A microbial supply chain for production of the anti-cancer drug vinblastine.

Monoterpene indole alkaloids (MIAs) are a diverse family of complex plant secondary metabolites with many medicinal properties, including the essential anti-cancer therapeutics vinblastine and vincristine1. As MIAs are difficult to chemically synthesize, the world's supply chain for vinblastine relies on low-yielding extraction and purification of the precursors vindoline and catharanthine from the plant Catharanthus roseus, which is then followed by simple in vitro chemical coupling and reduction to form vinblastine at an industrial scale2,3. Here, we demonstrate the de novo microbial biosynthesis of vindoline and catharanthine using a highly engineered yeast, and in vitro chemical coupling to vinblastine. The study showcases a very long biosynthetic pathway refactored into a microbial cell factory, including 30 enzymatic steps beyond the yeast native metabolites geranyl pyrophosphate and tryptophan to catharanthine and vindoline. In total, 56 genetic edits were performed, including expression of 34 heterologous genes from plants, as well as deletions, knock-downs and overexpression of ten yeast genes to improve precursor supplies towards de novo production of catharanthine and vindoline, from which semisynthesis to vinblastine occurs. As the vinblastine pathway is one of the longest MIA biosynthetic pathways, this study positions yeast as a scalable platform to produce more than 3,000 natural MIAs and a virtually infinite number of new-to-nature analogues.

Biosynthesis of natural and halogenated plant monoterpene indole alkaloids in yeast.

Monoterpenoid indole alkaloids (MIAs) represent a large class of plant natural products with marketed pharmaceutical activities against a wide range of indications, including cancer, malaria and hypertension. Halogenated MIAs have shown improved pharmaceutical properties; however, synthesis of new-to-nature halogenated MIAs remains a challenge. Here we demonstrate a platform for de novo biosynthesis of two MIAs, serpentine and alstonine, in baker's yeast Saccharomyces cerevisiae and deploy it to systematically explore the biocatalytic potential of refactored MIA pathways for the production of halogenated MIAs. From this, we demonstrate conversion of individual haloindole derivatives to a total of 19 different new-to-nature haloserpentine and haloalstonine analogs. Furthermore, by process optimization and heterologous expression of a modified halogenase in the microbial MIA platform, we document de novo halogenation and biosynthesis of chloroalstonine. Together, this study highlights a microbial platform for enzymatic exploration and production of complex natural and new-to-nature MIAs with therapeutic potential.

teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering.

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub.

Role of the Power Function Value in Linearity and Universality for Charged Aerosol Detectors: Theoretical Elucidations from a Validated Model.

High-throughput drug discovery on the microgram scale is now common, making analyte quantitation without molecule-specific calibration imperative. The charged aerosol detector (CAD) was invented to be a next-generation universal liquid chromatography (LC) detector with excellent response universality for nonvolatile analytes as well as sensitivity for nonchromophoric compounds. Although the CAD is a mass flow-sensitive detector, its response to mass is inherently nonlinear, which challenges traditional quantitation. In CAD software, there is a "power function value" (p) setting that can be used to linearize the signal through digital signal processing. The exact workings of this power function value algorithm remain unknown; however, its optimization is a crucial aspect of analytical method development for LC-CAD. Herein, we developed a theoretical relationship that can be used to predict the chromatogram (plus peak area, width, and height) at any p if the data are collected at p = 1. This model was validated using a diverse dataset comprising 1440 measurements including peak heights, areas, and widths. Predicted areas had an average error of less than 2% showing excellent agreement between calculated and experimental results. An open-access automated code is tested and provided, which predicts the power function value that produces the most linear response. It is vital to note that optimizing the power function value affects peaks of different heights disproportionately. Low-level impurities were shown to be minimized and eventually eliminated by increasing the power function value. This model provides an easy-to-implement tool (MATLAB or Excel) that assists in choosing the optimal p for each LC-CAD method, increasing the speed of method development and improving the accuracy of quantitative workflows.

Two-Dimensional SEC-SEC-UV-MALS-dRI Workflow for Streamlined Analysis and Characterization of Biopharmaceuticals.

