Design of Experiments (DoE) and Process Optimization. A Review of Recent Publications

High-Throughput Algorithmic Optimization of In Vitro Transcription for SARS-CoV-2 mRNA Vaccine Production

The in vitro transcription (IVT) of messenger ribonucleic acid (mRNA) from the linearized deoxyribonucleic acid (DNA) template of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant (B.1.617.2) was optimized for total mRNA yield and purity (by percent intact mRNA) utilizing machine learning in conjunction with automated, high-throughput liquid handling technology. An iterative Bayesian optimization approach successfully optimized 11 critical process parameters in 42 reactions across 5 experimental rounds. Once the optimized conditions were achieved, an automated, high-throughput screen was conducted to evaluate commercially available T7 RNA polymerases for rate and quality of mRNA production. Final conditions showed a 12% yield improvement and a 50% reduction in reaction time, while simultaneously significantly decreasing (up to 44% reduction) the use of expensive reagents. This novel platform offers a powerful new approach for optimizing IVT reactions for mRNA production.

Quality by design steered approach for co-encapsulation of timolol maleate and dorzolamide hydrochloride in injectable liposomes

Glaucoma is caused by high intraocular pressure, which can causes blindness. Combinations of timolol and dorzolamide are used for its treatment with a requirement of multiple dosing with dosing being twice or four times a day. Conventional eye drops have poor pre-corneal retention and is thus less available for action. This study utilizes principles of Quality by Design for formulation of injectable liposomes coloaded with timolol maleate and dorzolamide HCl, which overcomes limitations of conventional eye drops. For implementation of Quality by Design principles a systematic approach involving defining Quality Target Product Profile, identification of Critical Quality Attributes, mapping Critical Quality Attributes to Critical Process Parameters and Critical Material Attributes, Failure Mode and Effect Analysis based risk assessment, Taguchi screening, and 32 full factorial Design of Experiments design were utilized. A robust model for formulation of coloaded liposomes was successfully developed. Design of Experiments approach allowed to obtain optimized batch having particle size of 116.1 nm, encapsulation efficiency of dorzolamide HCl of 72.12 % and encapsulation efficiency of timolol maleate of 71.94 %. In-vitro drug release showed a sustained release for 4 days. The prepared formulation was in the desired osmolarity range. Biosafety was proved using histopathological characterization. In-vivo studies for assessing the Intra Ocular Pressure reduction showed that there was no significant difference in Intra Ocular Pressure reduction between prepared liposomes and marketed formulation but were superior than marketed formulation because of less fluctuations in Intra Ocular Pressure. Prepared coloaded injectable liposomes lays the foundation for further research in the area and can be translated from to bench side for commercial clinical use.

Design of experiments optimization of N,N,N-trimethyl chitosan synthesis using N,N-diisopropylethylamine base

This study presents a novel synthesis method of N,N,N-trimethyl chitosan (TMC) by using a non-nucleophilic base and optimizing the solvent system for enhanced scalability, while addressing critical factors such as viscosity management and stirring efficiency. The study objectives also included achieving high N,N,N-trimethylation without O-methylation while minimizing reagent use. Eight bases, three solvent systems, and varying levels of dilution were explored to mitigate viscosity challenges and gas evolution. 1H NMR spectroscopy was used to characterize the TMC products. The integral values of the peaks at 3.3, 3.0, and 2.8 ppm, corresponding to trimethyl, dimethyl, and monomethyl groups, were used to quantify the methylation degrees. The most promising initial results were obtained with N,N-diisopropylethylamine (DIPEA) base, and DMF as solvent. Using 6 eq methyl iodide (MeI) relative to chitosan and DIPEA as base, up to 68 % DTM was achieved. Applying Design of Experiments (DoE), the method was further optimized under diluted conditions, crucial for industrial scalability and viscosity control. Results from a full factorial design (32) revealed that diluted medium effectively prevented viscosity concerns, achieving a notably low viscosity of 5.9 cP in the reaction mixture, a 16-fold decrease in viscosity, compared to initial experiments. It was also established that both the MeI reagent and the base addition are significant factors for the DTM response, with both factors showing quadratic effects. The DoE model demonstrated high significance (R = 0.97), high precision for future prediction (Q2 = 0.87), good model validity (0.84) and excellent reproducibility (0.96). The results mark a notable advancement in TMC synthesis, offering an efficient and practical method with significant implications for industrial applications.

Drug Mechanism: A bioinformatic update

A drug Mechanism of Action (MoA) is a complex biological phenomenon that describes how a bioactive compound produces a pharmacological effect. The complete knowledge of MoA is fundamental to fully understanding the drug activity. Over the years, many experimental methods have been developed and a huge quantity of data has been produced. Nowadays, considering the increasing omics data availability and the improvement of the accessible computational resources, the study of a drug MoA is conducted by integrating experimental and bioinformatics approaches. The development of new in silico solutions for this type of analysis is continuously ongoing; herein, an updating review on such bioinformatic methods is presented. The methodologies cited are based on multi-omics data integration in biochemical networks and Machine Learning (ML). The multiple types of usable input data and the advantages and disadvantages of each method have been analyzed, with a focus on their applications. Three specific research areas (i.e. cancer drug development, antibiotics discovery, and drug repurposing) have been chosen for their importance in the drug discovery fields in which the study of drug MoA, through novel bioinformatics approaches, is particularly productive.

Flow-based bioconjugation of coumarin phosphatidylethanolamine probes: Optimised synthesis and membrane molecular dynamics studies

A series of phosphatidylethanolamine fluorescent probes head-labelled with 3-carboxycoumarin was prepared by an improved bioconjugation approach through continuous flow synthesis. The established procedure, supported by a design of experiment (DoE) set-up, resulted in a significant reduction in the reaction time compared to the conventional batch method, in addition to a minor yield increase. The characterization of these probes was enhanced by an in-depth molecular dynamics (MD) study of the behaviour of a representative probe of this family, 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine labelled with 3-carboxycoumarin (POPE-COUM), in bilayers of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC)/1-stearoyl-2-linoleoyl-sn-glycero-3-phosphocholine (SLPC) 2:1, mimicking the composition of the egg yolk lecithin membranes recently used experimentally by our group to study POPE-COUM as a biomarker of the oxidation state and integrity of large unilamellar vesicles (LUVs). The MD simulations revealed that the coumarin group is oriented towards the bilayer interior, leading to a relatively internal location, in agreement with what is observed in the nitrobenzoxadiazole fluorophore of commercial head-labelled NBD-PE probes. This behaviour is consistent with the previously stated hypothesis that POPE-COUM is entirely located within the LUVs structure. Hence, the delay on the oxidation of the probe in the oxygen radical absorbance capacity (ORAC) assays performed is related with the inaccessibility of the probe until alteration of the LUV structure occurs. Furthermore, our simulations show that POPE-COUM exerts very little global and local perturbation on the host bilayer, as evaluated by key properties of the unlabelled lipids. Together, our findings establish PE-COUM as suitable fluorescent lipid analogue probes.

