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Archive for the ‘Genome Biology’ Category


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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Narrative Building for the Future of LPBI Group: List of Talking Points

 

Exchange between Gail and Aviva

 

On Tuesday, June 25, 2019, 11:43:27 AM EDT, Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu> wrote:

https://www.terarecon.com/blog/beyond-the-screen-episode-6-next-generation-ai-companies-providing-physicians-a-starting-point-in-ai?utm_campaign=AuntMinnie%20June%202019

HOW can we get  Kevin Landwher of terarecon.com to create a Podcast for LPBI Group IP Assets, including a section on our forthcoming Genomics, Volume 2 

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-two-genomics-methodologies-ngs-bioinformatics-simulations-and-the-genome-ontology/

In response to this question we are in discussion on POINTS #1,2,3,4

 

From: Gail Thornton <gailsthornton@yahoo.com>

Reply-To: Gail Thornton <gailsthornton@yahoo.com>

Date: Sunday, June 30, 2019 at 8:38 AM

To: Aviva Lev-Ari <aviva.lev-ari@comcast.net>

Cc: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>, Rick Mandahl <rmandahl@gmail.com>, Amnon Danzig <amnon.danzig@gmail.com>

Subject: Please AUDIT PODCAST —>>>>>>>> Beyond the Screen Episode 6: Next Generation AI Companies Providing Physicians a Starting Point in AI

Aviva:

These videos from terarecon.com typically focus on one topic (not many as you’ve described below). 

If there are too many topics proposed to this company, they will not be interested.

My recommendation is for you to finalize Genomics, volume 2, and let’s see the story we have about that specific topic.

Gali 

 

On Tuesday, June 25, 2019, 11:43:27 AM EDT, Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu> wrote:

https://www.terarecon.com/blog/beyond-the-screen-episode-6-next-generation-ai-companies-providing-physicians-a-starting-point-in-ai?utm_campaign=AuntMinnie%20June%202019

HOW can we get  Kevin Landwher of terarecon.com to create a Podcast for LPBI Group IP Assets, including a section on our forthcoming Genomics, Volume 2 

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-two-genomics-methodologies-ngs-bioinformatics-simulations-and-the-genome-ontology/

 

On Saturday, June 29, 2019, 03:56:08 PM EDT, Aviva Lev-Ari <aviva.lev-ari@comcast.net> wrote:

 

POINT #1 for VIDEO coverage – Focus on Genomics, Volume 2

After 7/15, Prof. Feldman will be back in the US, stating to work on Part 5 in Genomics, Volume 2. We will Skype to discuss what to include in 5.1, 5.2, 5.3, 5.4

On 7/15, I am submitting my work on creation of Parts 1,2,3,4,6

Dr. Williams and Dr. Saha are working already on Part 7&8.

Below you have abbreviated eTOCs.

Go to URL of the Book to see what I placed already inside this book.

Dr. Williams and Prof. Feldman will compose 

Preface

Introduction to Volume 2

Volume Summary

Epilogue

Based on these four parts and the eTOCs you will have ample content for the video, which may start with the epitome of our book creation: Genomics Volume 2 (you interview the three Editors why it is Epitome)

POINT #2 or #3 or #4  for VIDEOs to Focus on coverage for Marketing LPBI Group

by DESCRIPTION of what was accomplished

 

  • Venture history/background
  • Venture milestones: all posts in the Journal with the Title
  • “We celebrate …..
  • 5-6 Titles like that, I may add two more
  • Site Statistics
  • Book articles cumulative views (Article Scoring System: Data Extract)
  • section on BioMed e-Series
  • section on List of Conference covered in Real Time
  • FIT Team input to Venture Valuation: top 5 or top 10 Factors in consensus 
  • the 3D graphs on Opportunity Maps: Gail, Rick, Amnon, Aviva – each explains their own outcome
  • section on Pipeline

Video on What is the Ideal Solution for the FUTURE of LPBI Group

  • Interviews with All FIT Members

For POINT #1:

To build the narrative for a VIDEO dedication to Genomics, Volume Two and Marketing campaign as a NEW BOOK on NGS, the Narrative will use content extracts to built a CASE for

Why GENOMICS Volume 2 – is the Epitome of all BioMed e-Series???????

