The drug or drug combination may not be official in any pharmacopoeias.
A proper analytical procedure for the drug may not be available in the literature due to patent regulations.
Analytical methods may not be available for the drug in the form of a formulation due to the interference caused by the formulation excipients.
Analytical methods for the quantitation of the drug in biological fluids may not be available.
Analytical methods for a drug in combination with other drugs may not be available.
The existing analytical procedures may require expensive reagents and solvents. It may also involve cumbersome extraction and separation procedures and these may not be reliable.
Method Validation - Limit of Detection, Quantitation limits and Robustnesslabgo
Prepared By: Shruti Vij (Senior Analyst) , Geeta Mathur(Senior Scientist) ,Khushbu ( Analyst)
This slide show contains detailed explanation of three characteristics of method validation- Limit of detection, Quantitation limits and Robustness. Limit of detection is the minimum amount of substance that can be detected but not measured, quantitation limit is the minimum amount of substance which can be detected and measured. Common approach to these procedures- signal to noise ratio has also been covered. Robustness is a characteristic which determines a method’s reliability when deliberate variations are induced in parameters.
The drug or drug combination may not be official in any pharmacopoeias.
A proper analytical procedure for the drug may not be available in the literature due to patent regulations.
Analytical methods may not be available for the drug in the form of a formulation due to the interference caused by the formulation excipients.
Analytical methods for the quantitation of the drug in biological fluids may not be available.
Analytical methods for a drug in combination with other drugs may not be available.
The existing analytical procedures may require expensive reagents and solvents. It may also involve cumbersome extraction and separation procedures and these may not be reliable.
Method Validation - Limit of Detection, Quantitation limits and Robustnesslabgo
Prepared By: Shruti Vij (Senior Analyst) , Geeta Mathur(Senior Scientist) ,Khushbu ( Analyst)
This slide show contains detailed explanation of three characteristics of method validation- Limit of detection, Quantitation limits and Robustness. Limit of detection is the minimum amount of substance that can be detected but not measured, quantitation limit is the minimum amount of substance which can be detected and measured. Common approach to these procedures- signal to noise ratio has also been covered. Robustness is a characteristic which determines a method’s reliability when deliberate variations are induced in parameters.
Rationale for the reporting and control of degradationDurgadevi Ganesan
Rationale for the reporting and control of degradation, Reporting procedure, Identification of degradation products, Threshold for degradation products in new drug products, Analytical procedure, Reporting degradation products contents of batches.
In this slide contains types of HPLC Columns, Plate theory and Van Deemter Equation.
Presented by : Malarvannan.M (Department of pharmaceutical analysis).
RIPER,anantpur.
New guidelines relating to elemental impurities from the International Conference on Harmonization (ICH), Q3D Guideline for Elemental Impurities have presented the pharmaceutical industry with new challenges. This new guidance has been developed to provide a global policy for limiting metal impurities qualitatively and quantitatively in drug products and ingredients.
This document is intended to provide guidance for registration applications on the content and qualification of impurities in new drug substances produced by chemical syntheses and not previously registered in a region or member state.
Rationale for the reporting and control of degradationDurgadevi Ganesan
Rationale for the reporting and control of degradation, Reporting procedure, Identification of degradation products, Threshold for degradation products in new drug products, Analytical procedure, Reporting degradation products contents of batches.
In this slide contains types of HPLC Columns, Plate theory and Van Deemter Equation.
Presented by : Malarvannan.M (Department of pharmaceutical analysis).
RIPER,anantpur.
New guidelines relating to elemental impurities from the International Conference on Harmonization (ICH), Q3D Guideline for Elemental Impurities have presented the pharmaceutical industry with new challenges. This new guidance has been developed to provide a global policy for limiting metal impurities qualitatively and quantitatively in drug products and ingredients.
This document is intended to provide guidance for registration applications on the content and qualification of impurities in new drug substances produced by chemical syntheses and not previously registered in a region or member state.
Decision Making Using The Analytic Hierarchy ProcessVaibhav Gaikwad
Analytic Hierarchy Process (AHP) is an
effective tool for dealing with complex decision making,
and may aid the decision maker to set priorities and
make the best decision. By reducing complex decisions
to a series of pairwise comparisons, and then
synthesizing the results, the AHP helps to capture both
subjective and objective aspects of a decision. In
addition, the AHP incorporates a useful technique for
checking the consistency of the decision maker’s
evaluations, thus reducing the bias in the decision
making process. In this paper we give special emphasis
to departure from consistency and its measurement and
to the use of absolute and relative measurement,
providing examples and justification for rank
preservation and reversal in relative measurement.
Time Series Anomaly Detection for .net and AzureMarco Parenzan
If you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET and Azure?
Time Series Anomaly Detection with .net and AzureMarco Parenzan
If you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
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.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
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.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
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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.
