Antibody Therapies for Acute Myeloid Leukemia: Unconjugated, Toxin-Conjugated, Radio-Conjugated and Multivalent Formats
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ENSG Gene Encodes Effector TCR Pathway Costimulation Inhibitory/Exhaustion Synapse/Adhesion Chemokines/Receptors
ENSG Gene Encodes Effector TCR pathway Costimulation Inhibitory/exhaustion Synapse/adhesion Chemokines/receptors ENSG00000111537 IFNG IFNg x ENSG00000109471 IL2 IL-2 x ENSG00000232810 TNF TNFa x ENSG00000271503 CCL5 CCL5 x x ENSG00000139187 KLRG1 Klrg1 x ENSG00000117560 FASLG Fas ligand x ENSG00000121858 TNFSF10 TRAIL x ENSG00000134545 KLRC1 Klrc1 / NKG2A x ENSG00000213809 KLRK1 Klrk1 / NKG2D x ENSG00000188389 PDCD1 PD-1 x x ENSG00000117281 CD160 CD160 x x ENSG00000134460 IL2RA IL-2 receptor x subunit alpha ENSG00000110324 IL10RA IL-10 receptor x subunit alpha ENSG00000115604 IL18R1 IL-18 receptor 1 x ENSG00000115607 IL18RAP IL-18 receptor x accessory protein ENSG00000081985 IL12RB2 IL-12 receptor x beta 2 ENSG00000186810 CXCR3 CXCR3 x x ENSG00000005844 ITGAL CD11a x ENSG00000160255 ITGB2 CD18; Integrin x x beta-2 ENSG00000156886 ITGAD CD11d x ENSG00000140678 ITGAX; CD11c x x Integrin alpha-X ENSG00000115232 ITGA4 CD49d; Integrin x x alpha-4 ENSG00000169896 ITGAM CD11b; Integrin x x alpha-M ENSG00000138378 STAT4 Stat4 x ENSG00000115415 STAT1 Stat1 x ENSG00000170581 STAT2 Stat2 x ENSG00000126561 STAT5a Stat5a x ENSG00000162434 JAK1 Jak1 x ENSG00000100453 GZMB Granzyme B x ENSG00000145649 GZMA Granzyme A x ENSG00000180644 PRF1 Perforin 1 x ENSG00000115523 GNLY Granulysin x ENSG00000100450 GZMH Granzyme H x ENSG00000113088 GZMK Granzyme K x ENSG00000057657 PRDM1 Blimp-1 x ENSG00000073861 TBX21 T-bet x ENSG00000115738 ID2 ID2 x ENSG00000176083 ZNF683 Hobit x ENSG00000137265 IRF4 Interferon x regulatory factor 4 ENSG00000140968 IRF8 Interferon -
Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse
Welcome to More Choice CD Marker Handbook For more information, please visit: Human bdbiosciences.com/eu/go/humancdmarkers Mouse bdbiosciences.com/eu/go/mousecdmarkers Human and Mouse CD Marker Handbook Human and Mouse CD Marker Key Markers - Human Key Markers - Mouse CD3 CD3 CD (cluster of differentiation) molecules are cell surface markers T Cell CD4 CD4 useful for the identification and characterization of leukocytes. The CD CD8 CD8 nomenclature was developed and is maintained through the HLDA (Human Leukocyte Differentiation Antigens) workshop started in 1982. CD45R/B220 CD19 CD19 The goal is to provide standardization of monoclonal antibodies to B Cell CD20 CD22 (B cell activation marker) human antigens across laboratories. To characterize or “workshop” the antibodies, multiple laboratories carry out blind analyses of antibodies. These results independently validate antibody specificity. CD11c CD11c Dendritic Cell CD123 CD123 While the CD nomenclature has been developed for use with human antigens, it is applied to corresponding mouse antigens as well as antigens from other species. However, the mouse and other species NK Cell CD56 CD335 (NKp46) antibodies are not tested by HLDA. Human CD markers were reviewed by the HLDA. New CD markers Stem Cell/ CD34 CD34 were established at the HLDA9 meeting held in Barcelona in 2010. For Precursor hematopoetic stem cell only hematopoetic stem cell only additional information and CD markers please visit www.hcdm.org. Macrophage/ CD14 CD11b/ Mac-1 Monocyte CD33 Ly-71 (F4/80) CD66b Granulocyte CD66b Gr-1/Ly6G Ly6C CD41 CD41 CD61 (Integrin b3) CD61 Platelet CD9 CD62 CD62P (activated platelets) CD235a CD235a Erythrocyte Ter-119 CD146 MECA-32 CD106 CD146 Endothelial Cell CD31 CD62E (activated endothelial cells) Epithelial Cell CD236 CD326 (EPCAM1) For Research Use Only. -
Prior Approaches and Future Directions Marna Williams1, Anna Spreafico2, Kapil Vashisht3, Mary Jane Hinrichs4
