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Archive for February, 2021

Comparing COVID-19 Vaccine Schedule Combinations, or “Com-COV” – First-of-its-Kind Study will explore the Impact of using eight different Combinations of Doses and Dosing Intervals for Different COVID-19 Vaccines

Posted in Biomarkers: Inflammation, Coronavirus Gene Expression, COVID-19, Immune-Mediation (independent immunopathology: lung and reticuloendothelial system), Mechanisms of infection by SARS-CoV-2, Population Health Management, Genetics & Pharmaceutical, SARS-CoV-2, Serology tests for coronavirus antibodies, T-cell response to SARS-CoV-2 infection, Treatment Protocols for COVID-19, Vaccinology on February 8, 2021| Leave a Comment »

Comparing COVID-19 Vaccine Schedule Combinations, or “Com-COV” – First-of-its-Kind Study will explore the Impact of using eight different Combinations of Doses and Dosing Intervals for Different COVID-19 Vaccines

Reporter: Aviva Lev-Ari, PhD, RN

 

The UK’s COVID-19 vaccine rollout commenced in December, and requires an individual to receive two doses of the same vaccine, either Pfizer/BioNTech’s BNT162b2 or AstraZeneca/Oxford’s ChAdOx1, with a maximum interval of 12 weeks between doses. As of February 3, 10 million first doses have been administered.

Com-COV has been classified as an “Urgent Public Health” study by the National Institutes for Health and Research (NIHR), and it’s hoped that the data produced may offer greater flexibility for vaccine delivery going forward.

“Given the inevitable challenges of immunizing large numbers of the population against COVID-19 and potential global supply constraints, there are definitely advantages to having data that could support a more flexible immunization program, if ever needed and approved by the medicines regulator,” –Jonathan Van-Tam, deputy chief medical officer and senior responsible officer for the study, said in a press release.

The study will run for a 13-month period and will recruit over 800 patients across eight sites in the UK, including London – St George’s and UCL, Oxford, Southampton, Birmingham, Bristol, Nottingham and Liverpool.

Com-COV has eight different arms that will test eight different combinations of doses and dose intervals. This is tentative and subject to change should more COVID-19 vaccines be approved for use in the UK. The eight arms include the following dose combinations:

  • Pfizer/BioNTech and Pfizer/BioNTech – 28 days apart
  • Pfizer/BioNTech and Pfizer/BioNTech – 12 weeks apart – (control group)
  • Oxford/AstraZeneca and Oxford/AstraZeneca – 28 days apart
  • Oxford/AstraZeneca and Oxford/AstraZeneca – 12 weeks apart – (control group)
  • Oxford/AstraZeneca and Pfizer/BioNTech – 28 days apart
  • Oxford/AstraZeneca and Pfizer/BioNTech – 12 weeks apart
  • Pfizer/BioNTech and Oxford/AstraZeneca – 28 days apart
  • Pfizer/BioNTech and Oxford/AstraZeneca – 12 weeks apart

Aside from the logistical benefits of using alternative vaccines, there is scientific value to exploring how different vaccines and doses affect the human immune system.

Dr Peter English, consultant in communicable disease control, pointed out that the antigen used across the currently authorized COVID-19 vaccines is the same Spike protein. Therefore, the immune system can be expected to respond just as well if a different product is used for boosting. “It is also the case that many vaccines work better if a different vaccine is used for boosting – an approach described as heterologous boosting,” English said, referencing previously successful trials using Hepatitis B vaccines.

“It is also even possible that by combining vaccines, the immune response could be enhanced giving even higher antibody levels that last longer; unless this is evaluated in a clinical trial we just won’t know,” added Van-Tam.

If warranted by the study data, the Medicines and Healthcare products Regulatory Agency may consider reviewing and authorizing modifications to the UK’s vaccine regimen approach – but only time will tell.

“We need people from all backgrounds to take part in this trial, so that we can ensure we have vaccine options suitable for all. Signing up to volunteer for vaccine studies is quick and easy via the NHS Vaccine Research Registry,” Professor Andrew Ustianowski, national clinical lead for the NIHR COVID Vaccine Research Program, said

SOURCE

First-of-its-Kind Study Will Test Combination of Different COVID-19 Vaccines | Technology Networks

https://www.technologynetworks.com/biopharma/news/first-of-its-kind-study-will-test-combination-of-different-covid-19-vaccines-345245?utm_campaign=NEWSLETTER_TN_Biopharma

WATCH VIDEO

Different Types of COVID-19 Vaccines With Dr Seth Lederman Video | Technology Networks

https://www.technologynetworks.com/biopharma/videos/different-types-of-covid-19-vaccines-with-dr-seth-lederman-345207

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Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2

Posted in Artificial Intelligence - General, Artificial Intelligence in Health Care - Tools & Innovations, Population Health Management, Genetics & Pharmaceutical, SARS-CoV-2, tagged , , , on February 4, 2021| Leave a Comment »