2. Peak Integration & Processing
Integration Process
Types of Peak Integration
Controlling the Integration Process
Challenges of Integration in Chromatography
Auto Integration v/s Manual Integration
Regulatory Perspective
Peak Integration & Data Integrity
Peak Integration inWarning Letters
References….
9/16/2015 2
3. Integration - Process of calculating an area
that is bounded in part or in whole by a
curved line.
The goal of chromatographic peak
integration is to obtain retention times,
heights and areas of these peaks.
9/16/2015 3
5. Processing – Process that measures data to
determine the identities and/or amounts of
separated components.
Processing methods define how software
detects, integrates, calibrates and
quantitates unprocessed, raw data from a 2D
channel.
9/16/2015 5
6. 9/16/2015 6
The Integration Process consists of the following:
1) Defines the initial baseline.
2) Continuously tracks and updates the baseline.
3) Identifies the start time for a peak and marks this
point with a vertical tick mark.
4) Finds the apex of each peak, creates a parabolic fit
for the peak top, and stores the retention time.
5) Identifies the end time for the peak, and marks this
point with a vertical tick mark.
6) Constructs a baseline.
7) Calculates the area, height, and peak width for each
peak
8. Common Integration Baseline Options :
1. Drop Perpendicular
2. Valley toValley
3. Tangential Skim
4. Exponential Skim
5. Gaussian Skim
9/16/2015 8
9. Figure 1. Baseline Profiles: 1. Drop Perpendicular, 2.Valley to Valley, 3.Tangential Skim, 4.
Exponential skim, 5. Gaussian Skim
9/16/2015 9
10. Drop Perpendicular :
The addition of a vertical
line from the valley
between the peaks to the
horizontal baseline.
The vertical line is drawn
between the start and
stop points of the peak
group.
9/16/2015 10
11. Valley toValley
sets start and
stop points at
the valley
between the
peaks, thus
integrating
each peak
separately.
9/16/2015 11
12. Tangential Skim
Separate the small peak
from the Parent peak
with separate baseline.
Parent peak is integrated
from its starting point to
the apparent end of the
peak group.
9/16/2015 12
13. Small peak baseline starts at the valley
between the peaks & ends where the signal
nears the baseline.
Area under the skimmed peak is added to the
parent peak not to the skimmed peak.
Small peak labeled as skim, Shoulder or Rider
peak.
9/16/2015 13
14. Exponential Skim :
Used to create curvature
in the skim line to
approximate the
underlying baseline of
the parent peak.
9/16/2015 14
15. Gaussian Skim:
Also referred as new
exponential skim method,
intent to reproduce the
Gaussian shape of the
parent peak.
9/16/2015 15
16. Following parameters used by processing software :
PeakThreshold(Detection Threshold or Slope
Sensitivity) - to determine if peak is detected or
not.
Decreasing slope sensitivity will result in detecting
smaller and broader peaks.
Height Reject - to set noise rejection.
All peaks whose heights are below this value will not
be reported.
9/16/2015 16
17. Peak width (Sampling rate) – sampling rate
(number of data points per second) that the
detector signal is sampled & to set an initial
sampling interval for the integrator to distinguish
peaks from baseline noise.
Controls the ability of the integrator to distinguish
peaks from baseline noise. In general, increasing the
peak width will result in broader peaks.
Faster chromatography needs higher sampling rate.
9/16/2015 17
18. Area Reject- to filter small peaks.
All peaks whose areas are below this value will not
be reported.
Shoulder - to specify the algorithm for shoulder
detection.
shoulders detected using the second derivative of
Peak.
Shoulders occur when two peaks are so close
together that no valley exists between them.
9/16/2015 18
19. Bunching (Smoothing)
Adding several consecutive data points to obtain
an average time slice value equivalent to slower
sampling rate.
Can also be used to reduce noise in
chromatogram.
9/16/2015 19
21. Clear separation of peaks – a fundamental
requirement of most accurate integration of
chromatographic peaks.
If clear separation not achieved, an intelligent
approach of selecting the baseline options thus
maintaining the accuracy of Quantitation to the
most.
Integration errors calculated using reference
calibration injections.
9/16/2015 21
22. Integration
Most important step in data analysis part of
Chromatography yet no clear Guidelines available.
Various errors can occur during integration which
include, but are not limited to, peak splitting,
adding area due to a coeluting interferant, failure to
detect a peak, excessive peak tailing due to failure
of the instrument response to return to baseline or a
rise in the baseline, and failure to separate peaks
9/16/2015 22
23. The drop and Gaussian skim methods produce
the least error in all situations.
The valley method consistently produces
negative errors for both peaks.
The skim method generates a significant
negative error for the shoulder peak.
Peak height also shown to be more accurate
than peak area
9/16/2015 23
24. However, the results may vary depending
upon
The resolution between peaks
Area and height of the peaks
Position of the peaks relative to principle
peak or with respect to each other.
Peak size
Complex Baseline
9/16/2015 24
25. Integration options are likely to generate
significantly different analytical results.
