The document proposes software solutions for drug research, including text mining, data warehousing, data mining, database development, and big data analytics. It discusses common challenges in drug research like the high costs and low success rates. It then describes various solutions like text mining patents and research to help identify new research opportunities and reduce duplication of efforts. It provides examples of how various pharmaceutical companies use data mining and warehousing techniques. Overall, the document pitches different IT solutions that can help pharmaceutical and life sciences companies address their research challenges and make their processes more efficient.
How AstraZeneca is Applying AI, Imaging & Data Analytics (AI-Driven Drug Deve...Nick Brown
Keynote AI Presentation given at AI-Driven Drug Development Summit Europe on 26th April 2023 in London. Overview around how AstraZeneca has been developing AI in the past 5+ years. Predominantly focused on R&D and how we are developing digital solutions & AI for right safety and right dose. AI examples include machine learning for safety assessment, augmenting digital pathology for image quantification & segmentation, understanding more about our drugs through advanced imaging modalities and first steps in applying AI for right dose - immunogenicity, adverse events and tolerability.
Strategies in Designing Clinicals for Fixed-Combination DrugMichael Swit
Presentation to DIA Annual Meeting in Philadelphia, in June 2006, with a focus on whether a factorial studies are required for combination drug and when alternative approaches have been acceptable.
Signal detection is a process used in pharmacovigilance to identify potential safety issues or new safety information associated with a medicinal product. The goal of signal detection is to detect signals, or potential safety concerns, as early as possible in order to allow for timely risk management and safety interventions.
Signal detection typically involves analyzing large amounts of safety data, including adverse event reports, clinical trial data, post-marketing surveillance data, and other sources of safety information. The data is analyzed using statistical methods and algorithms to identify any patterns or trends that may suggest a potential safety concern.
Once a potential safety concern is identified, further investigation is typically required to confirm the signal and assess the magnitude of the risk. This may involve conducting additional studies, analyzing the available data in more detail, or consulting with regulatory agencies and other stakeholders.
Signal detection is an ongoing process that continues throughout the life cycle of a medicinal product. The process is critical for ensuring the ongoing safety and effectiveness of medicinal products, and is an important component of pharmacovigilance activities.
How AstraZeneca is Applying AI, Imaging & Data Analytics (AI-Driven Drug Deve...Nick Brown
Keynote AI Presentation given at AI-Driven Drug Development Summit Europe on 26th April 2023 in London. Overview around how AstraZeneca has been developing AI in the past 5+ years. Predominantly focused on R&D and how we are developing digital solutions & AI for right safety and right dose. AI examples include machine learning for safety assessment, augmenting digital pathology for image quantification & segmentation, understanding more about our drugs through advanced imaging modalities and first steps in applying AI for right dose - immunogenicity, adverse events and tolerability.
Strategies in Designing Clinicals for Fixed-Combination DrugMichael Swit
Presentation to DIA Annual Meeting in Philadelphia, in June 2006, with a focus on whether a factorial studies are required for combination drug and when alternative approaches have been acceptable.
Signal detection is a process used in pharmacovigilance to identify potential safety issues or new safety information associated with a medicinal product. The goal of signal detection is to detect signals, or potential safety concerns, as early as possible in order to allow for timely risk management and safety interventions.
Signal detection typically involves analyzing large amounts of safety data, including adverse event reports, clinical trial data, post-marketing surveillance data, and other sources of safety information. The data is analyzed using statistical methods and algorithms to identify any patterns or trends that may suggest a potential safety concern.
Once a potential safety concern is identified, further investigation is typically required to confirm the signal and assess the magnitude of the risk. This may involve conducting additional studies, analyzing the available data in more detail, or consulting with regulatory agencies and other stakeholders.
Signal detection is an ongoing process that continues throughout the life cycle of a medicinal product. The process is critical for ensuring the ongoing safety and effectiveness of medicinal products, and is an important component of pharmacovigilance activities.
STEP (Stibo Enterprise Platform) TrailblazerStibo Systems
GET TO KNOW TRAILBLAZER
Stibo Systems’ Multi-domain master data management system allows companies to create a single authoritative view of their product and customer data, digital assets, and suppliers.
