Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules.

We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean: 50.9), and containing up to 54 (mean: 28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global and local physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and de novo generation of large (solvated) molecules with pharmaceutical and biological relevance.

Current State of Open Source Force Fields in Protein-Ligand Binding Affinity Predictions.

In drug discovery, the in silico prediction of binding affinity is one of the major means to prioritize compounds for synthesis. Alchemical relative binding free energy (RBFE) calculations based on molecular dynamics (MD) simulations are nowadays a popular approach for the accurate affinity ranking of compounds. MD simulations rely on empirical force field parameters, which strongly influence the accuracy of the predicted affinities. Here, we evaluate the ability of six different small-molecule force fields to predict experimental protein-ligand binding affinities in RBFE calculations on a set of 598 ligands and 22 protein targets. The public force fields OpenFF Parsley and Sage, GAFF, and CGenFF show comparable accuracy, while OPLS3e is significantly more accurate. However, a consensus approach using Sage, GAFF, and CGenFF leads to accuracy comparable to OPLS3e. While Parsley and Sage are performing comparably based on aggregated statistics across the whole dataset, there are differences in terms of outliers. Analysis of the force field reveals that improved parameters lead to significant improvement in the accuracy of affinity predictions on subsets of the dataset involving those parameters. Lower accuracy can not only be attributed to the force field parameters but is also dependent on input preparation and sampling convergence of the calculations. Especially large perturbations and nonconverged simulations lead to less accurate predictions. The input structures, Gromacs force field files, as well as the analysis Python notebooks are available on GitHub.

EXPRESS: Landmark Publications in Analytical Atomic Spectrometry: Fundamentals and Instrumentation Development.

The almost-two-centuries history of spectrochemical analysis has generated a body of literature so vast that it has become nearly intractable for experts, much less for those wishing to enter the field. Authoritative, focused reviews help to address this problem but become so granular that the overall directions of the field are lost. This broader perspective can be provided partially by general overviews but then the thinking, experimental details, theoretical underpinnings and instrumental innovations of the original work must be sacrificed. In the present compilation, this dilemma is overcome by assembling the most impactful publications in the area of analytical atomic spectrometry. Each entry was proposed by at least one current expert in the field and supported by a narrative that justifies its inclusion. The entries were then assembled into a coherent sequence and returned to contributors for a round-robin review.

The RIC Recruitment & Retention Materials Toolkit - a resource for developing community-informed study materials.

Clinical trials face many challenges with meeting projected enrollment and retention goals. A study's recruitment materials and messaging convey necessary key information and therefore serve as a critical first impression with potential participants. Yet study teams often lack the resources and skills needed to develop engaging, culturally tailored, and professional-looking recruitment materials. To address this gap, the Recruitment Innovation Center recently developed a Recruitment & Retention Materials Content and Design Toolkit, which offers research teams guidance, actionable tips, resources, and customizable templates for creating trial-specific study materials. This paper seeks to describe the creation and contents of this new toolkit.

A sensitive LC-MS/MS assay to quantitate free payload Aur0101 from ADC PYX-201 in rat and monkey plasma.

Aim: Aur0101 is a cytotoxic and small-molecule microtubule depolymerizing agent, and is the payload conjugated to antibody-drug conjugate PYX-201. Developing and validating a sensitive bioanalytical method to quantitate Aur0101 was novel and crucial in preclinical PYX-201 studies. Materials & methods: Reference standard Aur0101 and its stable isotope labelled internal standard Aur0101-d8 were used in this LC-MS/MS method. Results: This sensitive assay was validated at a lower limit of quantitation of 15 pg/ml and successfully applied to support preclinical rat and monkey toxicology studies. Preclinical plasma toxicokinetic parameters were presented. Conclusion: A sensitive and robust LC-MS/MS assay was validated for Aur0101 in rat and monkey plasma.

Broadening the Scope of Binding Free Energy Calculations Using a Separated Topologies Approach.

