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Archive for the ‘Pharmacogenomics’ Category

AI enabled Drug Discovery and Development: The Challenges and the Promise

Posted in Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Applications in Therapeutics, Drug Development Process, Drug Discovery Chemistry, Machine Learning, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmacogenomics on March 18, 2022| 1 Comment »

AI enabled Drug Discovery and Development: The Challenges and the Promise

Reporter: Aviva Lev-Ari, PhD, RN

 

Early Development

Caroline Kovac (the first IBM GM of Life Sciences) is the one who started in silico development of drugs in 2000 using a big db of substances and computer power. She transformed an idea into $2b business. Most of the money was from big pharma. She was asking what is are the new drugs they are planning to develop and provided the four most probable combinations of substances, based on in Silicon work. 

Carol Kovac

General Manager, Healthcare and Life Sciences, IBM

from speaker at conference on 2005

Carol Kovac is General Manager of IBM Healthcare and Life Sciences responsible for the strategic direction of IBM′s global healthcare and life sciences business. Kovac leads her team in developing the latest information technology solutions and services, establishing partnerships and overseeing IBM investment within the healthcare, pharmaceutical and life sciences markets. Starting with only two employees as an emerging business unit in the year 2000, Kovac has successfully grown the life sciences business unit into a multi-billion dollar business and one of IBM′s most successful ventures to date with more than 1500 employees worldwide. Kovac′s prior positions include general manager of IBM Life Sciences, vice president of Technical Strategy and Division Operations, and vice president of Services and Solutions. In the latter role, she was instrumental in launching the Computational Biology Center at IBM Research. Kovac sits on the Board of Directors of Research!America and Africa Harvest. She was inducted into the Women in Technology International Hall of Fame in 2002, and in 2004, Fortune magazine named her one of the 50 most powerful women in business. Kovac earned her Ph.D. in chemistry at the University of Southern California.

SOURCE

https://www.milkeninstitute.org/events/conferences/global-conference/2005/speaker-detail/1536

 

In 2022

The use of artificial intelligence in drug discovery, when coupled with new genetic insights and the increase of patient medical data of the last decade, has the potential to bring novel medicines to patients more efficiently and more predictably.

WATCH VIDEO

https://www.youtube.com/watch?v=b7N3ijnv6lk

SOURCE

https://engineering.stanford.edu/magazine/promise-and-challenges-relying-ai-drug-development?utm_source=Stanford+ALL

Conversation among three experts:

Jack Fuchs, MBA ’91, an adjunct lecturer who teaches “Principled Entrepreneurial Decisions” at Stanford School of Engineering, moderated and explored how clearly articulated principles can guide the direction of technological advancements like AI-enabled drug discovery.

Kim Branson, Global head of AI and machine learning at GSK.

Russ Altman, the Kenneth Fong Professor of Bioengineering, of genetics, of medicine (general medical discipline), of biomedical data science and, by courtesy, of computer science.

 

Synthetic Biology Software applied to development of Galectins Inhibitors at LPBI Group

https://pharmaceuticalintelligence.com/synthetic-biology-in-drug-discovery/

 

The Map of human proteins drawn by artificial intelligence and PROTAC (proteolysis targeting chimeras) Technology for Drug Discovery

Curators: Dr. Stephen J. Williams and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/10/21/the-map-of-human-proteins-drawn-by-artificial-intelligence-and-protac-proteolysis-targeting-chimeras-technology-for-drug-discovery/

Using Structural Computation Models to Predict Productive PROTAC Ternary Complexes

Ternary complex formation is necessary but not sufficient for target protein degradation. In this research, Bai et al. have addressed questions to better understand the rate-limiting steps between ternary complex formation and target protein degradation. They have developed a structure-based computer model approach to predict the efficiency and sites of target protein ubiquitination by CRNB-binding PROTACs. Such models will allow a more complete understanding of PROTAC-directed degradation and allow crafting of increasingly effective and specific PROTACs for therapeutic applications.

Another major feature of this research is that it a result of collaboration between research groups at Amgen, Inc. and Promega Corporation. In the past commercial research laboratories have shied away from collaboration, but the last several years have found researchers more open to collaborative work. This increased collaboration allows scientists to bring their different expertise to a problem or question and speed up discovery. According to Dr. Kristin Riching, Senior Research Scientist at Promega Corporation, “Targeted protein degraders have broken many of the rules that have guided traditional drug development, but it is exciting to see how the collective learnings we gain from their study can aid the advancement of this new class of molecules to the clinic as effective therapeutics.”

Literature Reviewed

Bai, N. , Riching K.M. et al. (2022) Modeling the CRLRA ligase complex to predict target protein ubiquitination induced by cereblon-recruiting PROTACs. J. Biol. Chem.

The researchers NanoBRET assays as part of their model validation. Learn more about NanoBRET technology at the Promega.com website.

SOURCE

https://www.promegaconnections.com/protac-ternary-complex/?utm_campaign=ms-2022-pharma_tpd&utm_source=linkedin&utm_medium=Khoros&utm_term=sf254230485&utm_content=030822ct-blogsf254230485&sf254230485=1

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Danny Bar-Zohar, MD –  New R&D Leader for new pipelines at Merck KGaA as Luciano Rossetti steps out

Posted in Drug Development Process, drug repurposing, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Investment, Pharmacogenomics, Scientist: Career considerations on October 15, 2020| Leave a Comment »

Danny Bar-Zohar, MD –  New R&D Leader for new pipelines at Merck KGaA as Luciano Rossetti steps out

Reporter: Aviva Lev-Ari, PhD, RN

 

Danny Bar-Zohar, MD – A Pharmaceutical Executive Profile in R&D: Ex-Novastis, Ex-Teva

Experience

  • Partner

    Syncona Limited Full-time

    Apr 2020 – Present

    7 mos

    London, England, United Kingdom

  • Novartis

    6 yrs 9 mos

    • Global Head, Clinical Development & Analytics

      May 2019 – Apr 2020

      1 yr

      Basel Area, Switzerland

    • Global Head, Neuroscience Development

      Mar 2017 – Sep 2019

      2 yrs 7 mos

      Basel Area, Switzerland

    Show 2 more roles 

  • Teva Pharmaceuticals

    6 yrs

    • Head of CNS & Pain Therapeutic Area, Global Branded Products

      Oct 2010 – Dec 2012

      2 yrs 3 mos

      Israel

    • Global Medical Director, Multiple Sclerosis

      Oct 2009 – Nov 2010

      1 yr 2 mos

    • Associate Director, Global Clinical Program Leader, MS Products

      2006 – Oct 2009

      3 yrs

      Responsible for the clinical development of the innovative MS franchise in Teva Pharmaceutical Industries

    Show fewer roles 

  • Search and Rescue Airborne Surgeon

    Israeli Air Force

    2004 – Dec 2009

    5 yrs

Education

  • Tel Aviv University

    Doctor of Medicine (M.D.)

    Graduated with honors

     – 

  • Tel Aviv University

    Bachelor of Science (B.Sc.)

    Medicine

     – 

    Graduated with Honors

     

SOURCE

https://www.linkedin.com/in/danny-bar-zohar-513904a/

 

Novartis vet Danny Bar-Zohar leaps back into R&D, taking over the development team at Merck KGaA as Luciano Rossetti steps out

John Carroll
Editor & Founder

After a brief stint as a biotech investor at Syncona, Novartis vet Danny Bar-Zohar is back in R&D, and he’s taking the lead position at Merck KGaA’s drug division.

Bar-Zohar had led late-stage clinical development across a variety of areas — neuroscience, immunology, oncology and ophthalmology, among others — before joining the migration of talent out of the Basel-based multinational. He had been at Novartis for 7 years, which followed an earlier chapter in research at Teva.

Luciano Rossetti
The scientist is taking the lead on development at Merck KGaA, in place of Luciano Rossetti, who had a mixed record in R&D that nevertheless marked a big improvement over the dismal run the company had endured earlier. Joern-Peter Halle will continue on as global head of research. Rossetti is retiring after 6 years of running the research group, which has extensive operations in Germany as well as Massachusetts.

Their PD-L1 Bavencio — allied with Pfizer — has had a few successes, and a whole slate of failures. Sprifermin was touted as a big potential advance in osteoarthritis, but Merck KGaA is now auctioning off that part of the portfolio. One of the few late-stage bright spots has been their MET inhibitor tepotinib, which won breakthrough status and now is under priority review. That drug faces a rival at Novartis — capmatinib — that won an accelerated OK at the FDA in May.

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There’s also a BTK inhibitor, evobrutinib, that’s being developed for MS. But that’s a very crowded field, and Sanofi has been bullish about its prospects in the same research niche after buying out Principia.

Moving back into mid-stage development, there’s a major program underway for bintrafusp alfa, a bifunctional fusion protein targeting TGF-β and PD-L1, which Merck KGaA has high hopes for.

