Comprehensive kinetic modeling and product distribution for pyrolysis of pulp and paper mill sludge

Pyrolysis holds immense potential for clean treatment of pulp and paper mill sludge (PPMS), enabling efficient energy and chemical recovery. However, current understanding of PPMS pyrolysis kinetics and product characteristics remains incomplete. This study conducted detailed modeling of pyrolysis kinetics for two typical PPMSs from a wastepaper pulp and paper mill, namely, deinking sludge (PPMS-DS) and sewage sludge (PPMS-SS), and analyzed comprehensively pyrolysis products. The results show that apparent activation energy of PPMS-DS (169.25-226.82 kJ/mol) and PPMS-SS (189.29-411.21 kJ/mol) pyrolysis undergoes significant change, with numerous parallel reactions present. A distributed activation energy model with dual logistic distributions proves to be suitable for modeling thermal decomposition kinetics of both PPMS-DS and PPMS-SS, with coefficient of determination >0.999 and relative root mean square error <1.99 %. High temperature promotes decomposition of solid organic materials in PPMS, and maximum tar yield for both PPMS-DS (53.90 wt%, daf) and PPMS-SS (56.48 wt%, daf) is achieved at around 500 °C. Higher levels of styrene (24.45 % for PPMS-DS and 14.71 % for PPMS-SS) and ethylbenzene (8.61 % for PPMS-DS and 8.33 % for PPMS-SS) are detected in tar and could be used as chemicals. This work shows great potential to propel development of PPMS pyrolysis technology, enabling green and sustainable production in pulp and paper industry.

Biobased short chain fatty acid production - Exploring microbial community dynamics and metabolic networks through kinetic and microbial modeling approaches

In recent years, there has been growing interest in harnessing anaerobic digestion technology for resource recovery from waste streams. This approach has evolved beyond its traditional role in energy generation to encompass the production of valuable carboxylic acids, especially volatile fatty acids (VFAs) like acetic acid, propionic acid, and butyric acid. VFAs hold great potential for various industries and biobased applications due to their versatile properties. Despite increasing global demand, over 90% of VFAs are currently produced synthetically from petrochemicals. Realizing the potential of large-scale biobased VFA production from waste streams offers significant eco-friendly opportunities but comes with several key challenges. These include low VFA production yields, unstable acid compositions, complex and expensive purification methods, and post-processing needs. Among these, production yield and acid composition stand out as the most critical obstacles impacting economic viability and competitiveness. This paper seeks to offer a comprehensive view of combining complementary modeling approaches, including kinetic and microbial modeling, to understand the workings of microbial communities and metabolic pathways in VFA production, enhance production efficiency, and regulate acid profiles through the integration of omics and bioreactor data.

Pharmacokinetic Analysis of [18F]FES PET in the Human Brain and Pituitary Gland

PURPOSE

Estrogen receptors (ER) are implicated in psychiatric disorders. We assessed if ER availability in the human brain could be quantified using 16α-[18F]-fluoro-17β-estradiol ([18F]FES) positron emission tomography (PET).

PROCEDURES

Seven post‑menopausal women underwent a dynamic [18F]FES PET scan with arterial blood sampling. A T1-weighted MRI was acquired for anatomical information. After one week, four subjects received a selective ER degrader (SERD), four hours before the PET scan. Pharmacokinetic analysis was performed using a metabolite-corrected plasma curve as the input function. The optimal kinetic model was selected based on the Akaike information criterion and standard error of estimated parameters. Accuracy of Logan graphical analysis and standardized uptake value (SUV) was determined via correlational analyses.

RESULTS

The reversible two-tissue compartment model (2T4k) model with fixed K1/k2 was preferred. The total volume of distribution (VT) could be more reliably estimated than the binding potential (BPND). A high correlation of VT with Logan graphical analysis was observed, but only a moderate correlation with SUV. SERD administration resulted in a reduced VT in the pituitary gland, but not in other regions.

CONCLUSIONS

The optimal quantification method for [18F]FES was the 2T4k with fixed K1/k2 or Logan graphical analysis, but specific binding was only observed in the pituitary gland.

Performance effectiveness of nano-lignin in production of gel with nano-chitosan for controlling release of salicylic acid

This work deals with assessing the performance of lignin nanoparticles (LNPs) in solving the problem of using salicylic acid as an agrochemical compound, via controlling its release. LNPs, obtained from black liquor, have been used to develop new delivery systems. Gels from chelating of LNPs with chitosan or chitosan nanoparticles (Cs-NPs) in presence or absence of cationic starch are investigated to achieve this essential aim. The nanoparticles are examined by TEM, ATR-FTIR, and XRD techniques. Based on measurements of swelling, encapsulation, release profile, release kinetic modeling of salicylic acid (SA), infrared spectroscopy, thermo-gravimetric analysis and scanning electron microscope the behavior of the investigated nanocomposite gels is assessed. The results show that the SA release profile of Cs-NPs and its nanocomposite with LNPs in phosphate-buffered saline (PBS) (7.4) (51.5-69.4 %) is higher than that of the mixture of water and ethanol (34.9-50.4 %). The release profile in PBS (7.4) demonstrates a trend of prolonged SA release over a 48-hour period. Best control of the SA-release can be achieved by CsNPs-LNPs nanocomposite. Comparing the results with previous literature demonstrates the promising characteristics of these examined gel nanocomposites. The release of SA from nanocomposites is regulated by a diffusion mechanism and follows the Ritger-Peppas and Higuchi models for kinetic release.

Elucidating uptake and metabolic fate of dipeptides in CHO cell cultures using 13C labeling experiments and kinetic modeling

The rapidly growing market of biologics including monoclonal antibodies has stimulated the need to improve biomanufacturing processes including mammalian host systems such as Chinese Hamster Ovary (CHO) cells. Cell culture media formulations continue to be enhanced to enable intensified cell culture processes and optimize cell culture performance. Amino acids, major components of cell culture media, are consumed in large amounts by CHO cells. Due to their low solubility and poor stability, certain amino acids including tyrosine, leucine, and phenylalanine can pose major challenges leading to suboptimal bioprocess performance. Dipeptides have the potential to replace amino acids in culture media. However, very little is known about the cleavage, uptake, and utilization kinetics of dipeptides in CHO cell cultures. In this study, replacing amino acids, including leucine and tyrosine by their respective dipeptides including but not limited to Ala-Leu and Gly-Tyr, supported similar cell growth, antibody production, and lactate profiles. Using 13C labeling techniques and spent media studies, dipeptides were shown to undergo both intracellular and extracellular cleavage in cultures. Extracellular cleavage increased with the culture duration, indicating cleavage by host cell proteins that are likely secreted and accumulate in cell culture over time. A kinetic model was built and for the first time, integrated with 13C labeling experiments to estimate dipeptide utilization rates, in CHO cell cultures. Dipeptides with alanine at the N-terminus had a higher utilization rate than dipeptides with alanine at the C-terminus and dipeptides with glycine instead of alanine at N-terminus. Simultaneous supplementation of more than one dipeptide in culture led to reduction in individual dipeptide utilization rates indicating that dipeptides compete for the same cleavage enzymes, transporters, or both. Dipeptide utilization rates in culture and cleavage rates in cell-free experiments appeared to follow Michaelis-Menten kinetics, reaching a maximum at higher dipeptide concentrations. Dipeptide utilization behavior was found to be similar in cell-free and cell culture environments, paving the way for future testing approaches for dipeptides in cell-free environments prior to use in large-scale bioreactors. Thus, this study provides a deeper understanding of the fate of dipeptides in CHO cell cultures through an integration of cell culture, 13C labeling, and kinetic modeling approaches providing insights in how to best use dipeptides in media formulations for robust and optimal mammalian cell culture performance.

Kinetics and toxicological potential of heavy metal leaching from asphalt pavements

The leaching of heavy metals from asphalt pavement has attracted increasing attention due to its associated environmental risks. Comprehending the leaching process is crucial for ensuring the safe utilization of asphalt pavement. This study investigates heavy metal leaching kinetics from asphalt pavements using tank-leaching tests and dynamic simulations employing both first and second-order kinetic models. Furthermore, this study reveals the toxicological potential of heavy metal leaching from asphalt pavement by assessing its temporal metal accessibility based on the obtained kinetic attributes. Six distinct asphalt mixtures were prepared and tested, each exhibiting two different gradations. The findings demonstrated that both kinetic models effectively elucidated the leaching process. Notably, the relatively stable final leaching stages primarily adhered to first-order kinetics, while the second-order kinetics provided a superior description of the more intricate initial leaching stages. In terms of toxicological potential, the results indicated that recycled waste-incorporated asphalt pavements, specifically bottom ash-incorporated asphalt and asphalt rubber, exhibited excessive heavy metal leaching for varying durations, ranging from several days to months under specific conditions. This study has provided valuable insights into the metal leaching kinetics of asphalt pavements and their associated toxicological impact, significantly advancing the current understanding of the consequences of heavy metal leaching from asphalt pavements.

Co-precipitation of iron and silicon: Reaction kinetics, elemental ratios and the influence of phosphorus

A sufficient supply of dissolved silicon (DSi) relative to dissolved phosphorus (DP) may decrease the likelihood of harmful algal blooms in eutrophic waters. Oxidative precipitation of Fe(II) at oxic-anoxic interfaces may contribute to the immobilization of DSi, thereby exerting control over the DSi availability in the overlying water. Nevertheless, the efficacy of DSi immobilization in this context remains to be precisely determined. To investigate the behavior of DSi during Fe(II) oxidation, anoxic solutions containing mixtures of aqueous Fe(II), DSi, and dissolved phosphorus (DP) were exposed to dissolved oxygen (DO) in the batch system. The experimental data, combined with kinetic reaction modeling, indicate that DSi removal during Fe(II) oxidation occurs via two pathways. At the beginning of the experiments, the oxidation of Fe(II)-DSi complexes induces the fast removal of DSi. Upon complete oxidation of Fe(II), further DSi removal is due to adsorption to surface sites of the Fe(III) oxyhydroxides. The presence of DP effectively competes with DSi via both of these pathways during the initial and later stages of the experiments, with as a result more limited removal of DSi during Fe(II) oxidation. Overall, we conclude that at near neutral pH the oxidation of Fe(II) has considerable capacity to immobilize DSi, where the rapid homogeneous oxidation of Fe(II)-DSi results in greater DSi removal compared to surface adsorption. Elevated DP concentration, however, effectively outcompetes DSi in co-precipitation interactions, potentially contributing to enhanced DSi availability within aquatic systems.

