scholarly journals PET-Specific Parameters and Radiotracers in Theoretical Tumour Modelling

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Matthew Jennings ◽  
Loredana G. Marcu ◽  
Eva Bezak
Keyword(s):  
Gene Expression ◽  
In Silico ◽  
Tumour Growth ◽  
Patient Specific ◽  
Computational Techniques ◽  
Accurate Information ◽  
Pet Ct ◽  
Ct Data ◽  
Specific Management

The innovation of computational techniques serves as an important step toward optimized, patient-specific management of cancer. In particular,in silicosimulation of tumour growth and treatment response may eventually yield accurate information on disease progression, enhance the quality of cancer treatment, and explain why certain therapies are effective where others are not.In silicomodelling is demonstrated to considerably benefit from information obtainable with PET and PET/CT. In particular, models have successfully integrated tumour glucose metabolism, cell proliferation, and cell oxygenation from multiple tracers in order to simulate tumour behaviour. With the development of novel radiotracers to image additional tumour phenomena, such as pH and gene expression, the value of PET and PET/CT data for use in tumour models will continue to grow. In this work, the use of PET and PET/CT information inin silicotumour models is reviewed. The various parameters that can be obtained using PET and PET/CT are detailed, as well as the radiotracers that may be used for this purpose, their utility, and limitations. The biophysical measures used to quantify PET and PET/CT data are also described. Finally, a list ofin silicomodels that incorporate PET and/or PET/CT data is provided and reviewed.

PO-0897: A new method to segment advanced stage lung tumors in joint PET-CT data using a patient-specific learning approach

2014 ◽  
Vol 111 ◽  
pp. S104
Author(s):  
V. Gupta ◽  
F. Frank Verhaegen ◽  
S. Venkatesan ◽  
P. Keswarpu ◽  
W. Van Elmpt
Keyword(s):  
Lung Tumors ◽  
New Method ◽  
Patient Specific ◽  
Learning Approach ◽  
Advanced Stage ◽  
Pet Ct ◽  
Ct Data ◽  
Specific Learning

scholarly journals In silico Drosophila Patient Model Reveals Optimal Combinatorial Therapies for Colorectal Cancer

2020 ◽  
Author(s):  
Mahnoor Naseer Gondal ◽  
Rida Nasir Butt ◽  
Osama Shiraz Shah ◽  
Zainab Nasir ◽  
Risham Hussain ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
In Silico ◽  
Network Models ◽  
Cancer Therapeutics ◽  
Patient Specific ◽  
Expression Data ◽  
Colorectal Tumorigenesis ◽  
Patient Model ◽  
Personalized Cancer

AbstractIn silico models of biomolecular regulation in cancer, annotated with patient-specific gene expression data can aid in the development of novel personalized cancer therapeutics strategies. Drosophila melanogaster is a well-established animal model that is increasingly being employed to evaluate preclinical personalized cancer therapies. Here, we report five Boolean network models of biomolecular regulation in cells lining the Drosophila midgut epithelium and annotate them with patient-specific mutation data to develop an in silico Drosophila Patient Model (DPM). The network models were validated against cell-type-specific RNA-seq gene expression data from the FlyGut-seq database and through three literature-based case studies on colorectal cancer. The results obtained from the study help elucidate cell fate evolution in colorectal tumorigenesis, validate cytotoxicity of nine FDA-approved cancer drugs, and devise optimal personalized drug treatment combinations. The proposed personalized therapeutics approach also helped identify synergistic combinations of chemotherapy (paclitaxel) with targeted therapies (pazopanib, or ruxolitinib) for treating colorectal cancer. In conclusion, this work provides a novel roadmap for decoding colorectal tumorigenesis and in the development of personalized cancer therapeutics through a DPM.

Effect of CT-based attenuation correction on uptake ratios in skeletal SPECT

2007 ◽  
Vol 46 (01) ◽  
pp. 38-42 ◽  
Author(s):  
V. Schulz ◽  
I. Nickel ◽  
A. Nömayr ◽  
A. H. Vija ◽  
C. Hocke ◽  
...  
Keyword(s):  
Vertebral Body ◽  
Facet Joint ◽  
One Dimension ◽  
Patient Specific ◽  
Data Sets ◽  
Facet Joints ◽  
Lumbar Vertebral Body ◽  
Ct System ◽  
Ct Data ◽  
Attenuation Corrected

