AI in Pharma: A Game-Changer?

AI could be a game-changer for pharma. AI can transform how we develop and deliver treatments, and how we envision the future of research and healthcare. Helping predict drug–target interactions, optimizing drug design, and ultimately saving time and costs, AI’s power lies in its ability to generate and analyze vast amounts of data to identify new therapeutic molecules with optimized properties in silico. With machine learning algorithms – and while optimizing properties and interactions with molecular targets – experimental work can be streamlined, enabling us to scan and prioritize large and diverse chemical spaces that a human cannot handle. 

And then there are clinical trials. Drafting study concepts and protocols, writing reports, automating regulatory processes, dossier filing, data extraction, auditing, and quality management are just some of the areas where AI could potentially help. AI can also leverage electronic health records, genetic data, and other sources to match eligible participants with suitable trials, and ensure a diverse representation of patients regardless of geographic location.

Much like the way AI can analyze large libraries of chemical compounds, it can also draw on large patient datasets to draw insights that may be missed, and help identify new areas of unmet need to determine which treatment protocols could yield the best outcomes for a particular patient population. Dedicated apps powered by AI can also help to track patient health metrics, medication adherence, and symptoms from anywhere using smartphones or wearable devices.

There is definitely no shortage of real potential. The question then, is how to implement the technology. Where do you start?

It goes without saying that you should be strategic about it. I advise focusing on where AI is expected to have a significant impact in your organization, such as drug discovery, clinical trial design, and landscape analysis. Where do your priorities lie and what is the most important area to your business? Implementation should be guided by a clear understanding of goals – be they to streamline processes or reduce costs. It’s also important to be smart about what we can and can’t do; the strengths of a pharma-based innovator lie in drug discovery rather than technological advancement, so don’t be scared to collaborate with tech providers to help bridge the gap.

One key consideration is the integration of AI into existing systems and workflows, whilst ensuring a human touch. This aspect is particularly critical for clinical trials. AI has vast benefits during trials, but human interaction is required to address participants’ concerns, provide clarifications, and establish trust, which will lead to better engagement and retention in trials. 

Wherever AI is implemented, workers will need to acquire new skills and new roles will need to be created, but AI data scientists should not become siloed. Your AI-generated data needs to be integrated into the drug development cycle if you want to get the true benefits. For example, it should be something that can continually inform researchers to help them make better decisions. 

When using AI in healthcare, there is also the challenge of ensuring data quality and privacy. AI infrastructure must be robust AI infrastructure, with the necessary data security. AI algorithms can also be designed to incorporate differential privacy techniques, while still allowing meaningful analysis of aggregate datasets. 

As computational capabilities expand and more diverse data becomes available, AI and machine learning models will only grow more robust. The increase in relevant datasets will teach the AI algorithms and performance will improve. And advances in algorithms and model architectures are already leading to highly sophisticated and efficient AI systems. 

There remain vast knowledge gaps in biological science that require wet-lab translational and clinical validation investigation. The essential role of human curiosity in scientific exploration will not change. As we embrace AI’s transformative potential in drug discovery and pharmaceuticals, it’s vital to remember the enduring importance of the human touch. Despite technological advances, healthcare remains fundamentally patient-centric, relying on empathy and understanding that only people can provide. Balancing AI’s impact with this human touch is key to truly revolutionizing patient care and outcomes.