That all marks some bright, though limited, prospects for Merck KGaA, highlighting the need to find something new to beef up the pipeline. Bar-Zohar will get a say in that.

AUTHOR
John Carroll

SOURCE

https://endpts.com/novartis-vet-danny-bar-zohar-leaps-back-into-rd-taking-over-the-team-at-merck-kgaa-as-luciano-rossetti-steps-out/

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CAR T-Cell Therapy Market: 2020 – 2027 – Global Market Analysis and Industry Forecast

Posted in Cancer and Current Therapeutics, Cancer Vaccines: Targeting Cancer Genes for Immunotherapy, CAR-T, combination immunotherapies., Immune Engineering, Immune Modulatory, Immunotherapy, Modulating Macrophages in Cancer Immunotherapy, Pharmaceutical Industry Competitive Intelligence, Pharmacogenomics, Synergistic Innate and Adaptive Immunotherapy on October 12, 2020| 1 Comment »

CAR T-CELL THERAPY MARKET: 2020 – 2027

G L O B A L  M A R K E T  A N A L Y S I S  A N D

I N D U S T R Y  F O R E C A S T

 

DISCLAIMER

LPBI Group’s decision to publish the Table of Contents of this Report does not imply endorsement of the Report

Aviva Lev-Ari, PhD, RN, Founder 1.0 & 2.0 LPBI Group

Guest Reporter: MIKE WOOD

Marketing Executive
BIOTECH FORECASTS

 

ABOUT BIOTECH FORECASTS

BIOTECH FORECASTS is a full-service market research and business- consulting firm primarily focusing on healthcare, pharmaceutical, and biotechnology industries. BIOTECH FORECASTS provides global as well as medium and small Pharmaceutical and Biotechnology businesses with unmatched quality of “Market Research Reports” and “Business Intelligence Solutions”. BIOTECH FORECASTS has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions, and achieve sustainable growth in their respective market domain.

UPDATED on 10/13/2020

CAR T-CELL THERAPY MARKET

Mike Wood

Mike Wood

Marketing Executive at Biotech Forecasts
1 article 

CAR T-cell therapy as a part of adoptive cell therapy (ACT), has become one of the most rapidly growing and promising fields in the Immuno-oncology. As compared to the conventional cancer therapies, CAR T-cell therapy is the single-dose solution for the treatment of various cancers, significantly for some lethal forms of hematological malignancies.

CAR T-cell therapy mainly involves the use of engineered T-cells, the process starts with the extraction of T-cells through leukapheresis, either from the patient (autologous) or a healthy donor (allogeneic). After the expression of a synthetic receptor (Chimeric Antigen Receptor) in the lab, the altered T-cells are expanded to the right dose and administered into the patient’s body. where they target and attach to a specific antigen on the tumor surface, to kill the cancerous cells by igniting the apoptosis.

The global CAR T-cell therapy market was valued at $734 million in 2019 and is estimated to reach $4,078 million by 2027, registering a CAGR of 23.91% from 2020 to 2027.

Factors that drive the market growth involve, (1) Increased in funding for R&D activities pertaining to cell and gene therapy. By H1 2020 cell and gene therapy companies set new records in the fundraising despite the pandemic crisis. For Instance, by June 2020 totaled $1,452 Million raised in Five IPOs including, Legend Biotech ($487M), Passage Bio ($284M), Akouos ($244M), Generation Bio ($230M), and Beam Therapeutics ($207M), which is 2.5 times the total IPO of 2019.

Moreover, in 2019 cell therapy companies specifically have raised $560 million of venture capital, including Century Therapeutics ($250M), Achilles Therapeutics Ltd. ($121M in series B), NKarta Therapeutics Inc. ($114M), and Tmunity Therapeutics ($75M in Series B).

(2) Increased in No. of Approved Products, By July 2020, there are a total of 03 approved CAR T-cell therapy products, including KYMRIAH®, YESCARTA®, and the most recently approved TECARTUS™ (formerly KTE-X19). Furthermore, two CAR T-cell therapies BB2121, and JCAR017 are expected to get the market approval by the end of 2020 or in early 2021.

Other factors that boost the market growth involves; (3) increase in government support, (4) ethical acceptance of Cell and Gene therapy for cancer treatment, (5) rise in the prevalence of cancer, and (6) an increase in awareness regarding the CAR T-cell therapy.

However, high costs associated with the treatment (KYMRIAH® cost around $475,000, and YESCARTA® costs $373,000 per infusion), long production hours, obstacles in treating solid tumors, and unwanted immune responses & potential side effects might hamper the market growth.

The report also presents a detailed quantitative analysis of the current market trends and future estimations from 2020 to 2027.

The forecasts cover 2 Approach Types, 5 Antigen Types, 5 Application Types, 4 Regions, and 14 Countries.

The report comes with an associated file covering quantitative data from all numeric forecasts presented in the report, as well as with a Clinical Trials Data File.

KEY FINDINGS

The report has the following key findings:

  • The global CAR T-cell therapy market accounted for $734 million in 2019 and is estimated to reach $4,078 million by 2027, registering a CAGR of 23.91% from 2020 to 2027.
  • By approach type the autologous segment was valued at $655.26 million in 2019 and is estimated to reach $ 3,324.52 million by 2027, registering a CAGR of 22.51% from 2020 to 2027.
  • By approach type, the allogeneic segment exhibits the highest CAGR of 32.63%.
  • Based on the Antigen segment CD19 was the largest contributor among the other segments in 2019.
  • The Acute lymphocytic leukemia (ALL) segment generated the highest revenue and is expected to continue its dominance in the future, followed by the Diffuse large B-cell lymphoma (DLBCL) segment.
  • North America dominated the global CAR T-cell therapy market in 2019 and is projected to continue its dominance in the future.
  • China is expected to grow the highest in the Asia-Pacific region during the forecast period.

TOPICS COVERED

The report covers the following topics:

  • Market Drivers, Restraints, and Opportunities
  • Porters Five Forces Analysis
  • CAR T-Cell Structure, Generations, Manufacturing, and Pricing Models
  • Top Winning Strategies, Top Investment Pockets
  • Analysis of by Approach Type, Antigen Type, Application, and Region
  • 51 Company Profiles, Product Portfolio, and Key Strategies
  • Approved Products Profiles, and list of Expected Approvals
  • COVID-19 Impact on the Cell and Gene Therapy Industry
  • CAR T-cell therapy clinical trials analysis from 1997 to 2019
  • Market analysis and forecasts from 2020 to 2027

FORECAST SEGMENTATION

By Approach Type

  • Autologous
  • Allogeneic

By Antigen Type

  • CD19
  • CD20
  • BCMA
  • MSLN
  • Others

By Application

  • Acute lymphoblastic leukemia (ALL)
  • Diffuse large B-Cell lymphoma (DLBCL)
  • Multiple Myeloma (MM)
  • Acute Myeloid Leukemia (AML)
  • Other Cancer Indications

By Region

  • North America: USA, Canada, Mexico
  • Europe: UK, Germany, France, Spain, Italy, Rest of Europe
  • Asia-Pacific: China, Japan, India, South Korea, Rest of Asia-Pacific
  • LAMEA: Brazil, South Africa, Rest of LAMEA

Contact at info@biotechforecasts.com for any Queries or Free Report Sample

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Published by

Mike Wood
Mike Wood
Marketing Executive at Biotech Forecasts
Published • 10h
1 article
The global CAR T-cell therapy market was valued at $734 million in 2019 and is estimated to reach $4,078 million by 2027, registering a CAGR of 23.91% from 2020 to 2027. celltherapy genetherapy immunotherapy cancertreatment cartcell regenerativemedicine biotech cancer

 

Table of Contents

 

CHAPTER 1: INTRODUCTION

1.1 REPORT DESCRIPTION 17
1.2 TOPICS COVERED 19
1.3 KEY MARKET SEGMENTS 20
1.4 KEY BENEFITS 21
1.5 RESEARCH METHODOLOGY 21
1.6 TARGET AUDIENCE 22
1.7 COMPANIES MENTIONED 23

CHAPTER 2: EXECUTIVE SUMMARY

2.1 EXECUTIVE SUMMARY 26
2.2 CXO PROSPECTIVE 29

CHAPTER 3: MARKET OVERVIEW

3.1 MARKET DEFINITION AND SCOPE 30
3.2 KEY FINDINGS 31
3.3 TOP INVESTMENT POCKETS 32
3.4 TOP WINNING STRATEGIES 33
3.4.1.Top winning strategies, by year, 2017-2019* 34
3.4.2.Top winning strategies, by development, 2017-2019*(%) 34
3.4.3.Top winning strategies, by company, 2017-2019* 35
3.5 TOP PLAYER POSITIONING, BY PIPELINE VOLUME, 2019 38
3.6 PORTERS FIVE FORCES ANALYSIS 39
3.7 COVID19 IMPACT ON CELL AND GENE THERAPY (CGT) INDUSTRY 41
3.8 MARKET DYNAMICS 46
3.8.1    Drivers 46
3.8.1.1   Increase in funding for R&D activities of CAR T-cell therapy 46
3.8.1.2   The rise in the prevalence of cancer 47
3.8.1.3   Increase in awareness regarding CAR T-cell therapy 47