 

forthcoming Genomics, Volume 2 

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-two-genomics-methodologies-ngs-bioinformatics-simulations-and-the-genome-ontology/

 

Aviva completed Parts 1,2,3,4,6, 

[5 is by Prof. Feldman] 

[7,8 are by Scientists on FIT]:

Latest in Genomics Methodologies for Therapeutics:

Gene Editing, NGS & BioInformatics,

Simulations and the Genome Ontology

 

2019

Volume Two

Prof. Marcus W. Feldman, PhD, Editor

Prof. Stephen J. Williams, PhD, Editor

And

Aviva Lev-Ari, PhD, RN, Editor 

https://pharmaceuticalintelligence.com/biomed-e-books/genomics-orientations-for-personalized-medicine/volume-two-genomics-methodologies-ngs-bioinformatics-simulations-and-the-genome-ontology/

Abbreviated eTOCs

Part 1: NGS

1.1 The Science

1.2 Technologies and Methodologies

1.3 Clinical Aspects

1.4 Business and Legal

 

Part 2: CRISPR for Gene Editing and DNA Repair

2.1 The Science

2.2 Technologies and Methodologies

2.3 Clinical Aspects

2.4 Business and Legal

 

Part 3: AI in Medicine

3.1 The Science

3.2 Technologies and Methodologies

3.3 Clinical Aspects

3.4 Business and Legal

3.5 Latest in Machine Learning (ML) Algorithms harnessed for Medical Diagnosis: Pattern Recognition & Prediction of Disease Onset

 

Part 4: Single Cell Genomics

4.1 The Science

4.2 Technologies and Methodologies

4.3 Clinical Aspects

4.4 Business and Legal

 

Part 5: Evolution Biology Genomics Modeling @Feldman Lab, Stanford University – Written and Curated by Prof. Marc Feldman

5.1

5.2

5.3

5.4

 

Part 6: Simulation Modeling in Genomics

6.1   Mutation Analysis – Gene Encoding

6.2   Mitochondrial Variations

6.3   Variant Analysis

6.4   Variant Detection in Hereditary Cancer Genes

6.5   Immuno-Informatics

6.6   RNA Sequencing

6.7   Complex Insertions and Deletions

6.8   Evolutionary Biology

6.9   Simulation Programs

6.10  A comparison of tools for the simulation of genomic next-generation sequencing data

 

Part 7: Applications of Genomics: Genotypes, Phenotypes and Complex Diseases

7.1 Genome-wide associations with complex diseases (GWAS)

7.2 Non-coding DNA and phenotypes—including diseases like cancer

7.3 Epigenomic associations with phenotypes including cancer

7.4 Rare variants and diseases

7.5 Population-level genomics and the meaning of group differences

7.6 Targeting drugs for complex diseases

 

Part 8: Epigenomics and Genomic Regulation

8.1  Genomic controls on epigenomics

8.2  The ENCODE project and gene regulation

8.3  Small interfering RNAs and gene expression

8.4  Epigenomics in cancer

8.5  Environmental epigenomics

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First Cost-Effectiveness Study of Multi-Gene Panel Sequencing in Advanced Non-Small Cell Lung Cancer Shows Moderate Cost-Effectiveness, Exposes Crucial Practice Gap

WASHINGTON (June 27, 2019) — The results of the first economic modeling study to estimate the cost-effectiveness of “multi-gene panel sequencing” (MGPS) as compared to standard-of-care, single-gene tests for patients with advanced non-small cell lung cancer (aNSCLC) show that the MGPS tests are moderately cost-effective but could deliver more value if patients with test results identifying actionable genetic mutations consistently received genetically guided treatments. The results of the study, which was commissioned by the Personalized Medicine Coalition (PMC), underline the need to align clinical practices with an era of personalized medicine in which physicians can use diagnostic tests to identify specific biological markers that inform targeted prevention and treatment plans.

