Model-Informed Precision Dosing: Background, Requirements, Validation, Implementation, and Forward Trajectory of Individualizing Drug Therapy.

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.

Using Prior Knowledge on Systems Through PBPK to Gain Further Insight into Routine Clinical Data on Trough Concentrations: The Case of Tacrolimus in Chronic Kidney Disease.

BACKGROUND: Routine therapeutic drug monitoring (TDM) relies heavily on measuring trough drug concentrations. Trough concentrations are affected not only by drug bioavailability and clearance, but also by various patient and disease factors and the volume of distribution. This often makes interpreting differences in drug exposure from trough data challenging. This study aimed to combine the advantages of top-down analysis of therapeutic drug monitoring data with bottom-up physiologically-based pharmacokinetic (PBPK) modeling to investigate the effect of declining renal function in chronic kidney disease (CKD) on the nonrenal intrinsic metabolic clearance ( CLint ) of tacrolimus as a case example. METHODS: Data on biochemistry, demographics, and kidney function, along with 1167 tacrolimus trough concentrations for 40 renal transplant patients, were collected from the Salford Royal Hospital's database. A reduced PBPK model was developed to estimate CLint for each patient. Personalized unbound fractions, blood-to-plasma ratios, and drug affinities for various tissues were used as priors to estimate the apparent volume of distribution. Kidney function based on the estimated glomerular filtration rate ( eGFR ) was assessed as a covariate for CLint using the stochastic approximation of expectation and maximization method. RESULTS: At baseline, the median (interquartile range) eGFR was 45 (34.5-55.5) mL/min/1.73 m 2 . A significant but weak correlation was observed between tacrolimus CLint and eGFR (r = 0.2, P < 0.001). The CLint declined gradually (up to 36%) with CKD progression. Tacrolimus CLint did not differ significantly between stable and failing transplant patients. CONCLUSIONS: Kidney function deterioration in CKD can affect nonrenal CLint for drugs that undergo extensive hepatic metabolism, such as tacrolimus, with critical implications in clinical practice. This study demonstrates the advantages of combining prior system information (via PBPK) to investigate covariate effects in sparse real-world datasets.

Physiologically Based Pharmacokinetic Modeling of Transporter-Mediated Hepatic Disposition of Imaging Biomarker Gadoxetate in Rats.

Physiologically based pharmacokinetic (PBPK) models are increasingly used in drug development to simulate changes in both systemic and tissue exposures that arise as a result of changes in enzyme and/or transporter activity. Verification of these model-based simulations of tissue exposure is challenging in the case of transporter-mediated drug-drug interactions (tDDI), in particular as these may lead to differential effects on substrate exposure in plasma and tissues/organs of interest. Gadoxetate, a promising magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 (OATP1B1) and multidrug resistance-associated protein 2 (MRP2). In this study, we developed a gadoxetate PBPK model and explored the use of liver-imaging data to achieve and refine in vitro-in vivo extrapolation (IVIVE) of gadoxetate hepatic transporter kinetic data. In addition, PBPK modeling was used to investigate gadoxetate hepatic tDDI with rifampicin i.v. 10 mg/kg. In vivo dynamic contrast-enhanced (DCE) MRI data of gadoxetate in rat blood, spleen, and liver were used in this analysis. Gadoxetate in vitro uptake kinetic data were generated in plated rat hepatocytes. Mean (%CV) in vitro hepatocyte uptake unbound Michaelis-Menten constant (Km,u) of gadoxetate was 106 µM (17%) (n = 4 rats), and active saturable uptake accounted for 94% of total uptake into hepatocytes. PBPK-IVIVE of these data (bottom-up approach) captured reasonably systemic exposure, but underestimated the in vivo gadoxetate DCE-MRI profiles and elimination from the liver. Therefore, in vivo rat DCE-MRI liver data were subsequently used to refine gadoxetate transporter kinetic parameters in the PBPK model (top-down approach). Active uptake into the hepatocytes refined by the liver-imaging data was one order of magnitude higher than the one predicted by the IVIVE approach. Finally, the PBPK model was fitted to the gadoxetate DCE-MRI data (blood, spleen, and liver) obtained with and without coadministered rifampicin. Rifampicin was estimated to inhibit active uptake transport of gadoxetate into the liver by 96%. The current analysis highlighted the importance of gadoxetate liver data for PBPK model refinement, which was not feasible when using the blood data alone, as is common in PBPK modeling applications. The results of our study demonstrate the utility of organ-imaging data in evaluating and refining PBPK transporter IVIVE to support the subsequent model use for quantitative evaluation of hepatic tDDI.

