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Item Cost avoidance of pharmacist-led deprescribing using STOPPFrail for older adults in nursing homes(Springer Nature, 2024-07-05) Hurley, Eoin; Byrne, Stephen; Walsh, Elaine; Foley, Tony; Woods, Noel; Dalton, Kieran; Irish Research CouncilBackground: The Screening Tool of Older Persons Prescriptions in Frail adults with limited life expectancy (STOPPFrail) criteria aim to reduce inappropriate/unnecessary medications in frail older adults, which should minimise adverse drug events and additional healthcare expenditure. Little is known about the economic outcomes of applying these criteria as an intervention. Aim: To evaluate cost avoidance of pharmacist-led application of STOPPFrail to frail older nursing home residents with limited life expectancy. Method: Pharmacist-identified STOPPFrail-defined potentially inappropriate medications that were deprescribed by patients’ general practitioners were assigned a rating by a multidisciplinary panel, i.e. the probability of an adverse drug event occurring if the medication was not deprescribed. The intervention’s net cost benefit and cost–benefit ratio were then determined by factoring in adverse drug event cost avoidance (calculated from probability of adverse drug event ratings), direct cost savings (deprescribed medication costs/reimbursement fees), and healthcare professionals’ salaries. Results: Of the 176 potentially inappropriate medications deprescribed across 69 patients, 65 (36.9%) were rated as having a medium or high probability of an adverse drug event occurring if not deprescribed. With €27,162 for direct cost savings, €61,336 for adverse drug event cost avoidance, and €2,589 for healthcare professionals' salary costs, there was a net cost benefit of €85,909 overall. The cost–benefit ratio was 33.2 and remained positive in all scenarios in sensitivity analyses. Conclusion: Pharmacist-led application of STOPPFrail to frail older nursing home residents is associated with significant cost avoidance. Wider implementation of pharmacist interventions in frail older nursing home residents should be considered to reduce potentially inappropriate medications and patient harm, alongside substantial cost savings for healthcare systems.Item Synthesis and reactivity of α-diazo-β-keto sulfonamides(Georg Thieme Verlag KG, 2024-06-19) Maguire, Anita R.; Judge, Evan; O'Shaughnessy, Keith A.; Lawrence, Simon E.; Collins, Stuart G.; Science Foundation Ireland; Irish Research Council; Higher Education Authority; Synthesis and Solid State Pharmaceutical Centre; European Regional Development FundCopper mediated reactions of α-diazo-β-keto sulfonamides 1 leads to a range of products including the alkyne sulfonamides 5, the enamines 6, and the α-halosulfonamides 7 and 11 with no evidence for intramolecular C–H insertion in any of the reactions, in contrast to the reactivity of the comparable α-diazo-β-keto sulfones. Use of copper(II) triflate (5 mol%) led to isolation of a series of alkyne sulfonamides 5 (up to 12%) and enamines 6 (up to 64%). Use of copper(II) chloride (5 mol%) formed, in addition, the α-halosulfonamides 7; use of stoichiometric amounts of copper(II) chloride/bromide enables facile halogenation of the β-keto sulfonamide to form the α-halosulfonamides 7 and 11 (up to 63%).Item Ten years of the manufacturing classification system: a review of literature applications and an extension of the framework to continuous manufacture(Taylor & Francis, 45418) Leane, Michael; Pitt, Kendal; Reynolds, Gavin; Tantuccio, Anthony; Moreton, Chris; Crean, Abina; Kleinebudde, Peter; Carlin, Brian; Gamble, John; Gamlen, Michael; Stone, Elaine; Kuentz, Martin; Gururajan, Bindhu; Khimyak, Yaroslav Z.; Van Snick, Bernd; Andersen, Sune; Misic, Zdravka; Peter, Stefanie; Sheehan, StephenThe MCS initiative was first introduced in 2013. Since then, two MCS papers have been published: the first proposing a structured approach to consider the impact of drug substance physical properties on manufacturability and the second outlining real world examples of MCS principles. By 2023, both publications had been extensively cited by over 240 publications. This article firstly reviews this citing work and considers how the MCS concepts have been received and are being applied. Secondly, we will extend the MCS framework to continuous manufacture. The review structure follows the flow of drug product development focussing first on optimisation of API properties. The exploitation of links between API particle properties and manufacturability using large datasets seems particularly promising. Subsequently, applications of the MCS for formulation design include a detailed look at the impact of percolation threshold, the role of excipients and how other classification systems can be of assistance. The final review section focusses on manufacturing process development, covering the impact of strain rate sensitivity and modelling applications. The second part of the paper focuses on continuous processing proposing a parallel MCS framework alongside the existing batch manufacturing guidance. Specifically, we propose that continuous direct compression can accommodate a wider range of API properties compared to its batch equivalent.Item Comparative analysis of chemical descriptors by machine learning reveals atomistic insights into solute–lipid interactions(ACS American Chemical Society, 2024-05-23) Lange, Justus Johann; Anelli, Andrea; Alsenz, Jochem; Kuentz, Martin; O'Dwyer, Patrick J.; Saal, Wiebke; Wyttenbach, Nicole; Griffin, Brendan T.; H2020 Marie Skłodowska-Curie Actions; Horizon 2020This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.Item Predictive computational models for assessing the impact of co-milling on drug dissolution(Elsevier, 2024-04-30) Pätzmann, Nicolas; O'Dwyer, Patrick J.; Beránek, Josef; Kuentz, Martin; Griffin, Brendan T.; HORIZON EUROPE Marie Sklodowska-Curie Actions; Horizon 2020Co-milling is an effective technique for improving dissolution rate limited absorption characteristics of poorly water-soluble drugs. However, there is a scarcity of models available to forecast the magnitude of dissolution rate improvement caused by co-milling. Therefore, this study endeavoured to quantitatively predict the increase in dissolution by co-milling based on drug properties. Using a biorelevant dissolution setup, a series of 29 structurally diverse and crystalline drugs were screened in co-milled and physically blended mixtures with Polyvinylpyrrolidone K25. Co-Milling Dissolution Ratios after 15 min (COMDR15 min) and 60 min (COMDR60 min) drug release were predicted by variable selection in the framework of a partial least squares (PLS) regression. The model forecasts the COMDR15 min (R2 = 0.82 and Q2 = 0.77) and COMDR60 min (R2 = 0.87 and Q2 = 0.84) with small differences in root mean square errors of training and test sets by selecting four drug properties. Based on three of these selected variables, applicable multiple linear regression equations were developed with a high predictive power of R2 = 0.83 (COMDR15 min) and R2 = 0.84 (COMDR60 min). The most influential predictor variable was the median drug particle size before milling, followed by the calculated drug logD6.5 value, the calculated molecular descriptor Kappa 3 and the apparent solubility of drugs after 24 h dissolution. The study demonstrates the feasibility of forecasting the dissolution rate improvements of poorly water-solube drugs through co-milling. These models can be applied as computational tools to guide formulation in early stage development.