The study, which was published yesterday in JCO Clinical Cancer Informatics, analyzed the clinical and economic value of using MGPS testing to identify patients with tumors that over-express genetic mutations that could be targeted by available therapies designed to inhibit the function of those genes — a mainstay of modern care for aNSCLC patients. Using data provided by Flatiron Health, researchers examined clinical and cost information associated with the care of 5,688 patients with aNSCLC treated between 2011 – 2016, separating them into cohorts who received MGPS tests that assess at least 30 genetic mutations at once and those who received only “single-marker genetic testing” (SMGT) of less than 30 genes.

Compared to SMGT, the MGPS testing strategy, including downstream treatment and monitoring of disease, incurred costs equal to $148,478 for each year of life that it facilitated, a level suggesting that MGPS is moderately cost-effective compared to commonly cited thresholds in the U.S., which range from $50,000 to $200,000 per life year (LY) gained.

The authors of the study point out, however, that physicians only prescribed a targeted therapy to some of the patients whose MGPS test results revealed actionable mutations. MGPS tests can only improve downstream patient outcomes if actionable results are used to put the patient on a targeted treatment regimen that is more effective than the therapy they would otherwise have been prescribed. It is therefore impossible for the cost of an MGPS test to translate into additional LYs if actionable results do not result in the selection of a targeted treatment regimen.

Although MGPS testing revealed actionable mutations in 30.1 percent of the patients in the study cohort, only 21.4 percent of patients who underwent MGPS testing received a targeted treatment.

The study’s authors calculated that if all MGPS-tested patients with actionable mutations had received a targeted therapy, MGPS testing would deliver measurably better value ($110,000 per LY gained).

“This research underlines the importance of ensuring that clinical practices keep pace with scientific progress in personalized medicine so that we can maximize the benefits of diagnostic tests that can improve patient care and make the health system more efficient by ensuring that safe and effective targeted therapies are prescribed to those patients who will benefit,” said PMC President Edward Abrahams.

The study’s authors include Dr. Lotte Steuten, Vice President and Head of Consulting, The Office of Health Economics, London, U.K., and Affiliate Associate Faculty Member, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center; Dr. Bernardo Goulart, Associate Faculty Member, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center; Dr. Neal Meropol, Vice President, Research Oncology, Flatiron Health; Dr. Daryl Pritchard, Senior Vice President, Science Policy, Personalized Medicine Coalition; and Dr. Scott Ramsey, Director, Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Cancer Research Center.

###

About the Personalized Medicine Coalition:

The Personalized Medicine Coalition, representing innovators, scientists, patients, providers and payers, promotes the understanding and adoption of personalized medicine concepts, services and products to benefit patients and the health system. For more information, please visit www.personalizedmedicinecoalition.org.

SOURCE

From: Personalized Medicine Coalition <pmc@personalizedmedicinecoalition.org>

Reply-To: “Christopher Wells (PMC)” <cwells@personalizedmedicinecoalition.org>

Date: Thursday, June 27, 2019 at 9:32 AM

To: Aviva Lev-Ari <AvivaLev-Ari@alum.berkeley.edu>

Subject: First Cost-Effectiveness Study of MGPS in aNSCLC Shows Moderate Cost-Effectiveness, Exposes Crucial Practice Gap

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Featuring Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI), The Dana Pe’er Lab

 

Reporter: Aviva Lev-Ari, PhD, RN

A lecture by Dana Pe’er is included, below in the eProceedings which I generated in Real Time on 6/14/2019 @MIT

eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PM ET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA

https://pharmaceuticalintelligence.com/2019/03/12/2019-koch-institute-symposium-machine-learning-and-cancer-june-14-2019-800-am-500-pmet-mit-kresge-auditorium-48-massachusetts-ave-cambridge-ma/

 

 

Memorial Sloan Kettering Cancer Center, Sloan Kettering Institute (SKI

https://www.mskcc.org/research/ski/about

 

Research Programs

Cancer Biology & Genetics Program

Our scientists study the molecular and genetic determinants of cancer predisposition, tumor development, and metastasis.

