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FDA Approves First Drug to Improve Growth in Children with Most Common Form of Dwarfism

Reporter: Aviva Lev-Ari, PhD, RN

The FDA has approved BioMarin Pharmaceutical’s Voxzogo (vosoritide), an injectable drug for increasing growth in children five years and up with achondroplasia, the most common form of dwarfism.

The FDA green light was supported by a 121-person phase 3 study showing children receiving Voxzogo grew 1.57 centimeters on average vs. placebo.  

This is the first approval of its kind for this pediatric population, but since Voxzogo was cleared under an accelerated pathway, the drug must still be evaluated in a confirmatory trial assessing final adult height.

The approval “fulfills an unmet medical need for more than 10,000 children in the United States,” said Theresa Kehoe, the FDA’s director of the Division of General Endocrinology within the Center for Drug Evaluation and Research.

For Immediate Release:

November 19, 2021

Today, the U.S. Food and Drug Administration approved Voxzogo (vosoritide) injection to improve growth in children five years of age and older with achondroplasia and open epiphyses (growth plates), meaning these children still have the potential to grow. Achondroplasia is the most common form of dwarfism. 

“Today’s approval fulfills an unmet medical need for more than 10,000 children in the United States and underscores the FDA’s commitment to help make new therapies available for rare diseases,” said Theresa Kehoe, M.D., director of the Division of General Endocrinology in the FDA’s Center for Drug Evaluation and Research. “With this action, children with short stature due to achondroplasia have a treatment option that targets the underlying cause of their short stature.” 

Achondroplasia is a genetic condition that causes severely short stature and disproportionate growth. The average height of an adult with achondroplasia is approximately four feet. People with achondroplasia have a genetic mutation that causes a certain growth regulation gene called fibroblast growth factor receptor 3 to be overly active, which prevents normal bone growth. Voxzogo works by binding to a specific receptor called natriuretic peptide receptor-B that reduces the growth regulation gene’s activity and stimulates bone growth. 

Voxzogo’s safety and efficacy in improving growth were evaluated in a year-long, double-blind, placebo-controlled, phase 3 study in participants five years and older with achondroplasia who have open epiphyses. In the study, 121 participants were randomly assigned to receive either Voxzogo injections under the skin or a placebo. Researchers measured the participants’ annualized growth velocity, or rate of height growth, at the end of the year. Participants who received Voxzogo grew an average 1.57 centimeters taller compared to those who received a placebo. 

The most common side effects of Voxzogo include injection site reactions, vomiting and decreased blood pressure. Voxzogo’s labeling also lists decreased blood pressure as a warning and precaution, which means it is a potentially serious side effect.

The FDA approved Voxzogo under the accelerated approval pathway, which allows for earlier approval of drugs that treat serious conditions and fill an unmet medical need, based on a surrogate or intermediate clinical endpoint. A condition of this accelerated approval is a post-marketing study that will assess final adult height. This application also received priority review designation.

The FDA granted the approval of Voxzogo to BioMarin.
 SOURCE

https://www.fda.gov/news-events/press-announcements/fda-approves-first-drug-improve-growth-children-most-common-form-dwarfism

Defective viral RNA sensing gene OAS1 linked to severe COVID-19

Reporter: Stephen J. Williams, Ph.D.

Source: https://www.science.org/doi/10.1126/science.abm3921

Defective viral RNA sensing linked to severe COVID-19

JOHN SCHOGGINS SCIENCE•28 Oct 2021•Vol 374, Issue 6567•pp. 535-536•DOI: 10.1126/science.abm39214,824

Why do some people with COVID-19 get sicker than others? Maybe exposure to a particularly high dose of the causative virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), accounts for the difference. Perhaps deficiencies in diet, exercise, or sleep contribute to worse illness. Although many factors govern how sick people become, a key driver of the severity of COVID-19 appears to be genetic, which is common for other human viruses and infectious agents (1). On page 579 of this issue, Wickenhagen et al. (2) show that susceptibility to severe COVID-19 is associated with a single-nucleotide polymorphism (SNP) in the human gene 2′-5′-oligoadenylate synthetase 1 (OAS1).The authors reasoned that SARS-CoV-2 should be inhibited by interferon-mediated antiviral responses, which are among the first cellular defense mechanisms produced in response to a viral infection. Interferons are a group of cytokines that induce the transcription of a large cadre of genes, many of which encode proteins with the potential to directly inhibit the invading virus. Wickenhagen et al. interrogated many hundreds of these putative antiviral proteins for their ability to suppress SARS-CoV-2 in cultured cells and found that OAS1 was particularly potent against SARS-CoV-2.OAS1 is an enzyme that is activated in the presence of double-stranded RNA, which is scattered along an otherwise singlestranded SARS-CoV-2 genome because of an assortment of RNA hairpins and other secondary structures. Once activated, OAS1 catalyzes the polymerization of adenosine triphosphate (ATP) into a second messenger, 2′-5′-oligoadenylate. This then triggers the conversion of ribonuclease L (RNaseL) into its active form so that it can cleave viral RNA, effectively blunting viral replication (3). Wickenhagen et al. found that OAS1 is expressed in respiratory tissues of healthy donors and COVID-19 patients and that it interacts with a region of the SARS-CoV-2 genome that contains double-stranded RNA secondary structures (see the figure).OAS1 exists predominantly as two isoforms in humans—a longer isoform (p46) and a shorter version (p42). Genetic variation dictates which isoform will be expressed. In humans, p46 is expressed in people who have a SNP that causes alternative splicing of the OAS1 messenger RNA (mRNA). This results in the utilization of a terminal exon that is not used to translate p42. Thus, the carboxyl terminus of the p46 OAS1 protein contains a distinct four–amino acid motif that forms a prenylation site. Prenylation is a posttranslational modification that targets proteins to membranes. In cell culture experiments, Wickenhagen et al. showed that only OAS1 p46, but not p42, could inhibit SARS-CoV-2. However, when the prenylation site of p46 was engineered into p42, this chimeric p42 protein was able to inhibit SARS-CoV-2, which strongly implicates a role for OAS1 specifically at membranes.Why are membranes important? SARS-CoV-2, like all coronaviruses, co-opts cellular membranes at the endoplasmic reticulum to form double-membrane vesicles, in which the virus replicates its genome. Thus, membrane-bound OAS1 p46 may be specifically activated by RNA viruses that form membrane-bound vesicles for replication. Indeed, the unrelated cardiovirus A, which also forms vesicular membranous structures, was inhibited by OAS1. Conversely, other respiratory RNA viruses, such as human parainfluenza virus type 3 and human respiratory syncytial virus, which do not use membrane-tethered vesicles for replication, were not inhibited by p46.Wickenhagen et al. examined a cohort of 499 COVID-19 patients hospitalized in the UK. Whereas all patients expressed OAS1, 42.5% of them did not express the antiviral p46 isoform. These patients were statistically more likely to have severe COVID-19 (be admitted to the intensive care unit). This suggests that OAS1 is an important antiviral factor in the control of SARS-CoV-2 infection and that its inability to activate RNaseL results in prolonged infections and severe disease, although other factors likely contribute. The authors also examined animals known to harbor different coronaviruses. They found evidence for prenylated OAS1 proteins in mice, cows, and camels. Notably, horseshoe bats, which are considered a possible reservoir for SARS-related coronaviruses (4), lack a prenylation motif in their OAS1 because of genomic changes that eliminated the critical four-amino acid motif. A horseshoe bat (Rhinolophus ferrumequinum) OAS1 was unable to inhibit SARS-CoV-2 infection in cell culture. Conversely, the black flying fox (Pteropus alecto)—a pteropid bat that is a reservoir for the Nipah and Hendra viruses, which can also infect humans—possesses a prenylated OAS1 that can inhibit SARS-CoV-2. These findings indicate that horseshoe bats may be genetically and evolutionarily primed to be optimal reservoir hosts for certain coronaviruses, like SARS-CoV-2.Other studies have now shown that the p46 OAS1 variant, which resides in a genomic locus inherited from Neanderthals (57), correlates with protection from COVID-19 severity in various populations (89). These findings mirror previous studies indicating that outcomes with West Nile virus (10) and hepatitis C virus (11) infection, both of which also use membrane vesicles for replication, are also associated with genetic variation at the human OAS1 locus. Another elegant functional study complements the findings of Wickenhagen et al. by also demonstrating that prenylated OAS1 inhibits multiple viruses, including SARS-CoV-2, and is associated with protection from severe COVID-19 in patients (12).There is a growing body of evidence that provides critical understanding of how human genetic variation shapes the outcome of infectious diseases like COVID-19. In addition to OAS1, genetic variation in another viral RNA sensor, Toll-like receptor 7 (TLR7), is associated with severe COVID-19 (1315). The effects appear to be exclusive to males, because TLR7 is on the X chromosome, so inherited deleterious mutations in TLR7 therefore result in immune cells that fail to produce normal amounts of interferon, which correlates with more severe COVID-19. Our knowledge of the host cellular factors that control SARS-CoV-2 is rapidly increasing. These findings will undoubtedly open new avenues into SARS-CoV-2 antiviral immunity and may also be beneficial for the development of strategies to treat or prevent severe COVID-19.

