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Computational Pharmacology

ABOUT    R&D ACTIVITIES    R&D PROGRAMS    PEOPLE    PUBLICATIONS


ABOUT

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.


RESEARCH AND DEVELOPMENT ACTIVITIES

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.


RESEARCH AND DEVELOPMENT PROGRAMS

COMPLETED PROJECTS

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


PEOPLE

RESEARCHERS

STUDENTS

  • Anastasia Tsyplakova, PhD candidate
  • Maria Kokkali, PhD candidate
  • Anastasios Nikolopoulos, PhD candidate
  • Ileana-Maria Theofili, PhD candidate
  • Nikodimos Chatzigeorgiou, PhD candidate


PUBLICATIONS

  • VD Karalis (2026) Gap-Entropy Testing (GET): A Framework for Structural and Distributional Differences, Applied Mathematics and Statistics 3 (2), 19, https://doi.org/10.53941/ams.2026.100019
  • M Kokkali, VD Karalis (2026) Vector‐Based Comparison and Average Slope Can Refine Bioequivalence Claims: A Machine and Deep Learning Approach, Biopharmaceutics & Drug Disposition, https://doi.org/10.1002/bdd.70023
  • M Kokkali, VD Karalis (2026) Vector‐based comparison as a novel framework for assessing multi‐endpoint clinical trials: Applications to actual data, Clinical and Translational Discovery 6 (4), e70180, https://onlinelibrary.wiley.com/doi/pdf/10.1002/ctd2.70180
  • A Nikolopoulos, VD Karalis (2026) The Use of Generative AI to Create Hybrid Populations for Bioequivalence Trials, Applied Mathematics and Statistics 3 (1), 3. https://doi.org/10.53941/ams.2026.100003
  • A Nikolopoulos, VD Karalis (2026) Implementation of Generative AI in Biomedical Research and Healthcare, Applied Biosciences 5 (2), 34, https://doi.org/10.3390/applbiosci5020034
  • N Papavramidou, VD Karalis (2026) Generative Data Augmentation in Clinical Studies: A Normalizing Flow Framework with an Inferential Bias–Variance Perspective, Applied Sciences 16(10), 4692, https://doi.org/10.3390/app16104692

  • AC Đorđević, V Karalis (2025) ARTIFICIAL INTELLIGENCE IN DRUG DEVELOPMENT, CLINICAL TRIALS, AND HEALTHCARE, Acta medica Medianae 64 (1).
  • V Karalis, A Catić-Đorđević (2025) Artificial Intelligence in drug development, clinical trials, and healthcare, Acta Medica Medianae 64 (1).
  • A Nikolopoulos, VD Karalis (2025) Artificial Intelligence Meets Bioequivalence: Using Generative Adversarial Networks for Smarter, Smaller Trials, Machine Learning and Knowledge Extraction 7 (2), 47.
  • A Nikolopoulos, VD Karalis (2025) Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs, Algorithms 18 (5), 266.
  • M Papacharalambous, V Karalis (2025) In Silico Κλινικές Μελέτες για τη Βελτιστοποίηση της Αντιπηκτικής Αγωγής, Pharmakeftiki 37 (1).
  • N Tsyplakova, G Ismailos, VD Karalis (2025) Optimising Pirfenidone Dosage Regimens in Idiopathic Pulmonary Fibrosis: Toward a Guide for Personalised Treatment, Xenobiotica, 55 (1), 25-36.
  • A Tsyplakova, A Catic-Djorđevic, N Stefanović, VD Karalis (2025) Optimizing Mycophenolate Therapy in Renal Transplant Patients Using Machine Learning and Population Pharmacokinetic Modeling, Medical Sciences 13 (4), 235.

