Leveraging QSP Models for MIPD: A Case Study for Warfarin/INR.

Warfarin dosing remains challenging due to substantial inter-individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model-informed precision dosing (MIPD) can help individualize warfarin dosing, requiring the selection of a suitable model. For models developed from clinical data, the dependence on the study design and population raises questions about generalizability. Quantitative system pharmacology (QSP) models promise better extrapolation abilities; however, their complexity and lack of validation on clinical data raise questions about applicability in MIPD. We have previously derived a mechanistic warfarin/international normalized ratio (INR) model from a blood coagulation QSP model. In this article, we evaluated the predictive performance of the warfarin/INR model in the context of MIPD using an external dataset with INR data from patients starting warfarin treatment. We assessed the accuracy and precision of model predictions, benchmarked against an empirically based reference model. Additionally, we evaluated covariate contributions and assessed the predictive performance separately in the more challenging outpatient data. The warfarin/INR model performed comparably to the reference model across various measures despite not being calibrated with warfarin initiation data. Including CYP2C9 and/or VKORC1 genotypes as covariates improved the prediction quality of the warfarin/INR model, even after assimilating 4 days of INR data. The outpatient INR exhibited higher unexplained variability, and predictions slightly exceeded observed values, suggesting that model adjustments might be necessary when transitioning from an inpatient to an outpatient setting. Overall, this research underscores the potential of QSP-derived models for MIPD, offering a complementary approach to empirical model development.

Bayesian framework for multi-source data integration-Application to human extrapolation from preclinical studies.

In preclinical investigations, for example, in in vitro, in vivo, and in silico studies, the pharmacokinetic, pharmacodynamic, and toxicological characteristics of a drug are evaluated before advancing to first-in-man trial. Usually, each study is analyzed independently and the human dose range does not leverage the knowledge gained from all studies. Taking into account all preclinical data through inferential procedures can be particularly interesting in obtaining a more precise and reliable starting dose and dose range. Our objective is to propose a Bayesian framework for multi-source data integration, customizable, and tailored to the specific research question. We focused on preclinical results extrapolated to humans, which allowed us to predict the quantities of interest (e.g. maximum tolerated dose, etc.) in humans. We build an approach, divided into four steps, based on a sequential parameter estimation for each study, extrapolation to human, commensurability checking between posterior distributions and final information merging to increase the precision of estimation. The new framework is evaluated via an extensive simulation study, based on a real-life example in oncology. Our approach allows us to better use all the information compared to a standard framework, reducing uncertainty in the predictions and potentially leading to a more efficient dose selection.

PBTK model-based analysis of CYP3A4 induction and the toxicokinetics of the pyrrolizidine alkaloid retrorsine in man.

Cytochrome P450 (CYP)3A4 induction by drugs and pesticides plays a critical role in the enhancement of pyrrolizidine alkaloid (PA) toxicity as it leads to increased formation of hepatotoxic dehydro-PA metabolites. Addressing the need for a quantitative analysis of this interaction, we developed a physiologically-based toxicokinetic (PBTK) model. Specifically, the model describes the impact of the well-characterized CYP3A4 inducer rifampicin on the kinetics of retrorsine, which is a prototypic PA and contaminant in herbal teas. Based on consumption data, the kinetics after daily intake of retrorsine were simulated with concomitant rifampicin treatment. Strongest impact on retrorsine kinetics (plasma AUC 24 and C max reduced to 67% and 74% compared to the rifampicin-free reference) was predicted directly after withdrawal of rifampicin. At this time point, the competitive inhibitory effect of rifampicin stopped, while CYP3A4 induction was still near its maximum. Due to the impacted metabolism kinetics, the cumulative formation of intestinal retrorsine CYP3A4 metabolites increased to 254% (from 10 to 25 nmol), while the cumulative formation of hepatic CYP3A4 metabolites was not affected (57 nmol). Return to baseline PA toxicokinetics was predicted 14 days after stop of a 14-day rifampicin treatment. In conclusion, the PBTK model showed to be a promising tool to assess the dynamic interplay of enzyme induction and toxification pathways.

Nonlinear Mixed-Effects Model of Z-Endoxifen Concentrations in Tamoxifen-Treated Patients from the CEPAM Cohort.