Author Manuscript Published OnlineFirst on June 16, 2020; DOI: 10.1158/1535-7163.MCT-19-0993 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Patient Selection Strategies to Maximize Therapeutic Index of Antibody Drug Conjugates: Prior Approaches and Future Directions Marna Williams1, Anna Spreafico2, Kapil Vashisht3, Mary Jane Hinrichs4 1Translational Medicine, Oncology, AstraZeneca, Gaithersburg, USA 2Drug Development Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada 3AstraZeneca, Gaithersburg, USA. 4Early Development Oncology, Ipsen LLC, USA Corresponding author: Marna Williams Email: [email protected] Postal address: One MedImmune Way, Gaithersburg, MD, 20878 Phone: 301-398-1241 Fax: 240-306-1545 Running title Patient Selection Strategies for Antibody Drug Conjugates Keywords Antibody drug conjugates; therapeutic index; patient selection; biomarkers; cancer Conflicts of interest This study was funded by AstraZeneca. MW, KV and MJH are all current or former employees of AstraZeneca with stock/stock options in AstraZeneca. AS has been Advisory Board Consultant to Merck, Bristol-Myers Squibb, Novartis, Oncorus, Janssen, and has received Grant/Research support from Novartis, Bristol-Myers Squibb, Symphogen AstraZeneca/Medimmune, Merck, Bayer, Surface Oncology, Northern Biologics, Janssen Oncology/Johnson & Johnson, Roche, Regeneron, and Alkermes. Word count: Abstract: 197 Manuscript: 5,268 (excluding abstract) + 683 figure/table legends and footnotes Figures/tables: 5 (as agreed) 1 Downloaded from mct.aacrjournals.org on September 24, 2021. © 2020 American Association for Cancer Research. Author Manuscript Published OnlineFirst on June 16, 2020; DOI: 10.1158/1535-7163.MCT-19-0993 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. -
For Acute Myeloid Leukemia
Published OnlineFirst September 21, 2010; DOI: 10.1158/1078-0432.CCR-10-0382 Published OnlineFirst on September 21, 2010 as 10.1158/1078-0432.CCR-10-0382 Clinical Therapy Clinical Cancer Research Sequential Cytarabine and α-Particle Immunotherapy with Bismuth-213–Lintuzumab (HuM195) for Acute Myeloid Leukemia Todd L. Rosenblat1, Michael R. McDevitt1, Deborah A. Mulford1, Neeta Pandit-Taskar1, Chaitanya R. Divgi1, Katherine S. Panageas1, Mark L. Heaney1, Suzanne Chanel1, Alfred Morgenstern2, George Sgouros1, Steven M. Larson1, David A. Scheinberg1, and Joseph G. Jurcic1 Abstract Purpose: Lintuzumab (HuM195), a humanized anti-CD33 antibody, targets myeloid leukemia cells and has modest single-agent activity against acute myeloid leukemia (AML). To increase the potency of the antibody without the nonspecific cytotoxicity associated with β-emitters, the α-particle–emitting ra- dionuclide bismuth-213 (213Bi) was conjugated to lintuzumab. This phase I/II trial was conducted to determine the maximum tolerated dose (MTD) and antileukemic effects of 213Bi-lintuzumab, the first targeted α-emitter, after partially cytoreductive chemotherapy. Experimental Design: Thirty-one patients with newly diagnosed (n = 13) or relapsed/refractory (n = 18) AML (median age, 67 years; range, 37-80) were treated with cytarabine (200 mg/m2/d) for 5 days followed by 213Bi-lintuzumab (18.5-46.25 MBq/kg). Results: The MTD of 213Bi-lintuzumab was 37 MB/kg; myelosuppression lasting >35 days was dose limiting. Extramedullary toxicities were primarily limited to grade ≤2 events, including infusion-related reactions. Transient grade 3/4 liver function abnormalities were seen in five patients (16%). Treatment- related deaths occurred in 2 of 21 (10%) patients who received the MTD. -
Tools for Cell Therapy and Immunoregulation
RnDSy-lu-2945 Tools for Cell Therapy and Immunoregulation Target Cell TIM-4 SLAM/CD150 BTNL8 PD-L2/B7-DC B7-H1/PD-L1 (Human) Unknown PD-1 B7-1/CD80 TIM-1 SLAM/CD150 Receptor TIM Family SLAM Family Butyrophilins B7/CD28 Families T Cell Multiple Co-Signaling Molecules Co-stimulatory Co-inhibitory Ig Superfamily Regulate T Cell Activation Target Cell T Cell Target Cell T Cell B7-1/CD80 B7-H1/PD-L1 T cell activation requires two signals: 1) recognition of the antigenic peptide/ B7-1/CD80 B7-2/CD86 CTLA-4 major histocompatibility complex (MHC) by the T cell receptor (TCR) and 2) CD28 antigen-independent co-stimulation induced by interactions between B7-2/CD86 B7-H1/PD-L1 B7-1/CD80 co-signaling molecules expressed on target cells, such