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Category : Nanomedicine

Northern youth group seeks speedy passage of Bill to establish National Innovation agency – Daily Sun

Sola Ojo, Kaduna

The leadership of Arewa Youth Assembly on Sunday appealed to the leadership of the 9th National Assembly to give speedy passage of the bill to establish a National Innovation Agency (NiNNOVA Establishment Bill 2020) which has already scaled through the first reading early July.

The bill, sponsored by the lawmaker representing Nnewi North/South/Ekwusigo Federal Constituency, Chris Emeka Azubogu, is expected to play a leading role in the development of Nigerias innovation ecosystem by coordinating, networking, fostering, and partnering different organizations from various fields such as academia, technology, industry, finance and investment.

The group in a statement through its Speaker, Mohammed salihu Danlami noted that, the main focus of the agency if established, would be on utilizing knowledge management to achieve innovation, which could be employed as the principal tool in improving quality of life and as a driving tool for an increasingly competitive economy.

According to Mohammed, NiNNOVA would undertake a broad-based and systematic approach in facilitating innovation development in Nigeria, both in terms of making improvements and pioneering new initiatives.

More precisely, NiNNOVA will focus on fostering strategic innovation and industry innovation, which will enhance national productivity, encourage economic restructuring and social development as well as promote national competitiveness by focusing on coordinating industrial clusters both at the policy and operational levels, promoting innovation culture and building up innovation systems, with a broader aim to transform Nigeria into an innovation-driven economy.

The National innovation Agency, if enacted, will coordinate innovation activities as well as administer funding to grow and support the innovation ecosystem and entrepreneurship in Nigeria. It shall exist to promote the development of efficient and innovative Nigerian systems within thematic areas such as technology, ICT, nanomedicine, nanotechnology, agriculture, agribusiness, biotechnology, health, education etc, he added..He continued, this is what Nigeria lacks but so desperately needs at the moment so that we can have a knowledge-based economy, commodity-based economy such as the one we currently operate does not get any country anywhere in terms of national development.

To buttress his point of view on the bill Mohammed sited example of South Korea, which developed an innovation-based economy and is not up to the size of a state in the Soviet Union, has about the economic size of the Soviet Union. China, Japan and India which are global economic giants do not have a drop of oil. India for instance rakes in a whopping 140 billion USD annually from ICT-based solutions and services.

This is just an inkling of what can be achieved through innovation. The Swedish Innovation Agency champions and coordinates innovation-based activities in Sweden for national development. The Thailand Innovation Agency plays a similar role in Thailand. The list is endless; virtually every country in the world has an Agency whose mandate is the coordination of innovation for national development.

In view of the crucial and critical role the National Innovation Agency will play in re-tooling our national economy for national development, we therefore urge the Honourable Speaker of the House of Representatives Rt. Hon Femi Gbajabiamila to please call for the second reading of this Bill and to expedite action for its eventual passage as such an agency will add huge value to Nigeria in terms of wealth creation and overall national competitiveness and development, the group added.

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Northern youth group seeks speedy passage of Bill to establish National Innovation agency - Daily Sun

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CLR 131 Leads a New Generation of Lipid-Based Cancer Drug Delivery Systems – OncLive

A novel compound that uses abundant lipids in cancer cell membranes to deliver a radioisotope to the tumor environment shows early signs of efficacy in a range of B-cell malignancies, including multiple myeloma.1,2

CLR 131 is a phospholipid-drug conjugate (PDC) designed to provide a payload of iodine-131 directly to the cytosol and cytoplasm of tumor cells.3 Cellectar Biosciences, a biopharmaceutical company based in Florham Park, New Jersey, is investigating the potential of CLR 131 in hematologic and solid tumors. The company also is exploring its PDC approach as a platform technology for other oncologic conjugates.4

Positive clinical trial data have been announced for patients with B-cell malignancies, 2 including multiple myeloma, and CLR 131 has secured fast track designation from the FDA for 3 separate indications.5-7 If it lives up to its potential, CLR 131 could be the first of many such drugs from Cellectar, with other payloads being explored.1

Meanwhile, the underlying technology shines a light on the broader use of lipids as vehicles for cancer therapies. With the advent of nanotechnology in medicine, lipid-based carriers have been designed to encapsulate drugs to improve delivery to the tumor site, in the hopes of reducing generalized toxicity and improving therapeutic effect.8-10

Several FDA-approved liposomal formulations of common chemotherapy drugs are on the market.11 Ongoing clinical efforts aim to improve the efficacy of some of these drugs; notably, daunorubicin plus cytarabine (CPX-351; Vyxeos)12 and liposomal irinotecan (Onivyde).13 CPX-351 was initially approved in 2017 in acute myeloid leukemia settings and Onivyde was cleared in 2015 for progressive metastatic pancreatic adenocarcinoma.

Additionally, newer lipid-based strategies aimed at overcoming the challenges of liposomal formulations are in development. These include SB05-EndoTAG-1 (SynCore Biotechnology), which combines paclitaxel with lipids14; mRNA-2416 (Moderna), which encodes OX40L in a lipid nanoparticle15; and Promitil (LipoMedix), a lipid-based form of mitomycin-C.16

Investigators have long sought more specific cancer drugs with reduced off-target toxicity and enhanced therapeutic efficacy. The development of molecularly targeted therapies has been one result, but new drug delivery systems may achieve similar goals. Thanks to the advent of nanotechnology, significant advances in the development of drug carrier technologies for cancer therapy have occurred in the past several decades.8-10

Broadly speaking, drug carriers are designed to shield drugs from interaction with healthy cells and facilitate accumulation at the tumor site. The latter is believed to occur as a result of the enhanced permeability and retention effect. Nanoparticles are too big to readily pass through the normal vasculature into healthy tissues but not the abnormal, leaky blood vessels characteristic of the tumor microenvironment. The lack of lymphatic drainage from tumor vessels adds to this effect.17

Nanoparticles prepared from natural polymers, such as lipids, proteins, and peptides, represent the most promising approach. In particular, liposomes are the most extensively studied type of nanoparticle drug carrier and account for first generation of FDA-approved lipidbased drug delivery systems.18

Liposomes are spherical vesicles composed of 1 or more phospholipid bilayers surrounding an aqueous core. Depending on its properties, a drug can be encapsulated within the core (a hydrophilic drug) or held in the bilayer (a hydrophobic drug) (Figure 1).8,11

Among their advantages over naked drugs, liposomes and other lipid-based delivery systems can reduce toxicity, prolong half-life in the circulation, and improve pharmacokinetics. Additionally, because of their biocompatibility with cell membranes, they are more readily taken up into cells via endocytosis. Because the drug remains behind a lipid barrier once inside the cell, being released only upon lysosomal degradation, it may avoid eviction from the cell by transporter pumps that play a large role in drug resistance.9,11,19

Chemotherapy Delivery

Beginning with the 1995 approval of doxorubicin hydrochloride liposome injection (Doxil) for the treatment of AIDS-related Kaposi sarcoma and, subsequently, multiple myeloma and ovarian cancer, severalliposomal formulations of conventional chemotherapies have become available.9,11

Despite better developed drug properties, some approved liposomal formulations only moderately improved patient survival compared with conventional chemotherapy.11 Their development revealed a number of inherent challenges. Early on, investigators discovered that liposomes were rapidly recognized and engulfed by macrophages, which led to their destruction by the mononuclear phagocyte system.10,20

Nevertheless, ongoing clinical development has demonstrated greater efficacy for several of these compounds. CPX-351 continued to show an overall survival (OS) benefit versus conventional 7 + 3 chemotherapy for patients with newly diagnosed high-risk/secondary acute myeloid leukemia in findings from a phase 3 trial (NCT01696084) presented at the 2020 European Hematology Association Virtual Congress.12

After a median follow-up of 60.65 months, the median OS was 9.33 months (95% CI, 6.37-11.86) and 5.95 months with CPX-351 and 7 + 3, respectively (HR, 0.70; 95% CI, 0.55-0.91). The estimated 3- and 5-year OS rates were also higher with CPX-351 versus 7 + 3, at 21% versus 9% and 18% versus 8%, respectively.12

The combination of Onivyde plus fluorouracil, leucovorin, and oxaliplatin (NALIRIFOX) demonstrated promising outcomes as a frontline treatment for patients with locally advanced or metastatic pancreatic ductal adenocarcinoma. Findings from a phase 1/2 study (NCT02551991) for 32 patients were presented at the European Society of Medical Oncology (ESMO) World Congress on Gastrointestinal Cancer 2020. The NALIRIFOX regimen resulted in a median progression-free survival of 9.2 months (95% CI, 7.69-11.96) and a median OS of 12.6 months (95% CI, 8.74-18.69). The overall response was 34.4% (95% CI, 18.6%-53.2%), consisting of 1 complete response (CR) and 10 partial responses (PRs).13

An international, randomized phase 3 trial (NAPOLI 3; NCT04083235) exploring the use of frontline NALIRIFOX compared with gemcitabine and nab-paclitaxel (Abraxane) in patients with metastatic pancreatic cancer is now under way.

Other Payloads

Besides chemotherapy, other cancer drugs can be contained within liposomes. Nucleic acidbased drugs, which include oligodeoxynucleotides, plasmid DNA, short interfering RNA, and messenger RNA (mRNA), can be used for gene therapy. However, the use of naked genetic material is challenging due to its large size, instability in the circulation, and susceptibility to degradation by nucleases. Lipid-based carriers offer a way to address these issues.20,21

Bio-Path Holdings is developing prexigebersen (BP1001), BP1002, and BP1003; the latter is still in preclinical testing. All 3 are liposome-encapsulated antisense oligonucleotides that inhibit synthesis of the GRB2, BCL2, and STAT3 proteins, respectively.22-24 Prexigebersen is most advanced in clinical development; Bio-Path recently announced an updated interim analysis of stage 1 of an ongoing phase 2 study in AML (NCT02781883).

Among 17 evaluable patients treated with a combination of prexigebersen and low-dose cytarabine (LDAC), 11 had a response, including 5 CRs.25 Moving forward, patients in stage 2 of the trial will be treated with a combination of prexigebersen, decitabine, and venetoclax, instead of LDAC, following initial safety testing of this combination in which 3 of 6 patients had a response.26

All the currently approved liposomal formulations rely on passive targeting of the tumor tissue through enhanced permeability and retention.9 However, the irregular tumor vasculature thought to be responsible for this effect can also work against effective drug delivery, as can the elevated fluid pressure surrounding the tumor.10,11

To further enhance active tumor-targeted drug delivery, development of functionalized liposomes has also been explored, in which properties of the liposome are engineered for improvements. This includes altering the type of lipid to affect the size or charge of the liposome or conjugating other drugs to the liposome surface. Immunoliposomes, for example, are generated by chemically coupling liposomes with antibodies or antibody fragments against cancer cellspecific antigens, such as EGFR.9,11,18,19

SB05-EndoTAG-1 encapsulates paclitaxel in positively charged liposomes. These are designed to interact with the negatively charged endothelial cells of newly formed blood vessels, releasing paclitaxel into these cells, killing them, and cutting off the tumors blood supply.14 Phase 3 trials are ongoing in locally advanced/metastatic pancreatic cancer (NCT03126435) and triple-negative breast cancer (NCT03002103).

