DATA - The next hurdle

In the high-stakes world of pharmaceuticals, the journey from molecule to market is a marathon requiring precision, agility and speed.

As the industry faces ever-increasing pressure to deliver new therapies faster, there is a growing recognition that the traditional methods of drug development must evolve. The solution lies in the heart of modern data science.

Data science is the backbone of drug discovery and development. It enables researchers to analyse vast data sets, identify potential drug candidates, and predict their efficacy and safety with unprecedented accuracy.

Yet, the true potential of data science in accelerating drug development can only be realised with the right tools, methodologies and practices.

Here, we explore three key areas that pharmaceutical companies must focus on to speed up their data science processes and, consequently, their drug development timelines.

Open-source software has revolutionised many industries, and life sciences is no exception. The flexibility and innovation offered by these tools can significantly enhance data analysis and model building.

However, the challenge lies in integrating these tools within a regulated environment where compliance and validation are paramount.

Pharmaceutical companies must take a number of steps ahead of implementation of open-source tools to deliver an effective integration that ensures compliance. This can include designing rigorous validation processes, involving regulatory experts to offer insight and advice from the beginning of the project and adopting tools with a proven track record in the industry.

Taking the above into consideration will enable companies to accelerate their data science processes and bring therapies to the market faster.

AI and machine learning are transforming drug development by enabling predictive modelling and advanced data analytics. However, the deployment of AI requires a focus on trust and transparency.

Models provide AI with the raw data from which to create insights – so the data has to be 100 percent accurate, reliable and transparent so data scientists, business leaders and regulatory auditors can fully understand the inputs and the outputs.

If the design, build and vetting process of models used can’t be trusted, then the insights generated cannot be relied on.

In any field this is critical, but in pharmaceuticals there is zero room for error when the insights from AI could be the basis for recommended dosages or detailed side effects that end up printed on the label of medicines.

The pharmaceutical industry must invest in the processes behind the technology. This includes ensuring validation and vetting procedures are regularly tested and updated and rigorous data governance practices are maintained.

The speed at which data scientists can build and test models directly impacts the drug development timeline. Traditional, cumbersome processes can slow down innovation, whereas agile, lightweight environments can significantly enhance productivity.

By enabling rapid model building and testing, these tools empower data scientists to work more effectively and accelerate the entire drug development process.

In the race to develop new therapies, speeding up the data science process is not just advantageous – it is essential. Pharmaceutical companies must embrace open-source tools within a regulated environment, build trustworthy AI models and adopt efficient model-building platforms.

As the industry continues to innovate, the integration of advanced data science techniques will play a crucial role in overcoming the challenges of drug development.

By focusing on the key areas highlighted, companies can not only accelerate their timelines but also improve the quality and efficacy of the therapies they bring to market.

The next hurdle

Building trust