Bayesian estimations of strong toxic signals [compatibility mode]Bhaswat Chakraborty
“Signals” of adverse drug reactions are, according to WHO, “reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously. Usually more than a single report is required to detect a signal, depending on the seriousness of the event and the quality of the information.” Once a signal is detected, one can then analyze and confirm it. In detecting signals from large adverse drug reaction (ADR) databases, however, one has to use a procedure that is sensitive (low false negativity) and specific (high true positivity) for the purpose. A whole range of statistical methods have been applied for data mining and signal detection (SD) in pharmacovigilance (PV). My talk would be on Bayesian methods for SD.
The US FDA uses a Bayesian data mining approach developed by William DuMouchel called multi-item gamma poisson shrinker (MGPS). WHO also uses a Bayesian method (Andrew Bate) based on a Bayesian confidence propagation neural network (BCPNN). These estimates provide shrinkage towards zero of the observed to expected number of ADRs, e.g., the empirical Bayesian geometric mean (EBGM) or information component (IC). These Bayesian estimators are robust measures of ADR-drug association.
Bayesian approaches are intuitively appealing when very small numbers are involved and where there is a need of continuous reassessment of probability of association with the acquisition of new data over time. Bayesian estimates such as EBGM are close to null hypothesis of independence even when the data is scarce. For example, if the EBGM is 5 for a drug-renal toxicity combination, then this drug-event combination occurred, on an average, 5 times more frequently than expected in the data set. Several examples Bayesian SD will be given from current research projects.
The slides from the keynote given by Dr. Dan Malone RPh, PhD at the First International Drug-Drug Interaction Knowledge Representation Workshop on October 6th 2014 (http://icbo14.com/sessions/drug-drug-interaction-knowledge-representation-workshop/). Posted with his permission.
Toward semantic modeling of pharmacogenomic knowledge for clinical and transl...Richard Boyce, PhD
A project update describing the semantic annotation of pharmacogenomics statements in drug product labeling. An innovative aspect of the work is the use of the W3C Open Annotation standard for publishing semantic annotations.
Bayesian estimations of strong toxic signals [compatibility mode]Bhaswat Chakraborty
“Signals” of adverse drug reactions are, according to WHO, “reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously. Usually more than a single report is required to detect a signal, depending on the seriousness of the event and the quality of the information.” Once a signal is detected, one can then analyze and confirm it. In detecting signals from large adverse drug reaction (ADR) databases, however, one has to use a procedure that is sensitive (low false negativity) and specific (high true positivity) for the purpose. A whole range of statistical methods have been applied for data mining and signal detection (SD) in pharmacovigilance (PV). My talk would be on Bayesian methods for SD.
The US FDA uses a Bayesian data mining approach developed by William DuMouchel called multi-item gamma poisson shrinker (MGPS). WHO also uses a Bayesian method (Andrew Bate) based on a Bayesian confidence propagation neural network (BCPNN). These estimates provide shrinkage towards zero of the observed to expected number of ADRs, e.g., the empirical Bayesian geometric mean (EBGM) or information component (IC). These Bayesian estimators are robust measures of ADR-drug association.
Bayesian approaches are intuitively appealing when very small numbers are involved and where there is a need of continuous reassessment of probability of association with the acquisition of new data over time. Bayesian estimates such as EBGM are close to null hypothesis of independence even when the data is scarce. For example, if the EBGM is 5 for a drug-renal toxicity combination, then this drug-event combination occurred, on an average, 5 times more frequently than expected in the data set. Several examples Bayesian SD will be given from current research projects.
The slides from the keynote given by Dr. Dan Malone RPh, PhD at the First International Drug-Drug Interaction Knowledge Representation Workshop on October 6th 2014 (http://icbo14.com/sessions/drug-drug-interaction-knowledge-representation-workshop/). Posted with his permission.
Toward semantic modeling of pharmacogenomic knowledge for clinical and transl...Richard Boyce, PhD
A project update describing the semantic annotation of pharmacogenomics statements in drug product labeling. An innovative aspect of the work is the use of the W3C Open Annotation standard for publishing semantic annotations.
