The document discusses the Collaborative Drug Discovery (CDD) platform, which aims to facilitate data sharing and collaboration in drug discovery. Key points:
- CDD allows users to securely store private data while selectively sharing subsets with collaborators. It also hosts public datasets totaling over 3 million compounds.
- CDD has been used to facilitate collaboration in neglected disease research, particularly for tuberculosis and malaria. It hosts over 15 public TB datasets totaling over 300,000 compounds.
- Analysis of TB and malaria hit compounds on the platform shows generally higher molecular weights and logP values compared to approved drugs. Many compounds also fail filtering for undesirable reactivity.
Dana Vanderwall, Associate Director of Cheminformatics at Bristol-Myers Squibb, presented at Drexel University for Jean-Claude Bradley's Chemical Information Retrieval class on December 2, 2010. This first part covers "Cheminformatics & The evolving relationship between data in the public domain & pharma" and includes a general discussion of modern drug discovery and the details of a malaria dataset recently released from the pharmaceutical industry to the public.
A talk given at the International Congress "Contrasts in Pharmacology 2.0" held in Turin, May 14-16 2015
It describes our work with Bigger datasets, working on Tuberculosis as well as other areas.
Dana Vanderwall, Associate Director of Cheminformatics at Bristol-Myers Squibb, presented at Drexel University for Jean-Claude Bradley's Chemical Information Retrieval class on December 2, 2010. This first part covers "Cheminformatics & The evolving relationship between data in the public domain & pharma" and includes a general discussion of modern drug discovery and the details of a malaria dataset recently released from the pharmaceutical industry to the public.
A talk given at the International Congress "Contrasts in Pharmacology 2.0" held in Turin, May 14-16 2015
It describes our work with Bigger datasets, working on Tuberculosis as well as other areas.
Neglected infectious diseases such as tuberculosis (TB) and malaria kill millions of people annually and the oral drugs used are subject to resistance requiring the urgent development of new therapeutics. Several groups, including pharmaceutical companies, have made large sets of antimalarial screening hit compounds and the associated bioassay data available for the community to learn from and potentially optimize. We have examined both intrinsic and predicted molecular properties across these datasets and compared them with large libraries of compounds screened against Mycobacterium tuberculosis in order to identify any obvious patterns, trends or relationships. One set of antimalarial hits provided by GlaxoSmithKline appears less optimal for lead optimization compared with two other sets of screening hits we examined. Active compounds against both diseases were identified to have larger molecular weight ([similar]350–400) and logP values of [similar]4.0, values that are, in general, distinct from the less active compounds. The antimalarial hits were also filtered with computational rules to identify potentially undesirable substructures. We were surprised that approximately 75–85% of these compounds failed one of the sets of filters that we applied during this work. The level of filter failure was much higher than for FDA approved drugs or a subset of antimalarial drugs. Both antimalarial and antituberculosis drug discovery should likely use simple available approaches to ensure that the hits derived from large scale screening are worth optimizing and do not clearly represent reactive compounds with a higher probability of toxicity in vivo.
There is an expanding interest in repurposing and repositioning of drugs as well as how in silico methods can assist these endeavors. Recent repurposing project tendering calls by the National Center for Advancing Translational Sciences (US) and the Medical Research Council (UK) have included compound information and pharmacological data. However none of the internal company development code names were assigned to chemical structures in the official documentation. This not only abrogates in silico analysis to support repurposing but consequently necessitates data gathering and curation to assign structures. We describe here the methods results and challenges associated with this, including the fact that ~40-50% of the code names remain completely blinded. In addition we describe the in silico predictions that are enabled once the structures are accessible. Consequently we suggest approaches to encourage earlier release of name to structure mappings into the public domain.
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuitytlnagy
This is a presentation that I gave for my chemistry seminar class last month on using ligand-comparison techniques to predict off-target effects in drug candidates early in the drug discovery pipeline.
CDD: Vault, CDD: Vision and CDD: Models for Drug Discovery CollaborationsSean Ekins
A talk given at SERMACS 7th Nov 2015 in Memphis, describes CDD Vault, CDD Vision and CDD Models. In addition it also describes how the software is used in large and smaller scale collaborations for drug discovery.
There are tens of thousands of man-made chemicals to which humans are exposed, but only a fraction of these have the extensive in vivo toxicity data used in most traditional risk assessments. This lack of data, coupled with concerns about testing costs, are driving the development of new methods for assessing the risk of toxicity.
This presentation by Dr. Richard Judson reviewed methods being used at the U.S. EPA to use zebrafish as an in vivo model of vertebrate developmental toxicity and build in vitro to in vivo models using human assays.
EPA is committed to sound science, and we are proud to have some of the world's best scientists, many of whom are internationally recognized as leaders in their fields. Not only are EPA's scientific experts vital to achieving our mission, but they are dedicated to sharing knowledge and contributing to their the scientific communities, which helps further advance the science that protects human health and the environment. Part of this includes giving presentations to other members of the scientific community. We have posted some of these presentations here so that more people have access.
