Green Equipment and Technology Industry Challenges



Green Equipment and Technology Industry Challenges Problem 1: The Use of PAT to Improve Product Sustainability Process analytical technologies have received much attention in pharmaceutical manufacturing to achieve both quality and manufacturing improvements. However, their full potential in process control applications has yet to be realized. Due to improved throughput and concepts such as Real Time Release Testing, it is believed that a sustainability and quality benefit can be obtained by reducing the processing energy and time to produce a product by leveraging advanced control techniques. Exemplification of ProblemNearly all controls in pharmaceutical plants are ‘recipe-based’ controls requiring manual sampling and manual interventions during processing. An example might be the end of a reaction, where a sample is taken and measured off-line by HPLC. The time taken to sample, analyze the sample, and report the result is often several hours. In addition, continuous secondary processing often uses outdated control algorithms, particularly for granulation where time-consuming off-line density or sieving tests on samples may be required to progress to the next step. The problem is to develop new analytical technologies that can be interfaced with a continuous process to allow increased knowledge of the process in real time. New sensors and interfaces are needed. PAT can be applied to liquid streams for continuous flow chemistry or powder/granules for continuous oral solid dose processing. Expected Output of ResearchThe output would demonstrate the use of PAT instruments and techniques (including MSPC) for on-line optimization and control of pharmaceutical and fine-chemical unit operations. The work should show how the use of the PAT instruments and techniques reduce the carbon footprint of the end product through a substantial reduction of energy use or a substantial reduction in cycle time. Ideally, one particular unit operation should be selected and the appropriate PAT tools outlined. Problem 2: Process IntegrationHistorically, pharmaceutical products have been developed looking at each stage mostly independently, based heavily on emulating laboratory conditions. However, it is believed that benefits may come from the better integration of unit operations and using systems approaches to improve manufacturing efficiency and thus sustainability. Exemplification of ProblemExamples of areas to explore integrated processing using systems approaches may include:Active Pharmaceutical Ingredient and Formulated Product Design Integrated batch synthesis development and optimization (e.g., using recycle loops as opposed to conventional batch processing to improve yield) to improve efficiency. Integrating solvent and materials recovery within a process rather than as a post-process options. Integrating batch with continuous unit operations to obtain the sustainability benefits of both processing techniques (for instance, using continuous reactors for short residence times while leveraging batch vessels for subsequent crystallizations which can have longer cycle times)Integrative heat and mass exchange networksPredictive modeling/optimization of pharmaceutical processes to investigate the sensitivity of perturbations to sustainability outcomes.Processes which effectively link API and Drug product production in an integrated manner. Real time process managementNovel Process sensing and Analytical Technologies (PAT) across several unit operations to improve throughput, react to perturbations, and minimize manual interventions for integrated processes. Expected Output of ResearchMethodologies to embed process systems engineering into pharmaceuticals in any of the topics described above, and related areas. Case studies demonstrating the applicability of the proposed methodologies.Problem 3: On-Line Mathematical Modeling of PAT dataProcess analytical technologies have received much attention in pharmaceutical manufacturing to achieve both quality and manufacturing improvements. However, their full potential in process control applications has yet to be realized. Reasons for this include the following: 1) instruments can vary in their sensitivity across batches resulting in changes in absolute measurements across multiple batches; 2) data is often noisy with multiple operations occurring in batch vessels; and 3) pharmaceuticals are manufactured in a highly regulated environment, requiring significant validation of proposed methodologies. Dryer monitoring provides an exemplification of these problems. Because drying is energy intensive and often a processing bottleneck, an inefficient drying process leads to a significant increase in cycle time and energy usage along with the added potential to impact the physical properties of the materials being dried.Exemplification of ProblemDuring the isolation of an API, N2 is typically blown over / through (blowdown) the material to remove solvent. Samples are taken and analyzed until a specified Loss on Drying (LOD) is achieved before vacuum is applied. These samples are not always representative of the material that is in the dryer (typically 25 g samples for a several hundred kg cake), which can lead to variability in the starting point for vacuum drying and variation in drying times while under vacuum. Agglomeration of material can also occur due to high solvent content remaining in the material prior to the start of vacuum drying.After partial drying with nitrogen, the dryer is placed under vacuum and heated to dry the material to acceptable residual solvent levels. Drying times are often indicated by indirect methods (such as cake temperatures, which can be imprecise across equipment) and the final discharge of a material from a dryer usually involves another small sample with analysis for either residual solvent by gas chromatography or loss on drying. Along with the lack of process information to determine the appropriate sampling interval, the time associated with getting a sample and analyzing the solvent content during both the blow down and vacuum drying has a negative impact on cycle time and energy usage. Data collected during a campaign at a GSK facility showed an increase in the average drying time of 10.3 hours due to these inefficiencies.Expected Output of ResearchAn