Artificial intelligence-powered electrochemical sensor: Recent advances, challenges, and prospects

Integrating artificial intelligence (AI) with electrochemical biosensors is revolutionizing medical treatments by enhancing patient data collection and enabling the development of advanced wearable sensors for health, fitness, and environmental monitoring. Electrochemical biosensors, which detect biomarkers through electrochemical processes, are significantly more effective. The integration of artificial intelligence is adept at identifying, categorizing, characterizing, and projecting intricate data patterns. As the Internet of Things (IoT), big data, and big health technologies move from theory to practice, AI-powered biosensors offer significant opportunities for real-time disease detection and personalized healthcare. Still, they also pose challenges such as data privacy, sensor stability, and algorithmic bias. This paper highlights the critical advances in material innovation, biorecognition elements, signal transduction, data processing, and intelligent decision systems necessary for developing next-generation wearable and implantable devices. Despite existing limitations, the integration of AI into biosensor systems shows immense promise for creating future medical devices that can provide early detection and improved patient outcomes, marking a transformative step forward in healthcare technology.

Web-BO: towards increased accessibility of Bayesian optimisation (BO) for chemistry

Historically, the chemical discovery process has predominantly been a matter of trial-and-improvement, where small modifications are made to a chemical system, guided by chemical knowledge, with the aim of optimising towards a target property or combination of properties. While a trial-and-improvement approach is frequently successful, especially when assisted by the help of serendipity, the approach is incredibly time- and resource-intensive. Complicating this further, the available chemical space that could, in theory, be explored is remarkably vast. As we are faced with near infinite possibilities and limited resources, we require improved search methods to effectively move towards desired optima, e.g. chemical systems exhibiting a target property, or several desired properties. Bayesian optimisation (BO) has recently gained significant traction in chemistry, where within the BO framework, prior knowledge is used to inform and guide the search process to optimise towards desired chemical targets, e.g. optimal reaction conditions to maximise yield, or optimal catalyst exhibiting improved catalytic activity. While powerful, implementing BO algorithms in practice is largely limited to interfacing via various APIs - requiring advanced coding experience and bespoke scripts for each optimisation task. Further, it is challenging to seamlessly link these with electronic lab notebooks via a graphical user interface (GUI). Ultimately, this limits the accessibility of BO algorithms. Here, we present Web-BO, a GUI to support BO for chemical optimisation tasks. We demonstrate its performance using an open source dataset and associated emulator, and link the platform with an existing electronic lab notebook, datalab. By providing a GUI-based BO service, we hope to improve the accessibility of data-driven optimisation tools in chemistry; https://suprashare.rcs.ic.ac.uk/web-bo/.

Evaluation of cavitation phenomena in three-way globe valve through computational analysis and visualization test

A three-way valve has a multi-port structure with three openings, which allows control of the fluid direction. However, owing to the complicated trim shape of the internal flow, an irregular fluid flow occurs, which hinders precise fluid flow control. In severe cases, cavitation induces mechanical damage owing to abrupt changes in the fluid direction. In this study conducted a computational fluid dynamics (CFD) analysis was performed to estimate the localized cavitation around the bottom plug of the three-way valve. To quantify localized cavitation, the percentage of cavitation (POC) was derived using the vapor volume fraction (VVF). The POC, defined by the cavitation occurrence zone with VVF > 0.5 divided by the volume of the cavitation danger zone, was 34.90%. Cavitation at this POC level could cause mechanical damage; therefore, a size optimization was performed. The lengths of the optimized waist and tail regions of the bottom plug were obtained wherein the POC level decreased by 19.06%. In addition, experiments were conducted using a flow visualization test setup. The experimental results were quantified into the POC employing the image gradients method, and the results were in good agreement with the CFD analysis.

Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis

Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.

Medium optimization to improve growth and iron uptake by Bacillus tequilensis ASFS1 using fractional factorial designs

Many notable applications have been described for magnetic nanoparticles in delivery of diverse drugs and bioactive compounds into cells, magnetofection for the treatment of cancer, photodynamic therapy, photothermal therapy, and magnetic particle imaging (MPI). In response to the growing demand for magnetic nanoparticles for drug delivery or biomedical imaging applications, more effective and eco-friendly methodologies are required for large-scale biosynthesis of this nanoparticles. The major challenge in the large-scale biomedical application of magnetic nanoparticles lies in its low efficiency and optimization of nanoparticle production can address this issue. In the current study, a prediction model is suggested by the fractional factorial designs. The present study aims to optimize culture media components for improved growth and iron uptake of this strain. The result of optimization for iron uptake by the strain ASFS1 is to increase the production of magnetic nanoparticles by this strain for biomedical applications in the future. In the present study, design of experiment method was used to probe the effects of some key medium components (yeast extract, tryptone, FeSO4, Na2-EDTA, and FeCl3) on Fe content in biomass and dried biomass of strain ASFS1. A 25-1 fractional factorial design showed that Na2-EDTA, FeCl3, yeast extract-tryptone interaction, and FeSO4-Na2-EDTA interaction were the most parameters on Fe content in biomass within the experimented levels (p < 0.05), while yeast extract, FeCl3, and yeast extract-tryptone interaction were the most significant factors within the experimented levels (p < 0.05) to effect on dried biomass of strain ASFS1. The optimum culture media components for the magnetic nanoparticles production by strain ASFS1 was reported to be 7.95 g L-1 of yeast extract, 5 g L-1 of tryptone, 75 μg mL-1 of FeSO4, 192.3 μg mL-1 of Na2-EDTA and 150 μg mL-1 of FeCl3 which was theoretically able to produce Fe content in biomass (158 μg mL-1) and dried biomass (2.59 mg mL-1) based on the obtained for medium optimization. Using these culture media components an experimental maximum Fe content in biomass (139 ± 13 μg mL-1) and dried biomass (2.2 ± 0.2 mg mL-1) was obtained, confirming the efficiency of the used method.