Analysts must decide which approach provides
better accuracy. However the approach must be
documented in respective SOP with a sound
scientific judgment.
Proper judgment is expected in selecting the
methodology for peak integration.
9/16/2015 25
26. Select smallest peak for Integration.
Set Minimum Area, Minimum height, peak slice,
Tailing/fronting sensitivity factor, valley to valley,
peak to valley etc.
Set minimum area to about 90% of the area of
smallest peak.
9/16/2015 26
27. Set Sensitivity value to about 33% of the peak
height of smallest peak that needs to be
integrated.
Set peak slice to about 20% of the width
(baseline width) of the smallest peak.
Peak slice parameter determine the width
from which several successive data points are
interpreted as peak or as noise.
9/16/2015 27
28. Set inhibit integration “ON” from start to the
end of the void volume & “OFF” after the void
volume.
Inhibit Integration detection parameter
serves to fade out certain chromatograms
area when set peak detection is disabled
9/16/2015 28
29. Other Integration parameters are :
Negative Peaks
Front rider to main peak
Lock Baseline
RiderThreshold
Rider skimming
VoidVolume treatment
Sensitivity
9/16/2015 29
30. For peaks with excessive tailing or broader peaks,
base to valley and valley to base to be used as
integration parameter.
Same integration parameter to be used through out
the sequence having same concentration of sample
and reference. However may vary from sample to
sample depending upon different peaks observed.
9/16/2015 30
31. Use Auto Integration as far as possible since manual
integration Not accepted by regulatory bodies
unless necessary.
Under no circumstances should manual
integration(i.e. peak shaving or peak
enhancement) be performed solely for the
purpose of meeting quality control criteria.
9/16/2015 31
32. The automatic integration may fail mainly for small
peaks close to the LLOQ.
Integrate manually as in line with your SOP.
Proper training required to analyst to comply with
the expectations of manual integration
9/16/2015 32
33. Acceptable cases for Manual Integration
Peak Missed
Poorly defined baseline
Peak splitting
Complicated chromatography due to sample
matrix interferences
Poor instrument integration
9/16/2015 33
34. Acceptable approach to manual integration :
Should document both the original and
manually integrated chromatograms
Analyst’s signature with date clearly specifying
the reason of manual integration.
Reviewer’s Signature with date
Review of hard raw data against the electronic
raw data.
9/16/2015 34
35. Expectations of Regulatory bodies :
Controlling of chromatographic peaks by
appropriate policies and standard operating
procedures with sound scientific approach.
Changing of Integration parameters to comply
with Quality control requirements is
unacceptable.
Use of same suitable Integration parameters for
a validated Analytical method as far as possible.
9/16/2015 35
37. 9/16/2015 37
Avoiding Data Integrity in your laboratory…??
Have a defined procedure in your laboratory
containing methods and procedures with the
recommended HPLC integration parameters.
Any manual integration should be approved
by the laboratory management.
38. HPLC Processing methods (including integration
parameters) and re-integration are executed
without a pre-defined, scientifically valid
procedure.Your analytical methods are not
locked to ensure that same integration
parameters are used on each analysis. A QC
operator interviewed during the inspection
stated that the integration are performed and
re-performed until the chromatographic peaks
are “good” but was unable to provide an
explanation for the manner in which integration
is performed.
9/16/2015 38
39. • The raw data retained does not include the
run sequence or the processing method used
to perform the peak integration.Your QC
personnel performed peak integration based
on analyst’s experience rather then by an
approved procedure.
9/16/2015 39
40. • Chromatography raw data does not include
the processing method used to produce the
final analytical result ; therefore it would not
be possible to detect if any modification to
the processing method is done.
9/16/2015 40
41. In addition, our investigators documented many
instances wit extensive manipulation of data with
no explanation regarding why the manipulation was
conducted.This manipulation would include
changing integration parameter or relabeling peaks
such that previously resolved peaks would not be
integrated and included in the calculation for
impurities.
9/16/2015 41
42. • HPLCs did not have the audit trails enabled;
some audit trails missing when peaks were
manually integrated, no SOP to describe
when manual integration is acceptable.
• And the series continues……………………
9/16/2015 42
43. USEPA Region 9 SOP #835,Chromatographic Integration Procedure,@
Revision 0, July 1, 1998.
Questions of Quality :Where Can I draw the line? By R.D. McDowall,
LCGC Europe,Volume 28, Issue 6.
Warning letters by FDA.
Taking the Pain Out of Chromatographic Peak Integration By Shaun
Quinn,1 Peter Sauter,1 Andreas Brunner, shawnAnderson,2 Fraser
McLeod1, 1Dionex Corporation,Germering, Germany; 2Dionex
Corporation, Sunnyvale, CA, USA.
Integration Errors in ChromatographicAnalysis, Part I: Peaks of
Approximately Equal Size, LCGC.
Integration Errors in ChromatographicAnalysis, Part II: Large Peak Size
Ratios, LCGC.
9/16/2015 43