With this latest release, Stibo Systems has added
Ability to quickly access new industry-specific features
New customizable dashboard that focuses on what matters most
Enhanced matching and linking capabilities for product and customer data
Easily classified product data with auto-classification
Create interactive tablet based publications with dynamic page content
Personalize selling by using pre-defined templates
And much more
its not my personal work presentation but taken from lecture ppt from university of San Diego, california.
Its about the drug discovery process, its development and its commercialization.
Strategies for Navigating ICH E9(R1) Webinar slidesMMS Holdings
https://info.mmsholdings.com/strategies-for-navigating-ich-e9-webinar?hsLang=en
The ICH E9(R1) Addendum on 'Estimands and Sensitivity Analysis in Clinical Trials' introduced a framework to align planning, design, conduct, analysis, and interpretation of clinical trials.
When defining the clinical question of interest, clarity is needed about 'intercurrent events' that affect either the interpretation or the existence of the measurements associated with the clinical question of interest, such as discontinuation of assigned treatment, use of an additional or alternative treatment and terminal events such as death.
The description of an estimand should reflect the clinical question of interest in respect of these intercurrent events, and the Addendum introduces strategies to reflect different questions of interest that might be posed. The choice of strategies can influence how more conventional attributes of a trial are reflected when describing the clinical question, for example the treatments, population or the variable (endpoint) of interest.
Role of Clinical Data Management in Risk-Based MonitoringClinosolIndia
Clinical Data Management (CDM) plays a significant role in the implementation of Risk-Based Monitoring (RBM) within clinical trials. RBM is an approach that focuses monitoring efforts on areas of highest risk, thereby optimizing resource allocation, enhancing data quality, and ensuring patient safety. Here's how CDM contributes to RBM
Successful market / customer access is a primary objective in healthcare marketing. This presentation provides the insight to successfully position, price and promote your pharmaceutical, medical device or clinical service in leading healthcare market segments.
This presentation is from www.healthcaremedicalpharmaceuticaldirectory.com, no subscription is required. Please visit us to see more presentations about the latest marketing trends in the healthcare industry.
John Baresky Healthcare Marketing Leader, Pharmaceutical Marketing, Digital Marketing Strategy, Content Marketing Strategy, Market Access Strategy, Healthcare RPA Software Marketing Strategy
www.healthcaremedicalpharmaceuticaldirectory.com
John G. Baresky
https://www.linkedin.com/in/johngbaresky
#baresky
CFTCC
2015 Learning about the IND/IDE Process and Reimbursements for New Drugs and Devices
Erin O'Reilly, PhD, RAC
Assoc. Director, Regulatory Affairs
Translational Medicine Institute
Introduces the basics of filing an Investigational New Drug (IND) Application with the FDA
Keynote presentation at Pharma MES Europe (September 26, 2023 in Berlin)
Current status of using Artificial Intelligence and Machine Learning in drug manufacturing. The presentation provides an overview about different AI models, maturity for implementing AI supported software solutions, potential uses cases, and challenges. A recent survey conducted amongst MES solution providers provides an overview about what to expect in the future.
STEP (Stibo Enterprise Platform) TrailblazerStibo Systems
GET TO KNOW TRAILBLAZER
Stibo Systems’ Multi-domain master data management system allows companies to create a single authoritative view of their product and customer data, digital assets, and suppliers.
With this latest release, Stibo Systems has added
Ability to quickly access new industry-specific features
New customizable dashboard that focuses on what matters most
Enhanced matching and linking capabilities for product and customer data
Easily classified product data with auto-classification
Create interactive tablet based publications with dynamic page content
Personalize selling by using pre-defined templates
And much more
its not my personal work presentation but taken from lecture ppt from university of San Diego, california.
Its about the drug discovery process, its development and its commercialization.
Strategies for Navigating ICH E9(R1) Webinar slidesMMS Holdings
https://info.mmsholdings.com/strategies-for-navigating-ich-e9-webinar?hsLang=en
The ICH E9(R1) Addendum on 'Estimands and Sensitivity Analysis in Clinical Trials' introduced a framework to align planning, design, conduct, analysis, and interpretation of clinical trials.