A Generalized Kinetic Model of Fractional Order Transport Dynamics with Transit Time Heterogeneity in Microvascular Space

The aim of this study is to develop and validate a unifying kinetic model for microvascular transport by introducing an impulse response function that incorporates essential physiological parameters and integrates key features of existing models. This new methodology combines a one-compartment model of fractional order with a model that uses the gamma distribution to describe the distribution of capillary transit times. Central to this model are two primary parameters: [Formula: see text], representing the kurtosis of residue times, and [Formula: see text], signifying the width of the distribution of capillary transit times within a tissue voxel. To validate this proposed model, data from dynamic contrast-enhanced magnetic resonance imaging (DCI-MRI) were employed and the findings were compared with three existing models. Using the Akaike information criterion for model selection, the results demonstrate that the integrative model, especially at elevated blood flow rates, frequently offers superior fits in comparison to constrained models.

A kinetic-assisted growth curve prediction method for Chlamydomonas reinhardtii incorporating transfer learning

Traditional predictions of microalgal growth states rely on empirical or easily implementable kinetic models, leading to significant biases and elevated cost. This study proposes a kinetic-assisted machine learning method for predicting the growth curve of microalgal biomass under small sample conditions. Firstly, a microalgae growth kinetic model is constructed based on the logistic model. A two-stage kinetic fitting strategy is specified to account for the light-dark ratio. The Box-Behnken method is employed for experimental design. Then, using Two-stage TrAdaboost.R2 algorithm, the kinetic model is utilized as the source domain, and the experimental design data serves as the target domain for training machine learning models. The results indicate that the proposed method outperforms a single machine learning model in terms of prediction and has the potential to rapidly estimate microalgal growth trends under different conditions and accurately predict harvested biomass, potentially reducing the need for laborious, expensive, and time-consuming laboratory trials.

Denim industry wastewater treatment by a heterogeneous solar-Fenton process catalyzed by Fe supported on recycled polyethylene terephthalate (PET) by ultrasonic modification

The textile industry is an important economic sector; however, its wastewater generates a great impact on the environment. A heterogeneous solar Fenton (HSF) process was evaluated for denim wastewater treatment. The catalyst was obtained through ultrasonic modification of recycled polyethylene terephthalate (PET) with Fe nanoparticles (PET/NPs- Fe3O4). The SFH process was optimized using surface response methodology with a face-centered central composite design considering the effects of the hydraulic retention time (10, 25, and 40 min), hydrogen peroxide dosage (500, 1000, and 1500 mg/L), and mass of the packed catalyst (4, 6 and 8 g) on the color, COD, and turbidity removal efficiencies. The operating conditions for maximum COD removal were H2O2 541.7 mg/L, HRT 33.9 min, and PET/NPs- Fe3O4 dose 7.9 g with solar radiation. The removal of 91.2% COD, 86.2% color, 90.4% turbidity, and 81.9% TOC was obtained at 14.2 kJ/L QUva. PET modification yielded 1.6 mg Fe/g PET, and the modification method does not allow Fe leaching. The effluent obtained from the SFH process complies with the maximum permissible limits in Mexican legislation in terms of COD, TOC, turbidity, and color and allows the reuse of PET.

Combined whole-body dynamic and static PET/CT with low-dose [18F]PSMA-1007 in prostate cancer patients

AIM

In addition to significant improvements in sensitivity and image quality, the recent introduction of long axial field-of-view (LAFOV) PET/CT scanners has enabled dynamic whole-body imaging for the first time. We aim herein to determine an appropriate acquisition time range for static low-dose [18F]PSMA-1007 PET imaging and to investigate the whole-body pharmacokinetics of [18F]PSMA-1007 by dynamic PET with the LAFOV Biograph Vision Quadra PET/CT in a group of prostate cancer patients.

METHODOLOGY

In total, 38 prostate cancer patients were enrolled in the analysis for staging or re-staging purposes. Thirty-four patients underwent dynamic whole-body PET/CT (60 min) followed by static whole-body PET/CT and four patients underwent static whole-body PET/CT only. The activity applied was 2 MBq/kg [18F]PSMA-1007. The static PET images of 10-min duration (PET-10) were reconstructed and further split into 8-min (PET-8), 6-min (PET-6), 5-min (PET-5), 4-min (PET-4), and 2-min (PET-2) duration groups. Comparisons were made between the different reconstructed scan times in terms of lesion detection rate and image quality based on SUV calculations of tumor lesions and the spleen, which served as background. Analysis of the dynamic PET/CT data was based on a two-tissue compartment model using an image-derived input function obtained from the descending aorta.

RESULTS

Analysis of lesion detection rate showed no significant differences when reducing PET acquisitions from 10 up to 5 min. In particular, a total of 169 lesions were counted with PET-10, and the corresponding lesion detection rates (95% CI for the 90% quantile of the differences in tumor lesions) for shorter acquisitions were 100% (169/169) for PET-8 (95% CI: 0-0), 98.8% (167/169) for PET-6 (95% CI: 0-1), 95.9% (162/169) for PET-5 (95% CI: 0-3), 91.7% (155/169) for PET-4 (95% CI: 1-2), and 85.2% (144/169) for PET-2 (95% CI: 1-6). With the exception of PET-2, the differences observed between PET-10 and the other shorter acquisition protocols would have no impact on any patient in terms of clinical management. Objective evaluation of PET/CT image quality showed no significant decrease in tumor-to-background ratio (TBR) with shorter acquisition times, despite a gradual decrease in signal-to-noise ratio (SNR) in the spleen. Whole-body quantitative [18F]PSMA-1007 pharmacokinetic analysis acquired with full dynamic PET scanning was feasible in all patients. Two-tissue compartment modeling revealed significantly higher values for the parameter k3 in tumor lesions and parotid gland compared to liver and spleen, reflecting a higher specific tracer binding to the PSMA molecule and internalization rate in these tissues, a finding also supported by the respective time-activity curves. Furthermore, correlation analysis demonstrated a significantly strong positive correlation (r = 0.72) between SUV and k3 in tumor lesions.

CONCLUSIONS

In prostate cancer, low-dose (2 MBq/kg) [18F]PSMA-1007 LAFOV PET/CT can reduce static scan time by 50% without significantly compromising lesion detection rate and objective image quality. In addition, dynamic PET can elucidate molecular pathways related to the physiology of [18F]PSMA-1007 in both tumor lesions and normal organs at the whole-body level. These findings unfold many of the potentials of the new LAFOV PET/CT technology in the field of PSMA-based diagnosis and theranostics of prostate cancer.

A nano-liposomal carrier containing p-coumaric acid for induction of targeted apoptosis on melanoma cells and kinetic modeling

There has been a growth in the use of plant compounds as biological products for the prevention and treatment of various diseases, including cancer. As a phenolic compound, p-Coumaric acid (p-CA) demonstrates preferrable biological effects such as anti-cancer activities. A nano-liposomal carrier containing p-CA was designed to increase the anticancer effectiveness of this compound on melanoma cells (A375). To determine the characteristics of synthesized liposomes, encapsulation efficiency was measured. In addition, the particle size was measured utilizing DLS, FTIR, and morphology examination using SEM. In vitro release was also studied through the dialysis method, while toxicity was evaluated using the MTT assay. To determine apoptotic characteristics, biotechnology tools like flow cytometry, real time PCR, and atomic force microscopy (AFM) were employed. The findings indicated that in the cells treated with the liposomal form of p-CA, the amount of elastic modulus was higher compared to its free form. Kinetic modeling indicated that the best fitting model was zero-order.

Kinetic Modeling Methods in Preclinical Positron Emission Tomography Imaging

There is an expanding number of applications for preclinical positron emission tomography (PET) imaging. Kinetic modeling of PET data provides rich multiparameter information on radiotracer uptake and binding in tissue from a single experiment. In this chapter, we provide a practical step-by-step protocol to assist with collection of PET data for kinetic modeling studies in rats and mice.

Synthesis and characterization of Alg/Gel/n-HAP/MNPs porous nanocomposite adsorbent for efficient water conservancy and removal of methylene blue in aqueous environments: Kinetic modeling and artificial neural network predictions

In this study, a new porous nanocomposite adsorbent for water conservancy was synthesized using the freeze-drying technique to adsorb a cationic dye (Methylene Blue) in an aqueous environment. The nanocomposite adsorbent was synthesized using natural polymers, gelatin, and sodium alginate, and hydroxyapatite and magnetic iron oxide nanoparticles was incorporated into the polymer network to improve mechanical properties and increase the surface-to-volume ratio. To confirm the structure and morphology of the sample, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscope (SEM) techniques were employed. In addition, the magnetic properties of the synthesis of MNPs and porous nanocomposite were determined using value stream mapping (VSM) and dynamic light scattering (DLS). The adsorption of Methylene Blue (MB) was studied as a function of effective physical and variable parameters, such as time, temperature, pH, and initial concentration. The synthesized porous nanocomposite adsorbent exhibited a high adsorption capacity of 473.2 mg g-1 and followed pseudo-second-order kinetics. Additionally, the maximum adsorption capacity was observed at an initial concentration of 534.9 mg g-1. The adsorbent was also sensitive to temperature changes and was well-described thermodynamically and isothermally by the Freundlich isotherm model. Two artificial neural networks (ANNs) were also developed to investigate the properties of the synthesized nanocomposites. In the first ANN, the properties of the nanocomposites, including pore size, porosity, compressive strength, and elastic modulus, were predicted based on the variations in the weight percentages of gelatin and hydroxyapatite. In the second ANN, the effects of changes in temperature and initial concentration on the adsorption of MB by the synthesized nanocomposite samples were predicted. The ANNs' predictions indicated that increasing the weight percentage of hydroxyapatite nanoparticles and gelatin enhances the physical, mechanical, and adsorption performance of the synthesized porous nanocomposites. The best results were achieved for the sample containing 40 wt % of gelatin and 30 wt % of hydroxyapatite nanoparticles. Furthermore, the ANN models demonstrated that increasing the temperature and initial concentration resulted in an increase in the amount of MB adsorbed.

