Chimia最新文献

{"title":"Implementing a Digital Transformation in Process Chemistry: Integrated Automation, Machine Learning, and Real-Time Analytics from Lab to Pilot Scale.","authors":"Elena Braconi, Jean-Philippe Krieger, Thomas Vent-Schmidt","doi":"10.2533/chimia.2026.583","DOIUrl":"https://doi.org/10.2533/chimia.2026.583","url":null,"abstract":"<p><p>Implementing a digital transformation in the Process Research and Development (PR&D) phase of an active ingredient offers significant opportunities to accelerate the journey from laboratory to manufacturing scale. Here, we report on three distinct initiatives undertaken at Syngenta to address concrete bottlenecks at different PR&D stages. First, Bayesian optimisation enabled efficient navigation of large reaction spaces with minimal experimental effort. Second, laboratory automation combined with multilinear calibration reduced hands-on time by ~85% and laid the foundation for autonomous closed-loop optimisation. Third, advances in Process Analytical Technology (PAT), including improved Multivariate Curve Resolution algorithms and modular Python-based pipelines, enabled real-time reaction monitoring in challenging industrial settings.</p>","PeriodicalId":9957,"journal":{"name":"Chimia","volume":"80 9","pages":"583-591"},"PeriodicalIF":1.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Getting Started with Lab Automation: Requirements, Misconceptions, and Practical Workflows.","authors":"Alessandro Castrogiovanni, Andreas Lingel","doi":"10.2533/chimia.2026.597","DOIUrl":"https://doi.org/10.2533/chimia.2026.597","url":null,"abstract":"<p><p>We herein provide a practical introduction for chemists with limited prior experience in automation, highlighting requirements, frequent misconceptions and common pitfalls encountered while setting up automated workflows. Key topics include the distinction between automation and autonomy, the development and integration effort typically required, and the central role of robust data capture and handling beyond the bench. Selected literature examples illustrate how substantial benefits can already be achieved with targeted automation of key elements, before moving to more connected end-to-end platforms.</p>","PeriodicalId":9957,"journal":{"name":"Chimia","volume":"80 9","pages":"597-601"},"PeriodicalIF":1.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modernizing Process Chemistry for Efficiency and Robustness.","authors":"Emmanuelle M D Allouche, Guillaume Coin, Thomas Duhamel, Christophe Girard, Sylvie Yolka, Guillaume Journot, Thomas Belser","doi":"10.2533/chimia.2026.602","DOIUrl":"https://doi.org/10.2533/chimia.2026.602","url":null,"abstract":"<p><p>Process chemistry evolves through the increasing integration of automation, real-time analytics, and predictive modelling into development workflows. In pharmaceutical process development, these tools provide an opportunity to move beyond empirical optimization toward a more informed and scalable approach. This article describes how automated laboratory platforms, Process Analytical Technology (PAT), structured data management, and modelling tools are used at Siegfried to improve development efficiency and process robustness to support more reliable transfer to manufacturing. Selected case studies illustrate the application of inline and in situ monitoring to reaction and crystallization development, while modelling supports rational scale-up of mixing, distillation, filtration, and centrifugation operations. Continuous flow chemistry is also discussed to widen the accessible process window while improving safety, control, and industrial relevance.</p>","PeriodicalId":9957,"journal":{"name":"Chimia","volume":"80 9","pages":"602-609"},"PeriodicalIF":1.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ketene, Diketene, and 1,3-Cyclobutanedione Chemistry - Industrial Relevance, Reactivity, and Emerging Applications of Cyclobutane Motifs.","authors":"Philipp Kohler, Christoph Taeschler, Arne Jan Stepen, Leo Schmid, Ulrich Mayerhoeffer","doi":"10.2533/chimia.2026.617","DOIUrl":"https://doi.org/10.2533/chimia.2026.617","url":null,"abstract":"<p><p>Ketene and diketene are cornerstone intermediates of industrial chemistry, enabling synthesis of a wide range of functional products. Beyond their established applications, side products of ketene dimerization provide access to 1,3-cyclobutanedione and triketene chemistry. This review highlights industrial relevance, reactivity, scalable access to 1,3-cyclobutanedione via the dione salt, and emerging applications of cyclobutane motifs in pharmaceuticals, emphasizing opportunities for further derivatization and process development.