Tamoxifen is widely used in patients with hormone receptor-positive breast cancer. The polymorphic enzyme CYP2D6 is primarily responsible for metabolic activation of tamoxifen, resulting in substantial interindividual variability of plasma concentrations of its most important metabolite, Z-endoxifen. The Z-endoxifen concentration thresholds below which tamoxifen treatment is less efficacious have been proposed but not validated, and prospective trials of individualized tamoxifen treatment to achieve Z-endoxifen concentration thresholds are considered infeasible. Therefore, we aim to validate the association between Z-endoxifen concentration and tamoxifen treatment outcomes, and identify a Z-endoxifen concentration threshold of tamoxifen efficacy, using pharmacometric modeling and simulation. As a first step, the CYP2D6 Endoxifen Percentage Activity Model (CEPAM) cohort was created by pooling data from 28 clinical studies (> 7,000 patients) with measured endoxifen plasma concentrations. After cleaning, data from 6,083 patients were used to develop a nonlinear mixed-effect (NLME) model for tamoxifen and Z-endoxifen pharmacokinetics that includes a conversion factor to allow inclusion of studies that measured total endoxifen but not Z-endoxifen. The final parent-metabolite NLME model confirmed the primary role of CYP2D6, and contributions from body weight, CYP2C9 phenotype, and co-medication with CYP2D6 inhibitors, on Z-endoxifen pharmacokinetics. Future work will use the model to simulate Z-endoxifen concentrations in patients receiving single agent tamoxifen treatment within large prospective clinical trials with long-term survival to identify the Z-endoxifen concentration threshold below which tamoxifen is less efficacious. Identification of this concentration threshold would allow personalized tamoxifen treatment to improve outcomes in patients with hormone receptor-positive breast cancer.

TLD-1, a novel liposomal doxorubicin, in patients with advanced solid tumors: Dose escalation and expansion part of a multicenter open-label phase I trial (SAKK 65/16).

BACKGROUND: TLD-1 is a novel liposomal doxorubicin that compared favorably to conventional doxorubicin liposomal formulations in preclinical models. This phase I first-in-human study aimed to define the maximum tolerated dose (MTD), recommended phase 2 dose (RP2D), safety and preliminary activity of TLD-1 in patients with advanced solid tumors. PATIENTS AND METHODS: We recruited patients with advanced solid tumors who failed standard therapy and received up to 3 prior lines of palliative systemic chemotherapy. TLD-1 was administered intravenously every 3 weeks up to a maximum of 9 cycles (6 for patients with prior anthracyclines) from a starting dose of 10 mg/m2, according to an accelerated titration design followed by a modified continual reassessment method. RESULTS: 30 patients were enrolled between November 2018 and May 2021. No dose-limiting toxicities (DLT) were observed. Maximum administered dose of TLD-1 was 45 mg/m2, RP2D was defined at 40 mg/m2. Most frequent treatment-related adverse events (TRAE) of any grade included palmar-plantar erythrodysesthesia (PPE) (50% of patients), oral mucositis (50%), fatigue (30%) and skin rash (26.7%). Most common G3 TRAE included PPE in 4 patients (13.3%) and oral mucositis in 2 (6.7%). Overall objective response rate was 10% in the whole population and 23.1% among 13 patients with breast cancer; median time-to-treatment failure was 2.7 months. TLD-1 exhibit linear pharmacokinetics, with a median terminal half-life of 95 h. CONCLUSIONS: The new liposomal doxorubicin formulation TLD-1 showed a favourable safety profile and antitumor activity, particularly in breast cancer. RP2D was defined at 40 mg/m2 administered every 3 weeks. (NCT03387917).

Index analysis: An approach to understand signal transduction with application to the EGFR signalling pathway.