Kinetic Modeling of Hepatic Metabolism and Simulation of Treatment Effects

Mathematical modeling is a promising strategy to fill the experimentally unapproachable knowledge gaps about the relative contribution of various molecular processes to cellular metabolic function. To this end, we developed detailed kinetic models of the central metabolism of different cell types, comprising multiple metabolic functionalities. We used the model to simulate metabolic changes in several cell types under different experimental settings in health and disease. In this way, we show that it is possible to decipher and characterize the relative influence of various metabolic pathways and enzymes to overall metabolic performance and phenotype.Quantitative Systems Metabolism (QSM™) allows quantitative assessment of metabolic functionality and metabolic profiling based on proteomic data. Here, we describe the technique, namely, molecular resolved kinetic modeling, underlying QSM™. We explain the necessary steps for the generation of cell-specific models to functionally interpret proteomic data and point out some unresolved challenges and open questions.

Comparison of parametric imaging and SUV imaging with [68 Ga]Ga-PSMA-11 using dynamic total-body PET/CT in prostate cancer

PURPOSE

Standardized uptake value (SUV) has been prevalently used to measure [68 Ga]Ga-PSMA-11 activity in prostate cancer, but it is susceptible to multiple factors. Parametric imaging allows for absolute quantification of tracer uptake and provides a better diagnostic accuracy that is crucial for lesion detection. However, the clinical significance of total-body parametric imaging of [68 Ga]Ga-PSMA-11 remains to be fully assessed. Therefore, the aim of our study is to delve into the diagnostic implications of total-body parametric imaging of [68 Ga]Ga-PSMA-11 PET/CT for patients with prostate cancer.

METHODS

Twenty prostate cancer patients were included and underwent a dynamic total-body [68 Ga]Ga-PSMA-11 PET/CT scan. An irreversible two-tissue compartment model (2T3k) was fitted for each tissue time-to-activity curve, and the net influx rate (Ki) was obtained. The image quality and semi-quantitative analysis of lesion-to-background ratio (LBR), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were compared between parametric images and SUV images.

RESULTS

Kinetic modeling using 2T3k demonstrated favorable model fitting in both normal organs and lesions. All of the lesions detected on SUV images (55-60 min) could be detected on Ki images. The correlation between Ki, SUVmean, and SUVmax in both normal organs and pathological lesions was found to be positive and statistically significant. Conversely, a moderate positive correlations were found between Ki and K1 (R = 0.69, P < 0.001; R = 0.61, P < 0.001) and Ki and k3 (R = 0.69, P < 0.001; R = 0.62, P < 0.001), in normal organs and pathological lesions, respectively. Visual assessment in Ki images showed less image noise and higher lesions conspicuity compared to SUV images. Ki image-derived LBR, SNR, and CBR of pathological lesions including primary tumors (PTs), lymph node metastases (LNMs) and bone metastases (BMs), exhibited remarkably higher folds (1.4-3.6 folds) compared to those derived from SUV of corresponding lesions.

CONCLUSIONS

Total-body parametric imaging of [68 Ga]Ga-PSMA-11 enhanced lesion contrast and improved lesion detectability compared to SUV images. This may potentially serve as an imaging biomarker and theranostic tool for precise diagnosis and treatment evaluation in prostate cancer patients.

Kinetics of phosphorous sorption to biochar-amended soils

Biochar has been investigated as a potential soil amendment for increasing P sorption to soils. Several studies of shown that coating biochar with Fe oxides can increase the amount of P sorbed to the biochar, yet little is known about the kinetics of P sorption to soils amended with Fe-coated biochar. In this study, the kinetics of P sorption are measured in four soils with contrasting surface properties and textures. In addition, a wood-based biochar, both unmodified (BC) and modified by chemical precipitation of Fe oxides (BCFe), was added to these four soils at a rate of 5% (w/w). P sorption to each soil with and without the unmodified or Fe-coated biochar was measured at incubation times ranging from 1 to 314 h. The data were fit using five different kinetic models to determine if the addition of the BC or BCFe significantly affected the amount of P sorption and the kinetic behavior of P sorption to the biochar-amended soils. Results showed that amending with BC had minimal impact on P sorption to the four soils, whereas the impact of the BCFe on P sorption varied depending on soil. In the low P sorbing soil, the BCFe nearly doubled the amount of P sorbed whereas in the high P sorbing soil, addition of the BCFe resulted in less-than-expected increases in P sorption. For each biochar and soil treatment, the same kinetic model provided the best fit to the observed sorption over time. In two soils, the kinetic model parameters were significantly different following the addition of the BC whereas the model parameters for all four soils were significantly different following addition of BCFe. This study provides new insights into P sorption kinetics to biochar-amended soils.

Performance evaluation of sulfidated nanoscale iron for hexavalent chromium removal from groundwater in sequential batch study

Chromium [Cr] contamination in groundwater is one of the serious environmental concerns due to the carcinogenicity of its water-soluble and mobile hexavalent [Cr(VI)] form. In spite of the existence of multiple precipitation and adsorption-based Cr(VI) remediation technologies, the usage of sulfidated nano zerovalent iron (S-nZVI) has recently attracted researchers due to its high selectivity. Although S-nZVI effectively immobilized Cr(VI), its long-term performance in multiple shifted equilibrium has not been explored. In this contribution, influences of S-nZVI dosage, initial concentration of Cr(VI), pH, ionic strength, total hardness, sulfate, carbonate, and silicate were probed in ultrapure water. Further experiments were performed in synthetic groundwater to investigate the effects of initial concentration of Cr(VI) in the pH range of 4-8 for 1 g L-1 S-nZVI dosage. Cr(VI) removal rate was quantified in groundwater without pH fixation. Finally, a comparative study between conventional nano zerovalent iron (nZVI) and S-nZVI was conducted in sequential batch reactors to investigate their respective efficiencies during repeated usage. Mechanistic interpretation of the processes governing the immobilization of Cr(VI) was done by integrating the results of these experiments with the metadata. While aggregation due to magnetic properties and rapid oxidation of Fe decreased the efficiency of nZVI with repeated usage, sulfidation minimized the passivation and favored an extended reducing environment because of continuous electron transfer from iron and sulfur components.

Contributions of Pharmaceuticals to DBP Formation and Developmental Toxicity in Chlorination of NOM-containing Source Water

Pharmaceuticals have been considered a priority group of emerging micropollutants in source waters in recent years, while their role in the formation and toxicity of disinfection byproducts (DBPs) during chlorine disinfection remains largely unclear. In this study, the contributions of natural organic matter (NOM) and pharmaceuticals (a mixture of ten representative pharmaceuticals) to the overall DBP formation and toxicity during drinking water chlorination were investigated. By innovatively "normalizing" chlorine exposure and constructing a kinetic model, we were able to differentiate and evaluate the contributions of NOM and pharmaceuticals to the total organic halogen (TOX) formation for source waters that contained different levels of pharmaceuticals. It was found that at a chlorine contact time of 1.0 h, NOM (2 mg/L as C) and pharmaceuticals (total 0.0062-0.31 mg/L as C) contributed 79.8-99.5% and 0.5-20.2%, respectively, of TOX. The toxicity test results showed that the chlorination remarkably increased the toxicity of the pharmaceutical mixture by converting the parent compounds into more toxic pharmaceutical-derived DBPs, and these DBPs might contribute significantly to the overall developmental toxicity of chlorinated waters. This study highlights the non-negligible role of pharmaceuticals in the formation and toxicity of overall DBPs in chlorinated drinking water.

Imaging glymphatic response to glioblastoma

BACKGROUND

The glymphatic system actively exchanges cerebrospinal fluid (CSF) and interstitial fluid (ISF) to eliminate toxic interstitial waste solutes from the brain parenchyma. Impairment of the glymphatic system has been linked to several neurological conditions. Glioblastoma, also known as Glioblastoma multiforme (GBM) is a highly aggressive form of malignant brain cancer within the glioma category. However, the impact of GBM on the functioning of the glymphatic system has not been investigated. Using dynamic contrast-enhanced magnetic resonance imaging (CE-MRI) and advanced kinetic modeling, we examined the changes in the glymphatic system in rats with GBM.

METHODS

Dynamic 3D contrast-enhanced T1-weighted imaging (T1WI) with intra-cisterna magna (ICM) infusion of paramagnetic Gd-DTPA contrast agent was used for MRI glymphatic measurements in both GBM-induced and control rats. Glymphatic flow in the whole brain and the olfactory bulb was analyzed using model-derived parameters of arrival time, infusion rate, clearance rate, and residual that describe the dynamics of CSF tracer over time.

RESULTS

3D dynamic T1WI data identified reduced glymphatic influx and clearance, indicating an impaired glymphatic system due to GBM. Kinetic modeling and quantitative analyses consistently indicated significantly reduced infusion rate, clearance rate, and increased residual of CSF tracer in GBM rats compared to control rats, suggesting restricted glymphatic flow in the brain with GBM. In addition, our results identified compromised perineural pathway along the optic nerves in GBM rats.

CONCLUSIONS

Our study demonstrates the presence of GBM-impaired glymphatic response in the rat brain and impaired perineural pathway along the optic nerves. Reduced glymphatic waste clearance may lead to the accumulation of toxic waste solutes and pro-inflammatory signaling molecules which may affect the progression of the GBM.

Cell-culture process optimization via model-based predictions of metabolism and protein glycosylation

In order to meet the rising demand for biologics and become competitive on the developing biosimilar market, there is a need for process intensification of biomanufacturing processes. Process development of biologics has historically relied on extensive experimentation to develop and optimize biopharmaceutical manufacturing. Experimentation to optimize media formulations, feeding schedules, bioreactor operations and bioreactor scale up is expensive, labor intensive and time consuming. Mathematical modeling frameworks have the potential to enable process intensification while reducing the experimental burden. This review focuses on mathematical modeling of cellular metabolism and N-linked glycosylation as applied to upstream manufacturing of biologics. We review developments in the field of modeling cellular metabolism of mammalian cells using kinetic and stoichiometric modeling frameworks along with their applications to simulate, optimize and improve mechanistic understanding of the process. Interest in modeling N-linked glycosylation has led to the creation of various types of parametric and non-parametric models. Most published studies on mammalian cell metabolism have performed experiments in shake flasks where the pH and dissolved oxygen cannot be controlled. Efforts to understand and model the effect of bioreactor-specific parameters such as pH, dissolved oxygen, temperature, and bioreactor heterogeneity are critically reviewed. Most modeling efforts have focused on the Chinese Hamster Ovary (CHO) cells, which are most commonly used to produce monoclonal antibodies (mAbs). However, these modeling approaches can be generalized and applied to any mammalian cell-based manufacturing platform. Current and potential future applications of these models for Vero cell-based vaccine manufacturing, CAR-T cell therapies, and viral vector manufacturing are also discussed. We offer specific recommendations for improving the applicability of these models to industrially relevant processes.

