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The document summarizes a project to redesign laboratory space at a medical center to improve efficiency and reduce turnaround time. A team analyzed the current layout and processes, identifying issues like separated test areas and long distances traveled between steps. They developed a new layout focused on consolidating related processes and equipment to minimize staff motion. Implementation led to estimated annual savings of over $50,000 from reduced time and inventory costs while improving staff satisfaction and capacity. Lessons included ensuring key stakeholders participated fully and having a detailed plan gained administrative support.
This document outlines the principles of Good Laboratory Practices (GLP). GLP provides a framework for conducting laboratory studies and ensuring quality and integrity of data. The key points covered include defining GLP and its purpose of certifying valid study steps. Ten GLP principles are described relating to laboratory organization, facilities, equipment, test systems, methods, and record keeping. Maintaining proper documentation, conducting quality assurance inspections, and retaining records and materials are emphasized. In conclusion, following GLP guidelines helps produce high quality data and ensures proper laboratory management.
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This document provides tips for organizing a laboratory at an organizational level. It recommends scheduling tasks using a shared calendar, making use of vertical space for storage, giving all equipment and supplies designated storage locations, using comprehensive labeling, tracking inventory electronically, storing documents digitally, maintaining the organized system through training and regular audits, and references a source on laboratory organization.
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GLP is a formal FDA regulation created in 1978 that provides principles for conducting laboratory studies in a standard, consistent manner. It aims to ensure quality and integrity of data submitted to the FDA. Key GLP principles include requirements for test facility organization, quality assurance programs, facilities, equipment, test systems, standard operating procedures, study conduct, reporting, and record keeping. GLP helps provide reliable results and protects study integrity and data for regulating products like drugs and pesticides.
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This document provides an overview of Good Laboratory Practices (GLP). It discusses that GLP aims to assure regulatory authorities that safety study data submitted to them are reliable and accurate. The document traces the history and development of GLP from cases of poor laboratory practices in the 1970s. It describes the key principles of GLP including requirements for facilities, equipment, test systems, personnel responsibilities, standard operating procedures, and reporting of study results. Adherence to GLP aims to produce high quality and integrity of nonclinical safety data.
Good Laboratory Practice (GLP) is a formal set of regulations established by the US FDA in 1976 to ensure that non-clinical safety studies are of high quality and are accurately reported. GLP covers the organizational processes and conditions under which non-clinical health and environmental safety studies are planned, performed, monitored, recorded, reported and archived. It aims to assure that study data accurately reflect the results obtained during the study and allows for mutual acceptance of data between countries. GLP requires strict adherence to standard operating procedures, record keeping, facilities management, and quality assurance programs to ensure the integrity of preclinical safety testing.
The document discusses key aspects of developing and implementing a Quality Assurance Project Plan (QAPP) for environmental sampling. It notes that a QAPP is required by the EPA to define the quality assurance program, and must be prepared and approved every four years. The QAPP is used to communicate, summarize, document, and audit the sampling process. Proper sample collection, handling, preservation, custody documentation and approved analytical methods are essential elements of quality assurance, along with standard operating procedures, certification programs, proficiency testing, data validation, and audits.
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NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
2. What will we be Covering?
• What is the Key to Success?
• About Spaulding Clinical Research
• Types of Studies
• Samples Processed
• Transitioning to ClinSpark
• Advantages of ClinSpark
• Decrease in Deviations
• Safety Labs and Reference Laboratories
• Handling Difficult Studies in the Processing Lab
• Example Studies
• Example Study 1 Data
• Sample Reporting, Tracking and Shipping
• Open Discussion/Questions?
4. About Spaulding Clinical
• Conducting studies with an E-Source system for 8 years
• June 2009 was Spaulding’s first study
• Initially used Logos Technologies E-Source Adas
• ClinSpark V1.0.0 validation completed April 2015
• First study utilizing ClinSpark started screening in April 2015 and 1st dose in
May 2015
• Virtually eliminated paper source documents for all studies
• Integrated lab data with an out-sourced reference laboratory
• Internal Reference Laboratory opened May 2016 and is fully integrated with
ClinSpark for easily accessible results
• Precise sample processing capabilities
5. Types of Studies
• Thorough QT
• Intensive Phase 1 ECG Trials
• PD/PD Characterization of QTC
• First-in-Human
• Single & Multiple Ascending Dose
• Adaptive/Accelerated Protocols
• Food Effect
• Drug/Drug Interactions
• Age & Gender Specific
• Patient Specific Population
• Bioavailability/Bioequivalence
6. Samples Processed
49061
57417
93365 92315
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
2014 2015 2016 2017
TOTAL SAMPLES PROCESSED
YEAR
Number of Samples Processed by Year Data is Based on 2014 – 2017
• Typical Study – 20 samples per subject/period
• Average five studies per quarter
• Slowest Quarter – 6,504 PK Sample Collections (2014)
• Busiest Quarter – 48,606 PK Sample Collections (2017)
• Busiest Sample Processing Days with ClinSpark
(Pre-dose – 12 hour draws)
• 2015 – 1006 Samples Collected
• 2016 – 795 Samples Collected
• 2017 – 999 Samples Collected
7. Transitioning to ClinSpark
• Started with a small group of staff and used the training environment for hands on
training
• This group of staff learned the system and the helped train all other study floor staff
• Choose one study to start with and gradually start all new studies on ClinSpark
• Our first study in ClinSpark for Day 1 dosing: 609 samples collected from 10 subjects
