the ideation phase of route design and will contribute to a reduced environmental impact of pharmaceutical production. The app can be accessed following the link: https://acsgcipr-predictpmi.shinyapps.io/pmi_calculator/

Reaction conditions that are generally applicable to a wide variety of substrates are highly desired, especially in the pharmaceutical and chemical industries 1 – 6 . Although many approaches are available to evaluate the general applicability of developed conditions, a universal approach to efficiently discover these conditions during optimizations is rare. Here we report the design, implementation and application of reinforcement learning bandit optimization models 7 – 10 to identify generally applicable conditions by efficient condition sampling and evaluation of experimental feedback. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions, with up to 31% improvement over baselines that mimic state-of-the-art optimization approaches. A palladium-catalysed imidazole C–H arylation reaction, an aniline amide coupling reaction and a phenol alkylation reaction were investigated experimentally to evaluate use cases and functionalities of the bandit optimization model in practice. In all three cases, the reaction conditions that were most generally applicable yet not well studied for the respective reaction were identified after surveying less than 15% of the expert-designed reaction space.

Reaction conditions that are generally applicable to a wide variety of substrates are highly desired. While many approaches exist to evaluate the general applicability of developed conditions, a universal approach to efficiently discover such conditions during optimizations de novo is rare. In this work, we report the design, implementation, and application of reinforcement learning bandit optimization models to identify generally applicable conditions in a variety of chemical transformations. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions. A palladium-catalyzed imidazole C–H arylation reaction and an aniline amide coupling reaction were investigated experimentally to demonstrate utilities of our learning model in practice.

The large and steadily growing demand for medicines combined with their inherent resource-intensive manufacturingnecessitates a relentless push for their sustainable production.Pharmaceutical companies are constantly seeking to perform reliablelife cycle assessments of their medicinal products and assess the truevalue of their sustainable development achievements; however, theyfind themselves impeded by the lack of a universal metric systemthat allows for objective quantification of the underlying coredenominators. Guided by the unambivalent purpose of the UnitedNations Sustainable Development Goal 12, which aims atsubstantially reducing production waste by 2030, and driven by avision to catalyze greener active pharmaceutical ingredient (API)manufacturing around the globe, the authors set out to overcomecurrent obstacles by defining an improved model for the metric named innovation green aspiration level, iGAL 2.0. We propose yieldand convergence as new key sustainability indicators and include a new formula for convergence with potential applicability incomputer assisted synthesis planning (CASP) algorithms. The improved statistical model of iGAL 2.0 represents a valuableextension to the common API process waste metrics, process mass intensity (PMI) and complete E factor (cEF), by putting thosemeasures into perspective: iGAL 2.0 enables determination of relative process greenness (RPG) to identify potentiallyunderperforming and environmentally concerning processes early and thereby deliver environmental value. At the same time, iGAL2.0 generates economic value since reduced waste correlates to lower API production costs. The metric is complemented by itsscorecard companion to highlight the impact of innovation on reductions of API manufacturing waste, enabling scientists to readilycommunicate the value of their work to their peers, managers, and the general public. We believe that iGAL 2.0 can readily beadopted by pharmaceuticalfirms around the globe and thereby empower and inspire their scientists to make meaningful and significant contributions to global sustainability

An expansive data set containing 33 substrates, 36 unique monophosphine ligands, and two solvents was produced for the NiCl2-6H(2)O catalyzed aryl (pseudo)halide borylation with tetrahydroxydiboron for a total of 1632 reactions. Exploratory data analysis revealed excellent reaction performance with simple triarylphosphines (P(p-F-Ph)(3) and P(p-Anis)(3)) and mixed aryl-alkyl phosphines (PPh2Cy), in addition to the previously established high performance with Cy-JohnPhos. The data were used to train machine learning models that predicted out of sample reaction performance with a root-mean-square error of 18.4. The important features extracted from the models identified three phosphine parameters that offered reliable reactivity thresholds for identifying optimal ligand performance. The predictive models showed reasonable performance for predicting reaction yields employing ligands not included in model training, while the important feature boundaries accurately classified the performance of 10 of the 12 external ligands examined.