Other types of lipid-based drug deliverysystems, beyond lyposomes, come with advantages and disadvantages. There are several major types of lipid nanoparticles; the lipid core may be solid, liquid, or both, and the core may contain single or multiple compartments of drug, among other distinctive features.8,19

Moderna Therapeutics is developing 2 lipid nanoparticle-based encapsulation systems that contain synthetic mRNAs encoding immunostimulatory proteins.27 Results from an ongoing study of mRNA-2416 (NCT03323398), in which the encapsulated mRNA encodes OX40L, were presented at the 2020 American Association for Cancer Research Virtual Meeting I. Despite being well tolerated, mRNA-2416 had modest antitumor activity, but it is hoped that this may be enhanced by combining it with durvalumab (Imfinzi), a PD-L1 inhibitor. This combination is being evaluated in part B of the study.15

Lipid-drug conjugates (LDCs), in which cancer drugs are linked with lipid molecules, are among the most promising types of lipid nanoparticle. LDCs also can facilitate the loading of hydrophobic drugs into other lipid-based carrier systems.8,28

Promitil is an LDC involving mitomycin-C that is further encapsulated in a pegylated liposomal carrier.16 In a phase 1a doseescalation study, toxicity was lower and dose tolerability higher than historical data for naked mitomycin-C. In the phase 1b portion of the trial in patients with advanced, chemorefractory colorectal cancer, Promitil was evaluated alone or combined with either capecitabine or capecitabine and bevacizumab (NCT01705002).

Among 36 response-evaluable patients, stable disease was observed in 42% at week 12. Median survival was 8.7 months, and adding capecitabine and bevacizumab to Promitil had no further effect. AEs were mostly mild to moderately severe.29

Cellectar Biosciences is developing a different kind of LDC. CLR 131 is a PDC, a proprietary mix of phospholipid ethers (PLEs) covalently linked to a cytotoxic radioactive isotope of iodine-131.3

PDCs offer a lipid-based carrier system with a unique feature: They exploit the altered lipid composition of cancer cell membranes to more actively target tumors. PLEs are naturally occurring lipids that are taken up into cells via lipid rafts, cholesterol-rich regions of the plasma membrane that play a key role in cell signaling. PLEs accumulate in cancer cells, in part because their cell membranes contain an enhanced number of lipid rafts.1,30-32

Thus, the lipid rafts on the surface of cancer cells are bound by multiple PDCs via their PLE moiety. When the lipid rafts eventually undergo transmembrane flipping, they deliver the PLEs and their radioactive payload into the cancer cell. Proposed advantages of this system include the PDCs ability to gain entry into a wide variety of cancer types and indiscriminately target all cells within a tumor without relying on expression of a specific antigen.1

Furthermore, the technology could offer considerable flexibility in the types of payloads that can be used and could be further refined via linker design (Figure 2).1 Cellectar has several other PDCs in preclinical development, including agents designed to produce cell cycle arrest, inhibit protein translation, and disrupt the cytoskeleton.33

CLR 131 has been granted orphan drug status in multiple myeloma, Ewing sarcoma, neuroblastoma, osteosarcoma, rhabdomyosarcoma, and lymphoplasmacytic lymphoma (LPL).34 CLR 131 also has fast track designation for multiple myeloma, diffuse large B-cell lymphoma (DLBCL), and LPL/Waldenstr.m macroglobulinemia (WM).5-7

The most recent fast track designation, for LPL/WM, follows positive results from the ongoing phase 2 CLOVER-1 trial (NCT02952508); Cellectar announced that all 4 treated participants with LPL/WM so far achieved an objective response, with 1 achieving CR.2,7,34

In this trial, patients with relapsed/refractory (R/R) B-cell lymphomas, multiple myeloma, and non-Hodgkin lymphoma (NHL) were treated with 3 doses of CLR 131: less than 50 mCi total body dose (TBD; an intentionally subtherapeutic dose), 50 mCi TBD, and 75 mCi TBD. Patients in both the multiple myeloma and NHL cohorts had a median age of 70 years and were heavily pretreated.34

The overall response rate (ORR) for patients with multiple myeloma (n = 33) was 34.5% across all doses (42.8% at the 75 mCi dose; 26.3%, 50 mCi). In patients with NHL, the ORR among 19 patients was 42% (43%, 75 mCi; 42%, 50 mCi). Subtype assessments demonstrated ORRs of 30% (with 1 CR) in patients with DLBCL and 33% for patients with chronic lymphocytic leukemia, small lymphocytic leukemia, and marginal zone lymphoma. CLR 131 was well tolerated across all dose groups.34

Cellectar simultaneously announced the completion of a phase 1 dose-escalation study of CLR 131 in patients with R/R multiple myeloma (NCT02278315). In this trial, 4 single-dose cohorts were examined (25, 37.5, 50, and 62.5 mCi TBD). The study was modified in 2018 to test fractionated doses (2 doses of 31, 37.5, or 40 mCi TBD, given 1 week apart). For both the single- and fractionated-dose cohorts, CLR 131 was administered as 30-minute intravenous infusions in combination with 40-mg weekly low-dose dexamethasone.34

All patients (n = 17) enrolled in the single-dose cohorts experienced clinical benefit, with 16 participants achieving stable disease. Pooled median OS from the 4 cohorts was 22 months.

Compared with patients administered the highest single dose of CLR 131, the cohort that received the lowest fractionated dose showed better tolerability and safety; despite receiving an 18% higher dose overall, these patients required less supportive care (such as blood transfusions) and had a 50% greater reduction in M protein levels, a surrogate marker of efficacy.34

The next fractionated-dose cohort, which received a total 75 mCi TBD (2 ~ 37.5 mCi TBD; n = 4), had a 50% PR rate, defined as at least a 50% decrease in M protein from baseline. The remaining 2 patients experienced a minimal response, defined as an M protein decrease between 25% and 49.9%.

The authors concluded that CLR 131 showed a clear dose response, with higher doses producing greater efficacy without unacceptable toxicity.35

1. A proprietary platform that specifically delivers oncologic warheads to tumor cells. Cellectar Biosciences. Accessed June 1, 2020. https://www.cellectar.com/technology

2. Cellectar Biosciences announces CLR 131 achieves primary efficacy endpoints from its phase 2 CLOVER-1 study in relapsed/refractory B-cell lymphomas and completion of the phase 1 relapsed/refractory multiple myeloma dose escalation study. News release. Cellectar Biosciences. February 19, 2020. Accessed June 1, 2020. bit.ly/2NZUflr

3. Longcor J, Oliver K, Friend J, Callandar N. Interim evaluation of a targeted radiotherapeutic, CLR 131, in relapsed/refractory diffuse large b cell lymphoma patients (R/R DLBCL). Presented at: 2019 European Society for Medical Oncology Congress; Barcelona, Spain; September 27-October 1, 2019. Abstract 5797. bit.ly/2VMpSDc

4. CLR 131. Cellectar Biosciences. Accessed May 25, 2020. http://www.cellectar.com/product-pipeline/clr-131

5. Cellectar receives FDA fast track designation for CLR 131 in relapsed or refractory multiple myeloma. News release. Cellectar Biosciences, Inc. May 13, 2020. Accessed May 25, 2020. https://www.cellectar.com/news-media/press-releases/detail/206/cellectar-receives-fda-fast-track-designation-for-clr-131

6. Cellectar receives FDA fast track designation for CLR 131 in diffuse large B-cell lymphoma. News release. Cellectar Biosciences. July 9, 2020. Accessed May 25, 2020. https://www.cellectar.com/news-media/press-releases/detail/211/cellectar-receives-fda-fast-track-designation-for-clr-131

7. Cellectar receives FDA fast track designation for CLR 131 in lymphoplasmacytic lymphoma/Waldenstroms macroglobulinemia. News release. Cellectar Biosciences. May 26, 2020. Accessed June 1, 2020. https://www.cellectar.com/news-media/press-releases/detail/238/cellectar-receives-fda-fast-track-designation-forclr-131

8. Alavi M, Hamidi M. Passive and active targeting in cancer therapy by liposomes and lipid nanoparticles. Drug Metab Pers Ther. 2019;34(1). doi:10.1515/dmpt-2018-0032

9. Yan W, Leung SS, To KK. Updates on the use of liposomes for active tumor targeting in cancer therapy. Nanomedicine (Lond). 2019;15(3):303-318. doi:10.2217/nnm-2019-0308

10. Jahan ST, Sadat SMA, Walliser M, Haddadi A. Targeted therapeutic nanoparticles: an immense promise to fight against cancer. J Drug Deliv. 2017;2017:9090325. doi:10.1155/2017/9090325

11. He H, Yuan D, Wu Y, Cao Y. Pharmacokinetics and pharmacodynamics modeling and simulation systems to support the development and regulation of liposomal drugs. Pharmaceutics. 2019;11(3):110. doi:10.3390/pharmaceutics11030110

12. Lancet JE, Uy GY, Newell LF, et al. Five-year final results of a phase 3 study of CPX-351 versus 7+3 in older adults with newly diagnosed high-risk/secondary acute myeloid leukemia. Presented at: 2020 European Hematology Association Virtual Congress; June 11-21, 2020. Abstract EP556.

13. Wainberg ZA, Bekaii-Saab T, Boland PM, et al. First-line liposomal irinotecan 5 fluorouracil/leucovorin oxaliplatin in patients with pancreatic ductal adenocarcinoma: primary analysis from a phase 1/2 study. Presented at: European Society of Medical Oncology World Congress on Gastrointestinal Cancer 2010; July 1-4, 2020. Abstract LBA-001.

