Category Archives: Genetic Therapy

From junior doctor to gene therapy CEO, how one persons drive to make rare disease curable is shaping the future of medicine.

It was a truly touching story to hear Dr Gaspar recant his early days working with young, severely immunocompromised patients at Great Ormond Street. Dr Gaspar knew that most of his patients would die within their first year without any therapy. Although bone marrow transplantation was an option for some patients, it was not without risk of complications.

Dr Gaspar was first told of a single patient candidate for gene therapy while doing his rounds in 1993. He was initially convinced it was a Senior Doctor prank on a trainee, but as the years passed Dr. Gaspar saw that one patient return to the hospital with a fully healthy immune system and he was inspired to return to the lab to explore.And he did. In 2001, he went on to test the innovations he and his colleague were exploring in human clinical trials. The success these approaches received garnered much interest: first beyond the academic setting and then across regions and continents. The next step was clear - a commercial entity was needed to deliver more solutions to more people.

The result was Orchard Therapeutics, a company dedicated to setting up a process that enables the treatment cells to travel so that patients, who are already living with debilitating diseases, do not have to. As a business, Orchard Therapeutics want to introduce as many therapies as possible and, in order to enable a broader reach, are eager to work with regulators to explore reimbursement models from single payments, to annualised payments or payment by results.

When asked what it is like as a clinician to see this level of success, Dr Gaspar replies that there is nothing more rewarding than treating a patient with a therapy that you have created it is, truly, an unbeatable moment.

Dr Gaspar has now moved on from his clinical duties to focus fully on his CEO responsibilities at Orchard Therapeutics, but remains certain that the options for clinicians when faced with rare genetic disease diagnoses, such as Severe Combined Immune Deficiency (SKID) and Metachromatic leukodystrophy (MLD), are different. The conversations with the families of patients will be entirely more positive in light of the new available options. The face of the disease is, thanks to Dr Gaspar and his colleagues work, now permanently changing for the better.

From this fireside chat, you can see the desire to introduce more curative therapies still burns bright.

Continued here:
Reflections of a Fireside Chat from the World Orphan Drug Congress 2020 - PMLiVE

Each month,TheClinical Advisormakes one new clinical feature available ahead of print. Dont forget to take the poll. The results will be published in the next months issue.

Major depressive disorder (MDD) is a common mental disorder that affects more than 264 million people worldwide and is a leading cause of disability, including death by suicide.1 MDD is a complicated disorder that involves the interaction of social, psychological, and biological factors.1 MDD can prevent patients from living healthy, productive lives and can complicate treatment of other comorbid conditions.1

Although MDD commonly is encountered in primary care settings, its treatment has become integrated into all fields of medicine due to its high prevalence. Cognitive behavioral therapy, interpersonal psychotherapy, and antidepressants, such as selective serotonin reuptake inhibitors and tricyclic antidepressants, are the mainstays of MDD treatment.1

Prescribing an antidepressant may be simple, but that does not make it easy. Efficacy and tolerability of antidepressants vary among patients, which can make it challenging to relieve patients symptoms.2 Although no genes have been associated with depression,3 several genetic variants may help clinicians predict how patients with MDD will metabolize antidepressants.4 Performing genetic testing of patients with MDD and matching patients with an antidepressant class based on identification of genetic variants that convey sensitivity to particular antidepressants could improve response to drug therapy in patients with MDD.5

The process of selecting an antidepressant should take into account cost, tolerability, adverse effect profiles, and patient preferences.2 When evaluating treatment options for patients with MDD, the current standard of care is to initiate an antidepressant at a starting dose and reassess effectiveness within 2 to 4 weeks, with adjustments to monitoring frequency dependent on the patients suicide and self-harm risk, comorbid conditions, age, and concomitant medication use.2

Several metrics are used to determine whether a selected antidepressant is working:

Clinicians can mitigate adverse effects by decreasing the dosage or switching to a different class of antidepressant. However, several weeks are needed after each change in drug or dose alteration to truly assess response. Finding and settling on a drug that produces a response with minimal adverse effects can take months. During the trial period, patients may become frustrated with the process and stop therapy and/or may be at increased risk for suicide or self-harm.

The study of drug metabolism in patients with MDD is a growing area of interest.3-5 A management approach incorporating pharmacogenetic testing in combination with clinical judgment may be superior to the standard trial and error method for finding an effective antidepressant regimen and could improve patient outcomes.5

Genome-wide association studies are used to identify single nucleotide polymorphisms (SNPs) in genes related to a particular disease or drug metabolism.6 Several laboratory testing companies offer pharmacogenetic panels to evaluate metabolism of drugs used to treat MDD.3 The FDA also has approved direct-to-consumer genetic testing panels (eg, 23andMe), which are widely available to the public without a health care providers prescription.7 A concern with these latter tests is that the results of these tests are reported directly from the company to the patient; thus, the patient decides whether or not to present the information to his or her health care provider. Many pharmacogenetic testing panels also include genes that have shown correlations with the pathogenesis of MDD, despite the lack of clinical research replicating the role of these genes in the disorder.3

From the October 2020 Issue of Clinical Advisor

Originally posted here:
Pharmacogenetic Testing: Does it Improve Therapy in Patients With MDD? - Clinical Advisor

New York, Sept. 09, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Precision Medicine Market: Focus on Ecosystem, Technology, Application, Country Data (21 Countries), and Competitive Landscape - Analysis and Forecast, 2020-2030" - https://www.reportlinker.com/p05965013/?utm_source=GNW By Application: Cancer, Infectious Disease, Neurology, Cardiovascular, Endocrinology, Gastroenterology and Other Applications By Region: North America, Europe, Asia-Pacific, Latin America, and Rest-of-the-World

Cross Segmentation

Applied Sciences: By Product, By Technology, By End User, By Region Precision Diagnostics: By Product, By Technology, By End User, By Region Precision Therapeutics: By Product, By Technology, By End User, By Region Digital Health and IT: By Product, By Technology, By End User, By Region

