Computational Pharmacology

Modeling and simulation in medicine and pharmaceutics


The Computational Pharmacology Group was established in 2018. The main focus of the group is on the area of applying modeling and simulation approaches to describe phenomena in medicine and pharmaceutics. Activity areas include modeling and simulation in drug development, in silico clinical trials, and the use of artificial intelligence methods to describe and unveil properties between medicines and pathophysiological conditions. In addition, the Computational Pharmacology group develops software tools for 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: Modeling 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: setting the appropriate sampling scheme and estimating the necessary sample size; less human exposure in clinical trials; reducing the cost of drug development; optimizing the clinical study design; allowing for more rapid development procedures; early selection of the most appropriate chemical compounds and/or formulations; gaining more information regarding the properties of the medicines; integrating in vitro and in vivo drug properties; and individualizing dose requirements. Today, the requirements of the European Medicines Agency and the US Food and Drug Administration make the application of MS methods an essential step in drug development.

Artificial Intelligence: One of the most rapidly rising activities of the group is to analyze and describe 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.

In silico clinical trials: One of the group’s activities is to develop in silico clinical trials (ISCT) in order to assist drug development. ISCT is an expression for clinical trials performed on computers or via computer simulations. ISCT allows predicting the outcome of a clinical trial and testing any condition that could potentially affect the outcome without performing the actual study. Thus, ISCT exhibits many advantages since it allows us to predict the in vivo performance of a medicine, investigate cases not able to be tested in actual practice, evaluate different conditions, optimize study design, adjust the most appropriate dosage regimen, and use a reduced sample size, thus minimizing time and cost during drug development.

Software products: The group works intensively on the development of software and applications used in the 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 students, and postdoctoral researchers, as well as pharmaceutical industry scientists, in the areas of modeling and simulation, software development, artificial intelligence and statistics, bioequivalence, population pharmacokinetic modeling, and any computational aspect of the drug development process.

Computational Pharmacology



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


  • 2023

    • Karalis, V.D. On the Interplay between Machine Learning, Population Pharmacokinetics, and Bioequivalence to Introduce Average Slope as a New Measure for Absorption Rate (2023) Applied Sciences (Switzerland), 13 (4), art. no. 2257, .
    • Karalis, V.D. Machine Learning in Bioequivalence: Towards Identifying an Appropriate Measure of Absorption Rate (2023) Applied Sciences (Switzerland), 13 (1), art. no. 418.
    • Karalis V. An In-Silico Approach Toward the Appropriate Absorption Rate Metric in Bioequivalence. Pharmaceuticals 2023, 16(5), 725;
    • Kousovista R, Karali G, Karalis V. Modeling the Double Peak Phenomenon in Drug Absorption Kinetics: The Case of Amisulpride. BioMedInformatics 2023, 3(1), 177-192;
    • Damnjanović I, Tsyplakova N, Stefanović N, Tošić T, Catić-Đorđević A, Karalis V. Joint Use of Population Pharmacokinetics and Machine Learning for Optimizing Antiepileptic Treatment in Pediatric Population. Ther Adv Drug Saf [2023]
    • Matsota P, Karalis V, Saranteas T, Kiospe F, Markantonis SL. Ropivacaine pharmacokinetics in the arterial and venous pools after ultrasound-guided continuous thoracic paravertebral nerve block. J Anaesth Clin Pharmacol 2022; doi: 10.4103/joacp.joacp_353_22

  • 2022
  • 2021
  • 2020
  • 2019
  • 2014-2018

    • 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-3174Basic Clin Pharmacol Toxicol.

      • 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.

  • 2013




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)