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Creating new drug candidates is a heroic endeavor, often taking over 10 years to bring a drug to market. New supercomputing-scale large language models (LLMs) that understand biology and chemistry text are helping scientists understand proteins, small molecules, DNA, and biomedical text. These state-of-the-art AI models help generate de novo proteins and molecules and predict the 3D structures of proteins. They can predict the binding structure of a small molecule to a protein and are offering scientists easier ways to engineer new candidate drugs and ultimately bring hope for patients. After Exscientia brought an AI-designed drug candidate to a clinical trial in 2021, several other companies have announced that their candidates are in trials. Within drug companies focused on AI-based discovery, there is publicly available information on about 160 discovery programs, of which 15 products are reportedly in clinical development. At the forefront of AI-based drug discovery are generative AI models for applications such as generating high-quality proteins. These large, powerful models learn from unlabeled data (such as sequencing data) on multi-GPU, multi-node, high-performance computing (HPC) infrastructures. With the novel NVIDIA BioNeMo Service, workflows for generative AI for biology are optimized and turnkey. You can focus on adapting AI models to the right drug candidates instead of dealing with configuration files and setting up supercomputing infrastructure. BioNeMo Service BioNeMo Service is a cloud service for generative AI in early drug discovery, featuring nine state-of-the-art large language and diffusion models in one place. The models in BioNeMo are accessible through a web interface or fully managed APIs and can be further trained and optimized on NVIDIA DGX Cloud. With BioNeMo Service, you can perform any of the following tasks: - Generate large libraries of proteins.
- Build property predictors using embeddings to refine protein libraries.
- Generate small molecules with specific properties.
- Rapidly and accurately predict and visualize the 3D structure for billions of proteins.
- Run large campaigns of ligand-to-small-molecule pose estimations.
- Download proteins, molecules, and predicted 3D structures.
Generative AI models in BioNeMo Service BioNeMo Service features nine AI generative models covering a wide spectrum of applications for developing AI drug discovery pipelines: - AlphaFold 2, ESMFold, and OpenFold for 3D protein structure prediction from a primary amino acid sequence
- ESM-1nv and ESM-2 for protein property predictions
- ProtGPT2 for protein generation
- MegaMolBART and MoFlow for small molecule generation
- DiffDock for predicting the binding structure of a small molecule to a protein
Researchers have found a surprisingly versatile workaround to create chemical compounds that could prove useful for medical imaging and drug development. The chemical mechanism, discovered by scientists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley, could also broaden our understanding of basic chemical reaction processes involving common helpers, called catalysts, like copper and gold. While studying chemical reactions of a gold-containing molecule, the research team happened upon a chemical mechanism that can be used to form trifluoromethyl (CF3) compounds and attach them to other chemical compounds. Their discovery could aid in the synthesis of new "radiotracers" - chemical compounds that contain a radioactive form, or isotope, of an element - for use with a noninvasive, high-resolution 3-D medical imaging technology known as PET (positron emission tomography) scanning. Drug companies have shown an increasing interest in incorporating CF3 compounds - which contain carbon and fluorine - in a range of pharmaceuticals. These compounds can make drugs more selective, effective, or potent. The antidepressant Prozac, HIV drug Sustiva, and anti-inflammatory Celebrex are among the examples of drugs containing CF3 compounds. So in testing the biological uptake of drugs that incorporate CF3 compounds, it's useful to incorporate fluorine-18 (18F), a radioactive isotope of fluorine in the CF3 compound as a sort of label or "tracer" that can be detected by PET scanners.