The emergence of complex biological modalities in the biopharmaceutical industry entails a significant expansion of the current analytical toolbox to address the need to deploy meaningful and reliable assays at an unprecedented pace. Size exclusion chromatography (SEC) is an industry standard technique for protein separation and analysis. Some constraints of traditional SEC stem from its restricted ability to resolve complex mixtures and notoriously long run times while also requiring multiple offline separation conditions on different pore size columns to cover a wider molecular size distribution. Two-dimensional liquid chromatography (2D-LC) is becoming an important tool not only to increase peak capacity but also to tune selectivity in a single online method. Herein, an online 2D-LC framework in which both dimensions utilize SEC columns with different pore sizes is introduced with a goal to increase throughput for biomolecule separation and characterization. In addition to improving the separation of closely related species, this online 2D SEC-SEC approach also facilitated the rapid analysis of protein-based mixtures of a wide molecular size range in a single online experimental run bypassing time-consuming deployment of different offline SEC methods. By coupling the second dimension with multiangle light scattering (MALS) and differential refractive index (dRI) detectors, absolute molecular weights of the separated species were obtained without the use of calibration curves. As illustrated in this report for protein mixtures and vaccine processes, this workflow can be used in scenarios where rapid development and deployment of SEC assays are warranted, enabling bioprocess monitoring, purity assessment, and characterization.

An improved workflow for the development of MS-compatible liquid chromatography assay purity and purification methods by using automated LC Screening instrumentation and in silico modeling.

The development of liquid chromatography UV and mass spectrometry (LC-UV-MS) assays in pharmaceutical analysis is pivotal to improve quality control by providing critical information about drug purity, stability, and presence and identity of byproducts and impurities. Analytical method development of these assays is time-consuming, which often causes it to become a bottle neck in drug development and poses a challenge for process chemists to quickly improve the chemistry. In this study, a systematic and efficient workflow was designed to develop purity assay and purification methods for a wide range of compounds including peptides, proteins, and small molecules with MS-compatible mobile phases (MP) by using automated LC screening instrumentation and in silico modeling tools. Initial LC MPs and chromatography column screening experiments enabled quick identification of conditions which provided the best resolution in the vicinity of the target compounds, which is further optimized using computer-assisted modeling (LC Simulator from ACD/Labs). The experimental retention times were in good agreement with the predicted retention times from LC Simulator (ΔtR < 7%). This workflow presents a practical workflow to significantly expedite the time needed to develop optimized LC-UV-MS methods, allowing for a facile, automatic method optimization and reducing the amount of manual work involved in developing new methods during drug development.

Polyploid giant cancer cells, cytokines and cytomegalovirus in breast cancer progression.

BACKGROUND: Breast cancer is the most common cancer among women. Accumulated evidence over the past decades indicates a very high prevalence of human cytomegalovirus (HCMV) in breast cancer. High-risk HCMV strains possess a direct oncogenic effect displayed by cellular stress, polyploid giant cancer cells (PGCCs) generation, stemness, and epithelial-to-mesenchymal transition (EMT) leading to cancer of aggressive phenotype. Breast cancer development and progression have been regulated by several cytokines where the latter can promote cancer cell survival, help in tumor immune evasion, and initiate the EMT process, thereby resulting in invasion, angiogenesis, and breast cancer metastasis. In the present study, we screened cytokines expression in cytomegalovirus-transformed HMECs (CTH cells) cultures infected with HCMV high-risk strains namely, HCMV-DB and BL, as well as breast cancer biopsies, and analyzed the association between cytokines production, PGCCs count, and HCMV presence in vitro and in vivo. METHODS: In CTH cultures and breast cancer biopsies, HCMV load was quantified by real-time qPCR. PGCCs count in CTH cultures and breast cancer biopsies was identified based on cell morphology and hematoxylin and eosin staining, respectively. CTH supernatants were evaluated for the production of TGF-ß, IL-6, IL1-ß, and IL-10 by ELISA assays. The above-mentioned cytokines expression was assessed in breast cancer biopsies using reverse transcription-qPCR. The correlation analyses were performed using Pearson correlation test. RESULTS: The revealed PGCCs/cytokine profile in our in vitro CTH model matched that of the breast cancer biopsies, in vivo. Pronounced cytokine expression and PGCCs count were detected in particularly CTH-DB cultures and basal-like breast cancer biopsies. CONCLUSIONS: The analysis of cytokine profiles in PGCCs present mostly in basal-like breast cancer biopsies and derived from CTH cells chronically infected with the high-risk HCMV strains might have the potential to provide novel therapies such as cytokine-based immunotherapy which is a promising field in cancer treatments.