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2

Reporter: Irina Robu, PhD

Artificial intelligence makes it imaginable for machines to learn from experience, adjust to new inputs and perform human-like tasks. Using the technologies, computer can be trained to achieve specific tasks by processing large amount of data and recognizing patterns. Scientists from NEC OncoImmunity use artificial intelligence to forecast designs for designing universal vaccines for COVID 19, that contain a broad spectrum of T-cell epitopes capable of providing coverage and protection across the global population. To help test their hypothesis, they profiled the entire SARS COV2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population using host infected cell surface antigen and immunogenicity predictors from NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. They use the epitope maps as a starting point for Monte Carlo simulation intended to identify the most significant epitope hotspot in the virus. Then they analyzed the antigen arrangement and immunogenic landscape to recognize a trend where SARS-COV-2 mutations are expected to have minimized potential to be accessible by host-infected cells, and subsequently noticed by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome.

By merging the antigen arrangement to the infected-host cell surface and immunogenicity estimates of the NEC Immune Profiler with a Monte Carlo and digital twin simulation, the researchers have outlined the entire SARS-CoV-2 proteome and recognized a subset of epitope hotspots that could be used  in a vaccine formulation to provide a wide-ranging coverage across the global population.

By using the database of HLA haplotypes of approximately 22,000 individuals to design  a “digital twin” type simulation to model how efficient various  combinations of hotspots would work in a varied human population. 

SOURCE

https://www.nature.com/articles/s41598-020-78758-5?utm_content=buffer4ebb7

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Google Cloud launches Vaccine Management Tools using ML & AI for Vaccine Distribution Efforts

Posted in Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, COVID-19, Machine Learning, Population Health Management, Genetics & Pharmaceutical, SARS-CoV-2, Vaccinology on February 2, 2021| Leave a Comment »

Google Cloud launches Vaccine Management Tools using ML & AI for Vaccine Distribution Efforts

Reporter: Aviva Lev-Ari, PhD, RN

 

Google Cloud announced Monday new artificial intelligence and machine learning tools to help with vaccine rollout efforts from vaccine information and scheduling, to distribution and analytics, to forecasting and modeling COVID-19 cases.

https://www.fiercehealthcare.com/tech/google-cloud-rolls-out-tools-for-vaccine-logistics-as-tech-giants-jump-into-distribution?utm_medium=nl&utm_source=internal&mrkid=993697&mkt_tok=eyJpIjoiWldZMVlXVmlNelprWXpNMyIsInQiOiJEQ3BsYnRMQTBPQU1HNDBqVFVhQnpKV3BlRUdIbXRBMWgwWFFEYktjWnc3XC9xWm9tNUNJcnNNR3M5cjNuZEhoYlFRQzZFTXAxU1NFUnFQc2o4Q09HYjBFMFRhejBMaWhuN1FLalU1U2xQQWV3bm1iZEtJQkk1aWRGVkVSOFVcL2tIIn0%3D

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Countries that are early adopters of ML methods

Posted in An executive's guide to AI, Artificial Intelligence - General, Machine Learning on February 2, 2021| Leave a Comment »

Countries that are early adopters of ML methods

Reporter: Aviva Lev-Ari, PhD, RN

 

Machine Learning Adoption by Country

Results of the survey appear in Figure 1 for the overall sample as well as countries that have 50 or more respondents. Overall, results show that the adoption rate of machine learning methods is 45%. Twenty-one percent of respondents indicate their company is exploring ML methods. Twenty percent of respondents indicate their company does not use ML methods.

Countries that are early adopters of ML methods include:

  1. Israel (63% adopt ML)
  2. Netherlands (57%)
  3. United States (56%)
  4. UK and Northern Ireland (54%)
  5. Germany (54%)
  6. Australia (53%)
  7. France (52%)
  8. China (52%)
  9. Taiwan (51%)
  10. Greece (49%)

Countries with the lowest adoption rate of ML methods include:

  1. Nigeria (23% adopt ML)
  2. Morocco (24%)
  3. Egypt (31%)
  4. Philippines (31%)
  5. Argentina (32%)

Countries with the highest percent of companies exploring ML methods include:

  1. Chile (36% are exploring ML methods)
  2. Sweden (35%)
  3. Malaysia (32%)
  4. South Korea (31%)
  5. Peru (29%)

Machine Learning Adoption Rates Around the World

Bob Hayes

A worldwide survey of data professionals showed that adoption of machine learning methods in their company is 45%. Twenty-one percent of survey respondents said their employer is exploring ML methods. ML adoption rates varied by country with Israel (63%), Netherlands (57%) and the United States (56%) showing the highest and Egypt (31%), Morocco (24%) and Nigeria (23%) showing the lowest adoption rate. ML adoption also varied by company size, with larger companies having higher adoption rates (61%) than medium (45%) and small (33%) companies.