References and Notes

1J. L. Casanova, Proc. Natl. Acad. Sci. U.S.A.112, E7118 (2015).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR2A. Wickenhagen et al., Science374, eabj3624 (2021).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR3H. Kristiansen, H. H. Gad, S. Eskildsen-Larsen, P. Despres, R. Hartmann, J. Interferon Cytokine Res.31, 41 (2011).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR4S. Lytras, W. Xia, J. Hughes, X. Jiang, D. L. Robertson, Science373, 968 (2021).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR5S. Zhou et al., Nat. Med.27, 659 (2021).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR6H. Zeberg, S. Pääbo, Proc. Natl. Acad. Sci. U.S.A.118, e2026309118 (2021).CROSSREFPUBMEDGOOGLE SCHOLAR7F. L. Mendez, J. C. Watkins, M. F. Hammer, Mol. Biol. Evol.30, 798 (2013).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR8A. R. Banday et al., medRxiv2021).GO TO REFERENCECROSSREFGOOGLE SCHOLAR9E. Pairo-Castineira et al., Nature591, 92 (2021).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR10J. K. Lim et al., PLOS Pathog.5, e1000321 (2009).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR11M. K. El Awady et al., J. Gastroenterol. Hepatol.26, 843 (2011).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR12F. W. Soveg et al., eLife10, e71047 (2021).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR13T. Asano et al., Sci. Immunol.6, eabl4348 (2021).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR14C. Fallerini et al., eLife10, e67569 (2021).CROSSREFPUBMEDGOOGLE SCHOLAR15C. I. van der Made et al., JAMA324, 663 (2020).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR

For more on COVID-19 Please see our Coronavirus Portal at

Moderna Vaccine Patent Application needs to include Names of Three NIH Scientists that Shared the Genome Sequence of SAR-Cov-2 with Moderna Early on

Reporter: Aviva Lev-Ari, PhD, RN

UPDATED on 11/12/2021

Within the filing, Moderna said it had “reached the good-faith determination” that three NIH scientists — John Mascola, Barney Graham and Kizzmekia Corbett — “did not co-invent” the sequence that prompts the body’s immune response to the coronavirus spike protein. The NIH, meanwhile, says the trio worked with Moderna at the outset of the pandemic to design the component in question.

In response to an Endpoints News request for comment, a Moderna spokesperson said the company has “all along” recognized the role the NIH played in developing the Covid-19 shot. But the spokesperson insisted only Moderna scientists invented mRNA-1273 — the codename for the company’s vaccine.

In the new book A Shot to Save the World out last month detailing the inventions of the mRNA Covid-19 vaccines, Wall Street Journal reporter Gregory Zuckerman wrote the three NIH scientists in question designed a sequence for a vaccine and sent it to Moderna. The biotech then used it to confirm their own designs and produce that vaccine.

Zuckerman wrote:

On Thursday, January 23, Wang packed his material in a container, trying hard to ensure it didn’t leak, and shipped it all to Kizzmekia Corbett, the government scientist who was doing similar work with other’s in Graham’s lab. Corbett, Graham and John Mascola chose an ideal spike-protein design and sent it to Moderna. The company’s scientists, relying on McLellan and Wang’s earlier work, had built their own spike-protein design. It matched the one from the government scientists, confirming they made the right choice. Moderna took their chosen sequence, employed some sophisticated computer software, and built an mRNA molecule capable of producing the stabilized spike protein. This would become Moderna’s vaccine antigen.

SOURCE

What Moderna says: The company argues that the NIH scientists — John Mascola, Barney Graham and Kizzmekia Corbett — were not part of selecting the messenger RNA sequence that became the Covid-19 shot authorized today. That sequence patent is essentially the heart of the product.

Moderna “has recognized the substantial role that the NIAID has played” in the vaccine development by including those scientists on other patents but “just because someone is an inventor on one patent application relating to our COVID-19 vaccine does not mean they are an inventor on every patent application relating to the vaccine,” it tweeted.

“Moderna remains the only company to have pledged not to enforce its COVID-19 intellectual property during the pandemic,” the company added.

It’s far from over: Moderna, which never brought a product to market before its effective Covid-19 shot, has received nearly $10 billion in government funding for the vaccine — a figure that advocates return to repeatedly when pressing for global access to patents and production.

SOURCE

From: POLITICO Pulse <pulse@email.politico.com>
Reply-To: “POLITICO, LLC” <reply-fe8c1d737662017574-630320_HTML-638333449-1376319-0@politicoemail.com>
Date: Friday, November 12, 2021 at 10:02 AM
To: Aviva Lev-Ari <Avivalev-ari@alum.Berkeley.edu>
Subject: Moderna vs. The Government

11/9/2021 and 11/11/2021

The NIH told the New York Times earlier this week that three of its scientists — John Mascola, Barney Graham, who recently retired, and Kizzmekia Corbett, who has since moved over to Harvard — worked with Moderna to design the genetic sequence that prompts the vaccine to produce an immune response.

“I think Moderna has made a serious mistake here in not providing the kind of co-inventorship credit to the people who played a major role in the development of the vaccine that they are now making a fair amount of money on. We did our best to try to resolve this and ultimately failed but we are not done,” NIH Director Francis Collins told Reuters in an interview yesterday.

Dr. Barney Graham, left, and his colleague at the time, Dr. Kizzmekia Corbett, right, explaining the role of spike proteins to President Biden at the National Institutes of Health in Bethesda, Md., in February 2021

The vaccine grew out of a four-year collaboration between Moderna and the N.I.H., the government’s biomedical research agency — a partnership that was widely hailed when the shot was found to be highly effective. A year ago this month, the government called it the “N.I.H.-Moderna Covid-19 vaccine.”

The agency says three scientists at its Vaccine Research Center — Dr. John R. Mascola, the center’s director; Dr. Barney S. Graham, who recently retired; and Dr. Kizzmekia S. Corbett, who is now at Harvard — worked with Moderna scientists to design the genetic sequence that prompts the vaccine to produce an immune response, and should be named on the “principal patent application.”

https://www.nytimes.com/2021/11/09/us/moderna-vaccine-patent.html?referringSource=articleShare

If the three agency scientists are named on the patent along with the Moderna employees, the federal government could have more of a say in which companies manufacture the vaccine, which in turn could influence which countries get access. It would also secure a nearly unfettered right to license the technology, which could bring millions into the federal treasury.

“Omitting N.I.H. inventors from the principal patent application deprives N.I.H. of a co-ownership interest in that application and the patent that will eventually issue from it.”

According to the NYT article,

But experts said the disputed patent was the most important one in Moderna’s growing intellectual property portfolio. It seeks to patent the genetic sequence that instructs the body’s cells to make a harmless version of the spike proteins that stud the surface of the coronavirus, which triggers an immune response.

While it has not publicly acknowledged the rift until now, the Biden administration has expressed frustration that Moderna has not done more to provide its vaccine to poorer nations even as it racks up huge profits.

Epidemiological measurement on COVID-19 pandemic may have statistical biases which might affect next variant responses

Reporter: Stephen J. Williams Ph.D.

