Amazon (not the rainforest)

 Now that Jeff Bezos anounces that he is to step aside as CEO, a summary in one figure:

The history, in one book:

Clinical utility of genetic testing for breast cancer

 Breast Cancer Risk Genes — Association Analysis in More than 113,000 Women

Genetic testing for breast cancer susceptibility is widely used, but for many genes, evidence of an association with breast cancer is weak, underlying risk estimates are imprecise, and reliable subtype-specific risk estimates are lacking.

However,

 We found strong evidence of an association with breast cancer risk (Bayesian false-discovery probability, <0.05) for protein-truncating variants in 9 genes, with a P value of less than 0.0001 for 5 genes (ATM, BRCA1, BRCA2, CHEK2, and PALB2) and a P value of less than 0.05 for the other 4 genes (BARD1, RAD51C, RAD51D, and TP53).

  None of the other 25 genes in the panel had a Bayesian false-discovery probability of less than 0.10. Of note, 19 genes had an upper limit of the 95% confidence interval of the odds ratio of less than 2.0, with 2.0 representing a proposed threshold for “pathogenic, moderate risk alleles”9; we therefore conclude that these genes are not informative for the prediction of breast cancer risk. We confirmed that missense variants in BRCA1, BRCA2, and TP53 that would be classified as pathogenic according to clinical guidelines are indeed associated with clinically significant risks. We also found that rare missense variants in CHEK2 overall, as well as variants in specific domains in ATM, are associated with moderate risk.

The summary:

 Variants in 8 genes — BRCA1, BRCA2, PALB2, BARD1, RAD51C, RAD51D, ATM, and CHEK2 — had a significant association with breast cancer risk.

 

Insights from Biotech on COVID-19

 Biotechnology in the Time of COVID-19

Our journey is far from over. In my mind, it is our responsibility as an industry, as a company, and as global citizens to help ensure a pandemic of this magnitude never happens again. I’ve worked in infectious diseases for my entire career—and I’ve been exposed to the disruption, the health crises, and the economic fallout they create. It is up to all of us to bring forward solutions. I hope you will join the many companies, worldwide health agencies, and nongovernmental organizations that are answering the call. Together, we can all use our skills to help serve people in this remarkably urgent time of need.

Great words by Stephane Bancel, CEO of Moderna 

Laboratory medicine as a data science (2)

 Recent evolutions of machine learning applications in clinical laboratory medicine

You'll find an interesting review about laboratory medicina and machine learning in this article, with applicatons to chemical chemistry, hematology and microbiology.

There has been a recent rise in various ML applications in the field of clinical laboratory medicine. Despite the potential of ML to ameliorate the efficiency of laboratory processes and optimize diagnostic workflows, translation into routine practice is still slow-going. There is a need to raise more awareness about the vast ML landscape among laboratory professionals. Educational programs dealing with theoretical ML concepts as well as their associated challenges and opportunities could stimulate wider acceptance and exploitation in the clinical laboratory. It is important to realize that ML will not  immediately function as a surrogate of the laboratory professional’s neural networks, but will rather act as a valuable supportive tool with the capability of increasing the odds on optimal outcomes for patients accessing health care.

 Margaret Huntington Boehner

The risk in a funambulist society

 The Great Risk Shift. The New Economic Insecurity and the Decline of the American Dream

Behavioral economics of markets

 The Community of Advantage. A Behavioural Economist's Defence of the Market

The threat of populism is among us

 The Populist Temptation: Economic Grievance and Political Reaction in the Modern Era

Antitrust

 The Antitrust Paradigm. Restoring a Competitive Economy

A plea for public patents on COVID prevention and treatment

 Funding of Pharmaceutical Innovation During and After the COVID-19 Pandemic

Extensive public investments also are being made in therapeutics. The 2 most prominent monoclonal antibodies (by Regeneron and Lilly) have come to market with substantial governmental support for product commercialization. Both products derive from therapeutic research platforms established with governmental support before the COVID-19 pandemic, but product commercialization and manufacturing received major additional investments in 2020. Separately, the National Institutes of Health (NIH) Rapid Acceleration of Diagnostics program has committed $1.5 billion to supporting development of diagnostic tests related to COVID-19. The specifics of the federal contracts largely remain confidential.

Why do they remain confidential? 

The lesson of the COVID-19 experience is that, when innovation in the life sciences is imperative, the traditional reliance on pharmaceutical industry prices and profits is jettisoned in favor of governmental grants and procurement. Sustained public funding for product development and commercialization will permit the sustained financing of innovation, a renewed attention to major public health needs, and the global position of the US pharmaceutical industry.

If there is public funding, why there aren't public patents? 

Technology assessment effectiveness

 Does the use of health technology assessment have an impact on the utilisation of health care resources? Evidence from two European countries

And the answer is YES.

This study suggests that medicine utilisation does respond to the positive recommendations of HTA bodies.However, if HTA capacity is organised primarily regionally, considerable effort may be required in coordination, to ensure consistent and rigorous assessments and adequate implementation of HTA findings.

 

AI in Health Care

 Artificial Intelligence in Health Care. Benefits and Challenges of Technologies to Augment Patient Care

This report is being jointly published by the Government Accountability Office (GAO) and the National Academy of Medicine (NAM). Part One of this joint publication is the full presentation of GAO’s Technology Assessment: Artificial Intelligence in Health Care: Benefits and Challenges of Technologies to Augment Patient Care. Part Two is the full presentation of NAM’s Special Publication: Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic.

