Busulfan (BU) dose adjustment following therapeutic drug monitoring contributes to better outcome of hematopoietic stem cell transplantation (HSCT). Further improvement could be achieved through genotype-guided BU dose adjustments. To investigate this aspect, polymorphism within glutathione S transferase genes were assessed. Particularly, promoter haplotypes of the glutathione S transferase A1 (GSTA1) were evaluated in vitro, with reporter gene assays and clinically, in a pediatric multi-center study (N =138) through association with BU pharmacokinetics (PK) and clinical outcomes. Promoter activity significantly differed between the GSTA1 haplotypes (p<0.001) supporting their importance in capturing PK variability. Four GSTA1 diplotype groups that significantly correlated with clearance (p=0.009) were distinguished. Diplotypes underlying fast and slow metabolizing capacity showed higher and lower BU clearance (ml/min/kg), respectively. GSTA1 diplotypes with slow metabolizing capacity were associated with higher incidence of sinusoidal obstruction syndrome, acute graft versus host disease and combined treatment-related toxicity (p<0.0005). Among other GST genes investigated, GSTP1 313GG correlated with acute graft versus host disease grade 1-4 (p=0.01) and GSTM1 non-null genotype was associated with hemorrhagic cystitis (p=0.003). This study further strengthens the hypothesis that GST diplotypes/genotypes could be incorporated into already existing population pharmacokinetic models for improving first BU dose prediction and HSCT outcomes. (No Clinicaltrials.gov identifier: NCT01257854. Registered 8 December 2010, retrospectively registered).

Cholinesterase inhibitors (CEIs) have been shown to improve cognitive functioning in Alzheimer's disease (AD) patients, but are associated with multiple side effects and only 20-40% of the patients clinically improve. In this study, we aimed to investigate the acute pharmacodynamic (PD) effects of administration of a single dose of galantamine on central nervous system (CNS) functioning in mild to moderate AD patients and its potential to predict long-term treatment response.

A hallmark of acute respiratory distress syndrome (ARDS) is hypoxaemic respiratory failure due to pulmonary vascular hyperpermeability. The tyrosine kinase inhibitor imatinib reversed pulmonary capillary leak in preclinical studies and improved clinical outcomes in hospitalized COVID-19 patients. We investigated the effect of intravenous (IV) imatinib on pulmonary edema in COVID-19 ARDS.

Joint replacement surgery of the lower extremities are common procedures in elderly persons who are at increased risk of postoperative falls. The use of mental state altering medications, such as opioids, antidepressants or benzodiazepines, can further contribute to impaired balance and risk of falls. The objective of the current systematic review was to evaluate the risk of the use of mental state altering medications on postoperative falls in patients undergoing total hip arthroplasty (THA) or total knee arthroplasty (TKA).

Over the past decade, several immunotherapies have been approved for the treatment of melanoma. The most prominent of these are the immune checkpoint inhibitors, which are antibodies that block the inhibitory effects on the immune system by checkpoint receptors, such as CTLA-4, PD-1 and PD-L1. Preclinically, blocking these receptors has led to increased activation and proliferation of effector cells following stimulation and antigen recognition, and subsequently, more effective elimination of cancer cells. Translation from preclinical to clinical outcomes in solid tumors has shown the existence of a wide diversity of individual patient responses, linked to several patient-specific parameters. We developed a quantitative systems pharmacology (QSP) model that looks at the mentioned checkpoint blockade therapies administered as mono-, combo- and sequential therapies, to show how different combinations of specific patient parameters defined within physiological ranges distinguish different types of virtual patient responders to these therapies for melanoma. Further validation by fitting and subsequent simulations of virtual clinical trials mimicking actual patient trials demonstrated that the model can capture a wide variety of tumor dynamics that are observed in the clinic and can predict median clinical responses. Our aim here is to present a QSP model for combination immunotherapy specific to melanoma.

