Accelerating the Discovery of Efficient High-Entropy Alloy Electrocatalysts: High-Throughput Experimentation and Data-Driven Strategies.

High-entropy alloys (HEAs) present both significant potential and challenges for developing efficient electrocatalysts due to their diverse combinations and compositions. Here, we propose a procedural approach that combines high-throughput experimentation with data-driven strategies to accelerate the discovery of efficient HEA electrocatalysts for the hydrogen evolution reaction (HER). This enables the rapid preparation of HEA arrays with various element combinations and composition ratios within a model system. The intrinsic activity of the HEA arrays is swiftly screened using scanning electrochemical cell microscopy (SECCM), providing precise composition-activity data sets for the HEA system. An ensemble machine learning (EML) model is then used to predict the activity database for the composition subspace of the system. Based on these database results, two groups of promising catalysts are recommended and validated through actual electrocatalytic evaluations. This procedural approach, which combines high-throughput experimentation with data-driven strategies, provides a new pathway to accelerate the discovery of efficient HEA electrocatalysts.

Evolution of Ferroelectric Properties in SmxBi1-xFeO3 via Automated Piezoresponse Force Microscopy across combinatorial spread libraries.

Combinatorial spread libraries offer an approach to explore the evolution of material properties over broad concentration, temperature, and growth parameter spaces. However, the traditional limitation of this approach is the requirement for the read-out of functional properties across the library. Here we develop automated piezoresponse force microscopy (PFM) for the exploration of combinatorial spread libraries and demonstrate its application in the SmxBi1-xFeO3 system with the ferroelectric-antiferroelectric morphotropic phase boundary. This approach relies on the synergy of the quantitative nature of PFM and the implementation of automated experiments that allow PFM-based sampling of macroscopic samples. The concentration dependence of pertinent ferroelectric parameters was determined and used to develop the mathematical framework based on the Ginzburg-Landau theory describing the evolution of these properties across the concentration space. We pose that a combination of automated scanning probe microscope and combinatorial spread library approach will emerge as an efficient research paradigm to close the characterization gap in high-throughput materials discovery. We make the data sets open to the community, and we hope that this will stimulate other efforts to interpret and understand the physics of these systems.

Radical Chlorination of Non-Resonant Heterobenzylic C-H Bonds and High-Throughput Diversification of Heterocycles.

Site-selective functionalization of the heterobenzylic C(sp3)-H bonds of pyridines and related heteroaromatic compounds presents challenges associated with the basic nitrogen atom and the variable reactivity among different positions on the heteroaromatic ring. Methods for functionalization of 2- and 4-alkylpyridines are increasingly available through polar pathways that leverage resonance stabilization of charge build-up at these positions. In contrast, functionalization of 3-alkylpyridines is largely inaccessible. Here, we report a photochemically promoted method for chlorination of non-resonant heterobenzylic C(sp3)-H sites in 3-alkylpyridines and related alkylheteroaromatics. Density functional theory calculations show that the optimal reactivity reflects a balance between the energetics of the two radical-chain propagation steps, with the preferred reagent consisting of an N-chlorosulfonamide. The operationally simple chlorination protocol enables access to heterobenzylic chlorides which serve as versatile intermediates in C-H cross-coupling reactions between heteroaromatic building blocks and diverse oxidatively sensitive nucleophiles using high-throughput experimentation.

Mapping Biomaterial Complexity by Machine Learning.

Biomaterials often have subtle properties that ultimately drive their bespoke performance. Given this nuanced structure-function behavior, the standard scientific approach of one experiment at a time or design of experiment methods is largely inefficient for the discovery of complex biomaterials. More recently, high-throughput experimentation coupled with machine learning methods has matured beyond expert users allowing scientists and engineers from diverse backgrounds to access these powerful data science tools. As a result, we now have the opportunity to strategically utilize all available data from high-throughput experiments to train efficacious models and map the structure-function behavior of biomaterials for their discovery. Herein, we discuss this necessary shift to data-driven determination of structure-function properties of biomaterials as we highlight how machine learning is leveraged in identifying physicochemical cues for biomaterials in tissue engineering, gene delivery, drug delivery, protein stabilization, and antifouling materials. We also discuss data-mining approaches that are coupled with machine learning to map biomaterial functions that reduce the load on experimental approaches for faster biomaterial discovery. Ultimately, harnessing the prowess of machine learning will lead to accelerated discovery and development of optimal biomaterial designs.

