Wednesday, November 29, 2023

NEJM Kadakia et al. on TCET

 AI summary from Chat GPT of Kadakia et al, TCET op ed, in NEJM on 11/30/2023.


The Op-Ed from the New England Journal of Medicine, written by Kushal T. Kadakia, Daniel B. Kramer, and Robert W. Yeh, provides a critical perspective on the Medicare coverage of emerging medical technologies, particularly focusing on the Breakthrough Devices Program and the Transitional Coverage for Emerging Technologies (TCET) pathway.

The authors discuss the challenges in bridging the gap between FDA authorization and Medicare coverage for new medical devices. They note that while the FDA authorizes nearly 3,000 new medical devices each year, including breakthrough devices intended to address unmet medical needs or life-threatening conditions, these devices often face delays in Medicare coverage and challenges in postmarketing surveillance. This delay affects patient access to new technologies, as insurance coverage determines accessibility.

The article reviews the Medicare Coverage of Innovative Technologies (MCIT) rule issued in 2021 by the Trump administration, which granted automatic Medicare coverage to all FDA-authorized breakthrough devices for four years after authorization. This rule, however, was criticized for potentially undermining safety and was subsequently repealed by the Biden administration.

In response, CMS proposed the TCET pathway in 2023, aiming to expedite access to breakthrough devices while preserving Medicare's role in evidence generation. TCET allows manufacturers to participate in consultative meetings with CMS before FDA authorization to create evidence-development plans, with guaranteed national coverage determinations within six months after FDA authorization.

Despite these efforts, the authors argue that TCET does not address fundamental issues in Medicare's coverage policy, such as the need for improved infrastructure for evidence generation and the limitations of implementing reforms through regulation rather than legislation. They suggest that broader collaborative efforts and legislative action are required to effectively evaluate and cover emerging medical technologies in Medicare.

In summary, while TCET represents a step towards expedited access to new technologies for Medicare beneficiaries, it has limitations and does not fully resolve the challenges of linking regulatory approval with patient access to emerging medical technologies.


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Here, I asked for a 150 word summary of the paired podcast.

In this interview, Stephen Morrissey from the New England Journal of Medicine speaks with Dr. Robert Yeh, a professor at Harvard Medical School and director at Beth Israel Deaconess Medical Center, about Medicare coverage for emerging medical technologies. Dr. Yeh explains that many medical devices, even after FDA approval, are not covered by Medicare. Coverage typically occurs through local coverage determinations by Medicare Administrative Contractors or, less frequently, through national coverage determinations by CMS.

Dr. Yeh discusses the 2016 introduction of the FDA's "Breakthrough Devices" designation for devices addressing unmet needs in life-threatening or debilitating conditions. About 900 devices have received this designation, with around 80 authorized, predominantly in the cardiovascular category.

The interview also covers the Trump administration's controversial MCIT proposal, which granted automatic Medicare coverage for four years to all breakthrough devices after FDA approval but was repealed by the Biden administration. This led to the proposal of the Transitional Coverage for Emerging Technologies (TCET), allowing manufacturers to engage with CMS prior to FDA approval, assuring a national coverage decision within six months of approval. This approach aims to expedite coverage decisions and involves CMS in evidence evaluation, potentially using real-world data.

However, Dr. Yeh notes limitations in the TCET, including its initial restriction to five devices per year and the potential underutilization, similar to the previously undersubscribed parallel review pathway. He also mentions the challenge of CMS's limited resources in evaluating a large number of devices.

Lastly, Dr. Yeh highlights the limitations of implementing reforms through regulation instead of legislation, noting the instability and changeability of rules and the challenges in passing relevant legislation given the current political climate.

Notepad: Interesting Methodology Documents

 In THAMBISETTY, STAT, ADU

he says we should be more proactive about detecting public health harms in ADU family drugs.   Should there even be a REMS?  If not, how do we manage harms like ARIA, plus the high incidence of random events in the very old.

https://www.statnews.com/2023/11/28/lecanemab-leqembi-aducanumab-donanemab-side-effects-aria-brain/

###

On the topic of HARMS, see position paper on measuring harms, which come in randomly at unknown rates, in RCTs.   JUNQUEIRA,  J Clin Epi 2023.

https://pubmed.ncbi.nlm.nih.gov/37100738/

CONSORT Harms 2022 statement, explanation, and elaboration: updated guideline for the reporting of harms in randomized trials

Daniela R Junqueira 1, 

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On the topic of imperfect gold standards, 2019 PLOS ONE

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0223832

Diagnostic test evaluation methodology: A systematic review of methods employed to evaluate diagnostic tests in the absence of gold standard – An update

Chinyereugo M. Umemneku Chikere 

At FDA MCED meeting 11/29, made the point that you can only show Cologuard is better than FIT, by having a 3rd test (colonoscopy) better than both.  The only sure paradigm to compare 2 tests, is to have 3 tests (!).

