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Pharma Leverages AI to Elevate Digital-Only Operations

WNS describes how pharma companies can gain competitive intelligence and use it to improve decision making.
The post Pharma Leverages AI to Elevate Digital-Only Operations appeared first on GEN – Genetic Engineering and Biotechnology News.

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By Akhilesh Ayer and Mark Halford

Pharma companies have long been playing a critical role in our lives while staying mostly in the background. However, COVID-19 has pushed the pharma industry firmly to the foreground. Governments and societies are pinning their hopes on the industry’s ability to help prevent the next epidemic while continuing to find cures for known health conditions and diseases.

Navigating a complex and fast-changing landscape

Akhilesh Ayer, Head of Research and Analytics, WNS

In the wake of the pandemic, pharma companies face a growing number of challenges, especially in relation to research and development (R&D). Costs have spiraled over the last decade. According to Statista, the pharma industry’s R&D spending in 2020 totaled about $200 billion globally. The journey from preclinical research to marketing can take anywhere between 12 to 18 years, pushing any kind of return on investment into the distant future and increasing the risk to company finances, particularly with respect to liquidity. And now, because of the precedent set by the pharma industry’s COVID-19 response, there is the expectation that essential vaccines will be developed quickly.

The growth of generics is eating away at market share, and regulations have become more complex. Pharma companies that fall foul of the rules can expect not just fines, but greater reputational damage than ever.

Mark Halford
Mark Halford, Senior VP, Client Services, Life Sciences and Healthcare, WNS

Developing drugs to combat new and sometimes little understood conditions is particularly time consuming and costly. And developing treatments that offer really significant improvements over existing products is also challenging. Meanwhile, the U.S. Food and Drug Administration (FDA) has signaled its desire to bring more competition to the pharma market in an attempt to reduce the cost of medicines.

To remain competitive, drug companies need to invest in their R&D systems to speed up the discovery process. As industries across the board move toward a digital-only world, pharma companies must account for the different technologies that are transforming the R&D process. Digital adoption is the future of the pharma industry, and it is essential thatpharma companies understand which technologies can deliver faster, more effective R&D.

Tapping into the immense potential of AI

Artificial intelligence (AI) offers forward-thinking pharma companies the opportunity to revolutionize their drug discovery and development processes. Hong Kong–based Insilico Medicine, for instance, has used AI and deep learning to design, synthesize, and validate a novel drug candidate in 46 days—15 times faster than what was previously thought possible.

As the Insilico Medicine case study demonstrates, it is already clear that AI, guided by humans, has the potential to manage the vast number of compound permutations needed for drug design. This fast-evolving technology enables the aggregation, harmonization, and analysis of multiple data sources needed for discovery, design, and clinical trials, thereby shortening the drug development process.

AI can increase novel drug discovery, minimize potential drug interactions, enhance the understanding of disease mechanisms, speed drug design, identify biomarkers, run preclinical experiments, design clinical trials, and provide deep insights more efficiently. It can be leveraged to predict the medicines that will ultimately work and the ones that will not, thus helping to reduce the investment in candidates unlikely to make it to market.

By becoming fully digital, pharma companies can gain real-time competitor insights and keep abreast of change in drug regulations across regions. With the use of AI and data analytics, they can streamline knowledge collation via available public and commercial data sources. This instantly provides R&D teams with insights needed on their competition as well as intelligence on local regulations, emerging conditions in a particular regime, and the release of new drugs into the market.

AI and data analytics can process vast amounts of data, some of which is unstructured, in a fraction of the time taken by human beings. AI can “learn” what trends and developments to look out for in the data and alert human beings only to what is relevant.

Advancing CI with AI and cloud technology

Cloud-based platforms allow pharma companies to become more agile, as they provide an immediate, interactive, and customizable way of storing competitor intelligence (CI) data. With a cloud-based platform, users can access insights across devices such as tablets, smartphones, or desktops—through highly secure and seamless authentication. This is particularly important for sales representatives or attendees of conferences and meetings, as vital information can be shared quickly among all relevant parties within the organization.

Pharma groups can integrate their cloud solutions across company systems in a way that combines both business intelligence tools and commercial data. These platforms are customizable so that they can display in-house and external news, competitor profiles, and sales and pricing information via easily accessible dashboards and layouts. Combining valuable data analytics insights, AI, and cloud-based platforms streamlines the retrieval of market insights for R&D teams.