Regional Segmentation

North America: U.S. and Canada Europe: Germany, France, U.K., Italy, Spain, and Rest-of-Europe Asia-Pacific: Japan, China, India, Australia, and Rest-of-Asia-Pacific Latin America: Brazil, Mexico, and Rest-of-Latin America Rest-of-the-World

Growth Drivers

Advancement of Sequencing Technologies Rising Prevalence of Chronic Diseases Growing Demand for Preventive Care Shifting the Significance in Medicine from Reaction to Prevention Reducing Adverse Drug Reactions Through Pharmacogenomics Test Potential to Reduce the Overall Healthcare Cost Across the Globe

Market Challenges

Unified Framework for Data Integration Limited Knowledge about Molecular Mechanism/ Interaction Lack of Robust Reimbursement Landscape Regulatory Hurdles

Market Opportunities

Targeted Gene Therapy Expansion into the Emerging Markets Collaborations and Partnerships Across Value Chain to Accelerate the Market Entry

Key Companies Profiled

Abbott Laboratories, Almac Group Ltd, Amgen Inc., ANGLE plc, Astellas Pharma Inc., Astra Zeneca PLC, ASURAGEN INC., Bio-Rad Laboratories, Inc., bioMrieux SA., Bristol-Myers Squibb Company, Cardiff Oncology, CETICS Healthcare Technologies GmbH, Danaher Corporation, Eli Lilly and Company Limited, Epic Sciences, Inc., F. Hoffmann-La Roche Ltd, GE Corporation, Gilead Sciences, Inc., GlaxoSmithKline Plc, Illumina, Inc., Intomics A/S, Johnson & Johnson Company, Konica Minolta, Inc., Laboratory Corporation of America, MDx Health, Inc., Menarini Silicon Biosystems, Inc., Merck KGaA, Myriad Genetics, Inc., Novartis AG, Oracle Corporation, Partek, Inc., Pfizer, Inc., QIAGEN N.V., Quest Diagnostics Inc, Randox Laboratories Ltd., Sanofi S.A., Sysmex Corporation, Teva Pharmaceuticals Industries Ltd., Thermo Fisher Scientific, Inc.

Key Questions Answered in this Report: What are the estimated and projected numbers for the global precision medicine market for 2020 and 2030? What are the drivers, challenges, and opportunities that are influencing the dynamics of the market? What is the competition layout of the market? What are the parameters on which competition mapping is carried out in the study? Which key development strategies are being followed and implemented by major players to help them sustain in the market? How are different segments of the market expected to perform during the forecast period from 2020 to 2030? The segments included in the comprehensive market study are: o product type o region o technology o application Which leading players are currently dominating the marke,t and what is the expected future scenario? Which companies are anticipated to be highly disruptive in the future, and why? How can the changing dynamics of the market impact the market share of different players operating in the market? What are the strategic recommendations offered in the study?

Market Overview

Precision medicine refers to the medicine developed as per an individuals genetic profile.It provides guidance regarding the prevention, diagnosis, and treatment of diseases.

The segmentation of the population is done depending on the genome structure of the individuals and their compatibility with a specific drug molecule.In the precision medicine market, the application of molecular biology is to study the cause of a patients disease at the molecular level, so that target-based therapies or individualized therapies can be applied to cure the patients health-related problems.

This industry is gaining traction due to the increasing awareness about healthcare among individuals, integration of smart devices such as smartphones and tablets into healthcare, and increasing collaborations and agreements of IT firms with the diagnostics and biopharmaceutical companies for the development of precision diagnostic tools.

The current precision medicine market is mainly dominated by several majors, such as Abbott Laboratories, Almac Group Ltd, Amgen Inc., ANGLE plc, Astellas Pharma Inc, Astra Zeneca PLC, ASURAGEN INC., Bio-Rad Laboratories, Inc., bioMrieux SA., Bristol-Myers Squibb Company, Cardiff Oncology, CETICS Healthcare Technologies GmbH, Danaher Corporation, Eli Lilly and Company Limited, Epic Sciences, Inc., F. Hoffmann-La Roche Ltd, GE Corporation, Gilead Sciences, Inc., GlaxoSmithKline Plc, Illumina, Inc. Intomics A/S, and Johnson & Johnson Company, Konica Minolta, Inc.

Within the research report, the market is segmented on the basis of product type, ecosystem application, and region, which highlight value propositions and business models useful for industry leaders and stakeholders. The research also comprises country-level analysis, go-to-market strategies of leading players, future opportunities, among others, to detail the scope and provide 360-degree coverage of the domain.

Competitive Landscape Major players, such as Abbott Laboratories, Almac Group Ltd, Amgen Inc., ANGLE plc, Astellas Pharma Inc, Astra Zeneca PLC, ASURAGEN INC., Bio-Rad Laboratories, Inc., bioMrieux SA., Bristol-Myers Squibb Company, Cardiff Oncology, CETICS Healthcare Technologies GmbH, Danaher Corporation, Eli Lilly and Company Limited, Epic Sciences, Inc., F. Hoffmann-La Roche Ltd, GE Corporation, Gilead Sciences, Inc., GlaxoSmithKline Plc, Illumina, Inc., Intomics A/S, Johnson & Johnson Company, Konica Minolta, Inc., Laboratory Corporation of America MDx Health, Inc., Menarini Silicon Biosystems, Inc., Merck & Co., Inc., Myriad Genetics, Inc., Novartis AG., Oracle Corporation, Partek, Inc., Pfizer, Inc., QIAGEN N.V., Quest Diagnostics Inc., Randox Laboratories Ltd., Sanofi SA, Sysmex Corporation, Teva Pharmaceuticals Industries Ltd., Thermo Fisher Scientific, Inc. including among others, led the number of key developments witnessed by the market. On the basis of region, North America is expected to retain a leading position throughout the forecast period 2020-2030, followed by Europe. Countries Covered North America U.S. Canada Europe Germany France Spain U.K. Italy Rest-of-Europe Asia-Pacific Japan China India Australia Rest-of-Asia-Pacific (RoAPAC) Latin America Brazil Mexico Rest-of-Latin America (RoLA) Rest-of-the-World Israel Saudi Arabia United Arab Emirates South Africa RussiaRead the full report: https://www.reportlinker.com/p05965013/?utm_source=GNW

About ReportlinkerReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

__________________________

See the article here:
Global Precision Medicine Market: Focus on Ecosystem, Technology, Application, Country Data (21 Countries), and Competitive Landscape - Analysis and...

