Pilar N. Ossorio

Managing Incidental Findings and Research Results in Genomic Research Involving Biobanks and Archived Data Sets

Biobanks and archived data sets collecting samples and data have become crucial engines of genetic and genomic research. Unresolved, however, is what responsibilities biobanks should shoulder to manage incidental findings and individual research results of potential health, reproductive, or personal importance to individual contributors (using “biobank” here to refer both to collections of samples and collections of data). This article reports recommendations from a 2-year project funded by the National Institutes of Health. We analyze the responsibilities involved in managing the return of incidental findings and individual research results in a biobank research system (primary research or collection sites, the biobank itself, and secondary research sites). We suggest that biobanks shoulder significant responsibility for seeing that the biobank research system addresses the return question explicitly. When reidentification of individual contributors is possible, the biobank should work to enable the biobank research system to discharge four core responsibilities to (1) clarify the criteria for evaluating findings and the roster of returnable findings, (2) analyze a particular finding in relation to this, (3) reidentify the individual contributor, and (4) recontact the contributor to offer the finding. We suggest that findings that are analytically valid, reveal an established and substantial risk of a serious health condition, and are clinically actionable should generally be offered to consenting contributors. This article specifies 10 concrete recommendations, addressing new biobanks as well as those already in existence.Genet Med 2012:14(4):361–384

Race and Genetics Controversies in Biomedical, Behavioral, and Forensic Sciences

Among biomedical scientists, there is a great deal of controversy over the nature of race, the relevance of racial categories for research, and the proper methods of using racial variables. This article argues that researchers and scholars should avoid a binary-type argument, in which the question is whether to use race always or never. Researchers should instead focus on developing standards for when and how to use racial variables. The article then discusses 1 context, criminology, in which the use of racial variables in behavioral genetics research could be particularly problematic. If genetic studies of criminalized behavior use forensic DNA databanks or forensic genetic profiles, they will be confounded by the many racial biases of the law enforcement and penal system.

Reflections on the Cost of "Low-Cost" Whole Genome Sequencing: Framing the Health Policy Debate

The future clinical applications of whole genome sequencing come with speculation and enthusiasm but require careful consideration of the true system costs and health benefits of the clinical uses of this exciting technology.

The Illusive Gold Standard in Genetic Ancestry Testing

New regulations on disclosure, authority, and responsibility would shape how genetic ancestry tests are used. Genetic ancestry testing is being applied in areas as diverse as forensics, genealogical research, immigration control, and biomedical research (1–3). Use of ancestry as a potential risk factor for disease is entrenched in clinical decision-making (4), so it is not surprising that techniques to determine genetic ancestry are increasingly deployed to identify genetic variants associated with disease and drug response (5). Recently, direct-to-consumer (DTC) personal genomics companies have used ancestry information to calculate individual risk profiles for a range of diseases and traits.

Clines Without Classes: How to Make Sense of Human Variation

This article examines Shiao, Bode, Beyer, and Selvig’s (2012) arguments in their article “The Genomic Challenge to the Social Construction of Race” and finds that their claims are based on fundamentally flawed interpretations of current genetic research. We discuss current genomic and genetic knowledge about human biological variation to demonstrate why and how Shiao et al.’s recommendations for future sociological studies and social policy, based on their inadequate understanding of genomic methods and evidence, are similarly flawed and will lead sociology astray.

The human genome as common heritage: common sense or legal nonsense?

This essay identifies two legal lineages underlying the common heritage concept, and applies each to the human genome. The essay notes some advantages and disadvantages of each approach, and argues that patenting of human genes would be allowable under either approach.

