Advanced separation of mineral oil aromatic hydrocarbons by number of aromatic rings using donor-acceptor-complex chromatography to extend on-line coupled liquid chromatography-gas chromatography.

An automated implementation for a subfractionation of mineral oil aromatic hydrocarbons (MOAH) into a mono-/di-aromatic fraction (MDAF) and a tri-/poly-aromatic fraction (TPAF) is presented, which is highly demanded by the European Food Safety Authority (EFSA) respecting the genotoxic and carcinogenic potential of MOAH. For this, donor-acceptor-complex chromatography (DACC) was used as a selective stationary phase to extend the conventional instrumental setup for the analysis of mineral oil hydrocarbons via on-line coupled liquid chromatography-gas chromatography-flame ionization detection (LC-GC-FID). A set of six new internal standards was introduced for the verification of the MOAH fractionation and a quantification of MDAF and TPAF, respectively. The automated DACC approach was applied to representative petrochemical references as well as to food samples, such as rice and infant formula, generally showing well conformity with results obtained by state-of-the-art analysis using two-dimensional GC (GCxGC). Relative deviations of DACC/LC-GC-FID compared to GCxGC-FID methods regarding the ≥ 3 ring MOAH content ranged between -50 and +6 % (median: -2 %, all samples, only values above limit of quantification). However, crucial deviations mainly result from "border-crossing" substances, e.g., dibenzothiophenes or partially hydrogenated MOAH. These substances can cause overestimations of ≥ 3 ring MOAH fraction during GCxGC analysis due to co-elution, which is mostly avoided using the DACC approach. Furthermore, the DACC approach can help to minimize underestimations of toxicologically relevant ≥ 3 ring MOAH caused by an unavoidable loss of MOAH during epoxidation, since natural olefins, such as terpenes, predominantly elute in MDAF, which was exemplarily shown for an olive oil and a terpene reference. The presented approach can be implemented easily in existing LC-GC-FID setup for an automated and advanced screening of MOAH to lower the need for elaborate GCxGC analysis also in routine environments.

AML consolidation therapy: timing matters.

PURPOSE: Infections due to severe neutropenia are the most common therapy-associated causes of mortality in patients with acute myeloid leukemia (AML). New strategies to lessen the severity and duration of neutropenia are needed. METHODS: Cytarabine is commonly used for AML consolidation therapy; we compared high- and intermediate-dose cytarabine administration on days 1, 2, and 3 (AC-123) versus days 1, 3, and 5 (AC-135) in consolidation therapy of AML. Recently, clinical trials demonstrated that high-dose AC-123 resulted in a shortened white blood cell (WBC) recovery time compared with high-dose AC-135. Our main hypothesis is that this is also the case for different cytarabine dosage, granulocyte colony-stimulating factor (G-CSF) administration, and cycle lengths. We analyzed 334 treatment schedules on virtual cohorts of digital twins. RESULTS: Comparison of 32,565 simulated consolidation cycles resulted in a reduction in the WBC recovery time for AC-123 in 99.6% of the considered cycles (median reduction 3.5 days) without an increase in the number of leukemic blasts (lower value in 94.2% of all cycles), compared to AC-135. CONCLUSION: Our numerical study supports the use of AC-123 plus G-CSF as standard conventional AML consolidation therapy to reduce the risk for life-threatening infectious complications.

Pharmacokinetic-pharmacodynamic modeling of maintenance therapy for childhood acute lymphoblastic leukemia.

In the treatment of childhood acute lymphoblastic leukemia (ALL), current protocols combine initial high-dose multiagent chemotherapy with prolonged oral therapy with 6-mercaptopurine (6MP) and low-dose methotrexate (MTX) maintenance therapy. Decades of research on ALL treatment have resulted in survival rates of approximately 90%. However, dose-response relationships vary widely between patients and insight into the influencing factors, that would allow for improved personalized treatment management, is insufficient. We use a detailed data set with measurements of thioguanine nucleotides and MTX in red blood cells and absolute neutrophil count (ANC) to develop pharmacokinetic models for 6MP and MTX, as well as a pharmacokinetic-pharmacodynamic (PKPD) model capable of predicting individual ANC levels and thus contributing to the development of personalized treatment strategies. Here, we show that integrating metabolite measurements in red blood cells into the full PKPD model improves results when less data is available, but that model predictions are comparable to those of a fixed pharmacokinetic model when data availability is not limited, providing further evidence of the quality of existing models. With this comprehensive model development leading to dynamics similar to simpler models, we validate the suitability of this model structure and provide a foundation for further exploration of maintenance therapy strategies through simulation and optimization.

