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Archive for August, 2020

Prime Editing as a New CRISPR Tool to Enhance Precision and Versatility

 

Reporter: Stephen J. Williams, PhD

 

CRISPR has become a powerful molecular for the editing of genomes tool in research, drug discovery, and the clinic

(see posts and ebook on this site below)

 

however, as discussed on this site

(see posts below)

there have been many instances of off-target effects where genes, other than the selected target, are edited out.  This ‘off-target’ issue has hampered much of the utility of CRISPR in gene-therapy and CART therapy

see posts

 

However, an article in Science by Jon Cohen explains a Nature paper’s finding of a new tool in the CRISPR arsenal called prime editing, meant to increase CRISPR specificity and precision editing capabilities.

PRIME EDITING PROMISES TO BE A CUT ABOVE CRISPR

By Jon Cohen | Oct 25th, 2019

Prime editing promises to be a cut above CRISPR Jon Cohen CRISPR, an extraordinarily powerful genome-editing tool invented in 2012, can still be clumsy. … Prime editing steers around shortcomings of both techniques by heavily modifying the Cas9 protein and the guide RNA. … ” Prime editing “well may become the way that disease-causing mutations are repaired,” he says.

Science Vol. 366, No. 6464; DOI: 10.1126/science.366.6464.406

The effort, led by Drs. David Liu and Andrew Anzalone at the Broad Institute (Cambridge, MA), relies on the modification of the Cas9 protein and guide RNA, so that there is only a nick in a single strand of the double helix.  The canonical Cas9 cuts both strands of DNA, and so relies on an efficient gap repair activity of the cell.  The second part, a new type of guide RNA called a pegRNA, contains an RNA template for a new DNA sequence to be added at the target location.  This pegRNA-directed synthesis of the new template requires the attachment of a reverse transcriptase enzymes to the Cas9.  So far Liu and his colleagues have tested the technology on over 175 human and rodent cell lines with great success.  In addition, they had also corrected mutations which cause Tay Sachs disease, which previous CRISPR systems could not do.  Liu claims that this technology could correct over 89% of pathogenic variants in human diseases.

A company Prime Medicine has been formed out of this effort.

Source: https://science.sciencemag.org/content/366/6464/406.abstract

 

Read an article on Dr. Liu, prime editing, and the companies that Dr. Liu has initiated including Editas Medicine, Beam Therapeutics, and Prime Medicine at https://www.statnews.com/2019/11/06/questions-david-liu-crispr-prime-editing-answers/

(interview by StatNews  SHARON BEGLEY @sxbegle)

As was announced, prime editing for human therapeutics will be jointly developed by both Prime Medicine and Beam Therapeutics, each focusing on different types of edits and distinct disease targets, which will help avoid redundancy and allow us to cover more disease territory overall. The companies will also share knowledge in prime editing as well as in accompanying technologies, such as delivery and manufacturing.

Reader of StatNews.: Can you please compare the pros and cons of prime editing versus base editing?

The first difference between base editing and prime editing is that base editing has been widely used for the past 3 1/2 years in organisms ranging from bacteria to plants to mice to primates. Addgene tells me that the DNA blueprints for base editors from our laboratory have been distributed more than 7,500 times to more than 1,000 researchers around the world, and more than 100 research papers from many different laboratories have been published using base editors to achieve desired gene edits for a wide variety of applications. While we are very excited about prime editing, it’s brand-new and there has only been one paper published thus far. So there’s much to do before we can know if prime editing will prove to be as general and robust as base editing has proven to be.

We directly compared prime editors and base editors in our study, and found that current base editors can offer higher editing efficiency and fewer indel byproducts than prime editors, while prime editors offer more targeting flexibility and greater editing precision. So when the desired edit is a transition point mutation (C to T, T to C, A to G, or G to A), and the target base is well-positioned for base editing (that is, a PAM sequence exists approximately 15 bases from the target site), then base editing can result in higher editing efficiencies and fewer byproducts. When the target base is not well-positioned for base editing, or when other “bystander” C or A bases are nearby that must not be edited, then prime editing offers major advantages since it does not require a precisely positioned PAM sequence and is a true “search-and-replace” editing capability, with no possibility of unwanted bystander editing at neighboring bases.

Of course, for classes of mutations other than the four types of point mutations that base editors can make, such as insertions, deletions, and the eight other kinds of point mutations, to our knowledge prime editing is currently the only approach that can make these mutations in human cells without requiring double-stranded DNA cuts or separate DNA templates.

Nucleases (such as the zinc-finger nucleases, TALE nucleases, and the original CRISPR-Cas9), base editors, and prime editors each have complementary strengths and weaknesses, just as scissors, pencils, and word processors each have unique and useful roles. All three classes of editing agents already have or will have roles in basic research and in applications such as human therapeutics and agriculture.

Nature Paper on Prime Editing CRISPR

Search-and-replace genome editing without double-strand breaks or donor DNA (6)

 

Andrew V. Anzalone,  Peyton B. Randolph, Jessie R. Davis, Alexander A. Sousa,

Luke W. Koblan, Jonathan M. Levy, Peter J. Chen, Christopher Wilson,

Gregory A. Newby, Aditya Raguram & David R. Liu

 

Nature volume 576, pages149–157(2019)

 

Abstract

Most genetic variants that contribute to disease1 are challenging to correct efficiently and without excess byproducts2,3,4,5. Here we describe prime editing, a versatile and precise genome editing method that directly writes new genetic information into a specified DNA site using a catalytically impaired Cas9 endonuclease fused to an engineered reverse transcriptase, programmed with a prime editing guide RNA (pegRNA) that both specifies the target site and encodes the desired edit. We performed more than 175 edits in human cells, including targeted insertions, deletions, and all 12 types of point mutation, without requiring double-strand breaks or donor DNA templates. We used prime editing in human cells to correct, efficiently and with few byproducts, the primary genetic causes of sickle cell disease (requiring a transversion in HBB) and Tay–Sachs disease (requiring a deletion in HEXA); to install a protective transversion in PRNP; and to insert various tags and epitopes precisely into target loci. Four human cell lines and primary post-mitotic mouse cortical neurons support prime editing with varying efficiencies. Prime editing shows higher or similar efficiency and fewer byproducts than homology-directed repair, has complementary strengths and weaknesses compared to base editing, and induces much lower off-target editing than Cas9 nuclease at known Cas9 off-target sites. Prime editing substantially expands the scope and capabilities of genome editing, and in principle could correct up to 89% of known genetic variants associated with human diseases.

 

 

From Anzolone et al. Nature 2019 Figure 1.

Prime editing strategy

Cas9 targets DNA using a guide RNA containing a spacer sequence that hybridizes to the target DNA site. We envisioned the generation of guide RNAs that both specify the DNA target and contain new genetic information that replaces target DNA nucleotides. To transfer information from these engineered guide RNAs to target DNA, we proposed that genomic DNA, nicked at the target site to expose a 3′-hydroxyl group, could be used to prime the reverse transcription of an edit-encoding extension on the engineered guide RNA (the pegRNA) directly into the target site (Fig. 1b, cSupplementary Discussion).

These initial steps result in a branched intermediate with two redundant single-stranded DNA flaps: a 5′ flap that contains the unedited DNA sequence and a 3′ flap that contains the edited sequence copied from the pegRNA (Fig. 1c). Although hybridization of the perfectly complementary 5′ flap to the unedited strand is likely to be thermodynamically favoured, 5′ flaps are the preferred substrate for structure-specific endonucleases such as FEN122, which excises 5′ flaps generated during lagging-strand DNA synthesis and long-patch base excision repair. The redundant unedited DNA may also be removed by 5′ exonucleases such as EXO123.

