Impact of type 2 diabetes on hospitalization and mortality in people with malignancy.

AIM: To compare the characteristics of and outcomes for people with malignancies with and without a co-diagnosis of diabetes. METHODS: Emergency department and hospital discharge data from a single centre for the period between 1 January 2015 and 31 December 2017 were used to identify people with a diagnosis of a malignancy and diabetes. Multivariate Cox regression models were used to estimate the effect of diabetes on all-cause mortality. A truncated negative binomial regression model was used to assess the impact of diabetes on length of hospital inpatient stay. Prentice-Williams-Peterson total time models were used to assess the effect of diabetes on number of emergency department re-presentations and inpatient re-admissions. RESULTS: Of 7004 people identified with malignancies, 1195 (17.1%) were also diagnosed with diabetes. A diagnosis of diabetes was associated with a greater number of inpatient re-admissions [adjusted hazard ratio 1.13 (95% CI 1.03, 1.24)], a greater number of emergency department re-presentations [adjusted hazard ratio 1.13 (95% CI 1.05, 1.22)] and longer length of stay [adjusted incidence rate ratio 1.14 (95% CI 1.04, 1.25)]. A co-diagnosis of diabetes was also associated with a 48% increased risk of all-cause mortality [adjusted hazard ratio 1.48 (95% CI 1.22-1.76)]. CONCLUSIONS: People with malignancies and diabetes had significantly more emergency department presentations, more inpatient admissions, longer length of hospital stay and higher rates of all-cause mortality compared to people with a malignancy without diabetes.

Prediction of Phase I single-dose pharmacokinetics using recombinant cytochromes P450 and physiologically based modelling.

1. Ten compounds from the Merck Research Laboratories pipeline were selected to evaluate the utility of using intrinsic clearance derived from recombinantly expressed cytochromes P450 (CYP) and physiologically based pharmacokinetic modelling to predict Phase I pharmacokinetics using simCYP. The compounds selected were anticipated to be eliminated predominantly by P450 metabolism. 2. There was a reasonable agreement between the predicted and actual clinical exposure with 80% of the predicted exposures being within three-fold of the observed values. Furthermore, prediction of C(t) (plasma concentration at a specified time point) and T(max) were acceptable with greater than or equal to 70% of the predicted data being within three-fold of the observed values. However, prediction of C(max) was unreliable and may have been due to error in predicting the time-dependent change in volume of distribution and/or error in estimating absorption rate. 3. Although it is acknowledged that research is needed to improve predictive performance, the data presented are supportive of using recombinant P450 intrinsic clearance and physiologically based pharmacokinetic modelling to predict Phase I pharmacokinetics for compounds eliminated by P450 metabolism.