In systems biology and pharmacology, large-scale kinetic models are used to study the dynamic response of a system to a specific input or stimulus. While in many applications, a deeper understanding of the input-response behaviour is highly desirable, it is often hindered by the large number of molecular species and the complexity of the interactions. An approach that identifies key molecular species for a given input-response relationship and characterises dynamic properties of states is therefore highly desirable. We introduce the concept of index analysis; it is based on different time- and state-dependent quantities (indices) to identify important dynamic characteristics of molecular species. All indices are defined for a specific pair of input and response variables as well as for a specific magnitude of the input. In application to a large-scale kinetic model of the EGFR signalling cascade, we identified different phases of signal transduction, the peculiar role of Phosphatase3 during signal activation and Ras recycling during signal onset. In addition, we discuss the challenges and pitfalls of interpreting the relevance of molecular species based on knock-out simulation studies, and provide an alternative view on conflicting results on the importance of parallel EGFR downstream pathways. Beyond the applications in model interpretation, index analysis is envisioned to be a valuable tool in model reduction.

Therapeutic drug monitoring of vedolizumab therapy in inflammatory bowel disease.

BACKGROUND: Therapeutic drug monitoring is effective for optimizing anti-tumor necrosis factor therapies in inflammatory bowel disease, but for vedolizumab, a gut-selective leucocyte migration inhibitor, data are scarce. METHODS: Observational cohort study including 116 bio-experienced inflammatory bowel disease patients treated with vedolizumab for active luminal disease. Biobanked trough blood samples (n = 676) covering 96% of patients were analyzed using a drug-binding immunofluorometric assay. Steroid-free treatment outcomes were classified by clinical disease activity indices and objective findings, primarily endoscopy. RESULTS: Patients with clinical remission to vedolizumab induction therapy (37%) had significantly higher trough levels than those without at weeks 6 (mean 34.1 vs 28.0 µg/mL, P = 0.03) and 10 (34.8 vs 27.5 µg/mL, P = 0.01). Optimal thresholds for discrimination were 32.4 µg/mL (AUCROC 0.66, P = 0.04) and 23.5 (AUCROC 0.67, P = 0.01), respectively. This positive association persisted during maintenance phase with 11.9 µg/mL (AUCROC 0.69, P < 0.01) associated with clinical remission (37%) and 15.3 (AUCROC 0.74, P < 0.001) for objective remission (46%). Stratification by temporal evolution of treatment effects revealed higher induction and maintenance vedolizumab levels in persistent and slow responders as compared to secondary or persistent failures. Pharmacokinetics was influenced by rare formation of anti-vedolizumab antibodies (2%), and to a lesser extent gender and albumin during induction, but not disease severity, concomitant steroids, or thiopurine metabolites. Switching to subcutaneous administrations resulted in 2.3-fold increase in steady-state trough levels. CONCLUSION: Our study supports maintaining adequate drug exposure being essential for sustained positive outcomes of vedolizumab and emphasizes individualized, therapeutic drug monitoring-based treatment regimens. Controlled trials and pharmacokinetic modeling are, however, needed.

Forecasting antimicrobial resistance evolution.

Antimicrobial resistance (AMR) is a major global health issue. Current measures for tackling it comprise mainly the prudent use of drugs, the development of new drugs, and rapid diagnostics. Relatively little attention has been given to forecasting the evolution of resistance. Here, we argue that forecasting has the potential to be a great asset in our arsenal of measures to tackle AMR. We argue that, if successfully implemented, forecasting resistance will help to resolve the antibiotic crisis in three ways: it will (i) guide a more sustainable use (and therefore lifespan) of antibiotics and incentivize investment in drug development, (ii) reduce the spread of AMR genes and pathogenic microbes in the environment and between patients, and (iii) allow more efficient treatment of persistent infections, reducing the continued evolution of resistance. We identify two important challenges that need to be addressed for the successful establishment of forecasting: (i) the development of bespoke technology that allows stakeholders to empirically assess the risks of resistance evolving during the process of drug development and therapeutic/preventive use, and (ii) the transformative shift in mindset from the current praxis of mostly addressing the problem of antibiotic resistance a posteriori to a concept of a priori estimating, and acting on, the risks of resistance.

Impact of Key Components of Intensified Ceftaroline Dosing on Pharmacokinetic/Pharmacodynamic Target Attainment.

