The effect of food and acid-reducing agents on the pharmacokinetic profile of capivasertib: Results from a randomized, crossover study.

AIMS: This two-part, adaptive study assessed the effect of food and an acid-reducing agent (rabeprazole) on the pharmacokinetics (PK) and safety of capivasertib, a potent AKT inhibitor, in clinical development for cancer treatment. METHODS: In Part 1, healthy participants (n = 24) were randomized to receive single-dose capivasertib after overnight fasting, a high-fat, high-calorie meal and with rabeprazole postovernight fasting in one of six treatment sequences. Based on Part 1 results, a new group of participants (n = 24) were randomized (Part 2) to receive capivasertib after overnight fasting, a low-fat, low-calorie meal and modified fasting (food restricted from 2 h before dosing to 1 h postdose) in one of six treatment sequences. Blood samples were collected for PK analyses. RESULTS: Following a high-fat, high-calorie meal, capivasertib exposure increased versus overnight fasting (geometric mean ratio [GMR] [90% confidence interval (CI)]: area under the concentration-time curve [AUCinf ] 1.32 [1.22, 1.43], maximum concentration [Cmax ] 1.23 [1.08, 1.41]), but was comparable to that postmodified fasting (GMR: AUCinf 1.13 [0.99, 1.29], Cmax 0.85 [0.70, 1.04]). AUCinf was similar and Cmax was lower with/without rabeprazole (GMR: AUCinf 0.94 [0.87, 1.02]), Cmax 0.73 [0.64, 0.84]). Capivasertib exposure was similar after a low-fat, low-calorie meal versus overnight fasting (GMR: AUCinf 1.14 [1.05, 1.25], Cmax 1.21 [0.99, 1.48]) or modified fasting (GMR: AUCinf 0.96 [0.88, 1.05], Cmax 0.86 [0.70, 1.06]). Safety was consistent with that in larger trials. CONCLUSIONS: This study demonstrates that administering capivasertib with food or acid-reducing agents does not lead to clinically relevant PK or safety profile changes.

Immediate Breast Cancer Reconstruction with or without Dermal Matrix or Synthetic Mesh Support: A Review and Network Meta-Analysis.

BACKGROUND: The use of acellular dermal matrices (ADMs) and synthetic mesh as part of implant-based breast reconstruction (IBBR) has been widely adopted. The authors investigated the clinical efficacy and safety of human ADM (HADM), xenograft ADM (XADM), and synthetic mesh as part of IBBR in postmastectomy patients as compared with previous standard implant reconstruction techniques using only a submuscular pocket for coverage. METHODS: A systematic search for randomized controlled trials and observational studies was performed. A frequentist network meta-analysis was conducted using the R packages netmeta and Shiny. RESULTS: Thirty-one of 2375 studies identified met the predefined inclusion criteria. Traditional submuscular placement (no ADM or mesh) had fewer overall complications compared with HADM [OR, 0.51; credible interval (CrI), 0.34 to 0.74], but there was no significant difference between no ADM or mesh and XADM (OR, 0.63; CrI, 0.29 to 1.32) or synthetic mesh (OR, 0.77; CrI, 0.44 to 1.30). No one treatment was superior with regards to implant loss. No ADM or mesh was associated with fewer infectious complications than HADM (OR, 0.6; CrI, 0.39 to 0.89). Both no ADM or mesh (OR, 0.45; CrI, 0.27 to 0.75) and XADM (OR, 0.46; CrI, 0.23 to 0.88) had reduced seroma compared with HADM. CONCLUSIONS: Selecting the appropriate IBBR should evaluate effectiveness, adverse events, and cost. Although it is difficult to select a universal ideal IBBR, evaluation using this network analysis may help guide both physicians and patients in their choice of procedure, especially in the case of HADM, which in this study was shown to be significantly predisposed to complications of infection and seroma. Randomized data are required comparing XADM versus synthetic meshes, given the similar risk profiles but significant cost discrepancy between the techniques.

COVID-19 and cosmetic tourism: A Google trends analysis of public interests and the experience from a tertiary plastic surgery centre.

AIMS: The primary aim of this study was to highlight the burden of cosmetic tourism on the Irish healthcare system during the COVID-19 pandemic. Our secondary aim was to examine the popularity of cosmetic tourism currently through Google Trends data analysis. METHODS: Data on all patients presenting to our unit with complications of cosmetic tourism were retrospectively reviewed to determine the level of care and resources used for each case. The 'Google Trends' data analysis tool was accessed and the popularity of common plastic surgery topics was analysed over the last two years. RESULTS: Eight patients were referred for treatment after experiencing complications following elective cosmetic surgery overseas. Complications included seroma1, infected collection3, wound dehiscence5 and implant extrusion.2 Length of stay ranged from 1 to 18 days with 63% of patients requiring invasive intervention. Google trends revealed that popularity for plastic surgery topics initially decreased at each lockdown date but increased to pre-pandemic interests after a short period. Related search topics for procedures including the terms, "Turkey" and "Lithuania" increasing in popularity by more than 5000%. CONCLUSIONS: This case series shows that despite the current pandemic cosmetic tourism is imposing a significant cost on healthcare resources. Analysis of Google Trends has shown that interest in these procedures is increasing despite travel restrictions and risks associated with the pandemic. The public need to be informed of the potentially negative consequences of cosmetic tourism and be discouraged from undertaking surgery overseas during the current crisis.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with improved data and modelling strategies.