Adverse Event Monitoring
• Identify relationships between drugs, diseases and devices and their associated events
• Use new filter options to search, visualize and export drug, device and disease-specific details
• Learn how new query language possibilities enable identification of specific drug- or device-related adverse events
Introduction to Argus Product Tab Screen in Pharmacovigilance or Drug Safety of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Literature Management for Pharmacovigilance: Outsource or in-house solution? ...Ann-Marie Roche
Pharmaceutical companies are required to screen scientific literature on a regular basis and this comes with many challenges, such as handling large amounts of data, building search strings and integrating EMA MLM results. Out-sourcing literature screening to service providers reduces the workload for the PV-team, but how does it impact the literature management process overall? Maybe it results in decreased oversight and additional activities like audits and reconciliation? And what about building the search strategy?
During this webinar our PV expert, Dr. Joyce De Langen spoke about the following:
• The importance of literature management in Pharmacovigilance and the challenges.
• An evaluation of the benefits and risks of outsourcing literature management versus alternative solutions.
About the speaker:
Joyce de Langen, Ph.D has more than 10 years of experience in the domain of pharmacovigilance and drug safety. Through her work in the pharmaceutical industry, academia and regulatory authorities, Joyce has developed a broad perspective and knowledge in pharmacovigilance and drug safety.
Literature Surveillance in Pharmacovigilance; Current Trends, Methods and Challenges
Please join Elizabeth E. Garrard, PharmD, founder and CEO of Garrard Safety Solutions, as she reviews key issues in literature surveillance for Pharmacovigilance.
Objectives:
• Understand the regulatory obligations, best sources and procedures for conducting literature surveillance.
• Appreciate some examples of when a safety signal was detected in the literature and its impact on the lifecycle of a drug.
• Understand when to start and where to look for emerging safety information.
• Setting up your search strategy, how to ensure your search strings are well balanced, recognizing the challenges between precision and sensitivity.
• What is the impact of the new literature monitoring by EMA of a number of substances in selected medical literature to identify suspected adverse reactions with medicines authorized in the European Union. Early insights into successes and issues.
• Discuss current methods that can increase the likelihood of early detection of a safety issue and minimize the issues surrounding.
• Realize the challenges we face including wide differences in quality, accuracy, and completeness in the scientific literature and how best to navigate these differences and maintain proper vigilance.
Argus Patient Screen Tab Training - Katalyst HLSKatalyst HLS
Introduction to Argus Patient Screen Tab in Pharmacovigilance or Drug Safety of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Piloting a Comprehensive Knowledge Base for Pharmacovigilance Using Standardi...Richard Boyce, PhD
A presentation of a new adverse drug event evidence base (Laertes - http://goo.gl/nZSqVw) within a standard framework for clinical research (OHDSI - www.ohdsi.org) made at the American Medical Informatics Association Joint Summits on Translational Research on 3/26/2015
Initial progress on the journey toward an open source potential drug-drug int...Richard Boyce, PhD
Presentation given at the 33rd VistA Community Meeting - George Mason University focusing on progress towards and open source potential drug interaction knowledge base
Reconciliation and Literature Review and Signal Detection_Katalyst HLSKatalyst HLS
Introduction Reconciliation and Literature Review and Signal Detection in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Pharmacovigilance Process Work Flow - Katalyst HLSKatalyst HLS
Introduction to Drug Safety & Pharmacovigilance Process Work Flow for Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Adverse Event Monitoring
• Identify relationships between drugs, diseases and devices and their associated events
• Use new filter options to search, visualize and export drug, device and disease-specific details
• Learn how new query language possibilities enable identification of specific drug- or device-related adverse events
Introduction to Argus Product Tab Screen in Pharmacovigilance or Drug Safety of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Literature Management for Pharmacovigilance: Outsource or in-house solution? ...Ann-Marie Roche
Pharmaceutical companies are required to screen scientific literature on a regular basis and this comes with many challenges, such as handling large amounts of data, building search strings and integrating EMA MLM results. Out-sourcing literature screening to service providers reduces the workload for the PV-team, but how does it impact the literature management process overall? Maybe it results in decreased oversight and additional activities like audits and reconciliation? And what about building the search strategy?
During this webinar our PV expert, Dr. Joyce De Langen spoke about the following:
• The importance of literature management in Pharmacovigilance and the challenges.
• An evaluation of the benefits and risks of outsourcing literature management versus alternative solutions.
About the speaker:
Joyce de Langen, Ph.D has more than 10 years of experience in the domain of pharmacovigilance and drug safety. Through her work in the pharmaceutical industry, academia and regulatory authorities, Joyce has developed a broad perspective and knowledge in pharmacovigilance and drug safety.
