We were cited
We were cited:
Doki K, Neuhoff S, Rostami-Hodjegan A, Homma M.Assessing potential drug-drug interactions between dabigatran etexilate and a P-gp inhibitor in renal impairment population using PBPK modeling. CPT: Pharmacometrics and Systems Pharmacology, 2019; doi:10.1002/psp4.12382.
Xie F, Gu J. Computational methods and applications for quantitative systems pharmacology. Quantitative Biology, 2019; doi:https://doi.org/10.1007/s40484-018-0161-6.
Ellison CA et al. Challenges in working towards an internal threshold of toxicological concern (iTTC) for use in the safety assessment of cosmetics: Discussions from the Cosmetics Europe iTTC Working Group workshop. Regulatory Toxicology and Pharmacology, 2019; 103:63-72.
Wang R et al. An integrated characterization of contractile, electrophysiological, and structural cardiotoxicity of Sophora tonkinensis Gapnep. in human pluripotent stem cell-derived cardiomyocytes. Stem Cell Research & Therapy, 2019; 10:20.
Polasek TM et al. What Does it Take to Make Model-Informed Precision Dosing Common Practice? Report from the 1st Asian Symposium on Precision Dosing. The AAPS Journal, 2019; 21:17.
Chastang A et al. Impact of hospital pharmacist interventions on the combination of citalopram or escitalopram with other QT-prolonging drugs. International Journal of Clinical Pharmacy, 2019; doi:https://doi.org/10.1007/s11096-018-0724-7.
Christophe B, Crumb W. Impact of disease state on arrhythmic event detection by action potential modelling in cardiac safety pharmacology. Journal of Pharmacological and Toxicological Methods, 2019; 96:15-26.
Polasek TM. Virtual twins for precision dosing in clinical drug development. Australasian Biotechnology, 2018; 28:42-43.
Liu S, Zhang R, Lu X. The Impact of Individuals' Attitudes Toward Health Websites on Their Perceived Quality of Health Information: An Empirical Study. Telemedicine and e-Health, 2018; doi:https://doi.org/10.1089/tmj.2018.0217.
Brouillette J, Cyr S, Fiset C. Mechanisms of Arrhythmia and Sudden Cardiac Death in Patients with Human Immunodeficiency Virus Infection. Canadian Journal of Cardiology, 2018; doihttps://doi.org/10.1016/j.cjca.2018.12.015.
Melillo N, Aarons L, Magni P, Darwich AS. Variance based global sensitivity analysis of physiologically based pharmacokinetic absorption models for BCS I–IV drugs. Journal of Pharmacokinetics and Pharmacodynamics, 2018; doi:https://doi.org/10.1007/s10928-018-9615-8.
Kowalska M, Fijałkowski Ł, Nowaczyk A. The Biological Activity Assessment of Potential Drugs Acting on Cardiovascular System Using Lipinski and Veber Rules. Journal of Education, Health and Sport, 2018; doi:http://dx.doi.org/10.5281/zenodo.2066519.
Polasek TM, Rayner CR, Peck RW, Rowland A, Kimko H, Rostami-Hodjegan A. Toward Dynamic Prescribing Information: Codevelopment of Companion Model‐Informed Precision Dosing Tools in Drug Development. Clinical Pharmacology in Drug Development, 2018; doi:https://doi.org/10.1002/cpdd.638.
Su X, Jiang X, Zhang S, Chen M. LSTM Power Mid-Term Power Load Forecasting with Meteorological Factors. In: Zhao Y., Wu TY., Chang TH., Pan JS., Jain L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2018. Smart Innovation, Systems and Technologies, 2019; vol 128. Springer, Cham.
Corpas-López V et al. A nanodelivered Vorinostat derivative is a promising oral compound for the treatment of visceral leishmaniasis. Pharmacological Research, 2018; doi:https://doi.org/10.1016/j.phrs.2018.11.039.