  • P Gkagkari, A Tagka, A Stratigos, V Karalis, A Kyritsi, A Vitsos, MC Rallis (2024) Differential Diagnosis of Irritant Versus Allergic Contact Dermatitis Based on Noninvasive Methods, Dermatology Practical & Conceptual 14 (4), e2024231.
  • M Gkousiaki, V Karalis, A Kyritsi, C Almpani, S Geronikolou, A Stratigos, M Christou Rallis, A Tagka (2024) Contact allergy caused by acrylates in nail cosmetics: A pilot study from Greece, Contact Dermatitis 90 (3), 273-279.
  • V Karalis (2024) Artificial intelligence in drug discovery and clinical practice, From Current to Future Trends in Pharmaceutical Technology, 215-255.
  • VD Karalis (2024) A Vector Theory of Assessing Clinical Trials: An Application to Bioequivalence, Journal of Cardiovascular Development and Disease 11 (7), 185.
  • V Karalis (2024) The Integration of Artificial Intelligence into Clinical Practice, Applied Biosciences 3 (1), 14-44.
  • A Karnaki, A Siamidi, V Karalis, N Lagopati, N Pippa, M Vlachou (2024) Thermoresponsive Hydrogels: Current Status and Future Perspectives, IntechOpen, DOI: 10.5772/intechopen.114986
  • M Kokkali, VD Karalis (2024) Average Slope vs. Cmax: Which Truly Reflects the Drug-Absorption Rate?, 2nd Panhellenic Congress of Medical Rhysics, (pdf file)
  • M Kokkali, V Karalis (2024) P. 6.3 AVERAGE SLOPE VS. CMAX: WHICH TRULY REFLECTS THE DRUG-ABSORPTION RATE?, Physica Medica 127, 104692.
  • A Kyritsi, A Tagka, A Stratigos, VD Karalis (2024) Machine Learning in Allergic Contact Dermatitis: Identifying (Dis) similarities between Polysensitized and Monosensitized Patients, BioMedInformatics 4 (2), 1348-1362.
  • A Kyritsi, A Tagka, A Stratigos, V Karalis (2024) Preservative contact allergy in occupational dermatitis: a machine learning analysis, Archives of Dermatological Research 316 (7), 372.
  • A Nikolopoulos, VD Karalis (2024) Implementation of a Generative AI Algorithm for Virtually Increasing the Sample Size of Clinical Studies, Applied Sciences 14 (11), 4570.
  • A Nikolopoulos, V Karalis (2024) P. 6.4 DEVELOPMENT OF A GENERATIVE ARTIFICIAL INTELLIGENCE ALGORITHM FOR THE REGENERATION OF VIRTUAL VOLUNTEERS IN BIOEQUIVALENCE STUDIES, Physica Medica 127, 104693.
  • D Papadopoulos, VD Karalis (2024) Introducing an Artificial Neural Network for Virtually Increasing the Sample Size of Bioequivalence Studies, Applied Sciences 14 (7), 2970.
  • D Papadopoulos, VD Karalis (2024) P. 6.7 ON THE INTEGRATION OF ARTIFICIAL NEURAL NETWORKS IN CLINICAL TRIALS, Physica Medica 127, 104696.
  • D Papadopoulos, G Karali, VD Karalis (2024) Bioequivalence Studies of Highly Variable Drugs: An Old Problem Addressed by Artificial Neural Networks, Applied Sciences 14 (12), 5279.

  • I Damnjanović, N Tsyplakova, N Stefanović, T Tošić, A Catić-Đorđević, V Karalis (2023) Joint use of population pharmacokinetics and machine learning for optimizing antiepileptic treatment in pediatric population, Therapeutic Advances in Drug Safety 14, 20420986231181337
  • 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]
  • C Daousani, V Karalis, YL Loukas, KH Schulpis, K Alexiou, Y Dotsikas (2023) Dried Blood Spots in Neonatal Studies: A Computational Analysis for the Role of the Hematocrit Effect, Pharmaceuticals 16 (8), 1126
  • 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; https://doi.org/10.3390/ph16050725
  • 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; https://doi.org/10.3390/biomedinformatics3010013
  • A Kyritsi, A Tagka, A Stratigos, V Karalis (2023) Analysis of Polysensitization and Monosensitization using Classic and Machine Learning Techniques, Free Radical Biology and Medicine 201, 49-50
  • Matsota P, Karalis V, Saranteas T, Kiospe F, Markantonis SL (2023) Ropivacaine pharmacokinetics in the arterial and venous pools after ultrasound-guided continuous thoracic paravertebral nerve block. Journal of Anaesthesiology Clinical Pharmacology.
  • D Papadopoulos, VD Karalis (2023) Variational Autoencoders for Data Augmentation in Clinical Studies, Applied Sciences 13 (15), 8793
  • SA Paschou, V Karalis, T Psaltopoulou, I Charitaki, A Sklirou, V Iconomidou, V Vasileiou, G Kassi, A Vryonidou, A Kokkinos, N Tentolouris, E Hatziaggelaki, I Trougakos, E Terpos, M-A Dimopoulos (2023) COVID-19 BNT162b2 mRNA vaccine for patients with type 2 diabetes mellitus, Hormones 22 (1), 17-18.
  • SA Paschou, V Karalis, T Psaltopoulou, I Charitaki, A Sklirou, V Iconomidou, V Vasileiou, G Kassi, A Vryonidou, A Kokkinos, N Tentolouris, E Hatziaggelaki, I Trougakos, E Terpos, M-A Dimopoulos (2023) Patients with type 2 diabetes mellitus present similar immunological response to COVID-19 BNT162b2 mRNA vaccine to healthy subjects: a prospective cohort study, Hormones 22 (1), 5-11.