Binding free energy calculations predict the potency of compounds to protein binding sites in a physically rigorous manner and see broad application in prioritizing the synthesis of novel drug candidates. Relative binding free energy (RBFE) calculations have emerged as an industry-standard approach to achieve highly accurate rank-order predictions of the potency of related compounds; however, this approach requires that the ligands share a common scaffold and a common binding mode, restricting the methods' domain of applicability. This is a critical limitation since complex modifications to the ligands, especially core hopping, are very common in drug design. Absolute binding free energy (ABFE) calculations are an alternate method that can be used for ligands that are not congeneric. However, ABFE suffers from a known problem of long convergence times due to the need to sample additional degrees of freedom within each system, such as sampling rearrangements necessary to open and close the binding site. Here, we report on an alternative method for RBFE, called Separated Topologies (SepTop), which overcomes the issues in both of the aforementioned methods by enabling large scaffold changes between ligands with a convergence time comparable to traditional RBFE. Instead of only mutating atoms that vary between two ligands, this approach performs two absolute free energy calculations at the same time in opposite directions, one for each ligand. Defining the two ligands independently allows the comparison of the binding of diverse ligands without the artificial constraints of identical poses or a suitable atom-atom mapping. This approach also avoids the need to sample the unbound state of the protein, making it more efficient than absolute binding free energy calculations. Here, we introduce an implementation of SepTop. We developed a general and efficient protocol for running SepTop, and we demonstrated the method on four diverse, pharmaceutically relevant systems. We report the performance of the method, as well as our practical insights into the strengths, weaknesses, and challenges of applying this method in an industrial drug design setting. We find that the accuracy of the approach is sufficiently high to rank order ligands with an accuracy comparable to traditional RBFE calculations while maintaining the additional flexibility of SepTop.

Detection of Off-Gassed Products From Molten Salts Using Laser-Induced Breakdown Spectroscopy.

The detection of off-gassed sodium from molten sodium nitrate (NaNO3) at temperatures between 330 °C and 505 °C and off-gassed calcium from molten lithium chloride-potassium chloride eutectic (LKE) mixtures at 510 °C with laser-induced breakdown spectroscopy (LIBS) was demonstrated. NaNO3 and LKE samples were melted in a custom-built crucible that promoted the generation of off-gassed products from the molten sample. The off-gassed products were analyzed with a LIBS system designed to probe the high-temperature environment. Na D emission lines, Na(I)588.99 nm and Na(I) 589.59 nm, were detected from the NaNO3 samples after reaching a temperature threshold, which indicated the occurrence of phase change. In LKE mixtures, the detection of Ca impurities at a concentration of 78 mg/kg was possible using the emission lines Ca(II) 393.66 nm and Ca(II) 395.85 nm. This work demonstrates the real-time monitoring capabilities of LIBS in high-temperature environments that simulate the conditions of molten salt reactors.

Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field.

We introduce the Open Force Field (OpenFF) 2.0.0 small molecule force field for drug-like molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF force fields are based on direct chemical perception, which generalizes easily to highly diverse sets of chemistries based on substructure queries. Like the previous OpenFF iterations, the Sage generation of OpenFF force fields was validated in protein-ligand simulations to be compatible with AMBER biopolymer force fields. In this work, we detail the methodology used to develop this force field, as well as the innovations and improvements introduced since the release of Parsley 1.0.0. One particularly significant feature of Sage is a set of improved Lennard-Jones (LJ) parameters retrained against condensed phase mixture data, the first refit of LJ parameters in the OpenFF small molecule force field line. Sage also includes valence parameters refit to a larger database of quantum chemical calculations than previous versions, as well as improvements in how this fitting is performed. Force field benchmarks show improvements in general metrics of performance against quantum chemistry reference data such as root-mean-square deviations (RMSD) of optimized conformer geometries, torsion fingerprint deviations (TFD), and improved relative conformer energetics (ΔΔE). We present a variety of benchmarks for these metrics against our previous force fields as well as in some cases other small molecule force fields. Sage also demonstrates improved performance in estimating physical properties, including comparison against experimental data from various thermodynamic databases for small molecule properties such as ΔHmix, ρ(x), ΔGsolv, and ΔGtrans. Additionally, we benchmarked against protein-ligand binding free energies (ΔGbind), where Sage yields results statistically similar to previous force fields. All the data is made publicly available along with complete details on how to reproduce the training results at https://github.com/openforcefield/openff-sage.