Deuterated Modifiers in Sub/Supercritical Fluid Chromatography for Streamlined NMR Structure Elucidation.

Isolation and chemical characterization of target components in fast-paced pharmaceutical laboratories can often be challenging, especially when dealing with mixtures of closely related, possibly unstable species. Traditionally, this process involves intense labor and manual intervention including chromatographic method development and optimization, fraction collection, and drying processes prior to NMR analyses for unambiguous structure elucidation. To circumvent these challenges, a foundational framework for the proper utilization of supercritical carbon dioxide (scCO2) and deuterated modifiers (CD3OD) in sub/supercritical fluid chromatography (SFC) is herein introduced. This facilitates a streamlined multicomponent isolation with minimized protic residues, further enabling immediate NMR analysis. In addition to bypassing tedious drying processes and minimizing analyte degradation, this approach (complementary to traditional reversed-phase liquid chromatography, RPLC) delivers highly efficient separations and automated fraction collection using readily available analytical/midscale SFC instrumentation. A series of diverse analytes across a wide spectrum of chemical properties (acid, basic, and neutral), combined with different stationary-phase columns in SFC are investigated using both a protic organic modifier (CH3OH) and its deuterated counterpart (CD3OD). The power of this framework is demonstrated with pharmaceutically relevant applications in the context of target characterization and analysis of complex multicomponent reaction mixtures from modern synthetic chemistry, demonstrating high isolation yields while reducing both the environmental footprint and manual intervention. This workflow enables unambiguous fast-paced structure elucidation on the analytical scale, providing results that are comparable to traditional, but time-consuming, RPLC purification approaches.

In Silico Method Development of Achiral and Chiral Tandem Column Reversed-phase Liquid Chromatography for Multicomponent Pharmaceutical Mixtures.

Tandem column liquid chromatography (LC) is a convenient, cost-effective approach to resolve multicomponent mixtures by serially coupling columns on readily available one-dimensional separation systems without specialized user training. Yet, adoption of this technique remains limited, mainly due to the difficulty in identifying optimal selectivity out of many possible tandem column combinations. At this point, method development and optimization require laborious "hit-or-miss" experimentation and "blind" screening when investigating different column selectivity without standard analytes. As a result, many chromatography practitioners end up combining two columns of similar selectivity, limiting the scope and potential of tandem column LC as a mainstay for industrial applications. To circumvent this challenge, we herein introduce a straightforward in silico multifactorial approach as a framework to expediently map the separation landscape across multiple tandem columns (achiral and chiral) and eluent combinations (isocratic and gradient elution) under reversed-phase LC conditions. Retention models were built using commercially available LC simulator software showcasing less than 2% difference between experimental and simulated retention times for analytes of interest in multicomponent pharmaceutical mixtures (e.g., metabolites and cyclic peptides).

Automated Hydrophobic Interaction Chromatography Screening Combined with In Silico Optimization as a Framework for Nondenaturing Analysis and Purification of Biopharmaceuticals.

The mounting complexity of new modalities in the biopharmaceutical industry entails a commensurate level of analytical innovations to enable the rapid discovery and development of novel therapeutics and vaccines. Hydrophobic interaction chromatography (HIC) has become one of the widely preferred separation techniques for the analysis and purification of biopharmaceuticals under nondenaturing conditions. Inarguably, HIC method development remains very challenging and labor-intensive owing to the numerous factors that are typically optimized by a "hit-or-miss" strategy (e.g., the nature of the salt, stationary phase chemistry, temperature, mobile phase additive, and ionic strength). Herein, we introduce a new HIC method development framework composed of a fully automated multicolumn and multieluent platform coupled with in silico multifactorial simulation and integrated fraction collection for streamlined method screening, optimization, and analytical-scale purification of biopharmaceutical targets. The power and versatility of this workflow are showcased by a wide range of applications including trivial proteins, monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs), oxidation variants, and denatured proteins. We also illustrate convenient and rapid HIC method development outcomes from the effective combination of this screening setup with computer-assisted simulations. HIC retention models were built using readily available LC simulator software outlining less than a 5% difference between experimental and simulated retention times with a correlation coefficient of >0.99 for pharmaceutically relevant multicomponent mixtures. In addition, we demonstrate how this approach paves the path for a straightforward identification of first-dimension HIC conditions that are combined with mass spectrometry (MS)-friendly reversed-phase liquid chromatography (RPLC) detection in the second dimension (heart-cutting two-dimensional (2D)-HIC-RPLC-diode array detector (DAD)-MS), enabling the analysis and purification of biopharmaceutical targets.

