The aim of this work was to simulate the effect of prostate-specific membrane antigen (PSMA)-positive total tumor volume (TTV) on the biologically effective doses (BEDs) to tumors and organs at risk in patients with metastatic castration-resistant prostate cancer who are undergoing Lu-PSMA radioligand therapy. A physiologically based pharmacokinetic model was fitted to the data of 13 patients treated with Lu-PSMA I&T (a PSMA inhibitor for imaging and therapy). The tumor, kidney, and salivary gland BEDs were simulated for TTVs of 0.1-10 L. The activity and peptide amounts leading to an optimal tumor-to-kidneys BED ratio were also investigated. When the TTV was increased from 0.3 to 3 L, the simulated BEDs to tumors, kidneys, parotid glands, and submandibular glands decreased from 22 ± 15 to 11.0 ± 6.0 Gy, 6.5 ± 2.3 to 3.7 ± 1.4 Gy, 11.0 ± 2.7 to 6.4 ± 1.9 Gy, and 10.9 ± 2.7 to 6.3 ± 1.9 Gy, respectively (where the subscripts denote that an α/β of 1.49, 2.5, or 4.5 Gy was used to calculate the BED). The BED to the red marrow increased from 0.17 ± 0.05 to 0.32 ± 0.11 Gy For patients with a TTV of more than 0.3 L, the optimal amount of peptide was 273 ± 136 nmol and the optimal activity was 10.4 ± 4.4 GBq. This simulation study suggests that in patients with large PSMA-positive tumor volumes, higher activities and peptide amounts can be safely administered to maximize tumor BEDs without exceeding the tolerable BED to the organs at risk.
The aim of this work was to investigate the effect of ligand amount, affinity and internalization of prostate-specific membrane antigen (PSMA)-specific ligands on the activity concentrations for PET/CT imaging and on the absorbed doses for therapy. A physiologically-based pharmacokinetic (PBPK) model for PSMA-specific ligands was implemented. Thirteen virtual patients with metastatic castration-resistant prostate cancer were analysed. Simulations were performed for different combinations of association rates kon (0.1–0.01 L/nmol/min), dissociation rates koff (0.1–0.0001 min−1), internalization rates λint (0.01–0.0001 min−1) and ligand amounts (1–1000 nmol). For imaging the activity was normalized to volume and injected activity (68Ga-PSMA at 1 h). For therapy the absorbed dose was calculated for 7.3 ± 0.3 GBq 177Lu-PSMA. The effect of the investigated parameters on therapy were larger compared to imaging. For imaging, the combination of properties leading to the highest tumour uptake was kon = 0.1 L/nmol/min, koff = 0.01 min−1 for typical ligand amounts (1–10 nmol). For therapy, the higher the internalization rate, the larger was the required ligand amount for optimal tumour-to-kidney ratios. The higher the affinity, the more important was the choice of the optimal ligand amount. PBPK modelling provides insight into the pharmacokinetics of PSMA-specific ligands. Further in silico and in vivo studies are required to verify the influence of the analysed parameters.
Gut-brain axis is a dynamic, complex, and bidirectional communication network between the gut and brain. Changes in the microbiota-gut-brain axis are responsible for developing various metabolic, neurodegenerative, and neuropsychiatric disorders. According to clinical and preclinical findings, the gut microbiota is a significant regulator of the gut-brain axis. In addition to interacting with intestinal cells and the enteric nervous system, it has been discovered that microbes in the gut can modify the central nervous system through metabolic and neuroendocrine pathways. The metabolites of the gut microbiome can modulate a number of diseases by inducing epigenetic alteration through DNA methylation, histone modification, and non-coding RNA-associated gene silencing. Short-chain fatty acids, especially butyrate, are well-known histone deacetylases inhibitors. Similarly, other microbial metabolites such as folate, choline, and trimethylamine-N-oxide also regulate epigenetics mechanisms. Furthermore, various studies have revealed the potential role of microbiome dysbiosis and epigenetics in the pathophysiology of depression. Hence, in this review, we have highlighted the role of gut dysbiosis in epigenetic regulation, causal interaction between host epigenetic modification and the gut microbiome in depression and suggest microbiome and epigenome as a possible target for diagnosis, prevention, and treatment of depression.
The aim of this work was to evaluate the sensitivity of time-integrated activity coefficients (TIACs) on the erroneously chosen prior knowledge in a physiologically based pharmacokinetic (PBPK) model used for treatment planning in peptide receptor radionuclide therapy (PRRT). Parameters of the PBPK model were fitted to the biokinetic data of 15 patients after the injection of (111)In-DTPAOC. The fittings were performed using fixed parameter values taken from literature as prior knowledge (reference case, Ref). The fixed parameters were gender, physical information (e.g., body weight), dissociation rate koff, dissociation constant KD, fraction of blood flow, and spleen and liver volumes. The fittings were repeated with changed fixed parameters (Changed). The relative deviations (RDs) of TIACs calculated from Changed and Ref were analyzed for kidneys, tumor, liver, spleen, remainder, whole body, and serum. A changed koff has the largest effect on RD, the largest RD values were found for changed koff = 0.001 L/min: RDkidneys = (3 ± 3)%, RDtumor = (0.5 ± 4)%, RDliver = (6 ± 9)%, RDspleen = (5 ± 5)%, RDremainder = (2 ± 31)%, RDserum = (-4 ± 25)%, and RDwholebody = (3 ± 16)%. For other changed parameters, the maximum RDs were <1%. The calculation of organ TIACs in PRRT using the PBPK model was little affected by assigning wrong prior knowledge to the evaluated patients. The calculation of bone marrow-absorbed doses could be affected by the inaccurate TIACs of serum and remainder in the case of an inadequate koff.
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