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Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies

Curator: Stephen J. Williams, PhD

 

 

From the POLICY FORUM ETHICS AND DIVERSITY Section of Science

Ethics of inclusion: Cultivate trust in precision medicine

 See all authors and affiliations

Science  07 Jun 2019:
Vol. 364, Issue 6444, pp. 941-942
DOI: 10.1126/science.aaw8299

Precision medicine is at a crossroads. Progress toward its central goal, to address persistent health inequities, will depend on enrolling populations in research that have been historically underrepresented, thus eliminating longstanding exclusions from such research (1). Yet the history of ethical violations related to protocols for inclusion in biomedical research, as well as the continued misuse of research results (such as white nationalists looking to genetic ancestry to support claims of racial superiority), continue to engender mistrust among these populations (2). For precision medicine research (PMR) to achieve its goal, all people must believe that there is value in providing information about themselves and their families, and that their participation will translate into equitable distribution of benefits. This requires an ethics of inclusion that considers what constitutes inclusive practices in PMR, what goals and values are being furthered through efforts to enhance diversity, and who participates in adjudicating these questions. The early stages of PMR offer a critical window in which to intervene before research practices and their consequences become locked in (3).

Initiatives such as the All of Us program have set out to collect and analyze health information and biological samples from millions of people (1). At the same time, questions of trust in biomedical research persist. For example, although the recent assertions of white nationalists were eventually denounced by the American Society of Human Genetics (4), the misuse of ancestry testing may have already undermined public trust in genetic research.

There are also infamous failures in research that included historically underrepresented groups, including practices of deceit, as in the Tuskegee Syphilis Study, or the misuse of samples, as with the Havasupai tribe (5). Many people who are being asked to give their data and samples for PMR must not only reconcile such past research abuses, but also weigh future risks of potential misuse of their data.

To help assuage these concerns, ongoing PMR studies should open themselves up to research, conducted by social scientists and ethicists, that examines how their approaches enhance diversity and inclusion. Empirical studies are needed to account for how diversity is conceptualized and how goals of inclusion are operationalized throughout the life course of PMR studies. This is not limited to selection and recruitment of populations but extends to efforts to engage participants and communities, through data collection and measurement, and interpretations and applications of study findings. A commitment to transparency is an important step toward cultivating public trust in PMR’s mission and practices.

From Inclusion to Inclusive

The lack of diverse representation in precision medicine and other biomedical research is a well-known problem. For example, rare genetic variants may be overlooked—or their association with common, complex diseases can be misinterpreted—as a result of sampling bias in genetics research (6). Concentrating research efforts on samples with largely European ancestry has limited the ability of scientists to make generalizable inferences about the relationships among genes, lifestyle, environmental exposures, and disease risks, and thereby threatens the equitable translation of PMR for broad public health benefit (7).

However, recruiting for diverse research participation alone is not enough. As with any push for “diversity,” related questions arise about how to describe, define, measure, compare, and explain inferred similarities and differences among individuals and groups (8). In the face of ambivalence about how to represent population variation, there is ample evidence that researchers resort to using definitions of diversity that are heterogeneous, inconsistent, and sometimes competing (9). Varying approaches are not inherently problematic; depending on the scientific question, some measures may be more theoretically justified than others and, in many cases, a combination of measures can be leveraged to offer greater insight (10). For example, studies have shown that American adults who do not self-identify as white report better mental and physical health if they think others perceive them as white (1112).

The benefit of using multiple measures of race and ancestry also extends to genetic studies. In a study of hypertension in Puerto Rico, not only did classifications based on skin color and socioeconomic status better predict blood pressure than genetic ancestry, the inclusion of these sociocultural measures also revealed an association between a genetic polymorphism and hypertension that was otherwise hidden (13). Thus, practices that allow for a diversity of measurement approaches, when accompanied by a commitment to transparency about the rationales for chosen approaches, are likely to benefit PMR research more than striving for a single gold standard that would apply across all studies. These definitional and measurement issues are not merely semantic. They also are socially consequential to broader perceptions of PMR research and the potential to achieve its goals of inclusion.