  • The authors reasoned that preferential 5′ flap excision and 3′ flap ligation could drive the incorporation of the edited DNA strand, creating heteroduplex DNA containing one edited strand and one unedited strand (Fig. 1c).
  • DNA repair to resolve the heteroduplex by copying the information in the edited strand to the complementary strand would permanently install the edit (Fig. 1c).
  • They had hypothesized that nicking the non-edited DNA strand might bias DNA repair to preferentially replace the non-edited strand.

Results

  • The authors evaluated the eukaryotic cell DNA repair outcomes of 3′ flaps produced by pegRNA-programmed reverse transcription in vitro, and performed in vitro prime editing on reporter plasmids, then transformed the reaction products into yeast cells (Extended Data Fig. 2).
  • Reporter plasmids encoding EGFP and mCherry separated by a linker containing an in-frame stop codon, +1 frameshift, or −1 frameshift were constructed and when plasmids were edited in vitro with Cas9 nickase, RT, and 3′-extended pegRNAs encoding a transversion that corrects the premature stop codon, 37% of yeast transformants expressed both GFP and mCherry (Fig. 1f, Extended Data Fig. 2).
  • They fused a variant of M—MLV-RT (reverse transcriptase) to Cas9 with an extended linker and this M-MLV RT fused to the C terminus of Cas9(H840A) nickase was designated as PE1. This strategy allowed the authors to generate a cell line containing all the required components of the primer editing system. They constructed 19 variants of PE1 containing a variety of RT mutations to evaluate their editing efficiency in human cells
  • Generated a pentamutant RT incorporated into PE1 (Cas9(H840A)–M-MLV RT(D200N/L603W/T330P/T306K/W313F)) is hereafter referred to as prime editor 2 (PE2).  These were more thermostable versions of RT with higher efficiency.
  • Optimized the guide (pegRNA) using a series of permutations and  recommend starting with about 10–16 nt and testing shorter and longer RT templates during pegRNA optimization.
  • In the previous attempts (PE1 and PE2 systems), mismatch repair resolves the heteroduplex to give either edited or non-edited products. So they next developed an optimal editing system (PE3) to produce optimal nickase activity and found nicks positioned 3′ of the edit about 40–90 bp from the pegRNA-induced nick generally increased editing efficiency (averaging 41%) without excess indel formation (6.8% average indels for the sgRNA with the highest editing efficiency) (Fig. 3b).
  • The cell line used to finalize and validate the system was predominantly HEK293T immortalized cell line
  • Together, their findings establish that PE3 systems improve editing efficiencies about threefold compared with PE2, albeit with a higher range of indels than PE2. When it is possible to nick the non-edited strand with an sgRNA that requires editing before nicking, the PE3b system offers PE3-like editing levels while greatly reducing indel formation.
  • Off Target Effects: Strikingly, PE3 or PE2 with the same 16 pegRNAs containing these four target spacers resulted in detectable off-target editing at only 3 out of 16 off-target sites, with only 1 of 16 showing an off-target editing efficiency of 1% or more (Extended Data Fig. 6h). Average off-target prime editing for pegRNAs targeting HEK3HEK4EMX1, and FANCFat the top four known Cas9 off-target sites for each protospacer was <0.1%, <2.2 ± 5.2%, <0.1%, and <0.13 ± 0.11%, respectively (Extended Data Fig. 6h).
  • The PE3 system was very efficient at editing the most common mutation that causes Tay-Sachs disease, a 4-bp insertion in HEXA(HEXA1278+TATC).

References

  1. Landrum, M. J. et al. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res44, D862–D868 (2016).
  2. Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science337, 816–821 (2012).
  3. Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science339, 819–823 (2013).

 

  1. Mali, P. et al. RNA-guided human genome engineering via Cas9. Science339, 823–826 (2013).
  2. Kosicki, M., Tomberg, K. & Bradley, A. Repair of double-strand breaks induced by CRISPR–Cas9 leads to large deletions and complex rearrangements.  Biotechnol. 36, 765–771 (2018).
  3. Anzalone, A.V., Randolph, P.B., Davis, J.R. et al.Search-and-replace genome editing without double-strand breaks or donor DNA. Nature576, 149–157 (2019). https://doi.org/10.1038/s41586-019-1711-4

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Tweets by @pharma_BI and @AVIVA1950 @ 2020 State of Possible Conference, MassBio’s VIRTUAL Annual Meeting, August 26 – 27, 2020

Reporter: Aviva Lev-Ari, PhD, RN

Real Time press coverage: Aviva Lev-Ari, PhD, RN

 

2020 State of Possible Conference, MassBio’s VIRTUAL Annual Meeting, August 26 – 27, 2020

Real Time press coverage: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/02/21/2020-state-of-possible-conference-massbios-annual-meeting-march-25-26-2020-sonesta-hotel-cambridge-ma/

 

Aviva Lev-Ari
@AVIVA1950

2020 State of Possible Conference, MassBio’s VIRTUAL Annual Meeting, August 26 – 27, 2020
2020 State of Possible Conference, MassBio’s VIRTUAL Annual Meeting, August 26 – 27, 2020 Leaders in Pharmaceutical Business Intelligence (LPBI) Group will cover this event in REAL TIME…
pharmaceuticalintelligence.com
2

Aviva Lev-Ari
@AVIVA1950

Thomas McCourt President Ironwood Pharmaceuticals, Inc. Clinical trials many are STUCK – solve problems calls for adoption of all companies to digital platforms Entrepreneurial spirit in Kendall square took away the prime position of CA Biotech

Aviva Lev-Ari
@AVIVA1950

Thomas McCourt President Ironwood Pharmaceuticals, Inc. GI disease in Patients – My Gi Health started in NIH – symptoms of GI diseases GI entrepreneurs to build a smart e-Tool to analyze the GI Symptoms  few thousand Patients

Aviva Lev-Ari
@AVIVA1950

Nick Dougherty Managing Director MassChallenge HealthTech Around the World communities, MA Biotech infrastructure  MassChallenge HealthTech: In Mexico, in Israel in Switzerland Becoming virtual instantly in MARCH 2020 More locations pick up scale up

1

Aviva Lev-Ari
@AVIVA1950

Naomi Fried Founder CEO Health Innovation Strategies MA best Hospitals: MGH-BWH, Beth Israel-Lahey Clinic, Stuart BC/BS Medical Schools Clusters in Biotech & Digital Health Counsel  Definition of Community changed in the COVID-19 Era

Aviva Lev-Ari
@AVIVA1950

Stephen Bernstein McDermott Will & Emery LLP Virtual Bench science Life sciences Products; Deploying a compound, provider responsible for the cost or how the Reimbursement will work Consumer & Patients: Specialty Pharmacy  collaborations by planning

Aviva Lev-Ari
@AVIVA1950

Stephen Bernstein McDermott Will & Emery LLP Webinars and Zooms allows communication we will see more innovations – Flatten the World Greater isolation US is expected to lead collaborate is NOW home based no travel creative  Virtual Clinical Trials

Aviva Lev-Ari
@AVIVA1950

Renée Deehan-Kenney PhD, VP, QuartzBio Precision Medicine Group Network biology modeling – helps inform chemistry models: Cell line in Mice translation to Human in pancreatic cancer cell line  Pessimistic view: Long way to go end point most time

Aviva Lev-Ari
@AVIVA1950

Rachel Hodos Senior AI Scientist BenevolentAI Panelist Pick Targets AI is a Possible dream trusted a drug in human