Conner TM, Reed RC, Zhang T. A Physiologically Based Pharmacokinetic Model for Optimally Profiling Lamotrigine Disposition and Drug–Drug Interactions. European Journal of Drug Metabolism and Pharmacokinetics, 2018; doi:https://doi.org/10.1007/s13318-018-0532-4.
Nothnagel L et al. Predictive PBPK modeling as a tool in the formulation of the drug candidate TMP-001. European Journal of Pharmaceutics and Biopharmaceutics, 2018; doi:https://doi.org/10.1016/j.ejpb.2018.11.012.
Shi J-Y et al. TMFUF: a triple matrix factorization-based unified framework for predicting comprehensive drug-drug interactions of new drugs. BMC Bioinformatics, 2018; doi: 19 (Suppl 14) :411.
Wojkowska-Mach et al. Antibiotic consumption and antimicrobial resistance in Poland; findings and implications. Antimicrobial Resistance & Infection Control, 2018; doi: https://doi.org/10.1186/s13756-018-0428-8.
Vaidhyanathan S et al. Bioequivalence comparison of pediatric Dasatinib formulations and elucidation of absorption mechanisms through integrated PBPK modeling. Journal of Pharmaceutical Sciences, 2018; doi: https://doi.org/10.1016/j.xphs.2018.11.005.
Fernandes FM et al. Assessment of the risk of QT-interval prolongation associated with potential drug-drug interactions in patients admitted to Intensive Care Units. Saudi Pharmaceutical Journal, 2018; doi: https://doi.org/10.1016/j.jsps.2018.11.003.
Svorc P. Chronobiology of Acid-Base Balance under General Anesthesia in Rat Model. IntechOpen, 2018; doi: 10.5772/intechopen.75174.
Bhattacharya P, Saha A, Basak S. Discovery of nano-piperolactam a: A non-steroidal contraceptive lead acting through down-regulation of interleukins. Nanomedicine: Nanotechnology, Biology and Medicine, 2018; doi:https://doi.org/10.1016/j.nano.2018.10.011.
Szafraniec J et al. Molecular Disorder of Bicalutamide-Amorphous Solid Dispersions Obtained by Solvent Methods. Pharmaceutics, 2018; 10:194.
Munawar S et al. Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities. Frontiers in Pharmacology, 2018; 9:1035.
Rhee S-j et al. Physiologically Based Pharmacokinetic Modeling of Fimasartan, Amlodipine, and Hydrochlorothiazide for the Investigation of Drug–Drug Interaction Potentials. Pharmaceutical Research, 2018; 35:236.
Savoji H et al. Cardiovascular disease models: A game changing paradigm in drug discovery and screening. Biomaterials, 2018; doi:https://doi.org/10.1016/j.biomaterials.2018.09.036.
Zakaria ZZ et al. Using Zebrafish for Investigating the Molecular Mechanisms of Drug-Induced Cardiotoxicity. BioMed Research International, 2018; Article ID 1642684.
Pentafragka C, Symillides M, McAllister M, Dressman J, Vertzoni M, Reppas C. The impact of food intake on the luminal environment and performance of oral drug products with a view to in vitro and in silico simulations: a PEARRL review. Journal of Pharmacy and Pharmacology, 2018; doi:https://doi.org/10.1111/jphp.12999.
Izumi-Nakaseko H et al. Application of human induced pluripotent stem cell-derived cardiomyocytes sheets with microelectrode array system to estimate antiarrhythmic properties of multi-ion channel blockers. Journal of Pharmacological Sciences, 2018; 137:372-378.
Goto A et al. Analysis of torsadogenic and pharmacokinetic profile of E-4031 in dogs bridging the gap of information between in vitro proarrhythmia assay and clinical observation in human subjects. Journal of Pharmacological Sciences, 2018; 137(2):237-240.
Guiastrennec B. Sonne DP, Bergstrand M, Vilsbøll T, Knop FK, Karlsson MO. Model- Based Prediction of Plasma Concentration and Enterohepatic Circulation of Total Bile Acids in Humans. CPT: Pharmacometrics & Systems Pharmacology, 2018; doi:10.1002/psp4.12325.