14. EndoTAG-1. SynCoreBio. Accessed June 2, 2020. https://www.syncorebio.com/en/focus-area/sb05-endotag-1/

15. Jimeno A, Gupta S, Sullivan R, et al. A phase 1/2, open-label, multicenter, dose escalation and efficacy study of mRNA-2416, a lipid nanoparticle encapsulated mRNA encoding human OX40L, for intratumoral injection alone or in combination with durvalumab for patients with advanced malignancies. Presented at: 2020 American Association for Cancer Research Virtual Meeting I; April 27-28, 2020. Accessed June 1, 2020. Abstract CT032. https://www.abstractsonline.com/pp8/#!/9045/presentation/10742

16. Technology. LipoMedix. Accessed July 5, 2020. http://lipomedix.com/Products/Technology

17. Golombek SK, May JN, Theek B, et al. Tumor targeting via EPR: strategies to enhance patient responses. Adv Drug Deliv Rev. 2018;130:17-38. doi:10.1016/j.addr.2018.07.007

18. Yingchoncharoen P, Kalinowski DS, Richardson DR. Lipid-based drug delivery systems in cancer therapy: what is available and what is yet to come. Pharmacol Rev. 2016;68(3):701-787. doi:10.1124/pr.115.012070

19. Battaglia L, Ugazio E. Lipid nano- and microparticles: an overview of patent-related research. J Nanomater. 2019:1-22. doi:10.1155/2019/2834941

20. Barba AA, Bochicchio S, Dalmoro A, Lamberti G. Lipid delivery systems for nucleic-acid-based-drugs: from production to clinical applications. Pharmaceutics. 2019;11(8):360. doi:10.3390/pharmaceutics11080360

21. Liposomes and lipid nanoparticles as delivery vehicles for personalized medicine. Exelead. November 16, 2018. Accessed June 1, 2020. https://www.exeleadbiopharma.com/news/liposomes-and-lipid-nanoparticles-as-delivery-vehicles-for-personalized-medicine

22. BP1002 (liposomal Bcl2) for follicular lymphoma and other forms of non-Hodgkins lymphoma. Bio-Path Holdings. Accessed June 1, 2020. http://www.dnabilize.com/bp1002/

23. Prexigebersen (liposomal Grb2 antisense) for acute myeloid leukemia (AML). Bio-Path Holdings. Accessed June 1, 2020. http://www.dnabilize.com/bp1001

24. BP1003 (liposomal Stat3) for pancreatic cancer. Bio-Path Holdings. Accessed June 1, 2020. http://www.dnabilize.com/bp1003/

25. Bio-Path announces clinical update to interim analysis of phase 2 prexigebersen trial in acute myeloid leukemia. News release. Bio-Path Holdings. March 6, 2019. Accessed June 1, 2020. http://www.biopathholdings.com/wp-content/uploads/2019/03/BPTH_Press_Release_20190306.pdf

26. Bio-Path Holdings provides clinical update and 2020 business outlook. News release. Bio-Path Holdings. January 8, 2020. Accessed June 1, 2020. http://www.biopathholdings.com/wp-content/uploads/2020/01/BPTH_2020_Business_Outlook.pdf

27. Modernas pipeline. Moderna. Accessed June 2, 2020. https://www.modernatx.com/pipeline

28. Sreekanth V, Bajaj A. Recent advances in engineering of lipid drug conjugates for cancer therapy. ACS Biomater. Sci. Eng. 2019;5(9):4148-4166. doi:10.1021/acsbiomaterials.9b00689

29. Gabizon AA, Tahover E, Golan T, et al. Pharmacokinetics of mitomycin-c lipidic prodrug entrapped in liposomes and clinical correlations in metastatic colorectal cancer patients. Published online January 18, 2020. Invest New Drugs. doi:10.1007/s10637-020-00897-3

30. Deming DA, Maher ME, Leystra AA, et al. Phospholipid ether analogs for the detection of colorectal tumors. PLoS One. 2014;9(10):e109668. doi:10.1371/journal.pone.0109668

31. Weichert JP, Clark PA, Kandela IK, et al. Alkylphosphocholine analogs for broad-spectrum cancer imaging and therapy. Sci Transl Med. 2014;6(240):240ra75. doi:10.1126/scitranslmed.3007646

32. Li YC, Park MJ, Ye SK, Kim CW, Kim YN. Elevated levels of cholesterol-rich lipid rafts in cancer cells are correlated with apoptosis sensitivity induced by cholesterol-depleting agents. Am J Pathol. 2006;168(4):1107-1118. doi:10.2353/ajpath.2006.050959

33. Multi-asset product portfolio for treatment of various cancers. Cellectar Biosciences. Accessed May 25, 2020. https://www.cellectar.com/product-pipeline

34. Annual Report. Cellectar Biosciences. Accessed June 1, 2020. bit.ly/2CwItfO

35. Longcor J, Ailawadhi S, Oliver K, Callander N, Stiff P. CLR 131 demonstrates high rate of activity in a phase 1, dose escalation study in patients with relapsed or refractory multiple myeloma (RRMM). Clin Lymphoma Myeloma Leuk. 2019;19(suppl 10):E356-E357. doi:10.1016/j.clml.2019.09.589

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CLR 131 Leads a New Generation of Lipid-Based Cancer Drug Delivery Systems - OncLive

Recommendation and review posted by Alexandra Lee Anderson

Nanomedicine and Tissue Engineering: State of the Art and …

Preface

Nanomedicine: From Concept to Reality; Rakhimol K. R., Robin Augustine, Sabu Thomas, and Nandakumar Kalarikkal

Tissue Engineering: Principles, Recent Trends and the Future; Ansuja P Mathew, Robin Augustine, Nandakumar Kalarikkal, and Sabu Thomas

Tailored Heating Superparamagnetic Nanoparticles for Hyperthermia Applications; Vanchna Singh and Varsha Banerjee

Silver Nanoparticles: Newly Emerging Antimicrobials in the 21st Century; M. Saravanan, Vinoy Jacob, K. Ravi Shankar, Karthik Deekonda, Jesu Arockiaraj, and P. Prakash

Tailoring Plasmon Resonances in Metal Nanospheres for Optical Diagnostics of Molecules and Cells; Krystyna Kolwas, Anastasiya Derkachova, and Daniel Jakubczyk

Recent Advances in Nanoparticulate Drug Delivery System for Antiviral Drugs; Dipali M. Dhoke, Rupesh V. Chikhale, Amit M. Pant, Sunil Menghani, Nilesh Rarokar, and Pramod B. Khedekar

Triggerable Liposomes: Newer Approach in Cytoplasmic Drug Delivery; Neeraj K. Sharma and Vimal Kumar

P-gp Inhibitors: A Potential Tool to Overcome Drug Resistance in Cancer Chemotherapy; Ankit Jain, Sanjay K. Jain

Formulation and Evaluation of Self-Nanoemulsifying Drug Delivery System (SNEDDS) for Oral Delivery of Ketoconazole; Poonam Verma, Sandeep K. Singh, Koshy M. Kymonil, and Shubhini A. Saraf

Recent Advances in the Application of Biopolymer Scaffolds for 3D Culture Of Cells; Mala Rajendra and Anantha Suganiya Selvaraj

Electrospun Matrices for Biomedical Applications: Recent Advances; Deepa P. Mohanan, Robin Augustine, Nandakumar Kalarikkal, Radhakrishnan E. K., and Sabu Thomas

Functionalization of Scaffolds with Biomolecules for Various Types of Tissue Engineering Applications; R. Selvakumar, Amitava Bhattacharyya, J. Gopinathan, R. Sournaveni, and Mamatha M. Pillai

Anti Microbial Nano Materials for Wound Dressings; Anantha Suganiya Selvaraj and Mala Rajendran

Cutaneous Wound Care: Grafts to Tissue Engineered Skin Substitutes; Robin Augustine, Bhavana Venugopal, Nandakumar Kalarikkal, and Sabu Thomas

Index

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Nanomedicine and Tissue Engineering: State of the Art and ...

Recommendation and review posted by Alexandra Lee Anderson

Healthcare Nanotechnology (Nanomedicine) Market 2020-2024 | Latest Industry Updates, Projections, Consumption Analysis, Investment Cost, Profits Data…

This Healthcare Nanotechnology (Nanomedicine) Market research report provide business experts opinion with efficient information on Healthcare Nanotechnology (Nanomedicine) market growth rate, emerging business environments, and the latest business-centric applications. This Healthcare Nanotechnology (Nanomedicine) industry report lists and studies the leading participants also provide insights with strategic industry Analysis of the key factors influencing the market dynamics. Healthcare Nanotechnology (Nanomedicine) market report focuses on to describe and analyze the value, market share, market competition landscape, SWOT analysis, and development plans in the next few years. The report also offers an in-depth analysis of the Healthcare Nanotechnology (Nanomedicine) market with prime emphasis on factors such as drivers, restraints, trends, and opportunities.

Scope of the Report:

As per the , the healthcare nanotechnology (nanomedicine) market includes products that are nanoformulations of the existing drugs or new drugs or are nanobiomaterials. The market is segmented by its application in the medical field, as drug delivery, biomaterials, active implants, diagnostic imaging, tissue regeneration, and other applications. The market is also segmented by its use in the treatment of diseases, like cardiovascular diseases, oncological diseases, neurological diseases, orthopedic diseases, infectious diseases, and other diseases.<

The Report Covers:

For More Information or Query or Customization Before Buying, Visit at https://www.industryresearch.co/enquiry/pre-order-enquiry/14099195

Key Market Trends:

The Growth of Nanomedicine is Expected to Provide High Opportunities for the Treatment of Neurological Diseases, Over the Forecast Period

A large number of brain disorders with neurological and psychological conditions result in short-term and long-term disabilities. Recent years observed a significant number of research studies being published on methods for the synthesis of nanoparticle-encapsulated drugs within in vivo and in vitro studies. The insufficient absorbance of oral drugs administered for a range of neurological conditions, such as Alzheimers disease, Parkinson disease, tumor, neuro-AIDS, among others, opens up the necessity of nanomedicine with stem cell therapy. Some of the registered nanoparticles for the complex CNS treatment are a gold nanoparticle, lipid nanoparticle, and chitosan nanoparticles.

Other than neurological diseases, research-based progress was found in the treatment of cancers, with the scientific communities identifying new metabolic pathways to find better drug combination using nanomedicine.

North America is Expected to Hold the Largest Share in the Market

In the United States, several companies are closely observing the developments in nanostructured materials across various applications in the healthcare industry, including medical devices, to improve efficiency and efficacy. In the United States, the National Nanotechnology Initiative (NNI), which was initiated in 2000, is among the supreme bodies that manage all nanotechnology-related activities. Under the NNI, several agencies are working in collaboration with companies and universities. For instance, nano-manufacturing in Small Business Innovation Research (SBIR) programs were developed for both commercial and public use. Companies are targeting the treatment of several cancer types and infectious diseases through immunotherapy, where nanoemulsion vaccines and drugs play a significant role. In the United States, one of the major challenges associated with nanotechnology is the ability to integrate nanoscale materials into new devices and systems, along with an application of novel properties at the nano-level. Thus, most of the companies are investing in R&D. Nanotechnology is likely to play a significant role in the delivery of drugs. In the recent strategic plan presented by the NNI in 2016, several programs were identified to further advance the research and development programs, over the forecast period.