New Opportunities for Organic Synthesis with Superheated Flow Chemistry

ConspectusFlow chemistry has brought a fresh breeze with great promises for chemical manufacturing, yet critical deterrents persist. To remain economically viable at production scales, flow processes demand quick reactions, which are actually not that common. Superheated flow technology stands out as a promising alternative poised to confront modern chemistry challenges. While continuous micro- and mesofluidic reactors offer uniform heating and rapid cooling across different scales, operating above solvent boiling points (i.e., operating under superheated conditions) significantly enhances reaction rates. Despite the energy costs associated with high temperatures, superheated flow chemistry aligns with sustainability goals by improving productivity (process intensification), offering solvent flexibility, and enhancing safety.However, navigating the unconventional chemical space of superheated flow chemistry can be cumbersome, particularly for neophytes. Expanding the temperature/pressure process window beyond the conventional boiling point under the atmospheric pressure limit vastly increases the optimization space. When associated with conventional trial-and-error approaches, this can become exceedingly wasteful, resource-intensive, and discouraging. Over the years, flow chemists have developed various tools to mitigate these challenges, with an increased reliance on statistical models, artificial intelligence, and experimental (kinetics, preliminary test reactions under microwave irradiation) or theoretical (quantum mechanics) a priori knowledge. Yet, the rationale for using superheated conditions has been slow to emerge, despite the growing emphasis on predictive methodologies.To fill this gap, this Account provides a concise yet comprehensive overview of superheated flow chemistry. Key concepts are illustrated with examples from our laboratory's research, as well as other relevant examples from the literature. These examples have been thoroughly studied to answer the main questions Why? At what cost? How? For what? The answers we provide will encourage educated and widespread adoption. The discussion begins with a demonstration of the various advantages arising from superheated flow chemistry. Different reactor alternatives suitable for high temperatures and pressures are then presented. Next, a clear workflow toward strategic adoption of superheated conditions is resorted either using Design of Experiments (DoE), microwave test chemistry, kinetics data, or Quantum Mechanics (QM). We provide rationalization for chemistries that are well suited for superheated conditions (e.g., additions to carbonyl functions, aromatic substitutions, as well as C-Y [Y = N, O, S, C, Br, Cl] heterolytic cleavages). Lastly, we bring the reader to a rational decision analysis toward superheated flow conditions. We believe this Account will become a reference guide for exploring extended chemical spaces, accelerating organic synthesis, and advancing molecular sciences.

Towards quantifying biomarkers for respiratory distress in preterm infants: Machine learning on mid infrared spectroscopy of lipid mixtures

Neonatal respiratory distress syndrome (nRDS) is a challenging condition to diagnose which can lead to delays in receiving appropriate treatment. Mid infrared (IR) spectroscopy is capable of measuring the concentrations of two diagnostic nRDS biomarkers, lecithin (L) and sphingomyelin (S) with the potential for point of care (POC) diagnosis and monitoring. The effects of varying other lipid species present in lung surfactant on the mid IR spectra used to train machine learning models are explored. This study presents a lung lipid model of five lipids present in lung surfactant and varies each in a systematic approach to evaluate the ability of machine learning models to predict the lipid concentrations, the L/S ratio and to quantify the uncertainty in the predictions using the jackknife + -after-bootstrap and variant bootstrap methods. We establish the L/S ratio can be determined with an uncertainty of approximately ±0.3 mol/mol and we further identify the 5 most prominent wavenumbers associated with each machine learning model.

Olaparib Process Development Employing Quality by Design (QbD) Principles

This study focuses on multivariate experimental design and statistical analysis to optimize the process of Olaparib 1. Quality by design (QbD) methodology was adopted for optimization of the Olaparib process consisting of three reaction steps: (1) amidation, (2) deprotection, and (3) acylation. Every chemical conversion was studied in isolation, employing risk assessment to identify key material attributes and key process parameters that may have the potential to impact the reaction. Thereafter, the screening design of experiment (DoE) was employed to scrutinize the factors that significantly impacted yield. Moving forward, the scrutinized factors which were found to impact the responses, the set of critical material attributes (CMAs) and critical process parameters (CPPs), were considered for optimization by applying I-Optimal design to define design space arriving at a robust setting wherein the predefined targets were supposedly optimal. To our delight, we got 95, 91, and 75% yield with more than 99% purity in amidation, deprotection, and acetylation, respectively, which enabled us to systematically identify design space to meet the desired quality target of the product consistently. More importantly, to distinguish the CMAs and CPPs, these elements ought to be monitored to have control of the quality parameter throughout the active pharmaceutical ingredient (API) value chain until commercial manufacturing followed by marketing. Eventually, we have developed a greener process in comparison to precedented one for Olaparib 1.

Harnessing Potential of ω-3 Polyunsaturated Fatty Acid with Nanotechnology for Enhanced Breast Cancer Therapy: A Comprehensive Investigation into ALA-Based Liposomal PTX Delivery

Our hypothesis posited that incorporating alpha-linolenic acid (ALA) into liposomes containing Paclitaxel (PTX) could augment cellular uptake, decrease the therapeutic dosage, and alleviate PTX-related side effects. Our investigation encompassed characterization of the liposomal formulation, encompassing aspects like particle size, surface morphology, chemical structure, drug release kinetics, and stability. Compatibility studies were performed through Fourier transform infrared spectroscopy (FTIR). By utilizing the Box-Behnken design (BBD), we developed ALA-based liposomes with satisfactory particle size and entrapment efficiency. It is noteworthy that ALA incorporation led to a slight increase in particle size but did not notably affect drug entrapment. In vitro drug release assessments unveiled a sustained release pattern, with ALA-PTX liposomes demonstrating release profiles comparable to PTX liposomes. Morphological examinations confirmed the spherical structure of the liposomes, indicating that substituting ALA with phosphatidylcholine did not alter the physicochemical properties. Cellular uptake investigations showcased enhanced uptake of ALA-based liposomes in contrast to PTX liposomes, likely attributed to the heightened fluidity conferred by ALA. Efficacy against MCF-7 cells demonstrated concentration-dependent reductions in cell viability, with ALA-PTX liposomes exhibiting the lowest IC50 value. Morphological analysis confirmed apoptotic changes in cells treated with all formulations, with ALA-PTX liposomes eliciting more pronounced changes, indicative of enhanced anticancer efficacy.