Deucravacitinib (BMS-986165) is a deuterated small-molecule TYK2 inhibitor developed for the treatment of numerous autoimmune disorders. While the first-generation discovery chemistry route to access deucravacitinib was concise and sufficient to access kilogram quantities of API, impurity control and cost-of-goods concerns necessitated the design of a new route. Once a new route was identified and demonstrated, each step was optimized for yield, purity, robustness, and sustainability. Key accomplishments include (1) the development of a novel cyclocondensation under mild conditions to afford a methylated 1,2,4-triazole with excellent regiocontrol, (2) the development of safe, homogeneous conditions to quench POCl3 following chlorination of a substrate that is sensitive to nucleophilic and basic conditions, (3) the discovery of a robust, scalable "dual-base" palladium-catalyzed C–N coupling reaction, and (4) mechanistic understanding to inform control strategies for a number of process-related impurities in an API step amidation mediated by EDC. Ultimately, the optimized commercial route was successfully scaled up to afford more than a metric ton of deucravacitinib for clinical and commercial use.

The use of low-energy deep-red (DR) and near-infrared (NIR) light to excite chromophores enables catalysis to ensue across barriers such as materials and tissues. Herein, we report the detailed photophysical characterization of a library of OsII polypyridyl photosensitizers that absorb low-energy light. By tuning ligand scaffold and electron density, we access a range of synthetically useful excited state energies and redox potentials.1 Introduction1.1 Scope1.2 Measuring Ground-State Redox Potentials1.3 Measuring Photophysical Properties1.4 Synthesis of Osmium Complexes2 Properties of Osmium Complexes2.1 Redox Potentials of Os(L)2-Type Complexes2.2 Redox Potentials of Os(L)3-Type Complexes2.3 UV/Vis Absorption and Emission Spectroscopy3 Conclusions

We report the development of an open-source experimental design via Bayesian optimization platform for multi-objective reaction optimization. Using high-throughput experimentation (HTE) and virtual screening data sets containing high-dimensional continuous and discrete variables, we optimized the performance of the platform by fine-tuning the algorithm components such as reaction encodings, surrogate model parameters, and initialization techniques. Having established the framework, we applied the optimizer to real-world test scenarios for the simultaneous optimization of the reaction yield and enantioselectivity in a Ni/photoredox-catalyzed enantioselective cross-electrophile coupling of styrene oxide with two different aryl iodide substrates. Starting with no previous experimental data, the Bayesian optimizer identified reaction conditions that surpassed the previously human-driven optimization campaigns within 15 and 24 experiments, for each substrate, among 1728 possible configurations available in each optimization. To make the platform more accessible to nonexperts, we developed a graphical user interface (GUI) that can be accessed online through a web-based application and incorporated features such as condition modification on the fly and data visualization. This web application does not require software installation, removing any programming barrier to use the platform, which enables chemists to integrate Bayesian optimization routines into their everyday laboratory practices.

Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of industrial processes to selecting conditions for the preparation of medicinal candidates 1 . Likewise, parameter optimization is omnipresent in artificial intelligence, from tuning virtual personal assistants to training social media and product recommendation systems 2 . Owing to the high cost associated with carrying out experiments, scientists in both areas set numerous (hyper)parameter values by evaluating only a small subset of the possible configurations. Bayesian optimization, an iterative response surface-based global optimization algorithm, has demonstrated exceptional performance in the tuning of machine learning models 3 . Bayesian optimization has also been recently applied in chemistry 4 – 9 ; however, its application and assessment for reaction optimization in synthetic chemistry has not been investigated. Here we report the development of a framework for Bayesian reaction optimization and an open-source software tool that allows chemists to easily integrate state-of-the-art optimization algorithms into their everyday laboratory practices. We collect a large benchmark dataset for a palladium-catalysed direct arylation reaction, perform a systematic study of Bayesian optimization compared to human decision-making in reaction optimization, and apply Bayesian optimization to two real-world optimization efforts (Mitsunobu and deoxyfluorination reactions). Benchmarking is accomplished via an online game that links the decisions made by expert chemists and engineers to real experiments run in the laboratory. Our findings demonstrate that Bayesian optimization outperforms human decisionmaking in both average optimization efficiency (number of experiments) and consistency (variance of outcome against initially available data). Overall, our studies suggest that adopting Bayesian optimization methods into everyday laboratory practices could facilitate more efficient synthesis of functional chemicals by enabling better-informed, data-driven decisions about which experiments to run.

Computerized Human Expert Method for Solubility Prediction (CHEM-SP) is a simple method that can be used to predict solubility in water and organic solvents, from two-dimensional (2-D) chemical structures in conjunction with a reference solubility library. Compared to other solubility prediction models, it is very simple to execute and produces results that are equivalent or better.

Photocatalysis driven by visible and ultraviolet irradiation is a fundamental tool for synthetic chemists. Recently, expansion of this tool to near-infrared (NIR) light has gained in popularity. Herein, we report the detailed photophysical characterization of a library of OsII polypyridyl photosensitizers that absorb NIR irradiation. By tuning ligand scaffold and electron density, we access a range of synthetically useful excited state energies and redox potentials.

