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Press Release for Five Bilingual BioMed e-Series

 

Version #1

Authors: Marcus W. Feldman with edits by Aviva Lev-Ari, PhD, RN

Leaders in Pharmaceutical Business Intelligence (LPBI) Group announces the publication of its two Kindle Editions on Amazon.com – its English Text Edition and its Spanish Audio Edition. The Spanish translation was performed by Montero Language Services, Madrid, Spain.

Eighteen volumes in the English Edition and 19 volumes in the Spanish Edition including 2,728 articles by biomedical professionals are available.

https://lnkd.in/ekWGNqA

The electronic books are collections of curated articles in biomedical science. The electronic Tables of Contents (eTOCs) of each volume was designed by a senior editor with expertise in the subjects covered in that volume. The curations use as sources published research findings in peer-reviewed scientific journals together with expert added interpretations.

The e-books are designed to make the latest research in the Five Bilingual BioMed e-Series – 37 volumes accessible to practicing health care professionals. These five e-Series cover the following medical specialties: cardiovascular diseases and therapies, genomics, cancer etiology and oncological therapies, immunology, and patient-centered precision medicine.

The material in these volumes can greatly enhance medical education and provide a resource for continued updating and education for health care professionals. In addition to the 37 e-books, LPBI has published more than 6,000 articles in its online scientific journal “PharmaceuticalIntelligence.com”, which has received 2.2 million views since its launch in 4/2012, Top articles had more than 15,000 views.

The authors of the curations are a team of eminent biomedical scholars assembled and led by Dr. Aviva Lev-Ari, Ph.D., R.N., Editor-in-Chief of the BioMed e-Series and the Journal, and LPBI Group’s founder.  Dr. Lev-Ari also contributed to many articles in these collections.

As LPBI Group’s leader, Dr. Aviva Lev-Ari, served as an invited Press/Media professional in +70 Global conferences, which she covered in-person in real-time and produced e-Proceedings and Tweet collections cited in many journal and book articles. Dr. Lev-Ari remarked that she was “gratified that the English language e-books had received more than 140,000 page-downloads. This is a strong indication that our work has been successful”. The Spanish Edition, offered at a 25% discount, will reach healthcare professionals in 22 Spanish speaking counties.

[345 words]

Version #2

Curators: Aviva Lev-Ari, PhD, RN and Stephen J. Williams, PhD

Leaders in Pharmaceutical Business Intelligence (LPBI) Group would like to announce the completion of the Translation in the Spanish-language of their’s Five Series of BioMedical E-Books, containing 18 Volumes total in the English-language. Translation was performed by Montero Language Services, Madrid, Spain.

Five Bilingual BioMed e-Series – 37 volumes

  • English-language Edition:  18 volumes in 17 books, and
  • Spanish-language Edition (EDICIÓN EN ESPAÑOL): 19 volumes in 19 books

All the details on book structure and content in the Spanish-language Edition is found in

https://pharmaceuticalintelligence.com/audio-english-spanish-biomed-e-series/

All the details on book structure and content in the English-language Edition is found in

https://pharmaceuticalintelligence.com/biomed-e-books/

All the 37 e-Books on Amazon.com are found in

https://lnkd.in/ekWGNqA

 

URLs for the Spanish-language Edition by e-Series:

 

Serie A: Enfermedades cardiovasculares ($385)

https://www.amazon.com/gp/product/B0BPR9L1ZX?ref_=dbs_p_pwh_rwt_anx_a_lnk

Serie B: Fronteras de la investigación genómica ($305)

https://www.amazon.com/dp/B0BQGZYZVT?binding=kindle_edition&ref=dbs_dp_rwt_sb_pc_tuk

Serie C: Cáncer y la oncología ($231)

https://www.amazon.com/dp/B0BQHMRK3C?binding=kindle_edition&ref=dbs_dp_rwt_sb_pc_tukn

Serie D: Biomedicina. Metabolómica, inmunología, enfermedades infecciosas, genómica reproductiva y endocr ($268)

https://www.amazon.com/dp/B0BR8P5TST

Serie E: Medicina centrada en el paciente ($217)

https://www.amazon.com/dp/B0BRGMCM8Q?binding=kindle_edition&ref=dbs_dp_rwt_sb_pc_tukn

 

URLs for the English-language Edition by e-Series:

 

Series A: Cardiovascular Diseases ($515)

https://www.amazon.com/gp/product/B07P981RCS?ref_=dbs_p_mng_rwt_ser_shvlr&storeType=ebooks

Series B: Frontiers in Genomics ($200)

https://www.amazon.com/gp/product/B0BSDPG2RX?ref_=dbs_p_pwh_rwt_anx_b_lnk&storeType=ebooks

Series C: Cancer & Oncology ($175)

https://www.amazon.com/gp/product/B0BSDWVB3H?ref_=dbs_p_mng_rwt_ser_shvlr&storeType=ebooks

Series D: Immunology ($325)

https://www.amazon.com/gp/product/B08VVWTNR4?ref_=dbs_p_pwh_rwt_anx_b_lnk&storeType=ebooks

Series E: Patient-Centered Medicine ($274)

https://www.amazon.com/gp/product/B0BSDW2K6C?ref_=dbs_p_mng_rwt_ser_shvlr&storeType=ebooks

 

These e-Books are  a comprehensive review of recent original research on Cardiovascular, Genomics, Cancer, Immunology and Precision Medicine written by Experts, Authors, Writers.  These e-books are ushered in the paradigm shifts in how we think about, diagnoses, diagnostics and therapeutics. The results of original research are gaining value added for the e-Reader by the Methodology of Curation. These Biomedical e-Books are well suited for medical and graduate school course curriculum development and as medical textbooks. 

All the articles in these e-books were published in

http://pharmaceuticalintelligence.com

an Open Access Online Scientific Journal is a scientific, medical and business multi expert authoring environment in several domains of  life sciences, pharmaceutical, healthcare & medicine industries. This journal was launched in 4/2012 by Dr. Aviva lev-Ari, PhD, RN who serves as the Editor-in-Chief to the Present.

The venture, DBA, LPBI Group operates as an online scientific intellectual exchange at their website http://pharmaceuticalintelligence.com and had published 37 Medical e-book in five Bilingual e-Series as Amazon’s Kindle Edition.

Crucial to innovation in the medical field is overcoming the scientific information overload. LPBI Group innovates by:

  • delivering curation and clinical interpretations of latest biological scientific findings with future goals of providing concept-driven search across the journal, the books and the conference e-proceedings on a networked blockchain platform. The curation methodology offers academic, clinical, and industrial research digital content globally accessible on the Internet and Web 3.0
  • providing a social platform for scientists and clinicians to enter into discussion using social media as evidenced by multiple comments to journal articles
  • continued updating to the article repository keeps the e-books content current. All new articles on these subjects, will continue to be incorporated, as published with periodical updates.
  • incorporation of information from conference covered in real time by EAWs
  • projects on Text analysis with machine learning
  • software application to drug discovery in the Galectins domain

 

2022 FDA Drug Approval List, 2022 Biological Approvals and Approved Cellular and Gene Therapy Products

Reporter: Aviva Lev-Ari, PhD, RN

SOURCE

Tal Bahar’s post on LinkedIn on 1/17/2023

Novel Drug Approvals for 2022

FDA’s Center for Drug Evaluation and Research (CDER)

New Molecular Entities (“NMEs”)

  • Some of these products have never been used in clinical practice. Below is a listing of new molecular entities and new therapeutic biological products that CDER approved in 2022. This listing does not contain vaccines, allergenic products, blood and blood products, plasma derivatives, cellular and gene therapy products, or other products that the Center for Biologics Evaluation and Research approved in 2022. 
  • Others are the same as, or related to, previously approved products, and they will compete with those products in the marketplace. See Drugs@FDA for information about all of CDER’s approved drugs and biological products. 

Certain drugs are classified as new molecular entities (“NMEs”) for purposes of FDA review. Many of these products contain active moieties that FDA had not previously approved, either as a single ingredient drug or as part of a combination product. These products frequently provide important new therapies for patients. Some drugs are characterized as NMEs for administrative purposes, but nonetheless contain active moieties that are closely related to active moieties in products that FDA has previously approved. FDA’s classification of a drug as an “NME” for review purposes is distinct from FDA’s determination of whether a drug product is a “new chemical entity” or “NCE” within the meaning of the Federal Food, Drug, and Cosmetic Act. 

INNOVATION   PREDICTABILITY   ACCESS FDA’s Center for Drug Evaluation and Research

January 2023

Table of Contents

 SOURCE

2022 Biological Approvals

The Center for Biologics Evaluation and Research (CBER) regulates products under a variety of regulatory authorities.  See the Development & Approval Process page for a description of what products are approved as Biologics License Applications (BLAs), Premarket Approvals (PMAs), New Drug Applications (NDAs) or 510Ks.

Biologics License Applications and Supplements

New BLAs (except those for blood banking), and BLA supplements that are expected to significantly enhance the public health (e.g., for new/expanded indications, new routes of administration, new dosage formulations and improved safety).

Other Applications Approved or Cleared by the Center for Biologics Evaluation and Research (CBER)

Medical devices involved in the collection, processing, testing, manufacture and administration of licensed blood, blood components and cellular products.

Key Resources

SOURCE

https://www.fda.gov/vaccines-blood-biologics/development-approval-process-cber/2022-biological-approvals

 

Approved Cellular and Gene Therapy Products

Below is a list of licensed products from the Office of Tissues and Advanced Therapies (OTAT).


Approved Products


 

Resources For You


SOURCE

https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products/approved-cellular-and-gene-therapy-products

 

2022 forecast: Cell, gene therapy makers push past regulatory, payer hurdles to set up high hopes for next year

There are five FDA-approved CAR-T treatments for blood cancers and two gene therapies to treat rare diseases now on the market in the U.S. The late-stage pipeline could produce several more cancer CAR-Ts and gene therapies to treat a range of diseases.

RELATED: ASH: Bristol Myers’ Breyanzi, Gilead’s Yescarta lock horns in race to move CAR-T therapy to earlier lymphoma

One of the biggest races to watch in the cell therapy space will be that between Gilead Sciences’ Yescarta and Bristol Myers Squibb’s Breyanzi, both of which are gunning to move their CAR-Ts into earlier lines of treatment in large B-cell lymphoma (LBCL). At ASH, both companies rolled out impressive data from their trials in the second-line setting, but Gilead could have the upper hand by virtue of its three-year head start in the market, analysts said. Gilead expects to hear from the FDA on a label expansion in the second-line setting in April.

Verily announced other organizational changes, 1/13/2023

Reporter: Aviva Lev-Ari, PhD, RN

The layoffs come just a few months after Verily raised $1 billion in an investment round led by Alphabet. At the time of the investment round, Verily said the $1 billion would be used to expand its business in precision health. 

In addition to the layoffs, Verily announced other organizational changes.

“We are making changes that refine our strategy, prioritize our product portfolio and simplify our operating model,” Gillett said in his email. “We will advance fewer initiatives with greater resources. In doing so, Verily will move from multiple lines of business to one centralized product organization with increasingly connected healthcare solutions.”

The company will specifically focus on AI and data science to accelerate learning and improving outcomes, with advancing precision health being the top overarching goal. In addition, the company will simplify how it works, “designing complexity out of Verily.” 

Among its product portfolio, Verily plans to “do fewer things” and focus its efforts within research and care. The company is “discontinuing the development of Verily Value Suite and some early-stage products, including our work in remote patient monitoring for heart failure and microneedles for drug delivery,” Gillet said. By eliminating Verily Value Suite, some staff will be redeployed elsewhere, while others will leave the company, Gillet said.

The 15% of eliminated staff include roles within discontinued programs and redundancy within the new, simplified organization. Gillet also announced leadership changes, including expanding the role of Amy Abernethy to become president of product development and chief medical officer. Scott Burke will expand his responsibilities as chief technology officer, adding hardware engineering and devices teams to his responsibilities, as well as serving as the bridge between product development and customer needs. Lisa Greenbaum will expand her responsibilities in a new chief commercial officer role, overseeing sales, marketing and corporate strategy teams.

Related Content

Google Health partners with iCAD in commercial AI imaging push
Former Google company Verily raises $1B
Google Health is no more?
Google’s Verily enters drug trials with big pharma
Google, Verily’s diabetes machine learning algorithm gets clinical testing
Walgreens teams up with Verily to tackle chronic conditions

SOURCE

https://healthexec.com/topics/patient-care/precision-medicine/verily-lays-15-workers-months-after-raising-1b?utm_source=newsletter&utm_medium=he_news

Use of Systems Biology for Design of inhibitor of Galectins as Cancer Therapeutic – Strategy and Software

Curator: Stephen J. Williams, Ph.D.

Below is a slide representation of the overall mission 4 to produce a PROTAC to inhibit Galectins 1, 3, and 9.

 

Using A Priori Knowledge of Galectin Receptor Interaction to Create a BioModel of Galectin 3 Binding

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Now after collecting literature from PubMed on “galectin-3” AND “binding” to determine literature containing kinetic data we generate a WordCloud on the articles.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

This following file contains the articles needed for BioModels generation.

https://pharmaceuticalintelligence.com/wp-content/uploads/2022/12/Curating-Galectin-articles-for-Biomodels.docx

 

From the WordCloud we can see that these corpus of articles describe galectin binding to the CRD (carbohydrate recognition domain).  Interestingly there are many articles which describe van Der Waals interactions as well as electrostatic interactions.  Certain carbohydrate modifictions like Lac NAc and Gal 1,4 may be important.  Many articles describe the bonding as well as surface  interactions.  Many studies have been performed with galectin inhibitors like TDGs (thio-digalactosides) like TAZ TDG (3-deoxy-3-(4-[m-fluorophenyl]-1H-1,2,3-triazol-1-yl)-thio-digalactoside).  This led to an interesting article

Dual thio-digalactoside-binding modes of human galectins as the structural basis for the design of potent and selective inhibitors

Affiliations 2016 Jul 15;6:29457.
 doi: 10.1038/srep29457. Free PMC article

Abstract

Human galectins are promising targets for cancer immunotherapeutic and fibrotic disease-related drugs. We report herein the binding interactions of three thio-digalactosides (TDGs) including TDG itself, TD139 (3,3′-deoxy-3,3′-bis-(4-[m-fluorophenyl]-1H-1,2,3-triazol-1-yl)-thio-digalactoside, recently approved for the treatment of idiopathic pulmonary fibrosis), and TAZTDG (3-deoxy-3-(4-[m-fluorophenyl]-1H-1,2,3-triazol-1-yl)-thio-digalactoside) with human galectins-1, -3 and -7 as assessed by X-ray crystallography, isothermal titration calorimetry and NMR spectroscopy. Five binding subsites (A-E) make up the carbohydrate-recognition domains of these galectins. We identified novel interactions between an arginine within subsite E of the galectins and an arene group in the ligands. In addition to the interactions contributed by the galactosyl sugar residues bound at subsites C and D, the fluorophenyl group of TAZTDG preferentially bound to subsite B in galectin-3, whereas the same group favored binding at subsite E in galectins-1 and -7. The characterised dual binding modes demonstrate how binding potency, reported as decreased Kd values of the TDG inhibitors from μM to nM, is improved and also offer insights to development of selective inhibitors for individual galectins.

Figures

Figure 1
 
Figure 2
 
Figure 3

 

 

Science Has A Systemic Problem, Not an Innovation Problem

Curator: Stephen J. Williams, Ph.D.