###

Excellent 2023 paper on Dec impact theory - review.  

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10004522/

PLoS One. 2023; 18(3): e0280582.

Published online 2023 Mar 10. doi: 10.1371/journal.pone.0280582

PMCID: PMC10004522

PMID: 36897859

Decision impact studies, evidence of clinical utility for genomic assays in cancer: A scoping review

Gillian Parker 2023


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FDA MCED - Discussion of spec sens and spectrum shift - something talked about since 1960s.

Test research vs diagnostic reserach "classic"

https://pubmed.ncbi.nlm.nih.gov/14981027/

Comparative Study Clin Chem

. 2004 Mar;50(3):473-6. doi: 10.1373/clinchem.2003.024752.

Test research versus diagnostic research

Karel G M Moons 1, Cornee J Biesheuvel, Diederick E Grobbee

Affiliations expand

PMID: 14981027 DOI: 10.1373/clinchem.2003.024752

Saturday, November 25, 2023

Notepad: SOFTWARE LISTING FOR AI RESEARCH AND WRITING

Many or most of these I've heard about through numerous videos on the Andy Stapleton channel.

Things I really use [xxx]
  • General Language AI
    • ChatGPT (Open AI) (have subscription) [XXX]
    • CLAUDE (Anthropic) (have free level) [XXX]
  • Science Research AI  (e.g. pubmed on AI steroids)
    • SCITE.AI (have subscription) [XXX]
      • Seems very powerful but also confusing to navigate
      • Shows real text where any number of documents cite to the index document.
      • For example, index is SMITH ET AL.  
      • It shows papers that cite SMITH with visible extracted text such as , "We were able to confirm and extend the results of SMITH ET AL" snippet.
    • Also noted and related:
      • ELICIT.org (Find seed paper, Andy Stapleton here. 1:35.
        • Then try taking that seed paper to LitMaps for context here. (forward and later works) 2:27
          • Click "Discover more related articles"
        • Put into manager like Mendeley (rec) or Zotero [*]
        • ConnectedPapers also gives prior and derivative works (as does SCITE)
        • DocAnalyzer.ai wlll analyze documents (although so will ChatGPT?)
          • "Upload collection of documents and chat with them" here see details 9:00.  Stapleton notes it can be finicky but that encourages you to be careful and logical.
      • One also sees mentioned...
      • ConsensusUS.org
      • JENNI.ai
      • SemanticScholar.org
      • and
      • Metaphor.Systems
      • SCI SPACE "literature review space"  https://scispace.com/  At YT.
      • There are numerous YT comparison videos with keywords like Semanticscholar or SciSpace.
      • See the "Andy Stapleton" YouTube channel
    • Don't Forget these free services:
  • Text clipper/paster:
    • Text Blaze (installed Chrome button, free level) [XXX]
    • https://dashboard.blaze.today/folder/LFrx9xrztJrguvJrYefs
    • This gives you small (free) or huge (pay) storage of text snippets and forms to plug into writings.  One YTer used it to manage a wide range of AI personas.
    • Also noted as alternatives to TextBlaze:
      • TextExpander.com
      • Espanso (free)
      • Getmagical.com
      • Oslash.com
    • Others I actually use, not necessarily "research tools"
      • I also have TabExtend which was a one time purchase and provides an html-like browser page for writing and storing links [XXX]
      • I have a subscription to READWISE for storing articles, [XXX] Readwise Reader, small subscription.   Free readers did not work as well for me.  Readwise auto-audio-reads all content, which I use.
      • I use FEEDLY as a blog and article auto aggregator to skim.[XXX}
      • I use a MS ONENOTE CLIPPER button to store articles in MS Office ONENOTE for client memos. [XXX]
      • I do most of my work in NOTION rather than One Note though. [XXX]
A channel, Science Grad School Hub, has released a "hub" for AI software like those above.  Video and website.


