What is Predictive Analytics?

Predictive analytics refers to using statistical algorithms, machine learning techniques, and historical data to forecast future events. In clinical trials, this means integrating diverse data sources – such as, past clinical trials, patient records, and real-world evidence (RWE) – to provide more accurate predictions about trial outcomes, patient responses, and potential risks³. By analyzing patterns from this data, predictive analytics offers a powerful tool for improving the efficiency, accuracy, and safety of clinical trials¹.

Data-Driven Decision-Making in Clinical Trials

Traditionally, clinical trials have been labor-intensive and costly, often taking years to yield results⁴. Researchers had to rely on historical outcomes, guesswork, and incomplete data to design trials and predict success. Predictive analytics changes this paradigm by enabling data-driven decision-making⁵. By analyzing data from clinical trials, and real-world data (RWD) – including but not limited to patient demographics, electronic health records (EHRs), and claims data-, predictive models can help physicians and researchers make informed decisions about trial design, patient selection, and potential treatment outcomes.

Predictive analytics is particularly valuable because it can integrate multiple data modalities, such as clinical, biological, genomic, biomarker, and imaging data. The ability to combine and analyze this wealth of information is central to predictive analytics’ potential to revolutionize the clinical trial process.

Key Applications of Predictive Analytics in Clinical Trials

1. Optimizing Clinical Trial Design: Predictive analytics can streamline clinical trial design by identifying the most effective methodologies and trial parameters, leading to trial designs that are likely to yield the most conclusive results with the least risk². Predictive models can also optimize dosage levels, intervention timing, and duration, reducing trial costs and timelines. This results in fewer unnecessary procedures, lower patient burden, and increased efficiency^5,6.
2. Improving and Accelerating Clinical Trial Execution: Predictive models can help overcome one of the biggest challenges in clinical trials, which is recruiting and retaining the right participants.
   i. Refine the target patient population, allowing researchers to narrow their focus and select patients most likely to benefit from the therapy, reducing variability, and increasing the chances of success²;
   ii. Forecast clinical outcomes, allowing the identification of early progressors and responders which can lead to faster go/no-go decisions²;
   iii. Predict adverse events (AE), highlighting patients who are at higher risk of suffering from an AE, and offering researchers a proactive approach that reduces the trial's overall risk and enhances patient care during the study⁷.
   Leveraging predictive analytics can lead to faster recruitment and better retention rates, thereby shortening trial timelines^4,6.
3. Speeding Up Drug Approvals: Predictive analytics can lead to faster, more efficient clinical trials, reducing the time and cost required to bring new drugs to market. By improving patient recruitment, optimizing trial design, and predicting outcomes and adverse events, predictive models help accelerate the overall trial process⁶. This means that new treatments reach patients sooner, benefiting both the patients who need innovative therapies and the companies that develop them, something especially important in fields like oncology and rare diseases, where time is often critical.

The Future of Clinical Trials

As the healthcare landscape continues to evolve, predictive analytics will play an increasingly central role in the clinical trial process, driving innovation and improving patient outcomes⁶.

As with any emerging technology, the adoption of predictive analytics in clinical trials requires collaboration between pharmaceutical companies, regulatory agencies, and healthcare providers. However, the potential benefits are too great to ignore. With predictive analytics at the helm, the future of clinical trials looks promising, offering a path to faster, safer, and more effective drug development.

Data-driven predictive analytics with SOPHiA DDM™

By leveraging on machine learning, SOPHiA DDM™ facilitates the integration and standardization of diverse data modalities – including but not limited to clinical, biological, radiomics, genomics, and digital pathology data – generating powerful insights to support you in accelerating drug development.

Our multimodal AI data analytics helps you optimize your clinical trial and enhance your post-launch access strategy, by predicting patient response to treatment, disease progression, risk of developing adverse events, and supporting treatment decision-making.

A great example of how SOPHiA GENETICS is spearheading innovation in cancer research by applying predictive analytics is the collaboration with UroCCR, the French Kidney Cancer Research Network, to develop a multimodal machine-learning model for predicting post-operative outcomes for individuals facing renal cell carcinoma (RCC). Using real-world prospective data from the UroCCR network, one of the world’s largest collaborative kidney cancer databases, this study showed that the AI model co-constructed by SOPHiA GENETICS and UroCCR provided a strong prediction for postoperative outcomes, outperforming the predictive performance of most usual risk scores. The results of this study have recently been published in npj Precision Oncology.

With a global network of 780+ institutions, across 70+ countries, and over 1.8 million genomics profiles analyzed to date, the SOPHiA DDM™ Platform accelerates the advances in the field of precision medicine. To learn more about SOPHiA DDM™ Multimodal Healthcare Analytics and our flagship programs visit our page. 

References

1. Visan AI, Negut I. Life. 2024;14(2):233. doi: 10.3390/life14020233
2. Tiwari PC, et al. Drug Dev Res. 2023;84(8):1652-1663. doi: 10.1002/ddr.22115
3. Clinical Trials Arena. Predictive analytics in drug development: state of play. Accessed on: September 2024. Available from: https://www.clinicaltrialsarena.com/features/predictive-analytics-drug-development/
4. Li X, et al. Clin Pharmacokinet. 2024. doi: 10.1007/s40262-024-01416-w
5. Paul D, et al. Drug Discov Today. 2021;26(1):80-93. doi: 10.1016/j.drudis.2020.10.010
6. Zhang B, et al. Commun Med. 2023;3(1):191. doi: 10.1038/s43856-023-00425-3
7. Yadav S, et al. Intelligent Pharmacy. 2024;2:367-380. doi: 10.1016/j.ipha.2024.02.009