Key Questions Answered in This Report:

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Detailed TOC of Healthcare Nanotechnology (Nanomedicine) Market 2019-2024:

1 INTRODUCTION1.1 Study Deliverables1.2 Study Assumptions1.3 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET DYNAMICS4.1 Market Overview4.2 Market Drivers4.2.1 Growing Prevalence of Cancer and Genetic and Cardiovascular Diseases4.2.2 Increasing Advancements in Nanoscale Technologies for Diagnostic Procedures4.2.3 Growing Preference for Personalized Medicines4.3 Market Restraints4.3.1 High Cost4.3.2 Stringent Regulations for Commercial Introduction4.4 Porters Five Forces Analysis4.4.1 Threat of New Entrants4.4.2 Bargaining Power of Buyers/Consumers4.4.3 Bargaining Power of Suppliers4.4.4 Threat of Substitute Products4.4.5 Intensity of Competitive Rivalry

5 MARKET SEGMENTATION5.1 By Application5.1.1 Drug Delivery5.1.2 Biomaterials5.1.3 Active Implants5.1.4 Diagnostic Imaging5.1.5 Tissue Regeneration5.1.6 Other Applications5.2 By Disease5.2.1 Cardiovascular Diseases5.2.2 Oncological Diseases5.2.3 Neurological Diseases5.2.4 Orthopedic Diseases5.2.5 Infectious Diseases5.2.6 Other Diseases5.3 Geography5.3.1 North America5.3.1.1 US5.3.1.2 Canada5.3.1.3 Mexico5.3.2 Europe5.3.2.1 France5.3.2.2 Germany5.3.2.3 UK5.3.2.4 Italy5.3.2.5 Spain5.3.2.6 Rest of Europe5.3.3 Asia-Pacific5.3.3.1 China5.3.3.2 Japan5.3.3.3 India5.3.3.4 Australia5.3.3.5 South Korea5.3.3.6 Rest of Asia-Pacific5.3.4 Middle East & Africa5.3.4.1 GCC5.3.4.2 South Africa5.3.4.3 Rest of Middle East & Africa5.3.5 South America5.3.5.1 Brazil5.3.5.2 Argentina5.3.5.3 Rest of South America

6 COMPETITIVE LANDSCAPE6.1 Company Profiles6.1.1 Sanofi SA6.1.2 Celegene Corporation6.1.3 CytImmune Sciences Inc.6.1.4 Johnson & Johnson6.1.5 Luminex Corporation6.1.6 Merck & Co. Inc.6.1.7 Nanobiotix6.1.8 Pfizer Inc.6.1.9 Starpharma Holdings Limited6.1.10 Taiwan Liposome Company Ltd

7 MARKET OPPORTUNITIES AND FUTURE TRENDS

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Nanomedicine Market 2020 Industry Share, Size, Consumption, Growth, Top Manufacturers, Type and Forecast to 2028 Bulletin Line – Bulletin Line

Most recent report on the global Nanomedicine market

A recent market study reveals that the global Nanomedicine market is likely to grow at a CAGR of ~XX% over the forecast period (2019-2029) largely driven by factors including, factor 1, factor 2, factor 3, and factor 4. The value of the global Nanomedicine market is estimated to reach ~US$ XX Bn/Mn by the end of 2029 owing to consistent focus on research and development activities in the Nanomedicine field.

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Competitive Outlook

The presented business intelligence report includes a SWOT analysis for the leading market players along with vital information including, revenue analysis, market share, pricing strategy of each market players.

market dynamics section of this report analyzes the impact of drivers and restraints on the global nanomedicine market. The impact of these drivers and restraints on the global nanomedicine market provides a view on the market growth during the course of the forecast period. Increasing research activities to improve the drug efficacy coupled with increasing government support are considered to be some of the major driving factors in this report. Moreover, few significant opportunities for the existing and new market players are detailed in this report.

Porters five forces analysis provides insights on the intensity of competition which can aid in decision making for investments in the global nanomedicine market. The market attractiveness section of this report provides a graphical representation for attractiveness of the nanomedicine market in four major regions North America, Europe, Asia-Pacific and Rest of the World, based on the market size, growth rate and industrial environment in respective regions, in 2012.

The global nanomedicine market is segmented on the basis of application and geography and the market size for each of these segments, in terms of USD billion, is provided in this report for the period 2011 2019. Market forecast for this applications and geographies is provided for the period 2013 2019, considering 2012 as the base year.

Based on the type of applications, the global nanomedicine market is segmented into neurological, cardiovascular, oncology, anti-inflammatory, anti-infective and other applications. Other applications include dental, hematology, orthopedic, kidney diseases, ophthalmology, and other therapeutic and diagnostic applications of nanomedicines. Nanoparticle based medications are available globally, which are aimed at providing higher bioavilability and hence improving the efficacy of drug. There have been increasing research activities in the nanomedicine filed for neurology, cardiovascular and oncology applications to overcome the barriers in efficient drug delivery to the target site. Moreover, the global nanomedicine market is also estimated and analyzed on the basis of geographic regions such as North America, Europe, Asia-Pacific and Rest of the World. This section describes the nanomedicine support activities and products in respective regions, thus determining the market dynamics in these regions.

The report also provides a few recommendations for the exisitng as well as new players to increase their market share in the global nanomedicine market. Some of the key players of this market include GE Healthcare, Mallinckrodt plc, Nanosphere Inc., Pfizer Inc., Merck & Co Inc., Celgene Corporation, CombiMatrix Corporation, Abbott Laboratories and others. The role of these market players in the global nanomedicine market is analyzed by profiling them on the basis of attributes such as company overview, financial overview, product portfolio, business strategies, and recent developments.

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Nanomedicine Market 2020 Industry Share, Size, Consumption, Growth, Top Manufacturers, Type and Forecast to 2028 Bulletin Line - Bulletin Line

Recommendation and review posted by Alexandra Lee Anderson

Nanomedicines Market Research On Present State & Future Growth Prospects to 2027 | Sirnaomics, BlueWillow Biologics, Cristal Therapeutics – Cole…

Nanomedicines Market research added by the insight partners, offers a comprehensive analysis of growth trends prevailing in the global business domain. This report also provides definitive data concerning market, size, commercialization aspects and revenue forecast of the industry. In addition, the study explicitly highlights the competitive status of key players within the projection timeline while focusing on their portfolio and regional expansion endeavors.

The application of nanotechnology in the medical field is called nanomedicine. Nanomedicine involves the use of nanoscale materials, such as biocompatible nanoparticles and nanorobots, for diagnosis, delivery, sensing or actuation purposes in a living organism. These can pass directly through the cellular membranes and interact with the cellular DNA and proteins, giving better desired results as compared to the traditional form of medicines. These nanomedicines are normally used across applications such as, diagnosis, targeted drug delivery and imaging.

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Major Players Included in this report are as follows

Nanomedicines Market: Regional analysis includes:

The global nanomedicines market is segmented on the basis of product, application and type. Based on product, the market is classified as therapeutics, regenerative medicine, in-vitro diagnostics, in-vivo diagnostics, and vaccines. On the basis of application, the market is classified as clinical oncology, infectious diseases, clinical cardiology, orthopedics, and other applications. On the basis of type, the market is categorized as nanoparticles, nanoshells, nanotubes and nanodevices.

This report on Nanomedicines Market delivers an in-depth analysis that also comprises an elaborate assessment of this business. Also, segments of the Nanomedicines Market have been evidently elucidated in this study, in addition to a basic overview pertaining to the markets current status as well as size, with respect to the profit and volume parameters. The study is ubiquitous of the major insights related to the regional spectrum of this vertical as well as the companies that have effectively gained a commendable status in the Nanomedicines Market.

The Nanomedicines Market research covers an exhaustive analysis of the following data:

Historical and future growth of the global Nanomedicines Market.

Segmentation of the Nanomedicines Market to highlight the growth prospects and trends impacting these segments.

Changing consumption behavior of customers across various regions.

Regional analysis on the basis of market share, growth outlook, and key countries.

Agreements, product launches, acquisitions, and R&D projects of different Nanomedicines Market players.

The Nanomedicines Market research addresses critical questions, such as

Why is region surpassing region in terms of value by the end of 2027?

How are the consumers using Nanomedicines for various purposes?

Which players are entering into collaborations in the market of the Nanomedicines?

At what rate has the global Nanomedicines Market been growing throughout the historic period?

In terms of value, which segment holds the largest share?

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A reanalysis of nanoparticle tumor delivery using classical pharmacokinetic metrics – Science Advances

Abstract

Nanoparticle (NP) delivery to solid tumors has recently been questioned. To better understand the magnitude of NP tumor delivery, we reanalyzed published murine NP tumor pharmacokinetic (PK) data used in the Wilhelm et al. study. Studies included in their analysis reporting matched tumor and blood concentration versus time data were evaluated using classical PK endpoints and compared to the unestablished percent injected dose (%ID) in tumor metric from the Wilhelm et al. study. The %ID in tumor was poorly correlated with standard PK metrics that describe NP tumor delivery (AUCtumor/AUCblood ratio) and only moderately associated with maximal tumor concentration. The relative tumor delivery of NPs was ~100-fold greater as assessed by the standard AUCtumor/AUCblood ratio than by %ID in tumor. These results strongly suggest that PK metrics and calculations can influence the interpretation of NP tumor delivery and stress the need to properly validate novel PK metrics against traditional approaches.

The theoretical advantages of nanoparticles (NPs) in cancer treatment include increased solubility, prolonged duration of exposure, selective delivery to the tumor, and an improved therapeutic index of the encapsulated or conjugated drug (1, 2). The number of available NP-based drug delivery systems for the treatment of cancer and other diseases has seen exponential growth in the past three decades. In 2017 alone, there were more than 300 nanomedicine patent filings, with more than half related to drug delivery (3). While the number of NP-based agents used clinically is still limited, the plethora that is emerging as potential therapeutic agents warrants the need for detailed studies of their unique pharmacology in animal models and in humans. Doxil, Onivyde, and Abraxane are the only members of this relatively new class of drugs that are approved by the Food and Drug Administration (FDA) for the treatment of solid tumors and currently available on the U.S. market. Despite the regulatory success of these drugs, the promise of NP-based agents for the treatment of cancer remains unfulfilled because of several factors including potential overall low tumor delivery (4, 5).