Oral drug absorption is a complex process depending on many factors, including the physicochemical properties of the drug, formulation characteristics and their interplay with gastrointestinal physiology and biology. Physiological-based pharmacokinetic (PBPK) models integrate all available information on gastro-intestinal system with drug and formulation data to predict oral drug absorption. The latter together with in vitro-in vivo extrapolation and other preclinical data on drug disposition can be used to predict plasma concentration-time profiles in silico. Despite recent successes of PBPK in many areas of drug development, an improvement in their utility for evaluating oral absorption is much needed. Current status of predictive performance, within the confinement of commonly available in vitro data on drugs and formulations alongside systems information, were tested using 3 PBPK software packages (GI-Sim (ver.4.1), Simcyp® Simulator (ver.15.0.86.0), and GastroPlus™ (ver.9.0.00xx)). This was part of the Innovative Medicines Initiative (IMI) Oral Biopharmaceutics Tools (OrBiTo) project. Fifty eight active pharmaceutical ingredients (APIs) were qualified from the OrBiTo database to be part of the investigation based on a priori set criteria on availability of minimum necessary information to allow modelling exercise. The set entailed over 200 human clinical studies with over 700 study arms. These were simulated using input parameters which had been harmonised by a panel of experts across different software packages prior to conduct of any simulation. Overall prediction performance and software packages comparison were evaluated based on performance indicators (Fold error (FE), Average fold error (AFE) and absolute average fold error (AAFE)) of pharmacokinetic (PK) parameters. On average, PK parameters (Area Under the Concentration-time curve (AUC0-tlast), Maximal concentration (Cmax), half-life (t1/2)) were predicted with AFE values between 1.11 and 1.97. Variability in FEs of these PK parameters was relatively high with AAFE values ranging from 2.08 to 2.74. Around half of the simulations were within the 2-fold error for AUC0-tlast and around 90% of the simulations were within 10-fold error for AUC0-tlast. Oral bioavailability (Foral) predictions, which were limited to 19 APIs having intravenous (i.v.) human data, showed AFE and AAFE of values 1.37 and 1.75 respectively. Across different APIs, AFE of AUC0-tlast predictions were between 0.22 and 22.76 with 70% of the APIs showing an AFE > 1. When compared across different formulations and routes of administration, AUC0-tlast for oral controlled release and i.v. administration were better predicted than that for oral immediate release formulations. Average predictive performance did not clearly differ between software packages but some APIs showed a high level of variability in predictive performance across different software packages. This variability could be related to several factors such as compound specific properties, the quality and availability of information, and errors in scaling from in vitro and preclinical in vivo data to human in vivo behaviour which will be explored further. Results were compared with previous similar exercise when the input data selection was carried by the modeller rather than a panel of experts on each in vitro test. Overall, average predictive performance was increased as reflected in smaller AAFE value of 2.8 as compared to AAFE value of 3.8 in case of previous exercise.

Persistent critical illness: baseline characteristics, intensive care course, and cause of death.

OBJECTIVES: Persistent critical illness (PerCI) is associated with high mortality and discharge to institutional care. Little is known about factors involved in its progression, complications and cause of death. We aimed to identify such factors and the time when the original illness was no longer the reason for intensive care unit (ICU) stay. DESIGN: Retrospective matched case-control study using an accepted PerCI definition (> 10 days in ICU). SETTING: Single-centre tertiary metropolitan ICU. PARTICIPANTS: All adult patients admitted during a 2-year period were eligible, matched on diagnostic code, gender, age and risk of death. MAIN RESULTS: Seventy-two patients staying > 10 days (PerCI cases) were matched to 72 control patients. The original illness was no longer a cause for continued ICU stay after a median of 10 days (interquartile range [IQR], 7-16) versus 2 days (IQR, 0-3); P < 0.001. Patients with PerCI were more likely to develop new sepsis (52.8% v 23.6%; P < 0.001), delirium (37.5% v 9.7%; P < 0.001), ICU-acquired weakness (15.3% v 0%, P = 0.001), and to be discharged to chronic care or rehabilitation (37.5% v 16.7%; P < 0.005). Death resulting from sepsis with multi-organ failure occurred in 16.7% v 8.3% of control patients (P = 0.13), and one-third of patients with PerCI were not mechanically ventilated on Day 10. CONCLUSION: PerCI likely results from complications acquired after ICU admission and mostly unrelated to the original illness; by Day 10, the original illness does not appear to be its cause, and new sepsis appears an important association.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 3: Identifying gaps in system parameters by analysing In Silico performance across different compound classes.