Study Practices, Improve Outcomes

Given the uncertainty and complexities of the current, early phase of PMR, the time is ripe for empirical studies that enable assessment and modulation of research practices and scientific priorities in light of their social and ethical implications. Studying ongoing scientific practices in real time can help to anticipate unintended consequences that would limit researchers’ ability to meet diversity recruitment goals, address both social and biological causes of health disparities, and distribute the benefits of PMR equitably. We suggest at least two areas for empirical attention and potential intervention.

First, we need to understand how “upstream” decisions about how to characterize study populations and exposures influence “downstream” research findings of what are deemed causal factors. For example, when precision medicine researchers rely on self-identification with U.S. Census categories to characterize race and ethnicity, this tends to circumscribe their investigation of potential gene-environment interactions that may affect health. The convenience and routine nature of Census categories seemed to lead scientists to infer that the reasons for differences among groups were self-evident and required no additional exploration (9). The ripple effects of initial study design decisions go beyond issues of recruitment to shape other facets of research across the life course of a project, from community engagement and the return of results to the interpretation of study findings for human health.

Second, PMR studies are situated within an ecosystem of funding agencies, regulatory bodies, disciplines, and other scholars. This partly explains the use of varied terminology, different conceptual understandings and interpretations of research questions, and heterogeneous goals for inclusion. It also makes it important to explore how expectations related to funding and regulation influence research definitions of diversity and benchmarks for inclusion.

For example, who defines a diverse study population, and how might those definitions vary across different institutional actors? Who determines the metrics that constitute successful inclusion, and why? Within a research consortium, how are expectations for data sharing and harmonization reconciled with individual studies’ goals for recruitment and analysis? In complex research fields that include multiple investigators, organizations, and agendas, how are heterogeneous, perhaps even competing, priorities negotiated? To date, no studies have addressed these questions or investigated how decisions facilitate, or compromise, goals of diversity and inclusion.

The life course of individual studies and the ecosystems in which they reside cannot be easily separated and therefore must be studied in parallel to understand how meanings of diversity are shaped and how goals of inclusion are pursued. Empirically “studying the studies” will also be instrumental in creating mechanisms for transparency about how PMR is conducted and how trade-offs among competing goals are resolved. Establishing open lines of inquiry that study upstream practices may allow researchers to anticipate and address downstream decisions about how results can be interpreted and should be communicated, with a particular eye toward the consequences for communities recruited to augment diversity. Understanding how scientists negotiate the challenges and barriers to achieving diversity that go beyond fulfilling recruitment numbers is a critical step toward promoting meaningful inclusion in PMR.

Transparent Reflection, Cultivation of Trust

Emerging research on public perceptions of PMR suggests that although there is general support, questions of trust loom large. What we learn from studies that examine on-the-ground approaches aimed at enhancing diversity and inclusion, and how the research community reflects and responds with improvements in practices as needed, will play a key role in building a culture of openness that is critical for cultivating public trust.

Cultivating long-term, trusting relationships with participants underrepresented in biomedical research has been linked to a broad range of research practices. Some of these include the willingness of researchers to (i) address the effect of history and experience on marginalized groups’ trust in researchers and clinicians; (ii) engage concerns about potential group harms and risks of stigmatization and discrimination; (iii) develop relationships with participants and communities that are characterized by transparency, clear communication, and mutual commitment; and (iv) integrate participants’ values and expectations of responsible oversight beyond initial informed consent (14). These findings underscore the importance of multidisciplinary teams that include social scientists, ethicists, and policy-makers, who can identify and help to implement practices that respect the histories and concerns of diverse publics.

A commitment to an ethics of inclusion begins with a recognition that risks from the misuse of genetic and biomedical research are unevenly distributed. History makes plain that a multitude of research practices ranging from unnecessarily limited study populations and taken-for-granted data collection procedures to analytic and interpretive missteps can unintentionally bolster claims of racial superiority or inferiority and provoke group harm (15). Sustained commitment to transparency about the goals, limits, and potential uses of research is key to further cultivating trust and building long-term research relationships with populations underrepresented in biomedical studies.

As calls for increasing diversity and inclusion in PMR grow, funding and organizational pathways must be developed that integrate empirical studies of scientific practices and their rationales to determine how goals of inclusion and equity are being addressed and to identify where reform is required. In-depth, multidisciplinary empirical investigations of how diversity is defined, operationalized, and implemented can provide important insights and lessons learned for guiding emerging science, and in so doing, meet our ethical obligations to ensure transparency and meaningful inclusion.

