Frequently Asked Questions

Our FAQs are intended to help you find out a bit more about the background to the ADDoPT project, and the vision for establishing the United Kingdom as a world-leading centre for the digital design of drug products from which it arises.

Q1: What is the ADDoPT project?

ADDoPT is a four year, £20.4m UK government-industry-academia collaboration designed to secure the UK’s position at the forefront of pharmaceutical development and manufacture through the establishment of robust manufacturing for current and next generation medicines built on UK excellence in process modelling, simulation, optimisation and control. ADDoPT stands for Advanced Digital Design of Pharmaceutical Therapeutics.

Q2: What is the ADDoPT project seeking to achieve?

The aim of the ADDoPT project is to secure the UK’s position at the forefront of pharmaceutical development and manufacture through more sophisticated definition, design and control of optimised pharmaceutical manufacturing processes using data analysis and first principle models in order to deliver new, higher quality medicines to patients, faster and more cost effectively.

The ADDoPT partners are working across the pharma value chain to define a system for top-down, knowledge-driven Digital Design and Control for drug products and their manufacturing processes. This will bring together a wide range of predictive models and insight from industrial case studies at four major pharmaceutical companies, allowing more targeted future experimentation, a better understanding of risk, and hence better design and scale-up for robust products and processes.

Digital Design combines research insight and qualitative and quantitative mechanistic modelling to provide links between raw materials, manufacturing processes and the needs of the patient. It spans all unit operations, processes and procedures during the manufacture of medicines and their impacts, both upstream on the efficiency of product and process design, and downstream on product performance.

Q3: Who is involved in the project?

The ADDoPT project consortium consists of twelve partners, comprising;

Leading businesses in the pharmaceutical value supply chain - Pfizer Ltd, GlaxoSmithKline Plc, AstraZeneca UK Limited, and Bristol-Myers Squibb Pharmaceuticals Limited;
Knowledge-driven small to medium sized enterprises (SMEs) - Process Systems Enterprise Ltd (PSE), Perceptive Engineering Ltd, and Britest Ltd; and
Specialist knowledge-based partners from UK universities and research centres - Cambridge Crystallographic Data Centre (CCDC), University of Leeds, University of Cambridge, STFC Hartree Centre, and the University of Strathclyde.

The project is being co-ordinated by Process Systems Enterprise Ltd (PSE).

Q4: How is the ADDoPT project funded?

The ADDoPT project is receiving £12.2 million of funding awarded competitively under the Advanced Manufacturing Supply Chain Initiative (AMSCI). The remainder of the €20.4 million project costs will be met through contribution of the consortium partners’ effort. The pharmaceutical industry partners in the project are contributing all of their effort to the project without external funding.

Though AMSCI is now closed to further bids, AMSCI funding supports manufacturing supply chains in England to encourage reshoring in the UK and improve global competitiveness through approved innovative, collaborative projects designed to establish strong, sustainable and balanced growth. Complementing the Regional Growth Fund, AMSCI offers flexible funding support for R&D, skills, training, capital finance and leveraging private sector investment. AMSCI is administered by Finance Birmingham Limited.

Q5: What is the timeframe for the project?

The ADDoPT project runs from 1st April 2015 until 30th June 2019.

Q6: How does the ADDoPT approach differ from current pharmaceutical design and manufacturing?

Pharmaceutical manufacturing technologies have not changed greatly in over 40 years and despite manufacturing organisations being data rich, ‘big data’ (see Q19) has not been effectively exploited in a widespread or systemic way in the sector. Process and product development is still essentially based upon a “make and test” approach, meaning long development cycles, non-robust scale-ups, and inefficient processes.

Some examples of the relatively poor benchmark metrics from which current pharmaceutical manufacturing systems and supply chains suffer include;

300,000 defect parts per million (2 sigma); this compares badly with the automotive (6,000 per million, 4 sigma) and semiconductor (3 per million, 6 sigma) industries.
Large in-process/supply chain inventories (up to a year’s worth of stock in the pipeline).
Often less than a few percent of value added time.
Often less than a few percent of the product reaching the part of the body where it is therapeutically active.