 

3.8.2    Restrains 48
3.8.2.1   The high cost of CAR T-cell therapy treatment 48
3.8.2.2   Unwanted immune responses and side effects 48
3.8.2.3   Long production time 48
3.8.2.4   Obstacles in treating solid tumors 49
3.8.3    Opportunities 49
3.8.3.1   Untapped potential for emerging markets 49

CHAPTER 4: CAR T-CELL THERAPY, A BRIEF INTRODUCTION

4.1 OVERVIEW 50
4.2 SIXTY YEARS HISTORY OF CAR T-CELL THERAPY 51
4.3 CAR T-CELL STRUCTURE AND GENERATIONS 53
4.4 CAR T-CELL MANUFACTURING PROCESSES 56
4.5 PRICING AND PAYMENT MODELS FOR CAR T-CELL THERAPIES 59

CHAPTER 5: CAR T-CELL THERAPY MARKET, BY APPROACH TYPE

5.1 OVERVIEW 61
5.1.1    Market size and forecast 62
5.2 AUTOLOGOUS 63
5.2.1    Key market trends 63
5.2.2    Key growth factors and opportunities 64
5.2.3    Market size and forecast 64
5.2.4    Market size and forecast by country 65
5.3 ALLOGENEIC 66
5.3.1    Key market trends 67
5.3.2    Key growth factors and opportunities 68
5.3.3    Market size and forecast 68
5.3.4    Market size and forecast by country 69

CHAPTER 6: CAR T-CELL THERAPY MARKET, BY ANTIGEN TYPE

6.1 OVERVIEW 70
6.1.1         Market size and forecast 71
6.2 CD19 72
6.2.1         Market size and forecast 73
6.2.2         Market size and forecast by country 74

 

6.3 CD20 75
6.3.1 Market size and forecast 76
6.3.2 Market size and forecast by country 77
6.4 BCMA 78
6.4.1 Market size and forecast 79
6.4.2 Market size and forecast by country 80
6.5 MSLN 81
6.5.1 Market size and forecast 82
6.5.2 Market size and forecast by country 83
6.6 OTHERS 84
6.6.1 Market size and forecast 85
6.6.2 Market size and forecast by country 86

CHAPTER 7: CAR T-CELL THERAPY MARKET, BY APPLICATION

7.1 OVERVIEW 87
7.1.1       Market size and forecast 88
7.2 ACUTE LYMPHOBLASTIC LEUKEMIA (ALL) 89
7.2.1       Market size and forecast 90
7.2.2       Market size and forecast by country 91
7.3 DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) 92
7.3.1       Market size and forecast 93
7.3.2       Market size and forecast by country 94
7.4 MULTIPLE MYELOMA (MM) 95
7.4.1       Market size and forecast 96
7.4.2       Market size and forecast by country 97
7.5 ACUTE MYELOID LEUKEMIA (AML) 98
7.5.1       Market size and forecast 99
7.5.2       Market size and forecast by country 100
7.6 OTHERS 101
7.6.1       Market size and forecast 102
7.6.2       Market size and forecast by country 103

CHAPTER 8: CAR T-CELL THERAPY MARKET, BY REGION

8.1 OVERVIEW 104
8.1.1       Market size and forecast 104
8.2 NORTH AMERICA 105
8.2.1       Key market trends 105
8.2.2       Key growth factors and opportunities 105

 

8.2.3       Market size and forecast, by country 106
8.2.4       Market size and forecast, by approach type 106
8.2.5       Market size and forecast, by antigen type 107
8.2.6 Market size and forecast, by application 107
8.2.6.1 U.S. market size and forecast, by approach type 108
8.2.6.2 U.S. market size and forecast, by antigen type 108
8.2.6.3 U.S. market size and forecast, by application 109
8.2.6.4 Canada market size and forecast, by approach type 110
8.2.6.5 Canada market size and forecast, by antigen type 110
8.2.6.6 Canada market size and forecast, by application 111
8.2.6.7 Mexico market size and forecast, by approach type 112
8.2.6.8 Mexico market size and forecast, by antigen type 112
8.2.6.9 Mexico market size and forecast, by application 113
8.3 EUROPE 114
8.4.1 Key market trends 114
8.4.2 Key growth factors and opportunities 114
8.4.3 Market size and forecast, by country 115
8.4.4 Market size and forecast, by approach type 115
8.4.5 Market size and forecast, by antigen type 116
8.4.6 Market size and forecast, by application 116
8.3.6.1 UK market size and forecast, by approach type 117
8.3.6.2 UK market size and forecast, by antigen type 117
8.3.6.3 UK market size and forecast, by application 118
8.3.6.4 Germany market size and forecast, by approach type 119
8.3.6.5 Germany market size and forecast, by antigen type 119
8.3.6.6 Germany market size and forecast, by application 120
8.3.6.7 France market size and forecast, by approach type 121
8.3.6.8 France market size and forecast, by antigen type 121
8.3.6.9 France market size and forecast, by application 122
8.3.6.10 Spain market size and forecast, by approach type 123
8.3.6.11 Spain market size and forecast, by antigen type 123
8.3.6.12 Spain market size and forecast, by application 124
8.3.6.13 Italy market size and forecast, by approach type 125
8.3.6.14 Italy market size and forecast, by antigen type 125
8.3.6.15 Italy market size and forecast, by application 126
8.3.6.16 Rest of Europe market size and forecast, by approach type 127
8.3.6.17 Rest of Europe market size and forecast, by antigen type 127
8.3.6.18 Rest of Europe market size and forecast, by application 128
8.4 ASIA-PACIFIC 129
8.4.1 Key market trends 129
8.4.2 Key growth factors and opportunities 129
8.4.3 Market size and forecast, by country 130
8.4.4 Market size and forecast, by approach type 130

 

8.4.5       Market size and forecast, by antigen type 131
8.4.6 Market size and forecast, by application 131
8.4.6.1 China market size and forecast, by approach type 132
8.4.6.2 China market size and forecast, by antigen type 132
8.4.6.3 China market size and forecast, by application 133
8.4.6.4 Japan market size and forecast, by approach type 134
8.4.6.5 Japan market size and forecast by antigen type 134
8.4.6.6 Japan market size and forecast, by application 135
8.4.6.7 India market size and forecast, by approach type 136
8.4.6.8 India market size and forecast, by antigen type 136
8.4.6.9 India market size and forecast, by application 137
8.4.6.10 South Korea market size and forecast, by approach type 138
8.4.6.11 South Korea market size and forecast, by antigen type 138
8.4.6.12 South Korea market size and forecast, by application 139
8.4.6.13 Rest of Asia-Pacific market size and forecast, by approach type 140
8.4.6.14 Rest of Asia-Pacific market size and forecast, by antigen type 140
8.4.6.15 Rest of Asia-Pacific market size and forecast, by application 141
8.5 LAMEA 142
8.5.1 Key market trends 142
8.5.2 Key growth factors and opportunities 142
8.5.3 Market size and forecast, by country 143
8.5.4 Market size and forecast, by approach type 143
8.5.5 Market size and forecast, by antigen type 144
8.5.6 Market size and forecast, by application 144
8.5.6.1 Brazil market size and forecast by approach type 145
8.5.6.2 Brazil market size and forecast, by antigen type 145
8.5.6.3 Brazil market size and forecast, by application 146
8.5.6.4 South Africa market size and forecast, by approach type 147
8.5.6.5 South Africa market size and forecast, by antigen type 147
8.5.6.6 South Africa market size and forecast, by application 148
8.5.6.7 Rest of LAMEA market size and forecast by approach type 149
8.5.6.8 Rest of LAMEA market size and forecast, by antigen type 149
8.5.6.9 Rest of LAMEA market size and forecast, by application 150

CHAPTER 9: CLINICAL TRIALS ANALYSIS & PRODUCT PROFILES

9.1 OVERVIEW 151
9.1.1      No. of Clinical Trials from 1997 to 2019 151
9.1.2      Clinical Trials from 1997 to 2019: Based on Approach Type 152
9.1.3      Clinical Trials from 1997 to 2019: Based on Antigen Type 153
9.1.4      Clinical Trials from 1997 to 2019: Based on Application 154
9.1.5      Clinical Trials from 1997 to 2019: Based on Region 155

 