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As much as 80 per cent of the cost of bringing vaccines to the developing world comes from ensuring that the medications are properly refrigerated and transported. A team of researchers from the University of Toronto, MIT, Harvard, and the University of Ottawa have developed a new portable drug-manufacturing system that uses two sets of freeze-dried pellets, which when mixed with water, are able to produce medications, vaccines and diagnostic tools virtually anywhere in the world. The team published a proof-of-principle paper in Cell that details the development of a drug manufacturing system that's able to produce on-site, on-demand therapeutics and biomolecules. “In essence, it’s like having a portable pharmacy that you can use to create the medications you need,” said Assistant Professor Keith Pardee of U of T’s Faculty of Pharmacy, co-lead author of the paper. Most vaccines need to maintain a consistent temperature to prevent spoilage and maintain their efficacy, which necessitates a cold chain from production to application. Despite these precautions and the attention paid to their transportation, the World Health Organization and United Nations Children’s Fund estimate that the amount of essential vaccines that end up wasted could be as high as 50 per cent. The possible applications for this new system, Pardee explains, are almost endless. “If, for example, the influenza vaccine developed in a given year is off target and doesn't fight the strains of the virus that emerge, the system we’ve developed can address that,” he said. “The current production chain for the influenza vaccine begins in late spring early summer for fall and winter application. If the formula is wrong, it would take months to change, produce, ship, and administer a vaccine that hits on the right strains. “Whereas with our system, in theory, once the proper strains are identified and a new formula developed, the vaccine could be produced anywhere in a matter of hours. The materials would already be on the shelf – they’d just need to be programmed to produce the vaccine. While this is just a proof-of-concept study, this could mean no prolonged production time, no timely and expensive shipping.”
Northwestern University scientists are experimenting with ways to eliminate a cancer-causing agent from gasoline by neutralizing the benzene compound found in gasoline. They developed a catalyst that effectively removed benzene from the other aromatic compounds in gasoline, making it cleaner and more efficient.
An estimated 137 billion gallons of gasoline were consumed in the United States last year, according to the U.S. Energy Information Administration, a daily average of about 375 million gallons. Within each gallon of gas is a chemical compound known as benzene, which has been recognized by the Environmental Protection Agency as a known contributor to cancer. A research team led by Northwestern's Tobin J. Marks has found a way to remove it.
"The gasoline we buy is one-third a mixture of aromatics, and benzene is one of them," said Marks, explaining that aromatics are necessary to improve gas octane numbers and fuel efficiency. "Only benzene is known to be cancer causing, and it's very difficult to remove. Our catalyst opened a whole new way to do that—and probably a very inexpensive way."
Marks is the Vladimir N. Ipatieff Research Professor of Chemistry in the Weinberg College of Arts and Sciences and professor of materials science and engineering in the McCormick School of Engineering and Applied Science.
"We could keep the cost of gasoline down," Marks added, "and a big environmental and health problem would be solved." He describes his team's catalyst as an organometallic molecule, which is not composed of an expensive platinum metal but an affordable, simple metal, which is absorbed onto a particular oxide support. After almost two years of research experimenting with the selective hydrogenation of benzene, the team created a catalyst that removed the benzene from the other aromatics with high selectivity.
"We really know what the catalyst structure looks like," Marks said, "the relative rates of reactions, how the catalyst and aromatics interact with each other and how selective the catalyst is." The research team, which includes scientists from Argonne National Laboratory and Universal Oil Products, released their findings in a paper featured on the cover of the June 3 issue of the Journal of the American Chemical Society.
The cover image depicts their catalyst, with a backdrop of the Chicago River and the architecture that towers over it along Michigan Avenue. "It's eye catching," Marks said. "We tried to blend science with something that looks a little bit different."
Small cationic amphiphilic α-helical peptides are emerging as agents for the treatment of cancer and infection, but they are costly and display unfavorable pharmacokinetics. Helical coordination complexes may offer a three-dimensional scaffold for the synthesis of mimetic architectures. However, the high symmetry and modest functionality of current systems offer little scope to tailor the structure to interact with specific biomolecular targets, or to create libraries for phenotypic screens.
Scientists now report the highly stereoselective asymmetric self-assembly of very stable, functionalized metallohelices. Their anti-parallel head-to-head-to-tail ‘triplex’ strand arrangement creates an amphipathic functional topology akin to that of the active sub-units of, for example, host-defence peptides and p53. The metallohelices display high, structure-dependent toxicity to the human colon carcinoma cell-line HCT116 p53++, causing dramatic changes in the cell cycle without DNA damage. They have lower toxicity to human breast adenocarcinoma cells (MDA-MB-468) and, most remarkably, they show no significant toxicity to the bacteria methicillin-resistant Staphylococcus aureus and Escherichia coli.