Li Z et al. Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative . Clinical Pharmacology & Therapeutics, 2018; doi:https://doi.org/10.1002/cpt.1184.
Stader F, Siccardi M, Battegay M, Kinvig H, Penny MA, Marzolini C. Repository Describing an Aging Population to Inform Physiologically Based Pharmacokinetic Models Considering Anatomical, Physiological, and Biological Age-Dependent Changes. Clinical Pharmacokinetics, 2018; doi:https://doi.org/10.1007/s40262-018-0709-7.
Shi J-Y, Shang X-Q, Gao K, Zhang S-W, Yiu S-M. An Integrated Local Classification Model of Predicting Drug-Drug Interactions via Dempster-Shafer Theory of Evidence. Scientific Reports, 2018; 8:11829.
Birhanu G. Dexamethasone loaded multi-layer poly-l-lactic acid/pluronic P123 composite electrospun nanofiber scaffolds for bone tissue engineering and drug delivery. Pharmaceutical Development and Technology , 2018; https://doi.org/10.1080/10837450.2018.1481429.
Kedzierska E, Dabkowska L, Krzanowski T, Gibula E, Orzelska-Gorka J, Wujec M. New drugs - from necessity to delivery. Current Issues in Pharmacy and Medical Sciences, 2018; 31:69-75.
Urooj S et al. Assessment of sustained release matrix tablets for quetiapine fumarate: in vitro studies. Acta Poloniae Pharmaceutica - Drug Research, 2018; 75:107-117.
Awad A, Trenfield SJ, Gaisford S, Basit AW. 3D printed medicines: A new branch of digital healthcare. European Journal of Pharmaceutics and Biopharmaceutics, 2018; https://doi.org/10.1016/j.ijpharm.2018.07.024.
Lozoya-Agullo I, González-Álvarez I, Merino-Sanjuán M, Bermejo M, González-Álvarez M. Preclinical Models for Colonic Absorption, Application to Controlled Release Formulation Development. European Journal of Pharmaceutics and Biopharmaceutics, 2018; https://doi.org/10.1016/j.ejpb.2018.07.008.
Hatton GB, Madla CM, Rabbie SC, Basit AW. All disease begins in the gut: Influence of gastrointestinal disorders and surgery on oral drug performance. International Journal of Pharmaceutics, 2018; https://doi.org/10.1016/j.ijpharm.2018.06.054.
Perryman Al, Patel JS, Russo R, Singleton E, Connell N, Ekins S, Freundlich JS. Naïve Bayesian Models for Vero Cell Cytotoxicity. Pharmaceutical Research, 2018; 35:170.
Muszkiewicz A, Liu X, Bueno-Orovio A, Lawson BAJ, Burrage K, Casadei B, Rodriguez B. From ionic to cellular variability in human atrial myocytes: an integrative computational and experimental study. American Journal of Physiology-Heart and Circulatory Physiology, 2018; 314:H895-H916.
Hunta S, Yooyativong T, Aunsri N. A Novel Integrated Action Crossing Method for Drug-Drug Interaction Prediction in Non-Communicable Diseases. Computer Methods and Programs in Biomedicine, 2018; doi: https://doi.org/10.1016/j.cmpb.2018.06.013.
Alqahtani S, Bukhari I, Albassam A, Alenazi M. An update on the potential role of intestinal first-pass metabolism for the prediction of drug–drug interactions: the role of PBPK modeling. Expert Opinion on Drug Metabolism & Toxicology, 2018; 14:625-634.
Martinez-Prat L et al. Comparison of Serological Biomarkers in Rheumatoid Arthritis and Their Combination to Improve Diagnostic Performance. Frontiers in Immunology, 2018; 1113.
Ravi G, Gupta Vishal N, Balamuralidhara V. Rivastigmine Tartrate Solid Lipid Nanoparticles Loaded Transdermal Film: An In vivo study. Research Journal of Pharmacy and Technology, 2018; 11:227.