Immunotherapy and immune checkpoint blocking antibodies such as anti-PD-1 are approved and significantly improve the survival of advanced non-small cell lung cancer (NSCLC) patients, but there has been little success in identifying biomarkers capable of separating the responders from non-responders before the onset of the therapy. In this study, we developed a quantitative system pharmacology (QSP) model to represent the anti-tumor immune response in human NSCLC that integrated our knowledge of tumor growth, antigen processing and presentation, T cell activation and distribution, antibody pharmacokinetics, and immune checkpoint dynamics. The model was calibrated with the available data and was used to identify potential biomarkers as well as patient-specific response based on the patient parameters. The model predicted that in addition to tumor mutational burden (TMB), a known biomarker for anti-PD-1 therapy in NSCLC, the number of effector T cells and regulatory T cells in the tumor and blood is a predictor of the responders. Furthermore, the model simulated a set of 12 patients with known TMB and MHC/antigen-binding affinity from a recent clinical trial ( ClinicalTrials.gov number, NCT02259621) on neoadjuvant nivolumab therapy in resectable lung cancer and predicted an augmented durable response in patients with adjuvant nivolumab treatment in addition to the clinical trial protocol of neoadjuvant nivolumab treatment followed by resection. Overall, the model provides a valuable framework to model tumor immunity and response to immune checkpoint blockers to enhance biomarker discovery and performing virtual clinical trials to aid in design and interpretation of the current trials with fewer patients.

Antibiotic exposure in intensive care patients with sepsis is frequently inadequate and is associated with poorer outcomes. Antibiotic dosing is challenging in the intensive care, as critically ill patients have altered and fluctuating antibiotic pharmacokinetics that make current one-size-fits-all regimens unsatisfactory. Real-time bedside dosing software is not available yet, and therapeutic drug monitoring is typically used for few antibiotic classes and only allows for delayed dosing adaptation. Thus, adequate and timely antibiotic dosing continues to rely largely on the level of pharmacokinetic expertise in the ICU. Therefore, we set out to assess the level of knowledge on antibiotic pharmacokinetics among these intensive care professionals.

This comprehensive observational study aimed to gain insight into adherence to nilotinib and the effect of (non)adherence on exposure (Cmin) and treatment outcomes.

An accurate estimated glomerular filtration rate (eGFR) is essential in drug dosing. This study demonstrates the limitations of indexed (ml/min/1.73 m2 ) and de-indexed (ml/min) eGFR based drug dosing in patients with obesity or underweight. This systematic study aimed to determine the most appropriate approach to estimate the GFR for standardized eGFR based drug dosing in these patients. (Raw) data of 12 studies were selected to investigate the accuracy and bias of both the indexed and de-indexed estimations of the Modification of Diet in Renal Disease (MDRD) study equation and the Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPI), and of the Cockcroft-Gault (CG) in patients with obesity or underweight. Accuracy was calculated as the proportion of eGFR values within 30% of the measured GFR (P30) using an inert tracer (e.g., iohexol, inulin, 51 Cr-EDTA, or iothalamate clearance). An accuracy of at least 80% was considered acceptable. GFR values estimated with the CG, MDRD, and CKD-EPI differ significantly within a patient with obesity or underweight regardless of whether it is indexed or de-indexed. All studies, with two exceptions, show that all three equations are inaccurate for patients with underweight or class II obesity (P30: 55%-94%). De-indexing eGFR improves not or modestly the accuracy, and mostly remains below the 80% (P30: 62%-100%). CG was highly inaccurate in obese and underweight patients (P30: 7%-82%). Although these results show that CG is obsolete, the accuracy of MDRD and CKD-EPI is low in patients with obesity or underweight and de-indexing is not the solution. Better education and more accurate methods for appropriate drug dosing (e.g., measured GFR with inert tracer, therapeutic drug monitoring, or 24-h creatinine clearance) are recommended.

The spice curcumin and its metabolites are widely used by cancer patients but have not shown proven health benefits in clinical studies, likely due to low plasma concentrations after oral intake. However, public interest in curcumin continues to grow, and companies claim enhanced absorption in their formulations. This study aims to determine if daily oral intake of curcumin leads to sufficient plasma concentrations for health effects. The study was registered in the Dutch Clinical Trial Register with ID NL5931.

