Track 3: Product and Process Development

Current Challenges in the Characterization of Complex Drug Formulations Containing Nanomaterials Moderators: Katherine Tyner, FDA and Henry Havel, Nanomedicines Alliance

Session Background • Drug products containing nanomaterials are often complex products

and are increasingly being submitted to the FDA

• Physicochemical properties (such as particle size) can significantly affect product performance and safety

• Specialized analytical methods are needed to characterize nanomaterials appropriately

• There is a current challenge to identify, characterize, and control the critical quality attributes of complex formulations containing nanomaterials

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Session Schedule • Quality Considerations and Regulatory Perspectives for Drug

Products Containing Nanomaterials – Katherine Tyner, FDA

• Nanoparticle Size Analysis: A Survey and Review – Marc Wolfgang, Cerulean Pharma

• Industrial Perspective on Nanomedicine Characterization Strategies – Donald Parson, BIND Therapeutics

• Panel Discussion

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Key Takeaways • Size dominated the characterization and control discussion

• Dynamic Light Scattering (DLS) is the most popular sizing technique

for measuring size • Pros: Fast, easy, wide range, sensitive to agglomeration • Cons: Low resolution, general assumptions, and transformative

calculations

• Must be supplemented with another technique. Cannot be used as a stand alone method (both for characterization and QC)

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Key Takeaways • Size is only one quality attribute

• Morphology, release, charge, etc. may also impact product performance

• What attributes are critical is product-dependent

• As a means to focus the physicochemical development, consider the clinical implications of the attribute

• There is a desire to have more interactions/public outreach by FDA to the public on the safety of nanomaterials

• There is a CDER/FDA guidance on drug products containing nanomaterials in the works

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Future Directions 1. A white paper is being formulated by NMA to be published with

regulatory and industry input on sizing techniques being used by industry

2. A comprehensive summary of the session will be disseminated in the conference report

3. Both FDA and industry are accumulating knowledge and will continue the conversation on: a) Both new and existing technologies b) The clinical importance

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Biosimilar Product Assessment – How Similar is Similar? Moderators: Anna Schwendeman, University of Michigan and Emanuela Lacana, FDA

Product development At what stages sponsors should do analytics.

• Showing similarity at small-scale is not sufficient, need also to do it for commercial manufacturing. Overall, should do analytics at every stage - before IND, for IND enabling, in PK/PD, and to support BLA. Want to see similarity at every step.

How many lots should we use for characterization studies? • Multiple reference lots. Some biosimilar developers have used 20-30 lots of reference

product over 4-5 years. Note that those lots that go into pivotal clinical studies should be part of the analytical similarity testing.

What is the different between Product Quality Attribute Assessment and Product Quality Risk Assessment?

• Attribute assessment is about criticality, and is done for each attribute. Risk assessment is about potential harm/impact, probability of occurrence, ability to detect. Risk assessment is done for each unit operation. This helps identify gaps in control strategy.

When should control strategy/risk assessment be done? Prior to validation/qualification? It’s difficult to do as a single exercise since need to piece together different strands of information.

• For new products, it’s done three times during clinical development. For biosimilars, usually twice, initially and after validation. In some respects, biosimilars are like breakthrough therapies, because of the condensed development cycle.

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Product development, cont In a situation when some lots of the reference product are shifted – do you include them

in the analysis? • It depends on where you are in the development program. If you’ve had 4-5 years of one

target range and then the marketed product changed, you should be allowed to keep the original target range. But if you are early in development, you could integrate the new reference lots in your database for analysis. It is important to talk to the agency as soon as the issue is noted.

• The agency’s goal is consistency across companies. One specific complication is with biosimilars approved in the late 1990s, because some aspects of the biologics’ action (e.g., role of fucosylation) were not as well understood then as they are now. Those are difficult conversations but we will have to have them.

How do you deal with analytical drift (e.g.,, due to instrumentation changes, personnel changes)?

• Use reference standard in assay, system suitability criteria (like in HPLC). • Consistency of the reference standard is important. We keep a master reference standard,

from which we make working reference standards.