  • Terpos, E., Fotiou, D., Karalis, V., Ntanasis-Stathopoulos, I., Sklirou, A.D., Gavriatopoulou, M., Malandrakis, P., Iconomidou, V.A., Kastritis, E., Trougakos, I.P., Dimopoulos, M.A. SARS-CoV-2 humoral responses following booster BNT162b2 vaccination in patients with B-cell malignancies (2022) American Journal of Hematology, 97 (10), pp. 1300-1308.
  • Kikionis, S., Koromvoki, M., Tagka, A., Polichronaki, E., Stratigos, A., Panagiotopoulos, A., Kyritsi, A., Karalis, V., Vitsos, A., Rallis, M., Ioannou, E., Roussis, V. Ulvan-Based Nanofibrous Patches Enhance Wound Healing of Skin Trauma Resulting from Cryosurgical Treatment of Keloids (2022) Marine Drugs, 20 (9), art. no. 551, .
  • Ntanasis-Stathopoulos, I., Karalis, V., Sklirou, A.D., Gavriatopoulou, M., Alexopoulos, H., Malandrakis, P., Trougakos, I.P., Dimopoulos, M.A., Terpos, E. Third Dose of the BNT162b2 Vaccine Results in Sustained High Levels of Neutralizing Antibodies Against SARS-CoV-2 at 6 Months Following Vaccination in Healthy Individuals (2022) HemaSphere, 6 (7), art. no. e747, .
  • Terpos, E., Karalis, V., Sklirou, A.D., Apostolakou, F., Ntanasis-Stathopoulos, I., Bagratuni, T., Iconomidou, V.A., Malandrakis, P., Korompoki, E., Papassotiriou, I., Trougakos, I.P., Dimopoulos, M.A. Third dose of the BNT162b2 vaccine results in very high levels of neutralizing antibodies against SARS-CoV-2: Results of a prospective study in 150 health professionals in Greece (2022) American Journal of Hematology, 97 (4), pp. E147-E150.
  • Kousovista, R., Athanasiou, C., Liaskonis, K., Ivopoulou, O., Karalis, V.D. Quantifying the effect of in-hospital antimicrobial use on the development of colistin-resistant Acinetobacter baumannii strains: a time series analysis (2022) European Journal of Hospital Pharmacy, 29 (2), pp. 66-71.
  • Paschou, S.A., Karalis, V., Psaltopoulou, T., Vasileiou, V., Charitaki, I., Bagratuni, T., Ktena, V., Papandroulaki, F., Gumeni, S., Kassi, G.N., Trougakos, I.P., Terpos, E., Dimopoulos, M.A. Patients With Autoimmune Thyroiditis Present Similar Immunological Response to COVID-19 BNT162b2 mRNA Vaccine With Healthy Subjects, While Vaccination May Affect Thyroid Function: A Clinical Study (2022) Frontiers in Endocrinology, 13, art. no. 840668, .
  • Terpos, E., Karalis, V., Ntanasis-Stathopoulos, I., Evangelakou, Z., Gavriatopoulou, M., Manola, M.S., Malandrakis, P., Gianniou, D.D., Kastritis, E., Trougakos, I.P., Dimopoulos, M.A. Comparison of Neutralizing Antibody Responses at 6 Months Post Vaccination with BNT162b2 and AZD1222 (2022) Biomedicines, 10 (2), art. no. 338, .
  • Papadopoulos, D., Ntanasis-Stathopoulos, I., Gavriatopoulou, M., Evangelakou, Z., Malandrakis, P., Manola, M.S., Gianniou, D.D., Kastritis, E., Trougakos, I.P., Dimopoulos, M.A., Karalis, V., Terpos, E. Predictive Factors for Neutralizing Antibody Levels Nine Months after Full Vaccination with BNT162b2: Results of a Machine Learning Analysis (2022) Biomedicines, 10 (2), art. no. 204, .