The disposition of NPs is dependent on the carrier and not on the therapeutic entity until the drug is released (6, 7). This complexity required the creation of nomenclature to describe NP pharmacokinetics (PK), including encapsulated or conjugated (the drug within or bound to the carrier), released (active drug that no longer associates with the carrier), and sum total or total (encapsulated/conjugated drug plus released drug) (6, 8). NPs act as prodrugs and are not active until the small-molecule (SM) drug is released from the carrier. In theory, the PK disposition of the drug after release from the carrier is the same as after administration of the SM formulation (6). Examples of various types of NPs include liposomes, polymeric micelles, fullerenes, carbon nanotubes, quantum dots, nanoshells, polymers, dendrimers, and conjugates, including antibody-drug conjugates (9). Thus, the types of NP carriers are vast and highly variable, and each type may have unique biological interactions and PK characteristics (10). As a result, detailed analytical studies must be performed to assess the disposition of encapsulated/conjugated and released forms of the drug in plasma, tumor, and tissues as part of PK and biodistribution studies in animals and patients (7). However, there are currently few, if any, robust and validated bioanalytical methods capable of quantifying released drug in tumors and tissues, which limits the ability to fully characterize the disposition of NP-based agents and compare them to conventional SM formulations (11). This has led to a limited number of published studies that evaluated the PK of NP encapsulated/conjugated and released drug in tumors. However, the use of modeling and simulation approaches to characterize this complex interplay is also emerging (12).

In theory, size-selective permeability of the tumor vasculature allows NPs to enter the tumor interstitial space, while suppressed lymphatic filtration prevents clearance, resulting in accumulation. This phenomenon, termed the enhanced permeability and retention (EPR) effect, may be exploited by NPs to deliver drugs to tumors (4, 5, 13). Unfortunately, progress in developing effective NPs relying on this approach has been hampered by heterogeneity of the EPR effect and lack of information on factors that influence EPR (4, 5, 14). Cancer cells in tumors are surrounded by a complex microenvironment composed of endothelial cells of the blood and lymphatic circulation, stromal fibroblasts, collagen, cells of the mononuclear phagocyte system, and other immune cells. Each of these components is a potential barrier to tumor delivery and intratumoral distribution of NPs and may be associated with variability in EPR (4, 1417). In addition, these potential barriers may be highly variable both within and across tumors, which further increases heterogeneity in the EPR effect. Thus, all solid tumors may not be conducive for treatment by NPs, which rely on EPR for delivery.

A workshop by the Alliance for Nanotechnology in Cancer concluded that there are major gaps in the understanding of factors that affect and inhibit EPR effect and NP tumor delivery, and new fundamental preclinical and clinical studies in this area are needed to effectively advance NP drug delivery and efficacy in solid tumors (4). Recent meta-analyses, described in detail below, have reported lower than expected NP tumor delivery, highlighting the potential limitations of current EPR-based NP delivery to tumors and the need to systematically evaluate NP disposition (18, 19).

Despite great promise, the impact of NPs on the treatment of solid tumors in patients, and in some cases, preclinical models, has been limited. To evaluate NP tumor delivery as compared to SM drugs, our group previously conducted a meta-analysis evaluating the plasma and tumor PK of NPs and SM anticancer agents using both standard PK parameters and a PK metric called relative distribution index over time (RDI-OT) that measures efficiency of tumor delivery (18). In general, standard PK parameters such as plasma and tumor Cmax and area under the time concentration curves (AUCs) were higher for NP agents than their respective SM drugs, as expected. However, when examining measures of tumor delivery efficiency, NPs underperform compared to SM drugs. AUCtumor/AUCplasma ratio was higher for the SM drug compared to the NP formulation for 14 of 17 datasets, and similar to this traditional PK approach, every SM tumor RDI-OT AUC06h value was also greater than that of its comparator NP. The lower efficiency of delivery seen with NPs compared with SMs suggests that even though NPs can deliver an overall greater total drug exposure to the tumor, there may be a limit to the extent or amount of NPs that can enter tumors (18). An important caveat to this conclusion, however, is that active, released NP drug concentrations were not evaluated, and without this key component of the PK analysis, it is impossible to infer potential advantages or disadvantages of the NP-mediated tumor delivery in comparison to SM. Regardless, the extent of NP-mediated tumor delivery estimated in our study, with a median AUCtumor/AUCplasma ratio of 0.4 (i.e., tumor exposure was 40% of plasma exposure), was still much higher than suggested in a recent study by Wilhelm et al. that attempted to relate NP tumor exposure to the injected dose, with a median estimated tumor value of 0.7% of the injected dose.

Wilhelm et al. (19) recently performed a meta-analysis evaluating the percentage of injected dose (%ID) of NPs that reaches the tumor from 117 published preclinical studies. The results of this analysis were somewhat unexpected and disappointing in that a median of only 0.7 %ID of NPs was found to be delivered to a solid tumor. The authors concluded that this overall low tumor delivery has negative consequences for the translation of nanotechnology for human use with respect to manufacturing, cost, toxicity, and imaging and therapeutic efficacy. However, there were several limitations to this study, such as highly variable study designs in the source publications, which included differences in dosing regimens, sampling schemes (especially limited sample numbers or short sampling durations), sample processing and analytical methods (limited data on exposures of active-released drug in tumors), and, in some cases, absence of matched blood PK data. The study was criticized in a follow-up perspective article by McNeil (20) that argued that the PK analysis used by Wilhelm et al. may be flawed because of the use of non-traditional methods. The tumor delivery efficiency in the Wilhelm et al. study was estimated using an unestablished PK metric, %ID in tumor, that was not supported by traditional PK analysis. The %ID in tumor parameter, calculated as %ID in tumor = (AUCtumor/tend)*tumor mass, is not a true measure of tissue exposure or delivery efficiency, because it reduces the time-concentration series to a single average drug mass value that neglects exposure time and does not relate tumor and systemic exposures. Further, the %ID in tumor metric is heavily influenced by the time points and total duration used in the estimation, and this single mass value does not reflect the overall PK disposition of a NP. Traditional comparison of AUCtumor to AUCblood (AUCtumor/AUCblood ratio) is considerably more meaningful because it takes into account the entire time-concentration series and relates tumor exposure to systemic exposure.

The goal of our current study was to compare the tumor disposition of NPs as depicted by the nonstandard %ID in tumor PK metric generated by Wilhelm et al. compared with standard PK metrics. In the present reanalysis, we compiled the source data from the 117 NP PK studies in mice that were evaluated in the original Wilhelm et al. study and then extracted and analyzed those studies that included matched tumor and blood concentration versus time data. We then compared established PK parameters resulting from the reanalysis of these extracted data to the %ID in tumor metric used in the prior study by Wilhelm et al. The %ID in tumor metric was found to correlate very poorly with established PK measures of exposure and delivery efficiency in tumors. These data refute the use of the exposure term %ID in tumor in the Wilhelm et al. study and suggest that the resulting conclusions regarding the efficiency of NP tumor distribution were misleading. The results of our present reanalysis support the use of established PK approaches and metrics to evaluate NP tumor delivery and stress the necessity to properly validate novel metrics against traditional PK metrics using standard methods.

From the 117 articles included in the data analysis by Wilhelm et al., 256 NP PK datasets were identified and evaluated. A total of 136 unique datasets contained sufficient data for calculation of both blood and tumor PK parameters and were included in the analysis. Each dataset included PK data collected following a single intravenous dose of a NP agent to tumor-bearing mice. The majority of included studies were conducted in xenograft models (120 of 136 datasets) with a smaller proportion in orthotopic models (13 of 136 datasets).

The relationship between the Wilhelm et al. %ID in tumor PK metric and established PK parameters, AUCtumor/AUCblood ratio, RDI-OT AUCtumor, and tumor Cmax for all NP types combined, is presented in Fig. 1. The Spearman correlation coefficients and Pearson correlation coefficients for these relationships are included in tables S1 and S2, respectively. Including different types of NPs together, there was no relationship between %ID in tumor and AUCtumor/AUCblood ratio, a weak relationship between %ID in tumor and RDI-OT AUCtumor, and a moderate relationship between %ID in tumor and tumor Cmax, based on value (see Materials and Methods for criteria). For all NP types combined, the median and interquartile range of values for %ID in tumor, AUCtumor/AUCblood ratio (as a percentage), RDI-OT AUCtumor, and tumor Cmax are presented in Table 1. The median (interquartile range) for %ID in tumor was 0.67% (0.36 to 1.19%) and that for AUCtumor/AUCblood ratio was 76.12% (48.79 to 158.81%).

Correlation plots for all datasets between %ID in tumor (per Wilhelm et al.) and AUCtumor/AUCblood ratio (%) (A), RDI-OT AUCtumor (B), and tumor Cmax (C). Plots are shown with all datasets (i, outliers shown as ) and with outliers excluded (ii). There was no relationship between %ID in tumor and AUCtumor/AUCblood ratio (%) [ = 0.183 all data (AD); = 0.151 excluding outliers (EO)] and a weak relationship between %ID in tumor and RDI-OT AUCtumor ( = 0.319 AD; = 0.289 EO). There was a moderate relationship between %ID in tumor and the tumor Cmax ( = 0.562 AD; = 0.572 EO).

The relationship between the Wilhelm et al. %ID in tumor estimation and established PK parameters, AUCtumor/AUCblood ratio, RDI-OT AUCtumor, and tumor Cmax, for the liposomal NP subset is presented in Fig. 2. The Spearman correlation coefficients and Pearson correlation coefficients for these relationships are included in tables S1 and S2, respectively. For the liposomal NP subset, there was no relationship between %ID in tumor and AUCtumor/AUCblood ratio, no relationship between %ID in tumor and RDI-OT AUCtumor, and a weak relationship between %ID in tumor and tumor Cmax, based on value (see Materials and Methods for criteria). For liposomes, the median and interquartile range of values for %ID in tumor, AUCtumor/AUCblood ratio as a percentage, RDI-OT AUCtumor, and tumor Cmax are presented in Table 1. The median (interquartile range) for %ID in tumor was 0.55% (0.31 to 2.17%) and that for AUCtumor/AUCblood ratio was 45.46% (31.16 to 63.48%).