SummaryThe aim of this study was to determine the clinical relevance of compensating SPECT data for patient specific attenuation by the use of CT data simultaneously acquired with SPECT/CT when analyzing the skeletal uptake of polyphosphonates (DPD). Furthermore, the influence of misregistration between SPECT and CT data on uptake ratios was investigated. Methods: Thirty-six data sets from bone SPECTs performed on a hybrid SPECT/CT system were retrospectively analyzed. Using regions of interest (ROIs), raw counts were determined in the fifth lumbar vertebral body, its facet joints, both anterior iliacal spinae, and of the whole transversal slice. ROI measurements were performed in uncorrected (NAC) and attenuation-corrected (AC) images. Furthermore, the ROI measurements were also performed in AC scans in which SPECT and CT images had been misaligned by 1 cm in one dimension beforehand (ACX, ACY, ACZ). Results: After AC, DPD uptake ratios differed significantly from the NAC values in all regions studied ranging from 32% for the left facet joint to 39% for the vertebral body. AC using misaligned pairs of patient data sets led to a significant change of whole-slice uptake ratios whose differences ranged from 3,5 to 25%. For ACX, the average left-to-right ratio of the facet joints was by 8% and for the superior iliacal spines by 31% lower than the values determined for the matched images (p <0.05). Conclusions: AC significantly affects DPD uptake ratios. Furthermore, misalignment between SPECT and CT may introduce significant errors in quantification, potentially also affecting leftto- right ratios. Therefore, at clinical evaluation of attenuation- corrected scans special attention should be given to possible misalignments between SPECT and CT.

Restaging of patients with lymphoma

2008 ◽  
Vol 47 (01) ◽  
pp. 37-42 ◽  
Author(s):  
T. Pfluger ◽  
V. Schneider ◽  
M. Hacker ◽  
N. Bröckel ◽  
D. Morhard ◽  
...  
Keyword(s):  
Low Dose ◽  
Non Hodgkin Lymphoma ◽  
Contrast Enhanced ◽  
Pet Ct ◽  
Significant Difference ◽  
18F Fdg ◽  
Sensitivity Specificity ◽  
Fdg Pet Ct

SummaryAim: Assessment of the clinical benefit of i.v. contrast enhanced diagnostic CT (CE-CT) compared to low dose CT with 20 mAs (LD-CT) without contrast medium in combined [18F]-FDG PET/CT examinations in restaging of patients with lymphoma. Patients, methods: 45 patients with non-Hodgkin lymphoma (n = 35) and Hodgkin's disease (n = 10) were included into this study. PET, LD-CT and CECT were analyzed separately as well as side-by-side. Lymphoma involvement was evaluated separately for seven regions. Indeterminate diagnoses were accepted whenever there was a discrepancy between PET and CT findings. Results for combined reading were calculated by rating indeterminate diagnoses according the suggestions of either CT or PET. Each patient had a clinical follow-up evaluation for >6 months. Results: Region-based evaluation suggested a sensitivity/specificity of 66/93% for LD-CT, 87%/91% for CE-CT, 95%/96% for PET, 94%/99% for PET/LD-CT and 96%/99% for PET/CE-CT. The data for PET/CT were obtained by rating indeterminate results according to the suggestions of PET, which turned out to be superior to CT. Lymphoma staging was changed in two patients using PET/ CE-CT as compared to PET/LD-CT. Conclusion: Overall, there was no significant difference between PET/LD-CT and PET/CE-CT. However, PET/CE-CT yielded a more precise lesion delineation than PET/LD-CT. This was due to the improved image quality of CE-CT and might lead to a more accurate investigation of lymphoma.

Appraisal of the role of In silico Methods in Pyrazole based drug design

2020 ◽  
Vol 20 ◽  
Author(s):  
Smriti Sharma ◽  
Vinayak Bhatia
Keyword(s):  
Drug Design ◽  
In Silico ◽  
Md Simulations ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Computational Techniques ◽  
Neuroprotective Drugs ◽  
Pharmacological Potential ◽  
Available Information ◽  
Anti Fungal

: Pyrazole and its derivatives are a pharmacologically significant active scaffold that have innumerable physiological and pharmacological activities. They can be very good targets for the discovery of novel anti-bacterial, anticancer, anti-inflammatory, anti-fungal, anti-tubercular, antiviral, antioxidant, antidepressant, anti-convulsant and neuroprotective drugs. This review focuses on the importance of in silico manipulations of pyrazole and its derivatives for medicinal chemistry. The authors have discussed currently available information on the use of computational techniques like molecular docking, structure-based virtual screening (SBVS), molecular dynamics (MD) simulations, quantitative structure activity relationship (QSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to drug design using pyrazole moieties. Pyrazole based drug design is mainly dependent on the integration of experimental and computational approaches. The authors feel that more studies need to be done to fully explore the pharmacological potential of the pyrazole moiety and in silico method can be of great help.

