Transforming Clinical Trials
Pattern Sciences unlocks the capability to efficiently and
accurately extract Real World Evidence (RWE) from Real World
Data (RWD) to enable next-generation clinical trial design.
Pattern Science's Real World Modeling Engine enables the creation of synthetic patient cohorts, which can be used to replace conventional control and treatment arms in clinical trials.
Traditional Clinical Trials Are Time Intensive & Expensive
Pattern Science's Real World Modeling Engine processes historical patient data into synthetic cohorts, which can be utilized in a vast array of clinical research.
Pattern Science is working to apply proprietary algorithms for three initial use cases, all of which have the potential to fundamentally change the way the world conducts clinical trials.
Gilead “whole heartedly supports [Pattern Sciences’] proposed studies” to compare two FDA approved oral HIV Pre-Exposure Prophylaxis therapies, Truvada and Descovy.
Synthetic Single-Arm Control Arm Trials
Pattern Science is creating advanced analysis methods to characterize existing data including historical control data, real world data, and the generation of a companion data sets to eliminate the need for traditional placebo groups, allowing for radically more efficient clinical trials where all participants receive the treatment.
Direct Head-to-Head Drug Comparisons
Although patients increasingly have multiple drug options available, there is often a lack of evidence from head-to-head clinical trials that allows for direct comparison of the efficacy (and/or safety) of one drug vs. another. Pattern Sciences is working to provide robust methodologies for accurate and efficient drug comparisons.
Multi-Arm Synthetic Clinical Trials
An expansion of Synthetic Cohorts, Multi-Arm Synthetic Clinical Trials would radically change the clinical research landscape by allowing for the combination and refinement of multiple treatment arms in the same study.