Learn more about Dr. Richard Judson - https://www.epa.gov/sciencematters/meet-epa-researcher-richard-judson
Learn more about EPA's Chemical Safety Research - https://www.epa.gov/chemical-research
There are tens of thousands of man-made chemicals to which humans are exposed, but only a fraction of these have the extensive in vivo toxicity data used in most traditional risk assessments. This lack of data, coupled with concerns about testing costs and animal use, are driving the development of new methods for assessing the risk of toxicity. These methods all start with the use of in vitro assays, e.g. for activity against the estrogen and androgen receptors (ER and AR) and targets in the steroidogenesis and thyroid signaling pathways. Because all individual assays are subject to a variety of noise processes and technology-specific assay artifacts, we have developed methods to create consensus predictions from multiple assays against the same target. The goal of these models is to both robustly predict in vivo activity, and also to provide quantitative estimates of uncertainty. This presentation by Dr. Richard Judson described these models and how they are validated against both in vitro and in vivo reference chemicals.
EPA is committed to sound science, and we are proud to have some of the world's best scientists, many of whom are internationally recognized as leaders in their fields. Not only are EPA's scientific experts vital to achieving our mission, but they are dedicated to sharing knowledge and contributing to their the scientific communities, which helps further advance the science that protects human health and the environment. Part of this includes giving presentations to other members of the scientific community. We have posted some of these presentations here so that more people have access.
Learn more about Dr. Richard Judson - https://www.epa.gov/sciencematters/meet-epa-researcher-richard-judson
Learn more about EPA's Chemical Safety Research - https://www.epa.gov/chemical-research
This lecture outlines the different strategies for finding a fragment hit and the subsequent elaboration strategies used in order to increase potency to develop a lead compound in drug discovery.
There are tens of thousands of man-made chemicals to which humans are exposed, but only a fraction of these have the extensive in vivo toxicity data used in most traditional risk assessments. This lack of data, coupled with concerns about testing costs and animal use, are driving the development of new methods for assessing the risk of toxicity. These methods include the use of in vitro high-throughput screening assays and computational models.
This presentation by Dr. Richard Judson reviewed a variety of high-throughput, non-animal methods being used at the U.S. EPA to screen chemicals for a variety of toxicity endpoints, including methods for providing mechanistic data like the Adverse Outcome Pathway.
EPA is committed to sound science, and we are proud to have some of the world's best scientists, many of whom are internationally recognized as leaders in their fields. Not only are EPA's scientific experts vital to achieving our mission, but they are dedicated to sharing knowledge and contributing to their the scientific communities, which helps further advance the science that protects human health and the environment. Part of this includes giving presentations to other members of the scientific community. We have posted some of these presentations here so that more people have access.
Learn more about Dr. Richard Judson - https://www.epa.gov/sciencematters/meet-epa-researcher-richard-judson
Learn more about EPA's Chemical Safety Research - https://www.epa.gov/chemical-research
Neglected infectious diseases such as tuberculosis (TB) and malaria kill millions of people annually and the oral drugs used are subject to resistance requiring the urgent development of new therapeutics. Several groups, including pharmaceutical companies, have made large sets of antimalarial screening hit compounds and the associated bioassay data available for the community to learn from and potentially optimize. We have examined both intrinsic and predicted molecular properties across these datasets and compared them with large libraries of compounds screened against Mycobacterium tuberculosis in order to identify any obvious patterns, trends or relationships. One set of antimalarial hits provided by GlaxoSmithKline appears less optimal for lead optimization compared with two other sets of screening hits we examined. Active compounds against both diseases were identified to have larger molecular weight ([similar]350–400) and logP values of [similar]4.0, values that are, in general, distinct from the less active compounds. The antimalarial hits were also filtered with computational rules to identify potentially undesirable substructures. We were surprised that approximately 75–85% of these compounds failed one of the sets of filters that we applied during this work. The level of filter failure was much higher than for FDA approved drugs or a subset of antimalarial drugs. Both antimalarial and antituberculosis drug discovery should likely use simple available approaches to ensure that the hits derived from large scale screening are worth optimizing and do not clearly represent reactive compounds with a higher probability of toxicity in vivo.
There is an expanding interest in repurposing and repositioning of drugs as well as how in silico methods can assist these endeavors. Recent repurposing project tendering calls by the National Center for Advancing Translational Sciences (US) and the Medical Research Council (UK) have included compound information and pharmacological data. However none of the internal company development code names were assigned to chemical structures in the official documentation. This not only abrogates in silico analysis to support repurposing but consequently necessitates data gathering and curation to assign structures. We describe here the methods results and challenges associated with this, including the fact that ~40-50% of the code names remain completely blinded. In addition we describe the in silico predictions that are enabled once the structures are accessible. Consequently we suggest approaches to encourage earlier release of name to structure mappings into the public domain.
Acceleration of Novel Drug Design via Prediction of Drug Candidate Promiscuitytlnagy
This is a presentation that I gave for my chemistry seminar class last month on using ligand-comparison techniques to predict off-target effects in drug candidates early in the drug discovery pipeline.
CDD: Vault, CDD: Vision and CDD: Models for Drug Discovery CollaborationsSean Ekins
A talk given at SERMACS 7th Nov 2015 in Memphis, describes CDD Vault, CDD Vision and CDD Models. In addition it also describes how the software is used in large and smaller scale collaborations for drug discovery.
There are tens of thousands of man-made chemicals to which humans are exposed, but only a fraction of these have the extensive in vivo toxicity data used in most traditional risk assessments. This lack of data, coupled with concerns about testing costs, are driving the development of new methods for assessing the risk of toxicity.
This presentation by Dr. Richard Judson reviewed methods being used at the U.S. EPA to use zebrafish as an in vivo model of vertebrate developmental toxicity and build in vitro to in vivo models using human assays.