Source: https://www.science.org/doi/10.1126/science.abi6602

From the jounal Science

Tackling the pandemic with (biased) data

CHRISTINA PAGEL AND CHRISTIAN A. YATESSCIENCE•22 Oct 2021•Vol 374, Issue 6566•pp. 403-404•DOI: 10.1126/science.abi66027,757

Accurate and near real-time data about the trajectory of the COVID-19 pandemic have been crucial in informing mitigation policies. Because choosing the right mitigation policies relies on an accurate assessment of the current state of the local epidemic, the potential ramifications of misinterpreting data are serious. Each data source has inherent biases and pitfalls in interpretation. The more data sources that are interpreted in combination, the easier it is to detect genuine changes in an epidemic. Recently, in many countries, this has involved disentangling the varying impact of rising but heterogeneous vaccination rates, relaxation of mitigations, and the emergence of new variants such as Delta.The exact data collected and their accuracy will vary by country. Typical data common to many countries are numbers of tests, confirmed cases, hospital and intensive care unit (ICU) admissions and occupancy, deaths, and vaccinations (1). Many countries additionally sequence a proportion of new positive tests to identify and track emerging variants. Some countries also now collect and publish data on infections, hospitalizations, and deaths by vaccination status (e.g., Israel and the UK). Stratifying all available data by different demographic factors (e.g., age, location, measures of deprivation, and ethnicity) is crucial for understanding patterns of spread, potential impact of policies, and efficacy of vaccines (age, timing of breakthrough infections, and prevalent variants).It is also necessary to be aware of what data are not being collected. For example, persistent symptoms of COVID-19 (Long Covid) were recognized as a long-term adverse outcome by the autumn of 2020. However, no simple diagnostic test has been associated with the up to 200 different reported symptoms (2). Counting Long Covid relies on a clinical diagnosis, based on a history of having had COVID-19 and a failure to fully recover, with development of some characteristic symptoms and with no obvious alternative cause (3). These features make it very difficult to measure routinely, and so it rarely is. As a result, Long Covid is often neglected in decision-making. Failure to account for the disease load associated with Long Covid may lead to an unnecessary long-term societal health burden.The feedback between different types of outcomes, different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, different mitigation policies (including vaccination), and individual risks (a combination of exposure and clinical risk) is complex and must be factored into both interpretation of data and the development of policy. Using all available data to quantify transmission is crucial to ensuring rapid and effective responses to early phases of renewed exponential growth and to evaluating mitigation measures. Relying too much on a single data source, or without disaggregating data, risks fundamentally misunderstanding the state of the epidemic.The inherent biases and lags in data are particularly important to understand from the point of view of policy-makers. Because of the natural time scales of COVID-19 disease progression (see the figure), policy changes can take several weeks to show up in the data. Purely reactive policy-making is likely to be ineffective. When cases are rising, increases in hospital admissions and deaths will follow. When a new variant is outcompeting existing strains, it is likely to become dominant without action to suppress. The precautionary principle suggests acting early and emphatically. Conversely, when releasing restrictions, governments must wait long enough to assess them before continuing with re-opening.The most up-to-date indicator of the state of the epidemic is typically the number of confirmed cases, as ascertained through testing of both symptomatic individuals and those tested frequently regardless of symptoms. Symptom-based testing is likely to pick up more adults and fewer younger individuals (4). Infections in children are harder to detect: children are more likely to be asymptomatic than adults, are harder to administer tests to (particularly young children), are often exposed to other viruses with similar symptoms, and can present with symptoms that are atypical in adults (e.g., abdominal pain or nausea). Children under 12 are not routinely offered the COVID-19 vaccination, and their mixing in schools provides ongoing opportunities for the virus to circulate, so it will be important for countries to track infections in children as accurately as possible. Other testing biases include accessibility, reporting lags, and the ability to act lawfully upon receiving a positive result. Substantial changes in the number of people seeking tests may further confound case figures (5). Case positivity rates may provide a more accurate reflection of the state of the epidemic (6) but are dependent on the mix of symptomatic and asymptomatic people being tested.SARS-CoV-2 variants have been an important driver of local epidemics in 2021. The four main SARS-CoV-2 variants of concern, to date, are B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma), and B.1.617.2 (Delta). Some have been more transmissible (Alpha), some have substantial resistance to previous infection or vaccines (Beta), and some have elements of both (Gamma and Delta) (7). Currently, the high transmissibility of Delta combined with some immune evasion has made it the world’s dominant variant. Determining which variants pose a substantial threat is difficult and takes time, particularly when many variants cocirculate. This is especially true for situations in which a dominant variant is declining, and a new one growing. While the declining variant remains dominant, its decrease masks increases in the new variant because case numbers remain unchanged or fall overall. Only when a new variant becomes dominant does its growth become apparent in aggregated case data, by which time it is, by definition, too late to contain its spread. This dynamic has been observed across the world with Delta over the latter half of 2021.With multiple variants circulating, there are, effectively, multiple epidemics occurring in parallel, and they must be tracked separately. This typically requires the availability of sequencing data, which is unfortunately limited in most countries. Sequencing takes time and so is typically a few weeks out of date. These lags, and the uncertainty in sampling, can lead to hesitancy in acting. The rapid path to dominance of the Delta variant in the UK highlights the need for action when a quickly growing variant represents a few percent (or less) of overall cases.Hospital admissions or occupancy data do not suffer the same biases associated with testing behaviors and provide unequivocal evidence of widespread transmission, its geography, and demographics. However, hospital admissions lag infections more than reported cases do, rendering these data less useful for proactive decision-making. Hospital data are also biased toward older people, who are more likely to suffer severe COVID-19, and now, unvaccinated populations. ICU occupancy data show a younger age profile than admissions because younger patients have a better chance of benefitting from the invasive treatment procedures (8).Deaths are the most lagged indicator, typically occurring 3 or more weeks after infection and with an additional lag in registration and reporting. Death data should never be used to inform real-time policy decisions. Instead, death figures can act as an eventual measure of the success of a country’s epidemic strategy and implementation. The age distribution of those who eventually die from COVID-19 is different from other metrics of the epidemic—skewed furthest toward older age groups (9). Those with clinical risk factors (such as immunodeficiency, obesity, or existing lung conditions), high exposure (health care workers and low-income workers), and the unvaccinated are overrepresented in COVID-19 deaths.In countries with high vaccination rates, vaccination has had a substantial impact—reducing COVID-19 cases, hospitalizations, and deaths. However, when looking at the raw numbers in highly vaccinated populations, it can be the case that more fully vaccinated people are dying of COVID-19 than unvaccinated. If these raw statistics are misinterpreted—or worse, deliberately misused—they can damage vaccine confidence. More vaccinated people may die than unvaccinated because such a high proportion of people are vaccinated (10). This does not mean vaccines are not effective at preventing death. Looking at the rates of death in vaccinated and unvaccinated individuals separately within age groups demonstrates that vaccines provide considerable protection against severe disease and death. This example illustrates how important it is to curate and manage the way in which data are presented.

COVID-19 progressionAn approximate timeline from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to various outcomes. When current infections show up in different data sources depends on this timeline. Collecting data for Long Covid, asymptomatic infection, and vaccine history will improve understanding of the pandemic.GRAPHIC: N. CARY/SCIENCE

Each country has established its own vaccination priority lists and dosing schedules to best achieve its goals (1112). Each of these strategies will manifest differently in the data. Additionally, many countries are using multiple vaccines in tandem and administer them differently for different demographics. Some countries are vaccinating adolescents, and others are not or not offering them the full approved dose. Most vaccines require two doses, spaced between 3 and 12 weeks apart, except for the Johnson & Johnson single-dose vaccine. This matters, particularly as variants spread, because different vaccines have different effectiveness after one and two doses, different timelines to full effectiveness, and different effectiveness against variants (13).Data published on the vaccination delivery itself must thus go beyond the raw numbers of people vaccinated. Vaccine uptake must be reported by whether fully or partially (one-dose in a two-dose regimen) vaccinated and using the whole population as a denominator. It is vital to disaggregate vaccine data by age, gender, and ethnicity as well as location so that it is possible, for example, to understand the impact of deprivation on vaccine coverage or vaccine hesitancy in particular demographics. When interpreting vaccination data, it is important to remember that there is also a lag between delivery and the build-up of immunity.Data on reinfection and post-vaccination (breakthrough) infection are also important to determine the relative benefits of infection-mediated and vaccine-mediated immunity and the length of protection offered. Studies that show those who were immunized earlier were acquiring COVID-19 with higher rates than those vaccinated more recently may suggest waning vaccine protection (14). Such studies have already prompted vaccine booster programs in some countries. However, any study that suggests waning immunity must be extremely careful to ensure that the “early” and “recent” subgroups are properly controlled. Differences in prior exposure, affluence, education level, age, and other demographic factors between these cohorts may be enough to explain the disparities in SARS-CoV-2 infection rates, even in the absence of waning immunity. Waning immunity must also be reported separately for different outcomes; for example, there might be waning in terms of preventing symptomatic infection but far less or none in preventing death (15). Additionally, there are ethical concerns about mass booster programs in high-income countries while many lower-income countries have been unable to procure vaccines.Moving into the vaccination era, reported cases, hospitalizations, and deaths should also be disaggregated by vaccination status (and by which vaccine), which will be easier in countries where national linked datasets exist. Additionally, incorporating Long Covid into routine reporting and policy-making is crucial. Consistent diagnostic criteria and well-controlled studies will be vital to this effort. These elusive data will be of critical importance to navigate our way successfully out of the pandemic.

