1. Shah Abhishek P ID no: 2012H14166P

2. INTRODUCTION It has become a very tedious, expensive and time consuming task tocollect and handle huge pharmacokinetic data. It is therefore, useful to develop pharmacokinetic simulation modelsfor the prediction of pharmacokinetic parameters. Pharmacokinetic simulation model is defined as a computationaland/or mathematical tool that interprets drug kinetics in livingenvironment under specific conditions . A predictive IVIVC can empower in vitro dissolution as a surrogatefor in vivo bioavailability / bioequivalence. 3. INTRODUCTION IVIVCs can decrease regulatory burden by decreasing the number ofbio-studies required in support of a drug product. This article presents a comprehensive overview of systematicprocedure for establishing and validating an in vitro - in vivocorrelation (IVIVC) level A, B, and C. A Level A IVIVC is an important mathematical tool that can help tosave time & cost during and after the development of a formulation. The IVIVC for a formulation is a mathematical relationship betweenan in vitro property of the formulation and its in vivo act. 4. BCS classification andexpectation for IVIVCClassExample Solubility Permeability IVIV correlation expectationIVIV correlation if dissolution rate is Verapamil, Proparanolol, slower than gastric emptying rate. 1 HighHighMetoprolol Otherwise limited or no correlationexpected.IVIV correlation expected If in vitro Carbamazepine, Ketoprofen, 2 Low Highdissolution rate is similar to in vivo Naproxen dissolution rate unless dose is very highAbsorption (permeability) is rate 3Ranitidine, Cimetidine, Atenolol High Lowdetermining otherwise no IVIV correlation with dissolution rate Furosemide,Limited or no IVIV correlation is 4 LowLow Hydrochlorothiazideexpected. 5. INTRODUCTION The Biopharmaceutics Classification System (BCS) is a predictiveapproach for developing correlation between physicochemicalcharacteristics of a drug formulation and in vivo bioavailability. TheBCS is not a direct IVIVC.The IVIVC is divided into five categories.1. Level A correlation 2. Level B correlation3. Level C correlation 4. Level D correlation5. Multiple level C correlation 6. Level A correlation, a higher level of correlation, elaborates point topoint relationship between in vitro dissolution and in vivo absorptionrate of drug from the formulation. level B correlation which compares MDTin vitro to MDTin vivo Level C correlation is a weak single point relationship having noreflection of dissolution or plasma profile and compares the amountof drug dissolved at one dissolution time-point to onepharmacokinetic parameter like t60% to AUC. 7. Due to the involvement of multiple time points multiple times,multiple level C is more producible than level C. It relates more thanone parameters. Level D correlation is helping in the development of a formulation. 8. Fast Release FormulationTime(h) Plasma drug conc AUC K*AUC Cp +(K*AUC) PDA PDR (mg/l)00 0 0 00 00.5 0.18 0.045 0.006 0.199.5 39.621 0.35 0.178 0.025 0.381948.912 0.66 0.683 0.096 0.753859.073 0.91 1.468 0.210 1.125667.744 1.12 2.483 0.350 1.477879.645 1.27 3.678 0.520 1.799090.716 1.28 4.953 0.693 1.979993.988 0.99 7.223 0.852.00100 98.36100.75 8.963 1.012.00100 99.07120.57 10.2831.711.71100 99.60150.38 11.7081.751.75100 99.61240.11 13.9121.401.40100 99.86 9. METHODOLOGY FormulationsThe formulations are abbreviated as follows: 1. Fast release formulation 2. Medium release formulation 3. Slow release formulation For the establishment of an IVIVC, the release governing excipientsin the formulations should be similar. Preferably. in vitro dissolution profiles should be determined with differentdissolution test conditions. The in vitro dissolution profiles leading toan IVIVC with the smallest prediction error (%). 10. The values of determination coefficients for IVIVC level A for fast,medium and slow release formulations were 0.9273, 0.9616 and0.9641, respectively. while 0.9885 and 0.9996 were the values of determinationcoefficients in case of IVIVC level B and C, respectively. 11. A sensitive and reliable in vitro dissolution method is used fordetermining the quality of a formulation. Such a dissolution method can be developed by serial modificationsin dissolution medium like the addition of a surfactant. Afterdevelopment of a reasonable dissolution medium, the in vitrodissolution testing is performed for suitable time period on theformulations selected. The dissolution samples are analyzedspectrophotometrically or chromatographically, to find out theamount of drug released. 12. where, Rt and Tt are the cumulative where m stands for thepercentage dissolved at time point t foramount of drug dissolved atreference and test products, respectively and time t.p is the number of pool points. 13. Out of model independent approaches, similarity factor analysis (apair wise procedure) is commonly chosen to compare the dissolutionprofiles. The two compared dissolution profiles are considered similar if f2 >50, and the mean difference between any dissolution sample notbeing greater than 15% (U.S. Department of Health and HumanServices, 1997). 