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Archive for the ‘3D Printing for Medical Application’ Category


Nanostraws Developed at Stanford Sample a Cell’s Contents without Damage

Reporter: Irina Robu, PhD

Cells within our bodies change over time and divide, with thousands of chemical reactions happening within cell daily. Nicholas Melosh, Associate Professor of Materials Science and Engineering, developed a new, non-destructive system for sampling cells with nanoscale straws which could help uncover mysteries about how cells function.

Currently, cells are sampled via lysing which ruptures the cell membrane which means that it can’t ever be sampled again. The sample system that Dr. Melosh invented banks on, on tiny tubes 600 times smaller than a strand of hair that allow researchers to sample a single cell at a time. The nanostraws penetrate a cell’s outer membrane, without damaging it, and draw out proteins and genetic material from the cell’s salty interior.

The Nanostraw sampling technique, according to Melosh, will knowingly impact our understanding of cell development and could result to much safer and operational medical therapies because the technique allows for long term, non-destructive monitoring. The sampling technique could also inform cancer treatments and answer questions about why some cancer cells are resistant to chemotherapy while others are not. The sampling platform on which the nanostraws are grown is tiny, similar to the size of a gumball. It’s called the Nanostraw Extraction (NEX) sampling system, and it was designed to mimic biology itself.

The goal of developing this technology was to make an impact in medical biology by providing a platform that any lab could build.

SOURCE

http://news.stanford.edu/2017/02/20/minuscule-nanostraws-sample-cells-contents-without-damage

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3D Printing Technique with Non-Contact Ultrasonic Manipulation Technology

Reporter: Irina Robu, PhD

The 3D printer we think more frequently in combination with PCBs is the DragonFly 2020 from Nano Dimension which works with different with all kinds of materials in addition to PCBs as they are a great 3D printing player in electronic space.

The Ultrasound Research group at Neurotechnology (http://www.neurotechnology.com) has proclaimed a new 3D printing method using ultrasonic manipulation which are totally hands off and non-contact tech behind it, permitting for the handling of parts and particles down to submillimeter range without causing damage to sensitive components. According to the project lead for Neurotechnology Ultrasound Research Group, Dr. Osvaldas Putkis, “Ultrasonic manipulation can handle a very large range of different materials, including metals, plastics and even liquids. Not only can it manipulate material particles, it can also handle components of various shapes. Other non-contact methods, like the ones based on magnetic or electrostatic forces, can’t offer such versatility”.

Since the work from the Ultrasound Research Group embodies a new technological application, Neurotechnology has filed a patent on their system. Neurotechnology describes ultrasonic manipulation as a “non-contact material handling method which uses ultrasonic waves to trap and move small particles and components.”  It is well known that ultrasonic manipulation of particles exploits the acoustic radiation force to deliver a contactless handling method for particles suspended in a fluid. In an ultrasonic standing wave field, the viscous torque induces the rotation of an object. Alongside the translation of particles due to the acoustic radiation force an additional controlled degree of rotation is obtainable. Consequently, there is a growing interest in spreading the field of application of ultrasonic particle manipulation to the deposition of micro and nanowires and for the assembly of micro objects.

Ultrasonic transducers are arranged in an array used to position electronic components in the creation of a PCB, utilizing a camera to detect accurate positioning. Continuing on with the hands-off theme, a laser solders the PCB components after their non-contact manipulation into placement. 3D printing and PCB manufacture are increasingly coming together, as advanced technologies benefit the creation of devices in electronics, including via 3D printed workstations for PCBs.

Even though their method works with all types of materials, we expect to see further applications beyond PCB assembly.

Source

https://3dprint.com/179097/neurotechnology-ultrasonic-3dp/

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Pharmacotyping Pancreatic Cancer Patients in the Future: Two Approaches – ORGANOIDS by David Tuveson and Hans Clevers and/or MICRODOSING Devices by Robert Langer

Curator: Aviva Lev-Ari, PhD, RN

 

This curation provides the resources for edification on Pharmacotyping Pancreatic Cancer Patients in the Future

 

  • Professor Hans Clevers at Clevers Group, Hubrecht University

https://www.hubrecht.eu/onderzoekers/clevers-group/

  • Prof. Robert Langer, MIT

http://web.mit.edu/langerlab/langer.html

Langer’s articles on Drug Delivery

https://scholar.google.com/scholar?q=Langer+on+Drug+Delivery&hl=en&as_sdt=0&as_vis=1&oi=scholart&sa=X&ved=0ahUKEwixsd2w88TTAhVG4iYKHRaIAvEQgQMIJDAA

organoids, which I know you’re pretty involved in with Hans Clevers. What are your plans for organoids of pancreatic cancer?