A latent variable approach to account for correlated inputs in global sensitivity analysis.

In drug development decision-making is often supported through model-based methods, such as physiologically-based pharmacokinetics (PBPK). Global sensitivity analysis (GSA) is gaining use for quality assessment of model-informed inference. However, the inclusion and interpretation of correlated factors in GSA has proven an issue. Here we developed and evaluated a latent variable approach for dealing with correlated factors in GSA. An approach was developed that describes the correlation between two model inputs through the causal relationship of three independent factors: the latent variable and the unique variances of the two correlated parameters. The latent variable approach was applied to a set of algebraic models and a case from PBPK. Then, this method was compared to Sobol's GSA assuming no correlations, Sobol's GSA with groups and the Kucherenko approach. For the latent variable approach, GSA was performed with Sobol's method. By using the latent variable approach, it is possible to devise a unique and easy interpretation of the sensitivity indices while maintaining the correlation between the factors. Compared methods either consider the parameters independent, group the dependent variables into one unique factor or present difficulties in the interpretation of the sensitivity indices. In situations where GSA is called upon to support model-informed decision-making, the latent variable approach offers a practical method, in terms of ease of implementation and interpretability, for applying GSA to models with correlated inputs that does not violate the independence assumption. Prerequisites and limitations of the approach are discussed.

Towards Further Verification of Physiologically-Based Kidney Models: Predictability of the Effects of Urine-Flow and Urine-pH on Renal Clearance.

In vitro-in vivo extrapolation (IVIVE) of renal excretory clearance (CLR) using the physiologically based kidney models can provide mechanistic insight into the interplay of multiple processes occurring in the renal tubule; however, the ability of these models to capture quantitatively the impact of perturbed conditions (e.g., urine flow, urine pH changes) on CLR has not been fully evaluated. In this work, we aimed to assess the predictability of the effect of urine flow and urine pH on CLR and tubular drug concentrations (selected examples). Passive diffusion clearance across the nephron tubule membrane was scaled from in vitro human epithelial cell line Caco-2 permeability data by nephron tubular surface area to predict the fraction reabsorbed and the CLR of caffeine, chloramphenicol, creatinine, dextroamphetamine, nicotine, sulfamethoxazole, and theophylline. CLR values predicted using mechanistic kidney model at a urinary pH of 6.2 and 7.4 resulted in prediction bias of 2.87- and 3.62-fold, respectively. Model simulations captured urine flow-dependent CLR, albeit with minor underprediction of the observed magnitude of change. The relationship between drug solubility, urine flow, and urine pH, illustrated in simulated intratubular concentrations of acyclovir and sulfamethoxazole, agreed with clinical data on tubular precipitation and crystal-induced acute kidney injury. This study represents the first systematic evaluation of the ability of the mechanistic kidney model to capture the impact of urine flow and urine pH on CLR and drug tubular concentrations with the aim of facilitating refinement of IVIVE-based mechanistic prediction of renal excretion.

A study of the dosage and duration for levobupivacaine infusion by the caudal-epidural route in infants aged 3-6 months.

BACKGROUND: The local anesthetic, levobupivacaine, is the safer enantiomer of racemic bupivacaine. Present protocols for levobupivacaine are based on studies and pharmacokinetic modeling with racemic bupivacaine. AIMS: The aim is to investigate total serum levobupivacaine concentrations after a caudalepidural loading dose followed by a maintenance infusion over 48 hours in infants aged 3-6 months. METHODS: The clinical trial was conducted in eight infants aged 3-6 months, undergoing bladder exstrophy repair. Pharmacokinetic modeling allowed optimization of clinical sampling to measure total levobupivacaine and α1 -acid glycoprotein and prediction of the effect of α1 -acid glycoprotein on levobupivacaine plasma protein binding. RESULTS: The observed median total levobupivacaine serum concentration was 0.30 mg/L (range: 0.20-0.70 mg/L) at 1 hour after the loading dose of 2 mg/kg. The median total levobupivacaine concentration after 47 hours of infusion, at 0.2 mg/kg/h, was 1.21 mg/L (0.07-1.85 mg/L). Concentrations of α1 -acid glycoprotein were found to rise throughout the study period. Pharmacokinetic modeling suggested that unbound levobupivacaine quickly reached steady state at a concentration of approximately 0.03 mg/L. CONCLUSION: The study allows the development of a pharmacokinetic model, combining levobupivacaine and α1 -acid glycoprotein data. Modeling indicates that unbound levobupivacaine quickly reaches steady state once the infusion is started. Simulations suggest that it may be possible to continue the infusion beyond 48 hours.