BACKGROUND AND OBJECTIVE: Ceftaroline fosamil is a ß-lactam antibiotic approved as a 600 mg twice daily dose (≤1 h infusion, 'standard dosing') or a 600 mg thrice daily dose (2 h infusion) to treat complicated skin and soft tissue infections caused by Staphylococcus aureus (minimum inhibitory concentration [MIC] 2-4 mg/L). We sought to systematically evaluate the relative impact of the three key components of the intensified dosing regimen (i.e. shortened dosing interval, prolonged infusion duration and increased total daily dose [TDD]) on the pharmacokinetic/pharmacodynamic (PK/PD) target attainment given different grades of bacterial susceptibility. METHODS: A population PK model was developed using data from 12 healthy volunteers (EudraCT-2012-005134-11) receiving standard or intensified dosing. PK/PD target attainment (ƒT>MIC = 35% and 100%) after 24 h was compared following systematically varied combinations of the (1) dosing interval (every 12 h [q12h]→ every 8 h [q8h]); (2) infusion duration (1 h→2 h); and (3) individual and total daily dose (400→900 mg, i.e. TDD 1200→1800 mg), as well as for varying susceptibility of S. aureus (MIC 0.032-8 mg/L). RESULTS: A two-compartment model with linear elimination adequately described ceftaroline concentrations (n = 274). The relevance of the dosing components dosing interval/infusion duration/TDD for ƒT>MIC systematically changed with pathogen susceptibility. For susceptible pathogens with MIC ≤1 mg/L, shortened dosing intervals appeared as the main driver of the improved target attainment associated with the intensified dosing regimen, followed by increased TDD and infusion duration. For less susceptible pathogens, the advantage of q8h dosing and 2 h infusions declined, and increased TDD improved ƒT>MIC the most. CONCLUSION: The analysis calls to mind consideration of dose increases when prolonging the infusion duration in the case of low bacterial susceptibility.

C-Reactive Protein as an Early Predictor of Efficacy in Advanced Non-Small-Cell Lung Cancer Patients: A Tumor Dynamics-Biomarker Modeling Framework.

In oncology, longitudinal biomarkers reflecting the patient's status and disease evolution can offer reliable predictions of the patient's response to treatment and prognosis. By leveraging clinical data in patients with advanced non-small-cell lung cancer receiving first-line chemotherapy, we aimed to develop a framework combining anticancer drug exposure, tumor dynamics (RECIST criteria), and C-reactive protein (CRP) concentrations, using nonlinear mixed-effects models, to evaluate and quantify by means of parametric time-to-event models the significance of early longitudinal predictors of progression-free survival (PFS) and overall survival (OS). Tumor dynamics was characterized by a tumor size (TS) model accounting for anticancer drug exposure and development of drug resistance. CRP concentrations over time were characterized by a turnover model. An x-fold change in TS from baseline linearly affected CRP production. CRP concentration at treatment cycle 3 (day 42) and the difference between CRP concentration at treatment cycles 3 and 2 were the strongest predictors of PFS and OS. Measuring longitudinal CRP allows for the monitoring of inflammatory levels and, along with its reduction across treatment cycles, presents a promising prognostic marker. This framework could be applied to other treatment modalities such as immunotherapies or targeted therapies allowing the timely identification of patients at risk of early progression and/or short survival to spare them unnecessary toxicities and provide alternative treatment decisions.

Towards Model-Informed Precision Dosing of Voriconazole: Challenging Published Voriconazole Nonlinear Mixed-Effects Models with Real-World Clinical Data.

BACKGROUND AND OBJECTIVES: Model-informed precision dosing (MIPD) frequently uses nonlinear mixed-effects (NLME) models to predict and optimize therapy outcomes based on patient characteristics and therapeutic drug monitoring data. MIPD is indicated for compounds with narrow therapeutic range and complex pharmacokinetics (PK), such as voriconazole, a broad-spectrum antifungal drug for prevention and treatment of invasive fungal infections. To provide guidance and recommendations for evidence-based application of MIPD for voriconazole, this work aimed to (i) externally evaluate and compare the predictive performance of a published so-called 'hybrid' model for MIPD (an aggregate model comprising features and prior information from six previously published NLME models) versus two 'standard' NLME models of voriconazole, and (ii) investigate strategies and illustrate the clinical impact of Bayesian forecasting for voriconazole. METHODS: A workflow for external evaluation and application of MIPD for voriconazole was implemented. Published voriconazole NLME models were externally evaluated using a comprehensive in-house clinical database comprising nine voriconazole studies and prediction-/simulation-based diagnostics. The NLME models were applied using different Bayesian forecasting strategies to assess the influence of prior observations on model predictivity. RESULTS: The overall best predictive performance was obtained using the aggregate model. However, all NLME models showed only modest predictive performance, suggesting that (i) important PK processes were not sufficiently implemented in the structural submodels, (ii) sources of interindividual variability were not entirely captured, and (iii) interoccasion variability was not adequately accounted for. Predictive performance substantially improved by including the most recent voriconazole observations in MIPD. CONCLUSION: Our results highlight the potential clinical impact of MIPD for voriconazole and indicate the need for a comprehensive (pre-)clinical database as basis for model development and careful external model evaluation for compounds with complex PK before their successful use in MIPD.

