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Eight Subcellular Pathologies driving Chronic Metabolic Diseases – Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics: Impact on Pharmaceuticals in Use

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Eight Subcellular Pathologies driving Chronic Metabolic Diseases – Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics: Impact on Pharmaceuticals in Use

Curators:

• Cardiovascular Expertise – Dr. Justin D. Pearlman, MD, PhD, FACC
• Pharmacology and Oncology Expertise – Dr. Stephen J. Williams, PhD
• Principal Investigator, initiator of the project – Aviva Lev-Ari, PhD, RN

 

THE VOICE of Aviva Lev-Ari, PhD, RN

In this curation we wish to present two breaking through goals:

Goal 1:

Exposition of a new direction of research leading to a more comprehensive understanding of Metabolic Dysfunctional Diseases that are implicated in effecting the emergence of the two leading causes of human mortality in the World in 2023: (a) Cardiovascular Diseases, and (b) Cancer

Goal 2:

Development of Methods for Mapping Bioelectronic Adjustable Measurements as potential new Therapeutics for these eight subcellular causes of chronic metabolic diseases. It is anticipated that it will have a potential impact on the future of Pharmaceuticals to be used, a change from the present time current treatment protocols for Metabolic Dysfunctional Diseases.

According to Dr. Robert Lustig, M.D, an American pediatric endocrinologist. He is Professor emeritus of Pediatrics in the Division of Endocrinology at the University of California, San Francisco, where he specialized in neuroendocrinology and childhood obesity, there are eight subcellular pathologies that drive chronic metabolic diseases.

These eight subcellular pathologies can’t be measured at present time.

In this curation we will attempt to explore methods of measurement for each of these eight pathologies by harnessing the promise of the emerging field known as Bioelectronics.

Unmeasurable eight subcellular pathologies that drive chronic metabolic diseases

1. Glycation
2. Oxidative Stress
3. Mitochondrial dysfunction [beta-oxidation Ac CoA malonyl fatty acid]
4. Insulin resistance/sensitive [more important than BMI], known as a driver to cancer development
5. Membrane instability
6. Inflammation in the gut [mucin layer and tight junctions]
7. Epigenetics/Methylation
8. Autophagy [AMPKbeta1 improvement in health span]

Diseases that are not Diseases: no drugs for them, only diet modification will help

Image source

Robert Lustig, M.D. on the Subcellular Processes That Belie Chronic Disease

https://www.youtube.com/watch?v=Ee_uoxuQo0I

 

Exercise will not undo Unhealthy Diet

Image source

Robert Lustig, M.D. on the Subcellular Processes That Belie Chronic Disease

https://www.youtube.com/watch?v=Ee_uoxuQo0I

 

These eight Subcellular Pathologies driving Chronic Metabolic Diseases are becoming our focus for exploration of the promise of Bioelectronics for two pursuits:

1. Will Bioelectronics be deemed helpful in measurement of each of the eight pathological processes that underlie and that drive the chronic metabolic syndrome(s) and disease(s)?
2. IF we will be able to suggest new measurements to currently unmeasurable health harming processes THEN we will attempt to conceptualize new therapeutic targets and new modalities for therapeutics delivery – WE ARE HOPEFUL

In the Bioelecronics domain we are inspired by the work of the following three research sources:

1. Biological and Biomedical Electrical Engineering (B2E2) at Cornell University, School of Engineering https://www.engineering.cornell.edu/bio-electrical-engineering-0
2. Bioelectronics Group at MIT https://bioelectronics.mit.edu/
3. The work of Michael Levin @Tufts, The Levin Lab

Michael Levin is an American developmental and synthetic biologist at Tufts University, where he is the Vannevar Bush Distinguished Professor. Levin is a director of the Allen Discovery Center at Tufts University and Tufts Center for Regenerative and Developmental Biology. Wikipedia

Born: 1969 (age 54 years), Moscow, Russia

Education: Harvard University (1992–1996), Tufts University (1988–1992)

