Miquel Galán - Bio and Pharmaceutical Technology: What can we learn from Chemical Engineering?


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Miquel Galán - Bio and Pharmaceutical Technology: What can we learn from Chemical Engineering?

  1. 1. 12th MEDITERRANEAN CONGRESS OF CHEMICAL ENGINEERING Bio and Pharma Technology: What Can We Learn From Chemical Engineering? Miquel Galan TELSTAR TECHNOLOGIES Innovation + R&D Dept. Terrassa, Terrassa SPAINBarcelona, November 2011
  2. 2. Chemical Engineering Chemical engineers apply the principles of chemistry, math, and physics to the design and operation of large-scale chemical manufacturing processes: • Translate processes developed in the lab into practical applications for the production of products (plastics, medicines, detergents, fuels, etc.). • Design plants to maximize productivity and minimize costs, and evaluate plant operations for performance and product quality quality. • Solve problems that occur during the daily plant operation, analyzing samples from the system and evaluating process parameters to determine the origin.M. Galan – Telstar Barcelona, November 2011 2
  3. 3. Pharma Industry / Chemical Industry • Very often, Pharmaceutical Industry is perceived as a particular case of the Chemical Industry: • Conventional pharmas rely on a chemical-based synthetic process p ocess to de elop small molec le drugs. develop small-molecule d gs • By contrast, biotechs use “biotechnology” to manufacture drugs, which involves the manipulation of microorganisms (such as bacteria) or biological substances (like enzymes) to perform a specific process. Biotech drug makers essentially use those microorganisms or highly complex proteins from genetically-modified living cells as components in medications to treat various diseases and t i di ti t t t i di d conditions, from cancer to rheumatoid arthritis to multiple sclerosis...M. Galan – Telstar Barcelona, November 2011 3
  4. 4. Pharma Industry Peculiarities • But Pharmaceutical and Biotech Industries have a very unique peculiarity: They are regulated i d t i Th l t d industriesM. Galan – Telstar Barcelona, November 2011 4
  5. 5. Regulation • The origins of regulation in the United States dates back to 1906 when President Roosevelt persuaded Congress to p p g pass the first Food and Drug Act and the FDA (Food and Drug Administration) was formed. First job: preventing the adulteration of food products and medicinal drug products. Thus the concept of having to prove product purity. • Response to a book, “The Jungle”, describing brutal sanitary conditions in stockyards and meat markets in Chicago. • Public outcry plus drop in meat sales prompted the FDA creation creation.M. Galan – Telstar Barcelona, November 2011 5
  6. 6. Regulation • By 1938, Congress passed the Food, Drug and Cosmetic Act (FD&C). The (FD&C) Th FDA now h d th mandate to ensure that had the d t t th t companies who supplied any food, drug or cosmetic products to the consumers also had to prove product safety. • Manufacturers had to submit an application and get approval from FDA prior marketing any new product. • I 1937 a T In Tennessee chemist d h i t developed an elixir f th t i f ti l d li i for throat infections. API (sulfamide) was poorly soluble in water. Best media for dissolution was diethyl glycol. Small dosages taken by the chemist to find sweet tasting mixture. mixture • 1 batch of 240 gallons fabricated. 107 people (many children) died. • The company refused to divulge any information: trade secret • After legislation, recall recovered 234 gallons legislationM. Galan – Telstar Barcelona, November 2011 6
  7. 7. Regulation • In 1962, the FDA issued its first set of GMPs (Good Manufacturing Practices) delineating g g ) g guidelines on how to produce, package, store, market and distribute Pharmaceuticals and Medical Devices. Manufacturers must prove product or device efficacy prior to market launch. • Clinical testing of products prior to commercialization was introduced and potential side effects had to be disclosed to physicians and general public • After-effect of the Thalidomide incident (thalidomide was a drug given to pregnant women to prevent morning sickness). The drug had horrific side effects on the embryo (many babies born with deformed or missing limbs)M. Galan – Telstar Barcelona, November 2011 7