Model-Informed Target Morning 17α-Hydroxyprogesterone Concentrations in Dried Blood Spots for Pediatric Congenital Adrenal Hyperplasia Patients.

Monitoring cortisol replacement therapy in congenital adrenal hyperplasia (CAH) patients is vital to avoid serious adverse events such as adrenal crises due to cortisol underexposure or metabolic consequences due to cortisol overexposure. The less invasive dried blood spot (DBS) sampling is an advantageous alternative to traditional plasma sampling, especially in pediatric patients. However, target concentrations for important disease biomarkers such as 17α-hydroxyprogesterone (17-OHP) are unknown using DBS. Therefore, a modeling and simulation framework, including a pharmacokinetic/pharmacodynamic model linking plasma cortisol concentrations to DBS 17-OHP concentrations, was used to derive a target morning DBS 17-OHP concentration range of 2-8 nmol/L in pediatric CAH patients. Since either capillary or venous DBS sampling is becoming more common in the clinics, the clinical applicability of this work was shown by demonstrating the comparability of capillary and venous cortisol and 17-OHP concentrations collected by DBS sampling, using a Bland-Altman and Passing-Bablok analysis. The derived target morning DBS 17-OHP concentration range is a first step towards providing improved therapy monitoring using DBS sampling and adjusting hydrocortisone (synthetic cortisol) dosing in children with CAH. In the future, this framework can be used to assess further research questions, e.g., target replacement ranges for the entire day.

Development of a project for interprofessional collaboration between medical and pharmacy students to improve medication safety in polypharmacy (PILLE).

Aim: Interprofessional collaboration is particularly relevant to patient safety in outpatient care with polypharmacy. The educational project "PILLE" is meant to give medical and pharmacy students an understanding of the roles and competencies needed for cooperation in the provision of healthcare and to enable interprofessional learning. Method: The curriculum is aimed at pharmacy and medical students and was developed in six steps according to the Kern cycle. It is comprised of an interprofessional seminar, a joint practical training in a simulated pharmacy, and a tandem job shadowing at a primary care practice. The project was implemented in three stages due to the pandemic: The interprofessional online seminar based on the ICAP model and the digital inverted classroom was held in the 2020 winter semester; the interprofessional practical training was added in the 2021 summer semester; and the interprofessional tandem job shadowing at a primary care practice in the 2021 winter semester. Attitudes toward interprofessional learning, among other things, was measured in the evaluation using the SPICE-2D questionnaire (Student Perceptions of Physician-Pharmacist Interprofessional Clinical Education). Results: In the first three semesters, a total of 105 students (46 pharmacy, 59 medicine) participated in the project, of which 78 participated in the evaluation (74% response rate). The students stated, in particular, that they had learned about the competencies and roles of the other profession and desired additional and more specific preparatory materials for the course sessions. The SPICE-2D questionnaire showed high values for both groups of students already in the pre-survey and these increased further as a result of the project. Conclusion: Joint case-based learning could be implemented under the conditions imposed by the pandemic. Online teaching is a low-threshold means to enable interprofessional exchange.

PBTK modeling of the pyrrolizidine alkaloid retrorsine to predict liver toxicity in mouse and rat.