Polyethyleneglycol (PEG) containing compounds are often non-crystalline, gel-like oils, which pose great challenges for handling, purification, and isolation, particularly on large scales. To overcome these isolation challenges, a new procedure has been established, which is based on the discovery that complexation with magnesium chloride (MgCl2) can transform these oily intermediates into solid complexes. This method significantly improves the handling of PEG compounds and leads a more processfriendly isolation for larger scale handling. Often, the resulting PEG-MgCl2 complexes could be used directly in subsequent transformations, such as a peptide coupling. Alternately, the inorganic Mg salt used in these weakly bonded complexes can be readily dissociated and removed from the desired PEG derivatives.

Intracellular accumulating of the hyperphosphorylated tau plays a pivotal role in neurodegeneration of Alzheimer disease (AD), but the mechanisms underlying the gradually aggravated tau hyperphosphorylation remain elusive. Here, we show that increasing intracellular tau could upregulate mRNA and protein levels of TRPC1 (transient receptor potential channel 1) with an activated store‐operated calcium entry (SOCE), an increased intraneuronal steady‐state [Ca 2+ ] i , an enhanced endoplasmic reticulum (ER) stress, an imbalanced protein kinases and phosphatase, and an aggravated tauopathy. Furthermore, overexpressing TRPC1 induced ER stress, kinases‐phosphatase imbalance, tau hyperphosphorylation and cognitive deficits in cultured neurons and mice, while pharmacological inhibiting or knockout TRPC1 attenuated the hTau‐induced deregulations in SOCE, ER homeostasis, kinases‐phosphatase balance, and tau phosphorylation level with improved synaptic and cognitive functions. Finally, an increased CCAAT‐enhancer‐binding protein (C/EBPβ) activity was observed in hTau‐overexpressing cells and the hippocampus of the AD patients, while downregulating C/EBPβ by siRNA abolished the hTau‐induced TRPC1 upregulation. These data reveal that increasing intracellular tau can upregulate C/EBPβ‐TRPC1‐SOCE signaling and thus disrupt phosphorylating system, which together aggravates tau pathologies leading to a chronic neurodegeneration.

In this study, new α-indolylacrylate derivatives were synthesized by the reaction of 2-substituted indoles with various pyruvates using a Brønsted acid ionic liquid catalyst in butyl acetate solvent. This is the first report on the application of pyruvate compounds for the synthesis of indolylacrylates. The acrylate derivatives could be obtained in good to excellent yields. A preliminary biological evaluation revealed their promising anticancer activity (IC50 = 9.73 μM for the compound 4l) and indicated that both the indole core and the acrylate moieties are promising for the development of novel anticancer drugs. The Lipinski's rule and Veber's parameters were assessed for the newly synthesized derivatives.

为探究生长于新疆的千叶蓍(Achillea millefolium L.)中的活性物质基础,本文对采自新疆哈密地区的千叶蓍开展了化学成分分离鉴定及其生物活性研究.利用柱层析色谱、制备型高效液相色谱等方法,从千叶蓍的全草中分离出二十种化合物.通过核磁共振和质谱等波谱学技术以及和文献数据对比,将其分别鉴定为猫眼草黄素(1)、矢车菊黄素(2)、槲皮素(3)、芹菜素(4)、芹黄素-7-O-β-D-葡萄糖苷(5)、(-)-芝麻素(6)、乙氧基阿魏酸(7)、咖啡酸乙酯(8)、香豆酸(9)、对羟基苯丙酸(10)、伞形花内酯(11)、7-甲氧基香豆素(12)、香草酸(13)、4-羟基苯甲酸(14)、二氢猕猴桃内酯(15)、paeoveitol B(16)、2-羟基-2-[(E)-1α,2β,3-三羟基-3-壬烷-5,7-二炔]-4H-吡喃(17)、吲哚-3-乙醛(i8)、β-谷甾醇(19)和3β-羟基-5α,8α-环二氧麦角甾-6,22-二烯麦角甾醇过氧化物(20).其中,化合物20为首次从本植物中分离得到,化合物6、8～10、12～13、15～18为首次从本属植物中分离得到.此外,初步评估了乙醇提取物、乙酸乙酯部位和化合物1～5、8～9、13～14和16的抗氧化及其抗菌活性.化合物3(IC50 =9.9±0.94μM)和8(IC50=21.29±1.65μM)表现出显著的DPPH自由基清除能力,化合物9、14和16表现出抑制白色念珠菌、大肠杆菌和金黄色葡萄球菌生长的能力.本文首次报道了分布于新疆的千叶蓍中化学成分及其活性,结果表明,酚类化合物如黄酮、苯丙素是该植物中的主要成分.此外,本研究发现千叶蓍中成分表现出一定的抗氧化和抗菌活性,为该植物作的药用价值提供了一定的科学依据.