Sustainable remediation of dye-contaminated wastewater using novel cross-linked Hex-CCP-co-PPT microspheres

The presence of dyes in contaminated water poses substantial dangers to the health of both humans and aquatic life. A process called precipitation polymerization was used to create unique cross-linked hexa-chlorocyclotriphosphazene-co-phenolphthalein (Hex-CCP-co-PPT) microspheres for the purpose of this research. Advanced methods such as X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and differential thermogravimetry (DTG) were used to characterise these microspheres. In a simulated solution, the performance of Hex-CCP-co-PPTs as a sorbent for removing MB dye was investigated, and the results showed an unprecedentedly high removal rate of 88.4% for MB. Temperature of 25 °C, a Hex-CCP-co-PPTs dose of 40 mg, an MB concentration of 20 ppm, an MB solution volume of 20 mL, a contact time of 40 min, and a pH of 9 were found to be the optimal experimental conditions. According to the results of the kinetic and adsorption analyses, the PSO and Langmuir adsorption models are the best ones to use. These models favour the chemi-sorption nature and mono-layered adsorption of MB in comparison to Hex-CCP-co-PPTs. Importantly, the thermodynamic analysis demonstrated that the process of removing MB by utilizing Hex-CCP-co-PPTs was endothermic and occurred spontaneously. These findings highlight the potential application of Hex-CCP-co-PPT microspheres in Algal Membrane Bioreactors (AMBRs) for the efficient and sustainable removal of dye from wastewater. This would contribute to the protection of ecosystems as well as the public's health.

Biodegradation of Congo Red Dye Using Lysinibacillus Species in a Moving Bed Biofilm Reactor: Continuous Study and Kinetic Evaluation

The objective of this work was to develop a low-cost and efficient biocarrier for biodegradation of azo dye (i.e., Congo red (CR) dye). The potential bacterial species, i.e., Lysinibacillus fusiformis KLM1 and Lysinibacillus macrolides KLM2, were isolated from the dye-contaminated site. These bacterial species were immobilized onto the polypropylene-polyurethane foam (PP-PUF) and employed in a moving bed biofilm reactor (MBBR) for the treatment of CR dye. The effectiveness of the MBBR was investigated by operating the bioreactor in a continuous mode at various initial CR dye concentrations (50-250 mg/L) for 113 days. The removal efficiency was found in the range of 88.4-64.6% when the initial dye concentration was varied from 50 to 250 mg/L. The maximum elimination capacity (EC) of 213.18 mg/L.d was found at 250 mg/L of CR dye concentration. In addition, the CR dye utilization rate in the MBBR was studied by using two kinetics, namely, first-order and second-order (Grau) models. The high regression coefficients (R2 > 0.97) and the satisfactory root mean square (RMSE) values (0.00096-0.02610) indicated the reasonable prediction of CR dye degradation rate by the Grau model.

Metabolic control analysis as a strategy to identify therapeutic targets, the case of cancer glycolysis

The use of kinetic modeling and metabolic control analysis (MCA) to identify possible therapeutic targets and to investigate the controlling and regulatory mechanisms in cancer glycolysis is here reviewed. The glycolytic pathway has been considered a target to decrease cancer cell growth; however, its occurrence in normal cells makes it difficult to design therapeutic strategies that target this pathway in pathological cells. Notwithstanding, the over-expression of all enzymes and transporters, as well as the expression of isoenzymes with different kinetic and regulatory properties in cancer cells, suggested a different distribution of the control of glycolytic flux than that observed in normal cells. Kinetic models of glycolysis are constructed with enzyme kinetics experimental data, validated with the steady-state metabolite concentrations and glycolytic fluxes; applying MCA, permitted us to identify the steps with the highest control of glycolysis in cancer cells, but low control in normal cells. The cancer glycolysis main controlling steps under several metabolic conditions were: glucose transport, hexokinase and hexose-6-phosphate isomerase (HPI); whereas in normal cells were: the first two and phosphofructokinase-1. HPI is the best therapeutic target because it exerts high control in cancer glycolytic flux, but not in normal cells. Furthermore, kinetic modeling also contributed to identifying new feed-back and feed-forward regulatory loops in cancer cells glycolysis, and to understanding the mode of metabolic action of glycolytic inhibitors. Thus, MCA and metabolic modeling allowed to propose new strategies for inhibiting glycolysis in cancer cells.

Design of a new fountain reactor for contamination degradation using advanced oxidation processes with hybrid techniques and modeling evaluation

Due to the water and energy crises, wastewater treatment systems that are more energy efficient and capable of large volume degradation are a priority. Photochemical decomposition methods have a significant impact on pollutant treatment. The use of these methods in conjunction with a novel designed reactor and hybridization processes can result in considerable treatment results. This research used a fountain system in a UV/H2O2 process to generate a belt-type liquid film with a low thickness and high mixing to remove methyl orange as a model pollutant. The flow rate, H2O2 concentration, temperature, and UV intensity were the parameters evaluated in this series of tests. After 90 minutes under optimum conditions, the maximum degradation of methyl orange was 99.73 percent. The efficiency of the purification process was increased to 99 percent in 75 minutes by using the optimum state of hybridization of UV/US/H2O2 processes. Two deep neural network models and a pseudo-first-order kinetic model were created to fit the experimental data. The results reveal a good fit between the experimental data and the model prediction. The discovered synergistic factor (1.168) and energy yield (2.65 g/kWh) demonstrated the high efficiency of the hybridization process and the outstanding function of the designed system, respectively.

Disentangling the impact of cerebrospinal fluid formation and neuronal activity on solute clearance from the brain

BACKGROUND

Despite recent attention, pathways and mechanisms of fluid transposition in the brain are still a matter of intense discussion and driving forces underlying waste clearance in the brain remain elusive. Consensus exists that net solute transport is a prerequisite for efficient clearance. The individual impact of neuronal activity and cerebrospinal fluid (CSF) formation, which both vary with brain state and anesthesia, remain unclear.

METHODS

To separate conditions with high and low neuronal activity and high and low CSF formation, different anesthetic regimens in naive rat were established, using Isoflurane (ISO), Medetomidine (MED), acetazolamide or combinations thereof. With dynamic contrast-enhanced MRI, after application of low molecular weight contrast agent (CA) Gadobutrol to cisterna magna, tracer distribution was monitored as surrogate for solute clearance. Simultaneous fiber-based Ca²⁺-recordings informed about the state of neuronal activity under different anesthetic regimen. T2-weighted MRI and diffusion-weighted MRI (DWI) provided size of subarachnoidal space and aqueductal flow as surrogates for CSF formation. Finally, a pathway and mechanism-independent two-compartment model was introduced to provide a measure of efficiency for solute clearance from the brain.

RESULTS

Anatomical imaging, DWI and Ca²⁺-recordings confirmed that conditions with distinct levels of neuronal activity and CSF formation were achieved. A sleep-resembling condition, with reduced neuronal activity and enhanced CSF formation was achieved using ISO+MED and an awake-like condition with high neuronal activity using MED alone. CA distribution in the brain correlated with the rate of CSF formation. The cortical brain state had major influence on tracer diffusion. Under conditions with low neuronal activity, higher diffusivity suggested enlargement of extracellular space, facilitating a deeper permeation of solutes into brain parenchyma. Under conditions with high neuronal activity, diffusion of solutes into parenchyma was hindered and clearance along paravascular pathways facilitated. Exclusively based on the measured time signal curves, the two-compartment model provided net exchange ratios, which were significantly larger for the sleep-resembling condition than for the awake-like condition.

CONCLUSIONS

Efficiency of solute clearance in brain changes with alterations in both state of neuronal activity and CSF formation. Our clearance pathway and mechanism agnostic kinetic model informs about net solute transport, solely based on the measured time signal curves. This rather simplifying approach largely accords with preclinical and clinical findings.

Measuring brain docosahexaenoic acid turnover as a marker of metabolic consumption

Docosahexaenoic acid (DHA, 22:6n-3) accretion in brain phospholipids is critical for maintaining the structural fluidity that permits proper assembly of protein complexes for signaling. Furthermore, membrane DHA can by released by phospholipase A₂ and act as substrate for synthesis of bioactive metabolites that regulate synaptogenesis, neurogenesis, inflammation, and oxidative stress. Thus, brain DHA is consumed through multiple pathways including mitochondrial β-oxidation, autoxidation to neuroprostanes, as well as enzymatic synthesis of bioactive metabolites including oxylipins, synaptamide, fatty-acid amides, and epoxides. By using models developed by Rapoport and colleagues, brain DHA loss has been estimated to be 0.07-0.26 μmol DHA/g brain/d. Since β-oxidation of DHA in the brain is relatively low, a large portion of brain DHA loss may be attributed to synthesis of autoxidative and bioactive metabolites. In recent years, we have developed a novel application of compound specific isotope analysis to trace DHA metabolism. By the use of natural abundance in ¹³C-DHA in food supply, we are able to trace brain phospholipid DHA loss in free-living mice with estimates ranging from 0.11 to 0.38 μmol DHA/g brain/d, in reasonable agreement with previous methods. This novel fatty acid metabolic tracing methodology should improve our understanding of the factors that regulate brain DHA metabolism.