Businesses are leveraging the power of machine learning methods to help them extract better quality information, increase productivity, reduce costs and extract more value from their data. As the amount of data continues to grow along with the processing power of technology, businesses will continue to incorporate ML into their business. Researchers have found different AI / ML adoption rates. In one study, adoption rate of ML Methods was 10%; in a 2020 study by McKinsey, adoption rate of AI was 50%. Still, another study found that 42% of companies were currently using AI and 40% of companies were planning on using AI in the next two years. Another 2020 study found that 59% of enterprises have machine learning initiatives either in production or at a proof-of-concept stage.

Current Analysis on Machine Learning Adoption

Kaggle conducted a worldwide survey in October 2020 of 20,036 data professionals (2020 Kaggle Machine Learning and Data Science Survey). The survey sample consisted of data professionals, including men (~79%) and women (~19%), from a variety of job titles (e.g., data scientist, business analyst, machine learning engineer, software developer) and company sizes. The survey asked a variety of questions, including “Does your current employer incorporate machine learning methods into their business?”

Figure 1. Machine Learning Adoption Rates across Countries. Click image to enlarge.

Figure 2. Adoption of ML Methods Across Company Size

Figures in:

http://businessoverbroadway.com/2021/02/01/machine-learning-adoption-rates-around-the-world/

Machine Learning Adoption by Company Size

We also looked at adoption rates by company size. Those results appear in Figure 2. Supporting prior studies, we found that larger companies have higher adoption rates about ML methods. The largest enterprise companies (10,000+ employees) reported ML adoption rates of 61%. The smallest companies (0-49 employees) reported adoption rates of 33%. Of the smallest companies, a little over a quarter of them (27%) indicate that they are exploring the use of ML methods.

Summary

Survey of data professionals showed that adoption rates of machine learning methods among businesses is 45%. About 21% of respondents indicated that their company is exploring machine learning methods with the hope of putting a model into production one day.

ML adoption rate varies by country and company size. Survey results reveal that early adopters come from large enterprise companies (adoption rate of 61%) and some countries including the United States, Israel, Netherlands and the UK and Northern Ireland.

Machine learning vendors, looking for inroads into businesses, could focus their marketing and sales efforts on small businesses as they have the highest percentage of companies who are exploring the use of ML methods.

SOURCE

http://businessoverbroadway.com/2021/02/01/machine-learning-adoption-rates-around-the-world/

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UPDATED to 1/1/2023 –>> February 1, 2021 – We Celebrate 1,924,400 e-Readers, 6,000 Scientific Journal Articles, 7,525 Scientific Comments, A Journal Ontology of 728 Medical & Life Sciences Research Categories, 10,440 Tags, Top Article 17,300 Views, Top Author 487,500 Views on PharmaceuticalIntelligence.com

Posted in LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management, LPBI Management on February 1, 2021| Leave a Comment »

UPDATED to 1/1/2023 –>> February 1, 2021 – We Celebrate 1,924,400 e-Readers, 6,000 Scientific Journal Articles, 7,525 Scientific Comments, A Journal Ontology of 728 Medical & Life Sciences Research Categories, 10,440 Tags, Top Article 17,300 Views, Top Author 487,500 Views on PharmaceuticalIntelligence.com

Reporter: Aviva Lev-Ari, PhD, RN

 

Update on 1/1/2023 by Srinivas Sriram and Abhisar Anand

1/1/2023- 2,205,188 views

Content

1/1/2023- 6,162 Posts

754 Categories

10,688 Tags

Top Articles by Views – Updated on 12/31/2022

  Original
Article Title Views July 2nd, 2021

UPDATED

Views December 31, 2022
Home page / Archives 765,595 824,332

Is the Warburg Effect the Cause or the

Effect of Cancer: A 21st Century View?

 17,365 17,553
Recent comprehensive review on the role of ultrasound in breast cancer management 16,246 17,163
Paclitaxel vs Abraxane (albumin-bound paclitaxel) 15,227 17,927
Do Novel Anticoagulants Affect the PT/INR? The Cases of XARELTO (rivaroxaban) and PRADAXA (dabigatran) 14,370 14,703
Apixaban (Eliquis): Mechanism of Action, Drug Comparison and Additional Indications 9,678 11,255
Clinical Indications for Use of Inhaled Nitric Oxide (iNO) in the Adult Patient Market: Clinical Outcomes after Use, Therapy Demand and Cost of Care 9,111 10,799
Our TEAM 6,740 6,918
Mesothelin: An early detection biomarker for cancer (By Jack Andraka) 6,623 6,703
Interaction of enzymes and hormones 6,017 6,582
Pyrroloquinoline quinone (PQQ) – an unproved supplement 5,956 8,954

 

Internet Exposure Indicator (IEI) Table

Note: To calculate % change, we used the formula: % Change = (Newer – Older)/Older * 100%
All calculations were rounded to the nearest 0.01%.

