Reckonect

Reckonect SAS is a company rooted in deeptech applied to biology. We combine AI-based information retrieval models and biomedical expertise to map disease progression from 3 perspectives: scale, causality and context of pathological development.

We are distinct from alternatives, noteworthy because we rely on avatars of pathologies and we can integrate heterogenous data (eg: multi-omics, semantic). See more at reckonect.com.

Drug screening for the identification of lead and mechanism of action, is expensive (several thousands of euros), long (3-5 years) with a high failure rate as only 4% will yield a licensed drug.
Reckonect’s AI based in silico drug screening for preclinical studies, drastically decreases both the duration and expenses of this step.

We offer several solutions for in silico drug screening, de novo positioning, indication repositioning or even identification of mechanism of action.
Ideally positioned upstream of the in vitro testing you designed; you alleviate the risk of failing the in-vitro tests and save both time and money by at least a factor of 100 folds.

A mechanistic disease progression model is a representation of all accessible relevant knowledge and validated experimental, preclinical, clinical data or even real-world evidence.
It is built upon the understanding of physiology in depth, especially on the expertise of the hierarchy of biological events.
The most promising model of disease progression are avatars and digital twins in healthcare.

Avatars as mechanistic disease progression models, is a game changer in diagnostics and therapeutic R&D.
They are known to drastically compress both time and expenses of basic, translational and clinical research in diagnostics and therapeutics discovery.
They are especially relevant on the following tasks: the identification of biomarkers and drivers of disease progression, the unravelling of the synergies or antagonisms of comorbidities or genetic background on a given disease progression.

Mechanistic disease progression avatars rationalize very early the R&D process with the complex physiological knowledge.
They are currently impacting profoundly therapeutic discovery as they to in silico drug screening, de novo indication positioning, and indication repositioning.

Disease is associated with a prototypical signature, aka several non-physiological alterations in the behavior of biological components (e.g. genes, lipids, proteins, metabolites, signaling networks, cell types, organs).
More precisely, this signature of biomarkers and drivers, is composed of distinct types and identities of components (e.g. a given cellular ecosystem for which several signaling are altered) and is specific of associated conditions such as comorbidities.

Although analysis of omics data by data scientists is still the main strategy to perform the discovery of biomarkers and drivers, it suffers from several caveats that mechanistic models resolve.
Nowadays, mechanistic models such as avatars of disease progression, are combined with data science tools.
Mechanistic models are a kind of avatars that is now combined with data science to offer the only way of integration of both biological cascades and physiological context, as well as the various organism levels (molecules, cell types, organs).
This dual strategy is proven to be extremely powerful as it facilitates empirical data integration (e.g. omics, imaging, semantics) and therefore impacts directly on candidate discovery, qualification, verification, validation and optimization.

Disease progression encompasses several levels of organization: molecular, cellular and tissular. When it comes to understand and target disease progression, one has to carefully consider the existence of comorbidities or existing conditions.
In such cases, the classical statistical data science on omics or imaging data, remains too reductionist to take into account comorbidities.
On the contrary, Reckonect can easily build and combined mechanistic disease progression models of several conditions to identify their potential synergies or antagonisms, using and empowering datascience workflows.