Reflecting on the 100th Anniversary of the Discovery of Insulin ePanel
Reflecting on the 100th Anniversary of the Discovery of Insulin ePanel
This year marks the 100th anniversary of the discovery and successful purification of insulin at the University of Toronto by Banting, Best, Collip and McLeod in 1921 (earning the Nobel Prize to Banting and McLeod in 1923). Over the last 100 years, the study of insulin has pioneered many of the important milestones of modern endocrinology. Insulin was not only the first hormone to be isolated but also the first to be measured by radioimmunoassay. It was also the first human protein to be sequenced, structurally characterized and then synthesized and manufactured for clinical use, affecting millions of lives.
To commemorate this important anniversary for the research community, Keystone Symposia and Frontiers Journals have collaborated on this special ePanel discussion featuring field leaders, past and present, to reflect on the evolution of the field and explore future directions.
Explore more emerging diabetes research & future directions at the upcoming virtual eSymposia:
Diabetes: Many Faces of the Disease
February 1-3, 2021 | 10:00AM EST | 3:00PM UTC
Speakers
Pierre De Meyts, MD, PhD
de Duve Institute
Jeffrey Holly, PhD
University of Bristol
Martin Ridderstrale, MD, PhD
Novo Nordisk Foundation
Jesse Roth, MD
Albert Einstein College of Medicine
Katherine Samaras, MD, PhD
Garvin Institute of Medical Research
Lori Sussel, PhD
University of Colorado Anschutz Medical Campus
Juleen R. Zierath, PhD
Karolinska Institutet
Mike Lawrence, PhD
The Walter and Eliza Hall Institute of Medical Research
Frontiers in Endocrinology Special Collections on Insulin
Submit Your Work
Frontiers in Endocrinology is organizing a special collection related to insulin and insulin-like peptides, with Jeff M.P. Holly and Pierre De Meyts as supervisory editors. Frontiers aim to bring together a collection of articles to celebrate the anniversary and the continued breadth of studies of this remarkable hormone.
Each of the 17 Specialty Sections of Frontiers in Endocrinology is calling for papers (reviews and mini-reviews) within the scope of each Specialty, and manuscripts will be edited by the Specialty Chief Editor. In the end all published articles will be collected into a unique commemorative eBook. The Chief Editors are inviting potential contributors, but spontaneous submissions are also welcome and should be submitted to the section that best fits the scope of the submission.
Multiple aspects of insulin and insulin-like peptides will be considered, including but not limited to, structure, evolution, mechanism of action and signaling, role in physiology, metabolism, lifespan, CNS function, pathophysiology of metabolic diseases, cancer and neurodegenerative diseases, and therapeutic applications. Articles with an original historical insight are also welcome.
Find out more here:
KS|QA: Francisco J. Quintana, PhD
Discover how Dr. Francisco Quintana of Harvard Medical School genetically engineered yeast as a probiotic treatment for inflammatory bowel disease (IBD) in this exclusive KSQA interview. Quintana’s synthetic yeast can not only sense inflammation in the colon or small intestine, they respond by degrading the inflammatory signal eATP, thereby dampening immune responses. The resulting suppression of intestinal inflammation reverses IBD pathology like fibrosis, and promotes recovery of a healthy gut microbiome in various mouse models of colitis and enteritis. The engineered yeast even out-perform FDA-approved treatments for IBD, without the adverse side effects of systemic immune suppression. Ultimately this innovative approach could transform treatment options and quality of life for patients suffering with various intestinal diseases driven by eATP, including IBD, as well as irradiation-induced intestinal fibrosis in cancer patients, and even graft versus host disease in transplant patients.
The idea was born six years ago on a flight from Boston to Vancouver, BC, where Quintana was seated next to colleague Dr. Sergio Peisajovich. The two were on their way to a Keystone Symposia meeting on Inflammatory Diseases, and spent the flight brainstorming about how they might combine their expertise in molecular immunology and protein engineering to correct pathological immune signaling. Now their “pie in the sky” ideas have become a reality. Passing pre-clinical studies with flying colors, the synthetic yeast are now ready to launch into clinical trials.
