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Update at PharmOut GMP & Validation Forum

• A multifaceted look into the future of pharmaceutical manufacturing.

• Focused presentations on:

– the end-user’s/patient perspective,

– innovative manufacturing strategies,

– disruptive technologies, and

– product value chain logistics.

• Based on the increased use of Big Data, Artificial Intelligence, and robotics in the industry.

Program Highlights


Stephen F. Kingsmore, President & CEO, Rady Children’s Institute for Genomic Medicine


World Record: 19.5 hour to sequence genome


How can Modern Technologies Address Pharmaceutical Manufacturing Challenges?

• Improve manufacturing efficiency: Increase process robustness, lower manufacturing costs for pharmaceutical products, increase supply chain flexibility

• Facilitate new clinical development – precision medicines: enable a wider range of doses without extensive alterations of the process, and convenient fixed‐combination dosage forms

• Support manufacturing of patient centric dosage forms: e.g. for special populations e.g. geriatrics

Sharmista Chatterjee, Ph.D., Division Director, Office of Process & Facilities, US FDA


• Integrated processing with fewer steps: No manual handling, increased safety, shorter processing times

• Smaller equipment and facilities: more flexible operation, lower capital costs, less work‐in‐progress materials, reduced environmental foot print, small batch sizes

• On‐line monitoring and control for increased product quality assurance in real‐time: Real Time Release Testing

• Benefits to both patients, and industry

• US FDA Have an Emerging Technology Team (ETT)

Continuous manufacturing (CM):


• “Emerging technology should be novel in the context of the pharmaceutical and related industries and it should have the potential to modernize the pharmaceutical manufacturing body of knowledge related to product quality. Emerging technology will be new to FDA in the context of pharmaceutical quality, with limited prior experience and knowledge”

Emerging Technology

PharmOut GMP & Validation Forum 2018

• First 3D printed drug product in 2015

• First approved drug product to switch from a legacy batch process to CM in 2016

• Four CM applications approved to date

More than 30 requests accepted to the Emerging Technology Program since the launch of the program in late 2014

Recent approvals in which ETT played a critical role:

https://www.fda.gov/AboutFDA/CentersOffices/OfficeofMedicalProductsandTobacco/CDER/ucm523228.htm



• First Digital Pill Approved. Designed to monitor medicine-taking.

• Millions of patients do not take drugs as prescribed.

• Experts estimate that so-called non-adherence or noncompliance to medication costs about $100 billion a year!

Some Recent Emerging Technologies Approved by US FDA:

https://www.nytimes.com/2017/11/13/health/digital-pill-fda.html

“a medication (Abilify)

embedded with a

sensor that can tell

doctors whether, and

when, patients take

their medicine”

https://www.nytimes.com/2017/11/13/health/digital-pill-fda.html


• First 3D printed drug product in 2015

• First US FDA‐approved drug product to switch from a legacy batch process to continuous manufacturing in 2016

• Four continuous manufacturing applications approved to date

• More than 30 requests accepted to the Emerging Technology Program since the launch of the program in late 2014

• For details regarding ETT activities, please visit the Emerging Technology Program Website


US FDA Progress:



• Continuous Manufacturing

• Model‐based control strategy for CM

• Continuous aseptic spray drying

• 3D printing manufacturing

• Ultra long‐acting oral formulation

• Advanced process control: predictive modeling for process monitoring; close loop bioreactor control; feed forward impurity control

US FDA Experiences:


US FDA Experiences:

• Comprehensive product testing using a single multi-attribute assay (multi‐attribute method)

• Next generation sequencing

• Continuous manufacturing for an upstream process

• Pharmacy on demand (a small manufacturing platform for continuous bioprocesses)

• Closed aseptic filling system

• Isolator and robotic arm for aseptic filling

• Novel container and closure system


KASA = Knowledge‐aided Assessment & Structured Application. Goal: Design of a computer aided interface for assessment of regulatory submissions that:

• Facilitates standardization of risk based assessment, ensures consistency

• Provides a standard template for communication of risk identification, and risk mitigation