Correlation plots for the liposome subset between %ID in tumor (per Wilhelm et al.) and AUCtumor/AUCblood ratio (%) (A), RDI-OT AUCtumor (B), and tumor Cmax (C). Plots are shown with all liposome datasets (i, outliers shown as ) and with outliers excluded (ii). There was no relationship between %ID in tumor and AUCtumor/AUCblood ratio (%) ( = 0.145 AD; = 0.023 EO) and no relationship between %ID in tumor and RDI-OT AUCtumor ( = 0.150 AD; = 0.029 EO). There was a weak relationship between %ID in tumor and the tumor Cmax ( = 0.412 AD; = 0.514 EO).

The relationship between the Wilhelm et al. %ID in tumor estimation and established PK parameters, AUCtumor/AUCblood ratio, RDI-OT AUCtumor, and tumor Cmax, for the polymeric NP subset is presented in Fig. 3. The Spearman correlation coefficients and Pearson correlation coefficients for these relationships are included in tables S1 and S2, respectively. For the polymeric NP subset, there was no relationship between %ID in tumor and AUCtumor/AUCblood ratio, a weak relationship between %ID in tumor and RDI-OT AUCtumor, and a moderate relationship between %ID in tumor and tumor Cmax, based on value (see Materials and Methods for criteria). For polymeric NPs, the median and interquartile range of values for %ID in tumor, AUCtumor/AUCblood ratio as a percentage, RDI-OT AUCtumor, and tumor Cmax are presented in Table 1. The median (interquartile range) for %ID in tumor was 0.68% (0.42 to 1.26%) and that for AUCtumor/AUCblood ratio was 143.94% (56.00 to 318.87%).

Correlation plots for the polymeric subset between %ID in tumor (per Wilhelm et al.) and AUCtumor/AUCblood ratio (%) (A), RDI-OT AUCtumor (B), and tumor Cmax (C). Plots are shown with all polymeric datasets (i, outliers shown as ) and with outliers excluded (ii). There was no relationship between %ID in tumor and AUCtumor/AUCblood ratio (%) ( = 0.094 AD; = 0.097 EO) and a weak relationship between %ID in tumor and RDI-OT AUCtumor ( = 0.422 AD; = 0.447 EO). There was a moderate relationship between %ID in tumor and the tumor Cmax ( = 0.547 AD; = 0.519 EO).

The relationship between the Wilhelm et al. %ID in tumor estimation and established PK parameters, AUCtumor/AUCblood ratio, RDI-OT AUCtumor, and tumor Cmax, for the inorganic NP subset is presented in Fig. 4. Spearman correlation coefficients and Pearson correlation coefficients for these relationships are included in tables S1 and S2, respectively. For inorganic NPs, there was no relationship between %ID in tumor and AUCtumor/AUCblood ratio, a weak relationship between %ID in tumor and RDI-OT AUCtumor, and a moderate relationship between %ID in tumor and tumor Cmax, based on value (see Materials and Methods for criteria). For inorganic NPs, the median and interquartile range of values for %ID in tumor, AUCtumor/AUCblood ratio as a percentage, RDI-OT AUCtumor, and tumor Cmax are presented in Table 1. The median (interquartile range) for %ID in tumor was 0.64% (0.35 to 1.14%) and that for AUCtumor/AUCblood ratio was 81.44% (55.01 to 135.92%).

Correlation plots for the inorganic subset between %ID in tumor (per Wilhelm et al.) and AUCtumor/AUCblood ratio (%) (A), RDI-OT AUCtumor (B), and tumor Cmax (C). Plots are shown with all inorganic datasets (i, outliers shown as ) and with outliers excluded (ii). There was no relationship between %ID in tumor and AUCtumor/AUCblood ratio (%) ( = 0.265 AD; = 0.243 EO) and a weak relationship between %ID in tumor and RDI-OT AUCtumor ( = 0.322 AD). There was a moderate relationship between %ID in tumor and the tumor Cmax ( = 0.618 AD; = 0.605 EO).

Currently, only three NP-based anticancer agents are FDA-approved for treatment of solid tumors. Both the pharmacology of NPs and the physiology of solid tumors are complex, and the interactions between the two are not fully understood. Recent analyses have questioned the utility of NPs for the treatment of solid tumors due to potential low tumor delivery efficiency and extent, especially the often-cited study by Wilhelm et al. (19) However, the conclusions of the study by Wilhelm et al. were based on a nonstandard PK metric, %ID in tumor, which was several orders of magnitude lower than other published PK metrics describing the tumor delivery efficiency of SM and NP drugs (18). To better characterize the delivery of drug-loaded NPs to solid tumors, we compiled and analyzed the source data from the published NP PK studies in mice used by the Wilhelm et al. study and evaluated the relationship between established PK parameters describing the tumor disposition of NP agents and the novel %ID in tumor metric. The goal of this study was to directly compare the relationship and absolute values of these PK metrics and consider how these values influence the interpretation of results.

Our findings reinforce the importance of adequate study design and PK metric selection when investigating NP PK. The calculation of %ID in tumor by Wilhelm et al. differs from the standard calculation of %ID. The conventional calculation of tissue %ID represents the amount of drug in the target tissue at a single time point and is calculated as follows%ID=100*(Amount of drug or decay corrected activity in tissue)/Dose

The calculation of %ID in tumor used by Wilhelm et al. begins with AUCtumor (in units of hours*%ID/g) and cancels units (dividing by tlast in hours and multiplying by tumor mass in grams) to arrive at final units of %ID. Given that the duration of PK studies are generally greater than 1 hour and the size of tumors in mouse models are typically less than 1 g, modifying or normalizing the AUCtumor by these values (e.g., divide by 72 hours, which is the duration of the PK study; multiply by 0.2 g, which is the size of the tumor) results in progressively smaller values. Rather than representing the total amount of drug in the tumor at a single time point (as used by conventional calculations of %ID), this nonstandard calculation actually describes the average amount of drug in the tumor within separate 1-hour intervals throughout the entire PK evaluation period.

By time-averaging and converting to drug mass, the Wilhelm et al. calculation excludes the important pharmacological concepts of drug concentration (i.e., law of mass action), exposure duration, and relative distribution (i.e., on/off target exposure) that are fundamental to understanding drug effect. Thus, the %ID in tumor metric is difficult to interpret, as it is not a measure of how much available drug distributes to the tumor, or even how much injected drug distributes to the tumor (as it has been interpreted). The inference from the %ID in tumor calculation is that perfect tumor uptake would be 100 %ID in tumor, but that would only be the case if the entire injected dose of drug instantaneously distributed to the tumor and remained in the tumor over the entire observation period without clearing, based on the calculations used. To clarify this point, using this calculation, systemic exposure itself upon intravenous injection would only be 100 %ID if the drug circulated indefinitely and never cleared. Obviously, this is a very flawed calculation. Established PK metrics that describe the extent and efficiency of NP tumor delivery take into account both the systemic (blood or plasma) and tumor exposure (i.e., drug concentration and duration, AUC). An example of standard PK metric and %ID in tumor calculations from blood and tumor concentration versus time profiles is shown in Fig. 5. The mock dataset portrayed by the solid lines represents approximately median values for %ID in tumor (0.7 %ID) and AUCtumor/AUCblood ratio (70%) assuming a tumor mass of 0.2 g. The dotted lines represent the approximate interquartile ranges. Given that the %ID in tumor metric ignores systemic exposure, any degree of change in AUCblood does not affect the calculation or interpretation of the %ID in tumor metric. In contrast, AUCtumor/AUCblood ratio is, by definition, sensitive to changes in either or both systemic exposure and target tissue exposure. These differences highlight the disconnect between the %ID in tumor metric and standard PK parameters and explain the lack of relationship between parameters identified in this analysis. This example and our results show how the use of non-standard PK metrics can markedly alter the interpretation of drug delivery to tumors.

The concentration versus time profile in blood is represented by the red symbols and lines. The concentration versus time profile in tumor is represented by the blue symbols and lines. The dotted red and blue lines represent the approximate variability in interquartile range for the blood and tumor concentration versus time profiles, respectively. The dashed gray line represents a constant tumor concentration of 3.5 %ID/g that yields the same AUCtumor (250 hours*%ID/g) as the actual tumor concentration versus time profile. The %ID in tumor calculated by Wilhelm et al. of 0.7% is the average %ID found in the tumor at every 1-hour interval throughout the entire PK evaluation period and is represented by the vertical white and green bar.

Not only was the %ID in tumor metric used by Wilhelm et al. a nonstandard calculation of %ID, it was also found not to be related to other standard PK parameters. The %ID in tumor metric used by Wilhelm et al. was not related to the more commonly and historically used PK metric describing the extent of tumor delivery (i.e., AUCtumor/AUCblood ratio). This observation was consistent for the full dataset and all three subsets (liposomes, polymeric NPs, or inorganic NPs), whether outliers were included or excluded. However, the %ID in tumor calculated by Wilhelm et al. could have been measuring a different process, such as efficiency of delivery. Similarly, there was a weak or no relationship between %ID in tumor and a metric of efficiency of tumor delivery (i.e., RDI-OT AUCtumor). Furthermore, the absolute values and resultant interpretations of these metrics differ substantially. The median %ID in tumor for all subsets combined was 0.67 %ID, while the median AUCtumor/AUCblood ratio was 76.12%. Per Wilhelm et al., this %ID in tumor was interpreted as only 7 of every 1000 administered NPs entering the tumor, a disappointingly low NP delivery. As described above, a more accurate description would be that an average of 0.67% of the injected dose could be found in the tumor at every 1-hour interval throughout the entire PK evaluation period. Using the more appropriate AUCtumor/AUCblood ratio metric from the same datasets, the PK results have a completely different and ultimately far more positive interpretation. For example, with an AUCtumor/AUCblood ratio of 76.12%, the overall exposure of NP in the tumor (AUCtumor) was 76.12% of the overall exposure in the plasma (AUCblood), which is a much more promising result.

There was a moderate relationship between %ID in tumor and tumor Cmax. Again, %ID in tumor resulted in substantially smaller absolute values (median, 0.67 %ID; interquartile range, 0.36 to 1.19 %ID) than tumor Cmax (median, 4.71 %ID/g; interquartile range, 2.65 to 7.97 %ID/g). Given that the tumor Cmax directly contributes to the calculation of AUCtumor and, in turn, %ID in tumor, the moderate relationship is expected. As opposed to the two previously described metrics (AUCtumor/AUCblood ratio and RDI-OT AUCtumor), both %ID in tumor and tumor Cmax exclusively evaluate the disposition of the NP in tumor without considering the systemic disposition and are therefore of lower utility to describe the extent or efficiency of NP tumor delivery.