EPA is committed to sound science, and we are proud to have some of the world's best scientists, many of whom are internationally recognized as leaders in their fields. Not only are EPA's scientific experts vital to achieving our mission, but they are dedicated to sharing knowledge and contributing to their the scientific communities, which helps further advance the science that protects human health and the environment. Part of this includes giving presentations to other members of the scientific community. We have posted some of these presentations here so that more people have access.
Learn more about Dr. Richard Judson - https://www.epa.gov/sciencematters/meet-epa-researcher-richard-judson
Learn more about EPA's Chemical Safety Research - https://www.epa.gov/chemical-research
There are tens of thousands of man-made chemicals to which humans are exposed, but only a fraction of these have the extensive in vivo toxicity data used in most traditional risk assessments. This lack of data, coupled with concerns about testing costs and animal use, are driving the development of new methods for assessing the risk of toxicity. These methods all start with the use of in vitro assays, e.g. for activity against the estrogen and androgen receptors (ER and AR) and targets in the steroidogenesis and thyroid signaling pathways. Because all individual assays are subject to a variety of noise processes and technology-specific assay artifacts, we have developed methods to create consensus predictions from multiple assays against the same target. The goal of these models is to both robustly predict in vivo activity, and also to provide quantitative estimates of uncertainty. This presentation by Dr. Richard Judson described these models and how they are validated against both in vitro and in vivo reference chemicals.
EPA is committed to sound science, and we are proud to have some of the world's best scientists, many of whom are internationally recognized as leaders in their fields. Not only are EPA's scientific experts vital to achieving our mission, but they are dedicated to sharing knowledge and contributing to their the scientific communities, which helps further advance the science that protects human health and the environment. Part of this includes giving presentations to other members of the scientific community. We have posted some of these presentations here so that more people have access.
Learn more about Dr. Richard Judson - https://www.epa.gov/sciencematters/meet-epa-researcher-richard-judson
Learn more about EPA's Chemical Safety Research - https://www.epa.gov/chemical-research
This lecture outlines the different strategies for finding a fragment hit and the subsequent elaboration strategies used in order to increase potency to develop a lead compound in drug discovery.
There are tens of thousands of man-made chemicals to which humans are exposed, but only a fraction of these have the extensive in vivo toxicity data used in most traditional risk assessments. This lack of data, coupled with concerns about testing costs and animal use, are driving the development of new methods for assessing the risk of toxicity. These methods include the use of in vitro high-throughput screening assays and computational models.
This presentation by Dr. Richard Judson reviewed a variety of high-throughput, non-animal methods being used at the U.S. EPA to screen chemicals for a variety of toxicity endpoints, including methods for providing mechanistic data like the Adverse Outcome Pathway.
EPA is committed to sound science, and we are proud to have some of the world's best scientists, many of whom are internationally recognized as leaders in their fields. Not only are EPA's scientific experts vital to achieving our mission, but they are dedicated to sharing knowledge and contributing to their the scientific communities, which helps further advance the science that protects human health and the environment. Part of this includes giving presentations to other members of the scientific community. We have posted some of these presentations here so that more people have access.
Learn more about Dr. Richard Judson - https://www.epa.gov/sciencematters/meet-epa-researcher-richard-judson
Learn more about EPA's Chemical Safety Research - https://www.epa.gov/chemical-research
Mel Reichman on Pool Shark’s Cues for More Efficient Drug DiscoveryJean-Claude Bradley
Mel Reichman, senior investigator and director of the LIMR Chemical Genomics Center at the Lankenau Institute for Medical Research presents at the chemistry department at Drexel University on November 12, 2009.
Modern drug discovery by high-throughput screening (HTS) begins with testing hundreds of thousands of compounds in biological assays. The confirmed hit rate for typical HTS is less than 0.5%; therefore, 99.5% of the costs of HTS are for generating null data. Orthogonal convolution of compound libraries (OCL) is 500% more efficient than present HTS practice. The OCL method combines 10 compounds per well. An advantage of this method is that each compound is represented twice in two separately arrayed pools. The potential for the approach to better enable academic centers of excellence to validate medicinally relevant biological targets is discussed.
dkNET Webinar: Illuminating The Druggable Genome With Pharos 10/23/2020dkNET
Abstract
Pharos (https://pharos.nih.gov/) is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied “dark” targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. In this webinar, Dr. Tudor Oprea will introduce how to use Pharos to find targets of interest for drug discovery.
The top 3 key questions that Pharos can answer:
1. What are the novel drug targets that may play a role in a specific disease?
2. What are the diseases that are related directly or indirectly to a drug target?
3. Find researchers that are related directly or indirectly to a drug target.
Presenter: Tudor Oprea, MD, PhD, Professor of Medicine, Chief of Translational Informatics Division & Internal Medicine, University of New Mexico
dkNET Webinar Information: https://dknet.org/about/webinar
The Karolinska Institute (KI) is the largest centre for medical education and research in Sweden and the home of the Nobel Prize in Physiology or Medicine.
KI consists of 22 departments and 600 research groups dedicated to improving human health through research and higher education.
The role of the Kohonen/Grafström team has been to guide the application, analysis, interpretation and storage of so called “omics” technology-derived data within the service-oriented subproject “ToxBank”.
Illuminating the Druggable Genome with Knowledge Engineering and Machine Lear...Jeremy Yang
Talk given at 14th Annual New Mexico BioInformatics, Science and Technology (NMBIST) Symposium, entitled Integrative Omics, on March 14-15, 2019. Most slides c/o IDG KMC PI Tudor Oprea, MD, PhD.