Cell Biology Program

Our researchers explore the molecular mechanisms that control normal cell behavior and how these mechanisms are disrupted in cancer.

Chemical Biology Program

Our scientists use chemical principles to investigate cutting-edge topics in biology and medicine.

Computational & Systems Biology Program

The goal of our research is to build computer models that simulate biological processes, from the molecular level up to the organism as a whole.

Developmental Biology Program

Our investigators study the mechanisms that control cell proliferation, cell differentiation, tissue patterning, and tissue morphogenesis.

Immunology Program

Our research is geared toward understanding how the immune system functions in all its complexity and how it can be harnessed to fight disease.

Molecular Biology Program

Our research is directed at understanding how cell growth is regulated and how the integrity of the genome is maintained.

Molecular Pharmacology Program

Our research program serves as a conduit for bringing basic science discoveries to preclinical and clinical evaluation.

Structural Biology Program

Our researchers are dedicated to understanding biology at the structural and mechanistic levels, and aiding the development of new cancer therapies.

Book traversal links for Research

 

The Dana Pe’er Lab

 

The Dana Pe'er Lab

The Pe’er lab combines single cell technologies, genomic datasets and machine learning algorithms to address fundamental questions in biomedical science. Empowered by recent breakthrough technologies like massive parallel single cell RNA-sequencing, we ask questions such as: How do multi-cellular organisms develop from a single cell, resulting in the vast diversity of progenitor and terminal cell types? How does a cell’s regulatory circuit control the dynamics of signal processing and how do these circuits rewire over the course of development? How does an ensemble of cells function together to execute a multi-cellular response, such as an immune response to pathogen or cancer? We will also address more medically oriented questions such as: How do regulatory circuits go awry in disease? What is the consequence of intra-tumor heterogeneity? Can we characterize the tumor immune eco-system to gain a better understanding of when or why immunotherapy works or does not work? A key goal is to use this characterization of the tumor immune eco-system to personalize immunotherapy.

Dana Pe'er, PhD

Dana Pe’er, PhD

Chair, Computational and Systems Biology Program, SKI; Scientific Director, Metastasis & Tumor Ecosystems Center

Research Focus

Computational Biologist Dana Pe’er combines single cell technologies, genomic datasets and machine learning techniques to address fundamental questions addressing regulatory cell circuits, cellular development, tumor immune eco-system, genotype to phenotype relations and precision medicine.

Education

PhD, Hebrew University, Jerusalem Israel

 

The Dana Pe’er Lab: Publications

View a full listing of Dana Pe’er’s journal articles.


Palantir characterizes cell fate continuities in human hematopoiesis. Setty M, Kiseliovas V, Levine J, Gayoso A, Mazutis L, Pe’er D. 2019, in press. Nature Biotechnology.

Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C, Prabhakaran S, Nainys J, Wu K, Kiseliovas V, Setty M, Choi K, Fromme RM, Dao P, McKenney PT, Wasti RC, Kadaveru K, Mazutis L, Rudensky AY, Pe’er D. Cell. 2018 Aug 23;174(5):1293-1308.e36. doi: 10.1016/j.cell.2018.05.060. PMID: 29961579

Recovering gene interactions from single-cell data using data diffusion. van Dijk D, Sharma R, Nainys J, Yim K, Kathail P, Carr AJ, Burdziak C, Moon KR, Chaffer CL, Pattabiraman D, Bierie B, Mazutis L, Wolf G, Krishnaswamy S, Pe’er D. Cell. 2018 Jul 26;174(3):716-729.e27. doi: 10.1016/j.cell.2018.05.061. PubMed PMID: 29961576

The Human Cell Atlas. Regev A et al. Elife. 2017 Dec 5;6. pii: e27041. doi: 10.7554/eLife.27041. PubMed PMID: 29206104

Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Wei SC, Levine JH, Cogdill AP, Zhao Y, Anang NAS, Andrews MC, Sharma P, Wang J, Wargo JA, Pe’er D, Allison JP. Cell. 2017 Sep 7;170(6):1120-1133.e17. doi: 10.1016/j.cell.2017.07.024. PMID: 28803728

Wishbone identifies bifurcating developmental trajectories from single-cell data. Setty M, Tadmor MD, Reich-Zeliger S, Angel O, Salame TM, Kathail P, Choi K, Bendall S, Friedman N, Pe’er D. Nat Biotechnol. 2016 Jun;34(6):637-45. doi: 10.1038/nbt.3569. PMID: 27136076

Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Levine JH, Simonds EF, Bendall SC, Davis KL, Amir el-AD, Tadmor MD, Litvin O, Fienberg HG, Jager A, Zunder ER, Finck R, Gedman AL, Radtke I, Downing JR, Pe’er D, Nolan GP. Cell. 2015 Jul 2;162(1):184-97. doi: 10.1016/j.cell.2015.05.047. PMID: 26095251

Interferon α/β enhances the cytotoxic response of MEK inhibition in melanoma. Litvin O, Schwartz S, Wan Z, Schild T, Rocco M, Oh NL, Chen BJ, Goddard N, Pratilas C, Pe’er D. Mol Cell. 2015 Mar 5;57(5):784-796. doi: 10.1016/j.molcel.2014.12.030. PMID: 25684207

Integration of genomic data enables selective discovery of breast cancer drivers. Sanchez-Garcia F, Villagrasa P, Matsui J, Kotliar D, Castro V, Akavia UD, Chen BJ, Saucedo-Cuevas L, Rodriguez Barrueco R, Llobet-Navas D, Silva JM, Pe’er D. Cell. 2014 Dec 4;159(6):1461-75. doi: 10.1016/j.cell.2014.10.048. PMID: 25433701

Conditional density-based analysis of T cell signaling in single-cell data. Krishnaswamy S, Spitzer MH, Mingueneau M, Bendall SC, Litvin O, Stone E, Pe’er D, Nolan GP. Systems biology. Science. 2014 Nov 28;346(6213):1250689. doi: 10.1126/science.1250689. PMID: 25342659

Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Bendall SC, Davis KL, Amir el-AD, Tadmor MD, Simonds EF, Chen TJ, Shenfeld DK, Nolan GP, Pe’er D. Cell. 2014 Apr 24;157(3):714-25. doi: 10.1016/j.cell.2014.04.005. PMID: 24766814

Book traversal links for The Dana Pe’er Lab

SOURCE

https://www.mskcc.org/research/ski/labs/dana-pe-er/publications

The Dana Pe’er Lab is one of four Labs of the Computational & Systems Biology Program

Computational biologists combine findings in biology with computer algorithms and databases to conduct biological research on powerful computers, using sophisticated software — so-called “dry” laboratories — in ways that complement and strengthen traditional laboratory and clinical research. The aim is to build computer models that simulate biological processes from the molecular level up to the organism as a whole and to use these models to make useful predictions.

 

Computational biology can help interpret detailed molecular profiles of cancerous and noncancerous cells, molecular response profiles of therapeutic agents, and a person’s genetic profile to assist in the development of better diagnostics and prognostics, as well as improved therapies. Intelligent use of computational methods using detailed molecular and genomic data is expected to reduce the trial and error of drug development and possibly lead to shorter, more accurate clinical trials.