</p>","PeriodicalId":9957,"journal":{"name":"Chimia","volume":"80 9","pages":"617-622"},"PeriodicalIF":1.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Danger to Care - On the Challenges of a CDMO in the Field of Hazardous Chemistry.","authors":"Luca Mantilli, Erika Lüthi, Sara Greco, Luca Allievi, Jens Wutke, Daniel Latassa, Max Lauwiner, Lucia Mafrici","doi":"10.2533/chimia.2026.623","DOIUrl":"https://doi.org/10.2533/chimia.2026.623","url":null,"abstract":"<p><p>Hazardous chemistry presents recurring challenges for contract development and manufacturing organizations (CDMOs) across the industry: highly exothermic reactions, toxic or carcinogenic and corrosive substances, unstable intermediates, high pressure/temperature processes, and stringent requirements for containment, exhaust gas purification, occupational safety, and change management. This white paper describes these challenges using key reaction classes and demonstrates which technical and organizational measures have proven effective in practice. Valsynthese SA serves as a concrete example of a Swiss CDMO whose core competence lies in handling hazardous chemicals/reactions, and which translates demanding chemical processes into robust, campaign-ready productions according to the guiding principle 'From Danger to Care' - with a focus on process safety, quality (ISO and GMP compliance), and reliable delivery capability. The focus is on reaction and substance classes whose industrialization is typically considered difficult or challenging: nitrations and nitrate esters,[1a] phosgenations,[1b] and hydrogenations[1c] as a common 'bridging reaction' in multi-step syntheses. Examples from isoprene processing[1d] and projects in the field of advanced materials for highly specific applications[1e]  are also discussed. For each class, typical risk factors such as exothermicity, gas release, sensitivity, toxicity, and corrosiveness, as well as the associated countermeasures such as heat dissipation, containment, detection, automation, (emergency) quench concepts, and process window robustness, are presented in a structured manner. Where appropriate, it is also determined when batch processes are suitable and when continuous or flow technologies can contribute to intrinsic risk reduction. The latter techniques are used at Valsynthese for very specific targets, such as for example, using MgFlow® technology[1f] for the on-site production of Grignard reagents or for continuous extractions in nitration processes.</p>","PeriodicalId":9957,"journal":{"name":"Chimia","volume":"80 9","pages":"623-630"},"PeriodicalIF":1.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast-Tracking Drug Substance Development with AI, HTE, and Digital Twins.","authors":"Aaron Johnson, Nichola McCann, Raphael Oeschger, Jens Schmidt, Simon Wagschal","doi":"10.2533/chimia.2026.576","DOIUrl":"https://doi.org/10.2533/chimia.2026.576","url":null,"abstract":"<p><p>The increasing structural complexity of active pharmaceutical ingredients (APIs) poses significant challenges for early-phase drug substance development, where speed and reliability are critical for clinical readiness. To address these limitations, we present an integrated, technology-driven framework combining AI-enabled route scouting, high-throughput experimentation (HTE), model-based process optimization (Design2OptimizeTM) and AI-assisted co-crystal prediction. Case studies demonstrating substantial benefits, including reductions in synthetic step count, material consumption, experimental effort, and overall development time will be discussed. This integrated approach de-risks early-phase development, supports more sustainable development practices, and enables strategic decision-making, offering a paradigm shift toward faster, smarter, and more resilient drug substance development.</p>","PeriodicalId":9957,"journal":{"name":"Chimia","volume":"80 9","pages":"576-582"},"PeriodicalIF":1.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building Shared Futures: Chemical Engineering and AI as Partners in Process Development.","authors":"Jasmine J, Amgad S Moussa","doi":"10.2533/chimia.2026.592","DOIUrl":"https://doi.org/10.2533/chimia.2026.592","url":null,"abstract":"<p><p>Chemical engineering has evolved by adapting to available tools - from empirical correlations to simulation platforms and high-throughput experimentation. Each transition reshaped what was computationally and experimentally affordable, determining which problems received attention. Artificial intelligence introduces a new shift in this cost structure: specifically, the cost of building mechanistic models and extracting structured knowledge from unstructured