Nguyen PTT et al. Development of a Physiologically Based Pharmacokinetic Model of Ethionamide in the Pediatric Population by Integrating Flavin-Containing Monooxygenase 3 Maturational Changes Over Time. The Journal of Clinical Pharmacology, 2018; https://doi.org/10.1002/jcph.1133.
Yao Y, Toshimoto K, Kim S-J, Yoshikado T Sugiyama Y. Quantitative Analysis of Complex Drug-Drug Interactions between Cerivastatin and Metabolism/Transport Inhibitors Using Physiologically Based Pharmacokinetic Modeling. Drug Metabolism and Disposition, 2018; doi:https://doi.org/10.1124/dmd.117.079210 .
Pellett JD, Dwaraknath S, Nauka E, Dalziel G. Chapter 18 – Accelerated Predictive Stability (APS) Applications: Packaging Strategies for Controlling Dissolution Performance.[in:] Qiu F, and Scrivens G. Accelerated Predictive Stability. Fundamentals and Pharmaceutical Industry Practices. Elsevier Inc, 2018; 383-401.
Yellepeddi V, Rower J, Liu X, Kumar S, Rashid J, Sherwin CMT. State-of-the-Art Review on Physiologically Based Pharmacokinetic Modeling in Pediatric Drug Development. Clinical Pharmacokinetics, 2018; doi:https://doi.org/10.1007/s40262-018-0677-y.
Effinger A, O'Driscoll CM, McAllister M, Fotaki N. Impact of gastrointestinal disease states on oral drug absorption – implications for formulation design – a PEARRL review. Journal of Pharmacy and Pharmacology, 2018; doi:https://doi.org/10.1111/jphp.12928.
Yamazaki S, Loi C-M, Kimoto E, Costales C, Varma MV. Application of Physiologically Based Pharmacokinetic Modeling in Understanding Bosutinib Drug-Drug Interactions: Importance of Intestinal P-Glycoprotein. Drug Metabolism and Disposition, 2018; doi:https://doi.org/10.1124/dmd.118.080424.
Boland JW, Johnson M, Ferreira D, Berry DJ In silico (computed) modelling of doses and dosing regimens associated with morphine levels above international legal driving limits. Palliative Medicine, 2018; doi:https://doi.org/10.1177/0269216318773956.
Galbusera F, Niemeyer F, Seyfried M, Bassani T, Casaroli G, Kienle A, Wilke H-J Exploring the Potential of Generative Adversarial Networks for Synthesizing Radiological Images of the Spine to be Used in In Silico Trials. Frontiers in Bioengineering and Biotechnology, 2018; 6:53.
Rautio J, Meanwell NA, Di L, Hageman MJ The expanding role of prodrugs in contemporary drug design and development. Nature Reviews Drug Discovery, 2018; doi:10.1038/nrd.2018.46.
Cai C, Fang J, Guo P, Wang Q, Hong H, Moslehi J, Cheng F In Silico Pharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications using Combined Classifiers. Journal of Chemical Information and Modeling, 2018; doi:10.1021/acs.jcim.7b0064.
Turner JR et al.Drug‐induced Proarrhythmia and Torsade de Pointes: A Primer for Students and Practitioners of Medicine and Pharmacy. The Journal of Clinical Pharmacology, 2018; doi:10.1002/jcph.1129.
Krupa A, Tabor Z, Tarasiuk J, Strach B, Pociecha K, Wyska E, Wroński S, Łyszczarz E, Jachowicz R. The impact of polymers on 3D microstructure and controlled release of sildenafil citrate from hydrophilic matrices. European Journal of Pharmaceutical Sciences, 2018; In press.
Yu H, Mao K-T, Shi J-Y, Huang H, Chen Z, Dong K, Yiu S-M. Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization. BMC Systems Biology, 2018; 12(Suppl 1): 14.
Zakaria Z, Badhan RKS. The impact of CYP2B6 polymorphisms on the interactions of efavirenz with lumefantrine: Implications for paediatric antimalarial therapy. European Journal of Pharmaceutical Sciences, 2018; 119: 90-101.