Enantioselective UHPLC Screening Combined with In Silico Modeling for Streamlined Development of Ultrafast Enantiopurity Assays.

Enantioselective chromatography has been the preferred technique for the determination of enantiomeric excess across academia and industry. Although sequential multicolumn enantioselective supercritical fluid chromatography screenings are widespread, access to automated ultra-high-performance liquid chromatography (UHPLC) platforms using state-of-the-art small particle size chiral stationary phases (CSPs) is an underdeveloped area. Herein, we introduce a multicolumn UHPLC screening workflow capable of combining 14 columns (packed with sub-2 µm fully porous and sub-3 µm superficially porous particles) with nine mobile phase eluent choices. This automated setup operates under a vast selection of reversed-phase liquid chromatography, hydrophilic interaction liquid chromatography, polar-organic mode, and polar-ionic mode conditions with minimal manual intervention and high success rate. Examples of highly efficient enantioseparations are illustrated from the integration of chiral screening conditions and computer-assisted modeling. Furthermore, we describe the nuances of in silico method development for chiral separations via second-degree polynomial regression fit using LC simulator (ACD/Labs) software. The retention models were found to be very accurate for chiral resolution of single and multicomponent mixtures of enantiomeric species across different types of CSPs, with differences between experimental and simulated retention times of less than 0.5%. Finally, we illustrate how this approach lays the foundation for a streamlined development of ultrafast enantioseparations applied to high-throughput enantiopurity analysis and its use in the second dimension of two-dimensional liquid chromatography experiments.

Glucagon-like peptide-1 receptor agonists as adjunctive treatment for type 1 diabetes: Renewed opportunities through tailored approaches?

Exogenous insulin has been the mainstay treatment for individuals living with type 1 diabetes (T1D). Although there has been tremendous growth in both pharmacological and technological advancements, insulin monotherapy has proven to be insufficient for maintaining optimal glycaemic targets for most adults with T1D. At present, there is still no breakthrough for the treatment of T1D. Adjunctive pharmacotherapies might therefore complement insulin management to achieve better glycaemic control, while possibly offering additional benefits. Recent interest in re-purposing glucagon-like peptide-1 receptor agonists (GLP-1RAs), a leading antihyperglycaemic medication class approved for type 2 diabetes, has prompted the field to seek extended potential for the T1D population. The adjunctive use of GLP-1RAs has been at the forefront of T1D research, albeit with some conflicting trial findings to date. However, the potential of GLP-1 agonism for T1D may have been underestimated, possibly from missed opportunities or categorized effects. Moreover, some GLP-1RAs have demonstrated extra-pancreatic potential with emerging multi-organ protection involving the heart, kidneys, liver and brain in varied cohorts, which may bode well for the growing T1D profile of comorbid complications. This narrative review aims to summarize and critically appraise the current evidence-based literature from large-scale randomized controlled trials and closed-loop system pilot studies that examined GLP-1RAs as adjunctive therapy for T1D. Furthermore, we outline uncharted opportunities with GLP-1 agonism using versatile approaches in selected T1D populations that may inspire and re-direct future research in this field.

A Concise Total Synthesis of the Fungal Isoquinoline Alkaloid TMC-120B.

Recent reports of antiepileptic activity of the fungal alkaloid TMC-120B have renewed the interest in this natural product. Previous total syntheses of TMC-120B comprise many steps and have low overall yields (11-17 steps, 1.5-2.9% yield). Thus, to access this compound more efficiently, we herein present a concise and significantly improved total synthesis of the natural product. Our short synthesis relies on two key cyclization steps to assemble the central scaffold: isoquinoline formation via an ethynyl-imino cyclization and an intramolecular Friedel-Crafts reaction to form the furanone.

[Healing communication: Therapeutic communication in internal medicine]. / La « communication thérapeutique ¼ en médecine interne.