Literature Surveillance in Pharmacovigilance; Current Trends, Methods and Challenges
Please join Elizabeth E. Garrard, PharmD, founder and CEO of Garrard Safety Solutions, as she reviews key issues in literature surveillance for Pharmacovigilance.
Objectives:
• Understand the regulatory obligations, best sources and procedures for conducting literature surveillance.
• Appreciate some examples of when a safety signal was detected in the literature and its impact on the lifecycle of a drug.
• Understand when to start and where to look for emerging safety information.
• Setting up your search strategy, how to ensure your search strings are well balanced, recognizing the challenges between precision and sensitivity.
• What is the impact of the new literature monitoring by EMA of a number of substances in selected medical literature to identify suspected adverse reactions with medicines authorized in the European Union. Early insights into successes and issues.
• Discuss current methods that can increase the likelihood of early detection of a safety issue and minimize the issues surrounding.
• Realize the challenges we face including wide differences in quality, accuracy, and completeness in the scientific literature and how best to navigate these differences and maintain proper vigilance.
Argus Patient Screen Tab Training - Katalyst HLSKatalyst HLS
Introduction to Argus Patient Screen Tab in Pharmacovigilance or Drug Safety of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Piloting a Comprehensive Knowledge Base for Pharmacovigilance Using Standardi...Richard Boyce, PhD
A presentation of a new adverse drug event evidence base (Laertes - http://goo.gl/nZSqVw) within a standard framework for clinical research (OHDSI - www.ohdsi.org) made at the American Medical Informatics Association Joint Summits on Translational Research on 3/26/2015
Initial progress on the journey toward an open source potential drug-drug int...Richard Boyce, PhD
Presentation given at the 33rd VistA Community Meeting - George Mason University focusing on progress towards and open source potential drug interaction knowledge base
Reconciliation and Literature Review and Signal Detection_Katalyst HLSKatalyst HLS
Introduction Reconciliation and Literature Review and Signal Detection in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Pharmacovigilance Process Work Flow - Katalyst HLSKatalyst HLS
Introduction to Drug Safety & Pharmacovigilance Process Work Flow for Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
This presentation outlines the process for dealing with adverse preclinical / nonclinical events in order to 1) optimize the chances of successful drug development, or 2) to create a scientific basis for early termination of drug development. Conclusion: There is no single answer for all problems.
Additional Considerations for Pesticide Formulations Containing Microbial Pes...OECD Environment
The seminar on Problem Formulation for the Risk Assessment of Biopesticides stemmed from a previous CRP-sponsored event on Innovating Microbial Pesticide Testing that identified the need for an overarching guidance document to determine when in vivo tests are necessary. Problem Formulation, a common practice in pesticide risk assessment, was highlighted as a useful approach for addressing uncertainties in data requirements for biopesticides.
The seminar featured presentations from various perspectives, including industry, regulatory bodies, and academia. Topics included the history and principles of Problem Formulation, industry perspectives on Problem Formulation and how it is applied internally for microbial pesticides, regulatory approaches, and specific case studies. The seminar provided an overview of the challenges, considerations, and potential solutions in harmonising Problem Formulation for biopesticide risk assessment. It emphasised the need for collaboration and discussion to develop Problem Formulation guidance for biopesticides.
Working the Science and Regulations Harder to Win Your Drug and Device CasesSara Dunlap
This webinar will teach critical scientific principles related to the regulatory framework as they pertain to drug and medical device litigation for seasoned in house and outside counsel alike. Examples of topics that will be covered include safety signaling and pharmacovigilance, epidemiological and randomized controlled trial study design, risk management principles, causality assessment, and the strategic role of regulatory guidelines and compliance.
PHARMACOVIGILANCE TERMINOLOGIES ASKED IN INTERVIEWS-
For more information regarding PHARMACOVIGILANCE, CLINICAL RESEARCH, CLINICAL DATA MANAGEMENT & DRUG REGULATORY AFFAIRS kindly contact us on 9028839789
Must be own work!!!!!! This is a 2 part assignment. There is a pap.docxrosemarybdodson23141
Must be own work!!!!!! This is a 2 part assignment. There is a paper and a power point.
