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Publisher: Springer-Verlag (Total: 2352 journals)

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Showing 1 - 200 of 2352 Journals sorted alphabetically
3D Printing in Medicine     Open Access   (Followers: 2)
3D Research     Hybrid Journal   (Followers: 21, SJR: 0.222, CiteScore: 1)
4OR: A Quarterly J. of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.825, CiteScore: 1)
AAPS J.     Hybrid Journal   (Followers: 23, SJR: 1.118, CiteScore: 4)
AAPS PharmSciTech     Hybrid Journal   (Followers: 7, SJR: 0.752, CiteScore: 3)
Abdominal Imaging     Hybrid Journal   (Followers: 17, SJR: 0.866, CiteScore: 2)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 4, SJR: 0.439, CiteScore: 0)
Academic Psychiatry     Full-text available via subscription   (Followers: 27, SJR: 0.53, CiteScore: 1)
Academic Questions     Hybrid Journal   (Followers: 8, SJR: 0.106, CiteScore: 0)
Accreditation and Quality Assurance: J. for Quality, Comparability and Reliability in Chemical Measurement     Hybrid Journal   (Followers: 29, SJR: 0.316, CiteScore: 1)
Acoustical Physics     Hybrid Journal   (Followers: 11, SJR: 0.359, CiteScore: 1)
Acoustics Australia     Hybrid Journal   (SJR: 0.232, CiteScore: 1)
Acta Analytica     Hybrid Journal   (Followers: 7, SJR: 0.367, CiteScore: 0)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1, SJR: 0.675, CiteScore: 1)
Acta Biotheoretica     Hybrid Journal   (Followers: 4, SJR: 0.284, CiteScore: 1)
Acta Diabetologica     Hybrid Journal   (Followers: 19, SJR: 1.587, CiteScore: 3)
Acta Endoscopica     Hybrid Journal   (Followers: 1)
acta ethologica     Hybrid Journal   (Followers: 4, SJR: 0.769, CiteScore: 1)
Acta Geochimica     Hybrid Journal   (Followers: 7, SJR: 0.24, CiteScore: 1)
Acta Geodaetica et Geophysica     Hybrid Journal   (Followers: 3, SJR: 0.305, CiteScore: 1)
Acta Geophysica     Hybrid Journal   (Followers: 11, SJR: 0.312, CiteScore: 1)
Acta Geotechnica     Hybrid Journal   (Followers: 7, SJR: 1.588, CiteScore: 3)
Acta Informatica     Hybrid Journal   (Followers: 5, SJR: 0.517, CiteScore: 1)
Acta Mathematica     Hybrid Journal   (Followers: 13, SJR: 7.066, CiteScore: 3)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2, SJR: 0.452, CiteScore: 1)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 6, SJR: 0.379, CiteScore: 1)
Acta Mathematica Vietnamica     Hybrid Journal   (SJR: 0.27, CiteScore: 0)
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal   (SJR: 0.208, CiteScore: 0)
Acta Mechanica     Hybrid Journal   (Followers: 21, SJR: 1.04, CiteScore: 2)
Acta Mechanica Sinica     Hybrid Journal   (Followers: 5, SJR: 0.607, CiteScore: 2)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 7, SJR: 0.576, CiteScore: 2)
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.638, CiteScore: 1)
Acta Neurochirurgica     Hybrid Journal   (Followers: 7, SJR: 0.822, CiteScore: 2)
Acta Neurologica Belgica     Hybrid Journal   (Followers: 1, SJR: 0.376, CiteScore: 1)
Acta Neuropathologica     Hybrid Journal   (Followers: 4, SJR: 7.589, CiteScore: 12)
Acta Oceanologica Sinica     Hybrid Journal   (Followers: 3, SJR: 0.334, CiteScore: 1)
Acta Physiologiae Plantarum     Hybrid Journal   (Followers: 3, SJR: 0.574, CiteScore: 2)
Acta Politica     Hybrid Journal   (Followers: 15, SJR: 0.605, CiteScore: 1)
Activitas Nervosa Superior     Hybrid Journal   (SJR: 0.147, CiteScore: 0)
adhäsion KLEBEN & DICHTEN     Hybrid Journal   (Followers: 8, SJR: 0.103, CiteScore: 0)
ADHD Attention Deficit and Hyperactivity Disorders     Hybrid Journal   (Followers: 25, SJR: 0.72, CiteScore: 2)
Adhesion Adhesives & Sealants     Hybrid Journal   (Followers: 9)
Administration and Policy in Mental Health and Mental Health Services Research     Partially Free   (Followers: 17, SJR: 1.005, CiteScore: 2)
Adsorption     Hybrid Journal   (Followers: 5, SJR: 0.703, CiteScore: 2)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 4, SJR: 0.698, CiteScore: 1)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 37, SJR: 0.956, CiteScore: 2)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 19, SJR: 0.812, CiteScore: 1)
Advances in Contraception     Hybrid Journal   (Followers: 3)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 59, SJR: 1.09, CiteScore: 1)
Advances in Gerontology     Partially Free   (Followers: 8, SJR: 0.144, CiteScore: 0)
Advances in Health Sciences Education     Hybrid Journal   (Followers: 30, SJR: 1.64, CiteScore: 2)
Advances in Manufacturing     Hybrid Journal   (Followers: 4, SJR: 0.475, CiteScore: 2)
Advances in Polymer Science     Hybrid Journal   (Followers: 45, SJR: 1.04, CiteScore: 3)
Advances in Therapy     Hybrid Journal   (Followers: 5, SJR: 1.075, CiteScore: 3)
Aegean Review of the Law of the Sea and Maritime Law     Hybrid Journal   (Followers: 6)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2, SJR: 0.517, CiteScore: 1)
Aerobiologia     Hybrid Journal   (Followers: 3, SJR: 0.673, CiteScore: 2)
Aesthetic Plastic Surgery     Hybrid Journal   (Followers: 11, SJR: 0.825, CiteScore: 1)
African Archaeological Review     Hybrid Journal   (Followers: 21, SJR: 0.862, CiteScore: 1)
Afrika Matematika     Hybrid Journal   (Followers: 1, SJR: 0.235, CiteScore: 0)
AGE     Hybrid Journal   (Followers: 7)
Ageing Intl.     Hybrid Journal   (Followers: 7, SJR: 0.39, CiteScore: 1)
Aggiornamenti CIO     Hybrid Journal   (Followers: 1)
Aging Clinical and Experimental Research     Hybrid Journal   (Followers: 3, SJR: 0.67, CiteScore: 2)
Agricultural Research     Hybrid Journal   (Followers: 6, SJR: 0.276, CiteScore: 1)
Agriculture and Human Values     Hybrid Journal   (Followers: 14, SJR: 1.173, CiteScore: 3)
Agroforestry Systems     Hybrid Journal   (Followers: 20, SJR: 0.663, CiteScore: 1)
Agronomy for Sustainable Development     Hybrid Journal   (Followers: 13, SJR: 1.864, CiteScore: 6)
AI & Society     Hybrid Journal   (Followers: 9, SJR: 0.227, CiteScore: 1)
AIDS and Behavior     Hybrid Journal   (Followers: 14, SJR: 1.792, CiteScore: 3)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 4, SJR: 0.862, CiteScore: 3)
Akupunktur & Aurikulomedizin     Full-text available via subscription   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 6, SJR: 0.531, CiteScore: 0)
Algebra Universalis     Hybrid Journal   (Followers: 2, SJR: 0.583, CiteScore: 1)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1, SJR: 1.095, CiteScore: 1)