New Target Prediction and Visualization Tools Incorporating Open Source Molec...Sean Ekins
SLAS talk 2015 on TB Mobile 2.0 a mobile app using open source fingerprints and Bayesian machine learning algorithm for tuberculosis target prediction.
Predicting Drug Candidates Safety : the Role and Usage of Knowledge BasesAureus Sciences
Context
Drug agencies encourage more and more the use of information technologies to improve models to predict the efficacy and safety of submitted drug candidates.
These models require various tools as well as reliable in silico, in vitro and in vivo data. The selection of qualitative experimental data is critical to the efficiency of the predictive models.
Aureus' Solutions
Aureus Sciences has developed a recognized expertise on building knowledge bases with industrial partners in a collaborative approach, for the organization and storage of experimental data to help the pharmaceutical industry improve predictive approaches to drug discovery and development projects.
Presentation from AAPS PharmSci360 (October 23, 2023) in which I describe highlights of my Springer/AAPS book Winning Grants (https://link.springer.com/book/10.1007/978-3-031-27516-6) - presenting a 'how to' guide on writing small business grants - e.g. NIH STTR and SBIR grants. Written by someone experienced in winning such grants.
Evaluating Multiple Machine Learning Models for Biodegradation and Aquatic To...Sean Ekins
The presentation was given at SETAC 2022 Nov 16 and describes our work on Evaluating Multiple Machine Learning Models for Biodegradation and Aquatic Toxicity.
We generated many models that are available to license in our MegaTox software. We found that the support vector machines performed the best after assessing many algorithms for both classification and regression models.
The authors of this work are Thomas R Lane, Fabio Urbina and Sean Ekins.
The contact is sean@collaborationspharma.com
A presentation at the Global Genes rare drug development symposium on governm...Sean Ekins
This presentation from June 12 2020 gives a brief overview of my experience of 15 years of applying for government grants to fund small companies. Prior to this I had no experience of applying for such grants. The bottom line for rare disease groups / families is find a scientist that can do this or assist you. please also see www.collaborationspharma.com
Leveraging Science Communication and Social Media to Build Your Brand and Ele...Sean Ekins
Slides from AAPS Careers session by Maren Katherina Preis, Kyle Bagin, Sean Ekins
Provides some clear steps on how you could use social media to help your career.
Oral presentation given in MEDI session at 2017 ACS in DC.
co-authors Kimberley M. Zorn, Mary A. Lingerfelt, Jair L. de Siqueira-Neto, Alex M. Clark, Sean Ekins
describes drug repurposing and machine learning - for more details see www.collaborationspharma.com
Assay Central: A New Approach to Compiling Big Data and Preparing Machine Lea...Sean Ekins
Oral presentation at 2017 ACS in DC - given by Kimberley Zorn
co-authors include Mary A. Lingerfelt, Alex M. Clark, Sean Ekins
for more details see www.collaborationspharma.com
Five Ways to Use Social Media to Raise Awareness for Your Paper or ResearchSean Ekins
Presentation given at the AAPS 2016 conference in Denver. Some of the slides are from AAPS, Some from Kudos and some from Figshare. One slide is from Tony Williams. All slides used with permission.
CDD: Vault, CDD: Vision and CDD: Models software for biologists and chemists ...Sean Ekins
A perspective on 12 yrs of CDD and developing products and collaborations.
A presentation given at the ACS meeting in San Diego - small business section
This presentation summarizes some early efforts on an open drug discovery collaboration between scientists in Brazil and the US. The amazing virus images were created by John Liebler and can be licensed from him http://www.artofthecell.com/animation/will-the-real-zika-virus-please-stand-up
The homology models were created with Swiss Model by Sean Ekins:
Marco Biasini, Stefan Bienert, Andrew Waterhouse, Konstantin Arnold, Gabriel Studer, Tobias Schmidt, Florian Kiefer, Tiziano Gallo Cassarino, Martino Bertoni, Lorenza Bordoli, Torsten Schwede. (2014). SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Research; (1 July 2014) 42 (W1): W252-W258; doi: 10.1093/nar/gku340.
Arnold K., Bordoli L., Kopp J., and Schwede T. (2006). The SWISS-MODEL Workspace: A web-based environment for protein structure homology modelling. Bioinformatics, 22,195-201.
Kiefer F, Arnold K, Künzli M, Bordoli L, Schwede T (2009). The SWISS-MODEL Repository and associated resources. Nucleic Acids Research. 37, D387-D392.
Guex, N., Peitsch, M.C., Schwede, T. (2009). Automated comparative protein structure modeling with SWISS-MODEL and Swiss-PdbViewer: A historical perspective. Electrophoresis, 30(S1), S162-S173.
Ensuring Chemical Structure, Biological Data and Computational Model Quality
A talk given at SLAS 2016 mon Jan 25th in San Diego
covers published work and recent forays with BIA 10-2474
Pros and cons of social networking for scientistsSean Ekins
Over the past 4 years I have been using social networking tools for scientists more inspired by Antony Williams. I realized I am using many tools and there are pros and cons of them. Here is my brief summary.
Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Mode...Sean Ekins
Slides from SERMACS 2015 meeting in Memphis 2015 describing a collaborative project with SRI International and Rutgers. The work was published in PLOS ONE http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141076
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Ocular injury ppt Upendra pal optometrist upums saifai etawah
Collaborative Drug Discovery: A Platform For Transforming Neglected Disease R&D and Beyond
1. Collaborative Drug Discovery: A Platform For Transforming Neglected Disease R&D and Beyond Sean Ekins Collaborative Drug Discovery, Burlingame, CA. Collaborations in Chemistry, Jenkintown, PA. Department of Pharmacology, University of Medicine & Dentistry of New Jersey-Robert Wood Johnson Medical School, Piscataway, NJ. School of Pharmacy, Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD. www.collaborativedrug.com
2. In the long history of human kind (and animal kind, too) those who have learned to collaborate and improvise most effectively have prevailed. Charles Darwin
3. What does "Collaboration" mean to you? Michael Pollastri • collaboration, to me, means that folks from disparate disciplines or skills work together towards the same end-goal. … A collaboration means free and open data sharing, transparent goals and intentions, and a relationship that allows open (frank) and constructive discussion. Markus Sitzmann • The internet is the perfect place to share (certain) data and many of the new technologies and format available at the Web (REST, SOAP etc.) are perfect to use data collaboratively.
4. Typical Lab: The Data Explosion Problem & Collaborations DDT Feb 2009
9. CDD: 2 way linking with ChemSpider www.chemspider.com
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11. > 15 public datasets for TB Including Novartis data on TB hits >300,000 cpds Patents, Papers Annotated by CDD Open to browse by anyone http://www.collaborativedrug.com/register Molecules with activity against
16. Bayesian Classification Models Good Bad active compounds with MIC < 5uM Laplacian-corrected Bayesian classifier models were generated using FCFP-6 and simple descriptors. 2 models 220,000 and >2000 compounds Ekins et al., Mol BioSyst, 6: 840-851, 2010
18. Bayesian Classification TB Models Leave out 50% x 100 Ekins et al., Mol BioSyst, 6: 840-851, 2010 65.47 ± 7.96 67.21 ± 7.05 66.85 ± 4.06 0.75 ± 0.01 0.73 ± 0.01 MLSMR dose response set (N = 2273) 77.13 ± 2.26 78.59 ± 1.94 78.56 ± 1.86 0.86 ± 0 0.86 ± 0 MLSMR All single point screen (N = 220463) Sensitivity Specificity Concordance Internal ROC Score External ROC Score Dateset (number of molecules)
19. Ekins and Freundlich, Pharm Res, In press 2011 100K library Novartis Data FDA drugs Additional test sets Suggests models can predict data from the same and independent labs Initial enrichment – enables screening few compounds to find actives 21 hits in 2108 cpds 34 hits in 248 cpds 1702 hits in >100K cpds
20. Novartis aerobic and anaerobic TB hits Ekins and Freundlich, Pharm Res, In press 2011 Anaerobic compounds showed statistically different and higher mean descriptor property values compared with the aerobic hits (e.g. molecular weight, logP, hydrogen bond donor, hydrogen bond acceptor, polar surface area and rotatable bond number) The mean molecular properties for the Novartis compounds are in a similar range to the MLSMR and TAACF-NIAID CB2 hits
21. CDD provides support to 3 groups with their respective 3 major pharma partners Extended BMGF Grant for TB
22. CDD is a partner on a 5 year project supporting >20 labs and proving cheminformatics support www.mm4tb.org More Medicines for Tuberculosis
23. Malaria data in CDD > 22,000 compounds Ekins, Hohman and Bunin in: Collaborative Computational Technologies for Biomedical Research , Edited by Sean Ekins, Maggie A. Z. Hupcey, Antony J. Williams.Published 2011 by John Wiley & Sons, Inc
26. Multiple antimalarial datasets Ekins and Williams Drug Disc Today 15; 812-815, 2010 Ekins and Williams, MedChemComm, 1: 325-330, 2010. screening hits in total are not ‘lead-like’ (MW < 350, LogP< 3) closest to ‘natural product lead-like’. Although GSK suggests that the compounds are “drug-like” the evidence for this is weak 5.8 ± 3.0 53.4 ± 21.2 0.2 ± 0.6 5.3 ± 1.5 1.8 ± 1.0 3.8 ± 1.6 341.6 ± 67.0 Antimalarial drugs (N = 14) 7.1 ± 7.7 90.6 ± 104.4 0.6 ± 0.9 5.4 ± 4.7 2.1 ± 3.4 2.2 ± 2.7 458.0 ± 298.6 Johns Hopkins Subset > 50% malaria inhibition at 96h (N = 165) 5.4 ± 9.6 96.0 ±139.8 0.3 ± 0.8 5.1 ± 5.5 2.4 ± 4.6 1.2 ± 3.4 349.1 ± 355.8 Johns Hopkins All FDA drugs (N = 2615) 5.6 ± 3.0 74.7 ± 37.9 0.4 ± 0.7 4.7 ± 2.1 1.2 ± 1.1 3.7 ± 2.0 398.2 ± 105.3 Novartis (N = 5695) 5.2 ±2.3 72.2 ±29.3 0.2 ± 0.4 4.9 ± 1.8 1.1 ± 0.8 3.8 ± 1.6 385.3 ± 71.2 St Jude (N = 1524) 7.2 ± 3.4 76.8 ± 30.0 0.8 ± 0.8 5.6 ± 2.0 1.8 ± 1.0 4.5 ± 1.6 478.2 ± 114.3 GSK data (N = 13,471) RBN PSA (Å 2 ) Lipinski rule of 5 alerts HBA HBD logP MW Dataset