Design and optimization of a cost-effective paper-based voltammetric sensor for the determination of trinitrotoluene (TNT) utilizing cysteamine-linked Fe3O4 @Au nanocomposite

This paper presents the development and optimization of a cost-effective paper electrochemical sensor for the detection of TNT using Fe3O4-Au core-shell nanoparticles modified with cysteamine (Fe3O4@Au/CA). The sensor was constructed by modifying a graphite paste with the aforementioned nanoparticles, which facilitated the formation of a Meisenheimer complex between cysteamine and TNT as an electron donor and an electron acceptor, respectively. The central composite design was employed to optimize four key parameters pH, modifier percentage, contact time, and buffer type to enhance the performance of the sensor. The detection limit was found to be 0.5 nM of TNT, while the linear range of the electrode response spanned from 0.002 μM to 10 μM. The simplicity and low cost of the sensor make it highly attractive for practical applications, particularly in scenarios where rapid and on-site TNT detection is required.

A Slug Flow Platform with Multiple Process Analytics Facilitates Flexible Reaction Optimization

Flow processing offers many opportunities to optimize reactions in a rapid and automated manner, yet often requires relatively large quantities of input materials. To combat this, the use of a flexible slug flow reactor, equipped with two analytical instruments, for low-volume optimization experiments are reported. A Buchwald-Hartwig amination toward the drug olanzapine, with 6 independent optimizable variables, is optimized using three different automated approaches: self-optimization, design of experiments, and kinetic modeling. These approaches are complementary and provide differing information on the reaction: pareto optimal operating points, response surface models, and mechanistic models, respectively. The results are achieved using <10% of the material that would be required for standard flow operation. Finally, a chemometric model is built utilizing automated data handling and three subsequent validation experiments demonstrate good agreement between the slug flow reactor and a standard (larger scale) flow reactor.

A numerical compass for experiment design in chemical kinetics and molecular property estimation

Kinetic process models are widely applied in science and engineering, including atmospheric, physiological and technical chemistry, reactor design, or process optimization. These models rely on numerous kinetic parameters such as reaction rate, diffusion or partitioning coefficients. Determining these properties by experiments can be challenging, especially for multiphase systems, and researchers often face the task of intuitively selecting experimental conditions to obtain insightful results. We developed a numerical compass (NC) method that integrates computational models, global optimization, ensemble methods, and machine learning to identify experimental conditions with the greatest potential to constrain model parameters. The approach is based on the quantification of model output variance in an ensemble of solutions that agree with experimental data. The utility of the NC method is demonstrated for the parameters of a multi-layer model describing the heterogeneous ozonolysis of oleic acid aerosols. We show how neural network surrogate models of the multiphase chemical reaction system can be used to accelerate the application of the NC for a comprehensive mapping and analysis of experimental conditions. The NC can also be applied for uncertainty quantification of quantitative structure-activity relationship (QSAR) models. We show that the uncertainty calculated for molecules that are used to extend training data correlates with the reduction of QSAR model error. The code is openly available as the Julia package KineticCompass.

OptiMS: An Accessible Program for Automating Mass Spectrometry Parameter Optimization and Configuration

Mass spectrometers have an enormous number of user-changeable parameters that drastically affect the observed mass spectrum. Using optimal parameters can significantly improve mass spectrometric data by increasing signal stability and signal-to-noise ratio, which decreases the limit of detection, thus revealing previously unobservable species. However, ascertaining optimal parameters is time-consuming, tedious, and made further challenging by the fact that parameters can act dependently on each other. Consequently, suboptimal parameters are frequently used during characterization, reducing the quality of results. OptiMS, an open-source, cross-platform program, was developed to simplify, accelerate, and more accurately determine optimal mass spectrometer parameters for a given system. It addresses common difficulties associated with existing software such as slow performance, high costs, and limited functionality. OptiMS efficacy was demonstrated through its application to multiple systems, quickly and successfully optimizing instrument parameters unassisted to maximize a user-defined metric, such as the intensity of a particular analyte. Additionally, among other features, OptiMS allows running of a sequence of predefined parameter configurations, reducing the workload of users wishing to obtain mass spectra under multiple sets of conditions.

Optimization of adaptation parameters from adhesion cell culture in serum-containing media to suspension in chemically defined media by superlative box design

A new design of experiments-superlative box design (SBD), was adopted to optimize the adaptation of Chinese hamster ovary cells from adhesion culture to serum-free suspension culture. It is a general trend to use a serum-free medium instead of a serum-containing medium. The advantage of serum-free medium (chemically defended) is that it does not contain unknown components and avoids safety issues. SBD requires fewer experiments while ensuring a sufficient number of experiments and uniformity in the distribution of experiments amongst all the factors. Six factors were considered in this experimental design with 43 runs plus three more repeating center runs. The cell line was adapted to serum-free media by gradually reducing serum, and from adherent to suspension by rotating at various speeds in a shake flask. Response surface methodology was applied to find the optimum condition. The optimized cell density reached 7.02 × 105 cells/mL, calculated by the quadratic model. Experiments validated the predicted cell adaptation with the maximum cell density. Three suspension runs were selected randomly to perform in the bioreactor to validate cell stability and production homogeneity. This study provides an efficient method to transfer adherent cells to suspension cells and is the first to successfully use SBD and establish a parameter quadratic optimization model.

Aerosolizable Pyrazinamide-Loaded Biodegradable Nanoparticles for the Management of Pulmonary Tuberculosis

Background: Pyrazinamide is a Biopharmaceutical Classification System class III antibiotic indicated for active tuberculosis. Methods: In the present work, pyrazinamide-loaded biodegradable polymeric nanoparticles (PNPs) based dry powder inhaler were developed using the double emulsion solvent evaporation technique and optimized using design of experiments to provide direct pulmonary administration with minimal side effects. Batches were characterized for various physicochemical and aerosol performance properties. Results: Optimized batch exhibited particle size of 284.5 nm, % entrapment efficiency of 71.82%, polydispersibility index of 0.487, zeta potential of -17.23 mV, and in vitro drug release at 4 hours of 79.01%. Spray-dried PNPs were evaluated for drug content, in vitro drug release, and kinetics. The particle mass median aerodynamic diameter was within the alveolar region's range (2.910 μm). In the trachea and lung, there was a 2.5- and 1.2-fold increase in in vivo deposition with respect to pure drug deposition, respectively. In vitro drug uptake findings showed that alveolar macrophages with pyrazinamide PNPs had a considerably higher drug concentration. Furthermore, accelerated stability studies were carried out for the optimized batch. Results indicated no significant change in the evaluation parameters, which showed stability of the formulation for at least a 6-month period. Conclusion: PNPs prepared using biodegradable polymers exhibited efficient pulmonary drug delivery with decent stability.