Caregiver-patient communication is a central element of the therapeutic relationship and a powerful placebo. While poorly adapted communication can have harmful effects, adequate communication offers many benefits for the patient and the caregiver. Therapeutic communication (TC), a set of strategies inspired by clinical hypnosis, is easy to learn and apply. In addition to reinforcing an empathetic attitude, TC appears to be effective in various clinical situations, particularly for its effects on pain and anxiety. In order to better understand the interest of CT in internal medicine, we have conducted a literature review on its efficacy during different medical procedures.

La communication soignant-e/soigné-e est un élément central de la relation thérapeutique et un puissant placebo dont il serait dommage de se priver. Mal adaptée, elle peut avoir des effets néfastes, efficace, elle offre de nombreux bénéfices pour les patient-e-s et soignant-e-s. La communication thérapeutique (CT), un ensemble de stratégies inspirées de l'hypnose clinique, est facile à apprendre et à appliquer. En plus de renforcer une attitude empathique, la CT semble efficace dans diverses situations cliniques, notamment pour ses effets sur la douleur et l'anxiété. Dans le but de mieux comprendre l'intérêt de la CT en médecine interne, nous avons effectué une revue de littérature démontrant son utilisation lors de gestes techniques auxquels les patient-e-s sont fréquemment confronté-e-s.

Separating insulin-mediated and non-insulin-mediated glucose uptake during and after aerobic exercise in type 1 diabetes.

Aerobic exercise in type 1 diabetes (T1D) causes rapid increase in glucose utilization due to muscle work during exercise, followed by increased insulin sensitivity after exercise. Better understanding of these changes is necessary for models of exercise in T1D. Twenty-six individuals with T1D underwent three sessions at three insulin rates (100%, 150%, 300% of basal). After 3-h run-in, participants performed 45 min aerobic exercise (moderate or intense). We determined area under the curve for endogenous glucose production (AUCEGP) and rate of glucose disappearance (AUCRd) over 45 min from exercise start. A novel application of linear regression of Rd across the three insulin sessions allowed separation of insulin-mediated from non-insulin-mediated glucose uptake before, during, and after exercise. AUCRd increased 12.45 mmol/L (CI = 10.33-14.58, P < 0.001) and 13.13 mmol/L (CI = 11.01-15.26, P < 0.001) whereas AUCEGP increased 1.66 mmol/L (CI = 1.01-2.31, P < 0.001) and 3.46 mmol/L (CI = 2.81-4.11, P < 0.001) above baseline during moderate and intense exercise, respectively. AUCEGP increased during intense exercise by 2.14 mmol/L (CI = 0.91-3.37, P < 0.001) compared with moderate exercise. There was significant effect of insulin infusion rate on AUCRd equal to 0.06 mmol/L per % above basal rate (CI = 0.05-0.07, P < 0.001). Insulin-mediated glucose uptake rose during exercise and persisted hours afterward, whereas non-insulin-mediated effect was limited to the exercise period. To our knowledge, this method of isolating dynamic insulin- and non-insulin-mediated uptake has not been previously employed during exercise. These results will be useful in informing glucoregulatory models of T1D. The study has been registered at www.clinicaltrials.gov as NCT03090451.NEW & NOTEWORTHY Separating insulin and non-insulin glucose uptake dynamically during exercise in type 1 diabetes has not been done before. We use a multistep process, including a previously described linear regression method, over three insulin infusion sessions, to perform this separation and can graph these components before, during, and after exercise for the first time.

In Silico Multifactorial Modeling for Streamlined Development and Optimization of Two-Dimensional Liquid Chromatography.