1. For this assignment you will research an infectious disease and create a PowerPoint presentation on the findings by providing the following for the chosen disease:
· disease name, means of transmission and usual reservoirs
· etiologic agent, its general characteristics and key tests for identification (be specific for this microbe!)
· historical information to include when and who isolated the microbe and any significance of its name
· signs and symptoms of the disease
· microbial virulence mechanisms contributing to the disease process
· control or treatment for the disease
· current outbreaks or cases, both globally and locally (include incidence figures for each)
· prevention, particularly current research about a vaccine or other means of control/prevention
· Minimum five reliable Internet references, plus any other references used.
Please submit the completed presentation by clicking the upload button below. Use this naming protocol for your presentation:
LastnameFirstnamePresentation
Your presentation will be graded using the following criteria:
Disease etiologic agent
2 points
Transmission
3 points
Reservoirs
3 points
General characteristics of microorganisms specific, such gram stain, shapes
3 points
Key tests for identification (specific)
3 points
Signs and symptoms of disease
3 points
Historical information
3 points
Virulence factors
3 points
Control/Treatment
3 points
Prevention/ Vaccine info, new trials?
3 points
Local cases or outbreaks (with incidence figures)
2 points
Global cases or outbreaks (with incidence figures)
2 points
Spelling/grammar errors and image as needed.
2 points
References: properly done in APA format Must have 5, so -1 point for each one missing
5 points
TOTAL
40 points
2. The report should contain the following information
COVER SHEET must have a title with the name of the disease and must have the names of student
TEXT (main body of info)—double -spaced, single-sided pages, 12 point font.
Project Content
A. What is the causative agent of the disease? Is it a bacterium, a virus, a prion, or a eukaryote?
1. If it is a bacterium, what are the characteristics of the cell (Gram-reaction?, cell shape and arrangement? metabolic capabilities?).
2. If it is a virus, what are its characteristics (DNA, positive-strand RNA, negative-strand RNA, or retrovirus? enveloped or naked? how large is it? does it form a provirus? anyunique characteristics of its multiplication cycle?).
3. If it is a prion, what is a prion? Wherein the body does it occur? What is the function of the normal-type protein?
4. If it is eukaryote, is it a fungus, an alga, a protozoan, a platyhelminth, or a nematode? Is it multicellular or unicellular? What is its life cycle?
B. History: How long have we known about this disease?
1. Describe the changes in our knowledge and attitudes toward this disease throughout history.
C. Epidemiology: D.
The usability of STAMP in drug development Arete-Zoe, LLC
Arete-Zoe in cooperation with Stuttgart University
Study authors: Veronika Valdova, Ronald L Sheckler, Asim Abdulkhaleq and Stefan Wagner (Jonathan M Fishbein)
Presentation of synopsis: Veronika Valdova
Presented at STAMP team meeting, PSCI, ACRES on February 26, 2016
PHARMACOVIGILANCE_SLIDE. Insight to pharmacovigilance, covering basics and va...ssharmapharmacy005
Insight to pharmacovigilance,
covering basics and various aspects, case processing types of ADR, basic terminologies
adr reporting dverse vent, types of adr, meddra
Clinical trials are the gold standard of evidence-based medicine. Properly designed clinical trials can lead to chance findings and potentially lead to erroneous conclusions.
Importantly, clinical trials can also be badly designed on purpose to increase the risk of false or chance findings leading to support misleading claims. Such techniques are frequently used by bad researchers and charlatans to substantiate their claims with biased clinical trials. It is therefore important to be weary of the limitations of clinical trials and understand how causal inference should be approach. In that presentation, I discuss the situations under which the risk of erroneous conclusions from clinical trials is increased and I discuss ways to identify and prevent bad clinical research.
The views expressed and presented in that presentation are my own views and may not represent the views of the National Institute for Health and Care Excellence.
Complex innovative trial designs are becoming increasingly used to improve the efficiency of the clinical development of new technologies. There is no agreed taxonomy of CID trials, these studies encompass a range of different approaches with some advantages but also some major drawbacks. This presentation discusses the issues associated with the conduct of complex innovative trial designs and the potential impact of CID trials on HTA methodological requirements and decisions.
The role of health technology assessment bodies in the value of cancer care i...Francois MAIGNEN
This presentation details the role of European HTA bodies in the value of new cancer therapies in Europe. The presentation also describes the NICE scientific advice activities and the activities of the HTA / regulatory parallel advice.