    A recent email, asking me to submit a survey, got me thinking about the malaise that scientists and industry professionals frequently bemoan: that innovation has been stymied for some reason and all sorts of convuluted processes must be altered to spur this mythical void of great new discoveries…..  and it got me thinking about our current state of science, and what is the perceived issue… and if this desert of innovation actually exists or is more a fundamental problem which we have created.

The email was from an NIH committee asking for opinions on recreating the grant review process …. now this on the same day someone complained to me about a shoddy and perplexing grant review they received.

The following email, which was sent out to multiple researchers, involved in either NIH grant review on both sides, as well as those who had been involved in previous questionnaires and studies on grant review and bias.  The email asked for researchers to fill out a survey on the grant review process, and how to best change it to increase innovation of ideas as well as inclusivity.  In recent years, there have been multiple survey requests on these matters, with multiple confusing procedural changes to grant format and content requirements, adding more administrative burden to scientists.

The email from Center for Scientific Review (one of the divisions a grant will go to before review {they set up review study sections and decide what section a grant should be  assigned to} was as follows:

Update on Simplifying Review Criteria: A Request for Information

https://www.csr.nih.gov/reviewmatters/2022/12/08/update-on-simplifying-review-criteria-a-request-for-information/

NIH has issued a request for information (RFI) seeking feedback on revising and simplifying the peer review framework for research project grant applications. The goal of this effort is to facilitate the mission of scientific peer review – identification of the strongest, highest-impact research. The proposed changes will allow peer reviewers to focus on scientific merit by evaluating 1) the scientific impact, research rigor, and feasibility of the proposed research without the distraction of administrative questions and 2) whether or not appropriate expertise and resources are available to conduct the research, thus mitigating the undue influence of the reputation of the institution or investigator.

Currently, applications for research project grants (RPGs, such as R01s, R03s, R15s, R21s, R34s) are evaluated based on five scored criteria: Significance, Investigators, Innovation, Approach, and Environment (derived from NIH peer review regulations 42 C.F.R. Part 52h.8; see Definitions of Criteria and Considerations for Research Project Grant Critiques for more detail) and a number of additional review criteria such as Human Subject Protections.

NIH gathered input from the community to identify potential revisions to the review framework. Given longstanding and often-heard concerns from diverse groups, CSR decided to form two working groups to the CSR Advisory Council—one on non-clinical trials and one on clinical trials. To inform these groups, CSR published a Review Matters blog, which was cross-posted on the Office of Extramural Research blog, Open Mike. The blog received more than 9,000 views by unique individuals and over 400 comments. Interim recommendations were presented to the CSR Advisory Council in a public forum (March 2020 videoslides; March 2021 videoslides). Final recommendations from the CSRAC (report) were considered by the major extramural committees of the NIH that included leadership from across NIH institutes and centers. Additional background information can be found here. This process produced many modifications and the final proposal presented below. Discussions are underway to incorporate consideration of a Plan for Enhancing Diverse Perspectives (PEDP) and rigorous review of clinical trials RPGs (~10% of RPGs are clinical trials) within the proposed framework.

Simplified Review Criteria

NIH proposes to reorganize the five review criteria into three factors, with Factors 1 and 2 receiving a numerical score. Reviewers will be instructed to consider all three factors (Factors 1, 2 and 3) in arriving at their Overall Impact Score (scored 1-9), reflecting the overall scientific and technical merit of the application.

  • Factor 1: Importance of the Research (Significance, Innovation), numerical score (1-9)
  • Factor 2: Rigor and Feasibility (Approach), numerical score (1-9)
  • Factor 3: Expertise and Resources (Investigator, Environment), assessed and considered in the Overall Impact Score, but not individually scored

Within Factor 3 (Expertise and Resources), Investigator and Environment will be assessed in the context of the research proposed. Investigator(s) will be rated as “fully capable” or “additional expertise/capability needed”. Environment will be rated as “appropriate” or “additional resources needed.” If a need for additional expertise or resources is identified, written justification must be provided. Detailed descriptions of the three factors can be found here.

Now looking at some of the Comments were very illuminating:

I strongly support streamlining the five current main review criteria into three, and the present five additional criteria into two. This will bring clarity to applicants and reduce the workload on both applicants and reviewers. Blinding reviewers to the applicants’ identities and institutions would be a helpful next step, and would do much to reduce the “rich-getting-richer” / “good ole girls and good ole boys” / “big science” elitism that plagues the present review system, wherein pedigree and connections often outweigh substance and creativity.

I support the proposed changes. The shift away from “innovation” will help reduce the tendency to create hype around a proposed research direction. The shift away from Investigator and Environment assessments will help reduce bias toward already funded investigators in large well-known institutions.

As a reviewer for 5 years, I believe that the proposed changes are a step in the right direction, refocusing the review on whether the science SHOULD be done and whether it CAN BE DONE WELL, while eliminating burdensome and unhelpful sections of review that are better handled administratively. I particularly believe that the de-emphasis of innovation (which typically focuses on technical innovation) will improve evaluation of the overall science, and de-emphasis of review of minor technical details will, if implemented correctly, reduce the “downward pull” on scores for approach. The above comments reference blinded reviews, but I did not see this in the proposed recommendations. I do not believe this is a good idea for several reasons: 1) Blinding of the applicant and institution is not likely feasible for many of the reasons others have described (e.g., self-referencing of prior work), 2) Blinding would eliminate the potential to review investigators’ biosketches and budget justifications, which are critically important in review, 3) Making review blinded would make determination of conflicts of interest harder to identify and avoid, 4) Evaluation of “Investigator and Environment” would be nearly impossible.

Most of the Comments were in favor of the proposed changes, however many admitted that it adds additional confusion on top of many administrative changes to formats and content of grant sections.

Being a Stephen Covey devotee, and just have listened to  The Four Principles of Execution, it became more apparent that issues that hinder many great ideas coming into fruition, especially in science, is a result of these systemic or problems in the process, not at the level of individual researchers or small companies trying to get their innovations funded or noticed.  In summary, Dr. Covey states most issues related to the success of any initiative is NOT in the strategic planning, but in the failure to adhere to a few EXECUTION principles.  Primary to these failures of strategic plans is lack of accounting of what Dr. Covey calls the ‘whirlwind’, or those important but recurring tasks that take us away from achieving the wildly important goals.  In addition, lack of  determining lead and lag measures of success hinder such plans.

In this case a lag measure in INNOVATION.  It appears we have created such a whirlwind and focus on lag measures that we are incapable of translating great discoveries into INNOVATION.

In the following post, I will focus on issues relating to Open Access, publishing and dissemination of scientific discovery may be costing us TIME to INNOVATION.  And it appears that there are systemic reasons why we appear stuck in a rut, so to speak.

The first indication is from a paper published by Johan Chu and James Evans in 2021 in PNAS:

 

Slowed canonical progress in large fields of science

Chu JSG, Evans JA. Slowed canonical progress in large fields of science. Proc Natl Acad Sci U S A. 2021 Oct 12;118(41):e2021636118. doi: 10.1073/pnas.2021636118. PMID: 34607941; PMCID: PMC8522281

 

Abstract

In many academic fields, the number of papers published each year has increased significantly over time. Policy measures aim to increase the quantity of scientists, research funding, and scientific output, which is measured by the number of papers produced. These quantitative metrics determine the career trajectories of scholars and evaluations of academic departments, institutions, and nations. Whether and how these increases in the numbers of scientists and papers translate into advances in knowledge is unclear, however. Here, we first lay out a theoretical argument for why too many papers published each year in a field can lead to stagnation rather than advance. The deluge of new papers may deprive reviewers and readers the cognitive slack required to fully recognize and understand novel ideas. Competition among many new ideas may prevent the gradual accumulation of focused attention on a promising new idea. Then, we show data supporting the predictions of this theory. When the number of papers published per year in a scientific field grows large, citations flow disproportionately to already well-cited papers; the list of most-cited papers ossifies; new papers are unlikely to ever become highly cited, and when they do, it is not through a gradual, cumulative process of attention gathering; and newly published papers become unlikely to disrupt existing work. These findings suggest that the progress of large scientific fields may be slowed, trapped in existing canon. Policy measures shifting how scientific work is produced, disseminated, consumed, and rewarded may be called for to push fields into new, more fertile areas of study.

So the Summary of this paper is

  • The authors examined 1.8 billion citations among 90 million papers over 241 subjects
  • found the corpus of papers do not lead to turnover of new ideas in a field, but rather the ossification or entrenchment of canonical (or older ideas)
  • this is mainly due to older paper cited more frequently than new papers with new ideas, potentially because authors are trying to get their own papers cited more frequently for funding and exposure purposes
  • The authors suggest that “fundamental progress may be stymied if quantitative growth of scientific endeavors is not balanced by structures fostering disruptive scholarship and focusing attention of novel ideas”

The authors note that, in most cases, science policy reinforces this “more is better” philosophy”,  where metrics of publication productivity are either number of publications or impact measured by citation rankings.  However, using an analysis of citation changes occurring in large versus smaller fields, it becomes apparent that this process is favoring the older, more established papers and a recirculating of older canonical ideas.

“Rather than resulting in faster turnover of field paradigms, the massive amounts of new publications entrenches the ideas of top-cited papers.”  New ideas are pushed down to the bottom of the citation list and potentially lost in the literature.  The authors suggest that this problem will intensify as the “annual mass” of new publications in each field grows, especially in large fields.  This issue is exacerbated by the deluge on new online ‘open access’ journals, in which authors would focus on citing the more highly cited literature. 

We maybe at a critical junction, where if many papers are published in a short time, new ideas will not be considered as carefully as the older ideas.  In addition,

with proliferation of journals and the blurring of journal hierarchies due to online articles-level access can exacerbate this problem

As a counterpoint, the authors do note that even though many molecular biology highly cited articles were done in 1976, there has been extremely much innovation since then however it may take a lot more in experiments and money to gain the level of citations that those papers produced, and hence a lower scientific productivity.

This issue is seen in the field of economics as well

Ellison, Glenn. “Is peer review in decline?” Economic Inquiry, vol. 49, no. 3, July 2011, pp. 635+. Gale Academic OneFile, link.gale.com/apps/doc/A261386330/AONE?u=temple_main&sid=bookmark-AONE&xid=f5891002. Accessed 12 Dec. 2022.

Abstract

Over the past decade, there has been a decline in the fraction of papers in top economics journals written by economists from the highest-ranked economics departments. This paper documents this fact and uses additional data on publications and citations to assess various potential explanations. Several observations are consistent with the hypothesis that the Internet improves the ability of high-profile authors to disseminate their research without going through the traditional peer-review process. (JEL A14, 030)

The facts part of this paper documents two main facts:

1. Economists in top-ranked departments now publish very few papers in top field journals. There is a marked decline in such publications between the early 1990s and early 2000s.

2. Comparing the early 2000s with the early 1990s, there is a decline in both the absolute number of papers and the share of papers in the top general interest journals written by Harvard economics department faculty.

Although the second fact just concerns one department, I see it as potentially important to understanding what is happening because it comes at a time when Harvard is widely regarded (I believe correctly) as having ascended to the top position in the profession.

The “decline-of-peer-review” theory I allude to in the title is that the necessity of going through the peer-review process has lessened for high-status authors: in the old days peer-reviewed journals were by far the most effective means of reaching readers, whereas with the growth of the Internet high-status authors can now post papers online and exploit their reputation to attract readers.

Many alternate explanations are possible. I focus on four theories: the decline-in-peer-review theory and three alternatives.

1. The trends could be a consequence of top-school authors’ being crowded out of the top journals by other researchers. Several such stories have an optimistic message, for example, there is more talent entering the profession, old pro-elite biases are being broken down, more schools are encouraging faculty to do cutting-edge research, and the Internet is enabling more cutting-edge research by breaking down informational barriers that had hampered researchers outside the top schools. (2)

2. The trends could be a consequence of the growth of revisions at economics journals discussed in Ellison (2002a, 2002b). In this more pessimistic theory, highly productive researchers must abandon some projects and/or seek out faster outlets to conserve the time now required to publish their most important works.

3. The trends could simply reflect that field journals have declined in quality in some relative sense and become a less attractive place to publish. This theory is meant to encompass also the rise of new journals, which is not obviously desirable or undesirable.

The majority of this paper is devoted to examining various data sources that provide additional details about how economics publishing has changed over the past decade. These are intended both to sharpen understanding of the facts to be explained and to provide tests of auxiliary predictions of the theories. Two main sources of information are used: data on publications and data on citations. The publication data include department-level counts of publications in various additional journals, an individual-level dataset containing records of publications in a subset of journals for thousands of economists, and a very small dataset containing complete data on a few authors’ publication records. The citation data include citations at the paper level for 9,000 published papers and less well-matched data that is used to construct measures of citations to authors’ unpublished works, to departments as a whole, and to various journals.

Inside Job or Deep Impact? Extramural Citations and the Influence of Economic Scholarship

Josh Angrist, Pierre Azoulay, Glenn Ellison, Ryan Hill, Susan Feng Lu. Inside Job or Deep Impact? Extramural Citations and the Influence of Economic Scholarship.

JOURNAL OF ECONOMIC LITERATURE

VOL. 58, NO. 1, MARCH 2020

(pp. 3-52)

So if innovation is there but it may be buried under the massive amount of heavily cited older literature, do we see evidence of this in other fields like medicine?

Why Isn’t Innovation Helping Reduce Health Care Costs?

 
 

National health care expenditures (NHEs) in the United States continue to grow at rates outpacing the broader economy: Inflation- and population-adjusted NHEs have increased 1.6 percent faster than the gross domestic product (GDP) between 1990 and 2018. US national health expenditure growth as a share of GDP far outpaces comparable nations in the Organization for Economic Cooperation and Development (17.2 versus 8.9 percent).

Multiple recent analyses have proposed that growth in the prices and intensity of US health care services—rather than in utilization rates or demographic characteristics—is responsible for the disproportionate increases in NHEs relative to global counterparts. The consequences of ever-rising costs amid ubiquitous underinsurance in the US include price-induced deferral of care leading to excess morbidity relative to comparable nations.

These patterns exist despite a robust innovation ecosystem in US health care—implying that novel technologies, in isolation, are insufficient to bend the health care cost curve. Indeed, studies have documented that novel technologies directly increase expenditure growth.

Why is our prolific innovation ecosystem not helping reduce costs? The core issue relates to its apparent failure to enhance net productivity—the relative output generated per unit resource required. In this post, we decompose the concept of innovation to highlight situations in which inventions may not increase net productivity. We begin by describing how this issue has taken on increased urgency amid resource constraints magnified by the COVID-19 pandemic. In turn, we describe incentives for the pervasiveness of productivity-diminishing innovations. Finally, we provide recommendations to promote opportunities for low-cost innovation.

 

 

Net Productivity During The COVID-19 Pandemic

The issue of productivity-enhancing innovation is timely, as health care systems have been overwhelmed by COVID-19. Hospitals in Italy, New York City, and elsewhere have lacked adequate capital resources to care for patients with the disease, sufficient liquidity to invest in sorely needed resources, and enough staff to perform all of the necessary tasks.

The critical constraint in these settings is not technology: In fact, the most advanced technology required to routinely treat COVID-19—the mechanical ventilator—was invented nearly 100 years ago in response to polio (the so-called iron lung). Rather, the bottleneck relates to the total financial and human resources required to use the technology—the denominator of net productivity. The clinical implementation of ventilators has been illustrative: Health care workers are still required to operate ventilators on a nearly one-to-one basis, just like in the mid-twentieth century. 