Quantitation of total antibody (tAb) from antibody drug conjugate (ADC) PYX-201 in rat and monkey plasma using an enzyme-linked immunosorbent assay (ELISA) and its application in preclinical studies.

PYX-201 is an investigative ADC oncology drug composed of a monoclonal human immunoglobulin G (IgG) antibody targeting the extra domain B splice variant of fibronectin (EDB + FN) conjugated to an auristatin payload through a cleavable linker. Effective measurement of PYX-201 tAb is the key to ADC drug PYX-201 preclinical pharmacokinetics (PK) assessment. PYX-201 monoclonal antibody (mAb) was used as the reference standard, goat anti-human IgG polyclonal antibody (pAb) or rabbit anti-human Kappa light chain mAb was employed as the capture antibody, and mouse mAb or goat pAb anti-human IgG the crystallizable fragment (Fc) (horseradish peroxidase (HRP)) was utilized as the detection antibody in this ELISA. This assay was validated with a dynamic range 250 - 10,000 ng/mL and 250 - 6000 ng/mL in rat and monkey K2EDTA plasma, respectively. PYX-201 tAb bioanalytical ELISA assay was reported for the first time in any biological matrix. This is the first time for a bioanalytical method to be validated for a tAb from an ADC drug targeting EDB + FN in any biological matrix.

Quantification of antibody-drug conjugate PYX-201 in rat and monkey plasma via ELISA and its application in preclinical studies.

Aim: PYX-201 is a novel antibody-drug conjugate targeting the extra domain B splice variant of fibronectin in the tumor microenvironment. Accurate quantification of PYX-201 is critical for PYX-201 pharmacokinetics profiling in preclinical studies. Materials & methods: ELISA was performed using reference standard PYX-201, mouse monoclonal anti-monomethyl auristatin E antibody, mouse IgG1, mouse monoclonal anti-human IgG horseradish peroxidase and donkey anti-human IgG horseradish peroxidase. Results: This assay was validated at 50.0-10,000 ng/ml in rat dipotassium EDTA plasma and 250-10,000 ng/ml in monkey dipotassium EDTA plasma. Conclusion: This is the first time for a PYX-201 bioanalytical assay in any matrix to be reported.

To Design Scalable Free Energy Perturbation Networks, Optimal Is Not Enough.

Drug discovery is accelerated with computational methods such as alchemical simulations to estimate ligand affinities. In particular, relative binding free energy (RBFE) simulations are beneficial for lead optimization. To use RBFE simulations to compare prospective ligands in silico, researchers first plan the simulation experiment, using graphs where nodes represent ligands and graph edges represent alchemical transformations between ligands. Recent work demonstrated that optimizing the statistical architecture of these perturbation graphs improves the accuracy of the predicted changes in the free energy of ligand binding. Therefore, to improve the success rate of computational drug discovery, we present the open-source software package High Information Mapper (HiMap)─a new take on its predecessor, Lead Optimization Mapper (LOMAP). HiMap removes heuristics decisions from design selection and instead finds statistically optimal graphs over ligands clustered with machine learning. Beyond optimal design generation, we present theoretical insights for designing alchemical perturbation maps. Some of these results include that for n number of nodes, the precision of perturbation maps is stable at n·ln(n) edges. This result indicates that even an "optimal" graph can result in unexpectedly high errors if a plan includes too few alchemical transformations for the given number of ligands and edges. And, as a study compares more ligands, the performance of even optimal graphs will deteriorate with linear scaling of the edge count. In this sense, ensuring an A- or D-optimal topology is not enough to produce robust errors. We additionally find that optimal designs will converge more rapidly than radial and LOMAP designs. Moreover, we derive bounds for how clustering reduces cost for designs with a constant expected relative error per cluster, invariant of the size of the design. These results inform how to best design perturbation maps for computational drug discovery and have broader implications for experimental design.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods.

