Merck was purposely cautious during the start of the pandemic when it came to vaccine development but is now slowly but surely ramping up its efforts into the clinic.
The U.S. Big Pharma, which has good form in creating a new vaccine against a sweeping threat after gaining approval for its Ebola vaccine, said in its second-quarter update that, via its recent buyout of Themis, it’s plotting a third-quarter start for human testing of its thus preclinical V591.
While behind the likes of AstraZeneca, Moderna, Pfizer/BioNTech and CureVac, Merck hopes its expertise and slightly different MOA may prove a tortoise over the hare victory.
The Themis/Merck vaccine works by tapping a measles virus vector platform based on a vector originally developed by scientists at the Institut Pasteur, a world-leading European vaccine research institute, and licensed exclusively to Themis.
And this is not the only shot on goal, as Merck has also teamed up with IAVI for another vaccine candidate,V590, that uses a recombinant vesicular stomatitis virus platform.
This is the same platform that was used for Merck’s approved Ebola vaccine, making it more of a known entity; human studies “are planned to start this year.” This also dovetails with a series of antivirals the company is working on, already in clinical testing.
Merck’s CEO Ken Frazier said: “This pandemic underscores the essential role of Merck and the biopharmaceutical industry in addressing the world’s greatest health challenges and underscores the importance of a health care ecosystem that incentivizes risk-taking and innovation. Ultimately, scientific and medical knowledge will help overcome this ongoing global pandemic, and that is why we must continue to trust and invest in breakthrough science.”
PAOG Advances FDA Application Process For Respiratory Cannabis Drug Treatment
Sandusky, OH – October 20, 2020 – OTC PR WIRE — PAO Group, Inc. (OTC PINK: PAOG) today announced the company plans to release a new key update this Friday, October 23, 2020, on its progress to advance an investigational New Drug application (IND) with the Food and Drug Administration (FDA). PAOG announced last week that it anticipates soon entering into an agreement with a contract research organization (CRO) making a major breakthrough in advancing PAOG’s RespRx treatment for Chronic Obstructive Pulmonary Disease (COPD) toward FDA approval.
On July 30, 2020, PAOG acquired RespRx from Kali-Extracts, Inc. (OTC PINK: KALY). RespRx is a cannabis treatment under development for COPD derived from a patented cannabis extraction method – U.S. Patent No. 9,199,960 entitled “METHOD AND APPARATUS FOR PROCESSING HERBACEOUS PLANT MATERIALS INCLUDING THE CANNABIS PLANT.”
In an initial scientific evaluation as a treatment for COPD, RespRx has demonstrated effecting significant increases in respiration rate, tidal volume and inspiratory air flow rate. Overall data from the evaluation demonstrated that RespRx can significantly improve inspiratory lung functions in instances of moderate pulmonary fibrosis.
Forward-Looking Statements: Certain statements in this news release may contain forward-looking information within the meaning of Rule 175 under the Securities Act of 1933 and Rule 3b-6 under the Securities Exchange Act of 1934, and are subject to the safe harbor created by those rules. All statements, other than statements of fact, included in this release, including, without limitation, statements regarding potential future plans and objectives of the company are forward-looking statements that involve risks and uncertainties. There can be no assurance that such statements will prove to be accurate and actual results and future events could differ materially from those anticipated in such statements. Technical complications, which may arise, could prevent the prompt implementation of any strategically significant plan(s) outlined above. The Company undertakes no duty to revise or update any forward-looking statements to reflect events or circumstances after the date of this release.
Machine learning uncovers potential new TB drugs
Machine learning is a computational tool used by many biologists to analyze huge amounts of data, helping them to identify potential new drugs. MIT researchers have now incorporated a new feature into these types of machine-learning algorithms, improving their prediction-making ability.
Using this new approach, which allows computer models to account for uncertainty in the data they’re analyzing, the MIT team identified several promising compounds that target a protein required by the bacteria that cause tuberculosis.
This method, which has previously been used by computer scientists but has not taken off in biology, could also prove useful in protein design and many other fields of biology, says Bonnie Berger, the Simons Professor of Mathematics and head of the Computation and Biology group in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).
“This technique is part of a known subfield of machine learning, but people have not brought it to biology,” Berger says. “This is a paradigm shift, and is absolutely how biological exploration should be done.”