This presentation is aimed at presenting the issues associated with subgroup analyses in clinical trials: the different types of subgroup analyses and the statistical issues associated with the conduct of subgroup analyses.
Clinical developments of medicines based on biomarkersFrancois MAIGNEN
The presentation provides an overview of the clinical development of new medicines based on biomarkers including basket, umbrella and platform trials. This is mostly relevant to oncology products.
This presentation explains the main features of medicines which will be developed and authorised via the adaptive pathways. It provides a definition of real world evidence and the caveats associated with the use and analysis of real world evidence in drug development.
The masking effect of measures of Disproportionality AnalysisFrancois MAIGNEN
Presentation on the three studies conducted on the masking effect of measures of disproportionality analysis (point estimates and confidence intervals).
CDSCO and Phamacovigilance {Regulatory body in India}NEHA GUPTA
The Central Drugs Standard Control Organization (CDSCO) is India's national regulatory body for pharmaceuticals and medical devices. Operating under the Directorate General of Health Services, Ministry of Health & Family Welfare, Government of India, the CDSCO is responsible for approving new drugs, conducting clinical trials, setting standards for drugs, controlling the quality of imported drugs, and coordinating the activities of State Drug Control Organizations by providing expert advice.
Pharmacovigilance, on the other hand, is the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other drug-related problems. The primary aim of pharmacovigilance is to ensure the safety and efficacy of medicines, thereby protecting public health.
In India, pharmacovigilance activities are monitored by the Pharmacovigilance Programme of India (PvPI), which works closely with CDSCO to collect, analyze, and act upon data regarding adverse drug reactions (ADRs). Together, they play a critical role in ensuring that the benefits of drugs outweigh their risks, maintaining high standards of patient safety, and promoting the rational use of medicines.
The Gram stain is a fundamental technique in microbiology used to classify bacteria based on their cell wall structure. It provides a quick and simple method to distinguish between Gram-positive and Gram-negative bacteria, which have different susceptibilities to antibiotics
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
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
263778731218 Abortion Clinic /Pills In Harare ,sisternakatoto
263778731218 Abortion Clinic /Pills In Harare ,ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group of receptionists, nurses, and physicians have worked together as a teamof receptionists, nurses, and physicians have worked together as a team wwww.lisywomensclinic.co.za/
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Light House Retreats: Plant Medicine Retreat Europe
Quantitative methods of signal detection - Parametric modelling of the time to onset of adverse drug reactions
1. www.diahome.org
Parametric modelling of time to onset
of adverse drug reactions using
parametric survival distributions
F. Maignen
Principal scientific administrator
European Medicines Agency
2nd DIA Conference on Signal Detection and Data Mining
17-18 November 2009
2. www.diahome.org
Plan of the presentation
1. Conflicts of interests and disclaimers
2. Background and rationale of the project
3. Materials and methods
4. Results
5. Interpretation and
discussion
6. Conclusions and
future directions
3. www.diahome.org
DIA Disclaimer
The views and opinions expressed in the following PowerPoint slides are
those of the individual presenter and should not be attributed to Drug
Information Association, Inc. (“DIA”), its directors, officers, employees,
volunteers, members, chapters, councils, Special Interest Area
Communities or affiliates, or any organization with which the presenter is
employed or affiliated.
These PowerPoint slides are the intellectual property of the individual
presenter and are protected under the copyright laws of the United
States of America and other countries. Used by permission. All rights
reserved. Drug Information Association, DIA and DIA logo are registered
trademarks or trademarks of Drug Information Association Inc. All other
trademarks are the property of their respective owners.
4. www.diahome.org
Conflicts of interest and more disclaimers
• P. Tsintis and M. Hauben have contributed to this study
• Other external contributions received from other Experts
• No external funding was received for this study
• I do not have any financial interests with the Pharmaceutical
industry or any IT software provider (declaration available from
the Agency)
• I thank the two Companies which have given their approval to
publish these results
• Disclaimer on the views expressed in this presentation wrt
European Medicines Agency
• No claim on a “better” safety profile on any medicinal product
mentioned in this work should be made.
5. www.diahome.org
Disclaimers (cont.)