Research

The worlds preeminent scientists say a theory from the Broad Institutes Alina Chan is too wild to be believed. But when the theory is about the possibility of COVID being man-made, is this science or censorship?

Illustration by Benjamen Purvis

In January, as she watched the news about a novel virus spreading out of control in China, Alina Chan braced for a shutdown. The molecular biologist at the Broad Institute of Harvard and MIT started stockpiling medicine and supplies. By the time March rolled around and a quarantine seemed imminent, shed bought hundreds of dollars worth of fillets from her favorite fishmonger in Cambridge and packed them into her freezer. Then she began to ramp down her projects in the lab, isolating her experimental cells from their cultures and freezing them in small tubes.

As prepared as she was for the shutdown, though, she found herself unprepared for the frustration of being frozen out of work. She paced the walls of her tiny apartment feeling bored and useless. Chan has been a puzzle demon since childhood, which was precisely what she loved about her workthe chance to solve fiendishly difficult problems about how viruses operate and how, through gene therapy, they could be repurposed to help cure devastating genetic diseases. Staring out her window at the eerily quiet streets of her Inman Square neighborhood, she groaned at the thought that it could be months before she was at it again. Her mind wandered back to 2003, when she was a teenager growing up in Singapore and the first SARS virus, a close relative of this coronavirus, appeared in Asia. It hadnt been anything like this. That one had been relatively easy to corral. How had this virus come out of nowhere and shut down the planet? Why was it so different? she asked herself.

Then it hit her: The worlds greatest puzzle was staring her in the face. Stuck at home, all she had to work with was her brain and her laptop. Maybe they were enough. Chan fired up the kettle for the first of what would become hundreds of cups of tea, stacked four boxes on her kitchen counter to raise her laptop to the proper height, pulled back her long dark hair, and began reading all of the scientific literature she could find on the coronavirus.

It wasnt long before she came across an article about the remarkable stability of the virus, whose genome had barely changed from the earliest human cases, despite trillions of replications. This perplexed Chan. Like many emerging infectious diseases, COVID-19 was thought to be zoonoticit originated in animals, then somehow found its way into people. At the time, the Chinese government and most scientists insisted the jump had happened at Wuhans seafood market, but that didnt make sense to Chan. If the virus had leapt from animals to humans in the market, it should have immediately started evolving to life inside its new human hosts. But it hadnt.

On a hunch, she decided to look at the literature on the 2003 SARS virus, which had jumped from civets to people. Bingo. A few papers mentioned its rapid evolution in its first months of existence. Chan felt the familiar surge of puzzle endorphins. The new virus really wasnt behaving like it should. Chan knew that delving further into this puzzle would require some deep genetic analysis, and she knew just the person for the task. She opened Google Chat and fired off a message to Shing Hei Zhan. He was an old friend from her days at the University of British Columbia and, more important, he was a computational god.

Do you want to partner on a very unusual paper? she wrote.

Sure, he replied.

One thing Chan noticed about the original SARS was that the virus in the first human cases was subtly differenta few dozen letters of genetic codefrom the one in the civets. That meant it had immediately morphed. She asked Zhan to pull up the genomes for the coronaviruses that had been found on surfaces in the Wuhan seafood market. Were they at all different from the earliest documented cases in humans?

Zhan ran the analysis. Nope, they were 100 percent the same. Definitely from humans, not animals. The seafood-market theory, which Chinese health officials and the World Health Organization espoused in the early days of the pandemic, was wrong. Chans puzzle detectors pulsed again. Shing, she messaged Zhan, this paper is going to be insane.

In the coming weeks, as the spring sun chased shadows across her kitchen floor, Chan stood at her counter and pounded out her paper, barely pausing to eat or sleep. It was clear that the first SARS evolved rapidly during its first three months of existence, constantly fine-tuning its ability to infect humans, and settling down only during the later stages of the epidemic. In contrast, the new virus looked a lot more like late-stage SARS. Its almost as if were missing the early phase, Chan marveled to Zhan. Or, as she put it in their paper, as if it was already well adapted for human transmission.

That was a profoundly provocative line. Chan was implying that the virus was already familiar with human physiology when it had its coming-out party in Wuhan in late 2019. If so, there were three possible explanations.

Perhaps it was just staggeringly bad luck: The mutations had all occurred in an earlier host species, and just happened to be the perfect genetic arrangement for an invasion of humanity. But that made no sense. Those mutations would have been disadvantageous in the old host.

Maybe the virus had been circulating undetected in humans for months, working out the kinks, and nobody had noticed. Also unlikely. Chinas health officials would not have missed it, and even if they had, theyd be able to go back now through stored samples to find the trail of earlier versions. And they werent coming up with anything.

That left a third possibility: The missing phase had happened in a lab, where the virus had been trained on human cells. Chan knew this was the third rail of potential explanations. At the time, conspiracy theorists were spinning bioweapon fantasies, and Chan was loath to give them any ammunition. But she also didnt want to play politics by withholding her findings. Chan is in her early thirties, still at the start of her career, and an absolute idealist about the purity of the scientific process. Facts were facts.

Or at least they used to be. Since the start of the pandemic, the Trump administration has been criticized for playing fast and loose with factsdenying, exaggerating, or spinning them to suit the presidents political needs. As a result, many scientists have learned to censor themselves for fear that their words will be misrepresented. Still, Chan thought, if she were to sit on scientific research just to avoid providing ammunition to conspiracy theorists or Trump, would she be any better than them?