High-throughput in silico workflow for optimization and characterization of multimodal chromatographic processes.

While high-throughput (HT) experimentation and mechanistic modeling have long been employed in chromatographic process development, it remains unclear how these techniques should be used in concert within development workflows. In this work, a process development workflow based on HT experiments and mechanistic modeling was constructed. The integration of HT and modeling approaches offers improved workflow efficiency and speed. This high-throughput in silico (HT-IS) workflow was employed to develop a Capto MMC polishing step for mAb aggregate removal. High-throughput batch isotherm data was first generated over a range of mobile phase conditions and a suite of analytics were employed. Parameters for the extended steric mass action (SMA) isotherm were regressed for the multicomponent system. Model validation was performed using the extended SMA isotherm in concert with the general rate model of chromatography using the CADET modeling software. Here, step elution profiles were predicted for eight RoboColumn runs across a range of ionic strength, pH, and load density. Optimized processes were generated through minimization of a complex objective function based on key process metrics. Processes were evaluated at lab-scale using two feedstocks, differing in composition. The results confirmed that both processes obtained high monomer yield (>85%) and removed ∼ 50 % $$ \sim 50\% $$ of aggregate species. Column simulations were then carried out to determine sensitivity to a wide range of process inputs. Elution buffer pH was found to be the most critical process parameter, followed by resin ionic capacity. Overall, this study demonstrated the utility of the HT-IS workflow for rapid process development and characterization.

Accelerating the Discovery of Oxygen Reduction Electrocatalysts: High-Throughput Screening of Element Combinations in Pt-Based High-Entropy Alloys.

The vast number of element combinations and the explosive growth of composition space pose significant challenges to the development of high-entropy alloys (HEAs). Here, we propose a procedural research method aimed at accelerating the discovery of efficient electrocatalysts for oxygen reduction reaction (ORR) based on Pt-based quinary HEAs. The method begins with an element library provided by a large language model (LLM), combined with microscale precursor printing and pulse high-temperature synthesis techniques to prepare multi-element combination HEA array in one step. Through high-throughput measurement using scanning electrochemical cell microscopy (SECCM), precise identification of highly active HEA element combinations and exploration of composition space for a specific combination are achieved. Advantageous element combinations are further validated in practical electrocatalytic evaluations. The contributions of individual element sites and the synergistic effects among elements of such HEAs in enhancing reaction activity are elucidated via density functional theory (DFT) calculations. This method integrates high-throughput experiments, practical catalyst validation, and DFT calculations, providing a new pathway for accelerating the discovery of efficient multi-element materials in the field of energy catalysis.

Predicting drug solubility in organic solvents mixtures: A machine-learning approach supported by high-throughput experimentation.

A novel approach based on supervised machine-learning is proposed to predict the solubility of drugs and drug-like molecules in mixtures of organic solvents. Similar to quantitative structure-property relationship (QSPR) models, different solvent types are identified by molecular descriptors, which, in this study, are considered as UNIFAC subgroups. To overcome the potential lack of UNIFAC subgroups for the complex Active Pharmaceutical Ingredients (APIs) currently developed in the pharmaceutical industry, the API molecule is considered as a unique entity in the proposed modelling approach. Therefore, API solubility is predicted as a function of temperature, functional subgroups of the solvents and composition of the solvent mixture; in turn, regressors' correlation is handled through Partial Least-Squares (PLS) regression. The method is developed and tested with experimental data of a real API and 14 organic solvents that are industrially employed for crystallisation. Solubility predictions are accurate and precise for single solvents, binary mixtures and ternary mixtures of organic solvents at different compositions and temperatures, with a determination coefficient R2 ≥ 0.90. To further test the applicability of the model, the proposed approach is applied to 9 literature organic solubility datasets of drugs and drug-like compounds and compared to benchmark solubility models in the literature. Results show that the proposed approach provides satisfactory predictions: the majority of validation and calibration data have R2 = 0.95-0.99; the ratio between RMSE (root mean squared error) of the proposed method and the range of measured solubility values is from 1 to 3 orders of magnitude smaller than the RMSE ratio obtained by the benchmark models.

Solvent Effects on the Chemo- and Site-Selectivity of Transition Metal-Catalyzed Nitrene Transfer Reactions: Alternatives to Chlorinated Solvents.