Introduction: Capecitabine is a widely prescribed oral anticancer agent. We studied medication adherence and explored its use in daily practice from a patients' perspective. Patients and Methods: Patients (n = 92) starting capecitabine were followed up to five 3-week cycles. Adherence was assessed using a pill count, pharmacy data and dosing information from the patients' medical file. Self-reported adherence was measured using the Medication Adherence Report Scale (MARS). At baseline and during week 2 of cycles 1, 3, and 5, patients filled out questionnaires about quality of life, symptoms, attitude toward medicines and disease and use in daily practice. Simultaneously, blood samples were taken to determine the area under the curve (AUC) of 5'-deoxy-5-fluorouridine (5'-DFUR), 5-fluorouracil (5-FU), and α-fluoro-β-alanine (FBAL) by a population pharmacokinetic model. Associations between AUCs and patient-reported symptoms were tested for cycles 3 and 5. Results: Most patients (84/92; 91%) had an adherence rate of ≥95 and ≤ 105%. The percentage of patients reporting any non-adherence behavior measured with MARS increased from 16% at cycle 1 to 29% at cycle 5. Symptoms were reported frequently and the dosing regimen was adjusted by the physician at least once in 62% of patients. In multivariate analysis the probability of an adjustment increased with the number of co-medication (OR 1.19, 95% CI: 1.03-1.39) and a stronger emotional response to the disease (OR 1.32, 95% CI: 1.10-1.59). The AUC of 5'-DFUR was associated with weight loss (OR 1.10, 95% CI: 1.01-1.19), AUC of FBAL with hand-foot syndrome (OR 0.90, 95% CI: 0.83-0.99), rhinorrhea (OR 1.21, 95% CI: 1.03-1.42 weight loss (OR 1.09, 95% CI: 1.00-1.20) and depression (OR 0.90, 95% CI: 0.82-0.99). Side effects were reported by one third of patients as the reason to discontinue treatment. Conclusion: Adherence to capecitabine was generally high. Nevertheless, adherence measured with MARS decreased over time Adherence management to support implementation of correct capecitabine use is specifically relevant in longer term treatment. In addition, it appears that adverse event management is important to support persistence. With the extending armamentarium of oral targeted anticancer agents and prolonged treatment duration, we expect the issue of medication adherence of increasing importance in oncology.

This study aimed to determine whether published pharmacokinetic (PK) models can adequately predict the PK profile of imatinib in a new indication, such as coronavirus disease 2019 (COVID-19). Total (bound + unbound) and unbound imatinib plasma concentrations obtained from 134 patients with COVID-19 participating in the CounterCovid study and from an historical dataset of 20 patients with gastrointestinal stromal tumor (GIST) and 85 patients with chronic myeloid leukemia (CML) were compared. Total imatinib area under the concentration time curve (AUC), maximum concentration (Cmax ) and trough concentration (Ctrough ) were 2.32-fold (95% confidence interval [CI] 1.34-3.29), 2.31-fold (95% CI 1.33-3.29), and 2.32-fold (95% CI 1.11-3.53) lower, respectively, for patients with CML/GIST compared with patients with COVID-19, whereas unbound concentrations were comparable among groups. Inclusion of alpha1-acid glycoprotein (AAG) concentrations measured in patients with COVID-19 into a previously published model developed to predict free imatinib concentrations in patients with GIST using total imatinib and plasma AAG concentration measurements (AAG-PK-Model) gave an estimated mean (SD) prediction error (PE) of -20% (31%) for total and -7.0% (56%) for unbound concentrations. Further covariate modeling with this combined dataset showed that in addition to AAG; age, bodyweight, albumin, CRP, and intensive care unit admission were predictive of total imatinib oral clearance. In conclusion, high total and unaltered unbound concentrations of imatinib in COVID-19 compared to CML/GIST were a result of variability in acute phase proteins. This is a textbook example of how failure to take into account differences in plasma protein binding and the unbound fraction when interpreting PK of highly protein bound drugs, such as imatinib, could lead to selection of a dose with suboptimal efficacy in patients with COVID-19.

Poly(ADP-ribose) polymerase inhibitors (PARPi), coupled to a DNA damaging agent is a promising approach to treating triple negative breast cancer (TNBC). However, not all patients respond; we hypothesize that non-response in some patients may be due to insufficient drug penetration. As a first step to testing this hypothesis, we quantified and visualized veliparib and carboplatin penetration in mouse xenograft TNBCs and patient blood samples.