Researchers atWashington University School of Medicine in St. Louis have created a massive online database that matches thousands of genes linked to cancer and other diseases with drugs that target those genes. Some of the drugs are approved by the U.S. Food and Drug Administration, while others are in clinical trials or just entering the drug development pipeline. The database was developed by identical twin brothers, Obi Griffith, PhD, and Malachi Griffith, PhD, whose interest in pairing drugs with genes is as much personal as it is scientific. Their mother died of breast cancer 17 years ago, just weeks before their high school graduation. “We wanted to create a comprehensive database that is user-friendly, something along the lines of a Google search engine for disease genes,” explained Malachi Griffith, a research instructor in genetics. “As we move toward personalized medicine, there’s a lot of interest in knowing whether drugs can target mutated genes in particular patients or in certain diseases, like breast or lung cancer. But there hasn’t been an easy way to find that information.” Details of the Drug Gene Interaction Database are reported online Oct. 13 inNature Methods. The database is weighted heavily toward cancer genes but also includes genes involved in Alzheimer’s disease, heart disease, diabetes and many other illnesses. The Griffiths created the database with a team of scientists at The Genome Institute at Washington University in St. Louis. The database is easy to search and geared toward researchers and physician-scientists who want to know whether errors in disease genes – identified through genome sequencing or other methods – potentially could be targeted with existing drug therapies. Additional genes included in the database could be the focus of future drug development efforts because they belong to classes of genes that are thought to make promising drug targets. “Developing the database was a labor of love for the Griffiths,” said senior author Richard K. Wilson, PhD, director of The Genome Institute. “There’s an amazing depth to this resource, which will be invaluable to researchers working to design better treatment options for patients.” Wilson and his colleagues caution that the database is intended for research purposes and that it does not recommend treatments. The primary purpose of the database is to further clinical research aimed at treating diseases more effectively.
Using a simple "drag-and-drop" computer interface and DNA self-assembly techniques, researchers have developed a new approach for drug development that could drastically reduce the time required to create and test medications.
In work supported by a National Science Foundation (NSF) Small Business Innovation Research grant, researchers fromParabon® NanoLabs of Reston, Va., recently developed and began evaluating a drug for combating the lethal brain cancer glioblastoma multiforme. Now, with the support of an NSF Technology Enhancement for Commercial Partnerships (TECP) grant, Parabon has partnered with Janssen Research & Development, LLC, part of the Janssen Pharmaceutical Companies of Johnson & Johnson, to use the technology to create and test the efficacy of a new prostate cancer drug. "We can now 'print,' molecule by molecule, exactly the compound that we want," says Steven Armentrout, the principal investigator on the NSF grants and co-developer of Parabon's technology. "What differentiates our nanotechnology from others is our ability to rapidly, and precisely, specify the placement of every atom in a compound that we design." The new technology is called the Parabon Essemblix™ Drug Development Platform, and it combines their computer-aided design (CAD) software called inSēquio™ with nanoscale fabrication technology. Scientists work within inSēquio™ to design molecular pieces with specific, functional components. The software then optimizes the design using the Parabon Computation Grid, a cloud supercomputing platform that uses proprietary algorithms to search for sets of DNA sequences that can self-assemble those components. "When designing a therapeutic compound, we combine knowledge of the cell receptors we are targeting or biological pathways we are trying to affect with an understanding of the linking chemistry that defines what is possible to assemble," says Hong Zhong, senior research scientist at Parabon and a collaborator on the grants. "It's a deliberate and methodical engineering process, which is quite different from most other drug development approaches in use today." With the resulting sequences, the scientists chemically synthesize trillions of identical copies of the designed molecules. The process, from conception to production, can be performed in weeks, or even days--much faster than traditional drug discovery techniques that rely on trial and error for screening potentially useful compounds. In vivo experiments, funded by the NSF SBIR award, validated the approach, and Parabon filed a provisional patent for its methods and compounds on May 4, 2011. The final applicationwas published in 2012. The process is characteristic of rational drug design, an effort to craft pharmaceuticals based on knowledge of how certain molecular pieces will work together in a biological system. For example, some molecules are good at finding cancer cells, while others are good at latching on to cancer cells, while still others are capable of killing cells. Working together as part of a larger molecule, these pieces could prove effective as a cancer treatment. While there are other methods to create multi-component compounds, they generally take more time, and, most important, the majority of them lack the precise control over size, charge and the relative placement of components enabled by the new technology. The recent TECP grant provided a supplement to Parabon to support further research that will help the novel technologies meet market demands.