Rowland A, van Dyk M, Hopkins AM, Mounzer R, Polasek TM, Rostami‐Hodjegan A, Sorich MJ. Physiologically‐based pharmacokinetic modelling to identify physiological and molecular characteristics driving variability in drug exposure. Clinical Pharmacology and Therapeutics, 2018; doi:https://doi.org/10.1002/cpt.1076.
Hens B, Talattof A, Paixão P, Bermejo M, Tsume Y, Löbenberg R,Amidon GL. Measuring the Impact of Gastrointestinal Variables on the Systemic Outcome of Two Suspensions of Posaconazole by a PBPK Model. The AAPS Journal, 2018; 20:57.
Kordbacheh E, Nazarian S, Sadeghi D, Hajizadeh A. An LTB-entrapped protein in PLGA nanoparticles preserves against enterotoxin of enterotoxigenic Escherichia coli. Iranian Journal of Basic Medical Sciences, 2018; 21: 517-524.
Romero L, Cano J, Gomis-Tena J, Trenor B, Sanz F, Pastor M, Saiz J. In Silico QT and APD Prolongation Assay for Early Screening of Drug-Induced Proarrhythmic Risk. Journal of Chemical Information and Modeling, 2018; doi:10.1021/acs.jcim.7b00440.
Dudefoi W. Titanium dioxide particles in food: characterization, fate in digestive fluids and impact on human gut microbiota (Doctoral dissertation), 2017; Retrieved from www.theses.fr: 2017NANT4019.
Mikkelsen CR, Jornil JR, Andersen LV, Banner J, Hasselstrøm JB. Distribution of Eight QT-Prolonging Drugs and Their Main Metabolites Between Postmortem Cardiac Tissue and Blood Reveals Potential Pitfalls in Toxicological Interpretation. Journal of Analytical Toxicology, 2018; doi:https://doi.org/10.1093/jat/bky018.
Gwozdzinski K, Azarderakhsh S, Imirzalioglu C, Falgenhauer L, Chakraborty T. An improved medium for colistin susceptibility testing. Journal of Clinical Microbiology, 2018; doi:10.1128/JCM.01950-17.
Mortensen HM, Chamberlin J, Joubert B, Angrish M, Sipes N, Lee JS, Euling SY.Leveraging human genetic and adverse outcome pathway (AOP) data to inform susceptibility in human health risk assessment. Mammalian Genome, 2018; doi:https://doi.org/10.1007/s00335-018-9738-7.
Guiastrennec B. Mechanism-based modeling of biological processes involved in oral absorption (Doctoral dissertation), 2018; Retrieved from DiVA Database: diva2:1178146.
Kordbacheh E, Nazarian S, Hajizadeh A, Sadeghi D. Entrapment of LTB protein in alginate nanoparticles protects against Enterotoxigenic Escherichia coli. APMIS, 2018; doi: 10.1111/apm.12815.
Hasani HJ, Ganesan A, Ahmed M, Barakat KH. Effects of protein-protein interactions and ligand binding on the ion permeation in KCNQ1 potassium channel. PLoS ONE, 2018; doi: https://doi.org/10.1371/journal.pone.0191905.
Dan G-A et al. Antiarrhythmic drugs–clinical use and clinical decision making: a consensus document from the European Heart Rhythm Association (EHRA) and European Society of Cardiology (ESC) Working Group on Cardiovascular Pharmacology, endorsed by the Heart Rhythm Society (HRS), Asia-Pacific Heart Rhythm Society (APHRS) and International Society of Cardiovascular Pharmacotherapy (ISCP). EP Europace, 2018; doi:https://doi.org/10.1093/europace/eux373.
Bown HK, Bonn C, Yohe S, Yadav DB, Patapoff TW, Daugherty A, Mrsny RJ. In vitro model for predicting bioavailability of subcutaneously injected monoclonal antibodies. Journal of Controlled Release, 2018; 273: 13-20.