Affiliation: University of Cape Town

Research interests: Allergy, Immunology, Cross Cultural Communication

Awards: Cozzarelli prize (2020)

Doctoral advisor: Clifford Tabin

Fields: Developmental biology, synthetic biology

SOURCE

https://www.google.com/search?q=Michael+Levin+%40Tufts&oq=Michael+Levin+%40Tufts&aqs=chrome..69i57j69i58.894128314j0j15&sourceid=chrome&ie=UTF-8

Most recent 20 Publications by Michael Levin, PhD

SOURCE

https://facultyprofiles.tufts.edu/michael-levin-1/publications

SCHOLARLY ARTICLE

The nonlinearity of regulation in biological networks

1 Dec 2023npj Systems Biology and Applications9(1)

Co-authorsManicka S, Johnson K, Levin M…1 more

VIEW PDF10.1038/S41540-023-00273-W

3

SCHOLARLY ARTICLE

Toward an ethics of autopoietic technology: Stress, care, and intelligence

1 Sep 2023BioSystems231

Co-authorsWitkowski O, Doctor T, Solomonova E…2 more

VIEW PDF10.1016/J.BIOSYSTEMS.2023.104964

SCHOLARLY ARTICLE

Closing the Loop on Morphogenesis: A Mathematical Model of Morphogenesis by Closed-Loop Reaction-Diffusion

14 Aug 2023Frontiers in Cell and Developmental Biology11:1087650

Co-authorsGrodstein J, McMillen P, Levin M

VIEW PDF10.3389/FCELL.2023.1087650

SCHOLARLY ARTICLE

Correcting instructive electric potential patterns in multicellular systems: External actions and endogenous processes.

30 Jul 2023Biochim Biophys Acta Gen Subj1867(10):130440

Co-authorsCervera J, Levin M, Mafe S

VIEW MORE INFO10.1016/J.BBAGEN.2023.130440

SCHOLARLY ARTICLE

Regulative development as a model for origin of life and artificial life studies

1 Jul 2023BioSystems229

Co-authorsFields C, Levin M

10.1016/J.BIOSYSTEMS.2023.104927

1

SCHOLARLY ARTICLE

The Yin and Yang of Breast Cancer: Ion Channels as Determinants of Left–Right Functional Differences

1 Jul 2023International Journal of Molecular Sciences24(13)

Co-authorsMasuelli S, Real S, McMillen P…3 more

VIEW PDF10.3390/IJMS241311121

SCHOLARLY ARTICLE

Bioelectricidad en agregados multicelulares de células no excitables- modelos biofísicos

Jun 2023Revista Española de Física32(2)

Co-authorsCervera J, Levin M, Mafé S

SCHOLARLY ARTICLE

Bioelectricity: A Multifaceted Discipline, and a Multifaceted Issue!

1 Jun 2023Bioelectricity5(2):75

Co-authorsDjamgoz MBA, Levin M

10.1089/BIOE.2023.0022.EDITORIAL

SCHOLARLY ARTICLE

Control Flow in Active Inference Systems – Part I: Classical and Quantum Formulations of Active Inference

1 Jun 2023IEEE Transactions on Molecular, Biological, and Multi-Scale Communications9(2):235-245

Co-authorsFields C, Fabrocini F, Friston K…4 more

10.1109/TMBMC.2023.3272150

2

SCHOLARLY ARTICLE

Control Flow in Active Inference Systems – Part II: Tensor Networks as General Models of Control Flow

1 Jun 2023IEEE Transactions on Molecular, Biological, and Multi-Scale Communications9(2):246-256

Co-authorsFields C, Fabrocini F, Friston K…4 more

10.1109/TMBMC.2023.3272158

2

SCHOLARLY ARTICLE

Darwin’s agential materials: evolutionary implications of multiscale competency in developmental biology

1 Jun 2023Cellular and Molecular Life Sciences80(6)

Co-authorsLevin M

VIEW PDF10.1007/S00018-023-04790-Z

1

SCHOLARLY ARTICLE

Morphoceuticals: Perspectives for discovery of drugs targeting anatomical control mechanisms in regenerative medicine, cancer and aging

