Offers
Industry
Pharma, biotech, and medical device companies struggle with long and costly drug R&D, access, and maintenance.
Quinten Health supports them across the medical product lifecycle.
Drug candidate screening and chemical lead optimization
U-SAR is a Quinten Health software solution which helps medicinal chemists save considerable amounts of time in their multiparametric molecular optimization process. Traditional SAR models trained on large chemical spaces systematically fail to predict molecular properties when it comes to compound optimization in limited chemical spaces. U-SAR turns the usual tedious and time consuming iterative multiparametric optimization process into an increasingly productive and converging experience through medicinal chemists’ cognitive augmentation.
Indication identification and prioritization
Quinten Health masters a pipeline of algorithms and methodologies, from clustering on real-world data to Natural Language Processing, which can be combined to hypothesize or confirm new indications for existing drugs. Such insights can guide the lifecycle and portfolio management and support drugs repurposing.
Patient profiling for biomarkers mining or predictive safety
Quinten Health delivers high-performance and interpretable patients profiling, e.g. using our proprietary machine learning algorithm Q-Finder®. We identify and characterize sub-groups of patients with a similar course of disease, with higher differentiation versus the standard of care, or with higher benefit / risk profiles for a drug, therapeutic strategy, or health policy. Q-Finder® has proven to be instrumental in a wide range of business applications, including biomarkers mining, drug responder's identification, or fast progressors profiling.
Diagnosis and prescription drivers
We design machine learning algorithms for specific disease diagnoses and train them on real-world data to enable deployment at the point of care. Diagnosis algorithms can be based on a combination of reported symptoms, text notes, biological data, and of course real-world data as reported in databases. They can be used for ICD codes proxies, for early diagnosis of rare diseases by physicians or to help patients self-diagnose their diseases.
Treatment patterns mining
Machine learning algorithms developed by Quinten Health allow for characterizing optimal treatment patterns based on patient’s medical history and characteristics. Disease progression can then be modeled in the real-world e.g. to detect unmet medical needs, predict realistic disease-level public health impact of new treatments and help positioning drugs under development.
Drug repurposing
Quinten Health masters a pipeline of algorithms and methodologies, from clustering on real-world data to Natural Language Processing, which can be combined to hypothesize or confirm new indications for existing drugs. Such insights can guide the lifecycle and portfolio management and support drugs repurposing.
Automated safety report and predictive safety
Quinten Health designs and develops bespoke solutions leveraging state-of-the-art and proprietary Natural Language Processing (NLP) algorithms to automate extraction of insights from the ever-growing medical literature. This allows to speed up systematic literature review processes with regulatory-grade standards.