Başçiftçi F, Avuçlu E. An expert system design to diagnose cancer by using a new method reduced rule base. Computer Methods and Programs in Biomedicine, 2018; 157: 113-120.
Alves VM, Braga RC, and Andrade CH. Computational Approaches for Predicting hERG Activity, in Computational Toxicology: Risk Assessment for Chemicals (ed Ekins). John Wiley & Sons, Inc., Hoboken, NJ, USA, 2018; doi: 10.1002/9781119282594.ch3.
Zakaria Z, Badhan R. Development of a Region-Specific Physiologically Based Pharmacokinetic Brain Model to Assess Hippocampus and Frontal Cortex Pharmacokinetics. Pharmaceutics, 2018; 10(1): 14.
Johnson TN, Bonner JJ, Tucker GT, Turner DB, Jamei M. Development and application of a physiologically-based model of paediatric oral drug absorption. European Journal of Pharmaceutical Sciences, 2018; https://doi.org/10.1016/j.ejps.2018.01.009
Conner TM et al. Physiologically based pharmacokinetic modeling of disposition and drug-drug interactions for valproic acid and divalproex. European Journal of Pharmaceutical Sciences, 2018; 111: 465-481.
Castilho ECD, Reis AMM, Borges TL, Siqueira LDC, Miasso AI. Potential drug–drug interactions and polypharmacy in institutionalized elderly patients in a public hospital in Brazil. Journal of Psychiatric and Mental Health Nursing, 2018; 25: 3-13.
Biffi A et al. Antidepressants and the risk of arrhythmia in elderly affected by a previous cardiovascular disease: a real-life investigation from Italy. European Journal of Clinical Pharmacology, 2018; 1(74): 119-129.
Kolanowski TJ, Antos CL, Guan K. Making human cardiomyocytes up to date: Derivation, maturation state and perspectives. International Journal of Cardiology, 2017; 241: 379-386.
Fonseca JC and López PG. Effect of compression force on critical quality attributes of immediate release tablets of furosemide. Revista Colombiana de Ciencias Químico - Farmacéuticas, 2017; 46(2): 235-255.
White WB, Hewitt LA, Mehdirad AA. Impact of the Norepinephrine Prodrug Droxidopa on the QTc Interval in Healthy Individuals. Clinical Pharmacology in Drug Development, 2017; doi:10.1002/cpdd.393
Mallick P. Utilizing in vitro transporter data in IVIVE-PBPK: an overview. ADMET and DMPK, 2017; 5(4): 201-211.
Muszkiewicz A, Liu X, Bueno-Orovio A, Lawson BAJ, Burrage K, Casadei B, Rodriguez B. From ionic to cellular variability in human atrial myocytes: an integrative computational and experimental study. American Journal of Physiology-Heart and Circulatory Physiology, 2017; doi:https://doi.org/10.1152/ajpheart.00477.2017
van Dyk M, Rowland A. Physiologically-based pharmacokinetic modeling as an approach to evaluate the effect of covariates and drug-drug interactions on variability in epidermal growth factor receptor kinase inhibitor exposure. Translational Cancer Research, 2017; doi:10.21037/tcr.2017.10.16
Lei CL, Wang K, Clerx M et al. Tailoring Mathematical Models to Stem-Cell Derived Cardiomyocyte Lines Can Improve Predictions of Drug-Induced Changes to Their Electrophysiology. Frontiers in Physiology, 2017; 8: 986.
Passini E, Britton OJ, Lu HR, Rohrbacher J, Hermans AN, Gallacher DJ, Greig RJH, Bueno-Orovio A, Rodriguez B. Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity. Frontiers in Physiology, 2017; 8: 668.
Poluzzi E, Raschi E, Diemberger I, De Ponti F. Drug-Induced Arrhythmia: Bridging the Gap Between Pathophysiological Knowledge and Clinical Practice. Drug Safety, 2017; 40(6): 461-464.
van Hassselt JGC, Iyengar R. Systems pharmacology-based identification of pharmacogenomic determinants of adverse drug reactions using human iPSC-derived cell lines. Current Opinion in Systems Biology, 2017; 4: 9-15.