When defining the clinical question of interest, clarity is needed about 'intercurrent events' that affect either the interpretation or the existence of the measurements associated with the clinical question of interest, such as discontinuation of assigned treatment, use of an additional or alternative treatment and terminal events such as death.
The description of an estimand should reflect the clinical question of interest in respect of these intercurrent events, and the Addendum introduces strategies to reflect different questions of interest that might be posed. The choice of strategies can influence how more conventional attributes of a trial are reflected when describing the clinical question, for example the treatments, population or the variable (endpoint) of interest.
Role of Clinical Data Management in Risk-Based MonitoringClinosolIndia
Clinical Data Management (CDM) plays a significant role in the implementation of Risk-Based Monitoring (RBM) within clinical trials. RBM is an approach that focuses monitoring efforts on areas of highest risk, thereby optimizing resource allocation, enhancing data quality, and ensuring patient safety. Here's how CDM contributes to RBM
Successful market / customer access is a primary objective in healthcare marketing. This presentation provides the insight to successfully position, price and promote your pharmaceutical, medical device or clinical service in leading healthcare market segments.
This presentation is from www.healthcaremedicalpharmaceuticaldirectory.com, no subscription is required. Please visit us to see more presentations about the latest marketing trends in the healthcare industry.
John Baresky Healthcare Marketing Leader, Pharmaceutical Marketing, Digital Marketing Strategy, Content Marketing Strategy, Market Access Strategy, Healthcare RPA Software Marketing Strategy
www.healthcaremedicalpharmaceuticaldirectory.com
John G. Baresky
https://www.linkedin.com/in/johngbaresky
#baresky
CFTCC
2015 Learning about the IND/IDE Process and Reimbursements for New Drugs and Devices
Erin O'Reilly, PhD, RAC
Assoc. Director, Regulatory Affairs
Translational Medicine Institute
Introduces the basics of filing an Investigational New Drug (IND) Application with the FDA
Keynote presentation at Pharma MES Europe (September 26, 2023 in Berlin)
Current status of using Artificial Intelligence and Machine Learning in drug manufacturing. The presentation provides an overview about different AI models, maturity for implementing AI supported software solutions, potential uses cases, and challenges. A recent survey conducted amongst MES solution providers provides an overview about what to expect in the future.
Big Data and its Impact on Industry (Example of the Pharmaceutical Industry)Hellmuth Broda
While we bemoan the ever increasing data tsunami new technologies allow to harvest the gold nuggets in the hay stack.
Using the example of the Pharmaceutical Industry some of the possible business uses for Big Data Analitics are outlined.
Big Data in Healthcare Made Simple: Where It Stands Today and Where It’s GoingHealth Catalyst
Health system leaders have questions about big data: When will I need it? How should I prepare? What’s the best way to use it? It’s important to separate the hype of big data from the reality. Where big data stands in healthcare today is a far cry from where it will be in the future. Right now, the best use cases are in academic- or research-focused healthcare institutions. Most healthcare organizations are still tackling issues with their transactional databases and learning how to use those databases effectively. But soon—once the issues of expertise and security have been addressed—big data will play a huge role in care management, predictive analytics, prescriptive analytics, and genomics for everyday patients. The transition to big data will be easier if health systems adopt a late-binding approach to the data now.
Surveys, a company’s best friend: the 5 ways they can have an influence on yo...Survmetrics
Surveys, a company’s best friend: The 5 ways they can have an influence on your sales.
Understanding customer data is beneficial to gain an advantage over the competition.
It comes as no surprise that surveys are a powerful ally to any business. No matter what industry you are in, it helps to clarify foggy situations.
Just think about when you want to analyze a specific aspect, you design a survey in hopes to receive feedback from your customers. If you haven’t considered doing this for your company, then it might be a good idea to start adopting this practice.
