| 1. |
- de Rouw, Nikki, et al.
(författare)
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Rethinking the Application of Pemetrexed for Patients with Renal Impairment : A Pharmacokinetic Analysis
- 2021
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Ingår i: Clinical Pharmacokinetics. - : ADIS INT LTD. - 0312-5963 .- 1179-1926. ; 60:5, s. 649-654
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Tidskriftsartikel (refereegranskat)abstract
- Background Pemetrexed is used for the treatment for non-small cell lung cancer and mesothelioma. Patients with renal impairment are withheld treatment with this drug as it is unknown what dose is well tolerated in this population. Objective The purpose of our study was to investigate the pharmacokinetics (PK) of pemetrexed in patients with renal impairment. Methods A population PK analysis of pemetrexed was performed using non-linear mixed-effects modelling with phase I data obtained from the manufacturer. Additionally, the impact of renal function on pemetrexed PK was assessed with a simulation study using the developed PK model and a previously developed PK model lacking the phase I data. Results The dataset included 548 paired observations of 47 patients, with a wide range of estimated glomerular filtration rates (eGFR; 14.4-145.6 mL/min). Pemetrexed PK were best described by a three-compartment model with eGFR (calculated using the Chronic Kidney Disease-Epidemiology Collaboration [CKD-EPI] formula) as a linear covariate on renal pemetrexed clearance. Using the developed model, we found that renal clearance accounts for up to 84% (95% confidence interval 69-98%) of total pemetrexed clearance, whereas the manufacturer previously reported a 50% contribution of renal clearance. Conclusion Renal function is more important for the clearance of pemetrexed than previously thought and this should be taken into account in patients with renal impairment. Furthermore, a third compartment may contribute to prolonged exposure to pemetrexed during drug washout.
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| 2. |
- Dieker, Jurgen, et al.
(författare)
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Autoantibodies against Modified Histone Peptides in SLE Patients Are Associated with Disease Activity and Lupus Nephritis
- 2016
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Ingår i: PLOS ONE. - : PUBLIC LIBRARY SCIENCE. - 1932-6203. ; 11:10
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Tidskriftsartikel (refereegranskat)abstract
- Persistent exposure of the immune system to death cell debris leads to autoantibodies against chromatin in patients with systemic lupus erythematosus (SLE). Deposition of antichromatin/ chromatin complexes can instigate inflammation in multiple organs including the kidney. Previously we identified specific cell death-associated histone modifications as targets of autoantibodies in SLE. In this study we addressed, in a large cohort of SLE patients and controls, the question whether plasma reactivities with specific histone peptides associated with serology and clinical features. Plasma from SLE patients with and without lupus nephritis, disease controls, and healthy controls, were tested in ELISA with histone H4 peptide acetylated at lysines 8, 12 and 16 (H4p(ac)), H2B peptide acetylated at lysine 12 (H2Bp(ac)), H3 peptide trimethylated at lysine 27 (H3p(me)), and their unmodified equivalents. SLE patients displayed a higher reactivity with the modified equivalent of each peptide. Reactivity with H4p(ac) showed both a high sensitivity (89%) and specificity (91%) for SLE, while H2Bp(ac) exhibited a high specificity (96%) but lower sensitivity (69%). Reactivity with H3p(me) appeared not specific for SLE. Anti-H4p(ac) and anti-H2Bp(ac) reactivity demonstrated a high correlation with disease activity. Moreover, patients reacting with multiple modified histone peptides exhibited higher SLEDAI and lower C3 levels. SLE patients with renal involvement showed higher reactivity with H2B/H2Bp(ac) and a more pronounced reactivity with the modified equivalent of H3p(me) and H2Bp(ac). In conclusion, reactivity with H4p(ac) and H2Bp(ac) is specific for SLE patients and correlates with disease activity, whereas reactivity with H2Bp(ac) is in particular associated with lupus nephritis.
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| 3. |
- Hermsen, Meyke, et al.
(författare)
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Convolutional Neural Networks for the Evaluation of Chronic and Inflammatory Lesions in Kidney Transplant Biopsies
- 2022
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Ingår i: American Journal of Pathology. - : ELSEVIER SCIENCE INC. - 0002-9440 .- 1525-2191. ; 192:10, s. 1418-1432
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Tidskriftsartikel (refereegranskat)abstract
- In kidney transplant biopsies, both inflammation and chronic changes are important features that predict long-term graft survival. Quantitative scoring of these features is important for transplant diagnostics and kidney research. However, visual scoring is poorly reproducible and labor intensive. The goal of this study was to investigate the potential of convolutional neural networks (CNNs) to quantify inflammation and chronic features in kidney transplant biopsies. A structure segmentation CNN and a lymphocyte detection CNN were applied on 125 whole-slide image pairs of periodic acid-Schiff- and CD3-stained slides. The CNN results were used to quantify healthy and sclerotic glomeruli, interstitial fibrosis, tubular atrophy, and inflammation within both nonatrophic and atrophic tubuli, and in areas of interstitial fibrosis. The computed tissue features showed high correlation with Banff lesion scores of five pathologists (A.A., A.Dend., J.H.B., J.K., and T.N.). Analyses on a small subset showed a moderate correlation toward higher CD3+ cell density within scarred regions and higher CD3+ cell count inside atrophic tubuli correlated with long-term change of estimated glomerular filtration rate. The presented CNNs are valid tools to yield objective quantitative information on glomeruli number, fibrotic tissue, and inflammation within scarred and non-scarred kidney parenchyma in a reproducible manner. CNNs have the potential to improve kidney transplant diagnostics and will benefit the community as a novel method to generate surrogate end points for large-scale clinical studies. (Am J Pathol 2022, 192: 1418-1432; https://doi.org/10.1016/j.ajpath.2022.06.009)
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| 4. |
- Hermsen, Meyke, et al.
