A guide to manufacturing CAR T cell therapies Vormittag, Philipp; Gunn, Rebecca; Ghorashian, Sara ...
Current opinion in biotechnology,
October 2018, 2018-10-00, 20181001, Volume:
53
Journal Article
Peer reviewed
Open access
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•CAR T cell clinical trial manufacturing data is investigated.•Three dominant process routes are uncovered.•The trend in CAR T cell manufacturing is towards automation.•Tailored ...off-the-shelf equipment for CAR T cell manufacturing is required.
In recent years, chimeric antigen receptor (CAR) modified T cells have been used as a treatment for haematological malignancies in several phase I and II trials and with Kymriah of Novartis and Yescarta of KITE Pharma, the first CAR T cell therapy products have been approved. Promising clinical outcomes have yet been tempered by the fact that many therapies may be prohibitively expensive to manufacture. The process is not yet defined, far from being standardised and often requires extensive manual handling steps. For academia, big pharma and contract manufacturers it is difficult to obtain an overview over the process strategies and their respective advantages and disadvantages. This review details current production processes being used for CAR T cells with a particular focus on efficacy, reproducibility, manufacturing costs and release testing. By undertaking a systematic analysis of the manufacture of CAR T cells from reported clinical trial data to date, we have been able to quantify recent trends and track the uptake of new process technology. Delivering new processing options will be key to the success of the CAR-T cells ensuring that excessive manufacturing costs do not disrupt the delivery of exciting new therapies to the wide possible patient cohort.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Macroautophagy (hereafter called autophagy) is a highly conserved physiological process that degrades over-abundant or damaged organelles, large protein aggregates and invading pathogens via the ...lysosomal system (the vacuole in plants and yeast). Autophagy is generally induced by stress, such as oxygen-, energy- or amino acid-deprivation, irradiation, drugs,
. In addition to non-selective bulk degradation, autophagy also occurs in a selective manner, recycling specific organelles, such as mitochondria, peroxisomes, ribosomes, endoplasmic reticulum (ER), lysosomes, nuclei, proteasomes and lipid droplets (LDs). This capability makes selective autophagy a major process in maintaining cellular homeostasis. The dysfunction of selective autophagy is implicated in neurodegenerative diseases (NDDs), tumorigenesis, metabolic disorders, heart failure,
. Considering the importance of selective autophagy in cell biology, we systemically review the recent advances in our understanding of this process and its regulatory mechanisms. We emphasize the 'cargo-ligand-receptor' model in selective autophagy for specific organelles or cellular components in yeast and mammals, with a focus on mitophagy and ER-phagy, which are finely described as types of selective autophagy. Additionally, we highlight unanswered questions in the field, helping readers focus on the research blind spots that need to be broken.
High-throughput sequencing has revolutionized microbial ecology, but read quality remains a considerable barrier to accurate taxonomy assignment and α-diversity assessment for microbial communities. ...We demonstrate that high-quality read length and abundance are the primary factors differentiating correct from erroneous reads produced by Illumina GAIIx, HiSeq and MiSeq instruments. We present guidelines for user-defined quality-filtering strategies, enabling efficient extraction of high-quality data and facilitating interpretation of Illumina sequencing results.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Translation of mRNAs is tightly regulated and constantly surveyed for errors. Aberrant translation can trigger co-translational protein and RNA quality control processes, impairments of which cause ...neurodegeneration by still poorly understood mechanism(s). Here we show that quality control of translation of mitochondrial outer membrane (MOM)-localized mRNA intersects with the turnover of damaged mitochondria, both orchestrated by the mitochondrial kinase PINK1. Mitochondrial damage causes stalled translation of complex-I 30 kDa subunit (C-I30) mRNA on MOM, triggering the recruitment of co-translational quality control factors Pelo, ABCE1, and NOT4 to the ribosome/mRNA-ribonucleoprotein complex. Damage-induced ubiquitination of ABCE1 by NOT4 generates poly-ubiquitin signals that attract autophagy receptors to MOM to initiate mitophagy. In the Drosophila PINK1 model, these factors act synergistically to restore mitophagy and neuromuscular tissue integrity. Thus ribosome-associated co-translational quality control generates an early signal to trigger mitophagy. Our results have broad therapeutic implications for the understanding and treatment of neurodegenerative diseases.