9.2 EXPECTED APPROVALS 156
9.3 APPROVED PRODUCTS PROFILES 157
9.3.1      KYMRIAH® 157
9.3.2      YESCARTA® 159
9.3.3      TECARTUS™ 161

CHAPTER 10: COMPANY PROFILES

10.1       Abbvie Inc. 162
10.2       Adaptimmune Therapeutics Plc 164
10.3 Allogene Therapeutics, Inc. 166
10.4 Amgen, Inc 168
10.5 Anixa Biosciences, Inc. 170
10.6 Arcellx, Inc. 172
10.7 Atara Biotherapeutics, Inc. 173
10.8 Autolus Therapeutics Plc. 175
10.9 Beam Therapeutics, Inc. 177
10.10 Bellicum Pharmaceuticals, Inc. 179
10.11 BioNtech SE 181
10.12 Bluebird Bio, Inc. 183
10.13 Carsgen Therapeutics, Ltd 185
10.14 Cartesian Therapeutics, Inc. 187
10.15 Cartherics Pty Ltd. 188
10.16 Celgene Corporation 189
10.17 Cellectis SA 191
10.18 Cellular Biomedicine Group, Inc. 193
10.19 Celularity, Inc. 195
10.20 Celyad SA 196
10.21 CRISPR Therapeutics AG 198
10.22 Eureka Therapeutics, Inc. 200
10.23 Fate Therapeutics, Inc. 201
10.24 Fortress Biotech, Inc 203
10.25 Gilead Sciences, Inc. 205
10.26 Gracell Biotechnology Ltd 207
10.27 icell Gene Therapeutics 208
10.28 Johnson & Johnson 209
10.29 Juventas Cell Therapy Ltd. 211
10.30 Kuur Therapeutics 212
10.31 Legend Biotech Corp. 213
10.32 Leucid Bio Ltd. 214
10.33 Minerva Biotechnologies Corp. 215

 

10.34     Molecular Medicine SPA (Molmed) 216
10.35     Nanjing Bioheng Biotech Co., Ltd. 218
10.36     Noile-Immune Biotech Inc. 219
10.37     Novartis AG 220
10.38     Oxford Biomedica PLC 222
10.39     Persongen Biotherapeutics (Suzhou) Co., Ltd. 224
10.40     Poseida Therapeutics, Inc. 226
10.41     Precigen, Inc. 227
10.42     Precision Biosciences, Inc. 229
10.43     Sorrento Therapeutics, Inc. 231
10.44     Takara Bio Inc. 233
10.45     Takeda Pharmaceutical Company Ltd. 235
10.46     TC Biopharm Ltd. 237
10.47     Tessa Therapeutics Pte Ltd. 238
10.48     Tmunity Therapeutics, Inc. 239
10.49     Unum Therapeutics Inc. 240
10.50     Xyphos Inc. 242
10.51     Ziopharm Oncology, Inc. 243

CHAPTER 11: CONCLUSION & STRATEGIC RECOMMENTATIONS

11.1     STRATEGIC RECOMMENDATIONS 245
11.2     CONCLUSION 247

 

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The Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Partnership on May 18, 2020

Posted in Coronavirus Gene Expression, COVID-19, Drug Development Process, Drug Discovery Chemistry, drug repurposing, Personal Health Applications: Tech Innovations serves HealhCare, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Informatics, Pharmaceutical R&D Investment, Pharmacogenomics, Pharmacovigilance, Population Health Management, Population Health Management, Genetics & Pharmaceutical, SARS-CoV-2, Serology tests for coronavirus antibodies, Vaccinology, Virus Infective Acute Respiratory Syndrome: SARS-CoV on May 18, 2020| 1 Comment »

The Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) Partnership on May 18, 2020: Leadership of AbbVie, Amgen, AstraZeneca, Bristol Myers Squibb, Eisai, Eli Lilly, Evotec, Gilead, GlaxoSmithKline, Johnson & Johnson, KSQ Therapeutics, Merck, Novartis, Pfizer, Roche, Sanofi, Takeda, and Vir. We also thank multiple NIH institutes (especially NIAID), the FDA, BARDA, CDC, the European Medicines Agency, the Department of Defense, the VA, and the Foundation for NIH

Reporter: Aviva Lev-Ari, PhD, RN

May 18, 2020

Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) An Unprecedented Partnership for Unprecedented Times

Francis S. Collins, MD, PhD1; Paul Stoffels, MD2
Article Information
JAMA. Published online May 18, 2020. doi:10.1001/jama.2020.8920
Author Affiliations

  • 1Office of the Director, National Institutes of Health, Bethesda, Maryland
  • 2Johnson & Johnson, New Brunswick, New Jersey

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It has been more than a century since the world has encountered a pandemic like coronavirus disease 2019 (COVID-19), and the rate of spread of COVID-19 around the globe and the associated morbidity and mortality have been staggering.1 To address what may be the greatest public health crisis of this generation, it is imperative that all sectors of society work together in unprecedented ways, with unprecedented speed. In this Viewpoint, we describe such a partnership.

First reported in Wuhan, China, in December 2019, COVID-19 is caused by a highly transmissible novel coronavirus, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). By March 2020, as COVID-19 moved rapidly throughout Europe and the US, most researchers and regulators from around the world agreed that it would be necessary to go beyond “business as usual” to contain this formidable infectious agent. The biomedical research enterprise was more than willing to respond to the challenge of COVID-19, but it soon became apparent that much-needed coordination among important constituencies was lacking.

Clinical trials of investigational vaccines began as early as January, but with the earliest possible distribution predicted to be 12 to 18 months away. Clinical trials of experimental therapies had also been initiated, but most, except for a trial testing the antiviral drug remdesivir,2 were small and not randomized. In the US, there was no true overarching national process in either the public or private sector to prioritize candidate therapeutic agents or vaccines, and no efforts were underway to develop a clear inventory of clinical trial capacity that could be brought to bear on this public health emergency. Many key factors had to change if COVID-19 was to be addressed effectively in a relatively short time frame.

On April 3, leaders of the National Institutes of Health (NIH), with coordination by the Foundation for the National Institutes of Health (FNIH), met with multiple leaders of research and development from biopharmaceutical firms, along with leaders of the US Food and Drug Administration (FDA), the Biomedical Advanced Research and Development Authority (BARDA), the European Medicines Agency (EMA), and academic experts. Participants sought urgently to identify research gaps and to discuss opportunities to collaborate in an accelerated fashion to address the complex challenges of COVID-19.

These critical discussions culminated in a decision to form a public-private partnership to focus on speeding the development and deployment of therapeutics and vaccines for COVID-19. The group assembled 4 working groups to focus on preclinical therapeutics, clinical therapeutics, clinical trial capacity, and vaccines (Figure). In addition to the founding members, the working groups’ membership consisted of senior scientists from each company or agency, the Centers for Disease Control and Prevention (CDC), the Department of Veterans Affairs (VA), and the Department of Defense.

Figure.

Accelerating COVID-19 Therapeutic Interventions and Vaccines

ACTIV’s 4 working groups, each with one cochair from NIH and one from industry, have made rapid progress in establishing goals, setting timetables, and forming subgroups focused on specific issues (Figure). The goals of the working group, along with a few examples of their accomplishments to date, include the following.

 

The Preclinical Working Group was charged to standardize and share preclinical evaluation resources and methods and accelerate testing of candidate therapies and vaccines to support entry into clinical trials. The aim is to increase access to validated animal models and to enhance comparison of approaches to identify informative assays. For example, through the ACTIV partnership, this group aims to extend preclinical researchers’ access to high-throughput screening systems, especially those located in the Biosafety Level 3 (BSL3) facilities currently required for many SARS-CoV-2 studies. This group also is defining a prioritization approach for animal use, assay selection and staging of testing, as well as completing an inventory of animal models, assays, and BSL 3/4 facilities.

 

The Therapeutics Clinical Working Group has been charged to prioritize and accelerate clinical evaluation of a long list of therapeutic candidates for COVID-19 with near-term potential. The goals have been to prioritize and test potential therapeutic agents for COVID-19 that have already been in human clinical trials. These may include agents with either direct-acting or host-directed antiviral activity, including immunomodulators, severe symptom modulators, neutralizing antibodies, or vaccines. To help achieve these goals, the group has established a steering committee with relevant expertise and objectivity to set criteria for evaluating and ranking potential candidate therapies submitted by industry partners. Following a rigorous scientific review, the prioritization subgroup has developed a complete inventory of approximately 170 already identified therapeutic candidates that have acceptable safety profiles and different mechanisms of action. On May 6, the group presented its first list of repurposed agents recommended for inclusion in ACTIV’s master protocol for adaptive clinical trials. Of the 39 agents that underwent final prioritization review, the group identified 6 agents—including immunomodulators and supportive therapies—that it proposes to move forward into the master protocol clinical trial(s) expected to begin later in May.