Aviva Lev-Ari
@AVIVA1950

Nora Khaldi Founder and CSO Nuritas AI biological data is early while relying on that knowledge identify drug safe for human is possible I believe AI – take a molecule to humans 99% working in humans AI teated and validated in vitro

Aviva Lev-Ari
@AVIVA1950

Juan Alvarez AVP, Merck High dimensional spaces sample comparison Find therapies for Humans in the absence of having Humans participating, data on human is BIASED by drugs history Drugable identify interventions translatable to Humans pathway-based

Aviva Lev-Ari
@AVIVA1950

Renée Deehan-Kenney PhD, VP, QuartzBio, Precision Medicine Group Chemistry in developing drugs is complex Network biology modeling – helps inform chemistry models: Cell line in Mice translation to Human in pancreatic cancer cell line

Aviva Lev-Ari
@AVIVA1950

Renée Deehan-Kenney PhD, VP, QuartzBio Precision Medicine Group technology enable company clinical data analysis of data clinical trials ML prior knowledge network biology drive inside MOA prioritize indications Chemistry in developing drugs complex

Aviva Lev-Ari
@AVIVA1950

Juan Alvarez AVP, Computational and Structural Chemistry Merck metabolomics evolving proteins analyzing data access to compute power data acquisition and storage – High dimensional spaces sample comparison

1

Aviva Lev-Ari
@AVIVA1950

Juan Alvarez AVP, Computational and Structural Chemistry Merck Drug Target identification Drug Discovery – ML since 1980s Identify molecules syntesis prediction physico space – physiological systems Transcriptomics, single cell biomarkers proteomics

Aviva Lev-Ari
@AVIVA1950

Renée Deehan-Kenney PhD, VP, QuartzBio Precision Medicine Group Network biology modeling – helps inform chemistry models: Cell line in Mice translation to Human in pancreatic cancer cell line  Pessimistic view: Long way to go end point most time

Aviva Lev-Ari
@AVIVA1950

Rachel Hodos Senior AI Scientist BenevolentAI Panelist Pick Targets AI is a Possible dream trusted a drug in human

Aviva Lev-Ari
@AVIVA1950

Nora Khaldi Founder and CSO Nuritas AI biological data is early while relying on that knowledge identify drug safe for human is possible I believe AI – take a molecule to humans 99% working in humans AI teated and validated in vitro

Aviva Lev-Ari
@AVIVA1950

Thomas McCourt President Ironwood Pharmaceuticals, Inc. Clinical trials many are STUCK – solve problems calls for adoption of all companies to digital platforms Entrepreneurial spirit in Kendall square took away the prime position of CA Biotech

Aviva Lev-Ari
@AVIVA1950

Thomas McCourt President Ironwood Pharmaceuticals, Inc. GI disease in Patients – My Gi Health started in NIH – symptoms of GI diseases GI entrepreneurs to build a smart e-Tool to analyze the GI Symptoms  few thousand Patients

Aviva Lev-Ari
@AVIVA1950

Nick Dougherty Managing Director MassChallenge HealthTech Around the World communities, MA Biotech infrastructure  MassChallenge HealthTech: In Mexico, in Israel in Switzerland Becoming virtual instantly in MARCH 2020 More locations pick up scale up

1

Aviva Lev-Ari
@AVIVA1950

Naomi Fried Founder CEO Health Innovation Strategies MA best Hospitals: MGH-BWH, Beth Israel-Lahey Clinic, Stuart BC/BS Medical Schools Clusters in Biotech & Digital Health Counsel  Definition of Community changed in the COVID-19 Era

Aviva Lev-Ari
@AVIVA1950

Stephen Bernstein McDermott Will & Emery LLP Virtual Bench science Life sciences Products; Deploying a compound, provider responsible for the cost or how the Reimbursement will work Consumer & Patients: Specialty Pharmacy  collaborations by planning

Aviva Lev-Ari
@AVIVA1950

Stephen Bernstein McDermott Will & Emery LLP Webinars and Zooms allows communication we will see more innovations – Flatten the World Greater isolation US is expected to lead collaborate is NOW home based no travel creative  Virtual Clinical Trials

Aviva Lev-Ari
@AVIVA1950

Kenneth Anderson Director, Multiple Myeloma Center Dana-Farber Cancer Institute Hematologic Division – African Americans  Change paradigm of clinical trials Geraldine Feraro was patient at DFCI Tom Bracow patient at DFCI  STEM for girls 6-12 grades

Aviva Lev-Ari
@AVIVA1950

Kenneth Anderson Director, Multiple Myeloma Center Dana-Farber Cancer Institute Multiple Myeloma – 23 drugs approved by FDA Dana-Farber Cancer Institute with Sanofi collaboration Foundations stepped forward to study Multiple Myeloma FDA motivated

Aviva Lev-Ari
@AVIVA1950

Juan Alvarez AVP, Merck High dimensional spaces sample comparison Find therapies for Humans in the absence of having Humans participating, data on human is BIASED by drugs history Drugable identify interventions translatable to Humans pathway-based

Aviva Lev-Ari
@AVIVA1950

Renée Deehan-Kenney PhD, VP, QuartzBio, Precision Medicine Group Chemistry in developing drugs is complex Network biology modeling – helps inform chemistry models: Cell line in Mice translation to Human in pancreatic cancer cell line

Aviva Lev-Ari
@AVIVA1950

Renée Deehan-Kenney PhD, VP, QuartzBio Precision Medicine Group technology enable company clinical data analysis of data clinical trials ML prior knowledge network biology drive inside MOA prioritize indications Chemistry in developing drugs complex

Aviva Lev-Ari
@AVIVA1950

Juan Alvarez AVP, Computational and Structural Chemistry Merck metabolomics evolving proteins analyzing data access to compute power data acquisition and storage – High dimensional spaces sample comparison

1

Aviva Lev-Ari
@AVIVA1950

Juan Alvarez AVP, Computational and Structural Chemistry Merck Drug Target identification Drug Discovery – ML since 1980s Identify molecules syntesis prediction physico space – physiological systems Transcriptomics, single cell biomarkers proteomics

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Most significant article published in the Society of Evolution, Medicine and Public Health won Prize: polygenic scores, polygenic adaptation, and human phenotypic differences

Reporter: Aviva Lev-Ari, PhD, RN 

 

UPDATED on 8/30/2020

Analysis of polygenic risk score usage and performance in diverse human populations

Abstract

A historical tendency to use European ancestry samples hinders medical genetics research, including the use of polygenic scores, which are individual-level metrics of genetic risk. We analyze the first decade of polygenic scoring studies (2008–2017, inclusive), and find that 67% of studies included exclusively European ancestry participants and another 19% included only East Asian ancestry participants. Only 3.8% of studies were among cohorts of African, Hispanic, or Indigenous peoples. We find that predictive performance of European ancestry-derived polygenic scores is lower in non-European ancestry samples (e.g. African ancestry samples: t = −5.97, df = 24, p = 3.7 × 10−6), and we demonstrate the effects of methodological choices in polygenic score distributions for worldwide populations. These findings highlight the need for improved treatment of linkage disequilibrium and variant frequencies when applying polygenic scoring to cohorts of non-European ancestry, and bolster the rationale for large-scale GWAS in diverse human populations.

SOURCE

https://www.nature.com/articles/s41467-019-11112-0

The Voice of Prof. Marcus W. Feldman

You might be interested in the paper “interpreting polygenic scores, polygenic adaptation, and human phenotypic differences” by N. Rosenberg, M. Edge, J. Pritchard, and M. Feldman, published in Evolution, Medicine and Public Health  (2019).    Rosenberg and Pritchard are my former PhD students, both full professors at Stanford, and M.Edge is a student of Rosenberg.