as antigen-presenting PD-L2/B7-DC PD-1 ICOS cells (APCs), and their T cell-expressed receptors. Engagement of the TCR in B7-H2/ICOS L 2Ig B7-H3 (Mouse) the absence of this second co-stimulatory signal typically results in T cell B7-H1/PD-L1 B7/CD28 Families 4Ig B7-H3 (Human) anergy or apoptosis. In addition, T cell activation can be negatively regulated Unknown Receptors by co-inhibitory molecules present on APCs. Therefore, integration of the 2Ig B7-H3 Unknown B7-H4 (Mouse) Receptors signals transduced by co-stimulatory and co-inhibitory molecules following TCR B7-H5 4Ig B7-H3 engagement directs the outcome and magnitude of a T cell response Unknown Ligand (Human) B7-H5 including the enhancement or suppression of T cell proliferation, B7-H7 Unknown Receptor differentiation, and/or cytokine secretion. -
Attachement B Summary
Summary - Paediatric Strategy Forum Medicinal Product Development for Acute Myeloid Leukaemia in Children and Adolescents 24-6-19 Paediatric Strategy Forum for Medicinal Product Development for Acute Myeloid Leukaemia in Children and Adolescents ACCELERATE in collaboration with the European Medicines Agency with participation of the Food and Drug Administration 11-12 April 2019 Summary Context: The fourth multi-stakeholder Paediatric Strategy Forum, organised by ACCELERATE in collaboration with the European Medicines Agency (EMA) with participation of the Food and Drug Administration (FDA) was held at Erasmus University, Rotterdam and focused on acute myeloid leukaemia (AML) in children and adolescents. Paediatric Strategy Forums are multi-stakeholder (clinicians, academics, patient advocates, pharmaceutical companies and regulators) scientific meetings on specific topics regarding paediatric oncology. The Forums aim to share information and advance learning, which may inform subsequent strategic and regulatory decisions on the development of medicines for children with cancer. The goal of this meeting was to facilitate development of innovative medicines for the treatment of children and adolescents with AML, and ultimately, by extension, introduce these medicines into the standard-of-care for children. The 5-year overall survival (OS) for paediatric AML currently is approximately 75%; however, the standard of care for frontline therapy for many decades has been daunorubicin and cytarabine. Furthermore, cardiotoxicity, due to anthracyclines, is an important long-term sequelae in a substantial proportion of survivors. AML is more frequent under the age of 3 years and its incidence initially declines and subsequently increases throughout young adulthood and is most frequent in the elderly. The occurrence of specific genetic alterations differs in AML in children compared to adults and the elderly. -
Predictive QSAR Tools to Aid in Early Process Development of Monoclonal Antibodies
Predictive QSAR tools to aid in early process development of monoclonal antibodies John Micael Andreas Karlberg Published work submitted to Newcastle University for the degree of Doctor of Philosophy in the School of Engineering November 2019 Abstract Monoclonal antibodies (mAbs) have become one of the fastest growing markets for diagnostic and therapeutic treatments over the last 30 years with a global sales revenue around $89 billion reported in 2017. A popular framework widely used in pharmaceutical industries for designing manufacturing processes for mAbs is Quality by Design (QbD) due to providing a structured and systematic approach in investigation and screening process parameters that might influence the product quality. However, due to the large number of product quality attributes (CQAs) and process parameters that exist in an mAb process platform, extensive investigation is needed to characterise their impact on the product quality which makes the process development costly and time consuming. There is thus an urgent need for methods and tools that can be used for early risk-based selection of critical product properties and process factors to reduce the number of potential factors that have to be investigated, thereby aiding in speeding up the process development and reduce costs. In this study, a framework for predictive model development based on Quantitative Structure- Activity Relationship (QSAR) modelling was developed to link structural features and properties of mAbs to Hydrophobic Interaction Chromatography (HIC) retention times and expressed mAb yield from HEK cells. Model development was based on a structured approach for incremental model refinement and evaluation that aided in increasing model performance until becoming acceptable in accordance to the OECD guidelines for QSAR models. -