 

The Clinical Trial Capacity Working Group is charged with assembling and coordinating existing networks of clinical trials to increase efficiency and build capacity. This will include developing an inventory of clinical trial networks supported by NIH and other funders in the public and private sectors, including contract research organizations. For each network, the working group seeks to identify their specialization in different populations and disease stages to leverage infrastructure and expertise from across multiple networks, and establish a coordination mechanism across networks to expedite trials, track incidence across sites, and project future capacity. The clinical trials inventory subgroup has already identified 44 networks, with access to adult populations and within domestic reach, for potential inclusion in COVID-19 trials. Meanwhile, the survey subgroup has developed 2 survey instruments to assess the capabilities and capacities of those networks, and its innovation subgroup has developed a matrix to guide deployment of innovative solutions throughout the trial life cycle.

 

The Vaccines Working Group has been charged to accelerate evaluation of vaccine candidates to enable rapid authorization or approval.4 This includes development of a harmonized master protocol for adaptive trials of multiple vaccines, as well as development of a trial network that could enroll as many as 100 000 volunteers in areas where COVID-19 is actively circulating. The group also aims to identify biomarkers to speed authorization or approval and to provide evidence to address cross-cutting safety concerns, such as immune enhancement. Multiple vaccine candidates will be evaluated, and the most promising will move to a phase 2/3 adaptive trial platform utilizing large geographic networks in the US and globally.5 Because time is of the essence, ACTIV will aim to have the next vaccine candidates ready to enter clinical trials by July 1, 2020.

References

1.

Desai  A .  Twentieth-century lessons for a modern coronavirus pandemic.   JAMA. Published online April 27, 2020. doi:10.1001/jama.2020.4165
ArticlePubMedGoogle Scholar

2.

NIH clinical trial shows remdesivir accelerates recovery from advanced COVID-19. National Institutes of Health. Published April 29, 2020. Accessed May 7, 2020. https://www.nih.gov/news-events/news-releases/nih-clinical-trial-shows-remdesivir-accelerates-recovery-advanced-covid-19

3.

NIH to launch public-private partnership to speed COVID-19 vaccine and treatment options. National Institutes of Health. Published April 17, 2020. Accessed May 7, 2020. https://www.nih.gov/news-events/news-releases/nih-launch-public-private-partnership-speed-covid-19-vaccine-treatment-options

4.

Corey  L , Mascola  JR , Fauci  AS , Collins  FS .  A strategic approach to COVID-19 vaccine R&D.   Science. Published online May 11, 2020. doi:10.1126/science.abc5312PubMedGoogle Scholar

5.

Angus  DC .  Optimizing the trade-off between learning and doing in a pandemic.   JAMA. Published online March 30, 2020. doi:10.1001/jama.2020.4984
ArticlePubMedGoogle Scholar

6.

Accelerating COVID-19 Therapeutic Interventions and Vaccines (ACTIV) portal. National Institutes of Health. Accessed May 15, 2020. https://www.nih.gov/ACTIV

7.

Accelerating Medicines Partnership (AMP). National Institutes of Health. Published February 4, 2014. Accessed May 7, 2020. https://www.nih.gov/research-training/accelerating-medicines-partnership-amp
SOURCE

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Advancing Drug Development – 12/12/2019, 8:30AM – 8:30PM at The University of Massachusetts Club, One Beacon Street, Boston, MA

Posted in Drug Delivery Platform Technology, Drug Development Process, Drug Discovery Chemistry, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical R&D Informatics, Pharmaceutical R&D Investment, Pharmacogenomics, Rapid automation of plasma protein pools on November 4, 2019| Leave a Comment »

Advancing Drug Development – 12/12/2019, 8:30AM – 8:30PM at The University of Massachusetts Club, One Beacon Street, Boston, MA

 

Reporter: Aviva Lev-Ari, PhD, RN

4th Advancing Drug Development Forum – Making the Impossible Possible – Harnessing Small Molecule Drug Development scheduled to take place December 12th, 2019 at The University of Massachusetts Club, in Boston, Massachusetts from 8:30 AM – 8:30 PM.

http://advdrug.com/agenda/

 

Scientists are more than just chipping away and kicking down the barricades to develop complex small molecule products better and faster.  Successful companies are spending quality time finding novel and clever approaches and powerful technologies to better support their knowledgeable teams.  Often it takes establishing strong partnerships with 1 or more specialized service providers, cleverly combining resources – always striving to raise the bar in order to make life threatening diseases more of a chronic and tolerable disease or eradicated completely.

Hear from key opinion leaders in pharma, biotech, the investment community and innovative service providers on how they are meeting the challenges. Keep in mind, it takes being open-minded, flexible and willing sometimes to redesigning a new formulation that better enhances bioavailability, optimizes drug-delivery profiles, reduces dosing frequency, or improves the patient experience to have the potential to deliver quicker returns on investments than developing a completely new drug.

PROGRAM AGENDA Thursday, December 12, 2019
8:30 AM Registration and Networking Continental Breakfast
9:00 AM Welcome Address and Opening Remarks
Kevin Bittorf, Ph.D., & Shelly Amster
9:15 AM Opening VC Keynote
9:45 AM Bridging the Gap between Experimentation and Implementation
Panel Discussion
10:15 AM Refreshment Break
10:45 AM Cross-Talk Between Clin-Ops and Tech-Ops
Panel Discussion
11:15 AM The Cost of Speed and Value in Drug Development
Panel Discussion
12:00 PM Networking Luncheon
1:00 PM Advances in the Delivery of Therapeutics to the Brain
Academic Keynote
Mansoor M. Amiji, Ph.D., University Distinguished Professor, Professor of Pharmaceutical Sciences & Professor of Chemical Engineering, Northeastern University
1:30 PM Advancing Drug Delivery and Controlled Release
Panel Discussion
2:00 PM Drowning in DATA
2:30 PM Disruptive AI Technologies Improving Drug Development
3:00 PM Refreshment Break
3:30 PM Small Specialty VS Full Service – What Makes Sense for US?
Panel Discussion
4:00 PM Fireside Chat
Michael Bonney, Executive Chair, Kaleido Biosciences
Heinrich Schlieker, Ph.D., SVP Technical Operations, Sage Therapeutics
5:00 PM – 8:00 PM Networking Social
Direct electronic communication with Shelly Amster

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

Posted in Advanced Computing Platform, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Applications in Therapeutics, Big Data & Analytics, BioIT: BioInformatics, BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics, Biotechnology, Cancer Genomics, Cardiovascular Pharmacogenomics, Cell Biology, Clinical Genomics, Computational Biology/Systems and Bioinformatics, Data Science, Genome Biology, Genomic Analysis of EKG Electrophysiology Genomics, Genomic Endocrinology, Genomic Expression, Genomic Testing: Methodology for Diagnosis, Genomics Pharmacy, Immuno-Oncology & Genomics, Nutrigenomics, Personalized and Precision Medicine & Genomic Research, Pharmacogenomics, Preimplantation Genetic Diagnosis and Reproductive Genomics, Reproductive Andrology, Embryology, Genomic Endocrinology, Preimplantation Genetic Diagnosis and Reproductive Genomics, RNA Biology, RNA Biology, Cancer and Therapeutics, Single Cell Genomics, Single-cell sequencing, Uncategorized, tagged , , , , , on July 16, 2019| Leave a Comment »

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

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

4.2.5

4.2.5   scPopCorn: A New Computational Method for Subpopulation Detection and their Comparative Analysis Across Single-Cell Experiments, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics

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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Verily kicked off Project Baseline in April 2017, with a health study geared to gather health data from 10,000 people over four years – Partnership with Big Pharma on Clinical Trials announced on 5/21/2019

Posted in Health Care System by Country, HealthCare IT, Pharmaceutical R&D Informatics, Pharmacogenomics, United States on May 21, 2019| Leave a Comment »

Verily kicked off Project Baseline in April 2017, with a health study geared to gather health data from 10,000 people over four years – Partnership with Big Pharma on Clinical Trials announced on 5/21/2019

 

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 5/22/2019

On Tuesday morning, Verily, Alphabet’s unit focused on life sciences, announced that it had formed alliances with Novartis, Sanofi, Otsuka, and Pfizer to work on clinical trials. What are those drug giants getting out of the deal? STAT sat down with Scarlet Shore, who leads Verily’s project Baseline, to learn about the company’s vision for the clinical trial of the future. The conversation took place at CNBC’s “Healthy Returns” conference, where the partnerships were unveiled.