INTERNET EXPOSURE INDICATORS (IEI) % change
2/3/2022-1/1/2023		 On 1/1/2023 % change
2/1/2021-2/3/2022		 On 2/3/2022 % change
2/1/2021-7/1/2021		 On 2/1/2021 On 7/1/2021
Total # of Journal Article Views 5.52% increase 2,205,188 8.59% increase 2,089,759 4.10% increase 1,924,462 2,003,415
# Categories 1.48% increase 754 2.06% increase 743 1.10% increase 728 736
# Tags 0.33% increase 10,688 2.03% increase 10,653 1.00% increase 10,441 10,545
TOP AUTHORS VIEWS          
Aviva Lev-Ari 5.46% increase 536,209 9.73% increase 508,457 5.04% increase 463,371 486,733
Larry H Bern 7.46% increase 401,503 9.22% increase 373,618 4.30% increase 342,084 356,793

S J Williams

PA

 6.93% increase 77,537 8.55% increase 72,513 2.69% increase 66,800 68,594
Tilda Barliya 4.03% increase 72,751 5.64% increase 69,934 2.85% increase 66,203 68,093
Dr. Sudipta Saha 8.19% increase 42,291 6.23% increase 39,088 2.36% increase 36,797 37,665
Dror Nir 2.67% incease 39,337 6.84% incease 38,314 4.75% increase 35,862 37,564
Demet Sag Ph.D. CRA GCP 3.32% increase 20,694 5.10% increase 20,030 2.79% increase 19,058 19,590
Ritu Saxena 1.39% increase 17,571 1.90% increase 17,330 0.96% increase 17,007 17,170
Gail S Thornton 10.57% increase 20,027 15.63% increase 18,112 7.05% increase 15,664 16,768
Irina Robu 2.81% increase 10,495 13.18% increase 10,208 9.34% increase 9,019  

Comparison of Internet Exposure Indicators (IEI’s) With Bar Graphs

Total Views
January 1, 2023 2,205,188
February 3, 2022 2,089,759

 

Total Categories
January 1, 2023 754
February 3, 2022 743

Total Tags
January 1, 2023 10,688
February 3, 2022 10,653

Total Posts
January 1, 2023 6,162
February 3, 2022 6,112

Progression of Top Author’s Views from 2012-2022

Updated January 1st, 2023

Author

2012

2013

2014

2015

2016

2017

2018

Aviva Lev-Ari

29,969

64,810

68,960

66,996

54,816

46,724

39,070

Larry H Bernstein

10,133

31,960

41,996

59,005

58,545

41,557

37,077

S J Williams PA

1,002

7,402

7,207

9,123

11,108

8,382

6,091

Tilda Barliya

2,854

14,453

9,761

9,762

7,402

7,784

6,859

Dr. Sudipta Saha

3,937

9,923

1,406

1,591

2,396

4,346

3,453

Dror Nir

1,979

6,901

5,162

5,666

3,423

3,595

4,071

Demet Sag Ph.D. CRA GCP

 

2,069

2,739

4,102

3,128

2,168

1,862

Ritu Saxena

3,445

5,392

2,455

2,190

1,289

761

820

Gail S Thornton

 

 

 

 

2,040

2,971

4,156

Irina Robu

 

 

 

232

515

601

896

 

Author

2019

2020

2021

2022

Total

Aviva Lev-Ari

34,505

52,386

46,923

31,050

536,209

Larry H Bernstein

25,089

33,432

32,629

30,080

401,503

S J Williams PA

5,735

9,523

6,345

5,619

77,537

tildabarliya

4,472

3,190

3,159

3,055

72,751

Dr. Sudipta Saha

4,517

4,023

3,276

3,423

42,291

Dror Nir

3,436

2,324

1,713

1,067

39,337

Demet Sag Ph.D. CRA GCP

1,206

1,617

1,086

717

20,694

Ritu Saxena

345

277

341

256

17,571

Gail S Thornton

3,164

2,989

2,619

2,088

20,027

Irina Robu

1,743

4,460

1,729

319

10,495

 

 

 

@@@@@@

Update on 3/12/2022 by Srinivas Sriram and Abhisar Anand

2/3/2022 – 2,089,759 views

7,932 comments

Content

2/3/2022 – 6,112 Posts

743 Categories

10,653 Tags

Internet Exposure Indicator (IEI) Table

Note: To calculate % change, we used the formula: % Change = (Newer – Older)/Older * 100%
All calculations were rounded to the nearest 0.01%.