 Policy Options to Address Challenges or Enhance Benefits of AI to Augment Patient Care

Policy Option	Opportunities	Considerations
Collaboration (report p. 32) Policymakers could encourage interdisciplinary collaboration between developers and health care providers.	Could result in AI tools that are easier to implement and use within a providers’ existing workflow. Could help implement tools on a larger scale. Approaches to encourage collaboration include agencies seeking input from innovators. For example, agencies have used a challenge format to encourage the public to develop innovative technologies.	May result in the creation of tools that are specific to one hospital or provider. Providers may not have time to both collaborate and treat patients.
Data Access (report p. 33) Policymakers could develop or expand high-quality data access mechanisms.	A “data commons”–a cloud based-platform where users can store, share, access, and interact with data–could be one approach. More high-quality data could facilitate the development and testing of AI tools. Could help developers address bias concerns by ensuring data are representative, transparent and equitable.	Cybersecurity and privacy risks could increase, and threats would likely require additional precautions. Would likely require large amounts of resources to successfully coordinate across different domains and help address interoperability issues. Organizations with proprietary data could be reluctant to participate.
Best Practices (report p. 34) Policymakers could encourage relevant stakeholders and experts to establish best practices (such as standards) for development, implementation, and use of AI technologies.	Could help providers deploy AI tools by providing guidance on data, interoperability, bias, and implementation, among other things. Could help improve scalability of AI tools by ensuring data are formatted to be interoperable. Could address concerns about bias by encouraging wider representation and transparency.	Could require consensus from many public- and private-sector stakeholders, which can be time- and resource-intensive. Some best practices may not be widely applicable because of differences across institutions and patient populations.
Interdisciplinary Education (report p. 35) Policymakers could create opportunities for more workers to develop interdisciplinary skills.	Could help providers use tools effectively. Could be implemented in a variety of ways, including through changing academic curriculums or through grants.	Employers and university leaders may have to modify their existing curriculums, potentially increasing the length of medical training.
Oversight Clarity (report p. 36) Policymakers could collaborate with relevant stakeholders to clarify appropriate oversight mechanisms.	Predictable oversight could help ensure that AI tools remain safe and effective after deployment and throughout their lifecycle. A forum consisting of relevant stakeholders could help recommend additional mechanisms to ensure appropriate oversight of AI tools.	Soliciting input and coordinating among stakeholders, such as hospitals, professional organizations, and agencies, may be challenging. Excess regulation could slow the pace of innovation.
Status quo (report p. 37) Policymakers could maintain the status quo (i.e., allow current efforts to proceed without intervention).	Challenges may be resolved through current efforts. Some hospitals and providers are already using AI to augment patient care and may not need policy-based solutions to continue expanding these efforts. Existing efforts may prove more beneficial than new options.	The challenges described in this report may remain unresolved or be exacerbated. For example, fewer AI tools may be implemented at scale and disparities in use of AI tools may increase.

Source: GAO.

Health systems during the pandemic

 Health system responses to COVID-19

The Health System Response Monitor (HSRM) platform, a major initiative led by the European Observatory on Health Systems and Policies, the WHO Regional Office for Europe and the European Commission has published an issue that explains what's goin on in health services in the current pandemic, under the following issues:

• Covid-19 and health systems resilience
• Preventing transmission 
• Ensuring sufficient workforce capacity 
• Providing health services effectively 
• Paying for services 
• Governance 

CRISPR therapeutic success

 Treatment by CRISPR-Cas9 Gene Editing — A Proof of Principle

CRISPR-Cas9 Gene Editing for Sickle Cell Disease and β-Thalassemia

Transfusion-dependent β-thalassemia (TDT) and sickle cell disease (SCD) are severe monogenic diseases with severe and potentially life-threatening manifestations. BCL11A is a transcription factor that represses γ-globin expression and fetal hemoglobin in erythroid cells. We performed electroporation of CD34+ hematopoietic stem and progenitor cells obtained from healthy donors, with CRISPR-Cas9 targeting the BCL11A erythroid-specific enhancer. Approximately 80% of the alleles at this locus were modified, with no evidence of off-target editing. After undergoing myeloablation, two patients — one with TDT and the other with SCD — received autologous CD34+ cells edited with CRISPR-Cas9 targeting the same BCL11A enhancer. More than a year later, both patients had high levels of allelic editing in bone marrow and blood, increases in fetal hemoglobin that were distributed pancellularly, transfusion independence, and (in the patient with SCD) elimination of vaso-occlusive episodes. 

A pivotal moment. Great.

Prioritizing vaccines

 Who Goes First? Government Leaders and Prioritization of SARS-CoV-2 Vaccines

In US,

The vice president, Senate majority leader, House speaker, surgeon general, and members of Congress were among the first people vaccinated against Covid-19 in the United States. The president-elect, incoming first lady, vice president-elect, and her husband were also vaccinated in the first phase. Yet prioritization frameworks created by expert panels and adopted by states do not grant government leaders any special status, and giving them priority raises important questions of fairness and transparency. As vaccination proceeds, will other political or institutional leaders, or even celebrities and elite athletes, decide that prioritization plans don’t apply to them?

In another country, military personnel stole 300 vaccines that were for health personnel and administered themselves. I suggest you may guess which is this country....

Josep Segú, NYC