 

On Aug 28, 2020, at 4:36 PM, Horowitz, Barbara Natterson <natterson-horowitz@fas.harvard.edu> wrote:

Dear Dr. Rosenberg,

It is my pleasure in my role as President of the International Society for Evolution, Medicine and Public Health to inform you that your 2019 EMPH article, “Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences” has won The George C. Williams Prize which is awarded each year to the first author of the most significant article published in the Society’s flagship journal, Evolution, Medicine and Public Health.  

The Prize recognizes the contributions of George C. Williams to evolutionary medicine and aims to encourage and highlight important research in this growing field. It includes $5,000 and an invitation to present at the online lecture series, Club EvMed. The Prize is made possible by donations from Doris Williams, Randolph Nesse, and other supporters of EMPH.

The winning article:

 

Interpreting polygenic scores, polygenic adaptation, and human phenotypic differences

Evolution, Medicine, and Public Health, Volume 2019, Issue 1, 2019, Pages 26–34, https://doi.org/10.1093/emph/eoy036
Published:
27 December 2018

Article history

SOURCE

Abstract

Recent analyses of polygenic scores have opened new discussions concerning the genetic basis and evolutionary significance of differences among populations in distributions of phenotypes. Here, we highlight limitations in research on polygenic scores, polygenic adaptation and population differences. We show how genetic contributions to traits, as estimated by polygenic scores, combine with environmental contributions so that differences among populations in trait distributions need not reflect corresponding differences in genetic propensity. Under a null model in which phenotypes are selectively neutral, genetic propensity differences contributing to phenotypic differences among populations are predicted to be small. We illustrate this null hypothesis in relation to health disparities between African Americans and European Americans, discussing alternative hypotheses with selective and environmental effects. Close attention to the limitations of research on polygenic phenomena is important for the interpretation of their relationship to human population differences.

INTRODUCTION

We are currently witnessing a surge in public interest in the intersection of evolutionary genetics with such topics as cognitive phenotypes, disease, race and heritability of human traits [1–7]. This attention emerges partly from recent advances in genomics, including the introduction of polygenic scores—the aggregation of estimated effects of genome-wide variants to predict the contribution of a person’s genome to a phenotypic trait [8–10]—and a new focus on polygenic adaptations, namely adaptations that have occurred by natural selection on traits influenced by many genes [11–13].

Theories involving natural selection have long been applied in the scientific literature to explain mean phenotypic differences among human populations [14–16]. Although new tools for statistical analysis of polygenic variation and polygenic adaptation provide opportunities for studying human evolution and the genetic basis of traits, they also generate potential for misinterpretation. In the past, public attention to research on human variation and its possible evolutionary basis has often been accompanied by claims that are not justified by the research findings [17]. Recognizing pitfalls in the interpretation of new research on human variation is therefore important for advancing discussions on associated sensitive and controversial topics.

The contribution of polygenic score distributions to phenotype distributions. Two populations are considered, populations 1 (red) and 2 (blue). Each population has a distribution of genetic propensities, which are treated as accurately estimated in the form of polygenic scores (left). The genetic propensity distribution and an environment distribution sum to produce a phenotype distribution (right). All plots have the same numerical scale. (A) Environmental differences amplify an underlying difference in genetic propensities. (B) Populations differ in their phenotypes despite having no differences in genetic propensity distributions. (C) Environmental differences obscure a difference in genetic propensities opposite in direction to the difference in phenotype means. (D) Similarity in phenotype distributions is achieved despite a difference in genetic propensity distributions by an intervention that reduces the environmental contribution for individuals with polygenic scores above a threshold. (E) Within populations, heritability is high, so that genetic variation explains the majority of phenotypic variation; however, the difference between populations is explained by an environmental difference. Panels (A–C and E) present independent normal distributions for genotype and environment that sum to produce normal distributions for phenotype. In (D), (genotype, environment) pairs are simulated from independent normal distributions and a negative constant—reflecting the effect of a medication or other intervention—is added to environmental contributions associated with simulated genotypic values that exceed a threshold

Summary

These limitations illustrate that much of the complexity embedded in use of polygenic scores—the effects of the environment on phenotype and its relationship to genotype, the proportion of variance explained, and the peculiarities of the underlying GWAS data that have been used to estimate effect sizes—is obscured by the apparent simplicity of the single values computed for each individual for each phenotype. Consequently, in using polygenic scores to describe genomic contributions to traits, particularly traits for which the total contribution of genetic variation to trait variation, as measured by heritability, is low—but even if it is high (Fig. 1E)—a difference in polygenic scores between populations provides little information about potential genetic bases for trait differences between those populations.

Unlike heritability, which ranges from 0 to 1 and therefore makes it obvious that the remaining contribution to phenotypic variation is summarized by its difference from 1, the limited explanatory role of genetics is not embedded in the nature of the polygenic scores themselves. Although polygenic scores encode knowledge about specific genetic correlates of trait variation, they do not change the conceptual framework for genetic and environmental contribution to population differences. Attributions of phenotypic differences among populations to genetic differences should therefore be treated with as much caution as similar genetic attributions from heritability in the pre-genomic era.

 

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AI in Drug Discovery: Data Science and Core Biology @Merck &Co, Inc., @GNS Healthcare, @QuartzBio, @Benevolent AI and Nuritas

Reporters: Aviva Lev-Ari, PhD, RN and Irina Robu, PhD

 

Inclusion of this Scientific Report was inspired by 

2020 State of Possible Conference, MassBio’s VIRTUAL Annual Meeting, August 26 – 27, 2020

https://pharmaceuticalintelligence.com/2020/02/21/2020-state-of-possible-conference-massbios-annual-meeting-march-25-26-2020-sonesta-hotel-cambridge-ma/

 

10:15 AM – 10:45 AM EDT
(30 Min)

Science Breakout: AI and Drug Discovery: Marrying Data Science with Core Biology

Colin HillChief Executive Officer, President, Chairman, and Co-Founder GNS Healthcare Moderator 

Juan Alvarez AVP, Computational and Structural Chemistry Merck & Co., Inc. Panelist

  • Drug Target identification
  • Drug Discovery – ML since 1980s
  • Identify moleduces
  • syntesis prediction
  • physico space
  • evolving proteins
  • analysing data

Renée Deehan-Kenney PhD, VP, QuartzBio Precision Medicine Group Panelist

  • technology enable company clinical data analysis of data clical trials 
  • ML prior knowledge
  • network biology 
  • drive inside 
  • MOA prioritize indications 

Rachel Hodos Senior AI Scientist Benevolent AI Panelist

Nora Khaldi Founder and CSO Nuritas Panelist

 

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Heart Failure in Women With Hypertensive Disorders of Pregnancy

Reporter: Aviva Lev-Ari, PhD, RN

 

Heart Failure in Women With Hypertensive Disorders of Pregnancy

Insights From the Cardiovascular Disease in Norway Project
Originally publishedhttps://doi.org/10.1161/HYPERTENSIONAHA.120.15654Hypertension. ;0

Hypertensive disorders of pregnancy (HDP) have been associated with heart failure (HF). It is unknown whether concurrent pregnancy complications (small-for-gestational-age or preterm delivery) or recurrent HDP modify HDP-associated HF risk. In this cohort study, we included Norwegian women with a first birth between 1980 and 2004. Follow-up occurred through 2009. Cox models examined gestational hypertension and preeclampsia in the first pregnancy as predictors of a composite of HF-related hospitalization or HF-related death, with assessment of effect modification by concurrent small-for-gestational-age or preterm delivery. Additional models were stratified by final parity (1 versus ≥2 births) and tested associations with recurrent HDP. Among 508 422 women, 565 experienced incident HF over a median 11.8 years of follow-up. After multivariable adjustment, gestational hypertension in the first birth was not significantly associated with HF (hazard ratio, 1.41 [95% CI, 0.84–2.35], P=0.19), whereas preeclampsia was associated with a hazard ratio of 2.00 (95% CI, 1.50–2.68, P<0.001). Among women with HDP, risks were not modified by concurrent small-for-gestational-age or preterm delivery (Pinteraction=0.42). Largest hazards of HF were observed in women whose only lifetime birth was complicated by preeclampsia and women with recurrent preeclampsia. HF risks were similar after excluding women with coronary artery disease. In summary, women with preeclampsia, especially those with one lifetime birth and those with recurrent preeclampsia, experienced increased HF risk compared to women without HDP. Further research is needed to clarify causal mechanisms.