Chan knew she had to move forward and make her findings public. In the final draft of her paper, she torpedoed the seafood-market theory, then laid out a case that the virus seemed curiously well adapted to humans. She mentioned all three possible explanations, carefully wording the third to emphasize that if the novel coronavirus did come from a lab, it would have been the result of an accident in the course of legitimate research.

On May 2, Chan uploaded the paper to a site where as-yet-unpublished biology papers known as preprints are shared for open peer review. She tweeted out the news and waited. On May 16, the Daily Mail, a British tabloid, picked up her research. The very next day, Newsweek ran a story with the headline Scientists Shouldnt Rule Out Lab as Source of Coronavirus, New Study Says.

And that, Chan says, is when shit exploded everywhere.

Alina Chan, a molecular biologist at the Broad Institute, says we cant rule out the possibility that the novel coronavirus originated in a labeven though she knows its a politically radioactive thing to say. / Photo by Mona Miri

Chan had come to my attention a week before the Newsweek story was published through her smart and straightforward tweets, which I found refreshing at a time when most scientists were avoiding any serious discussion about the possibility that COVID-19 had escaped from a biolab. Id written a lot about genetic engineering and so-called gain-of-function researchthe fascinating, if scary, line of science in which scientists alter viruses to make them more transmissible or lethal as a way of assessing how close those viruses are to causing pandemics. I also knew that deadly pathogens escape from biolabs with surprising frequency. Most of these accidents end up being harmless, but many researchers have been infected, and people have died as a result.

For years, concerned scientists have warned that this type of pathogen research was going to trigger a pandemic. Foremost among them was Harvard epidemiologist Marc Lipsitch, who founded the Cambridge Working Group in 2014 to lobby against these experiments. In a series of policy papers, op-eds, and scientific forums, he pointed out that accidents involving deadly pathogens occurred more than twice a week in U.S. labs, and estimated that just 10 labs performing gain-of-function research over a 10-year period would run a nearly 20 percent risk of an accidental release. In 2018, he argued that such a release could lead to global spread of a virulent virus, a biosafety incident on a scale never before seen.

Thanks in part to the Cambridge Working Group, the federal government briefly instituted a moratorium on such research. By 2017, however, the ban was lifted and U.S. labs were at it again. Today, in the United States and across the globe, there are dozens of labs conducting experiments on a daily basis with the deadliest known pathogens. One of them is the Wuhan Institute of Virology. For more than a decade, its scientists have been discovering coronaviruses in bats in southern China and bringing them back to their lab in Wuhan. There, they mix genes from different strains of these novel viruses to test their infectivity in human cells and lab animals.

When word spread in January that a novel coronavirus had caused an outbreak in Wuhanwhich is a thousand miles from where the bats that carry this lineage of viruses are naturally foundmany experts were quietly alarmed. There was no proof that the lab was the source of the virus, but the pieces fit.

Despite the evidence, the scientific community quickly dismissed the idea. Peter Daszak, president of EcoHealth Alliance, which has funded the work of the Wuhan Institute of Virology and other labs searching for new viruses, called the notion preposterous, and many other experts echoed that sentiment.

That wasnt necessarily what every scientist thought in private, though. They cant speak directly, one scientist told me confidentially, referring to the virology communitys fear of having their comments sensationalized in todays politically charged environment. Many virologists dont want to be hated by everyone in the field.

There are other potential reasons for the pushback. Theres long been a sense that if the public and politicians really knew about the dangerous pathogen research being conducted in many laboratories, theyd be outraged. Denying the possibility of a catastrophic incident like this, then, could be seen as a form of career preservation. For the substantial subset of virologists who perform gain-of-function research, Richard Ebright, a Rutgers microbiologist and another founding member of the Cambridge Working Group, told me, avoiding restrictions on research funding, avoiding implementation of appropriate biosafety standards, and avoiding implementation of appropriate research oversight are powerful motivators. Antonio Regalado, biomedicine editor of MIT Technology Review, put it more bluntly. If it turned out COVID-19 came from a lab, he tweeted, it would shatter the scientific edifice top to bottom.

Thats a pretty good incentive to simply dismiss the whole hypothesis, but it quickly amounted to a global gaslighting of the mediaand, by proxy, the public. An unhealthy absolutism set in: Either you insisted that any questions about lab involvement were absurd, or you were a tool of the Trump administration and its desperation to blame China for the virus. I was used to social media pundits ignoring inconvenient or politically toxic facts, but Id never expected to see that from some of our best scientists.

Which is why Chan stood out on Twitter, daring to speak truth to power. It is very difficult to do research when one hypothesis has been negatively cast as a conspiracy theory, she wrote. Then she offered some earnest advice to researchers, suggesting that most viral research should be done with neutered viruses that have had their replicating machinery removed in advance, so that even if they escaped confinement, they would be incapable of making copies of themselves. When these precautions are not followed, risk of lab escape is exponentially higher, she explained, adding, I hope the pandemic motivates local ethics and biosafety committees to think carefully about how they can reduce risk. She elaborated on this in another tweet several days later: Id alsopersonallyprefer if high biosafety level labs were not located in the most populous cities on earth.

How Safe Are Bostons Biolabs?

As one of the world centers of biotech, the Hub is peppered with academic and corporate labs doing research on pathogens. Foremost among them is Boston Universitys National Emerging Infectious Diseases Laboratories (NEIDL), the only lab in the city designated as BSL-4 (the highest level of biosafety and the same level as the Wuhan Institute of Virology). It is one of just a dozen or so in the United States equipped to work with live versions of the worlds most dangerous viruses, including Ebola and Marburg. Researchers there began doing so in 2018 after a decade of controversy: Many locals objected to the risks of siting such a facility in the center of a major metropolitan area.