1 Jun 2023Drug Discovery Today28(6)

Co-authorsPio-Lopez L, Levin M

VIEW PDF10.1016/J.DRUDIS.2023.103585

1

SCHOLARLY ARTICLE

Morphoceuticals: Perspectives for discovery of drugs targeting anatomical control mechanisms in regenerative medicine, cancer and aging

Jun 2023DRUG DISCOVERY TODAY28(6):1-13

Co-authorsPio-Lopez L, Levin M

VIEW MORE INFO10.1016/J.DRUDIS.2023.103585THIS

SCHOLARLY ARTICLE

Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine

12 May 2023Patterns4(5)

Co-authorsMathews J, Chang A, Devlin L…1 more

VIEW PDF10.1016/J.PATTER.2023.100737

3

SCHOLARLY ARTICLE

Making and breaking symmetries in mind and life

14 Apr 2023Interface Focus13(3)

Co-authorsSafron A, Sakthivadivel DAR, Sheikhbahaee Z…3 more

VIEW PDF10.1098/RSFS.2023.0015

SCHOLARLY ARTICLE

The scaling of goals from cellular to anatomical homeostasis: an evolutionary simulation, experiment and analysis

14 Apr 2023Interface Focus13(3)

Co-authorsPio-Lopez L, Bischof J, LaPalme JV…1 more

VIEW PDF10.1098/RSFS.2022.0072

1

SCHOLARLY ARTICLE

The collective intelligence of evolution and development

Apr 2023Collective Intelligence2(2):263391372311683SAGE Publications

Co-authorsWatson R, Levin M

VIEW PDF10.1177/26339137231168355

SCHOLARLY ARTICLE

Bioelectricity of non-excitable cells and multicellular pattern memories: Biophysical modeling

13 Mar 2023Physics Reports1004:1-31

Co-authorsCervera J, Levin M, Mafe S

10.1016/J.PHYSREP.2022.12.003

2

SCHOLARLY ARTICLE

There’s Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-Scale Machines

1 Mar 2023Biomimetics8(1)

Co-authorsBongard J, Levin M

VIEW PDF10.3390/BIOMIMETICS8010110

4

SCHOLARLY ARTICLE

Transplantation of fragments from different planaria: A bioelectrical model for head regeneration

7 Feb 2023Journal of Theoretical Biology558

Co-authorsCervera J, Manzanares JA, Levin M…1 more

VIEW PDF10.1016/J.JTBI.2022.111356

SCHOLARLY ARTICLE

Bioelectric networks: the cognitive glue enabling evolutionary scaling from physiology to mind

1 Jan 2023Animal Cognition

Co-authorsLevin M

VIEW PDF10.1007/S10071-023-01780-3

2

SCHOLARLY ARTICLE

Biological Robots: Perspectives on an Emerging Interdisciplinary Field

1 Jan 2023Soft Robotics

Co-authorsBlackiston D, Kriegman S, Bongard J…1 more

10.1089/SORO.2022.0142

1

SCHOLARLY ARTICLE

Cellular Competency during Development Alters Evolutionary Dynamics in an Artificial Embryogeny Model

1 Jan 2023Entropy25(1)

Co-authorsShreesha L, Levin M

VIEW PDF10.3390/E25010131

5

SCHOLARLY ARTICLE

Endless forms most beautiful 2.0: teleonomy and the bioengineering of chimaeric and synthetic organisms (July, blac073, 2022)

1 Jan 2023BIOLOGICAL JOURNAL OF THE LINNEAN SOCIETY138(1):141

Co-authorsClawson WP, Levin M

VIEW PDFVIEW MORE INFO10.1093/BIOLINNEAN/BLAC116

SCHOLARLY ARTICLE

Future medicine: from molecular pathways to the collective intelligence of the body

1 Jan 2023Trends in Molecular Medicine

Co-authorsLagasse E, Levin M

10.1016/J.MOLMED.2023.06.007

THE VOICE of Dr. Justin D. Pearlman, MD, PhD, FACC

PENDING

THE VOICE of  Stephen J. Williams, PhD

Ten TakeAway Points of Dr. Lustig’s talk on role of diet on the incidence of Type II Diabetes