Organoids are a really terrific model of a patient’s tumour that you generate from tissue that is either removed at the time of surgery or when they get a small needle biopsy. Culturing the tissue and observing an outgrowth of it is usually successful and when you have the cells, you can perform molecular diagnostics of any type. With a patient-derived organoid, you can sequence the exome and the RNA, and you can perform drug testing, which I call ‘pharmacotyping’, where you’re evaluating compounds that by themselves or in combination show potency against the cells. A major goal of our lab is to work towards being able to use organoids to choose therapies that will work for an individual patient – personalized medicine.

Organoids could be made moot by implantable microdevices for drug delivery into tumors, developed by Bob Langer. These devices are the size of a pencil lead and contain reservoirs that release microdoses of different drugs; the device can be injected into the tumor to deliver drugs, and can then be carefully dissected out and analyzed to gain insight into the sensitivity of cancer cells to different anticancer agents. Bob and I are kind of engaged in a friendly contest to see whether organoids or microdosing devices are going to come out on top. I suspect that both approaches will be important for pharmacotyping cancer patients in the future.

From the science side, we use organoids to discover things about pancreatic cancer. They’re great models, probably the best that I know of to rapidly discover new things about cancer because you can grow normal tissue as well as malignant tissue. So, from the same patient you can do a comparison easily to find out what’s different in the tumor. Organoids are crazy interesting, and when I see other people in the pancreatic cancer field I tell them, you should stop what you’re doing and work on these because it’s the faster way of studying this disease.

SOURCE

Other related articles on Pancreatic Cancer and Drug Delivery published in this Open Access Online Scientific Journal include the following:

 

Pancreatic Cancer: Articles of Note @PharmaceuticalIntelligence.com

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/05/26/pancreatic-cancer-articles-of-note-pharmaceuticalintelligence-com/

Keyword Search: “Pancreatic Cancer” – 275 Article Titles

https://pharmaceuticalintelligence.wordpress.com/wp-admin/edit.php?s=Pancreatic+Cancer&post_status=all&post_type=post&action=-1&m=0&cat=0&paged=1&action2=-1

Keyword Search: Drug Delivery: 542 Articles Titles

https://pharmaceuticalintelligence.wordpress.com/wp-admin/edit.php?s=Drug+Delivery&post_status=all&post_type=post&action=-1&m=0&cat=0&paged=1&action2=-1

Keyword Search: Personalized Medicine: 597 Article Titles

https://pharmaceuticalintelligence.wordpress.com/wp-admin/edit.php?s=Personalized+Medicine&post_status=all&post_type=post&action=-1&m=0&cat=0&paged=1&action2=-1

  • Cancer Biology & Genomics for Disease Diagnosis, on Amazon since 8/11/2015

http://www.amazon.com/dp/B013RVYR2K

 

 

VOLUME TWO WILL BE AVAILABLE ON AMAZON.COM ON MAY 1, 2017

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Regulatory MicroRNAs in Aberrant Cholesterol Transport and Metabolism

Curator: Marzan Khan, B.Sc

Aberrant levels of lipids and cholesterol accumulation in the body lead to cardiometabolic disorders such as atherosclerosis, one of the leading causes of death in the Western World(1). The physical manifestation of this condition is the build-up of plaque along the arterial endothelium causing the arteries to constrict and resist a smooth blood flow(2). This obstructive deposition of plaque is merely the initiation of atherosclerosis and is enriched in LDL cholesterol (LDL-C) as well foam cells which are macrophages carrying an overload of toxic, oxidized LDL(2). As the condition progresses, the plaque further obstructs blood flow and creates blood clots, ultimately leading to myocardial infarction, stroke and other cardiovascular diseases(2). Therefore, LDL is referred to as “the bad cholesterol”(2).

Until now, statins are most widely prescribed as lipid-lowering drugs that inhibit the enzyme 3-hydroxy-3methylgutaryl-CoA reductase (HMGCR), the rate-limiting step in de-novo cholesterol biogenesis (1). But some people cannot continue with the medication due to it’s harmful side-effects(1). With the need to develop newer therapeutics to combat cardiovascular diseases, Harvard University researchers at Massachusetts General Hospital discovered 4 microRNAs that control cholesterol, triglyceride, and glucose homeostasis(3)

MicroRNAs are non-coding, regulatory elements approximately 22 nucleotides long, with the ability to control post-transcriptional expression of genes(3). The liver is the center for carbohydrate and lipid metabolism. Stringent regulation of endogenous LDL-receptor (LDL-R) pathway in the liver is crucial to maintain a minimal concentration of LDL particles in blood(3). A mechanism whereby peripheral tissues and macrophages can get rid of their excess LDL is mediated by ATP-binding cassette, subfamily A, member 1 (ABCA1)(3). ABCA1 consumes nascent HDL particles- dubbed as the “good cholesterol” which travel back to the liver for its contents of triglycerides and cholesterol to be excreted(3).

