Computational Pharmacology


The Computational Pharmacology group was established in 2018. The main focus of the group is on the area of applying modelling and simulation approaches to describe phenomena in medicine and pharmaceutics. Activity areas include modelling & simulation in drug development, in silico clinical trials, use of artificial intelligence methods to describe and unveil properties between drugs and pathophysiological conditions, and quantitative system pharmacology. In addition, the Computational Pharmacology group develops software tools for their use in drug development. Successful applications include the in vitro - in vivo simulator which allows the prediction of the outcome of a bioequivalence using only in vitro data. The group also provides assistance in the R&D departments of pharmaceutical industries and offers training programs, related to any computational methods, to scientists working in the medicine/pharmaceutics area.


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.

Computational Pharmacology


Ongoing projects

 Development of a Triple Combination Tablet for the treatment of Hypertension 3CT4Hypertension (ΤΕ1ΔΚ 561, ΕΣΠΑ 2014-2020)


  • 2013-2019

    • Macheras, P., Karalis, V., Valsami, G., (2013). Keeping a critical eye on the science and the regulation of oral drug absorption: A review. J Pharm Sci. 102: 3018-36.
    • Karalis, V., (2013). The Role of the Upper Sample Size Limit in Two-Stage Bioequivalence Designs. Int J Pharm. 456:87-94.
    • Karalis, V., Bialer, M., Macheras, P., (2013). Quantitative Assessment of the Switchability of Generic Products. Eur J Pharm Sci. 50:476-483.
    • Karalis, V., Macheras, P., (2014). On the Statistical Model of the Two-Stage Designs in Bioequivalence Assessment. J Pharm Pharmacol. 66(1):48-52.
    • Karalis, V., Macheras, P., Bialer, M., (2014). Generic Products of Antiepileptic Drugs: A Perspective on Bioequivalence, Bioavailability and Formulation Switches Using Monte Carlo Simulations. CNS Drugs. 28:69-77.
    •      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


For any information regarding the Group, please contact:

Computational Pharmacology Group
Institute of Applied and Computational Mathematics
Foundation for Research and Technology - Hellas
Nikolaou Plastira 100, Vassilika Vouton,
GR 700 13 Heraklion, Crete

Tel: +30 2810 391800
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. (Mrs. Maria Papadaki)

Tel.: +30 2810 391805
E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it. (Mrs. Yiota Rigopoulou)

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