First-principles calculation of the intrinsic aqueous solubility of crystalline druglike molecules

We demonstrate that the intrinsic aqueous solubility of crystalline druglike molecules can be estimated with reasonable accuracy from sublimation free energies calculated using crystal lattice simulations and hydration free energies calculated using the 3D Reference Interaction Site Model (3D-RISM) of the Integral Equation Theory of Molecular Liquids (IET). The solubilities of 25 crystalline druglike molecules taken from different chemical classes are predicted by the model with a correlation coefficient of R = 0.85 and a root mean square error (RMSE) equal to 1.45 log(10) S units, which is significantly more accurate than results obtained using implicit continuum solvent models. The method is not directly parametrized against experimental solubility data, and it offers a full computational characterization of the thermodynamics of transfer of the drug molecule from crystal phase to gas phase to dilute aqueous solution.PostprintPeer reviewe

Anesthesia and Sedation Practices Among Neurointerventionalists during Acute Ischemic Stroke Endovascular Therapy

Background and Purpose: Intra-arterial reperfusion therapies are expanding frontiers in acute ischemic stroke (AIS) management but there is considerable variability in clinical practice. The use of general anesthesia (GA) is one example. We aimed to better understand sedation practices in AIS. Methods: An online survey was distributed to the 68 active members of the Society of Vascular and Interventional Neurology (SVIN). Survey development was based on discussions at the SVIN Endovascular Stroke Round Table Meeting (Chicago, IL, 2008). The final survey contained 12 questions. Questions were developed as single and multiple-item responses; with an option for a free-text response. Results: There was a 72% survey response rate (N = 49/68). Respondents were interventional neurologists in practice 1–5 years (71.4%, N = 35). The mean (±SD) AIS interventions performed per year at the respondents’ institutions was 42.5 ± 25, median 35.0 (IQR 20, 60). The most frequent anesthesia type used was GA (anesthesia team), then conscious sedation (nurse administered), monitored anesthesia care (anesthesia team), and finally local analgesia alone. There was a preference for GA because of eliminating movement (65.3% of respondents; N = 32/49), perceived procedural safety (59.2%, N = 29/49), and improved procedural efficacy (42.9%, N = 21/49). However, cited limitations to GA included risk of time delay (69.4%, N = 34), of propagating cerebral ischemia due to hypoperfusion or other complications (28.6%, N = 14), and lack of adequate anesthesia workforce (20.4%, N = 7). Conclusions: The most frequent type of anesthesia used by Neurointerventionalists for AIS interventions is GA. Prior to making GA standard of care during AIS intervention, more data are needed about effects on clinical outcomes

The Role of Pharmacists in Caring for Young People with Chronic Illness

PurposeTo explore the perceived and potential roles of pharmacists in the care of young people aged 10-24 years with chronic illness, through the exemplar of juvenile arthritis, from the perspectives of UK community and hospital pharmacists, health service commissioners, rheumatology health professionals and lay advocates.MethodsA sequential mixed methods study design comprising: focus groups with community and hospital pharmacists; telephone interviews with pharmacy and rheumatology stakeholders and commissioners, and multidisciplinary group discussions to prioritize roles generated by the first two qualitative phases.ResultsThe high priority roles for pharmacists, identified by pharmacists and rheumatology staff, were: developing generic healthcare skills among young people; transferring information effectively across care interfaces; building trusting relationships with young people; helping young people to find credible online health information, and the need to develop specialist expertise. Participants identified associated challenges for pharmacists in supporting young people with chronic illness. These challenges included parents collecting prescription refills alone, thus reducing opportunities to engage, and pharmacist isolation from the wider healthcare team.ConclusionsThis study has led to the identification of specific enhancements to pharmacy services for young people which have received the endorsement of a wide range of stakeholders. These suggestions could inform the next steps in developing the contribution of community and hospital pharmacy to support young people with chronic illness in the optimal use of their medication

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge"in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms and were trained on a relatively small data set of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility data sets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge data sets, with the best model, a graph convolutional neural network, resulting in an RMSE of 0.86 log units. Critical analysis of the models reveals systematic differences between the performance of models using certain feature sets and training data sets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modeling complex chemical spaces from sparse training data sets

Editorial Message

We compare a range of computational methods for the prediction of sublimation thermodynamics (enthalpy, entropy and free energy of sublimation). These include a model from theoretical chemistry that utilizes crystal lattice energy minimization (with the DMACRYS program) and QSPR models generated by both machine learning (Random Forest and Support Vector Machines) and regression (Partial Least Squares) methods. Using these methods we investigate the predictability of the enthalpy, entropy and free energy of sublimation, with consideration of whether such a method may be able to improve solubility prediction schemes. Previous work has suggested that the major source of error in solubility prediction schemes involving a thermodynamic cycle via the solid state is in the modeling of the free energy change away from the solid state. Yet contrary to this conclusion other work has found that the inclusion of terms such as the enthalpy of sublimation in QSPR methods does not improve the predictions of solubility. We suggest the use of theoretical chemistry terms, detailed explicitly in the methods section, as descriptors for the prediction of the enthalpy and free energy of sublimation. A dataset of 158 molecules with experimental sublimation thermodynamics values and some CSD refcodes has been collected from the literature and is provided with their original source references

Blinded predictions and post-hoc analysis of the second solubility challenge data : exploring training data and feature set selection for machine and deep learning models

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state-of-the-art, the American Chemical Society organised a “Second Solubility Challenge” in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019, but which have not previously been reported. These models were based on computationally inexpensive molecular descriptors and traditional machine learning algorithms, and were trained on a relatively small dataset of 300 molecules. In the second part of the article, to test the hypothesis that predictions would improve with more advanced algorithms and higher volumes of training data, we compare these original predictions with those made after the deadline using deep learning models trained on larger solubility datasets consisting of 2999 and 5697 molecules. The results show that there are several algorithms that are able to obtain near state-of-the-art performance on the solubility challenge datasets, with the best model, a graph convolutional neural network, resulting in a RMSE of 0.86 log units. Critical analysis of the models reveal systematic di↵erences between the performance of models using certain feature sets and training datasets. The results suggest that careful selection of high quality training data from relevant regions of chemical space is critical for prediction accuracy, but that other methodological issues remain problematic for machine learning solubility models, such as the difficulty in modelling complex chemical spaces from sparse training datasets

Circulation-driven variability of Atlantic anthropogenic carbon transports and uptake

The ocean absorbs approximately a quarter of the carbon dioxide currently released to the atmosphere by human activities (Canth). A disproportionately large fraction accumulates in the North Atlantic due to the combined effects of transport by the Atlantic Meridional Overturning Circulation (AMOC) and air–sea exchange. However, discrepancies exist between modelled and observed estimates of the air–sea exchange due to unresolved ocean transport variability. Here we quantify the strength and variability of Canth transports across 26.5° N in the North Atlantic between 2004 and 2012 using circulation measurements from the RAPID mooring array and hydrographic observations. Over this period, decreasing circulation strength tended to decrease northward Canth transport, while increasing Canth concentrations (preferentially in the upper limb of the overturning circulation) tended to increase northward Canth transport. These two processes compensated each other over the 8.5-year period. While ocean transport and air–sea Canth fluxes are approximately equal in magnitude, the increasing accumulation rate of Canth in the North Atlantic combined with a stable ocean transport supply means we infer a growing contribution from air–sea Canth fluxes over the period. North Atlantic Canth accumulation is thus sensitive to AMOC strength, but growing atmospheric Canth uptake continues to significantly impact Canth transports