in 14 hours
• Reaction to ClinSpark from Staff:
• “What I enjoy about ClinSpark is it’s simplistic user interface, along with it’s accessibility to
data” – Clinical Research Sample Specialist
• “It makes my job easier and more enjoyable” – Clinical Research Technician
• “Major improvement for Spaulding” – Clinical Research Sample Specialist
• “It has been very easy for me as a new employee to navigate and use ClinSpark and I rarely
fought with it” – Clinical Research Sample Specialist
8. Advantages of ClinSpark
• Decrease in Staff Needed for Studies
• Minimal Training for Staff
• Training Environment
• PC Brower and Mobile Devices
• Multi-Site Support
• Exceptional Reporting Functions
• Audit Trails on all Data
• Flexibility in Study Builds and Design
• Decrease in Deviations During Sample
Processing (See Next Slide for more details)
• External Device Configuration
• Export Activity Plan Details to Excel
• Ability to Configure Simple & Complex
Sample Processing Steps
• Pre-labeled Sample Tubes
• Barcoded Specimens
• Countdown Timers
• Capability to Filter Items
• Sample Packaging Containers Module
• Create and Manage Shipments
• Safety Lab Reporting and Reviews
• Process Unscheduled Samples with a Unique
Barcode
• Bi-directional Interface for Safety Labs (HL7,
CDISC LAB and more)
10. Safety Labs and Reference Laboratories
• The sample processing module is not exclusive to PK sample processing; this
module can be used for processing and tracking safety labs
• Lab codes and orders are created in the lab module
• Lab codes are assigned to determine what tests should be conducted on each sample
• Lab orders are generated to accompany samples to their destination
• Lab codes and sample processing paths can be assigned for one sample
• Multiple reference laboratories may be used including an in-house laboratory
• Using an interface with an outsourced or internal reference lab allows results to be
viewed once reported and an electronic signature for review of the results
• Signoff on the review of lab values can be restricted to role assignments
• Review date/time, significance if necessary, repeat necessary and any notes from the reviewer
are captured upon signing off on lab data
• Lab orders can be placed on hold or deleted if sample(s) do not require processing
11. Handling Difficult Studies in the Processing Lab
• Complex Studies and Sample Processing
• When configuring a sample path all steps are calculated off of the collection time in
seconds allowing you to be as accurate as possible
• Create a study specific sample path for studies to accommodate many requests
• Create your own steps for processing such as clot, flash freeze and additive if needed
and any sample processing step created will report out in the sample processing report
(See example study 1 on next slide)
• Timers and filters in the sample processing module allow staff to know what the most
time sensitive samples are to process across all studies or a single study
• Auto-calculate items within forms; this allows you to not only capture the data but also
provide staff with the correct calculation (See example study 2 on next slide)
• Unscheduled events can be added and a barcode is created to have the correct sample
path attached so there is complete sample tracking for even the unexpected during a
study
• Studies with multiple periods can be built out entirely and allow labeling to occur
weeks before dosing
• SAD/MAD studies are easily adjustable within ClinSpark and allow adjustments to an
activity plan from cohort to cohort
12. Example Studies
• Example Study 1:
• At each time point collect two samples;
• One whole blood sample is collected and within 5 minutes of collection a derivatizing agent is added to the
sample, within 15-30 minutes of collection sample will be placed on a rocker for a minimum of 2 hours and a
maximum of 3 hours, after samples are taken off the rocker the whole blood is transferred evenly between
two aliquots and frozen within 30-60 minutes after transfer
• One whole blood sample is collected and within 30 minutes of collection sample will be centrifuged, transfer
plasma evenly between two aliquots and freeze within 20-30 minutes of transfer
• See next slide for data
• Example Study 2:
• Enter the following upon interval completion;
• Step 1 – Enter the time/date
• Step 2 – Weight of urinary storage container with cap (grams to the tenth)
• Step 3 – Weight of sample in capped storage container (grams to the tenth)
• Step 4 – Weight of sample minus the weight of capped storage container (grams to the nearest tenth)
• Step 5 – (Step 3*0.02) Volume of Additive to add = X mL
• Step 6 – Staff adds the indicated amount (x mL) of additive to the urine
• Step 7 – Staff processes sample per protocol requirement
13. Example Study 1 Data
• Five studies with the same overall sample
collection and processing design:
• Average of 500 samples on day 1 of each
study
• 1 Adas study Vs. 4 ClinSpark studies and a
learning curve
• Switching to ClinSpark improved greatly
on data reported out to the sponsor as
well as identifying areas of improvement
to decrease overall errors
• Previous system only allowed Centrifuge,
Transfer and Freeze Time to be reported in
sample processing
• Ability to capture data for all steps necessary
within sample processing using barcode
scans and eliminate the use of collection
sign off; simplistic reporting
93.28%
94.41%
98.24%
98.87%
99.47%
90.00%
91.00%
92.00%
93.00%
94.00%
95.00%
96.00%
97.00%
98.00%
99.00%
100.00%
Study 1 Q1-2014 Study 2 Q2-2015 Study 3 Q4-2015 Study 4 Q2-2017 Study 5 Q2-2017
Sample Processing %
14. Sample Reporting, Tracking and Shipping
• Each barcode scan is documented as the step time for any given sample processing task created
• Batched steps will record the first scan not the last scan
• Reports include all steps that were configured in ClinSpark sample processing in addition to
providing time taken to each step configured
• Example: Time taken to Time Centrifuged
• Using the device integration you can assign centrifuges and freezers as a location for samples
• This would be a sample processing step and require a barcode scan
• Calibration information can be added and tracked within ClinSpark
• In addition to sample processing tasks you can also create packaging and shipment tasks for studies
• Sample Containers (packaging) – allows for you to batch samples into a container such as a gird box
and have one barcode print for the contents of the grid box
• Shipments can be created and then containers are added to the shipment to ensure appropriate
samples are included in the shipment
• Sample processing reports are easily generated and can be used for shipment manifests
• Shipment locations in addition to shipment date and date of receipt can be easily tracked in
ClinSpark in the shipment module