SOURCE

https://www.ahajournals.org/doi/10.1161/HYPERTENSIONAHA.120.15654?utm_campaign=sciencenews20-21&utm_source=weekly-sn&utm_medium=email&utm_content=phd08-26-20&j=72055108&sfmc_sub=1648404797&l=7991033_HTML&u=633892723&mid=10171707&jb=0

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Did FDA Reverse Course on Convalescent Plasma Therapy for COVID-19?

Reporter: Stephen J. Williams, PhD

 

Starting with a timeline of recent announcements by the FDA on convalescent plasma therapy

April 16, 2020

FDA STATEMENT

Coronavirus (COVID-19) Update: FDA Encourages Recovered Patients to Donate Plasma for Development of Blood-Related Therapies

 

As part of the all-of-America approach to fighting the COVID-19 pandemic, the U.S. Food and Drug Administration has been working with partners across the U.S. government, academia and industry to expedite the development and availability of critical medical products to treat this novel virus. Today, we are providing an update on one potential treatment called convalescent plasma and encouraging those who have recovered from COVID-19 to donate plasma to help others fight this disease.

Convalescent plasma is an antibody-rich product made from blood donated by people who have recovered from the disease caused by the virus. Prior experience with respiratory viruses and limited data that have emerged from China suggest that convalescent plasma has the potential to lessen the severity or shorten the length of illness caused by COVID-19. It is important that we evaluate this potential therapy in the context of clinical trials, through expanded access, as well as facilitate emergency access for individual patients, as appropriate.

The response to the agency’s recently announced national efforts to facilitate the development of and access to convalescent plasma has been tremendous. More than 1,040 sites and 950 physician investigators nationwide have signed on to participate in the Mayo Clinic-led expanded access protocol. A number of clinical trials are also taking place to evaluate the safety and efficacy of convalescent plasma and the FDA has granted numerous single patient emergency investigational new drug (eIND) applications as well.

Source: https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-encourages-recovered-patients-donate-plasma-development-blood

August 23, 2020

 

Recommendations for Investigational COVID-19 Convalescent Plasma

 

  • FDA issues guidelines on clinical trials and obtaining emergency enrollment concerning convalescent plasma

FDA has issued guidance to provide recommendations to health care providers and investigators on the administration and study of investigational convalescent plasma collected from individuals who have recovered from COVID-19 (COVID-19 convalescent plasma) during the public health emergency.

The guidance provides recommendations on the following:

Because COVID-19 convalescent plasma has not yet been approved for use by FDA, it is regulated as an investigational product.  A health care provider must participate in one of the pathways described below.  FDA does not collect COVID-19 convalescent plasma or provide COVID-19 convalescent plasma.  Health care providers or acute care facilities should instead obtain COVID-19 convalescent plasma from an FDA-registered blood establishment.

Excerpts from the guidance document are provided below.

Background

The Food and Drug Administration (FDA or Agency) plays a critical role in protecting the United States (U.S.) from threats including emerging infectious diseases, such as the Coronavirus Disease 2019 (COVID-19) pandemic.  FDA is committed to providing timely guidance to support response efforts to this pandemic.

One investigational treatment being explored for COVID-19 is the use of convalescent plasma collected from individuals who have recovered from COVID-19.  Convalescent plasma that contains antibodies to severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 (the virus that causes COVID-19) is being studied for administration to patients with COVID-19. Use of convalescent plasma has been studied in outbreaks of other respiratory infections, including the 2003 SARS-CoV-1 epidemic, the 2009-2010 H1N1 influenza virus pandemic, and the 2012 MERS-CoV epidemic.

Although promising, convalescent plasma has not yet been shown to be safe and effective as a treatment for COVID-19. Therefore, it is important to study the safety and efficacy of COVID-19 convalescent plasma in clinical trials.

Pathways for Use of Investigational COVID-19 Convalescent Plasma

The following pathways are available for administering or studying the use of COVID-19 convalescent plasma:

  1. Clinical Trials

Investigators wishing to study the use of convalescent plasma in a clinical trial should submit requests to FDA for investigational use under the traditional IND regulatory pathway (21 CFR Part 312). CBER’s Office of Blood Research and Review is committed to engaging with sponsors and reviewing such requests expeditiously. During the COVID-19 pandemic, INDs may be submitted via email to CBERDCC_eMailSub@fda.hhs.gov.

  1. Expanded Access

An IND application for expanded access is an alternative for use of COVID-19 convalescent plasma for patients with serious or immediately life-threatening COVID-19 disease who are not eligible or who are unable to participate in randomized clinical trials (21 CFR 312.305). FDA has worked with multiple federal partners and academia to open an expanded access protocol to facilitate access to COVID-19 convalescent plasma across the nation. Access to this investigational product may be available through participation of acute care facilities in an investigational expanded access protocol under an IND that is already in place.

Currently, the following protocol is in place: National Expanded Access Treatment Protocol

  1. Single Patient Emergency IND

Although participation in clinical trials or an expanded access program are ways for patients to obtain access to convalescent plasma, for various reasons these may not be readily available to all patients in potential need. Therefore, given the public health emergency that the COVID-19 pandemic presents, and while clinical trials are being conducted and a national expanded access protocol is available, FDA also is facilitating access to COVID-19 convalescent plasma for use in patients with serious or immediately life-threatening COVID-19 infections through the process of the patient’s physician requesting a single patient emergency IND (eIND) for the individual patient under 21 CFR 312.310. This process allows the use of an investigational drug for the treatment of an individual patient by a licensed physician upon FDA authorization, if the applicable regulatory criteria are met.  Note, in such case, a licensed physician seeking to administer COVID-19 convalescent plasma to an individual patient must request the eIND (see 21 CFR 312.310(b)).

To Obtain a Single Patient Emergency IND  

The requesting physician may contact FDA by completing Form FDA 3926 (https://www.fda.gov/media/98616/download) and submitting the form by email to CBER_eIND_Covid-19@FDA.HHS.gov.

FACT SHEET FOR PATIENTS AND PARENTS/CAREGIVERS EMERGENCY USE AUTHORIZATION (EUA) OF COVID-19 CONVALESCENT PLASMA FOR TREATMENT OF COVID-19 IN HOSPITALIZED PATIENTS

  • FDA issues fact sheet for patients on donating plasma

August 23, 2020

 

FDA Issues Emergency Use Authorization for Convalescent Plasma as Potential Promising COVID–19 Treatment, Another Achievement in Administration’s Fight Against Pandemic

 

Today, the U.S. Food and Drug Administration issued an emergency use authorization (EUA) for investigational convalescent plasma for the treatment of COVID-19 in hospitalized patients as part of the agency’s ongoing efforts to fight COVID-19. Based on scientific evidence available, the FDA concluded, as outlined in its decision memorandum, this product may be effective in treating COVID-19 and that the known and potential benefits of the product outweigh the known and potential risks of the product.