Algorithmica     Hybrid Journal   (Followers: 9, SJR: 0.56, CiteScore: 1)
Allergo J.     Full-text available via subscription   (Followers: 1, SJR: 0.234, CiteScore: 0)
Allergo J. Intl.     Hybrid Journal   (Followers: 2)
Alpine Botany     Hybrid Journal   (Followers: 5, SJR: 1.11, CiteScore: 3)
ALTEX : Alternatives to Animal Experimentation     Open Access   (Followers: 3)
AMBIO     Hybrid Journal   (Followers: 10, SJR: 1.569, CiteScore: 4)
American J. of Cardiovascular Drugs     Hybrid Journal   (Followers: 16, SJR: 0.951, CiteScore: 3)
American J. of Community Psychology     Hybrid Journal   (Followers: 29, SJR: 1.329, CiteScore: 2)
American J. of Criminal Justice     Hybrid Journal   (Followers: 9, SJR: 0.772, CiteScore: 1)
American J. of Cultural Sociology     Hybrid Journal   (Followers: 17, SJR: 0.46, CiteScore: 1)
American J. of Dance Therapy     Hybrid Journal   (Followers: 5, SJR: 0.181, CiteScore: 0)
American J. of Potato Research     Hybrid Journal   (Followers: 2, SJR: 0.611, CiteScore: 1)
American J. of Psychoanalysis     Hybrid Journal   (Followers: 21, SJR: 0.314, CiteScore: 0)
American Sociologist     Hybrid Journal   (Followers: 14, SJR: 0.35, CiteScore: 0)
Amino Acids     Hybrid Journal   (Followers: 8, SJR: 1.135, CiteScore: 3)
AMS Review     Partially Free   (Followers: 4)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7, SJR: 0.211, CiteScore: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 5, SJR: 0.536, CiteScore: 1)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Analysis of Verbal Behavior     Hybrid Journal   (Followers: 6)
Analytical and Bioanalytical Chemistry     Hybrid Journal   (Followers: 32, SJR: 0.978, CiteScore: 3)
Anatomical Science Intl.     Hybrid Journal   (Followers: 3, SJR: 0.367, CiteScore: 1)
Angewandte Schmerztherapie und Palliativmedizin     Hybrid Journal  
Angiogenesis     Hybrid Journal   (Followers: 3, SJR: 2.177, CiteScore: 5)
Animal Cognition     Hybrid Journal   (Followers: 20, SJR: 1.389, CiteScore: 3)
Annales françaises de médecine d'urgence     Hybrid Journal   (Followers: 1, SJR: 0.192, CiteScore: 0)
Annales Henri Poincaré     Hybrid Journal   (Followers: 3, SJR: 1.097, CiteScore: 2)
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4, SJR: 0.438, CiteScore: 0)
Annali dell'Universita di Ferrara     Hybrid Journal   (SJR: 0.429, CiteScore: 0)
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1, SJR: 1.197, CiteScore: 1)
Annals of Biomedical Engineering     Hybrid Journal   (Followers: 17, SJR: 1.042, CiteScore: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 4, SJR: 0.932, CiteScore: 1)
Annals of Data Science     Hybrid Journal   (Followers: 12)
Annals of Dyslexia     Hybrid Journal   (Followers: 10, SJR: 0.85, CiteScore: 2)
Annals of Finance     Hybrid Journal   (Followers: 32, SJR: 0.579, CiteScore: 1)
Annals of Forest Science     Hybrid Journal   (Followers: 7, SJR: 0.986, CiteScore: 2)
Annals of Global Analysis and Geometry     Hybrid Journal   (Followers: 1, SJR: 1.228, CiteScore: 1)
Annals of Hematology     Hybrid Journal   (Followers: 15, SJR: 1.043, CiteScore: 2)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12, SJR: 0.413, CiteScore: 1)
Annals of Microbiology     Hybrid Journal   (Followers: 11, SJR: 0.479, CiteScore: 2)
Annals of Nuclear Medicine     Hybrid Journal   (Followers: 5, SJR: 0.687, CiteScore: 2)
Annals of Operations Research     Hybrid Journal   (Followers: 10, SJR: 0.943, CiteScore: 2)
Annals of Ophthalmology     Hybrid Journal   (Followers: 12)
Annals of Regional Science     Hybrid Journal   (Followers: 8, SJR: 0.614, CiteScore: 1)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annals of Solid and Structural Mechanics     Hybrid Journal   (Followers: 9, SJR: 0.239, CiteScore: 1)
Annals of Surgical Oncology     Hybrid Journal   (Followers: 15, SJR: 1.986, CiteScore: 4)
Annals of Telecommunications     Hybrid Journal   (Followers: 9, SJR: 0.223, CiteScore: 1)
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1, SJR: 1.495, CiteScore: 1)
Antonie van Leeuwenhoek     Hybrid Journal   (Followers: 5, SJR: 0.834, CiteScore: 2)
Apidologie     Hybrid Journal   (Followers: 4, SJR: 1.22, CiteScore: 3)
APOPTOSIS     Hybrid Journal   (Followers: 9, SJR: 1.424, CiteScore: 4)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2, SJR: 0.294, CiteScore: 1)
Applications of Mathematics     Hybrid Journal   (Followers: 2, SJR: 0.602, CiteScore: 1)
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 44, SJR: 0.571, CiteScore: 2)
Applied Biochemistry and Microbiology     Hybrid Journal   (Followers: 18, SJR: 0.21, CiteScore: 1)
Applied Categorical Structures     Hybrid Journal   (Followers: 5, SJR: 0.49, CiteScore: 0)
Applied Composite Materials     Hybrid Journal   (Followers: 49, SJR: 0.58, CiteScore: 2)
Applied Entomology and Zoology     Partially Free   (Followers: 6, SJR: 0.422, CiteScore: 1)
Applied Geomatics     Hybrid Journal   (Followers: 3, SJR: 0.733, CiteScore: 3)
Applied Geophysics     Hybrid Journal   (Followers: 9, SJR: 0.488, CiteScore: 1)
Applied Intelligence     Hybrid Journal   (Followers: 13, SJR: 0.6, CiteScore: 2)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4, SJR: 0.319, CiteScore: 1)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 8, SJR: 0.886, CiteScore: 1)
Applied Mathematics - A J. of Chinese Universities     Hybrid Journal   (SJR: 0.17, CiteScore: 0)
Applied Mathematics and Mechanics     Hybrid Journal   (Followers: 5, SJR: 0.461, CiteScore: 1)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 66, SJR: 1.182, CiteScore: 4)
Applied Physics A     Hybrid Journal   (Followers: 10, SJR: 0.481, CiteScore: 2)
Applied Physics B: Lasers and Optics     Hybrid Journal   (Followers: 24, SJR: 0.74, CiteScore: 2)
Applied Psychophysiology and Biofeedback     Hybrid Journal   (Followers: 8, SJR: 0.519, CiteScore: 2)
Applied Research in Quality of Life     Hybrid Journal   (Followers: 12, SJR: 0.316, CiteScore: 1)
Applied Solar Energy     Hybrid Journal   (Followers: 22, SJR: 0.225, CiteScore: 0)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 7, SJR: 0.542, CiteScore: 1)
Aquaculture Intl.     Hybrid Journal   (Followers: 26, SJR: 0.591, CiteScore: 2)
Aquarium Sciences and Conservation     Hybrid Journal   (Followers: 2)
Aquatic Ecology     Hybrid Journal   (Followers: 36, SJR: 0.656, CiteScore: 2)
Aquatic Geochemistry     Hybrid Journal   (Followers: 4, SJR: 0.591, CiteScore: 1)