Accounting for inter-correlation between enzyme abundance: a simulation study to assess implications on global sensitivity analysis within physiologically-based pharmacokinetics.

Physiologically based pharmacokinetic (PBPK) models often include several sets of correlated parameters, such as organ volumes and blood flows. Because of recent advances in proteomics, it has been demonstrated that correlations are also present between abundances of drug-metabolising enzymes in the liver. As the focus of population PBPK has shifted the emphasis from the average individual to theoretically conceivable extremes, reliable estimation of the extreme cases has become paramount. We performed a simulation study to assess the impact of the correlation between the abundances of two enzymes on the pharmacokinetics of drugs that are substrate of both, under assumptions of presence or lack of such correlations. We considered three semi-physiological models representing the cases of: (1) intravenously administered drugs metabolised by two enzymes expressed in the liver; (2) orally administered drugs metabolised by CYP3A4 expressed in the liver and gut wall; (3) intravenously administered drugs that are substrates of CYP3A4 and OATP1B1 in the liver. Finally, the impact of considering or ignoring correlation between enzymatic abundances on global sensitivity analysis (GSA) was investigated using variance based GSA on a reduced PBPK model for repaglinide, substrate of CYP3A4 and CYP2C8. Implementing such correlations can increase the confidence interval for population pharmacokinetic parameters (e.g., AUC, bioavailability) and impact the GSA results. Ignoring these correlations could lead to the generation of implausible parameters combinations and to an incorrect estimation of pharmacokinetic related parameters. Thus, known correlations should always be considered in building population PBPK models.

Variance based global sensitivity analysis of physiologically based pharmacokinetic absorption models for BCS I-IV drugs.

Regulatory agencies have a strong interest in sensitivity analysis for the evaluation of physiologically-based pharmacokinetic (PBPK) models used in pharmaceutical research and drug development and regulatory submissions. One of the applications of PBPK is the prediction of fraction absorbed and bioavailability for drugs following oral administration. In this context, we performed a variance based global sensitivity analysis (GSA) on in-house PBPK models for drug absorption, with the aim of identifying key parameters that influence the predictions of the fraction absorbed and the bioavailability for neutral, acidic and basic compounds. This analysis was done for four different classes of drugs, defined according to the Biopharmaceutics Classification System, differentiating compounds by permeability and solubility. For class I compounds (highly permeable, highly soluble), the parameters that mainly influence the fraction absorbed are related to the formulation properties, for class II compounds (highly permeable, lowly soluble) to the dissolution process, for class III (lowly permeable, highly soluble) to both absorption process and formulation properties and for class IV (lowly permeable, lowly soluble) to both absorption and dissolution processes. Considering the bioavailability, the results are similar to those for the fraction absorbed, with the addition that parameters related to gut wall and liver clearance influence as well the predictions. This work aimed to give a demonstration of the GSA methodology and highlight its importance in improving our understanding of PBPK absorption models and in guiding the choice of parameters that can safely be assumed, estimated or require data generation to allow informed model prediction.

Simultaneous Assessment In Vitro of Transporter and Metabolic Processes in Hepatic Drug Clearance: Use of a Media Loss Approach.