ACKNOWLEDGEMENTS
• No external source of funding was used to perform this study. The
implementation of EudraVigilance was undertaken by the EudraVigilance team
at the EMEA lead by Dr Sabine Brosch. The following authors: FM has no
conflicts of interest with the pharmaceutical industry (declaration of interest
available from EMEA). P. Tsintis contributed to the study when he was working
for the EMEA. M. Hauben is also working in Department of Medicine, Risk
Management Strategy, Pfizer Inc., New York, New York University School of
Medicine, Departments of Community and Preventive Medicine and
Pharmacology, New York Medical College, Valhalla, New York, USA and for the
School of Information Systems, Computing and Mathematics, Brunel University,
London, England . None of the authors have any conflict of interests with any
statistical software provider. Valuable comments on this work were received
from Nils Feltelius, Hans-Georg Eichler, Francesco Pignatti, Xavier Kurz, Jim
Slattery and Anders Sundström.
DISCLAIMER
• The views expressed in this presentation are the personal views of the author(s)
and may not be understood or quoted as being made on behalf of or reflecting
the position of the European Medicines Agency or one of its committees or
working parties.
7. www.diahome.org
Background
• The time to onset of adverse drug reactions is directly connected to the
underlying mechanism of the toxicity associated with a medicine (DoTS
classification)
• The current quantitative methods do not integrate any information concerning
the underlying toxic mechanism of the suspected medicinal product (some rare
studies conducted by A. Bate and E. Van Puijenbroek).
• Current methods used to analyse the reported time to onset of adverse drug
reactions in Pharmacovigilance
• Simple histograms (or LOESS)
– Only provide a partial view of the evolution of the risk
– The visualisation of the risk highly depends on the number of bins and
bandwidth
– Difficult to find a “risk window”
– Output can be awful (LOESS).
• Other non-parametric methods
– Kaplan-Meier estimate of the survivor function: can be difficult
to interpret and difficult to actually visualise the exact
evolution of the risk.
• Find patterns of toxicity (true signals) via the hazards
8. www.diahome.org
Rationale for the study: hazard and hazard
functions
• The hazard expresses the risk that something happens at a
certain time t (does not help a lot).
• The hazard function specifies the instantaneous rate at which
events / failures occur for items which survived until time t.
• Some recent classifications of adverse drug reactions (DoTS)
includes the time relatedness as one key elements the
classification.
• Therefore (in theory) the hazard should be directly connected to
the underlying mechanism of the toxic effect resulting in an
adverse drug reaction.
• Parametric survival distributions have a hazard function which is
specified by a function (in opposition to non-parametric methods
such as CPH).
9. www.diahome.org
Hazard fcts of parametric survival dist.
Kalbfleisch and Prentice. The statistical analysis
of failure time data. Second ed. Wiley and
sons.
10. www.diahome.org
Reported hazard of occurrence: a phenomenon
involving several mechanisms
• P(occur.)*P(diag./occur.)*P(rep./diag.)(1)
• P = prob. failure conditional on survival
until time t.
• Lim f(x)*g(x) = Lim f(x)*Lim g(x)
• Then when we take Lim t -> 0 (1)
becomes.
h(occur.)*h(diag./occur.)*h(rep./diag.)
PD
Toxicology profile
Efficacy / duration tt
Monitoring and
“RM” activities
Awareness
Awareness
Reporting mechanisms
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Materials and methods
• We have used parametric survival distributions to perform a
modelling of the reported time to onset to compute and plot the
corresponding hazard functions for signal detection purposes (in
a broad sense).
• The objective is to illustrate (and better understand the elements
of interpretation of) the use of hazard functions for signal
detection purposes using two real examples.
• Study conducted on a spontaneous reported database
(EudraVigilance).
• Two examples have been used
in the study:
Liver injuries associated with Bosentan
Infections associated with the use
of TNF alpha inhibitors
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Criteria used to interpret the results
• The idea is to use a convergence of available
evidence together with the hazard functions
of the reported time to onset to assess
whether there is a signal:
– Existing signal
– Pharmacodynamic properties of the products
– Bradford-Hill criteria which have been used to
interpret the results of data mining algorithms.