INTERNET EXPOSURE INDICATORS (IEI) % change
2/1/2021-2/3/2022		 On 2/3/2022 % change
2/1/2021-7/1/2021		 On 2/1/2021 On 7/1/2021
Total # of Journal Article Views 8.59% increase 2,089,759 4.10% increase 1,924,462 2,003,415
# Categories 2.06% increase 743 1.10% increase 728 736
# Tags 2.03% increase 10,653 1.00% increase 10,441 10,545
# Comments 5.41% increase 7,932 4.27% increase 7,525 7,846
TOP AUTHORS VIEWS          
2012pharmaceutical 9.73% increase 508,457 5.04% increase 463,371 486,733
larryhbern 9.22% increase 373,618 4.30% increase 342,084 356,793
sjwilliamspa 8.55% increase 72,513 2.69% increase 66,800 68,594
tildabarliya 5.64% increase 69,934 2.85% increase 66,203 68,093
Dr. Sudipta Saha 6.23% increase 39,088 2.36% increase 36,797 37,665
Dror Nir 6.84% incease 38,314 4.75% increase 35,862 37,564
Demet Sag Ph.D. CRA GCP 5.10% increase 20,030 2.79% increase 19,058 19,590
ritusaxena 1.90% increase 17,330 0.96% increase 17,007 17,170
Gail S Thornton 15.63% increase 18,112 7.05% increase 15,664 16,768
Irina Robu 13.18% increase 10,208 9.34% increase 9,019 9,861

Comparison of Internet Exposure Indicators (IEI’s) With Bar Graphs

Total Views
February 1, 2021 1,924,462
February 3, 2022 2,089,759
Total Comments
February 1, 2021 7,525
February 3, 2022 7,932
Total Categories
February 1, 2021 728
February 3, 2022 743
Total Tags
February 1, 2021 10,441
February 3, 2022 10,653
Total Posts
February 1, 2021 6,001
February 3, 2022 6,112

Progression of Top Author’s Views from 2012-2022 – updated February 3rd, 2022

Author 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Total
2012pharmaceutical 29,969 64,810 68,960 66,996 54,816 46,724 39,070 34,505 52,386 46,993 505,229
larryhbern 10,133 31,960 41,996 59,005 58,545 41,557 37,077 25,089 33,432 32,629 371,423
sjwilliamspa 1,002 7,402 7,207 9,123 11,108 8,382 6,091 5,735 9,523 6,345 71,918
tildabarliya 2,854 14,453 9,761 9,762 7,402 7,784 6,859 4,472 3,190 3,159 69,696
Dr. Sudipta Saha 3,937 9,923 1,406 1,591 2,396 4,346 3,453 4,517 4,023 3,276 38,868
Dror Nir 1,979 6,901 5,162 5,666 3,423 3,595 4,071 3,436 2,324 1,713 38,270
Demet Sag Ph.D. CRA GCP   2,069 2,739 4,102 3,128 2,168 1,862 1,206 1,617 1,086 19,977
ritusaxena 3,445 5,392 2,455 2,190 1,289 761 820 345 277 341 17,315
Gail S Thornton         2,040 2,971 4,156 3,164 2,989 2,619 17,939
Irina Robu       232 515 601 896 1,743 4,460 1,729 10,176

Update on 7/1/2021 by Srinivas Sriram and Abhisar Anand

2/1/2021 – 1,924,462 views
7/1/2021 – 2,003,639 views 

7,555 comments

Content

2/1/2021 – 6,001 Posts
7/1/2021 – 6,056 Posts

736 Categories

10,545 Tags

 

Top Articles by Views 2/1/2021 vs 7/1/2021

 

Line Graph Depicting Top Authors Progression by Views Ending 2021-07-01
Date of Production: 2021-07-01

Top Posts for all days ending 2021-07-01 (Summarized)
Date of Production: 2021-07-01

All Time

Title   Views
Home page / Archives More stats 765,494
Is the Warburg Effect the Cause or the Effect of Cancer:

 

A 21st Century View?

 More stats 17,365
Recent comprehensive review on the role of ultrasound in

 

breast cancer management

 More stats 16,244
Paclitaxel vs Abraxane (albumin-bound paclitaxel) More stats 15,220
Do Novel Anticoagulants Affect the PT/INR? The Cases of

 

XARELTO (rivaroxaban) and PRADAXA (dabigatran)

 More stats 14,362
Apixaban (Eliquis): Mechanism of Action, Drug

 

Comparison and Additional Indications

 More stats 9,672
Clinical Indications for Use of Inhaled Nitric Oxide (iNO) in

 

the Adult Patient Market: Clinical Outcomes after Use,

Therapy Demand and Cost of Care

 More stats 9,107
Our TEAM More stats 6,739
Mesothelin: An early detection biomarker for

 

cancer (By Jack Andraka)

 More stats 6,623
Interaction of enzymes and hormones More stats 6,013
Pyrroloquinoline quinone (PQQ) – an unproved supplement

 

 More stats 5,951

Top Posts for all days ending 2021-02-01 (Summarized)

All Time

Title   Views
Home page / Archives More stats 738,554
Is the Warburg Effect the Cause or the Effect of Cancer:

 

A 21st Century View?