Styrene-acrylonitrile-copolymer and acrylonitrile-butadiene-styrene-copolymer: a study on extractable and migratable oligomers.

Styrene-acrylonitrile-copolymer (SAN) and acrylonitrile-butadiene-styrene-copolymer (ABS) are gaining in importance as food contact materials. Oligomers and other non-intentionally added substances can migrate into foodstuffs. Five SAN and four ABS samples from the German market and manufacturers were extracted and the extractable oligomers were characterised by high performance liquid chromatography-mass spectrometry/ultraviolet detection/chemiluminescence nitrogen detection/fluorescence detection and gas chromatography-mass spectrometry. Trimers, formed from acrylonitrile and styrene units, were determined to be the dominating group of extractable oligomers in SAN and ABS in concentrations of about 4900-15800 mg/kg material. Furthermore, styrene-acrylonitrile dimers, styrene oligomers, styrene monomer and ethylbenzene were identified in the sample extracts. Migration testing with three consecutive migrations for multiple use articles was performed for two SAN articles. Migration of trimers into water, 3% acetic acid, 10% and 20% ethanol under hot-fill conditions (70°C, 2 h) was not detectable above 9 µg/dm2, while 50% ethanol acting as a food simulant for milk (124 µg/dm2 trimers during the third migration) was shown to overestimate the actual migration into milk (< 11 µg/dm2 trimers at 70°C, 2 h). 2-Amino-3-methyl-1-naphthalenecarbonitrile (AMNC), an oligomer degradation product and a primary aromatic amine, was detected in all material sample extracts (0.3-17.1 mg/kg material) and was released into food simulants in low amounts (< 0.014 µg/dm2 during the third migration into 50% ethanol at 70°C, 2 h).

Model-Based Optimal AML Consolidation Treatment.

OBJECTIVE: Neutropenia is an adverse event commonly arising during intensive chemotherapy of acute myeloid leukemia (AML). It is often associated with infectious complications. Mathematical modeling, simulation, and optimization of the treatment process would be a valuable tool to support clinical decision making, potentially resulting in less severe side effects and deeper remissions. However, until now, there has been no validated mathematical model available to simulate the effect of chemotherapy treatment on white blood cell (WBC) counts and leukemic cells simultaneously. METHODS: We developed a population pharmacokinetic/pharmacodynamic (PK/PD) model combining a myelosuppression model considering endogenous granulocyte-colony stimulating factor (G-CSF), a PK model for cytarabine (Ara-C), a subcutaneous absorption model for exogenous G-CSF, and a two-compartment model for leukemic blasts. This model was fitted to data of 44 AML patients during consolidation therapy with a novel Ara-C plus G-CSF schedule from a phase II controlled clinical trial. Additionally, we were able to optimize treatment schedules with respect to disease progression, WBC nadirs, and the amount of Ara-C and G-CSF. RESULTS: The developed PK/PD model provided good prediction accuracies and an interpretation of the interaction between WBCs, G-CSF, and blasts. For 14 patients (those with available bone marrow blast counts), we achieved a median 4.2-fold higher WBC count at nadir, which is the most critical time during consolidation therapy. The simulation results showed that relative bone marrow blast counts remained below the clinically important threshold of 5%, with a median of 60% reduction in Ara-C. CONCLUSION: These in silico findings demonstrate the benefits of optimized treatment schedules for AML patients. SIGNIFICANCE: Until 2017, no new drug had been approved for the treatment of AML, fostering the optimal use of currently available drugs.

Optimized and Personalized Phlebotomy Schedules for Patients Suffering From Polycythemia Vera.

Polycythemia vera (PV) is a slow-growing type of blood cancer, where the production of red blood cells (RBCs) increase considerably. The principal treatment for targeting the symptoms of PV is bloodletting (phlebotomy) at regular intervals based on data derived from blood counts and physician assessments based on experience. Model-based decision support can help to identify optimal and individualized phlebotomy schedules to improve the treatment success and reduce the number of phlebotomies and thus negative side effects of the therapy. We present an extension of a simple compartment model of the production of RBCs in adults to capture patients suffering from PV. We analyze the model's properties to show the plausibility of its assumptions. We complement this with numerical results using exemplary PV patient data. The model is then used to simulate the dynamics of the disease and to compute optimal treatment plans. We discuss heuristics and solution approaches for different settings, which include constraints arising in real-world applications, where the scheduling of phlebotomies depends on appointments between patients and treating physicians. We expect that this research can support personalized clinical decisions in cases of PV.