And Heterodimeric Interactions Between the Gene Products of PKD1 and PKD2 (Polycystic Kidney Disease͞yeast Two-Hybrid System͞protein–Protein Interactions)
Proc. Natl. Acad. Sci. USA Vol. 94, pp. 6965–6970, June 1997 Medical Sciences Homo- and heterodimeric interactions between the gene products of PKD1 and PKD2 (polycystic kidney diseaseyyeast two-hybrid systemyprotein–protein interactions) LEONIDAS TSIOKAS*†,EMILY KIM†‡,THIERRY ARNOULD*, VIKAS P. SUKHATME*, AND GERD WALZ*§ *Renal Division, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215; and ‡Laboratory of Molecular and Developmental Neuroscience, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 Communicated by Irving M. London, Massachusetts Institute of Technology, Cambridge, MA, May 1, 1997 (received for review January 3, 1997) ABSTRACT PKD1 and PKD2 are two recently identified clarified the nature of the aberrant gene products caused by genes that are responsible for the vast majority of autosomal mutations of PKD1 and PKD2. Renal cysts are thought to arise polycystic kidney disease, a common inherited disease that through a process of persistent epithelial proliferation related causes progressive renal failure. PKD1 encodes polycystin, a to the lack of terminal differentiation. Both abnormal growth large glycoprotein that contains several extracellular motifs factor responsiveness (8–12) and the elevated expression of indicative of a role in cell–cell or cell–matrix interactions, and certain oncogenes appear to support this hypothesis (13–16). the PKD2 encodes a protein with homology to a voltage- Recently, loss of heterozygosity was discovered within a subset activated calcium channel and to PKD1. It is currently un- of cysts for two closely linked polymorphic markers located known how mutations of either protein functionally cause within the PKD1 gene, indicating that cyst formation in autosomal polycystic kidney disease. -
Advances and Limitations of Antibody Drug Conjugates for Cancer
biomedicines Review Advances and Limitations of Antibody Drug Conjugates for Cancer Candice Maria Mckertish and Veysel Kayser * Sydney School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; [email protected] * Correspondence: [email protected]; Tel.: +61-2-9351-3391 Abstract: The popularity of antibody drug conjugates (ADCs) has increased in recent years, mainly due to their unrivalled efficacy and specificity over chemotherapy agents. The success of the ADC is partly based on the stability and successful cleavage of selective linkers for the delivery of the payload. The current research focuses on overcoming intrinsic shortcomings that impact the successful devel- opment of ADCs. This review summarizes marketed and recently approved ADCs, compares the features of various linker designs and payloads commonly used for ADC conjugation, and outlines cancer specific ADCs that are currently in late-stage clinical trials for the treatment of cancer. In addition, it addresses the issues surrounding drug resistance and strategies to overcome resistance, the impact of a narrow therapeutic index on treatment outcomes, the impact of drug–antibody ratio (DAR) and hydrophobicity on ADC clearance and protein aggregation. Keywords: antibody drug conjugates; drug resistance; linkers; payloads; therapeutic index; target specific; ADC clearance; protein aggregation Citation: Mckertish, C.M.; Kayser, V. Advances and Limitations of Antibody Drug Conjugates for 1. Introduction Cancer. Biomedicines 2021, 9, 872. Conventional cancer therapy often entails a low therapeutic window and non-specificity https://doi.org/10.3390/ of chemotherapeutic agents that consequently affects normal cells with high mitotic rates biomedicines9080872 and provokes an array of adverse effects, and in some cases leads to drug resistance [1]. -