Retrorsine is a hepatotoxic pyrrolizidine alkaloid (PA) found in herbal supplements and medicines, food and livestock feed. Dose-response studies enabling the derivation of a point of departure including a benchmark dose for risk assessment of retrorsine in humans and animals are not available. Addressing this need, a physiologically based toxicokinetic (PBTK) model of retrorsine was developed for mouse and rat. Comprehensive characterization of retrorsine toxicokinetics revealed: both the fraction absorbed from the intestine (78%) and the fraction unbound in plasma (60%) are high, hepatic membrane permeation is dominated by active uptake and not by passive diffusion, liver metabolic clearance is 4-fold higher in rat compared to mouse and renal excretion contributes to 20% of the total clearance. The PBTK model was calibrated with kinetic data from available mouse and rat studies using maximum likelihood estimation. PBTK model evaluation showed convincing goodness-of-fit for hepatic retrorsine and retrorsine-derived DNA adducts. Furthermore, the developed model allowed to translate in vitro liver toxicity data of retrorsine to in vivo dose-response data. Resulting benchmark dose confidence intervals (mg/kg bodyweight) are 24.1-88.5 in mice and 79.9-104 in rats for acute liver toxicity after oral retrorsine intake. As the PBTK model was built to enable extrapolation to different species and other PA congeners, this integrative framework constitutes a flexible tool to address gaps in the risk assessment of PA.

Deriving mechanism-based pharmacodynamic models by reducing quantitative systems pharmacology models: An application to warfarin.

Quantitative systems pharmacology (QSP) models integrate comprehensive qualitative and quantitative knowledge about pharmacologically relevant processes. We previously proposed a first approach to leverage the knowledge in QSP models to derive simpler, mechanism-based pharmacodynamic (PD) models. Their complexity, however, is typically still too large to be used in the population analysis of clinical data. Here, we extend the approach beyond state reduction to also include the simplification of reaction rates, elimination of reactions, and analytic solutions. We additionally ensure that the reduced model maintains a prespecified approximation quality not only for a reference individual but also for a diverse virtual population. We illustrate the extended approach for the warfarin effect on blood coagulation. Using the model-reduction approach, we derive a novel small-scale warfarin/international normalized ratio model and demonstrate its suitability for biomarker identification. Due to the systematic nature of the approach in comparison with empirical model building, the proposed model-reduction algorithm provides an improved rationale to build PD models also from QSP models in other applications.

Cell-level systems biology model to study inflammatory bowel diseases and their treatment options.

To help understand the complex and therapeutically challenging inflammatory bowel diseases (IBDs), we developed a systems biology model of the intestinal immune system that is able to describe main aspects of IBD and different treatment modalities thereof. The model, including key cell types and processes of the mucosal immune response, compiles a large amount of isolated experimental findings from literature into a larger context and allows for simulations of different inflammation scenarios based on the underlying data and assumptions. In the context of a large and diverse virtual IBD population, we characterized the patients based on their phenotype (in contrast to healthy individuals, they developed persistent inflammation after a trigger event) rather than on a priori assumptions on parameter differences to a healthy individual. This allowed to reproduce the enormous diversity of predispositions known to lead to IBD. Analyzing different treatment effects, the model provides insight into characteristics of individual drug therapy. We illustrate for anti-TNF-α therapy, how the model can be used (i) to decide for alternative treatments with best prospects in the case of nonresponse, and (ii) to identify promising combination therapies with other available treatment options.

A prognostic baseline blood biomarker and tumor growth kinetics integrated model in paclitaxel/platinum treated advanced non-small cell lung cancer patients.