27. TB Compound libraries and filter failures Filtering using SMARTs filters to remove thiol reactives, false positives etc at University of New Mexico (http://pasilla.health.unm.edu/tomcat/biocomp/smartsfilter) Ekins et al., Mol Biosyst, 6: 2316-2324, 2010
29. Ekins and Freundlich, Pharm Res, In press 2011 Correlation between the number of SMARTS filter failures and the number of Lipinski violations for different types of rules sets with FDA drug set from CDD (N = 2804) Suggests # of Lipinski violations may also be an indicator of undesirable chemical features that result in reactivity
30. Summary Active compounds vs Mtb and P. Falciparum have higher mean molecular weights and logP values A high proportion of compounds that fail the Abbott filters for reactivity when compared to drugs and antimalarials Understanding the chemical properties and characteristics of compounds = better compounds for lead optimization. St Jude and Novartis datasets should be screened vs Mtb as their property space is close to TB actives GSK compounds may not be an ideal starting point for lead optimization for malaria
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34. 2D Similarity search with “hit” from screening Export database and use for 3D searching with a pharmacophore or other model Suggest approved drugs for testing - may also indicate other uses if it is present in more than one database Suggest in silico hits for in vitro screening Key databases of structures and bioactivity data FDA drugs database Repurpose FDA drugs in silico Ekins S, Williams AJ, Krasowski MD and Freundlich JS, Drug Disc Today, In press 2011
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36. RELEVANT PAPERS Ekins S, Williams AJ, Krasowski MD and Freundlich JS, In silico repositioning of approved drugs for rare and neglected diseases, Drug Disc Today, In press 2011. Ekins S and Freundlich JS, Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets, Pharm Res, In press 2011. Lamichhane G, Freundlich JS, Ekins S , Wickramaratne N, Nolan, S and Bishai WR, Essential Metabolites of M. tuberculosis and their small molecule mimics, Mbio, 2: e00301-10, 2011. Ekins S , Freundlich JS, Choi I, Sarker M and Talcott C, Computational Databases, Pathway and Cheminformatics Tools for Tuberculosis Drug Discovery, Trends In Microbiology, 19: 65-74, 2011. Ekins S and Williams AJ, Meta-analysis of molecular property patterns and filtering of public datasets of antimalarial “hits” and drugs, MedChemComm, 1: 325-330, 2010. Ekins S , Kaneko T, Lipinski CA, Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, Ernst S, Yang J, Goncharoff N, Hohman M and Bunin BA, Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis, Mol Biosyst, 6: 2316-2324, 2010. Rishi R. Gupta, Gifford, EM, Liston T, Waller CL, Hohman M, Bunin BA and Ekins S , Using open source computational tools for predicting human metabolic stability and additional ADME/Tox properties, Drug Metab Dispos, 38: 2083-2090, 2010. Ekins S and Williams AJ, When Pharmaceutical Companies Publish Large Datasets: An Abundance of riches or fool’s gold? Drug Disc Today, 15; 812-815, 2010. Ekins S , Gupta R, Gifford E, Bunin BA, Waller CL, Chemical Space: missing pieces in cheminformatics, Pharm Res, 27: 2035-2039, 2010. Ekins S . and Williams AJ, Reaching out to collaborators: crowdsourcing for pharmaceutical research, Pharm Res, 27: 393-395, 2010. Ekins S and Williams AJ, Precompetitive Preclinical ADME/Tox Data: Set It Free on The Web to Facilitate Computational Model Building to Assist Drug Development. Lab On A Chip, 10: 13-22, 2010. Ekins S , Bradford J, Dole K, Spektor A, Gregory K, Blondeau D, Hohman M and Bunin BA, A Collaborative Database and Computational Models for Tuberculosis Drug Discovery, Mol BioSyst, 6: 840-851, 2010. Williams AJ, Tkachenko V, Lipinski C, Tropsha A and Ekins S , Free online resources enabling crowdsourced drug discovery, Drug Discovery World, Winter 2009/10, 33-39. Louise-May S, Bunin B and Ekins S , Towards integrated web-based tools in drug discovery, Touch Briefings - Drug Discovery, 6: 17-21, 2009. Hohman M, Gregory K, Chibale K, Smith PJ, Ekins S and Bunin B, Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery, Drug Disc Today, 14: 261-270, 2009.