Hepatocyte drug depletion-time assays are well established for determination of metabolic clearance in vitro. The present study focuses on the refinement and evaluation of a "media loss" assay, an adaptation of the conventional depletion assay involving centrifugation of hepatocytes prior to sampling, allowing estimation of uptake in addition to metabolism. Using experimental procedures consistent with a high throughput, a selection of 12 compounds with a range of uptake and metabolism characteristics (atorvastatin, cerivastatin, clarithromycin, erythromycin, indinavir, pitavastatin, repaglinide, rosuvastatin, saquinavir, and valsartan, with two control compounds-midazolam and tolbutamide) were investigated in the presence and absence of the cytochrome P450 inhibitor 1-aminobenzotriazole and organic anion transporter protein inhibitor rifamycin SV in rat hepatocytes. Data were generated simultaneously for a given drug, and provided, through the use of a mechanistic cell model, clearance terms characterizing metabolism, active and passive uptake, together with intracellular binding and partitioning parameters. Results were largely consistent with the particular drug characteristics, with active uptake, passive diffusion, and metabolic clearances ranging between 0.4 and 777, 3 and 383, and 2 and 236 µl/min per milligram protein, respectively. The same experiments provided total and unbound drug cellular partition coefficients ranging between 3.8 and 254 and 2.3 and 8.3, respectively, and intracellular unbound fractions between 0.014 and 0.263. Following in vitro-in vivo extrapolation, the lowest prediction bias was noted using uptake clearance, compared with metabolic clearance or apparent clearance from the media loss assay alone. This approach allows rapid and comprehensive characterization of hepatocyte drug disposition valuable for prediction of hepatic processes in vivo.

Implications of intercorrelation between hepatic CYP3A4-CYP2C8 enzymes for the evaluation of drug-drug interactions: a case study with repaglinide.

AIMS: Statistically significant positive correlations are reported for the abundance of hepatic drug-metabolizing enzymes. We investigate, as an example, the impact of CYP3A4-CYP2C8 intercorrelation on the predicted interindividual variabilities of clearance and drug-drug interactions (DDIs) for repaglinide using physiologically based pharmacokinetic (PBPK) modelling. METHODS: PBPK modelling and simulation were employed using Simcyp Simulator (v15.1). Virtual populations were generated assuming intercorrelations between hepatic CYP3A4-CYP2C8 abundances derived from observed values in 24 human livers. A repaglinide PBPK model was used to predict PK parameters in the presence and absence of gemfibrozil in virtual populations, and the results were compared with a clinical DDI study. RESULTS: Coefficient of variation (CV) of oral clearance was 52.5% in the absence of intercorrelation between CYP3A4-CYP2C8 abundances, which increased to 54.2% when incorporating intercorrelation. In contrast, CV for predicted DDI (as measured by AUC ratio before and after inhibition) was reduced from 46.0% in the absence of intercorrelation between enzymes to 43.8% when incorporating intercorrelation: these CVs were associated with 5th/95th percentiles (2.48-11.29 vs. 2.49-9.69). The range of predicted DDI was larger in the absence of intercorrelation (1.55-77.06) than when incorporating intercorrelation (1.79-25.15), which was closer to clinical observations (2.6-12). CONCLUSIONS: The present study demonstrates via a systematic investigation that population-based PBPK modelling incorporating intercorrelation led to more consistent estimation of extreme values than those observed in interindividual variabilities of clearance and DDI. As the intercorrelations more realistically reflect enzyme abundances, virtual population studies involving PBPK and DDI should avoid using Monte Carlo assignment of enzyme abundance.

Drug disposition and modelling before and after gastric bypass: immediate and controlled-release metoprolol formulations.

AIMS: The aim of the present study was to evaluate the disposition of metoprolol after oral administration of an immediate and controlled-release formulation before and after Roux-en-Y gastric bypass (RYGB) surgery in the same individuals and to validate a physiologically based pharmacokinetic (PBPK) model for predicting oral bioavailability following RYGB. METHODS: A single-dose pharmacokinetic study of metoprolol tartrate 200 mg immediate release and controlled release was performed in 14 volunteers before and 6-8 months after RYGB. The observed data were compared with predicted results from the PBPK modelling and simulation of metoprolol tartrate immediate and controlled-release formulation before and after RYGB. RESULTS: After administration of metoprolol immediate and controlled release, no statistically significant difference in the observed area under the curve (AUC(0-24 h)) was shown, although a tendency towards an increased oral exposure could be observed as the AUC(0-24 h) was 32.4% [95% confidence interval (CI) 1.36, 63.5] and 55.9% (95% CI 5.73, 106) higher following RYGB for the immediate and controlled-release formulation, respectively. This could be explained by surgery-related weight loss and a reduced presystemic biotransformation in the proximal gastrointestinal tract. The PBPK values predicted by modelling and simulation were similar to the observed data, confirming its validity. CONCLUSIONS: The disposition of metoprolol from an immediate-release and a controlled-release formulation was not significantly altered after RYGB; there was a tendency to an increase, which was also predicted by PBPK modelling and simulation.