Aquatic Sciences     Hybrid Journal   (Followers: 13, SJR: 1.109, CiteScore: 3)
Arabian J. for Science and Engineering     Hybrid Journal   (Followers: 5, SJR: 0.303, CiteScore: 1)
Arabian J. of Geosciences     Hybrid Journal   (Followers: 2, SJR: 0.319, CiteScore: 1)
Archaeological and Anthropological Sciences     Hybrid Journal   (Followers: 21, SJR: 1.052, CiteScore: 2)
Archaeologies     Hybrid Journal   (Followers: 12, SJR: 0.224, CiteScore: 0)
Archiv der Mathematik     Hybrid Journal   (Followers: 1, SJR: 0.725, CiteScore: 1)
Archival Science     Hybrid Journal   (Followers: 65, SJR: 0.745, CiteScore: 2)
Archive for History of Exact Sciences     Hybrid Journal   (Followers: 7, SJR: 0.186, CiteScore: 1)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3, SJR: 0.909, CiteScore: 1)
Archive for Rational Mechanics and Analysis     Hybrid Journal   (SJR: 3.93, CiteScore: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 6, SJR: 0.79, CiteScore: 2)
Archives and Museum Informatics     Hybrid Journal   (Followers: 153, SJR: 0.101, CiteScore: 0)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6, SJR: 1.41, CiteScore: 5)
Archives of Dermatological Research     Hybrid Journal   (Followers: 7, SJR: 1.006, CiteScore: 2)
Archives of Environmental Contamination and Toxicology     Hybrid Journal   (Followers: 14, SJR: 0.773, CiteScore: 2)
Archives of Gynecology and Obstetrics     Hybrid Journal   (Followers: 17, SJR: 0.956, CiteScore: 2)
Archives of Microbiology     Hybrid Journal   (Followers: 9, SJR: 0.644, CiteScore: 2)
Archives of Orthopaedic and Trauma Surgery     Hybrid Journal   (Followers: 9, SJR: 1.146, CiteScore: 2)
Archives of Osteoporosis     Hybrid Journal   (Followers: 2, SJR: 0.71, CiteScore: 2)
Archives of Sexual Behavior     Hybrid Journal   (Followers: 10, SJR: 1.493, CiteScore: 3)
Archives of Toxicology     Hybrid Journal   (Followers: 17, SJR: 1.541, CiteScore: 5)
Archives of Virology     Hybrid Journal   (Followers: 5, SJR: 0.973, CiteScore: 2)
Archives of Women's Mental Health     Hybrid Journal   (Followers: 16, SJR: 1.274, CiteScore: 3)
Archivio di Ortopedia e Reumatologia     Hybrid Journal  
Archivum Immunologiae et Therapiae Experimentalis     Hybrid Journal   (Followers: 2, SJR: 0.946, CiteScore: 3)
ArgoSpine News & J.     Hybrid Journal  
Argumentation     Hybrid Journal   (Followers: 6, SJR: 0.349, CiteScore: 1)
Arid Ecosystems     Hybrid Journal   (Followers: 2, SJR: 0.2, CiteScore: 0)
Arkiv för Matematik     Hybrid Journal   (Followers: 2, SJR: 0.766, CiteScore: 1)
Arnold Mathematical J.     Hybrid Journal   (Followers: 1, SJR: 0.355, CiteScore: 0)
Arthropod-Plant Interactions     Hybrid Journal   (Followers: 2, SJR: 0.839, CiteScore: 2)
Arthroskopie     Hybrid Journal   (Followers: 1, SJR: 0.131, CiteScore: 0)
Artificial Intelligence and Law     Hybrid Journal   (Followers: 11, SJR: 0.937, CiteScore: 2)
Artificial Intelligence Review     Hybrid Journal   (Followers: 18, SJR: 0.833, CiteScore: 4)
Artificial Life and Robotics     Hybrid Journal   (Followers: 9, SJR: 0.226, CiteScore: 0)
Asia Europe J.     Hybrid Journal   (Followers: 5, SJR: 0.504, CiteScore: 1)
Asia Pacific Education Review     Hybrid Journal   (Followers: 12, SJR: 0.479, CiteScore: 1)
Asia Pacific J. of Management     Hybrid Journal   (Followers: 16, SJR: 1.185, CiteScore: 2)
Asia-Pacific Education Researcher     Hybrid Journal   (Followers: 13, SJR: 0.353, CiteScore: 1)
Asia-Pacific Financial Markets     Hybrid Journal   (Followers: 3, SJR: 0.187, CiteScore: 0)
Asia-Pacific J. of Atmospheric Sciences     Hybrid Journal   (Followers: 19, SJR: 0.855, CiteScore: 1)
Asian Business & Management     Hybrid Journal   (Followers: 9, SJR: 0.378, CiteScore: 1)
Asian J. of Business Ethics     Hybrid Journal   (Followers: 10)
Asian J. of Criminology     Hybrid Journal   (Followers: 6, SJR: 0.543, CiteScore: 1)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 5, SJR: 0.548, CiteScore: 1)
AStA Wirtschafts- und Sozialstatistisches Archiv     Hybrid Journal   (Followers: 5, SJR: 0.183, CiteScore: 0)
ästhetische dermatologie & kosmetologie     Full-text available via subscription  
Astronomy and Astrophysics Review     Hybrid Journal   (Followers: 22, SJR: 3.385, CiteScore: 5)

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Journal Cover
AAPS Journal
Journal Prestige (SJR): 1.118
Citation Impact (citeScore): 4
Number of Followers: 23  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Online) 1550-7416
Published by Springer-Verlag Homepage  [2352 journals]
  • Model-Based Conditional Weighted Residuals Analysis for Structural Model
    • Authors: Moustafa M. A. Ibrahim; Sebastian Ueckert; Svetlana Freiberga; Maria C. Kjellsson; Mats O. Karlsson
      Abstract: Nonlinear mixed effects models are widely used to describe longitudinal data to improve the efficiency of drug development process or increase the understanding of the studied disease. In such settings, the appropriateness of the modeling assumptions is critical in order to draw correct conclusions and must be carefully assessed for any substantial violations. Here, we propose a new method for structure model assessment, based on assessment of bias in conditional weighted residuals (CWRES). We illustrate this method by assessing prediction bias in two integrated models for glucose homeostasis, the integrated glucose-insulin (IGI) model, and the integrated minimal model (IMM). One dataset was simulated from each model then analyzed with the two models. CWRES outputted from each model fitting were modeled to capture systematic trends in CWRES as well as the magnitude of structural model misspecifications in terms of difference in objective function values (ΔOFVBias). The estimates of CWRES bias were used to calculate the corresponding bias in conditional predictions by the inversion of first-order conditional estimation method’s covariance equation. Time, glucose, and insulin concentration predictions were the investigated independent variables. The new method identified correctly the bias in glucose sub-model of the integrated minimal model (IMM), when this bias occurred, and calculated the absolute and proportional magnitude of the resulting bias. CWRES bias versus the independent variables agreed well with the true trends of misspecification. This method is fast easily automated diagnostic tool for model development/evaluation process, and it is already implemented as part of the Perl-speaks-NONMEM software.