Reverse Screening Bioinformatics Approach to Identify Potential Anti Breast Cancer Targets Using Thymoquinone from Neutraceuticals Black Cumin Oil

2019 ◽  
Vol 19 (5) ◽  
pp. 599-609 ◽  
Author(s):  
Sumathi Sundaravadivelu ◽  
Sonia K. Raj ◽  
Banupriya S. Kumar ◽  
Poornima Arumugamand ◽  
Padma P. Ragunathan
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cell Cycle ◽  
Cell Death ◽  
In Silico ◽  
Chemotherapeutic Agents ◽  
Normal Cells ◽  
Black Cumin ◽  
In Silico Docking ◽  
Wide Range

Background: Functional foods, neutraceuticals and natural antioxidants have established their potential roles in the protection of human health and diseases. Thymoquinone (TQ), the main bioactive component of Nigella sativa seeds (black cumin seeds), a plant derived neutraceutical was used by ancient Egyptians because of their ability to cure a variety of health conditions and used as a dietary food supplement. Owing to its multi targeting nature, TQ interferes with a wide range of tumorigenic processes and counteracts carcinogenesis, malignant growth, invasion, migration, and angiogenesis. Additionally, TQ can specifically sensitize tumor cells towards conventional cancer treatments (e.g., radiotherapy, chemotherapy, and immunotherapy) and simultaneously minimize therapy-associated toxic effects in normal cells besides being cost effective and safe. TQ was found to play a protective role when given along with chemotherapeutic agents to normal cells. Methods: In the present study, reverse in silico docking approach was used to search for potential molecular targets for cancer therapy. Various metastatic and apoptotic targets were docked with the target ligand. TQ was also tested for its anticancer activities for its ability to cause cell death, arrest cell cycle and ability to inhibit PARP gene expression. Results: In silico docking studies showed that TQ effectively docked metastatic targets MMPs and other apoptotic and cell proliferation targets EGFR. They were able to bring about cell death mediated by apoptosis, cell cycle arrest in the late apoptotic stage and induce DNA damage too. TQ effectively down regulated PARP gene expression which can lead to enhanced cancer cell death. Conclusion: Thymoquinone a neutraceutical can be employed as a new therapeutic agent to target triple negative breast cancer which is otherwise difficult to treat as there are no receptors on them. Can be employed along with standard chemotherapeutic drugs to treat breast cancer as a combinatorial therapy.

scholarly journals THU0008 DEVELOPMENT OF A NOVEL TRANSLATIONAL IN SILICO INDICATION DISCOVERY FRAMEWORK: EXEMPLIFIED BY THE CLINICAL COMPOUND CENERIMOD

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 216.2-217
Author(s):  
D. Hartl ◽  
M. Keller ◽  
A. Klenk ◽  
M. Murphy ◽  
M. Martinic ◽  
...  
Keyword(s):  
Gene Expression ◽  
Animal Models ◽  
Clinical Response ◽  
Gene Expression Data ◽  
In Silico ◽  
Drug Candidate ◽  
Expression Data ◽  
Computer Algorithms ◽  
Clinical Patient ◽  
Link Type