SOURCE

https://www.statnews.com/2019/05/21/four-of-the-worlds-largest-drug-companies-are-teaming-with-verily-here-is-what-they-get/?utm_source=STAT+Newsletters&utm_campaign=1630aad75d-Readout_COPY_03&utm_medium=email&utm_term=0_8cab1d7961-1630aad75d-150237109

Novartis, Otsuka, Pfizer, Sanofi join Verily’s Project Baseline

“Evidence generation through research is the backbone of improving health outcomes. We need to be inclusive and encourage diversity in research to truly understand health and disease, and to provide meaningful insights about new medicines, medical devices and digital health solutions,” said Jessica Mega, M.D., Verily’s chief medical and scientific officer, in the statement. “Novartis, Otsuka, Pfizer and Sanofi have been early adopters of advanced technology and digital tools to improve clinical research operations, and together we’re taking another step towards making research accessible and generating evidence to inform better treatments and care.”
Jessica Mega, M.D., Verily’s chief medical and scientific officer, in the statement. “Novartis, Otsuka, Pfizer and Sanofi have been early adopters of advanced technology and digital tools to improve clinical research operations, and together we’re taking another step towards making research accessible and generating evidence to inform better treatments and care.”
SOURCE

 

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The Journey of Antibiotic Discovery

Posted in Allergy and Infectious Diseases, Antibiotic resistance, Artificial Intelligence Applications in Health Care, Artificial Intelligence in Health Care - Tools & Innovations, Biological Networks, Electronic Health Record, Genomics Pharmacy, Health Care System by Country, Health Economics and Outcomes Research, Health Law & Patient Safety, Healthcare Reform, Human Immune System in Health and in Disease, Infectious Disease & New Antibiotic Targets, Infectious Disease Immunodiagnostics, Metabolomics, Microbiologial genetics, Microbiology, microbiome, Nutrigenomics, Personal Health Applications: Tech Innovations serves HealhCare, Pharmacogenomics, Population Health Management, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, Proteomics, tagged , , , , , on May 19, 2019| Leave a Comment »

The Journey of Antibiotic Discovery

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

 

The term ‘antibiotic’ was introduced by Selman Waksman as any small molecule, produced by a microbe, with antagonistic properties on the growth of other microbes. An antibiotic interferes with bacterial survival via a specific mode of action but more importantly, at therapeutic concentrations, it is sufficiently potent to be effective against infection and simultaneously presents minimal toxicity. Infectious diseases have been a challenge throughout the ages. From 1347 to 1350, approximately one-third of Europe’s population perished to Bubonic plague. Advances in sanitary and hygienic conditions sufficed to control further plague outbreaks. However, these persisted as a recurrent public health issue. Likewise, infectious diseases in general remained the leading cause of death up to the early 1900s. The mortality rate shrunk after the commercialization of antibiotics, which given their impact on the fate of mankind, were regarded as a ‘medical miracle’. Moreover, the non-therapeutic application of antibiotics has also greatly affected humanity, for instance those used as livestock growth promoters to increase food production after World War II.

 

Currently, more than 2 million North Americans acquire infections associated with antibiotic resistance every year, resulting in 23,000 deaths. In Europe, nearly 700 thousand cases of antibiotic-resistant infections directly develop into over 33,000 deaths yearly, with an estimated cost over €1.5 billion. Despite a 36% increase in human use of antibiotics from 2000 to 2010, approximately 20% of deaths worldwide are related to infectious diseases today. Future perspectives are no brighter, for instance, a government commissioned study in the United Kingdom estimated 10 million deaths per year from antibiotic resistant infections by 2050.

 

The increase in antibiotic-resistant bacteria, alongside the alarmingly low rate of newly approved antibiotics for clinical usage, we are on the verge of not having effective treatments for many common infectious diseases. Historically, antibiotic discovery has been crucial in outpacing resistance and success is closely related to systematic procedures – platforms – that have catalyzed the antibiotic golden age, namely the Waksman platform, followed by the platforms of semi-synthesis and fully synthetic antibiotics. Said platforms resulted in the major antibiotic classes: aminoglycosides, amphenicols, ansamycins, beta-lactams, lipopeptides, diaminopyrimidines, fosfomycins, imidazoles, macrolides, oxazolidinones, streptogramins, polymyxins, sulphonamides, glycopeptides, quinolones and tetracyclines.

 

The increase in drug-resistant pathogens is a consequence of multiple factors, including but not limited to high rates of antimicrobial prescriptions, antibiotic mismanagement in the form of self-medication or interruption of therapy, and large-scale antibiotic use as growth promotors in livestock farming. For example, 60% of the antibiotics sold to the USA food industry are also used as therapeutics in humans. To further complicate matters, it is estimated that $200 million is required for a molecule to reach commercialization, with the risk of antimicrobial resistance rapidly developing, crippling its clinical application, or on the opposing end, a new antibiotic might be so effective it is only used as a last resort therapeutic, thus not widely commercialized.

 

Besides a more efficient management of antibiotic use, there is a pressing need for new platforms capable of consistently and efficiently delivering new lead substances, which should attend their precursors impressively low rates of success, in today’s increasing drug resistance scenario. Antibiotic Discovery Platforms are aiming to screen large libraries, for instance the reservoir of untapped natural products, which is likely the next antibiotic ‘gold mine’. There is a void between phenotanypic screening (high-throughput) and omics-centered assays (high-information), where some mechanistic and molecular information complements antimicrobial activity, without the laborious and extensive application of various omics assays. The increasing need for antibiotics drives the relentless and continuous research on the foreground of antibiotic discovery. This is likely to expand our knowledge on the biological events underlying infectious diseases and, hopefully, result in better therapeutics that can swing the war on infectious diseases back in our favor.

 

During the genomics era came the target-based platform, mostly considered a failure due to limitations in translating drugs to the clinic. Therefore, cell-based platforms were re-instituted, and are still of the utmost importance in the fight against infectious diseases. Although the antibiotic pipeline is still lackluster, especially of new classes and novel mechanisms of action, in the post-genomic era, there is an increasingly large set of information available on microbial metabolism. The translation of such knowledge into novel platforms will hopefully result in the discovery of new and better therapeutics, which can sway the war on infectious diseases back in our favor.

 

References:

 

https://www.mdpi.com/2079-6382/8/2/45/htm

 

https://www.ncbi.nlm.nih.gov/pubmed/19515346

 

https://www.ajicjournal.org/article/S0196-6553(11)00184-2/fulltext

 

https://www.ncbi.nlm.nih.gov/pubmed/21700626

 

http://www.med.or.jp/english/journal/pdf/2009_02/103_108.pdf

 

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LIVE 13th Annual BioPharma and Healthcare Summit, Thursday, May 9, 2019, Marriott Hotel, Cambridge, MA

Posted in BioTechnology - Venture Creation, BioTechnology - Venture Creation, Venture Capital, Conference Coverage with Social Media, Drug Development Process, drug repurposing, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Informatics, Pharmaceutical R&D Investment, Pharmacodynamics and Pharmacokinetics, Pharmacogenomics, Pharmacovigilance, Value-based Drug Pricing on April 30, 2019| 2 Comments »

LIVE 13th Annual BioPharma and Healthcare Summit, Thursday, May 9, 2019, Marriott Hotel, Cambridge, MA

 

http://www.usaindiachamber.org

8:40 AM – 9:10 AM Registration and Networking
9:10 AM – 9:20 AM Welcome address: Karun Rishi, President, USAIC

Opening comments: Dr Andrew Plump, President R&D and Director, Takeda Pharmaceuticals
9:20 AM – 9:40 AM Fireside Chat

  • Mark Abdoo, Acting Deputy Commissioner, U.S. Food and Drug Administration
  • Dr Eswara Reddy, Drug Controller General of India, Central Drug Control Organization

Moderator: Sanat Chattopadhyay, President, Merck Manufacturing Division; Merck & Co.
9:40 AM – 10:00 AM Presentation on CAR (chimeric antigen receptor) T-cell Therapies
Dr. Carl June, Director of Translational Research, Abramson Cancer Center University of Pennsylvania Moderator: Dr. Raju Kucherlapati, Professor of Genetics, Harvard Medical School
10:00 AM – 10:50 AM Panel Discussion: Oncology – The Emperor of BioPharma Development

Panelists:

Moderator: Dr. Christiana Bardon, Managing Director, MPM Capital
10:50 AM – 11:20 AM Networking Break
11:20 AM – 12:10 PM Panel Discussion: Future of Clinical Trials and Drug Development

Panelists:

Moderator: Dr. William Chin, Professor of Medicine, Emeritus, Harvard Medical School
12:10 PM – 1:00 PM Panel Discussion: Manufacturing in the Future

Panelists:

  • Hari Bhartia, Founder and Co-Chairman, Jubilant Bhartia Group
  • Mark Abdoo, Acting Deputy Commissioner, U.S. Food and Drug Administration
  • Dr. Paul McKenzie, Executive Vice President, Pharma Operations & Technology, Biogen
  • Sanat Chattopadhyay, President, Merck Manufacturing Division; Merck & Co.
  • Vinay Ranade, Chief Executive Officer, Reliance Life Sciences

