Predicting Solvent-Dependent Nucleophilicity Parameter with a Causal Structure Property Relationship.

Solvent-dependent reactivity is a key aspect of synthetic science, which controls reaction selectivity. The contemporary focus on new, sustainable solvents highlights a need for reactivity predictions in different solvents. Herein, we report the excellent machine learning prediction of the nucleophilicity parameter N in the four most-common solvents for nucleophiles in the Mayr's reactivity parameter database (R2 = 0.93 and 81.6% of predictions within ±2.0 of the experimental values with Extra Trees algorithm). A Causal Structure Property Relationship (CSPR) approach was utilized, with focus on the physicochemical relationships between the descriptors and the predicted parameters, and on rational improvements of the prediction models. The nucleophiles were represented with a series of electronic and steric descriptors and the solvents were represented with principal component analysis (PCA) descriptors based on the ACS Solvent Tool. The models indicated that steric factors do not contribute significantly, because of bias in the experimental database. The most important descriptors are solvent-dependent HOMO energy and Hirshfeld charge of the nucleophilic atom. Replacing DFT descriptors with Parameterization Method 6 (PM6) descriptors for the nucleophiles led to an 8.7-fold decrease in computational time, and an ∼10% decrease in the percentage of predictions within ±2.0 and ±1.0 of the experimental values.

A Coalescing Filter for Liquid-Liquid Separation and Multistage Extraction in Continuous-Flow Chemistry.

Presented here is the design and performance of a coalescing liquid-liquid filter, based on low-cost and readily available meltblown nonwoven substrates for separation of immiscible phases. The performance of the coalescer was determined across three broad classes of fluid mixtures: (i) immiscible organic/aqueous systems, (ii) a surfactant laden organic/aqueous system with modification of the type of emulsion and interfacial surface tension through the addition of sodium chloride, and (iii) a water-acetone/toluene system. The first two classes demonstrated good performance of the equipment in effecting separation, including the separation of a complex emulsion system for which a membrane separator, operating through transport of a preferentially wetting fluid through the membrane, failed entirely. The third system was used to demonstrate the performance of the separator within a multistage liquid-liquid counterflow extraction system. The performance, robust nature, and scalability of coalescing filters should mean that this approach is routinely considered for liquid-liquid separations and extractions within the fine chemical and pharmaceutical industry.

Modelled impact of virtual fractional flow reserve in patients undergoing coronary angiography (VIRTU-4).

BACKGROUND: The practical application of 'virtual' (computed) fractional flow reserve (vFFR) based on invasive coronary angiogram (ICA) images is unknown. The objective of this cohort study was to investigate the potential of vFFR to guide the management of unselected patients undergoing ICA. The hypothesis was that it changes management in >10% of cases. METHODS: vFFR was computed using the Sheffield VIRTUheart system, at five hospitals in the North of England, on 'all-comers' undergoing ICA for non-ST-elevation myocardial infarction acute coronary syndrome (ACS) and chronic coronary syndrome (CCS). The cardiologists' management plan (optimal medical therapy, percutaneous coronary intervention (PCI), coronary artery bypass surgery or 'more information required') and confidence level were recorded after ICA, and again after vFFR disclosure. RESULTS: 517 patients were screened; 320 were recruited: 208 with ACS and 112 with CCS. The median vFFR was 0.82 (0.70-0.91). vFFR disclosure did not change the mean number of significantly stenosed vessels per patient (1.16 (±0.96) visually and 1.18 (±0.92) with vFFR (p=0.79)). A change in intended management following vFFR disclosure occurred in 22% of all patients; in the ACS cohort, there was a 62% increase in the number planned for medical management, and in the CCS cohort, there was a 31% increase in the number planned for PCI. In all patients, vFFR disclosure increased physician confidence from 8 of 10 (7.33-9) to 9 of 10 (8-10) (p<0.001). CONCLUSION: The addition of vFFR to ICA changed intended management strategy in 22% of patients, provided a detailed and specific 'all-in-one' anatomical and physiological assessment of coronary artery disease, and was accompanied by augmentation of the operator's confidence in the treatment strategy.

Determining Phase Separation Dynamics with an Automated Image Processing Algorithm.

The problems of extracting products efficiently from reaction workups are often overlooked. Issues such as emulsions and rag layer formation can cause long separation times and slow production, thus resulting in manufacturing inefficiencies. To better understand science within this area and to support process development, an image processing methodology has been developed that can automatically track the interface between liquid-liquid phases and provide a quantitative measure of the separation rate of two immiscible liquids. The algorithm is automated and has been successfully applied to 29 cases. Its robustness has been demonstrated with a variety of different liquid mixtures that exhibit a wide range of separation behavior-making such an algorithm suited to high-throughput experimentation. The information gathered from applying the algorithm shows how issues resulting from poor separations can be detected early in process development.

Parameter estimation for closed-loop lumped parameter models of the systemic circulation using synthetic data.

