A platform to simulate trials and foster collaboration

« jinkō helped my team explore the best treatment combinations for our PhII clinical trial, with an unlimited number of trial designs at limited cost. »

« I use jinkō to build models for our clinical team. My non-modeler colleagues can then run their in silico clinical trials with full autonomy, which frees my time to cover more projects in our pipeline. »

« Thanks to jinkō we can provide the rationale for dose selection, and thereby meet regulators’ expectations. »

« We provide clinicians with clear visualisations of the impact of a given variable on the study outcome. This is particularly useful to refine inclusion and exclusion criteria. »

« I enjoy jinkō’s collaborative, transparent approach to literature review. Everything is traceable, meaning I can always easily go back to the primary knowledge source and therefore have full confidence in what has gone into the disease model. »

« I can generate the exact clinical data required by each HTA body to feed my HEOR model. »

Our offering

jinkō   models

 

01

Access disease models across a wide range of therapeutic areas

02

Generate your own population or upload existing virtual patients

03

Test different experimental protocols on specific patient populations

jinkō  platform

Combining the powers of data science, biology and medicine, jinkō predicts
clinical outcomes before trials in human subjects

Manage your knowledge and stay up to date through collaborative curation

Design and simulate with best-in-class model library, virtual patients and simple protocol design

Analyze and derisk: intuitive interface
for clinical trial experts

Try jinkō

Managed services

We create and analyze knowledge-based models
to solve your R&D questions

Diversity of expertise in terms of scientific domains (pharma, biostats, medicine, clinical development, applied maths, computer science, etc.)

Numerous diseases knowledge

across different applications

oncology, infectious diseases, immunology, rare diseases, cardiovascular diseases, etc.

40+

Biomodelers

Features   jinkō   platform

Module 01

Knowledge management 

Jinkō’s knowledge management feature allows scientific knowledge to be extracted from trusted sources to create a complete, fully transparent knowledge model. 

Scientific extracts and claims are expertly curated and scored by scientists, in a systematic and collective fashion, ensuring the knowledge underpinning any model is as robust and comprehensive as possible. 

These extracts and claims are then added to a fully collaborative document editor, alongside the global description of the model and its mathematical equations. This creates  structured and transparent documentation, all the way to the scientific sources, including comments from internal and external experts involved in the research.

Module 02

Modeling & simulation 

Building on nova’s 10+ years of modeling expertise,  jinkō incorporates best practice in modeling and in-silico simulation to streamline R&D workflows and support their decision-making.

Even those without modeling expertise can quickly run trials on various populations, choose their measures of interest and apply different  scenarios to trial arms in which  individual patients can be their  own control.  This allows investigators to drill down into precise treatment effects and identify best-responders. 

Jinkō’s collaborative features make it easy for modelers and clinicians to collaborate and communicate at every step of the simulation, be it to discuss the model itself, or the results of a first in silico trial.

Module 03

Data visualization & analytics

Investigators can explore the results of a simulation on an easy-to-use interface, which offers a choice of methods to highlight measures of interest. 

They can compare results from different arms and their respective controls to evaluate the effects of treatments and create visuals that can be shared. 

With jinkō’s automated impact analysis, it is easy to quickly identify patient descriptors that generate the most variability in the model, and to refine eligibility criteria. This allows researchers to immediately see the effect of these descriptors on simulation results, thereby identifying trial design improvements and increasing the probability of success.

Solutions for clinical development issues

Applications from discovery to market

Go further

Do you want to learn more
about in silico clinical trials ?
Take a look at our ebook