• 1. Techniques to Increase ConversionsFor Your SaaS BusinessHiten ShahKISSmetrics Webinar • February 22nd, 2011
• 2. Twitter Hash Tag #measure
• 3. SaaS: Software as a Service
• 4. SaaS Sales Models• Customer Self Service (sign up and pay online) • Examples: 37signals, Zoho, Dropbox• Transactional Sales (inside sales)
• 5. SaaS Pricing Models• Paid Sign Up (no free trial or free plan) • Examples: CrazyEgg, Mozy• Free Trials (timed trials) • Examples: 37signals, Backupify• Freemium (free forever → upgrades) • Examples: YouSendIt, LogMeIn, Evernote
• 6. CrazyEgg Pricing BeforeFreemium (free forever → upgrades) Visited Site Free Sign Up Upgraded
• 7. CrazyEgg Pricing AfterPaid Sign Up (no free trial or free plan) Visited Site Sign Up Pay
• 8. CrazyEgg Results• Sign ups decrease by 10x• Doubled revenue in the first month• 2 years later, 25% of the revenue is from free customers• There is a free plan in CrazyEgg’s future =)
• 9. Funnel Testing & Optimization
• 10. Macro Conversions“ It’s important not to get distracted by intermediate metrics like the click-through rate of the button itself...we only care about the customer behaviors that lead to something useful” Eric Ries, The Lean Startup
• 11. Activation• Web analytics application • Install javascript code and start tracking data• Project management application • Create first project and invite a person• Customer relationship management application • Add their first contact and follow up with them• Customer support application • Receive and respond to a customer support request
• 12. SaaS Macro Conversion Funnels
• 13. 5 Step Process for Funnel Optimization
• 14. Baseline your macro conversions
• 15. Identify optimization opportunities• Which step has the highest drop off?
• 16. Gather qualitative data• Identify conversion barriers• Surveying • Survey.io, KISSinsights, Wufoo, SurveyMonkey, etc...• Usability Testing • In-person, UserTesting.com, FiveSecondTest, etc...• Example • Task completion survey http://kiss.ly/biUD9L • SaaS plans page survey http://kiss.ly/f9ZlAb
• 17. Create and implement A/B tests• Many ways to improve funnels• Modify your steps • Combine, remove or change the order of steps. • Add UI elements to guide users• Test your designs • Headlines, images, buttons, microcopy, etc... • Try radical variations
• 18. Measure against your baseline, rinse & repeat
• 19. Segmenting Your Customers
• 20. Account type segmentation http://rowfeeder.com/plans
• 21. Company size segmentation http://box.net/pricing
• 22. Custom segmentation
• 23. Custom segmentation data
• 24. Testing Your Call to Actions
• 25. Funnel A/B Testing Rules!
• 26. Survey, segment, analyze & then create tests 27. Segment and analyze
• 28. Don’t copy “best practices” blindly
• 29. Learn what works best for your customers
• 30. 3 Techniques for Increasing SaaS Conversions
According to FDA Draft Guidance for Industry in Electronic Submission and Study Data Technical Conformance Guide, the pharmaceutical companies will need to provide CDISC Electronic submission to FDA. The paper will explain Data Standard Catalog which will dictate FDA Standards. The paper will discuss how to prepare CDISC electronic submission and what to prepare in CDISC electronic submission.
Research trends in different pharmaceutical areas: Natural product chemistry
Imtiaj Hossain Chowdhury
B’Pharm (Jahangirnagar University), M’Pharm (Jahangirnagar University)
Master’s in Public Health (American International University Bangladesh)
Discovery on Target 2014 - The Industry's Preeminent Event on Novel Drug TargetsJaime Hodges
Cambridge Healthtech Institute's 12th Annual Discovery on Target will showcase current and emerging “hot” targets for the pharmaceutical industry, October 8 – 10, 2014 in Boston, MA. Spanning three days, the meeting will bring together more than 900 global attendees, including scientists/technologists, executives, directors, and managers from biopharma, academic, and healthcare organizations. In 2014 the event is comprised of 14 conference tracks which include Epigenetic Readers, Ubiquitin Proteasome, Big Data Discovery, GPCR Drug Discovery, RNAi-Screens-Functional-Genomics, PPI Targets, Protein-Targets, Histone-Methyltransferases-Demethylases, Drug Transporters, Maximizing Efficiency, GPCR Therapeutics, Genomics Screening, Cancer Metabolism and Membrane Production. The 2014 event will offer 200+ scientific presentations across 14 conference tracks, 1 Symposium and 15 conference short courses, 40+ interactive breakout discussion groups, an exhibit hall of 40+ companies, and dedicated poster viewing and networking sessions.