Synergistic interactions between sewage sludge, polypropylene, and high-density polyethylene during co-pyrolysis: An investigation based on iso-conversional model-free methods and master plot analysis

Sewage sludge (SS) is a hazardous by-product of wastewater treatment processes that requires careful management for minimal environmental impacts and effective resource recovery. Through thermochemical processes such as pyrolysis, clean energy is recovered from SS in the form of bio-oil, biogas, and biochar. To improve the yield and quality of products, the co-pyrolysis of more than two materials is increasingly gaining interest. Here, the thermal behaviour, kinetics, and synergistic interactions during the co-pyrolysis of SS with polypropylene (PP) and high-density polyethylene (HDPE) were comparatively evaluated with thermogravimetric analysis at different mixing ratios and heat rates. Activation energies and reaction mechanisms were determined through iso-conversional model-free methods and master plot analysis. Evolved gases were monitored with thermogravimetric-mass spectrometry. Increased volatile conversion and degradation rates, and reduced activation energies during co-pyrolysis were mediated by synergistic interactions between H-radicals of PP/HDPE and oxygenated intermediates of SS. Contrary to the pyrolysis of SS, PP and HDPE, the co-pyrolysis processes are predominantly diffusion-controlled. Insights into the co-pyrolysis processes of SS/PP and SS/HDPE gained from this work provide the theoretical support for subsequent investigation, facilitate design of waste-to-energy reactor, and aid the adoption of the technology to harness the bioenergy potential of the feedstocks.

Development of novel kinetic model based on microbiome and biochar for in-situ remediation of total petroleum hydrocarbons (TPHs) contaminated soil

A novel kinetic model has been developed to explain the degradation of total petroleum hydrocarbons. Microbiome engineered biochar amendment may result in a synergistic impact on degradation of total petroleum hydrocarbons (TPHs). Therefore, the present study analyzed the potential of hydrocarbon-degrading bacteria A designated as Aeromonas hydrophila YL17 and B as Shewanella putrefaciens Pdp11 morphological characterized as rod shaped, anaerobic and gram-negative immobilized on biochar, and the degradation efficiency was measured by gravimetric analysis and gas chromatography-mass spectrometry (GC-MS). Whole genome sequencing of both strains revealed the existence of genes responsible for hydrocarbon degradation. In 60 days remediation setup, the treatment consisting of immobilization of both strains on biochar proved more efficient with less half-life and better biodegradation potentials compared to biochar without strains for decreasing the content of TPHs and n-alkanes (C₁₂-C₁₈). Enzymatic content and microbiological respiration showed that biochar acted as a soil fertilizer and carbon reservoir and enhanced microbial activities. The removal efficiency of hydrocarbons was found to be a maximum of 67% in soil samples treated with biochar immobilized with both strains (A + B), followed by biochar immobilized with strain B 34%, biochar immobilized with strain A 29% and with biochar 24%, respectively. A 39%, 36%, and 41% increase was observed in fluorescein diacetate (FDA) hydrolysis, polyphenol oxidase and dehydrogenase activities in immobilized biochar with both strains as compared to control and individual treatment of biochar and strains. An increase of 35% was observed in the respiration rate with the immobilization of both strains on biochar. While a maximum colony forming unit (CFU/g) was found 9.25 with immobilization of both strains on biochar at 40 days of remediation. The degradation efficiency was due to synergistic effect of both biochar and bacteria based amendment on the soil enzymatic activity and microbial respiration.

Modeling the kinetics of perfluorooctanoic and perfluorooctane sulfonic acid biodegradation by Acidimicrobium sp. Strain A6 during the feammox process

Per- and polyfluoroalkyl substances (PFAS) are emerging contaminants of concern due to their health effects and persistence in the environment. Although perfluoroalkyl acids (PFAAs), such as perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) are very difficult to biodegrade because they are completely saturated with fluorine, it has recently been shown that Acidimicrobium sp. A6 (A6), which oxidizes ammonium under iron reducing conditions (Feammox process), can defluorinate PFAAs. A kinetic model was developed and tested in this study using results from previously published laboratory experiments, augmented with results from additional incubations, to couple the Feammox process to PFAS defluorination. The experimental results show higher Feammox activity and PFAS degradation in the A6 enrichment cultures than in the highly enriched A6 cultures. The coupled experimental and modeling results show that the PFAS defluorination rate is proportional to the rate of ammonium oxidation. The ammonium oxidation rate and the defluorination rate increase monotonically, but not linearly, with increasing A6 biomass. Given that different experiments had different level of Feammox activity, the parameters required to simulate the Feammox varied between A6 cultures. Nonetheless, the kinetic model was able to simulate an anaerobic incubation system and show that PFAS defluorination is proportional to the Feammox activity.

Design of a generic method for single dual-tracer PET imaging acquisition in clinical routine

Using different tracers in PET imaging can bring complementary information on tumor heterogeneities. Ideally, PET images of different tracers should be acquired simultaneously to avoid the bias induced by movement and physiological changes between sequential acquisitions. Previous studies have demonstrated the feasibility of recovering separated PET signals or parameters of two or more tracers injected (quasi-)simultaneously in a single acquisition. In this study, a generic framework in the context of dual-tracer PET acquisition is proposed where no strong kinetic assumptions nor specific tuning of parameters are required. The performances of the framework were assessed through simulations involving the combination of [¹⁸F]FCH and [¹⁸F]FDG injections, two protocols (90 and 60-minute acquisition durations) and various activity ratios between the two injections. Preclinical experiments with the same radiotracers were also conducted. Results demonstrate the ability of the method both to extract separated arterial input functions (AIF) from noisy image-derived input function (IDIF) and to separate the dynamic signals and further estimate kinetic parameters.&#xD;The compromise between bias and variance associated with the estimation of net influx rateK_ishows that it is preferable to use the second injected radiotracer with twice the activity of the first for both 90-minute [¹⁸F]FCH+[¹⁸F]FDG and 60-minute [¹⁸F]FDG+[¹⁸F]FCH protocols. In these optimal settings, the weighted mean-squared-error of the estimated AIF was always less than 7%. TheK_ibias was similar to the one of single-tracer acquisitions; below 5%. Compared to single-tracer results, the variance ofK_iwas twice more for 90-minute dual-tracer scenario and four times more for the 60-minute scenario. The generic design of the method makes it easy to use for other pairs of radiotracers and even for more than two tracers. The absence of strong kinetic assumptions and tuning parameters makes it suitable for a possible use in clinical routine.

Kinetic modeling of xylonic acid production by Gluconobacter oxydans: effects of hydrodynamic conditions

In this study, the synthesis of xylonic acid from xylose by Gluconobacter oxydans NL71 has been investigated. According to the relationship between oxygen transfer rate and oxygen uptake rate, three different kinetic models of product formation were established and the nonlinear fitting was carried out. The results showed that G. oxydans has critical dissolved oxygen under different strain concentrations, and the relationship between respiration intensity and dissolved oxygen conformed to the Monod equation [Formula: see text]. The maximum reaction rate per unit cell mass and the theoretical maximum specific productivity of G. oxydans obtained by the kinetic model are 0.042 mol/L/h and 6.97 g/g_x/h, respectively. These results will assist in determining the best balance between the airflow rate and cell concentration in the reaction and improve the production efficiency of xylonic acid.

Bayesian kinetic modeling for tracer-based metabolomic data

BACKGROUND

Stable Isotope Resolved Metabolomics (SIRM) is a new biological approach that uses stable isotope tracers such as uniformly [Formula: see text]-enriched glucose ([Formula: see text]-Glc) to trace metabolic pathways or networks at the atomic level in complex biological systems. Non-steady-state kinetic modeling based on SIRM data uses sets of simultaneous ordinary differential equations (ODEs) to quantitatively characterize the dynamic behavior of metabolic networks. It has been increasingly used to understand the regulation of normal metabolism and dysregulation in the development of diseases. However, fitting a kinetic model is challenging because there are usually multiple sets of parameter values that fit the data equally well, especially for large-scale kinetic models. In addition, there is a lack of statistically rigorous methods to compare kinetic model parameters between different experimental groups.

RESULTS

We propose a new Bayesian statistical framework to enhance parameter estimation and hypothesis testing for non-steady-state kinetic modeling of SIRM data. For estimating kinetic model parameters, we leverage the prior distribution not only to allow incorporation of experts' knowledge but also to provide robust parameter estimation. We also introduce a shrinkage approach for borrowing information across the ensemble of metabolites to stably estimate the variance of an individual isotopomer. In addition, we use a component-wise adaptive Metropolis algorithm with delayed rejection to perform efficient Monte Carlo sampling of the posterior distribution over high-dimensional parameter space. For comparing kinetic model parameters between experimental groups, we propose a new reparameterization method that converts the complex hypothesis testing problem into a more tractable parameter estimation problem. We also propose an inference procedure based on credible interval and credible value. Our method is freely available for academic use at https://github.com/xuzhang0131/MCMCFlux .

CONCLUSIONS

Our new Bayesian framework provides robust estimation of kinetic model parameters and enables rigorous comparison of model parameters between experimental groups. Simulation studies and application to a lung cancer study demonstrate that our framework performs well for non-steady-state kinetic modeling of SIRM data.

Elucidating yeast glycolytic dynamics at steady state growth and glucose pulses through kinetic metabolic modeling

Microbial cell factories face changing environments during industrial fermentations. Kinetic metabolic models enable the simulation of the dynamic metabolic response to these perturbations, but their development is challenging due to model complexity and experimental data requirements. An example of this is the well-established microbial cell factory Saccharomyces cerevisiae, for which no consensus kinetic model of central metabolism has been developed and implemented in industry. Here, we aim to bring the academic and industrial communities closer to this consensus model. We developed a physiology informed kinetic model of yeast glycolysis connected to central carbon metabolism by including the effect of anabolic reactions precursors, mitochondria and the trehalose cycle. To parametrize such a large model, a parameter estimation pipeline was developed, consisting of a divide and conquer approach, supplemented with regularization and global optimization. Additionally, we show how this first mechanistic description of a growing yeast cell captures experimental dynamics at different growth rates and under a strong glucose perturbation, is robust to parametric uncertainty and explains the contribution of the different pathways in the network. Such a comprehensive model could not have been developed without using steady state and glucose perturbation data sets. The resulting metabolic reconstruction and parameter estimation pipeline can be applied in the future to study other industrially-relevant scenarios. We show this by generating a hybrid CFD-metabolic model to explore intracellular glycolytic dynamics for the first time. The model suggests that all intracellular metabolites oscillate within a physiological range, except carbon storage metabolism, which is sensitive to the extracellular environment.

