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Quality by Design Approach to Pharmacokinetic Modeling. A Case Study On Cyclosporin-A

Quality by Design Approach to Pharmacokinetic Modeling. A Case Study On Cyclosporin-A. Eric Lueshen, Cierra Hall, Andrej Mošať and Andreas Linninger University of Illinois at Chicago, Department of Bioengineering 2010 AIChE Annual Meeting November 11, 2010. Quality by Design.

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Quality by Design Approach to Pharmacokinetic Modeling. A Case Study On Cyclosporin-A

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  1. Quality by Design Approach to Pharmacokinetic Modeling. A Case Study On Cyclosporin-A Eric Lueshen, Cierra Hall, Andrej Mošať and Andreas Linninger University of Illinois at Chicago, Department of Bioengineering 2010 AIChE Annual Meeting November 11, 2010

  2. Quality by Design • Systematic approach to pharmaceutical development. • Based on sound science and quality risk management. • Begins with predefined objectives and emphasizes product and process understanding and process control. Companies must demonstrate an understanding of the sciencebehind all of the processes that support their proposed drug. Model-Based Understanding

  3. Deficiencies with Classical Pharmacokinetics • Data fitting and black-box models. • Results are hard to scale. • No mass conservation. • Equations sometimes based on unobservable phenomena such as specific binding. • Derive very little information in terms of drug kinetics and biotransport phenomena. Rigorous engineering approach needed for the development of Physiologically-Based Pharmacokinetic (PBPK) models to address these deficiencies. Black-box

  4. Specific Aims • Develop an automated methodology for the rigorous assessment of a multivariate Physiologically-Based Pharmacokinetic (PBPK) model of an organism such as a rat based on first principles. • Apply the developed methodology towards improved scaling which can account for pathological conditions and differences in individual body composition.

  5. Therapy DesignCase Study: Cyclosporin A Therapeutic Window Effect of drug administration method for same dosage (6 mg/kg).

  6. Methodology for Discovering Transport & Kinetic Parameters Observe Physical Phenomena • Obtain experimental data. • Construct closed vasculature flow network. Create a PBPK Model Discover Kinetics • Compute steady state blood • flow rates and pressures. • Select organ-specific PBPK • model and generate equations. • Estimate parameters via • kinetic inversion. • Optimize transport and • kinetic parameters. • Evaluate quality of each • PBPK model.

  7. Step 1: Bioaccumulation of Cyclosporine A in Different Organs • Male Sprague Dawley Rats • 277 +/- 15 g • 2 minute bolus injection into femoral vein • 12 organs & blood measured • 3 dose groups: • 1.2, 6, 30 mg/kg 1. Tanaka, et al. Drug Metabolism and Disposition, 2000.

  8. Step 2: Construction of the Closed Vasculature Flow Network for each Analyzed Species Left Forelimb Right Forelimb Brain Heart Input Heart Output Stomach + Intestines Liver L. Kidney R. Kidney L. Hindlimb R. Hindlimb Tail Lumped Other

  9. Left Forelimb Right Forelimb Brain Heart Output Heart Input Stomach + Intestines Liver L. Kidney R. Kidney L. Hindlimb R. Hindlimb Tail Lumped Other Step 3: Compute Steady State Flow Rates & Pressures Constituitive Equations Flow is conserved throughout the model Pressure drops are proportional to volumetric flow rate Hagen-Poiseuille equation mmHg MAP = 104 mmHg MVP = 2 mmHg

  10. Step 4: Organ-Specific PBPK Model & Equation Generation CyA clearance into metabolite: Kidney: Mass transfer into/from tissue: Drug clearance: Blood compartments: Tissue bound drug compartments: Hct: Hematocrit CyA: Cyclosporin A CyAT: Cyclosporin A bound to Tissue MCyA: Metabolized Cyclosporin A

  11. Step 5: Parameter Estimation via Kinetic Inversion Objective function for space xj time tj Conservation balances: Optimization of k-values Satisfactory Unsatisfactory Global Solution 2. W. Tang, et al. Industrial and Engineering Chemistry Research, 2005. 3. M. Somayaji, et al. Computers and Chemical Engineering, 2008.

  12. Step 6: Solution of the Kinetic Inversion Problem • Kinetic rates (hr-1) - 9 of 30 • kmuscle = 47.18 • kskin = 54.03 • kspleen = 35.22 • kfat = 96.11 • kgut = 31.20 • kheart = 27.58 • kliver = 118.04 • kkidney = 70.09 • kbrain = 7.03

  13. Optimal Therapy Selection 9 Dosage [μg/ml] 6 3 2 4 6 8 Duration of injection [hr]

  14. Knowledge Gain • First principles kinetics allows model to be scalable. • Determines the global transport mechanisms of drugs based on biochemical, anatomical, and physiological data. • Information is able to be derived from experimental data. • No longer reliant on pure data fitting. • Conservation of mass ensures model fidelity. • Accurate prediction of dose curves. • Improve introduction of new drugs. • Variety of therapy designs including administration mode (oral, intravenous; bolus, continuous), dosage interval, concentration and total drug amount.

  15. Individual Weight Age Future Plans • Develop biochemical, anatomical and physiological (BAP) scaling laws. • Create a library of models for animals commonly used in typical drug trials.

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