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Giantic network links all known compounds and reactions together. Northwestern University scientists have connected 250 years of organic chemical knowledge into one giant computer network called Chematica — a chemical “Google” on steroids. A decade in the making, the software optimizes syntheses of drug molecules and other important compounds, combines long (and expensive) syntheses of compounds into shorter and more economical routes, and identifies suspicious chemical recipes that could lead to chemical weapons. The number of possible synthetic pathways leading to the desired target of a synthesis can be astronomical (10E19 pathways within five synthetic steps). The Chematica network comprises some seven million chemicals connected by a similar number of reactions. A family of algorithms that searches and analyzes the network allows the chemist at his or her computer to easily tap into this vast compendium of chemical knowledge. And the system learns from experience, as more data and algorithms are added to its knowledge base.
A critical early step in drug discovery is the screening of a chemical library. Typically, promising compounds are identified in a primary screen and then more fully characterized in a dose–response analysis with 7–10 data points per compound. Recently, at team of scientists have introduced a robust microfluidic approach that increases the number of data points to approximately 10,000 per compound. The system exploits Taylor–Aris dispersion to create concentration gradients, which are then segmented into picoliter microreactors by droplet-based microfluidics. The large number of data points results in IC50 values that are highly precise (± 2.40% at 95% confidence) and highly reproducible (CV = 2.45%, n = 16). In addition, the high resolution of the data reveals complex dose–response relationships unambiguously.
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Using a new machine-learning algorithm, MIT researchers have identified a powerful antibiotic that can kill a wide range of species of pathogenic bacteria, including some that are resistant to all known antibiotics. The computer model, which can screen more than a hundred million chemical compounds in a matter of days, is designed to pick out potential antibiotics that kill bacteria using different mechanisms than those of existing drugs. "We wanted to develop a platform that would allow us to harness the power of artificial intelligence to usher in a new age of antibiotic drug discovery," says James Collins, the Termeer Professor of Medical Engineering and Science in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering. "Our approach revealed this amazing molecule which is arguably one of the more powerful antibiotics that has been discovered." In their new study, the researchers also identified several other promising antibiotic candidates, which they plan to test further. They believe the model could also be used to design new drugs, based on what it has learned about chemical structures that enable drugs to kill bacteria. "The machine learning model can explore, in silico, large chemical spaces that can be prohibitively expensive for traditional experimental approaches," says Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science in MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). In this case, the researchers designed their model to look for chemical features that make molecules effective at killing E. coli. To do so, they trained the model on about 2,500 molecules, including about 1,700 FDA-approved drugs and a set of 800 natural products with diverse structures and a wide range of bioactivities. Once the model was trained, the researchers tested it on the Broad Institute’s Drug Repurposing Hub, a library of about 6,000 compounds. The model picked out one molecule that was predicted to have strong antibacterial activity and had a chemical structure different from any existing antibiotics. Using a different machine-learning model, the researchers also showed that this molecule would likely have low toxicity to human cells. This recently identified molecule, which the researchers decided to call halicin, after the fictional artificial intelligence system from “2001: A Space Odyssey,” has been previously investigated as possible diabetes drug. The researchers tested it against dozens of bacterial strains isolated from patients and grown in lab dishes, and found that it was able to kill many that are resistant to treatment, including Clostridium difficile, Acinetobacter baumannii, and Mycobacterium tuberculosis. The drug worked against every species that they tested, with the exception of Pseudomonas aeruginosa, a difficult-to-treat lung pathogen. To test halicin’s effectiveness in living animals, the researchers used it to treat mice infected with