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.
DOE-NCI Pilots presentation at the Frederick National Laboratory Advisory Com...Warren Kibbe
May 2016 FNLAC presentation of the DOE-NCI partnership around three pilots focused on existing projects in NCI and existing NSCI directives and activities in DOE.
5 Cutting-Edge Trends in Molecular DiagnosticsBruce Carlson
Despite the focus on novelty in this field, it is near 2 decades old. Yet a lot is changing. A look at a few trends that could change molecular diagnostics.
With the recent announcement that GlaxoSmithKline have released a huge tranche of whole cell malaria screening data to the public domain, accompanied by a corresponding publication, this raises some issues for consideration before this exemplar instance becomes a trend. We have examined the data from a high level, by studying the molecular properties, and consider the various alerts presently in use by major pharma companies. We acknowledge the potential value of such data but also raise the issue of the actual value of such datasets released into the public domain. We also suggest approaches that could enhance the value of such datasets to the community and theoretically offer more immediate benefit to the search for leads for other neglected diseases.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
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From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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2. Drug Research: Common Challenges
EXPENSIVE! - Drug research is expensive. A new drug takes
around 15 years and $1.2b from concept to market.
LOW SUCCESS RATE! - The success ratio is extremely low
with most candidate molecules being abandoned midway. Two
out of three submissions with regulatory authorities result in
failures.
LOW GROWTH RATE! - The typical growth rate has reduced
from 13% to 5%. Limited resources.
DUPLICATION OF EFFORT! - Companies often end up
duplicating research effort as they fail to determine if similar
research is taking place somewhere else.
OMICS EMIT UNMANAGEABLE DATA! - Newer technologies
have come in, that deal at gene and cell level. Resulting data is
Voluminous, in Various formats and gets piled up at a blistering
pace. Drug research faces challenges in leveraging these
technologies in a timely, effective , efficient and optimum
manner.
Accommodator Consultancy Services
Lucknow
3. Our Offerings in IT in Life Sciences
TEXT MINING SOLUTIONS
DATA WAREHOUSE SOLUTIONS
DATA MINING SOLUTIONS
DATABASE DEVELOPMENT SOLUTIONS
BIG DATA ANALYTICS
CANCER SOLUTIONS
Accommodator Consultancy Services
Lucknow
4. TEXT MINING SOLUTIONS
Philosophy – Researchers to be able to find new information found in the
various scientific reports and papers published around the world and then
absorb that information into their ongoing work and give direction to their
work by gathering and analyzing trends.
Areas Covered:
Patents
Research papers
Publications
Specialized web sites such as Pubchem, Pubmed covering millions of articles
Social media sites such as Facebook, Twitter, Instagram, blogging forums etc,
Internal collection of documents and information.
Deliverable: A set of programs that would automatically run and prepare
Reports and documents with relevant summarized and detailed information
downloaded from above mentioned sources based on input keywords and
events.
Accommodator Consultancy Services
Lucknow
5. WHY PATENT MINING
Patent information of Novel bioactive chemical structures related to drug
discovery exceed those in journals by at least five-fold.
Patents encompass academic, as well as commercial, global med. chem.
output.
Targets, assays, mechanisms of action, disease descriptions and in-vivo
data.
~ 70% of data initially patent-only, some never disclosed elswhere.
Include synthetic descriptions and other useful enabling information.
Precede journal or meeting reports by ~ 1.5 to 5 years.
Can be complementary to papers (e.g. larger SAR matrix).
Intersect with papers at chemistry, target, disease, author and citation levels
IP exploitable for Neglected Tropical Disease research becoming ”open”.
Accommodator Consultancy Services
Lucknow
7. DATA MINING SOLUTIONS
Definition - Data Mining is an interdisciplinary subfield of computer science that
discovers patterns in large data sets involving methods at the intersection of
artificial intelligence, machine learning, statistics, and database systems.