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I use an older program called EndNote that is not as well web integrated.  I had troubles getting Zotero to put hyperlinks in my bibliography the way I wanted.  (For example, my Monthly Headlines Report is designed to be links only as the reference footnote).

Wednesday, November 22, 2023

Claude Appraises CDK4/6 drugs as a area of need for novel biomarkers, then CHAT GPT

 Let's say you are a company that can elucidate cellular cancer pathways.  Where should you invest your R&D time?

In this case, CHAT GPT probably has the better answer.  

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I am a genomic test expert and consultant. I am working with a company that can elucidate the cell biology of cancer cells (for example ten patients with breast or lung cancer, each cancer may have a different cell biology activation or suppression fingerprint). The company is looking for good use cases for its technology. i am wondering about the CDK4/6 family of drugs. These are used in cases that are ER positive HER2 negative, but that's a huge group of patients and not a very tight way to predict CDK4/6 responders. Please discuss the mechanism of action of CDK4/6 drugs, and the current standard of care for biomarkers to select better responders (if there are any existing good biomarkers.) If the CDk4/6 class of drugs is expensive, but with many treatment failures, then it could be a high need for better predictive biomarkers based on new forms of technology that elucidate cell biology pathways in each patient's personal cancer. I understand you are not a doctor and I am just looking for helpful insights based on your body of knowledge.

CLAUDE:

Here are a few key points regarding CDK4/6 inhibitors and the potential to improve response prediction:

Mechanism of Action:

  • CDK4/6 are kinases that regulate cell cycle progression and proliferation. In many cancers, CDK4/6 are overactivated, leading to uncontrolled growth.
  • CDK4/6 inhibitors (e.g. palbociclib, ribociclib, abemaciclib) bind to and inhibit CDK4/6, blocking proliferation signals in cancer cells.

Current Use and Limitations:

  • CDK4/6 inhibitors are standard-of-care for metastatic ER+ HER2- breast cancer in combination with endocrine therapy. However, intrinsic and acquired resistance affects a substantial number of patients.
  • Beyond ER/PR status, there are currently no validated predictive biomarkers to select patients most likely to respond to CDK4/6 inhibitors. More precise selection could maximize benefit and avoid overtreatment.

Potential for Cell Biology Profiling:

  • Different signaling networks and cell cycle abnormalities likely drive reliance on CDK4/6 signaling across patients' cancers. Elucidating these molecular alterations could better predict dependence on CDK4/6 for proliferation.
  • Techniques that provide in-depth, personalized cell biology profiles could uncover patient subsets with cell cycle dysregulation making their cancers especially susceptible to CDK4/6 inhibition.

In summary, CDK4/6 inhibitors fill an important unmet need but better response biomarkers are needed.

New cell biology profiling approaches that uncover patient-specific cancer cell dependencies may enable more precise CDK4/6 inhibitor patient selection and improve outcomes.


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CHAT GPT 4

CDK4/6 inhibitors are a class of drugs used in the treatment of certain types of cancers, most notably hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) breast cancer. These drugs work by targeting cyclin-dependent kinases 4 and 6 (CDK4/6), which are proteins that play a crucial role in cell division. By inhibiting these kinases, CDK4/6 inhibitors can interrupt the cell cycle, thereby preventing cancer cells from proliferating. In the context of breast cancer, estrogen receptor (ER) positive status is one of the key determinants for the use of CDK4/6 inhibitors. These drugs are typically used in combination with hormonal therapies, such as aromatase inhibitors or fulvestrant, as they can synergistically suppress the proliferation of cancer cells that depend on hormonal signals for growth. The standard of care for selecting patients who may benefit from CDK4/6 inhibitors primarily includes determining the hormone receptor status and the HER2 status. However, the biomarker landscape for predicting response to CDK4/6 inhibitors is still evolving. Some of the potential biomarkers that are being investigated include: Rb Expression: Retinoblastoma (Rb) protein is a key downstream effector of CDK4/6, and its presence is crucial for the efficacy of CDK4/6 inhibitors. Loss of Rb function might indicate resistance to these drugs. Cyclin D1 Amplification: Overexpression of cyclin D1, which forms a complex with CDK4/6, might be associated with a better response to CDK4/6 inhibitors. p16INK4a Loss: The tumor suppressor p16INK4a inhibits CDK4/6, and loss of this protein could theoretically suggest a potential benefit from CDK4/6 inhibition, although this is not yet a standard predictive biomarker. PIK3CA Mutations: These mutations are common in ER-positive breast cancer and may predict sensitivity to CDK4/6 inhibitors, although evidence is still emerging. Since CDK4/6 inhibitors are indeed expensive and not all patients respond to them, there is a significant interest in developing better predictive biomarkers. Technologies that elucidate cell biology pathways and the molecular profile of each patient's cancer, such as next-generation sequencing, proteomics, and single-cell analyses, could potentially identify more precise biomarkers. These could enable the stratification of patients into subgroups that are more likely to benefit from CDK4/6 inhibitors, thus optimizing treatment outcomes and cost-effectiveness. Given your expertise and the technology at your disposal, exploring the intricate networks of cell signaling and gene expression in cancer cells could uncover novel predictive biomarkers. You might consider focusing on how the various pathways interact with the CDK4/6 axis and how these interactions may predict the efficacy of CDK4/6 inhibitors. Such a personalized approach to cancer treatment could be a game-changer in oncology, offering more targeted and effective therapies for patients.