High levels of resources required for implementation of health care technologies constrain the scalability of patient care—such as during respiratory disease outbreaks such as COVID-19. Thus, research to reduce health care costs is the same kind of research we urgently require to promote health care access for patients with COVID-19.

Types Of Innovation And Their Relationship To Expenditure Growth

The widespread use of novel medical technologies has been highlighted as a central driver of NHE growth in the US. We believe that the continued expansion of health care costs is largely the result of innovation that tends to have low productivity (exhibit 1). We argue that these archetypes—novel widgets tacked on to existing workflows to reinforce traditional care models—are exactly the wrong properties to reduce NHEs at the systemic level.

Exhibit 1: Relative productivity of innovation subtypes

Source: Authors’ analysis.

Content Versus Process Innovation

Content (also called technical) innovation refers to the creation of new widgets, such as biochemical agents, diagnostic tools, or therapeutic interventions. Contemporary examples of content innovation include specialty pharmaceuticalsmolecular diagnostics, and advanced interventions and imaging.

These may be contrasted with process innovations, which address the organized sequences of activities that implement content. Classically, these include clinical pathways and protocols. They can address the delivery of care for acute conditions, such as central line infections, sepsis, or natural disasters. Alternatively, they can target chronic conditions through initiatives such as team-based management of hypertension and hospital-at-home models for geriatric care. Other processes include hiring staffdelegating labor, and supply chain management.

Performance-Enhancing Versus Cost-Reducing Innovation

Performance-enhancing innovations frequently create incremental outcome gains in diagnostic characteristics, such as sensitivity or specificity, or in therapeutic characteristics, such as biomarkers for disease status. Their performance gains often lead to higher prices compared to existing alternatives.  

Performance-enhancing innovations can be compared to “non-inferior” innovations capable of achieving outcomes approximating those of existing alternatives, but at reduced cost. Industries outside of medicine, such as the computing industry, have relied heavily on the ability to reduce costs while retaining performance.

In health care though, this pattern of innovation is rare. Since passage of the 2010 “Biosimilars” Act aimed at stimulating non-inferior innovation and competition in therapeutics markets, only 17 agents have been approved, and only seven have made it to market. More than three-quarters of all drugs receiving new patents between 2005 and 2015 were “reissues,” meaning they had already been approved, and the new patent reflected changes to the previously approved formula. Meanwhile, the costs of approved drugs have increased over time, at rates between 4 percent and 7 percent annually.

Moreover, the preponderance of performance-enhancing diagnostic and therapeutic innovations tend to address narrow patient cohorts (such as rare diseases or cancer subtypes), with limited clear clinical utility in broader populations. For example, the recently approved eculizimab is a monoclonal antibody approved for paroxysmal nocturnal hemoglobinuria—which effects 1 in 10 million individuals. At the time of its launch, eculizimab was priced at more than $400,000 per year, making it the most expensive drug in modern history. For clinical populations with no available alternatives, drugs such as eculizimab may be cost-effective, pending society’s willingness to pay, and morally desirable, given a society’s values. But such drugs are certainly not cost-reducing.

Additive Versus Substitutive Innovation

Additive innovations are those that append to preexisting workflows, while substitutive innovations reconfigure preexisting workflows. In this way, additive innovations increase the use of precedent services, whereas substitutive innovations decrease precedent service use.

For example, previous analyses have found that novel imaging modalities are additive innovations, as they tend not to diminish use of preexisting modalities. Similarly, novel procedures tend to incompletely replace traditional procedures. In the case of therapeutics and devices, off-label uses in disease groups outside of the approved indication(s) can prompt innovation that is additive. This is especially true, given that off-label prescriptions classically occur after approved methods are exhausted.

Eculizimab once again provides an illustrative example. As of February 2019, the drug had been used for 39 indications (it had been approved for three of those, by that time), 69 percent of which lacked any form of evidence of real-world effectiveness. Meanwhile, the drug generated nearly $4 billion in sales in 2019. Again, these expenditures may be something for which society chooses to pay—but they are nonetheless additive, rather than substitutive.

Sustaining Versus Disruptive Innovation

Competitive market theory suggests that incumbents and disruptors innovate differently. Incumbents seek sustaining innovations capable of perpetuating their dominance, whereas disruptors pursue innovations capable of redefining traditional business models.

In health care, while disruptive innovations hold the potential to reduce overall health expenditures, often they run counter to the capabilities of market incumbents. For example, telemedicine can deliver care asynchronously, remotely, and virtually, but large-scale brick-and-mortar medical facilities invest enormous capital in the delivery of synchronous, in-house, in-person care (incentivized by facility fees).

The connection between incumbent business models and the innovation pipeline is particularly relevant given that 58 percent of total funding for biomedical research in the US is now derived from private entities, compared with 46 percent a decade prior. It follows that the growing influence of eminent private organizations may favor innovations supporting their market dominance—rather than innovations that are societally optimal.

Incentives And Repercussions Of High-Cost Innovation

Taken together, these observations suggest that innovation in health care is preferentially designed for revenue expansion rather than for cost reduction. While offering incremental improvements in patient outcomes, therefore creating theoretical value for society, these innovations rarely deliver incremental reductions in short- or long-term costs at the health system level.

For example, content-based, performance-enhancing, additive, sustaining innovations tend to add layers of complexity to the health care system—which in turn require additional administration to manage. The net result is employment growth in excess of outcome improvement, leading to productivity losses. This gap leads to continuously increasing overall expenditures in turn passed along to payers and consumers.

Nonetheless, high-cost innovations are incentivized across health care stakeholders (exhibit 2). From the supply side of innovation, for academic researchers, “breakthrough” and “groundbreaking” innovations constitute the basis for career advancement via funding and tenure. This is despite stakeholders’ frequent inability to generalize early successes to become cost-effective in the clinical setting. As previously discussed, the increasing influence of private entities in setting the medical research agenda is also likely to stimulate innovation benefitting single stakeholders rather than the system.

Exhibit 2: Incentives promoting low-value innovation

Source: Authors’ analysis adapted from Hofmann BM. Too much technology. BMJ. 2015 Feb 16.

From the demand side of innovation (providers and health systems), a combined allure (to provide “cutting-edge” patient care), imperative (to leave “no stone unturned” in patient care), and profit-motive (to amplify fee-for-service reimbursements) spur participation in a “technological arms-race.” The status quo thus remains as Clay Christensen has written: “Our major health care institutions…together overshoot the level of care actually needed or used by the vast majority of patients.”

Christensen’s observations have been validated during the COVID-19 epidemic, as treatment of the disease requires predominantly century-old technology. By continually adopting innovation that routinely overshoots the needs of most patients, layer by layer, health care institutions are accruing costs that quickly become the burden of society writ large.

Recommendations To Reduce The Costs Of Health Care Innovation

Henry Aaron wrote in 2002 that “…the forces that have driven up costs are, if anything, intensifying. The staggering fecundity of biomedical research is increasing…[and] always raises expenditures.” With NHEs spiraling ever-higher, urgency to “bend the cost curve” is mounting. Yet, since much biomedical innovation targets the “flat of the [productivity] curve,” alternative forms of innovation are necessary.

The shortcomings in net productivity revealed by the COVID-19 pandemic highlight the urgent need for redesign of health care delivery in this country, and reevaluation of the innovation needed to support it. Specifically, efforts supporting process redesign are critical to promote cost-reducing, substitutive innovations that can inaugurate new and disruptive business models.

Process redesign rarely involves novel gizmos, so much as rejiggering the wiring of, and connections between, existing gadgets. It targets operational changes capable of streamlining workflows, rather than technical advancements that complicate them. As described above, precisely these sorts of “frugal innovations” have led to productivity improvements yielding lower costs in other high-technology industries, such as the computing industry.

Shrank and colleagues recently estimated that nearly one-third of NHEs—almost $1 trillion—were due to preventable waste. Four of the six categories of waste enumerated by the authors—failure in care delivery, failure in care coordination, low-value care, and administrative complexity—represent ripe targets for process innovation, accounting for $610 billion in waste annually, according to Shrank.

Health systems adopting process redesign methods such as continuous improvement and value-based management have exhibited outcome enhancement and expense reduction simultaneously. Internal processes addressed have included supply chain reconfiguration, operational redesign, outlier reconciliation, and resource standardization.

Despite the potential of process innovation, focus on this area (often bundled into “health services” or “quality improvement” research) occupies only a minute fraction of wallet- or mind-share in the biomedical research landscape, accounting for 0.3 percent of research dollars in medicine. This may be due to a variety of barriers beyond minimal funding. One set of barriers is academic, relating to negative perceptions around rigor and a lack of outlets in which to publish quality improvement research. To achieve health care cost containment over the long term, this dimension of innovation must be destigmatized relative to more traditional manners of innovation by the funders and institutions determining the conditions of the research ecosystem.

Another set of barriers is financial: Innovations yielding cost reduction are less “reimbursable” than are innovations fashioned for revenue expansion. This is especially the case in a fee-for-service system where reimbursement is tethered to cost, which creates perverse incentives for health care institutions to overlook cost increases. However, institutions investing in low-cost innovation will be well-positioned in a rapidly approaching future of value-based care—in which the solvency of health care institutions will rely upon their ability to provide economically efficient care.

Innovating For Cost Control Necessitates Frugality Over Novelty

Restraining US NHEs represents a critical step toward health promotion. Innovation for innovation’s sake—that is content-based, incrementally effective, additive, and sustaining—is unlikely to constrain continually expanding NHEs.

In contrast, process innovation offers opportunities to reduce costs while maintaining high standards of patient care. As COVID-19 stress-tests health care systems across the world, the importance of cost control and productivity amplification for patient care has become apparent.

As such, frugality, rather than novelty, may hold the key to health care cost containment. Redesigning the innovation agenda to stem the tide of ever-rising NHEs is an essential strategy to promote widespread access to care—as well as high-value preventive care—in this country. In the words of investors across Silicon Valley: Cost-reducing innovation is no longer a “nice-to-have,” but a “need-to-have” for the future of health and overall well-being this country.

So Do We Need A New Way of Disseminating Scientific Information?  Can Curation Help?

We had high hopes for Science 2.0, in particular the smashing of data and knowledge silos. However the digital age along with 2.0 platforms seemed to excaccerbate this somehow. We still are critically short on analysis!



Old Science 1.0 is still the backbone of all scientific discourse, built on the massive amount of experimental and review literature. However this literature was in analog format, and we moved to a more accesible digital open access format for both publications as well as raw data. However as there was a structure for 1.0, like the Dewey decimal system and indexing, 2.0 made science more accesible and easier to search due to the newer digital formats. Yet both needed an organizing structure; for 1.0 that was the scientific method of data and literature organization with libraries as the indexers. In 2.0 this relied on an army mostly of volunteers who did not have much in the way of incentivization to co-curate and organize the findings and massive literature.



The Intenet and the Web is rapidly adopting a new “Web 3.0” format, with decentralized networks, enhanced virtual experiences, and greater interconnection between people. Here we start the discussion what will the move from Science 2.0, where dissemination of scientific findings was revolutionized and piggybacking on Web 2.0 or social media, to a Science 3.0 format. And what will it involve or what paradigms will be turned upside down?

We have discussed this in other posts such as

Will Web 3.0 Do Away With Science 2.0? Is Science Falling Behind?

and

Curation Methodology – Digital Communication Technology to mitigate Published Information Explosion and Obsolescence in Medicine and Life Sciences

For years the pharmaceutical industry has toyed with the idea of making innovation networks and innovation hubs

It has been the main focus of whole conferences

Tales from the Translational Frontier – Four Unique Approaches to Turning Novel Biology into Investable Innovations @BIOConvention #BIO2018

However it still seems these strategies have not worked

Is it because we did not have an Execution plan? Or we did not understand the lead measures for success?

Other Related Articles on this Open Access Scientific Journal Include:

Old Industrial Revolution Paradigm of Education Needs to End: How Scientific Curation Can Transform Education

Analysis of Utilizing LPBI Group’s Scientific Curation Platform as an Educational Tool: New Paradigm for Student Engagement

Global Alliance for Genomics and Health Issues Guidelines for Data Siloing and Sharing

Multiple Major Scientific Journals Will Fully Adopt Open Access Under Plan S

eScientific Publishing a Case in Point: Evolution of Platform Architecture Methodologies and of Intellectual Property Development (Content Creation by Curation) Business Model 

Chemistry Nobelist Carolyn Bertozzi’s years at UC Berkeley

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 12/8/2022

Watch the Nobel Prize lectures in chemistry

Watch now!

Carolyn R. BertozziThe Bioorthogonal Chemistry Journey, from Laboratory to Life
Morten MeldalMolecular Click Adventures, a Leap from Shoulders of Giants
K. Barry SharplessClick Chemistry: the Certainty of Chance

 

Press release: The Nobel Prize in Chemistry 2022

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English (pdf)
Swedish
Swedish (pdf)
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5 October 2022

The Royal Swedish Academy of Sciences has decided to award the Nobel Prize in Chemistry 2022 to

Carolyn R. Bertozzi
Stanford University, CA, USA

Morten Meldal
University of Copenhagen, Denmark

K. Barry Sharpless
Scripps Research, La Jolla, CA, USA

“for the development of click chemistry and bioorthogonal chemistry”

It just says click – and the molecules are coupled together

The Nobel Prize in Chemistry 2022 is about making difficult processes easier. Barry Sharpless and Morten Meldal have laid the foundation for a functional form of chemistry – click chemistry – in which molecular building blocks snap together quickly and efficiently. Carolyn Bertozzi has taken click chemistry to a new dimension and started utilising it in living organisms.

Chemists have long been driven by the desire to build increasingly complicated molecules. In pharmaceutical research, this has often involved artificially recreating natural molecules with medicinal properties. This has led to many admirable molecular constructions, but these are generally time consuming and very expensive to produce.

“This year’s Prize in Chemistry deals with not overcomplicating matters, instead working with what is easy and simple. Functional molecules can be built even by taking a straightforward route,” says Johan Åqvist, Chair of the Nobel Committee for Chemistry.

Barry Sharpless – who is now being awarded his second Nobel Prize in Chemistry – started the ball rolling. Around the year 2000, he coined the concept of click chemistry, which is a form of simple and reliable chemistry, where reactions occur quickly and unwanted by-products are avoided.

Shortly afterwards, Morten Meldal and Barry Sharpless – independently of each other – presented what is now the crown jewel of click chemistry: the copper catalysed azide-alkyne cycloaddition. This is an elegant and efficient chemical reaction that is now in widespread use. Among many other uses, it is utilised in the development of pharmaceuticals, for mapping DNA and creating materials that are more fit for purpose.

Carolyn Bertozzi took click chemistry to a new level. To map important but elusive biomolecules on the surface of cells – glycans – she developed click reactions that work inside living organisms. Her bioorthogonal reactions take place without disrupting the normal chemistry of the cell.

These reactions are now used globally to explore cells and track biological processes. Using bioorthogonal reactions, researchers have improved the targeting of cancer pharmaceuticals, which are now being tested in clinical trials.

Click chemistry and bioorthogonal reactions have taken chemistry into the era of functionalism. This is bringing the greatest benefit to humankind.

https://www.nobelprize.org/prizes/chemistry/2022/press-release/

 

Carolyn Bertozzi’s Years at Berkeley

By Robert Sanders, Media relations| OCTOBER 5, 2022

Carolyn Bertozzi as a young professor at UC Berkeley. (Photo courtesy of College of Chemistry)

Carolyn Bertozzi, a professor at Stanford University who today shared the 2022 Nobel Prize in Chemistry, spent her formative and most creative years at UC Berkeley.