The good news? Before opening, NEIDL undertook one of the most thorough risk assessments in history, learning from the mistakes of other facilities. Even Lynn Klotz, a senior science fellow at the Washington, DCbased Center for Arms Control and Non-Proliferation, who advised local groups that opposed NEIDL, told the medical website Contagion that the lab likely has the best possible security protocols and measures in place.

But the reality, Klotz added, is that most lab accidents are caused by human error, and there is only so much that can be done through good design and protocols to proactively prevent such mistakes. (Or to guard against an intentional release by a disgruntled researcher, as allegedly happened in the anthrax attacks of 2001.) Rutgers molecular biologist Richard Ebright, a longtime critic of potentially dangerous pathogen research, says the risks introduced by NEIDL are not low enough and definitely not worth the negligible benefits.

Still, risk is relative. Klotz has estimated the chance of a pathogen escape from a BSL-4 lab at 0.3 percent per year, and NEIDL is probably significantly safer than the typical BSL-4 lab. And if catching a deadly pathogen is your fear, well, currently you run a good risk of finding one in your own neighborhood. Until that gets cleared up, the citys biolabs are probably among the safer spaces in town.

Chan had started using her Twitter account this intensely only a few days earlier, as a form of outreach for her paper. The social platform has become the way many scientists find out about one anothers work, and studies have shown that attention on Twitter translates to increased citations for a paper in scientific literature. But its a famously raw forum. Many scientists are not prepared for the digital storms that roil the Twitterverse, and they dont handle it well. Chan dreaded it at first, but quickly took to Twitter like a digital native. Having Twitter elevates your work, she says. And I think its really fun to talk to nonscientists about that work.

After reading her tweets, I reviewed her preprint, which I found mind-blowing, and wrote her to say so. She thanked me and joked that she worried it might be career suicide.

It wasnt long before it began to look like she might be right.

Speaking her mind, it turns outeven in the face of censurewas nothing new for Chan, who is Canadian but was raised in Singapore, one of the more repressive regimes on earth. Her parents, both computer science professionals, encouraged free thinking and earnest inquiry in their daughter, but the local school system did not. Instead, it was a pressure-cooker of a system that rewarded students for falling in line, and moved quickly to silence rebels.

That was a bad fit for Chan. You have to bow to teachers, she says. Sometimes teachers from other classes would show up and ask me to bow to them. And I would say, No, youre not my teacher. Back then they believed in corporal punishment. A teacher could just take a big stick and beat you in front of the class. I got whacked so many times.

Still, Chan rebelled in small ways, skipping school and hanging out at the arcade. She also lost interest in her studies. I just really didnt like school. And I didnt like all the extracurriculars they pack you with in Singapore, she says. That changed when a teacher recruited her for math Olympiads, in which teams of students compete to solve devilishly hard arithmetic puzzles. I really loved it, she says. You just sit in a room and think about problems.

Chan might well have pursued a career in math, but then she came up against teams from China in Olympiad competitions. They would just wipe everyone else off the board, she says. They were machines. Theyd been trained in math since they could walk. Theyd hit the buzzer before you could even comprehend the question. I thought, Im not going to survive in this field.

Chan decided to pursue biology instead, studying at the University of British Columbia. I liked viruses from the time I was a teen, she says. I remember the first time I learned about HIV. I thought it was a puzzle and a challenge. That instinct took her to Harvard Medical School as a postdoc, where the puzzle became how to build virus-like biomolecules to accomplish tasks inside cells, and then to Ben Devermans lab at the Broad Institute. When I see an interesting question, I want to spend 100 percent of my time working on it, she says. I get really fixated on answering scientific questions.

Deverman, for his part, says he wasnt actively looking to expand his team when Chan came along, but when opportunities to hire extraordinary people fall in my lap, he takes them. Alina brings a ton of value to the lab, he explains, adding that she has an ability to pivot between different topics and cut to the chase. Nowhere was that more on display than with her coronavirus work, which Deverman was able to closely observe. In fact, Chan ran so many ideas past him that he eventually became a coauthor. She is insightful, determined, and has the rare ability to explain complex scientific findings to other scientists and to the public, he says.

Those skills would prove highly useful when word got out about her coronavirus paper.

If Chan had spent a lifetime learning how to pursue scientific questions, she spent most of the shutdown learning what happens when the answers you come up with are politically radioactive. After the Newsweek story ran, conservative-leaning publications seized on her paper as conclusive evidence that the virus had come from a lab. Everyone focused on the one line, Chan laments. The tabloids just zoomed in on it. Meanwhile, conspiracists took it as hard evidence of their wild theories that there had been an intentional leak.

Chan spent several exhausting days putting out online fires with the many people who had misconstrued her findings. I was so naive, she tells me with a quick, self-deprecating laugh. I just thought, Shouldnt the world be thinking about this fairly? I really have to kick myself now.

Even more troubling, though, were the reactions from other scientists. As soon as her paper got picked up by the media, luminaries in the field sought to censure her. Jonathan Eisen, a well-known professor at UC Davis, criticized the study in Newsweek and on his influential Twitter account, writing, Personally, I do not find the analysis in this new paper remotely convincing. In a long thread, he argued that comparing the new virus to SARS was not enough to show that it was preadapted to humans. He wanted to see comparisons to the initial leap of other viruses from animals to humans.

Moments later, Daszak piled on. The NIH had recently cut its grant to his organization, EcoHealth Alliance, after the Trump administration learned that some of it had gone to fund the Wuhan Institute of Virologys work. Daszak was working hard to get it restored and trying to stamp out any suggestion of a lab connection. He didnt hold back on Chan. This is sloppy research, he tweeted, calling it a poorly designed phylogenetic study with too many inferences and not enough data, riding on a wave of conspiracy to drive a higher impact. Peppering his tweets with exclamation points, he attacked the wording of the paper, arguing that one experiment it cited was impossible, and told Chan she didnt understand her own data. Afterward, a Daszak supporter followed up his thread with a GIF of a mike drop.