Quantitative assessment of translocator protein (TSPO) in the non-human primate brain and clinical translation of [18F]LW223 as a TSPO-targeted PET radioligand

OBJECTIVES

Translocator protein 18 kDa (TSPO) positron emission tomography (PET) can be harnessed for the non-invasive detection of macrophage-driven inflammation. [¹⁸F]LW223, a newly reported TSPO PET tracer which was insensitive to rs6971 polymorphism, showed favorable performance characteristics in a recent imaging study involving a rat myocardial infarction model. To enable quantitative neuroimaging with [¹⁸F]LW223, we conducted kinetic analysis in the non-human primate (NHP) brain. Further, we sought to assess the utility of [¹⁸F]LW223-based TSPO imaging in a first-in-human study.

METHODS

Radiosynthesis of [¹⁸F]LW223 was accomplished on an automated module, whereas molar activities, stability in formulation, lipophilicity and unbound free fraction (f_u) of the probe were measured. Brain penetration and target specificity of [¹⁸F]LW223 in NHPs were corroborated by PET-MR imaging under baseline and pre-blocking conditions using the validated TSPO inhibitor, (R)-PK11195, at doses ranging from 5 to 10 mg/kg. Kinetic modeling was performed using one-tissue compartment model (1TCM), two-tissue compartment model (2TCM) and Logan graphical analyses, using dynamic PET data acquisition, arterial blood collection and metabolic stability testing. Clinical PET scans were performed in two healthy volunteers (HVs). Regional brain standard uptake value ratio (SUVr) was assessed for different time intervals.

RESULTS

[¹⁸F]LW223 was synthesized in non-decay corrected radiochemical yields (n.d.c. RCYs) of 33.3 ± 6.5% with molar activities ranging from 1.8 ± 0.7 Ci/µmol (n = 11). [¹⁸F]LW223 was stable in formulation for up to 4 h and LogD_7.4 of 2.31 ± 0.13 (n = 6) and f_u of 5.80 ± 1.42% (n = 6) were determined. [¹⁸F]LW223 exhibited good brain penetration in NHPs, with a peak SUV value of ca. 1.79 in the whole brain. Pre-treatment with (R)-PK11195 substantially accelerated the washout and attenuated the area under the time-activity curve, indicating in vivo specificity of [¹⁸F]LW223 towards TSPO. Kinetic modeling demonstrated that 2TCM was the most suitable model for [¹⁸F]LW223-based neuroimaging. Global transfer rate constants (K₁) and total volumes of distribution (V_T) were found to be 0.10 ± 0.01 mL/cm³/min and 2.30 ± 0.17 mL/cm³, respectively. Dynamic PET data analyses across distinct time windows revealed that the V_T values were relatively stable after 60 min post-injection. In a preliminary clinical study with two healthy volunteers, [¹⁸F]LW223 exhibited good brain uptake and considerable tracer retention across all analyzed brain regions. Of note, an excellent correlation between SUVr with V_T was obtained when assessing the time interval from 20 to 40 min post tracer injection (SUVr_(20-40 min), R² = 0.94, p < 0.0001), suggesting this time window may be suitable to estimate specific binding to TSPO in human brain.

CONCLUSION

Our findings indicate that [¹⁸F]LW223 is suitable for quantitative TSPO-targeted PET imaging in higher species. Employing state-of-the-art kinetic modeling, we found that [¹⁸F]LW223 was effective in mapping TSPO throughout the NHP brain, with best model fits obtained from 2TCM and Logan graphical analyses. Overall, our results indicate that [¹⁸F]LW223 exhibits favorable tracer performance characteristics in higher species, and this novel imaging tool may hold promise to provide effective neuroinflammation imaging in patients with neurological disease.

Purification and thermodynamic characterization of acid protease with novel properties from Melilotus indicus leaves

A thermostable acid protease from M. indicus leaves was purified 10-fold using a 4-step protocol. We were able to isolate a purified protease fraction with a molecular weight of 50 kDa and exhibited maximal protease activity at pH 4.0 and 40 °C. Structural analysis revealed that the protease is monomeric and non-glycosylated. The addition of epoxy monocarboxylic acid, iodoacetic acid, and dimethyl sulfoxide significantly reduced protease activity while dramatically increasing the inhibition of Mn²⁺, Fe²⁺, and Cu²⁺. The activation energy of the hydrolysis reaction (33.33 kJ mol^-1) and activation energy (E_d = 105 kJ mol^-1), the standard enthalpy variation of reversible protease unfolding (2.58 kJ/mol) were calculated after activity measurements at various temperatures. Thermal inactivation of the pure enzyme followed first-order kinetics. The half-life (t_1/2) of the pure enzyme at 50 °C, 60 °C, and 70 °C was 385, 231, and 154 min, respectively. Thermodynamic parameters (entropy and enthalpy) suggested that the protease was highly thermostable. This is the first report on the thermodynamic parameters of proteases produced by M. indicus. The novel protease appears to be particularly thermostable and may be important for industrial applications based on these thermodynamic properties.

Effect of free nitrous acid on nitritation process: Microbial community, inhibitory kinetics, and functional biomarker

This work comprehensively deciphered the effect of free nitrous acid (FNA) on the microbial community, inhibitory kinetics, and nitrifiers in nitritation process. Nitritation was first successfully achieved through selective inhibition of free ammonia (FA) on nitrite oxidizers (NOB). Then, batch tests clearly showed that FNA significantly inhibits the ammonia oxidation rate (r_su) and the growth rate (μ) of ammonia oxidizers (AOB), which was well described by the Hellinga model (K_I = 0.222 mg·L^-1). The structural equation model indicated that FNA was significantly and negatively associated with r_su, μ, Nitrosomonas, Commamons, Nitrospira, and Nitrotoga and positively correlated with Paracoccus. Furthermore, Nitrosomonas significantly drove the ammonia utilization and growth of AOB and was identified as the most important functional biomarker indicating the nitritation in response to FNA levels using random forest model. This study provides helpful information on the kinetics of the mechanism underlying the FNA inhibition on nitrification.

A dynamic kinetic model captures cell-free metabolism for improved butanol production

Cell-free systems are useful tools for prototyping metabolic pathways and optimizing the production of various bioproducts. Mechanistically-based kinetic models are uniquely suited to analyze dynamic experimental data collected from cell-free systems and provide vital qualitative insight. However, to date, dynamic kinetic models have not been applied with rigorous biological constraints or trained on adequate experimental data to the degree that they would give high confidence in predictions and broadly demonstrate the potential for widespread use of such kinetic models. In this work, we construct a large-scale dynamic model of cell-free metabolism with the goal of understanding and optimizing butanol production in a cell-free system. Using a combination of parameterization methods, the resultant model captures experimental metabolite measurements across two experimental conditions for nine metabolites at timepoints between 0 and 24 h. We present analysis of the model predictions, provide recommendations for butanol optimization, and identify the aldehyde/alcohol dehydrogenase as the primary bottleneck in butanol production. Sensitivity analysis further reveals the extent to which various parameters are constrained, and our approach for probing valid parameter ranges can be applied to other modeling efforts.

Analysis of degradation kinetic modeling and mechanism of chlorinated-halonitromethanes under UV/monochloramine treatment

Chlorinated-halonitromethanes (Cl-HNMs) including chloronitromethane (CNM), dichloronitromethane (DCNM), and trichloronitromethane (TCNM) are nitrogenous disinfection by-products, which have high cytotoxicity and genotoxicity to human. This study aimed to investigate the degradation kinetic modeling and mechanism of Cl-HNMs under monochloramine activated by ultraviolet of 254 nm (UV/NH₂Cl) treatment. The first-principle kinetic model of UV/NH₂Cl process was developed to simulate Cl-HNMs degradation. Of note, the second-order rate constants of Cl-HNMs reacting with HO• (∼10⁸ M^-1 s^-1), Cl• (k_{Cl•,CNM or DCNM} = ∼10¹⁰ M^-1 s^-1, k_Cl•,TCNM = ∼10² M^-1 s^-1), Cl₂•^- (k_{Cl•,CNM or DCNM} = ∼10⁹ M^-1 s^-1, k_Cl•,TCNM = ∼10¹ M^-1 s^-1), ClO• (∼10⁵-10⁶ M^-1 s^-1) and CO₃•^- (∼10⁶-10⁷ M^-1 s^-1) were obtained by the first-principle kinetic model. Overall, Cl-HNMs degradation under UV/NH₂Cl treatment was successfully predicted by the kinetic model under various conditions. It was found that UV (>60%) was dominant in Cl-HNMs degradation, followed by HO• (3.8%-24.5%), reactive chlorine species (RCS, 0.9%-28.8%) and CO₃•^- (0-26.1%). Among the contributions of RCS, Cl• and Cl₂•^- were main radicals in the degradation of CNM and DCNM, while ClO• was responsible for the abatement of TCNM. The minimum EE/O values under UV/NH₂Cl treatment were approximately 30% lower than those under UV treatment. Finally, the possible degradation pathways were proposed, including hemolytic/heterolytic cleavage of Cl-HNMs by UV irradiation, hydrogen abstraction/electron transfer of CNM and DCNM and adduct reaction of TCNM by free radicals. This study based on the kinetic model is beneficial to predict and control the concentrations of Cl-HNMs under UV/NH₂Cl treatment.

Tumor-to-blood ratio for assessment of fibroblast activation protein receptor density in pancreatic cancer using [(68)Ga]Ga-FAPI-04

PURPOSE

[⁶⁸Ga]Ga-FAPI PET/CT has been widely used in clinical diagnosis and radiopharmaceutical therapy. In this study, tumor-to-blood ratio (TBR) was evaluated as a powerful tool for semiquantitative assessment of [⁶⁸Ga]Ga-FAPI-04 tumor uptake and as an effective index for tumors with high FAP expression in theranostics.

METHODS

Nine patients with pancreatic cancer underwent a 60-min dynamic PET/CT scan by total-body PET/CT (with a long AFOV of 194 cm) after injection of [⁶⁸Ga]Ga-FAPI-04. After dynamic PET/CT scan, three patients received chemotherapy and underwent the second dynamic scan to evaluate treatment response. Time-activity curves (TACs) were obtained by drawing regions of interest for primary pancreatic lesions and metastatic lesions. The lesion TACs were fitted using four compartment models by the software PMOD PKIN kinetic modeling. The preferred pharmacokinetic model for [⁶⁸Ga]Ga-FAPI-04 was evaluated based on the Akaike information criterion. The correlations between simplified methods for quantification of [⁶⁸Ga]Ga-FAPI-04 (SUVs; tumor-to-blood ratios [TBRs]) and the total distribution volume (V_t) estimates obtained from pharmacokinetic analysis were calculated.

