Amid the excitement around the success of the development and manufacture of COVID-19 vaccines, it can be easy to forget the huge developments in other fields of research. One, which is creating a buzz in the pharmaceutical manufacturing sector, is the rise of personalized medicine.
Many readers will be familiar with the growing prominence of the potential for using genetic or other biomarker information to tailor medical treatments to the individual characteristics of each patient. In the pharmaceutical sector, biopharma companies are already developing targeted therapies and conducting innovative research based on the increasing understanding of genetic variation and its effects on the safety and effectiveness of the candidate drug. This approach is growing in prominence with very real potential to treat previously incurable diseases and minimize human suffering on a grand scale.
There will no longer need to be a “one-size-fits-all” approach to medical intervention. Instead we’ll have personalized or precision treatments that we know will be effective for certain individuals and not others. Since Watson and Crick’s discovery of the double helix in 1953, there have been many astounding applications for our knowledge of the genome, with profound implications. Not least for medicine.
Now, personalized medicine has the potential to have the biggest impact yet. According to one source, the U.S. Food and Drug Administration (FDA) has received hundreds of “active investigational drug applications involving gene therapies” already, with more than 40 expected to be productized by the end of 2022.1
Scientists have been working hard to understand the human genome. In the UK, for example, a project known as the 100,000 Genomes Project2 has set out to analyze the genomes of people with rare diseases and cancer to identify opportunities for intervention targeted at the individual level, or at small groups of individuals.
While a lot of progress has been made, we’re still in the pioneering early stages of development. Autologous stem cell transplants, where a unique product is created for a single cancer patient, has been one of the earliest successful examples.
Not all examples will need to be produced in such small measures. Precision medicines are being made in batches and matched to groups of patients with certain genetic attributes. As this technology moves from the fringes of modern medicine to play a more central role, it will mean manufacturers will need to produce a wider variety of complex drugs and each on a smaller scale.
With the advancement in technology and bioengineering, also comes additional financial risk. The cost of genetically modified material can be exceptionally high. Certain biologic medicines can be in excess of $100,000 per milliliter. While not all personalized medicines are produced from living organisms, or contain components from living organisms, this gives an indication of what can potentially be at stake.
Losses have the potential to be huge, underlining the need for meticulous planning and scheduling of complex processes. For the major biopharma manufacturers, understanding how to integrate new approaches into their business-model will be vital.
Shifting business models
This is supported by the conversations we’ve been having with our customers. Many are planning for this which is likely to be disruptive to pharmaceutical manufacturing supply chains. From the standpoint of manufacturers and supply chains, personalized medicine brings a number of challenges.
For example, the vast ecosystem of biopharma manufacturers are well adapted to large batch runs, as we have seen so successfully in the mass manufacture of COVID-19 vaccines. A personalized approach would require a shift of production to small runs of complex medicines whether that’s for precision medicine, which could involve numerous variations of treatments, or autologous treatments, unique to individual patients.
This method of manufacture requires a more patient-centric approach, almost mimicking the short runs of lab-based manufacturing seen during clinical trials. In some examples, treatments won’t even start production until a patient is found.
This marks a significant shift in the business models of biopharma manufacturers, which traditionally rely on forecasting demand ahead of time before pushing products to wholesalers, and in turn on to physicians and pharmacies.
For contract manufacturers who have historically focused on economies of scale, this presents significant questions about their approach to manufacturing, as well as their systems and processes.
Modifying the supply chain
As manufacturers get to grips with the mass production of personalized medicines and patients, healthcare providers and pharmacies take up the innovative new treatments, the wider supply chain must also adapt to get the right treatment to the right patient.
For example, if a unique cancer treatment is delivered to the wrong clinic or patient, or even without the correct product identification, this can lead to significant delays in patients’ treatments, not to mention potential life endangerment. Although these errors are currently uncommon, if demand for more widespread personalized medicines continues to grow as expected, the supply chain will need to evolve to improve controls and visibility.