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Statistical Considerations What are good limits for the confidence interval? What is relevant for biosimilarity?

• Traditionally, 90-110% are used in statistics. But it is difficult to decide what is relevant clinically. Must discuss with the agency. Should use scientific understanding to identify where the differences are. Statistics should be used as a tool, one tool among many. But it is also important that the agency uses consistent approach across different biosimilars manufacturers.

If you have two biosimilar companies with different variabilities, what should the agency do?

• The number of lots for the reference product is important. With two few lots (e.g., 4-6), may not capture all of variability.

With a poor analytical method, equivalence criteria could be wide, so there is a disincentive to use sensitive methods.

• A bad assay is not a justification for a failed statistical test. • A low-sensitivity assay could indeed be easier to pass. • FDA statisticians tried to design acceptance limits using a good number of lots, so sponsors

would not be penalized for having more information.

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Statistical considerations, cont. Tiers 1, 2, 3 – are they general or product specific?

• Product specific. Not all highly critical attributes will be evaluated with a tier 1 statistical method. Generally, method-related or mechanism of action related go to tier 1. For some biosimilars it could be binding, for others effectors function assay. For narrow therapeutic index products, it could be protein content in tier 1. Sequence is highly critical but it cannot be quantified, hard to do statistics on that.

Equivalence acceptance range. Is statistical guidance from FDA coming out soon? It is difficult to link analytical difference to clinical difference. Some guidance is needed.

• Guidance is in the works. . If there is an expectation among reviewers for a certain range for tier 1 acceptance, it would be

helpful for developers of biosimilars to know it. It’s hard enough to hit the target. Even harder when you don’t know what the target range is.

• Guidance should provide sufficient advice, but cannot be so specific to cover all possible examples What to do if confidence intervals overlap, so equivalence is inconclusive?

• Would need to see additional data to justify, or increase the number of lots, or use an orthogonal method, or tighten manufacturing control strategy.

The agency’s goal is to make this program successful. Criteria are tight so that clinicians would have confidence in biosimilars. Especially in oncology in cases of curative therapies, switching from an originator to a biosimilar could meet clinician’s resistance unless can show that FDA has done a good job approving biosimilars.

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Palatability and Swallowability-Benefits of Transdisciplinary Learning Moderators: Arzu Selen, FDA and Rob Ju, AbbVie Speakers: Stefan Baier, PepsiCo and Julie Lorenz, Zoetis

Take-Home Messages • Discussed science and methods for assessing mouthfeel (texture, tribology) and

palatability (taste masking, e-tongue) • Agree that information obtained is most valuable in early formulation

development (screening) • No single method can replace in vivo assessments (manage expectations)

• Opportunities for cross-disciplinary learning for formulation development and continued dialog • Industry: Food, Consumer, Human and Animal Healthcare • Academia • Regulatory Agencies • Need more venues for open discussion and sharing

• Need for continued research and collaboration on how to further apply in-vitro

methods to formulation development

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Alternatives for Content Uniformity Acceptance Criteria and Stratified Sampling Moderators: Geoffrey Wu, FDA and Siva Vaithiyalingam, Teva

Topic I: Uniformity of Dosage Unit USP <905> Maintaining Relevance

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• Jon Clark, USP • Compendial Expectations (USP <905>):

• Fixed units for testing (n = 30) • Zero tolerance: No units outside of 75% to 125% • Determination test: Is the batch acceptable?

• Industry Needs: • Flexibility in sample size • Zero tolerance criteria is unproductive • Strategy if batch seems unacceptable on determination

test • Initiatives:How should we consider manufacturing data that

indicates high probability of final product compliance? • Real Time Release Testing (QbD Based) • Large N sampling of units at final product

Topic II: Recommendation: Modern Approaches to Sampling and Testing • James Drennen, Duquesne University • ISPE proposed framework for BU/CU testing

• Provide increased confidence that future samples drawn from the batch will comply with USP <905>

• Link blend and content uniformity • Process design and qualification • Continued process verification

• PAT methods offer value • Product Quality and Process Understanding

• Facilitate appropriate sampling • Enhance statistical confidence

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Topic III: Statistical Considerations for Establishing AC for CU and Stratified Sampling • Alex Viehmann, FDA • Sampling method needs to be defined:

• Dictates the ability to quantitate between/within variance components

• Distribution needs to be evaluated • Dictates the method to apply

• Attribute (spec range) needs to be relevant for the product • Is 85-115% appropriate for all drug products?