Paclitaxel/platinum chemotherapy, the backbone of standard first-line treatment of advanced non-small cell lung cancer (NSCLC), exhibits high interpatient variability in treatment response and high toxicity burden. Baseline blood biomarker concentrations and tumor size (sum of diameters) at week 8 relative to baseline (RS8) are widely investigated prognostic factors. However, joint analysis of data on demographic/clinical characteristics, blood biomarker levels, and chemotherapy exposure-driven early tumor response for improved prediction of overall survival (OS) is clinically not established. We developed a Weibull time-to-event model to predict OS, leveraging data from 365 patients receiving paclitaxel/platinum combination chemotherapy once every three weeks for ≤six cycles. A developed tumor growth inhibition model, combining linear tumor growth and first-order paclitaxel area under the concentration-time curve-induced tumor decay, was used to derive individual RS8. The median model-derived RS8 in all patients was a 20.0% tumor size reduction (range from -78% to +15%). Whereas baseline carcinoembryonic antigen, cytokeratin fragments, and thyroid stimulating hormone levels were not significantly associated with OS in a subset of 221 patients, and lactate dehydrogenase, interleukin-6 and neutrophil-to-lymphocyte ratio levels were significant only in univariate analyses (p value < 0.05); C-reactive protein (CRP) in combination with RS8 most significantly affected OS (p value < 0.01). Compared to the median population OS of 11.3 months, OS was 128% longer at the 5th percentile levels of both covariates and 60% shorter at their 95th percentiles levels. The combined paclitaxel exposure-driven RS8 and baseline blood CRP concentrations enables early individual prognostic predictions for different paclitaxel dosing regimens, forming the basis for treatment decision and optimizing paclitaxel/platinum-based advanced NSCLC chemotherapy.

Ulcerative Colitis and Acute Severe Ulcerative Colitis Patients Are Overlooked in Infliximab Population Pharmacokinetic Models: Results from a Comprehensive Review.

Ulcerative colitis (UC) is part of the inflammatory bowels diseases, and moderate to severe UC patients can be treated with anti-tumour necrosis α monoclonal antibodies, including infliximab (IFX). Even though treatment of UC patients by IFX has been in place for over a decade, many gaps in modelling of IFX PK in this population remain. This is even more true for acute severe UC (ASUC) patients for which early prediction of IFX pharmacokinetic (PK) could highly improve treatment outcome. Thus, this review aims to compile and analyse published population PK models of IFX in UC and ASUC patients, and to assess the current knowledge on disease activity impact on IFX PK. For this, a semi-systematic literature search was conducted, from which 26 publications including a population PK model analysis of UC patients receiving IFX therapy were selected. Amongst those, only four developed a model specifically for UC patients, and only three populations included severe UC patients. Investigations of disease activity impact on PK were reported in only 4 of the 14 models selected. In addition, the lack of reported model codes and assessment of predictive performance make the use of published models in a clinical setting challenging. Thus, more comprehensive investigation of PK in UC and ASUC is needed as well as more adequate reports on developed models and their evaluation in order to apply them in a clinical setting.

Microdialysis of Voriconazole and its N-Oxide Metabolite: Amalgamating Knowledge of Distribution and Metabolism Processes in Humans.

PURPOSE: Voriconazole is an essential antifungal drug whose complex pharmacokinetics with high interindividual variability impedes effective and safe therapy. By application of the minimally-invasive sampling technique microdialysis, interstitial space fluid (ISF) concentrations of VRC and its potentially toxic N-oxide metabolite (NO) were assessed to evaluate target-site exposure for further elucidating VRC pharmacokinetics. METHODS: Plasma and ISF samples of a clinical trial with an approved VRC dosing regimen were analyzed for VRC and NO concentrations. Concentration-time profiles, exposure assessed as area-under-the-curve (AUC) and metabolic ratios of four healthy adults in plasma and ISF were evaluated regarding the impact of multiple dosing and CYP2C19 genotype. RESULTS: VRC and NO revealed distribution into ISF with AUC values being ≤2.82- and 17.7-fold lower compared to plasma, respectively. Intraindividual variability of metabolic ratios was largest after the first VRC dose administration while interindividual variability increased with multiple dosing. The CYP2C19 genotype influenced interindividual differences with a maximum 6- and 24-fold larger AUCNO/AUCVRC ratio between the intermediate and rapid metabolizer in plasma and ISF, respectively. VRC metabolism was saturated/auto-inhibited indicated by substantially decreasing metabolic concentration ratios with increasing VRC concentrations and after multiple dosing. CONCLUSION: The feasibility of the simultaneous microdialysis of VRC and NO in vivo was demonstrated and provided new quantitative insights by leveraging distribution and metabolism processes of VRC in humans. The exploratory analysis suggested substantial dissimilarities of VRC and NO pharmacokinetics in plasma and ISF. Ultimately, a thorough understanding of target-site pharmacokinetics might contribute to the optimization of personalized VRC dosing regimens.