  8. 8. Regulation • In 1976 the FDA issued new cGMPs. These proposals were declared substantive, which meant that non-compliance with the new regulations had now become directly a prosecutable criminal act. The ‘c’ indicates that the regulations are in constant evolution: what is perfectly acceptable today, may become passé tomorrow. The agency can decide to review each situation on a case-by-case basis within the context of the ‘current’ practices. Producers must prove current product purity, safety, efficacy, and consistency… • Before 1976, each time the FDA tried to prosecute, had to prove that , p , p each point it was trying to prosecute was what Congress had in mind when passed the 1938 FD&C Act. • FDA enforcement Agency. Representatives are called “investigators”. They have the authority to ask any record they feel may be pertinent to an audit: In 1992 in a letter to a large US Pharma manufacturer: “the FDA is entitled to any document it wants and we will bring in the marshals with guns and we can take what we want”M. Galan – Telstar Barcelona, November 2011 8
  9. 9. Validation How all of this is tied together? • Simply put, validation or proving that the system or equipment will do what it is designed to do, time after time, every ti time,… ensuring product purity, safety, efficacy, and i d t it f t ffi d consistency. • In 1987, the FDA issued its Guideline on General Principles of Process Validation. Validation was defined as: “establishing documented evidence which provides a high degree of assurance that a specific process will consistently produce a product meeting its pre-determined specifications and quality attributes”M. Galan – Telstar Barcelona, November 2011 9
  10. 10. Validation • (FDA) Establishing documented evidence which provides a high degree of assurance that a specific process will consistently produce a product meeting its predetermined l d d d d specifications and quality attributes. • (EC GMP Guide) The action of proving, in accordance with the principles of Good Manufacturing Practice, that any p p g , y procedure, process, equipment, material, activity or system actually leads to the expected results. Prove that a specific process does what is intended to do!M. Galan – Telstar Barcelona, November 2011 10
  11. 11. Compliance Aspects of Validation • Well-Defined, Planned and Documented Studies • Adherence to Protocols • Performance of all Tests and Procedures. • Proper Reporting of Failures If it isn’t documented - it is not done! i ’t d t d i td ! A risk analysis, a decision, training for employees, an inspection, an operation (i.e. cleaning step) is considered “done” only if documents prove it.M. Galan – Telstar Barcelona, November 2011 11
  12. 12. Validation vs. Qualification Process Validation Equipment QualificationM. Galan – Telstar Barcelona, November 2011 12
  13. 13. GEP (Good Engineering Practices) vs. Validation GEP VALIDATION Time From Planning (order) to From Planning to Scratch Commissioning Scope Everything All critical systems Objective Fulfill specifications Documented evidence that process is under control Approach Should be planned Written detailed plan with all (heuristic ?) testing criteria Changes Record, add notes Systematic recording Documented impact analysis Structure Can be very Particular DQ IQ No media involved OQ Water substitutes media PQ Process media involved Responsibility Vendor BuyerM. Galan – Telstar Barcelona, November 2011 13
  14. 14. Validation: Life Cycle Specification S ifi ti Qualification (DQ IQ OQ PQ) (DQ,IQ,OQ,PQ) Process Validation Change Control Periodic RevalidationM. Galan – Telstar Barcelona, November 2011 14
  15. 15. Key Regulatory Bodies • US • FDA (Food and Drug Administration) • EUROPE • EMA (European Medicines Agency) • I di id l C Individual Countries t i • UK – MHRA (Medicines and Healthcare products Regulatory Agency) • S i – AEMPS (A Spain (Agencia E i Española d l M di ñ l del Medicamento y Productos Sanitarios) • … • JAPAN • PMDA (Pharmaceuticals and Medical Devices Agency)M. Galan – Telstar Barcelona, November 2011 15
  16. 16. Regulatory? Advisors? • Regulators (=regulations) • US, European, Japanese, others • Influential Bodies (=advice, opinion, guidance) • PIC/S ISPE PDA, PHSS (formerly PS), ... PIC/S, ISPE, PDA PS) • Industry associations (=information) y ( ) • EFPIA (European Federation of Pharmaceutical Industries Associations) • A Associations from individual countries: (AEFI etc) i ti f i di id l t i (AEFI, t ) PIC/S: Pharmaceutical Inspection Convention and Pharmaceutical Inspection Co-operation Scheme ISPE: International Society of Pharmaceutical Engineers PDA: Parenteral Drug Association PHSS: Pharmaceutical and Healthcare Sciences SocietyM. Galan – Telstar Barcelona, November 2011 16