Our study has several limitations and factors to consider. The source studies included in this analysis were limited to those previously identified and evaluated by Wilhelm et al. to provide a direct comparison of PK metric results and interpretations. There are many additional published NP PK studies that did not meet the selection criteria or were not identified in the initial evaluation. In addition, the calculations completed in this analysis rely on the quality and accuracy of the data collected and published by the authors in the source studies. The study designs, analytical methods, and measured moieties may all influence the results and interpretation of PK data. For example, simply excluding those studies with no matching blood concentration data reported decreased the overall sample size of our analysis by approximately one-third relative to the original analysis by Wilhelm et al. Another important issue is that most of these studies measured total drug (i.e., encapsulated plus released), and not the biologically active, released drug fraction. Although encapsulated drug dominates the total drug profile for most NP formulations, and therefore, NP-encapsulated tumor uptake can be inferred from the total drug profile, it is the released drug fraction that correlates with toxicity and efficacy (7).

Despite these limitations, our study provides direct comparison of PK metrics calculated from identical source data and highlights how the interpretation of NP PK results can be markedly influenced by the differing PK metrics selected. For example, the median (interquartile range) for %ID in tumor was 0.67 %ID (0.36 to 1.19%) and that for AUCtumor/AUCblood ratio was 76.12% (48.79 to 158.81%). The median values for %ID in tumor and AUCtumor/AUCblood ratio were 113-fold different, and thus, metric selection greatly influences the interpretation of the results and the conclusion of the study. Optimal study design, including analysis of both tumor and blood concentrations, is critical to understanding the efficiencies and deficiencies of NP tumor delivery.

To fully evaluate the current and potential impact of NPs on the treatment of solid tumors, more detailed and extensive meta-analyses, modeling, and statistical comparisons, ideally using PK datasets that include all drug fractions (i.e., total, encapsulated, and released drug), are needed to evaluate and predict what NP formulation attributes, dosing regimens, and animal model characteristics are associated with high tumor delivery and efficacy of NPs for solid tumor treatment.

All 117 articles included in the data analysis by Wilhelm et al. (19) were accessed and reviewed. Each identifiable dataset was given a unique identifier, and data were extracted from published text, tables, and figures for inclusion in a comprehensive database. Retrieved information included NP specifications (NP type and encapsulated or conjugated drug) and PK study data (dose, route, regimen, analytical methods, and concentration versus time data for tumor and blood or plasma). When available, concentration data were preferentially sourced from published text or tables (including the Supplementary Materials). If numerical concentration data were not published in text or tables, WebPlotDigitizer version 3.12 (Ankit Rohatgi, Austin, TX) was used to extract data from concentration versus time plots.

Following data extraction, the raw concentration versus time data were used to calculate various PK metrics for each unique dataset. When needed, data were converted to units of %ID/g using assumptions published by Wilhelm et al. The tumor AUC and delivery efficiency (%ID) were calculated per Wilhelm et al. (19). For clarity, the Wilhelm et al. delivery efficiency metric is described as %ID in tumor throughout this analysis. In addition, the blood AUC was calculated by the linear trapezoidal rule (to match tumor AUC calculations) from 0 to tlast. The ratio of tumor AUC to blood AUC was calculated as followsAUCtumor/AUCbloodratio(%)=100*AUCtumor(hours*%ID/gtumor)/AUCblood(hours*%ID/gblood)

The RDI-OT, used to evaluate the efficiency of tumor delivery from systemic circulation, is calculated as the ratio of tumor concentration to blood concentration at the same time point (e.g., 24 hours) (18). The area under the tumor RDI-OT curve (RDI-OT AUCtumor) from 0 to tlast was calculated using the linear trapezoidal rule for each dataset. Last, the tumor Cmax was determined by visual inspection.

After data extraction and PK metric calculation, each unique dataset was assessed for inclusion in the final analysis. Datasets were excluded if there were missing, incomplete, insufficient (i.e., <3 time points), or unmatched tumor and blood data, or if units could not be converted to %ID/g. In addition, datasets representing NPs administered by nonintravenous routes (i.e., intraperitoneal or subcutaneous), to animals other than mice, or those with duplicate data were excluded.

All remaining datasets were evaluated in the final analysis. For each metric, outliers were identified by the Grubbs test (P < 0.01). The correlation between PK metrics used by Wilhelm et al. (%ID in tumor) and standard PK metrics (AUCtumor/AUCblood ratio and tumor Cmax) and tumor delivery efficiency metrics (RDI-OT AUCtumor) was estimated using Spearmans rank correlation coefficients () and Pearson correlation coefficients (r). For each comparison, and r were determined with all datasets and after exclusion of outliers. Correlation coefficients between metrics were interpreted as follows: or |r| < 0.3, no relationship; 0.3 or |r| < 0.5, weak relationship; 0.5 or |r| < 0.7, moderate relationship; 0.7 or |r|, strong relationship (21). The median and interquartile range for each metric were also determined.

Last, datasets included all NPs and three NP subsets defined as liposomes and solid lipid NPs (liposome subset); polymeric NPsincluding micelles, hydrogels, and dendrimers(polymeric subset); and inorganic, graphene, hybrid, or other NPs (inorganic subset). Statistical analysis as above was repeated for each NP type subset.

V. V. Ambardekar, S. T. Stern, NBCD pharmacokinetics and bioanalytical methods to measure drug release, in Non-Biological Complex Drugs; the Science and the Regulatory Landscape (Springer International Publishing, ed. 1, 2015), pp. 261287.

Acknowledgments: Funding: This study was supported by NIH Carolina Center of Cancer Nanotechnology Excellence 1U54CA19899-01 Pilot Grant and T32 Carolina Cancer Nanotechnology Training Program 1T32CA196589 and R01CA184088. Author contributions: L.S.L.P., S.T.S., A.V.K., and W.C.Z. designed the study. L.S.L.P. collected the data. L.S.L.P. and A.M.D. performed the statistical analysis. L.S.L.P. and W.C.Z. drafted the manuscript. All authors contributed to the interpretation of the results and to the final manuscript text. This manuscript reflects the views of the authors and should not be construed to represent the US Food and Drug Administration's views or policies. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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A reanalysis of nanoparticle tumor delivery using classical pharmacokinetic metrics - Science Advances

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Healthcare Nanotechnology Market 2020 Global Industry Brief Analysis by Top Countries Data with Market Size, Growth Drivers, Investment Opportunity…

Healthcare Nanotechnology Market 2020 Research Report cover detailed competitive outlook including the Healthcare Nanotechnology Industry share and company profiles of the key participants operating in the global market. It provides key analysis on the market status of the Healthcare Nanotechnology manufacturers with best facts and figures, meaning, definition, SWOT analysis, expert opinions and the latest developments across the globe. The Report also calculate the market size, Healthcare Nanotechnology Sales, Price, Revenue, Gross Margin, cost structure and growth rate. The report considers the revenue generated from the sales and technologies by various application segments.

COVID-19 can affect the global economy in three main ways: by directly affecting production and demand, by creating supply chain and market disruption, and by its financial impact on firms and financial markets.

Final Report will add the analysis of the impact of COVID-19 on this industry.

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Short Description About Healthcare Nanotechnology Market :

It is defined as the study of controlling, manipulating and creating systems based on their atomic or molecular specifications. As stated by the US National Science and Technology Council, the essence of nanotechnology is the ability to manipulate matters at atomic, molecular and supra-molecular levels for creation of newer structures and devices. Generally, this science deals with structures sized between 1 to 100 nanometer (nm) in at least one dimension and involves in modulation and fabrication of nanomaterials and nanodevices.

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The research covers the current Healthcare Nanotechnology market size of the market and its growth rates based on 5-year records with company outline ofKey players/manufacturers:

Scope of the Healthcare Nanotechnology Market Report:

Nanotechnology is becoming a crucial driving force behind innovation in medicine and healthcare, with a range of advances including nanoscale therapeutics, biosensors, implantable devices, drug delivery systems, and imaging technologies.

The classification of Healthcare Nanotechnology includes Nanomedicine, Nano Medical Devices, Nano Diagnosis and Other product. And the sales proportion of Nanomedicine in 2017 is about 86.5%, and the proportion is in increasing trend from 2013 to 2017.

The global Healthcare Nanotechnology market is valued at 160800 million USD in 2018 and is expected to reach 255500 million USD by the end of 2024, growing at a CAGR of 9.7% between 2019 and 2024.

The Asia-Pacific will occupy for more market share in following years, especially in China, also fast growing India and Southeast Asia regions.

North America, especially The United States, will still play an important role which cannot be ignored. Any changes from United States might affect the development trend of Healthcare Nanotechnology.

Europe also play important roles in global market, with market size of xx million USD in 2019 and will be xx million USD in 2024, with a CAGR of xx%.

This report studies the Healthcare Nanotechnology market status and outlook of Global and major regions, from angles of players, countries, product types and end industries; this report analyzes the top players in global market, and splits the Healthcare Nanotechnology market by product type and applications/end industries.

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Report further studies the market development status and future Healthcare Nanotechnology Market trend across the world. Also, it splits Healthcare Nanotechnology market Segmentation by Type and by Applications to fully and deeply research and reveal market profile and prospects.

Major Classifications are as follows:

Major Applications are as follows:

Geographically, this report is segmented into several key regions, with sales, revenue, market share and growth Rate of Healthcare Nanotechnology in these regions, from 2014 to 2024, covering

This Healthcare Nanotechnology Market Research/Analysis Report Contains Answers to your following Questions

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Major Points from Table of Contents:

1. Market Overview1.1 Healthcare Nanotechnology Introduction1.2 Market Analysis by Type1.3 Market Analysis by Applications1.4 Market Dynamics1.4.1 Market Opportunities1.4.2 Market Risk1.4.3 Market Driving Force

2.Manufacturers Profiles

2.4.1 Business Overview2.4.2 Healthcare Nanotechnology Type and Applications2.4.2.1 Product A2.4.2.2 Product B

3.Global Healthcare Nanotechnology Sales, Revenue, Market Share and Competition By Manufacturer (2019-2020)

3.1 Global Healthcare Nanotechnology Sales and Market Share by Manufacturer (2019-2020)3.2 Global Healthcare Nanotechnology Revenue and Market Share by Manufacturer (2019-2020)3.3 Market Concentration Rates3.3.1 Top 3 Healthcare Nanotechnology Manufacturer Market Share in 20203.3.2 Top 6 Healthcare Nanotechnology Manufacturer Market Share in 20203.4 Market Competition Trend