Genome-wide association studies (GWASs) meta-analysis carried out by the researchers disclosed 4 microRNAs –(miR-128-1, miR-148a, miR-130b, and miR-301b) to lie close to single-nucleotide polymorphisms (SNPs) associated with abnormal metabolism and transport of lipids and cholesterol(3) Experimental analyses carried out on relevant cell types such as the liver and macrophages have proven that these microRNAs bind to the 3’ UTRs of both LDL-R and ABCA1 transporters, and silence their activity. Overexpression of miR-128-1 and miR148a in mice models caused circulating HDL-C to drop. Corroborating the theory under investigation further, their inhibition led to an increased clearance of LDL from the blood and a greater accumulation in the liver(3).

That the antisense inhibition of miRNA-128-1 increased insulin signaling in mice, propels us to hypothesize that abnormal expression of miR-128-1 might cause insulin resistance in metabolic syndrome, and defective insulin signaling in hepatic steatosis and dyslipidemia(3)

Further examination of miR-148 established that Liver-X-Receptor (LXR) activation of the Sterol regulatory element-binding protein 1c (SREBP1c), the transcription factor responsible for controlling  fatty acid production and glucose metabolism, also mediates the expression of miR-148a(4,5) That the promoter region of miR-148 contained binding sites for SREBP1c was shown by chromatin immunoprecipitation combined with massively parallel sequencing (ChIP-seq)(4). More specifically, SREBP1c attaches to the E-box2, E-box3 and E-box4 elements on miR-148-1a promoter sites to control its expression(4).

Earlier, the same researchers- Andres Naars and his team had found another microRNA called miR-33 to block HDL generation, and this blockage to reverse upon antisense targeting of miR-33(6).

These experimental data substantiate the theory of miRNAs being important regulators of lipoprotein receptors and transporter proteins as well as underscore the importance of employing antisense technologies to reverse their gene-silencing effects on LDL-R and ABCA1(4). Such a therapeutic approach, that will consequently lower LDL-C and promote HDL-C seems to be a promising strategy to treat atherosclerosis and other cardiovascular diseases(4).

References:

1.Goedeke L1,Wagschal A2,Fernández-Hernando C3, Näär AM4. miRNA regulation of LDL-cholesterol metabolism. Biochim Biophys Acta. 2016 Dec;1861(12 Pt B):. Biochim Biophys Acta. 2016 Dec;1861(12 Pt B):2047-2052

https://www.ncbi.nlm.nih.gov/pubmed/26968099

2.MedicalNewsToday. Joseph Nordgvist. Atherosclerosis:Causes, Symptoms and Treatments. 13.08.2015

http://www.medicalnewstoday.com/articles/247837.php

3.Wagschal A1,2, Najafi-Shoushtari SH1,2, Wang L1,2, Goedeke L3, Sinha S4, deLemos AS5, Black JC1,6, Ramírez CM3, Li Y7, Tewhey R8,9, Hatoum I10, Shah N11, Lu Y11, Kristo F1, Psychogios N4, Vrbanac V12, Lu YC13, Hla T13, de Cabo R14, Tsang JS11, Schadt E15, Sabeti PC8,9, Kathiresan S4,6,8,16, Cohen DE7, Whetstine J1,6, Chung RT5,6, Fernández-Hernando C3, Kaplan LM6,10, Bernards A1,6,16, Gerszten RE4,6, Näär AM1,2. Genome-wide identification of microRNAs regulating cholesterol and triglyceride homeostasis. . Nat Med.2015 Nov;21(11):1290

https://www.ncbi.nlm.nih.gov/pubmed/26501192

4.Goedeke L1,2,3,4, Rotllan N1,2, Canfrán-Duque A1,2, Aranda JF1,2,3, Ramírez CM1,2, Araldi E1,2,3,4, Lin CS3,4, Anderson NN5,6, Wagschal A7,8, de Cabo R9, Horton JD5,6, Lasunción MA10,11, Näär AM7,8, Suárez Y1,2,3,4, Fernández-Hernando C1,2,3,4. MicroRNA-148a regulates LDL receptor and ABCA1 expression to control circulating lipoprotein levels. Nat Med. 2015 Nov;21(11):1280-9.

https://www.ncbi.nlm.nih.gov/pubmed/26437365

5.Eberlé D1, Hegarty B, Bossard P, Ferré P, Foufelle F. SREBP transcription factors: master regulators of lipid homeostasis. Biochimie. 2004 Nov;86(11):839-48.