Papers J Clin Pathol: First Published As 10.1136/Jcp.49.7.539 on 1 July 1996
Clin Pathol 1996;49:539-544 539 Papers J Clin Pathol: first published as 10.1136/jcp.49.7.539 on 1 July 1996. Downloaded from Differential expression of T cell antigens in normal peripheral blood lymphocytes: a quantitative analysis by flow cytometry L Ginaldi, N Farahat, E Matutes, M De Martinis, R Morilla, D Catovsky Abstract diagnosis by comparison with findings in Aims-To obtain reference values of the normal counterparts. level of expression of T cell antigens on ( Clin Pathol 1996;49:539-544) normal lymphocyte subsets in order to disclose differences which could reflect Keywords: flow cytometry, T cell antigens, peripheral their function or maturation stages, or blood lymphocytes. both. Methods-Peripheral blood from 15 healthy donors was processed by flow The most common use of flow cytometry is to cytometry with triple colour analysis. For determine the percent of "positive" or "nega- each sample phycoerythrin (PE) conju- tive" cells for each antigen in different cell gated CD2, CD4, CD5, CD8, and CD56 populations. However, valuable information is monoclonal antibodies were combined lost when the relative intensities of the positive with Cy5-R-phycoerythrin (TC) conju- cells, reflecting antigenic densities, are not considered. A cell population with a well gated CD3 and fluorescein isothiocyanate http://jcp.bmj.com/ (FITC) conjugated CD7; CD2- and defined phenotype, positive for one antigen, CD7-PE were also combined with might be heterogeneous with regard to its level CD3-TC and CD4-FITC. Standard mi- of expression. This may reflect different crobeads with different capacities to bind functional or maturational states, or both, or mouse immunoglobulins were used to identify subpopulations on the basis of the dif- convert the mean fluorescence intensity ferent numbers of molecules of antigen per cell. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
CD29 Identifies IFN-Γ–Producing Human CD8+ T Cells With
+ CD29 identifies IFN-γ–producing human CD8 T cells with an increased cytotoxic potential Benoît P. Nicoleta,b, Aurélie Guislaina,b, Floris P. J. van Alphenc, Raquel Gomez-Eerlandd, Ton N. M. Schumacherd, Maartje van den Biggelaarc,e, and Monika C. Wolkersa,b,1 aDepartment of Hematopoiesis, Sanquin Research, 1066 CX Amsterdam, The Netherlands; bLandsteiner Laboratory, Oncode Institute, Amsterdam University Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands; cDepartment of Research Facilities, Sanquin Research, 1066 CX Amsterdam, The Netherlands; dDivision of Molecular Oncology and Immunology, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands; and eDepartment of Molecular and Cellular Haemostasis, Sanquin Research, 1066 CX Amsterdam, The Netherlands Edited by Anjana Rao, La Jolla Institute for Allergy and Immunology, La Jolla, CA, and approved February 12, 2020 (received for review August 12, 2019) Cytotoxic CD8+ T cells can effectively kill target cells by producing therefore developed a protocol that allowed for efficient iso- cytokines, chemokines, and granzymes. Expression of these effector lation of RNA and protein from fluorescence-activated cell molecules is however highly divergent, and tools that identify and sorting (FACS)-sorted fixed T cells after intracellular cytokine + preselect CD8 T cells with a cytotoxic expression profile are lacking. staining. With this top-down approach, we performed an un- + Human CD8 T cells can be divided into IFN-γ– and IL-2–producing biased RNA-sequencing (RNA-seq) and mass spectrometry cells. Unbiased transcriptomics and proteomics analysis on cytokine- γ– – + + (MS) analyses on IFN- and IL-2 producing primary human producing fixed CD8 T cells revealed that IL-2 cells produce helper + + + CD8 Tcells.