opportunity to assist in using PAT instruments for on-line control would focus on using online sensors and mathematical models to optimize and control the blowdown and vacuum drying process. The focus would be to minimize drying times and remove the need for sampling. The expected output would include:Development of models to better understand and predict VLE during blow down and vacuum drying to identify improvements and speed implementation.Advanced signal processing to detect endpoint of blowdown and drying stages. (Figure 1)Feedback control of this signal to a distributed control system to make an active control loop and minimize cycle time across dryingAn analysis of the use of ‘relative’ signals versus absolute signalsThe amount of validation required to implement in a GMP manufacturing facilityThe methodology should also be applicable to signals for other unit operations. GSK can provide additional data. Other unit operations (such as distillation and crystallization) would also provide useful case studies. The output of this research would involve an industrializable solution for implementation in a pharmaceutical plant. Figure 1Problem 4: Evaluation of benefits for drug substance continuous manufacturingPharmaceutical and fine chemical scale-up chemical synthesis facilities are dominated by batch processing. Due to this constraint, processes are often designed to accommodate a batch scale-up methodology rather than optimizing environmental performance. While this is done for all unit operations, of particular interest is around chemical transformations and crystallizations, which often dictate the complexity of subsequent purification operations and have the greatest impact on mass and energy usage. It is desired to evaluate the mass efficiency and energy savings possible when flow chemistry is considered by first intent to be the selected processing method used. Exemplification of ProblemMass and energy inefficiencies due to batch scale-up paradigms result from primarily 2 areasExtra solvent is added to systems. This can occur both because large batch vessels require a certain volume to achieve good agitation, or in order to serve as thermal ballast for highly exothermic reactions in large batch vessels to prevent temperature excursionsChemical transformation ‘workarounds’ are developed to enable batch scale-up, but at the expense of atom economy and therefore process efficiency. There have been several publications which have examined the use of flow chemistry to solve challenging problems, some of which are attached here:The papers above all address instances where a transformation has been used to develop synthetic reactions, but little focus has been dedicated to the exact benefit gain or as to how the use of an individual reaction facilitated an entire synthetic scheme to deliver a route benefit. A common theme is a workaround for safety purposes, but it is unclear what total benefit this workaround delivers. Expected outcome of researchWe would like to understand the specific mass and energy benefits for continuous reactions and crystallizations through:The integration of flow chemistry into an entire synthetic design with a clear comparison of benefits relative to a baseline case. The PIs are encouraged to select a synthesis or GSK could provide some API structures which could be used as targets for a synthetic scheme. A base case would be a comparable batch process which would be otherwise designed. Mass intensity and energy considerations are outputs of this comparative work, along with details of the flow chemistry synthetic design. The evaluation of certain reaction classes, such as those indicated in the aforementioned papers (e.g., nitration, diazotizations, microwave, etc), and direct mass and energy comparisons of these processes versus those run in batch mode. The description of how a continuous crystallizer would look like to be applicable to different crystallization methods (e.g. cooling, antisolvent, reactive). Key criteria for scaling up. Considerations of how to minimise API loss in start-up, maximise throughput (by shortening residence time), PAT techniques and strategies to deal with deviations from steady state.While this is one industry challenge, it likely requires shared chemistry and chemical engineering skill sets to complete. Problem 5: Develop a more detailed and real-world representative ROI analysis for e2e manufacturingExemplification of ProblemThe traditional approach to the manufacturing of APIs has been largely based on batch processes. One of the challenges faced when considering whether continuous processes are a better option is the inability to compare like for like processes between a batch and a continuous process. ?Most current models utilize synthetic routes that were developed in batch mode and therefore not optimal flow processes.? As a result the true benefits of a continuous process are often not realized as fudge factors and suboptimal assumptions are forced to be used in order to complete the model. The development of a solution that will allow for us to compare the benefits offered by one technology option over the other on a like for like basis will allow us to make more informed decisions on the application of continuous processing for future products. Expected outcome of researchA mathematical model that can be used to assess the relative benefits of e2e continuous processing compared to traditional batch processing utilizing processes amenable for either. The model will also allow us to determine the optimal combination of continuous process steps with traditional batch process steps. This model should allow for the input of a number of different variables such that many different scenarios can be explored. Problem 6: Assess the feasibility of laser induced crystallization in microspheresExemplification of ProblemWithin the pharmaceutical sector probably the most important step in the manufacturing process is the particle forming step which controls not only formulation properties (flow-ability, compressibility, particle size as examples) but also bioavailability and ultimately efficacy (through dissolution rates).? Historically this process has been performed as a batch operation, most times with exquisite control of particle form and size.? However with the strong desire to move to continuous processing