• Allows leveraging prior knowledge

• Emphasizes lifecycle knowledge management

US FDA Innovative Review Approaches:

https://www.fda.gov/downloads/AboutFDA/CentersOffices/OfficeofMedicalProductsandTobacco/CDER/UCM598727.pdf?utm_campaign=FDA%E2%80%99s+Office+of+Pharmaceutical+Quality+%28OPQ%29+issues+annual+report+for+201&utm_medium=email&utm_source=Eloqua

https://www.fda.gov/downloads/AboutFDA/CentersOffices/OfficeofMedicalProductsandTobacco/CDER/UCM598727.pdf?utm_campaign=FDA%E2%80%99s+Office+of+Pharmaceutical+Quality+(OPQ)+issues+annual+report+for+201&utm_medium=email&utm_source=Eloqua

Interest Group 2: Filtration


Annex 1; PUPSIT revision and comments submitted

90 comments from PDA Working Group on draft Annex 1

PDA PUPSIT Initiatives:

• PDA/BPOG Memorandum of Understanding

• Filter manufacturers joint statement

• Definition of known and potential filter failures modes

• Filter blocking trial initiative. Masking studies - protocol establishment and tests at PDA TRI

• Best practice design of a PUPSIT

• Risk assessment template


Current Draft Annex 1:

8.84 The integrity of the sterilized filter assembly should be verified by testing before use, in case of damage and loss of integrity caused by processing, and should be verified by online testing immediately after use by an appropriate method such as a bubble point, diffusive flow, water intrusion or pressure hold test. It is recognised that for small batch sizes, this may not be possible; in these cases an alternative approach may be taken as long as a formal risk assessment has been performed and compliance is achieved. There should be written integrity test methods, including acceptance criteria, and failure investigation procedures and justified conditions under which the filter integrity test can be repeated. Results of the integrity tests (including failed and repeated tests) should be included in the batch record.

PDA Comments:

8.84 The filter and filter assembly preparation, sterilization, and use for sterilization of the product should be qualified to ensure that the filter and assembly maintain their integrity throughout the entire process. This should include a well‐documented risk based assessment of and corresponding control strategy implementation to address potential filter and assembly defects and filtration failures caused by manufacture, handling, storage, sterilization, and use of the filter and assembly prior to and during product filtration. Control strategies should include efforts to prevent such defects and failures, as well as test the filter and assembly at appropriate phases of the process, including testing prior to the filter sterilization, immediately after use, and where the risk assessment indicates the need, after the filter sterilization.


Current Draft Annex 1:

8.85 The integrity of critical sterile gas and air vent filters in the filter assembly should be verified by testing after use. The integrity of non-critical air or gas vent filters should be confirmed and recorded at appropriate intervals.

PDA Comments:

8.85 Filter and assembly integrity methods and systems should be designed, installed and operated to be effective for their purpose and where appropriate, fit for use in an aseptic process and controlled environment. There should be written integrity test methods, including acceptance criteria, failure investigation procedures and justified conditions under which the filter integrity test can be repeated. Results of the integrity tests (including failed and repeated tests) should be included in the batch record.

One main argument made for PUPSIT, is the blinding of a flaw, so the post‐use test cannot find the flaw

• Blocking trial test protocol → review and approval by the filter suppliers.

• Once approved we send it to MHRA → review and approval by MHRA.

• In the meantime we collect 10” sterilizing grade filter samples from the filter manufacturers. These filters need to be failed filters for testing.

• Once the protocol is approved and filters are collected, PDA will start the trial work in PDA TRI.

• The results of the tests will be published without naming the filter types and companies.

Interest Group 2: Filtration



Amy D. Wilson, Biogen: Improving Operational Performance Using a Resilience Engineering Approach

Practice 1 – Anticipate dynamic risk

• Critical Steps -Focus attention where risk is greatest

• Pre‐Job Briefs -Ensure a plan that recognizes the here and now

Practice 2 – Observe Work‐as‐Done

• Work Observation & Risk Conversation

• Humble Inquiry


Practice 3 – Apply Systems Thinking to Investigations

Practice 4 – Learn also from successes

• What went well and why?

• What can be improved and how?