 

1. 25% of US children have fatty liver
2. Type II diabetes can be manifested from fatty live with 151 million  people worldwide affected moving up to 568 million in 7 years
3. A common myth is diabetes due to overweight condition driving the metabolic disease
4. There is a trend of ‘lean’ diabetes or diabetes in lean people, therefore body mass index not a reliable biomarker for risk for diabetes
5. Thirty percent of ‘obese’ people just have high subcutaneous fat.  the visceral fat is more problematic
6. there are people who are ‘fat’ but insulin sensitive while have growth hormone receptor defects.  Points to other issues related to metabolic state other than insulin and potentially the insulin like growth factors
7. At any BMI some patients are insulin sensitive while some resistant
8. Visceral fat accumulation may be more due to chronic stress condition
9. Fructose can decrease liver mitochondrial function
10. A methionine and choline deficient diet can lead to rapid NASH development

 

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Artificial Intelligence (AI) Used to Successfully Determine Most Likely Repurposed Antibiotic Against Deadly Superbug Acinetobacter baumanni

Posted in Antibiotic resistance, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Applications in Therapeutics, Drug Development Process, drug repurposing, Evidence-based decision-making, Infectious Disease & New Antibiotic Targets, Inflammatory Pathophysiology, Pharmaceutical Drug Discovery, Transformative Technologies in Healthcare, tagged Acinetobacter baumanni, AI, Antibiotic resistance, Artificial intelligence, Artificial Intelligence ( AI), artificial intelligence in drug design, drug screens, in silico, rational drug design, superbug, WHO on May 26, 2023| Leave a Comment »

Artificial Intelligence (AI) Used to Successfully Determine Most Likely Repurposed Antibiotic Against Deadly Superbug Acinetobacter baumanni

Reporter: Stephen J. Williams, Ph.D.

The World Health Organization has identified 3 superbugs, or infective micororganisms displaying resistance to common antibiotics and multidrug resistance, as threats to humanity:

Three bacteria were listed as critical:

• Acinetobacter baumannii bacteria that are resistant to important antibiotics called carbapenems. Acinetobacter baumannii are highly-drug resistant bacteria that can cause a range of infections for hospitalized patients, including pneumonia, wound, or blood infections.
• Pseudomonas aeruginosa, which are resistant to carbapenems. Pseudomonas aeruginosa can cause skin rashes and ear infectious in healthy people but also severe blood infections and pneumonia when contracted by sick people in the hospital.
• Enterobacteriaceae — a family of bacteria that live in the human gut — that are resistant to both carbepenems and another class of antibiotics, cephalosporins.

 

It has been designated critical need for development of  antibiotics to these pathogens.  Now researchers at Mcmaster University and others in the US had used artificial intelligence (AI) to screen libraries of over 7,000 chemicals to find a drug that could be repurposed to kill off the pathogen.

Liu et. Al. (1) published their results of an AI screen to narrow down potential chemicals that could work against Acinetobacter baumanii in Nature Chemical Biology recently.

Abstract

Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.

Schematic workflow for incorporation of AI for antibiotic drug discovery for A. baumannii from 1. Liu, G., Catacutan, D.B., Rathod, K. et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol (2023). https://doi.org/10.1038/s41589-023-01349-8

Figure source: https://www.nature.com/articles/s41589-023-01349-8

Article Source: https://www.nature.com/articles/s41589-023-01349-8

1. Liu, G., Catacutan, D.B., Rathod, K. et al.Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat Chem Biol (2023). https://doi.org/10.1038/s41589-023-01349-8

 

 

For reference to WHO and lists of most pathogenic superbugs see https://www.scientificamerican.com/article/who-releases-list-of-worlds-most-dangerous-superbugs/

The finding was first reported by the BBC.