The Pharmacokinetics of the CYP3A Substrate Midazolam in Morbidly Obese Patients Before and One Year After Bariatric Surgery.

PURPOSE: Bariatric surgery is nowadays commonly applied as treatment for morbid obesity (BMI > 40 kg/m(2)). As information about the effects of this procedure on a drug's pharmacokinetics is limited, we aimed to evaluate the pharmacokinetics of CYP3A probe substrate midazolam after oral and intravenous administration in a cohort of morbidly obese patients that was studied before and 1 year post bariatric surgery. METHODS: Twenty morbidly obese patients (aged 26-58 years) undergoing bariatric surgery participated in the study of which 18 patients returned 1 year after surgery. At both occasions, patients received 7.5 mg oral and 5 mg intravenous midazolam separated by 160 ± 48 min. Per patient and occasion, a mean of 22 blood samples were collected. Midazolam concentrations were analyzed using population pharmacokinetic modeling. RESULTS: One year after bariatric surgery, systemic clearance of midazolam was higher [0.65 (7%) versus 0.39 (11%) L/min, mean ± RSE (P < 0.01), respectively] and mean oral transit time (MTT) was faster [23 (20%) versus 51 (15%) minutes (P < 0.01)], while oral bioavailability was unchanged (0.54 (9%)). Central and peripheral volumes of distribution were overall lower (P < 0.05). CONCLUSIONS: In this cohort study in morbidly obese patients, systemic clearance was 1.7 times higher 1 year after bariatric surgery, which may potentially result from an increase in hepatic CYP3A activity per unit of liver weight. Although MTT was found to be faster, oral bioavailability remained unchanged, which considering the increased systemic clearance implies an increase in the fraction escaping intestinal first pass metabolism.

Meta-analysis of the turnover of intestinal epithelia in preclinical animal species and humans.

Due to the rapid turnover of the small intestinal epithelia, the rate at which enterocyte renewal occurs plays an important role in determining the level of drug-metabolizing enzymes in the gut wall. Current physiologically based pharmacokinetic (PBPK) models consider enzyme and enterocyte recovery as a lumped first-order rate. An assessment of enterocyte turnover would enable enzyme and enterocyte renewal to be modeled more mechanistically. A literature review together with statistical analysis was employed to establish enterocyte turnover in human and preclinical species. A total of 85 studies was identified reporting enterocyte turnover in 1602 subjects in six species. In mice, the geometric weighted combined mean (WX) enterocyte turnover was 2.81 ± 1.14 days (n = 169). In rats, the weighted arithmetic mean enterocyte turnover was determined to be 2.37 days (n = 501). Humans exhibited a geometric WX enterocyte turnover of 3.48 ± 1.55 days for the gastrointestinal epithelia (n = 265), displaying comparable turnover to that of cytochrome P450 enzymes in vitro (0.96-4.33 days). Statistical analysis indicated humans to display longer enterocyte turnover as compared with preclinical species. Extracted data were too sparse to support regional differences in small intestinal enterocyte turnover in humans despite being indicated in mice. The utilization of enterocyte turnover data, together with in vitro enzyme turnover in PBPK modeling, may improve the predictions of metabolic drug-drug interactions dependent on enzyme turnover (e.g., mechanism-based inhibition and enzyme induction) as well as absorption of nanoparticle delivery systems and intestinal metabolism in special populations exhibiting altered enterocyte turnover.

Diagnosing an overcrowded emergency department from its Electronic Health Records.