These are issues the sector has been able to tolerate during the “blockbuster drug era”, but as this draws to a close (projected patent expiry on products worth $267bn between 2011 – 16 was noted by market analysts in 2010 ) the economies of scale of big pharma are rapidly diminishing.

ADDoPT seeks to define a new paradigm for the model-based design and control of drug products and their manufacturing processes, where improved process understanding results in more robust and efficient processes, greater speed to market, and a supply chain capable of providing better quality and more diversified products to the patient at a lower cost. The digital design approach aims to reduce defects by an order of magnitude, halve stocks, double the value added time and double the amount of delivered therapeutic product through a step change in product and process design.

Q7: Why is the project important to the future of the UK pharmaceutical manufacturing industry?

The UK pharmaceuticals industry is arguably poised potentially to benefit from the big data revolution but not so well-equipped to deal with trends in global competition, and changes in expectations from patient and regulators.

Whilst the pharma sector has increasingly used outsourcing/offshoring to reduce its costs, the associated supply chain risks and costs of mitigation are substantially higher. Personalised medicines require a shift from “one-size-fits-all” type products and manufacturing processes to more flexible products/processes.

Regulators such as the European Medicines Agency (EMA) and the US FDA are supportive of Quality by Design , an approach that aims to ensure the quality of medicines by employing statistical, analytical and risk-management methodology in the design, development and manufacturing of medicines. One of the goals of Quality by Design is to ensure that all sources of variability affecting a process are identified, explained and managed by appropriate measures. This enables the finished medicine to consistently meet its predefined characteristics from the start - so that it is 'right first time'.

The ADDoPT partners believe that the UK pharmaceutical manufacturing industry’s competitive position will be significantly enhanced by the better knowledge of drug products and their manufacturing processes/value chains, and more efficient methods for design/control of a wider range of manufacturing process which the project will deliver.

Q8: What are the key deliverables for the project?

Key project deliverables are:

An overall systems framework defining; key activities in pharmaceutical product manufacturing and process development; inputs, outputs, workflows; computational tools, their applicability; gaps and development needs (updated in the light of project developments).
Development of an ontology and model for clinical and commercial manufacturing data establishing agreed parameters for investigation, data formats, and analytical methods (using a Cross-Industry Standard Process for Data Mining) in order to produce a final service analytics system.
Detailed mechanistic models (in ten sub-thematic areas – see Q9) developed and documented for the processes, products and product performance underpinning the Digital Design and Control approach.
Development and application of predictive tools that exploit our understanding of crystalline structures to support the design of more robust manufacturing processes and the identification of the most appropriate controls.
Advanced systems for process control and optimisation of pharmaceutical processes incorporating hybrid modelling to combine the benefits of mechanistic models with the classical data driven techniques used in automation.
Four industrial case variously covering both batch and continuously operated manufacturing processes, existing and yet to be designed processes, and in-house and outsourced manufacture by UK-based CMOs.
A knowledge management and exploitation plan, and a dissemination strategy and implementation plan including engagement with MHRA innovation office to discuss how models developed can be integrated within the Chemistry Manufacturing Control (CMC) sections of new product registration.
Training of process operators in advanced control and operation, research technicians and scientists in data analytics and fundamentals, and stakeholders in awareness of digital design methods, totalling more than 300 individuals trained at NVQ level 3 - 5

Q9: What process / product areas will ADDoPT focus on?

ADDoPT spans all unit operations, processes and operation procedures during final stage primary and secondary manufacture of medicines. These are organised along ten sub-themes, seven of which focus on key manufacturing unit operations, and three which deal with underpinning fundamental physical and chemical properties in relation to their impact upstream on the efficiency of product and process design and downstream on product performance.

These are respectively

Control of solid form in final stage crystallisation
Prediction of particle properties in spray drying
Agitated filter drying, filtration, washing and drying
Milling, attrition, breakage, mechanical properties
Blending and dry granulation of formulation ingredients
Particle and granule compaction and tableting
Granule and tablet coating

and

Solid form physical and chemical properties, and critical quality attributes
The solution process environment, and the particle/solution interface
Particle flow properties, particle/particle and particle/surface binding

Q10: Is ADDoPT about batch or continuous processing? What about flexible manufacturing?

