Understanding the practical limitations of chemical reactions is critically important for efficiently planning the synthesis of compounds in pharmaceutical, agrochemical, and specialty chemical research and development. However, literature reports of the scope of new reactions are often cursory and biased toward successful results, severely limiting the ability to predict reaction outcomes for untested substrates. We herein illustrate strategies for carrying out large-scale surveys of chemical reactivity by using a material-sparing nanomole-scale automated synthesis platform with greatly expanded synthetic scope combined with ultrahigh-throughput matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS).

Determining the net charge and protonation states populated by a small molecule in an environment of interest or the cost of altering those protonation states upon transferto another environment is a prerequisite for predicting its physicochemical and pharmaceutical properties. The environment of interest can be aqueous, an organic solvent, a protein binding site, or a lipid bilayer. Predicting the protonation state of a small molecule is essential to predicting its interactions with biological macromolecules using computational models. Incorrectly modeling the dominant protonation state, shifts in dominant protonation state, or the population of significant mixtures of protonation states can lead to large modeling errors that degrade the accuracy of physical modeling. Low accuracy hinders the use of physical modeling approaches for molecular design. For small molecules, the acid dissociation constant (pKa) is the primary quantity needed to determine the ionic states populated by a molecule in an aqueous solution at a given pH. As a part of SAMPL6 community challenge, we organized a blind pKa prediction component to assess the accuracy with which contemporary pKa prediction methods can predict this quantity, with the ultimate aim of assessing the expected impact on modeling errors this would induce. While a multitude of approaches for predicting pKa values currently exist, predicting the pKas of drug-like molecules can be difficult due to challenging properties such as multiple titratable sites, heterocycles, and tautomerization. For this challenge, we focused on set of 24 small molecules selected to resemble selective kinase inhibitors—an important class of therapeutics replete with titratable moieties. Using a Sirius T3 instrument that performs automated acid- base titrations, we used UV absorbance-based pKa measurements to construct a high-quality experimental reference dataset of macroscopic pKas for the evaluation of computational pKa prediction methodologies that was utilized in the SAMPL6 pKa challenge. For several compounds in which the microscopic protonation states associated with macroscopic pKas were ambiguous, we performed follow-up NMR experiments to disambiguate the microstates involved in the transition. This dataset provides a useful standard benchmark dataset for the evaluation of pKa prediction methodologies on kinase inhibitor-like compounds.

Organic electrosynthesis is a rapidly evolving field, providing powerful methods to assemble targets of interest in organic synthesis. Concerns around the scalability of electrochemical methods remain the biggest reason behind their scarce implementation in manufacturing routes for the pharmaceutical industry. To fill this gap, we report a workflow describing the key reaction parameters toward the successful scale-up of an organic electrosynthetic method from milligram to kilogram scale. The reaction used to demonstrate our workflow and scale-up in a flow setting was the oxidation of a thioether to its corresponding sulfone, a fragment of interest in an active pharmaceutical ingredient under development. The use of online flow nuclear magnetic resonance spectroscopy, offline ion chromatography, cyclic voltammetry, and density functional theory calculations provided insight into the reaction mechanism and side reactions.

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Recent developments in two-dimensional liquid chromatography (2D-LC) now make separation and analysis of very complex mixtures achievable. Despite being such a powerful chromatographic tool, current 2D-LC technology requires a series of arduous method development activities poorly suited for a fast-paced industrial environment. Recent introductions of new technologies including active solvent modulation and a support for multicolumn 2D-LC are helping to overcome this stigma. However, many chromatography practitioners believe that the lack of a systematic way to effectively optimize 2D-LC separations is a missing link in securing the viability of 2D-LC as a mainstay for industrial applications. In this work, a computer-assisted modeling approach that dramatically simplifies both offline and online 2D-LC method developments is introduced. Our methodology is based on mapping the separation landscape of pharmaceutically relevant mixtures across both first (1 D) and second (2 D) dimensions using LC Simulator (ACD/Labs) software. Retention models for 1 D and 2 D conditions were built using a minimal number of multifactorial modeling experiments (2 × 2 or 3 × 3 parameters: gradient slope, column temperature, and different column and mobile phase combinations). The approach was first applied to online 2D-LC analysis involving achiral and chiral separations of complex mixtures of enantiomeric species. In these experiments, the retention models proved to be quite accurate for both the 1 D and 2 D separations, with retention time differences between experiments and simulations of less than 3.5%. This software-based concept was also demonstrated for offline 2D-LC purification of drug substances.

we used UV absorbance-based pK a measurements to construct a high-quality experimental reference dataset 36 of macroscopic pK a s for the evaluation of computational pK a prediction methodologies that was utilized in CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint (which . http://dx.doi.org/10.1101/368787 doi: bioRxiv preprint first posted online Jul. 13, 2018;

Matrix-assisted laser desorption/ionization (MALDI) coupled with a time-of-flight (TOF) mass-spectrometry (MS) detector is acknowledged to be very useful for analysis of biological molecules. At the same time, hydrogen-deuterium exchange (HDX) is a well-known technique for studying protein higher-order structure. However, coupling MALDI with HDX has been challenging because of undesired back-exchange reactions during analysis. In this report, we survey an approach that utilizes MALDI coupled with an automated sample preparation to compare global conformational changes of proteins under different solution conditions using differential HDX. A nonaqueous matrix was proposed for MALDI sample preparation to minimize undesirable back-exchange. An automated experimental setup based on the use of a liquid-handling robot and automated data acquisition allowed for tracking protein conformational changes as a difference in the number of protons exchanged to deuterons at specified solution conditions. Experimental time points to study the deuteration-labeling kinetics were obtained in a fully automated manner. The use of a nonaqueous matrix solution allowed experimental error to be minimized to within 1% RSD. We applied this newly developed MALDI-HDX workflow to study the effect of several common excipients on insulin folding stability. The observed results were corroborated by literature data and were obtained in a high-throughput and automated manner. The proposed MALDI-HDX approach can also be applied in a high-throughput manner for batch-to-batch higher-order structure comparison, as well as for the optimization of protein chemical modification reactions.

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.