      PubDate: 2019-02-27
      DOI: 10.1208/s12248-019-0305-2
      Issue No: Vol. 21, No. 3 (2019)
  • A Reappraisal of Sedimentation Nonideality Coefficients for the Analysis
           of Weak Interactions of Therapeutic Proteins
    • Authors: Sumit K. Chaturvedi; Peter Schuck
      Abstract: The study of weak or colloidal interactions of therapeutic proteins in different formulations allows prediction and optimization of protein stability. Various biophysical techniques have been applied to determine the second osmotic virial coefficient B2 as it reflects on the macromolecular distance distribution that governs solution behavior at high concentration. In the present work, we exploit a direct link predicted by hydrodynamic theory between B2 and the nonideality of sedimentation, commonly measured in sedimentation velocity analytical ultracentrifugation through the nonideality coefficient of sedimentation, kS. Using sedimentation equilibrium analytical ultracentrifugation for independent measurement of B2, we have examined the dependence of kS on B2 for model proteins in different buffers. The data exhibit the expected linear relationship and highlight the impact of protein shape on the magnitude of the nonideality coefficient kS. Recently, measurements of kS have been considerably simplified allowing higher throughput and simultaneous polydispersity assessment at higher protein concentrations. Thus, sedimentation velocity may offer a useful approach to compare the impact of formulation conditions on weak interactions and simultaneously on higher-order structure of therapeutic proteins.
      PubDate: 2019-02-27
      DOI: 10.1208/s12248-019-0307-0
      Issue No: Vol. 21, No. 3 (2019)
  • A Density-Changing Centrifugation Method for Efficient Separation of Free
           Drugs from Drug-Loaded Particulate Delivery Systems
    • Authors: Lu Han; Hongyan Zhan; Xun Sun; Zhi-Rong Zhang; Li Deng
      Abstract: Commonly used separation techniques, such as ultracentrifugation, chromatography, and membrane separation, have inherent drawbacks that limit their usage. Herein, we introduced a new separation method, density-changing centrifugation (DCC), which is based on trisodium citrate (TC) and ultracentrifugation. Paclitaxel-loaded cationic solid lipid nanoparticles (SLNs/PTX) and doxorubicin-loaded PEGylated liposomes (Lipo/Dox) were prepared as model drug delivery particulates. After optimizing TC concentration and centrifugal conditions, DCC showed superior separation efficiency and accuracy over common ultracentrifugation and ultrafiltration methods and displayed comparable or even better separation efficiency compared with size-exclusion chromatography, as demonstrated by the determination of encapsulation efficiency, Tyndall effect, transmittance, and drug recovery. DCC was also proven to minimally impact the size distribution, surface morphology, and thermal properties of the nanoparticles and liposomes, and moreover, it did not affect the determination of drug concentrations. Together, DCC has been demonstrated as a neat and effective method for the separation of free drugs from drug-loaded SLNs and liposomes, which shall be of great benefit for the development of particulate based delivery systems.
      PubDate: 2019-02-25
      DOI: 10.1208/s12248-019-0306-1
      Issue No: Vol. 21, No. 3 (2019)
  • First-Principles and Empirical Approaches to Predicting In Vitro
           Dissolution for Pharmaceutical Formulation and Process Development and for
           Product Release Testing
    • Authors: Nikolay Zaborenko; Zhenqi Shi; Claudia C. Corredor; Brandye M. Smith-Goettler; Limin Zhang; Andre Hermans; Colleen M. Neu; Md Anik Alam; Michael J. Cohen; Xujin Lu; Leah Xiong; Brian M. Zacour
      Abstract: This manuscript represents the perspective of the Dissolution Working Group of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) and of two focus groups of the American Association of Pharmaceutical Scientists (AAPS): Process Analytical Technology (PAT) and In Vitro Release and Dissolution Testing (IVRDT). The intent of this manuscript is to show recent progress in the field of in vitro predictive dissolution modeling and to provide recommended general approaches to developing in vitro predictive dissolution models for both early- and late-stage formulation/process development and batch release. Different modeling approaches should be used at different stages of drug development based on product and process understanding available at those stages. Two industry case studies of current approaches used for modeling tablet dissolution are presented. These include examples of predictive model use for product development within the space explored during formulation and process optimization, as well as of dissolution models as surrogate tests in a regulatory filing. A review of an industry example of developing a dissolution model for real-time release testing (RTRt) and of academic case studies of enabling dissolution RTRt by near-infrared spectroscopy (NIRS) is also provided. These demonstrate multiple approaches for developing data-rich empirical models in the context of science- and risk-based process development to predict in vitro dissolution. Recommendations of modeling best practices are made, focused primarily on immediate-release (IR) oral delivery products for new drug applications. A general roadmap is presented for implementation of dissolution modeling for enhanced product understanding, robust control strategy, batch release testing, and flexibility toward post-approval changes.