Niederer SA, de Oliveira BL, Curtis MJ. The opportunities and challenges for biophysical modelling of beneficial and adverse drug actions on the heart. Current Opinion in Systems Biology, 2017; 4: 29-34.
Parikh J, Gurev V, Rice JJ. Novel Two-Step Classifier for Torsades de Pointes Risk Stratification from Direct Features. Frontiers in Physiology, 2017; 8: 816.
McMillan B, Gavaghan DJ, Mirams GR. Early afterdepolarisation tendency as a simulated pro-arrhythmic risk indicator. Toxicology Research, 2017; 6: 912-921.
Lane JD, Tinker A. Have the Findings from Clinical Risk Prediction and Trials Any Key Messages for Safety Pharmacology?. Frontiers in Physiology, 2017; 8: 890.
Hartung T et al. Systems Toxicology: Real World Applications and Opportunities. Chemical Research in Toxicology, 2017; 30(4): 870-882.
Corsi C et al. Noninvasive quantification of blood potassium concentration from ECG in hemodialysis patients. Scientific Reports, 2017; 7: 42492.
Rao RT, Scherholz ML, Hartmanshenn C, Bae SA, Androulakis IP. On the analysis of complex biological supply chains: From process systems engineering to quantitative systems pharmacology. Computers & Chemical Engineering, 2017; 107: 100-110.
Li M, Ramos LG. Drug-Induced QT Prolongation And Torsades de Pointes. Pharmacy and Therapeutics, 2017; 42(7): 473-477.
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Benatar A. The Pediatric Electrocardiogram. Annals of Cardiology and Cardiovascular Diseases, 2017; 2(1): 1009.
Britton OJ, Abi-Gerges N, Page G, Ghetti A, Miller PE, Rodriguez B. Quantitative Comparison of Effects of Dofetilide, Sotalol, Quinidine, and Verapamil between Human Ex vivo Trabeculae and In silico Ventricular Models Incorporating Inter-Individual Action Potential Variability. Frontiers in Physiology, 2017; 8: 597.
Izumi-Nakaseko H, Nakamura Y, Wada T, Ando K, Kanda Y, Sekino Y, Sugiyama A. Characterization of human iPS cell-derived cardiomyocyte sheets as a model to detect drug-induced conduction disturbance. The Journal of Toxicological Sciences, 2017; 42(2): 183-192.
Britton OJ, Bueno-Orovio A, Virág L, Varró A, Rodriguez B. The Electrogenic Na+/K+ Pump Is a Key Determinant of Repolarization Abnormality Susceptibility in Human Ventricular Cardiomyocytes: A Population-Based Simulation Study. Frontiers in Physiology, 2017; 8: 278.
Ritchie HE, Oakes DJ, Kennedy D, Polson JW. Early Gestational Hypoxia and Adverse Developmental Outcomes. Birth Defects Research, 2017; 109(17): 1358–1376.
Gueta I, Loebstein R, Markovits N, Kamari Y, Halkin H, Livni G, Yarden-Bilavsky H. Voriconazole-induced QT prolongation among hemato-oncologic patients: clinical characteristics and risk factors. European Journal of Clinical Pharmacology, 2017; 73(9): 1181-1185.
Dobchev D, Karelson M. Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?. Expert Opinion on Drug Discovery , 2016; 11(7): 627-639.
Soneson C, Robinson MD. iCOBRA: open, reproducible, standardized and live method benchmarking. Nature Methods, 2016; 13(4): 283.
Danielsson B, Collin J, Bergman GJ, Borg N, Salmi P, Fastbom J. Antidepressants and antipsychotics classified with torsades de pointes arrhythmia risk and mortality in older adults - a Swedish nationwide study. British Journal of Clinical Pharmacology, 2016; 81(4): 773-783.