Practice 5 – Expand how you learn

Improving Operational Performance Using a Resilience Engineering Approach

Anticipate Monitor Respond Learn

Commitment needed


Other topics/presentations:

• Continuous Processing Strategies, Manufacturing 4.0

• Facility design, Isolator Technology

• Outsourcing (CMO, laboratory, supply chain)

• Big Data/Analytics, “Data is the new oil”

• Immunotherapy, Cell and Gene Therapy, personal medicines

• Multi-Attribute Method (MAM) - UPLC/MS

• The Human Side of Innovation


1. Personal Edge & Devices

2. Company Staged Assets

3. End User Engagement

4. Global Network Industry Orchestration

5. Hybrid Cloud Ecosystems

6. Blockchain and Trusted Data Sharing

7. Security Intelligence

8. Artificial Intelligence

Malcolm Postings, IVQIA: 8 areas for Innovation


Proximity/edge: Process data at the point of data generation.

Device: “Wearable” technology. Smart glasses, fit bands, smart watches, earbuds, VR/AR headsets, shoe insoles, smart fabrics.

• Need to be linked to the cloud.

Internet of Things: The network of devices embedded with sensors, electronics, software etc.

Proximity/edge computing


• Voice assisted technology: (questioning and listening e.g. clinical trials, batch records. Directive e.g. “tell me how to change out the filter”).

• Virtual Reality/Augmented Reality:

• Virtual: training e.g. aseptic technique.

• Augmented/mixed: design review, in field maintenance/repair, headset with projection e.g. Microsoft Hololens (with hard hat solution!)

Proximity/edge computing


• Research and development

• From experimentation to full-scale manufacturing

• Predictive modelling

• Operational efficiencies

• Must have a value-oriented perspective on where to direct analytics

• A new innovation capability

Advanced Data Analytics

https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-

insights/how-pharma-can-accelerate-business-impact-from-advanced-analytics

https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/how-pharma-can-accelerate-business-impact-from-advanced-analytics


• Social networking – goes both ways. Using social media to “recruit” people for trials, but also as a feedback mechanism for those on the trial.

• Precision medicine – customised/tailored healthcare. All about molecular diagnostics, imaging, and analytics.

• Data mining – descriptive, predictive and prescriptive.

• Virtual Trials – patients interacting with mobile phones and telemedicine devices with investigators e.g. Science37 with NORA® - Network Oriented Research Assistant.

End User Engagement


Internet of Things: The network of devices embedded with sensors, electronics, software etc

• Projected to reach $USD7.1 trillion by 2020

• “Big Data”…should be “Fast Data”

• Intelligent algorithms, data mining, statistical techniques.

• Monitor the facility

• Monitor the supply chain

• Monitor the patient

Global Network Industry Orchestration


Cloud computing environment.

• Mix of on-premises, private cloud and third-party, public cloud services

• Orchestration between the two platforms

Fog computing:

• Uses edge devices to carry out computation, storage, communication locally.

• May also use smaller Edge Clouds (cloudlets).

• Supports IoT concepts.

Hybrid Cloud Ecosystems


• Distributed Ledger– Proof of Work (PoW): computational power, hashing algorithms e.g.

SHA-256.

– Proof of Stake (PoS): miners hold a large amount of a token.

– Delegated Proof of Stake (DoPS): vote for delegates to maintain the network.

• Secure supply chain solution – one of the most important use cases for our industry

• Data Sharing – secure, immutable…trusted.

• Data has value - “new gold”.

• Token economy

Blockchain and Trusted Data Sharing


Authorisation

• Device Authentication

• Human Authentication (biometrics)

Cybersecurity

• Ransomware

Security Intelligence


• Machine Learning (ML): Discovery of drug candidates e.g. SITraN and BenevolentAI for MND in the UK.

• Virtual assistants: Lab assistants for data analysis e.g. H2O.ai. Open source.

• Advanced processing

• Automation

Artificial Intelligence (AI)


“Innovation has nothing to do with how many R&D dollars you have. When Apple came up with the Mac, IBM was spending at least 100 times more on R&D. It's not about money. It's

about the people you have, how you're led, and how much you get it. “

Steve Jobs

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