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•Enhanced translation of OXPHOS-related mRNAs on mitochondrial surface under stress•Severe mitochondrial damage induces translational stalling on mitochondrial surface•Stalled ribosomes recruit co-translational quality control machinery•Remodeling of stalled ribosome/mRNP generates signals that trigger mitophagy
Removal of damaged mitochondria is essential for maintaining cellular vitality, but the earliest signal that initiates the mitophagy process is not well defined. Wu et al. show that mitochondrial damage causes stalled translation of OXPHOS-related mRNAs on the mitochondrial surface. Co-translational quality control of stalled ribosomes generates ubiquitin-containing signals that trigger mitophagy.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Ubiquitin chains of distinct topologies control the stability, interactions, or localization of many proteins in eukaryotic cells, and thus play an essential role in cellular information transfer. It ...has recently been found that ubiquitin chains can be combined to produce branched conjugates that are characterized by the presence of at least two linkages within the same polymer. Akin to their homotypic counterparts, branched chains elicit a wide array of biological outputs, further expanding the versatility, specificity, and efficiency of ubiquitin-dependent signaling. This review discusses emerging understanding of the synthesis and function of branched ubiquitin chains.
Branched ubiquitin chains, characterized by a subunit modified by one, two, or more residues, can be an abundant fraction of the total ubiquitin chain pool.Branched chains can be synthesized via different routes including a single E3, the collaborative effort of two or more E3s, or by the combined action of two or more E2 enzymes.Branched chains are often detected by effector proteins or complexes that contain multiple ubiquitin-binding domains to read out the distinct linkages or higher ubiquitin concentrations afforded by these conjugates.Branched chains improve the versatility, complexity, and specificity of ubiquitin-dependent signaling by prioritizing substrate processing, favoring coincidence detection, or enabling editing of ubiquitin functions.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
We present primary results from the Sequencing Quality Control (SEQC) project, coordinated by the US Food and Drug Administration. Examining Illumina HiSeq, Life Technologies SOLiD and Roche 454 ...platforms at multiple laboratory sites using reference RNA samples with built-in controls, we assess RNA sequencing (RNA-seq) performance for junction discovery and differential expression profiling and compare it to microarray and quantitative PCR (qPCR) data using complementary metrics. At all sequencing depths, we discover unannotated exon-exon junctions, with >80% validated by qPCR. We find that measurements of relative expression are accurate and reproducible across sites and platforms if specific filters are used. In contrast, RNA-seq and microarrays do not provide accurate absolute measurements, and gene-specific biases are observed for all examined platforms, including qPCR. Measurement performance depends on the platform and data analysis pipeline, and variation is large for transcript-level profiling. The complete SEQC data sets, comprising >100 billion reads (10Tb), provide unique resources for evaluating RNA-seq analyses for clinical and regulatory settings.
The purpose of this global study is to investigate the validity of Dr. Ishikawa's statement that "95% of problems in processes can be solved using the 7 Quality Control (QC) tools" in organizations. ...An online survey instrument was developed, disseminated, and responded to by a total of 456 senior quality professionals from five different continents. The main finding of this article suggests that less than 25% of participants perceived that the seven tools of QC can solve above 95% of quality problems while 40% of quality professionals stated that they had incorrectly applied the tools "right first time" during the problem solving. Pareto analysis was the most widely used tool across all sectors while the least used tools are scatter diagrams and stratification. The seven QC tools were widely utilized in production or manufacturing areas but least applied in IT and finance functions. The common benefits from the use of seven basic tools of QC in all sectors include: providing structure to the problem-solving efforts; aids problem solving and helps in problem definition, measurement, and analysis. This article presents a list of critical success factors (CSFs) required for the proper application of the seven QC tools including having management support and a commitment to tool usage as well as having a continuous improvement initiative to encourage tool usage. This article is the first global research focused on investigating Dr. Ishikawa's statement: "95% of problems in processes can be solved using the 7 QC tools." The findings further facilitate an important first step toward understanding the applicability, benefits, CSFs, and challenges to utilizing these tools in organizations across sectors and globally.
Conditional DETR for Fast Training Convergence Meng, Depu; Chen, Xiaokang; Fan, Zejia ...