ADDoPT will develop tools and methodologies that will improve robustness of both batch and continuous manufacturing processes with an emphasis on batch. The pharma sector has a huge installed asset base for batch manufacturing that will exist for several decades, so it is important to maximise value and quality that can be obtained from batch processes. For companies moving toward more flexible forms of manufacturing (such as modular units, and compartmentalised equipment) it is worth noting that the project’s industrial case studies variously cover both batch and continuously operated manufacturing processes, existing and yet to be designed processes, and in-house and outsourced manufacture.

The project consortium is well-positioned to make use of insights from previous collaborations and to transfer knowledge into new flexible manufacturing approaches through the extensive involvement of partners in related projects and activities. Examples include the EU Seventh Framework project ‘F3 Factory’ which studied fast, flexible, sustainable modular production technology (and which included ADDoPT partners AstraZeneca and Britest) and the UK AMSCI-funded REMEDIES (RE-configuring MEDIcines End-to-end Supply) project, which is identifying innovative ways for clinical and commercial supply chains to capitalise on new technologies with the potential to improve medicine manufacturing and supply, and offer more personalised, faster and cheaper drug delivery. ADDoPT partners GSK, AstraZeneca, PSE, Britest and Perceptive Engineering are all actively involved in REMEDIES.

Use of the ADDoPT systems framework will support the transition to flexible manufacturing by reducing the risks associated with process design and equipment selection, as well as enabling the development of high fidelity process control models, facilitating rapid commissioning of new production lines.

Q11: When will the ADDoPT project deliver tangible benefits and what will these entail?

ADDoPT aligns with the Medicines Manufacturing Industry Partnership’s (MMIP) vision to help to secure the UK’s position at the forefront of pharmaceutical development and manufacture by supporting the design and control of robust, optimised manufacturing processes driven by data analytics and first principle models. The new approach is closely aligned to UK government industrial strategy (specifically the Strategy for Life Sciences and building on Big Data). ADDoPT builds on UK excellence in big data, mechanistic modelling, process optimisation and control to establish a highly competitive UK knowledge value supply chain for the pharmaceutical sector that will seek to:

protect UK drug manufacturing and support future growth
encourage reshoring of existing pharmaceutical production
contribute to job creation and safeguarding in the pharmaceutical supply chain
enhance UK skills and capabilities through the training of operators and scientists in new design and control tools and methodologies
get new innovative medicines to market as efficiently as possible to ensure access for patients.

Q12: What are the key challenges in Digital Design for Pharmaceuticals?

Conventional, largely empirically based, modelling capability in the pharmaceutical industry is approaching the limiting point of incremental development. Digital Design combines research insight and qualitative and quantitative mechanistic modelling to provide links between raw materials, manufacturing processes and the needs of the patient. It spans all unit operations, processes and procedures during the manufacture of medicines and their impacts, both upstream on the efficiency of product and process design, and downstream on product performance.

Key challenges are to improve quality systems, real time release/performance, materials properties, particle attributes, surface chemistry, formulation, processing ‘rules’, and the Manufacturing Classification System (an aid to product and process development facilitating the transition between clinical and commercial manufacturing sites providing a common understanding of risk).

Q13: How will the wider manufacturing industries benefit from the outputs of this project? What about biotech and suppliers?

The skills, tools and services that will be developed and delivered within ADDoPT will not only provide the in-house manufacturing facilities of the pharmaceutical partners with a competitive advantage, but the entire UK pharma sector including the very large contract manufacturing organisation (CMO) subsector, as well as a range of small and medium sized enterprises. The project will engage with the whole UK pharmaceutical community including CMOs, contract research organisations (CRO), process analytical technology (PAT) suppliers, and equipment manufacturers.

Beyond the scope of the current project there is great potential to leverage the UK Digital Design eco-system created for pharma into other manufacturing sectors which face similar issues. Incorporating appropriate modelling techniques for bioprocessing would enable the ADDoPT systems framework to be expanded to biotech companies in the future. The same tools and methodologies should be readily applied to other formulated industries, e.g. specialty chemicals, agrochemicals, food, and fast moving consumer goods. These sectors have comparable manufacturing processes, similar regulatory frameworks and recruit heavily from pharma companies and universities involved in pharma research.

Q14: How is the ADDoPT Project structured?