Temperature-Transferable Coarse-Grained Models for Volumetric Properties of Poly(lactic Acid)

A new coarse-grained (CG) model, for which each monomer is mapped as one bead at its center of mass, was developed for simulating the volumetric properties of the polylactide (PLA) bulk. The three bonded CG potentials are first parametrized against the strain energies of the dimer, trimer, and tetramer, and the nonbonded CG potentials are then optimized to match the melt densities of the decamer. With the derived CG potentials, molecular dynamics (MD) simulations are found to reproduce thermal expansion and glass transition. By rescaling the dihedral and nonbonded potentials with temperature-independent factors, the glass transition temperature (Tg) is also satisfactorily restored with little modifications on the volumetric expansive coefficients at both rubbery and glassy states. Therefore, the finally optimized CG potentials exhibit excellent temperature transferability, as rationalized by the Simha-Boyer relation. Furthermore, it is confirmed that the dihedral torsions and nonbonded interactions play key roles in glass transition. Also, the simulated bulk moduli and conformational properties in a wide temperature range compare well with the referenced data. The proposed multiscale scheme has great potential in simulating thermo-mechanical properties of PLA and other polymers.

Direct Heterocycle C-H Alkenylation via Dual Catalysis Using a Palladacycle Precatalyst: Multifactor Optimization and Scope Exploration Enabled by High-Throughput Experimentation

One of the major challenges in developing catalytic methods for C-C bond formation is the identification of generally applicable reaction conditions, particularly if multiple substrate structural classes are involved. Pd-catalyzed direct arylation reactions are powerful transformations that enable direct functionalization of C-H bonds; however, the corresponding direct alkenylation reactions, using vinyl (pseudo) halide electrophiles, are less well developed. Inspired by process development efforts toward GSK3368715, an investigational active pharmaceutical ingredient, we report that a Pd(II) palladacycle derived from tri-tert-butylphosphine and Pd(OAc)2 is an effective single-component precatalyst for a variety of direct alkenylation reactions. High-throughput experimentation identified optimal solvent/base combinations for a variety of HetAr-H substrate classes undergoing C-H activation without the need for cocatalysts or stoichiometric silver bases (e.g., Ag2CO3). We propose this reaction proceeds via a dual cooperative catalytic mechanism, where in situ-generated Pd(0) supports a canonical Pd(0)/(II) cross-coupling cycle and the palladacycle effects C-H activation via CMD in a redox-neutral cycle. In all, 192 substrate combinations were tested with a hit rate of approximately 40% and 24 isolated examples. Importantly, this method was applied to prepare a key intermediate in the synthesis of GSK3368715 on multigram scale.

Using design of experiments to guide genetic optimization of engineered metabolic pathways

Design of experiments (DoE) is a term used to describe the application of statistical approaches to interrogate the impact of many variables on the performance of a multivariate system. It is commonly used for process optimization in fields such as chemical engineering and material science. Recent advances in the ability to quantitatively control the expression of genes in biological systems open up the possibility to apply DoE for genetic optimization. In this review targeted to genetic and metabolic engineers, we introduce several approaches in DoE at a high level and describe instances wherein these were applied to interrogate or optimize engineered genetic systems. We discuss the challenges of applying DoE and propose strategies to mitigate these challenges.

ONE-SENTENCE SUMMARY

This is a review of literature related to applying Design of Experiments for genetic optimization.

Plant leaf mucilage/carrageenan/Eudragit® NE30D blended films: Optimization, characterization, and pharmaceutical application

Mucilage of C. pareira leaves was utilized, being manufactured for use in pharmaceutical products. Carrageenan and Eudragit® NE30D were used to combined. Glycerin was used as a plasticizer at a concentration of 20 % w/w based on the amount of polymer used. Computer software optimized its characteristics, including tensile properties, moisture uptake, and erosion; the optimal formulation was 1.4:1.2:2.8. The percentages of optimization error ranged from 8.48 to 13.80 %. Propranolol HCl was mixed to an optimal formulation. The film layer was tight, homogeneous, and smooth, with no holes. DSC thermogram showed no interaction peaks at 101.33 °C and 170.50 °C. Propranolol HCl concentration in the film ranged from 2.18 to 2.20 mg/cm2. Propranolol HCl was quickly released from the film. The kinetic model for the release profile was first-order kinetic. Although propranolol HCl had a high-release profile, its skin permeation was limited. The permeation lag time, Jss, and Kp were 1.60-2.65 h, 0.0182-0.0338 μg/cm2/h, and 9.10-15.35 cm/h, respectively. A significant amount of propranolol HCl residue was found on the skin's surface. Glycerin appeared to influence propranolol HCl permeability. Therefore, the plant leaf mucilage/carrageenan/Eudragit® NE30D blended film can be utilized in pharmaceutical applications to control drug release from its film layer.

Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows

Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a "user guide" for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure-property relationships in soft materials.

Optimization of oil yield of Pelargonium graveolens L'Hér using Box-Behnken design in relation to its antimicrobial activity and in silico study

Pelargonium graveolens L'Hér is an important species of genus Pelargonium with an economic value. The unique rose scent of its oil is used in perfume and cosmetic industry. The oil is characterized by the presence of citronellol, geraniol and rose oxide. Fresh aerial parts of P. graveolens at GC-MS analysis of four seasons revealed that autumn constituted the highest yield of the oil. For the first time, optimization of the yield of extracted oil of P. graveolens was performed employing 3-level Box-Behnken design using 3-factors. The GC-MS analysis of the essential oil was performed for the 17-runs. The optimized extraction of the oil was performed employing numerical optimization and studied for antimicrobial, Minimum Inhibitory Concentration (MIC) and biofilm inhibitory activities. The 3 factors followed rank (plant material amount > water volume > NaCl percent in water), in their magnitude of effect on increasing yield of the oil. Increasing the plant material amount increased the yield of the oil by 6-folds compared to NaCl percent in water. The optimized yield of oil (4 ml) was obtained from extraction criteria (150 g of plant, 750 ml of water and 3.585% (26.85 g) of NaCl). Computational docking was performed to overcome the multi-drug resistant Gram-negative bacilli targeting undecaprenyl pyrophosphate synthase (UPPS). The optimized oil exhibited a promising inhibitory activity against Gram-negative bacteria (K. pneumonia and P. aeruginosa) with significant antibiofilm action (P < 0.05). Moreover, it exerted a synergistic effect when combined with various antibiotics (Cefoxitin, Cloxacillin, Oxacillin and Vancomycin) against MRSA clinical strains.