Source: https://www.bbc.com/news/health-65709834

By James Gallagher

Health and science correspondent

Scientists have used artificial intelligence (AI) to discover a new antibiotic that can kill a deadly species of superbug.

The AI helped narrow down thousands of potential chemicals to a handful that could be tested in the laboratory.

The result was a potent, experimental antibiotic called abaucin, which will need further tests before being used.

The researchers in Canada and the US say AI has the power to massively accelerate the discovery of new drugs.

It is the latest example of how the tools of artificial intelligence can be a revolutionary force in science and medicine.

• AI breakthrough could spark medical revolution

Stopping the superbugs

Antibiotics kill bacteria. However, there has been a lack of new drugs for decades and bacteria are becoming harder to treat, as they evolve resistance to the ones we have.

More than a million people a year are estimated to die from infections that resist treatment with antibiotics.The researchers focused on one of the most problematic species of bacteria – Acinetobacter baumannii, which can infect wounds and cause pneumonia.

You may not have heard of it, but it is one of the three superbugs the World Health Organization has identified as a “critical” threat.

It is often able to shrug off multiple antibiotics and is a problem in hospitals and care homes, where it can survive on surfaces and medical equipment.

Dr Jonathan Stokes, from McMaster University, describes the bug as “public enemy number one” as it’s “really common” to find cases where it is “resistant to nearly every antibiotic”.

 

Artificial intelligence

To find a new antibiotic, the researchers first had to train the AI. They took thousands of drugs where the precise chemical structure was known, and manually tested them on Acinetobacter baumannii to see which could slow it down or kill it.

This information was fed into the AI so it could learn the chemical features of drugs that could attack the problematic bacterium.

The AI was then unleashed on a list of 6,680 compounds whose effectiveness was unknown. The results – published in Nature Chemical Biology – showed it took the AI an hour and a half to produce a shortlist.

The researchers tested 240 in the laboratory, and found nine potential antibiotics. One of them was the incredibly potent antibiotic abaucin.

Laboratory experiments showed it could treat infected wounds in mice and was able to kill A. baumannii samples from patients.

However, Dr Stokes told me: “This is when the work starts.”

The next step is to perfect the drug in the laboratory and then perform clinical trials. He expects the first AI antibiotics could take until 2030 until they are available to be prescribed.

Curiously, this experimental antibiotic had no effect on other species of bacteria, and works only on A. baumannii.

Many antibiotics kill bacteria indiscriminately. The researchers believe the precision of abaucin will make it harder for drug-resistance to emerge, and could lead to fewer side-effects.

 

In principle, the AI could screen tens of millions of potential compounds – something that would be impractical to do manually.

“AI enhances the rate, and in a perfect world decreases the cost, with which we can discover these new classes of antibiotic that we desperately need,” Dr Stokes told me.

The researchers tested the principles of AI-aided antibiotic discovery in E. coli in 2020, but have now used that knowledge to focus on the big nasties. They plan to look at Staphylococcus aureus and Pseudomonas aeruginosa next.

“This finding further supports the premise that AI can significantly accelerate and expand our search for novel antibiotics,” said Prof James Collins, from the Massachusetts Institute of Technology.

He added: “I’m excited that this work shows that we can use AI to help combat problematic pathogens such as A. baumannii.”

Prof Dame Sally Davies, the former chief medical officer for England and government envoy on anti-microbial resistance, told Radio 4’s The World Tonight: “We’re onto a winner.”

She said the idea of using AI was “a big game-changer, I’m thrilled to see the work he (Dr Stokes) is doing, it will save lives”.

Other related articles and books published in this Online Scientific Journal include the following:

Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases, Reproductive Genomic Endocrinology

(3 book series: Volume 1, 2&3, 4)

https://www.amazon.com/gp/product/B08VVWTNR4?ref_=dbs_p_pwh_rwt_anx_b_lnk&storeType=ebooks

 

 

 

 

 

 

 

 

 

 

• The Immune System, Stress Signaling, Infectious Diseases and Therapeutic Implications:

 

• Series D, VOLUME 2

Infectious Diseases and Therapeutics

and

• Series D, VOLUME 3

The Immune System and Therapeutics

(Series D: BioMedicine & Immunology) Kindle Edition.

