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- ItemSynthesis and evaluation of aromatic BDSF bioisosteres on biofilm formation and colistin sensitivity in pathogenic bacteria(Elsevier, 2023-09-23) Gómez, Andromeda-Celeste; Horgan, Conor; Yero, Daniel; Bravo, Marc; Daura, Xavier; O'Driscoll, Michelle; Gibert, Isidre; O'Sullivan, Timothy P.; Irish Research Council; Ministerio de Ciencia e Innovación; Agència de Gestió d'Ajuts Universitaris i de RecercaThe diffusible signal factor family (DSF) of molecules play an important role in regulating intercellular communication, or quorum sensing, in several disease-causing bacteria. These messenger molecules, which are comprised of cis-unsaturated fatty acids, are involved in the regulation of biofilm formation, antibiotic tolerance, virulence and the control of bacterial resistance. We have previously demonstrated how olefinic N-acyl sulfonamide bioisosteric analogues of diffusible signal factor can reduce biofilm formation or enhance antibiotic sensitivity in a number of bacterial strains. This work describes the design and synthesis of a second generation of aromatic N-acyl sulfonamide bioisosteres. The impact of these compounds on biofilm production in Acinetobacter baumannii, Escherichia coli, Burkholderia multivorans, Burkholderia cepacia, Burkholderia cenocepacia, Pseudomonas aeruginosa and Stenotrophomonas maltophilia is evaluated, in addition to their effects on antibiotic tolerance. The ability of these molecules to increase survival rates on co-administration with colistin is also investigated using the Galleria infection model.
- ItemOrganocatalytic asymmetric peroxidation of g,d-unsaturated ß-keto esters - A novel route to chiral cycloperoxides(2023-05-24) Hennessy, Mary C.; Hirenkumar, Gandhi; O'Sullivan, Timothy P.; Irish Research Council; Science Foundation IrelandA methodology for the asymmetric peroxidation of g,d-unsaturated ß-keto esters is presented. Using a cinchona-derived organocatalyst, the target d-peroxy-ß-keto esters were obtained in high enantiomeric ratios of up to 95:5. Additionally, these d-peroxy esters can be readily reduced to chiral d-hydroxy-ß-keto esters without impacting the ß-keto ester functionality. Importantly, this chemistry opens up a concise route to chiral 1,2-dioxolanes, a common motif in many bioactive natural products, via a novel P2O5-mediated cyclisation of the corresponding d-peroxy-ß-hydroxy esters.
- ItemLeveraging the use of in vitro and computational methods to support the development of enabling oral drug products: An InPharma commentary(Elsevier B.V., 2023-07-13) Reppas, Christos; Kuentz, Martin; Bauer-Brandl, Annette; Carlert, Sara; Dallmann, André; Dietrich, Shirin; Dressman, Jennifer; Ejskjaer, Lotte; Frechen, Sebastian; Guidetti, Matteo; Holm, René; Holzem, Florentin Lukas; Karlsson, Εva; Kostewicz, Edmund; Panbachi, Shaida; Paulus, Felix; Senniksen, Malte Bøgh; Stillhart, Cordula; Turner, David B.; Vertzoni, Maria; Vrenken, Paul; Zöller, Laurin; Griffin, Brendan T.; O'Dwyer, Patrick J.; Horizon 2020Due to the strong tendency towards poorly soluble drugs in modern development pipelines, enabling drug formulations such as amorphous solid dispersions, cyclodextrins, co-crystals and lipid-based formulations are frequently applied to solubilize or generate supersaturation in gastrointestinal fluids, thus enhancing oral drug absorption. Although many innovative in vitro and in silico tools have been introduced in recent years to aid development of enabling formulations, significant knowledge gaps still exist with respect to how best to implement them. As a result, the development strategy for enabling formulations varies considerably within the industry and many elements of empiricism remain. The InPharma network aims to advance a mechanistic, animal-free approach to the assessment of drug developability. This commentary focuses current status and next steps that will be taken in InPharma to identify and fully utilize 'best practice' in vitro and in silico tools for use in physiologically based biopharmaceutic models.
- ItemAdvancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation(Elsevier B.V., 2023-09-29) Murray, Jack D.; Lange, Justus J.; Bennett-Lenane, Harriet; Holm, René; Kuentz, Martin; O'Dwyer, Patrick J.; Griffin, Brendan T.; Irish Research Council; Horizon 2020Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.
- ItemEnhancing the clinical pharmacy service of a large teaching hospital: Development of a new clinical prioritisation tool(Elsevier, 2023-09-21) Clarke, Rebecca; Colleran, Maeve; Melanophy, Gail; Bermingham, MargaretBackground: The number and complexity of patients being admitted to hospitals is rising and some patients may not receive a full clinical pharmacy review or be reviewed as regularly as needed during their inpatient stay. This is a risk factor for medication errors. Clinical prioritisation identifies patients who are high-risk and most in need of a pharmacist review, targeting finite pharmacy resources to patients who will benefit the most. Objectives: Assess and enhance clinical prioritisation within a hospital pharmacy department. Methods: The study was conducted in a large urban academic teaching hospital. A cross-sectional survey of clinical pharmacists in the hospital was conducted to establish the patient clinical criteria they prioritise in their work. A clinical prioritisation tool was developed based on survey findings and was integrated into an existing electronic pharmacy care interface. A pre- and post-intervention study was conducted, consisting of data collection for five days pre- and five days post-implementation of the tool. Quantitative data were analysed using descriptive and inferential statistics. Qualitative data were analysed by thematic analysis. Results: Of 39 eligible pharmacists, 37 (95%) responded to the survey. The top-rated prioritisation criteria, including medicines reconciliation tasks and high-risk medicines, helped to inform the content of the clinical prioritisation tool. Post-intervention, there were more Level 1 complex patients reviewed by pharmacists and fewer Level 3 stable patients compared to pre-intervention. Tool sensitivity ranged from 51 to 88%, depending on the experience of the pharmacist using the tool. High levels of satisfaction with clinical prioritisation were reported by those using the tool. Conclusion: This newly developed clinical prioritisation tool has the potential to support pharmacists in identifying and reviewing patients in a more targeted manner than practice prior to tool development. Continued development and validation of the tool is essential, with a focus on developing a fully automated tool.