Feed-Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction

Density functional theory (DFT) is a powerful tool to model transition state (TS) energies to predict selectivity in chemical synthesis. However, a successful multistep synthesis campaign must navigate energetically narrow differences in pathways that create some limits to rapid and unambiguous application of DFT to these problems. While powerful data science techniques may provide a complementary approach to overcome this problem, doing so with the relatively small data sets that are widespread in organic synthesis presents a significant challenge. Herein, we show that a small data set can be labeled with features from DFT TS calculations to train a feed-forward neural network for predicting enantioselectivity of a Negishi cross-coupling reaction with P-chiral hindered phosphines. This approach to modeling enantioselectivity is compared with conventional approaches, including exclusive use of DFT energies and data science approaches, using features from ligands or ground states with neural network architectures.

Drilling into "Quality by Design" Approach for Analytical Methods

The need for consistency in analytical method development reinforces the dependence of pharmaceutical product development and manufacturing on robust analytical data. The Analytical Quality by Design (AQbD), akin to the product Quality by Design (QbD) endows a high degree of confidence to the method quality developed. AQbD involves the definition of the analytical target profile as starting point, followed by the identification of critical method variables and critical analytical attributes, supported on risk assessment and design of experiment tools for the establishment of a method operable design region and control strategy of the method. This systematic approach moves away from reactive troubleshooting to proactive failure reduction. The objective of this review is to highlight the elements of the AQbD framework and provide an overview of their implementation status in various analytical methods used in the pharmaceutical field. These methodologies include but are not limited to, high-performance liquid chromatography, UV-Vis spectrophotometry, capillary electrophoresis, supercritical fluid chromatography, and high-performance thin-layer chromatography. Finally, a critical appraisal is provided to highlight how regulators have encouraged AQbD principles application to boost the prevention of method failures and a better understanding of the method operable design region (MODR) and control strategy, ultimately resulting in cost-effectiveness and regulatory flexibility.

A Customized Bayesian Algorithm to Optimize Enzyme-Catalyzed Reactions

Design of experiments (DoE) plays an important role in optimizing the catalytic performance of chemical reactions. The most commonly used DoE relies on the response surface methodology (RSM) to model the variable space of experimental conditions with the fewest number of experiments. However, the RSM leads to an exponential increase in the number of required experiments as the number of variables increases. Herein we describe a Bayesian optimization algorithm (BOA) to optimize the continuous parameters (e.g., temperature, reaction time, reactant and enzyme concentrations, etc.) of enzyme-catalyzed reactions with the aim of maximizing performance. Compared to existing Bayesian optimization methods, we propose an improved algorithm that leads to better results under limited resources and time for experiments. To validate the versatility of the BOA, we benchmarked its performance with biocatalytic C-C bond formation and amination for the optimization of the turnover number. Gratifyingly, up to 80% improvement compared to RSM and up to 360% improvement vs previous Bayesian optimization algorithms were obtained. Importantly, this strategy enabled simultaneous optimization of both the enzyme's activity and selectivity for cross-benzoin condensation.

Bayesian Optimization-Assisted Screening to Identify Improved Reaction Conditions for Spiro-Dithiolane Synthesis

Bayesian optimization (BO)-assisted screening was applied to identify improved reaction conditions toward a hundred-gram scale-up synthesis of 2,3,7,8-tetrathiaspiro[4.4]nonane (1), a key synthetic intermediate of 2,2-bis(mercaptomethyl)propane-1,3-dithiol [tetramercaptan pentaerythritol]. Starting from the initial training set (ITS) consisting of six trials sampled by random screening for BO, suitable parameters were predicted (78% conversion yield of spiro-dithiolane 1) within seven experiments. Moreover, BO-assisted screening with the ITS selected by Latin hypercube sampling (LHS) further improved the yield of 1 to 89% within the eight trials. The established conditions were confirmed to be satisfactory for a hundred grams scale-up synthesis of 1.

Increasing Higher Alcohols and Acetates in Low-Alcohol Beer by Proteases

The market of non-alcoholic and low-alcohol beer has grown continuously thanks to the advocacy for healthy and responsible drinking. Non-alcoholic and low-alcohol products usually possess less higher alcohols and acetates and more aldehyde off-flavors due to the manufacturing processes. The employment of non-conventional yeasts partially mitigates this problem. In this study, we used proteases to optimize the wort amino acid profile for better aroma production during yeast fermentation. The design of experiments was applied to increase the leucine molar fraction, aiming to boost 3-methylbutan-1-ol and 3-methylbutyl acetate (banana-like aromas). This led to an increase from 7% to 11% leucine in wort after protease treatment. The aroma output in the subsequent fermentation, however, was yeast-dependent. An 87% increase of 3-methylbutan-1-ol and a 64% increase of 3-methylbutyl acetate were observed when Saccharomycodes ludwigii was used. When Pichia kluyveri was employed, higher alcohols and esters from valine and isoleucine were increased: 58% more of 2-methylpropyl acetate, 67% more of 2-methylbutan-1-ol, and 24% more of 2-methylbutyl acetate were observed. Conversely, 3-methylbutan-1-ol decreased by 58% and 3-methylbutyl acetate largely remained the same. Apart from these, the amounts of aldehyde intermediates were increased to a varying extent. The impact of such increases in aromas and off-flavors on the perception of low-alcohol beer remains to be evaluated by sensory analysis in future studies.