  17. 17. FDA • 21CFR 210 & 211 (cGMPs) • 21CFR 11 • FDA Guidelines (Guidance for Industry) G id li (G id f I d t ) http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm • Process Validation: General Principles and Practices (2011) • Sterile Drug Products Produced by Aseptic Processing — Current Good Manufacturing Practice (2004) • Q7A Good Manufacturing Practice Guidance for Active Q g Pharmaceutical Ingredients (2001) • Guide To Inspections of Lyophilization of Parenterals (1993) • FDA Powers (21CFR210 1(b)) (21CFR210.1(b)) “The failure to comply with any regulation...in the manufacture, processing, packaging or holding of a drug shall render such drug to be adulterated...and such drug as well as the person who is adulterated and responsible for the failure to comply shall be subject to regulatory action.” Role extends to drugs that are to be used in USA, wherever they USA are manufacturedM. Galan – Telstar Barcelona, November 2011 17
  18. 18. EMA • European Medicines Agency (formerly EMEA) • Established 22 J l 1993 Located in London July 1993. • In charge of coordinating scientific resources in Member States, to evaluate and supervise medicinal products for human & veterinary use • According to EMA opinions, EC authorizes products and arbitrates between member states • Each EU member has it’s own Authority • E hM Each Member E b Estate i implements the directive as national l h di i i l lawM. Galan – Telstar Barcelona, November 2011 18
  19. 19. Validation without understanding A whole industry has grown up around process validation: with a proliferation of validation p p protocols, , validation reports, and validation documentation; but there are still processes that work poorly. We have lost the goal, which is that before trying to demonstrate the process reliably does what its y g p y supposed to do, we must “know” the process in depth.M. Galan – Telstar Barcelona, November 2011 19
  20. 20. Validation without understanding • The traditional approach presupposes that if nothing is changed from the validation batches, everything will remain the same. h • But this assumption is false, because neither ingredients nor processing conditions can remain fixed fixed… There will be small changes from batch-to-batch, there may be further changes over time, that operators can introduce, or the equipment will be moved from one site to another. There will be a new supplier for a certain material, and this new material may be within specifications… y p It was never real that everything could be kept the same !M. Galan – Telstar Barcelona, November 2011 20
  21. 21. Managing variability Inputs + Process Outputs Variable + Inflexible (fixed) Variable Inputs I Process P Outputs O Variable + Adjustable Constant Inputs Process Outputs Does it look new to Chemical Engineers?M. Galan – Telstar Barcelona, November 2011 21
  22. 22. Quality in pharmaceutical products • Automated manufacturing facilities dominate the biomedical industries. industries Inert and active ingredients are mixed… They are mixed compressed into tablets, filled into capsules or dissolved in liquids that may be subsequently lyophilized… And they are tracked throughout the packaging, and delivery processes. • Problems in these automated steps can result in large quantities of pills, capsules, vials, bottles bags … that must be pills capsules vials bottles, bags, quarantined, retested, rejected, reprocessed, or destroyed, all at significant expense. • Of course, the worst case scenario would be that defective manufactured products were not detected, but were inappropriately shipped f use b patients who, at b t would i i t l hi d for by ti t h t best, ld receive ineffective medications, but potentially might receive toxic or harmful products.M. Galan – Telstar Barcelona, November 2011 22