Model-Based Simulation of Maintenance Therapy of Childhood Acute Lymphoblastic Leukemia.

Acute lymphoblastic leukemia is the most common malignancy in childhood. Successful treatment requires initial high-intensity chemotherapy, followed by low-intensity oral maintenance therapy with oral 6-mercaptopurine (6MP) and methotrexate (MTX) until 2-3 years after disease onset. However, intra- and inter-individual variability in the pharmacokinetics (PK) and pharmacodynamics (PD) of 6MP and MTX make it challenging to balance the desired antileukemic effects with undesired excessive myelosuppression during maintenance therapy. A model to simulate the dynamics of different cell types, especially neutrophils, would be a valuable contribution to improving treatment protocols (6MP and MTX dosing regimens) and a further step to understanding the heterogeneity in treatment efficacy and toxicity. We applied and modified a recently developed semi-mechanistic PK/PD model to neutrophils and analyzed their behavior using a non-linear mixed-effects modeling approach and clinical data obtained from 116 patients. The PK model of 6MP influenced the accuracy of absolute neutrophil count (ANC) predictions, whereas the PD effect of MTX did not. Predictions based on ANC were more accurate than those based on white blood cell counts. Using the new cross-validated mathematical model, simulations of different treatment protocols showed a linear dose-effect relationship and reduced ANC variability for constant dosages. Advanced modeling allows the identification of optimized control criteria and the weighting of specific influencing factors for protocol design and individually adapted therapy to exploit the optimal effect of maintenance therapy on survival.

Mathematical models for cytarabine-derived myelosuppression in acute myeloid leukaemia.

We investigate the personalisation and prediction accuracy of mathematical models for white blood cell (WBC) count dynamics during consolidation treatment using intermediate or high-dose cytarabine (Ara-C) in acute myeloid leukaemia (AML). Ara-C is the clinically most relevant cytotoxic agent for AML treatment. We extend a mathematical model of myelosuppression and a pharmacokinetic model of Ara-C with different hypotheses of Ara-C's pharmacodynamic effects. We cross-validate the 12 model variations using dense WBC count measurements from 23 AML patients. Surprisingly, the prediction accuracy remains satisfactory in each of the models despite different modelling hypotheses. Therefore, we compare average clinical and calculated WBC recovery times for different Ara-C schedules as a successful methodology for model discrimination. As a result, a new hypothesis of a secondary pharmacodynamic effect on the proliferation rate seems plausible. Furthermore, we demonstrate the impact of treatment timing on subsequent nadir values based on personalised predictions as a possibility for influencing/controlling myelosuppression.

A mathematical model of white blood cell dynamics during maintenance therapy of childhood acute lymphoblastic leukemia.

Acute lymphoblastic leukemia is the most common malignancy in childhood and requires prolonged oral maintenance chemotherapy to prevent disease relapse after remission induction with intensive intravenous chemotherapy. In maintenance therapy, drug doses of 6-mercaptopurine (6-MP) and methotrexate (MTX) are adjusted to achieve sustained antileukemic activity without excessive myelosuppression. However, uncertainty exists regarding timing and extent of drug dose responses and optimal dose adaptation strategies. We propose a novel comprehensive mathematical model for 6-MP and MTX pharmacokinetics, pharmacodynamics and myelosuppression in acute lymphoblastic maintenance therapy. We personalize and cross-validate the mathematical model using clinical data and propose a real-time algorithm to predict chemotherapy responses with a clinical decision support system as a potential future application.

Optimal control for selected cancer chemotherapy ODE models: a view on the potential of optimal schedules and choice of objective function.

In this article, four different mathematical models of chemotherapy from the literature are investigated with respect to optimal control of drug treatment schedules. The various models are based on two different sets of ordinary differential equations and contain either chemotherapy, immunotherapy, anti-angiogenic therapy or combinations of these. Optimal control problem formulations based on these models are proposed, discussed and compared. For different parameter sets, scenarios, and objective functions optimal control problems are solved numerically with Bock's direct multiple shooting method. In particular, we show that an optimally controlled therapy can be the reason for the difference between a growing and a totally vanishing tumor in comparison to standard treatment schemes and untreated or wrongly treated tumors. Furthermore, we compare different objective functions. Eventually, we propose an optimization-driven indicator for the potential gain of optimal controls. Based on this indicator, we show that there is a high potential for optimization of chemotherapy schedules, although the currently available models are not yet appropriate for transferring the optimal therapies into medical practice due to patient-, cancer-, and therapy-specific components.