data. This perspective argues that AI enables correction of two long-standing imbalances: the limited ability to distinguish exploratory (data-sparse) from exploitative (data-rich) problem modes, and an entrenched preference for experimentation over mechanistic reasoning. We propose a unified framework combining two complementary AI-enabled architectures - the Data Pipeline and the Insight Pipeline - and introduce a four-axis diagnostic for selecting between them based on physical-law knowability, data availability, regime novelty, and reliability constraints. In fine chemical manufacturing, where formalized physical understanding is valuable, yet scarce, large language models offer a practical path to accelerate know-how generation within this framework. A recommended workflow for large language model (LLM)-assisted modeling is outlined, emphasizing auditability, competing hypotheses, uncertainty quantification, and information-rich experimentation. We hope this supports a renewed modeling-first culture in chemical engineering.</p>","PeriodicalId":9957,"journal":{"name":"Chimia","volume":"80 9","pages":"592-596"},"PeriodicalIF":1.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial.","authors":"Lucie Lovelle, Stefan Hildbrand","doi":"10.2533/chimia.2026.573","DOIUrl":"https://doi.org/10.2533/chimia.2026.573","url":null,"abstract":"","PeriodicalId":9957,"journal":{"name":"Chimia","volume":"80 9","pages":"573"},"PeriodicalIF":1.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of Certified Reference Materials for Pharmaceutical Impurities using Quantitative NMR.","authors":"Nieves Pérez Payán, Christine Hellriegel, Matthias Giezendanner, Alexander Rueck, Lukas Rigger, Uma Sreenivasan","doi":"10.2533/chimia.2026.631","DOIUrl":"https://doi.org/10.2533/chimia.2026.631","url":null,"abstract":"<p><p>Pharmaceutical impurities are unintended substances in finished drug products, that may arise during manufacturing, degradation, or through interactions with excipients or packaging materials. As they are neither the drug substance nor an excipient, they can potentially impact safety, efficacy, and overall quality of the product. We describe the synthesis routes of different paclitaxel related impurities. Paclitaxel is a highly oxygenated taxane diterpenoid with a complex tetracyclic core and eleven stereocenters. We utilize quantitative nuclear magnetic resonance (qNMR) as a primary method for value assignment, complemented by comparison to compendial standards when available and developed certified reference materials (CRM) in accordance with ISO 17034, ensuring traceability to the SI (Système international d'unités).</p>","PeriodicalId":9957,"journal":{"name":"Chimia","volume":"80 9","pages":"631-637"},"PeriodicalIF":1.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanochemistry <i>via</i> Bead-milling: an Emerging Tool Towards Greener and Scalable Organic Synthesis.","authors":"Khalil Yamani, Benjamin Martin","doi":"10.2533/chimia.2026.2026_610","DOIUrl":"https://doi.org/10.2533/chimia.2026.2026_610","url":null,"abstract":"<p><p>Mechanochemistry has emerged as a powerful approach to reduce solvent consumption, improve process mass intensity (PMI), and access enhanced reactivity under highly concentrated or solvent-free conditions. However, the translation of mechanochemical transformations from small-scale discovery to relevant and industrially meaningful scale remains a central challenge. This review highlights bead-milling as an emerging technology for scalable mechanochemical organic synthesis, with a green metric analysis of the solution-based methodology versus the mechanochemical process. Diverse organic transformations are discussed to illustrate the opportunities and limitations of this platform. Across these studies, bead mills enable enhanced mass transfer, short reaction times, reduced solvent use, and favourable green metrics compared with conventional solution-phase processes. At the same time, successful implementation requires careful optimization of mechanical parameters such as rotor speed or bead size, observation of the rheology of the reaction mixture over time, as well as rigorous assessment of process safety and reproducibility. Overall, bead-mill mechanochemistry offers a promising route towards more sustainable, intensified, and scalable organic synthesis, and may become an important enabling technology for future active pharmaceutical ingredient (API) manufacturing.</p>","PeriodicalId":9957,"journal":{"name":"Chimia","volume":"80 9","pages":"610-616"},"PeriodicalIF":1.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149104752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}