Transition metal-catalyzed, non-enzymatic nitrene transfer (NT) reactions to selectively transform C-H and C=C bonds to new C-N bonds are a powerful strategy to streamline the preparation of valuable amine building blocks. However, many catalysts for these reactions use environmentally unfriendly solvents that include dichloromethane, chloroform, 1,2-dichloroethane and benzene. We developed a high-throughput experimentation (HTE) protocol for heterogeneous NT reaction mixtures to enable rapid screening of a broad range of solvents for this chemistry. Coupled with the American Chemical Society Pharmaceutical Roundtable (ACSPR) solvent tool, we identified several attractive replacements for chlorinated solvents. Selected catalysts for NT were compared and contrasted using our HTE protocol, including silver supported by N-dentate ligands, dinuclear Rh complexes and Fe/Mn phthalocyanine catalysts.

Phenoxythiazoline (FTz)-Cobalt(II) Precatalysts Enable C(sp2 )-C(sp3 ) Bond-Formation for Key Intermediates in the Synthesis of Toll-like Receptor 7/8 Antagonists.

Evaluation of the relative rates of the cobalt-catalyzed C(sp2 )-C(sp3 ) Suzuki-Miyaura cross-coupling between the neopentylglycol ester of 4-fluorophenylboronic acid and N-Boc-4-bromopiperidine established that smaller N-alkyl substituents on the phenoxyimine (FI) supporting ligand accelerated the overall rate of the reaction. This trend inspired the design of optimal cobalt catalysts with phenoxyoxazoline (FOx) and phenoxythiazoline (FTz) ligands. An air-stable cobalt(II) precatalyst, (FTz)CoBr(py)3 was synthesized and applied to the cross-coupling of an indole-5-boronic ester nucleophile with a piperidine-4-bromide electrophile that is relevant to the synthesis of reported toll-like receptor (TLR) 7/8 antagonist molecules including afimetoran. Addition of excess KOMe⋅B(Oi Pr)3 improved catalyst lifetime due to attenuation of alkoxide basicity that otherwise resulted in demetallation of the FI chelate. A first-order dependence on the cobalt precatalyst and a saturation regime in nucleophile were observed, supporting turnover-limiting transmetalation and the origin of the observed trends in N-imine substitution.

High-Throughput Experimentation and Machine Learning-Assisted Optimization of Iridium-Catalyzed Cross-Dimerization of Sulfoxonium Ylides.

A novel and convenient approach that combines high-throughput experimentation (HTE) with machine learning (ML) technologies to achieve the first selective cross-dimerization of sulfoxonium ylides via iridium catalysis is presented. A variety of valuable amide-, ketone-, ester-, and N-heterocycle-substituted unsymmetrical E-alkenes are synthesized in good yields with high stereoselectivities. This mild method avoids the use of diazo compounds and is characterized by simple operation, high step-economy, and excellent chemoselectivity and functional group compatibility. The combined experimental and computational studies identify an amide-sulfoxonium ylide as a carbene precursor. Furthermore, a comprehensive exploration of the reaction space is also performed (600 reactions) and a machine learning model for reaction yield prediction has been constructed.

Ruthenium-Catalyzed Aminocarbonylation with Isocyanates Through Weak Coordinating Groups.

Introducing amide functional groups under mild conditions has growing importance owing to the prevalence of such moiety in biologically active molecules. Herein, we disclose a mild protocol for the directed ruthenium-catalyzed C-H aminocarbonylation with isocyanates as the amidating agents developed through high-throughput experimentation (HTE). The redox-neutral and base-free reaction is guided by weakly Lewis basic functional groups, including anilides, lactams and carbamates to access anthranilamide derivatives. The synthetic utility of this transformation is reflected by large-scale synthesis and late-stage functionalization.

High-Throughput Screening of Streptavidin-Binding Proteins in Self-Assembled Solid Films for Directed Evolution of Materials.

Evolution has shaped the development of proteins with an incredible diversity of properties. Incorporating proteins into materials is desirable for applications including biosensing; however, high-throughput selection techniques for screening protein libraries in materials contexts is lacking. In this work, a high-throughput platform to assess the binding affinity for ordered sensing proteins was established. A library of fusion proteins, consisting of an elastin-like polypeptide block, one of 22 variants of rcSso7d, and a coiled-coil order-directing sequence, was generated. All selected variants had high binding in films, likely due to the similarity of the assay to magnetic bead sorting used for initial selection, while solution binding was more variable. From these results, both the assembly of the fusion proteins in their operating state and the functionality of the binding protein are key factors in the biosensing performance. Thus, the integration of directed evolution with assembled systems is necessary to the design of better materials.