• Quality levels = Risk Based • Should not be static across all products

• Firm needs to clearly identify the assurance the plan provides

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Panel Discussion • Specific test method should be selected on a case-

by-case basis • Currently there is no official position about large

sampling size testing. • Very important to determine proper confidence and

coverage early on; once determined, flexibility can be achieved later on (e.g., scale-up)

• Acceptance criteria should be determined based on expected product-performance (i.e., clinically relevant): science- and risk-based approach

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The Science of Tech Transfer/Scale-up Moderators: Grace McNally, FDA and Ilgaz Akseli, Boehringer Ingelheim

Session Premise

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Manufacturing high quality drug products cost-effectively necessitates a multidisciplinary approach focused on fundamental pharmaceutical science, materials and engineering expertise, innovative materials science methodology advanced computer-based predictive tools and a supportive quality system.

Speakers:

Implementing Fundamental Pharmaceutical Science and Materials/Engineer Expertise in Scale-Up – -------Ecevit Bilgili, New Jersey Institute of Technology

Using Material Science Methodology and Modeling Predictive Tools to Enable Scale-up. – Alberto Cuitino, Rutgers University Integrated Product and Process Understanding and Control that Enables Flexible and Efficient Tech

Transfer to Different Sites – San Kiang, Consultant, BMS Risk Assessment and Troubleshooting Using Fundamental Science and Predictive Tools – -Stephen Conway, Merck Mitigating Product Risk in Manufacturing or During Formulation Development and Identify Opportunities

to Maximize Efficiency -Ilgaz Akseli, Boehringer Ingelheim Regulatory Perspective: Advantages of Science Based Pharmaceutical Development in Regulatory Review

– Sharmista Chatterjee, FDA

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Themes and Take Aways Presentations described modeling case studies for fluid bed granulation, coating processes, powder and particle behaviors, and bi-layer tablet compression, big data computing, virtual drugs and virtual patients.

• Simulations and modeling increases process understanding; can

shorten development timelines and reduce development costs.

• Use of scale-up rules, modeling and fundamental understanding of underlying physical transformations is key to successful scale-up.

• Basic science and predictive modeling aids trouble shooting and root cause analysis; focuses risk assessments.

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Themes and Take Aways • Modeling helps identify key variables for which commercial scale

controls can be more easily established.

• Improve DOE design with model assisted validation; this reduces the number of experiments at scale, which saves time and money.

• Regulatory Perspective: common issues with regulatory submissions • inadequate data to support scaled-up parameters, differences in

equipment capacity utilization and potential raw material variability are not considered; scale-up risks and appropriate detection and mitigation techniques not described.

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Panel Discussion • Staff skilled in modeling and simulation is essential to effectively develop,

scale-up and transfer processes in the pharmaceutical industry

• Multi-disciplinary experts in process/product development and manufacturing; engage a diversity of backgrounds for success!

• • Development of standards for models and model sharing.

• How to determining the need for IPCs after a product problem develops;

quantitative risk assessments may help in decision making

• Model information in regulatory submissions: Guidance in FDA – EMA Points To Consider document regarding high impact and low impact models.

.

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Recommendations • Integration of multidisciplinary approaches, including modeling and

simulation, in R&D will greatly improve scientific product and process understanding & robustness and the likelihood of successful Tech Transfer and Scale-up in the commercial plant.

• Providing supporting scale up information in the submission facilitates

evaluation of the proposed manufacturing process and minimizes IR (Information Request) cycles; Explore process design space ( QbD) and support CMC section in NDA and MAA with a demonstrated mechanistic understanding of the process.

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