I N S I L I A N C E
AI FOR DRUG (RE)POSITIONING, CLINICAL DIAGNOSIS & TREATMENTS
HYPERFOCUS. Because AI for Drug (Re)positioning, Clinical Diagnosis & Treatments needs results, PoC by PoC, to create trust
Patients want precise diagnosis, drug treatments (7 000 orphan diseases), or more efficient treatments. They deserve a more fertile R&D while at best only 1 out of 14 drug-candidates entering the clinical phase will get an FDA approval (Dowden & Munro - 2019) and while its overall $165B budget in 2019 led to only 48 of them.
Drug (re)positioning is a pharmaceutical R&D strategy which can reduce risks, processing time and costs but has to be very selective about drug-candidates in order to create significant medical then economic values.
Artificial Intelligence can match, analyse and simulate various structured and unstructured Health (Big) Data exhaustively and quickly in order to help discover patterns and to provide decisive knowledge, hypotheses and predictions.
Because every stakeholder of a pharmaceutical or medical project needs to build trust in AI solutions, INSILIANCE hyperfocuses its mission, objectives and expertise to AI for Drug (Re)positioning, Clinical Diagnosis & Treatments.
MEDICAL & ECONOMIC VALUE. Our mission is to reduce risks and costs and increase medical benefit and economic value of private-public projects
Drug (Re)positioning, Clinical Diagnosis & Treatments are human and data-driven environments with a massive of public and private health data such as medical images, genomic profiles, molecular, diseases and genes structures and signatures, scientific literature, patents, Real-World Evidence, etc.
Artificial Intelligence is not a magical technology. But it can really help biologists, MDs and executives get a more decisive knowledge from various health data with a quicker process in every phase of research due to a cultural, strategic and operational shift, from trials & errors to predictions.
INSILIANCE, its experts and its customized solutions can improve lateral thinking, serendipity and decision-making in consortiums and own preclinical projects for reducing risks and costs, increasing medical benefit of drugs and improving private and public economic models.
HALF-OPEN INNOVATION. Validating health data, algorithms design and results requires a full integration of researchers, MDs & executives partners’ experience and needs
Medicine is an art. Experience, expertise, intuition, serendipity, sense of others, transparency, confidentiality and ethics are crucial for the efficiency and the effectiveness of AI for Drug (Re)positioning, Clinical Diagnosis & Treatments projects.
And every partner in an AI project wishes to avoid any "black box" effect throughout the process, from the selection of health data sets to the analysis of results, including the algorithm fine-tuning.
It is no surprise that with a name composed of Insilico and Alliance, INSILIANCE always associates biologists, MDs and pharmacists with computer and BioData scientists and, whenever needed, executives, ethical referents and regulators in Half-Open Innovation (innovation pulled by partners) projects and task forces.
CHAMPOLLION.AI.PLATFORM INSIDE. Your projects always need to get quicker, more in-depth and unexpected crucial information about responses to treatments
Like hieroglyphs, pharmacology, "Omics", Scientific Literature, Pharmaceutical Patents, Real-World evidence and other types of data can better and quicker deliver their full potential of key knowledge for Drug (re)positioning, Clinical Diagnosis & Treatments.
With consortiums or confidential bilateral projects and Half-Open Innovation approaches, because a quick POC by POC implementation of AI is essential, every collaborative project takes benefit from a flexible AI platform for delivering universal and customized services.
With pioneer-partners, INSILIANCE is developing its CHAMPOLLION.AI.PLATFORM and its expertise in order to bring short term value-added AI solutions to their projects and to its team who will soon launch a complete preclinical R&D for own account.