Background:To explore the full therapeutic spectrum of a drug it is crucial to consider its potential effectiveness in all diseases. Serendipitous clinical observations have often shown that approved drugs and those in development to be efficacious in indications different to those originally tested for. Traditional approaches to match a drug candidate with possible indications are mostly based on matching drug mechanistic knowledge with disease pathophysiology. Proof-of-concept trials or elaborate pre-clinical studies in animal models do not allow for a broad assessment due to high costs and slow progress. Gene expression changes in patients or animal models represent a good proxy to comprehensively assess both disease and drug effects. Furthermore, this data type can be integrated with a plethora of publicly available data.Objectives:Generation of a novel in silico framework to support the selection and expansion of potential indications which associate with a compound or approved drug. The framework was exemplified by the clinical compound cenerimod, a potent, selective, and orally active sphingosine-1-phosphate receptor 1 modulator (Piali et al., 2017).Methods:A total of ~13’000 public patient gene expression datasets from ~140 diseases were evaluated against cenerimod gene expression data generated in mouse disease models. To improve comparability of studies across platforms and species, computer algorithms (neural networks) were trained and employed to reduce noise within the data sets and improve signal. The predicted response to cenerimod for individual patients was contrasted against clinical patient characteristics.Results:The neural network algorithm efficiently reduced experimental noise and improved sensitivity in the gene expression data. The results predicted cenerimod to be efficacious in several auto-immune diseases foremost SLE. Additionally, focused analysis on individual patients rather than disease cohorts revealed potential determinants predictive of maximal clinical response, with the highest predicted clinical response for cenerimod in patients with severe inflammatory endotype and/or high SLE Disease Activity Index (SLEDAI).Conclusion:Combining preclinical compound data with the wealth of public disease gene expression data, provides great potential to support indication selection. The novel in silico framework identified SLE as a prime potential indication for cenerimod and supported the cenerimod phase 2b clinical trial in patients with SLE (CARE study,NCT03742037).References:[1]Piali, L., Birker-Robaczewska, M., Lescop, C., Froidevaux, S., Schmitz, N., Morrison, K., … Nayler, O. (2017). Cenerimod, a novel selective S1P1 receptor modulator with unique signaling properties. Pharmacology Research & Perspectives, 5(6), 1–12.https://doi.org/10.1002/prp2.370Disclosure of Interests:Dominik Hartl Shareholder of: Idorsia shares, Employee of: Idorsia employee, Marcel Keller Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Axel Klenk Shareholder of: Idorsia option/shares, Employee of: Idorsia employee, Mark Murphy Shareholder of: Idorsia shares and stock options, Employee of: Idorsia employee, Marianne Martinic Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Gabin Pierlot Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Peter Groenen Shareholder of: Idorsia options/shares, Employee of: Idorsia employee, Daniel Strasser Shareholder of: Idorsia options/shares, Employee of: Idorsia employee

scholarly journals A method for evaluation of patient-specific lean body mass from limited-coverage CT images and its application in PERCIST: comparison with predictive equation

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jingjie Shang ◽  
Zhiqiang Tan ◽  
Yong Cheng ◽  
Yongjin Tang ◽  
Bin Guo ◽  
...  
Keyword(s):  
Lean Body Mass ◽  
Body Mass ◽  
Whole Body ◽  
Patient Specific ◽  
Predictive Equation ◽  
Reference Standard ◽  
Pet Ct ◽  
Before And After ◽  
Percist 1.0 ◽  
Limited Coverage

Abstract Background Standardized uptake value (SUV) normalized by lean body mass ([LBM] SUL) is recommended as metric by PERCIST 1.0. The James predictive equation (PE) is a frequently used formula for LBM estimation, but may cause substantial error for an individual. The purpose of this study was to introduce a novel and reliable method for estimating LBM by limited-coverage (LC) CT images from PET/CT examinations and test its validity, then to analyse whether SUV normalised by LC-based LBM could change the PERCIST 1.0 response classifications, based on LBM estimated by the James PE. Methods First, 199 patients who received whole-body PET/CT examinations were retrospectively retrieved. A patient-specific LBM equation was developed based on the relationship between LC fat volumes (FVLC) and whole-body fat mass (FMWB). This equation was cross-validated with an independent sample of 97 patients who also received whole-body PET/CT examinations. Its results were compared with the measurement of LBM from whole-body CT (reference standard) and the results of the James PE. Then, 241 patients with solid tumours who underwent PET/CT examinations before and after treatment were retrospectively retrieved. The treatment responses were evaluated according to the PE-based and LC-based PERCIST 1.0. Concordance between them was assessed using Cohen’s κ coefficient and Wilcoxon’s signed-ranks test. The impact of differing LBM algorithms on PERCIST 1.0 classification was evaluated. Results The FVLC were significantly correlated with the FMWB (r=0.977). Furthermore, the results of LBM measurement evaluated with LC images were much closer to the reference standard than those obtained by the James PE. The PE-based and LC-based PERCIST 1.0 classifications were discordant in 27 patients (11.2%; κ = 0.823, P=0.837). These discordant patients’ percentage changes of peak SUL (SULpeak) were all in the interval above or below 10% from the threshold (±30%), accounting for 43.5% (27/62) of total patients in this region. The degree of variability is related to changes in LBM before and after treatment. Conclusions LBM algorithm-dependent variability in PERCIST 1.0 classification is a notable issue. SUV normalised by LC-based LBM could change PERCIST 1.0 response classifications based on LBM estimated by the James PE, especially for patients with a percentage variation of SULpeak close to the threshold.

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