2021 IEEE/CVF International Conference on Computer Vision (ICCV),
2021-Oct.
Conference Proceeding
Open access
The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow ...training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty.Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101. Code is available at https://github.com/Atten4Vis/ConditionalDETR.
Poor outcomes for people with diabetes have necessitated targeted quality improvement (QI) projects to affect change. Project ECHO Diabetes is a tele-education and tele-mentoring program for ...community health clinics in California and Florida serving under-resourced patients with diabetes who lack routine access to specialty care. The model provided a unique opportunity to undertake a multi-site QI initiative, where 12 community clinics participated in measuring and improving quality of care. Using a modified-Delphi approach, clinic providers rated various QI measures on their validity and feasibility. A total of 18 measures were selected, and each clinic chose its own measures to track throughout the one-year initiative. The number of measures each clinic focused on ranged from 1 to 9, with a median of 3. Quarterly, clinics met remotely to report performance based on selected measures and discuss their efforts, successes, and setbacks. At the end of the QI year, clinics reported widespread improvements across structural, process, and outcome measures (Table) . Remarkable improvement ideas came from the sharing of best practices during the remote tele-ECHO clinic sessions and the changes made in the data systems used to track important measures. The Project ECHO Diabetes tele-education model has the potential to improve care for people living with diabetes in clinics serving under-resourced communities.
Disclosure
C.Wang: None. L.Figg: None. C.Anez-zabala: None. E.Sheehan: None. S.C.Westen: None. C.E.Deremer: Research Support; Pfizer-BMS Alliance, Stock/Shareholder; Portola Pharmaceuticals, Inc. A.Bernier: None. W.T.Donahoo: None. A.F.Walker: None. E.Lewit: n/a. C.Clark: None. D.M.Maahs: Advisory Panel; Abbott Diabetes, Eli Lilly and Company, Medtronic, Novo Nordisk, Sanofi, Consultant; Aditx Therapeutics, Inc., Biospex. M.J.Haller: Advisory Panel; SAB Biotherapeutics , Consultant; MannKind Corporation, Sanofi. A.Addala: None. R.Lal: Advisory Panel; Provention Bio, Inc., Consultant; Abbott Diabetes, Biolinq, Capillary Biomedical, Inc., Deep Valley Labs, Gluroo, Morgan Stanley, Tidepool. N.Cuttriss: Advisory Panel; American Diabetes Association, Board Member; American Youth Understanding Diabetes Abroad , Consultant; Cecelia Health, Leona M. and Harry B. Helmsley Charitable Trust, Med-IQ. L.G.Baer: None.
Funding
The Leona M. and Harry B. Helmsley Charitable Trust
Background: The T1D Exchange Quality Improvement Collaborative (T1DX-QI) is a national quality improvement network focused on improving health outcomes for patients with diabetes by sharing and using ...data to refine best practices among 41 participating clinics. Due to Covid-restrictions, engagement among learning health networks has shifted to remote platforms, and may limit engagement among members. Clinics participate in regular collaborative-wide calls and one-on-one QI check-ins with a QI coach. Clinics can also participate in Committees and have access to an EMR-based data benchmarking platform.
Methods: T1DX-QI tracked network engagement quarterly, using the measures of: 1) Collaborative Calls; 2) Coaching Calls; 3) Committee participation; and 4) Data Benchmarking Platform Use. A radar chart (Figure 1) displays the percentage of clinics meeting the four engagement measures by quarter.
Results: Findings show an increase in data benchmarking platform use among clinics from 75% in Q1 to 86.7% in Q4. The remaining measures sustain an 85% engagement or higher from Q1-Q4, with increases in subsequent quarters. Concurrently, the number of clinics participating increased from 30 in Q1 to 41 at Q4.
Conclusions: Over the past year, T1DX-QI growth increased and engagement among clinics increased and sustained across various measured components. Future tracking will include data sharing via other platforms.
Disclosure
E.L.Ospelt: None. O.Ebekozien: None. A.Mungmode: None. N.Rioles: None. S.Rompicherla: None. O.Odugbesan: n/a. H.Hardison: None. C.Rowe: None. A.Wirsch: None. N.Noor: None.
Funding
The Leona M. and Harry B. Helmsley Charitable Trust
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