What to expect from the next decade of drug development

“Change is the only constant.” These words of wisdom from the ancient Greek philosopher Heraclitus still hold true today. It is most certainly true of the pharmaceutical sector where change is critical for survival. The race for a cure across complex therapeutic areas and individual diseases is highly competitive, placing enormous pressure squarely on the drug development machine. This is especially difficult as the mechanism of action of diseases is progressively better understood, blurring the lines between distinct diseases and moving the treatment space into multiplex target treatment, regardless of disease.

While this past decade has witnessed major innovation in this space, particularly around technology such as artificial intelligence (AI) and machine learning aimed at streamlining and removing risks from the drug development process, this is only just the beginning. As we move into a new decade, we should expect continued evolution of these technological innovations, even more pharmaceutical-technology partnerships and continued innovations in clinical trial models, all with the goal of expediting drug development.

In the past decade, enthusiasm for AI has grown within the pharmaceutical sector, as it has the potential to drastically reduce the amount of time required to develop a new drug. It can handle vast amounts of data and be used in target identification, big data analytics, forecasting, patient matching, automating molecule design and more. Numerous pharmaceutical organizations have embraced and adopted AI approaches, such as machine learning, investing heavily in the belief that AI will reduce costs, shorten timelines and lead to new and better drugs.

An important premise of AI is that it can support and analyse vast amounts of data. Crucial facets of the drug development pathway, such as biomarker discovery, outlier identification, and the creation of synthetic control arms, can all benefit from the utilization of AI systems and algorithms, as well. Data sharing should increase in the next few years. We are only just beginning to tap the value of AI in gathering, managing and intelligently using industry data.

It is well understood that integrating AI into the drug-discovery phase can have a huge impact on the creation of safer and more effective drugs by supporting data-driven decision-making around which potential compounds in the pipeline should be further developed and tested. The traditional, early drug-discovery stage of research, when potential disease targets are identified and testing occurs to understand whether a drug candidate can impact the identified target, can be a long and complex process, regularly taking four to six years to complete. AI has the potential to compress that timeline and optimize the process, leading to improved or more targeted molecules or compounds for development.

Unfortunately, a real impact on compressing these timelines has not yet been achieved and there are different points of view on why this is. One line of thought is that with new technology comes new complexity, requiring more decision-making. The human decision-making process isn’t evolving with technology. Since AI fundamentally changes how decisions are made, then human decision-making/ways of working need to evolve with that. Adoption of AI technology is as much about the change management process of human interaction with data, as it is about the technology. A successful outcome would be to evolve traditional drug-discovery processes to benefit from AI, as opposed to bolting AI onto the drug discovery process. By utilizing AI properly, drug discovery has the potential to be revolutionized. It is essential for expediting drug development and will lead the establishment of “Drug Development 2.0”.

Digital technology and biopharmaceutical company partnerships will continue to gain traction in the next 10 years in ways that seek to shorten and streamline the drug development process, while bringing vital treatment to patients who need it most. At its most basic level, pharmaceutical/technology collaborations help pharmaceutical companies adopt a more agile mindset to drug development, even before tangible benefits to the development process are realized. These collaborations have the potential to result in more efficient and cost-effective bench-to-bedside solutions.

Beyond technological innovations such as AI and machine learning for drug discovery and early development, innovations will occur in clinical trial models to reduce costs and put potential therapies on a faster track to marketing authorization. One growing trend is the emergence of virtual clinical trials as a way to reduce spend and patient burden, while increasing patient retention by making trials and treatments as accessible and attractive to patients as possible.

Traditional clinical trials have multiple physical study sites and require multiple patient visits to a site in order to meet a study's protocol. In virtual clinical trials, patients may participate from the comfort of their own home and record data remotely through monitoring devices, smartphones applications and more. Trials are beginning to incorporate smart contracts that allow patients to share their health data online with whomever they choose and be able to transparently see who requests and receives access to their personal health information. Virtual clinical trials also take advantage of online social media engagement platforms for critical actions such as recruitment, informed consent, patient counselling, measuring clinical endpoints and measuring adverse events.

Virtually conducted clinical trials offer opportunities for a more patient-centred approach. Adoption of virtual clinical trials will expand in the new decade as technological innovations emerge, making it possible to efficiently and effectively collect, monitor and collate data remotely. Virtual clinical trial models, AI and technology partnerships are a few of the crucial trends that will impact drug development in the new decade, speeding the way to getting much-needed medicines to the patients who need them.