Acknowledgments

C.P. and C.A.Y. are both members of Independent SAGE: www.independentsage.org.

References and Notes

1M. Roser et al., Our World in Data (2021); https://bit.ly/3kepLgw.GO TO REFERENCEGOOGLE SCHOLAR2H. E. Davis et al., E. Clin. Med.38, 101019 (2021).GO TO REFERENCEGOOGLE SCHOLAR3M. Sivan, S. Taylor, BMJ371, m4938 (2020).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR4S. M. Moghadas et al., Proc. Natl. Acad. Sci. U.S.A.117, 17513 (2020).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR5J. Wise, BMJ370, m3678 (2020).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR6D. Dowdy, G. D’Souza, COVID-19 Testing: Understanding the “Percent Positive” (2020); https://bit.ly/3CeN8wl.GO TO REFERENCEGOOGLE SCHOLAR7C. E. Gómez et al., Vaccines (Basel)9, 243 (2021).CROSSREFPUBMEDGOOGLE SCHOLAR8A. B. Docherty et al., BMJ369, 1985 (2020).GO TO REFERENCECROSSREFPUBMEDGOOGLE SCHOLAR9Office for National Statistics, Deaths registered weekly in England and Wales by age and sex: covid-19 (2021); https://bit.ly/3Ci2obS.

For articles on Issues of Bias in Science on this Open Access Journal see

From @Harvardmed Center for Bioethics: The Medical Ethics of the Corona Virus Crisis

Live Notes from @HarvardMed Bioethics: Authors Jerome Groopman, MD & Pamela Hartzband, MD, discuss Your Medical Mind

The Vibrant Philly Biotech Scene: Proteovant Therapeutics Using Artificial Intelligence and Machine Learning to Develop PROTACs

Reporter: Stephen J. Williams, Ph.D.

It has been a while since I have added to this series but there have been a plethora of exciting biotech startups in the Philadelphia area, and many new startups combining technology, biotech, and machine learning. One such exciting biotech is Proteovant Therapeutics, which is combining the new PROTAC (Proteolysis-Targeting Chimera) technology with their in house ability to utilize machine learning and artificial intelligence to design these types of compounds to multiple intracellular targets.

PROTACs (which actually is under a trademark name of Arvinus Operations, but is also refered to as Protein Degraders. These PROTACs take advantage of the cell protein homeostatic mechanism of ubiquitin-mediated protein degradation, which is a very specific targeted process which regulates protein levels of various transcription factors, protooncogenes, and receptors. In essence this regulated proteolyic process is needed for normal cellular function, and alterations in this process may lead to oncogenesis, or a proteotoxic crisis leading to mitophagy, autophagy and cellular death. The key to this technology is using chemical linkers to associate an E3 ligase with a protein target of interest. E3 ligases are the rate limiting step in marking the proteins bound for degradation by the proteosome with ubiquitin chains.

Model of PROTAC Ternarary Complex

A review of this process as well as PROTACs can be found elsewhere in articles (and future articles) on this Open Access Journal.

Protevant have made two important collaborations:

  1. Oncopia Therapeutics: came out of University of Michigan Innovation Hub and lab of Shaomeng Wang, who developed a library of BET and MDM2 based protein degraders. In 2020 was aquired by Riovant Sciences.
  2. Riovant Sciences: uses computer aided design of protein degraders

Proteovant Company Description:

Proteovant is a newly launched development-stage biotech company focusing on discovery and development of disease-modifying therapies by harnessing natural protein homeostasis processes. We have recently acquired numerous assets at discovery and development stages from Oncopia, a protein degradation company. Our lead program is on track to enter IND in 2021. Proteovant is building a strong drug discovery engine by combining deep drugging expertise with innovative platforms including Roivant’s AI capabilities to accelerate discovery and development of protein degraders to address unmet needs across all therapeutic areas. The company has recently secured $200M funding from SK Holdings in addition to investment from Roivant Sciences. Our current therapeutic focus includes but is not limited to oncology, immunology and neurology. We remain agnostic to therapeutic area and will expand therapeutic focus based on opportunity. Proteovant is expanding its discovery and development teams and has multiple positions in biology, chemistry, biochemistry, DMPK, bioinformatics and CMC at many levels. Our R&D organization is located close to major pharmaceutical companies in Eastern Pennsylvania with a second site close to biotech companies in Boston area.

Protein degradation

Source: Protevant

The ubiquitin proteasome system (UPS) is responsible for maintaining protein homeostasis. Targeted protein degradation by the UPS is a cellular process that involves marking proteins and guiding them to the proteasome for destruction. We leverage this physiological cellular machinery to target and destroy disease-causing proteins.

Unlike traditional small molecule inhibitors, our approach is not limited by the classic “active site” requirements. For example, we can target transcription factors and scaffold proteins that lack a catalytic pocket. These classes of proteins, historically, have been very difficult to drug. Further, we selectively degrade target proteins, rather than isozymes or paralogous proteins with high homology. Because of the catalytic nature of the interactions,  it is possible to achieve efficacy at lower doses with prolonged duration while decreasing dose-limiting toxicities.

Biological targets once deemed “undruggable” are now within reach.

About Riovant Sciences: from PRNewsWire https://www.prnewswire.com/news-releases/roivant-unveils-targeted-protein-degradation-platform-301186928.html

Roivant develops transformative medicines faster by building technologies and developing talent in creative ways, leveraging the Roivant platform to launch “Vants” – nimble and focused biopharmaceutical and health technology companies. These Vants include Proteovant but also Dermovant, ImmunoVant,as well as others.

Roivant’s drug discovery capabilities include the leading computational physics-based platform for in silico drug design and optimization as well as machine learning-based models for protein degradation.

The integration of our computational and experimental engines enables the rapid design of molecules with high precision and fidelity to address challenging targets for diseases with high unmet need.

Our current modalities include small molecules, heterobifunctionals and molecular glues.

Roivant Unveils Targeted Protein Degradation Platform

– First therapeutic candidate on track to enter clinical studies in 2021

– Computationally-designed degraders for six targets currently in preclinical development

– Acquisition of Oncopia Therapeutics and research collaboration with lab of Dr. Shaomeng Wang at the University of Michigan to add diverse pipeline of current and future compounds

Clinical-stage degraders will provide foundation for multiple new Vants in distinct disease areas

– Platform supported by $200 million strategic investment from SK Holdings

Other articles in this Vibrant Philly Biotech Scene on this Online Open Access Journal include:

The Vibrant Philly Biotech Scene: PCCI Meeting Announcement, BioDetego Presents Colon Cancer Diagnostic Tool

The Vibrant Philly Biotech Scene: Focus on KannaLife Sciences and the Discipline and Potential of Pharmacognosy

The Vibrant Philly Biotech Scene: Focus on Vaccines and Philimmune, LLC

The Vibrant Philly Biotech Scene: Focus on Computer-Aided Drug Design and Gfree Bio, LLC

Philly Biotech Scene: Biobots and 3D BioPrinting (Now called Allevi)

Philly Biotech Scene: November 2015 PCCI Meeting Showcasing ViFant (Penn Center For Innovation)

Spark Therapeutics’ $4.8Billion deal Confirmed as Biggest VC-backed Exit in Philadelphia

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

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

UPDATED on 11/5/2021

Introducing Isomorphic Labs

I believe we are on the cusp of an incredible new era of biological and medical research. Last year DeepMind’s breakthrough AI system AlphaFold2 was recognised as a solution to the 50-year-old grand challenge of protein folding, capable of predicting the 3D structure of a protein directly from its amino acid sequence to atomic-level accuracy. This has been a watershed moment for computational and AI methods for biology.
Building on this advance, today, I’m thrilled to announce the creation of a new Alphabet company –  Isomorphic Labs – a commercial venture with the mission to reimagine the entire drug discovery process from the ground up with an AI-first approach and, ultimately, to model and understand some of the fundamental mechanisms of life.