14. Mean dissolution time, a model independent approach, would becalculated from dissolution data to determine . level B correlation For level C correlation, time required for a specific value of meandissolution like t50% (time required to dissolve 50% of total drug) isrequired which is calculated from the curve of zero order equation 15. Obtention of in vivo absorption data To establish IVIVC, in vivo absorption data is also required whichmeans that pharmacokinetic and bioavailability study is also to bedone. Pharmacokinetic study reflects the performance of drug inbody. It involves the study of the influence of body on the drug, thatis, absorption, distribution, metabolism and excretion. Where as thebioavailability indicates the extent to which a drug reaches systemiccirculation and is commonly assessed by evaluating AUC (area underplasma drug concentration-time curve), Cmax (maximum plasmadrug concentration) and tmax (time required to achieve Cmax). 16. Generally, single dose cross over experimental design with a washoutperiod of one week, is adopted to obtain absorption data forestablishing IVIVC. Thus for three formulations with differentrelease rates, a three treatment cross over study design is allocated. 17. Analysis of in vivo data The association between the in vitro and in vivo data is statedmathematically by a linear or nonlinear correlation. But, the plasma drug concentration data cannot be related directly tothe obtained in vitro release data; they are needed to be convertedfirst to the fundamental in vivo release data using pharmacokineticcompartment model analysis or linear system analysis. Based on a pharmacokinetic compartment analysis, the in vivoabsorption kinetics can be evaluated using various pharmacokineticparameters. 18. To develop level A correlation, in vivo absorption profiles of theformulations from the individual plasma concentration versus timedata are also evaluated using Wagner-Nelson equation. 19. For the establishment of level B correlation, mean residence time(MRT) is calculated from AUC and AUMC as the followingMRT is the first moment of drug distribution phase in body. It indicatesthe mean time for drug substance to transit through the body andinvolves in many kinetic processes 20. While level C correlation is established using a pharmacokineticparameter like AUC, Cmax or tmax. 21. IVIVC development and its analysis Level A IVIVC correlates the entire in vitro and in vivo profiles(Rasool et al., 2010). In order to develop level A correlation, thefraction of drug absorbed (Fa) for a formulation is plotted against itsfraction of drug dissolved (Fd), where Fa and Fd are plotted along x-axis and y-axis, respectively. 22. This curve provides basic information of the relationship between Faand Fd, either it is linear or non-linear. Also plots can also be plottedin the form of combination of two formulations like (a) slow/moderate (b) moderate / fast and (c) slow / fast or in combination ofthree formulations like fast/moderate/slow. Finally the regression analysis is performed for each curve toevaluate strength of correlation. The correlation is considered asefficient as the value of determination coefficient is closer to 1. 23. The predictability of the correlation is analyzed by calculating itsprediction error (%). The deviation between prediction and observation is assessed eitherwith internal validation or with external validation. Internal validation elaborates the predictability of data that has beenemployed in the model development and is recommended for allIVIVC analysis External validation depends on how efficiently theIVIVC predicts additional data sets. This predictability is considered better than the internal one. Externalpredictability is analyzed in the following conditions. 24. 1. When no conclusion is obtained from internal prediction.2. When correlation is established for drugs having narrow therapeuticwindow.3. When two formulations with different release rates are involved incorrelation development 25. The predictability of a correlation in regulatory context ischaracterized in terms of the prediction error (%) using AUC andCmax as the following: 26. According to FDA guidelines, the correlation is consideredpredictive if mean prediction error across formulations is less than10% for AUC and Cmax and the prediction error (%) for anyformulation is less than 15 for AUC and Cmax. Level B correlation is developed between MDT and MRT of aformulation. The MDT and MRT are plotted along x-axis and y-axis,respectively. The regression analysis is performed for this curve. 27. Level C correlation, a single point correlation, can be established bydrawing a curve between t50% and pharmacokinetic parameter areplotted along x-axis and y-axis, respectively following regressionanalysis. 28. Conclusion A Level A IVIVC is an important mathematical tool that can help tosave time and cost during and after the development of aformulation. 29. What the caterpillar calls THE END,Butterfly calls it.THE BEGINNING !!!