Insights from CI have always enabled pharma companies to identify their core strengths in relation to their competitors. As well as transforming drug development, AI can enhance CI to cope with rapidly evolving markets and greater disruption. Detailed, accurate, and timely CI can help companies target unmet needs to transform not just their offerings, but entire business models. The analysis of the competitive environment can also help companies focus their R&D efforts. This helps benchmark performance, identify strengths, and highlight priority areas. Superimposing results from business intelligence and CI helps pharma companies identify new avenues to differentiate their brands and fulfill market needs as they emerge.

Cloud computing can be used to complement AI-powered tools as they harvest data from millions of website pages to draw out valuable insights by tracking a competitor’s entire digital footprint, both on and off that competitor’s site. Together, technologies can measure everything from changes in the price of competitors’ products to new appointments. Natural language processing can analyze the sentiment that competitors’ brands are experiencing on social media, and it can read customer reviews on a wide variety of platforms and convert it into usable data and actionable information.

Cloud computing enables companies to effortlessly increase or reduce their storage requirements for CI data. The insights that they develop can also be easily shared across all departments, from R&D to finance, on a variety of devices using intuitive interfaces such as dashboards.

Factoring in the obvious challenges

As pharma companies rush to adopt these new technologies, they are bound to run into obstacles. For example, current patent laws usually prevent AI from being acknowledged as an “inventor.” Hence, AI inventions are not protected as well as traditional innovations. Also, data stores, including stores of genetic code, are becoming larger and more varied. Consequently, security risks are evolving, necessitating new governance measures.

AI will be limited by processing speeds, requiring pharma companies to adopt new technologies in their endeavor of maximizing AI impact. As competitors possibly compress years of product research, development, and launch efforts into mere months, companies will need to ensure that their CI can keep pace.

Even the largest pharma companies need to look for trusted technology partners to help them benefit fully from AI. As they focus on their core competencies, they can work alongside these partners, with their specialist knowledge and capabilities, to draw up a road map for the adoption of AI. Their partner organization should be able to identify the right solutions and then scale them up rapidly in a controlled environment.

CI platforms, such as the PRECIZON platform from WNS, have built-in readiness for the rapid acceleration of R&D enabled by AI. The pace of the market will increasingly demand machine learning intelligence for real-time user recommendations and content classification. Delivered on the cloud, such technologies can be integrated into other core systems with personalized content available through all devices. As AI increases pipeline pace, such scalable enterprise tools are becoming vital. They can ensure that automated systems foster collaboration and deliver insights.

Ranking is a machine learning technique for recommendation systems such as intelligent CI. In what are known as “clustered approaches,” information about user behavior can be utilized to recommend items. These recommendations are generated with user-user or item-item similarity. Based on these similarity measures, the resulting suggestions are provided to the user. By predicting these user preferences, the portal delivers effortless user engagement.

The time for innovation is now

Interestingly, these innovative technologies feed off of each other. As the growth of AI and automation drives various facets of pharma operations to become digital-only, enterprises will increasingly find themselves storing data on cloud-based platforms. They need to start now by integrating these new technologies into their everyday workflows to stay relevant and ahead of the competition.

Innovation has always been at the heart of the pharma industry. However, the urgency for new drugs is greater than ever, even as risks and uncertainties continue to mount. Implemented correctly, AI and other technologies will enable agile, forward-thinking companies to manage these risks, exploit new opportunities, and deliver for their employees, shareholders, and patients around the world.

 

Akhilesh Ayer is the head of research and analytics at WNS, and Mark Halford is the company’s senior vice president of client services, life sciences and healthcare.

The post Pharma Leverages AI to Elevate Digital-Only Operations appeared first on GEN - Genetic Engineering and Biotechnology News.

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VIRI: Enrollment Complete in FORTRESS Trial; Results Expected in September 2022…

By David Bautz, PhD
NASDAQ:VIRI
READ THE FULL VIRI RESEARCH REPORT
Business Update
FORTRESS Trial Fully Enrolled; Topline Results in September 2022
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By David Bautz, PhD

NASDAQ:VIRI

READ THE FULL VIRI RESEARCH REPORT

Business Update

FORTRESS Trial Fully Enrolled; Topline Results in September 2022

On April 28, 2022, Virios Therapeutics, Inc. (NASDAQ: VIRI) announced that it has completed enrollment of 425 fibromyalgia patients into the Phase 2b FORTRESS (Fibromyalgia Outcome Research Trial Evaluating Synergistic Suppression of Herpes Simplex Virus-1) trial, a randomized, double blind, placebo controlled study of IMC-1. The primary endpoint of the trial is reduction in pain and secondary endpoints include change in fatigue, sleep disturbance, global health status, and patient functionality (NCT04748705). An outline of the trial is shown below.