 More stats 17,296
Recent comprehensive review on the role of ultrasound in

 

breast cancer management

 More stats 15,696
Paclitaxel vs Abraxane (albumin-bound paclitaxel) More stats 14,640
Do Novel Anticoagulants Affect the PT/INR? The Cases of

 

XARELTO (rivaroxaban) and PRADAXA (dabigatran)

 More stats 14,098
Apixaban (Eliquis): Mechanism of Action, Drug

 

Comparison and Additional Indications

 More stats 9,003
Clinical Indications for Use of Inhaled Nitric Oxide (iNO) in

 

the Adult Patient Market: Clinical Outcomes after Use,

Therapy Demand and Cost of Care

 More stats 8,642
Our TEAM More stats 6,672
Mesothelin: An early detection biomarker for

 

cancer (By Jack Andraka)

 More stats 6,587
Interaction of enzymes and hormones More stats 5,747
Biochemistry of the Coagulation Cascade and

 

Platelet Aggregation: Nitric Oxide: Platelets, Circulatory

Disorders, and Coagulation Effects

 More stats 5,426

 

Top Authors by Views 2/1/2021 vs 7/1/2021

Top Authors for all days ending 2021-07-01 (Summarized)
Date of Production: 2021-07-01

All Time

Author   Views
2012pharmaceutical

 

Aviva Lev-Ari, PhD, RN

   486,733
larryhbern 356,793
sjwilliamspa 68,594
tildabarliya   68,093
Dr. Sudipta Saha 37,665
Dror Nir   37,564
Demet Sag, Ph.D., CRA, GCP 19,590
ritusaxena 17,170
Gail S Thornton 16,768
     
Irina Robu 9,861

 

Top Authors for all days ending 2021-02-01 (Summarized)

All Time

Author   Views
2012pharmaceutical

 

Aviva Lev-Ari, PhD, RN

   463,371
larryhbern 342,084
tildabarliya   66,800
sjwilliamspa 66,203
Dror Nir   36,797
Dr. Sudipta Saha 35,862
Demet Sag, Ph.D., CRA, GCP 19,058
ritusaxena 17,007
Gail S Thornton 15,664
     
Irina Robu 9,019

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Rise of a trio of mutated viruses hints at an increase in transmissibility, speeding the virus’ leaps from one host to the next

Posted in Coronavirus Gene Expression, COVID-19, Mechanisms of infection by SARS-CoV-2, Population Health Management, Genetics & Pharmaceutical, SARS-CoV-2, Seasonality & Environmental Factors in Resurgence, T-cell response to SARS-CoV-2 infection, Vaccinology, Virus Infective Acute Respiratory Syndrome: SARS-CoV on February 1, 2021| Leave a Comment »

Rise of a trio of mutated viruses hints at an increase in transmissibility, speeding the virus’ leaps from one host to the next

Reporter: Aviva Lev-Ari, PhD, RN

“We have uncontrolled viral spread in much of the world,” says Adam Lauring, an infectious disease physician and virologist at the University of Michigan. “So the virus has a lot of opportunity to evolve.”

“The variants may be more transmissible, but physics has not changed,” says Müge Çevik, an infectious disease physician at the University of St. Andrews in Scotland.

Many changes don’t affect the virus’ function, and some even harm SARS-CoV-2’s ability to multiply, but they keep happening. “Viruses mutate; that’s what they do,” says Akiko Iwasaki, an immunologist at Yale School of Medicine in Connecticut.

U.K., Brazil, and South Africa. In the United Kingdom, variant B.1.1.7 likely drove the region’s record-setting spike of COVID-19 cases in January. The variant is now circulating in more than 60 countries, including the United States—and projections suggest it will become the most common virus variety in the U.S. by mid-March.

An independently arising lineage called P.1 might also be driving a wave of cases in Manaus, Brazil, where it accounted for nearly half of new COVID-19 infections in December. On January 26, Minnesotan officials reported the first U.S. case of P.1 in a resident who previously traveled to Brazil. And a third lineage raising alarms, known as B.1.351, was first spotted amid a December wave of infections in South Africa. On January 28, the first known U.S. cases of the variant were reported in South Carolina.

One specific mutation, known as N501Y, popped up independently in all three variants, suggesting it could provide an advantage to the virus. “That’s a sign that there is natural selection going on,” Lauring says. The N501Y mutation affects the virus’ spike protein, which is the key it uses to unlock entry into its host’s cells.

Another possibility is that new variants cause people who are infected to harbor more copies of the virus. This results in greater viral “shedding” in airborne droplets spewed when people talk, sing, cough, and breath.

mutations in 501Y.V2 could diminish the effectiveness of antibodies in the blood of people previously infected with the virus. But understanding whether that could lead to more re-infections, or if it could affect vaccine efficacy.

Dramatically scale up production of high-filtration masks for the general public.

