Verify or refute the use of Non Linear Mixed Effect Model for Interferon effect on HCV Hila David Shimrit Vashdi Project Advisors: Prof. Avidan Neumann Dr. Rachel Levy Drummer

Introduction Biomathematical Model is a valuable tool for science and it has implications on medicine and economy. It is often used to characterize diseases and drug’s behavior at the human body. Finding the right model for HCV treatment will have a great medical and economic influence.

Hepatitis C Virus HCV is a Single Strand Ribonucleic acid (RNA), belongs to the Flaviviridae family. Its genome is 9.6 kb size, and encoding to a polyprotein of 3,000 amino acids, produced by cellular and viral proteases.

Interferon α IFN- α is an anti-viral treatment for HCV. It’s a Glycoprotein, naturally secreted from cells in a response to viral infection. The Glycoprotein attach to membrane receptors which starts a cellular signals sequence. Those signals cause expression of anti-viral genes.

Pegylated-Interferon α Polyethylen glycol (peg) is a polymer which improves the pharmacokinetiks & pharmacodynamics of proteins its attached to. Two variants of pegylated-IFN α were tested, pegasys and pegIntron, differ each other with three features which effect their behavior: average molecular weight. branching. Link to the Interferon.

PharmacoKinetics Study of the absorption, spreading, metabolism and elimination of a drug. Its important to understand the IFN-α pharmacokinetics in order to efficiently predict the patients response to the treatment, since it’s a critical stage of the disease. The equations describes the concentration of the drug as a function of time. The first relates to the bolus and the second to the serum. Bifn- the drug concentration at the bolus. Inj- the drug dose. Kbs- spreading drug rate. Sifn- drug concentration at the serum. Cifn- drug elimination rate.

Project Goal Running a simulation with virtual patients, using Non-Linear Mixed Effect Model in order to verify or refute the use of the individual model for IFN-α effect on HCV.

Individual PK The data is blood samples collected for each patient separately and the estimation of the parameters is done for each patient specifically. Attributes: Independency of the patients. More complicated to implement.

Population PK Estimation of population parameters by treating all data as if it arose from homogeneous population. It can also identify the sources of variability that explain differences in the parameters between patients. Attributes: More objective. Easier to implement. More powerful (under some assumptions).

Non Linear Mixed Effect Model for PK A method based on population PK. NLME makes a one stage analysis and evaluate the population parameters that enable determine the PK and PD simultaneously. The NLME combine both approaches, the individual and population PK. It fits the best model under statistic population assumptions and can combine together parameters with different influence.

MONOLIX PROGRAM Monolix is a new software for the analysis of Non-linear mixed effect models, used especially at clinical experiments and pharmacokinetics processes. Monolix requires to define the data and the model and to fix some parameters used for the algorithms. The output is the estimation of the individual parameters, the maximal likelihood and the residuals.

Working process Analysis of Individual Experimental Data Kinetics graphs. Individual parameters. Creating data for virtual patients Simulated Individual kinetic profiles. Adding noise to the simulated Individual kinetic profiles. Running the population approach NLME Individual parameters out of population parameters. Comparison of the methods Comparing the two methods individual parameters results. *The working process was done for each treatment group of patients.

Step 1 – kinetics graphs pegIntronpegasys The drug concentration was measured during the first week of the treatment at 21 patients treated with pegasys and 10 patients treated with pegintron.

Step 2 - individual parameters Running the real data with the model equations at the Madonna. Finding the combination of the parameters values that will make the best fit of the real data to the model for each patient. pegIntronpegasys

Step 3- creating virtual patients Creating 100 combinations of parameters for each treatment. Simulating the kinetic profiles according to the parameters of the individual patients. Adding noise (uniform distribution) on the data outcomes from the kinetic profiles.

Step 4 - virtual patient’s individual fit Running the virtual patients data at the Madonna and finding the individual fit and parameters to every patient. pegIntronpegasys

pegIntron CifnKbsinjCifnKbsinj Mean , ,506.6 s.d , ,591.8 Minimum1.367E , , maximum , ,502 median , ,172.2 Virtual parameters according to the individual approach

Cifn Histogram pegasys pegIntron

Inj Histogram pegasys pegIntron

Kbs Histogram pegIntron Pegasys

Step 5 – population fit Running the simulated data in monolix program in order to estimate the population parameters and the outcomes individual parameters

Individual fit- Individual approach vs. population approach pegIntronpegasys Red- individual approach Blue- population approach Blue- individual Pink- population

conclusions At the dynamic model, we can see clear differences at Cifn and Inj between the treatments, while the absorption from the bolus to the serum (Kbs) is similar. Under the restriction of running the programs for only one injection and for limited number of patients, the model used at the monolix succeed predicting the individual fits, but still the individual approach find a better fit.

Thanks Prof. Avidan Neumann Dr. Rachel Levy Drummer