Moderator: Professor N. Venkat Venkatraman, Boston University Questrom School of Business
1:00 PM – 1:50 PM Lunch
1:50 PM – 1:55 PM Video message from Suresh Prabhu, Hon’ble Minister of Commerce & Industry, Gov. of India
1:55 PM – 2:45 PM Panel Discussion: One in a million – Emerging trends in Rare Diseases

Panelists:

Moderator: Dr. Samarth Kulkarni, Chief Executive Officer, CRISPR Therapeutics
2:45 PM – 3:20 PM Networking & Tea Break
3:20 PM – 3:50 PM Fireside Chat: Value and Access – The ongoing debate

Moderator: Dr Andrew Plump, President R&D, Takeda Pharmaceuticals
3:50 PM – 4:10 PM India update on Clinical Trial Regulations

  • Arun Singhal, Additional Secretary, Ministry of Health & Family Welfare, India
  • Dr Eswara Reddy, Drug Controller General of India, Central Drug Control Organization
4:10 PM – 5:00 PM Panel Discussion: Research and Development Strategies and Trends

Panelists:

Moderator: Dr. Martin Mackay, Co-Founde, Rallybio
5:00 PM – 5:05 PM Closing Remarks
5:05 PM – 6:15 PM Cocktails & Networking Reception

Aviva Lev-Ari, PhD, RN & Leaders in Pharmaceutical Business Intelligence (LPBI) Group

will cover the event in Real Time

REAL TIME COVERAGE USING SOCIAL MEDIA

 

LIVE Images taken by @AVIVA1950

 

 

 

9:10 AM – 9:20 AM

Welcome address: Karun Rishi, President, USAIC

Opening comments: Dr Andrew Plump, President R&D and Director, Takeda Pharmaceuticals

  • tomorrow announcement @Shire
  • India 1.3Billion in India, each person is a potential patient in the largest democracy in the World
  • China – transformation takes place every day
  • The Patient and the Pricing of Drugs the biggest issue missing the ball dialoguing on Panel today

9:20 AM – 9:40 AM

Fireside Chat

  • Mark Abdoo, Acting Deputy Commissioner, U.S. Food and Drug Administration (FDA)
  • Dr Eswara Reddy, Drug Controller General of India (DCGI), Central Drug Control Organization

Moderator: Sanat Chattopadhyay, President, Merck Manufacturing Division; Merck & Co.

9:40 AM – 10:00 AM Presentation on CAR (chimeric antigen receptor) T-cell Therapies
Dr. Carl June, Director of Translational Research, Abramson Cancer Center University of Pennsylvania Moderator: Dr. Raju Kucherlapati, Professor of Genetics, Harvard Medical School

  • Video on child with recurrent twice of leukhimia
  • T-cell HIV Virus infect

 

10:00 AM – 10:50 AM

Panel Discussion: Oncology – The Emperor of BioPharma Development

Panelists:

  1. solid vs blood tumors
  2. T-Cells amplification microenvironment and biology
  3. PD-1 in combination therapies thousand Trials
  4. Biomarker allows to check response in conjunction with genomics data brings insights
  5. Tumors World, Biomarkers in Immuno oncology respond to PD-1 no response to other drug
  6. stratify patients
  1. Protein experimental data compound design from simulations of VIRTUAL compounds,
  2. how to incentivise to take on new innovations
  1. more that one single administration by injection
  2. response rates different even in one patient let alone among patients
  3. detection gene
  4. CAR-T glioblastoma
  5. pancreatic cancer good responses in combination therapies
  6. immunr repertoire biology so complex that biomarkers are limited

Moderator: Dr. Christiana Bardon, Managing Director, MPM Capital

  • 30% patinets with complete cure

10:50 AM – 11:20 AM Networking Break11:20 AM – 12:10 PM

Panel Discussion: Future of Clinical Trials and Drug Development

Panelists:

  1. endpoints need to be redefined it effect price of drug development
  2. in Oncology – Basket and Umbrellas Trial – two stufies approval for melanoma, biomarker
  3. Is response rate is 30% va 50% and Phase 3 is negative Kertuda when worked at Merck dose ranging last phase when response dropped from 60% to 30% in the case of Study C3
  4. 30% of the cost of the study – 30% was translational
  5. CRO model appropriate oversite vs douplication of tasks
  • Dr. Bruce Chabner, Director of Clinical Research, Mass General Hospital Cancer Center
  1. Old paradigm Phase 1,2,3 – off the board now, New drugs do not need the old paradigm
  2. Phase i1 changed if genomics is involved multiple cohorts at same time
  3. FDA play amazing role
  4. patient selection is key
  5. mutations in rare disease vs mutations in cancer
  6. immunotherapy and endogenic drugs with chemo in RENAL cancer
  7. check-points – lung cancer understood money spent to find responders
  8. HOW to select which cheno therapy — no improvement today vs past
  9. 40 drugs approved by accelerated approval one came back on the market
  10. Financial burden of being in a clinical trial
  11. Foundation gives money to Institutions to reimburse patients for flights, meals, acommodation, Pharma are reluctant to participants due to potential accusation of bias id Pharma pays Patients that participate in Clinical Trials
  1. FDA recognizes approval process – systems involved AFTER approval for reimbursement and monitoring after market
  2. regulatory by countires are different
  3. which factors are sacrifiable in the long tern in clinical trial design
  1. Safety – benefit risk is what physicians work with every day
  2. Drugs paradign of small molecules does not hold is you have a drug that deliver entire organelle – how you dose for half life how you prive the rate of replication in the body
  3. Surrogate markers
  4. Taking a drug off the market ->>  conditional approvals [approval can be taken back or require additional studies] not a favorable view of Pharma in the present to support Conditional approval vs accelerated approval

 

  1. speed
  2. differentiation from competition
  3. drug development in crisis is CVD not cancer, US and the rest of the world – lowest investment in drugs is CVD
  4. Studies designed by Physicians using SAME design
  5. need to create experts to use ML in the course of clinical trial design
  6. regulators as Partners not as Barriers
  7. Proof of efficacy is a burden on the developers of the drug not on the Regulatory
  8. Increase use of advertising to recruit
  9. 70% OF PATIENTS WILLING TO PARTICIPATE  lives to far from site of trials
  10. Telecommunication between administrators of study ans clinical Trials participants
  11. Back when I was at Pfizer, designing study – patients burden relieved more willingness to participate
  12. Preferrs to run studies in house vs use CRO they are not effective in monitoring like study run in house

Moderator: Dr. William Chin, Professor of Medicine, Emeritus, Harvard Medical School

  • Probability of success to clinic has not changed
  • challenge is design and execution in clinical trials
  • changes in drug modalities: RNA, DNA,
  • which combination to use
  • how to find the many patients needed
  • Basket and Umbrellas Trial

12:10 PM – 1:00 PM

Panel Discussion: Manufacturing in the Future

Panelists:

  • Hari Bhartia, Founder and Co-Chairman, Jubilant Bhartia Group
  1. supply change
  2. blockchain
  3. quality by design
  4. CPK
  5. productivity will go up variability will decrease
  6. manufacturng must happen in India
  7. Genetics price selection
  8. Secure system, data quality the data logic and the analytics
  9. infrastructure in manufacturing is not completed yet
  10. Training by augmented reality Turnover high in India
  11. cyber security – digitization and central control
  12. demonstration data offense
  • Mark Abdoo, Acting Deputy Commissioner, U.S. Food and Drug Administration
  1. next 10 years India and China will improve regulatory activities and match better the US requirements
  2. review foreign hosts
  3. skills and location of hosts:
  4. India: Standards and unannounced inspections and
  5. China: same
  6. Blockchain is experienced as experimentation at FDA across each all parts of the Agency
  • Dr. Paul McKenzie, Executive Vice President, Pharma Operations & Technology, Biogen
  1. raw material to patients: Pharma very slow than other industries Reliable needs be very high, relationships
  2. Hurrican in PortoRIco affected supply chain
  3. Reality, every one HAVE to be in China
  4. Platforming for each modality for Scaling out vs Scaling up
  5. diversify vs modality x
  6. build capacity and capabilities customization of ultra filtration different in two plants lowers standardizations
  7. Training on Demand, Virtually, documnetation needs to change to electronic
  8. Continueous manufacturing Academic contribution
  • Vinay Ranade, Chief Executive Officer, Reliance Life Sciences
  1. Pharma was slow in India the manufacturing
  2. infantile diarreha vaccine 70,000 in 4 years needs that drug,
  3. massive intellectual capital in India
  4. How to implement and make best use of data to improve processes
  5. cyber security was not experiences
  1. Phase 1 scaling out vs up – it is different in vaccine field
  2. ML, Block chain, supply chain and manufacturing will be adapted in supply chain
  3. Apply analytics and relationships in manufacturing
  4. obsolescence and upgrades
  5. capture data electronically
  6. cyber security can be a hazard hard to mitigate when all systems are down
  7. significant challenges in manufacturing and data security