Philosophy – The overall goal of the data mining process is to extract
information from a data set and transform it into an understandable structure for
further use.
Areas Covered:
Virtual HTS and HCS Data
Predictive Toxicology
Life sciences and health related issues trending on social media
FDA datasets
Micro-biomes
Chemo-genomics
Predicting and preventing diseases through gene analysis
Both big an small molecules
Deliverable: Converting raw data into actionable information after detecting
patterns and trends, and applying a number of verified algorithms.
Benefits: Improves prediction of early stage drug safety testing. Data mining
(as opposed to conventional statistical analysis) can uncover patterns and
relationships in large data volumes that are completely unexpected. Patterns
can be used to extrapolate and predict.
Accommodator Consultancy Services
Lucknow
9. DATA MINING CASE STUDIES
@ Roche:
Used DM techniques to set up models for the diagnostic of diabetes high risk group
to analyze existing samples sets (including Diabetes II patients and healthy subjects),
to identify the factors (age, sex, race, height, weight, BMI value, ADA value) that may
cause Diabetes II, and predict the probability of the subjects developing Diabetes II in
the next 7 and half years, in order to take preventive measures a traditional statistical
methods are not as accurate as DM methods.
@ GSK:
Data Mining Human Gut Microbiota for therapeutic targets. This could lead to a
systems-level understanding of the global physiology of the host–microbiota
superorganism in health and disease. Such knowledge will provide a platform for the
identification and development of new therapeutic strategies for chronic diseases
possibly involving microbial as well as human-host targets that improve upon existing
probiotics, prebiotics or antibiotics
used text analytics to analyze public discussion boards on BabyCenter.com and
WhattoExpect.com, to learn what factors motivate parents to either go ahead or delay
vaccinating their children for diseases like measles and mumps.
Data mining was used to identify unrecognized drug interaction (pravastatin and
paroxetine) that suggested raising blood glucose level manifold. However this would
need a careful crystallization of the problem statement by experts to make the
exercise useful.
Accommodator Consultancy Services
Lucknow
10. DATA MINING CASE STUDIES
@ Bayer:
GI adverse effect of short term Aspirin use. Meta analysis of AE comparison with
similar drugs for mktg. & drug improvement.
@ Pfizer:
Uses mining to determine if certain AE’s are being reported with greater frequency
than expected.
large-scale semantic Web-based data mining and network methods to seek to
uncover previously undiscovered historical links between chemical compounds,
drugs, biological pathways, targets, genes and diseases.
By using big data to bring together genomic data, clinical trials and EMR data,
Pfizer was able to develop precise drug ‘Xalkori’ which proves very effective for
around 5% of patients suffering from cancer who suffer mutation of their ALK gene.
Through data mining, this sub section of population was identified which had a
healthy lifestyle, yet got affected by cancer.
It funded a study that would use genomic data mining to identify antigens in NTS
(non-typhoidal salmonella) that may be used as targets for vaccine development.
@ Johnson & Johnson:
Has built an open source data management system called Transmart. The idea is
to combine genomic data sets, from internal and external sources, using the
platform's data standards and processing capabilities. This facilitates data mining
which provides immense opportunities.
Accommodator Consultancy Services
Lucknow
11. DATA MINING CASE STUDIES
@ Novartis:
In HTS, used Ontology Based Pattern Identification (OPI) algorithm to predict
patters by which they were able to find out 1500 scaffold families with significant
structure-HTS activity profile relationships.
@ Astra Zeneca
It uses data-mining tools to identify plausible preclinical Gastro Intestinal effects
that may be associated with nausea and that could be of potential use in its
prediction. A total of 86 marketed drugs were used in this analysis, and the main
outcome was a confirmation that nausogenic and non-nausogenic drugs can be
clearly separated based on their preclinical GI observations. .
Accommodator Consultancy Services
Lucknow
12. CHEMOGENOMICS DATA MINING
Chemogenomics is rapidly emerging as a way of helping discover new disease therapies and
uncovering new uses for existing drugs.