RESULTS

In total, 9 primary lesions and 25 metastatic lesions were evaluated. The reversible two-tissue compartment model (2TCM) was the most appropriate model among the four compartment models. The total distribution volume V_t values derived from 2TCM varied significantly in pathological lesions and background regions. A strong positive correlation was observed between TBR_mean and V_t from the 2TCM model in pathological lesions (R²=0.92, P<0.001). The relative difference range for TBR_mean was 2.1% compared to the reduction rate of V_t in the patients who were treated with chemotherapy.

CONCLUSIONS

A strong positive correlation was observed between TBR_mean and V_t for [⁶⁸Ga]Ga-FAPI-04. TBR_mean reflects FAP receptor density better than SUV_mean and SUV_max, and would be the preferred measurement tool for semiquantitative assessment of [⁶⁸Ga]Ga-FAPI-04 tumor uptake and as a means for evaluating treatment response.

Kinetic Evaluation of the Hypoxia Radiotracers [18F]FMISO and [18F]FAZA in Dogs with Spontaneous Tumors Using Dynamic PET/CT Imaging

Purpose

We evaluated the kinetics of the hypoxia PET radiotracers, [18F]fluoromisonidazole ([18F]FMISO) and [18F]fluoroazomycin-arabinoside ([18F]FAZA), for tumor hypoxia detection and to assess the correlation of hypoxic kinetic parameters with static imaging measures in canine spontaneous tumors.

Methods

Sixteen dogs with spontaneous tumors underwent a 150-min dynamic PET scan using either [18F]FMISO or [18F]FAZA. The maximum tumor-to-muscle ratio (TMR_max) > 1.4 on the last image frame was used as the standard threshold to determine tumor hypoxia. The tumor time-activity curves were analyzed using irreversible and reversible two-tissue compartment models and graphical methods. TMR_max was compared with radiotracer trapping rate (k ₃), influx rate (K _i), and distribution volume (V _T).

Results

Tumor hypoxia was detected in 7/8 tumors in the [18F]FMISO group and 4/8 tumors in the [18F]FAZA group. All hypoxic tumors were detected at > 120 min with [18F]FMISO and at > 60 min with [18F]FAZA. [18F]FAZA showed better fit with the reversible model. TMR_max was strongly correlated with the irreversible parameters (k ₃ and K _i) for [18F]FMISO at > 90 min and with the reversible parameter (V _T) for [18F]FAZA at > 120 min.

Conclusions

Our results showed that [18F]FAZA provided a promising alternative radiotracer to [18F]FMISO with detecting the presence of tumor hypoxia at an earlier time (60 min), consistent with its favorable faster kinetics. The strong correlation between TMR_max over the 90-150 min and 120-150 min timeframes with [18F]FMISO and [18F]FAZA, respectively, with kinetic parameters associated with tumor hypoxia for each radiotracer, suggests that a static scan measurement (TMR_max) is a good alternative to quantify tumor hypoxia.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13139-022-00780-4.

Masks thermal degradation as an alternative of waste valorization on the COVID-19 pandemic: A kinetic study

The COVID-19 pandemic generated a new dynamic around waste management. Personal protective equipment such as masks, gloves, and face shields were essential to prevent the spread of the disease. However, despite the increase in waste, no technical alternatives were foreseen for the recovery of these wastes, which are made up of materials that can be valued for energy recovery. It is essential to design processes such as waste to energy to promote the circular economy. Therefore, techniques such as pyrolysis and thermal oxidative decomposition of waste materials need to be studied and scaled up, for which kinetic models and thermodynamic parameters are required to allow the design of this reaction equipment. This work develops kinetic models of the thermal degradation process by pyrolysis as an alternative for energy recovery of used masks generated by the COVID-19 pandemic. The wasted masks were isolated for 72 h for virus inactivation and characterized by FTIR-ATR spectroscopy, elemental analysis, and determinate the higher calorific value (HCV). The composition of the wasted masks included polypropylene, polyethylene terephthalate, nylon, and spandex, with higher calorific values than traditional fuels. For this reason, they are susceptible to value as an energetic material. Thermal degradation was performed by thermogravimetric analysis at different heating rates in N₂ atmosphere. The gases produced were characterized by gas chromatography and mass spectrometry. The kinetic model was based on the mass loss of the masks on the thermal degradation, then calculated activation energies, reaction orders, pre-exponential factors, and thermodynamic parameters. Kinetics models such as Coats and Redfern, Horowitz and Metzger, Kissinger-Akahira-Sunose were studied to find the best-fit models between the experimental and calculated data. The kinetic and thermodynamic parameters of the thermal degradation processes demonstrated the feasibility and high potential of recovery of these residues with conversions higher than 89.26% and obtaining long-chain branched hydrocarbons, cyclic hydrocarbons, and CO₂ as products.

Development of a minimally invasive simultaneous estimation method for quantifying translocator protein binding with [18F]FEPPA positron emission tomography

BACKGROUND

The purpose of this study was to assess the feasibility of using a minimally invasive simultaneous estimation method (SIME) to quantify the binding of the 18-kDa translocator protein tracer [¹⁸F]FEPPA. Arterial sampling was avoided by extracting an image-derived input function (IDIF) that was metabolite-corrected using venous blood samples. The possibility of reducing scan duration to 90 min from the recommended 2-3 h was investigated by assuming a uniform non-displaceable distribution volume (V_ND) to simplify the SIME fitting.

RESULTS

SIME was applied to retrospective data from healthy volunteers and was comprised of both high-affinity binders (HABs) and mixed-affinity binders (MABs). Estimates of global V_ND and regional total distribution volume (V_T) from SIME were not significantly different from values obtained using a two-tissue compartment model (2CTM). Regional V_T estimates were greater for HABs compared to MABs for both the 2TCM and SIME, while the SIME estimates had lower inter-subject variability (41 ± 17% reduction). Binding potential (BP_ND) values calculated from regional V_T and brain-wide V_ND estimates were also greater for HABs, and reducing the scan time from 120 to 90 min had no significant effect on BP_ND. The feasibility of using venous metabolite correction was evaluated in a large animal model involving a simultaneous collection of arterial and venous samples. Strong linear correlations were found between venous and arterial measurements of the blood-to-plasma ratio and the remaining [¹⁸F]FEPPA fraction. Lastly, estimates of BP_ND and the specific distribution volume (i.e., V_S = V_T - V_ND) from a separate group of healthy volunteers (90 min scan time, venous-scaled IDIFs) agreed with estimates from the retrospective data for both genotypes.

CONCLUSIONS

The results of this study demonstrate that accurate estimates of regional V_T, BP_ND and V_S can be obtained by applying SIME to [¹⁸F]FEPPA data. Furthermore, the application of SIME enabled the scan time to be reduced to 90 min, and the approach worked well with IDIFs that were scaled and metabolite-corrected using venous blood samples.

Predicting the Simultaneous Oxidation of Ammonia, Nitrite, and m-cresol and Microbial Growth in a Sequencing Batch Reactor with a Kinetic Model Using Inhibition and Inactivation Effects

The kinetic model derived in this study was able to adequately predict the simultaneous oxidation of ammonia, nitrite, and m-cresol and microbial growth using nitrifying sludge in a sequencing batch reactor. Time-varying inhibition and inactivation effects were successfully incorporated in the process kinetics to account for the past cell exposure history to m-cresol increasing concentrations (up to 150 mg C L^-1). The initial concentration of the microbial species (ammonia and nitrite oxidizers, heterotrophs) was evaluated using pyrosequencing of DNA samples of the consortium. These measurements allowed to establish a model that explicitly handles specific reaction rates and to enhance the practical identifiability of the model parameters. A single simulation run was used to adequately predict the kinetic behavior of the main variables throughout the 242 cycles using a single set of initial conditions in the first cycle. This kind of dynamic model may be used as a helpful predictive tool to improve nitrification by avoiding the occurrence of severely repetitive inhibitive conditions due to the presence of inhibitive/toxic aromatic compounds.

First total-body kinetic modeling and parametric imaging of dynamic 68Ga-FAPI-04 PET in pancreatic and gastric cancer

Background: Fibroblast activation protein inhibitor (FAPI) is an ideal diagnostic and therapeutic target in malignant tumors. However, the knowledge of kinetic modelling and parametric imaging of ⁶⁸Ga-FAPI is limited. Purpose: To explore the pharmacokinetics of ⁶⁸Ga-FAPI-04 PET/CT in pancreatic cancer and gastric cancer and to conduct parametric imaging of dynamic total-body data compared to standardized uptake value (SUV) imaging. Methods: Dynamic total-body ⁶⁸Ga-FAPI-04 PET/CT (with an AFOV of 194 cm) was performed on thirteen patients. The lesion time activity curves (TACs) were fitted by three-compartment models and multigraphical models. The kinetics parameters derived from the two-tissue reversible compartment model (2T4K) and multigraphical models were analyzed. Parametric 𝑉𝑡 imaging was generated using the 2T4K and Logan model, and their performances were evaluated compared to SUV images. Results: 2T4K had the lowest Akaike Information Criterion (AIC) value and its fitting curves matched excellently with the origin TACs. Visual assessment revealed that the 𝑉𝑡 (2T4K) images and 𝑉𝑡 (Logan with SC) images both showed less image noise and higher lesions conspicuity compared to SUV images. Objective image quality assessment demonstrated that parametric 𝑉𝑡 (2T4K) images and parametric 𝑉𝑡 (Logan with SC) images had a 5.0-fold and 5.0-fold higher average signal-to-noise ratio (SNR), and 3.6-fold and 4.1-fold higher average contrast-to-noise ratio (CNR) compared to conventional SUV images, respectively. In addition, no significant differences in SNR and CNR of pathological lesions were observed between parametric 𝑉𝑡 (2T4K) images and Parametric 𝑉𝑡 (Logan with SC) images (p all ＞0.05). Conclusion: The 2T4K model was the preferred compartment model. Total-body parametric imaging of ⁶⁸Ga-FAPI-04 PET yielded superior quantification beyond SUV with enhanced lesion contrast, which may serve as a promising imaging method for early diagnosis, to better reflect tumor characterization, or to allow evaluation of treatment response. 𝑉𝑡 (2T4K) images are comparable in image quality and superior to 𝑉𝑡 (Logan with SC) images in lesions conspicuity, however 𝑉𝑡 (Logan with SC) images presented an appealing alternative to 𝑉𝑡 (2T4K) images due to their simplicity.