  • Markantonis, S.-L., Markou, N., Karagkounis, A., Koutrafouri, D., Stefanatou, H., Kousovista, R., Karalis, V. The Pharmacokinetics of Levetiracetam in Critically Ill Adult Patients: An Intensive Care Unit Clinical Study (2022) Applied Sciences (Switzerland), 12 (3), art. no. 1208, .
  • Terpos, E., Karalis, V., Ntanasis-Stathopoulos, I., Apostolakou, F., Gumeni, S., Gavriatopoulou, M., Papadopoulos, D., Malandrakis, P., Papanagnou, E.-D., Korompoki, E., Kastritis, E., Papassotiriou, I., Trougakos, I.P., Dimopoulos, M.A. Sustained but Declining Humoral Immunity against SARS-CoV-2 at 9 Months Postvaccination with BNT162b2: A Prospective Evaluation in 309 Healthy Individuals (2022) HemaSphere, 6 (1), p. E677.
  • Paschou, S.A., Karalis, V., Psaltopoulou, T., Charitaki, I., Sklirou, A.D., Iconomidou, V.A., Vasileiou, V., Kassi, G.N., Vryonidou, A., Kokkinos, A., Tentolouris, N., Hatziaggelaki, E., Trougakos, I.P., Terpos, E., Dimopoulos, M.-A. COVID-19 BNT162b2 mRNA vaccine for patients with type 2 diabetes mellitus (2022) Hormones, .
  • Paschou, S.A., Karalis, V., Psaltopoulou, T., Charitaki, I., Sklirou, A.D., Iconomidou, V.A., Vasileiou, V., Kassi, G.N., Vryonidou, A., Kokkinos, A., Tentolouris, N., Hatziaggelaki, E., Trougakos, I.P., Terpos, E., Dimopoulos, M.A. Patients with type 2 diabetes mellitus present similar immunological response to COVID-19 BNT162b2 mRNA vaccine to healthy subjects: a prospective cohort study (2022) Hormones, .
  • Ntousi, S., Karalis, V. SARS-CoV-2 coronavirus: Pathogenesis, Pharmacotherapy and Treatment with Monoclonal Antibodies (2022) Pharmakeftiki, 34 (2), pp. 39-67.
  • Naoum, G., Markantonis, S.L., Fanerou, E., Siagkas, G., Petropoulos, F., Zafiris, E., Kousovista, R., Karalis, V. On the Association between Gastrointestinal Symptoms and Extragastric Manifestations (2022) Gastroenterology Research and Practice, 2022, art. no. 8379579, .
  • Rosati, M., Terpos, E., Agarwal, M., Karalis, V., Bear, J., Burns, R., Hu, X., Papademetriou, D., Ntanasis-Stathopoulos, I., Trougakos, I.P., Dimopoulos, M.-A., Pavlakis, G.N., Felber, B.K. Distinct neutralization profile of spike variants by antibodies induced upon SARS-CoV-2 infection or vaccination (2022) American Journal of Hematology, 97 (1), pp. E3-E7.
  • Robinson, P.C., Terkeltaub, R., Pillinger, M.H., Shah, B., Karalis, V., Karatza, E., Liew, D., Imazio, M., Cornel, J.H., Thompson, P.L., Nidorf, M. Consensus Statement Regarding the Efficacy and Safety of Long-Term Low-Dose Colchicine in Gout and Cardiovascular Disease (2022) American Journal of Medicine, 135 (1), pp. 32-38.