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38. Searching for TB molecular mimics Lamichhane G, Freundlich JS, Ekins S , Wickramaratne N, Nolan, S and Bishai WR, Mbio, 2: e00301-10, 2011
39. >10 fold Enrichment with TB Bayesian model Filtering a 100K compound library Ekins et al., Mol Biosyst, 6: 2316-2324, 2010 82 (4.82) 107 (6.29) 9.95 (0.58) 600 70 (4.11) 92 (5.41) 8.29 (0.49) 500 58 (3.41) 77 (4.52) 6.63 (0.39) 400 54 (3.17) 64 (3.76) 4.98 (0.29) 300 42 (2.47) 48 (2.82) 3.32 (0.19) 200 24 (1.41) 23 (1.35) 1.66 (0.10) 100 0 0 0 0 dose response Bayesian model (%) single point screening (200k) Bayesian model (%) Random hit rate (%) Number of compounds screened
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Editor's Notes
CDD Experienced Team Innovates and Executes Barry Bunin, PhD (Pres. & Cofounder as first Eli Lilly EIR) Libraria (CEO, Pres.-CSO), Arris Pharmaceuticals (Sr. Scientist), Genentech, UC Berkeley (Ellman), Columbia University, author. Moses Hohman, PhD (Director Software Engineering) Northwestern Assoc. Director of Bioinformatics, Thoughtworks, Inc., U of Chicago (PhD), Harvard ( magna cum laude, Physics) Sylvia Ernst, PhD (Director Community Growth & Sales) Left 800-lb Gorillas: Accelrys-Scitegic, MDL-Elsevier-Beilstein Peter Cohan (BOD & Overall Sales Strategy) Symyx (VP Bus Dev & President-Discovery Tools), MDL (VP Customer Marketing), www.secondderivative.com, author. Omidyar Network, Founders Fund, & Lilly (BOD observers) WSGR (Corporate Counsel), Rina Accountancy (GAAP compliance) Partners: Hub Consortium Members, ChemAxon, DNDi, MMV, Sandler Center… CDD SAB: Christopher Lipinski PhD, James McKerrow, MD PhD, David Roos PhD, Adam Renslo PhD, Wes Van Voorhis, MD PhD
CDD Experienced Team Innovates and Executes Barry Bunin, PhD (Pres. & Cofounder as first Eli Lilly EIR) Libraria (CEO, Pres.-CSO), Arris Pharmaceuticals (Sr. Scientist), Genentech, UC Berkeley (Ellman), Columbia University, author. Moses Hohman, PhD (Director Software Engineering) Northwestern Assoc. Director of Bioinformatics, Thoughtworks, Inc., U of Chicago (PhD), Harvard ( magna cum laude, Physics) Sylvia Ernst, PhD (Director Community Growth & Sales) Left 800-lb Gorillas: Accelrys-Scitegic, MDL-Elsevier-Beilstein Peter Cohan (BOD & Overall Sales Strategy) Symyx (VP Bus Dev & President-Discovery Tools), MDL (VP Customer Marketing), www.secondderivative.com, author. Omidyar Network, Founders Fund, & Lilly (BOD observers) WSGR (Corporate Counsel), Rina Accountancy (GAAP compliance) Partners: Hub Consortium Members, ChemAxon, DNDi, MMV, Sandler Center… CDD SAB: Christopher Lipinski PhD, James McKerrow, MD PhD, David Roos PhD, Adam Renslo PhD, Wes Van Voorhis, MD PhD
CDD Experienced Team Innovates and Executes Barry Bunin, PhD (Pres. & Cofounder as first Eli Lilly EIR) Libraria (CEO, Pres.-CSO), Arris Pharmaceuticals (Sr. Scientist), Genentech, UC Berkeley (Ellman), Columbia University, author. Moses Hohman, PhD (Director Software Engineering) Northwestern Assoc. Director of Bioinformatics, Thoughtworks, Inc., U of Chicago (PhD), Harvard ( magna cum laude, Physics) Sylvia Ernst, PhD (Director Community Growth & Sales) Left 800-lb Gorillas: Accelrys-Scitegic, MDL-Elsevier-Beilstein Peter Cohan (BOD & Overall Sales Strategy) Symyx (VP Bus Dev & President-Discovery Tools), MDL (VP Customer Marketing), www.secondderivative.com, author. Omidyar Network, Founders Fund, & Lilly (BOD observers) WSGR (Corporate Counsel), Rina Accountancy (GAAP compliance) Partners: Hub Consortium Members, ChemAxon, DNDi, MMV, Sandler Center… CDD SAB: Christopher Lipinski PhD, James McKerrow, MD PhD, David Roos PhD, Adam Renslo PhD, Wes Van Voorhis, MD PhD
CDD Experienced Team Innovates and Executes Barry Bunin, PhD (Pres. & Cofounder as first Eli Lilly EIR) Libraria (CEO, Pres.-CSO), Arris Pharmaceuticals (Sr. Scientist), Genentech, UC Berkeley (Ellman), Columbia University, author. Moses Hohman, PhD (Director Software Engineering) Northwestern Assoc. Director of Bioinformatics, Thoughtworks, Inc., U of Chicago (PhD), Harvard ( magna cum laude, Physics) Sylvia Ernst, PhD (Director Community Growth & Sales) Left 800-lb Gorillas: Accelrys-Scitegic, MDL-Elsevier-Beilstein Peter Cohan (BOD & Overall Sales Strategy) Symyx (VP Bus Dev & President-Discovery Tools), MDL (VP Customer Marketing), www.secondderivative.com, author. Omidyar Network, Founders Fund, & Lilly (BOD observers) WSGR (Corporate Counsel), Rina Accountancy (GAAP compliance) Partners: Hub Consortium Members, ChemAxon, DNDi, MMV, Sandler Center… CDD SAB: Christopher Lipinski PhD, James McKerrow, MD PhD, David Roos PhD, Adam Renslo PhD, Wes Van Voorhis, MD PhD