After graduating from Harvard University in 1988, she earned her Ph.D. in chemistry from Berkeley in 1993 and, following postdoctoral and faculty positions elsewhere, returned to join the chemistry faculty and Berkeley Lab in 1996.

For 19 years, until 2015 — the year she left to help lead Stanford’s Sarafan ChEM-H institute — she developed at Berkeley the chemical biology techniques for which she received the Nobel Prize. She calls these techniques bioorthogonal chemistry, building off the “click chemistry” developed by her Nobel Prize co-winners, K. Barry Sharpless of Scripps Research in La Jolla, California, and Morten Meldal of the University of Copenhagen in Denmark.

Carolyn Bertozzi is a true trailblazer in chemical biology,” said Doug Clark, dean of the College of Chemistry. “Her lab is among the most prolific in the field, consistently producing innovative and enabling chemical approaches, inspired by organic synthesis, for the study of complex biomolecules in living cells. Carolyn’s work and spirit embody what is best about the scientific tradition and history of the College of Chemistry and of UC Berkeley.”

Carolyn Bertozzi, now the Anne T. and Robert M. Bass Professor in the School of Humanities and Sciences and a professor of chemistry at Stanford University. (Photo courtesy of Stanford University)

During a video press conference this morning from Stanford, Bertozzi, 55, described bioorthogonal chemistry as chemical reactions “not interacting with or interfering with biology.”

“What that means in practice is that we basically develop pairs of chemical groups, and those pairs of groups are perfectly suited for each other,” she said. “And when they encounter each other, they want to react and form a bond, and they love each other so much that you can surround those chemical groups with thousands of other chemicals — that’s what you have in biological systems, in your cells, in your body, there’s thousands of chemicals — but these two chemicals that are bioorthogonal will ignore all of that. And they’ll find each other and form a bond with each other, do chemistry with each other.”

Bertozzi’s rationale for developing these reactions was to study the sugars that coat the outside of cells — a field called glycobiology — that has been a passion of hers since her graduate student days at Berkeley. At Berkeley, she worked in the lab of Mark Bednarski, a young assistant professor and a rising star in the field of chemical biology, at the time a relatively new field in which the biochemical processes inside cells are manipulated and studied using techniques of organic chemistry.

In a 2011 interview, Bertozzi discussed the role Berkeley played in her career.

“I credit the UC Berkeley environment for catalyzing my interests in chemical biology and glycobiology from the outset, as I first learned about the opportunities in these fields as a graduate student in this very department,” she said. “I was encouraged to join the lab of a new professor, Mark Bednarski, and he introduced me to the chemistry and biology of sugars. I have been enraptured by this still-burgeoning area of science ever since, in light of the critical roles that sugars play in cell signaling, organ development, immunobiology and in numerous diseases.”

A friend and former colleague of Bertozzi’s at Berkeley, Matt Francis, now chair of the Department of Chemistry, was one of the first to congratulate Bertozzi today after the streamed announcement from Stockholm at 2:45 a.m. PDT, which he was watching. He immediately texted her congratulations.

Carolyn Bertozzi in 2001. (Photo credit: Peg Skorpinski)

“As soon as I heard her name in Swedish, I sent it, and I got an emoji back immediately — the shocked face emoji,” he said. “She’s a total rock star, and this is well deserved.”

Francis came to Berkeley in 2001, when Bertozzi was already well known for her research, and she was a critical academic mentor, he said.

“She did more than just do great science. She really mentored a lot of us who are on the faculty now and helped us get our groups off the ground and was always there to talk to us,” he said. “She was just a great colleague.”

She is equally known for mentoring students at both Berkeley and Stanford. She and Berkeley chemistry colleague Judith Klinman also were instrumental in establishing a chemical biology major within the chemistry department, which currently enrolls half the 480 undergraduates majoring in chemistry in the department.

During the Stanford press conference, Bertozzi explained what led to her Nobel Prize-winning work.

“Bioorthogonal chemistry was a tool that my lab created originally to study cell surface sugars — in fact, to image cell surface sugars using microscopes,” she said. “But then, it turned out to be so useful just as a platform for studying biology that lots of other labs picked up on it and started using those same chemistries to study other molecules, like proteins DNA and RNA. And they, and it turns out you, can study these molecules in live cells and in laboratory animals. And the most exciting development is now there’s a pharmaceutical company doing these chemistries inside the body of human cancer patients as a means to deliver drugs to cancers. So, the field has really progressed a long way in the last 25 years, and it’s very exciting for me to see this.”

She emphasized that her work built on that of co-winners Sharpless and Meldal.

“Before the advent of bioorthogonal chemistry and the related chemistry that professors Sharpless and Meldal developed, which they call click chemistry, there was really no way to study certain biological processes. They were just invisible to the scientists,” she said. “But these chemistries make those processes visible, and we have benefited from that — specifically, to study cell surface sugars.”

A photo of Carolyn Bertozzi taken the morning of Oct. 5, 2022, shortly after she heard that she had won the 2022 Nobel Prize in Chemistry. (Image credit: Andrew Brodhead)

The click chemistry reactions Sharpless and Meldal developed involved copper, however, which is often toxic to living cells. According to Francis, Bertozzi found a novel way around using copper.

“Carolyn’s lab came up with a way around it where they built strain into one of the molecules. In other words, they spring-loaded that molecule so it made it much more readily reactive without the copper,” he said. “And that is now what most people use to label live cell surfaces. It’s called strain promoted click chemistry. She really changed the way people think about the chemistry that we could do in a living organism.”

Francis said that copper-based click chemistry is arguably still faster and is used today in situations without living cells, but Bertozzi’s copperless click chemistry — as well as her previous work on the Bertozzi-Staudinger ligation — is the only technique that works in living cells.

Much of her research while at Berkeley was done in collaboration with scientists at Berkeley Lab. She was one of six Berkeley Lab scientists who led the establishment of the Molecular Foundry, a nanoscience research facility that provides scientists from around the world with access to cutting-edge expertise and instrumentation, and she served as its director from 2006 until 2010.

“It was a privilege to watch how the success of her (Bertozzi’s) discoveries unfolded here on the Berkeley campus and beyond,” said Clark, who also is a faculty scientist at Berkeley Lab. “On behalf of the College of Chemistry community, we extend our heartiest congratulations to Carolyn for her spectacular work and this well-deserved honor.”

https://news.berkeley.edu/2022/10/05/chemistry-nobelist-carolyn-bertozzis-years-at-uc-berkeley/

RELATED INFORMATION

Technion #1 in Europe in Field of AI for 2nd Straight Year

Reporter: Aviva Lev-Ari, PhD, RN

For the second year in a row, the Technion is ranked first in Europe in the field of artificial intelligence (AI) according to CSRankings, which are highly regarded for their metrics-based ranking of top computer science institutions. The repeat win further solidifies the Technion’s position as a leading institution in AI. It was also ranked 16th in the world in AI and 10th in the world in the subfield of learning systems. 

The Technion recruits researchers and students from all Technion units for interdisciplinary AI research by increasing the number of new programs and initiatives in its various fields with leading companies, top universities, and research institutions around the world. It is also establishing its own AI community to empower the student body and researchers in all fields of AI and deepening their collaborations with others doing related work.  

The Technion’s Tech.AI Center for Artificial Intelligence, established in 2020, is the greatest source of AI innovation and research on campus. Tech.AI includes approximately 150 researchers and aims to apply advanced methodologies and tools at the forefront of AI in a variety of fields including data science, medical research, mechanical engineering, civil engineering, architecture, biology, and more.  

To further facilitate AI research and collaborations, a recent agreement was signed to establish a Zimin Institute at the Technion for AI Solutions in Healthcare that will operate as part of Tech.AI. The Institute will promote interdisciplinary projects and work to develop technologies based on big data and computational learning in order to improve human health and healthcare, with an emphasis on proposals that have an applied AI component.  

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Endoglin Protein Interactome Profiling Identifies TRIM21 and Galectin-3 as New Binding Partners

Curator: Stephen J. Williams, Ph.D.

First please see the summary of LPBI efforts into development of inhibitors of Galectin-3 for cancer therapeutics

Mission 4: Use of Systems Biology for Design of inhibitor of Galectins as Cancer Therapeutic – Strategy and Software

The following paper in Cells describes the discovery of protein interactors of endoglin, which is recruited to membranes at the TGF-β receptor complex upon TGF-β signaling. Interesting a carbohydrate binding protein, galectin-3, and an E3-ligase, TRIM21, were found to be unique interactors within this complex.

Gallardo-Vara E, Ruiz-Llorente L, Casado-Vela J, Ruiz-Rodríguez MJ, López-Andrés N, Pattnaik AK, Quintanilla M, Bernabeu C. Endoglin Protein Interactome Profiling Identifies TRIM21 and Galectin-3 as New Binding Partners. Cells. 2019 Sep 13;8(9):1082. doi: 10.3390/cells8091082. PMID: 31540324; PMCID: PMC6769930.

Abstract

Endoglin is a 180-kDa glycoprotein receptor primarily expressed by the vascular endothelium and involved in cardiovascular disease and cancer. Heterozygous mutations in the endoglin gene (ENG) cause hereditary hemorrhagic telangiectasia type 1, a vascular disease that presents with nasal and gastrointestinal bleeding, skin and mucosa telangiectases, and arteriovenous malformations in internal organs. A circulating form of endoglin (alias soluble endoglin, sEng), proteolytically released from the membrane-bound protein, has been observed in several inflammation-related pathological conditions and appears to contribute to endothelial dysfunction and cancer development through unknown mechanisms. Membrane-bound endoglin is an auxiliary component of the TGF-β receptor complex and the extracellular region of endoglin has been shown to interact with types I and II TGF-β receptors, as well as with BMP9 and BMP10 ligands, both members of the TGF-β family. To search for novel protein interactors, we screened a microarray containing over 9000 unique human proteins using recombinant sEng as bait. We find that sEng binds with high affinity, at least, to 22 new proteins. Among these, we validated the interaction of endoglin with galectin-3, a secreted member of the lectin family with capacity to bind membrane glycoproteins, and with tripartite motif-containing protein 21 (TRIM21), an E3 ubiquitin-protein ligase. Using human endothelial cells and Chinese hamster ovary cells, we showed that endoglin co-immunoprecipitates and co-localizes with galectin-3 or TRIM21. These results open new research avenues on endoglin function and regulation.

Source: https://www.mdpi.com/2073-4409/8/9/1082/htm

Endoglin is an auxiliary TGF-β co-receptor predominantly expressed in endothelial cells, which is involved in vascular development, repair, homeostasis, and disease [1,2,3,4]. Heterozygous mutations in the human ENDOGLIN gene (ENG) cause hereditary hemorrhagic telangiectasia (HHT) type 1, a vascular disease associated with nasal and gastrointestinal bleeds, telangiectases on skin and mucosa and arteriovenous malformations in the lung, liver, and brain [4,5,6]. The key role of endoglin in the vasculature is also illustrated by the fact that endoglin-KO mice die in utero due to defects in the vascular system [7]. Endoglin expression is markedly upregulated in proliferating endothelial cells involved in active angiogenesis, including the solid tumor neovasculature [8,9]. For this reason, endoglin has become a promising target for the antiangiogenic treatment of cancer [10,11,12]. Endoglin is also expressed in cancer cells where it can behave as both a tumor suppressor in prostate, breast, esophageal, and skin carcinomas [13,14,15,16] and a promoter of malignancy in melanoma and Ewing’s sarcoma [17]. Ectodomain shedding of membrane-bound endoglin may lead to a circulating form of the protein, also known as soluble endoglin (sEng) [18,19,20]. Increased levels of sEng have been found in several vascular-related pathologies, including preeclampsia, a disease of high prevalence in pregnant women which, if left untreated, can lead to serious and even fatal complications for both mother and baby [2,18,19,21]. Interestingly, several lines of evidence support a pathogenic role of sEng in the vascular system, including endothelial dysfunction, antiangiogenic activity, increased vascular permeability, inflammation-associated leukocyte adhesion and transmigration, and hypertension [18,22,23,24,25,26,27]. Because of its key role in vascular pathology, a large number of studies have addressed the structure and function of endoglin at the molecular level, in order to better understand its mechanism of action.

 Galectin-3 Interacts with Endoglin in Cells

Galectin-3 is a secreted member of the lectin family with the capacity to bind membrane glycoproteins like endoglin and is involved in the pathogenesis of many human diseases [52]. We confirmed the protein screen data for galectin-3, as evidenced by two-way co-immunoprecipitation of endoglin and galectin-3 upon co-transfection in CHO-K1 cells. As shown in Figure 1A, galectin-3 and endoglin were efficiently transfected, as demonstrated by Western blot analysis in total cell extracts. No background levels of endoglin were observed in control cells transfected with the empty vector (Ø). By contrast, galectin-3 could be detected in all samples but, as expected, showed an increased signal in cells transfected with the galectin-3 expression vector. Co-immunoprecipitation studies of these cell lysates showed that galectin-3 was present in endoglin immunoprecipitates (Figure 1B). Conversely, endoglin was also detected in galectin-3 immunoprecipitates (Figure 1C).

Cells 08 01082 g001 550

Figure 1. Protein–protein association between galectin-3 and endoglin. (AC). Co-immunoprecipitation of galectin-3 and endoglin. CHO-K1 cells were transiently transfected with pcEXV-Ø (Ø), pcEXV–HA–EngFL (Eng) and pcDNA3.1–Gal-3 (Gal3) expression vectors. (A) Total cell lysates (TCL) were analyzed by SDS-PAGE under reducing conditions, followed by Western blot (WB) analysis using specific antibodies to endoglin, galectin-3 and β-actin (loading control). Cell lysates were subjected to immunoprecipitation (IP) with anti-endoglin (B) or anti-galectin-3 (C) antibodies, followed by SDS-PAGE under reducing conditions and WB analysis with anti-endoglin or anti-galectin-3 antibodies, as indicated. Negative controls with an IgG2b (B) and IgG1 (C) were included. (D) Protein-protein interactions between galectin-3 and endoglin using Bio-layer interferometry (BLItz). The Ni–NTA biosensors tips were loaded with 7.3 µM recombinant human galectin-3/6xHis at the C-terminus (LGALS3), and protein binding was measured against 0.1% BSA in PBS (negative control) or 4.1 µM soluble endoglin (sEng). Kinetic sensorgrams were obtained using a single channel ForteBioBLItzTM instrument.

Cells 08 01082 g002 550

Figure 2.Galectin-3 and endoglin co-localize in human endothelial cells. Human umbilical vein-derived endothelial cell (HUVEC) monolayers were fixed with paraformaldehyde, permeabilized with Triton X-100, incubated with the mouse mAb P4A4 anti-endoglin, washed, and incubated with a rabbit polyclonal anti-galectin-3 antibody (PA5-34819). Galectin-3 and endoglin were detected by immunofluorescence upon incubation with Alexa 647 goat anti-rabbit IgG (red staining) and Alexa 488 goat anti-mouse IgG (green staining) secondary antibodies, respectively. (A) Single staining of galectin-3 (red) and endoglin (green) at the indicated magnifications. (B) Merge images plus DAPI (nuclear staining in blue) show co-localization of galectin-3 and endoglin (yellow color). Representative images of five different experiments are shown.