  23. 23. Quality in pharmaceutical products • Conventional pharmaceutical manufacturing is generally accomplished using batch processing with laboratory testing conducted on collected samples to evaluate quality This quality. conventional approach has been successful in providing quality pharmaceuticals to the public. • However, significant opportunities exist for improving pharmaceutical development, manufacturing, and quality assurance through innovation in product and p g p process development, process analysis, and process control. Source: Gold Sheet 2009M. Galan – Telstar Barcelona, November 2011 23
  24. 24. Quality in pharmaceutical products • Pharmaceutical industry has been hesitant to introduce innovations i t th manufacturing sector: i ti into the f t i t • Regulatory uncertainty, resulting from the perception that existing regulatory system is rigid and unfavorable to the introduction of innovative systems For example, many systems. example manufacturing procedures are treated as being frozen and many process changes are managed through regulatory submissions. • Efficient pharmaceutical manufacturing is a critical part of an effective health care system. The health of persons and animals system depends on the availability of safe, effective, and affordable medicines.M. Galan – Telstar Barcelona, November 2011 24
  25. 25. PAT (Process Analytical Technology) Guidance for Industry PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance (FDA, September 2004) “The Agency considers PAT to be a system for designing The designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and in process processes, with the goal of ensuring final product quality. …….. The goal of PAT is to enhance understanding and control the manufacturing process which is consistent with process, our current drug quality system: quality cannot be tested into products; it should be built-in or should be by design.”M. Galan – Telstar Barcelona, November 2011 25
  26. 26. What is PAT? A system for: • designing, analyzing, and controlling manufacturing • timely measurements (i.e., during processing) • critical quality and performance attributes • raw and in-process materials • processes “Analytical” includes: • integrated chemical, physical, microbiological, g ,p y , g , mathematical, and risk analysis Focus of PAT is Understanding and Controlling the manufacturing ProcessM. Galan – Telstar Barcelona, November 2011 26
  27. 27. PAT = Process understanding A process is well understood when: • all critical sources of variability are identified and explained • variability is managed by the process • product quality attributes can be accurately and reliably predicted Accurate and Reliable predictions reflect process A t d R li bl di ti fl t understanding Process Understanding inversely proportional to riskM. Galan – Telstar Barcelona, November 2011 27
  28. 28. PAT Tools: Process Control Tools • Monitor the state of a process and actively manipulate it to maintain a desired state state. • Strategies should accommodate: • attributes of input materials • the ability and reliability of process analyzers to measure critical attributes • achievement of process end points to ensure consistent quality • End points = achievement of the desired material attribute (not process “time”)M. Galan – Telstar Barcelona, November 2011 28
  29. 29. Terminology There are 4 categories of sampling and analyzing: • “off – line”: Sample is extracted, tagged and sent to the laboratory to analyze. y y • “at – line”: Sample is extracted from the process and analyzed close to th fabrication flow. l t the f b i ti fl • “on – line: Sample is extracted from the process, but it can be on reinserted without affecting quality. • “in – line”: Sample is not extracted from the process.M. Galan – Telstar Barcelona, November 2011 29
  30. 30. PAT: “Right First Time” • Historically, the emphasis of the PAT applications have been on the following: a e bee o e o o g • Enable process understanding • Identify and remove the sources of variability • Monitor processes on-line to provide real time data for information p p purposes • Determine process endpoints in chemical reactions, drying, etc. to allow better timing of the off-line release samples • As the reliability and performance of the Process Analytical systems improve, the potential for use of PAT as an Integral part of pharmaceutical processes increases. Within this context, PAT is increasingly used to: • R l Replace off-line fi l product tests with at-line or online PAT b ff li final d tt t ith t li li based d release tests • Provide the basis for Process Control Strategy • Enable Continuous Quality Verification and Real Time ReleaseM. Galan – Telstar Barcelona, November 2011 30