Berger and Bryan Bryson, an assistant professor of biological engineering at MIT and a member of the Ragon Institute of MGH, MIT, and Harvard, are the senior authors of the study, which appears today in Cell Systems. MIT graduate student Brian Hie is the paper’s lead author.
Machine learning is a type of computer modeling in which an algorithm learns to make predictions based on data that it has already seen. In recent years, biologists have begun using machine learning to scour huge databases of potential drug compounds to find molecules that interact with particular targets.
One limitation of this method is that while the algorithms perform well when the data they’re analyzing are similar to the data they were trained on, they’re not very good at evaluating molecules that are very different from the ones they have already seen.
To overcome that, the researchers used a technique called Gaussian process to assign uncertainty values to the data that the algorithms are trained on. That way, when the models are analyzing the training data, they also take into account how reliable those predictions are.
For example, if the data going into the model predict how strongly a particular molecule binds to a target protein, as well as the uncertainty of those predictions, the model can use that information to make predictions for protein-target interactions that it hasn’t seen before. The model also estimates the certainty of its own predictions. When analyzing new data, the model’s predictions may have lower certainty for molecules that are very different from the training data. Researchers can use that information to help them decide which molecules to test experimentally.
Another advantage of this approach is that the algorithm requires only a small amount of training data. In this study, the MIT team trained the model with a dataset of 72 small molecules and their interactions with more than 400 proteins called protein kinases. They were then able to use this algorithm to analyze nearly 11,000 small molecules, which they took from the ZINC database, a publicly available repository that contains millions of chemical compounds. Many of these molecules were very different from those in the training data.
Using this approach, the researchers were able to identify molecules with very strong predicted binding affinities for the protein kinases they put into the model. These included three human kinases, as well as one kinase found in Mycobacterium tuberculosis. That kinase, PknB, is critical for the bacteria to survive, but is not targeted by any frontline TB antibiotics.
The researchers then experimentally tested some of their top hits to see how well they actually bind to their targets, and found that the model’s predictions were very accurate. Among the molecules that the model assigned the highest certainty, about 90 percent proved to be true hits — much higher than the 30 to 40 percent hit rate of existing machine learning models used for drug screens.
The researchers also used the same training data to train a traditional machine-learning algorithm, which does not incorporate uncertainty, and then had it analyze the same 11,000 molecule library. “Without uncertainty, the model just gets horribly confused and it proposes very weird chemical structures as interacting with the kinases,” Hie says.
The researchers then took some of their most promising PknB inhibitors and tested them against Mycobacterium tuberculosis grown in bacterial culture media, and found that they inhibited bacterial growth. The inhibitors also worked in human immune cells infected with the bacterium.
A good starting point
Another important element of this approach is that once the researchers get additional experimental data, they can add it to the model and retrain it, further improving the predictions. Even a small amount of data can help the model get better, the researchers say.
“You don’t really need very large data sets on each iteration,” Hie says. “You can just retrain the model with maybe 10 new examples, which is something that a biologist can easily generate.”
This study is the first in many years to propose new molecules that can target PknB, and should give drug developers a good starting point to try to develop drugs that target the kinase, Bryson says. “We’ve now provided them with some new leads beyond what has been already published,” he says.
The researchers also showed that they could use this same type of machine learning to boost the fluorescent output of a green fluorescent protein, which is commonly used to label molecules inside living cells. It could also be applied to many other types of biological studies, says Berger, who is now using it to analyze mutations that drive tumor development.
The research was funded by the U.S. Department of Defense through the National Defense Science and Engineering Graduate Fellowship; the National Institutes of Health; the Ragon Institute of MGH, MIT, and Harvard’ and MIT’s Department of Biological Engineering.
To make mini-organs grow faster, give them a squeeze
The closer people are physically to one another, the higher the chance for exchange, of things like ideas, information, and even infection. Now researchers at MIT and Boston Children’s Hospital have found that, even in the microscopic environment within a single cell, physical crowding increases the chance for interactions, in a way that can significantly alter a cell’s health and development.
In a paper published today in the journal Cell Stem Cell, the researchers have shown that physically squeezing cells, and crowding their contents, can trigger cells to grow and divide faster than they normally would.