A. baumannii, a bacterium that has infected many U.S. soldiers stationed in Iraq and Afghanistan. The strain of A. baumannii that they used is resistant to all known antibiotics, but application of a halicin-containing ointment completely cleared the infections within 24 hours. Preliminary studies suggest that halicin kills bacteria by disrupting their ability to maintain an electrochemical gradient across their cell membranes. This gradient is necessary, among other functions, to produce ATP (molecules that cells use to store energy), so if the gradient breaks down, the cells die. This type of killing mechanism could be difficult for bacteria to develop resistance to, the researchers say. “When you’re dealing with a molecule that likely associates with membrane components, a cell can’t necessarily acquire a single mutation or a couple of mutations to change the chemistry of the outer membrane. Mutations like that tend to be far more complex to acquire evolutionarily,” Stokes says. The researchers also found that E. coli did not develop any resistance to halicin during a 30-day treatment period. In contrast, the bacteria started to develop resistance to the antibiotic ciprofloxacin within one to three days, and after 30 days, the bacteria were about 200 times more resistant to ciprofloxacin than they were at the beginning of the experiment.
Dana-Farber scientists report that a compound able to reverse the allegiance of innate immune system cells – turning them from tumor enablers into tumor opponents – caused breast tumors in mice to shrink and withdraw from distant metastases. In a new study in the journal Nature, Dana-Farber Cancer Institute scientists report that a compound able to reverse the allegiance of innate immune system cells -- turning them from tumor enablers into tumor opponents -- caused breast tumors in mice to shrink and withdraw from distant metastases. When combined with chemotherapy or another immunotherapy, the new compound significantly extended the period of tumor remission. The findings suggest a way to bring the full repertoire of the immune system to bear on cancer in humans, the authors said. "Most current forms of cancer immunotherapy influence the behavior of T cells -- white blood cells that are part of the adaptive immune system -- by 'teaching' them to attack tumor cells or removing impediments to such an attack," said the study's lead author Jennifer Guerriero, PhD, of Dana-Farber. "This strategy has been effective against several types of cancer, but generally only a subset of patients benefit. We wanted to see if harnessing both arms of the immune system could produce superior results." The targets of the new study were innate immune system cells known as tumor-associated macrophages (TAMs). They're often found deeply embedded within tumors, but although they're part of the immune system -- the body's defense against disease -- they frequently promote tumor growth. In doing so, they're responding to cues issued by the tumor itself. The roles that macrophages play -- whether protective or destructive -- depend on signals from their environment. In wound healing, for example, macrophages marshal the elements of the immune system that clear away damaged tissue and restore the affected area. Tumor macrophages manage to hijack some of these supportive functions for their own purposes. Not without reason is cancer sometimes referred to as a wound that doesn't heal. In previous research, the Dana-Farber scientists and their colleagues showed that a compound known as TMP195 could convert TAMs from aiding tumor growth to organizing an attack on it. A selective, first-in-class, class IIa HDAC inhibitor, TMP195 switches the macrophage response by altering gene activity within TAMs. In this current study, TMP195 sharply reduced the rate of tumor growth in mice with breast tumors, researchers found. They next combined TMP195 with various chemotherapy regimens and with a form of immunotherapy known as T-cell checkpoint blockade. In both cases, the combinations produced longer-lasting remissions of breast cancer than TMP195 alone did. "Once they've undergone conversion, macrophages act as the orchestrators of the immune system attack on the tumor," said Anthony Letai, MD, PhD, of Dana-Farber, co-senior author of the study with Michael A. Nolan, PhD, of GlaxoSmithKline. "Our findings demonstrate that class IIa HDAC inhibitors can be an effective way of harnessing the anti-tumor potential of macrophages in cancer therapy. "The future of cancer treatment is likely to involve combinations of therapies that act on both the innate and adaptive arms of the immune system, as well as therapies, such as chemotherapy, radiation therapy, or targeted therapy, that act on cancer cells themselves," he continued. "The ability to engage the innate immune system is an exciting new front in cancer therapy."