Endoglin associates with the cullin-type E3 ligase TRIM21
Cells 08 01082 g003 550

Figure 3.Protein–protein association between TRIM21 and endoglin. (AE) Co-immunoprecipitation of TRIM21 and endoglin. A,B. HUVEC monolayers were lysed and total cell lysates (TCL) were subjected to SDS-PAGE under reducing (for TRIM21 detection) or nonreducing (for endoglin detection) conditions, followed by Western blot (WB) analysis using antibodies to endoglin, TRIM21 or β-actin (A). HUVECs lysates were subjected to immunoprecipitation (IP) with anti-TRIM21 or negative control antibodies, followed by WB analysis with anti-endoglin (B). C,D. CHO-K1 cells were transiently transfected with pDisplay–HA–Mock (Ø), pDisplay–HA–EngFL (E) or pcDNA3.1–HA–hTRIM21 (T) expression vectors, as indicated. Total cell lysates (TCL) were subjected to SDS-PAGE under nonreducing conditions and WB analysis using specific antibodies to endoglin, TRIM21, and β-actin (C). Cell lysates were subjected to immunoprecipitation (IP) with anti-TRIM21 or anti-endoglin antibodies, followed by SDS-PAGE under reducing (upper panel) or nonreducing (lower panel) conditions and WB analysis with anti-TRIM21 or anti-endoglin antibodies. Negative controls of appropriate IgG were included (D). E. CHO-K1 cells were transiently transfected with pcDNA3.1–HA–hTRIM21 and pDisplay–HA–Mock (Ø), pDisplay–HA–EngFL (FL; full-length), pDisplay–HA–EngEC (EC; cytoplasmic-less) or pDisplay–HA–EngTMEC (TMEC; cytoplasmic-less) expression vectors, as indicated. Cell lysates were subjected to immunoprecipitation with anti-TRIM21, followed by SDS-PAGE under reducing conditions and WB analysis with anti-endoglin antibodies, as indicated. The asterisk indicates the presence of a nonspecific band. Mr, molecular reference; Eng, endoglin; TRIM, TRIM21. (F) Protein–protein interactions between TRIM21 and endoglin using Bio-layer interferometry (BLItz). The Ni–NTA biosensors tips were loaded with 5.4 µM recombinant human TRIM21/6xHis at the N-terminus (R052), and protein binding was measured against 0.1% BSA in PBS (negative control) or 4.1 µM soluble endoglin (sEng). Kinetic sensorgrams were obtained using a single channel ForteBioBLItzTM instrument.

Table 1. Human protein-array analysis of endoglin interactors1.

Accession #Protein NameCellular Compartment
NM_172160.1Potassium voltage-gated channel, shaker-related subfamily, beta member 1 (KCNAB1), transcript variant 1Plasma membrane
Q14722
NM_138565.1Cortactin (CTTN), transcript variant 2Plasma membrane
Q14247
BC036123.1Stromal membrane-associated protein 1 (SMAP1)Plasma membrane
Q8IYB5
NM_173822.1Family with sequence similarity 126, member B (FAM126B)Plasma membrane, cytosol
Q8IXS8
BC047536.1Sciellin (SCEL)Plasma membrane, extracellular or secreted
O95171
BC068068.1Galectin-3Plasma membrane, mitochondrion, nucleus, extracellular or secreted
P17931
BC001247.1Actin-binding LIM protein 1 (ABLIM1)Cytoskeleton
O14639
NM_198943.1Family with sequence similarity 39, member B (FAM39B)Endosome, cytoskeleton
Q6VEQ5
NM_005898.4Cell cycle associated protein 1 (CAPRIN1), transcript variant 1Cytosol
Q14444
BC002559.1YTH domain family, member 2 (YTHDF2)Nucleus, cytosol
Q9Y5A9
NM_003141.2Tripartite motif-containing 21 (TRIM21)Nucleus, cytosol
P19474
BC025279.1Scaffold attachment factor B2 (SAFB2)Nucleus
Q14151
BC031650.1Putative E3 ubiquitin-protein ligase SH3RF2Nucleus
Q8TEC5
BC034488.2ATP-binding cassette, sub-family F (GCN20), member 1 (ABCF1)Nucleus
Q8NE71
BC040946.1Spliceosome-associated protein CWC15 homolog (HSPC148)Nucleus
Q9P013
NM_003609.2HIRA interacting protein 3 (HIRIP3)Nucleus
Q9BW71
NM_005572.1Lamin A/C (LMNA), transcript variant 2Nucleus
P02545
NM_006479.2RAD51 associated protein 1 (RAD51AP1)Nucleus
Q96B01
NM_014321.2Origin recognition complex, subunit 6 like (yeast) (ORC6L)Nucleus
Q9Y5N6
NM_015138.2RNA polymerase-associated protein RTF1 homolog (RTF1)Nucleus
Q92541
NM_032141.1Coiled-coil domain containing 55 (CCDC55), transcript variant 1Nucleus
Q9H0G5
BC012289.1Protein PRRC2B, KIAA0515Data not available
Q5JSZ5

1 Microarrays containing over 9000 unique human proteins were screened using recombinant sEng as a probe. Protein interactors showing the highest scores (Z-score ≥2.0) are listed. GeneBank (https://www.ncbi.nlm.nih.gov/genbank/) and UniProtKB (https://www.uniprot.org/help/uniprotkb) accession numbers are indicated with a yellow or green background, respectively. The cellular compartment of each protein was obtained from the UniProtKB webpage. Proteins selected for further studies (TRIM21 and galectin-3) are indicated in bold type with blue background.

Note: the following are from NCBI Genbank and Genecards on TRIM21

 From Genbank: https://www.ncbi.nlm.nih.gov/gene?Db=gene&Cmd=DetailsSearch&Term=6737

TRIM21 tripartite motif containing 21 [ Homo sapiens (human) ]

Gene ID: 6737, updated on 6-Sep-2022

Summary

Official Symbol TRIM21provided by HGNC Official Full Name tripartite motif containing 21provided by HGNC Primary source HGNC:HGNC:11312 See related Ensembl:ENSG00000132109MIM:109092;AllianceGenome:HGNC:11312 Gene type protein coding RefSeq status REVIEWED Organism Homo sapiens Lineage Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Euarchontoglires; Primates; Haplorrhini; Catarrhini; Hominidae; Homo Also known as SSA; RO52; SSA1; RNF81; Ro/SSA Summary This gene encodes a member of the tripartite motif (TRIM) family. The TRIM motif includes three zinc-binding domains, a RING, a B-box type 1 and a B-box type 2, and a coiled-coil region. The encoded protein is part of the RoSSA ribonucleoprotein, which includes a single polypeptide and one of four small RNA molecules. The RoSSA particle localizes to both the cytoplasm and the nucleus. RoSSA interacts with autoantigens in patients with Sjogren syndrome and systemic lupus erythematosus. Alternatively spliced transcript variants for this gene have been described but the full-length nature of only one has been determined. [provided by RefSeq, Jul 2008] Expression Ubiquitous expression in spleen (RPKM 15.5), appendix (RPKM 13.2) and 24 other tissues See more Orthologs mouseall NEW Try the new Gene table
Try the new Transcript table

Genomic context

See TRIM21 in Genome Data Viewer Location:   11p15.4 Exon count:   7

Annotation releaseStatusAssemblyChrLocation
110currentGRCh38.p14 (GCF_000001405.40)11NC_000011.10 (4384897..4393702, complement)
110currentT2T-CHM13v2.0 (GCF_009914755.1)11NC_060935.1 (4449988..4458819, complement)
105.20220307previous assemblyGRCh37.p13 (GCF_000001405.25)11NC_000011.9 (4406127..4414932, complement)

Chromosome 11 – NC_000011.10Genomic Context describing neighboring genes

Bibliography

Related articles in PubMed

  1. TRIM21 inhibits the osteogenic differentiation of mesenchymal stem cells by facilitating K48 ubiquitination-mediated degradation of Akt.Xian J, et al. Exp Cell Res, 2022 Mar 15. PMID 35051432
  2. A Promising Intracellular Protein-Degradation Strategy: TRIMbody-Away Technique Based on Nanobody Fragment.Chen G, et al. Biomolecules, 2021 Oct 14. PMID 34680146, Free PMC Article
  3. Induced TRIM21 ISGylation by IFN-β enhances p62 ubiquitination to prevent its autophagosome targeting.Jin J, et al. Cell Death Dis, 2021 Jul 13. PMID 34257278, Free PMC Article
  4. TRIM21 Polymorphisms are associated with Susceptibility and Clinical Status of Oral Squamous Cell Carcinoma patients.Chuang CY, et al. Int J Med Sci, 2021. PMID 34220328, Free PMC Article
  5. TRIM21 inhibits porcine epidemic diarrhea virus proliferation by proteasomal degradation of the nucleocapsid protein.Wang H, et al. Arch Virol, 2021 Jul. PMID 33900472, Free PMC Article

From GeneCard:https://www.genecards.org/cgi-bin/carddisp.pl?gene=TRIM21

Entrez Gene Summary for TRIM21 Gene

  • This gene encodes a member of the tripartite motif (TRIM) family. The TRIM motif includes three zinc-binding domains, a RING, a B-box type 1 and a B-box type 2, and a coiled-coil region. The encoded protein is part of the RoSSA ribonucleoprotein, which includes a single polypeptide and one of four small RNA molecules. The RoSSA particle localizes to both the cytoplasm and the nucleus. RoSSA interacts with autoantigens in patients with Sjogren syndrome and systemic lupus erythematosus. Alternatively spliced transcript variants for this gene have been described but the full-length nature of only one has been determined. [provided by RefSeq, Jul 2008]

GeneCards Summary for TRIM21 Gene

TRIM21 (Tripartite Motif Containing 21) is a Protein Coding gene. Diseases associated with TRIM21 include Heart Block, Congenital and Sjogren Syndrome. Among its related pathways are Cytosolic sensors of pathogen-associated DNA and KEAP1-NFE2L2 pathway. Gene Ontology (GO) annotations related to this gene include identical protein binding and ligase activity. An important paralog of this gene is TRIM6.

UniProtKB/Swiss-Prot Summary for TRIM21 Gene

E3 ubiquitin-protein ligase whose activity is dependent on E2 enzymes, UBE2D1, UBE2D2, UBE2E1 and UBE2E2. Forms a ubiquitin ligase complex in cooperation with the E2 UBE2D2 that is used not only for the ubiquitination of USP4 and IKBKB but also for its self-ubiquitination. Component of cullin-RING-based SCF (SKP1-CUL1-F-box protein) E3 ubiquitin-protein ligase complexes such as SCF(SKP2)-like complexes. A TRIM21-containing SCF(SKP2)-like complex is shown to mediate ubiquitination of CDKN1B (‘Thr-187’ phosphorylated-form), thereby promoting its degradation by the proteasome. Monoubiquitinates IKBKB that will negatively regulates Tax-induced NF-kappa-B signaling. Negatively regulates IFN-beta production post-pathogen recognition by polyubiquitin-mediated degradation of IRF3. Mediates the ubiquitin-mediated proteasomal degradation of IgG1 heavy chain, which is linked to the VCP-mediated ER-associated degradation (ERAD) pathway. Promotes IRF8 ubiquitination, which enhanced the ability of IRF8 to stimulate cytokine genes transcription in macrophages. Plays a role in the regulation of the cell cycle progression. Enhances the decapping activity of DCP2. Exists as a ribonucleoprotein particle present in all mammalian cells studied and composed of a single polypeptide and one of four small RNA molecules. At least two isoforms are present in nucleated and red blood cells, and tissue specific differences in RO/SSA proteins have been identified. The common feature of these proteins is their ability to bind HY RNAs.2. Involved in the regulation of innate immunity and the inflammatory response in response to IFNG/IFN-gamma. Organizes autophagic machinery by serving as a platform for the assembly of ULK1, Beclin 1/BECN1 and ATG8 family members and recognizes specific autophagy targets, thus coordinating target recognition with assembly of the autophagic apparatus and initiation of autophagy. Acts as an autophagy receptor for the degradation of IRF3, hence attenuating type I interferon (IFN)-dependent immune responses (PubMed:26347139162978621631662716472766168805111802269418361920186413151884514219675099). Represses the innate antiviral response by facilitating the formation of the NMI-IFI35 complex through ‘Lys-63’-linked ubiquitination of NMI (PubMed:26342464). ( RO52_HUMAN,P19474 )

Molecular function for TRIM21 Gene according to UniProtKB/Swiss-Prot

Function:

  • E3 ubiquitin-protein ligase whose activity is dependent on E2 enzymes, UBE2D1, UBE2D2, UBE2E1 and UBE2E2.
    Forms a ubiquitin ligase complex in cooperation with the E2 UBE2D2 that is used not only for the ubiquitination of USP4 and IKBKB but also for its self-ubiquitination.
    Component of cullin-RING-based SCF (SKP1-CUL1-F-box protein) E3 ubiquitin-protein ligase complexes such as SCF(SKP2)-like complexes.
    A TRIM21-containing SCF(SKP2)-like complex is shown to mediate ubiquitination of CDKN1B (‘Thr-187’ phosphorylated-form), thereby promoting its degradation by the proteasome.
    Monoubiquitinates IKBKB that will negatively regulates Tax-induced NF-kappa-B signaling.
    Negatively regulates IFN-beta production post-pathogen recognition by polyubiquitin-mediated degradation of IRF3.
    Mediates the ubiquitin-mediated proteasomal degradation of IgG1 heavy chain, which is linked to the VCP-mediated ER-associated degradation (ERAD) pathway.
    Promotes IRF8 ubiquitination, which enhanced the ability of IRF8 to stimulate cytokine genes transcription in macrophages.
    Plays a role in the regulation of the cell cycle progression.

Endoglin Protein Interactome Profiling Identifies TRIM21 and Galectin-3 as New Binding Partners

Gallardo-Vara E, Ruiz-Llorente L, Casado-Vela J, Ruiz-Rodríguez MJ, López-Andrés N, Pattnaik AK, Quintanilla M, Bernabeu C. Endoglin Protein Interactome Profiling Identifies TRIM21 and Galectin-3 as New Binding Partners. Cells. 2019 Sep 13;8(9):1082. doi: 10.3390/cells8091082. PMID: 31540324; PMCID: PMC6769930.

Abstract

Endoglin is a 180-kDa glycoprotein receptor primarily expressed by the vascular endothelium and involved in cardiovascular disease and cancer. Heterozygous mutations in the endoglin gene (ENG) cause hereditary hemorrhagic telangiectasia type 1, a vascular disease that presents with nasal and gastrointestinal bleeding, skin and mucosa telangiectases, and arteriovenous malformations in internal organs. A circulating form of endoglin (alias soluble endoglin, sEng), proteolytically released from the membrane-bound protein, has been observed in several inflammation-related pathological conditions and appears to contribute to endothelial dysfunction and cancer development through unknown mechanisms. Membrane-bound endoglin is an auxiliary component of the TGF-β receptor complex and the extracellular region of endoglin has been shown to interact with types I and II TGF-β receptors, as well as with BMP9 and BMP10 ligands, both members of the TGF-β family. To search for novel protein interactors, we screened a microarray containing over 9000 unique human proteins using recombinant sEng as bait. We find that sEng binds with high affinity, at least, to 22 new proteins. Among these, we validated the interaction of endoglin with galectin-3, a secreted member of the lectin family with capacity to bind membrane glycoproteins, and with tripartite motif-containing protein 21 (TRIM21), an E3 ubiquitin-protein ligase. Using human endothelial cells and Chinese hamster ovary cells, we showed that endoglin co-immunoprecipitates and co-localizes with galectin-3 or TRIM21. These results open new research avenues on endoglin function and regulation.
 