For over a decade DeepMind has been in the vanguard of advancing the state-of-the-art in AI, often using games as a proving ground for developing general purpose learning systems, like AlphaGo, our program that beat the world champion at the complex game of Go. We are at an exciting moment in history now where these techniques and methods are becoming powerful and sophisticated enough to be applied to real-world problems including scientific discovery itself. One of the most important applications of AI that I can think of is in the field of biological and medical research, and it is an area I have been passionate about addressing for many years. Now the time is right to push this forward at pace, and with the dedicated focus and resources that Isomorphic Labs will bring.

An AI-first approach to drug discovery and biology
The pandemic has brought to the fore the vital work that brilliant scientists and clinicians do every day to understand and combat disease. We believe that the foundational use of cutting edge computational and AI methods can help scientists take their work to the next level, and massively accelerate the drug discovery process. AI methods will increasingly be used not just for analysing data, but to also build powerful predictive and generative models of complex biological phenomena. AlphaFold2 is an important first proof point of this, but there is so much more to come. 
At its most fundamental level, I think biology can be thought of as an information processing system, albeit an extraordinarily complex and dynamic one. Taking this perspective implies there may be a common underlying structure between biology and information science – an isomorphic mapping between the two – hence the name of the company. Biology is likely far too complex and messy to ever be encapsulated as a simple set of neat mathematical equations. But just as mathematics turned out to be the right description language for physics, biology may turn out to be the perfect type of regime for the application of AI.

What’s next for Isomorphic Labs
This is just the beginning of what we hope will become a radical new approach to drug discovery, and I’m incredibly excited to get this ambitious new commercial venture off the ground and to partner with pharmaceutical and biomedical companies. I will serve as CEO for Isomorphic’s initial phase, while remaining as DeepMind CEO, partially to help facilitate collaboration between the two companies where relevant, and to set out the strategy, vision and culture of the new company. This will of course include the building of a world-class multidisciplinary team, with deep expertise in areas such as AI, biology, medicinal chemistry, biophysics, and engineering, brought together in a highly collaborative and innovative environment. (We are hiring!
As pioneers in the emerging field of ‘digital biology’, we look forward to helping usher in an amazingly productive new age of biomedical breakthroughs. Isomorphic’s mission could not be a more important one: to use AI to accelerate drug discovery, and ultimately, find cures for some of humanity’s most devastating diseases.

SOURCE

https://www.isomorphiclabs.com/blog

DeepMind creates ‘transformative’ map of human proteins drawn by artificial intelligence

DeepMind plans to release hundreds of millions of protein structures for free

James Vincent July 22, 2021 11:00 am

AI research lab DeepMind has created the most comprehensive map of human proteins to date using artificial intelligence. The company, a subsidiary of Google-parent Alphabet, is releasing the data for free, with some scientists comparing the potential impact of the work to that of the Human Genome Project, an international effort to map every human gene.

Proteins are long, complex molecules that perform numerous tasks in the body, from building tissue to fighting disease. Their purpose is dictated by their structure, which folds like origami into complex and irregular shapes. Understanding how a protein folds helps explain its function, which in turn helps scientists with a range of tasks — from pursuing fundamental research on how the body works, to designing new medicines and treatments.
 “the culmination of the entire 10-year-plus lifetime of DeepMind” 
Previously, determining the structure of a protein relied on expensive and time-consuming experiments. But last year DeepMind showed it can produce accurate predictions of a protein’s structure using AI software called AlphaFold. Now, the company is releasing hundreds of thousands of predictions made by the program to the public.
“I see this as the culmination of the entire 10-year-plus lifetime of DeepMind,” company CEO and co-founder Demis Hassabis told The Verge. “From the beginning, this is what we set out to do: to make breakthroughs in AI, test that on games like Go and Atari, [and] apply that to real-world problems, to see if we can accelerate scientific breakthroughs and use those to benefit humanity.”



Two examples of protein structures predicted by AlphaFold (in blue) compared with experimental results (in green). 
Image: DeepMind


There are currently around 180,000 protein structures available in the public domain, each produced by experimental methods and accessible through the Protein Data Bank. DeepMind is releasing predictions for the structure of some 350,000 proteins across 20 different organisms, including animals like mice and fruit flies, and bacteria like 
E. coli. (There is some overlap between DeepMind’s data and pre-existing protein structures, but exactly how much is difficult to quantify because of the nature of the models.) Most significantly, the release includes predictions for 98 percent of all human proteins, around 20,000 different structures, which are collectively known as the human proteome. It isn’t the first public dataset of human proteins, but it is the most comprehensive and accurate.

If they want, scientists can download the entire human proteome for themselves, says AlphaFold’s technical lead John Jumper. “There is a HumanProteome.zip effectively, I think it’s about 50 gigabytes in size,” Jumper tells The Verge. “You can put it on a flash drive if you want, though it wouldn’t do you much good without a computer for analysis!”
 “anyone can use it for anything” 
After launching this first tranche of data, DeepMind plans to keep adding to the store of proteins, which will be maintained by Europe’s flagship life sciences lab, the European Molecular Biology Laboratory (EMBL). By the end of the year, DeepMind hopes to release predictions for 100 million protein structures, a dataset that will be “transformative for our understanding of how life works,” according to Edith Heard, director general of the EMBL.
The data will be free in perpetuity for both scientific and commercial researchers, says Hassabis. “Anyone can use it for anything,” the DeepMind CEO noted at a press briefing. “They just need to credit the people involved in the citation.”

The benefits of protein folding


Understanding a protein’s structure is useful for scientists across a range of fields. The information can help design new medicines, synthesize novel enzymes that break down waste materials, and create crops that are resistant to viruses or extreme weather. Already, DeepMind’s protein predictions are being used for medical research, including studying the workings of SARS-CoV-2, the virus that causes COVID-19.
 “it will definitely have a huge impact for the scientific community” 
New data will speed these efforts, but scientists note it will still take a lot of time to turn this information into real-world results. “I don’t think it’s going to be something that changes the way patients are treated within the year, but it will definitely have a huge impact for the scientific community,” Marcelo C. Sousa, a professor at the University of Colorado’s biochemistry department, told The Verge.
Scientists will have to get used to having such information at their fingertips, says DeepMind senior research scientist Kathryn Tunyasuvunakool. “As a biologist, I can confirm we have no playbook for looking at even 20,000 structures, so this [amount of data] is hugely unexpected,” Tunyasuvunakool told The Verge. “To be analyzing hundreds of thousands of structures — it’s crazy.”

Notably, though, DeepMind’s software produces predictions of protein structures rather than experimentally determined models, which means that in some cases further work will be needed to verify the structure. DeepMind says it spent a lot of time building accuracy metrics into its AlphaFold software, which ranks how confident it is for each prediction.

Example protein structures predicted by AlphaFold.
Image: DeepMind
Predictions of protein structures are still hugely useful, though. Determining a protein’s structure through experimental methods is expensive, time-consuming, and relies on a lot of trial and error. That means even a low-confidence prediction can save scientists years of work by pointing them in the right direction for research.
Helen Walden, a professor of structural biology at the University of Glasgow, tells The Verge that DeepMind’s data will “significantly ease” research bottlenecks, but that “the laborious, resource-draining work of doing the biochemistry and biological evaluation of, for example, drug functions” will remain.
Sousa, who has previously used data from AlphaFold in his work, says for scientists the impact will be felt immediately. “In our collaboration we had with DeepMind, we had a dataset with a protein sample we’d had for 10 years, and we’d never got to the point of developing a model that fit,” he says. “DeepMind agreed to provide us with a structure, and they were able to solve the problem in 15 minutes after we’d been sitting on it for 10 years.”