Today’s action follows the FDA’s extensive review of the science and data generated over the past several months stemming from efforts to facilitate emergency access to convalescent plasma for patients as clinical trials to definitively demonstrate safety and efficacy remain ongoing.

The EUA authorizes the distribution of COVID-19 convalescent plasma in the U.S. and its administration by health care providers, as appropriate, to treat suspected or laboratory-confirmed COVID-19 in hospitalized patients with COVID-19.

Alex Azar, Health and Human Services Secretary:
“The FDA’s emergency authorization for convalescent plasma is a milestone achievement in President Trump’s efforts to save lives from COVID-19,” said Secretary Azar. “The Trump Administration recognized the potential of convalescent plasma early on. Months ago, the FDA, BARDA, and private partners began work on making this product available across the country while continuing to evaluate data through clinical trials. Our work on convalescent plasma has delivered broader access to the product than is available in any other country and reached more than 70,000 American patients so far. We are deeply grateful to Americans who have already donated and encourage individuals who have recovered from COVID-19 to consider donating convalescent plasma.”

Stephen M. Hahn, M.D., FDA Commissioner:
“I am committed to releasing safe and potentially helpful treatments for COVID-19 as quickly as possible in order to save lives. We’re encouraged by the early promising data that we’ve seen about convalescent plasma. The data from studies conducted this year shows that plasma from patients who’ve recovered from COVID-19 has the potential to help treat those who are suffering from the effects of getting this terrible virus,” said Dr. Hahn. “At the same time, we will continue to work with researchers to continue randomized clinical trials to study the safety and effectiveness of convalescent plasma in treating patients infected with the novel coronavirus.”

Scientific Evidence on Convalescent Plasma

Based on an evaluation of the EUA criteria and the totality of the available scientific evidence, the FDA’s Center for Biologics Evaluation and Research determined that the statutory criteria for issuing an EUA criteria were met.

The FDA determined that it is reasonable to believe that COVID-19 convalescent plasma may be effective in lessening the severity or shortening the length of COVID-19 illness in some hospitalized patients. The agency also determined that the known and potential benefits of the product, when used to treat COVID-19, outweigh the known and potential risks of the product and that that there are no adequate, approved, and available alternative treatments.

 

August 24, 2020

Donate COVID-19 Plasma

 

  • FDA posts video and blog about how to donate plasms if you had been infected with COVID

 

https://youtu.be/PlX15rWdBbY

 

 

Please go to https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/donate-covid-19-plasma

to read more from FDA

 

 

August 25, 2020

 

CLINICAL MEMORANDUM From: , OBRR/DBCD/CRS To: , OBRR Through: , OBRR/DBCD , OBRR/DBCD , OBRR/DBCD/CRS Re: EUA 26382: Emergency Use Authorization (EUA) Request (original request 8/12/20; amended request 8/23/20) Product: COVID-19 Convalescent Plasma Items reviewed: EUA request Fact Sheet for Health Care Providers Fact Sheet for Recipients Sponsor: Robert Kadlec, M.D. Assistant Secretary for Preparedness and Response (ASPR) Office of Assistant Secretary for Preparedness and Response (ASPR) U.S. Department of Health and Human Services (HHS) EXECUTIVE SUMMARY COVID-19 Convalescent Plasma (CCP), an unapproved biological product, is proposed for use under an Emergency Use Authorization (EUA) under section 564 of the Federal Food, Drug, and Cosmetic Act (the Act),(21 USC 360bbb-3) as a passive immune therapy for the treatment of hospitalized patients with COVID-19, a serious or life-threatening disease. There currently is no adequate, approved, and available alternative to CCP for treating COVID-19. The sponsor has pointed to four lines of evidence to support that CCP may be effective in the treatment of hospitalized patients with COVID-19: 1) History of convalescent plasma for respiratory coronaviruses; 2) Evidence of preclinical safety and efficacy in animal models; 3) Published studies of the safety and efficacy of CCP; and 4) Data on safety and efficacy from the National Expanded Access Treatment Protocol (EAP) sponsored by the Mayo Clinic. Considering the totality of the scientific evidence presented in the EUA, I conclude that current data for the use of CCP in adult hospitalized patients with COVID-19 supports the conclusion that CCP meets the “may be effective” criterion for issuance of an EUA from section 564(c)(2)(A) of the Act. It is reasonable to conclude that the known and potential benefits of CCP outweigh the known and potential risks of CCP for the proposed EUA. Current data suggest the largest clinical benefit is associated with high-titer units of CCP administered early course of the disease.

Source: https://www.fda.gov/media/141480/download

 

And Today August 26, 2020

  • A letter, from Senator Warren, to Commissioner Hahn from Senate Committee asking for documentation for any communication between FDA and White House

August 25, 2020 Dr. Stephen M. Hahn, M.D. Commissioner of Food and Drugs U.S. Food and Drug Administration 10903 New Hampshire Avenue Silver Spring, MD 20993 Dear Commissioner Hahn: We write regarding the U.S. Food and Drug Administration’s (FDA) troubling decision earlier this week to issue an Emergency Use Authorization (EUA) for convalescent plasma as a treatment for coronavirus disease 2019 (COVID-19).1 Reports suggests that the FDA granted the EUA amid intense political pressure from President Trump and other Administration officials, despite limited evidence of convalescent plasma’s effectiveness as a COVID-19 treatment.2 To help us better understand whether the issuance of the blood plasma EUA was motivated by politics, we request copies of any and all communications between FDA and White House officials regarding the blood plasma EUA.

Source: https://www.warren.senate.gov/imo/media/doc/2020.08.25%20Letter%20to%20FDA%20re%20Blood%20Plasma%20EUA.pdf

…….. which may have been a response to this article

FDA chief walks back comments on effectiveness of coronavirus plasma treatment

 

From CNBC: https://www.cnbc.com/2020/08/25/fda-chief-walks-back-comments-on-effectiveness-of-coronavirus-plasma-treatment.html

PUBLISHED TUE, AUG 25 202010:45 AM EDTUPDATED TUE, AUG 25 20204:12 PM EDT

Berkeley Lovelace Jr.@BERKELEYJR

Will Feuer@WILLFOIA

KEY POINTS

  • The authorization will allow health-care providers in the U.S. to use the plasma to treat hospitalized patients with Covid-19.
  • The FDA’s emergency use authorization came a day after President Trump accused the agency of delaying enrollment in clinical trials for vaccines or therapeutics.
  • The criticism from Trump and action from the FDA led some scientists to believe the authorization, which came on the eve of the GOP national convention, was politically motivated.

FDA Commissioner Dr. Stephen Hahn is walking back comments on the benefits of convalescent plasma, saying he could have done a better job of explaining the data on its effectiveness against the coronavirus after authorizing it for emergency use over the weekend.

Commisioners responses over Twitter

https://twitter.com/SteveFDA/status/1298071603675373569?s=20

https://twitter.com/SteveFDA/status/1298071619236245504?s=20

August 26, 2020

In an interview with Bloomberg’s , FDA Commissioner Hahn reiterates that his decision was based on hard evidence and scientific fact, not political pressure.  The whole interview is at the link below:

https://www.bloomberg.com/news/articles/2020-08-25/fda-s-hahn-vows-to-stick-to-the-science-amid-vaccine-pressure?sref=yLCixKPR

Some key points:

  • Dr. Hahn corrected his initial statement about 35% of people would be cured by convalescent plasma. In the interview he stated:

I was trying to do what I do with patients, because patients often understand things in absolute terms versus relative terms. And I should’ve been more careful, there’s no question about it. What I was trying to get to is that if you look at a hundred patients who receive high titre, and a hundred patients who received low titre, the difference between those two particular subset of patients who had these specific criteria was a 35% reduction in mortality. So I frankly did not do a good job of explaining that.