References and Notes

  1. C. P. Jones et al Ethn. Dis. 18496 (2008).
  2. C. C. GravleeA. L. NonC. J. Mulligan
  3. S. A. Kraft et al Am. J. Bioeth. 183 (2018).
  4. A. E. Shields et al Am. Psychol. 6077 (2005).

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Parkinson’s Disease (PD), characterized by both motor and non-motor system pathology, is a common neurodegenerative disorder affecting about 1% of the population over age 60. Its prevalence presents an increasing social burden as the population ages. Since its introduction in the 1960’s, dopamine (DA)-replacement therapy (e.g., L-DOPA) has remained the gold standard treatment. While improving PD patients’ quality of life, the effects of treatment fade with disease progression and prolonged usage of these medications often (>80%) results in side effects including dyskinesias and motor fluctuations. Since the selective degeneration of A9 mDA neurons (mDANs) in the substantia nigra (SN) is a key pathological feature of the disease and is directly associated with the cardinal motor symptoms, dopaminergic cell transplantation has been proposed as a therapeutic strategy.

 

Researchers showed that mammalian fibroblasts can be converted into embryonic stem cell (ESC)-like induced pluripotent stem cells (iPSCs) by introducing four transcription factors i.e., Oct4, Sox2, Klf4, and c-Myc. This was then accomplished with human somatic cells, reprogramming them into human iPSCs (hiPSCs), offering the possibility of generating patient-specific stem cells. There are several major barriers to implementation of hiPSC-based cell therapy for PD. First, probably due to the limited understanding of the reprogramming process, wide variability exists between the differentiation potential of individual hiPSC lines. Second, the safety of hiPSC-based cell therapy has yet to be fully established. In particular, since any hiPSCs that remain undifferentiated or bear sub-clonal tumorigenic mutations have neoplastic potential, it is critical to eliminate completely such cells from a therapeutic product.

 

In the present study the researchers established human induced pluripotent stem cell (hiPSC)-based autologous cell therapy. Researchers reported a platform of core techniques for the production of mDA progenitors as a safe and effective therapeutic product. First, by combining metabolism-regulating microRNAs with reprogramming factors, a method was developed to more efficiently generate clinical grade iPSCs, as evidenced by genomic integrity and unbiased pluripotent potential. Second, a “spotting”-based in vitro differentiation methodology was established to generate functional and healthy mDA cells in a scalable manner. Third, a chemical method was developed that safely eliminates undifferentiated cells from the final product. Dopaminergic cells thus produced can express high levels of characteristic mDA markers, produce and secrete dopamine, and exhibit electrophysiological features typical of mDA cells. Transplantation of these cells into rodent models of PD robustly restored motor dysfunction and reinnervated host brain, while showing no evidence of tumor formation or redistribution of the implanted cells.

 

Together these results supported the promise of these techniques to provide clinically applicable personalized autologous cell therapy for PD. It was recognized by researchers that this methodology is likely to be more costly in dollars and manpower than techniques using off-the-shelf methods and allogenic cell lines. Nevertheless, the cost for autologous cell therapy may be expected to decrease steadily with technological refinement and automation. Given the significant advantages inherent in a cell source free of ethical concerns and with the potential to obviate the need for immunosuppression, with its attendant costs and dangers, it was proposed that this platform is suitable for the successful implementation of human personalized autologous cell therapy for PD.

 

References:

 

https://www.jci.org/articles/view/130767/pdf?elqTrackId=2fd7d0edee744f9cb6d70a686d7b273b

 

https://www.ncbi.nlm.nih.gov/pubmed/31714896

 

https://www.ncbi.nlm.nih.gov/pubmed/23666606

 

https://www.ncbi.nlm.nih.gov/pubmed/27343168

 

https://www.ncbi.nlm.nih.gov/pubmed/21495962

 

https://www.ncbi.nlm.nih.gov/pubmed/28083784

 

https://www.ncbi.nlm.nih.gov/pubmed/20336395

 

https://www.ncbi.nlm.nih.gov/pubmed/28585381

 

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Effective humoral immune responses to infection and immunization are defined by high-affinity antibodies generated as a result of B cell differentiation and selection that occurs within germinal centers (GC). Within the GC, B cells undergo affinity maturation, an iterative and competitive process wherein B cells mutate their immunoglobulin genes (somatic hypermutation) and undergo clonal selection by competing for T cell help. Balancing the decision to remain within the GC and continue participating in affinity maturation or to exit the GC as a plasma cell (PC) or memory B cell (MBC) is critical for achieving optimal antibody avidity, antibody quantity, and establishing immunological memory in response to immunization or infection. Humoral immune responses during chronic infections are often dysregulated and characterized by hypergammaglobulinemia, decreased affinity maturation, and delayed development of neutralizing antibodies. Previous studies have suggested that poor antibody quality is in part due to deletion of B cells prior to establishment of the GC response.