 
Endoglin is an auxiliary TGF-β co-receptor predominantly expressed in endothelial cells, which is involved in vascular development, repair, homeostasis, and disease [1,2,3,4]. Heterozygous mutations in the human ENDOGLIN gene (ENG) cause hereditary hemorrhagic telangiectasia (HHT) type 1, a vascular disease associated with nasal and gastrointestinal bleeds, telangiectases on skin and mucosa and arteriovenous malformations in the lung, liver, and brain [4,5,6]. The key role of endoglin in the vasculature is also illustrated by the fact that endoglin-KO mice die in utero due to defects in the vascular system [7]. Endoglin expression is markedly upregulated in proliferating endothelial cells involved in active angiogenesis, including the solid tumor neovasculature [8,9]. For this reason, endoglin has become a promising target for the antiangiogenic treatment of cancer [10,11,12]. Endoglin is also expressed in cancer cells where it can behave as both a tumor suppressor in prostate, breast, esophageal, and skin carcinomas [13,14,15,16] and a promoter of malignancy in melanoma and Ewing’s sarcoma [17]. Ectodomain shedding of membrane-bound endoglin may lead to a circulating form of the protein, also known as soluble endoglin (sEng) [18,19,20]. Increased levels of sEng have been found in several vascular-related pathologies, including preeclampsia, a disease of high prevalence in pregnant women which, if left untreated, can lead to serious and even fatal complications for both mother and baby [2,18,19,21]. Interestingly, several lines of evidence support a pathogenic role of sEng in the vascular system, including endothelial dysfunction, antiangiogenic activity, increased vascular permeability, inflammation-associated leukocyte adhesion and transmigration, and hypertension [18,22,23,24,25,26,27]. Because of its key role in vascular pathology, a large number of studies have addressed the structure and function of endoglin at the molecular level, in order to better understand its mechanism of action.
 

 Galectin-3 Interacts with Endoglin in Cells

Galectin-3 is a secreted member of the lectin family with the capacity to bind membrane glycoproteins like endoglin and is involved in the pathogenesis of many human diseases [52]. We confirmed the protein screen data for galectin-3, as evidenced by two-way co-immunoprecipitation of endoglin and galectin-3 upon co-transfection in CHO-K1 cells. As shown in Figure 1A, galectin-3 and endoglin were efficiently transfected, as demonstrated by Western blot analysis in total cell extracts. No background levels of endoglin were observed in control cells transfected with the empty vector (Ø). By contrast, galectin-3 could be detected in all samples but, as expected, showed an increased signal in cells transfected with the galectin-3 expression vector. Co-immunoprecipitation studies of these cell lysates showed that galectin-3 was present in endoglin immunoprecipitates (Figure 1B). Conversely, endoglin was also detected in galectin-3 immunoprecipitates (Figure 1C).
Figure 1. Protein–protein association between galectin-3 and endoglin. (AC). Co-immunoprecipitation of galectin-3 and endoglin. CHO-K1 cells were transiently transfected with pcEXV-Ø (Ø), pcEXV–HA–EngFL (Eng) and pcDNA3.1–Gal-3 (Gal3) expression vectors. (A) Total cell lysates (TCL) were analyzed by SDS-PAGE under reducing conditions, followed by Western blot (WB) analysis using specific antibodies to endoglin, galectin-3 and β-actin (loading control). Cell lysates were subjected to immunoprecipitation (IP) with anti-endoglin (B) or anti-galectin-3 (C) antibodies, followed by SDS-PAGE under reducing conditions and WB analysis with anti-endoglin or anti-galectin-3 antibodies, as indicated. Negative controls with an IgG2b (B) and IgG1 (C) were included. (D) Protein-protein interactions between galectin-3 and endoglin using Bio-layer interferometry (BLItz). The Ni–NTA biosensors tips were loaded with 7.3 µM recombinant human galectin-3/6xHis at the C-terminus (LGALS3), and protein binding was measured against 0.1% BSA in PBS (negative control) or 4.1 µM soluble endoglin (sEng). Kinetic sensorgrams were obtained using a single channel ForteBioBLItzTM instrument.
Figure 2. Galectin-3 and endoglin co-localize in human endothelial cells. Human umbilical vein-derived endothelial cell (HUVEC) monolayers were fixed with paraformaldehyde, permeabilized with Triton X-100, incubated with the mouse mAb P4A4 anti-endoglin, washed, and incubated with a rabbit polyclonal anti-galectin-3 antibody (PA5-34819). Galectin-3 and endoglin were detected by immunofluorescence upon incubation with Alexa 647 goat anti-rabbit IgG (red staining) and Alexa 488 goat anti-mouse IgG (green staining) secondary antibodies, respectively. (A) Single staining of galectin-3 (red) and endoglin (green) at the indicated magnifications. (B) Merge images plus DAPI (nuclear staining in blue) show co-localization of galectin-3 and endoglin (yellow color). Representative images of five different experiments are shown.
  
Endoglin associates with the cullin-type E3 ligase TRIM21
 
Figure 3. Protein–protein association between TRIM21 and endoglin. (AE) Co-immunoprecipitation of TRIM21 and endoglin. A,B. HUVEC monolayers were lysed and total cell lysates (TCL) were subjected to SDS-PAGE under reducing (for TRIM21 detection) or nonreducing (for endoglin detection) conditions, followed by Western blot (WB) analysis using antibodies to endoglin, TRIM21 or β-actin (A). HUVECs lysates were subjected to immunoprecipitation (IP) with anti-TRIM21 or negative control antibodies, followed by WB analysis with anti-endoglin (B). C,D. CHO-K1 cells were transiently transfected with pDisplay–HA–Mock (Ø), pDisplay–HA–EngFL (E) or pcDNA3.1–HA–hTRIM21 (T) expression vectors, as indicated. Total cell lysates (TCL) were subjected to SDS-PAGE under nonreducing conditions and WB analysis using specific antibodies to endoglin, TRIM21, and β-actin (C). Cell lysates were subjected to immunoprecipitation (IP) with anti-TRIM21 or anti-endoglin antibodies, followed by SDS-PAGE under reducing (upper panel) or nonreducing (lower panel) conditions and WB analysis with anti-TRIM21 or anti-endoglin antibodies. Negative controls of appropriate IgG were included (D). E. CHO-K1 cells were transiently transfected with pcDNA3.1–HA–hTRIM21 and pDisplay–HA–Mock (Ø), pDisplay–HA–EngFL (FL; full-length), pDisplay–HA–EngEC (EC; cytoplasmic-less) or pDisplay–HA–EngTMEC (TMEC; cytoplasmic-less) expression vectors, as indicated. Cell lysates were subjected to immunoprecipitation with anti-TRIM21, followed by SDS-PAGE under reducing conditions and WB analysis with anti-endoglin antibodies, as indicated. The asterisk indicates the presence of a nonspecific band. Mr, molecular reference; Eng, endoglin; TRIM, TRIM21. (F) Protein–protein interactions between TRIM21 and endoglin using Bio-layer interferometry (BLItz). The Ni–NTA biosensors tips were loaded with 5.4 µM recombinant human TRIM21/6xHis at the N-terminus (R052), and protein binding was measured against 0.1% BSA in PBS (negative control) or 4.1 µM soluble endoglin (sEng). Kinetic sensorgrams were obtained using a single channel ForteBioBLItzTM instrument.
 
Table 1. Human protein-array analysis of endoglin interactors1.
Accession # Protein Name Cellular Compartment
NM_172160.1 Potassium voltage-gated channel, shaker-related subfamily, beta member 1 (KCNAB1), transcript variant 1 Plasma membrane
Q14722
NM_138565.1 Cortactin (CTTN), transcript variant 2 Plasma membrane
Q14247
BC036123.1 Stromal membrane-associated protein 1 (SMAP1) Plasma membrane
Q8IYB5
NM_173822.1 Family with sequence similarity 126, member B (FAM126B) Plasma membrane, cytosol
Q8IXS8
BC047536.1 Sciellin (SCEL) Plasma membrane, extracellular or secreted
O95171
BC068068.1 Galectin-3 Plasma membrane, mitochondrion, nucleus, extracellular or secreted
P17931
BC001247.1 Actin-binding LIM protein 1 (ABLIM1) Cytoskeleton
O14639
NM_198943.1 Family with sequence similarity 39, member B (FAM39B) Endosome, cytoskeleton
Q6VEQ5
NM_005898.4 Cell cycle associated protein 1 (CAPRIN1), transcript variant 1 Cytosol
Q14444
BC002559.1 YTH domain family, member 2 (YTHDF2) Nucleus, cytosol
Q9Y5A9
NM_003141.2 Tripartite motif-containing 21 (TRIM21) Nucleus, cytosol
P19474
BC025279.1 Scaffold attachment factor B2 (SAFB2) Nucleus
Q14151
BC031650.1 Putative E3 ubiquitin-protein ligase SH3RF2 Nucleus
Q8TEC5
BC034488.2 ATP-binding cassette, sub-family F (GCN20), member 1 (ABCF1) Nucleus
Q8NE71
BC040946.1 Spliceosome-associated protein CWC15 homolog (HSPC148) Nucleus
Q9P013
NM_003609.2 HIRA interacting protein 3 (HIRIP3) Nucleus
Q9BW71
NM_005572.1 Lamin A/C (LMNA), transcript variant 2 Nucleus
P02545
NM_006479.2 RAD51 associated protein 1 (RAD51AP1) Nucleus
Q96B01
NM_014321.2 Origin recognition complex, subunit 6 like (yeast) (ORC6L) Nucleus
Q9Y5N6
NM_015138.2 RNA polymerase-associated protein RTF1 homolog (RTF1) Nucleus
Q92541
NM_032141.1 Coiled-coil domain containing 55 (CCDC55), transcript variant 1 Nucleus
Q9H0G5
BC012289.1 Protein PRRC2B, KIAA0515 Data not available
Q5JSZ5
1 Microarrays containing over 9000 unique human proteins were screened using recombinant sEng as a probe. Protein interactors showing the highest scores (Z-score ≥2.0) are listed. GeneBank (https://www.ncbi.nlm.nih.gov/genbank/) and UniProtKB (https://www.uniprot.org/help/uniprotkb) accession numbers are indicated with a yellow or green background, respectively. The cellular compartment of each protein was obtained from the UniProtKB webpage. Proteins selected for further studies (TRIM21 and galectin-3) are indicated in bold type with blue background.
  

Note: the following are from NCBI Genbank and Genecards on TRIM21

TRIM21 tripartite motif containing 21 [ Homo sapiens (human) ]

Gene ID: 6737, updated on 6-Sep-2022

Summary
Official Symbol
TRIM21provided by HGNC
Official Full Name
tripartite motif containing 21provided by HGNC
Primary source
HGNC:HGNC:11312
See related
Ensembl:ENSG00000132109 MIM:109092; AllianceGenome:HGNC:11312
Gene type
protein coding
RefSeq status
REVIEWED
Organism
Homo sapiens
Lineage
Eukaryota; Metazoa; Chordata; Craniata; Vertebrata; Euteleostomi; Mammalia; Eutheria; Euarchontoglires; Primates; Haplorrhini; Catarrhini; Hominidae; Homo
Also known as
SSA; RO52; SSA1; RNF81; Ro/SSA
Summary
This gene encodes a member of the tripartite motif (TRIM) family. The TRIM motif includes three zinc-binding domains, a RING, a B-box type 1 and a B-box type 2, and a coiled-coil region. The encoded protein is part of the RoSSA ribonucleoprotein, which includes a single polypeptide and one of four small RNA molecules. The RoSSA particle localizes to both the cytoplasm and the nucleus. RoSSA interacts with autoantigens in patients with Sjogren syndrome and systemic lupus erythematosus. Alternatively spliced transcript variants for this gene have been described but the full-length nature of only one has been determined. [provided by RefSeq, Jul 2008]
Expression
Ubiquitous expression in spleen (RPKM 15.5), appendix (RPKM 13.2) and 24 other tissues See more
Orthologs
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Try the new Transcript table
Genomic context
 
See TRIM21 in Genome Data Viewer
Location:
11p15.4
Exon count:
7
Annotation release Status Assembly Chr Location
110 current GRCh38.p14 (GCF_000001405.40) 11 NC_000011.10 (4384897..4393702, complement)
110 current T2T-CHM13v2.0 (GCF_009914755.1) 11 NC_060935.1 (4449988..4458819, complement)
105.20220307 previous assembly GRCh37.p13 (GCF_000001405.25) 11 NC_000011.9 (4406127..4414932, complement)

Chromosome 11 – NC_000011.10Genomic Context describing neighboring genes

Neighboring gene olfactory receptor family 52 subfamily B member 4 Neighboring gene olfactory receptor family 52 subfamily B member 3 pseudogene Neighboring gene olfactory receptor family 51 subfamily R member 1 pseudogene Neighboring gene olfactory receptor family 52 subfamily P member 2 pseudogene

 

Entrez Gene Summary for TRIM21 Gene

  • This gene encodes a member of the tripartite motif (TRIM) family. The TRIM motif includes three zinc-binding domains, a RING, a B-box type 1 and a B-box type 2, and a coiled-coil region. The encoded protein is part of the RoSSA ribonucleoprotein, which includes a single polypeptide and one of four small RNA molecules. The RoSSA particle localizes to both the cytoplasm and the nucleus. RoSSA interacts with autoantigens in patients with Sjogren syndrome and systemic lupus erythematosus. Alternatively spliced transcript variants for this gene have been described but the full-length nature of only one has been determined. [provided by RefSeq, Jul 2008]

GeneCards Summary for TRIM21 Gene

TRIM21 (Tripartite Motif Containing 21) is a Protein Coding gene. Diseases associated with TRIM21 include Heart Block, Congenital and Sjogren Syndrome. Among its related pathways are Cytosolic sensors of pathogen-associated DNA and KEAP1-NFE2L2 pathway. Gene Ontology (GO) annotations related to this gene include identical protein binding and ligase activity. An important paralog of this gene is TRIM6.