There are large structure activity databases set up by pharmaceutical companies and
commercial vendors. These databases can be mined to derive insights into common properties or
structural features among ligands linked to common features of the receptors to which they bind.
These insights can then used for the rational compilation of screening sets or the knowledge-based
synthesis of chemical libraries to accelerate lead finding.
Can be used to reposition drugs and find new applications for existing
drugs/molecules/compounds.
Four Canadian government research funding agencies will spend around US$6.7 million to
create a cloud computing facility and data mining tools that will enable researchers to access and
use data from the International Cancer Genome Consortium.
DM could lead to a systems-level understanding of the global physiology of the host–microbiota
superorganism in health and disease. Such knowledge will provide a platform for the identification
and development of new therapeutic strategies for chronic diseases possibly involving microbial as
well as human-host targets that improve upon existing probiotics, prebiotics or antibiotics
We can collect or organize known GPCR and non GPCR ligands and mining models can be
trained based on such properties. New compounds can automatically be classified as ligand or non
ligand based compound.
Design and knowledge based synthesis of chemical libraries targeting subfamily of purinergic
GPCR . Chemical scoffolds can be synthesized.
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Lucknow
13. DATA WAREHOUSE SOLUTIONS
Definition – Central repository created by integrating data from disparate
sources, with past and current data for both operational and strategic decision
making and senior management reporting such as annual comparisons of budget
per scientist.
Goal – to enable users appropriate access to a homogenized, comprehensive
and consistent view of the organization, supporting forecasting and decision-
making processes at the enterprise level..
Areas Covered:
Bioinformatics research
Finance
HR
Marketing
Disease Management etc
Deliverable: Central repository of useful and actionable data integrated from
multiple departments and sources and available to end users for operational and
strategic decision making in an efficient and effective manner.
Benefits: Better use of internal resources, Reduction in critical time path for
statistical analysis. Standard exchange of data with CRO’s, partners and
regulatory agencies. Cross trial analysis and leveraged use of historical data.
Globalization and knowledge sharing. Facilitates open source drug development.
Compliance with regulatory authorities.
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15. DWH @ NOVARTIS
Prominent DWH – FDA’s Janus, Johnson and Johnson, Pfizer,
Novartis’ Avalon, GSK and Roche
DWH Use Cases:
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16. DWH USE CASES
Novartis:
Tell me everything about a given structure
Collect comprehensive data of corporate interest in a single place.
Data grouped by chemical structure.
Standardized data dictionary to describe data.
Chemical structure conventions are unified.
Computed descriptors would be available
Given a substructure give me useful calculated descriptors.
Assays physical properties and calculated descriptors are represented uniformly.
Will support changing row model between batch, compound and bioactive.
Find all compounds in stock with some publicly known activity.
Integrate structured in house data with external data.
Set the row model by active substance.
Pre defined task based query to automate this kind of query.
FDA Janus:
Janus creates an integrated data platform for most commercial tools for review, analysis and reporting.
It reduces overall cost of information gathering and submissions, development process as well as review and analysis
of information.
It provides a common data model that is based on the SDTM standard to represent four classes of clinical data
submitted to regulatory agencies: tabulation datasets, patient profiles, listings, etc.
It provides central access to standardized data, and provides common data views across collaborative partners.
It supports cross-trial analyses for data mining and helps detect clinical trends and address clinical hypotheses, and
performs more advanced, robust analysis. This enables the ability to contrast and compare data from multiple clinical
trials to help improve efficacy and safety.
It facilitates a more efficient review process and ability to locate and query data more easily through automated
processes and data standards.
It provides a potentially broader data view for all clinical trials with proper security, de-identified patient data, and
proper agreements in place to share data.Accommodator Consultancy Services
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17. ERP v/s DWH
People confuse between ERP and DWH. They are
different as shown below:
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ERP DWH
Detailed Summarized
Facilitate data entry &
storage
Facilitate quick analysis
Used by Operations Used by Strategists
End users need to be
trained
Generalist end users
No AdHoc reporting Facilitates ad hoc reports
ERP for biochemical less
available
Easily integrates and
stores biochemical data
18. DATABASE SOLUTIONS
Drug discovery analytics is traditionally performed on
Relational Database Management Systems. However
with new discoveries, it does not remain an optimal
choice. Discoveries require newer technologies.