 

In fact the impact of chronic infections on B cell fate decisions in the GC remains poorly understood. To address this question, researchers used single-cell transcriptional profiling of virus-specific GC B cells to test the hypothesis that chronic viral infection disrupted GC B cell fate decisions leading to suboptimal humoral immunity. These studies revealed a critical GC differentiation checkpoint that is disrupted by chronic infection, specifically at the point of dark zone re-entry. During chronic viral infection, virus-specific GC B cells were shunted towards terminal plasma cell (PC) or memory B cell (MBC) fates at the expense of continued participation in the GC. Early GC exit was associated with decreased B cell mutational burden and antibody quality. Persisting antigen and inflammation independently drove facets of dysregulation, with a key role for inflammation in directing premature terminal GC B cell differentiation and GC exit. Thus, the present research defines GC defects during chronic viral infection and identify a critical GC checkpoint that is short-circuited, preventing optimal maturation of humoral immunity.

 

Together, these studies identify a key GC B cell differentiation checkpoint that is dysregulated during chronic infection. Further, it was found that the chronic inflammatory environment, rather than persistent antigen, is sufficient to drive altered GC B cell differentiation during chronic infection even against unrelated antigens. However, the data also indicate that inflammatory circuits are likely linked to perception of antigen stimulation. Nevertheless, this study reveals a B cell-intrinsic program of transcriptional skewing in chronic viral infection that results in shunting out of the cyclic GC B cell process and early GC exit with consequences for antibody quality and hypergammaglobulinemia. These findings have implications for vaccination in individuals with pre-existing chronic infections where antibody responses are often ineffective and suggest that modulation of inflammatory pathways may be therapeutically useful to overcome impaired humoral immunity and foster affinity maturation during chronic viral infections.

 

References:

 

https://www.biorxiv.org/content/10.1101/849844v1

 

https://www.ncbi.nlm.nih.gov/pubmed/25656706

 

https://www.ncbi.nlm.nih.gov/pubmed/27653600

 

https://www.ncbi.nlm.nih.gov/pubmed/26912368

 

https://www.ncbi.nlm.nih.gov/pubmed/26799208

 

https://www.ncbi.nlm.nih.gov/pubmed/23001146

 

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scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Present day technological advances have facilitated unprecedented opportunities for studying biological systems at single-cell level resolution. For example, single-cell RNA sequencing (scRNA-seq) enables the measurement of transcriptomic information of thousands of individual cells in one experiment. Analyses of such data provide information that was not accessible using bulk sequencing, which can only assess average properties of cell populations. Single-cell measurements, however, can capture the heterogeneity of a population of cells. In particular, single-cell studies allow for the identification of novel cell types, states, and dynamics.

 

One of the most prominent uses of the scRNA-seq technology is the identification of subpopulations of cells present in a sample and comparing such subpopulations across samples. Such information is crucial for understanding the heterogeneity of cells in a sample and for comparative analysis of samples from different conditions, tissues, and species. A frequently used approach is to cluster every dataset separately, inspect marker genes for each cluster, and compare these clusters in an attempt to determine which cell types were shared between samples. This approach, however, relies on the existence of predefined or clearly identifiable marker genes and their consistent measurement across subpopulations.

 

Although the aligned data can then be clustered to reveal subpopulations and their correspondence, solving the subpopulation-mapping problem by performing global alignment first and clustering second overlooks the original information about subpopulations existing in each experiment. In contrast, an approach addressing this problem directly might represent a more suitable solution. So, keeping this in mind the researchers developed a computational method, single-cell subpopulations comparison (scPopCorn), that allows for comparative analysis of two or more single-cell populations.