Over its lifetime, the project can be broadly divided into three phases:

Start-up Phase (years 1-2): Assembly of available tools and rapidly providing these in an industrially compatible format together with associated training.
Integration and Further Development Phase (years 2-3): Development of a multi-scale modelling framework and toolkit. This integrates tools developed in Phase 1 within a holistic structure linking to big data resources encompassing both modelling and experimental data.
Consolidation and Sustainability Phase (years 3-4): Integration of the latest developments in the evolving big data and predictive modelling platform and robust interconnections between the tools developed. Continuous improvement of the user interface so the tools can be used in manufacturing on a day-to-day basis and links to High Performance Computing (HPC) resources can be efficiently implemented.

As with all major consortium based projects of this nature, there are several components (work packages) that bring together the relevant skills/expertise of the consortium partners to focus on the specific challenges of the project. For ADDoPT, there are eight separate work packages as follows:

WP1 – This identifies key activities in pharmaceutical product manufacturing and process development, and for each determines the currently available computational tools and experimental methods for performing the task, their range of applicability and their interoperability with other tools.

WP2 – Four case studies reflecting different products and processes at different stages, including mature products (where manufacturing optimisation is the key and where benefits can be realised within the project timeframe) and new product development (where quality, manufacturability, digital characterisation and customisation together with linking process parameters with product quality without extensive experimentation are the key).

WP3 – Developing the relevant big data and analytics platform

WP4 – Building of the mechanistic model-based infrastructure and development of models for existing and emerging unit operations.

WP5 – Solid form particle design aimed at optimising primary and secondary manufacturing with respect to product performance.

WP6 – Development of advanced control and monitoring strategies critical in bringing the digital design approach into manufacturing.

WP7 – Development and execution of strategies for knowledge management, dissemination and an implementation plan, i.e. preparing the ground to maximise impact and successful exploitation. This work package will also provide training for up to 350 people on the use of selected Digital Design tools.

WP8 – Consortium project management and reporting.

Q15: What does ‘data analysis and first principle models’ mean?

In the context of ADDoPT we are particularly interested in the challenge and potential gains bound up in the analysis of ‘big’ data sets held within different parts of a business, and across businesses at different stages of the value supply chain, and how these can be integrated with more fundamental scientific and engineering insight to improve the fidelity of predictive computational models and the performance of advanced control systems which underpin digital design and control systems. For more on Big Data, see Q19.

Advanced Process Modelling involves applying detailed, high-fidelity mathematical models of process equipment and phenomena, usually within an optimisation framework, to provide accurate predictive information for decision support in process innovation, design and operation. The resultant models are used to explore the process decision space to enable better, faster and safer decisions by reducing uncertainty. The approach differs significantly from that of traditional process simulation.

Advanced process modelling is a combination of three elements:

mathematical models based on chemical engineering first-principles theory
experimental data – laboratory, pilot or operating plant – used to fit the empirical parameters in the model (or 'validate' the model)
advanced solution techniques – for example, optimisation – to exploit the rich information in the model and its predictive capability.

Much of the predictive power of advanced process models results from the combination of first-principles chemical engineering, physics and chemistry with observed ("real-life") data. A properly-constructed model will have predictive accuracy well beyond the area in which it was fitted, allowing – for example – scale-up, or optimisation of processes for different operating conditions.

Q16: What does this mean for patients in the future?

The ADDoPT project aims to improve how innovative medicines enter the market and to help patients receive the medicine they need. Pharmaceutical manufacturing systems and supply chains currently lag behind other major manufacturing sectors in terms of benchmark quality metrics (see Q6 for more details). It is hoped that the new digital designs developed through ADDoPT will lead to improved process efficiency and robustness compared with ‘make-and-test’ development as well as increased speed to market.

The digital design approach aspires to ‘right first time’ manufacture, designing quality into products and processes from the offset through validated, predictive modelling and control systems based on fundamental material properties and process insight. This step change in product and process design has the potential to reduce manufacturing defects and the volumes of stocks held, as well as increasing the value added time in production and the amount of therapeutic product delivered effectively to the patient. With personalised medicines looking to revolutionise the way patients are treated, it is hoped that digital design in manufacturing will make more diverse formulations and flexible processes financially viable for the first time.

