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Free Patent Litigation Docket Reports Data




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Copyright © DrugPatentWatch. Originally published at Free Patent Litigation Docket Reports Data

The Chinese patent litigation system is less impenetrable than it was only a few years ago.The USPTO Office of the Chief Economist has releases a comprehensive set of patent litigation data files for public use.

The Patent Litigation Dataset has been updated as of December 2019 and now contains detailed patent litigation data on 81,350 unique district court cases filed during the period 1963-2016. OCE and partners at the University of San Diego Law School collected all of the data from the Public Access to Court Electronics Records (PACER) and RECAP, an independent project designed to serve as a repository for litigation data sourced from PACER. The final output datasets, provided in six different files, include information on the litigating parties involved and their attorneys; the cause of action; the court location; important dates in the litigation history; and descriptions of all documents submitted in a given case, which cover more than 5 million separate documents contained in the case docket reports. There is also a new sixth file with hand-coded information on patent-in-suit and case type for most cases filed between 2003 and 2016.

A document describing the original data release is available and can be cited as: Marco, A., A. Tesfayesus, A. Toole (2017). “Patent Litigation Data from US District Court Electronic Records (1963-2015).” USPTO Economic Working Paper No. 2017-06. Available at SSRN: There is also an addendum to the original document, which describes changes to the Dataset and can be accessed below.

A document describing the hand-coded patent and case type variables can be cited as Schwartz, D., T. Sichelman, R. Miller (2019). “USPTO Patent Number and Case Code File Dataset Documentation.” USPTO Economic Working Paper No. 2019-05. Available at SSRN:

DrugPatentWatch has tools to allow a deeper dive into drug patent litigation, with district court cases going back ten year and PTAB cases as well. For more information, preview the litigation module, or contact us.

Copyright © DrugPatentWatch. Originally published at Free Patent Litigation Docket Reports Data



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.

Better predictions

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.


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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.

Packed in

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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3 Questions: Ram Sasisekharan on hastening vaccines and treatments




Covid-19 has brought much of the world to a halt this year. However, it is just one of the many infectious diseases without a vaccine that affect millions of people around the world. The development of therapeutics for these infectious diseases has mostly been overlooked by pharmaceutical companies in favor of higher-margin therapies for the developed world. Creating therapeutics for these viral pathogens can take years of clinical and regulatory assessment before they become available to those who need them. 

Ram Sasisekharan, the Alfred H. Caspary Professor of Biological Engineering, has been at MIT since 1996 and is a principal investigator of the Antimicrobial Resistance Interdisciplinary Research Group at the Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore. His work seeks to condense the time taken to develop therapeutics down from many years to a matter of months.

Q: How has your work helped to speed up the development of therapeutics for infectious diseases?

A: I have always had a fascination with solving problems. My career progressed as I began looking for solutions that other people hadn’t investigated before. On the way, I took an interest in regulatory science, in terms of how drugs are developed, tested for safety and efficacy, and brought to market.

It is not as easy as simply thinking that you have a vaccine or therapy to conquer a disease and then releasing it to the public. Before you can do so, all the levels of non-clinical and clinical trials have to come together, which is not a fast process. Current drug development models are very linear and can take five to 10 years — and an extraordinary amount of money — before they pan out. A lot of drugs will fail, so we need to take a lot of shots on goal before we can deliver the right therapy and vaccines.

The better we understand a disease, the faster we can move. For example, dengue, Zika, and yellow fever broadly belong to the same family of viruses, which allows us to use what we learn in one of these viruses for the others, to some extent. We found that these viruses bind to carbohydrates (or glycans) on the surface of cells, and this knowledge builds on my past work analyzing and characterizing glycans and related complex biologics. The same could be said for flu viruses like H5N1 and coronaviruses like SARS-CoV-2.

It is clear that once you have an outbreak, you must be able to combat the virus and quickly find a countermeasure. Many infectious diseases are also drug-resistant, as this is the natural way a pathogen evolves to escape and continue to survive. All these factors need to be put into a strategy for optimal drug development that is rapid, effective, and can prevent drug resistance. For these reasons, we have focused on the development of engineered antibodies that directly target the pathogen.