Application of quality-by-design for adopting an environmentally green fluorogenic determination of benoxinate hydrochloride in eye drops and artificial aqueous humour

Herein, a sensitive and selective spectrofluorimetric method has been developed for the determination of the ocular local anesthetic benoxinate hydrochloride (BEN-HCl) in eye drops and artificial aqueous humour. The proposed method is based on the interaction of fluorescamine with the primary amino group of BEN-HCl at room temperature. Following the excitation of the reaction product at 393 nm, the emitted relative fluorescence intensity (RFI) was measured at 483 nm. The key experimental parameters were carefully examined and optimized by adopting an analytical quality-by-design approach. The method used a two-level full factorial design (2⁴ FFD) to obtain the optimum RFI of the reaction product. The calibration curve was linear at the range of 0.10-1.0 μg/mL of BEN-HCl with sensitivity down to 0.015 μg/mL. The method was applied for analyzing the BEN-HCl eye drops and could also assess its spiked levels in artificial aqueous humour with high % recoveries (98.74-101.37%) and low SD values (≤ 1.11). To investigate the green profile of the proposed method, a greenness assessment was performed with the aid of the Analytical Eco-Scale Assessment (ESA) and GAPI. The developed method obtained a very high ESA rating score in addition to being sensitive, affordable, and environmentally sustainable. The proposed method was validated according to ICH guidelines.

Nickel-Catalyzed Isomerization of Homoallylic Alcohols

Nickel-catalyzed isomerizations of homoallylic alcohols and a bishomoallylic alcohol are presented. These isomerization reactions occur in the presence of a simple nickel catalyst that does not require addition of an exogenous ligand. The corresponding ketone products are generated in ≤98% yield.

Versatile Triphenylphosphine-Containing Polymeric Catalysts and Elucidation of Structure-Function Relationships

Synthetic polymers are a modular solution to bridging the two most common classes of catalysts: proteins and small molecules. Polymers offer the synthetic versatility of small-molecule catalysts while simultaneously having the ability to construct microenvironments mimicking those of natural proteins. We synthesized a panel of polymeric catalysts containing a novel triphenylphosphine acrylamide monomer and investigated how their properties impact the rate of a model Suzuki-Miyaura cross-coupling reaction. Systematic variation of polymer properties, such as the molecular weight, functional density, and comonomer identity, led to tunable reaction rates and solvent compatibility, including full conversion in an aqueous medium. Studies with bulkier substrates revealed connections between polymer parameters and reaction conditions that were further elucidated with a regression analysis. Some connections were substrate-specific, highlighting the value of the rapidly tunable polymer catalyst. Collectively, these results aid in building structure-function relationships to guide the development of polymer catalysts with tunable substrates and environmental compatibility.

A field guide to flow chemistry for synthetic organic chemists

Flow chemistry has unlocked a world of possibilities for the synthetic community, but the idea that it is a mysterious "black box" needs to go. In this review, we show that several of the benefits of microreactor technology can be exploited to push the boundaries in organic synthesis and to unleash unique reactivity and selectivity. By "lifting the veil" on some of the governing principles behind the observed trends, we hope that this review will serve as a useful field guide for those interested in diving into flow chemistry.

Influence of Poloxamer on the Dissolution and Stability of Hot-Melt Extrusion-Based Amorphous Solid Dispersions Using Design of Experiments

The current study aimed to see the effects of poloxamer P407 on the dissolution performance of hydroxypropyl methylcellulose acetate succinate (AquaSolve™ HPMC-AS HG)-based amorphous solid dispersions (ASD). A weakly acidic, poorly water-soluble active pharmaceutical ingredient (API), mefenamic acid (MA), was selected as a model drug. Thermal investigations, including thermogravimetry (TG) and differential scanning calorimetry (DSC), were conducted for raw materials and physical mixtures as a part of the pre-formulation studies and later to characterize the extruded filaments. The API was blended with the polymers using a twin shell V-blender for 10 min and then extruded using an 11-mm twin-screw co-rotating extruder. Scanning electron microscopy (SEM) was used to study the morphology of the extruded filaments. Furthermore, Fourier-transform infrared spectroscopy (FT-IR) was performed to check the intermolecular interactions of the components. Finally, to assess the in vitro drug release of the ASDs, dissolution testing was conducted in phosphate buffer (0.1 M, pH 7.4) and hydrochloric acid-potassium chloride (HCl-KCl) buffer (0.1 M, pH 1.2). The DSC studies confirmed the formation of the ASDs, and the drug content of the extruded filaments was observed to be within an acceptable range. Furthermore, the study concluded that the formulations containing poloxamer P407 exhibited a significant increase in dissolution performance compared to the filaments with only HPMC-AS HG (at pH 7.4). In addition, the optimized formulation, F3, was stable for over 3 months when exposed to accelerated stability studies.

A Brief Introduction to Chemical Reaction Optimization

From the start of a synthetic chemist's training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing the scientist to develop practical skills and some chemical intuition. This procedure is often kept long into a researcher's career, as new recipes are developed based on similar reaction protocols, and intuition-guided deviations are conducted through learning from failed experiments. However, when attempting to understand chemical systems of interest, it has been shown that model-based, algorithm-based, and miniaturized high-throughput techniques outperform human chemical intuition and achieve reaction optimization in a much more time- and material-efficient manner; this is covered in detail in this paper. As many synthetic chemists are not exposed to these techniques in undergraduate teaching, this leads to a disproportionate number of scientists that wish to optimize their reactions but are unable to use these methodologies or are simply unaware of their existence. This review highlights the basics, and the cutting-edge, of modern chemical reaction optimization as well as its relation to process scale-up and can thereby serve as a reference for inspired scientists for each of these techniques, detailing several of their respective applications.

Interpretable artificial neural networks for retrospective QbD of pharmaceutical tablet manufacturing based on a pilot-scale developmental dataset

As the pharmaceutical industry increasingly adopts the Pharma 4.0. concept, there is a growing need to effectively predict the product quality based on manufacturing or in-process data. Although artificial neural networks (ANNs) have emerged as powerful tools in data-rich environments, their implementation in pharmaceutical manufacturing is hindered by their black-box nature. In this work, ANNs were developed and interpreted to demonstrate their applicability to increase process understanding by retrospective analysis of developmental or manufacturing data. The in vitro dissolution and hardness of extended-release, directly compressed tablets were predicted from manufacturing and spectroscopic data of pilot-scale development. The ANNs using material attributes and operational parameters provided better results than using NIR or Raman spectra as predictors. ANNs were interpreted by sensitivity analysis, helping to identify the root cause of the batch-to-batch variability, e.g., the variability in particle size, grade, or substitution of the hydroxypropyl methylcellulose excipient. An ANN-based control strategy was also successfully utilized to mitigate the batch-to-batch variability by flexibly operating the tableting process. The presented methodology can be adapted to arbitrary data-rich manufacturing steps from active substance synthesis to formulation to predict the quality from manufacturing or development data and gain process understanding and consistent product quality.