In parallel with the FORTRESS trial, Virios is continuing the chronic toxicology studies of IMC-1 in two animal species. The results of these studies are required by regulators before Virios will be allowed to dose patients for one year or more, which is the plan for the Phase 3 program. The results of the chronic toxicology studies should be known around the time of the completion of the FORTRESS trial, thus the company should be able to move into a final Phase 3 program following completion of the current study, pending positive results.

Testing Combination Antiviral Therapy for the Treatment of Long COVID

In February 2022, Virios announced a collaboration with the Bateman Horne Center (BHC) to test combination antiviral therapy for the treatment of Long COVID. Following an infection with SARS-CoV-2, the virus that causes COVID-19, approximately 30% of patients will experience symptoms that last for weeks or months, which is referred to as Long COVID. The range of symptoms varies from patient to patient, however the most commonly reported (from a recent meta analysis) were fatigue (58%), headache (44%), attention disorder (27%), hair loss (25%), and dyspnea (24%) (Lopez-Leon et al., 2021).

The main theories for what might be causing ...

Full story available on Benzinga.com

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Type-I interferon stops immune system ‘going rogue’ during viral infections

Hamilton, ON (May 17, 2022) – McMaster University researchers have found not only how some viral infections cause severe tissue damage, but also how…

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Hamilton, ON (May 17, 2022) – McMaster University researchers have found not only how some viral infections cause severe tissue damage, but also how to reduce that damage.

Credit: Georgia Kirkos/McMaster University

Hamilton, ON (May 17, 2022) – McMaster University researchers have found not only how some viral infections cause severe tissue damage, but also how to reduce that damage.

 

They have discovered how Type I interferon (IFN) stops the immune system ‘going rogue’ and attacking the body’s own tissues when fighting viral infections, including COVID-19.

 

Their paper was published in the journal PLOS Pathogens today.

  

Senior author Ali Ashkar said IFN is a well-known anti-viral signalling molecule released by the body’s cells that can trigger a powerful immune response against harmful viruses.

 

“What we have found is that it is also critical to stop white blood cells from releasing protease enzymes, which can damage organ tissue. It has this unique dual function to kick start an immune response against a viral infection on the one hand, as well as restrain that same response to prevent significant bystander tissue damage on the other,” he said.

 

The research team investigated IFN’s ability to regulate a potentially dangerous immune response by testing it on both flu and the HSV-2 virus, a highly prevalent sexually transmitted pathogen, using mice. Data from COVID-19 patients in Germany, including post-mortem lung samples, was also used in the study.

 

“For many viral infections, it is not actually the virus that causes most of the tissue damage, it is our heightened immune activation towards the virus,” said Ashkar, a professor of medicine at McMaster.

  

First co-author of the study and PhD student Emily Feng said: “Our body’s immune response is trying to fight off the virus infection, but there’s a risk of damaging innocent healthy tissue in the process. IFNs regulates the immune response to only target tissues that are infected.

 

“By discovering the mechanisms the immune system uses that can inadvertently cause tissue damage, we can intervene during infection to prevent this damage and not necessarily have to wait until vaccines are developed to develop life-saving treatments,” she added.

 

“This applies not just to COVID-19, but also other highly infectious viruses such as flu and Ebola, which can cause tremendous and often life-threatening damage to the body’s organs,” said first study co-author Amanda Lee, a family medicine resident. 

 

Ashkar said the release of harmful proteases is the result of a ‘cytokine storm’, which is life-threatening inflammation sometimes triggered by viral infections. It has been a common cause of death in patients with COVID-19, but treatment has been developed to prevent and suppress the cytokine storm.

 

Ashkar said that steroids like dexamethasone are already used to rein in an extreme immune response to viral infections. The authors used doxycycline in their study, an antibiotic used for bacterial infections and as an anti-inflammatory agent, inhibits the function of proteases causing the bystander tissue damage.

 

Lee added: “This has the potential in the future to be used to alleviate virus-induced life-threatening inflammation and warrants further research.” 

 

The study was funded by the Canadian Institutes of Health Research.