  31. 31. Process Control Strategy: The Current State • Traditionally, Process Control achieved through tight control of Critical and Key Process Parameters at pre-determined setpoints or ranges t i t • The premise for this approach is the assumed or established relationship between the Process Parameters (Process Inputs) and Critical and Key Product Attributes ( Process Outputs) • This control strategy doesn’t allow startup or mid-course doesn t correction to account for variation in starting materials or process upsets • No flexibility within or between production runs to utilize the concept of the “Design Space” • Process Output specifications are most often met, but can be subject to considerable variationsM. Galan – Telstar Barcelona, November 2011 31
  32. 32. Indirect Control Limited Control Variable Y = f(X) Input 1 Raw Materials Input 2 Outputt 1 Process Parameters: Product Attributes: Temperature, pH, Input 3 Process Outputt 2 Potency, Particle size, etc. , Reaction Time, etc Time Input 4 Typically, Typically no direct Measured & tightly controlled at measurement or control predetermined setpoints or ranges during the process. Usually variableM. Galan – Telstar Barcelona, November 2011 32
  33. 33. “Advanced” Process Control • Mathematically advanced control algorithms that use predictive, adaptive, and optimization techniques to control multi-inputs, multi-output processes. • Control strategies that utilize PAT, process models or other techniques, to manipulate Process Parameters (Xs) within any required constraints, in order to actively control one or more q , y Drug Product Attributes (Ys) at a especified setpoint or within a tight range. • Although a new concept in the Pharma Industry, this is a “mature technology” commonly used in all other industrial sectors (chemical, petrochemical, etc.) to improve quality, consistency, and process efficiency.M. Galan – Telstar Barcelona, November 2011 33
  34. 34. “Advanced” Process Control • Advanced Process Control provides a new and promising paradigm for controlling Pharmaceutical processes processes. • These applications aim to address some of the most technically pp y challenging control problems in our industry that can provide tangible quality and business benefits. • Important technical challenges to implement this methodology in the classical environment of “Validated Process”.M. Galan – Telstar Barcelona, November 2011 34
  35. 35. This is not PAT ! • 2005 – 2006: PAT was the hot issue, but the message was not “well sold”. • Th concept was being presented with too much focus on The b i d ih hf technological advances: • Management perception was, mainly, costs for expensive a ag p p o a , a y, o o p analytical instruments (at-line): NIR Chemometrics Ch t i Multivariate analysis …M. Galan – Telstar Barcelona, November 2011 35
  36. 36. QbD: Quality-by-Design • Quality by Design (QbD) is an initiative of the United States Food and Drug Administration, and the biomedical industries it g , regulates, intended to integrate the quality process through research, development, manufacturing and distribution. • When properly implemented, Quality by Design should improve speed to market; reduce product variation; improve operating efficiency and reduce costs at all stages of the process. QbD ( b (Quality-by-Design) l b ) ⇔ QbT ( l b b (Quality-by-Testing) )M. Galan – Telstar Barcelona, November 2011 36
  37. 37. QbD: Quality-by-Design • QbD consists of three key elements: • the use of Design Space to establish elastic quality standards; • the use of Risk Assessment to define the boundaries of those standards; • and the implementation of Process Analytical Technology (PAT) to monitor and adjust to those standards. • The resulting cost controls and regulatory streamlining should significantly increase the efficiency of the industry.M. Galan – Telstar Barcelona, November 2011 37
  38. 38. How a process can be measured? • By using sensors able to measure the desired property. • With models ih d lM. Galan – Telstar Barcelona, November 2011 38
  39. 39. Sensors • PAT “stamped” analyzers have proliferated (NIR, Raman, etc) • First presented “success case studies” (2005) were based in processes where the regulatory aperture (“not ask if analyzers to get knowledge were added into the process”) allowed direct process ) applications: blending, coating, etc • Typically all them were stirred processes. A single sensor could acquire batch representative data • Unfortunately, more complex processes (lyophilization, biological processes, etc) continued stuck to the traditional way due to a lack of available sensorsM. Galan – Telstar Barcelona, November 2011 39