UniProtKB/Swiss-Prot Summary for TRIM21 Gene

E3 ubiquitin-protein ligase whose activity is dependent on E2 enzymes, UBE2D1, UBE2D2, UBE2E1 and UBE2E2. Forms a ubiquitin ligase complex in cooperation with the E2 UBE2D2 that is used not only for the ubiquitination of USP4 and IKBKB but also for its self-ubiquitination. Component of cullin-RING-based SCF (SKP1-CUL1-F-box protein) E3 ubiquitin-protein ligase complexes such as SCF(SKP2)-like complexes. A TRIM21-containing SCF(SKP2)-like complex is shown to mediate ubiquitination of CDKN1B (‘Thr-187’ phosphorylated-form), thereby promoting its degradation by the proteasome. Monoubiquitinates IKBKB that will negatively regulates Tax-induced NF-kappa-B signaling. Negatively regulates IFN-beta production post-pathogen recognition by polyubiquitin-mediated degradation of IRF3. Mediates the ubiquitin-mediated proteasomal degradation of IgG1 heavy chain, which is linked to the VCP-mediated ER-associated degradation (ERAD) pathway. Promotes IRF8 ubiquitination, which enhanced the ability of IRF8 to stimulate cytokine genes transcription in macrophages. Plays a role in the regulation of the cell cycle progression. Enhances the decapping activity of DCP2. Exists as a ribonucleoprotein particle present in all mammalian cells studied and composed of a single polypeptide and one of four small RNA molecules. At least two isoforms are present in nucleated and red blood cells, and tissue specific differences in RO/SSA proteins have been identified. The common feature of these proteins is their ability to bind HY RNAs.2. Involved in the regulation of innate immunity and the inflammatory response in response to IFNG/IFN-gamma. Organizes autophagic machinery by serving as a platform for the assembly of ULK1, Beclin 1/BECN1 and ATG8 family members and recognizes specific autophagy targets, thus coordinating target recognition with assembly of the autophagic apparatus and initiation of autophagy. Acts as an autophagy receptor for the degradation of IRF3, hence attenuating type I interferon (IFN)-dependent immune responses (PubMed:26347139162978621631662716472766168805111802269418361920186413151884514219675099). Represses the innate antiviral response by facilitating the formation of the NMI-IFI35 complex through ‘Lys-63’-linked ubiquitination of NMI (PubMed:26342464). ( RO52_HUMAN,P19474 )

Molecular function for TRIM21 Gene according to UniProtKB/Swiss-Prot

Function:
  • E3 ubiquitin-protein ligase whose activity is dependent on E2 enzymes, UBE2D1, UBE2D2, UBE2E1 and UBE2E2.
    Forms a ubiquitin ligase complex in cooperation with the E2 UBE2D2 that is used not only for the ubiquitination of USP4 and IKBKB but also for its self-ubiquitination.
    Component of cullin-RING-based SCF (SKP1-CUL1-F-box protein) E3 ubiquitin-protein ligase complexes such as SCF(SKP2)-like complexes.
    A TRIM21-containing SCF(SKP2)-like complex is shown to mediate ubiquitination of CDKN1B (‘Thr-187’ phosphorylated-form), thereby promoting its degradation by the proteasome.
    Monoubiquitinates IKBKB that will negatively regulates Tax-induced NF-kappa-B signaling.
    Negatively regulates IFN-beta production post-pathogen recognition by polyubiquitin-mediated degradation of IRF3.
    Mediates the ubiquitin-mediated proteasomal degradation of IgG1 heavy chain, which is linked to the VCP-mediated ER-associated degradation (ERAD) pathway.
    Promotes IRF8 ubiquitination, which enhanced the ability of IRF8 to stimulate cytokine genes transcription in macrophages.
    Plays a role in the regulation of the cell cycle progression.

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The drug efflux pump MDR1 promotes intrinsic and acquired resistance to PROTACs in cancer cells

Reporter: Stephen J. Williams, PhD.
Below is one of the first reports  on the potential mechanisms of intrinsic and acquired resistance to PROTAC therapy in cancer cells.
Proteolysis-targeting chimeras (PROTACs) are a promising new class of drugs that selectively degrade cellular proteins of interest. PROTACs that target oncogene products are avidly being explored for cancer therapies, and several are currently in clinical trials. Drug resistance is a substantial challenge in clinical oncology, and resistance to PROTACs has been reported in several cancer cell models. Here, using proteomic analysis, we found intrinsic and acquired resistance mechanisms to PROTACs in cancer cell lines mediated by greater abundance or production of the drug efflux pump MDR1. PROTAC-resistant cells were resensitized to PROTACs by genetic ablation of ABCB1 (which encodes MDR1) or by coadministration of MDR1 inhibitors. In MDR1-overexpressing colorectal cancer cells, degraders targeting either the kinases MEK1/2 or the oncogenic mutant GTPase KRASG12C synergized with the dual epidermal growth factor receptor (EGFR/ErbB)/MDR1 inhibitor lapatinib. Moreover, compared with single-agent therapies, combining MEK1/2 degraders with lapatinib improved growth inhibition of MDR1-overexpressing KRAS-mutant colorectal cancer xenografts in mice. Together, our findings suggest that concurrent blockade of MDR1 will likely be required with PROTACs to achieve durable protein degradation and therapeutic response in cancer.

INTRODUCTION

Proteolysis-targeting chimeras (PROTACs) have emerged as a revolutionary new class of drugs that use cancer cells’ own protein destruction machinery to selectively degrade essential tumor drivers (1). PROTACs are small molecules with two functional ends, wherein one end binds to the protein of interest, whereas the other binds to an E3 ubiquitin ligase (23), bringing the ubiquitin ligase to the target protein, leading to its ubiquitination and subsequent degradation by the proteasome. PROTACs have enabled the development of drugs against previously “undruggable” targets and require neither catalytic activity nor high-affinity target binding to achieve target degradation (4). In addition, low doses of PROTACs can be highly effective at inducing degradation, which can reduce off-target toxicity associated with high dosing of traditional inhibitors (3). PROTACs have been developed for a variety of cancer targets, including oncogenic kinases (5), epigenetic proteins (6), and, recently, KRASG12C proteins (7). PROTACs targeting the androgen receptor or estrogen receptor are avidly being evaluated in clinical trials for prostate cancer (NCT03888612) or breast cancer (NCT04072952), respectively.
However, PROTACs may not escape the overwhelming challenge of drug resistance that befalls so many cancer therapies (8). Resistance to PROTACs in cultured cells has been shown to involve genomic alterations in their E3 ligase targets, such as decreased expression of Cereblon (CRBN), Von Hippel Lindau (VHL), or Cullin2 (CUL2) (911). Up-regulation of the drug efflux pump encoded by ABCB1—MDR1 (multidrug resistance 1), a member of the superfamily of adenosine 5′-triphosphate (ATP)–binding cassette (ABC) transporters—has been shown to convey drug resistance to many anticancer drugs, including chemotherapy agents, kinase inhibitors, and other targeted agents (12). Recently, PROTACs were shown to be substrates for MDR1 (1013), suggesting that drug efflux represents a potential limitation for degrader therapies. Here, using degraders (PROTACs) against bromodomain and extraterminal (BET) bromodomain (BBD) proteins and cyclin-dependent kinase 9 (CDK9) as a proof of concept, we applied proteomics to define acquired resistance mechanisms to PROTAC therapies in cancer cells after chronic exposure. Our study reveals a role for the drug efflux pump MDR1 in both acquired and intrinsic resistance to protein degraders in cancer cells and supports combination therapies involving PROTACs and MDR1 inhibitors to achieve durable protein degradation and therapeutic responses.

Fig. 1. Proteomic characterization of degrader-resistant cancer cell lines.
(A) Workflow for identifying protein targets up-regulated in degrader-resistant cancer cells. Single-run proteome analysis was performed, and changes in protein levels among parent and resistant cells were determined by LFQ. m/z, mass/charge ratio. (B and C) Cell viability assessed by CellTiter-Glo in parental and dBET6- or Thal SNS 032–resistant A1847 cells treated with increasing doses of dBET6 (B) or Thal SNS 032 (C) for 5 days. Data were analyzed as % of DMSO control, presented as means ± SD of three independent assays. Growth inhibitory 50% (GI50) values were determined using Prism software. (D to G) Immunoblotting for degrader targets and downstream signaling in parental A1847 cells and their derivative dBET6-R or Thal-R cells treated with increasing doses of dBET6 or Thal SNS 032 for 4 hours. The dBET6-R and Thal-R cells were continuously cultured in 500 nM PROTAC. Blots are representative, and densitometric analyses are means ± SD from three blots, each normalized to the loading control, GAPDH. DC50 values, quantitating either (E) the dose of dBET6 that reduces BRD2, BRD3, or BRD4 or (G) the dose of Thal SNS 032 that reduces CDK9 protein levels 50% of the DMSO control treatment, were determined with Prism software. Pol II, polymerase II. (H to K) Volcano plot of proteins with increased or reduced abundance in dBET6-R (H) or Thal-R (I) A1847 cells relative to parental cells. Differences in protein log2 LFQ intensities among degrader-resistant and parental cells were determined by paired t test permutation-based adjusted P values at FDR of <0.05 using Perseus software. The top 10 up-regulated proteins in each are shown in (J) and (K), respectively. FC, fold change. (L and M) ABCB1 log2 LFQ values in dBET6-R cells from (H) and Thal-R cells from (I) compared with those in parental A1847 cells. Data are presented as means ± SD from three independent assays. By paired t test permutation-based adjusted P values at FDR of <0.05 using Perseus software, ***P ≤ 0.001. (N) Cell viability assessed by CellTiter-Glo in parental and MZ1-resistant SUM159 cells treated with increasing doses of MZ1 for 5 days. Data were analyzed as % of DMSO control, presented as means of three independent assays. GI50 values were determined using Prism software. (O and P) Immunoblotting for degrader targets and downstream signaling in parental or MZ1-R SUM159 cells treated with increasing doses of MZ1 for 24 hours. The MZ1-R cells were continuously cultured in 500 nM MZ1. Blots are representative, and densitometric analyses are means ± SD from three blots, each normalized to the loading control, GAPDH. DC50 values were determined in Prism software. (Q and R) Top 10 up-regulated proteins (Q) and ABCB1 log2 LFQ values (R) in MZ1-R cells relative to parental SUM159 cells

Fig. 2. Chronic exposure to degraders induces MDR1 expression and drug efflux activity.
(A) ABCB1 mRNA levels in parental and degrader-resistant cell lines as determined by qRT-PCR. Data are means ± SD of three independent experiments. ***P ≤ 0.001 by Student’s t test. (B) Immunoblot analysis of MDR1 protein levels in parental and degrader-resistant cell lines. Blots are representative of three independent experiments. (C to E) Immunofluorescence (“IF”) microscopy of MDR1 protein levels in A1847 dBET6-R (C), SUM159 MZ1-R (D), and Thal-R A1847 cells (E) relative to parental cells. Nuclear staining by DAPI. Images are representative of three independent experiments. Scale bars, 100 μm. (F) Drug efflux activity in A1847 dBET6-R, SUM159 MZ1-R, and Thal-R A1847 cells relative to parental cells (Par.) using rhodamine 123 efflux assays. Bars are means ± SD of three independent experiments. ***P ≤ 0.001 by Student’s t test. (G) Intracellular dBET6 levels in parental or dBET-R A1847 cells transfected with a CRBN sensor and treated with increasing concentrations of dBET6. Intracellular dBET6 levels measured using the CRBN NanoBRET target engagement assay. Data were analyzed as % of DMSO control, presented as means ± SD of three independent assays. *P ≤ 0.05, **P ≤ 0.01, and ***P ≤ 0.001 by Student’s t test. (H and I) FISH analysis of representative drug-sensitive parental and drug-resistant A1847 (H) and SUM159 (I) cells using ABCB1 and control XCE 7 centromere probes. Images of interphase nuclei were captured with a Metasystems Metafer microscope workstation, and the raw images were extracted and processed to depict ABCB1 signals in magenta, centromere 7 signals in cyan, and DAPI-stained nuclei in blue. (J and K) CpG methylation status of the ABCB1 downstream promoter (coordinates: chr7.87,600,166-87,601,336) by bisulfite amplicon sequencing in parent and degrader-resistant A1847 (J) and SUM159 (K) cells. Images depict the averaged percentage of methylation for each region of the promoter, where methylation status is depicted by color as follows: red, methylated; blue, unmethylated. Schematic of the ABCB1 gene with the location of individual CpG sites is shown. Graphs are representative of three independent experiments. (L and M) Immunoblot analysis of MDR1 protein levels after short-term exposure [for hours (h) or days (d) as indicated] to BET protein degraders dBET6 or MZ1 (100 nM) in A1847 (L) and SUM159 (M) cells, respectively. Blots are representative of three independent experiments. (N to P) Immunoblot analysis of MDR1 protein levels in A1847 and SUM159 cells after long-term exposure (7 to 30 days) to BET protein degraders dBET6 (N), Thal SNS 032 (O), or MZ1 (P), each at 500 nM. Blots are representative of three independent experiments. (Q and R) Immunoblot analysis of MDR1 protein levels in degrader-resistant A1847 (Q) and SUM159 (R) cells after PROTAC removal for 2 or 7 days. Blots are representative of three independent experiments.

 

Fig. 3. Blockade of MDR1 activity resensitizes degrader-resistant cells to PROTACs.
(A and B) Cell viability by CellTiter-Glo assay in parental and degrader-resistant A1847 (A) and SUM159 (B) cells transfected with control siRNA or siRNAs targeting ABCB1 and cultured for 120 hours. Data were analyzed as % of control, presented as means ± SD of three independent assays. ***P ≤ 0.001 by Student’s t test. (C and D) Immunoblot analysis of degrader targets after ABCB1 knockdown in parental and degrader-resistant A1847 (C) and SUM159 (D) cells. Blots are representative, and densitometric analyses using ImageJ are means ± SD of three blots, each normalized to the loading control, GAPDH. (E) Drug efflux activity, using the rhodamine 123 efflux assay, in degrader-resistant cells after MDR1 inhibition by tariquidar (0.1 μM). Data are means ± SD of three independent experiments. ***P ≤ 0.001 by Student’s t test. (F to H) Cell viability by CellTiter-Glo assay in parental and dBET6-R (F) or Thal-R (G) A1847 cells or MZ1-R SUM159 cells (H) treated with increasing concentrations of tariquidar. Data are % of DMSO control, presented as means ± SD of three independent assays. GI50 value determined with Prism software. (I to K) Immunoblot analysis of degrader targets after MDR1 inhibition (tariquidar, 0.1 μM for 24 hours) in parental and degrader-resistant A1847 cells (I and J) and SUM159 cells (K). Blots are representative, and densitometric analyses are means ± SD from three blots, each normalized to the loading control, GAPDH. (L and M) A 14-day colony formation assessed by crystal violet staining of (L) A1847 cells or (M) SUM159 cells treated with degrader (0.1 μM; dBET6 or MZ1, respectively) and MDR1 inhibitor tariquidar (0.1 μM). Images are representative of three biological replicates. (N) Immunoblotting for MDR1 in SUM159 cells stably expressing FLAG-MDR1 after selection with hygromycin. (O) Long-term 14-day colony formation assay of SUM159 cells expressing FLAG-MDR1 that were treated with DMSO, MZ1 (0.1 μM), or MZ1 and tariquidar (0.1 μM) for 14 days, assessed by crystal violet staining. Representative images of three biological replicates are shown. (P and Q) RT-PCR (P) and immunoblot (Q) analysis of ABCB1 mRNA and MDR1 protein levels, respectively, in parental or MZ1-R HCT116, OVCAR3, and MOLT4 cells.

 

Fig. 4. Overexpression of MDR1 conveys intrinsic resistance to degrader therapies in cancer cells.
(A) Frequency of ABCB1 mRNA overexpression in a panel of cancer cell lines, obtained from cBioPortal for Cancer Genomics using Z-score values of >1.2 for ABCB1 mRNA levels (30). (B) Immunoblot for MDR1 protein levels in a panel of 10 cancer cell lines. Blots are representative of three independent experiments. (C) Cell viability by CellTiter-Glo assay in cancer cell lines expressing high or low MDR1 protein levels and treated with Thal SNS 032 for 5 days. Data were analyzed as % of DMSO control, presented as means ± SD of three independent assays. GI50 values were determined with Prism software. (D to F) Immunoblot analysis of CDK9 in MDR1-low (D) or MDR1-high (E) cell lines after Thal SNS 032 treatment for 4 hours. Blots are representative, and densitometric analyses using ImageJ are means ± SD from three blots, each normalized to the loading control, GAPDH. DC50 value determined with Prism. (G and H) Immunoblotting of control and MDR1-knockdown DLD-1 cells treated for 4 hours with increasing concentrations of Thal SNS 032 [indicated in (H)]. Blots are representative, and densitometric analysis data are means ± SD from three blots, each normalized to the loading control, GAPDH. DC50 value determined with Prism. (I) Drug efflux activity using rhodamine 123 efflux assays in DLD-1 cells treated with DMSO or 0.1 μM tariquidar. Data are means ± SD of three independent experiments. ***P ≤ 0.001 by Student’s t test. (J) Intracellular Thal SNS 032 levels, using the CRBN NanoBRET target engagement assay, in MDR1-overexpressing DLD-1 cells treated with DMSO or 0.1 μM tariquidar and increasing doses of Thal SNS 032. Data are % of DMSO control, presented as means ± SD of three independent assays. **P ≤ 0.01 and ***P ≤ 0.001 by Student’s t test. (K to N) Immunoblotting in DLD-1 cells treated with increasing doses of Thal SNS 032 (K and L) or dBET6 (M and N) alone or with tariquidar (0.1 μM) for 4 hours. Blots are representative, and densitometric analyses are means ± SD from three blots, each normalized to the loading control, GAPDH. DC50 value of Thal SNS 032 for CDK9 reduction (L) or of dBET6 for BRD4 reduction (N) determined with Prism. (O to T) Bliss synergy scores based on cell viability by CellTiter-Glo assay, colony formation, and immunoblotting in DLD-1 cells treated with the indicated doses of Thal SNS 032 (O to Q) or dBET6 (R to T) alone or with tariquidar. Cells were treated for 14 days for colony formation assays and 24 hours for immunoblotting.