https://www.ncbi.nlm.nih.gov/pubmed/15589694

6.Harvard Medical School. News. MicoRNAs and Metabolism.

https://hms.harvard.edu/news/micrornas-and-metabolism

7. MGH – Four microRNAs identified as playing key roles in cholesterol, lipid metabolism

http://www.massgeneral.org/about/pressrelease.aspx?id=1862

 

Other related articles published in this Open Access Online Scientific Journal include the following:

 

  • Cardiovascular Diseases, Volume Three: Etiologies of Cardiovascular Diseases: Epigenetics, Genetics and Genomics,

on Amazon since 11/29/2015

http://www.amazon.com/dp/B018PNHJ84

 

HDL oxidation in type 2 diabetic patients

Larry H. Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2015/11/27/hdl-oxidation-in-type-2-diabetic-patients/

 

HDL-C: Target of Therapy – Steven E. Nissen, MD, MACC, Cleveland Clinic vs Peter Libby, MD, BWH

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2014/11/07/hdl-c-target-of-therapy-steven-e-nissen-md-macc-cleveland-clinic-vs-peter-libby-md-bwh/

 

High-Density Lipoprotein (HDL): An Independent Predictor of Endothelial Function & Atherosclerosis, A Modulator, An Agonist, A Biomarker for Cardiovascular Risk

Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/03/31/high-density-lipoprotein-hdl-an-independent-predictor-of-endothelial-function-artherosclerosis-a-modulator-an-agonist-a-biomarker-for-cardiovascular-risk/

 

Risk of Major Cardiovascular Events by LDL-Cholesterol Level (mg/dL): Among those treated with high-dose statin therapy, more than 40% of patients failed to achieve an LDL-cholesterol target of less than 70 mg/dL.

Reporter: Aviva Lev-Ari, PhD., RN

https://pharmaceuticalintelligence.com/2014/07/29/risk-of-major-cardiovascular-events-by-ldl-cholesterol-level-mgdl-among-those-treated-with-high-dose-statin-therapy-more-than-40-of-patients-failed-to-achieve-an-ldl-cholesterol-target-of-less-th/

 

LDL, HDL, TG, ApoA1 and ApoB: Genetic Loci Associated With Plasma Concentration of these Biomarkers – A Genome-Wide Analysis With Replication

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/12/18/ldl-hdl-tg-apoa1-and-apob-genetic-loci-associated-with-plasma-concentration-of-these-biomarkers-a-genome-wide-analysis-with-replication/

 

Two Mutations, in the PCSK9 Gene: Eliminates a Protein involved in Controlling LDL Cholesterol

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/04/15/two-mutations-in-a-pcsk9-gene-eliminates-a-protein-involve-in-controlling-ldl-cholesterol/

Artherogenesis: Predictor of CVD – the Smaller and Denser LDL Particles

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2012/11/15/artherogenesis-predictor-of-cvd-the-smaller-and-denser-ldl-particles/

 

A Concise Review of Cardiovascular Biomarkers of Hypertension

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/04/25/a-concise-review-of-cardiovascular-biomarkers-of-hypertension/

 

Triglycerides: Is it a Risk Factor or a Risk Marker for Atherosclerosis and Cardiovascular Disease ? The Impact of Genetic Mutations on (ANGPTL4) Gene, encoder of (angiopoietin-like 4) Protein, inhibitor of Lipoprotein Lipase

Reporters, Curators and Authors: Aviva Lev-Ari, PhD, RN and Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2016/03/13/triglycerides-is-it-a-risk-factor-or-a-risk-marker-for-atherosclerosis-and-cardiovascular-disease-the-impact-of-genetic-mutations-on-angptl4-gene-encoder-of-angiopoietin-like-4-protein-that-in/

 

Excess Eating, Overweight, and Diabetic

Larry H Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2015/11/15/excess-eating-overweight-and-diabetic/

 

Obesity Issues

Larry H. Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2015/11/12/obesity-issues/

 

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3D Printing for Surgical Planning: The Clinical and Economic Promise using Quantitative Clinical Evidence

Reporter: Aviva Lev-Ari, PhD, RN

The Clinical and Economic Promise of 3D Printing for Surgical Planning

M A K I N G  T H E  C A S E  T H R O U G H  Q U A N T I TAT I V E CLINICAL EVIDENCE

Stratasys engaged Quorum Consulting, experts in health economics and outcomes research, to conduct a comprehensive analysis of the clinical and economic evidence on 3D printing for surgical planning. This white paper, authored by Quorum Consulting, summarizes the result of that analysis.