Kissoyan KAB, Bazzi W, Hadi U, Matar GM. The inhibition of Pseudomonas aeruginosa biofilm formation by micafungin and the enhancement of antimicrobial agent effectiveness in BALB/c mice. Biofouling, 2016; 32(7): 779-786.
Gotta V, Yu Z, Cools F, van Ammel K, Gallacher DJ, Visser SAG, Sannajust F, Morissette P, Danhof M, van der Graaf PH. Application of a systems pharmacology model for translational prediction of hERG-mediated QTc prolongation. Pharmacology Research & Perspectives, 2016; 4(6): e00270.
Cooper BM, Putnam D. Polymers for siRNA Delivery: A Critical Assessment of Current Technology Prospects for Clinical Application. ACS Biomaterials Science & Engineering, 2016; 2(11): 1837-1850.
Kügler P. Early Afterdepolarizations with Growing Amplitudes via Delayed Subcritical Hopf Bifurcations and Unstable Manifolds of Saddle Foci in Cardiac Action Potential Dynamics. PLoS ONE, 2016; 11(3): e0151178.
Lin J, Li H-X, Qin L, Du Z-H, Xia J, Li J-L. A novel mechanism underlies atrazine toxicity in quails (Coturnix Coturnix coturnix): triggering ionic disorder via disruption of ATPases. Oncotarget, 2016; 51(7): 83880-83892.
Lin J, Li H-X, Xia J, Li X-N, Jiang X-Q, Zhu S-Y, Ge J, Li J-L. The chemopreventive potential of lycopene against atrazine-induced cardiotoxicity: modulation of ionic homeostasis. Scientific Reports, 2016; 6: 24855.
Terker AS, Zhang C, Erspamer KJ, Gamba G, Yang C-L, Ellison DH. Unique chloride-sensing properties of WNK4 permit the distal nephron to modulate potassium homeostasis. Kidney International, 2016; 89(1): 127-134.
Templeton I, Ravenstijn P, Sensenhauser C, Snoeys J. A physiologically based pharmacokinetic modeling approach to predict drug–drug interactions between domperidone and inhibitors of CYP3A4. Biopharmaceutics and Drug Disposition, 2016; 37(1): 15-27.
Knight-Schrijver VR, Chelliah V, Cucurull-Sanchez L, Le Novère N. The promises of quantitative systems pharmacology modelling for drug development. Computational and Structural Biotechnology Journal, 2016; 14: 363-370.
Trayanova NA, Chang KC. How computer simulations of the human heart can improve anti-arrhythmia therapy. The Journal of Physiology, 2016; 594(9): 2483-2502.
Gawehn E, Hiss JA, Schneider G. Deep Learning in Drug Discovery. Molecular Informatics, 2016; 35(1): 3-14.
Sánchez-López VA, Brennan-Bourdon LM, Rincón-Sánchez AR, Islas-Carbajal MC, Navarro-Ruíz A, Huerta-Olvera SG. Prevalence of Potential Drug-Drug Interactions in Hospitalized Surgical Patients. Journal of Pharmacy and Pharmacology, 2016; 4: 658-666.
Le Guennec J-Y et al. Inter-individual variability and modeling of electrical activity: a possible new approach to explore cardiac safety?. Scientific Reports, 2016; Article number: 37948.
Didziapetris R, Lanevskij K. Compilation and physicochemical classification analysis of a diverse hERG inhibition database. Journal of Computer-Aided Molecular Design, 2016; Article First Online: 25 October 2016.
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Muszkiewicz A et al. Variability in cardiac electrophysiology: Using experimentally-calibrated populations of models to move beyond the single virtual physiological human paradigm. Progress in Biophysics and Molecular Biology, 2016; Vol. 173, Issue 19: 2819-2832.
Dubois VFS et al. Pharmacokinetic–pharmacodynamic modelling of drug-induced QTc interval prolongation in man: prediction from in vitro human ether-à-go-go-related gene binding and functional inhibition assays and conscious dog studies. British Journal of Pharmacology, 2016; Vol. 173, Issue 19: 2819-2832.
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