Therapeutic Performance Mapping System (TPMS) technology is an advanced top-down approach that combines systems biology and AI with applications throughout all stages of the drug discovery and development process. It integrates comprehensive pharmacological, biological, and clinical knowledge to generate mathematical models capable of simulating human (patho)physiology in silico.
TPMS mathematical models are constructed using a combination of machine learning techniques, Artificial Neural Networks and Sampling Methods. These models are trained and validated using a training set that contains comprehensive information about drugs, targets, indications, and adverse events defined as clinical elements of the network. Through machine learning algorithms, the models learn from the training data and capture complex relationships within the network. This enables the models to simulate human pathophysiology and predict the clinical responses to different drugs and interventions. By leveraging these mathematical models, TPMS technology provides a powerful tool for understanding and analyzing the intricate pharmacological mechanisms of human body's response to therapies.
Unveiling the Key Elements of TPMS Technology
The methodology is systematic, and applies data and mechanistic-driven analysis to offers clinical oriented results. To achieve clinical-oriented results, TPMS technology incorporates crucial components:
- The Biological Effector Database: Our proprietary database serves as a crucial resource that translates medical terminologies into molecular terms. It aids in the comprehensive understanding of the underlying molecular mechanisms associated with various medical conditions and adverse drug reactions.
- Patient Data Integration: TPMS technology allows for the inclusion of patients' data collected from public repositories or provided by the client. By incorporating this information, virtual patient cohorts with diverse profiles can be constructed, enhancing the applicability of the models to real-world scenarios.
- Supervised Machine Learning: TPMS technology employs supervised machine learning techniques to leverage information about drugs, targets, and clinical elements within the network. This includes the utilization of molecular fingerprints associated with adverse drug reactions and indications from the proprietary database. These machine learning algorithms enable the prediction of clinical outcomes and facilitate informed decision-making.
TPMS technology has undergone extensive validation at various levels. Internally, experimental validation has been conducted through in vitro and in vivo studies, ensuring the accuracy and reliability of the predictions. Additionally, the technology has been involved in numerous competitive grants, including 22 European Union projects, further demonstrating its robustness and effectiveness.
All our policies and operating procedures are certified to be in accordance with the ISO 9001:2015 quality management standard and with the ISO 27001:2013 information security management standard. We are constantly working to further refine our technology and create new tools in order to better meet the needs of our clients.