 

Fig. 5. Repurposing dual kinase/MDR1 inhibitors to overcome degrader resistance in cancer cells.
(A and B) Drug efflux activity by rhodamine 123 efflux assays in degrader-resistant [dBET-R (A) or Thal-R (B)] A1847 cells after treatment with tariquidar, RAD001, or lapatinib (each 2 μM). Data are means ± SD of three independent experiments. *P ≤ 0.05 by Student’s t test. (C and D) CellTiter-Glo assay for the cell viability of parental, dBET6-R, or Thal-R A1847 cells treated with increasing concentrations of RAD001 (C) or lapatinib (D). Data were analyzed as % of DMSO control, presented as means ± SD of three independent assays. GI50 values were determined with Prism software. (E to I) Immunoblot analysis of degrader targets in parental (E), dBET6-R (F and G), and Thal-R (H and I) A1847 cells treated with increasing concentrations of RAD001 or lapatinib for 4 hours. Blots are representative, and densitometric analyses are means ± SD from three blots, each normalized to the loading control, GAPDH. DC50 value of dBET6 for BRD4 reduction (G) or of Thal SNS 032 for CDK9 reduction (I) determined with Prism. (J) Immunoblotting for cleaved PARP in dBET6-R or Thal-R A1847 cells treated with RAD001, lapatinib, or tariquidar (each 2 μM) for 24 hours. Blots are representative of three independent blots. (K to N) Immunoblotting for BRD4 in DLD-1 cells treated with increasing doses of dBET6 alone or in combination with either RAD001 or lapatinib [each 2 μM (K and L)] or KU-0063794 or afatinib [each 2 μM (M and N)] for 4 hours. Blots are representative of three independent experiments and, in (L), are means ± SD from three blots, each normalized to the loading control, GAPDH. DC50 value for BRD4 reduction (L) determined in Prism. (O) Colony formation by DLD-1 cells treated with DMSO, dBET6 (0.1 μM), lapatinib (2 μM), afatinib (2 μM), RAD001 (2 μM), KU-0063794 (2 μM), or the combination of inhibitor and dBET6 for 14 days. Images representative of three independent assays. (P and Q) Immunoblotting for CDK9 in DLD-1 cells treated with increasing doses of Thal SNS 032 and/or RAD001 (2 μM) or lapatinib (2 μM) for 4 hours. Blots are representative, and densitometric analyses are means ± SD from three blots, each normalized to the loading control, GAPDH. DC50 value for CDK9 reduction determined with Prism (Q). (R) Colony formation in DLD-1 cells treated with DMSO, Thal SNS 032 (0.5 μM), lapatinib (2 μM), and/or RAD001 (2 μM) as indicated for 14 days.

 

Fig. 6. Combining MEK1/2 degraders with lapatinib synergistically kills MDR1-overexpressing KRAS-mutant CRC cells and tumors.
(A and B) ABCB1 expression in KRAS-mutant CRC cell lines from cBioPortal (30) (A) and MDR1 abundance in select KRAS-mutant CRC cell lines (B). (C) Cell viability assessed by CellTiter-Glo in CRC cells treated with increasing doses of MS432 for 5 days, analyzed as % of DMSO control. GI50 value determined with Prism software. (D) Colony formation by CRC cells 14 days after treatment with 1 μM MS432. (E) MEK1/2 protein levels assessed by immunoblot in CRC lines SKCO1 (low MDR1) or LS513 (high MDR1) treated with increasing doses of MS432 for 4 hours. (F) Rhodamine 123 efflux in LS513 cells treated with DMSO, 2 μM tariquidar, or 2 μM lapatinib. (G and H) Immunoblotting analysis in LS513 cells treated with increasing doses of MS432 alone or in combination with tariquidar (0.1 μM) or lapatinib (5 μM) for 24 hours. DC50 value for MEK1 levels determined with Prism. (I) Immunoblotting in LS513 cells treated with DMSO, PD0325901 (0.01 μM), lapatinib (5 μM), or the combination for 48 hours. (J and K) Immunoblotting in LS513 cells treated either with DMSO, MS432 (1 μM), tariquidar (0.1 μM) (J), or lapatinib (5 μM) (K), alone or in combination. (L) Bliss synergy scores determined from cell viability assays (CellTiter-Glo) in LS513 cells treated with increasing concentrations of MS432, lapatinib, or the combination. (M and N) Colony formation by LS513 cells (M) and others (N) treated with DMSO, lapatinib (2 μM), MS432 (1 μM), or the combination for 14 days. (O and P) Immunoblotting in LS513 cells treated with increasing doses of MS934 alone (O) or combined with lapatinib (5 μM) (P) for 24 hours. (Q and R) Tumor volume of LS513 xenografts (Q) and the body weights of the tumor-bearing nude mice (R) treated with vehicle, MS934 (50 mg/kg), lapatinib (100 mg/kg), or the combination. n = 5 mice per treatment group. In (A) to (R), blots and images are representative of three independent experiments, and quantified data are means ± SD [SEM in (Q) and (R)] of three independent experiments; ***P ≤ 0.001 by Student’s t test.

 

Fig. 7. Lapatinib treatment improves KRASG12C degrader therapies in MDR1-overexpressing CRC cell lines.
(A and B) Colony formation by SW1463 (A) or SW837 (B) cells treated with DMSO, LC-2 (1 μM), or MRTX849 (1 μM) for 14 days. Images representative of three independent assays. (C to E) Immunoblotting in SW1463 cells (C and D) and SW837 cells (E) treated with DMSO, LC-2 (1 μM), tariquidar (0.1 μM) (C), or lapatinib (5 μM) (D and E) alone or in combination for 48 hours. Blots are representative of three independent experiments. (F and G) Bliss synergy scores based on CellTiter-Glo assay for the cell viability of SW1463 (F) or SW837 (G) cells treated with increasing concentrations of LC-2, lapatinib, or the combination. Data are means of three experiments ± SD. (H and I) Colony formation of SW1463 (H) or SW837 (I) cells treated as indicated (−, DMSO; LC-2, 1 μM; lapatinib, 2 μM; tariquidar, 0.1 μM) for 14 days. Images representative of three independent assays. (J) Rationale for combining lapatinib with MEK1/2 or KRASG12C degraders in MDR1-overexpressing CRC cell lines. Simultaneous blockade of MDR1 and ErbB receptor signaling overcomes degrader resistance and ErbB receptor kinome reprogramming, resulting in sustained inhibition of KRAS effector signaling.

SOURCE

Other articles in this Open Access Scientific Journal on PROTAC therapy in cancer include

Accelerating PROTAC drug discovery: Establishing a relationship between ubiquitination and target protein degradation

The Vibrant Philly Biotech Scene: Proteovant Therapeutics Using Artificial Intelligence and Machine Learning to Develop PROTACs

The Map of human proteins drawn by artificial intelligence and PROTAC (proteolysis targeting chimeras) Technology for Drug Discovery

Cancer Policy Related News from Washington DC and New NCI Appointments

Reportor: Stephen J. Williams, PhD.

Biden to announce appointees to Cancer Panel, part of initiative to cut death rate

The president first launched the initiative in 2016 as vice president.

By Mary Kekatos

July 13, 2022, 3:00 PM

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America This Morning

America This Morning

President Joe Biden will announce Wednesday his appointees to the President’s Cancer Panel, ABC News can exclusively reveal.

The Cancer Panel is part of Biden’s Cancer Moonshot Initiative, which was relaunched in February, with a goal of slashing the national cancer death rate by 50% over the next 25 years.MORE: Biden relaunches cancer ‘moonshot’ initiative to help cut death rate

Biden will appoint Dr. Elizabeth Jaffee, Dr. Mitchel Berger and Dr. Carol Brown to the panel, which will advise him and the White House on how to use resources of the federal government to advance cancer research and reduce the burden of cancer in the United States.

Jaffee, who will serve as chair of the panel, is an expert in cancer immunology and pancreatic cancer, according to the White House. She is currently the deputy director of the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University and previously led the American Association for Cancer Research.

PHOTO: In this Sept. 8, 2016, file photo, Dr. Elizabeth M. Jaffee of the Pancreatic Dream Team attends Stand Up To Cancer (SU2C), a program of the Entertainment Industry Foundation (EIF), in Hollywood, Calif.
In this Sept. 8, 2016, file photo, Dr. Elizabeth M. Jaffee of the Pancreatic Dream Team attends Stand Up To Cancer (SU2C), a program of the Entertainment Industry Foundation (EIF), in Hollywood, Calif.ABC Handout via Getty Images, FILE

Berger, a neurological surgeon, directs the University of California, San Francisco Brain Tumor Center and previously spent 23 years at the school as a professor of neurological surgery.

Brown, a gynecologic oncologist, is the senior vice president and chief health equity officer at Memorial Sloan Kettering Cancer Center in New York City. According to the White House, much of her career has been focused on eliminating cancer care disparities due to racial, ethnic, cultural or socioeconomic factors.

Additionally, First Lady Jill Biden, members of the Cabinet and other administration officials are holding a meeting Wednesday of the Cancer Cabinet, made up of officials across several governmental departments and agencies, the White House said.

The Cabinet will introduce new members and discuss priorities in the battle against cancer including closing the screening gap, addressing potential environmental exposures, reducing the number of preventable cancer and expanding access to cancer research.MORE: Long Island school district found to have higher rates of cancer cases: Study

It is the second meeting of the cabinet since Biden relaunched the initiative in February, which he originally began in 2016 when he was vice president.

Both Jaffee and Berger were members of the Blue Ribbon Panel for the Cancer Moonshot Initiative led by Biden.

The initiative has personal meaning for Biden, whose son, Beau, died of glioblastoma — one of the most aggressive forms of brain cancer — in 2015.

“I committed to this fight when I was vice president,” Biden said at the time, during an event at the White House announcing the relaunch. “It’s one of the reasons why, quite frankly, I ran for president. Let there be no doubt, now that I am president, this is a presidential, White House priority. Period.”

The initiative has several priority actions including diagnosing cancer sooner; preventing cancer; addressing inequities; and supporting patients, caregivers and survivors.

PHOTO: In this June 14, 2016, file photo, Dr. Carol Brown, physician at Memorial Sloan Kettering Cancer Center, gives a presentation, at The White House Summit on The United State of Women, in Washington, D.C.
In this June 14, 2016, file photo, Dr. Carol Brown, physician at Memorial Sloan Kettering Cancer Center, gives a presentation, at The White House Summit on The United State of Women, in Washington, D.C.NurPhoto via Getty Images, FILE

The White House has also issued a call to action to get cancer screenings back to pre-pandemic levels.

More than 9.5 million cancer screenings that would have taken place in 2020 were missed due to the COVID-19 pandemic, according to the National Institutes of Health.MORE: Louisiana’s ‘Cancer Alley’ residents in clean air fight

“We have to get cancer screenings back on track and make sure they’re accessible to all Americans,” Biden said at the time.

Since the first meeting of the Cancer Cabinet, the Centers for Disease Control and Prevention has issued more than $200 million in grants to cancer prevention programs, the Centers for Medicaid & Medicare Services implemented a new model to reduce the cost of cancer care, and the U.S. Patent and Trademark Office said it will fast-track applications for cancer immunotherapies.

ABC News’ Sasha Pezenik contributed to this report.

Biden to tap prominent Harvard cancer surgeon to head National Cancer Institute

Monica Bertagnolli brings leadership experience in cancer clinical trials funded by the $7 billion research agency

headshot of Monica Bertagnolli
Monica BertagnolliASCO; GLENN DAVENPORT

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President Joe Biden is expected to pick cancer surgeon Monica Bertagnolli as the next director of the National Cancer Institute (NCI). Bertagnolli, a physician-scientist at Brigham and Women’s Hospital, the Dana-Farber Cancer Center, and Harvard Medical School, specializes in gastrointestinal cancers and is well known for her expertise in clinical trials. She will replace Ned Sharpless, who stepped down as NCI director in April after nearly 5 years.

The White House has not yet announced the selection, first reported by STAT, but several cancer research organizations closely watching for the nomination have issued statements supporting Bertagnolli’s expected selection. She is “a national leader” in clinical cancer research and “a great person to take the job,” Sharpless told ScienceInsider.

With a budget of $7 billion, NCI is the largest component of the National Institutes of Health (NIH) and the world’s largest funder of cancer research. Its director is the only NIH institute director selected by the president. Bertagnolli’s expected appointment, which does not require Senate confirmation, drew applause from the cancer research community

Margaret Foti, CEO of the American Association for Cancer Research, praised Bertagnolli’s “appreciation for … basic research” and “commitment to ensuring that such treatment innovations reach patients … across the United States.” Ellen Sigal, chair and founder of Friends of Cancer Research, says Bertagnolli “brings expertise the agency needs at a true inflection point for cancer research.”

Bertagnolli, 63, will be the first woman to lead NCI. Her lab research on tumor immunology and the role of a gene called APC in colorectal cancer led to a landmark trial she headed showing that an anti-inflammatory drug can help prevent this cancer. In 2007, she became the chief of surgery at the Dana-Farber Brigham Cancer Center.

She served as president of the American Society of Clinical Oncology in 2018 and currently chairs the Alliance for Clinical Trials in Oncology, which is funded by NCI’s National Clinical Trials Network. The network is a “complicated” program, and “Monica will have a lot of good ideas on how to make it work better,” Sharpless says.

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One of Bertagnolli’s first tasks will be to shape NCI’s role in Biden’s reignited Cancer Moonshot, which aims to slash the U.S. cancer death rate in half within 25 years. NCI’s new leader also needs to sort out how the agency will mesh with a new NIH component that will fund high-risk, goal-driven research, the Advanced Research Projects Agency for Health (ARPA-H).

Bertagnolli will also head NCI efforts already underway to boost grant funding rates, diversify the cancer research workplace, and reduce higher death rates for Black people with cancer.

The White House recently nominated applied physicist Arati Prabhakar to fill another high-level science position, director of the White House Office of Science and Technology Policy (OSTP). But still vacant is the NIH director slot, which Francis Collins, acting science adviser to the president, left in December 2021. And the administration hasn’t yet selected the inaugural director of ARPA-H.

Correction, 22 July, 9 a.m.: This story has been updated to reflect that Francis Collins is acting science adviser to the president, not acting director of the White House Office of Science and Technology Policy.

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