A design of experiments screen reveals that Clostridium novyi-NT spore germinant sensing is stereoflexible for valine and its analogs

Although Clostridium novyi-NT is an anti-cancer bacterial therapeutic which germinates within hypoxic tumors to kill cancer cells, the actual germination triggers for C. novyi-NT are still unknown. In this study, we screen candidate germinants using combinatorial experimental designs and discover by serendipity that D-valine is a potent germinant, inducing 50% spore germination at 4.2 mM concentration. Further investigation revealed that five D-valine analogs are also germinants and four of these analogs are enantiomeric pairs. This stereoflexible effect of L- and D-amino acids shows that spore germination is a complex process where enantiomeric interactions can be confounders. This study also identifies L-cysteine as a germinant, and hypoxanthine and inosine as co-germinants. Several other amino acids promote (L-valine, L-histidine, L-threonine and L-alanine) or inhibit (L-arginine, L-glycine, L-lysine, L-tryptophan) germination in an interaction-dependent manner. D-alanine inhibits all germination, even in complex growth media. This work lays the foundation for improving the germination efficacy of C. novyi-NT spores in tumors.

A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0

With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems in industrial chemistry and chemical engineering. Therefore, this review provides an overview of the application of AI techniques, in particular machine learning, in chemical design, synthesis, and process optimization over the past years. In this review, the focus is on the application of AI for structure-function relationship analysis, synthetic route planning, and automated synthesis. Finally, we discuss the challenges and future of AI in making chemical products.

Modelling and prediction of the effect of operational parameters on the fate of contaminants of emerging concern in WWTPs

Wastewater treatment plants (WWTPs) provide a barrier against the discharge of contaminants of emerging concern (CECs) into the environment. The removal of CECs is highly WWTP-specific and the underlying mechanisms are still poorly understood, hampering the optimization of biological treatment steps for their removal. To fill this knowledge gap, we assessed the influence of four operational parameters of activated sludge biological treatment, namely total suspended solids, temperature, pH and redox conditions, on the sorption and biodegradation of four CECs under controlled laboratory conditions. Design of Experiments was used to better address the factors influencing CECs removal and interactions among operational parameters. The derived statistical models showed results in concordance with previous studies and indicated how sorption and biodegradation of the investigated CECs depend on most tested parameters and few of their interactions. The predictions of the developed models have been compared with literature values, indicating how the tested parameters are responsible for most of the variability of sorption, while they could not reliably generalize biodegradation rates. The developed models were also implemented as an extension of a mechanistic biological treatment model, successfully describing the dynamic behaviour of a large-scale WWTP, which was observed during a three-day continuous monitoring campaign. Compared to a traditional modelling approach, the one including the developed models showed on average almost a three-fold uncertainty reduction, favouring its use to aid WWTP managers and regulators for improved assessment of CEC fate and removal. Finally, the models highlighted that, while higher temperatures and solids concentrations generically favoured CECs removal, removal efficiency vary significantly due to operational parameters and no globally optimum conditions for CECs removal exist. The use of these models opens the door to the combined dynamic management of both traditional contaminants and CECs in WWTPs.

NMR chiral recognition of lipoic acid by cinchonidine CSA: A stereocenter beyond the organic function

Alpha-lipoic acid is a natural product that possesses distinct pharmacological properties. Lipoic acid is a short-chain fatty acid containing an asymmetric carbon at five bonds of distance to the organic function. Herein, we developed a nuclear magnetic resonance protocol to access the chiral recognition of lipoic acid in a simple and rapid procedure employing cinchonidine as a cheap chiral solvation agent in deuterated chloroform. To optimize this method, a statistical design of the experimental model was performed to produce a clear understanding of the optimal concentration, temperature, and molar ratio parameters. Based on the obtained spectra, the cinchonidine H₈ -H₉ scalar coupling indicated a conformational preference in the chiral discrimination procedure. Density functional theory calculations established a proximity between the asymmetric center of lipoic acid and the aromatic moiety of cinchonidine, clarifying possible conformations in this ion-pair interaction. The described protocol demonstrates how far is far enough to chiral discrimination using a chiral solvation agent, expanding the method to compounds that contain a remote stereocenter.

Rapid and Efficient Optimization of Poly(1,2-Ethanediol Citrate) Synthesis Based on Magic Squares' Various Methods

New biomaterials among aliphatic polyesters are in demand due to their potential applications in tissue engineering. There is a challenge not only to design scaffolds to regenerate defects in load-bearing tissues but also to ensure a proper blood supply to the reconstructed tissues. Poly-(1,2-ethanediol citrate) is one of the novel citrate-based polymers that could have the desired properties for cell scaffold fabrication and for enhancing cell adhesion. Both citric acid and 1,2-ethanediol are used in medicine and are fully resorbable by cells. This work aimed to synthesize poly(1,2-ethanediol citrate) in a catalyzed reaction with water removed by the Dean-Stark apparatus. The polyester structure was characterized by FTIR and NMR spectroscopy, and the HMBC experiment was performed to support the theory of successful polymer synthesis. The molecular weight was determined for the products obtained at 140 °C. The process was described via non-linear mathematical models. The influence of temperature and catalyst content on the degree of esterification and the conversion of acid groups in citric acid is described. The optimal process parameters are determined at 140 °C and 3.6% of p-toluenesulfonic acid content. The presented results are the starting point for scaffold design and scaling-up the process.

The design of experiments (DoE) in optimization of an aerobic flow Pd-catalyzed oxidation of alcohol towards an important aldehyde precursor in the synthesis of phosphatidylinositide 3-kinase inhibitor (CPL302415)

Herein, we describe the development of a green, scalable flow Pd-catalyzed aerobic oxidation for the key step in the synthesis of CPL302415, which is a new PI3Kδ inhibitor. Applying this environmental-friendly, sustainable catalytic oxidation we significantly increased product yield (up to 84%) and by eliminating of workup step, we improved the waste index and E factor (up to 0.13) in comparison with the stoichiometric synthesis. The process was optimized by using the DoE approach.