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306 results for “Personalized therapy”
ValRun: GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration
<p><strong>VaLRun: </strong></p> <p><strong>Raw data of "GMP-grade Manufacturing and Quality Control of a Non-Virally engineered Advanced Therapy Medicinal Product for Personalized Treatment of Age-Related Macular Degeneration"</strong></p> <p>(Excel-, pdf-, GraphPad-files, mp4 videos and a READ-ME text file)</p> <p>The introduction of new therapeutics requires validation of Good Manufacturing Practice (GMP)-grade manufacturing including suitable quality controls. This is challenging for Advanced Therapy Medicinal Products (ATMP) with personalized batches. We have developed a person-alized, cell-based gene therapy to treat age-related macular degeneration and established a vali-dation strategy of the GMP-grade manufacture for the ATMP; manufacturing and quality control were challenging due to a low cell number, batch-to-batch variability and short production duration. Instead of patient iris pigment epithelial cells, human donor tissue was used to produce the transfected cell product (“tIPE”). We implemented an extended validation of 104 tIPE productions. Procedure, operators and devices have been validated and qualified by determining cell number, viability, extracellular DNA, sterility, duration, temperature and volume. Transfected autologous cells were transplanted to rabbits verifying feasibility of the treatment. A container has been engineered to insure a safe transport from the production to the surgery site. Criteria for successful validation and qualification were based on tIPE’s Critical Quality Attributes and Process Parameters, its manufacture and release criteria. The validated process and qualified operators are essential to bring the ATMP into clinic and offer a general strategy for the transfer to other manufacture centers and personalized ATMPs.</p>
In silico prediction of ARB resistance: A first step in creating personalized ARB therapy
<p><strong>AT1R Model preparation</strong><br> The crystal structure of human AT1R bound to olmesartan (PDB: 4ZUD) was downloaded from the RCSB Protein Data Bank. 4ZUD contains apocytochrome b562RIL fused to the amino terminus, and many of the flexible regions, as well as helix 8, are not resolved. In order to generate an appropriate starting structure, olmesartan and the apocytochrome b562RIL fusion were removed from 4ZUD, and the missing regions were added to the protein with MOE software (Chemical Computing Group ULC, Montreal, Canada). Specifically, the N-Terminus (residues 1 to 25), intracellular loop 2 (residues 134 to 140), extracellular loop 2 (residues 186 to 188), intracellular loop 3 (residues 223 to 234), and helix 8 (residues 305 to 316) were added to the AT1R in accordance to the human AT1R sequence and PDB:4YAY. The remaining carboxyl-tail of the AT1R (residues 317 to 359) was not modeled. The AT1R model then underwent an energy minimization within MOE using the Amber10:Extended Huckel Theory (EHT) force field.</p> <p><strong>Molecular dynamic (MD) simulations and analysis</strong><br> The MOE minimized AT1R was loaded into CHARMM-GUI. An 80 Å by 80 Å lipid bi-layer composed of 13% cholesterol and 87% Phosphatidylcholine (POPC) was generated around the receptor. Water was packed 17.5 Å above and below the lipid bi-layer, and 150 mM Na+ and Cl- ions were added to the system via Monte-Carlo ion placing. The all-atom CHARMM C36 force field for proteins and ions, and the CHARMM TIP3P force field for water were selected. A hard non-bonded cutoff of 8.0 angstroms was utilized. All molecular dynamics simulations were performed using the PMEMD module of the AMBER16 package with support for MPI multi-process control and GPU acceleration code. Orthorhombic periodic boundary conditions with a constant pressure of 1 atm was set via the NPT ensemble and temperature was set to 310.15°K (37°C) using Langevin dynamics. The SHAKE algorithm was used to constrain bonds containing hydrogens. The dynamics were propagated using Langevin dynamics with Langevin damping coefficient of 1 ps-1 and a time step of 2 fs. Before the production run, the AT1R model was minimized for 5000 steps using the steepest descent method and then equilibrated for 600 ps. The protein coordinates were saved in 10 ps intervals. The production run lasted 150 ns, at which point all three replicas were stable for at least the last 20 ns.</p>
Personalized AZithromycin/metronidAZole Therapy in Pediatric Crohn's Disease (CD)
ClinicalTrials.gov study NCT04186247. IPD Sharing: YES. Countries: 4. Publications: 1.