  • Cardozo, B., Karatza, E., Karalis, V. Osteoporosis treatment with risedronate: a population pharmacokinetic model for the description of its absorption and low plasma levels (2021) Osteoporosis International, 32 (11), pp. 2313-2321.
  • Terpos, E., Karalis, V., Ntanasis-Stathopoulos, I., Gavriatopoulou, M., Gumeni, S., Malandrakis, P., Papanagnou, E.-D., Kastritis, E., Trougakos, I.P., Dimopoulos, M.A. Robust neutralizing antibody responses 6 months post vaccination with bnt162b2: A prospective study in 308 healthy individuals (2021) Life, 11 (10), art. no. 1077, .
  • Karalia, D., Siamidi, A., Karalis, V., Vlachou, M. 3d‐printed oral dosage forms: Mechanical properties, computational approaches and applications (2021) Pharmaceutics, 13 (9), art. no. 1401, .
  • Terpos, E., Trougakos, I.P., Karalis, V., Ntanasis-Stathopoulos, I., Sklirou, A.D., Bagratuni, T., Papanagnou, E.-D., Patseas, D., Gumeni, S., Malandrakis, P., Korompoki, E., Dimopoulos, M.A. Comparison of neutralizing antibody responses against SARS-CoV-2 in healthy volunteers who received the BNT162b2 mRNA or the AZD1222 vaccine: Should the second AZD1222 vaccine dose be given earlier?(2021) American Journal of Hematology, 96 (9), pp. E321-E324.
  • Terpos, E., Trougakos, I.P., Karalis, V., Ntanasis-Stathopoulos, I., Gumeni, S., Apostolakou, F., Sklirou, A.D., Gavriatopoulou, M., Skourti, S., Kastritis, E., Korompoki, E., Papassotiriou, I., Dimopoulos, M.A. Kinetics of anti-SARS-CoV-2 antibody responses 3 months post complete vaccination with BNT162B2; a prospective study in 283 health workers (2021) Cells, 10 (8), art. no. 1942, .
  • Terpos, E., Politou, M., Ntanasis-Stathopoulos, I., Karalis, V., Merkouri, E., Fotiou, D., Gavriatopoulou, M., Malandrakis, P., Kastritis, E., Trougakos, I.P., Dimopoulos, M.A. High prevalence of anti-pf4 antibodies following chadox1 ncov-19 (Azd1222) vaccination even in the absence of thrombotic events (2021) Vaccines, 9 (7), art. no. 712.
  • Kontostathi, M., Isou, S., Mostratos, D., Vasdekis, V., Demertzis, N., Kourounakis, A., Vitsos, A., Kyriazi, M., Melissos, D., Tsitouris, C., Karalis, E., Klamarias, L., Dania, F., Papaioannou, G.-T., Roussis, V., Polychronopoulos, E., Anastassopoulou, J., Theophanides, T., Rallis, M.-C., Black, H.S. Influence of Omega-3 Fatty Acid-Rich Fish Oils on Hyperlipidemia: Effect of Eel, Sardine, Trout, and Cod Oils on Hyperlipidemic Mice (2021) Journal of Medicinal Food, 24 (7), pp. 749-755.
  • Kyritsi, A., Kikionis, S., Tagka, A., Koliarakis, N., Evangelatou, A., Papagiannis, P., Stratigos, A., Karalis, V., Dallas, P., Vitsos, A., Ioannou, E., Roussis, V., Rallis, M. Management of acute radiodermatitis in non-melanoma skin cancer patients using electrospun nanofibrous patches loaded with pinus halepensis bark extract (2021) Cancers, 13 (11), art. no. 2596, .
  • Karatza, E., Karalis, V. Investigating the Impact of Gastric Emptying on Pharmacokinetic Parameters Using Delay Differential Equations and Principal Component Analysis (2021) European Journal of Drug Metabolism and Pharmacokinetics, 46 (3), pp. 451-458.
  • Vlachou, M., Karalis, V. An in vitro–in vivo simulation approach for the prediction of bioequivalence (2021) Materials, 14 (3), art. no. 555, pp. 1-27.
  • Kousovista, R., Karali, G., Vlasopoulou, K., Karalis, V. Validation of population pharmacokinetic models: a comparison of internal and external validation approaches for hydrochlorothiazide (2021) Xenobiotica, 51 (12), pp. 1372-1388.
  • Kousovista, R., Athanasiou, C., Liaskonis, K., Ivopoulou, O., Ismailos, G., Karalis, V. Correlation between acinetobacter baumannii resistance and hospital use of meropenem, cefepime, and ciprofloxacin: Time series analysis and dynamic regression models (2021) Pathogens, 10 (4), art. no. 480.
  • Karatza, E., Ismailos, G., Karalis, V. Colchicine for the treatment of COVID-19 patients: efficacy, safety, and model informed dosage regimens (2021) Xenobiotica, 51 (6), pp. 643-656.
  • Karatza, E., Ismailos, G., Marangos, M., Karalis, V. Optimization of hydroxychloroquine dosing scheme based on COVID-19 patients’ characteristics: a review of the literature and simulations (2021) Xenobiotica, 51 (2), pp. 127-138.