  40. 40. Why using a model? • The engineers that built this bridge did not use trial and error. • The models told them how to do it right the first time. • The Treasury (taxpayers) cannot afford “too expensive” bridges. • Politicians cannot accept collapses.M. Galan – Telstar Barcelona, November 2011 40
  41. 41. Which type of model? Mechanistic models vs. Empirical /Statistical • A mechanistic model is derived from the knowledge about the underlying science (physics, etc) of the unit operation. (physics operation • If there is no knowledge about the mechanism, there is only the option of traditional statistical DoE • Statistical models: maximize knowledge getting a robust design space • Mechanistic models: “in-line” monitoring of the required variables controlling the process even in the case of variabilitiesM. Galan – Telstar Barcelona, November 2011 41
  42. 42. Freeze Drying Case • Freeze drying, also called lyophilization, is a drying process where the wet product is first frozen to a solid phase and subsequently dried to vapour phase through sublimation, that sublimation is, without passing through the liquid phase, by exposing it to a low partial pressure (vacuum) of water vapor.M. Galan – Telstar Barcelona, November 2011 42
  43. 43. Lyophilization Challenges • Collapse: • Speed of the process: 5ºC Speed x 2M. Galan – Telstar Barcelona, November 2011 43
  44. 44. Lyophilization Process Definition Parameters • It is usually specified the recipe (Shelf temperatures and chamber pressures vs. time) but this doesn t guarantee vs doesn’t that the sublimation parameters are constant Temperature, pressure and time are intensive variables (not scalable) • For Primary Drying, what it would be desirable is knowing (and controlling !!) • The sublimation front temperature (to avoid collapse) • The sublimation speed (to optimize productivity).M. Galan – Telstar Barcelona, November 2011 44
  45. 45. Classical Temperature Monitoring • Insertion of a thin thermocouple (or a more bulky Pt-100) in few vials is a widely used method to “measure” the product temperature during the process process. Disadvantages: • Intrusive for the product • Influence ice nucleation => morphology => sublimation > • Problems concerning the sterility of the product • Impossible with automatic loading • Using a thermocouple we can measure the temperature only in one point.M. Galan – Telstar Barcelona, November 2011 45
  46. 46. Primary Drying: temperature measurement • What is product temperature? Discrete temperature probes don’t measure real temperature: sublimation front moves during primary drying. • The most critical parameter is ice temperature at sublimation front (Tice). Collapse and/or melting, and sublimation speed depend di d d directly on Tice. tl Primary Drying Heated shelf at -10ºC -25 -24 -20 -15 -10 Dry product Frozen interface moving Frozen product downwards -25 -24 -20 -15 -10 Heated shelf at -10ºC Temperature ºCM. Galan – Telstar Barcelona, November 2011 46
  47. 47. Soft-sensors In many engineering applications it is desirable to have estimates of hard-to- measure or non-measurable quantities. A soft sensor combines a priori knowledge about the physical system (mathematical model) with experimental data (in-line measurements) to provide an in-line estimation of the sought quantities in line quantities. input output Process Soft Sensor State estimate Patent pendingM. Galan – Telstar Barcelona, November 2011 47
  48. 48. Soft-sensors input output Process Soft Sensor State estimate 1) Introducing a small perturbation Specific parameters of the model equations not known 2) Acquiring system response 3) Solving the equations to “reproduce” this response 4) Variables of interest can be calculatedM. Galan – Telstar Barcelona, November 2011 48
  49. 49. Regression Analysis ResultsM. Galan – Telstar Barcelona, November 2011 49