      PubDate: 2019-02-21
      DOI: 10.1208/s12248-019-0297-y
      Issue No: Vol. 21, No. 3 (2019)
  • Moringa Isothiocyanate Activates Nrf2: Potential Role in Diabetic
    • Authors: David Cheng; Linbo Gao; Shan Su; Davit Sargsyan; Renyi Wu; Ilya Raskin; Ah-Ng Kong
      Abstract: Moringa isothiocyanate (MIC-1) is the main active isothiocyanate found in Moringa oleifera, a plant consumed as diet and traditional herbal medicine. Compared to sulforaphane (SFN), MICs are less studied and most work have focused on its anti-inflammatory activity. The purpose of this study is to better understand the Nrf2-ARE antioxidant activity of MIC-1 and its potential in diabetic nephropathy. MIC-1 showed little toxicity from 1.25–5 μM. MIC-1 activated Nrf2-ARE at similar levels to SFN. MIC-1 also increased gene expression of downstream Nrf2 genes NQO1, HO-1, and GCLC. Protein expression of HO-1 and GCLC was elevated in MIC-1-treated cells versus control. MIC-1 suppressed pro-inflammatory cytokines in LPS-stimulated macrophages. MIC-1 reduced levels of reactive oxygen species in high glucose (HG)-treated human renal proximal tubule HK-2 cells. RNA-seq was performed to examine the transcriptome in HK-2 cells exposed to HG with or without MIC-1. Ingenuity Pathway Analysis (IPA) of RNA-seq on HK-2 cells exposed to HG identified TGFβ1 and NQO1 regulation as potentially impacted and treatment of HG-exposed HK-2 cells with MIC-1 reversed the gene expression of these two pathways. Results implicate that the transcriptional regulator TGFβ1 signaling is activated by HG and that MIC-1 can inhibit HG-stimulated TGFβ1 activation. In summary, MIC-1 activates Nrf2-ARE signaling, increases expression of Nrf2 target genes, and suppresses inflammation, while also reducing oxidative stress and possibly TGFβ1 signaling in high glucose induced renal cells. Taken together, it appears that one potential therapeutic strategy for managing DN and is currently under development in clinic is Nrf2 activation.
      PubDate: 2019-02-19
      DOI: 10.1208/s12248-019-0301-6
      Issue No: Vol. 21, No. 2 (2019)
  • Activation of Protein Kinase A Stimulates SUMOylation, Expression, and
           Transport Activity of Organic Anion Transporter 3
    • Authors: Haoxun Wang; Jinghui Zhang; Guofeng You
      Abstract: Organic anion transporter 3 (OAT3) plays a vital role in removing a broad variety of anionic drugs from kidney, thus avoiding their possible toxicity in the body. We earlier established that activation of protein kinase C (PKC) enhances OAT3 ubiquitination, which promotes OAT3 internalization from the cell plasma membrane to intracellular endosomes and consequent degradation. As a result, OAT3 expression and transport activity are reduced. In the current study, we discovered that protein kinase A (PKA) had an opposite effect to PKC on the regulation of OAT3. We showed that activation of PKA by Bt2-cAMP stimulated OAT3 transport activity, which was largely caused by an enhanced plasma membrane expression of the transporter, kinetically reflected as an augmented maximal transport velocity Vmax without notable alteration in substrate-binding affinity Km. Additionally, we showed that PKA activation accelerated the rate of OAT3 recycling from intracellular compartments to the plasma membrane and decelerated the rate of OAT3 degradation. We further showed that OAT3 is subjected to post-translational modification by SUMO-2 and SUMO-3 not by SUMO-1. PKA activation enhanced OAT3 SUMOylation, which was accompanied by a reduced OAT3 ubiquitination. Finally, insulin-like growth factor 1 significantly stimulated OAT3 transport activity and SUMOylation through PKA signaling pathway. In conclusion, this is the first demonstration that PKA stimulated OAT3 expression and transport activity by altering the trafficking kinetics of OAT3 possibly through the crosstalk between SUMOylation and ubiquitination. Our studies are consistent with a remote sensing and signaling model for transporters (Wu et al. in Mol Pharmacol. 79(5):795–805, 2011).
      PubDate: 2019-02-13
      DOI: 10.1208/s12248-019-0303-4
      Issue No: Vol. 21, No. 2 (2019)
  • Incurred Sample Reanalysis: Time to Change the Sample Size
    • Authors: Piotr J. Rudzki; Przemysław Biecek; Michał Kaza
      Abstract: Reliable results of pharmacokinetic and toxicokinetic studies are vital for correct decision making during drug discovery and development. Thus, ensuring high quality of bioanalytical methods is of critical importance. Incurred sample reanalysis (ISR)—one of the tools used to validate a method—is included in the bioanalytical regulatory recommendations. The methodology of this test is well established, but the estimation of the sample size is still commented on and contested. We have applied the hypergeometric distribution to evaluate ISR test passing rates in different clinical study sizes. We have tested both fixed rates of the clinical samples—as currently recommended by FDA and EMA—and a fixed number of ISRs. Our study revealed that the passing rate using the current sample size calculation is related to the clinical study size. However, the passing rate is much less dependent on the clinical study size when a fixed number of ISRs is used. Thus, we suggest using a fixed number of ISRs, e.g., 30 samples, for all studies. We found the hypergeometric distribution to be an adequate model for the assessment of similarities in original and repeated data. This model may be further used to optimize the sample size needed for the ISR test as well as to bridge data from different methods. This paper provides a basis to re-consider current ISR recommendations and implement a more statistically rationalized and risk-controlled approach.
      PubDate: 2019-02-11
      DOI: 10.1208/s12248-019-0293-2
      Issue No: Vol. 21, No. 2 (2019)
  • Dissolution and Translational Modeling Strategies Toward Establishing an
           In Vitro - In Vivo Link—a Workshop Summary Report
    • Authors: Tycho Heimbach; Sandra Suarez-Sharp; Maziar Kakhi; Nico Holmstock; Andrés Olivares-Morales; Xavier Pepin; Erik Sjögren; Eleftheria Tsakalozou; Paul Seo; Min Li; Xinyuan Zhang; Ho-Pi Lin; Timothy Montague; Amitava Mitra; Denise Morris; Nikunjkumar Patel; Filippos Kesisoglou
      Abstract: ABSTRACT This publication summarizes the proceedings of day 2 of a 3-day workshop on “Dissolution and Translational Modeling Strategies Enabling Patient-Centric Product Development.” Patient-centric drug product development from a drug product quality perspective necessitates the establishment of clinically relevant drug product specifications via an in vitro-in vivo link. Modeling and simulation offer a path to establish this link; in this regard, physiologically based modeling has been implemented successfully to support regulatory decision-making and drug product labeling. In this manuscript, case studies of physiologically based biopharmaceutics modeling (PBBM) applied to drug product quality are presented and summarized. These case studies exemplify a possible path to achieve an in vitro-in vivo link and encompass (a) development of biopredictive dissolution methods to support biowaivers, (b) model-informed formulation selection, (c) predicting clinical formulation performance, and (d) defining a safe space for regulatory flexibility via virtual bioequivalence (BE). Workflows for the development and verification of absorption models/PBBM and for the establishment of a safe space using dissolution as an input are described with examples. Breakout session discussions on topics, such as current challenges and some best practices in model development and verification, are included as part of the Supplementary material.