Physics-based models can be applied to describe mechanisms in both health and disease, which has the potential to accelerate the development of personalized medicine. The aim of this study was to investigate the feasibility of personalizing a model of systemic hemodynamics by estimating model parameters. We investigated the feasibility of estimating model parameters for a closed-loop lumped parameter model of the left heart and systemic circulation using the step-wise subset reduction method. This proceeded by first investigating the structural identifiability of the model parameters. Secondly, we performed sensitivity analysis to determine which parameters were most influential on the most relevant model outputs. Finally, we constructed a sequence of progressively smaller subsets including parameters based on their ranking by model output influence. The model was then optimized to data for each set of parameters to evaluate how well the parameters could be estimated for each subset. The subsequent results allowed assessment of how different data sets, and noise affected the parameter estimates. In the noiseless case, all parameters could be calibrated to less than 10-3% error using time series data, while errors using clinical index data could reach over 100%. With 5% normally distributed noise the accuracy was limited to be within 10% error for the five most sensitive parameters, while the four least sensitive parameters were unreliably estimated for waveform data. The three least sensitive parameters were particularly challenging to estimate so these should be prioritized for measurement. Cost functions based on time series such as pressure waveforms, were found to give better parameter estimates than cost functions based on standard indices used in clinical assessment of the cardiovascular system, for example stroke volume (SV) and pulse pressure (PP). Averaged parameter estimate errors were reduced by several orders of magnitude by choosing waveforms for noiseless synthetic data. Also when measurement data were noisy, the parameter estimation procedure based on continuous waveforms was more accurate than that based on clinical indices. By application of the stepwise subset reduction method we demonstrated that by the addition of venous pressure to the cost function, or conversely fixing the systemic venous compliance parameter at an accurate value improved all parameter estimates, especially the diastolic filling parameters which have least influence on the aortic pressure.

Machine learning with physicochemical relationships: solubility prediction in organic solvents and water.

Solubility prediction remains a critical challenge in drug development, synthetic route and chemical process design, extraction and crystallisation. Here we report a successful approach to solubility prediction in organic solvents and water using a combination of machine learning (ANN, SVM, RF, ExtraTrees, Bagging and GP) and computational chemistry. Rational interpretation of dissolution process into a numerical problem led to a small set of selected descriptors and subsequent predictions which are independent of the applied machine learning method. These models gave significantly more accurate predictions compared to benchmarked open-access and commercial tools, achieving accuracy close to the expected level of noise in training data (LogS ± 0.7). Finally, they reproduced physicochemical relationship between solubility and molecular properties in different solvents, which led to rational approaches to improve the accuracy of each models.

The role of venous valves in pressure shielding.

BACKGROUND: It is widely accepted that venous valves play an important role in reducing the pressure applied to the veins under dynamic load conditions, such as the act of standing up. This understanding is, however, qualitative and not quantitative. The purpose of this paper is to quantify the pressure shielding effect and its variation with a number of system parameters. METHODS: A one-dimensional mathematical model of a collapsible tube, with the facility to introduce valves at any position, was used. The model has been exercised to compute transient pressure and flow distributions along the vein under the action of an imposed gravity field (standing up). RESULTS: A quantitative evaluation of the effect of a valve, or valves, on the shielding of the vein from peak transient pressure effects was undertaken. The model used reported that a valve decreased the dynamic pressures applied to a vein when gravity is applied by a considerable amount. CONCLUSION: The model has the potential to increase understanding of dynamic physical effects in venous physiology, and ultimately might be used as part of an interventional planning tool.

Closing the loop: modelling of heart failure progression from health to end-stage using a meta-analysis of left ventricular pressure-volume loops.

INTRODUCTION: The American Heart Association (AHA)/American College of Cardiology (ACC) guidelines for the classification of heart failure (HF) are descriptive but lack precise and objective measures which would assist in categorising such patients. Our aim was two fold, firstly to demonstrate quantitatively the progression of HF through each stage using a meta-analysis of existing left ventricular (LV) pressure-volume (PV) loop data and secondly use the LV PV loop data to create stage specific HF models. METHODS AND RESULTS: A literature search yielded 31 papers with PV data, representing over 200 patients in different stages of HF. The raw pressure and volume data were extracted from the papers using a digitising software package and the means were calculated. The data demonstrated that, as HF progressed, stroke volume (SV), ejection fraction (EF%) decreased while LV volumes increased. A 2-element lumped parameter model was employed to model the mean loops and the error was calculated between the loops, demonstrating close fit between the loops. The only parameter that was consistently and statistically different across all the stages was the elastance (Emax). CONCLUSIONS: For the first time, the authors have created a visual and quantitative representation of the AHA/ACC stages of LVSD-HF, from normal to end-stage. The study demonstrates that robust, load-independent and reproducible parameters, such as elastance, can be used to categorise and model HF, complementing the existing classification. The modelled PV loops establish previously unknown physiological parameters for each AHA/ACC stage of LVSD-HF, such as LV elastance and highlight that it this parameter alone, in lumped parameter models, that determines the severity of HF. Such information will enable cardiovascular modellers with an interest in HF, to create more accurate models of the heart as it fails.

Expansion of the Ligand Knowledge Base for Chelating P,P-Donor Ligands (LKB-PP).

We have expanded the ligand knowledge base for bidentate P,P- and P,N-donor ligands (LKB-PP, Organometallics2008, 31, 1372-1383) by 208 ligands and introduced an additional steric descriptor (nHe8). This expanded knowledge base now captures information on 334 bidentate ligands and has been processed with principal component analysis (PCA) of the descriptors to produce a detailed map of bidentate ligand space, which better captures ligand variation and has been used for the analysis of ligand properties.