It was an old and familiar dynamic: threatened silverback male attempts to bully a junior female member of the tribe. As a postdoc, Chan was in a vulnerable position. The world of science is still a bit medieval in its power structure, with a handful of institutions and individuals deciding who gets published, who gets positions, who gets grants. Theres little room for rebels.

What happened next was neither old nor familiar: Chan didnt back down. Sorry to disrupt mike drop, she tweeted, providing a link to a paper in the prestigious journal Nature that does that exact experiment you thought was impossible. Politely but firmly, she justified each point Daszak had attacked, showing him his mistakes. In the end, Daszak was reduced to arguing that she had used the word isolate incorrectly. In a coup de grce, Chan pointed out that actually the word had come from online data provided by GenBank, the NIHs genetic sequence database. She offered to change it to whatever made sense. At that point, Daszak stopped replying. He insists, however, that Chan is overinterpreting her findings.

With Eisen, Chan readily agreed to test her hypothesis by finding other examples of viruses infecting new hosts. Within days, a perfect opportunity came along when news broke that the coronavirus had jumped from humans to minks at European fur farms. Sure enough, the mink version began to rapidly mutate. You actually see the rapid evolution happening, Chan said. Just in the first few weeks, the changes are quite drastic.

Chan also pointed out to Eisen that the whole goal of a website such as bioRxiv (pronounced bioarchive)where she posted the paperis to elicit feedback that will make papers better before publication. Good point, he replied. Eventually he conceded that there was a lot of interesting analysis in the paper and agreed to work with Chan on the next draft.

The Twitter duels with her powerful colleagues didnt rattle Chan. I thought Jonathan was very reasonable, she says. I really appreciated his expertise, even if he disagreed with me. I like that kind of feedback. It helped to make our paper better.

With Daszak, Chan is more circumspect. Some people have trouble keeping their emotions in check, she says. Whenever I saw his comments, Id just think, Is there something I can learn here? Is there something hes right about that I should be fixing? Ultimately, she decided, there was not.

By late May, both journalists and armchair detectives interested in the mystery of the coronavirus were discovering Chan as a kind of Holmes to our Watson. She crunched information at twice our speed, zeroing in on small details wed overlooked, and became a go-to for anyone looking for spin-free explications of the latest science on COVID-19. It was thrilling to see her reasoning in real time, a reminder of why Ive always loved science, with its pursuit of patterns that sometimes leads to exciting revelations. The website CNET featured her in a story about a league of scientists-turned-detectives who were using genetic sequencing technologies to uncover COVID-19s origins. After it came out, Chan added scientist-turned-detective to her Twitter bio.

Shes lived up to her new nom de tweet. As the search for the source of the virus continued, several scientific teams published papers identifying a closely related coronavirus in pangolinsanteater-like animals that are heavily trafficked in Asia for their meat and scales. The number of different studies made it seem as though this virus was ubiquitous in pangolins. Many scientists eagerly embraced the notion that the animals might have been the intermediate hosts that had passed the novel coronavirus to humans. It fit their preexisting theories about wet markets, and it would have meant no lab had been involved.

As Chan read the pangolin papers, she grew suspicious. The first one was by a team that had analyzed a group of the animals intercepted by anti-smuggling authorities in southern China. They found the closely related virus in a few of them, and published the genomes for that virus. Some of the other papers, though, were strangely ambiguous about where their data was coming from, or how their genomes had been constructed. Had they really taken samples from actual pangolins?

Once again, Chan messaged Shing Hei Zhan. Shing, somethings weird here, she wrote. Zhan pulled up the raw data from the papers and compared the genomes they had published. Individual copies of a virus coming from different animals should have small differences, just as individuals of a species have genetic differences. Yet the genomes in all of the pangolin papers were perfect matchesthe authors were all simply using the first groups data set. Far from being ubiquitous, the virus had been found only in a few pangolins who were held together, and it was unclear where they had caught it. The animals might have even caught it from their own smuggler.

Remarkably, one group of authors in Nature even appeared to use the same genetic sequences from the other paper as if it were confirmation of their own discovery. These sequences appear to be from the same virus (Pangolin-CoV) that we identified in the present study.

Chan called them out on Twitter: Of course its the same Pangolin-CoV, you used the same dataset! For context, she later added, Imagine if clinical trials were playing fast and loose with their patient data; renaming patients, throwing them into different datasets without clarification, possibly even describing the same patient multiple times across different studies unintentionally.

She and Zhan posted a new preprint on bioRxiv dismantling the pangolin papers. Confirmation came in June when the results of a study of hundreds of pangolins in the wildlife trade were announced: Not a single pangolin had any sign of a coronavirus. Chan took a victory lap on Twitter: Supports our hypothesis all this time. The pangolin theory collapsed.

Chan then turned her Holmesian powers on bigger game: Daszak and the Wuhan Institute of Virology. Daszak had been pleading his case everywhere from 60 Minutes to the New York Times and has been successful in rallying sympathy to his cause, even getting 77 Nobel laureates to sign a letter calling for the NIH to restore EcoHealth Alliances funding.

In several long and detailed tweetorials, Chan began to cast a cloud of suspicion on the WIVs work. She pointed out that scientists there had discovered a virus that is more than 96 percent identical to the COVID-19 coronavirus in 2013 in a mineshaft soon after three miners working there had died from a COVID-like illness. The WIV didnt share these findings until 2020, even though the goal of such work, Chan pointed out, was supposedly to identify viruses with the potential to cause human illnesses and warn the world about them.

Even though that virus had killed three miners, Daszak said it wasnt considered a priority to study at the time. We were looking for SARS-related virus, and this one was 20 percent different. We thought it was interesting, but not high risk. So we didnt do anything about it and put it in the freezer, he told a reporter from Wired. It was only in 2020, he maintained, that they started looking into it once they realized its similarity to COVID-19. But Chan pointed to an online database showing that the WIV had been genetically sequencing the mine virus in 2017 and 2018, analyzing it in a way they had done in the past with other viruses in preparation for running experiments with them. Diplomatic yet deadpan, she wrote, I think Daszak was misinformed.