of API’s the need for crystallization methods amenable for continuous operation while still providing exquisite control will be essential.? The current paradigm of continuous crystallization methods are plagued by fouling, clogging and most devastatingly form changes of the saturated API slurry solution over extended run times.? A new mode for controlling crystallization in? a continuous manner and obviating the current methods’ drawbacks will be a tremendous advance and enabling technology in the continuous processing arena.Expected outcome of researchA working laboratory scale prototype demonstrating the ability to bring about controlled crystallization and particle formation through laser induction of supersaturated microspheres in a continuous manner.? Ideally the prototype would be developed and designed in such a manner as to be readily scalable to manufacturing scales as well. ?Problem 7: Assessing the impact of biopharmaceutical processing on the carbon footprint and how different technologies present different outcomes (Aligned to the existing work on the bio harm value chain sustainability roadmap)Exemplification of ProblemThe current biopharmaceutical process has a significant carbon footprint due to both the direct process operations as well as the indirect process operations. In general, the indirect process impact is >70% of the overall contribution. As we are developing new technologies we want to understand the potential impact of this technology on the overall footprint rather; it considers the combinatorial impact. Expected outcome of researchA model that allows us to understand the implications of technology choices to the overall carbon footprint of the process. (Collective sustainability measure)Report capturing the sustainability measure for each individual technology choiceSuggested approaches in order to maximize the sustainability of disposable technologies – technologies or approachesA simulation model that allows for us to calculate the overall sustainability of each process design. Problem 8: Identifying methods of reducing the impact of disposable biopharmaceutical processing technology on the environment (e.g. disposable bio-reactors)Exemplification of ProblemWith the increase usage of disposable based technologies for biopharmaceutical processes, a new challenge will arise impacting on our ability to create sustainable manufacturing processes. It needs focus to minimise the impact of this new tech.Expected outcome of researchA report and a model that allows us to make informed decisions on the design of our processes such that we can consider sustainable manufacturing over the entire product/plant lifecycle.Problem 9: Addressing the challenge of the cold supply chain required to support biopharmaceutical productsExemplification of ProblemLooking to alternative methods either through new formulations or new technologies that can help minimise the impact of the cold chain.Expected outcome of researchIdentification and development of alternative methods in order to mitigate the impact of cold chain on future formulations.Alternative formulation methods that limits the requirement for cold chain managementAlternative technologies that allows for the accurate and cost effective solutions in order to main thermal stabilityAlternative technologies that allows for the localized supply of product thus limiting the burden of the supply chain. Problem 10: Demonstrate both the ability to scale up the production of controlled API microspheres and the tunability of the platform in the production of single and co-formulated crystalline API drug productExemplification of ProblemThis work would follow on the successful completion of PoC work (Phase 1) being conducted by Prof. Saif Khan’s group at NUS.? This work has many potential applications but ultimately could allow for precise control over drug product manufacture utilizing a continuous formulation process with a small footprint, thus enabling smaller and more efficient production lines with built-in quality and minimal reject waste.? Depending on the tunability and robustness of the microsphere size, the platform could also potentially have applications across multiple routes of administration, including inhaled, parenteral, and oral solid dose.Expected outcome of researchThe size limits for microsphere creation will be determined for multiple representative GSK and/or model compounds, as well as multiple excipients for spherical granule formation with the active molecules (breadth of platform with pure API and API/excipient combinations).? A lab-scale pilot kit will also be created to address a first step in scale-up, including downstream operations such as isolation, filtration, and drying of the microspheres (scale-up).? Analytical tests on the manufactured microspheres and subsequent downstream testing and formulation of the final drug product will also be reported, with predictive properties identified and used to control microsphere properties and production (quality).Problem 11: Automate the development of drug product formulations through the use of models that predict compatibility and dissolution profile with a standard set of excipients based on the physical and chemical properties of the API, combined with tableting equipment with built-in analytics that can automatically iterate to a final drug product formulation meeting the required specifications (which would be part of the input to the development algorithm)Exemplification of ProblemAutomated API formulation equipment would drastically reduce the amount of time, energy, and API / excipients required to perform development work on drug product formulation, making the process of development much more efficient.Expected outcome of researchWorking models suitable for several classes of API compounds that allow for rapid formulation screening, testing and finalization for oral solid dosage products that can be robustly manufactured at commercial scale. Reduce the amount of API needed during early formulation development activities, reduce the time taken to develop formulations, and reduce analytical burden. ??? Models would ideally be broad in scope,?but initially may be coupled to one particular vendor’s equipment.Feedback into primary – API property requirements. ................
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