How AI and Tech is changing Pharma — A Look at What’s to Come

Artificial intelligence and other advances in technology are having a huge impact on every sector of human activity. New ways of researching, manufacturing, commercialising and delivering products and services appear every day, with more than a few paradigm-shifting technological breakthroughs seemingly on the horizon.

The intersection of technology and medicine has always been an important one. The way we identify, diagnose and treat diseases is fundamentally linked to technology. From the creation of insulin to the discovery of penicillin, the synthesis of the polio and smallpox vaccines, and the recent and extraordinarily rapid release of the Covid-19 vaccines, technology drives forward medicine and, with it, the pharmaceutical industry.

In this article, I examine three key ways technology, and especially artificial intelligence (AI) and machine learning (ML), are changing the face of Pharma. The first concerns technologies that will impact the way diseases are identified and treated. The second concerns the explosion of data being made available to the pharmaceutical industry and its analysis with AI and ML. And the third looks at how technology is changing the Pharmaceutical industry’s relationship to its customers, including payers, patients and practitioners. 

Smaller, smarter and individualised: Pharma’s new product offering

Pharma’s product offering evolves in step with technology. Vaccines, aspirin, chemotherapy and antiretrovirals were all the result of significant technological and biotechnological breakthroughs. The industry is now on the cusp of some astonishing new technologies that will fundamentally change the way diseases are identified, diagnosed, tracked and treated.

 
Nanotechnology
Nanotechnology refers to the use of technology on a nanoscopic scale. That is, at the atomic, molecular or supramolecular level.

One key difficulty in identifying and treating diseases stems from the fact that viruses, bacteria and other pathogens operate largely at the cellular and subcellular level. They are too small for us to see or interact with directly.

Thus, we rely on chemical compounds to signal their presence and intervene on a molecular level. But this presents many disadvantages, chief among them that there are few ways to effectively target the offending lifeforms or molecules. Healthy cells and systems get caught in the cross-fire, and this is what causes unwanted side effects.

Nanotechnology promises to change this by allowing for medical interventions on a cellular and even molecular basis.

For example, pharmacists hope to someday soon target individual viruses, bacteria and cancer cells, leaving healthy tissues intact and unharmed.

In the more distant future, a secondary, programmable synthetic immune system may work in conjunction with our own to prevent the vast majority of diseases from ever taking hold. Creating, commercialising, and updating this synthetic immune system will fall to future pharmaceutical companies.

 
3D Printing.
Pills have come a long way from the rudimentary hodgepodges of medical ingredients formed into little balls or pillula by ancient Romans. For example, by using different substrates and pill shapes, it’s possible to control, to a limited degree, the release of different substances.

3D printing, however, will enable drug manufacturers to do this with far greater sophistication. By building pills layer by layer, complex and mathematically elaborate structures can be created within the substance of the pill itself. Different compounds can be added together for multi-functional pills that were previously impossible or very difficult to synthesise.

Perhaps one of the most exciting potential outcomes of 3D printing is the democratisation of the pill manufacturing process. In the not-too-distant future, pharmaceutical companies may be responsible for producing and distributing to pharmacies and even clinics the key ingredients necessary to print pills on-site and on-demand.

This not only shifts the burden of production away from the pharmaceutical industry, it is likely to dramatically reduce the number of discarded drugs (currently a $2.8bn problem for the American healthcare system).

Another exciting prospect relates to the personalisation of medicine. Historically, it hasn’t been commercially practical to produce small-batch drugs in most manufacturing sites. Mass-production takes precedence over personalisation. 3D printing will allow pharmacists to produce pills for specific populations or even specific individuals, with exact timeframes for release based on the person’s profile or unique combinations of medication to treat cancers and other complex, multi-pill ailments.

 
Big data and machine learning

More data is created, exchanged and analysed every minute today than ever before. And a big chunk of it is medical in nature.

This is partly due to the use of more sophisticated instruments in clinics and hospitals, which can collect greater and more complex information on patients and diseases.

But another sizable (and rapidly growing) contribution comes from everyday wearables. Smart watches, smart rings and other devices equipped with sensors are enabling users and, through data-sharing agreements, medical and pharmaceutical organisations to gather and analyse information on heart rates and heart rate variability, body temperature, sleep patterns, and more. The number of smartwatches has doubled since 2017, and future models are sure to have more sophisticated sensors.