Continued adoption of two-dimensional liquid chromatography (2D-LC) in industrial laboratories will depend on the development of approaches to make method development for 2D-LC more systematic, less tedious, and less reliant on user expertise. In this paper, we build on previous efforts in these directions by describing the use of multifactorial modeling software that can help streamline and simplify the method development process for 2D-LC. Specifically, we have focused on building retention models for second dimension (2D) separations involving variables including gradient time, temperature, organic modifier blending, and buffer concentration using LC simulator (ACD/Labs) software. Multifactorial retention modeling outcomes are illustrated as resolution map planes or cubes that enable straightforward location of 2D conditions that maximize resolution while minimizing analysis time. We also illustrate the practicality of this approach by identifying conditions that yield baseline separation of all compounds co-eluting from a first dimension (1D) separation using a single combination of 2D stationary phase and elution conditions. The multifactorial retention models were found to be very accurate for both the 1D and 2D separations, with differences between experimental and simulated retention times of less than 0.5%. Pharmaceutical applications of this approach for multiple heartcutting 2D-LC were demonstrated using IEC-IEC or achiral RPLC-chiral RPLC for 2D separations of multicomponent mixtures. The framework outlined here should help make 2D-LC method development more systematic and streamline development and optimization for a variety of 2D-LC applications in both industry and academia.

Alleviating carbohydrate counting with a FiASP-plus-pramlintide closed-loop delivery system (artificial pancreas): Feasibility and pilot studies.

AIM: To assess whether a FiASP-and-pramlintide closed-loop system has the potential to replace carbohydrate counting with a simple meal announcement (SMA) strategy (meal priming bolus without carbohydrate counting) without degrading glycaemic control compared with a FiASP closed-loop system. MATERIALS AND METHODS: We conducted a 24-hour feasibility study comparing a FiASP system with full carbohydrate counting (FCC) with a FiASP-and-pramlintide system with SMA. We conducted a subsequent 12-day outpatient pilot study comparing a FiASP-and-placebo system with FCC, a FiASP-and-pramlintide system with SMA, and a FiASP-and-placebo system with SMA. Basal-bolus FiASP-and-pramlintide were delivered at a fixed ratio (1 U:10 µg). Glycaemic outcomes were measured, surveys evaluated gastrointestinal symptoms and diabetes distress, and participant interviews helped establish a preliminary coding framework to assess user experience. RESULTS: Seven participants were included in the feasibility analysis. Time spent in 3.9-10 mmol/L was similar between both interventions (81%-84%). Four participants were included in the pilot analysis. Time spent in 3.9-10 mmol/L was similar between the FiASP-and-placebo with FCC and FiASP-and-pramlintide with SMA interventions (70%), but was lower in the FiASP-and-placebo with SMA intervention (60%). Time less than 3.9 mmol/L and gastrointestinal symptoms were similar across all interventions. Emotional distress was moderate at baseline, after the FiASP-and-placebo with FCC and SMA interventions, and fell after the FiASP-and-pramlintide with SMA intervention. SMA reportedly afforded participants flexibility and reduced mealtime concerns. CONCLUSIONS: The FiASP-and-pramlintide system has the potential to substitute carbohydrate counting with SMA without degrading glucose control.

Reducing the need for carbohydrate counting in type 1 diabetes using closed-loop automated insulin delivery (artificial pancreas) and empagliflozin: A randomized, controlled, non-inferiority, crossover pilot trial.

AIM: To assess whether adding empagliflozin to closed-loop automated insulin delivery could reduce the need for carbohydrate counting in type 1 diabetes (T1D) without worsening glucose control. MATERIALS AND METHODS: In an open-label, crossover, non-inferiority trial, 30 adult participants with T1D underwent outpatient automated insulin delivery interventions with three random sequences of prandial insulin strategy days: carbohydrate counting, simple meal announcement (no carbohydrate counting) and no meal announcement. During each sequence of prandial insulin strategies, participants were randomly assigned empagliflozin (25 mg/day) or not, and crossed over to the comparator. Mean glucose for carbohydrate counting without empagliflozin (control) was compared with no meal announcement with empagliflozin (in the primary non-inferiority comparison) and simple meal announcement with empagliflozin (in the conditional primary non-inferiority comparison). RESULTS: Participants were aged 40 ± 15 years, had 27 ± 15 years diabetes duration and HbA1c of 7.6% ± 0.7% (59 ± 8 mmol/mol). The system with no meal announcement and empagliflozin was not non-inferior (and thus reasonably considered inferior) to the control arm (mean glucose 10.0 ± 1.6 vs. 8.5 ± 1.5 mmol/L; non-inferiority p = .94), while simple meal announcement and empagliflozin was non-inferior (8.5 ± 1.4 mmol/L; non-inferiority p = .003). Use of empagliflozin on the background of automated insulin delivery with carbohydrate counting was associated with lower mean glucose, corresponding to a 14% greater time in the target range. While no ketoacidosis was observed, mean fasting ketones levels were higher on empagliflozin (0.22 ± 0.18 vs. 0.13 ± 0.11 mmol/L; p < .001). CONCLUSIONS: Empagliflozin added to automated insulin delivery has the potential to eliminate the need for carbohydrate counting and improves glycaemic control in conjunction with carbohydrate counting, but does not allow for the elimination of meal announcement.

