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Effective transfer of information is vital for rational drug therapy. This is particularly important for older patients, who have a high prevalence of polypharmacy and are managed by multidisciplinary teams. We aimed to assess medicine information exchange (MIE) networks in geriatric medicine wards and whether they are associated with prescribing patterns.We conducted network analysis in acute geriatric medicine wards from four hospitals to characterize MIE networks among multidisciplinary team members. Corresponding patient data were collected to analyze high-risk prescribing in conjunction with network characteristics.We found that junior doctors, senior nurses and pharmacists were central to MIE across all four hospitals. Doctors were more likely than other professions to receive medicines information in three hospitals. Reciprocity and the tendency to communicate within one's own profession also influenced network formation. No difference was observed in prescribing practice between hospitals.Understanding MIE networks can identify gaps in multidisciplinary communication that can be addressed. Networks may identify targets for dissemination of interventions to improve prescribing.

Frailty, a syndrome of decreased physiological reserve that is prevalent in old age, impacts on clinical pharmacology. The aims of the study were to (1) determine whether frailty affects the pharmacokinetics of gentamicin and (2) assess the accuracy of different estimates of body size and renal clearance as estimates of gentamicin pharmacokinetics in older inpatients.This was an observational study of gentamicin pharmacokinetics in a cohort of Australian hospital inpatients aged ≥65 years, who were administered prophylactic intravenous gentamicin.Of the 31 participants, 14 were frail and 17 non frail on the Reported Edmonton Frail Scale. The mean volume of distribution of gentamicin was 14.8 ± 1.4 l in frail participants and 15.3 ± 2.2 l in non frail (NS). Volume of distribution correlated best with lean bodyweight. Gentamicin clearance was significantly lower in frail participants (46.6 ± 10.7 ml min(-1)) than in non frail (58.2 ± 12.4 ml min(-1), P=0.01). The Cockcroft Gault estimate of creatinine clearance calculated using ideal bodyweight gave the best estimate of gentamicin clearance (mean error -0.15 ml min(-1), 95% CI -2.67, 2.39). The Cockcroft Gault creatinine clearance calculated using actual bodyweight and the estimated glomerular filtration rate from the modified diet in renal disease equation overestimated gentamicin clearance, with mean errors of -10.15 ml min(-1) (95%CI -13.60, -6.71) and -18.86 ml min(-1) (95% CI -22.45, -15.27), respectively. The Cockcroft Gault creatinine clearance calculated using lean bodyweight underestimated gentamicin clearance (mean error 6.54 ml min(-1), 95% CI 4.18, 8.90).Frail older people have significantly lower gentamicin clearance than non frail. The best estimate of gentamicin clearance is obtained from the Cockcroft Gault creatinine clearance calculated using ideal bodyweight.

The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the problem of increasing resistance to our frontline treatments. The Open Source Malaria (OSM) consortium has been developing compounds ("Series 4") that have potent activity against Plasmodium falciparum in vitro and in vivo and that have been suggested to act through the inhibition of PfATP4, an essential membrane ion pump that regulates the parasite’s intracellular Na+ concentration. The structure of PfATP4 is yet to be determined. In the absence of structural information about this target, a public competition was created to develop a model that would allow the prediction of anti-PfATP4 activity among Series 4 compounds, thereby reducing project costs associated with the unnecessary synthesis of inactive compounds.In the first round, in 2016, six participants used the open data collated by OSM to develop moderately predictive models using diverse methods. Notably, all submitted models were available to all other participants in real time. Since then further bioactivity data have been acquired and machine learning methods have rapidly developed, so a second round of the competition was undertaken, in 2019, again with freely-donated models that other participants could see. The best-performing models from this second round were used to predict novel inhibitory molecules, of which several were synthesised and evaluated against the parasite. One such compound, containing a motif that the human chemists familiar with this series would have dismissed as ill-advised, was active. The project demonstrated the abilities of new machine learning methods in the prediction of active compounds where there is no biological target structure, frequently the central problem in phenotypic drug discovery. Since all data and participant interactions remain in the public domain, this research project “lives” and may be improved by others.

We have previously shown that the consumption of GABA-enriched oolong tea is effective in reducing stress in a student cohort. However, key constituent content has not been previously investigated, especially as applied to a standard cup of tea. Further, it has not been substantiated whether it is the suggested GABA content or other constituents that lead to these observed changes in stress behaviour. Using reverse-phase HPLC, we determined the actual content of four chemicals known to influence stress in 200 mL cups of regular or GABA-enriched oolong tea brewed to manufacturer’s instructions. We found eight times as much γ-aminobutyric acid (GABA) and 1.5 times as much caffeine in GABA-enriched oolong tea as in regular oolong tea. In contrast, there was 10 times less epigallocatechin gallate (EGCG), and half as much theanine in the GABA-enriched tea. Thus, there are changes in multiple constituents in GABA-enriched oolong tea that may contribute to the biological effects we observed in students consuming these teas.