Identification of clinically relevant T cell receptors for personalized T cell therapy using combinatorial algorithms
<p>Single-cell RNA (scRNA) and single-cell TCR (scTCR)-sequencing data data for patients number 11, 12 and 14 from the article " Identification of clinically relevant T-cell receptors for personlized T-cell therapy". </p> <p>Each compressed file contains two subfolders. One is for the scRNA-seq (GEX-sequencing) and the other for the scTCR-seq (VDJ-sequencing). </p> <p>In the original article, scRNA-seq and scTCR-seq were aligned to the GRCh38 reference genome using <em>cellranger count </em>(10X Genomics, version 3.0.1) and <em>vdj </em>(10X Genomics, version 3.1.0) respectively. The subsequent data processing was performed using Seurat library (V.4.3.0) on R Statistical Software (V.4.0.3).</p>
Microbiome-based biomarkers to guide personalized microbiome-based therapies for Parkinson's disease
<p><strong>Abstract: </strong>We address an unmet challenge in Parkinson’s disease: the lack of biomarkers to identify the right patients for the right therapy, which is a main reason clinical trials for disease modifying treatments have all failed. The gut microbiome is a new target for treatment of neurodegenerative diseases. Our aim was to develop microbiome-based biomarkers to guide patient selection for microbiome-based clinical trials. We used microbial taxa that are robustly associated with PD across studies and at high significance as dysbiotic features of PD. Using individual-level taxonomic relative abundance data, we classified patients according to their dysbiotic features, effectively defining microbiome-based subtypes of PD. We show that not all persons with PD have a dysbiotic microbiome, and not all dysbiotic PD microbiomes have the same features. Grounded in robust and reproducible data from differential abundance studies, we propose an intuitive and easily modifiable method to identify the optimal candidates for microbiome-based clinical trials, and subsequently, for treatments that are personalized for each individual’s dysbiotic features. We demonstrate the method for PD. The concept, and the method, is generalizable for any disease with a microbiome component.</p> <p><strong>Zenodo</strong> <strong>content: </strong>In this Zenodo archive we provide (a) the method described step by step, which can be implemented in Microsoft Excel (we used v.16.84 (RRID:SCR_016137) <a href="https://www.microsoft.com/en-gb/">https://www.microsoft.com/en-gb/</a>) or in R (we used v4.3.3 (RRID:SCR_001905) <a title="https://www.r-project.org/ Cmd+Click or tap to follow the link" href="https://www.r-project.org/">https://www.r-project.org/</a>); and (b) data used to generate the results, tables and figures (except figure 2). Data for creating figure 2 can be found in source data (doi: 10.5281/zenodo.7246185) and the method is described by Wallen et al 2022 (DOI: <u><a href="https://doi.org/10.1038/s41467-022-34667-x" target="_blank" rel="noopener">10.1038/s41467-022-34667-x</a></u>). All data used here were extracted from the original source data reported by<strong> </strong>Wallen et. al. 2022 (DOI: <u><a href="https://doi.org/10.1038/s41467-022-34667-x" target="_blank" rel="noopener">10.1038/s41467-022-34667-x</a></u>) which can be found on Zenodo (DOI: 10.5281/zenodo.7246185).</p>
Supplementary file 23 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 22 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 21 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 19 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 17 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 18 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 16 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 15 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 2 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 10 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 3 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 5 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 14 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 4 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
Supplementary file 13 for the Cochrane review Psychological therapies for people with borderline personality disorder
<p>A Trial Sequential Analysis figure</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.