Scientists have created a map of all 1,578 licensed drugs and their mechanisms of action - as a means of identifying 'uncharted waters' in the search for future treatments.
Their analysis of drugs licensed through the Food and Drug Administration reveals that 667 separate proteins in the human body have had drugs developed against them - just an estimated 3.5% of the 20,000 human proteins. And as many as 70 per cent of all targeted drugs created so far work by acting on just four families of proteins - leaving vast swathes of human biology untouched by drug discovery programs. The study is the most comprehensive analysis of existing drug treatments across all diseases ever conducted. It was jointly led by scientists at The Institute of Cancer Research, London, which also funded the research. The new map reveals areas where human genes and the proteins they encode could be promising targets for new treatments - and could also be used to identify where a treatment for one disease could be effective against another. The new data, published in a paper in the journal Nature Reviews Drug Discovery, could be used to improve treatments for all human aliments - as diverse as cancer, mental illness, chronic pain and infectious disease. Scientists brought together vast amounts of information from huge datasets including the canSAR database at The Institute of Cancer Research (ICR), the ChEMBL database from the European Bioinformatics Institute (EMBL-EBI) in Cambridge and the University of New Mexico's DrugCentral database. They matched each drug with prescribing information and data from published scientific papers, and built up a comprehensive picture of how existing medicines work - and where the gaps and opportunities for the future lie. The researchers discovered that there are 667 unique human proteins targeted by existing approved drugs, and identified a further 189 drug targets in organisms that are harmful to humans, such as bacteria, viruses and parasites.
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How does consciousness work? Few questions if any could be more profound. Lipid solubility appears to be one key clue to anesthesia. The empirical cornerstone of anesthesiology is a 100 year old rule of thumb known as the Meyer-Overton relationship. It provides that the potency of general anesthetics (GAs), regardless of their size or structure, is approximately proportional to how soluble they are in lipids. Since that time, studies have suggested that GAs can also bind to lipid-like parts of proteins, presumably those near or embedded within cell membranes.
The first real stab at explaining the "how" of anesthetics, as opposed to just the "where", has now been taken by Turin and his colleagues Efthimios Skoulakis and Andrew Horsfield. Their new work, just published in PNAS, suggests that volatile anesthetics operate by perturbing the internal electronic structure of proteins. This would lead to changes in electron currents in those proteins, in cells, and in the organism. They don't just theorize about these effects, they actually measure the electron currents in anesthetized flies using a technique known as electron spin resonance (often called electron paramagnetic resonance).
ESR is similar to nuclear magnetic resonance, the techno-phenomenon at heart of the modern MRI machine. The main difference is that in ESR excited electron spins are measured instead of proton resonance. Typically, microwaves are applied in the presence of a magnetic field to a sample (or whole organism) inside the resonator cavity of an ESR spectrometer.What Turin and colleagues have shown is that the total amount of free electron spins in fruit flies increases when they are exposed to general anesthetics. The amount of free spins generated during anesthesia is independent of melanin content and far larger than any signal previously measured from free radicals which are the other source of spin.
To account for the fact that a very broad class of compounds act as volatile anesthetics the researchers propose a unitary mechanism for their action involving electrons. They note that the smallest among them, Xenon (Xe), presents a puzzle to chemical theories of anesthetic action. Xe is a wonderful (if expensive) anesthetic but it has no biologically relevant chemistry to speak of— it is completely inert. Furthermore, it persists as a perfect sphere of electron density and so is devoid of any possibly interesting shape. However, as Turin and colleagues point out, "Xe has physics". In particular, it can conduct electrons, as the IBM researchers who first used a scanning tunneling microscope to write the company's logo in Xe atoms found out.
