Synth pops

Accelerating innovation possibilities throughout the life sciences

The pharmaceutical industry is facing an innovation imperative. As the demand for new treatments grows and the complexity of drug development increases, traditional approaches are proving less sustainable.

With research and development costs averaging over $2.6 billion per drug, according to estimates by Pharmaceutical Research and Manufacturers of America, synthetic data offers a transformative solution that can help pharmaceutical companies innovate faster and more efficiently. So, what is it?

Synthetic data refers to artificially generated datasets and is a form of generative AI that maintains the statistical properties of real-world data without compromising privacy.

In highly regulated sectors like life sciences, where patient confidentiality is paramount, synthetic data can be a game-changer. It can open new pathways for clinical trials, drug repurposing and commercialisation strategies.

Recruiting a diverse and representative patient population is a critical yet often challenging aspect of clinical trials. Recent reports highlight that 80% of clinical trials fail to meet enrolment deadlines, and 15% to 20% of trial sites fail to enrol a single patient.

Synthetic data provides a way to simulate patient populations, allowing researchers to test drug efficacy across a wider range of demographics, genetic profiles and health conditions without needing real patients in every scenario.

By utilising synthetic data, pharmaceutical companies can run virtual trials to complement real-world studies, reducing the time and cost associated with trial recruitment.

This approach not only accelerates the process but also enables researchers to explore treatment effects in underrepresented or rare disease populations, improving the inclusivity and validity of clinical trials.

The potential for drug repurposing – finding new uses for existing medications – has also been magnified in recent years, especially in response to crises like the COVID-19 pandemic. Drug repurposing can shorten development timelines by up to 60% compared to traditional pathways.

Synthetic data plays a pivotal role in accelerating these efforts by allowing companies to simulate how a drug might perform in treating different conditions.

Through data synthesis, pharmaceutical firms can simulate complex scenarios where a drug interacts with different diseases or patient populations, quickly identifying promising repurposing candidates. This ability to test hypotheses with synthetic data helps researchers move beyond the constraints of real-world data, which is often limited in availability and scope.

But bringing a drug to market is only half the battle. Understanding the behaviours of healthcare providers, patients and stakeholders is crucial to successful commercialisation.

Synthetic data allows companies to model various market scenarios, simulating how different factors – such as prescription patterns, provider preferences, or patient adherence – may affect product uptake.

Pharmaceutical companies that embrace advanced analytics, including the use of synthetic data, can boost commercial success significantly. Platforms like SAS Data Maker provide the tools to generate and analyse synthetic data sets at scale, offering actionable insights into prescription trends, regional sales dynamics and market access barriers.

By leveraging synthetic data, companies can refine their go-to-market strategies with more precision, leading to faster adoption and broader market penetration.

The promise of synthetic data extends far beyond just protecting patient privacy – it is a catalyst for faster innovation.

Whether simulating patient populations, accelerating drug repurposing efforts, or fine-tuning market strategies, synthetic data empowers pharmaceutical companies to innovate with greater speed and confidence.

When time-to-market is critical and the cost of failure is high, the ability to harness synthetic data may well determine which companies lead the future of life sciences.

With synthetic data, organisations can scale their capabilities and drive forward the next wave of innovation.

Kayt Leonard is Global Health and Life Sciences Advisor at SAS.
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