Wade Aubry1,2, Raj Stewart1 , Chance Scott1 , Jeffrey Chu1

The modern emphasis on evidence-based medicine centers on three core tenets: • Best available research findings • Clinical expertise • Patient value Incorporating cutting-edge technology alongside these principles – often delicately balancing material innovation against scientific rigor, state-of-the-art professional training and experience, and attempts to provide the best care while respecting patient perspectives – is a challenge. 3D printing, however, aligns with the first two tenets, and when appropriately employed, may inform and indirectly influence the third.1

1 Quorum Consulting, Inc., San Francisco, CA, USA

2 University of California, San Francisco; San Francisco, CA, USA

 

3D printing was used in surgical planning applications in a wide range of specialties including cardiothoracic, orthopedic, neurological, reconstructive and transplant surgeries, as well as gastroenterology and surgical oncology. When examining these use cases, five general benefits emerge in association with 3D printing for surgical planning:

  • Patient communication
  • Anatomic familiarity
  • Procedure practice
  • Procedure selection
  • Patient selection / rule-out

 

INDICATION-SPECIFIC UTILIZATION AND EVIDENCEBASED EFFECTIVENESS DATA / RESULTS

  • Cardiothoracic surgery
  • Neurosurgery
  • Reconstructive surgeries

 

CONCLUSION

In a healthcare environment continuing to shift towards value- and outcome-contingent systems that penalize providers for inefficiencies and suboptimal outcomes in rendered care, 3D printed models for surgical planning – with their ability to facilitate procedural efficiency, improve treatment outcomes, and reduce downstream re-intervention costs – offer high potential value. Patients, clinicians and hospitals all have a vested interest in quality, affordable patient care and service, and surgical planning with 3D models appeals to each of these stakeholders.

Accordingly, results and trends from published literature and healthcare data support the effectiveness of 3D printing for surgical planning. As shown for several surgical procedures, clinicians with access to 3D printed models are able to provide better, more efficient care likely to improve patient outcomes and reduce the need for additional surgical interventions. Procedures that would most justify the financial and resource cost in creating 3D printed patient models are those with long operating times, high Relative Value Units (RVUs), greater risk and uncertainty, and risk of complications. Concurrently, this quality care is also potentially less costly and more profitable to providers. Amidst the growing commercial market for 3D printers and related technologies, there are some key differentiators when evaluating utility for surgical planning. As reflected in clinician surveys, the most effective 3D models should capably depict complex, fine anatomy with high fidelity to actual patient physiologies. This degree of fidelity crosses several characteristics:

  • Accurate depiction of a variety of colors
  • Simulation of multiple textures
  • Manipulability,

including the ability to be dissected or probed with surgical instruments.22 Given these real-world requirements, next generation multi-material and multi-color 3D printers likely represent the best option for facilities and clinicians. Viewed objectively, additional data addressing the quantitative impact of 3D printed models is needed. Preferably, this data will be generated from well-designed, patient outcome-oriented studies. However, in the interim, the tide of evidence favors 3D printed models for surgical planning, particularly for leading-edge clinicians and healthcare administrators who are able to recognize its value.

A Brief RVU Primer:

Relative Value Units (RVUs) are used by Medicare to determine reimbursement rates for a given service:

• For each service, Medicare determines the cost value of three primary components – physician’s work, practice expenses and malpractice insurance.

• These three components are then adjusted based on differences in living and business costs nationwide, using a factor called the Geographic Practice Cost Index (GPCI).

• The adjusted values are multiplied by an annual conversion factor, established by the U.S. Congress, and totaled to calculate final reimbursement rates.

SOURCE

http://s3.amazonaws.com/engineering.whitepapers/Stratasys/SurgicalPlanningPromise_Quorum_WP.pdf

From: Medical Design & Outsourcing <newsletters@e.medicaldesignandoutsourcing.com>

Reply-To: <newsletters@e.medicaldesignandoutsourcing.com>

Date: Wednesday, February 15, 2017 at 2:00 PM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: The Clinical and Economic Promise of Surgical Planning Using 3D Printing

Other related articles published in this Open Access Online Scientific Journal including the following:

Curator: Aviva Lev-Ari, PhD, RN

 

Technologies for Patient-centered Medicine: From R&D in Biologics to New Medical Devices

 

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One blood sample can be tested for a comprehensive array of cancer cell biomarkers: R&D at WPI

Curator: Marzan Khan, B.Sc

 

A team of mechanical engineers at Worcester Polytechnic Institute (WPI) have developed a fascinating technology – a liquid biopsy chip that captures and detects metastatic cancer cells, just from a small blood sample of cancer patients(1). This device is a recent development in the scientific field and holds tremendous potential that will allow doctors to spot signs of metastasis for a variety of cancers at an early stage and initiate an appropriate course of treatment(1).