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Does the asthma-chronic obstructive pulmonary disease overlap syndrome (ACOS) exist? A narrative review from epidemiology and practice.

Asthma and chronic obstructive pulmonary disease (COPD) have traditionally been approached as separate entities that must be researched and treated separately. There is growing recognition, however, that a substantial proportion of patients with obstructive lung disease have characteristics of both asthma and COPD (termed the asthma-COPD overlap syndrome (ACOS)). Lung disease experts have difficulty defining ACOS, and many still resist accepting the possibility that asthma and COPD may be linked. It is likely that practicing clinicians may be equally confused about how to identify and treat ACOS. This narrative review aims to clarify concepts of ACOS definition, argues that the best way to understand ACOS is to view the chronic lung disease process longitudinally rather than cross-sectionally, and presents evidence that ACOS can be the end result of the natural history of severe asthma. The review also points out the serious gaps in knowledge regarding therapy for ACOS and presents emerging data supporting the intracellular respiratory pathogen Chlamydia pneumoniae as a possible common etiologic agent in severe asthma and ACOS.

Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks [Article v0.1].

Free energy calculations are rapidly becoming indispensable in structure-enabled drug discovery programs. As new methods, force fields, and implementations are developed, assessing their expected accuracy on real-world systems (benchmarking) becomes critical to provide users with an assessment of the accuracy expected when these methods are applied within their domain of applicability, and developers with a way to assess the expected impact of new methodologies. These assessments require construction of a benchmark-a set of well-prepared, high quality systems with corresponding experimental measurements designed to ensure the resulting calculations provide a realistic assessment of expected performance when these methods are deployed within their domains of applicability. To date, the community has not yet adopted a common standardized benchmark, and existing benchmark reports suffer from a myriad of issues, including poor data quality, limited statistical power, and statistically deficient analyses, all of which can conspire to produce benchmarks that are poorly predictive of real-world performance. Here, we address these issues by presenting guidelines for (1) curating experimental data to develop meaningful benchmark sets, (2) preparing benchmark inputs according to best practices to facilitate widespread adoption, and (3) analysis of the resulting predictions to enable statistically meaningful comparisons among methods and force fields. We highlight challenges and open questions that remain to be solved in these areas, as well as recommendations for the collection of new datasets that might optimally serve to measure progress as methods become systematically more reliable. Finally, we provide a curated, versioned, open, standardized benchmark set adherent to these standards (PLBenchmarks) and an open source toolkit for implementing standardized best practices assessments (arsenic) for the community to use as a standardized assessment tool. While our main focus is free energy methods based on molecular simulations, these guidelines should prove useful for assessment of the rapidly growing field of machine learning methods for affinity prediction as well.

Collaborative Assessment of Molecular Geometries and Energies from the Open Force Field.

Force fields form the basis for classical molecular simulations, and their accuracy is crucial for the quality of, for instance, protein-ligand binding simulations in drug discovery. The huge diversity of small-molecule chemistry makes it a challenge to build and parameterize a suitable force field. The Open Force Field Initiative is a combined industry and academic consortium developing a state-of-the-art small-molecule force field. In this report, industry members of the consortium worked together to objectively evaluate the performance of the force fields (referred to here as OpenFF) produced by the initiative on a combined public and proprietary dataset of 19,653 relevant molecules selected from their internal research and compound collections. This evaluation was important because it was completely blind; at most partners, none of the molecules or data were used in force field development or testing prior to this work. We compare the Open Force Field "Sage" version 2.0.0 and "Parsley" version 1.3.0 with GAFF-2.11-AM1BCC, OPLS4, and SMIRNOFF99Frosst. We analyzed force-field-optimized geometries and conformer energies compared to reference quantum mechanical data. We show that OPLS4 performs best, and the latest Open Force Field release shows a clear improvement compared to its predecessors. The performance of established force fields such as GAFF-2.11 was generally worse. While OpenFF researchers were involved in building the benchmarking infrastructure used in this work, benchmarking was done entirely in-house within industrial organizations and the resulting assessment is reported here. This work assesses the force field performance using separate benchmarking steps, external datasets, and involving external research groups. This effort may also be unique in terms of the number of different industrial partners involved, with 10 different companies participating in the benchmark efforts.