 

The performance of scPopCorn was tested in three distinct settings. First, its potential was demonstrated in identifying and aligning subpopulations from single-cell data from human and mouse pancreatic single-cell data. Next, scPopCorn was applied to the task of aligning biological replicates of mouse kidney single-cell data. scPopCorn achieved the best performance over the previously published tools. Finally, it was applied to compare populations of cells from cancer and healthy brain tissues, revealing the relation of neoplastic cells to neural cells and astrocytes. Consequently, as a result of this integrative approach, scPopCorn provides a powerful tool for comparative analysis of single-cell populations.

 

This scPopCorn is basically a computational method for the identification of subpopulations of cells present within individual single-cell experiments and mapping of these subpopulations across these experiments. Different from other approaches, scPopCorn performs the tasks of population identification and mapping simultaneously by optimizing a function that combines both objectives. When applied to complex biological data, scPopCorn outperforms previous methods. However, it should be kept in mind that scPopCorn assumes the input single-cell data to consist of separable subpopulations and it is not designed to perform a comparative analysis of single cell trajectories datasets that do not fulfill this constraint.

 

Several innovations developed in this work contributed to the performance of scPopCorn. First, unifying the above-mentioned tasks into a single problem statement allowed for integrating the signal from different experiments while identifying subpopulations within each experiment. Such an incorporation aids the reduction of biological and experimental noise. The researchers believe that the ideas introduced in scPopCorn not only enabled the design of a highly accurate identification of subpopulations and mapping approach, but can also provide a stepping stone for other tools to interrogate the relationships between single cell experiments.

 

References:

 

https://www.sciencedirect.com/science/article/pii/S2405471219301887

 

https://www.tandfonline.com/doi/abs/10.1080/23307706.2017.1397554

 

https://ieeexplore.ieee.org/abstract/document/4031383

 

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0927-y

 

https://www.sciencedirect.com/science/article/pii/S2405471216302666

 

 

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eProceedings for BIO 2019 International Convention, June 3-6, 2019 Philadelphia Convention Center; Philadelphia PA, Real Time Coverage by Stephen J. Williams, PhD @StephenJWillia2

 

CONFERENCE OVERVIEW

Real Time Coverage of BIO 2019 International Convention, June 3-6, 2019 Philadelphia Convention Center; Philadelphia PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/05/31/real-time-coverage-of-bio-international-convention-june-3-6-2019-philadelphia-convention-center-philadelphia-pa/

 

LECTURES & PANELS

Real Time Coverage @BIOConvention #BIO2019: Machine Learning and Artificial Intelligence: Realizing Precision Medicine One Patient at a Time, 6/5/2019, Philadelphia PA

Reporter: Stephen J Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/05/real-time-coverage-bioconvention-bio2019-machine-learning-and-artificial-intelligence-realizing-precision-medicine-one-patient-at-a-time/

 

Real Time Coverage @BIOConvention #BIO2019: Genome Editing and Regulatory Harmonization: Progress and Challenges, 6/5/2019. Philadelphia PA

Reporter: Stephen J Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/05/real-time-coverage-bioconvention-bio2019-genome-editing-and-regulatory-harmonization-progress-and-challenges/

 

Real Time Coverage @BIOConvention #BIO2019: Precision Medicine Beyond Oncology June 5, 2019, Philadelphia PA

Reporter: Stephen J Williams PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/05/real-time-coverage-bioconvention-bio2019-precision-medicine-beyond-oncology-june-5-philadelphia-pa/

 

Real Time @BIOConvention #BIO2019:#Bitcoin Your Data! From Trusted Pharma Silos to Trustless Community-Owned Blockchain-Based Precision Medicine Data Trials, 6/5/2019, Philadelphia PA

Reporter: Stephen J Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/05/real-time-bioconvention-bio2019bitcoin-your-data-from-trusted-pharma-silos-to-trustless-community-owned-blockchain-based-precision-medicine-data-trials/

 

Real Time Coverage @BIOConvention #BIO2019: Keynote Address Jamie Dimon CEO @jpmorgan June 5, 2019, Philadelphia, PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/05/real-time-coverage-bioconvention-bio2019-keynote-address-jamie-dimon-ceo-jpmorgan-june-5-philadelphia/

 