A conceptual framework for incorporating machine learned ligand prediction into predictive route comparisons, to enable greener chemistry outcomes.

BackgroundSjögren’s syndrome (SS) is one of the most common autoimmune diseases. Main symptoms include dry eyes and mouth, followed by difficulty swallowing and serious systemic manifestations including neurological, pulmonary and musculoskeletal effects.1 However, SS is not homogeneous; it affects patients in different ways, with varying symptoms and severity. Identifying discrete patient types and understanding their commonalities and differences is integral to enhancing disease knowledge, identifying areas of unmet need, defining the size of the affected population and determining appropriate treatment approach.ObjectivesUsing data gathered by the SS Foundation through a survey of over 3000 patients with SS, analyses were performed to: 1) describe patient characteristics and treatment in a real-world setting and 2) categorise patients based on baseline characteristics.MethodsThe analytical approach used clustering techniques to synthesise the mixed-data–type survey in two separate analyses. Data were filtered to include patients 40–65 years of age. Analyses were performed firstly to identify relationships between features—related to demographic, disease state, treatments, etc—and secondly to identify clusters of patients. A Bayesian network model was used to evaluate feature correlations and the probability of a specific survey answer given one or many preliminary conditions. With respect to patient clustering, the model used was based on Gower distance and Partitioning Around Medoids, which each can include both numerical and categorical data.ResultsWe evaluated comorbidities, symptoms and treatment to identify traits that differed most significantly among patient types. Four discrete patient clusters were identified (disease severity measured by median EULAR SS Patient Reported Index score2; Table 1): Recent: Most recently diagnosed, least severe disease manifestation Slower progressing: Longest time since diagnosis, second–lowest disease severity Second–second: Second in time since of diagnosis, second in disease severity Most severe: Most severe disease manifestations and comorbidities The most differentiating comorbidities across all clusters included gastroesophageal reflux disease, fibromyalgia and Raynaud’s syndrome (Table 2). The symptoms that impacted patients’ lives the most and that were differentiating across clusters were brain fog, fatigue and forgetfulness. The treatments that were most contrasting were oral comfort agents, followed by DMARDS and secretagogues (Figure 1). The patient cluster definitions above suggest some contradictions, because not all significant cluster characteristics progressively worsened with increasing disease severity.ConclusionThese analyses show differences in disease characteristics and treatment across patient clusters; however, more work is required to validate these clusters.References[1] Ienopoli S, et al. Oral and Maxillofac Surg Clin North Am2014;26:91-9. [2] Seror R, et al. Ann Rheum Dis2011;70:968-72.AcknowledgementProfessional medical writing: Fiona Boswell, PhD, Caudex; funding: Bristol-Myers SquibbDisclosure of InterestsCarlos Gonzalez-Najera Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, Steven Taylor: None declared, Sean Crawford Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, Premkumar Narasimhan Employee of: Bristol-Myers Squibb, Xiao Shao Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb, Jun Li Shareholder of: Bristol-Myers Squibb, Employee of: Bristol-Myers Squibb

Herein we describe a green-by-design approach to route selection and development, assisted by predictive analytics and historical data. In order to aid the selection of more efficient strategies, we created a user-friendly web application, the “PMI Prediction Calculator,” to foretell the probable efficiencies of proposed synthetic routes, prior to their evaluation in the laboratory. This tool can also be used to benchmark the outcome performance of a developed process. We expect that use of this app will bring greater awareness of sustainability during the ideation phase of route design and will contribute to a reduced environmental impact of pharmaceutical production. The app can be accessed following the link:https://acsgcipr-predictpmi.shinyapps.io/pmi_calculator/

This paper expands our work predicting Process Mass Intensity (PMI), as a methodology for exploring the potential efficiency of proposed synthetic routes. In the present work, we integrate a method for predicting the PMI contributions of high complexity reagents, needed to enable certain transformations. We focus on ligands for metal catalyzed reactions - and develop an approach for predicting which ligands may function in CN couplings - as a proof of concept. We leverage this to enable the integration of the PMI contribution of the ligands into a predictions of a routes efficiency, enabling an understanding of the holistic impact of a route decision..