Proposal for a Manufacturing Classification System (MCS) Michael Leane1, Kendal Pitt2, Kiren Vyas2, Stuart Charlton1, Gavin Reynolds3, Richard Storey3, Conrad Davies4. 1 Bristol-Myers Squibb, Moreton, UK 2 ; GlaxoSmithKline, Ware, UK; 3 Astra Zeneca, Macclesfield, UK; 4 Pfizer, Sandwich, UK. With contributions from delegates to the Mat Sci / PEFDM seminar: “BCS to MCS: Predictions From Materials Science to Manufacturing” EMCC, Nottingham, May 2013.

What do we need to get out of this meeting? The story so far: Outcome from May seminar and subsequent discussions. Outline description of a MCS based on processing route. Discussion: How should we define the classes? How do we put materials into the different classes? Future steps towards a white paper.

Why do we need an MCS? Current costs of failure are high. Regulators see importance of material properties in QBD. Identify if API has desirable properties for drug product development. Could provide a common understanding of risk. Defines what are the “right particles” and best process. Aid development and subsequent transfer to manufacturing. Fits with QBD principles and potential of obtaining regulatory relief on the development of dosage forms by demonstrating that the properties of the ingoing API and excipients are within established ranges for the manufacturing process.

MCS Based on Processing Route

MCS Based on Processing Route Class I Direct compression. Class II: Dry Granulation, Class III: Wet Granulation, Class IV: Specialised Technologies Needed. Assumes there is a preference for simpler manufacturing routes. Builds on prior knowledge e.g. Hancock’s direct compression criteria could form the foundation of MCS Class I. Data needed (from literature / sharing of non-competitive data) to construct similar for the other classes. Ultimate aim of prediction from previous experience.

Class 1 Direct compression Assumes there is a preference for simpler manufacturing routes. Ultimate aim of prediction from previous experience. Prior knowledge available. Do we agree with Hancock’s criteria? Update needed?

Remaining Classes Class II: Roller compaction. Can we set similar boundaries? Class III: Wet granulation. Can we set similar boundaries? Class IV: Specialised manufacturing processes reserved for materials which cannot be processed using the first three conventional routes. What would be included in this class?

Example of a white paper It is proposed to draft a similar white paper by 1H 2014. Would you like to take part?

Risk Analysis Risk analysis score based on relevant API properties and drug product target attributes (link to TPP) . Overall score used to identify appropriate manufacturing methods. or Examples: Direct Compress Dry Granulate Wet Granulate Other Technology 1000 5000 15000 Flow¹ x Drug loading Bulk density Tensile strength² 41 x 10 = 410 1.0 DC 50 x 30 = 3333 0.3 1.5 DG 55 x 30 = 10313 0.2 0.8 WG ¹ Effective angle of internal friction; ² At ~0.85 solid fraction.

Risk Analysis Alternatively, the “risk factor” of various processes can be compared using analytical testing or experiences during particle production processes e.g. particle consistency, physical properties. Material X: low risk factor for all 3 selected manufacturing processes. Material Y: low risk factor for wet granulation, medium for dry granulation and high for direct compression. Material Z: medium risk factor for wet granulation and high for dry granulation and direct compression.

Failure Modes Another option is to consider physical properties and ‘intermediate’ material attributes as a way to map new APIs and products. These can be classified based on key failure modes (e.g. poor flow). Could use parallel coordinates or spider diagrams as a way of plotting multiple attributes. Using this approach to plot data may help to identify appropriate ranges/zones of L/M/H risk and therefore generate a common framework for understanding risk associated with running a particular set of material properties on a particular process.

Example: Process guidance for high drug loading using Rise time as API property. Material Rise time (s) Neat material Compression Avicel PH102 1.1 Excellent Hydrous Lactose 0.32 Poor Drug Y 0.21 Laminated :WG developed Drug Z 0.41 Good: DC developed Drug X 0.39 Poor : WG developed Drug X Hydrate 0.45 Good : DC developed

Manufacturing Robustness Robustness being in terms of not impacting the dosage form Quality Target Product Profile (QTPP) by secondary manufacturing processing conditions. If any conditions below are not met then particle is not robust Condition 1: Stability : API must be chemically (degradation) and physically (form) stable during secondary manufacturing transformations. Condition 2: Dissolution: API solubility, dose and permeability are such that it is a DCS 1, 2A or 3. (i.e. absorption is not solubility limited). Condition 3: Content uniformity & particle size: Dose and particle size distribution meet Rohr’s analysis. Condition 4: Content uniformity & segregation: Passes segregation test

Manufacturing Robustness Data collected on prototype formulations

Next Steps Gain agreement for preferred options. Gather and share data linking material attributes to process selection. Generate ‘maps’ based on key failure modes for different manufacturing routes across a diverse range of compounds Share and plot data (phys prop based, no compound info) on ‘maps’. Publish as a consortium paper to provide a frame of reference of level of risk vs process type. This will build ‘prior art’ and be a literature reference that can be used to articulate risk in a regulatory submission. Gain alignment with pharmaceutical scientists in other countries.