Emergency department overcrowding is a complex problem that persists globally. Data of visits constitute an opportunity to understand its dynamics. However, the gap between the collected information and the real-life clinical processes, and the lack of a whole-system perspective, still constitute a relevant limitation. An analytical pipeline was developed to analyse one-year of production data following the patients that came from the ED (n = 49,938) at Uppsala University Hospital (Uppsala, Sweden) by involving clinical experts in all the steps of the analysis. The key internal issues to the ED were the high volume of generic or non-specific diagnoses from non-urgent visits, and the delayed decision regarding hospital admission caused by several imaging assessments and lack of hospital beds. Furthermore, the external pressure of high frequent re-visits of geriatric, psychiatric, and patients with unspecified diagnoses dramatically contributed to the overcrowding. Our work demonstrates that through analysis of production data of the ED patient flow and participation of clinical experts in the pipeline, it was possible to identify systemic issues and directions for solutions. A critical factor was to take a whole systems perspective, as it opened the scope to the boundary effects of inflow and outflow in the whole healthcare system.

Exploring Hospital Overcrowding with an Explainable Time-to-Event Machine Learning Approach.

Emergency department (ED) overcrowding is a complex problem that is intricately linked with the operations of other hospital departments. Leveraging ED real-world production data provides a unique opportunity to comprehend this multifaceted problem holistically. This paper introduces a novel approach to analyse healthcare production data, treating the length of stay of patients, and the follow up decision regarding discharge or admission to the hospital as a time-to-event analysis problem. Our methodology employs traditional survival estimators and machine learning models, and Shapley additive explanations values to interpret the model outcomes. The most relevant features influencing length of stay were whether the patient received a scan at the ED, emergency room urgent visit, age, triage level, and the medical alarm unit category. The clinical insights derived from the explanation of the models holds promise for increase understanding of the overcrowding from the data. Our work demonstrates that a time-to-event approach to the over- crowding serves as a valuable initial to uncover crucial insights for further investigation and policy design.

Exploring the discrepancies between clinical trials and real-world data: A small-cell lung cancer study.

The potential of real-world data to inform clinical trial design and supplement control arms has gained much interest in recent years. The most common approach relies on reproducing control arm outcomes by matching real-world patient cohorts to clinical trial baseline populations. However, recent studies pointed out that there is a lack of replicability, generalisability, and consensus. In this article, we propose a novel approach that aims to explore and examine these discrepancies by concomitantly investigating the impact of selection criteria and operations on the measurements of outcomes from the patient data. We tested the approach on a dataset consisting of small-cell lung cancer patients receiving platinum-based chemotherapy regimens from a real-world data cohort (n = 223) and six clinical trial control arms (n = 1224). The results showed that the discrepancy between real-world and clinical trial data potentially depends on differences in both patient populations and operational conditions (e.g., frequency of assessments, and censoring), for which further investigation is required. Discovering and accounting for confounders, including hidden effects of differences in operations related to the treatment process and clinical trial study protocol, would potentially allow for improved translation between clinical trials and real-world data. Continued development of the method presented here to systematically explore and account for these differences could pave the way for transferring learning across clinical studies and developing mutual translation between the real-world and clinical trials to inform clinical study design.

Population Pharmacokinetic Analysis and Simulation of Alternative Dosing Regimens for Biosimilars to Adalimumab and Etanercept in Patients with Rheumatoid Arthritis.

Efficacy to biologics in rheumatoid arthritis (RA) patients is variable and is likely influenced by each patient's circulating drug levels. Using modelling and simulation, the aim of this study was to investigate whether adalimumab and etanercept biosimilar dosing intervals can be altered to achieve therapeutic drug levels at a faster/similar time compared to the recommended interval. RA patients starting subcutaneous Amgevita or Benepali (adalimumab and etanercept biosimilars, respectively) were recruited and underwent sparse serum sampling for drug concentrations. Drug levels were measured using commercially available kits. Pharmacokinetic data were analysed using a population approach (popPK) and potential covariates were investigated in models. Models were compared using goodness-of-fit criteria. Final models were selected and used to simulate alternative dosing intervals. Ten RA patients starting the adalimumab biosimilar and six patients starting the etanercept biosimilar were recruited. One-compartment PK models were used to describe the popPK models for both drugs; no significant covariates were found. Typical individual parameter estimates were used to simulate altered dosing intervals for both drugs. A simulation of dosing the etanercept biosimilar at a lower rate of every 10 days reached steady-state concentrations earlier than the usual dosing rate of every 7 days. Simulations of altered dosing intervals could form the basis for future personalised dosing studies, potentially saving costs whilst increasing efficacy.