Metastasis occurs when cancer cells break away from their site of origin and spread to other parts of the body via the lymph or the bloodstream, where they give rise to secondary tumors(2). By this time, the cancer is at an advanced stage and it becomes increasingly difficult to fight the disease. The cells that are shed by primary and metastatic cancers are called circulating tumor cells (CTCs) and their numbers lie in the range of 1–77,200/m(3). The basis of the liquid biopsy chip test is to capture these circulating tumor cells in the patient’s blood and identify the cell type through specific interaction with antibodies(4).

The chip is comprised of individual test units or small elements, about 3 millimeters wide(4). Each small element contains a network of carbon nanotube sensors in a well which are functionalized with antibodies(4). These antibodies will bind cell-surface antigens or protein markers unique for each type of cancer cell. Specific interaction between a cell surface protein and its corresponding antibody is a thermodynamic event that causes a change in free energy which is transduced into electricity(3). This electrical signature is picked up by the semi-conducting carbon nanotubes and can be seen as electrical spikes(4). Specific interactions create an increase in electrical signal, whereas non-specific interactions cause a decrease in signal or no change at all(4). Capture efficiency of cancer cells with the chip has been reported to range between 62-100%(4).

The liquid biopsy chip is also more advanced than microfluidics for several reasons. Firstly, the nanotube-chip arrays can capture as well as detect cancer cells, while microfluidics can only capture(4). Samples do not need to be processed for labeling or fixation, so the cell structures are preserved(4). Unlike microfluidics, these nanotubes will also capture tiny structures called exosomes spanning the nanometer range that are produced from cancer cells and carry the same biomarkers(4).

Pancreatic cancer is the fourth leading cause of cancer-associated deaths in the United states, with a survival window of 5 years in only 6% of the cases with treatment(5). In most patients, the disease has already metastasized at the time of diagnosis due to the lack of early-diagnostic markers, affecting some of the major organs such as liver, lungs and the peritoneum(5,6). Despite surgical resection of the primary tumor, the recurrence of local and metastatic tumors is rampant(5). Metastasis is the major cause of mortality in cancers(5). The liquid biopsy chip, that identifies CTCs can thus become an effective diagnostic tool in early detection of cancer as well as provide information into the efficacy of treatment(3). At present, ongoing experiments with this device involve testing for breast cancers but Dr. Balaji Panchapakesan and his team of engineers at WPI are optimistic about incorporating pancreatic and lung cancers into their research.

REFERENCES

1.Nanophenotype. Researchers build liquid biopsy chip that detects metastatic cancer cells in blood: One blood sample can be tested for a comprehensive array of cancer cell biomarkers. 27 Dec 2016. Genesis Nanotechnology,Inc

https://genesisnanotech.wordpress.com/2016/12/27/researchers-build-liquid-biopsy-chip-that-detects-metastatic-cancer-cells-in-blood-one-blood-sample-can-be-tested-for-a-comprehensive-array-of-cancer-cell-markers/

2.Martin TA, Ye L, Sanders AJ, et al. Cancer Invasion and Metastasis: Molecular and Cellular Perspective. In: Madame Curie Bioscience Database [Internet]. Austin (TX): Landes Bioscience; 2000-2013.

https://www.ncbi.nlm.nih.gov/books/NBK164700/

3.F Khosravi, B King, S Rai, G Kloecker, E Wickstrom, B Panchapakesan. Nanotube devices for digital profiling of cancer biomarkers and circulating tumor cells. 23 Dec 2013. IEEE Nanotechnology Magazine 7 (4), 20-26

Nanotube devices for digital profiling of cancer biomarkers and circulating tumor cells

4.Farhad Khosravi, Patrick J Trainor, Christopher Lambert, Goetz Kloecker, Eric Wickstrom, Shesh N Rai and Balaji Panchapakesan. Static micro-array isolation, dynamic time series classification, capture and enumeration of spiked breast cancer cells in blood: the nanotube–CTC chip. 29 Sept 2016. Nanotechnology. Vol 27, No.44. IOP Publishing Ltd

http://iopscience.iop.org/article/10.1088/0957-4484/27/44/44LT03/meta

5.Seyfried, T. N., & Huysentruyt, L. C. (2013). On the Origin of Cancer Metastasis. Critical Reviews in Oncogenesis18(1-2), 43–73.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3597235/

6.Deeb, A., Haque, S.-U., & Olowoure, O. (2015). Pulmonary metastases in pancreatic cancer, is there a survival influence? Journal of Gastrointestinal Oncology6(3), E48–E51. http://doi.org/10.3978/j.issn.2078-6891.2014.114

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4397254/

Other related articles published in this Open Access Online Scientific Journal include the following:

 