4.Global Healthcare Nanotechnology Market Analysis by Regions

4.1 Global Healthcare Nanotechnology Sales, Revenue and Market Share by Regions4.1.1 Global Healthcare Nanotechnology Sales and Market Share by Regions (2014-2019)4.1.2 Global Healthcare Nanotechnology Revenue and Market Share by Regions (2014-2019)4.2 North America Healthcare Nanotechnology Sales and Growth Rate (2014-2019)4.3 Europe Healthcare Nanotechnology Sales and Growth Rate (2014-2019)4.4 Asia-Pacific Healthcare Nanotechnology Sales and Growth Rate (2014-2019)4.6 South America Healthcare Nanotechnology Sales and Growth Rate (2014-2019)4.6 Middle East and Africa Healthcare Nanotechnology Sales and Growth Rate (2014-2019)

5.Healthcare Nanotechnology Market Forecast (2020-2024)5.1 Global Healthcare Nanotechnology Sales, Revenue and Growth Rate (2020-2024)5.2 Healthcare Nanotechnology Market Forecast by Regions (2020-2024)5.3 Healthcare Nanotechnology Market Forecast by Type (2020-2024)5.3.1 Global Healthcare Nanotechnology Sales Forecast by Type (2020-2024)5.3.2 Global Healthcare Nanotechnology Market Share Forecast by Type (2020-2024)5.4 Healthcare Nanotechnology Market Forecast by Application (2020-2024)5.4.1 Global Healthcare Nanotechnology Sales Forecast by Application (2020-2024)5.4.2 Global Healthcare Nanotechnology Market Share Forecast by Application (2020-2024)

6.Sales Channel, Distributors, Traders and Dealers6.1 Sales Channel6.1.1 Direct Marketing6.1.2 Indirect Marketing6.1.3 Marketing Channel Future Trend6.2 Distributors, Traders and Dealers

7.Research Findings and Conclusion

8.Appendix8.1 Methodology8.2 Data Source

Continued..

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Healthcare Nanotechnology Market 2020 Global Industry Brief Analysis by Top Countries Data with Market Size, Growth Drivers, Investment Opportunity...

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Nanomedicine Market 2020 Recent Industry Developments and Growth Strategies Adopted by Top Key Players Worldwide and Assessment to 2025 – Bulletin…

The Nanomedicine Market research report is one of the most comprehensive report about business strategies adopted by different players in this Market. This research study gives the potential headway openings that prevails in the global market. It offers detailed research and analysis of key aspects of the Nanomedicine Market.

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Impact of COVID- 19 on Nanomedicine Market

Due to the pandemic, we have included a special section on the Impact of COVID 19 on the Nanomedicine Market, which would mention How the Covid-19 is Affecting the Industry, Market Trends and Potential Opportunities in the COVID-19 Landscape, Key Regions and Proposal Nanomedicine Market Players to battle Covid-19 Impact.

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Competitive Landscape:

The competitive analysis of major market players is another notable feature of the Nanomedicine Market industry report; it identifies direct or indirect competitors in the market.

Key CompaniesGE HealthcareJohnson & JohnsonMallinckrodt plcMerck & Co. Inc.Nanosphere Inc.Pfizer Inc.Sigma-Tau Pharmaceuticals Inc.Smith & Nephew PLCStryker CorpTeva Pharmaceutical Industries Ltd.UCB (Union chimique belge) S.A

Key parameters which define the competitive landscape of the Nanomedicine Market:

Revenue and Market Share by Player

Production and Share by Player

Average Price by Player

Base Distribution, Sales Area and Product Type by Player

Concentration Rate

Manufacturing Base

Mergers & Acquisitions, Expansion

Market Segmentation:

The segmentation is used to decide the target market into smaller sections or segments like product type, application, and geographical regions to optimize marketing strategies, advertising technique and global as well as regional sales efforts of Nanomedicine Market.

Geographically, the report includes the research on production, consumption, revenue, market share and growth rate, and forecast of the following regions:

United States

Central and South America (Brazil, Mexico, Colombia)

Europe (Germany, UK, France, Italy, Spain, Russia, Poland)

China

Japan

India

Southeast Asia (Malaysia, Singapore, Philippines, Indonesia, Thailand, Vietnam)

Middle East and Africa (Saudi Arabia, United Arab Emirates, Turkey, Egypt, South Africa, Nigeria)

The Research Report Provides:

An overview of the Nanomedicine Market

Comprehensive analysis of the market

The segment that accounted for a large market share in the past

The segment that is anticipated to account for a dominant market share by forecasted period

Emerging market segments and regional markets

Segmentations up to the second and/or third level

Analyses of recent developments in the market

Events in the market scenario in past few years

Historical, current, and estimated market size in terms of value and volume

Competitive analysis, with company overview, products, revenue, and strategies

Strategic recommendations to help companies increase their market presence

Lucrative opportunities in the market

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Overview: Along with a broad overview of the Nanomedicine Market, this section gives you the details overview, an idea about the nature and contents of the research study.

Analysis on Strategies of Leading Players: Market players can use this analysis to gain competitive advantage over their competitors in the Nanomedicine Market.

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Nanomedicine Market 2020 Recent Industry Developments and Growth Strategies Adopted by Top Key Players Worldwide and Assessment to 2025 - Bulletin...

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Report: New PET radiotracer proven safe and effective in imaging malignant brain tumors – Tdnews

IMAGE:Representative maximum-intensity projection PET images of a healthy human volunteer injected with 64Cu-NOTA-EB-RGD at 1, 8, and 24 hours after injection. Axial MRI and PET slices of glioblastoma patient injectedview more

Credit: Jingjing Zhang et al., Peking Union Medical College Hospital, Beijing, China/Xiaoyuan Chen et al., Laboratory of Molecular Imaging and Nanomedicine, NIBIB/NIH, Bethesda, USA

A first-in-human study presented at the Society of Nuclear Medicine and Molecular Imaging 2020 Annual Meeting has demonstrated the safety, favorable pharmacokinetic and dosimetry profile of 64Cu-EBRGD, a new, relatively long-lived PET tracer, in patients with glioblastomas. The radiotracer proved to be a superior, high-contrast imaging diagnostic in patients, visualizing tumors that express low or moderate levels of v3 integrin with high sensitivity.

Glioblastoma is the most common and most aggressive primary malignant brain tumor in adults, with 17,000 diagnoses annually. It is a highly diffuse and invasive disease that is personally devastating and virtually incurable. Once diagnosed, most patients survive less than 15 months, and fewer than five percent survive five years.

The 64Cu-EBRGD radiotracer presented in this study has several unique qualities. The peptide sequence Arg-Gly-Asp (RGD) specifically targets the cell surface receptor v3 integrin, which is overexpressed in glioblastomas. To slow clearance, Evans Blue (EB) dye, which reversibly binds to circulating albumin, is bound to RGD, significantly enhancing target accumulation and retention. The addition of the 64Cu label to EBRGD provides persistent, high-contrast diagnostic images in glioblastoma patients.

This first-in-human, first-in-class study included three healthy volunteers who underwent whole-body 64Cu-EBRGD PET/CT. Safety dataincluding vital signs, physical examination, electrocardiography, laboratory parameters and adverse eventswere collected after one day and after one week. Regions of interest were drawn, time-activity curves were obtained and dosimetry was calculated. Two patients with recurrent glioblastoma also underwent 64Cu-EBRGD PET/CT. Seven sets of brain PET and PET/CT scans were obtained over two consecutive days. Tumor-to-background ratios were calculated for the target tumor lesion and normal brain tissue. One week after radiotracer administration, the patient underwent surgical treatment, and immunohistochemical staining of tumor samples was performed.

64Cu-EBRGD was well-tolerated in patients with no adverse symptoms immediately or up to one week after administration. The mean effective dose of 64Cu-EBRGD was very similar to the effective dose of an 18F-FDG scan. Injection of 64Cu-EBRGD to the patients with recurrent glioblastoma showed high accumulation at the tumor with continuously increased tumor-to-background contrast over time. Post-operative pathology revealed World Health Organization grade IV glioblastoma, and immunohistochemical staining showed moderate expression of the v3 integrin.

In this study, we have demonstrated a potential radiotheranostic agent that is safe, sensitive and highly selective in humans, which infers a future diagnostic tool and breakthrough targeted radiotherapy for glioblastoma patients, said Jingjing Zhang, MD, PhD, of Peking Union Medical College Hospital, Beijing, China. We believe this innovative use of 64Cu-EBRGD will significantly improve therapeutic efficacy and patient outcomes.

64Cu-labeled EBRGD represents a viable model compound for therapeutic applications since 177Lu, 90Y or 225Ac can be substituted for 64Cu, said Deling Li, MD, of Beijing Tiantan Hospital, Capital Medical University, Beijing, China. We are currently studying the 177Lu homolog to treat glioblastoma and other v3 integrin expressing cancers, including non-small cell lung, melanoma, renal and bone, and hope to build on the current wave of radiotherapies like 177Lu-DOTATATE.

###

Abstract 349. First-in-Human Study of a 64Cu-Labeled Long-acting Integrin v3 Targeting Molecule 64Cu-NOTA-EB-RGD in Healthy Volunteers and GBM Patients, Jingjing Zhang, Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China, and THERANOSTICS Center for Radiomolecular Precision Oncology, ENETS Center of Excellence, Zentralklinik Bad Berka, Bad Berka, Germany; Deling Li, Department of Neurosurgery Beijing, Tiantan Hospital, Beijing City, China; Gang Nu, National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Institutes of Health (NIH), Bethesda, Maryland; Richard Baum, THERANOSTICS Center for Radiomolecular Precision Oncology, ENETS Center of Excellence, Zentralklinik Bad Berka, Bad Berka, Germany; Zhaohui Zhu, Department of Nuclear Medicine, Peking Union Medic, Beijing, China; and Xiaoyuan Chen, NIBIB/NIH, Bethesda, Maryland. SNMMIs 67th Annual Meeting, July 11-14, 2020.

Molecular Targeting Technologies, Inc., received an exclusive worldwide commercialization license from NIH for rights that, in part, cover EBRGD radiotherapeutics using various radionuclides. Glioblastoma treatment is among its potential uses.

All 2020 SNMMI Annual Meeting abstracts can be found online at http://jnm.snmjournals.org/content/61/supplement_1.toc.

About the Society of Nuclear Medicine and Molecular Imaging

The Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and medical organization dedicated to advancing nuclear medicine and molecular imaging, vital elements of precision medicine that allow diagnosis and treatment to be tailored to individual patients in order to achieve the best possible outcomes.

SNMMIs members set the standard for molecular imaging and nuclear medicine practice by creating guidelines, sharing information through journals and meetings and leading advocacy on key issues that affect molecular imaging and therapy research and practice. For more information, visit http://www.snmmi.org.

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Report: New PET radiotracer proven safe and effective in imaging malignant brain tumors - Tdnews

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