Analyzing Experimental Data By Regression. Annals of the Institute of Mathematical Statistics 30:A9–14.Īllen and Cady (1982). Posterior probabilities for choosing a regression model. Drug Intelligence, Illinois.Īkaike (1978). Fundamentals of Clinical Pharmacokinetics. Kinetics of pharmacologic effects in man: The anticoagulant action of warfarin. Computational problems of compartmental models with Michaelis-Menten-type elimination.
A semicompartmental modeling approach for pharmacodynamic data assessment. Direct linear plotting method for estimation of pharmacokinetic parameters. Corticosteroid pharmacodynamics: models for a broad array of receptor-mediated pharmacologic effects. Pharmacokinetics: Statistical moment calculations. Pharmacokinetic and Pharmacodynamic Data Analysis: Concepts and Applications, 5 th ed. Kinetic Data Analysis: Design and Analysis of Enzyme and Pharmacokinetic Experiments. J Pharmacokin Biopharm 21:457.Įndrenyi, ed. Comparison of four basic models of indirect pharmacodynamic responses. Noncompartmental determination of mean residence time and steady-state volume of distribution during multiple dosing, J Pharm Sci 80:202.ĭayneka, Garg and Jusko (1993). Estimation of statistical moments and steady-state volume of distribution for a drug given by intravenous infusion. ESTRIP, A BASIC computer program for obtaining initial polyexponential parameter estimates. Conditional Weighted Residuals (CWRES): A Model Diagnostic for the FOCE Method, Pharmaceutical Research, DOI:10.1007/s1109-x.īrown and Manno (1978). Additional references are provided below. Phoenix Workfl ows.References for the WinNonlin operational objects are provided in their descriptions. Manages fi les and analysis runs for SAS, Tables, and listings and help prepare theĮlectronic signatures, reason for change, PKS Administration tool,Īnalysis automation.
“Investing in an infrastructure for early drug development will ultimately cut development costs and accelerate development time by increasing the level of knowledge and the confi dence in making program design decisions much earlier in the drug development process.“ Phase I Phase II Time/Money Confidence of Approval Phase III Current Desired (4) To effectively leverage intellectual capitalįigure 1: Inversion of the Confi dence Curve in Drug Development Rational and cost-effective drug development Simulation technologies as part of a more Needs, the creation of a comprehensive andĪccessible pharmacology data warehouse isĮssential to applying advanced modeling and In addition to meeting regulatory compliance Up the scientifi c resources needed to adoptīenefi ts for Clinical Pharmacology and PK Groups On emerging in-silico techniques like trialĪutomation efforts leading to signifi cant “Pharsight Knowledgebase Server offers an integrated solution to the key challenges facing pharmacokinetic researchers and the IT/QA groups supporting them: Regulatory Compliance, Increased Researcher Productivity, and Knowledge Management for Early Drug Development.”Ĭompanies will be able to fully capitalize Regulatory-compliant pharmacokinetic research The Phoenix-based family of analysis toolsĭerived information, across a large set of – and they must do so while collecting andĪbove, Pharsight, the leading developer of The return on the investment in creating it Intellectual capital this type of research Represent a critical gap in compliance with Preparation and review workfl ow also may PK data and analysis activities are often
Used in support of regulatory submissions. Stricter controls over electronic data being Generated by early-phase drug development. “For drug development organizations, the challenge of managing and leveraging the intellectual capital and maximizing the return on the investment in creating it, often boils down to collecting and managing clinical data effi ciently and in compliance with strict FDA regulations.”Īctivities, these systems do not adequatelyĪddress the need for a centralized, secure Whileĭocumentum®-based document management systems This fl ood of new data - relating to both Skilled scientifi c researchers must becomeĬritical skilled resources needed to learn Has been made in effectively handling this Optimizing the value of Pharmacology Knowledge in Early Drug Development Primary factors for the 80% attrition rateĪbsorption, distribution, metabolism, and
That characterize the kinetics and dynamics “concentration-vs.-time” and “concentration-vs.-effect” data Is being directed at measuring, modeling, The Challenge of Leveraging Pharmacology Data in Early Drug DevelopmentĪctive site and the effects they produce - is