Exploring the multifunctional roles of quantum dots for unlocking the future of biology and medicine.

With recent advancements in nanomedicines and their associated research with biological fields, their translation into clinically-applicable products is still below promises. Quantum dots (QDs) have received immense research attention and investment in the four decades since their discovery. We explored the extensive biomedical applications of QDs, viz. Bio-imaging, drug research, drug delivery, immune assays, biosensors, gene therapy, diagnostics, their toxic effects, and bio-compatibility. We unravelled the possibility of using emerging data-driven methodologies (bigdata, artificial intelligence, machine learning, high-throughput experimentation, computational automation) as excellent sources for time, space, and complexity optimization. We also discussed ongoing clinical trials, related challenges, and the technical aspects that should be considered to improve the clinical fate of QDs and promising future research directions.

Self-driving laboratories: A paradigm shift in nanomedicine development.

Nanomedicines have transformed promising therapeutic agents into clinically approved medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA vaccines against COVID-19, which were made possible by lipid nanoparticle technology. Despite the success of nanomedicines to date, their design remains far from trivial, in part due to the complexity associated with their preclinical development. Herein, we propose a nanomedicine materials acceleration platform (NanoMAP) to streamline the preclinical development of these formulations. NanoMAP combines high-throughput experimentation with state-of-the-art advances in artificial intelligence (including active learning and few-shot learning) as well as a web-based application for data sharing. The deployment of NanoMAP requires interdisciplinary collaboration between leading figures in drug delivery and artificial intelligence to enable this data-driven design approach. The proposed approach will not only expedite the development of next-generation nanomedicines but also encourage participation of the pharmaceutical science community in a large data curation initiative.

High-Throughput Optimization of Photochemical Reactions using Segmented-Flow Nanoelectrospray-Ionization Mass Spectrometry.

Within the realm of drug discovery, high-throughput experimentation techniques enable the rapid optimization of reactions and expedited generation of drug compound libraries for biological and pharmacokinetic evaluation. Herein we report the development of a segmented flow mass spectrometry-based platform to enable the rapid exploration of photoredox reactions for early-stage drug discovery. Specifically, microwell plate-based photochemical reaction screens were reformatted to segmented flow format to enable delivery to nanoelectrospray ionization-mass spectrometry analysis. This approach was demonstrated for the late-stage modification of complex drug scaffolds, as well as the subsequent structure-activity relationship evaluation of synthesized analogs. This technology is anticipated to expand the robust capabilities of photoredox catalysis in drug discovery by enabling high-throughput library diversification.

Isotherm model discrimination for multimodal chromatography using mechanistic models derived from high-throughput batch isotherm data.

In this work, we have examined an array of isotherm formalisms and characterized them based on their relative complexities and predictive abilities with multimodal chromatography. The set of isotherm models studied were all based on the stoichiometric displacement framework, with considerations for electrostatic interactions, hydrophobic interactions, and thermodynamic activities. Isotherm parameters for each model were first determined through twenty repeated fits to a set of mAb - Capto MMC batch isotherm data spanning a range of loading, ionic strength, and pH as well as a set of mAb - Capto Adhere batch data at constant pH. The batch isotherm data were used in two ways-spanning the full range of loading or consisting of only the high concentration data points. Predictive ability was defined through the model's capacity to capture prominent changes in salt gradient elution behavior with respect to pH for Capto MMC or unique elution patterns and yield losses with respect to gradient slope for Capto Adhere. In both cases, model performance was quantified using a scoring metric based on agreement in peak characteristics for column predictions and accuracy of fit for the batch data. These scores were evaluated for all twenty isotherm fits and their corresponding column predictions, thereby producing a statistical distribution of model performances. Model complexity (number of isotherm parameters) was then considered through use of the Akaike information criterion (AIC) calculated from the score distributions. While model performance for Capto MMC benefitted substantially from removal of low protein concentration data, this was not the case for Capto Adhere; this difference was likely due to the qualitatively different shapes of the isotherms between the two resins. Surprisingly, the top-performing (high accuracy with minimal number of parameters) isotherm model was the same for both resins. The extended steric mass action (SMA) isotherm (containing both protein-salt and protein-protein activity terms) accurately captured both the pH-dependent elution behavior for Capto MMC as well as loss in protein recovery with increasing gradient slope for Capto Adhere. In addition, this isotherm model achieved the highest median score in both resin systems, despite it lacking any explicit hydrophobic stoichiometric terms. The more complex isotherm models, which explicitly accounted for both electrostatic and hydrophobic interaction stoichiometries, were ill-suited for Capto MMC and had lower AIC model likelihoods for Capto Adhere due to their increased complexity. Interestingly, the ability of the extended SMA isotherm to predict the Capto Adhere results was largely due to the protein-salt activity coefficient, as determined via isotherm parameter sensitivity analyses. Further, parametric studies on this parameter demonstrated that it had a major impact on both binding affinity and elution behavior, therein fully capturing the impact of hydrophobic interactions. In summary, we were able to determine the isotherm formalisms most capable of consistently predicting a wide range of column behavior for both a multimodal cation-exchange and multimodal anion-exchange resin with high accuracy, while containing a minimized set of model parameters.