Why protein folding is so difficult

Proteins are constructed from chains of amino acids, which come in 20 different varieties in the human body. As any individual protein can be comprised of hundreds of individual amino acids, each of which can fold and twist in different directions, it means a molecule’s final structure has an incredibly large number of possible configurations. One estimate is that the typical protein can be folded in 10^300 ways — that’s a 1 followed by 300 zeroes.

 Protein folding has been a “grand challenge” of biology for decades 

Because proteins are too small to examine with microscopes, scientists have had to indirectly determine their structure using expensive and complicated methods like nuclear magnetic resonance and X-ray crystallography. The idea of determining the structure of a protein simply by reading a list of its constituent amino acids has been long theorized but difficult to achieve, leading many to describe it as a “grand challenge” of biology.
In recent years, though, computational methods — particularly those using artificial intelligence — have suggested such analysis is possible. With these techniques, AI systems are trained on datasets of known protein structures and use this information to create their own predictions.

DeepMind’s AlphaFold software has significantly increased the accuracy of computational protein-folding, as shown by its performance in the CASP competition. 
Image: DeepMind
Many groups have been working on this problem for years, but DeepMind’s deep bench of AI talent and access to computing resources allowed it to accelerate progress dramatically. Last year, the company competed in an international protein-folding competition known as CASP and blew away the competition. Its results were so accurate that computational biologist John Moult, one of CASP’s co-founders, said that “in some sense the problem [of protein folding] is solved.”

DeepMind’s AlphaFold program has been upgraded since last year’s CASP competition and is now 16 times faster. “We can fold an average protein in a matter of minutes, most cases seconds,” says Hassabis.

@@@@@@@

The company also released the underlying code for AlphaFold last week as open-source, allowing others to build on its work in the future.

@@@@@@@

Liam McGuffin, a professor at Reading University who developed some of the UK’s leading protein-folding software, praised the technical brilliance of AlphaFold, but also noted that the program’s success relied on decades of prior research and public data. “DeepMind has vast resources to keep this database up to date and they are better placed to do this than any single academic group,” McGuffin told The Verge. “I think academics would have got there in the end, but it would have been slower because we’re not as well resourced.”

Why does DeepMind care?

Many scientists The Verge spoke to noted the generosity of DeepMind in releasing this data for free. After all, the lab is owned by Google-parent Alphabet, which has been pouring huge amounts of resources into commercial healthcare projects. DeepMind itself loses a lot of money each year, and there have been numerous reports of tensions between the company and its parent firm over issues like research autonomy and commercial viability.

Hassabis, though, tells The Verge that the company always planned to make this information freely available, and that doing so is a fulfillment of DeepMind’s founding ethos. He stresses that DeepMind’s work is used in lots of places at Google — “almost anything you use, there’s some of our technology that’s part of that under the hood” — but that the company’s primary goal has always been fundamental research.
 “There’s many ways value can be attained.” 

“The agreement when we got acquired is that we are here primarily to advance the state of AGI and AI technologies and then use that to accelerate scientific breakthroughs,” says Hassabis. “[Alphabet] has plenty of divisions focused on making money,” he adds, noting that DeepMind’s focus on research “brings all sorts of benefits, in terms of prestige and goodwill for the scientific community. There’s many ways value can be attained.”
Hassabis predicts that AlphaFold is a sign of things to come — a project that shows the huge potential of artificial intelligence to handle messy problems like human biology.

“I think we’re at a really exciting moment,” he says. “In the next decade, we, and others in the AI field, are hoping to produce amazing breakthroughs that will genuinely accelerate solutions to the really big problems we have here on Earth.”


SOURCE

https://www.theverge.com/platform/amp/2021/7/22/22586578/deepmind-alphafold-ai-protein-folding-human-proteome-released-for-free?__twitter_impression=true

Potential Use of Protein Folding Predictions for Drug Discovery

PROTAC Technology: Opportunities and Challenges

  • Hongying Gao
  • Xiuyun Sun
  • Yu Rao*

Cite this: ACS Med. Chem. Lett. 2020, 11, 3, 237–240Publication Date:March 12, 2020https://doi.org/10.1021/acsmedchemlett.9b00597Copyright © 2020 American Chemical Society

Abstract

PROTACs-induced targeted protein degradation has emerged as a novel therapeutic strategy in drug development and attracted the favor of academic institutions, large pharmaceutical enterprises (e.g., AstraZeneca, Bayer, Novartis, Amgen, Pfizer, GlaxoSmithKline, Merck, and Boehringer Ingelheim, etc.), and biotechnology companies. PROTACs opened a new chapter for novel drug development. However, any new technology will face many new problems and challenges. Perspectives on the potential opportunities and challenges of PROTACs will contribute to the research and development of new protein degradation drugs and degrader tools.

Although PROTAC technology has a bright future in drug development, it also has many challenges as follows:
(1)
Until now, there is only one example of PROTAC reported for an “undruggable” target; (18) more cases are needed to prove the advantages of PROTAC in “undruggable” targets in the future.
(2)
“Molecular glue”, existing in nature, represents the mechanism of stabilized protein–protein interactions through small molecule modulators of E3 ligases. For instance, auxin, the plant hormone, binds to the ligase SCF-TIR1 to drive recruitment of Aux/IAA proteins and subsequently triggers its degradation. In addition, some small molecules that induce targeted protein degradation through “molecular glue” mode of action have been reported. (21,22) Furthermore, it has been recently reported that some PROTACs may actually achieve target protein degradation via a mechanism that includes “molecular glue” or via “molecular glue” alone. (23) How to distinguish between these two mechanisms and how to combine them to work together is one of the challenges for future research.
(3)
Since PROTAC acts in a catalytic mode, traditional methods cannot accurately evaluate the pharmacokinetics (PK) and pharmacodynamics (PD) properties of PROTACs. Thus, more studies are urgently needed to establish PK and PD evaluation systems for PROTACs.
(4)
How to quickly and effectively screen for target protein ligands that can be used in PROTACs, especially those targeting protein–protein interactions, is another challenge.
(5)
How to understand the degradation activity, selectivity, and possible off-target effects (based on different targets, different cell lines, and different animal models) and how to rationally design PROTACs etc. are still unclear.
(6)
The human genome encodes more than 600 E3 ubiquitin ligases. However, there are only very few E3 ligases (VHL, CRBN, cIAPs, and MDM2) used in the design of PROTACs. How to expand E3 ubiquitin ligase scope is another challenge faced in this area.

PROTAC technology is rapidly developing, and with the joint efforts of the vast number of scientists in both academia and industry, these problems shall be solved in the near future.

PROTACs have opened a new chapter for the development of new drugs and novel chemical knockdown tools and brought unprecedented opportunities to the industry and academia, which are mainly reflected in the following aspects:
(1)
Overcoming drug resistance of cancer. In addition to traditional chemotherapy, kinase inhibitors have been developing rapidly in the past 20 years. (12) Although kinase inhibitors are very effective in cancer therapy, patients often develop drug resistance and disease recurrence, consequently. PROTACs showed greater advantages in drug resistant cancers through degrading the whole target protein. For example, ARCC-4 targeting androgen receptor could overcome enzalutamide-resistant prostate cancer (13) and L18I targeting BTK could overcome C481S mutation. (14)
(2)
Eliminating both the enzymatic and nonenzymatic functions of kinase. Traditional small molecule inhibitors usually inhibit the enzymatic activity of the target, while PROTACs affect not only the enzymatic activity of the protein but also nonenzymatic activity by degrading the entire protein. For example, FAK possesses the kinase dependent enzymatic functions and kinase independent scaffold functions, but regulating the kinase activity does not successfully inhibit all FAK function. In 2018, a highly effective and selective FAK PROTAC reported by Craig M. Crews’ group showed a far superior activity to clinical candidate drug in cell migration and invasion. (15) Therefore, PROTAC can expand the druggable space of the existing targets and regulate proteins that are difficult to control by traditional small molecule inhibitors.
(3)
Degrade the “undruggable” protein target. At present, only 20–25% of the known protein targets (include kinases, G protein-coupled receptors (GPCRs), nuclear hormone receptors, and iron channels) can be targeted by using conventional drug discovery technologies. (16,17) The proteins that lack catalytic activity and/or have catalytic independent functions are still regarded as “undruggable” targets. The involvement of Signal Transducer and Activator of Transcription 3 (STAT3) in the multiple signaling pathway makes it an attractive therapeutic target; however, the lack of an obviously druggable site on the surface of STAT3 limited the development of STAT3 inhibitors. Thus, there are still no effective drugs directly targeting STAT3 approved by the Food and Drug Administration (FDA). In November 2019, Shaomeng Wang’s group first reported a potent PROTAC targeting STAT3 with potent biological activities in vitro and in vivo. (18) This successful case confirms the key potential of PROTAC technology, especially in the field of “undruggable” targets, such as K-Ras, a tricky tumor target activated by multiple mutations as G12A, G12C, G12D, G12S, G12 V, G13C, and G13D in the clinic. (19)
(4)
Fast and reversible chemical knockdown strategy in vivo. Traditional genetic protein knockout technologies, zinc-finger nuclease (ZFN), transcription activator-like effector nuclease (TALEN), or CRISPR-Cas9, usually have a long cycle, irreversible mode of action, and high cost, which brings a lot of inconvenience for research, especially in nonhuman primates. In addition, these genetic animal models sometimes produce phenotypic misunderstanding due to potential gene compensation or gene mutation. More importantly, the traditional genetic method cannot be used to study the function of embryonic-lethal genes in vivo. Unlike DNA-based protein knockout technology, PROTACs knock down target proteins directly, rather than acting at the genome level, and are suitable for the functional study of embryonic-lethal proteins in adult organisms. In addition, PROTACs provide exquisite temporal control, allowing the knockdown of a target protein at specific time points and enabling the recovery of the target protein after withdrawal of drug treatment. As a new, rapid and reversible chemical knockdown method, PROTAC can be used as an effective supplement to the existing genetic tools. (20)

