Modelling & Simulation in drug development: Modelling and simulation (MS) approaches represent an integral part of drug development starting from in vitro testing up to all phases of the clinical procedure. The benefits of applying MS methods in drug development include the following: set the appropriate sampling scheme and estimate the necessary sample size, less human exposure in clinical trials, reduce the cost of drug development, optimize the clinical study design, allow for more rapid development procedures, early selection of the most appropriate chemical compounds and/or formulations, gain more information regarding the properties of the medicines, integrate in vitro and in vivo drug properties, individualize dose requirements. Today, the requirements of the European Medicines Agency (EMA) and the US-Food and Drug Administration (FDA) made the application of MS methods an essential step in drug development.
Artificial Intelligence - Applied Statistics: One of group’s activities is to apply any statistical methodology to analyze real world problems in medicine and pharmacy. However, real world situations can be rather complex and very often cannot be analyzed using typical inferential statistics like t-tests, general linear models (e.g., analysis of variance), the correlation coefficient of Pearson etc. Thus, additional methodologies are required such as artificial intelligence, machine learning, multivariate statistics, non-linear mixed effect modeling, non-parametric methods, classification techniques, Monte Carlo methods, signal analysis, and procedures for analyzing time series data. All types of statistical analyses are used in the drug development process and clinical problems.
Quantitative systems pharmacology: Quantitative systems pharmacology (QSP) is a discipline that utilizes mathematical models and computing in order to describe biological systems, disease processes, and drug pharmacokinetics and pharmacodynamics. In case of pharmacokinetic and pharmacodynamic modeling, QSP can also allow the identification and quantification of several sources of variability (e.g., between- and within-subject, inter-occasion variability, etc.) in drug concentrations of the population under study. QSP modelling has numerous advantages such as incorporating unbalanced designs, modelling sparse data (e.g., only two or three samples per subject), and examining the role of patient-specific covariates (such as gender, age, body weight).
Software Products: The group works intensively on the development of software and applications used in R&D departments of pharmaceutical industries. Among others, the group has developed an in vitro – in vivo simulation tool (IVIVS) which is a flexible tool able to predict the in vivo behavior of a formulation and/or the bioequivalence outcome based only on in vitro data. This IVIVS app is useful for selecting the most appropriate formulation during development and the most appropriate clinical trial design and sample size.
Education and Training : The group contributes to the education and training of undergraduate, graduate and post-graduate students, PhD candidates and postdoctoral researchers, as well as pharmaceutical industry scientists in the area of modelling and simulation, software development, artificial intelligence and statistics, bioequivalence, population pharmacokinetic modelling, and any computational aspect in drug development process.
Development of a Triple Combination Tablet for the treatment of Hypertension 3CT4Hypertension (ΤΕ1ΔΚ 561, ΕΣΠΑ 2014-2020)
Karatza E. Karalis V. Modelling gastric emptying: a pharmacokinetic model simultaneously describing distribution of losartan and its active metabolite EXP-3174. Basic Clin Pharmacol Toxicol [doi: 10.1111/BCPT.13321]
· Vangelis Karalis, Collaborating Researcher
· Georgia Karali, Collaborating Researcher
· Eleni Karatza, PhD candidate
· Ourania Kousovista, PhD candidate