      PubDate: 2019-02-11
      DOI: 10.1208/s12248-019-0298-x
      Issue No: Vol. 21, No. 2 (2019)
  • Multiplexed Gene Expression as a Characterization of Bioactivity for
           Interferon Beta (IFN-β) Biosimilar Candidates: Impact of Innate Immune
           Response Modulating Impurities (IIRMIs)
    • Authors: Eduardo F. Mufarrege; Lydia A. Haile; Marina Etcheverrigaray; Daniela I. Verthelyi
      Abstract: Recombinant human interferon-β (rhIFN-β) therapy is the first-line treatment in relapsing-remitting forms of multiple sclerosis (MS). The mechanism of action underlying its therapeutic activity is only partially understood as IFN-βs induce the expression of over 1000 genes modifying multiple immune pathways. Currently, assessment of potency for IFN-β products is based on their antiviral effect, which is not linked to its therapeutic effect. Here, we explore the use of a multiplexed gene expression system to more broadly characterize IFN-β bioactivity. We find that MM6 cells stimulated with US-licensed rhIFN-βs induce a dose-dependent and reproducible pattern of gene expression. This pattern of gene expression was used to compare the bioactivity profile of biosimilar candidates with the corresponding US-licensed rhIFN-β products, Rebif and Betaseron. While the biosimilar candidate for Rebif matched the pattern of gene expression, there were differences in the expression of a subset of interferon-inducible genes including CXCL-10, CXCL-11, and GBP1 induced by the biosimilar candidate for Betaseron. Assessment of product impurities in both products suggested that the difference was rooted in the presence of innate immune response modulating impurities (IIRMIs) in the licensed product. These studies indicate that determining the expression levels for an array of reporter genes that monitor different pathways can be informative as part of the demonstration of biosimilarity or comparability for complex immunomodulatory products such as IFN-β, but the sensitivity of each gene to potential impurities in the product should be examined to fully understand the results.
      PubDate: 2019-02-08
      DOI: 10.1208/s12248-019-0300-7
      Issue No: Vol. 21, No. 2 (2019)
  • Estimation of Solid Tumor Doubling Times from Progression-Free Survival
           Plots Using a Novel Statistical Approach
    • Authors: Katherine Kay; Keith Dolcy; Robert Bies; Dhaval K. Shah
      Abstract: ABSTRACT Tumor doubling time can significantly affect the outcome of anticancer therapy, but it is very challenging to determine. Here, we present a statistical approach that extracts doubling times from progression-free survival (PFS) plots, which inherently contains information regarding the growth of solid tumors. Twelve cancers were investigated and multiple PFS plots were evaluated for each type. The PFS plot showing fastest tumor growth was deemed to best represent the inherent growth kinetics of the solid tumor, and selected for further analysis. The exponential tumor growth rates were extracted from each PFS plot, along with associated variabilities, which ultimately allowed for the estimation of solid tumor doubling times. The mean simulated doubling times for pancreatic cancer, melanoma, hepatocellular carcinoma (HCC), renal cell carcinoma, triple negative breast cancer, non-small cell lung cancer, hormone receptor positive (HR+) breast cancer, human epidermal growth factor receptor-2 positive (HER-2+) breast cancer, gastric cancer, glioblastoma multiforme, colorectal cancer, and prostate cancer were 5.06, 3.78, 3.06, 2.67, 2.38, 2.40, 4.31, 4.12, and 3.84 months, respectively. For all cancers, clinically reported doubling times were within the estimated ranges. For all cancers, except HCC, the growth rates were best characterized by a log-normal distribution. For HCC, the gamma distribution best described the data. The statistical approach presented here provides a qualified method for extracting tumor growth rates and doubling times from PFS plots. It also allows estimation of the distributional characteristics for tumor growth rates and doubling times in a given patient population.
      PubDate: 2019-02-08
      DOI: 10.1208/s12248-019-0302-5
      Issue No: Vol. 21, No. 2 (2019)
  • Development of a New Inhaler for High-Efficiency Dispersion of Spray-Dried
           Powders Using Computational Fluid Dynamics (CFD) Modeling
    • Authors: Worth Longest; Dale Farkas
      Abstract: Computational fluid dynamics (CFD) modeling offers a powerful tool for the development of drug delivery devices using a first principles approach but has been underutilized in the development of pharmaceutical inhalers. The objective of this study was to develop quantitative correlations for predicting the aerosolization behavior of a newly proposed dry powder inhaler (DPI). The dose aerosolization and containment (DAC) unit DPI utilizes inlet and outlet air orifices designed to maximize the dispersion of spray-dried powders, typically with low air volumes (~ 10 mL) and relatively low airflow rates (~ 3 L/min). Five DAC unit geometries with varying orifice outlet sizes, configurations, and protrusion distances were considered. Aerosolization experiments were performed using cascade impaction to determine mean device emitted dose (ED) and mass median aerodynamic diameter (MMAD). Concurrent CFD simulations were conducted to predict both flow field-based and particle-based dispersion parameters that captured different measures of turbulence. Strong quantitative correlations were established between multiple measures of turbulence and the experimentally observed aerosolization metrics of ED and MMAD. As expected, increasing turbulence produced increased ED with best case values reaching 85% of loaded dose. Surprisingly, decreasing turbulence produced an advantageous decrease in MMAD with values as low as approximately 1.6 μm, which is in contrast with previous studies. In conclusion, CFD provided valuable insights into the performance of the DAC unit DPI as a new device including a two-stage aerosolization process offering multiple avenues for future enhancements.