On Amazon.com since September 4, 2017

(English Edition) Kindle Edition – as one Book

https://www.amazon.com/dp/B075CXHY1B $115

 

Bacterial multidrug resistance problem solved by a broad-spectrum synthetic antibiotic

The Journey of Antibiotic Discovery

FDA cleared Clever Culture Systems’ artificial intelligence tech for automated imaging, analysis and interpretation of microbiology culture plates speeding up Diagnostics

Artificial Intelligence: Genomics & Cancer

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Mission 4: Use of Systems Biology for Design of inhibitor of Galectins as Cancer Therapeutic – Strategy and Software

Posted in Artificial Intelligence in CANCER, Artificial Intelligence in Medicine - Applications in Therapeutics, BioIT: BioInformatics, BioSimilars, BioTechnology - Venture Creation, Cancer - General, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, Cancer Informatics, cancer metabolism, Cell Biology, Signaling & Cell Circuits, Computational Biology/Systems and Bioinformatics, Genetics & Pharmaceutical, Genome Biology, Genomic Expression, Glycobiology: Biopharmaceutical Production, Glycobiology: Biopharmaceutical Production, Pharmacodynamics and Pharmacokinetics, Innovations, LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management, MicroEngineering Cell-Tissue & Systems, Natural Language Processing (NLP), Phosphorylation, S-nitrosylation, Signaling, Signaling & Cell Circuits, Small Molecules in Development of Therapeutic Drugs, Ubiquitin, Ubiquitinylation, tagged angiogenesis, Cancer, carbohydrate, galectin-3, LPBI, metastasis, PROTAC, Synthetic biology, systems biology on December 13, 2022| Leave a Comment »

Use of Systems Biology for Design of inhibitor of Galectins as Cancer Therapeutic – Strategy and Software

 

 

Curator: Stephen J. Williams, Ph.D.

Below is a slide representation of the overall mission 4 to produce a PROTAC to inhibit Galectins 1, 3, and 9.

 

Using A Priori Knowledge of Galectin Receptor Interaction to Create a BioModel of Galectin 3 Binding

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Now after collecting literature from PubMed on “galectin-3” AND “binding” to determine literature containing kinetic data we generate a WordCloud on the articles.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

This following file contains the articles needed for BioModels generation.

https://pharmaceuticalintelligence.com/wp-content/uploads/2022/12/Curating-Galectin-articles-for-Biomodels.docx

 

From the WordCloud we can see that these corpus of articles describe galectin binding to the CRD (carbohydrate recognition domain).  Interestingly there are many articles which describe van Der Waals interactions as well as electrostatic interactions.  Certain carbohydrate modifictions like Lac NAc and Gal 1,4 may be important.  Many articles describe the bonding as well as surface  interactions.  Many studies have been performed with galectin inhibitors like TDGs (thio-digalactosides) like TAZ TDG (3-deoxy-3-(4-[m-fluorophenyl]-1H-1,2,3-triazol-1-yl)-thio-digalactoside).  This led to an interesting article

Sci Rep

 

. 

Dual thio-digalactoside-binding modes of human galectins as the structural basis for the design of potent and selective inhibitors

Tung-Ju Hsieh ¹, Hsien-Ya Lin ^1 2, Zhijay Tu ¹, Ting-Chien Lin ^1 2, Shang-Chuen Wu ^1 3, Yu-Yao Tseng ¹, Fu-Tong Liu ⁴, Shang-Te Danny Hsu ^1 3, Chun-Hung Lin ^1 2 3 5

Affiliations 2016 Jul 15;6:29457.

 doi: 10.1038/srep29457.