SOURCE

PROTAC Technology: Opportunities and Challenges
  • Hongying Gao
  • Xiuyun Sun
  • Yu Rao*

Cite this: ACS Med. Chem. Lett. 2020, 11, 3, 237–240

Goal in Drug Design: Eliminating both the enzymatic and nonenzymatic functions of kinase.

Work-in-Progress

Induction and Inhibition of Protein in Galectins Drug Design

Work-in-Progress

Screening Proteins in DeepMind’s AlphaFold DataBase

The company also released the underlying code for AlphaFold last week as open-source, allowing others to build on its work in the future.

Work-in-Progress

Other related research published in this Open Access Online Journal include the following:

Synthetic Biology in Drug Discovery

Peroxisome proliferator-activated receptor (PPAR-gamma) Receptors Activation: PPARγ transrepression  for Angiogenesis in Cardiovascular Disease and PPARγ transactivation for Treatment of Diabetes

Breakthrough Procedure in Aortic Valve Repair: VIDEO: How to Perform a Transcaval TAVR Procedure

Reporter: Aviva Lev-Ari, PhD, RN

VIEW VIDEO

https://www.dicardiology.com/videos/video-how-perform-transcaval-tavr-procedure

Tiberio Frisoli, M.D., interventional structural cardiologist, senior staff physician, Henry Ford Hospital, explains how his center performs transcaval transcatheter aortic valve replacement (TAVR) access for patients who have suboptimal abdominal aortic and femoral vascular anatomy. Transcaval access was pioneered at Henry Ford Hospital and involves using femoral vein access and then using a surgical radio frequency cutter to bore a hole from the interior venacava into the aorta to allow the TAVR delivery catheter to path through. 

This procedure was developed to enable more patients to receive TAVR via the preferred femoral access route. Some patients are not candidates for femoral artery access because of calcified lesions and heart atherosclerotic plaque, which narrows the vessel lumen, and makes it difficult to thread catheters through. The transcaval access technique can bypass the restricted arteries or heavy calcified plaques to still enable a minimally invasive procedure without the need for surgery. 

This video was produced in partnership from Henry Ford Hospital.

Related Transcaval TAVR Content:

VIDEO: Transcaval Access in TAVR Procedures — Interview with Adam Greenbaum, M.D.

How to Perform Transcaval TAVR Access

VIDEO: Walk Through of the Henry Ford Hospital Structural Heart Cath Lab

Study Deems Transcaval Valve Replacement Pioneered at Henry Ford Hospital Successful

First Transcaval Aortic Valve Replacement Performed in Europe

Additional articles and videos on Henry Ford Hospital 

Find more structural heart technology content

SOURCE

https://www.dicardiology.com/videos/video-how-perform-transcaval-tavr-procedure

Comparative Study: Four SARS-CoV-2 vaccines induce quantitatively different antibody responses against SARS-CoV-2 variants

Reporter: Aviva Lev- Ari, PhD, RN

Marit J. van Gils, A. H. Ayesha Lavell, Karlijn van der Straten, Brent Appelman, Ilja Bontjer, Meliawati Poniman, Judith A. Burger, Melissa Oomen, Joey H. Bouhuijs, Lonneke A. van Vught, Marleen A. Slim, Michiel Schinkel, Elke Wynberg, Hugo D.G. van Willigen, Marloes Grobben, Khadija Tejjani, Jonne Snitselaar, Tom G. Caniels, Amsterdam UMC COVID-19 S3/HCW study group, Alexander P. J. Vlaar, Maria Prins, Menno D. de Jong, Godelieve J. de Bree, Jonne J. Sikkens, Marije K. Bomers, Rogier W. Sanders doi: https://doi.org/10.1101/2021.09.27.21264163

Abstract

Emerging and future SARS-CoV-2 variants may jeopardize the effectiveness of vaccination campaigns. We performed a head-to-head comparison of the ability of sera from individuals vaccinated with either one of four vaccines (BNT162b2, mRNA-1273, AZD1222 or Ad26.COV2.S) to recognize and neutralize the four SARS-CoV-2 variants of concern (VOCs; Alpha, Beta, Gamma and Delta). Four weeks after completing the vaccination series, SARS-CoV-2 wild-type neutralizing antibody titers were highest in recipients of BNT162b2 and mRNA-1273 (median titers of 1891 and 3061, respectively), and substantially lower in those vaccinated with the adenovirus vector-based vaccines AZD1222 and Ad26.COV2.S (median titers of 241 and 119, respectively). VOCs neutralization was reduced in all vaccine groups, with the largest (5.8-fold) reduction in neutralization being observed against the Beta variant. Overall, the mRNA vaccines appear superior to adenovirus vector-based vaccines in inducing neutralizing antibodies against VOCs four weeks after the final vaccination.

Figure 2:Binding and neutralization titers post-vaccination against VOCs.

(A) Median with interquartile range of binding titers to wild-type and VOCs S proteins represented as mean fluorescence intensity (MFI) of 1:100,000 diluted sera collected four-five weeks after full vaccination for the four vaccination groups. The lower cutoff for binding was set at an MFI of 10 (grey shading). Vaccine groups are indicated by colors with BNT162b2 in green, mRNA-1273 in purple, AZD1222 in orange and Ad26.COV2.S in blue. (B) Median with interquartile range of half-maximal neutralization (ID50) titers of D614G and VOCs pseudoviruses for sera collected after full vaccination for the four vaccination groups. The lower cutoff for neutralization was set at an ID50 of 100 (grey shading). Vaccine groups are indicated by colors with BNT162b2 in green, mRNA-1273 in purple, AZD1222 in orange and Ad26.COV2.S in blue. (C) Median ID50 neutralization of D614G and VOCs plotted against the reported vaccine efficacy against symptomatic infection25,1217. Vaccine groups are indicated by colors with BNT162b2 in green, mRNA-1273 in purple, AZD1222 in orange and Ad26.COV2.S in blue. Circles represent WT data, squares for Alpha, diamond for Beta, nabla triangle for Gamma and delta triangle for Delta. Spearman’s rank correlation coefficient with p value are indicated. The result of the AZD1222 phase 3 trial conducted in South Africa, demonstrating poor (10%) efficacy against Beta variant, is not shown.