Q17: What do you mean by 'top down digital design and control'?

The top-down approach starts with an assessment of industrial pharmaceutical manufacturing processes: to alleviate manufacturing bottlenecks, identify gaps, and subsequently put together the relevant elements of the existing science base, and UK excellence in modelling, simulation, optimisation and control so that they can be used by practitioners on a day-to-day basis to accelerate the translation of molecules into medicinal products. This will provide the UK with the transformational capability confidently to accelerate the development of robust manufacturing processes for effective supply of current and the next generation medicines.

We are using big data and digital design to build on recent cross-industry efforts to develop a Manufacturing Classification System (MCS) as an aid to product and process development, facilitating the transition between clinical and commercial manufacturing sites, and providing a common understanding of risk.

Specifically, this means;

Harnessing world-leading computationally-based product and process development research
Improving manufacturing through novel data and model-based control, real-time data analytics and operations algorithms to make progress towards zero defect manufacturing;
Developing and disseminating digital design tools so that knowledge-driven, integrated product and process design methods are effectively taken up and used in the pharmaceutical industry
Integrating leading scientists, university research groups, manufacturers and SMEs with the UK’s advanced E-infrastructure research facilities to optimise pharmaceutical clinical and commercial manufacturing processes
Building on existing UK excellence in multi-scale modelling and advanced simulation to accelerate the development of clinical and commercial manufacturing processes.

Q18: What do you mean by 'Big Data'?

Information technology and the data it generates and feeds off permeate most aspects of modern life. The field of pharmaceutical product development, manufacture and performance evaluation is no exception to this, but most organisations in the supply chain would recognise the paradox of being data rich but information poor. Vast amounts of data are either deliberately created or incidentally generated right across the development, scale-up, manufacturing and use phases of a drug product’s life-cycle. This offers a wealth of insight and business transforming value if we just knew where and how to look for it, but the challenge is not straightforward.

Data may be internal to the organisation or held externally, it may be available in the public domain or proprietary, it may be structured or unstructured, and different structures may be more or less compatible with one another. Overcoming these difficulties needs specialist expertise, in high performance computing (HPC), data science, data visualization, and pioneering fields such as cognitive (‘learning as it goes’) computing, but the potential rewards are substantial: allowing decision makers to take advantage of the data to which they have access, driving performance and growth with a sharper focus on when and where to act.

More information on the potential of Big Data and the positive impact of using it is available in this publication from the STFC Hartree Centre (pdf format download).

Q19: What plans are there to engage with the broader pharma supply chain and other industries that could benefit from the digital design concept?

A key feature of the project dissemination activities is to engage with as broad a stakeholder group as possible to share the knowledge and information generated through ADDoPT and seek their input to the successful development and implementation of the digital design concept. In this way we seek to ensure that wider UK pharmaceutical eco-system has the opportunity to contribute to and benefit from the outputs of the ADDoPT project, Stakeholders are encouraged to engage with the project through a range of channels, for example stakeholder events, information from this website, academic and industry conferences, etc.

Toward the end of the project we will be seeking to establish mechanisms by which the generic outputs of the digital design concept as applied to pharma can be translated for use in other industry sectors.

Q20: Are there plans to reach out to global regulators? What about biotech and suppliers?

Regulators such as the European Medicines Agency (EMA) and the US FDA are supportive of Quality by Design (QbD), an approach that aims to ensure the quality of medicines by employing statistical, analytical and risk-management methodology in their design, development and manufacturing. One of the goals of Quality by Design is to ensure that all sources of variability affecting a process are identified, explained and managed by appropriate measures. This enables the finished medicine to consistently meet its predefined characteristics from the start - so that it is 'right first time'. We believe that the pharmaceutical manufacturing industry’s ability to deliver QbD will be significantly enhanced by the better knowledge of drug products and their manufacturing processes/value chains, and more efficient methods for the design and control of a wider range of manufacturing process which the project will deliver. Our dissemination strategy and implementation plans include engagement with the UK’s MHRA (Medicines & Healthcare products Regulatory Agency) Innovation Office to discuss how the ADDoPT systems framework can be integrated within the Chemistry Manufacturing Control (CMC) sections of new product registration.