Designing and developing a novel treatment from inception to clinic is an accomplishment that most scientist can only hope to accomplish by the end of their career, but for Quintana, this is just the beginning. He envisions adapting the platform against many other diseases, “simply” by engineering the yeast to sense different signals and/or respond with different actions, targeted to each disease case. It may not be so simple, but the highly modifiable yeast platform is suited for the challenge.
The yeast might even be engineered to treat diseases outside the gut itself. With an avid interest in the gut-brain axis, Quintana is eager to develop probiotics that trigger signals in the gut that will act to modulate activities in the brain, for the treatment of multiple sclerosis and other CNS disorders. In light of the recent explosion of evidence pointing to the gut’s central role in educating systemic immune responses, this yeast probiotic platform is a powerful tool for modifying gut signaling to correct immune pathology throughout the body and in the context of diverse diseases. From autoimmune diseases to chronic inflammatory conditions, these innovative immune-modulating yeast have vast potential across all fields of medicine.
Artificial Intelligence/Machine Learning Approaches to Drive Innovations in Drug Discovery and Development
Drug discovery and development is a costly and lengthy process that involves balancing multiple attributes of molecules to achieve desired clinical efficacy and safety. Among those attributes, absorption, distribution, metabolism, and excretion (ADME) properties, nonclinical safety profiles, and pharmacokinetics/pharmacodynamics (PK/PD) characteristics are key components to the molecules’ success. Recent advancement in Artificial Intelligence/Machine Learning (AI/ML) techniques has the potential to increase probability of success and speed up decision-making in drug discovery and development. The AI/ML model works by learning from large datasets that contain chemical structure information and their corresponding experimental readouts and then elucidates the underlying relationships between chemical structure and biological responses. The AI/ML models can be subsequently used to predict the likely biological responses of a molecule that has never been tested, or sometimes not even synthesized, entirely based on its chemical structure. In the ADME area, discovery scientists apply such AI/ML models to identify small molecules (out of hundreds of thousands of theoretical options) with the most promising ADME profiles to synthesize, test, and advance into the pipeline.
With the advent of advanced AI/ML techniques, such as the “Graph Convolutional Neural Network”, the ADME field is enjoying a renaissance of sorts. Recently, Lilly scientists have executed state-of-the-art multi-task neural network modeling and have observed consistently improved predictions over traditional ML methods, thereby decreasing cycle time and traditional costs of drug discovery. Albeit incremental, consistent improvement of the new neural network method over traditional ML methods illustrates the potential of emerging AI/ML techniques to enhance the accuracy of ADME property predictions. To speed up innovation, we have initiated an interdisciplinary effort to tackle ADME, nonclinical safety, and PK/PD problems that can likely benefit from applying the state-of-the-art AI/ML techniques. Currently, our toxicology team is actively working on extracting morphological features from historical in-house rat primary hepatocyte cell images using AI/ML techniques to improve understanding of translation between in vitro phenotypic changes and in vivo toxicity observations. Similarly, our PK/PD modelers are experimenting with AI/ML methodologies to build models that predict patient response time course and simulate the effects of untested dosing regimens without requiring additional clinical trials. Collectively, these efforts will integrate AI/ML into various aspects of early drug discovery to enhance the probability of success and speed up decision-making to move potential medicines into development.
Integrating in vitro, preclinical, literature information and pharmacokinetic-pharmacodynamic modeling in development of neutralizing antibodies for treatment of COVID-19
During a new pandemic such as COVID-19, there is a need to develop drug treatments as quickly as possible. However, a new disease means that limited information would be available. With paucity of data, the objective was to predict the likely efficacy and required human dose of neutralizing monoclonal antibodies (mAb) for treatment of COVID-19. The use of pharmacokinetic-pharmacodynamic modeling and simulation is presented as the ideal platform to incorporate any available information and identify the efficacious human dose.
