Unlock the power of clinico-molecular data
The pharmaceutical industry is turning to artificial intelligence (AI) techniques such as machine learning and neural networks to extract insights about drugs, diseases and outcomes from clinical and molecular data. However, it is challenging to apply AI reliably and meaningfully to biomedicine because:
- AI applied to healthcare requires a high-quality and complex integration of different types of data that do not normally appear interlinked in typical sources
- AI’s predictive power and capability to produce meaningful insights is well-established with linear, repetitive patterns, while biomedicine data exhibits a different nature with complex and scattered features
- Healthcare records are incomplete and variable, so human intervention by highly specialized medical experts is needed to decide which techniques will yield the most meaningful and accurate interpretations.
Molecular Health merges clinical, molecular and drug data, and filters it through its highly-specialized data knowledge-base and human domain expertise to choose the right machine learning model that will optimize and ensure the quality and reliability of data insights to increase your success in discovering, developing and commercializing drugs.
- Better predict drug response and resistance
- The merging of clinical and molecular data allows you to form a better understanding of how drug candidates will perform in the real world before full real-world evidence emerges and is aggregated.
- Design more successful trials
- An improved understanding of the clinical and molecular signals and pathways that explain and predict drug safety and efficacy will enable you to design trials that are more targeted, more precisely stratified, and potentially more likely to succeed.
- Use molecular evidence for market acceptance
- Payers want to know which patients will respond to your drug, and whether the outcome will be better than with other treatments. Through in silico science delivered by MH, you can develop a deep understanding of the clinical and molecular mode of action, identify and validate biomarkers to support your therapy’s value, and find treatment combinations that have the potential to improve efficacy and safety as well as identifying repurposing opportunities for existing drugs.