As a result, there is a huge quantity of medical data being made available to the industry.

When this abundance of data is paired with the astounding power of today’s most sophisticated learning algorithms, the insights are extraordinary. Here are just a few ways the data explosion, combined with AI, is already having an impact on Pharma:

Personalised medicine.

Humans are complex organisms. Tiny differences in genome and environment can dramatically impact how patients respond to treatment. Of course, viruses and bacteria are highly complex, too, and constantly changing.

Supercharged data analysis is the key to unlocking personalised medicine—and we’re very nearly there.

Someday soon, physicians may be able to connect directly with individuals’ wearables, feed the data into a machine learning algorithm already primed with their global medical data, and come up with a highly individualised treatment approach.

Ensuring personalisation and privacy are balanced is a key concern, although there are many ways businesses can achieve this.

 
Digital twins.

Digital twins are a digital representation of some real world system or process. The pharmaceutical industry is already experimenting with using digital twins for a variety of purposes.

For instance, Atos paired with Siemens and GlaxoSmithKline to create a digital twin of the vaccine creation process itself. Using dozens of sensors that collect real-world data about the manufacturing process in real-time, the group created a “live, in-silica replica” or the physical vaccine process to optimise processes and test out new production configurations.

Other companies are experimenting with human digital twins to augment or replace patient control subjects for clinical trials. Industry leader Unlearn, for example, recently signed on with Merck KGaA to improve the efficiency and speed of immunology trials.

 
Faster drug discovery.

Finally, machine learning is being used to great effect in drug discovery. From target identification and validation to compound screening and lead discovery, and preclinical and clinical development, the combination of more data with sophisticated AI is helping industry leaders to develop molecules more quickly than ever before.

For example, predicting and determining bioactivity has historically been done via ‘high-throughput’ screening experiments. Today, it’s possible to use such experiments to scan thousands of compounds and detect their bioactivity levels vis-à-vis specific proteins. The process, however, is expensive and time-consuming, and should thus only be conducted on the most likely compound-protein pairs. Machine learning algorithms have successfully been used to improve this bioactivity prediction, thus significantly reducing the number of failed experiments.

This is just one way machine learning and the vast quantities of medical and biochemical data available to pharmaceutical companies today is being used to speed drug discovery.

 
Pharma and its customers, from patients to practitioners

The final way in which AI and Tech is having a profound impact on the pharmaceutical industry has to do with the industry’s relationships with the world. The industry is in the middle of several changes. I explore some of the most important below.

Customer-centricity

More and more pharmaceutical companies have come to recognize the importance of being customer-centric, as opposed to product-centric. There’s a lot of data to support why this is. According to one survey by McKinsey, for example, prescribers who are satisfied with the patient-prescription journey are 70% more likely to prescribe a drug in the future.

Practitioners want a highly personalised experience, with access to information and representatives through multiple, connected channels. Managing this level of personalisation and interaction with a traditional team of salespersons and pharmaceutical reps is impossible.

But thanks to advances in client relationship platforms powered by AI and machine learning, pharmaceutical companies are able to be more customer-centric and provide better experiences to practitioners and patients.

AI in Sales and Marketing

AI-augmented sales forces have 50% more leads and appointments and up to four times as many prescriber conversations. They are able to target practitioners more effectively, intercede earlier with practitioners who are thinking of switching to another manufacturer, and can provide a more personalised and holistic experience for practitioners across channels.

Applied to marketing, AI is enabling pharmaceutical companies to rapidly identify key opinion leaders and thought leaders using public (e.g. social media) data, allow for faster reimbursement, create more content marketing materials, improve customer segmentation and personalised marketing, and more.

Beyond the Pill and DTx

“Beyond the pill” refers to a pharmaceutical commercial and business strategy, as well as an approach to treatment, in which “the pill”, as a stand-in for any traditional pharmaceutical product, including pills, creams, gels, and other molecules, exist within a more holistic treatment framework, one which can include additional medtech products, healthcare services, outcomes management, lifestyle and behavioural modification, and more.

When the goal is to establish tight-knit collaborations with providers of these additional products or services and take ownership of the holistic approach, it is known as “owning a disease.”

Sanofi, Novo Nordisk and Eli Lilly, for example, have executed strategies for “owning diabetes”, while Fresenius, DaVita, Baxter/Gambro and Amgen have done so with end-stage renal disease.