SOURCE

 https://doi.org/10.1101/2021.09.27.21264163

A laboratory for the use of AI for drug development has been launched in collaboration with Pfizer, Teva, AstraZeneca, Mark and Amazon

Reporter: Aviva Lev-Ari, PhD, RN

AION Labs unites pharma, technology and funds companies including IBF to invest in startups to integrate developments in cloud computing and artificial intelligence to improve drug development capabilities. An alliance of four leading pharmaceutical companies –  
AION Labs
 , the first innovation lab of its kind in the world and a pioneer in the process of adopting cloud technologies, artificial intelligence and computer science to solve the R&D challenges of the pharma industry, today announces its launch.
AstraZeneca ,  
Mark ,  
Pfizer  and 
Teva  – and two leading companies in the field of high-tech and biotech investments, respectively – AWS ( 
Amazon Web Services Inc ) and the Israeli investment fund IBF ( 
Israel Biotech Fund ) – which joined together to establish groundbreaking ventures Through artificial intelligence and computer science to change the way new therapies are discovered and developed.  “We are excited to launch the new innovation lab in favor of discoveries of drugs and medical devices using groundbreaking computational tools,” said Matti Gil, CEO of AION Labs. We are prepared and ready to make a difference in the process of therapeutic discoveries and their development. 
With a strong pool of talent from Israel and the world, cloud technology and artificial intelligence at the heart of our activities and a significant commitment by the State of Israel, we are ready to contribute to the health and well-being of the human race and promote industry in Israel. 
I thank the partners for the trust, and it is an honor for me to lead such a significant initiative. ” 
In addition, AION Labs has announced a strategic partnership with X  
BioMed  , an independent biomedical research institute operating in Heidelberg, Germany. 
BioMed X has a proven track record in advancing research innovations in the field of biomedicine at the interface between academic research and the pharmaceutical industry. 
BioMed X’s innovation model, based on global mass sourcing and incubators to cultivate the most brilliant talent and ideas, will serve as the R & D engine to drive AION Labs’ enterprise model.

SOURCE

Greylock Partners Announces Unique $500 Million Venture to act as Seed Capital Funding for Earliest Stage Startups

Reporter: Stephen J. Williams, Ph.D.

Greylock Partners CEO Reid Hoffman announces a $500 million fund to help the earliest stage startups find capital.

See video below:

https://www.bloomberg.com/multimedia/api/embed/iframe?id=798828e9-7850-4c83-9348-a35d5fad3e1c

https://www.bloomberg.com/news/videos/2021-09-24/intv-sara-guoh-greylock-partners-video

See transcript from Bloomberg.com

00:00This is a lot of money for seed stage deals which is typicallysmaller. Why do you want to make seed such a priority.

00:09So see it has always been a priority for us. We’ve been activeat this stage for a long time and some of our biggest wins

00:15historically have been incubation and seed. So I think companieslike Workday and Palo Alto Networks and more recently abnormal

00:21and Snorkel. And then this year 70 percent of our investmentsyou must mints or seeds before we announce this fund. And so

00:29when we saw this level of opportunity we also want to make surewe had enough funding to really back entrepreneurs and to

00:36support them through their journey and make sure entrepreneursalso know they have different options at the seed for the type

00:41of partners they work with. Now at the seed stage you’re talkingabout companies in their infancy. How early are you investing. I

00:49mean is this ideas on a napkin stage with a couple ofentrepreneurs that you believe in or is it beyond that.

00:58So there definitely is a whole range. We don’t catch everysingle person. Like the day they left their job. Right. But you

01:04know abnormal was to see it in 2018 when it was a slide deck andtwo co-founders. We backed another company recently and self on

01:12first capital. That was a repeat founder we have history with.Similarly no product yet. Just an idea and an early team. And so

01:20the range of when we do see it really depends on when weencounter companies. We do like to get to know people as early

01:26as possible. And sometimes that’s the right time for us to writethe check. Obviously Greylock is a multi-stage venture venture

01:32capital firm and I think founders might have the question here.You know if you give me the seed funding we’ll follow on and

01:38reserves come out of that same bucket. And what could this meanin terms of a longer term relationship with Greylock. What’s the

01:46answer to that. So the first thing I’d start with is seeds forus our core investments. Right. So many firms look at them as

01:54options to then follow on. We look at seeds as investments we’retrying to make money on. We’re building a relationship for the

02:01long term to begin with. Right. So. So I’d start with that thenI’d say it is a third of our fund. So it is a big piece of our

02:09investing. And and you know there are many instances where wethen follow on and invest even more because our conviction

02:16continues or even grows. But the point of us doing seed is notjust a follow on it’s to make that investment. How big is each

02:24deal. I mean would you say that seed is the new series A.I think I think that.

02:33Well let’s see the market data would tell us that round sizesoverall have increased for the same level of progress. And I

02:41think that makes sense right. And the reason being the markethas become a lot smarter at the attractiveness of early stage

02:48technology opportunities. And so great returns in tech venturecapital over many years mean there’s more capital than ever and

02:57people are savvier about software and Internet companies. ButI’d say there is you know I think kind of the noble creature

03:04doesn’t matter so much. We think of it as being the firstinstitutional partner to go to a set of founders. The world is

03:12changing quickly. I mean we’re still in the middle of apandemic. And who would’ve known that you know working from home

03:16was going to be a thing 18 months ago. What are the trends thatyou are most excited about right now that you’re doubling down

03:22on at the seed stage.Yeah. So we invest across the technology spectrum business

03:30consumer. The one you just mentioned in terms of just the seachange of the pandemic in terms of how we do our work together

03:36as one. I’m really excited about but we’ve been we’ve beeninvesting in let’s say just this. There’s a shortage globally

03:44because the pandemic. But even before of human connection andand intimacy and people look for it online. And so we invest in

03:53companies like Dischord and Common ROOM and Promotion that helppeople connect more online. So that’s when we’ll continue to

04:00invest in. And then of course we’re investing across all of yourusual range of SAS social data A.I. etc. and then spending more

04:10and more time in fintech and crypto in particular. Now what arethe potential problems with seed stage. Is that at a certain

04:16point as the company develops maybe they pivot they change. Overtime they could potentially ultimately compete with another one

04:23of your core portfolio companies. How do you manage that.So it’s a good question but it is also something that doesn’t

04:30only happen at the scene and funnily enough Greylock has been aninvestor in several companies that were like great companies

04:37post pivot right. So like first semester and discord and nextdoor after they decided to be what they are today. And so that

04:46you know I’d start with the premise of our our philosophy isthat the company should do what’s best for the company. And we

04:53know our our philosophy is to be fully behind companies and notto go invest in a bunch of competitors in a sector just because

04:59we like this sector. But if that were to happen you know wewould we would just divide those interests within the firm and

05:06like make sure that there’s no information flow and just addressit in a reasonable way. I’ve talked with many of your partners

05:12over the years about investing in more women. And I’m curioushow you look at it as an opportunity to potentially you know

05:22spread the wealth a little bit across more women entrepreneurspeople of color people who historically haven’t gotten a chance

05:29in Silicon Valley and Silicon Valley hasn’t benefited from theirideas.

05:34OK. So I’d say this is an issue that’s near and dear to myheart. We are working on it. Two of the last three founders I

05:40backed are women. One is the seed stage founder. One of thefounders. I backed at the seed stage is Hispanic. But. But I

05:49would say you know one thing I want to make sure is clear. Likeyou want to back great founders from diverse backgrounds across

05:56the spectrum. And like we wouldn’t like do it more in seedbecause seed isn’t important. Because it is important to us.

06:02Right. It’s just across the portfolio. This is a priority.

From TechStartups

Source: https://techstartups.com/2021/09/22/greylock-partners-raises-500-million-invest-seed-stage-startups/

Greylock Partners raises $500 million to invest in seed-stage startups

Nickie LouisePOSTED ON SEPTEMBER 22, 2021


Greylock Partners has raised $500 million to invest exclusively in seed-stage startups. The announcement comes a year after the firm raised $1 billion for its 16th flagship fund to invest in early- and growth-stage tech startups.

Guo and general partner Saam Motamedi said in an interview the fund is part of an expansion of a $1.1 billion fund, which we reported last year, to $1.6 billion, The Information reported. The funding is among the industry’s largest devoted to seed investments, which often represent a startup’s first outside capital.

The pool of funds will give the 56-year-old venture capital firm the ability to write large checks at “lean-in valuations” and emphasize its commitment to early-stage investing, said general partner Sarah Guo. In a thread post on Twitter, Greylock said, “We at @GreylockVC  are excited to announce we’ve raised $500M dedicated to seed investing. This is the industry’s largest pool of venture capital dedicated to backing founders at day one.”

Press Release from Grelock

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