The Human Machine
A Patient-Centric View on Pharma’s Tech-Led Future
Peter Freed, PhD, Head of Global Pharma Customer Technical Support (CTS), Roquette
A future where medications are tailored to individual needs, doctors instantly know whether a prescription has been followed, or where treatments reach patients faster, could be just around the corner thanks to the pharma industry’s accelerating use of artificial intelligence (AI). In this article, Peter Freed Ph.D., Head of Global Pharma Customer Technical Support (CTS) at Roquette Pharma Solutions explores what this tech revolution means for patients, patentors and drug producers and ponders the benefits of better collaborations between human and machine.
Imagine a world where medications could be precisely tailored to each individual patient, where doctors could know at the touch of a button whether a prescribed medication has been taken as planned, or where treatments safely reach those in need faster than ever before. Such a world is not so far away now that the AI revolution has officially reached the world of pharma.
Following its mainstream breakout in 2023, artificial intelligence (AI) and its capacity to revolutionize drug manufacturing has dominated news headlines, with industry commentators hailing its potential to transition pharma’s scientific approach from “descriptive” to “predictive.”
Technologies like this could usher in a new era for pharmaceuticals that transforms how and when patients receive care, and where science stays one step ahead of health challenges. But amid all the new possibilities it unlocks, it’s crucial that we maintain a grounded, people-centered perspective.
Here, we explore what pharma’s latest tech revolution means for patients, patentors and the many parties involved in production – from ethical considerations to smarter and safer manufacturing practices, and personalized medicines – and the new heights the industry could reach through closer collaboration between human and machine.
Safety first: Ethical considerations and regulatory challenges
Before drug producers reap any of the rewards offered by AI, they must first consider the duty they have to protect patients and their privacy. AI models need data – and lots of it. The practice of scraping the internet for information which is then “recycled” by AI is already controversial, but the conversation becomes more fraught when dealing with sensitive data like patient records. As such, several countries have already enacted rules requiring that such information be exclusively stored on domestic servers to ensure it doesn’t make its way into AI datasets.
Pharma producers must therefore ensure all data – but especially that of patients – is securely stored, and that any tools they employ have limits and fail safes baked in to avoid privacy breaches. Ethical and secure data practices like these are essential for maintaining trust and protecting patient rights – a fundamental responsibility for the pharmaceutical industry.
The key here, as with most discussions of AI’s potential, is to view these technologies as complements to, rather than replacements for, human oversight. Global governance structures contain nuances, dilemmas and illogical contradictions that the AI machine may never be able to fully comprehend, making it all the more important to allow humans and machines to work together in pursuit of patient needs. A compelling example of this kind of collaboration can be found in the field of drug discovery and development.
Inspiring human innovation: AI’s potential in drug development
Once regulatory hurdles are cleared, drug manufacturers can begin to really explore AI’s ace card: efficiency. Specially developed models can survey and assess thousands of data points in the blink of an eye – teeing up experts to perform invaluable qualitative analysis. The speed and breadth of AI’s information-processing capabilities, alongside the experience and judgment of human operators holds immense potential to accelerate the discovery of promising new actives or excipients, and their development into fully approved drugs. A great example of this potential in action is Pfizer’s use of AI to determine the optimal crystal structure that would allow its COVID-19 treatment, PAXLOVID™, to be administered in a more convenient oral format rather than via traditional intravenous methods.
As well as this creating a new competitive edge for pharma manufacturers, ultimately such efficiencies will help deliver better outcomes for patients. Accelerated drug discovery timelines means earlier access to groundbreaking therapies for people battling life-threatening and chronic conditions, allowing clinicians to treat diseases earlier in their progression for better prognoses and faster symptom relief.
Making it better: AI’s capacity to optimize manufacturing safety and efficiency
The many benefits of AI models continue as we move from drug discovery to actual production. This is perhaps the area in which it is easiest to envisage the application of smart technologies, given how neatly they slot into the ongoing industrial digitalization movement.
Take predictive maintenance and quality control, for example. Armed with data from existing or historical manufacturing processes, equipment sensor data, or quality-testing results, AI models can be taught to read the future – or rather predict events such as equipment failure, product quality deviations, or process anomalies. The value of this kind of proactive maintenance and quality assurance is immense, not only because it unlocks reduced production costs and increases efficiency, but because it can help reduce health and safety risks for the people working on site.
Importantly too, it once again helps patients access safe, effective treatments more efficiently, minimizing the risk of delays through mechanical or administrative error.
Compliance through science: Boosting patient health outcomes
The potential of AI is vast, with the ability to transform the pharmaceutical industry as we know it. But it all boils down to one critical purpose: its capacity to enhance patient care. Each year, companies invest billions into devising new and improved methods to make treatments safer, more efficient and efficacious. AI is set to turbocharge the value of this investment, unlocking new possibilities, streamlining processes and unlocking an unprecedented level of patient insight.
It is well known for instance that tailoring medications to an individual’s needs and preferences can vastly improve patient compliance – a seismic issue thought to cause at least 100,000 preventable deaths each year. Yet despite their advantages, customized drugs have remained rare, costly and niche, with logistical barriers keeping them firmly out of the mainstream. Now, however, mass-produced personalized medicine could finally become a reality thanks to the combination of AI and other new technologies, such as rapidly advancing 3D printing techniques.
Offering the potential to produce custom medicines virtually on-demand, 3D drug printing technology is blazing a trail for a new pharmaceutical business model, where medicines are manufactured according to actual patient need, rather than solely on the outcomes of a cost-benefit analysis. AI models are an important auxiliary to this process, responsible for aggregating and analyzing the in-depth patient data that makes personalization possible.
AI equally has a role in the initial collection of this data. Beyond the already-familiar side of wearable tech, such as smartwatches, drug manufacturers have also been experimenting with smart pills fitted with trackers that allow patients and their clinicians to confirm whether a medication has been taken.
Of all the impressive technology and innovation that AI has unlocked within pharma, it is this relatively simple application that best exemplifies the day-to-day impact it could have on patients’ lives. For those who have Alzheimer’s disease, psychiatric disorders such as schizophrenia or any other conditions that could impair one’s ability to keep track of medication schedules, these tiny, AI-equipped pills could make a life-changing difference.
Holding on to humanity: The future of AI in pharma
The view ahead for the use of AI in pharma is bright, if a little hazy. Many of the technologies making waves across the industry today were unheard of just 10 to 15 years ago. Given the rapid pace of change, it is difficult to say just how much machine learning and AI might shape the drug development industry moving forward.
But one thing is certain. Whatever their eventual uses, these technologies should be applied to support and protect human life – whether it’s by finding ways to enhance existing treatments or unlock new ones, speed up patient access or improve medication compliance. Amid all the excitement, our industry must always remember the real people these innovations are set to impact. By focusing on patient needs and ethical practices at every stage of their implementation, the pharma industry can create a future where technological excellence and human ingenuity combine to generate better health outcomes.
Author Bio
Peter Freed, Ph.D., is the Head of Global Pharma Customer Technical Support at Roquette, overseeing pharmaceutical services worldwide. Previously, he served as Director of Formulation Development at Pace® Life Sciences and Emerson Resources, leading teams in drug product development, cGMP manufacturing, and scientific research. With a Ph.D. in Pharmaceutical Sciences from Temple University, Peter specializes in ternary amorphous spray-dried dispersions and solubility enhancement. His extensive experience spans across multiple pharmaceutical disciplines, making him a recognized expert in formulation development and manufacturing processes.