Liquid Biopsy Chip detects an array of metastatic cancer cell markers in blood – R&D @Worcester Polytechnic Institute, Micro and Nanotechnology Lab

Reporters: Tilda Barliya, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/12/28/liquid-biopsy-chip-detects-an-array-of-metastatic-cancer-cell-markers-in-blood-rd-worcester-polytechnic-institute-micro-and-nanotechnology-lab/

 

Trovagene’s ctDNA Liquid Biopsy urine and blood tests to be used in Monitoring and Early Detection of Pancreatic Cancer

Reporters: David Orchard-Webb, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/07/06/trovagenes-ctdna-liquide-biopsy-urine-and-blood-tests-to-be-used-in-monitoring-and-early-detection-of-pancreatic-cancer/

 

Liquid Biopsy Assay May Predict Drug Resistance

Curator: Larry H. Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/2015/11/06/liquid-biopsy-assay-may-predict-drug-resistance/


New insights in cancer, cancer immunogenesis and circulating cancer cells

Larry H. Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2016/04/15/new-insights-in-cancer-cancer-immunogenesis-and-circulating-cancer-cells/

 

Prognostic biomarker for NSCLC and Cancer Metastasis

Larry H. Bernstein, MD, FCAP, Curato

https://pharmaceuticalintelligence.com/2016/03/24/prognostic-biomarker-for-nsclc-and-cancer-metastasis/

 

Monitoring AML with “cell specific” blood test

Larry H. Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2016/01/23/monitoring-aml-with-cell-specific-blood-test/

 

Diagnostic Revelations

Larry H. Bernstein, MD, FCAP, Curator

https://pharmaceuticalintelligence.com/2015/11/02/diagnostic-revelations/

 

Circulating Biomarkers World Congress, March 23-24, 2015, Boston: Exosomes, Microvesicles, Circulating DNA, Circulating RNA, Circulating Tumor Cells, Sample Preparation

Reporter: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2015/03/03/circulating-biomarkers-world-congress-march-23-24-2015-boston-exosomes-microvesicles-circulating-dna-circulating-rna-circulating-tumor-cells-sample-preparation/

 

 

 

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LIVE 11/16 1:15PM – 2:45PM – The 12th Annual Personalized Medicine Conference, HARVARD MEDICAL SCHOOL, Joseph B. Martin Conference Center, 77 Avenue Louis Pasteur, Boston

 

Leaders in Pharmaceutical Business intelligence (LPBI) Group

Covering in Real Time using Social Media this Event on

Personalized Medicine

Aviva Lev-Ari, PhD, RN, Founder LPBI Group & Editor-in-Chief

http://pharmaceuticalintelligence.com

Streaming LIVE @ HARVARD MEDICAL SCHOOL,

Joseph B. Martin Conference Center

@pharma_BI

@AVIVA1950

November 16

#PMConf

1:15 p.m. — Update: Kraft Precision Medicine Accelerator & Trials Challenge Award

An update on the activities of the Kraft Precision Medicine Accelerator and interviews with the winners of Harvard Business School’s “Precision Trials Challenge,” sponsored by the Kraft Precision Medicine Accelerator.

  • Presenter: Richard Hamermesh, D.B.A., Faculty Co-Chair, Kraft Precision Medicine Accelerator, Harvard Business School
  • Winner: MatchMiner
    • Team Lead: Ethan Cerami, Ph.D., Director, Knowledge Systems Group, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute
  1. 17,000 patients now are genome sequenced
  2. Rational clinical trial design
  3. enroll patient n trial
  4. clinical decision support
  5. Trial-Centric Matching vs Patient Centric Matching
  6. Open Source PLATFORM
  7. Clinical Trial Markup Language (CTML)
  8. MatchMiner, Knowledge System, The Hyve

 

 

 

  • Runner Up: No Patients Left Behind
    • Team Lead: Gavin MacBeath, Ph.D., Co-Founder and Senior Vice President, Merrimack Pharmaceuticals
  1. Company brings Drug and biomarker assay match – for patient assignment to Trial
  2. Protein based not genomic based test

 

  • Runner Up: iCare for Cancer Patients
    • Team Lead: Leylah Drusbosky, Ph.D., Associate Professor of Medicine, University of Florida, Scientific Director, iCare for Cancer Patients
  1. iCare for Cancer Patients
  2. Protein Network Map: Drug interaction and Genomic aberrations
  3. Functional interactions
  4. Predictive SImulation Technology

Computational Biology Model: Proliferation, SUrvival apoptosis

VIrtual Cancer Clinical Trial Simulator – Bring new drugs to the RIGHT patient population: DIsease onhibition score

1:45 p.m. — Keynote Speaker
“Reforming Clinical Trials: How Alternative Trial Designs May Reshape Regulatory Review”

Traditional clinical trial designs are often too cumbersome and expensive to study the efficacy of personalized medicine products and services in sub-populations of patients. Yet there is no consensus on which methods have the most promise to speed trials and lower costs. During her keynote address, Dr. Woodcock will explore which of the latest progressive designs she believes are best suited to demonstrate the efficacy of personalized medicine based on past successes and proposed reforms.