For good measure, almost in passing, Chan pointed out a detail no one else had noticed: COVID-19 contains an uncommon genetic sequence that has been used by genetic engineers in the past to insert genes into coronaviruses without leaving a trace, and it falls at the exact point that would allow experimenters to swap out different genetic parts to change the infectivity. That same sequence can occur naturally in a coronavirus, so this was not irrefutable proof of an unnatural origin, Chan explained, only an observation. Still, it was enough for one Twitter user to muse, If capital punishment were as painful as what Alina Chan is doing to Daszak/WIV regarding their story, it would be illegal.

Daszak says that indeed he had been misinformed and was unaware that that virus found in the mine shaft had been sequenced before 2020. He also says that a great lab, with great scientists, is now being picked apart to search for suspicious behavior to support a preconceived theory. If you believe, deep down, something fishy went on, then what you do is you go through all the evidence and you try to look for things that support that belief, he says, adding, That is not how you find the truth.

Many of the points in Chans tweetorials had also been made by others, but she was the first reputable scientist to put it all together. That same week, Londons Sunday Times and the BBC ran stories following the same trail of breadcrumbs that Chan had laid out to suggest that there had been a coverup at the WIV. The story soon circulated around the world. In the meantime, the WIV has steadfastly denied any viral leak. Lab director Yanyi Wang went on Chinese television and described such charges as pure fabrication, and went on to explain that the bat coronavirus from 2013 was so different than COVID that it could not have evolved into it this quickly and that the lab only sequenced it and didnt obtain a live virus from it.

To this day, there is no definitive evidence as to whether the virus occurred naturally or had its origins in a lab, but the hypothesis that the Wuhan facility was the source is increasingly mainstream and the science behind it can no longer be ignored. And Chan is largely to thank for that.

In late spring, Chan walked through the tall glass doors of the Broad Institute for the first time in months. As she made her way across the gleaming marble foyer, her sneaker squeaks echoed in the silence. It was like the zombie apocalypse version of the Broad; all the bright lights but none of the people. It felt all the weirder that she was wearing her gym clothes to work.

A few days earlier, the Broad had begun letting researchers back into their labs to restart their projects. All computer work still needed to be done remotely, but bench scientists such as Chan could pop in just long enough to move along their cell cultures, provided they got tested for the virus every four days.

In her lab, Chan donned her white lab coat and took inventory, throwing out months of expired reagents and ordering new materials. Then she rescued a few samples from the freezer, took her seat at one of the tissue-culture hoodsstainless steel, air-controlled cabinets in which cell engineers do their workand began reviving some of her old experiments.

She had mixed emotions about being back. It felt good to free her gene-therapy projects from their stasis, and she was even more excited about the new project she and Deverman were working on: an online tool that allows vaccine developers to track changes in the viruss genome by time, location, and other characteristics. It came out of my personal frustration at not being able to get answers fast, she says.

On the other hand, she missed being all-consumed by her detective work. I wanted to stop after the pangolin preprint, she says, but this mystery keeps drawing me back in. So while she waits for her cell cultures to grow, shes been sleuthing on the sideonly this time she has more company: Increasingly, scientists have been quietly contacting her to share their own theories and papers about COVID-19s origins, forming something of a growing underground resistance. Theres a lot of curiosity, she says. People are starting to think more deeply about it. And they have to, she says, if we are going to prevent future outbreaks: Its really important to find out where this came from so it doesnt happen again.

That is what keeps Chan up at nightthe possibility of new outbreaks in humans from the same source. If the virus emerged naturally from a bat cave, there could well be other strains in existence ready to spill over. If they are closely related, whatever vaccines we develop might work on them, too. But that might not be the case with manipulated viruses from a laboratory. Someone could have been sampling viruses from different caves for a decade and just playing mix-and-match in the lab, and those viruses could be so different from one another that none of our vaccines will work on them, she says. Either way, We need to find where this came from, and close it down.

Whatever important information she finds, we can be sure Chan will share it with the world. Far from being shaken by the controversy her paper stirred, she is more committed than ever to holding a line that could all too easily be overrun. Scientists shouldnt be censoring themselves, she says. Were obliged to put all the data out there. We shouldnt be deciding that its better if the public doesnt know about this or that. If we start doing that, we lose credibility, and eventually we lose the publics trust. And thats not good for science. In fact, it would cause an epidemic of doubt, and that wouldnt be good for any of us.

Read more from the original source:
Was COVID-19 Manmade? Meet the Scientist Behind the Theory - Boston magazine

The hunt for the Grail just got a lot easier. In fact, in a few weeks youll likely be able to pick up a portion of it on Robin Hood, no coconut horses required.

Grail, the monstrously backed liquid biopsy biotech, has filed for an IPO. No pricing details have been disclosed, but if history is any indicator, the company will have a chance to cap 6 months of booming pandemic-era offerings the way the billion-dollar Dark Knight films would cap the summer blockbuster in their heyday. CEO Hans Bishop has penciled in $100 million for the raise, but that figure has become a standard placeholder until biotechs can gauge exactly how much they can raise.

The S-1 lifts a lid on a group that has operated in relative obscurity over the past few years, known primarily by way of the big-name investors Jeff Bezos, Illumina and ARCH Venture among them who poured in $1.6 billion while it was still a private company. It shows, for one, that the company is planning a faster rollout than some may have expected. And it reveals that Bishop, long one of the industrys highest paid executives, got a princely sum to return from a post-Juno sabbatical.

Like Third Rock-backed Thrive and a few lesser-known rivals, Grail is trying to develop a blood test that can detect cancers more cheaply and earlier than conventional diagnostics can. Its a goal that could transform cancer treatment, allowing doctors to begin therapy when its most effective and catch potentially fatal malignancies while theyre still treatable, but it comes with major technological and financial hurdles.

