Automated High Throughput pKa and Distribution Coefficient Measurements of Pharmaceutical Compounds for the SAMPL8 Blind Prediction Challenge

The goal of the SAMPL (Statistical Assessment of the Modeling of Proteins and Ligands) challenge is to improve the accuracy of current computational models to estimate free energy of binding, deprotonation, distribution and other associated physical properties that are useful for the design of new pharmaceutical products. New experimental datasets of physicochemical properties provide opportunities for prospective evaluation of computational prediction methods. Here, aqueous pKa and a range of bi-phasic logD values for a variety of pharmaceutical compounds were determined through a streamlined automated process to be utilized in the SAMPL8 physical property challenge. The goal of this paper is to provide an in-depth review of the experimental methods utilized to create a comprehensive data set for the blind prediction challenge. The significance of this work involves the use of high throughput experimentation equipment and instrumentation to produce acid dissociation constants for twenty-three drug molecules, as well as distribution coefficients for eleven of those molecules.

Fan Stability With Leading Edge Damage: Blind Prediction and Validation

This paper considers the impact of a damaged leading edge on the stall margin and stall inception mechanisms of a transonic, low pressure ratio fan. The damage takes the form of a squared-off leading edge over the upper half of the blade. Full-annulus, unsteady CFD simulations are used to explain the stall inception mechanisms for the fan at low- and high-speed operating points. A combination of steady and unsteady simulations show that the fan is predicted to be sensitive to leading edge damage at low speed, but insensitive at high speed. This blind prediction aligns well with rig test data. The difference in response is shown to be caused by the change between subsonic and supersonic flow regimes at the leading edge. Where the inlet relative flow is subsonic, rotating stall is initiated by growth and propagation of a subsonic leading edge flow separation. This separation is shown to be triggered at higher mass flow rates when the leading edge is damaged, reducing the stable flow range. Where the inlet relative flow is supersonic, the flow undergoes a supersonic expansion around the leading edge, creating a supersonic flow patch terminated by a shock on the suction surface. Rotating stall is triggered by growth of this separation, which is insensitive to leading edge shape. This creates a marked difference in sensitivity to damage at low- and high-speed operating points.

Quantum–mechanical property prediction of solvated drug molecules: what have we learned from a decade of SAMPL blind prediction challenges?

Joint academic–industrial projects supporting drug discovery are frequently pursued to deploy and benchmark cutting-edge methodical developments from academia in a real-world industrial environment at different scales. The dimensionality of tasks ranges from small molecule physicochemical property assessment over protein–ligand interaction up to statistical analyses of biological data. This way, method development and usability both benefit from insights gained at both ends, when predictiveness and readiness of novel approaches are confirmed, but the pharmaceutical drug makers get early access to novel tools for the quality of drug products and benefit of patients. Quantum–mechanical and simulation methods particularly fall into this group of methods, as they require skills and expense in their development but also significant resources in their application, thus are comparatively slowly dripping into the realm of industrial use. Nevertheless, these physics-based methods are becoming more and more useful. Starting with a general overview of these and in particular quantum–mechanical methods for drug discovery we review a decade-long and ongoing collaboration between Sanofi and the Kast group focused on the application of the embedded cluster reference interaction site model (EC-RISM), a solvation model for quantum chemistry, to study small molecule chemistry in the context of joint participation in several SAMPL (Statistical Assessment of Modeling of Proteins and Ligands) blind prediction challenges. Starting with early application to tautomer equilibria in water (SAMPL2) the methodology was further developed to allow for challenge contributions related to predictions of distribution coefficients (SAMPL5) and acidity constants (SAMPL6) over the years. Particular emphasis is put on a frequently overlooked aspect of measuring the quality of models, namely the retrospective analysis of earlier datasets and predictions in light of more recent and advanced developments. We therefore demonstrate the performance of the current methodical state of the art as developed and optimized for the SAMPL6 pKa and octanol–water log P challenges when re-applied to the earlier SAMPL5 cyclohexane-water log D and SAMPL2 tautomer equilibria datasets. Systematic improvement is not consistently found throughout despite the similarity of the problem class, i.e. protonation reactions and phase distribution. Hence, it is possible to learn about hidden bias in model assessment, as results derived from more elaborate methods do not necessarily improve quantitative agreement. This indicates the role of chance or coincidence for model development on the one hand which allows for the identification of systematic error and opportunities toward improvement and reveals possible sources of experimental uncertainty on the other. These insights are particularly useful for further academia–industry collaborations, as both partners are then enabled to optimize both the computational and experimental settings for data generation.