Methods
Candidate selection and effective therapeutic dose were supported by expanding two innovative, open-science initiatives1,2. The first approach used a physiologically-based pharmacokinetic (PBPK) model, incorporating various physicochemical drug properties to predict mAb clearance and tissue distribution. The PBPK model was then used to estimate mAb exposures to maintain concentrations above the IC90 of in vitro neutralization in lung interstitial fluid for up to 4 weeks in 90% of patients.
The second approach used a target cell-limited SARS-CoV-2 viral dynamic model developed from literature.3 This method evaluated viral clearance as a function of mAb dose and lung concentration by including:
• uninfected target cell population
• free virus and subsequent viral replication
• free virus elimination (host immune system)
• infected target cell death
• in vitro neutralization data
Results
The PBPK modeling approach determined that a clinical dose of 175 - 500 mg was expected to maintain target mAb lung concentrations for over 28 days in 90% of patients. The viral dynamic model predicted a 700 mg dose would achieve maximum viral elimination. Clinical trial results later confirmed the accuracy of model predictions for PK, viral clearance and ultimately the authorized dose of 700 mg bamlanivimab.
Conclusions
The accelerated selection of bamlanivimab as the first neutralizing mAb drug candidate to enter clinical evaluation and accurate prediction of the maximum therapeutic dose for treatment of COVID-19 in the absence of data from animal model of disease was supported by a pharmacokinetic-pharmacodynamic modeling and simulation framework.
References:
1. Jones et al. CPT:PSP 2019; 8(10): 738-47
2. Shah and Betts. MAbs 2013; 5(2): 297-305
3. Kim KS et al. medRxiv; 27 Mar 2020
Understanding and De-risking Drug-induced Injection Site Reactions (ISRs) with an Immunocompetent Human Skin Model
Upon subcutaneous (SQ) injection of therapeutic drugs, a local inflammatory reaction, commonly referred to as an injection site reaction (ISR), may develop at the injection site. ISRs are characterized by one or more of the following, erythema, edema, pruritus, pain, and induration. Therefore, ISRs are highly undesirable as they result in a suboptimal patient experience and represent a significant barrier for patient compliance, particularly for frequently administered therapeutics. Despite a well-recognized and long-standing unmet need, the development of validated model systems that can enable the identification of safer therapeutic candidates that have favorable ISR profiles has proven to be elusive. Here we use standardized immunocompetent human skin explants from live donors containing epidermis, dermis, and hypodermis to enable subcutaneous injection (up to 100 µL), to assess the extent to which therapeutic molecules trigger an ISR-like inflammatory response at the site of injection. To enable broad utility agnostic to therapeutic molecule classes, mechanism of action, and stage of development, we leveraged two complementary readouts: multiplex cytokine profiling to broadly survey pro-inflammatory responses and mast cell degranulation by immunofluorescence of whole skin sections. Compared to skin explants injected with vehicle control (phosphate buffered saline), the injection of the Mas-related G-protein coupled receptor member X2 (MRGPRX2) agonist compound 48/80, or Kineret (i.e., a commercial biologic that is a known competitive inhibitor of the IL-1R1 and triggers intense ISRs in up to 70% of patients) induced statistically significant and reproducible exteriorization of mast cell cytoplasmic granules. Moreover, we observe a dose-response of mast cell degranulation upon injection with different concentrations of compound 48/80 in the tissue. Further, SQ injections of molecules known to cause ISRs in the clinic produced a pro-inflammatory cytokine response assayed by multiplex profiling that is consistent with the clinical observation. Our results strongly suggest that these standardized human skin explants from multiple donors, maintained in an immunocompetent state, can be used for the identification and selection of safer and better tolerated therapeutics by predicting the extent to which candidate drugs can trigger ISRs when injected in patients.