CDD Experienced Team Innovates and Executes Barry Bunin, PhD (Pres. & Cofounder as first Eli Lilly EIR) Libraria (CEO, Pres.-CSO), Arris Pharmaceuticals (Sr. Scientist), Genentech, UC Berkeley (Ellman), Columbia University, author. Moses Hohman, PhD (Director Software Engineering) Northwestern Assoc. Director of Bioinformatics, Thoughtworks, Inc., U of Chicago (PhD), Harvard ( magna cum laude, Physics) Sylvia Ernst, PhD (Director Community Growth & Sales) Left 800-lb Gorillas: Accelrys-Scitegic, MDL-Elsevier-Beilstein Peter Cohan (BOD & Overall Sales Strategy) Symyx (VP Bus Dev & President-Discovery Tools), MDL (VP Customer Marketing), www.secondderivative.com, author. Omidyar Network, Founders Fund, & Lilly (BOD observers) WSGR (Corporate Counsel), Rina Accountancy (GAAP compliance) Partners: Hub Consortium Members, ChemAxon, DNDi, MMV, Sandler Center… CDD SAB: Christopher Lipinski PhD, James McKerrow, MD PhD, David Roos PhD, Adam Renslo PhD, Wes Van Voorhis, MD PhD
CDD Experienced Team Innovates and Executes Barry Bunin, PhD (Pres. & Cofounder as first Eli Lilly EIR) Libraria (CEO, Pres.-CSO), Arris Pharmaceuticals (Sr. Scientist), Genentech, UC Berkeley (Ellman), Columbia University, author. Moses Hohman, PhD (Director Software Engineering) Northwestern Assoc. Director of Bioinformatics, Thoughtworks, Inc., U of Chicago (PhD), Harvard ( magna cum laude, Physics) Sylvia Ernst, PhD (Director Community Growth & Sales) Left 800-lb Gorillas: Accelrys-Scitegic, MDL-Elsevier-Beilstein Peter Cohan (BOD & Overall Sales Strategy) Symyx (VP Bus Dev & President-Discovery Tools), MDL (VP Customer Marketing), www.secondderivative.com, author. Omidyar Network, Founders Fund, & Lilly (BOD observers) WSGR (Corporate Counsel), Rina Accountancy (GAAP compliance) Partners: Hub Consortium Members, ChemAxon, DNDi, MMV, Sandler Center… CDD SAB: Christopher Lipinski PhD, James McKerrow, MD PhD, David Roos PhD, Adam Renslo PhD, Wes Van Voorhis, MD PhD
CDD Experienced Team Innovates and Executes Barry Bunin, PhD (Pres. & Cofounder as first Eli Lilly EIR) Libraria (CEO, Pres.-CSO), Arris Pharmaceuticals (Sr. Scientist), Genentech, UC Berkeley (Ellman), Columbia University, author. Moses Hohman, PhD (Director Software Engineering) Northwestern Assoc. Director of Bioinformatics, Thoughtworks, Inc., U of Chicago (PhD), Harvard ( magna cum laude, Physics) Sylvia Ernst, PhD (Director Community Growth & Sales) Left 800-lb Gorillas: Accelrys-Scitegic, MDL-Elsevier-Beilstein Peter Cohan (BOD & Overall Sales Strategy) Symyx (VP Bus Dev & President-Discovery Tools), MDL (VP Customer Marketing), www.secondderivative.com, author. Omidyar Network, Founders Fund, & Lilly (BOD observers) WSGR (Corporate Counsel), Rina Accountancy (GAAP compliance) Partners: Hub Consortium Members, ChemAxon, DNDi, MMV, Sandler Center… CDD SAB: Christopher Lipinski PhD, James McKerrow, MD PhD, David Roos PhD, Adam Renslo PhD, Wes Van Voorhis, MD PhD
CDD Experienced Team Innovates and Executes Barry Bunin, PhD (Pres. & Cofounder as first Eli Lilly EIR) Libraria (CEO, Pres.-CSO), Arris Pharmaceuticals (Sr. Scientist), Genentech, UC Berkeley (Ellman), Columbia University, author. Moses Hohman, PhD (Director Software Engineering) Northwestern Assoc. Director of Bioinformatics, Thoughtworks, Inc., U of Chicago (PhD), Harvard ( magna cum laude, Physics) Sylvia Ernst, PhD (Director Community Growth & Sales) Left 800-lb Gorillas: Accelrys-Scitegic, MDL-Elsevier-Beilstein Peter Cohan (BOD & Overall Sales Strategy) Symyx (VP Bus Dev & President-Discovery Tools), MDL (VP Customer Marketing), www.secondderivative.com, author. Omidyar Network, Founders Fund, & Lilly (BOD observers) WSGR (Corporate Counsel), Rina Accountancy (GAAP compliance) Partners: Hub Consortium Members, ChemAxon, DNDi, MMV, Sandler Center… CDD SAB: Christopher Lipinski PhD, James McKerrow, MD PhD, David Roos PhD, Adam Renslo PhD, Wes Van Voorhis, MD PhD
CDD Experienced Team Innovates and Executes Barry Bunin, PhD (Pres. & Cofounder as first Eli Lilly EIR) Libraria (CEO, Pres.-CSO), Arris Pharmaceuticals (Sr. Scientist), Genentech, UC Berkeley (Ellman), Columbia University, author. Moses Hohman, PhD (Director Software Engineering) Northwestern Assoc. Director of Bioinformatics, Thoughtworks, Inc., U of Chicago (PhD), Harvard ( magna cum laude, Physics) Sylvia Ernst, PhD (Director Community Growth & Sales) Left 800-lb Gorillas: Accelrys-Scitegic, MDL-Elsevier-Beilstein Peter Cohan (BOD & Overall Sales Strategy) Symyx (VP Bus Dev & President-Discovery Tools), MDL (VP Customer Marketing), www.secondderivative.com, author. Omidyar Network, Founders Fund, & Lilly (BOD observers) WSGR (Corporate Counsel), Rina Accountancy (GAAP compliance) Partners: Hub Consortium Members, ChemAxon, DNDi, MMV, Sandler Center… CDD SAB: Christopher Lipinski PhD, James McKerrow, MD PhD, David Roos PhD, Adam Renslo PhD, Wes Van Voorhis, MD PhD