  • Vlachou, M., Siamidi, A., Goula, E., Georgas, P., Pippa, N., Karalis, V., Sentoukas, T., Pispas, S. Probing the release of the chronobiotic hormone melatonin from hybrid calcium alginate hydrogel beads (2020) Acta Pharmaceutica, 70 (4), pp. 527-538.
  • Karatza, E., Karalis, V. Delay differential equations for the description of Irbesartan pharmacokinetics: A population approach to model absorption complexities leading to dual peaks (2020) European Journal of Pharmaceutical Sciences, 153, art. no. 105498.
  • Karalis, V., Ismailos, G., Karatza, E. Chloroquine dosage regimens in patients with COVID-19: Safety risks and optimization using simulations (2020) Safety Science, 129, art. no. 104842, .
  • Karatza, E., Markantonis, S.L., Savvidou, A., Verentzioti, A., Siatouni, A., Alexoudi, A., Gatzonis, S., Mavrokefalou, E., Karalis, V. Pharmacokinetic and pharmacodynamic modeling of levetiracetam: investigation of factors affecting the clinical outcome (2020) Xenobiotica, 50 (9), pp. 1090-1100.
  • Karatza, E., Karalis, V. Modelling gastric emptying: A pharmacokinetic model simultaneously describing distribution of losartan and its active metabolite EXP-3174 (2020) Basic and Clinical Pharmacology and Toxicology, 126 (3), pp. 193-202.
  • Ioannidis, K., Papachristos, A., Skarlatinis, I., Kiospe, F., Sotiriou, S., Papadogeorgaki, E., Plakias, G., Karalis, V.D., Markantonis, S.L. Do we need to adopt antifungal stewardship programmes? (2020) European Journal of Hospital Pharmacy, 27 (1), pp. 14-18.

  • Kotroni, E., Simirioti, E., Kikionis, S., Sfiniadakis, I., Siamidi, A.,
  • Karalis, V., Vitsos, A., Vlachou, M., Ioannou, E., Roussis, V., Rallis, M. In vivo evaluation of the anti-inflammatory activity of electrospun micro/nanofibrous patches loaded with Pinus halepensis bark extract on hairless mice skin (2019) Materials, 12 (16), art. no. 2596, .
  • Soulele, K., Karalis, V. On the population pharmacokinetics and the enterohepatic recirculation of total ezetimibe (2019) Xenobiotica, 49 (4), pp. 446-456.
  • Soulele, K., Karalis, V. Development of a joint population pharmacokinetic model of ezetimibe and its conjugated metabolite (2019) European Journal of Pharmaceutical Sciences, 128, pp. 18-26.
  • Daousani, C., Karalis, V., Malenović, A., Dotsikas, Y. Hematocrit effect on dried blood spots in adults: a computational study and theoretical considerations (2019) Scandinavian Journal of Clinical and Laboratory Investigation, 79 (5), pp. 325-333.

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