  • FDA colleagues had frank discussion after the statement was made.  He is not asking for other people in HHS to retract their statements, only is concerned that FDA has correct information for physicians and patients
  • Hahn is worried that people will not enroll due to chance they may be given placebo
  • He gave no opinion when asked if FDA should be an independent agency

 

For more articles on COVID19 please go to our Coronavirus Portal at

https://pharmaceuticalintelligence.com/coronavirus-portal/

 

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UC Berkeley accelerates bio-preservation research as part NSF center

Reporter: Irina Robu, PhD

National Science Foundation funded a new research center which focuses on advancing methods for storing and preserving biological cells and tissues. The new center will be directed by John Bischof at the University of Minnesota  and co-directed by Mehmet Toner at Massachusetts General Hospital in collaboration with researchers from UC Berkeley and UC Riverside. It is known that the inability to extend shelf life of biological tissues means that patients in Florida can’t receive a heart or lung from California. And when thawing any cell culture, the survival rate of the cells are about  60 percent, which is considered normal .

NSF Engineering Research Centers were founded to bring together academic and industry partners in interdisciplinary collaborations to tackle complex, long-range engineering challenges. The anticipation is that the centers will brood whole new industries with new systems-level technologies. In 2025, the award for ATP-Bio may be renewed for an additional five years.

SOURCE

https://engineering.berkeley.edu/news/2020/08/uc-berkeley-part-of-26m-nsf-center-to-accelerate-research-in-bio-preservation/

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Surgical Planning and 3D bioprinting

Reporter: Irina Robu, PhD

The cardiovascular team at SSM Health Cardinal Glennon Children’s Hospital found a solution for better surgical planning using 3D printing. As a pediatric center, Glennon Children’s Hospital deals with the most complex patients, which requires surgeries within days or weeks of birth. According to the center, one of the pediatric patients was an infant diagnosed in utero via fetal ultrasound with an unusual form of switch of great arteries. Deoxygenated blue blood entered the right atrium which connected to the left ventricle, then to the aorta and the oxygenated red blood entered the left atrium which connects to the right ventricle and then to the pulmonary artery. The pediatric patients had a very large ventricular septal defect connecting both ventricles and severe narrowing between the left ventricle and the aorta.

It is obvious that the patient was fairly blue as deoxygenated blood was directed toward the aorta. The balloon atrial septostomy made in the first few days of life. Yet, the tachycardia persisted. The surgical team from SSM Health Cardinal Glennon Children’s Hospital, led by Charles Huddleston, MD used 3D printing to identify the anatomy of the patient clearly and provided them with the ability to repair the mitral valve. It seems that the neonatal atrial switch appeared to be the best plan, even if the operation proved challenging.

The team knew that they could go into the procedure knowing that the tissue can be safely removed without damage to the mitral valve. The team was able to show that the 3D model was essential in determining the optimal surgical approach and with the help of the 3D printed heart model, the neonatal atrial switch, the VSD closure and the subaortic stenosis resection was performed effectively on a 20-day infant. The surgery allowed the mitral valve function to remain intact. The pediatric patient cardiac function improved gradually and is expected to have an excellent recovery.

SOURCE

https://www.javelin-tech.com/3d/surgical-planning-3d-printed-heart/

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Toaster Sized Machine Detects COVID-19

Reporter: Irina Robu, PhD

DnaNudge, a small UK-based DNA testing company designed a toaster sized machine that can detect COVID-19 in 90 min without lab analysis. The machine invented by Christofer Toumazou, professor at Imperial College was designed to aid people tailor their diet based on heredity, but changed the design due to the pandemic. The machine needs a nose swab or some saliva to detect traces of coronavirus. It can even spot other diseases such as the flu and a common virus infection called Respiratory Syncytial Virus (RSV). It will also notify the operator if a proper sample has been taken or if a test needs to be retaken.

Currently, the UK National Health Service ordered 5,000 of the machines, as well as cartridges to start testing coronavirus patients, as part of a $211 million contract. They are hoping that the machine designed by DNANudge states that can prove helpful in triaging potential COVID patients.

SOURCE

 https://futurism.com/neoscope/machine-covid-90-minutes?mc_eid=8eae667eea

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Clustering of Country-Based Data in COVID-19 Infections by Coronavirus outbreak features – First wave, Data up to date 28/5/2020

Authors: Akad Doha, Markman Ofer and Lefkort Jared

 

This study investigated connections between the infection cycles of countries around the world. Utilizing factors such as the Day of Maximum Infections, the Total Infections and the Day of Maximum Infections, and Deaths and Recoveries per Million. In addition, countries that have completed the infection cycle were compared to understand similarities and differences amongst the aforementioned factors and others.

Note: All variables are reportedly up to date 28/5.

The variables:

Country

State / status – The state of the outbreak

Daily_peak – Maximum number of new daily infections

Total_at_daily_peak – The number of infections from the beginning of the outbreak to the maximum day of the new infections.

Death_per_m – The deaths per million people

Recovered_per_m – The recovery cases per million people

Continent – Continent

Time_to_peak- Time from day to the maximum day of new infections.

Break_time – Time in days from the maximum day for new infections until fading (only in countries that have significantly decreased the number of infections, which means that they can be considered in the end)

Total_time- Time from the day of first outbreak to the end.

 

 

Clustering:

Figure 1. Classification 1, Clustering Based on the variables – the number of new daily infections , the number of infections from the beginning of the outbreak to the maximum day of infections , the deaths per million people , the recoveries per million people , the time to the maximum day for new infections.

Cluster 1 – red – characterized by:

  • The number of new daily maximum infections below average
  • The number of infections from the beginning of the outbreak to the maximum daily infections below average
  • Deaths per million persons below average
  • Recoveries per million less than the number of deaths and below average.

Cluster 2 – blue – characterized by:

  • The number of new daily infections usually above average Deaths per million people above average
  • Recoveries per million above average yet less than deaths
  • Time to the maximum day for new infections less than average.

 

Figure 2. Classification 2, Clustering Based on the variables – the number of new daily infections, the number of infections from the beginning of the outbreak to the maximum day of infections, the deaths per million people , the recovery cases per million people.

Cluster 1 (red): The number of new daily infections is less than average, the number of infections from the beginning of the outbreak to the maximum day of the new infections is almost average, deaths to one million people on average, recovery cases per million people above average

Cluster 2 ( green): the number of new daily maximum infections above average, the number of infections from the beginning of the outbreak to the maximum daily infections most often above average, yet less than the maximum daily new infections, the deaths per million above average, the recoveries per million above average, but less than deaths.

Cluster 3 (blue): maximum number of new daily infections smaller than average and smaller than cluster 1 , the number of infections since the beginning of the outbreak to the maximum new infections below the average, deaths per million people below average, recoveries per million people under the average and lower than deaths.

 

Figure 3. Classification 3, Cluster (clustering) Based on all variables for countries that have already completed the outbreak cycle.

Cluster 1 (red): maximum number of daily new infections above average, number of infections from the initial outbreak to the maximum day of new infections above average, recoveries per million people below average, the fading time below average, and total time to completion of outbreak circle below average.

Cluster 2 ( blue ): maximum number of daily new infections below average, number of infections from the initial outbreak to the maximum day of new infections less than average, fading time usually above average and not necessarily over cluster 1, and the total time to the end of the outbreak cycle above average.