• PMID: 27416897
 
• PMCID: PMC4945863
 
• DOI: 10.1038/srep29457

Free PMC article

Abstract

Human galectins are promising targets for cancer immunotherapeutic and fibrotic disease-related drugs. We report herein the binding interactions of three thio-digalactosides (TDGs) including TDG itself, TD139 (3,3′-deoxy-3,3′-bis-(4-[m-fluorophenyl]-1H-1,2,3-triazol-1-yl)-thio-digalactoside, recently approved for the treatment of idiopathic pulmonary fibrosis), and TAZTDG (3-deoxy-3-(4-[m-fluorophenyl]-1H-1,2,3-triazol-1-yl)-thio-digalactoside) with human galectins-1, -3 and -7 as assessed by X-ray crystallography, isothermal titration calorimetry and NMR spectroscopy. Five binding subsites (A-E) make up the carbohydrate-recognition domains of these galectins. We identified novel interactions between an arginine within subsite E of the galectins and an arene group in the ligands. In addition to the interactions contributed by the galactosyl sugar residues bound at subsites C and D, the fluorophenyl group of TAZTDG preferentially bound to subsite B in galectin-3, whereas the same group favored binding at subsite E in galectins-1 and -7. The characterised dual binding modes demonstrate how binding potency, reported as decreased Kd values of the TDG inhibitors from μM to nM, is improved and also offer insights to development of selective inhibitors for individual galectins.

Figures

Figure 1. Chemical structures of L3, TDG…

 

Figure 2. Structural comparison of the carbohydrate…

 

Figure 3. ¹⁹ F-NMR spectra of TAZTDG…

 

 

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The Vibrant Philly Biotech Scene: Proteovant Therapeutics Using Artificial Intelligence and Machine Learning to Develop PROTACs

Posted in Artificial Intelligence in CANCER, Artificial Intelligence in Medicine - Applications in Therapeutics, Autophagy, Autophagy-Modulating Proteins, BioIT: BioInformatics, Cell Biology, Signaling & Cell Circuits, Cell death pathways, Chemical Biology and its relations to Metabolic Disease, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Investment in Technological Breakthrough, Machine Learning, Proteolysis, Proteosome, Signaling, Signaling & Cell Circuits, Technology Transfer: Biotech and Pharmaceutical, Ubiquitin, Ubiquitinylation, Unfolded Protein Response (UPR), tagged Artificial intelligence, biotech startup, early ventures, Machine Learning, philadelphia, PROTAC, Proteovant, Therapeutic innovations, ubiquitin-proteosome, University of Michigan, Venture capital on October 26, 2021| Leave a Comment »

The Vibrant Philly Biotech Scene: Proteovant Therapeutics Using Artificial Intelligence and Machine Learning to Develop PROTACs

Reporter: Stephen J. Williams, Ph.D.

It has been a while since I have added to this series but there have been a plethora of exciting biotech startups in the Philadelphia area, and many new startups combining technology, biotech, and machine learning. One such exciting biotech is Proteovant Therapeutics, which is combining the new PROTAC (Proteolysis-Targeting Chimera) technology with their in house ability to utilize machine learning and artificial intelligence to design these types of compounds to multiple intracellular targets.

PROTACs (which actually is under a trademark name of Arvinus Operations, but is also refered to as Protein Degraders. These PROTACs take advantage of the cell protein homeostatic mechanism of ubiquitin-mediated protein degradation, which is a very specific targeted process which regulates protein levels of various transcription factors, protooncogenes, and receptors. In essence this regulated proteolyic process is needed for normal cellular function, and alterations in this process may lead to oncogenesis, or a proteotoxic crisis leading to mitophagy, autophagy and cellular death. The key to this technology is using chemical linkers to associate an E3 ligase with a protein target of interest. E3 ligases are the rate limiting step in marking the proteins bound for degradation by the proteosome with ubiquitin chains.

Structure-Based PROTAC Design: Project Overview

A review of this process as well as PROTACs can be found elsewhere in articles (and future articles) on this Open Access Journal.