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Bosentan – liver injuries
• Logical course of events some occurrences need
careful interpretation (blood bilirubin inc. and
[hyper]bilirubinemia)
• Pattern AST/ALT unusual for liver injuries (but not for
mitochondrial injuries from hepatocytes) but
consistent with clinical safety data
• Residual and constant risk of liver failure
• Consistent with the putative mechanism of toxicity
(dose-dpt)
• Consistent with the safety profile of bosentan (lack of
independence)
• Influence of the risk minimisation activities
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TNF inhibitors - infections
• Striking similarities (early risk of UTI, sepsis,
pneumonia and herpes zoster) and differences
between products (TB and cellulitis)
• Consistent with the PD properties of the products and
results of clinical trials
• Differences could be explained by:
– PD/PK differences (half life of adalimumab significantly
longer than for the other two products, etanercept also binds
TNF beta, infliximab inhibits IFN gamma)
• Probable influence of RM activities / monitoring of the
patients (provided that the side-effect can be
detected / prevented - cf Bosentan).
28. www.diahome.org
TNF inhibitors - TB
• TNF alpha plays an important role in the control of
granulomatous infections
• Main difference is observed between infliximab / etanercept on
the one hand and adalimumab on the other
• Different PD properties between the products would implies
different profiles between infliximab and etanercept
• Different PK profile between adalimumab and the
other two products
• Awareness and risk minimisation: adalimumab
has been authorised after the first two products
when the risk of TB was established and
recommendations to monitor the patients had been
published (shift of risk of TB? Different reporting
pattern?).
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Summary of the main results
• Pattern consistent with the logical course of action of the toxicity
(bosentan)
• Hazard consistent with the suspected mechanism of the toxicity
(bosentan – dose dependent)
• Consistency of the reported hazard of occurrence of the infections
across the 3 TNFs
• Consistency of the reported hazard of tuberculosis for infliximab
• Differences between the TNFs products (TB) could be explained by:
– Different pharmacological properties
– Different pharmacokinetic properties
– Different monitoring of the patients / reporting mechanisms
• Patterns consistent with the known safety profile of the product (two
analyses not completely independent)
• Hazards certainly influenced by awareness and risk management
activities
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Factors influencing the modelling
• Nplicates
– Method sensitive to duplication
like any other DMA
– Consider the cases of extreme
duplication
– Duplication vs clusters
• Data quality
– Accuracy of the dates
– Completeness and precision does not
mean accuracy
• Good documentation and FUp of the cases
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Statistical issues and important limitations
• The work is still preliminary. Interpretation is
still based on explanations which involve
documented pharmacological or reporting
behaviours which can be subjective
• Issue with censoring and competing risks
• Absence of hypothesis testing +++
• Great difficulty to choose a suitable
comparator to build the hypothesis testing.
• Performances need to be tested
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Statistical issues and important limitations
• Approach limited by the number and quality of the reports
• Influence of the reporting mechanisms +++
– since the modelling was performed on spontaneous
reporting data, the hazard does not have the usual
interpretation as an instantaneous probability of failure
conditional on survival to time t.
– As far as the spontaneous reports are concerned, the
hazard reflects a mixture of reporting behaviour and natural
history which cannot be disentangled.
• Multi-state model?
– A “complete model” would not be devoid of any limitations or would
rely on strong assumptions which may not be met.
– Spontaneous reporting does not collect all the information needed
to build such model).
– Situation dependent
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Conclusions
• Encouraging work which illustrates the
potential use of hazard functions in signal
detection
• Inherent limitations of the spontaneous
reporting
• A lot of data manipulation
• Carefully consider the influence of reporting
mechanisms (biases) and data quality (cliché)
• Some statistical issues to be addressed
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Future directions
• Better understand the reporting mechanisms
• Test the approach to discriminate true signals from
confounding (find negative examples)
• Build a test of hypothesis
• Use it in specific situations where a “shift” of the
hazard function could reflect an underlying /
intercurrent event
• Assess the performances on a larger scale of data
• Potentially able to disantangle the reporting
mechanisms by comparing functions from different
sources of collection of information
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Acknowledgements
• Thank you to the persons who have
supported me in this work (list not limitative)
– Jim Slattery
– Xavier Kurz
– JM Dogne
– Anders Sundstrom
– Eugene Van Puijenbroek
– HG Eichler
Change to: In addition to DIA’s continued presence in North America, Europe, and Japan, DIA also serves the following regions:
India (remove italics from Mumbai, India, October 16-18, 2006)
Middle East (7th Annual Middle East Regulatory Conference, Dubai, UNITED ARAB EMIRATES, November 14-16, 2006)
China
Remove Central and Eastern Europe since it’s referenced at the top.
Latin America