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The mission of the pharmaceutical company of the near future is clear: to become the major personalized medicine provider to individual groups of patients by providing a value-based approach to the market alongside the traditional volume-based proposition. The key to success is smart manufacturing, which retunes the process and right-sizes it for smaller production runs, while collecting and analyzing data for constant improvement in the patient/drug relationship.

Historically, developing a drug from pre-clinical testing all the way through to human testing has been a moonshot. Only 0.1 percent of these drugs find their way to final-line human test subjects, and then, only one in five of those ever goes into production[1].

It can take 6-9 months to manufacture a drug, stock inventory, and then fill the supply chain. Even then, drugs pass their expiry date, or inventory runs down to the point where supply mismatches demand.

With the odds of clinical success so stacked, pharma is now focusing on where its manufacturing processes can become more effective, especially given the incentive towards continuous production, which alleviates the current issue of flow-through of the production and distribution chain.

There's also a priority on greater control over manufacturing variables once a drug is certified, as a bulwark against the greater global R&D variables of disease mutations, vectors and treatment methods, which are less controllable but need to be handled.

This is coupled with a sea change in creating new drugs which are specific to smaller, more specialized groups of patients. It's a complex transition involving all areas of the value chain, but particularly manufacturing, where IOT devices and data monitoring and analysis systems are critical.

The requirements of smart manufacturing are to:

  • Adapt the manufacturing process for better quality drugs; smaller, individualized production runs or continuous production; and gain more flexibility for further adaption and repurposing of production runs
  • Use data analytics to steer towards predictive production, with end-to-end visibility and tracking of the supply chain to better manage existing inventories, right-size production runs to react more rapidly to novel patient or market requirements
  • Meet new regulatory environments and Good Manufacturing Practices

Smart manufacturing is essentially about making machines talk. If we gain insight into what a machine is doing, add granular control over functionality, and generate a start-to-finish process which is trouble-free, then new smart manufacturing processes are born.

These processes will be unique to each pharma company, and indeed, each organization will have a high number of medicine manufacturing requirements that are individual to each drug, and in the future, maybe even to each patient.

There are now two clear tracks for pharma companies: they're continuing to pursue existing traditional high-volume business, but now also new opportunities from the nascent market of drugs-on-demand.

Drugs-on-demand and precision medicine

The concept of on-demand has been demonstrated by MIT and is very promising[2]. MIT custom-built all of the machinery necessary to produce drugs at any location, at a moment's notice, on-demand.

According to Bob Roehr of Scientific American magazine, "Patients could, one day, potentially obtain pills from a machine that is able to take raw materials and synthesize them into drugs in a matter of hours."

"What’s more, the products would not be one-size-fits-all but a drug dose calibrated to each person’s needs based on factors like age, body weight and genetic variations that affect how one metabolizes and clears the drug as well as takes into account potential interactions with other medicines."

Coupled with the flexibility of on-demand, there's also the advancement of Precision Medicine, which uses the analysis of genomic data integrated with electronic medical records and other data sets to make exacting conclusions about the suitability of certain medicines compared to others.

The key here is in the ability to move from laboratory to production scale. New simulation tools help to assess the required conditions or impacts to a successful and flexible production.

Continuous manufacture

Medical outcomes are improved because the patient's treatment is modeled on their own personal medical history, rather than the more generalist medical history of large groups of cohorts targeted by one-size-fits-all medicines. This model also more closely tracks the course of medical recovery, and continually revises dosages based on how well the patient is recovering.

This drugs-on-demand model shows the way towards the discrete medical treatments of the future. For now, it's just a concept, but it illustrates how smart manufacturing processes can aim for a more personalized approach that provides better value to the patient. The entire value chain will be affected, bringing with it new models and opportunities.

So, now the challenge is – in effect – to cross-subsidize the development of new short-run processes with capital from the traditional revenue base because it provides diversification and access to new markets, not because it replaces them.

Machines-as-a-Service (MaaS)

Pharma is right in the sweet spot of the Industry 4.0 revolution, where smart machines will be the key factor in delivering the precision required to produce personalized medicine.

Pharma manufacturers worldwide can connect their various machines, devices, tools and equipment to the cloud and develop their very own industrial Internet of Things (IOT).

These provide the ability to generate and process data, enable better monitoring and improvement of manufacturing process, as well as bringing more flexibility to manage.

This Machines-as-a-Service (MaaS) effectively measures machine performance versus business goals, ensuring a tight control on business expectations versus the reality on the production floor.

All machines must reliably work 24x7, and so the same rules that govern volume production still apply to short runs: make sure that potential issues are ironed out before production, linked with an ability to very quickly isolate and resolve anything bad occurring along the manufacturing line. This only works when production controllers have a single pane of glass, dashboard view into all areas, supporting not just a greater degree of automation, but also the ability to quickly analyze data and make decisions.

Flow-through production, when applied to personalized medicines, requires granular measurement and control at each stage of the process, communicated in near real-time. Short runs are about small production volumes, but also about applying multiple dynamic variables to production that, for example, would see several granular dosages produced rather than two or three big ones. It's even possible that specific mixes of dosages could be produced for certain components. Quality control in this model is doubly important, as each medicine will have characteristics that are unique to a specific group of patients. So, pharmacovigilance will increasingly depend on test devices that proactively monitor the QA process and are able to expand this process the more personalized medicines being produced.

Pharma 'launch factories' – the production sandbox of the future

Smart manufacturing is embodied in the new concept of launch factories[3]. Although these can be physical manufacturing centers, the concept also embraces scrutinizing the entire drug research, development and manufacturing lifecycle through continuous improvement.

This helps understand where deltas between today's and tomorrow's development needs are, with a goal of faster launches, higher drug uptake, improved predictability of results and continuous learning that acts as a force multiplier.

There's also the potential to grow production flexibility using modularized assembly lines with short cleaning time or pre-sterilized single-use systems that become the building blocks to serve many different types of production.

These huge evolutions of manufacturing technologies are enabling more on-demand and effective drug production that is beginning to meet today's standard of maximized healthcare treatments and outcomes, and also, a pledge to treat every single person with the best program available.

6 recommendations for smart manufacturing in pharma:

  1. Processes: go beyond today's one-and-done processes to treat launches as ongoing, rather than one-off events
  2. Data: utilize data more effectively, especially from real world evidence, to instruct meaningful KPIs rather than ones based purely on revenue and production milestones
  3. HR: new approaches necessitate new talent and deploying existing skill bases to locations where they're more valuable. Also, develop a whole ecosystem of partners outside of an organization.
  4. Analysis: as data volume and complexity increases, develop better methodologies that use data to power better decision-making and explore ways to monetize the data.
  5. Methodologies: move from a focus on short-term performance and troubleshooting to a more strategic, holistic longer-term view
  6. Simulation: begin including systems that can simulate the testing and design of launches, increasing successful outcomes when they go into production.

 

Source : https://www.contractpharma.com/issues/2018-01-01/view_features/pharma-industry-outlook