Pharma, Big Pharma

 White Market Drugs. BIG PHARMA AND THE HIDDEN HISTORY OF ADDICTION IN AMERICA

A book to understand the contribution to addiction over a century.

By showing how the twenty-first-century opioid crisis is only the most recent in a long history of similar crises of addiction to pharmaceuticals, Herzberg forces us to rethink our most basic ideas about drug policy and addiction itself—ideas that have been failing us catastrophically for over a century.

This is the outline:

Introduction
The First Crisis
1 Drug wars and white markets
2 “Legitimate addicts” in the first drug war
3 Preventing blockbuster opioids
The Second Crisis
4 Opioids out, barbiturates in
5 A new crisis and a new response
6 White markets, under control
The Third Crisis
7 White market apocalypse
Conclusion: Learning from the past
Appendix: White market sales and overdose rates, 1870–2015

Human genome 20 years later

Complicated legacies: The human genome at 20

On genome and precision medicine:

Debates about precision medicine (PM), which uses genetic information to target interventions, commonly focus on whether we can “afford” PM (17), but focusing only on affordability, not also value, risks rejecting technologies that might make health care more efficient. Affordability is a question of whether we can pay for an intervention given its impact on budgets, whereas value can be measured by the health outcomes achieved per dollar spent for an intervention. Ideally, a PM intervention both saves money and improves outcomes; however, most health care interventions produce better outcomes at higher cost, and PM is no exception. By better distinguishing affordability and value, and by considering how we can address both, we can further the agenda of achieving affordable and valuable PM.

The literature has generally not shown that PM is unaffordable or of low value; however, it has also not shown that PM is a panacea for reducing health care expenditures or always results in high-value care (17). Understanding PM affordability and value requires evidence on total costs and outcomes as well as potential cost offsets, but these data are difficult to capture because costs often occur up front while beneficial outcomes accrue over time (18). Also, PM could result in substantial downstream implications because of follow-up interventions, not only for patients but also for family members who may have inherited the same genetic condition. Emerging PM tests could be used for screening large populations and could include genome sequencing of all newborns, liquid biopsy testing to screen for cancers in routine primary care visits, and predictive testing for Alzheimer's disease in adults. These interventions may provide large benefits, but they are likely to require large up-front expenditures.

 

 

Post COVID-19

 Post Corona. From Crisis to Opportunity

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.