 

The Christina Leslie Lab

The John Chodera Lab

The Dana Pe'er Lab

The Joao Xavier Lab

 

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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:

@Handles

@pharma_BI

@AVIVA1950

@BIOConvention

# Hashtags

#BIO2019 (official meeting hashtag)

 

 

 

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

Reporter: Stephen J. Williams, PhD @StephenJWillia2

 

  • taking patient concerns and voices from anecdotal to data driven system
  • talked about patient accrual hearing patient voice not only in ease of access but reporting toxicities
  • at FDA he wants to remove barriers to trial access and accrual; also talk earlier to co’s on how they should conduct a trial

Digital tech

  • software as medical device
  • regulatory path is mixed like next gen sequencing
  • wearables are concern for FDA (they need to recruit scientists who know this tech

Opioids

  • must address the crisis but in a way that does not harm cancer pain patients
  • smaller pain packs “blister packs” would be good idea

Clinical trial modernization

  • for Alzheimers disease problem is science
  • for diabetes problem is regulatory
  • different diseases calls for different trial design
  • have regulatory problems with rare diseases as can’t form control or placebo group, inhumane. for example ras tumors trials for MEK inhibitors were narrowly focused on certain ras mutants
Realizing the Promise of Gene Therapies for Patients Around the World

103ABC, Level 100

Speakers
Lots of promise, timeline is progressing faster but we need more education on use of the gene therapy
Regulatory issues: Cell and directly delivered gene based therapies have been now approved. Some challenges will be the ultrarare disease trials and how we address manufacturing issues.  Manufacturing is a big issue at CBER and scalability.  If we want to have global impact of these products we need to address the manufacturing issues
 of scalability.
Pfizer – clinical grade and scale is important.
Aventis – he knew manufacturing of biologics however gene therapy manufacturing has its separate issues and is more complicated especially for regulatory purposes for clinical grade as well as scalability.  Strategic decision: focusing on the QC on manufacturing was so important.  Had a major issue in manufacturing had to shut down and redesign the system.
Albert:  Manufacturing is the most important topic even to the investors.  Investors were really conservative especially seeing early problems but when academic centers figured out good efficacy then they investors felt better and market has exploded.  Now you can see investment into preclinical and startups but still want mature companies to focus on manufacturing.  About $10 billion investment in last 4 years.

How Early is Too Early? Valuing and De-Risking Preclinical Opportunities

109AB, Level 100

Speakers
Valuing early-stage opportunities is challenging. Modeling will often provide a false sense of accuracy but relying on comparable transactions is more art than science. With a long lead time to launch, even the most robust estimates can ultimately prove inaccurate. This interactive panel will feature venture capital investors and senior pharma and biotech executives who lead early-stage transactions as they discuss their approaches to valuing opportunities, and offer key learnings from both successful and not-so-successful experiences.
Dr. Schoenbeck, Pfizer:
  • global network of liaisons who are a dedicated team to research potential global startup partners or investments.  Pfizer has a separate team to evaluate academic laboratories.  In Most cases Pfizer does not initiate contact.  It is important to initiate the first discussion with them in order to get noticed.  Could be just a short chat or discussion on what their needs are for their portfolio.

Question: How early is too early?

Luc Marengere, TVM:  His company has early stage focus, on 1st in class molecules.  The sweet spot for their investment is a candidate selected compound, which should be 12-18 months from IND.  They will want to bring to phase II in less than 4 years for $15-17 million.  Their development model is bad for academic labs.  During this process free to talk to other partners.

Dr. Chaudhary, Biogen:  Never too early to initiate a conversation and sometimes that conversation has lasted 3+ years before a decision.  They like build to buy models, will do convertible note deals, candidate compound selection should be entering in GLP/Tox phase (sweet spot)

Merck: have MRL Venture Fund for pre series A funding.  Also reiterated it is never too early to have that initial discussion.  It will not put you in a throw away bin.  They will have suggestions and never like to throw out good ideas.

Michael Hostetler: Set expectations carefully ; data should be validated by a CRO.  If have a platform, they will look at the team first to see if strong then will look at the platform to see how robust it is.

All noted that you should be completely honest at this phase.  Do not overstate your results or data or overhype your compound(s).  Show them everything and don’t have a bias toward compounds you think are the best in your portfolio.  Sometimes the least developed are the ones they are interested in.  Also one firm may reject you however you may fit in others portfolios better so have a broad range of conversations with multiple players.

 

 

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