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Penetran Cl

  CAS No.:   Cat No.: BADC-00305   Purity: ≥ 98%.(TLC) 4.5  

Penetran Cl functions as a membrane-permeable ADC cytotoxin payload, facilitating intracellular delivery of cytotoxic agents in ADCs. It disrupts cellular function leading to tumor cell apoptosis, making it a versatile component in targeted antibody-drug conjugates for cancer treatment.

Penetran Cl

Structure of

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Category
ADC Cytotoxin
Molecular Formula
C14H14NOCl .HCl
Molecular Weight
284.18
Shipping
Room temperature
Shipping
Store at +4 °C, in dark place.

* For research and manufacturing use only. We do not sell to patients.

Size Price Stock Quantity
-- $-- In stock

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Capabilities & Facilities

Popular Publications Citing BOC Sciences Products
Solubility
Water, ethanol and DMSO
Melting Point
185-187 °C.
Appearance
white to light yellow solid.
Shipping
Room temperature
Storage
Store at +4 °C, in dark place.
1. Secondary leukemia in patients with germline transcription factor mutations (RUNX1, GATA2, CEBPA)
Christopher N Hahn, Hamish S Scott, Anna L Brown Blood . 2020 Jul 2;136(1):24-35. doi: 10.1182/blood.2019000937.
Recognition that germline mutations can predispose individuals to blood cancers, often presenting as secondary leukemias, has largely been driven in the last 20 years by studies of families with inherited mutations in the myeloid transcription factors (TFs) RUNX1, GATA2, and CEBPA. As a result, in 2016, classification of myeloid neoplasms with germline predisposition for each of these and other genes was added to the World Health Organization guidelines. The incidence of germline mutation carriers in the general population or in various clinically presenting patient groups remains poorly defined for reasons including that somatic mutations in these genes are common in blood cancers, and our ability to distinguish germline (inherited or de novo) and somatic mutations is often limited by the laboratory analyses. Knowledge of the regulation of these TFs and their mutant alleles, their interaction with other genes and proteins and the environment, and how these alter the clinical presentation of patients and their leukemias is also incomplete. Outstanding questions that remain for patients with these germline mutations or their treating clinicians include: What is the natural course of the disease? What other symptoms may I develop and when? Can you predict them? Can I prevent them? and What is the best treatment? The resolution of many of the remaining clinical and biological questions and effective evidence-based treatment of patients with these inherited mutations will depend on worldwide partnerships among patients, clinicians, diagnosticians, and researchers to aggregate sufficient longitudinal clinical and laboratory data and integrate these data with model systems.
2. Dilated cardiomyopathy: the complexity of a diverse genetic architecture
Dale J Hedges, Ana Morales, Ray E Hershberger Nat Rev Cardiol . 2013 Sep;10(9):531-47. doi: 10.1038/nrcardio.2013.105.
Remarkable progress has been made in understanding the genetic basis of dilated cardiomyopathy (DCM). Rare variants in >30 genes, some also involved in other cardiomyopathies, muscular dystrophy, or syndromic disease, perturb a diverse set of important myocardial proteins to produce a final DCM phenotype. Large, publicly available datasets have provided the opportunity to evaluate previously identified DCM-causing mutations, and to examine the population frequency of sequence variants similar to those that have been observed to cause DCM. The frequency of these variants, whether associated with dilated or hypertrophic cardiomyopathy, is greater than estimates of disease prevalence. This mismatch might be explained by one or more of the following possibilities: that the penetrance of DCM-causing mutations is lower than previously thought, that some variants are noncausal, that DCM prevalence is higher than previously estimated, or that other more-complex genomics underlie DCM. Reassessment of our assumptions about the complexity of the genomic and phenomic architecture of DCM is warranted. Much about the genomic basis of DCM remains to be investigated, which will require comprehensive genomic studies in much larger cohorts of rigorously phenotyped probands and family members than previously examined.
3. Identifying breast cancer susceptibility genes - a review of the genetic background in familial breast cancer
Camilla Wendt, Sara Margolin Acta Oncol . 2019 Feb;58(2):135-146. doi: 10.1080/0284186X.2018.1529428.
Heritage is the most important risk factor for breast cancer. About 15-20% of breast cancer is familial, referring to affected women who have one or more first- or second-degree relatives with the disease. The heritable component in these families is substantial, especially in families with aggregation of breast cancer with low age at onset. Identifying breast cancer susceptibility genes: Since the discovery of the highly penetrant autosomal dominant susceptibility genes BRCA1 and BRCA2 in the 1990s, several more breast cancer genes that confer a moderate to high risk of breast cancer have been identified. Furthermore, during the last decade, advances in genomic technologies have led to large scale genotyping in genome-wide association studies that have identified a considerable amount of common low penetrance loci. In total, the high risk genes, BRCA1, BRCA2, TP53, STK11, CD1 and PTEN account for approximately 20% of the familial risk. Moderate risk variants account for up to 5% of the inherited familial risk. The more than 180 identified low-risk loci explain 18% of the familial risk. Altogether more than half of the genetic background in familial breast cancer remains unclear. Other genes and low risk loci that explain a part the remaining fraction will probably be identified. Clinical aspects and future perspectives: Definitive clinical recommendations can be drawn only for carriers of germline variants in a limited number of high and moderate risk genes for which an association with breast cancer has been established. Future progress in evaluating previously identified breast cancer candidate variants and low risk loci as well as exploring new ones can play an important role in improving individual risk prediction in familial breast cancer.

The molarity calculator equation

Mass (g) = Concentration (mol/L) × Volume (L) × Molecular Weight (g/mol)

The dilution calculator equation

Concentration (start) × Volume (start) = Concentration (final) × Volume (final)

This equation is commonly abbreviated as: C1V1 = C2V2

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Historical Records: Fmoc-Gly-Gly-Phe-Gly-CH2-O-CH2-Gly-CH2-O-CH2-Cbz | Fmoc-Gly-Gly-Phe-OtBu | Aniline-MPB-amino-C3-PBD | Fmoc-Val-Cit-PAB | DM3 | Spliceostatin C | MC-vc-PAB-Auristatin 0101 | Spliceostatin B | Bis-PEG4-NHS ester | Auristatin T | Penetran Cl
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