Simultaneous dynamic glucose-enhanced (DGE) MRI and fiber photometry measurements of glucose in the healthy mouse brain

Glucose is the main energy source in the brain and its regulated uptake and utilization are important biomarkers of pathological brain function. Glucose Chemical Exchange Saturation Transfer (GlucoCEST) and its time-resolved version Dynamic Glucose-Enhanced MRI (DGE) are promising approaches to monitor glucose and detect tumors, since they are radioactivity-free, do not require ¹³C labeling and are is easily translatable to the clinics. The main principle of DGE is clear. However, what remains to be established is to which extent the signal reflects vascular, extracellular or intracellular glucose. To elucidate the compartmental contributions to the DGE signal, we coupled it with FRET-based fiber photometry of genetically encoded sensors, a technique that combines quantitative glucose readout with cellular specificity. The glucose sensor FLIIP was used with fiber photometry to measure astrocytic and neuronal glucose changes upon injection of D-glucose, 3OMG and L-glucose, in the anaesthetized murine brain. By correlating the kinetic profiles of the techniques, we demonstrate the presence of a vascular contribution to the signal, especially at early time points after injection. Furthermore, we show that, in the case of the commonly used contrast agent 3OMG, the DGE signal actually anticorrelates with the glucose concentration in neurons and astrocytes.

Feasibility of using abbreviated scan protocols with population-based input functions for accurate kinetic modeling of [18F]-FDG datasets from a long axial FOV PET scanner

BACKGROUND

Accurate kinetic modeling of 18F-fluorodeoxyglucose ([18F]-FDG) positron emission tomography (PET) data requires accurate knowledge of the available tracer concentration in the plasma during the scan time, known as the arterial input function (AIF). The gold standard method to derive the AIF requires collection of serial arterial blood samples, but the introduction of long axial field of view (LAFOV) PET systems enables the use of non-invasive image-derived input functions (IDIFs) from large blood pools such as the aorta without any need for bed movement. However, such protocols require a prolonged dynamic PET acquisition, which is impractical in a busy clinical setting. Population-based input functions (PBIFs) have previously shown potential in accurate Patlak analysis of [18F]-FDG datasets and can enable the use of shortened dynamic imaging protocols. Here, we exploit the high sensitivity and temporal resolution of a LAFOV PET system and explore the use of PBIF with abbreviated protocols in [18F]-FDG total body kinetic modeling.

METHODS

Dynamic PET data were acquired in 24 oncological subjects for 65 min following the administration of [18F]-FDG. IDIFs were extracted from the descending thoracic aorta, and a PBIF was generated from 16 datasets. Five different scaled PBIFs (sPBIFs) were generated by scaling the PBIF with the AUC of IDIF curve tails using various portions of image data (35-65, 40-65, 45-65, 50-65, and 55-65 min post-injection). The sPBIFs were compared with the IDIFs using the AUCs and Patlak Ki estimates in tumor lesions and cerebral gray matter. Patlak plot start time (t*) was also varied to evaluate the performance of shorter acquisitions on the accuracy of Patlak Ki estimates. Patlak Ki estimates with IDIF and t* = 35 min were used as reference, and mean bias and precision (standard deviation of bias) were calculated to assess the relative performance of different sPBIFs. A comparison of parametric images generated using IDIF and sPBIFs was also performed.

RESULTS

There was no statistically significant difference between AUCs of the IDIF and sPBIFs (Wilcoxon test: P > 0.05). Excellent agreement was shown between Patlak Ki estimates obtained using sPBIF and IDIF. Using the sPBIF55-65 with the Patlak model, 20 min of PET data (i.e., 45 to 65 min post-injection) achieved < 15% precision error in Ki estimates in tumor lesions compared to the estimates with the IDIF. Parametric images reconstructed using the IDIF and sPBIFs with and without an abbreviated protocol were visually comparable. Using Patlak Ki generated with an IDIF and 30 min of PET data as reference, Patlak Ki images generated using sPBIF55-65 with 20 min of PET data (t* = 45 min) provided excellent image quality with structural similarity index measure > 0.99 and peak signal-to-noise ratio > 55 dB.

CONCLUSION

We demonstrate the feasibility of performing accurate [18F]-FDG Patlak analysis using sPBIFs with only 20 min of PET data from a LAFOV PET scanner.

Drug characteristics derived from kinetic modeling: combined 11C-UCB-J human PET imaging with levetiracetam and brivaracetam occupancy of SV2A

BACKGROUND

Antiepileptic drugs, levetiracetam (LEV) and brivaracetam (BRV), bind to synaptic vesicle glycoprotein 2A (SV2A). In their anti-seizure activity, speed of brain entry may be an important factor. BRV showed faster entry into the human and non-human primate brain, based on more rapid displacement of SV2A tracer ¹¹C-UCB-J. To extract additional information from previous human studies, we developed a nonlinear model that accounted for drug entry into the brain and binding to SV2A using brain ¹¹C-UCB-J positron emission tomography (PET) data and the time-varying plasma drug concentration, to assess the kinetic parameter K₁ (brain entry rate) of the drugs.

METHOD

Displacement (LEV or BRV p.i. 60 min post-tracer injection) and post-dose scans were conducted in five healthy subjects. Blood samples were collected for measurement of drug concentration and the tracer arterial input function. Fitting of nonlinear differential equations was applied simultaneously to time-activity curves (TACs) from displacement and post-dose scans to estimate 5 parameters: K₁ (drug), K₁(¹¹C-UCB-J, displacement), K₁(¹¹C-UCB-J, post-dose), free fraction of ¹¹C-UCB-J in brain (f_ND(¹¹C-UCB-J)), and distribution volume of ¹¹C-UCB-J (V_T(UCB-J)). Other parameters (K_D(drug), K_D(¹¹C-UCB-J), f_P(drug), f_P(¹¹C-UCB-J, displacement), f_P(¹¹C-UCB-J, post-dose), f_ND(drug), k_off(drug), k_off(¹¹C-UCB-J)) were fixed to literature or measured values.

RESULTS

The proposed model described well the TACs in all subjects; however, estimates of drug K₁ were unstable in comparison with ¹¹C-UCB-J K₁ estimation. To provide a conservative estimate of the relative speed of brain entry for BRV vs. LEV, we determined a lower bound on the ratio BRV K₁/LEV K₁, by finding the lowest BRV K₁ or highest LEV K₁ that were statistically consistent with the data. Specifically, we used the F test to compare the residual sum of squares with fixed BRV K₁ to that with floating BRV K₁ to obtain the lowest possible BRV K₁; the same analysis was performed to find the highest LEV K₁. The lower bound of the ratio BRV K₁/LEV K₁ was ~ 7.

CONCLUSIONS

Under appropriate conditions, this advanced nonlinear model can directly estimate entry rates of drugs into tissue by analysis of PET TACs. Using a conservative statistical cutoff, BRV enters the brain at least sevenfold faster than LEV.

MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling

BACKGROUND

Kinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (K_m), is necessary, and global optimization algorithms have long been used for parameter estimation. However, the conventional global optimization approach has three problems: (i) It is computationally demanding. (ii) It often yields unrealistic parameter values because it simply seeks a better model fitting to experimentally observed behaviors. (iii) It has difficulty in identifying a unique solution because multiple parameter sets can allow a kinetic model to fit experimental data equally well (the non-identifiability problem).

RESULTS

To solve these problems, we propose the Machine Learning-Aided Global Optimization (MLAGO) method for K_m estimation of kinetic modeling. First, we use a machine learning-based K_m predictor based only on three factors: EC number, KEGG Compound ID, and Organism ID, then conduct a constrained global optimization-based parameter estimation by using the machine learning-predicted K_m values as the reference values. The machine learning model achieved relatively good prediction scores: RMSE = 0.795 and R² = 0.536, making the subsequent global optimization easy and practical. The MLAGO approach reduced the error between simulation and experimental data while keeping K_m values close to the machine learning-predicted values. As a result, the MLAGO approach successfully estimated K_m values with less computational cost than the conventional method. Moreover, the MLAGO approach uniquely estimated K_m values, which were close to the measured values.

CONCLUSIONS

MLAGO overcomes the major problems in parameter estimation, accelerates kinetic modeling, and thus ultimately leads to better understanding of complex cellular systems. The web application for our machine learning-based K_m predictor is accessible at https://sites.google.com/view/kazuhiro-maeda/software-tools-web-apps , which helps modelers perform MLAGO on their own parameter estimation tasks.

Designing drug occupancy studies with PET neuroimaging: Sample size, occupancy ranges and analytical methods

Molecular neuroimaging is today considered essential for evaluation of novel CNS drugs; it is used to quantify blood-brain barrier permeability, verify interaction with key target and determine the drug dose resulting in 50% occupancy, IC₅₀. In spite of this, there has been limited data available to inform on how to optimize study designs. Through simulations, we here evaluate how IC₅₀ estimation is affected by the (i) range of drug doses administered, (ii) number of subjects included, and (iii) level of noise in the plasma drug concentration measurements. Receptor occupancy is determined from PET distribution volumes using two different methods: the Lassen plot and Likelihood estimation of occupancy (LEO). We also introduce and evaluate a new likelihood-based estimator for direct estimation of IC₅₀ from PET distribution volumes. For estimation of IC₅₀, we find very limited added benefit in scanning individuals who are given drug doses corresponding to less than 40% receptor occupancy. In the range of typical PET sample sizes (5-20 subjects) each extra individual clearly reduces the error of the IC₅₀ estimate. In all simulations, likelihood-based methods gave more precise IC₅₀ estimates than the Lassen plot; four times the number of subjects were required for the Lassen plot to reach the same IC₅₀ precision as LEO.