Investigating the Connections Between Delivery of Care, Reablement, Workload, and Organizational Factors in Home Care Services: Mixed Methods Study.

BACKGROUND: Home care is facing increasing demand due to an aging population. Several challenges have been identified in the provision of home care, such as the need for support and tailoring support to individual needs. Goal-oriented interventions, such as reablement, may provide a solution to some of these challenges. The reablement approach targets adaptation to disease and relearning of everyday life skills and has been found to improve health-related quality of life while reducing service use. OBJECTIVE: The objective of this study is to characterize home care system variables (elements) and their relationships (connections) relevant to home care staff workload, home care user needs and satisfaction, and the reablement approach. This is to examine the effects of improvement and interventions, such as the person-centered reablement approach, on the delivery of home care services, workload, work-related stress, home care user experience, and other organizational factors. The main focus was on Swedish home care and tax-funded universal welfare systems. METHODS: The study used a mixed methods approach where a causal loop diagram was developed grounded in participatory methods with academic health care science research experts in nursing, occupational therapy, aging, and the reablement approach. The approach was supplemented with theoretical models and the scientific literature. The developed model was verified by the same group of experts and empirical evidence. Finally, the model was analyzed qualitatively and through simulation methods. RESULTS: The final causal loop diagram included elements and connections across the categories: stress, home care staff, home care user, organization, social support network of the home care user, and societal level. The model was able to qualitatively describe observed intervention outcomes from the literature. The analysis suggested elements to target for improvement and the potential impact of relevant studied interventions. For example, the elements "workload" and "distress" were important determinants of home care staff health, provision, and quality of care. CONCLUSIONS: The developed model may be of value for informing hypothesis formulation, study design, and discourse within the context of improvement in home care. Further work will include a broader group of stakeholders to reduce the risk of bias. Translation into a quantitative model will be explored.

Application of Process Mining for Modelling Small Cell Lung Cancer Prognosis.

Process mining is a relatively new method that connects data science and process modelling. In the past years a series of applications with health care production data have been presented in process discovery, conformance check and system enhancement. In this paper we apply process mining on clinical oncological data with the purpose of studying survival outcomes and chemotherapy treatment decision in a real-world cohort of small cell lung cancer patients treated at Karolinska University Hospital (Stockholm, Sweden). The results highlighted the potential role of process mining in oncology to study prognosis and survival outcomes with longitudinal models directly extracted from clinical data derived from healthcare.

Trends in oral drug bioavailability following bariatric surgery: examining the variable extent of impact on exposure of different drug classes.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT: Changes to oral drug bioavailability have been observed post bariatric surgery. However, the magnitude and the direction of changes have not been assessed systematically to provide insights into the parameters governing the observed trends. Understanding these can help with dose adjustments. WHAT THIS STUDY ADDS: Analysis of drug characteristics based on a biopharmaceutical classification system is not adequate to explain observed trends in altered oral drug bioavailability following bariatric surgery, although the findings suggest solubility to play an important role. AIMS: To identify the most commonly prescribed drugs in a bariatric surgery population and to assess existing evidence regarding trends in oral drug bioavailability post bariatric surgery. METHODS: A retrospective audit was undertaken to document commonly prescribed drugs amongst patients undergoing bariatric surgery in an NHS hospital in the UK and to assess practice for drug administration following bariatric surgery. The available literature was examined for trends relating to drug permeability and solubility with regards to the Biopharmaceutics Classification System (BCS) and main route of elimination. RESULTS: No significant difference in the 'post/pre surgery oral drug exposure ratio' (ppR) was apparent between BCS class I to IV drugs, with regards to dose number (Do) or main route of elimination. Drugs classified as 'solubility limited' displayed an overall reduction as compared with 'freely soluble' compounds, as well as an unaltered and increased ppR. CONCLUSION: Clinical studies establishing guidelines for commonly prescribed drugs, and the monitoring of drugs exhibiting a narrow therapeutic window or without a readily assessed clinical endpoint, are warranted. Using mechanistically based pharmacokinetic modelling for simulating the multivariate nature of changes in drug exposure may serve as a useful tool in the further understanding of postoperative trends in oral drug exposure and in developing practical clinical guidance.