The approach we take at SMART is to use the regulatory framework as a design constraint. By starting with that framework in mind and working with the regulators each step of the way, we can rapidly develop, produce, and evaluate antibody candidates for safety and efficacy in humans.

Most recently, through Tychan, a biotech company founded by Professor Ooi Eng Eong and myself, we have developed in just four months the first monoclonal antibody (mAb) that targets SARS-CoV-2. Announced in June, this was the first clinical trial tackling the coronavirus in Singapore and it will evaluate the TY027 mAb regarding its safety and efficacy. Preliminary results from the phase 1 trials show that no adverse events have been observed after the infusion of TY027 in healthy volunteers.

First, we used computational methods to target functionally important, and evolutionarily stable, regions on the virus. Then our bioprocessing methods made antibodies in a matter of weeks, instead of the many months that are usually required if working with a master cell bank. Previously, we developed the world’s first anti-yellow fever virus antibody. Right now, this is in phase 2 clinical trials.

Q: Why is Singapore an ideal location to investigate these diseases?

A: I became involved at SMART after bird flu had been a big concern in Asia and countries like Singapore in particular. When you need to solve a problem, you need to get as close to its source as possible. I thought we should go to Singapore to understand how we can use our research to predict how the bird flu virus can gain a foothold in humans.

Singapore is now a leading biomedical sciences hub at the heart of Asia with world-class manufacturing capabilities and an extensive and integrated research ecosystem. At SMART, I can collaborate with local researchers who are not only familiar with the local issues but have also developed expertise in their fields.

In parallel, I became interested in how these viruses adapt to humans, and what effective countermeasures could be taken. We began developing a platform for antibody design, rather than using other established approaches. Our approach acknowledges that there are known structures and databases we actively use to very rapidly design, engineer and assemble antibodies to test in an iterative fashion. We then pick the best design to go forward and develop as a drug candidate. 

In the case of infectious diseases like dengue and Zika, being at the source means that we can get a sense of the scale, scope, and seriousness of the problem or the outbreak when it appears around you. Dengue, for example, is endemic in Southeast Asia, while Zika first appeared in Brazil but eventually hit Singapore.

In Boston, saying dengue is important would be the furthest thing from people’s minds, but because of globalization and global warming, that’s likely to change. When you’re looking at dengue or bird flu that’s prevalent at that time, getting samples, and talking to clinician-scientists on the front line, you can learn the crucial nuances of the disease, its properties, and the gravity of an outbreak. Being armed with this information gives you the ability to move fast.

What’s more, the significance of the problem becomes much higher, the more you understand the implications of this disease; the farther away you are, the harder it is to grasp this significance. If you truly want to understand the issue, you need to be close to the source and grasp the elements that define what the problem statement is and what a pragmatic solution would be.

Q: What impact will the Covid-19 outbreak have on our approach to infectious disease therapeutics development?

A: Big pharmaceutical companies do not generally get involved in developing therapeutics for infectious diseases; instead, they tend to make huge investments in high-margin mass-market drugs, like cancer therapies. In infectious diseases, there is usually no mass market — though that is clearly not the case at the moment — and the drugs under development must also be accessible and affordable. Because of this, drugs for infectious diseases are not usually the sort of things the big companies will take on. It doesn’t help that outbreaks tend to be cyclical, so there is an urgent effort for developing a vaccine for an emerging infectious disease at one moment, then the crisis passes, and the need gets forgotten.

Consider antimicrobial resistance, which is an enormous global threat. Bacteria are learning to withstand the drugs that are used against them. Yet this is still not an exciting enough problem for pharmaceutical companies to invest in, compared to solving heart disease or the cancer problem.

I think Covid-19 is going to change the way these things are considered, given the significance of the economic and lifestyle impact it’s been having this year. We are still in the first wave and have months to go before any of the prospective solutions play out.

Covid-19 has hit at the core of national security for many influential countries. The pandemic has forced them to grapple with the idea that they might come under attack from another virus at any point, and they don’t want to be in a situation again of not being able to respond. The question that is ahead of us all is: How can we bring treatments for infectious diseases to market faster and more efficiently than we have done so far? It looks like we all have a lot more work to do.


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