  50. 50. Advantages & Limitations Advantages: • Consistent results up to the end of primary drying • Both for R&D and production • Robust monitoring tool. Capable to help in assessing production process variations Limitations: • Indirect (?) measuring method • Inaccuracy slightly increases at the end of primary drying (if there are large heterogeneities between vials) • Model (as it is) only valid for vials and bulk, but not applicable for lyophilization of granulesM. Galan – Telstar Barcelona, November 2011 50
  51. 51. Closing the loop: From Monitoring to Control DPE output • Front temperature (and T profile vs. time) Lyo-Driver • Mass Flux of water vapor (control system) • Effective diffusivity • Heat transfer coefficientM. Galan – Telstar Barcelona, November 2011 51
  52. 52. Closed Loop Control: the innovation Goal: Goal determination of an optimal heating shelf control strategy for primary drying in order to minimize the drying time avoiding to jeopardize the integrity of the material. f PROCESS PRESSURE RISE Tfluid, Batch Parameters, etc. DPE Tproduct, Thickness, ? Tmax,etc. CONTROLLER CONTROLLED Tfluid PROCESS PROCES PROCESccS MODEL Tproduct Gain ISE Patent pending LyoDriverM. Galan – Telstar Barcelona, November 2011 52
  53. 53. Some experimental results 50 40 30 Tset_point 20 Tfliud 10 T,°C 0 Tthermocouple -10 10 -20 -30 Tmax -40 TDPE -50 0 5 10 15 20 25 30 35 40 time,h Tfluid,sp Tprod,max T_fluid TB, °C T_thermocoupleM. Galan – Telstar Barcelona, November 2011 53
  54. 54. Case Study (1/5) The recipe development and transfer of a formulation proposed to lyophilize a protein has been studied. Its main excipients being y p p p g mannitol, sucrose and a buffer. By means of DSC and Freeze Drying Microscope collapse temperature was determined: -26ºCM. Galan – Telstar Barcelona, November 2011 54
  55. 55. Case Study (2/5) A cycle driven by LyoDriver was launched in an industrial lyophilizer, establishing the maximum product temperature at -32ºC (safety reasons). Primary d y g t e was de ed longer o purpose. Opt u primary a y drying time as defined o ge on pu pose Optimum p ay drying temperature profile can be observed in the figure 2 40 20 1.5 PP/PB T,°C 0 1 -20 0.5 05 -40 -60 0 0 10 20 30 40 Time,h Tfluid,sp Tprod,max T_fluid TB, °C TC1, °C End time PP/PBM. Galan – Telstar Barcelona, November 2011 55
  56. 56. Case Study (3/5) Delivered cycle by LyoDriver: Sublimation flowM. Galan – Telstar Barcelona, November 2011 56
  57. 57. Case Study (4/5) With the obtained results a second cycle (NO CONTROL, JUST MONITORING!) with the shown recipe was launched, with a more “conservative” approach j just at the beginning of the primary drying (as it would be done in the g g p y y g( production units), but with the optimum recipe parameters found by LyoDriver in the rest of the primary drying. Sublimation flowM. Galan – Telstar Barcelona, November 2011 57
  58. 58. Case Study (5/5) The maximum shelf temperature at the end of primary drying was deliberately not respected in this recipe (-25ºC instead of -30ºC delivered by L D i b LyoDriver).) 2 40 rature,°C C 20 1.5 15 PP/PB 0 1 Temper -20 0.5 -40 40 -60 0 0 10 20 30 40 Product overheat Time,h Tfluid,sp Tfluid sp Tprod,max Tprod max T_fluid T fluid TB, C TB °C TC1, C TC1 °C End time PP/PBM. Galan – Telstar Barcelona, November 2011 58
  59. 59. Advantages & Limitations Advantages: • Physically based predictive control algorithm. • Control action is determined taking into account the real dynamic response of the heating/cooling system • Predicts potentially damaging temperature overshoots anticipating the control. Fastest possible response Limitations: • Indirect (?) method • N d some parameters from the plant (cooling&heating Needs t f th l t( li &h ti speed) • Only valid for primary drying y p y y gM. Galan – Telstar Barcelona, November 2011 59
  60. 60. Advantages Production monitoring • Detailed tracking of primary drying kinetics allow process improvement maximizing productivity without impairing product quality. • Additional information on primary drying ending • C l d i Cycle design space definition (t ki d fi iti (taking i t account product, container into t d t t i and lyo capabilities) extremely simplified • Monitoring gives extra information on machine characterization, so scale up or just process transfer simplified, helping to generate robust support documentation Closed loop control • Optimum cycle determined in a single run (development tool) • Constant quality no matter of intrinsic “process input” variations • Much more robust process understanding has an inverse relationship with the risk of producing a poor quality product. Significantly less restrictive regulatory approaches and scrutiny should be expectedM. Galan – Telstar Barcelona, November 2011 60