To see whether this property would apply to all anesthetics, and not just Xe, Turin used a modeling technique called density functional theory to show that Xe and other anesthetics effect the highest occupied molecular orbit (HOMO) of the alpha helices common to membrane proteins. The HOMO level for organic molecules or semiconductors is analogous to what the valence band maximum is to inorganic semiconductors. Intriguingly, while all the anesthetics were found to extend the alpha helix HOMO level, similar molecules with strong convulsant effects on the brain, but no anesthetic effects, had the smallest HOMO effect.
Imperial College London researchers have developed a new method for analyzing biological samples based on their chemical makeup that could transform the way medical scientists examine diseased tissue.
When tests are carried out on a patient’s tissue today, such as looking for cancer, the test has to be interpreted by a histology specialist, which can take weeks to get a full result.
Scientists have proposed using mass spectrometry imaging (MSI), which uses technologies that reveal how hundreds or thousands of chemical components are distributed in a tissue sample. But currently proposed MSI workflows are subject to several limitations, including nonoptimized raw data preprocessing, imprecise image coregistration, and limited pattern recognition capabilities.
In PNAS, the Imperial College London researchers have now outlined a comprehensive new strategy for effectively processing MSI data and building a database of tissue types. In MSI, a beam moves across the surface of a sample, producing a pixelated image. Each pixel contains data on thousands of chemicals present in that part of the sample. By analyzing many samples and comparing them to the results of traditional histological analysis, a computer can learn to identify different types of tissue.
A single test taking a few hours can provide much more detailed information than standard histological tests, for example showing not just if a tissue is cancerous, what the type and sub-type of cancer, which can be important for choosing the best treatment. The technology can also be applied in research to offer new insights into cancer biology.
According to Kirill Veselkov, M.D., corresponding author of the study from the Department of Surgery and Cancer at Imperial College London, “MSI is an extremely promising technology, but the analysis required to provide information that doctors or scientists can interpret easily is very complex. This work overcomes some of the obstacles to translating MSI’s potential into the clinic. It’s the first step towards creating the next generation of fully automated histological analysis.”
The technology will also be useful in drug development. To study where a new drug is absorbed in the body, pharmaceutical scientists attach a radioactive label to the drug molecule, then look at where the radiation can be detected in a laboratory animal. If the label is detached when the drug is processed in the body, it is impossible to determine how and where the drug has been metabolized. MSI would allow researchers to look for the drug and any metabolic products in the body, without using radioactive labels.
Although isoniazid and rifampin, the two front-line TB drugs, came into use in 1952 and 1967 respectively, new TB infections still occur at the rate of roughly one per second. At any moment about a third of the existing human population is infected, mostly with inactive, latent TB, although active TB still kills over one million people each year. Russia, Africa, China and Southeast Asia have been especially hard hit by the epidemic. Increased urbanization, public health complacency and immunity-weakening HIV have been major enablers of TB’s spread in recent decades. But the bacterium that causes TB (Mycobacterium tuberculosis - Mtb) also happens to be unusually well adapted for persisting in humans. Among other strategies, it frequently reverts to a dormant, non-replicating state and also creates attack-resistant cell colonies called biofilms, which contain a high proportion of non-replicating TB. Compared to ordinary, fast-replicating TB, these other forms of TB are much less susceptible to existing drugs. Effective TB therapy thus requires months to years of regular dosing. But many patients quit before completing such long courses of treatment and end up incubating drug-resistant TB strains. Some strains are now “extensively drug-resistant” (XDR) and virtually untreatable?and usually fatal. “The big challenge here has been to find a drug that clears TB infection more quickly, which means it has to be effective against both replicating and non-replicating TB,” said Wang, now also a scientist at the California Institute for Biomedical Research (CALIBR), a non-profit organization founded by Schultz for the early-stage development of new medicines. Most existing TB drugs work poorly against non-replicating TB, having been developed principally for their ability to kill actively replicating TB. Wang therefore set up a different kind of screening test?one to detect compounds that block TB’s