This classification is done based on a small number of countries since there are a lack of countries who have completed the outbreak circle, so we will use it only to understand what kinds of classifications we receive if there is a fading time and total time.

Figure 4. World map by classification 1:

The map shows that the countries of Asia, Northeastern Europe, Africa, Central America and South America, and some of North America are classified by Cluster 1, which means that they have Cluster 1 characteristics.

Western Europe, Eastern South America, part of North America belongs to Cluster 2. (Please refer to Cluster properties in explanation of Figure 3)

 

Figure 5. World Map by Classification 2:

Northern North America, South America, the Middle East, parts of Europe, and North Asia are classified as Cluster 3.

Western Europe, Southeastern America, and some of North America are classified as Cluster 2.

East Asia, Africa, parts of Northern Europe, parts of South America and Central America are classified into Cluster 3. (Please refer to Cluster properties in explanation of Figure 2).

 

Figure 6. Summary Classification – Combining the two classifications 1 and 2:

Cluster 1 (red) is characterized by a maximum number of new infections larger than average (highest number of maximum daily infections), the number of infections since the beginning of the outbreak to the day of maximum new daily infections more than or equal to the average, deaths above average and above cluster 4, recoveries per million people over the average, yet less than deaths.

Cluster 2 (green) is characterized by the maximum number of daily new infections close to average and tends to be above average in most cases, the number of infections since the beginning of the outbreak to the day of maximum new daily infections almost average, deaths mostly at or above average, but below cluster 1, recoveries per million above average and greater than the deaths.

Cluster 3 (blue) is characterized by a maximum number of new infections below average, the number of infections since the beginning of the outbreak to the day of maximum new daily infections less than or equal to the average, deaths below average (lowest deaths) , recoveries per million people below average and less than deaths.

Cluster 4 (Purple) is characterized by a maximum number of new infections below average, the number of infections since the beginning of the outbreak to the day of maximum new daily infections below average, deaths above average and above clusters 2 and 3, recoveries per million above average and above deaths (greatest amount of recoveries)

 

Figure 7. Distribution of time until the maximum day of New infections by the summary classification.

Cluster 3 has the highest average time up to the maximum day for new infections, followed by Cluster 1, then Cluster 2 and Cluster 4 with the lowest average.

 

Figure 8. The world map is classified according to the summery classification:

Southern South America, parts of North America, and Western Europe are classified as Cluster 1.

Table 1. countries in first cluster:

Status Country
Ongoing USA
Subsiding Belgium
Subsiding UK
Subsiding Italy
Ongoing Brazil
Subsiding France
Subsiding Spain

 

Western South America, parts of North America, the Middle East, North Asia and some parts of Europe are classified as Cluster 2.

Table 2. countries in second cluster:

status country status country
ongoing Panama ongoing Russia
completed Norway subsiding Turkey
subsiding Germany reemerged Iran
ongoing Peru ongoing Canada
subsiding Netherlands ongoing Saudi Arabia
ongoing Sweden ongoing Chile
completed Israel subsiding Portugal
completed Austria subsiding Ecuador
    subsiding Denmark

 

Parts of America, Africa, East Asia and parts of Europe are classified into Cluster 3.

Table 3. countries in second cluster:

status country status country
ongoing South Africa ongoing Poland
ongoing Philippines ongoing Mexico
ongoing Dominican Republic ongoing India
ongoing Egypt ongoing Pakistan
completed South Korea ongoing Bangladesh
subsiding Czechia ongoing Ukraine
ongoing Argentina ongoing Indonesia
ongoing Algeria subsiding Romania
subsiding Finland completed Japan
subsiding Hungary ongoing Colombia
    completed China

 

Small parts of Western Europe are classified into Cluster 4. (Please refer to Cluster properties in explanation of Figures 6 and 7)

Table 4. countries in second cluster:

status country
completed Switzerland
completed Ireland

 

Interesting discovery:

While searching the variables that contribute to a clearer picture of the world situation, some countries were found to have a day that repeats every week, characterized by the minimum number of deceased from coronavirus. These countries include: The United States, Brazil, the Netherlands, Sweden, and Israel.

In addition, India had a day characterized by a maximum number of new infections that repeats every week.

Peru had a devoted day that repeats every week characterized by a minimum number of new infections.

Statistical insights appendix:

 

Figure 9. The quantum of the quantitative variables

We can see that:

  1. The maximum number of new daily infections in most countries is less than 10000 people. In individual cases over 10000.
  2. The number of deaths from the virus in most countries is less than 200 people per million.
  3. The number of people who have recovered from the virus in most countries are under 2000 people per million.
  4. The maximum time to date for new infections varies by country and there is no common reservation for a number of days, but from the chart it can be assumed that most countries are below 80 days for maximum full outbreak.
  5. The number of infections from the beginning of the outbreak to the maximum day for new infections in most countries does not exceed 250000 infections.

 

Relationships and adjustments between variables:

 

Figure 10. Correlation between the different variables

The most prominent correlations between the variables are:

  1. The number of new daily infections in the maximum day for new infections and the number of infections from the beginning of the outbreak to the maximum day for new infections. Indicates a strong positive correlation.
  2. Between the number of deaths and the number of recoveries a moderate positive correlation exists.
  3. Between the number recoveries per million and the time to maximum day of new infections a moderate negative correlation exists.

 

Figure 11. Correlation of all variables Countries that completed the outbreak cycle:

The most prominent correlations between the variables are:

  1. The number of new daily infections in the maximum day for new infections and the number of infections from the beginning of the outbreak to the maximum day for new infections. Indicates a very strong positive correlation.
  2. Between the number of deaths and the number of recoveries correlates strong positive.
  3. The number of infections that have healed, the maximum number of new daily cases and the number of infections from the beginning of the outbreak to the maximum day of new infections has a negative medium correlation.
  4. Between the time of the outbreak fading and the time of the complete outbreak cycle there is a very strong positive correlation.
  5. The maximum number of daily new infections and outbreak fading time and all the time of outbreak cycle has a strong negative correlation.
  6. Between the number of infections from the onset of the outbreak to the maximum day for new infections, the time of outbreak fading and the whole time of the complete outbreak cycle has a very strong negative correlation.

* consider that the correlations are based on a small number of countries, so there may be biases in the correctness of adjustment with the true situation. If there were more countries that have completed the outbreak cycle would have been more precise – recommends future research.

 

Figure 12. Diagram of the correlation between variables by PCA analysis (For all countries)

 

The diagram shows the relationships between all variables, they can be interpreted as follows:

  • As the total number of infections from the onset of the outbreak to the maximum day for new infections increases, the number of maximum new daily infections increases.
  • As the number of deaths increases, the number of recovered patients also increases.
  • As the time to the maximum day for new infections decreases, the number of recovered patients increases.
  • The variables depicted in red represent those that are significant to understanding the world data, and conversely, the variables in blue are less significant, but are also necessary in understanding the data. Therefore, subsequently, one analysis was performed including the maximum day for new infections variable, and one was performed without it.

 

Figure 13. Diagram of the correlation between variables by PCA analysis (Countries that have completed the outbreak cycle)

Chart is prepared to show the connections of the variables with two variables that were found only in countries that have completed the outbreak cycle, 1. Fading time 2. The total time to completion.

  • As the time between the reduction of infection rates and the day of maximum infections increases, so does the total length of the infection cycle. And it seems that a negative relationship exists between this relationship and time to the maximum day of new infections.
  • As the fading time and time to end decreases, the total number of infections in the maximum day of new infections and new daily infections number increases (very interesting).

Reference:

The data was collected from:

https://ourworldindata.org/covid-deaths

 

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