Today Grail revealed plans to roll out its first major test, called Galleri, next year, setting them up to potentially beat Thrive to market. That promise, the company acknowledges, comes in advance of the actual data to support that test and the rollout could be delayed if Covid-19 again blocks trials, as they did earlier this year. Grail is currently running a trial, called Pathfinder, to see whether the test can prospectively help doctors detect cancer early. They will also use a subset of data an observational study that provided their first validation last year.

The company also plans to roll out a second test that will aid traditional diagnoses in the second half of next year. Subsequent trial data could help expand the indication.

To oversee final development and launch, Grail hired Bishop as CEO last year. The serial executive had worked with ARCHs Bob Nelsen at Juno Therapeutics, the CAR-T company that Celgene bought for $9 billion. Bishop, who at one point made $88 million in a year at Juno, took home a pay package of $64 million last year, most of it in stock.

For an intra-industry comparison, thats a little more than the $59 million Moderna CEO Stephan Bancel earned after the mRNA biotechs IPO and its a little less than the $70 million Sareptas Doug Ingram made, a figure that ranked him 10th in Bloombergs list of top ten CEOs.

Still, the S-1 also points to the obstacles will face as it looks to transition from a cash-raising company to a profit-generating one. Notably, they will have to secure reimbursement from insurers, particularly in Medicaid, and they acknowledge they will probably not have broad-based coverage and reimbursement at the initial commercial launch for Galleri. Long-term, if patients have to pay out-of-pocket, doctors may be unwilling to order it.

They acknowledge, too, that if competitors products do not perform as intended, the market for our products could be impaired i.e., if another company has faulty tests, payers and doctors may discount the entire approach. Which means that, although Grail may be racing Thrive and a handful of others to the finish line, theyre also depending on them to deliver.

View post:
UPDATED: $1.6 billion liquid biopsy player Grail files IPO, revealing 2021 commercial plans and a Midas-sized $65M pay package for Hans Bishop -...

Some biotechs struggle for cash. Recursion, lately, seems to be swimming in it.

Today, having already raised over $180 million in the last two years, the Salt Lake City-based AI drug developer announced a $239 million Series D. Thats more than any US or European biotech has raised in a round this year and more than all but two biotechs raised last year. (Mabwell, a Shanghai-based antibody developer, raised $278 million in a Series A in April.)

The round came with an important point of validation for Recursion and a potential long-term source of revenue: a new partnership with Bayer. That deal, centered on fibrotic diseases, will pay Recursion $30 million upfront for the partnership, along with $100 million in milestones for each of up to 10 programs the companies could pursue. So its an up to $1 billion deal, even if its vanishingly unlikely to reach that. Leaps by Bayer, the German pharmas venture arm, also led the Series D, contributing a $50 million equity investment.

The investment-partnership represents one of the largest rounds, if not the single largest round, for an artificial-intelligence focused biotech and cements Recursion as one of the major players in a nascent field that has produced many small startups but few heavyweights. It also points to where Big Pharma and major VCs may spread their focus as machine learning approaches advance. The first small companies to make headlines and sign deals used machine learning to screen vast libraries of molecules in search of ones that can hit targets that drug companies have long tried to hit. More recently, though, a couple upstarts have raised significant cash and scored prominent partnerships using advanced computational tools to study cells more closely and come up with new targets themselves.

What Recursion does is really hard so I wouldnt say there is going to be a deluge of companies, Zachary Bogue, founder of early Recursion backer DCVC, told Endpoints News. But this idea of biology as a platform and using AI as a drug discovery is the new frontier in biotech.

Roughly, thats what Recursion does. In a 100,000 square-foot warehouse in downtown Salt Lake City, robots take petri dishes of different cell types and knock out different genes, taking constant pictures in the process. Humans cant easily tell the difference between most of those pictures, but computers can, and with enough images and hundreds of different measurements on each, they can pick up patterns to indicate what can make a cell sick and which genes, when targeted, can make them healthy. They can then identify and tweak molecules or compounds that hit those targets.

So far, theyve used that approach to identify molecules to bring into clinical development for several rare neurological conditions and hereditary cancer syndrome, pulling compounds from Ohio State, Takeda, and co-founder Dean Lis labs. But the company lists a bevy of preclinical disease areas on its pipeline, and Recursion CEO Chris Gibson said that, with the Bayer deal, they would begin to look to partner with big companies where clinical development is more complex, such as in neurology and oncology.

Weve been talking about this internally as the beginning of a second act for the company, he said in an interview with Endpoints.

Other companies have scored large amounts of capital with similar approaches. Most notably, Daphne Kollers startup Insitro has nabbed two $100 million-plus rounds in a span of 13 months, plus a Gilead collaboration on NASH. In January, star Canadian researcher Brendan Freys Deep Genomics raised $40 million into the clinic. Meanwhile, the rhetoric from the handful of drug pharma executives who talk openly about machine learning, such as GlaxoSmithKlines Hal Barron, has centeredon approaches that help uncover not just new molecules but also new targets.

Under the deal with Bayer, Recursion will use their system to build models for different fibrotic diseases, following guidance from Bayer fibrosis experts at the pharma. They will then use Bayers library of small molecules and their own internal ability to screen and develop molecules to come up with preclinical candidates. Around that point, Gibson said, theyll hand things off to Germany.

Although Leaps by Bayer described their $50 million contribution to the Series D as our big bet in terms of digital drug discovery, Bayer itself has also invested in the molecule-screening side of the AI biotech world, as have other major companies. In January, Bayer and the UK-based Exscientia signed a discovery collaboration for up to 240 million. Exscientia has also teamed with Sumitomo, Sanofi and Celgene, the last of which included a $25 million upfront payment. Atomwise and Insilico have also signed multiple big-name partnerships, although they have been largely milestone-heavy, with little upfront disclosed.

Follow this link:
Recursion nabs $239M and an up to $1B partnership with Bayer as AI race heats up - Endpoints News