Real Time Coverage @BIOConvention #BIO2019: Chat with @FDA Commissioner, & Challenges in Biotech & Gene Therapy June 4, 2019, Philadelphia, PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/04/real-time-coverage-bioconvention-bio2019-chat-with-fda-commissioner-challenges-in-biotech-gene-therapy-june-4-philadelphia/

 

Falling in Love with Science: Championing Science for Everyone, Everywhere June 4 2019, Philadelphia PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/04/real-time-coverage-bioconvention-bio2019-falling-in-love-with-science-championing-science-for-everyone-everywhere/

 

Real Time Coverage @BIOConvention #BIO2019: June 4 Morning Sessions; Global Biotech Investment & Public-Private Partnerships, 6/4/2019, Philadelphia PA

Reporter: Stephen J Williams PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/04/real-time-coverage-bioconvention-bio2019-june-4-morning-sessions-global-biotech-investment-public-private-partnerships/

 

Real Time Coverage @BIOConvention #BIO2019: Understanding the Voices of Patients: Unique Perspectives on Healthcare; June 4, 2019, 11:00 AM, Philadelphia PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/04/real-time-coverage-bioconvention-bio2019-understanding-the-voices-of-patients-unique-perspectives-on-healthcare-june-4/

 

Real Time Coverage @BIOConvention #BIO2019: Keynote: Siddhartha Mukherjee, Oncologist and Pulitzer Author; June 4 2019, 9AM, Philadelphia PA

Reporter: Stephen J. Williams, PhD. @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/04/real-time-coverage-bioconvention-bio2019-keynote-siddhartha-mukherjee-oncologist-and-pulitzer-author-june-4-9am-philadelphia-pa/

 

Real Time Coverage @BIOConvention #BIO2019:  Issues of Risk and Reproduceability in Translational and Academic Collaboration; 2:30-4:00 June 3, 2019, Philadelphia PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/03/real-time-coverage-bioconvention-bio2019-issues-of-risk-and-reproduceability-in-translational-and-academic-collaboration-230-400-june-3-philadelphia-pareal-time-coverage-bioconvention-bi/

 

Real Time Coverage @BIOConvention #BIO2019: What’s Next: The Landscape of Innovation in 2019 and Beyond. 3-4 PM June 3, 2019, Philadelphia PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/03/real-time-coverage-bioconvention-bio2019-whats-next-the-landscape-of-innovation-in-2019-and-beyond-3-4-pm-june-3-philadelphia-pa/

 

Real Time Coverage @BIOConvention #BIO2019: After Trump’s Drug Pricing Blueprint: What Happens Next? A View from Washington; June 3, 2019 1:00 PM, Philadelphia PA

Reporter: Stephen J. Williams, PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/03/real-time-coverage-bioconvention-bio2019-after-trumps-drug-pricing-blueprint-what-happens-next-a-view-from-washington-june-3-2019-100-pm-philadelphia-pa/

 

Real Time Coverage @BIOConvention #BIO2019: International Cancer Clusters Showcase June 3, 2019, Philadelphia PA

Reporter: Stephen J. Williams PhD @StephenJWillia2

https://pharmaceuticalintelligence.com/2019/06/03/real-time-coverage-bioconvention-bio2019-international-cancer-clusters-showcase-june-3-philadelphia-pa/

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Real Time Coverage @BIOConvention #BIO2019: Genome Editing and Regulatory Harmonization: Progress and Challenges

Reporter: Stephen J Williams, PhD @StephenJWillia2

 

Genome editing offers the potential of new and effective treatments for genetic diseases. As companies work to develop these treatments, regulators are focused on ensuring that any such products meet applicable safety and efficacy requirements. This panel will discuss how European Union and United States regulators are approaching therapeutic use of genome editing, issues in harmonization between these two – and other – jurisdictions, challenges faced by industry as regulatory positions evolve, and steps that organizations and companies can take to facilitate approval and continued efforts at harmonization.

 

CBER:  because of the nature of these gene therapies, which are mainly orphan, there is expedited review.  Since they started this division in 2015, they have received over 1500 applications.

Spark: Most of the issues were issues with the primary disease not the gene therapy so they had to make new endpoint tests so had talks with FDA before they entered phase III.   There has been great collaboration with FDA,  now they partnered with Novartis to get approval outside US.  You should be willing to partner with EU pharmas to expedite the regulatory process outside US.  In China the process is new and Brazil is behind on their gene therapy guidance.  However there is the new issue of repeat testing of your manufacturing process, as manufacturing of gene therapies had been small scale before. However he notes that problems with expedited review is tough because you don’t have alot of time to get data together.  They were lucky that they had already done a randomized trial.

