The analytical method presented excellent linearity (R\({}^{2}\) > 0.990), high recoveries (75-87%), increased repeatability (0.5-5.6%) and low LOD values (15 pg/mg in the case of skin and IVDs) in all matrices studied. Plasma, skin and AF samples, presented a similar temporal variation pattern in the concentration of ZOL, with a peak at 30-60 min and then was quickly reduced approaching baseline levels. ZOL was not detected in NP tissues. Physiological concentrations of ZOL were non-cytotoxic and unable to increase ROS production in rabbit AF cells. Finally, ZOL provoked an anabolic phenotype in AF IVD cells. **Acknowledgments:** This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme \"Human Resources Development, Education and Lifelong Learning 2014-2020\" in the context of the project \"Study of antitumor compounds' accumulation in intervertebral disc tissues and their effects on cellular aging\" (MIS 5047829). _8.7. Machine Leaning Techniques for the Correlation of Retention Factors of Biomimetic Chromatography with Pharmacokine Properties_  
**P. Danias *, F. Tsopelas and A. Pappa**  
Laboratory of Inorganic and Analytical Chemistry, School of Chemical Engineering, National Technical University of Athens, Iroon Polytechniou 9, 15780 Athens, Greece  
* Correspondence: pan.danias@hotmail.com  
Biomimetic chromatography, involving Immobilized Artificial Membrane (IAM) and Immobilized Artificial Protein Chromatography as well as micellar chromatography, has gained considerable attention due to its ability to simulate biological processes, in particular for rapid screening of candidate drugs in the early stages of drug discovery. Biomimetic chromatographic indices can be used for the prediction of pharmacokinetic properties, such as volume of distribution, plasma protein binding (%PPB), Human Intestinal Absorption (HIA), and Blood-Brain Barrier (BBB) Penetration [1]. In some cases, however, standard computational techniques such as linear and non-linear regression seem to be insufficient to successfully correlate biomimetic indices with pharmacokinetic properties, such as volume of distribution and BBB penetration. Machine learning and Artificial Intelligence techniques have the potential to provide an insight of the underlying molecular mechanism and construct algorithms with superior predictive performance [2,3]. In the present work several Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence Techniques were used to construct models based on biomimetic chromatographic indices with certain pharmacokinetic properties. For this purpose, retention factors of over than 100 structurally diverse drugs were considered. Additional physicochemical properties, such as positively and negatively charged molecular fractions, topological polar surface area, and molecular weight were tested. Validation of the constructed models was carried out using appropriate test sets. ## References  
* Tsopelas et al. 2020 Tsopelas, F.; Danias, P.; Pappa, A.; Tsantili-Kakoulidou, A. Biopartitioning micellar chromatography under different conditions: Insight into the retention mechanism and the potential to model biological processes. _J. Chromatogr. A_**2020**, _1621_, 461027. * Kumar et al. 2017 Kumar, R.; Sharma, A.; Siddiqui, M.H. ; Tiwari, R.K. Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques. _Curr. Drug Discov. Technol._**2017**, _14_, 244-254. * Ghahramani 2015 Ghahramani, Z. Probabilistic machine learning and artificial intelligence. _Nat. Cell Biol._**2015**, _521_, 452-459.