Three Physiologically Based Pharmacokinetic software packages (GI-Sim, Simcyp® Simulator, and GastroPlus™) were evaluated as part of the Innovative Medicine Initiative Oral Biopharmaceutics Tools project (OrBiTo) during a blinded "bottom-up" anticipation of human pharmacokinetics. After data analysis of the predicted vs. measured pharmacokinetics parameters, it was found that oral bioavailability (Foral) was underpredicted for compounds with low permeability, suggesting improper estimates of intestinal surface area, colonic absorption and/or lack of intestinal transporter information. Foral was also underpredicted for acidic compounds, suggesting overestimation of impact of ionisation on permeation, lack of information on intestinal transporters, or underestimation of solubilisation of weak acids due to less than optimal intestinal model pH settings or underestimation of bile micelle contribution. Foral was overpredicted for weak bases, suggesting inadequate models for precipitation or lack of in vitro precipitation information to build informed models. Relative bioavailability was underpredicted for both high logP compounds as well as poorly water-soluble compounds, suggesting inadequate models for solubility/dissolution, underperforming bile enhancement models and/or lack of biorelevant solubility measurements. These results indicate areas for improvement in model software, modelling approaches, and generation of applicable input data. However, caution is required when interpreting the impact of drug-specific properties in this exercise, as the availability of input parameters was heterogeneous and highly variable, and the modellers generally used the data "as is" in this blinded bottom-up prediction approach.

IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 2: An introduction to the simulation exercise and overview of results.

Orally administered drugs are subject to a number of barriers impacting bioavailability (Foral), causing challenges during drug and formulation development. Physiologically-based pharmacokinetic (PBPK) modelling can help during drug and formulation development by providing quantitative predictions through a systems approach. The performance of three available PBPK software packages (GI-Sim, Simcyp®, and GastroPlus™) were evaluated by comparing simulated and observed pharmacokinetic (PK) parameters. Since the availability of input parameters was heterogeneous and highly variable, caution is required when interpreting the results of this exercise. Additionally, this prospective simulation exercise may not be representative of prospective modelling in industry, as API information was limited to sparse details. 43 active pharmaceutical ingredients (APIs) from the OrBiTo database were selected for the exercise. Over 4000 simulation output files were generated, representing over 2550 study arm-institution-software combinations and approximately 600 human clinical study arms simulated with overlap. 84% of the simulated study arms represented administration of immediate release formulations, 11% prolonged or delayed release, and 5% intravenous (i.v.). Higher percentages of i.v. predicted area under the curve (AUC) were within two-fold of observed (52.9%) compared to per oral (p.o.) (37.2%), however, Foral and relative AUC (Frel) between p.o. formulations and solutions were generally well predicted (64.7% and 75.0%). Predictive performance declined progressing from i.v. to solution and immediate release tablet, indicating the compounding error with each layer of complexity. Overall performance was comparable to previous large-scale evaluations. A general overprediction of AUC was observed with average fold error (AFE) of 1.56 over all simulations. AFE ranged from 0.0361 to 64.0 across the 43 APIs, with 25 showing overpredictions. Discrepancies between software packages were observed for a few APIs, the largest being 606, 171, and 81.7-fold differences in AFE between SimCYP and GI-Sim, however average performance was relatively consistent across the three software platforms.

IMI - oral biopharmaceutics tools project - evaluation of bottom-up PBPK prediction success part 1: Characterisation of the OrBiTo database of compounds.

Predicting oral bioavailability (Foral) is of importance for estimating systemic exposure of orally administered drugs. Physiologically-based pharmacokinetic (PBPK) modelling and simulation have been applied extensively in biopharmaceutics recently. The Oral Biopharmaceutical Tools (OrBiTo) project (Innovative Medicines Initiative) aims to develop and improve upon biopharmaceutical tools, including PBPK absorption models. A large-scale evaluation of PBPK models may be considered the first step. Here we characterise the OrBiTo active pharmaceutical ingredient (API) database for use in a large-scale simulation study. The OrBiTo database comprised 83 APIs and 1475 study arms. The database displayed a median logP of 3.60 (2.40-4.58), human blood-to-plasma ratio of 0.62 (0.57-0.71), and fraction unbound in plasma of 0.05 (0.01-0.17). The database mainly consisted of basic compounds (48.19%) and Biopharmaceutics Classification System class II compounds (55.81%). Median human intravenous clearance was 16.9L/h (interquartile range: 11.6-43.6L/h; n=23), volume of distribution was 80.8L (54.5-239L; n=23). The majority of oral formulations were immediate release (IR: 87.6%). Human Foral displayed a median of 0.415 (0.203-0.724; n=22) for IR formulations. The OrBiTo database was found to be largely representative of previously published datasets. 43 of the APIs were found to satisfy the minimum inclusion criteria for the simulation exercise, and many of these have significant gaps of other key parameters, which could potentially impact the interpretability of the simulation outcome. However, the OrBiTo simulation exercise represents a unique opportunity to perform a large-scale evaluation of the PBPK approach to predicting oral biopharmaceutics.