Taking aims seriously: repository research and limits on the duty to return individual research findings

Most discussions of researchers’ duties to return incidental findings or research results to research participants or repository contributors fail to provide an adequate theoretical grounding for such duties. Returning findings is a positive duty, a duty to help somebody. Typically, such duties are specified narrowly such that helping is only a duty when it poses little or no risk or burden to the helper and does not interfere with her legitimate aims. Under current budgetary and personnel constraints, and with currently available information technology, routine return of individual findings from research using repository materials would constitute a substantial burden on the scientific enterprise and would seriously frustrate the aims of both scientists and specimen/data contributors. In most cases, researchers’ limited duties to help repository contributors probably can be fulfilled by some action less demanding than returning individual findings. Furthermore, the duty-to-return issue should be analyzed as a conflict between (possibly) helping some contributors now and (possibly) helping a greater number of people who would benefit in the future from the knowledge produced by research.Genet Med 2012:14(4):461–466

The Challenge of Regulating Clinical Decision Support Software after 21st Century Cures

Clinical decision support (CDS) software broadly refers to software that assists healthcare providers in combining patient-specific data with general sources of medical knowledge to make better diagnostic and treatment decisions in the clinical setting. The 21st-Century Cures Act strips FDA of jurisdiction to regulate some (not all) CDS software. To qualify for this exclusion from FDA regulation, 21 U.S.C. § 360j(o)(1)(E)(iii) requires that the software must be intended to enable “the health care professional to independently review the basis” for its recommendations so that it is “not the intent that such health care professional rely primarily” on the software’s recommendations when making diagnostic and treatment decisions about individual patients. This article explores whether this is a workable standard as applied to advanced CDS software that uses machine learning to glean insights from real-world clinical experience and then applies these insights to improve the quality of patient care. We conclude that the standard Congress set out in 21st-Century Cures is potentially workable, but only if FDA takes additional steps to clarify the standards of transparency that CDS software must meet before it can escape FDA regulation. Transparency in this context includes algorithmic transparency, physician access to the underlying data that the software relies on in rendering decisions, and business transparency as reflected in the terms of contracts between CDS software vendors and users. FDA’s recent draft guidance on CDS software leaves a crucial question unresolved. This question cuts to the very heart of what is wrong with the US healthcare system and how to fix it: Is the problem simply that doctors are not heeding existing medical evidence — for example, by ignoring warnings in FDA-approved drug labeling or failing to keep up with findings in the peer-reviewed medical literature? Or is the problem that the existing evidence base is itself inadequate and flawed — for example, because FDA-approved labeling relies on contrived clinical trials that fail to reflect real patients, or because peer-reviewed literature is skewed by publication biases that favor studies in which the treatment worked, or because clinical practice guidelines can be captured by commercial interests? FDA’s draft guidance on CDS software — perhaps as an unintended consequence — would expedite market entry for simple CDS software that promotes physician conformity with the existing medical evidence base, while imposing higher regulatory hurdles that delay the clinical translation of machine-learning software that may be our best hope to overcome flaws in current medical evidence? Is this the right path forward?

Fairness in Manufacturing Cellular Therapies

ISSN: 1526-5161 (Print) 1536-0075 (Online) Journal homepage: http://www.tandfonline.com/loi/uajb20 Fairness in Manufacturing Cellular Therapies Amritava Das, Krishanu Saha & Pilar N. Ossorio To cite this article: Amritava Das, Krishanu Saha & Pilar N. Ossorio (2018) Fairness in Manufacturing Cellular Therapies, The American Journal of Bioethics, 18:4, 68-70, DOI: 10.1080/15265161.2018.1445792 To link to this article: https://doi.org/10.1080/15265161.2018.1445792

Genotype-Driven Recruitment Without Deception

ISSN: 1526-5161 (Print) 1536-0075 (Online) Journal homepage: http://www.tandfonline.com/loi/uajb20 Genotype-Driven Recruitment Without Deception Pilar Ossorio & Marsha Mailick To cite this article: Pilar Ossorio & Marsha Mailick (2017) Genotype-Driven Recruitment Without Deception, The American Journal of Bioethics, 17:4, 60-61, DOI: 10.1080/15265161.2017.1284924 To link to this article: http://dx.doi.org/10.1080/15265161.2017.1284924