Commercial RDBMS have kept pace by introducing
newer features (such as column store indexes)
We design the RDBMS to consolidate data from
disparate sources to facilitate analytics. We also convert
existing DBMS systems to leverage newly introduced
features.
We also undertake performance enhancements,
provide additional security and other maintenance
tasks.
Accommodator Consultancy Services
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19. BIG DATA SOLUTIONS
Definition – A collection of data sets so large and complex that it becomes difficult
to process using on-hand database management tools or traditional data processing
applications. The challenges include capture, curation, storage, search, sharing,
transfer, analysis and visualization. The trend to larger data sets is due to the
additional information derivable from analysis of a single large set of related data, as
compared to separate smaller sets with the same total amount of data, allowing
correlations to be found to "spot business trends, determine quality of research,
prevent diseases, link legal citations, combat crime, and determine real-time roadway
traffic conditions.
Philosophy – To handle such huge data generated by Omics, regular computers
are used that are networked/set up in such a way to make it loss proof and leverage
individuals processors to work in synergy and solve bigger problems Companies have
started offering cloud storage for big data and publicly available.
Areas Covered:
Finding cause of diseases
Repositioning of drugs
Prescription of more effective drugs and procedures.
Deliverable - We collect information about possible sources of data for related
research area. We analyze the data for volume variety and velocity. We do a small
pilot prototype of the big data set up using source big data on cloud. We set up
programs to collect and process the data and then try to solve the hypothesis
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20. BIG DATA SOLUTIONS
Accommodator Consultancy Services
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Use Case 1: Researchers found that previously undetected mutations in a single
gene (called LMX1B) triggered focal segmental glomerulosclerosis (FSGS), a
disease that scars the kidneys’ filtering system. This was possible after genome data
was collected and compared for healthy and diseased individuals.
Use Case 2: Big data approach already has predicted the efficacy of drug
repurposing for treating colitis — a form of inflammatory bowel disease — small-cell
lung cancer and other conditions, according to Scott Saywell, vice president,
corporate development, NuMedii.
Use Case 3: For patients, the use of big data analytics in drug development results
in less trial and error when physicians prescribe drugs. This tighter targeting of
drugs to disease also results in fewer side effects.
According to new draft policy by Dept. of Biotechnology, Govt. of India, genome
based prescription and treatment will be top priority in next few years.
The draft policy envisages converting half of hospitals currently engaged in
treatment of human diseases to that of prediction and prevention of diseases using
genomic tools.
It also aims to provide all available genetic screening tests to general public at
affordable prices.
Genome data processing and analysis has been possible by Big Data as genome
(and other omics technologies) for just one individual results in data that tops 80
story building when translated on a paper.
21. Cancer Solutions
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• We offer collaborate with CDRI and ITRI for providing cancer patients data
for further research.
• We do research on National Cancer Data Repository providing consultancy
on cancer drugs and assisting in cancer research with a goal of
personalized cancer solutions.
• Any other assistance you would need on this subject.
22. Value that Accommodator
Consultancy would add
Accommodator Consultancy Services
Lucknow
We have vast experience in data analysis, text and data mining
and dealing in technologies compatible with biochemical
substances having delivered successful projects throughout the
world. We will take the IT and statistics worries away from you
so you can concentrate on pure research.
We have the skills to be able to work with large volumes of data
and Big Data (Hadoop) source systems.
Vast experience in developing, using and configuring different
kinds of bioinformatics software.
Team consists of chemist, data warehouse and data mining
professional and senior cancer surgeon.
We firmly believe in providing great value in our service/product
offering.
23. Questions/Comments?
Accommodator Consultancy Services
Lucknow
In the interest of keeping material short, only a simple summary
has been provided. Please do not hesitate to ask any
questions/clarification for further details.
Our contact details:
Ankur Khanna: Director Technical
945 166 8432
Dr Vibhor Mahendru: Director Business Development
800 536 5132
THANK YOU