Designing and implementing clinical studies and similar consumer studies is expensive, intrusive, and can introduce critical delays in getting products to market. The stringent regulatory requirements for clinical trials make them particularly challenging.
Machine learning enables you to build a model based on study data (whether from a single study or multiple studies) and then apply that model to gain insights. Establish useful relationships that might otherwise remain hidden. Predict likely behaviour in new studies. As a result, you can design further phases of study to be more efficient and provide valuable information to product design and development.
Alchemite™ machine learning has unique capabilities in its ability to handle sparse, noisy data (as often found in real-world studies), its speed, and its accurate quantification of uncertainty in predictions, enabling rational decision-making.
Webinar – Machine learning for efficient clinical studies design
In this recorded webinar, see how machine learning (ML) can enable faster progress and valuable insights, drawing on experience including a project at BAT that applied ML as a tool when optimising studies on the pharmacokinetic response of participants using a new product.
Potential applications in clinical studies
- Study design and protocol optimisation – harness deep data insights to improve the probability of success from non-clinical translation throughout the clinical development plan.
- Enable selection of participant populations for investigation, including patient enrichment strategies and adaptive design.
- Identify useful surrogate clinical measures, for example, when using digital health technologies.
- Find outliers in the data quickly, enabling more effective adverse event signal detection or prediction.
- Identify and analyse sub-populations within your studies.
- Support simulation and product design based on real-world evidence (RWE) / real-world data (RWD) approaches.
Other types of study
Alchemite™ can be applied to extract more value and insight from any research study. Examples include: consumer studies, product trials, and population studies for applications in healthcare or personalised medicine.
What is unique about Alchemite™?
- A machine learning approach that works for complex, real-world data, particularly where data is sparse (e.g., due to aggregating results from multiple studies).
- Proven in a wide range of practical applications in life sciences and chemistry.
- Accurate uncertainty quantification enables rational decision making.
- Provides effective guidance on next steps in a study or experiment (using Bayesian optimisation capabilities).
- Supported by the Intellegens Science Team with extensive application experience, including for clinical study projects.