persistence-related ability to form biofilms. Because experiments with live TB require a special (level 3) biosafety facility, Wang used a related but non-disease-causing mycobacterium for his initial, high-throughput test. Screening a diverse library of 70,000 compounds, he quickly found one, dubbed TCA1, that stood out for its ability to inhibit mycobacterial biofilms. Tests in Jacobs’s biosafety level 3-certified laboratory confirmed that TCA1 also has powerful activity against TB. “Surprisingly, it turned out to kill both non-replicating and replicating TB,” Wang said. In cell culture tests, TCA1 on its own killed more than 99.9% of ordinary, actively replicating TB bacteria within three weeks, and in combination with isoniazid or rifampin, could kill 100% within that period. TCA1 also showed strong effectiveness against drug-resistant TB strains, removing all signs of one common strain within a week when combined with isoniazid. Against a highly fatal “super-bug” strain from South Africa, which resists all conventional TB drugs, the new compound on its own had a kill rate of more than 99.999% within three weeks. As expected, TCA1 also showed potent effects against non-replicating TB. Tests in mice confirmed TCA1′s effectiveness and suggested that the combination of TCA1 and isoniazid could be more powerful than existing drug regimens. TCA1 showed no sign of toxicity or adverse side effects in cell culture and mouse experiments, and also showed almost no tendency to induce drug resistance in TB. Working with the laboratories of Gurdyal S. Besra and Klaus FA¼tterer at the University of Birmingham, UK, Katarina Mikusova at Comenius University in Slovakia, and Kai Johnson at Ecole Polytechnique FA©dA©rale de Lausanne (EPFL) in Switzerland, the team next did structural and biochemical analyses to determine how the new compound kills Mtb so efficiently. The researchers found it works in an apparently unique way, largely by targeting two Mtb enzymes, one supporting TB replication and the other TB dormancy and persistence. “I don’t know of any other antibiotic that kills replicating bacteria through one pathway and non replicating bacteria through another, as this one does,” Wang said.
Scientists at EPFL and the University of Geneva have developed a microfluidic device smaller than a domino that can simultaneously measure up to 768 biomolecular interactions. Inside our cells in the body, molecules are constantly binding and separating from one another. It’s this game of constant flux that drives gene expression asides essentially every other biological process. Understanding the specific details of how these interactions take place is thus crucial to our overall understanding of the fundamental mechanisms of living organisms. There are millions of possible combinations of molecules, however; determining all of them would be a Herculean task. Various tools have been developed to measure the degree of affinity between a strand of DNA and its transcription factor. They provide an indication of the strength of the affinity between them. Commercial devices, however, have one main drawback: many preliminary manipulations are necessary before an experiment can be carried out, and even then, the experiment can only focus on a dozen interactions at a time. As part of his doctoral research at the California Institute of Technology (Caltech), Sebastian Maerkl designed a device that he named “MITOMI” — a small device containing hundreds of microfluidic channels equipped with pneumatic valves. The new version, “k-MITOMI,” was developed in the context of the SystemsX.ch RTD DynamiX in cooperation with the University of Geneva. k-MITOMI has 768 chambers, each one with a valve that allows DNA and transcription factors to interact in a very carefully controlled manner. “In traditional methods, we generally manage to determine if an interaction takes place or not, and then we restart the experiment with another gene or another transcription factor,” Maerkl explains. “Our device goes much further, because it allows us to measure the affinity and kinetics of the interaction.” The strength of the device lies in a sort of “push-button” in its microreactors. A protein substrate is immobilized on the device; above it circulates a solution containing DNA moelcules. The push-button is activated at regular intervals of a few milliseconds, trapping protein-DNA complexes that form on the surface of the device. “Then we close the lid, and fluorescence reveals the exact number of bound molecules,” explains Maerkl. “We can also observe how long these molecules remain bound.” http://tinyurl.com/d79bwwb
Efforts to create customised medicines using 3D printer-based technology are being developed in Glasgow. Researchers have used a £1,250 system to create a range of organic compounds and inorganic clusters - some of which are used to create cancer treatments. Longer term, the scientists say the process could be used to make customised medicines. It is predicted that this technique will be used by pharmaceutical firms within five years, and by the public within 20.
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