Protevant have made two important collaborations:

1. Oncopia Therapeutics: came out of University of Michigan Innovation Hub and lab of Shaomeng Wang, who developed a library of BET and MDM2 based protein degraders. In 2020 was aquired by Riovant Sciences.
2. Riovant Sciences: uses computer aided design of protein degraders

Proteovant Company Description:

Proteovant is a newly launched development-stage biotech company focusing on discovery and development of disease-modifying therapies by harnessing natural protein homeostasis processes. We have recently acquired numerous assets at discovery and development stages from Oncopia, a protein degradation company. Our lead program is on track to enter IND in 2021. Proteovant is building a strong drug discovery engine by combining deep drugging expertise with innovative platforms including Roivant’s AI capabilities to accelerate discovery and development of protein degraders to address unmet needs across all therapeutic areas. The company has recently secured $200M funding from SK Holdings in addition to investment from Roivant Sciences. Our current therapeutic focus includes but is not limited to oncology, immunology and neurology. We remain agnostic to therapeutic area and will expand therapeutic focus based on opportunity. Proteovant is expanding its discovery and development teams and has multiple positions in biology, chemistry, biochemistry, DMPK, bioinformatics and CMC at many levels. Our R&D organization is located close to major pharmaceutical companies in Eastern Pennsylvania with a second site close to biotech companies in Boston area.

Protein degradation

Source: Protevant

The ubiquitin proteasome system (UPS) is responsible for maintaining protein homeostasis. Targeted protein degradation by the UPS is a cellular process that involves marking proteins and guiding them to the proteasome for destruction. We leverage this physiological cellular machinery to target and destroy disease-causing proteins.

Unlike traditional small molecule inhibitors, our approach is not limited by the classic “active site” requirements. For example, we can target transcription factors and scaffold proteins that lack a catalytic pocket. These classes of proteins, historically, have been very difficult to drug. Further, we selectively degrade target proteins, rather than isozymes or paralogous proteins with high homology. Because of the catalytic nature of the interactions,  it is possible to achieve efficacy at lower doses with prolonged duration while decreasing dose-limiting toxicities.

Biological targets once deemed “undruggable” are now within reach.

About Riovant Sciences: from PRNewsWire https://www.prnewswire.com/news-releases/roivant-unveils-targeted-protein-degradation-platform-301186928.html

Roivant develops transformative medicines faster by building technologies and developing talent in creative ways, leveraging the Roivant platform to launch “Vants” – nimble and focused biopharmaceutical and health technology companies. These Vants include Proteovant but also Dermovant, ImmunoVant,as well as others.

Roivant’s drug discovery capabilities include the leading computational physics-based platform for in silico drug design and optimization as well as machine learning-based models for protein degradation.

The integration of our computational and experimental engines enables the rapid design of molecules with high precision and fidelity to address challenging targets for diseases with high unmet need.

Our current modalities include small molecules, heterobifunctionals and molecular glues.

Roivant Unveils Targeted Protein Degradation Platform

– First therapeutic candidate on track to enter clinical studies in 2021

– Computationally-designed degraders for six targets currently in preclinical development

– Acquisition of Oncopia Therapeutics and research collaboration with lab of Dr. Shaomeng Wang at the University of Michigan to add diverse pipeline of current and future compounds

– Clinical-stage degraders will provide foundation for multiple new Vants in distinct disease areas

– Platform supported by $200 million strategic investment from SK Holdings

Other articles in this Vibrant Philly Biotech Scene on this Online Open Access Journal include:

The Vibrant Philly Biotech Scene: PCCI Meeting Announcement, BioDetego Presents Colon Cancer Diagnostic Tool

The Vibrant Philly Biotech Scene: Focus on KannaLife Sciences and the Discipline and Potential of Pharmacognosy

The Vibrant Philly Biotech Scene: Focus on Vaccines and Philimmune, LLC

The Vibrant Philly Biotech Scene: Focus on Computer-Aided Drug Design and Gfree Bio, LLC

Philly Biotech Scene: Biobots and 3D BioPrinting (Now called Allevi)

Philly Biotech Scene: November 2015 PCCI Meeting Showcasing ViFant (Penn Center For Innovation)

Spark Therapeutics’ $4.8Billion deal Confirmed as Biggest VC-backed Exit in Philadelphia

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