Based on:

Why some coronavirus variants are more contagious‹and how we can stop them

PUBLISHED 

https://www.nationalgeographic.com/science/2021/01/why-some-coronavirus-variants-are-more-contagious/?cmpid=org=ngp::mc=crm-email::src=ngp::cmp=editorial::add=SpecialEdition_20210129

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A Platform called VirtualFlow: Discovery of Pan-coronavirus Drugs help prepare the US for the Next Coronavirus Pandemic

Posted in COVID-19, Drug Discovery Chemistry, Mechanisms of infection by SARS-CoV-2, Pharmaceutical Analytics, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical R&D Informatics, Pharmaceutical R&D Investment, Population Health Management, Genetics & Pharmaceutical, SARS-CoV-2, Virtual Drug Molecule Screening targeting SAR-CoV-2 proteins, Virus Infective Acute Respiratory Syndrome: SARS-CoV on February 1, 2021| Leave a Comment »

A Platform called VirtualFlow: Discovery of Pan-coronavirus Drugs help prepare the US for the Next Coronavirus Pandemic

Reporter: Aviva Lev-Ari, PhD, RN

 

ARTICLE|ONLINE NOW, 102021

A multi-pronged approach targeting SARS-CoV-2 proteins using ultra-large virtual screening

Open AccessPublished:January 04, 2021DOI:https://doi.org/10.1016/j.isci.2020.102021

SOURCE

https://www.cell.com/iscience/fulltext/S2589-0042(20)31218-9

 

The work was made possible in large part by about $1 million in cloud computing hours awarded by Google through a COVID-19 research grant program.

The work reported, below was sponsored by

  • a Google Cloud COVID-19 research grant. Funding was also provided by the
  • Fondation Aclon,
  • National Institutes of Health (GM136859),
  • Claudia Adams Barr Program for Innovative Basic Cancer Research,
  • Math+ Berlin Mathematics Research Center,
  • Templeton Religion Trust (TRT 0159),
  • U.S. Army Research Office (W911NF1910302), and
  • Chleck Family Foundation

 

Harvard University, AbbVie form research alliance to address emergent viral diseases

 Research

This article is part of Harvard Medical School’s continuing coverage of medicine, biomedical research, medical education and policy related to the SARS-CoV-2 pandemic and the disease COVID-19.

Harvard University and AbbVie today announced a $30 million collaborative research alliance, launching a multi-pronged effort at Harvard Medical School to study and develop therapies against emergent viral infections, with a focus on those caused by coronaviruses and by viruses that lead to hemorrhagic fever.

The collaboration aims to rapidly integrate fundamental biology into the preclinical and clinical development of new therapies for viral diseases that address a variety of therapeutic modalities. HMS has led several large-scale, coordinated research efforts launched at the beginning of the COVID-19 pandemic.

“A key element of having a strong R&D organization is collaboration with top academic institutions, like Harvard Medical School, to develop therapies for patients who need them most,” said Michael Severino, vice chairman and president of AbbVie. “There is much to learn about viral diseases and the best way to treat them. By harnessing the power of collaboration, we can develop new therapeutics sooner to ensure the world is better prepared for future potential outbreaks.”

“The cataclysmic nature of the COVID-19 pandemic reminds us how vital it is to be prepared for the next public health crisis and how critical collaboration is on every level—across disciplines, across institutions and across national boundaries,” said George Q. Daley, dean of Harvard Medical School. “Harvard Medical School, as the nucleus of an ecosystem of fundamental discovery and therapeutic translation, is uniquely positioned to propel this transformative research alongside allies like AbbVie.”

AbbVie will provide $30 million over three years and additional in-kind support leveraging AbbVie’s scientists, expertise and facilities to advance collaborative research and early-stage development efforts across five program areas that address a variety of therapeutic modalities:

  • Immunity and immunopathology—Study of the fundamental processes that impact the body’s critical immune responses to viruses and identification of opportunities for therapeutic intervention.

Led by Ulirich Von Andrian, the Edward Mallinckrodt Jr. Professor of Immunopathology in the Blavatnik Institute at HMS and program leader of basic immunology at the Ragon Institute of MGH, MIT and Harvard, and Jochen Salfeld, vice president of immunology and virology discovery at AbbVie.

  • Host targeting for antiviral therapies—Development of approaches that modulate host proteins in an effort to disrupt the life cycle of emergent viral pathogens.

Led by Pamela Silver, the Elliot T. and Onie H. Adams Professor of Biochemistry and Systems Biology in the Blavatnik Institute at HMS, and Steve Elmore, vice president of drug discovery science and technology at AbbVie.

  • Antibody therapeutics—Rapid development of therapeutic antibodies or biologics against emergent pathogens, including SARS-CoV-2, to a preclinical or early clinical stage.

Led by Jonathan Abraham, assistant professor of microbiology in the Blavatnik Institute at HMS, and by Jochen Salfeld, vice president of immunology and virology discovery at AbbVie.

  • Small molecules—Discovery and early-stage development of small-molecule drugs that would act to prevent replication of known coronaviruses and emergent pathogens.

Led by Mark Namchuk, executive director of therapeutics translation at HMS and senior lecturer on biological chemistry and molecular pharmacology in the Blavatnik Institute at HMS, and Steve Elmore, vice president of drug discovery science and technology at AbbVie.

  • Translational development—Preclinical validation, pharmacological testing, and optimization of leading approaches, in collaboration with Harvard-affiliated hospitals, with program leads to be determined.