Moderator: Professor N. Venkat Venkatraman, Boston University Questrom School of Business

  • How can Pharma become leaner
  • heterogenuious environment for production
  • cyber security

1:00 PM – 1:50 PMLunch1:50 PM – 1:55 PM Video message from Suresh Prabhu, Hon’ble Minister of Commerce & Industry, Gov. of India1:55 PM – 2:45 PM

Panel Discussion: One in a million – Emerging trends in Rare Diseases

Panelists:

  1. worked with Academic community on how to treat rare disease in the future
  1. Show clinical benefit and impact multiplemyeloma
  2. patients becoming activists
  3. access
  4. foundation by patients
  5. Patient to get cloud
  • Dr. Dhaval Patel, Executive Vice President  and Chief Scientific Officer, UCB
  1. if a modality will cure a disease justify innovation Model for payment: Mortgage Model
  2. Access INDEX pricing – US will benchmark the price in other parts of the world
  3. Gene therapy is not only got monognenic diseases but for
  4. decrease work involved in development of drugs
  • Dr. James Wilson, Director – Gene Therapy Program, University of Pennsylvania
  1. tension between physicians and development of the perfect drug.
  2. AV
  3. Protein replacement therapy repeated infusion gene therapy infrastructure develop in China for China, Develop in India for India vs develop in US for India or China
  4. Cost of manufacturing to decrease
  • Dr. Timothy Yu, Assistant Professor in Pediatrics, Harvard Medical School
  1. Scalability beyond the one case: the mechanism for the drug has generability for other aptients iwth same mutation the method has no limit
  2. Molecular type of mutation Spice Switching strategy, just-in-time manufacturing

Moderator: Dr. Samarth Kulkarni, Chief Executive Officer, CRISPR Therapeutics

  1. Rare diseases, potential for cure
  2. Academia, Hospitals, biotech
  3. commercial model of the disease

2:45 PM – 3:20 PMNetworking & Tea Break3:20 PM – 3:50 PM

Fireside Chat: Value and Access – The ongoing debate

  1. since 2003 testify in the House, against Canadian  David Brenner was asked about importation from Canada of breast cancer tamoxiphen at a lower price than in the US.
  2. From importation crisis to Obama Care – stable system Medicare Part D – drug coverage for Olderly
  3. After Obama – Price is part of doing business REBATES $100Billion the valur of REBATES
  4. Co-Insurance
  1. right for innovation will be preserved
  2. price increase
  3. give and take
  4. Co-pay – We need lower co-pay
  5. with current administration, sink finding the Well instead of Well funding the sick
  6. CHange is coming, co-pay will change
  1. Genzyme days vs 2019
  2. changes how drugs are priced?
  3. Flaws of the system:Gevernment induce prices that will change
  4. $800,000 drug is now $80 [ala Regeneron] – R&D was $2Billion
  5. CO-pay for hospital stay is lower than co-pay on drugs – 10% twice a year

Moderator: Dr Andrew Plump, President R&D, Takeda Pharmaceuticals

3:50 PM – 4:10 PM

India update on Clinical Trial Regulations

  • Arun Singhal, Additional Secretary, Ministry of Health & Family Welfare, India
  1. Each patient deserve access to healthcare in India
  2. experimenting
  • Dr Eswara Reddy, Drug Controller General of India, Central Drug Control Organization
  1. Time line for Application approval for drugs, if approved in another country 60 days
  2. Gov’t hospitals can import New drugs which have not been permitted in India

4:10 PM – 5:00 PM

Panel Discussion: Research and Development Strategies and Trends

Panelists:

  1. Neuroscience – Pharma understand biomarkers and now genetics
  2. Vaccines – across species in the animal WORLD
  1. Attempt not to tweak the PIPELINE: CVD, NEUROSCIENCE AND CANCER
  2. 485 Teams doing R&D convluence of interests to develop cure
  3. Modularity – BioMolecule — multimodality biophysical biochemical protein degradation – rewire disease cells with biomolecules combing propertitie of permiability of small molecules
  4. PHARMACOLOGICAL PREVENTION – biotech is inspiring only Pharma can solve
  1. immunooncology – mutation signature – marker protein signature — that group of diseases respond to
  2. colon cancer and multiple myeloma — understanding of the biology was deep

Moderator: Dr. Martin Mackay, Co-Founder, Rallybio

5:00 PM – 5:05 PM Closing Remarks

5:05 PM – 6:15 PM Cocktails & Networking Reception

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Cholesterol Lowering Novel PCSK9 drugs: Praluent [Sanofi and Regeneron] vs Repatha [Amgen] – which drug cuts CV risks enough to make it cost-effective?

Posted in Atherogenic Processes & Pathology, Epigenetics and Cardiovascular Risks, Fatty acids, Frontiers in Cardiology and Cardiovascular Disorders, inflammation independent of lipid levels, Lipid metabolism, Lipidomics, Lipids, Origins of Cardiovascular Disease, PCSK9 Inhibitor Therapy, Pharmaceutical R&D Informatics, Pharmacogenomics, Pharmacotherapy of Cardiovascular Disease, Systemic Inflammatory Diseases as CVD Risk, Value-based Drug Pricing on March 12, 2018| Leave a Comment »

 Cholesterol Lowering Novel PCSK9 drugs: Praluent [Sanofi and Regeneron] vs Repatha [Amgen] – which drug cuts CV risks enough to make it cost-effective?

Reporter: Aviva Lev-Ari, PhD, RN

 

UPDATED on 4/5/2022

Early PCSK9 Inhibition in AMI Yields Plaque Regression

Ted Bosworth

April 05, 2022

https://www.medscape.com/viewarticle/971582?spon=2&uac=93761AJ&impID=4140595&sso=true&faf=1&src=WNL_mdpls_220405_mscpedit_card

https://www.mdedge.com/cardiology/article/253443/lipid-disorders/early-pcsk9-inhibition-ami-yields-plaque-regression

 

UPDATED on 1/15/2019

In the patent fight over PCSK9 inhibitors, the Supreme Court refused to hear Amgen’s appeal of a 2017 court decision allowing Sanofi and Regeneron to continue selling alirocumab (Praluent). Amgen still has a new patent trial starting in Delaware federal court next month, FiercePharma reports.

Amgen’s Repatha hits wall at SCOTUS but presses ahead—new price breaks included

by Arlene Weintraub |

Amgen has been trying since 2015 to protect its PCSK9 cholesterol drug Repatha by keeping Sanofi and Regeneron’s rival Praluent off the market, even going as far as to ask the U.S. Supreme Court to review an ongoing patent fight.

But that attempt fell short this week as SCOTUS refused to hear the company’s appeal of a 2017 court decision allowing Sanofi and Regeneron to continue selling its head-to-head rival.

Amgen isn’t giving up the fight, though. The company is prepping for a new patent trial starting in Delaware federal court next month. And it’s responding to long-standing criticism of the high cost of PCSK9 drugs, which hit the market in 2015 at list prices of about $14,000 a year.

Amgen had already brought the price of the biweekly version of Repatha down to $5,850 per year before discounts and rebates, and late Monday it said it would lower cost of the monthly injectable dose to that same level.

SOURCE

UPDATED on 11/13/2018

ODYSSEY OUTCOMES: Alirocumab Cost-effective at $6000 a Year

Marlene Busko

November 11, 2018

CHICAGO — Treatment with the proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor alirocumab (Praluent, Sanofi/Regeneron) is cost-effective at $6319 a year when the willingness-to-pay threshold is the generally accepted $100,000 per quality-adjusted life-year (QALY), new research reports.

Deepak L. Bhatt, MD, MPH, Brigham and Women’s Hospital Heart and Vascular Center, Harvard Medical School, Boston, Massachusetts, presented these cost-effectiveness findings for alirocumab, based on data from the ODYSSEY OUTCOMES trial, here at the American Heart Association (AHA) 2018 Scientific Sessions

As previously reported, results from ODYSSEY OUTCOMES were presented at American College of Cardiology (ACC) 2018 Annual Scientific Session in March and the study was published November 7 in the New England Journal of Medicine.

Strengths of the current cost analysis include that it used actual trial data as opposed to modeling estimates, Bhatt pointed out to theheart.org | Medscape Cardiology.

SOURCE

https://www.medscape.com/viewarticle/904744?nlid=126063_3866&src=WNL_mdplsfeat_181113_mscpedit_card&uac=93761AJ&spon=2&impID=1799507&faf=1

 

Did Amgen’s Repatha cut CV risks enough to make it cost-effective? Analysts say no

Sanofi, Regeneron’s Praluent pulls off PCSK9 coup with 29% cut to death risks in most vulnerable patients
SEE our curations on PCSK9 drugs:

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