CHAT GPT 3.5 (About as good)

Certainly, I can provide you with information on the mechanism of action of CDK4/6 inhibitors and the current state of biomarker selection for their use in breast cancer treatment.


CDK4/6 (Cyclin-Dependent Kinase 4 and 6) inhibitors are a class of drugs used in the treatment of hormone receptor-positive (HR+), HER2-negative breast cancer. These inhibitors include drugs like palbociclib, ribociclib, and abemaciclib. The mechanism of action of CDK4/6 inhibitors involves targeting specific proteins involved in cell cycle regulation. Here's how they work:


Cell Cycle Regulation: CDK4 and CDK6 are enzymes that play a crucial role in regulating the cell cycle. They promote cell cycle progression by phosphorylating proteins that drive the cell from the G1 phase to the S phase, where DNA replication occurs.


Cyclin Binding: CDK4 and CDK6 require the binding of specific cyclin proteins (cyclin D) to become active. Once active, these CDKs phosphorylate the retinoblastoma protein (Rb), which releases E2F transcription factors, allowing the cell to enter the S phase and initiate DNA replication.


Inhibition by CDK4/6 Inhibitors: CDK4/6 inhibitors, such as palbociclib, ribociclib, and abemaciclib, selectively block the activity of CDK4 and CDK6. By doing so, they prevent the phosphorylation of Rb and the release of E2F, thus inhibiting cell cycle progression and cell proliferation.


Now, regarding biomarkers for selecting better responders to CDK4/6 inhibitors in breast cancer treatment:


As of my last knowledge update in January 2022, there is no single, well-established biomarker that is routinely used to predict which breast cancer patients will respond best to CDK4/6 inhibitors. However, ongoing research is focused on identifying potential biomarkers to improve patient selection. Some factors that are considered in the decision to use CDK4/6 inhibitors include:


Hormone Receptor Status: CDK4/6 inhibitors are primarily used in HR+ breast cancer patients, so the presence of hormone receptors (estrogen receptor-positive and/or progesterone receptor-positive) is a fundamental criterion for their use.


Progression-Free Interval: The time elapsed between the completion of previous endocrine therapy and disease recurrence (progression-free interval) can influence the decision to use CDK4/6 inhibitors.


Molecular Profiling: In some cases, molecular profiling of the tumor may provide additional information on genetic alterations or pathways that could guide treatment decisions. However, specific biomarkers for CDK4/6 inhibitor response have not been widely adopted in clinical practice.


Clinical Factors: Clinical factors, such as the patient's overall health, age, and treatment history, are also considered when determining the use of CDK4/6 inhibitors.


It's important to note that personalized medicine approaches, such as elucidating cell biology pathways in individual patient tumors, are gaining momentum in oncology. Advancements in genomic and proteomic technologies are helping researchers identify potential biomarkers that could improve the selection of patients who are likely to benefit from CDK4/6 inhibitors. However, the development and validation of such biomarkers are ongoing processes and may not yet be part of standard clinical practice.


Ultimately, the decision to use CDK4/6 inhibitors in breast cancer treatment should be made in consultation with a medical oncologist who can consider all available clinical and molecular information to determine the most appropriate treatment plan for each patient. Additionally, it's essential to stay updated on the latest research and clinical guidelines, as the field of oncology is continually evolving.