(författare)
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Deep Learning-Based Histopathologic Assessment of Kidney Tissue
- 2019
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Ingår i: Journal of the American Society of Nephrology. - : AMER SOC NEPHROLOGY. - 1046-6673 .- 1533-3450. ; 30:10, s. 1968-1979
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Tidskriftsartikel (refereegranskat)abstract
- Background The development of deep neural networks is facilitating more advanced digital analysis of histopathologic images. We trained a convolutional neural network for multiclass segmentation of digitized kidney tissue sections stained with periodic acid-Schiff (PAS). Methods We trained the network using multiclass annotations from 40 whole-slide images of stained kidney transplant biopsies and applied it to four independent data sets. We assessed multiclass segmentation performance by calculating Dice coefficients for ten tissue classes on ten transplant biopsies from the Radboud University Medical Center in Nijmegen, The Netherlands, and on ten transplant biopsies from an external center for validation. We also fully segmented 15 nephrectomy samples and calculated the networks glomerular detection rates and compared network-based measures with visually scored histologic components (Banff classification) in 82 kidney transplant biopsies. Results The weighted mean Dice coefficients of all classes were 0.80 and 0.84 in ten kidney transplant biopsies from the Radboud center and the external center, respectively. The best segmented class was "glomeruli" in both data sets (Dice coefficients, 0.95 and 0.94, respectively), followed by "tubuli combined" and "interstitium." The network detected 92.7% of all glomeruli in nephrectomy samples, with 10.4% false positives. In whole transplant biopsies, the mean intraclass correlation coefficient for glomerular counting performed by pathologists versus the network was 0.94. We found significant correlations between visually scored histologic components and network-based measures. Conclusions This study presents the first convolutional neural network for multiclass segmentation of PAS-stained nephrectomy samples and transplant biopsies. Our network may have utility for quantitative studies involving kidney histopathology across centers and provide opportunities for deep learning applications in routine diagnostics.
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| 5. |
- Hermsen, Meyke, et al.
(författare)
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Quantitative assessment of inflammatory infiltrates in kidney transplant biopsies using multiplex tyramide signal amplification and deep learning
- 2021
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Ingår i: Laboratory Investigation. - : Springer Nature. - 0023-6837 .- 1530-0307. ; 101:8, s. 970-982
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Tidskriftsartikel (refereegranskat)abstract
- Delayed graft function (DGF) is a strong risk factor for development of interstitial fibrosis and tubular atrophy (IFTA) in kidney transplants. Quantitative assessment of inflammatory infiltrates in kidney biopsies of DGF patients can reveal predictive markers for IFTA development. In this study, we combined multiplex tyramide signal amplification (mTSA) and convolutional neural networks (CNNs) to assess the inflammatory microenvironment in kidney biopsies of DGF patients (n = 22) taken at 6 weeks post-transplantation. Patients were stratified for IFTA development (<10% versus >= 10%) from 6 weeks to 6 months post-transplantation, based on histopathological assessment by three kidney pathologists. One mTSA panel was developed for visualization of capillaries, T- and B-lymphocytes and macrophages and a second mTSA panel for T-helper cell and macrophage subsets. The slides were multi spectrally imaged and custom-made python scripts enabled conversion to artificial brightfield whole-slide images (WSI). We used an existing CNN for the detection of lymphocytes with cytoplasmatic staining patterns in immunohistochemistry and developed two new CNNs for the detection of macrophages and nuclear-stained lymphocytes. F1-scores were 0.77 (nuclear-stained lymphocytes), 0.81 (cytoplasmatic-stained lymphocytes), and 0.82 (macrophages) on a test set of artificial brightfield WSI. The CNNs were used to detect inflammatory cells, after which we assessed the peritubular capillary extent, cell density, cell ratios, and cell distance in the two patient groups. In this cohort, distance of macrophages to other immune cells and peritubular capillary extent did not vary significantly at 6 weeks post-transplantation between patient groups. CD163(+) cell density was higher in patients with >= 10% IFTA development 6 months post-transplantation (p < 0.05). CD3(+)CD8(-)/CD3(+)CD8(+) ratios were higher in patients with <10% IFTA development (p < 0.05). We observed a high correlation between CD163(+) and CD4(+)GATA3(+) cell density (R = 0.74, p < 0.001). Our study demonstrates that CNNs can be used to leverage reliable, quantitative results from mTSA-stained, multi spectrally imaged slides of kidney transplant biopsies. This study describes a methodology to assess the microenvironment in sparse tissue samples. Deep learning, multiplex immunohistochemistry, and mathematical image processing techniques were incorporated to quantify lymphocytes, macrophages, and capillaries in kidney transplant biopsies of delayed graft function patients. The quantitative results were used to assess correlations with development of interstitial fibrosis and tubular atrophy.
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