The low response rate of immune checkpoint blockade in breast cancer has highlighted the need for predictive biomarkers to identify responders. While a number of clinical trials are ongoing, testing all possible combinations is not feasible. In this study, a quantitative systems pharmacology model is built to integrate immune-cancer cell interactions in patients with breast cancer, including central, peripheral, tumour-draining lymph node (TDLN) and tumour compartments. The model can describe the immune suppression and evasion in both TDLN and the tumour microenvironment due to checkpoint expression, and mimic the tumour response to checkpoint blockade therapy. We investigate the relationship between the tumour response to checkpoint blockade therapy and composite tumour burden, PD-L1 expression and antigen intensity, including their individual and combined effects on the immune system, using model-based simulations. The proposed model demonstrates the potential to make predictions of tumour response of individual patients given sufficient clinical measurements, and provides a platform that can be further adapted to other types of immunotherapy and their combination with molecular-targeted therapies. The patient predictions demonstrate how this systems pharmacology model can be used to individualize immunotherapy treatments. When appropriately validated, these approaches may contribute to optimization of breast cancer treatment.

The antitumor drug nilotinib has a large inter- and intra individual variability in pharmacokinetics. Adherence to treatment may substantially influence plasma levels and has been recognized as the most important determinant of treatment failure in chronic myeloid leukemia (CML). A better understanding of the various factors contributing to the efficacy of treatment is essential for the development of interventions to optimize the treatment of chronic phase CML (CP-CML) with a protein kinase inhibitor like nilotinib.

The likelihood of a tumor recurrence in patients with T3-4N0-1 non-small cell lung cancer following multimodality treatment remains substantial, mainly due distant metastases. As pathological complete responses (pCR) in resected specimens are seen in only a minority (28-38%) of patients following chemoradiotherapy, we designed the INCREASE trial (EudraCT-Number: 2019-003454-83; Netherlands Trial Register number: NL8435) to assess if pCR rates could be further improved by adding short course immunotherapy to induction chemoradiotherapy. Translational studies will correlate changes in loco-regional and systemic immune status with patterns of recurrence.

The major complication of COVID-19 is hypoxaemic respiratory failure from capillary leak and alveolar oedema. Experimental and early clinical data suggest that the tyrosine-kinase inhibitor imatinib reverses pulmonary capillary leak.

Quantitative characterization of the tumor microenvironment, including its immuno-architecture, is important for developing quantitative diagnostic and predictive biomarkers, matching patients to the most appropriate treatments for precision medicine, and for providing quantitative data for building systems biology computational models able to predict tumor dynamics in the context of immune checkpoint blockade therapies. The intra- and inter-tumoral spatial heterogeneities are potentially key to the understanding of the dose-response relationships, but they also bring challenges to properly parameterizing and validating such models. In this study, we developed a workflow to detect CD8+ T cells from whole slide imaging data, and quantify the spatial heterogeneity using multiple metrics by applying spatial point pattern analysis and morphometric analysis. The results indicate a higher intra-tumoral heterogeneity compared with the heterogeneity across patients. By comparing the baseline metrics with PD-1 blockade treatment outcome, our results indicate that the number of high-density T cell clusters of both circular and elongated shapes are higher in patients who responded to the treatment. This methodology can be applied to quantitatively characterize the tumor microenvironment, including immuno-architecture, and its heterogeneity for different cancer types.

Adherence to pharmacological therapy is a complex and multifactorial issue that can substantially alter the outcome of treatment. Especially when using long-term medication, cancer patients have adherence rates similar to those of patients with other diseases. The consequences of poor adherence are poor health outcomes and increased health care costs. Only few studies have focused on the use of oral anticancer agents in daily practice. Information about the reasons for nonadherence is essential for the development of interventions that may improve adherence. This report presents the CAPER-capecitabine protocol, which is designed to study the adherence to capecitabine and the influence of patient attitudes towards medication and self-reported side effects. Furthermore, the relationships between patient characteristics, disease characteristics, side effects, quality of life, patient beliefs and attitudes towards disease and medication, dose adjustments, reasons for discontinuation, and plasma concentration of three of the main metabolites, including the active compound 5-fluorouracil, will be explored.

Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis.