First Health Pharma prioritizes the development of compounds with optimal physico-chemical profile, from the early phases of drug discovery pipeline, to increase the chances of research project success


The process for researching and developing new medicines (R&D) requires a major expenditure in terms of time and cost. That is why thorough evaluation of drug hits and identification of lead compounds at an early stage is of great importance to the pharmaceutical industry.

There are thousands or even millions of compounds that may be tested and assessed at early stages of research and development process, but only few of them will be ultimately approved and subjected to further drug investigation during the clinical testing.

The probability of clinical success (the Likelihood of Approval from Phase I) is currently estimated to be less than 12%.

With many drugs tested in Phase I trials that fail to success, attrition is definitely a major issue in the pharmaceutical industry. The approach to reduce drug attrition rate may include the control of physicochemical properties of compounds and consequent identification of compounds with optimal physicochemical properties.


Since from the early stages, First Health Pharmaceuticals research and development pipeline, integrates a series of internally developed Artificial Intelligence predictive models to monitor the principal physicochemical properties such as water solubility and membrane permeability.

The Artificial Intelligence models based on algorithms such as artificial neural network and random forest, are constantly updated with experimental data coming from the tested compounds to increase the reliability of the predictions.

In advanced phases the data on physicochemical properties and ADME characterization are combined to assess the probability of success (POS) for a specific compound, identifying the the suitable candidates for the further development process.