Chemical Space Exploration with Active Learning and Alchemical Free Energies.

Drug discovery can be thought of as a search for a needle in a haystack: searching through a large chemical space for the most active compounds. Computational techniques can narrow the search space for experimental follow up, but even they become unaffordable when evaluating large numbers of molecules. Therefore, machine learning (ML) strategies are being developed as computationally cheaper complementary techniques for navigating and triaging large chemical libraries. Here, we explore how an active learning protocol can be combined with first-principles based alchemical free energy calculations to identify high affinity phosphodiesterase 2 (PDE2) inhibitors. We first calibrate the procedure using a set of experimentally characterized PDE2 binders. The optimized protocol is then used prospectively on a large chemical library to navigate toward potent inhibitors. In the active learning cycle, at every iteration a small fraction of compounds is probed by alchemical calculations and the obtained affinities are used to train ML models. With successive rounds, high affinity binders are identified by explicitly evaluating only a small subset of compounds in a large chemical library, thus providing an efficient protocol that robustly identifies a large fraction of true positives.

Surface Anchoring of the Kingella kingae Galactan Is Dependent on the Lipopolysaccharide O-Antigen.

Kingella kingae is a leading cause of bone and joint infections and other invasive diseases in young children. A key K. kingae virulence determinant is a secreted exopolysaccharide that mediates resistance to serum complement and neutrophils and is required for full pathogenicity. The K. kingae exopolysaccharide is a galactofuranose homopolymer called galactan and is encoded by the pamABC genes in the pamABCDE locus. In this study, we sought to define the mechanism by which galactan is tethered on the bacterial surface, a prerequisite for mediating evasion of host immune mechanisms. We found that the pamD and pamE genes encode glycosyltransferases and are required for synthesis of an atypical lipopolysaccharide (LPS) O-antigen. The LPS O-antigen in turn is required for anchoring of galactan, a novel mechanism for association of an exopolysaccharide with the bacterial surface. IMPORTANCE Kingella kingae is an emerging pediatric pathogen and produces invasive disease by colonizing the oropharynx, invading the bloodstream, and disseminating to distant sites. This organism produces a uniquely multifunctional exopolysaccharide called galactan that is critical for virulence and promotes intravascular survival by mediating resistance to serum and neutrophils. In this study, we established that at least some galactan is anchored to the bacterial surface via a novel structural interaction with an atypical lipopolysaccharide O-antigen. Additionally, we demonstrated that the atypical O-antigen is synthesized by the products of the pamD and pamE genes, located downstream of the gene cluster responsible for galactan biosynthesis. This work addresses how the K. kingae exopolysaccharide can mediate innate immune resistance and advances understanding of bacterial exopolysaccharides and lipopolysaccharides.

Molecular basis for polysaccharide recognition and modulated ATP hydrolysis by the O antigen ABC transporter.

O antigens are ubiquitous protective extensions of lipopolysaccharides in the extracellular leaflet of the Gram-negative outer membrane. Following biosynthesis in the cytosol, the lipid-linked polysaccharide is transported to the periplasm by the WzmWzt ABC transporter. Often, O antigen secretion requires the chemical modification of its elongating terminus, which the transporter recognizes via a carbohydrate-binding domain (CBD). Here, using components from A. aeolicus, we identify the O antigen structure with methylated mannose or rhamnose as its cap. Crystal and cryo electron microscopy structures reveal how WzmWzt recognizes this cap between its carbohydrate and nucleotide-binding domains in a nucleotide-free state. ATP binding induces drastic conformational changes of its CBD, terminating interactions with the O antigen. ATPase assays and site directed mutagenesis reveal reduced hydrolytic activity upon O antigen binding, likely to facilitate polymer loading into the ABC transporter. Our results elucidate critical steps in the recognition and translocation of polysaccharides by ABC transporters.