SOURCE

https://hms.harvard.edu/news/joining-forces

 

 

A Screen Door Opens

Virtual screen finds compounds that could combat SARS-CoV-2

By STEPHANIE DUTCHEN  Research

This article is part of Harvard Medical School’s continuing coverage of medicine, biomedical research, medical education, and policy related to the SARS-CoV-2 pandemic and the disease COVID-19.

Less than a year ago, Harvard Medical School researchers and international colleagues unveiled a platform called VirtualFlow that could swiftly sift through more than 1 billion chemical compounds and identify those with the greatest promise to become disease-specific treatments, providing researchers with invaluable guidance before they embark on expensive and time-consuming lab experiments and clinical trials.

Propelled by the urgent needs of the pandemic, the team has now pushed VirtualFlow even further, conducting 45 screens of more than 1 billion compounds each and ranking the compounds with the greatest potential for fighting COVID-19—including some that are already approved by the FDA for other diseases.

“This was the largest virtual screening effort ever done,” said VirtualFlow co-developer Christoph Gorgulla, research fellow in biological chemistry and molecular pharmacology in the labs of Haribabu Arthanari and Gerhard Wagner in the Blavatnik Institute at HMS.

The results were published in January in the open-access journal iScience.

The team searched for compounds that bind to any of 15 proteins on SARS-CoV-2 or two human proteins, ACE2 and TMPRSS2, known to interact with the virus and enable infection.

Researchers can now explore on an interactive website the 1,000 most promising compounds from each screen and start testing in the lab any ones they choose.

The urgency of the pandemic and the sheer number of candidate compounds inspired the team to release the early results to the scientific community.

“No one group can validate all the compounds as quickly as the pandemic demands,” said Gorgulla, who is also an associate of the Department of Physics at Harvard University. “We hope that our colleagues can collectively use our results to identify potent inhibitors of SARS-CoV-2.”

In most cases, it will take years to find out whether a compound is safe and effective in humans. For some of the compounds, however, researchers have a head start.

Hundreds of the most promising compounds that VirtualFlow flagged are already FDA approved or being studied in clinical or preclinical trials for other diseases. If researchers find that one of those compounds proves effective against SARS-CoV-2 in lab experiments, the data their colleagues have already collected could save time establishing safety in humans.

Other compounds among VirtualFlow’s top hits are currently being assessed in clinical trials for COVID-19, including several drugs in the steroid family. In those cases, researchers could build on the software findings to investigate how those drug candidates work at the molecular level—something that’s not always clear even when a drug works well.

It shows what we’re capable of computationally during a pandemic.

Hari Arthanari

In another effort to speed successful transitions from computer to clinic, the team has joined in Harvard’s partnership with pharmaceutical company AbbVie.

If any successful drugs eventually arise from the VirtualFlow screens, they would complement other strategies in the fight against COVID-19. For instance, drugs that halt viral replication or prevent it from entering cells could help those who cannot be vaccinated.

The nature of the screens also raises the likelihood that drugs developed against current forms of SARS-CoV-2 would work against future mutations or other coronaviruses, the authors say.

Computing power

The work was made possible in large part by about $1 million in cloud computing hours awarded by Google through a COVID-19 research grant program.

Gorgulla and colleagues used that power to screen how well the compounds bound to more than 40 sites across the 15 viral proteins as well as to the two human proteins—effectively trying 1 billion 3D puzzle pieces in 40 places each.

The team selected the sites because blocking them with compounds would either prevent SARS-CoV-2 from entering cells, prevent the virus from replicating and thus lower viral load, reduce the virus’s ability to evade or subvert our immune defenses, or disrupt viral assembly and packaging.

Many of the sites have not yet been investigated by other groups, as far as the team is aware.

“We’re exploring new avenues on how to tackle the virus. This is one of the most exciting aspects of our project,” said Gorgulla.

One benefit of studying dozens of sites on 17 proteins at once is the possibility of identifying or developing multiple drugs that, when combined, combat SARS-CoV-2 infection on different fronts. Such drug cocktails would have a better chance of halting the virus in its tracks and overcoming future mutations than a single drug that works on one protein site.

Some sites included in the screen are shared among other coronaviruses of worldwide concern. Any compound that binds to one of those sites has a “high” chance of also combating SARS, MERS, or as-yet-unknown coronavirus diseases, said Arthanari.

“We are in a position to discover pan-coronavirus drugs and help prepare for the next coronavirus pandemic, should there ever be one,” said Gorgulla.

Regardless of the outcomes, the work raises the benchmark for virtual screening and demonstrates the current power of computing in biological research.

“It shows what we’re capable of computationally during a pandemic,” said Arthanari.

The project has also brought special satisfaction to team members who don’t typically work on projects so closely tied to human health.

SOURCE

https://hms.harvard.edu/news/screen-door-opens?utm_source=Silverpop&utm_medium=email&utm_term=field_news_item_1&utm_content=HMNews02012021

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