Sidley Austin:  EU regulatory you make application with advance therapy you don’t have a national option, the regulation body assesses a committee to see if has applicability. Then it goes to a safety committee.  EU has been quicker to approve these advance therapies. Twenty five percent of their applications are gene therapies.  Companies having issues with manufacturing.  There can be issues when the final application is formalized after discussions as problems may arise between discussions, preliminary applications, and final applications.

Sarepta: They have a robust gene therapy program.  Their lead is a therapy for DMD (Duchenne’s Muscular Dystrophy) where affected males die by 25. Japan and EU have different regulatory applications and although they are similar and data can be transferred there is more paperwork required by EU.  The US uses an IND for application. Global feedback is very challenging, they have had multiple meetings around the world and takes a long time preparing a briefing package….. putting a strain on the small biotechs.  No company wants to be either just EU centric or US centric they just want to get out to market as fast as possible.

 

Please follow LIVE on TWITTER using the following @ handles and # hashtags:

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@pharma_BI

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# Hashtags

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Real Time @BIOConvention #BIO2019:#Bitcoin Your Data! From Trusted Pharma Silos to Trustless Community-Owned Blockchain-Based Precision Medicine Data Trials

Reporter: Stephen J Williams, PhD @StephenJWillia2
Speakers

As care for lifestyle-driven chronic diseases expands in scope, prevention and recovery are becoming the new areas of focus. Building a precision medicine foundation that will promote ownership of individuals’ health data and allow for sharing and trading of this data could prove a great blockchain.

At its core, blockchain may offer the potential of a shared platform that decentralizes healthcare interactions ensuring access control, authenticity and integrity, while presenting the industry with radical possibilities for value-based care and reimbursement models. Panelists will explore these new discoveries as well as look to answer lingering questions, such as: are we off to a “trustless” information model underpinned by Bitcoin cryptocurrency, where no central authority validates the transactions in the ledger, and anyone whose computers can do the required math can join to mine and add blocks to your data? Would smart contracts begin to incentivize “rational” behaviors where consumers respond in a manner that makes their data interesting?

Moderator:  Cybersecurity is extremely important in the minds of healthcare CEOs.  CEO of Kaiser Permenente has listed this as one of main concerns for his company.

Sanjeey of Singularity: There are Very few companies in this space.  Singularity have collected thousands of patient data.  They wanted to do predictive health care, where a patient will know beforehand what health problems and issues to expect.  Created a program called Virtual Assistant. As data is dynamic, the goal was to provide Virtual Assistant to everyone.

Benefits of blockchain: secure, simple to update, decentralized data; patient can control their own data, who sees it and monetize it.

Nebular Genetics: Company was founded by Dr. George Church, who had pioneered the next generation sequencing (NGS) methodology.  The company goal is to make genomics available to all but this currently is not the case as NGS is not being used as frequently.

The problem is a data problem:

  • data not organized
  • data too parsed
  • data not accessible

Blockchain may be able to alleviate the accessibiltiy problem.  Pharma is very interested in the data but expensive to collect.  In addition many companies just do large scale but low depth sequencing.  For example 23andme (which had recently made a big deal with Lilly for data) only sequences about 1% of genome.

There are two types of genome sequencing companies

  1.  large scale and low depth – like 23andme
  2. smaller scale but higher depth – like DECODE and some of the EU EXOME sequencing efforts like the 1000 Project

Simply Vital Health: Harnesses blockchain to combat ineffeciencies in hospital records. They tackle the costs after acute care so increase the value based care.  Most of healthcare is concentrated on the top earners and little is concentrated on the majority less affluent and poor.  On addressing HIPAA compliance issues: they decided to work with HIPAA and comply but will wait for this industry to catch up so the industry as a whole can lobby to affect policy change required for blockchain technology to work efficiently in this arena.  They will only work with known vendors: VERY Important to know where the data is kept and who are controlling the servers you are using.  With other blockchain like Etherium or Bitcoin, the servers are anonymous.

Encrypgen: generates new blockchain for genomic data and NGS companies.

 

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