A rise in personalized medicine will require individual end-to-end tracking, from manufacture to customer delivery, unique packaging requirements, increased temperature monitoring, and additional customs checks, to name just a few services.
Smart technology is on hand to offer solutions to quality control and product integrity challenges. For example, monitoring and tracking systems can provide real-time access to data such as temperature, location, shock and orientation. This allows for regular checks and urgent responses if problems do arise.
Adapting the manufacturing process
The shift towards personalized therapies will require a move away from large batch production to a much smaller volume production. Precision medicines simply cannot be made in the same scale with the reliability, speed, and traceability required for the safety and efficacy of the treatment.
In order to adapt for this shift in production methods, a number of additional considerations will be essential across the biopharma supply chain, with strategies required to be even stronger to cope with the increased complexities.
Some leading biopharma manufacturing experts have suggested that continuous manufacturing technologies are a solution to the challenge of manufacturing drugs cost-effectively on a relatively small scale.
This process, which moves away from traditional batch manufacturing, allows drugs to be manufactured non-stop and potentially provides greater flexibility to adapt to personalized medicines. When seeking to scale up production, continuous manufacturing allows for drug manufacture to be tailored to demand.
For example, if a personalized medicine is targeted at a small number of patients, fewer specialized reactors would be needed to manufacture it. If demand increased, the process can be extended, or more reactors devoted in parallel to existing production. With medical advancements accelerating, this approach could help to get personalized drugs to commercial manufacture and into the hands of patients more quickly.
While a move to continuous manufacturing may be a solution for some, others argue that existing processes and systems can help manufacturers shift development to personalized medicines. Some believe advanced planning and scheduling (APS) tools, processes and systems that serve us so well now for mass production might struggle. We disagree.
The manufacturing processes may change, and the batches might get smaller, but APS will play a fundamental role, with manufacturing constraints still dictated by finite equipment, labor and materials. The effective management and planning of these will still be the biggest factor in ensuring reliable, safe and efficient production of personalized medicines.
While the process required to create these new classes of drugs may be different and more complex, the need to manage labor, equipment and materials will remain key. In fact, with added complexity, a robust APS will likely become even more critical.
It will be essential for pharma manufacturers to create a real-time single plan. And reducing lead times and optimizing resources will allow manufacturers to validate processes while maximizing the number of batches that can actually fit in the schedule. This becomes even more important when manufacturing lots of smaller batches.
Scenario planning will also be critical, as with a greater variety of medicines to manufacture, there will be a greater variety of potential scenarios to plot. Taking these decisions based on data will make for more effective decision-making.
Some major manufacturers are discovering to their cost that if production falls behind schedule, or is not properly audited, whole batches can be rendered useless, or worse, dangerous. We’ve already heard how costly this can be, and in this new world of patient-centric medicines, it may even have a direct knock-on effect to a patient’s treatment. Matching complex production processes, a hallmark of the production of personalized medicines, against upcoming preventative maintenance and repairs means this risk is mitigated. This will only become more critical in the pharma sector where controls are likely to get yet tighter as new complex and advanced technologies are rolled out.
New horizons for the sector
As researchers continue to develop our understanding of how an individual’s molecular biology influences their unique response to a given drug, personalized medicines offer new horizons for the medical sector.
While the potential benefits to patients increase substantially, the risk of error or delays is also multiplied. As manufacturers and logistics firms get to grips with increasingly complex production processes and supply chains, the cost of mistakes could lead to delays or even life endangerment.
A move away from a “one-size-fits-all” means that current manufacturing processes will have to adapt for precision production. As with all complex processes, getting a single vision of the truth is vital to make decisions effectively, particularly at this early stage with processes and systems still very much in development.
These challenges are only likely to increase exponentially as personalized medicines become used more widely. The growth of AI and machine learning may assist to make the biopharma manufacturing process look very different ten years from now, but in the meantime, getting the right systems in place to manage complex processes will be critical to increase efficiency, reduce human error, and enhance production of precision medicines.