  61. 61. Models as control tools • It is possible to design a process with a consistent output, despite a very variable input p y p • With a mechanistic model, a powerful analysis of the correlation bet een p ocess pa amete s co elation between process parameters and process output p ocess o tp t can be done • The simulations allow identifying key parameters and spend the limited resources where most gain is expectedM. Galan – Telstar Barcelona, November 2011 61
  62. 62. QbD Advantages The pharmaceutical industry will benefit: • Q alit by Design ens es better design of products with an Quality b ensures bette p od cts ith expectation of fewer problems in manufacturing. • It reduces the number of manufacturing supplements for post- market changes – relying on process and risk understanding with k t h l i d i k d t di ith commensurate risk mitigation. • It allows implementation of new technology to improve manufacturing without extraordinary regulatory scrutiny. f h d l • A possible reduction in overall costs of manufacturing – and less waste – is probable. • QbD promises less hassle during review –translated as reduced deficiencies and quicker approvals. • It may improve interaction with Regulatory Authorities allowing y p g y g industry to deal with them on a science level instead of on a process level. • Continuous improvements in products and manufacturing processes are viable and significant outcomes of QbD.M. Galan – Telstar Barcelona, November 2011 62
  63. 63. QbD Advantages • The FDA reported the benefits of implementing Quality by Design for the Food and Drug Administration as consisting of these enhancements to pharmaceutical manufacturing: th h t t h ti l f t i • It enhances scientific foundation for review. • QbD will provide for better coordination across review, compliance and inspection. • It will also improve information in regulatory submissions. • Better consistency will result along with improvements in quality of review. • More flexibility in decision making will be a result that is beneficial to the industry and FDA. • QbD ensures decisions will be made on science and not on merely empirical information. empi ical info mation • It involves various disciplines in decision making. • Resources will be used to address higher risks.M. Galan – Telstar Barcelona, November 2011 63
  64. 64. Traditional vs. QbD (FDA’s View)M. Galan – Telstar Barcelona, November 2011 64
  65. 65. Regulatory Expectations for Production • In January 2011 FDA published: FDA Guidance for Industry - Process Validation: y General Principles and Practices: “More advanced strategies, which may involve the use of process analytical technology (PAT), can include timely analysis and control loops t adjust th processing l to dj t the i conditions so that the output remains constant. Manufacturing systems of this type can provide a higher degree of process control than non-PAT systems. In the case of a strategy using PAT the PAT, approach to process qualification will differ from that used in other process designs.” designsM. Galan – Telstar Barcelona, November 2011 65
  66. 66. Same focus? • FDA: Process knowledge “Go home and do the homework” • EMA BfArM: PAT submission – yes! EMA, “…but we continue as we are used to” • Industry: Design Space - QbD “Less controls – more flexibility” • Patient: Quality “I rely on safe drugs” l f d ” (BfArM: Bundesinstitut für Arzneimittel und Medizinprodukte)M. Galan – Telstar Barcelona, November 2011 66
  67. 67. EQUIPMENT AND PROCESS MODELLING • The engineers that built this bridge did not use trial and error. • The models told them how to do it right the first time. • The Treasury (taxpayers) cannot afford “too e pe s e b dges expensive” bridges. • Politicians cannot accept collapses. llM. Galan – Telstar Barcelona, November 2011 67
  68. 68. EQUIPMENT AND PROCESS MODELLING • The engineers that developed this process did not use trial and error. • The models told them how to do it right the first time. • The Patients cannot afford “too expensive” medicines. • Reg. Authorities cannot accept collapses. llM. Galan – Telstar Barcelona, November 2011 68
  69. 69. Thank you for your attention Any question? Miquel Galan mgalan@telstar.euM. Galan – Telstar Barcelona, November 2011 69