Nickel-Catalyzed Electroreductive Coupling of Alkylpyridinium Salts and Aryl Halides.

An electrochemical, nickel-catalyzed reductive coupling of alkylpyridinium salts and aryl halides is reported. High-throughput experimentation (HTE) was employed for rapid reaction optimization and evaluation of a broad scope of pharmaceutically relevant structurally diverse aryl halides, including complex drug-like substrates. In addition, the transformation is compatible with both primary and secondary alkylpyridinium salts with distinct conditions. Mechanistic insights were critical to enhance the efficiency of coupling using secondary alkylpyridinium salts. Systematic comparisons of the electrochemical and non-electrochemical methods revealed the complementary scope and efficiency of the two approaches.

Overcoming Biopharmaceutical Interferents for Quantitation of Host Cell DNA Using an Automated, High-Throughput Methodology.

The rapid development of biologics and vaccines in response to the current pandemic has highlighted the need for robust platform assays to characterize diverse biopharmaceuticals. A critical aspect of biopharmaceutical development is achieving a highly pure product, especially with respect to residual host cell material. Specifically, two important host cell impurities of focus within biopharmaceuticals are residual DNA and protein. In this work, a novel high-throughput host cell DNA quantitation assay was developed for rapid screening of complex vaccine drug substance samples. The developed assay utilizes the commercially available, fluorescent-sensitive Picogreen dye within a 96-well plate configuration to allow for a cost effective and rapid analysis. The assay was applied to in-process biopharmaceutical samples with known interferences to the dye, including RNA and protein. An enzymatic digestion pre-treatment was found to overcome these interferences and thus allow this method to be applied to wide-ranging, diverse analyses. In addition, the use of deoxycholate in the digestion treatment allowed for disruption of interactions in a given sample matrix in order to more accurately and selectively quantitate DNA. Critical analytical figures of merit for assay performance, such as precision and spike recovery, were evaluated and successfully demonstrated. This new analytical method can thus be successfully applied to both upstream and downstream process analysis for biologics and vaccines using an innovative and automated high-throughput approach.

Accelerated and Concerted Aza-Michael Addition and SuFEx Reaction in Microdroplets in Unitary and High-Throughput Formats.

The sulfur fluoride exchange (SuFEx) reaction is significant in drug discovery, materials science, and chemical biology. Conventionally, it involves installation of SO2 F followed by fluoride exchange by a catalyst. We report catalyst-free Aza-Michael addition to install SO2 F and then SuFEx reaction with amines, both occurring in concert, in microdroplets under ambient conditions. The microdroplet reaction is accelerated by a factor of ∼104 relative to the corresponding bulk reaction. We suggest that the superacidic microdroplet surface assists SuFEx reaction by protonating fluorine to create a good leaving group. The reaction scope was established by performing individual reactions in microdroplets of 18 amines in four solvents and confirmed using high-throughput desorption electrospray ionization experiments. The study demonstrates the value of microdroplet-assisted accelerated reactions in combination with high-throughput experimentation for characterization of reaction scope.

Highly-automated, high-throughput replication of yeast-based logic circuit design assessments.

We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of Saccharomyces cerevisiae by Gander et al. Our experimental campaign used a novel high-throughput experimentation framework developed under Defense Advanced Research Projects Agency's Synergistic Discovery and Design program: a remote robotic lab at Strateos executed a parameterized experimental protocol. Using this protocol and robotic execution, we generated two orders of magnitude more flow cytometry data than the original experiments. We discuss our results, which largely, but not completely, agree with the original report and make some remarks about lessons learned. Graphical Abstract.