While squeezing something to make it grow may sound counterintuitive, the team has an explanation: Squeezing acts to wring water out of a cell. With less water to swim in, proteins and other cell constituents are packed closer together. And when certain proteins are brought in close proximity, they can trigger cell signaling and activate genes within the cell.
In their new study, the scientists found that squeezing intestinal cells triggered proteins to cluster along a specific signaling pathway, which can help cells maintain their stem-cell state, an undifferentiated state in which they can quickly grow and divide into more specialized cells. Ming Guo, associate professor of mechanical engineering at MIT, says that if cells can simply be squeezed to promote their “stemness,” they can then be directed to quickly build up miniature organs, such as artificial intestines or colons, which could then be used as platforms to understand organ function and test drug candidates for various diseases, and even as transplants for regenerative medicine.
Guo’s co-authors are lead author Yiwei Li, Jiliang Hu, and Qirong Lin from MIT, and Maorong Chen, Ren Sheng, and Xi He of Boston Children’s Hospital.
To study squeezing’s effect on cells, the researchers mixed various cell types in solutions that solidified as rubbery slabs of hydrogel. To squeeze the cells, they placed weights on the hydrogel’s surface, in the form of either a quarter or a dime.
“We wanted to achieve a significant amount of cell size change, and those two weights can compress the cell by something like 10 to 30 percent of their total volume,” Guo explains.
The team used a confocal microscope to measure in 3D how individual cells’ shapes changed as each sample was compressed. As they expected, the cells shrank with pressure. But did squeezing also affect the cell’s contents? To answer this, the researchers first looked to see whether a cell’s water content changed. If squeezing acts to wring water out of a cell, the researchers reasoned that the cells should be less hydrated, and stiffer as a result.
They measured the stiffness of cells before and after weights were applied, using optical tweezers, a laser-based technique that Guo’s lab has employed for years to study interactions within cells, and found that indeed, cells stiffened with pressure. They also saw that there was less movement within cells that were squeezed, suggesting that their contents were more packed than usual.
Next, they looked at whether there were changes in the interactions between certain proteins in the cells, in response to cells being squeezed. They focused on several proteins that are known to trigger Wnt/β-catenin signaling, which is involved in cell growth and maintenance of “stemness.”
“In general, this pathway is known to make a cell more like a stem cell,” Guo says. “If you change this pathway’s activity, how cancer progresses and how embryos develop have been shown to be very different. So we thought we could use this pathway to demonstrate how cell crowding is important.”
A “refreshing” path
To see whether cell squeezing affects the Wnt pathway, and how fast a cell grows, the researchers grew small organoids — miniature organs, and in this case, clusters of cells that were collected from the intestines of mice.
“The Wnt pathway is particularly important in the colon,” Guo says, pointing out that the cells that line the human intestine are constantly being replenished. The Wnt pathway, he says, is essential for maintaining intestinal stem cells, generating new cells, and “refreshing” the intestinal lining.
He and his colleagues grew intestinal organoids, each measuring about half a millimeter, in several Petri dishes, then “squeezed” the organoids by infusing the dishes with polymers. This influx of polymers increased the osmotic pressure surrounding each organoid and forced water out of their cells. The team observed that as a result, specific proteins involved in activating the Wnt pathway were packed closer together, and were more likely to cluster to turn on the pathway and its growth-regulating genes.
The upshot: Those organoids that were squeezed actually grew larger and more quickly, with more stem cells on their surface than those that were not squeezed.
“The difference was very obvious,” Guo says. “Whenever you apply pressure, the organoids grow even bigger, with a lot more stem cells.”
He says the results demonstrate how squeezing can affect an organoid’s growth. The findings also show that a cell’s behavior can change depending on the amount of water that it contains.
“This is very general and broad, and the potential impact is profound, that cells can simply tune how much water they have to tune their biological consequences,” Guo says.
Going forward, he and his colleagues plan to explore cell squeezing as a way to speed up the growth of artificial organs that scientists may use to test new, personalized drugs.
“I could take my own cells and transfect them to make stem cells that can then be developed into a lung or intestinal organoid that would mimic my own organs,” Guo says. “I could then apply different pressures to make organoids of different size, then try different drugs. I imagine there would be a lot of possibilities.”
This research is supported, in part, by the National Cancer Institute and the Alfred P. Sloan Foundation.
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