      PubDate: 2019-02-07
      DOI: 10.1208/s12248-018-0281-y
      Issue No: Vol. 21, No. 2 (2019)
  • Systemic Bioequivalence Is Unlikely to Equal Target Site Bioequivalence
           for Nanotechnology Oncologic Products
    • Authors: Jessie L.-S. Au; Ze Lu; Roberto A. Abbiati; M. Guillaume Wientjes
      Abstract: Approval of generic drugs by the US Food and Drug Administration (FDA) requires the product to be pharmaceutically equivalent to the reference listed drug (RLD) and demonstrate bioequivalence (BE) in effectiveness when administered to patients under the conditions in the RLD product labeling. Effectiveness is determined by drug exposure at the target sites. However, since such measurement is usually unavailable, systemic exposure is assumed to equal target site exposure and systemic BE to equal target site BE. This assumption, while it often applies to small molecule drug products that are readily dissolved in biological fluids and systemically absorbed, is unlikely to apply to nanotechnology products (NP) that exist as heterogeneous systems and are subjected to dimension- and material-dependent changes. This commentary provides an overview of the intersecting and spatial-dependent processes and variables governing the delivery and residence of oncologic NP in solid tumors. In order to provide a quantitative perspective of the collective effects of these processes, we used quantitative systems pharmacology (QSP) multi-scale modeling to capture the physicochemical and biological events on several scales (whole-body, organ/suborgan, cell/subcellular, spatial locations, time). QSP is an emerging field that entails using modeling and computation to facilitate drug development; an analogous approach (i.e., model-informed drug development) is advocated by to FDA. The QSP model-based simulations illustrated that small changes in NP attributes (e.g., size variations during manufacturing, interactions with proteins in biological milieu) could lead to disproportionately large differences in target site exposure, rending systemic BE unlikely to equal target site BE.
      PubDate: 2019-02-01
      DOI: 10.1208/s12248-019-0296-z
      Issue No: Vol. 21, No. 2 (2019)
  • Translational Framework Predicting Tumour Response in Gemcitabine-Treated
           Patients with Advanced Pancreatic and Ovarian Cancer from Xenograft
    • Authors: Maria Garcia-Cremades; Celine Pitou; Philip W. Iversen; Iñaki F. Troconiz
      Abstract: The aim of this evaluation was to predict tumour response to gemcitabine in patients with advanced pancreas or ovarian cancer using pre-clinical data obtained from xenograft tumour-bearing mice. The approach consisted of building a translational model combining pre-clinical pharmacokinetic–pharmacodynamic (PKPD) models and parameters, with dosing paradigms used in the clinics along with clinical PK models to derive tumour profiles in humans driving overall survival. Tumour growth inhibition simulations were performed using drug effect parameters obtained from mice, system parameters obtained from mice after appropriate scaling, patient PK models for gemcitabine and carboplatin, and the standard dosing schedules given in the clinical scenario for both types of cancers. Tumour profiles in mice were scaled by body weight to their equivalent values in humans. As models for survival in humans showed that tumour size was the main driver of the hazard rate, it was possible to describe overall survival in pancreatic and ovarian cancer patients. Simulated tumour dynamics in pancreatic and ovarian cancer patients were evaluated using available data from clinical trials. Furthermore, calculated metrics showed values (maximal tumour regression [0–17%] and tumour size ratio at week 12 with respect to baseline [− 9, − 4.5]) in the range of those predicted with the clinical PKPD models. The model-informed Drug Discovery and Development paradigm has been successfully applied retrospectively to gemcitabine data, through a semi-mechanistic translational approach, describing the time course of the tumour response in patients from pre-clinical studies.
      PubDate: 2019-01-31
      DOI: 10.1208/s12248-018-0291-9
      Issue No: Vol. 21, No. 2 (2019)
  • Predicting Overall Survival and Progression-Free Survival Using Tumor
           Dynamics in Advanced Breast Cancer Patients
    • Authors: Hyeong-Seok Lim; Wan Sun; Kourosh Parivar; Diane Wang
      Abstract: ABSTRACT Prediction of survival endpoints, e.g., overall survival (OS) and progression-free survival (PFS), based on early observations, i.e., tumor size, may facilitate early decision making in oncology drug development. In this paper, using data from six randomized trials for first- or second-line advanced breast cancer (ABC) treatments with various mechanisms of action, tumor size change from baseline at different observation time points was evaluated as a predictor for survival endpoints using different modeling approaches. The aim is to establish a predictive model where tumor size change from baseline can be used as a treatment independent predictive marker for PFS and OS in first- and second-line ABC. The results showed that tumor size change at single time point (TSP) or up to certain time points as a time-varying covariate (TSTVC) were significant predictors for OS and PFS in the survival models along with other covariates identified for each line of treatment. TSP and TSTVC models performed similarly for first-line treatments; TSTVC performed significantly better for second-line treatments. Eight weeks was selected as the recommended early evaluation time of tumor size change to predict OS and PFS in both first- and second-line treatment, while better prediction can be achieved for first-line OS by using 16 weeks tumor size change. The result of this study is treatment independent and can be used to predict the outcome of the clinical trials using early readout of tumor size change for the classes of drugs that have been evaluated in this study.
      PubDate: 2019-01-30
      DOI: 10.1208/s12248-018-0290-x
      Issue No: Vol. 21, No. 2 (2019)
  • Dissolution Testing in Drug Product Development: Workshop Summary Report
    • Authors: Andreas Abend; David Curran; Jesse Kuiper; Xujin Lu; Hanlin Li; Andre Hermans; Pramod Kotwal; Dorys A. Diaz; Michael J. Cohen; Limin Zhang; Erika Stippler; German Drazer; Yiqing Lin; Kimberly Raines; Lawrence Yu; Carrie A. Coutant; Haiyan Grady; Johannes Krämer; Sarah Pope-Miksinski; Sandra Suarez-Sharp
      Abstract: This publication summarizes the proceedings and key outcomes of the first day (“Day 1”) of the 3-day workshop on “Dissolution and Translational Modeling Strategies Enabling Patient-Centric Product Development.” The overall aims of the workshop were to foster a productive dialog between industry and regulatory agencies and to discuss current strategies toward the development and implementation of clinically relevant dissolution specifications as an integral part of enhanced drug product understanding and effective drug product life-cycle management. The Day 1 podium presentations covered existing challenges and concerns for implementing highly valuable, yet often unique and novel experimental dissolution setups as quality control tools. In addition, several podium presentations highlighted opportunities to replace conventional dissolution testing with surrogate test methods to enable robust drug product and process understanding within the context of quality by design (QbD), new manufacturing technologies, and real-time release testing (RTRT). The topics covered on Day 1 laid the foundation for subsequent discussions which focused on the challenges related to establishing an in vitro–in vivo link and approaches for establishing clinically relevant drug product specifications which are becoming an expectation in regulatory submissions. Clarification of dissolution-related terminology used inconsistently among the scientific community, and the purpose of various testing approaches were key discussion topics of the Day 1 breakout sessions. The outcome of these discussions along with creative ways to overcome challenges related to bridging “exploratory dissolution approaches” with methods suitable for end-product control testing are captured within this report.