 

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Editors:

Pictures of Ali Ashkar and Emily Feng may be found at https://bit.ly/3wmSw0D

  

 

 


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mRNA vaccines like Pfizer and Moderna fare better against COVID-19 variants of concern

A comparison of four COVID-19 vaccinations shows that messenger RNA (mRNA) vaccines — Pfizer-BioNTech and Moderna — perform better against the World…

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A comparison of four COVID-19 vaccinations shows that messenger RNA (mRNA) vaccines — Pfizer-BioNTech and Moderna — perform better against the World Health Organization’s variants of concern (VOCs) than viral vector vaccines — AstraZeneca and J&J/Janssen. Although they all effectively prevent severe disease by VOCs, the research, publishing May 17th in the open access journal PLOS Medicine, suggests that people receiving a viral vector vaccine are more vulnerable to infection by new variants.

Credit: Carlos Reusser Monsalvez, Flickr (CC0, https://creativecommons.org/publicdomain/zero/1.0/)

A comparison of four COVID-19 vaccinations shows that messenger RNA (mRNA) vaccines — Pfizer-BioNTech and Moderna — perform better against the World Health Organization’s variants of concern (VOCs) than viral vector vaccines — AstraZeneca and J&J/Janssen. Although they all effectively prevent severe disease by VOCs, the research, publishing May 17th in the open access journal PLOS Medicine, suggests that people receiving a viral vector vaccine are more vulnerable to infection by new variants.

By March 2022, COVID-19 had caused over 450 million confirmed infections and six million reported deaths. The first vaccines approved in the US and Europe that protect against serious infection are Pfizer-BioNTech and Moderna, which deliver genetic code, known as mRNA, to the bodies’ cells, whereas Oxford/AstraZeneca and J&J/Janssen are viral vector vaccines that use a modified version of a different virus — a vector — to deliver instructions to our cells. Three vaccines are delivered as two separate injections a few weeks apart, and J&J/Janssen as a single dose.

Marit J. van Gils at the University of Amsterdam, Netherlands, and colleagues, took blood samples from 165 healthcare workers, three and four weeks after first and second vaccination respectively, and for J&J/Janssen at four to five and eight weeks after vaccination. Samples were collected before, and four weeks after a Pfizer-BioNTech booster.

Four weeks after the initial two doses, antibody responses to the original SARS-CoV-2 viral strain were highest in recipients of Moderna, followed closely by Pfizer-BioNTech, and were substantially lower in those who received viral vector vaccines. Tested against the VOCs – Alpha, Beta, Gamma, Delta and Omicron – neutralizing antibodies were higher in the mRNA vaccine recipients compared to those who had viral vector vaccines. The ability to neutralize VOCs was reduced in all vaccine groups, with the greatest reduction against Omicron. The Pfizer-BioNTech booster increased antibody responses in all groups with substantial improvement against VOCs, including Omicron.

The researchers caution that their AstraZeneca group was significantly older, because of safety concerns for the vaccine in younger age groups. As immune responses tend to weaken with age, this could affect the results. This group was also smaller because the Dutch government halted use for a period.

van Gils concludes, “Four COVID-19 vaccines induce substantially different antibody responses.”

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In your coverage, please use this URL to provide access to the freely available paper in PLOS Medicine:

http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1003991

Citation: van Gils MJ, Lavell A, van der Straten K, Appelman B, Bontjer I, Poniman M, et al. (2022) Antibody responses against SARS-CoV-2 variants induced by four different SARS-CoV-2 vaccines in health care workers in the Netherlands: A prospective cohort study. PLoS Med 19(5): e1003991. https://doi.org/10.1371/journal.pmed.1003991

 

Author Countries: The Netherlands, United States

 

Funding: This work was supported by the Netherlands Organization for Scientific Research (NWO) ZonMw (Vici grant no. 91818627 to R.W.S., S3 study, grant agreement no. 10430022010023 to M.K.B.; RECoVERED, grant agreement no. 10150062010002 to M.D.d.J.), by the Bill & Melinda Gates Foundation (grant no. INV002022 and INV008818 to R.W.S. and INV-024617 to M.J.v.G.), by Amsterdam UMC through the AMC Fellowship (to M.J.v.G.) and the Corona Research Fund (to M.K.B.), and by the European Union’s Horizon 2020 program (RECoVER, grant no. 101003589 to M.D.d.J). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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