  • Introduction: Steve BMS
  • Keynote: Janet Woodcock, M.D., Director, Center for Drug Evaluation and Research, U.S. Food and Drug Administration
  1. Not just the trial, but the Development Program
  2. knowledge that underpins the program vs Novel Trial Design
  3. +50%  of Trials FAIL at Phase 3 – simulate  best and worse scenarios
  4. Cut off points – affect the result of the Human Trial
  5. Outcome measures for disease have never or rarely, been tested
  6. Murphy’s law operate
  7. robust vs fragile design
  8. DEsign – conduct a seamless, adaptive development program
  9. Trade off – for benefits vs burden of Disease – Functionality vs Longevity – give up life for better functionality when aive
  10. More patient enroll or tril goes longer, treatment gets better and Endpoints needs to be revised
  11. heterogenious progression, fast progression
  12. DIsease heterogeniety — REDUCTION by beter Patient selection – more homogenious to reach similar progression
  13. Natural History: rare diseas vs heterogeneous
  14. Progression not known because longitudinal studies are limited or study is not representative
  15. projection of results of trial may be difict: Pharmacodynamic Markers (efficacy) dificult to reproduce
  16. Trial Design: Phase 1,2,3
  17. Alternatives: “Extended Phase 1 COhort” –>> Approval
  18. Endpoint (cancer): Response rate, Progression Free Survival (PFS) Time
  19. Mechanistics hypothesis, natural history data on non responding patients
  20. N-of -1 looking at disease trajectory
  21. Oncology: “basket” trials with biomarker defined targets across histologic diagnoses : NCI “MATCH” trial
  22. Emergencies: EBOLA trial
  23. DOse-finding can be randomized, adaptive,, include placedbo arm
  24. Serious, Rare or Uncommon DIsease wiht Existing Standard of Care (SOC) vs Placebo
  25. Common Diseaase with SOC
  26. Biomarkers: Predictive of Response: Magnitude of the response not Yes or No — Highest Biomarket Cutoff
  27. RWE – Rare WOrld Evidence
  28. Knowledge tht UNDERLIE – biological knowledge
  29. CUrative therappy with PM – promise is there

2:15 p.m. — Fireside Chat

  • Moderator: Alexander Vadas, Ph.D., Managing Director and Partner, L.E.K. Consulting
  1. Companion Diagnostics
  • Peer M. Schatz, M.B.A., CEO, QIAGEN (15 years around)
  1. PM and experience
  2. Value chain of PM does not work – Diagnostics is 2% of the HealthCare expenses. Reimbursement by COst of Production
  3. 30x smaller then Pharmaceuticals
  4. Standards to evaluate the value of diagnostics
  5. Biomarkers – 60,000 distinctive tests
  6. Benefits of Diagnostics not recognized
  7. HealthCare 2% spent on DIagnostics and Monitoring
  8. Value of information of Molecualr DIagnostics
  9. Lower quality evidence
  10. Reward value of diagnostics – Patients
  11. For LABs  diagnostics is a profit looser
  12. Molecular Diagnostics – new, PM is known in 1999 – AMP launched its Journal
  13. Value based Medicine, systems of rewards is to blame
  14. Reimbursement – Diagnostics and regulatory
  15. 30 Pharma Partnerships
  16. Industry organization
  17. Biopsy to Microbiome
  18. 70% of Cancer care is done at COmmunity Hospitals
  19. Human genomics data doubles annoually
  20. Data needs, 34% in accordance, 70% accordance with diagnosis – CONCORDANCE POOR CROSS GENOMICS OR AXON LABS
  21. PM recognize the value of information DIagnosis, improvemnet in Patient Diagnosis

Reply to interview by Alexander Vadas, Ph.D., Managing Director and Partner, L.E.K. Consulting

  • Education of Pathologists in Genomic Pathology
  • Approval of Companion Diagnostics in China requires infrastructure in regulatory interface with each Country
  • Seamless interaction with Pharma
  • If tests in Pathology are too expensive, LABS will not be able to be profitable
  • NGS – Variance – vs a frontline test Designed for Reimbursability and profitaility

2:45 p.m. — Networking Break

 

 

#PMConf

SOURCE

http://www.personalizedmedicinecoalition.org/Conference/November_16_Program

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