Claude Summarizes Dagogo-Jack 2023

 Integrated Radiology, Pathology, and Pharmacy Program to

Accelerate Access to Osimertinib

Ibiayi Dagogo-Jack, MD et al.

CLAUDE

I am a genomics expert. You are also a genomics expert. please summarize this paper in detail. I expect a detailed summary to be circa 500 words.

BQ

Here is a detailed 447-word summary of the key points from the research paper:

This paper describes the development and evaluation of an integrated radiology, pathology, and pharmacy program to accelerate access to the EGFR inhibitor osimertinib for patients with EGFR-mutant non-small cell lung cancer (NSCLC). The goal was to minimize delays in initiating optimal targeted therapy by optimizing molecular testing workflows and enabling early pharmacy engagement.

The intervention involved parallel workflows combining interventional radiology to obtain tumor samples, rapid nucleic acid extraction and EGFR mutation testing on frozen tumor tissue, coordination between pathology and molecular diagnostics teams, and early engagement of an internal specialty pharmacy. The specialty pharmacy team initiated prior authorization paperwork and insurance reviews for osimertinib prior to biopsy.

The study included 222 patients who underwent testing via this pathway from 2020-2021. EGFR mutations were detected in 22% (n=49). For EGFR-mutant cases, the median turnaround time from biopsy to results was 1 day. Thirty-one EGFR-mutant patients (63%) were prescribed osimertinib through the pathway. The median time from osimertinib prescription to dispensation was 3 days, with 42% receiving the medication within 48 hours. The median time from biopsy to osimertinib dispensation was 5 days; 10% of patients received osimertinib within 24 hours of results.

Compared to historical cohorts, the integrated pathway significantly reduced the median time from biopsy to results (1 day vs. 7 days) and biopsy to treatment initiation (5 days vs. 23 days) for EGFR-mutant cases. However, patients requiring financial assistance experienced delays in accessing osimertinib.

In summary, coupling rapid molecular workflows with early pharmacy engagement and management of insurance/financial issues led to more timely access to targeted therapy for patients with EGFR-mutant NSCLC. Although resource intensive, this integrated approach ensures patients receive optimal first-line treatment without unnecessary delays following diagnosis.




Editorials Expanding the Multidisciplinary Team in Prediagnostic Care of Lung Cancer: How Else Can We Improve Time to Targeted Treatment in Metastatic Non–Small-Cell Lung Cancer? Kathryn F. Mileham, MD, FACP1 and Carol J. Farhangfar, MBA, PhD

I am a genomics expert. You are also a genomics expert. please summarize this OP ED paper in detail. I expect a detailed summary to be circa 500 words, but it could be less.


 

Here is a 447-word summary of the key points from this editorial:


This editorial by Mileham and Farhangfar comments on a study by Dagogo-Jack et al. that described an integrated radiology, pathology, and pharmacy workflow to accelerate access to the EGFR inhibitor osimertinib for patients with EGFR-mutant metastatic non-small cell lung cancer (NSCLC).


The authors commend Dagogo-Jack et al. for achieving a median time-to-treatment (TTT) initiation of just 5 days, significantly shorter than typical real-world timeframes. They note this was accomplished through coordinated parallel workflows for tissue acquisition and handling, prompt in-house testing, and early pharmacy engagement for insurance authorization.


However, the authors discuss concerns about broader applicability and practicality of implementing such an approach, given its reliance on resource-intensive components like specialty pharmacy, customized assays, and dedicated coordination. Adoption would require systems change across multiple disciplines. Also, the focus on EGFR testing only addresses 15% of lung cancer cases.


The authors suggest more practical strategies could still improve TTT by:


Optimizing platforms for faster test turnaround time from standard specimen types

Strategic use of existing resources like pharmacy technicians for streamlined prior authorization

Better integrating diagnostics with electronic medical records to prompt faster treatment decisions

The impact of rapid TTT on patient outcomes like progression-free or overall survival remains unclear. However, assessing effects on quality of life and patient-reported outcomes like anxiety should be feasible on a shorter timeframe.


Ultimately, the takeaway is that all centers should evaluate workflow inefficiencies and communication gaps to enhance coordinated, multidisciplinary efforts around precision diagnosis and treatment in lung cancer. This may lead to incremental improvements in TTT even without replicating the entire approach.


In summary, while resource-intensive components limit generalizability, the concepts emphasize the need for cross-disciplinary collaboration and evaluation of process bottlenecks, which could benefit TTT optimization at diverse centers.