Introducing Multifactorial Peak Crossover in Analytical and Preparative Chromatography via Computer-Assisted Modeling.

Modern pharmaceutical processes can often lead to multicomponent mixtures of closely related species that are difficult to resolve under chromatographic conditions, and even worse in preparative scale settings. Despite recent improvements in column technology and instrumentation, there remains an urgent need for creating innovative approaches that address challenging coelutions of critical pair and poor chromatographic productivity of purification methods. Herein, we overcome these challenges by introducing a simple and practical technique named multifactorial peak crossover (MPC) via computer-assisted chromatographic modeling. The approach outlined here focuses on mapping the separation landscape of pharmaceutical mixtures to quickly identify spaces of peak coelution crossings which enables one to conveniently switch the elution order of target analytes. Diverse examples of MPC diagrams as a function of column temperature, mobile phase gradient or a multifactorial combination in reversed phase and ion exchange chromatography (RPLC and IEC) modes are generated using ACD Laboratories/LC Simulator software and corroborated with experimental data match (overall retention time differences of less than 1%). This powerful MPC technique allows us to gain massive productivity increases (shorter cycle time and higher sample loading) for purification of pharmaceuticals by selectively switching the elution order of target components away from undesired tailing peaks and coelution spaces. MPC chromatography dramatically reduces the time spent developing productive analytical and preparative scale separations. In addition, we illustrate how this new MPC concept can be used to gain substantial improvements of the signal-to-noise ratio, enabling straightforward ppb detection of low-level target components with direct impact in the quantitation of metabolites and potential genotoxic impurities (PGIs). These innovations are of paramount importance in order to facilitate efficient isolation, characterization, and quantitation of drug substances in the development of new medicines.

The Emergence of Universal Chromatographic Methods in the Research and Development of New Drug Substances.

Manufacturing process development of new drug substances in the pharmaceutical industry combines numerous chemical challenges beyond the efficient synthesis of complex molecules. Optimization of a synthetic route involves the screening of multiple reaction variables with a desired outcome that not only depends on an increased product yield but is also highly influenced by the removal efficacy of residual chemicals and reaction byproducts during the subsequent synthetic route. Consequently, organic chemists must survey a wide array of synthetic variables to develop a highly productive, green, and cost-effective manufacturing process. The time constraints of developing robust quantitative methods prior to each processing step can easily lead to sample analysis becoming a bottleneck in synthetic route development. In this regard, conventional "on demand" analytical method development and optimization approaches, traditionally used for guiding synthetic chemistry efforts, become unsustainable. This Account introduces recent efforts to address the aforementioned challenges through the development and implementation of generic or more universal chromatographic methods that can cover a broad spectrum of targeted compound classes. Such generic methods require significant resolving power to enable baseline resolution of multicomponent mixtures in a single experimental run without additional method customization but must be simple enough to allow for routine use by chemists, chemical engineers and other researchers with little experience in chromatographic method development. These powerful analytical methodologies are often employed to minimize the time spent developing new analytical assays, while also facilitating method transfer to manufacturing facilities and application in regulatory settings. Diverse examples of universal and fit-for-purpose analytical procedures are presented herein, illustrating the power of modern readily available analytical technology for streamlining the development of new drug substances in organic chemistry laboratories across both academic and industrial sectors. With recent advances in analytical instrumentation and column technologies, universal chromatographic methods are quickly becoming a proactive and effective strategy to accelerate the discovery and implementation of new synthetic methodologies, especially but not limited to laboratories where the synthetic process route is undergoing rapid change and optimization. Targets of these generic methods include analysis of organic solvents, acid and basic additives, nucleotide species, palladium scavengers, impurity mapping, enantiopurity, synthetic intermediates, active pharmaceutical ingredients and their counterions, dehalogenation byproducts, and mixtures of organohalogenated pharmaceuticals, among other chemicals used or formed in process chemistry reactions.