      PubDate: 2019-01-28
      DOI: 10.1208/s12248-018-0288-4
      Issue No: Vol. 21, No. 2 (2019)
  • A Receiver Operating Characteristic Framework for Non-adherence Detection
           Using Drug Concentration Thresholds—Application to Simulated Risperidone
           Data in Schizophrenic Patients
    • Abstract: Non-adherence to antipsychotic medication is a primary factor in disease relapse in schizophrenic patients. We sought to evaluate if plasma concentrations of the antipsychotic risperidone can be used as a predictor of treatment adherence and to identify the optimal plasma concentration threshold to reliably distinguish between adherent and non-adherent patients. A population pharmacokinetic model was used to simulate plasma risperidone steady-state trough concentrations in 1000 virtual patients, where 60% of the patients were 100% adherent to their medication, while 40% of the patients were non-adherent to their medication. The probability of adherence was assessed by receiver operating characteristic (ROC) analysis on Ctrough. The area under the ROC curve (AUCROC) was used to identify the optimal Ctrough threshold. Single vs multiple Ctrough at steady state was also evaluated. After a single risperidone Ctrough measurement, the AUCROC (95% CI) was estimated to be 0.71 (0.69–0.72) and the optimal Ctrough threshold accounting for the lowest number of adherent and non-adherent misclassifications was estimated to be 11.9 ng/mL. After multiple Ctrough measurements, the AUCROC (95% CI) increased up to 0.85 (0.84–0.87) for three Ctrough measurements. The optimal probability threshold to reliably discriminate between adherent and non-adherent patients was estimated to be 0.51. Using this model which is reflective of typical adherence to antipsychotic medication, we found that three consecutive steady-state Ctrough measurements are needed for an accurate and precise diagnostic test to discriminate between patients who are adherent or non-adherent to treatment.
      PubDate: 2019-03-14
  • Pharmacokinetic Properties of Humanized IgG1 and IgG4 Antibodies in
           Preclinical Species: Translational Evaluation
    • Abstract: ABSTRACT Assessment of the factors that regulate antibody exposure–response relationships in the relevant animal models is critical for the design of successful translational strategies from discovery to the clinic. Depending on the specific clinical indication, preclinical development paradigms may require that the efficacy or dosing-related attributes for the existing antibody be assessed in various species when cross-reactivity of the lead antibody to the intended species is justified. Additionally, with the success of monoclonal antibodies for management of various human conditions, a parallel interest in therapeutic use of these novel modalities in various veterinary species has followed. The protective role of neonatal Fc receptor (FcRn) in regulation of IgG homeostasis and clearance is now well recognized and the “nonspecific clearance” of antibodies through bone marrow-derived phagocytic and vascular endothelial cells (via lysosomal processes) is modulated by interactions with FcRn receptors. In this study, we have attempted to examine the PK properties of human IgG antibodies in dog and monkey. These studies establish a translational framework for evaluation of IgG antibody PK properties across species.
      PubDate: 2019-03-13
  • Variability Attribution for Automated Model Building
    • Abstract: We investigated the possible advantages of using linearization to evaluate models of residual unexplained variability (RUV) for automated model building in a similar fashion to the recently developed method “residual modeling.” Residual modeling, although fast and easy to automate, cannot identify the impact of implementing the needed RUV model on the imprecision of the rest of model parameters. We used six RUV models to be tested with 12 real data examples. Each example was first linearized; then, we assessed the agreement in improvement of fit between the base model and its extended models for linearization and conventional analysis, in comparison to residual modeling performance. Afterward, we compared the estimates of parameters’ variabilities and their uncertainties obtained by linearization to conventional analysis. Linearization accurately identified and quantified the nature and magnitude of RUV model misspecification similar to residual modeling. In addition, linearization identified the direction of change and quantified the magnitude of this change in variability parameters and their uncertainties. This method is implemented in the software package PsN for automated model building/evaluation with continuous data.
      PubDate: 2019-03-08
  • Scaling Drug Clearance from Adults to the Young Children for Drugs
           Undergoing Hepatic Metabolism: A Simulation Study to Search for the
           Simplest Scaling Method
    • Abstract: Previous research showed that scaling drug clearance from adults to children based on body weight alone is not accurate for all hepatically cleared drugs in very young children. This study systematically assesses the accuracy of scaling methods that, in addition to body weight, also take age-based variables into account for drugs undergoing hepatic metabolism in children younger than five years, namely scaling with (1) a body weight-based function using an age-dependent exponent (ADE) and (2) a body weight-based function with fixed exponent of 0.75 (AS0.75) combined with isoenzyme maturation functions (MFPBPK) similar to those implemented in physiologically based pharmacokinetic (PBPK) models (AS0.75 + MFPBPK). A PBPK-based simulation workflow was used, including hypothetical drugs with a wide range of properties and metabolized by different isoenzymes. Adult clearance values were scaled to seven typical children between one day and four years. Prediction errors of ± 50% were considered reasonably accurate. Isoenzyme maturation was found to be an important driver of changes in hepatic metabolic clearance in children younger than five years, which prevents the systematic accuracy of ADE scaling. AS0.75 + MFPBPK, when accounting for maturation of isoenzymes and microsomal protein per gram of liver (MPPGL), can reasonably accurately scale hepatic metabolic clearance for all low and intermediate extraction ratio drugs except for drugs binding to alpha-1-acid glycoprotein in neonates. As differences in the impact of changes in system-specific parameters on drugs with different properties yield differences in clearance ontogeny, it is unlikely that for the remaining drugs, scaling methods that do not take drug properties into account will be systematically accurate.
      PubDate: 2019-03-08
  • Dissolution Edge Charts for Immediate Release Products and Their
           Applications: a Simulation Study to Aid the Setting of Specifications
    • Abstract: One of the most commonly used methods to establish the clinical relevance of dissolution is to align the dissolution specifications with pivotal clinical batches. The objective of the study was to create edge charts for the dissolution of immediate release (IR) drug products to quantitatively establish the bases for setting clinically relevant and discriminating dissolution specifications and to clarify which stage in the US Pharmacopoeia (USP) <711> acceptance tables should be targeted. The simulations of dissolution data were performed on a batch of IR products with 1,000,000 units. The desired acceptance criterion was Q = 80% of the label claim at 30 min. A total of 110 scenarios for IR data were generated, which included various combinations of two determinants: the batch mean and SD (standard deviation). For each scenario, the dissolution data were tested based on USP three-stage procedures to determine the pass/fail at each stage. This process was repeated 10,000 times. The failure rate at each stage for each scenario was calculated as the percentage of failed replicates across 10,000 trials. Contour plots, named edge charts, were created to demonstrate the relationship between the dissolution failure rates and the two determinants (mean and SD). The edge lines represent the failure rates for the given combinations of the mean and SD. The edge charts can provide a quantitative estimate based on the observed dissolution data and provide fundamental support for recommendations on using USP stage 2 as a target for setting the acceptance limit(s).
      PubDate: 2019-03-05
School of Mathematical and Computer Sciences
Heriot-Watt University
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