In-Vitro-In-Vivo Correlation and Applications

Dive into the essence of In-Vitro-In-Vivo Correlation (IVIVC) with our presentation. Uncover the definition, significance, key parameters, methods, and levels, including the innovative realm of in-vitro-in-silico integration

1. In-Vitro-In-Vivo Correlation (IVIVC) Guru Gobind Singh College of Pharmacy , Yamunanagar By Dr. Rameshwar Dass

2. Contents Definition of IVIVC Why require IVIVC Parameters and methods of correlation Levels of IVIVC Generation of in-vitro release profile Generation of in-vivo release profile Predictability Error and Issues In-vitro in silico in-vivo Correlation Applications

3. Definition of IVIVC In-vitro in-vivo correlations (IVIVC) It is the inter-relationship b/w an in-vitro property (such as dissolution) and an in-vivo response. Valid in-vitro and in-vivo methods valid IVIVC

4. Why require IVIVC To find change in process effects Effect of site change Effect of formulation and For biowaiver of BA/BE testing To minimize unnecessary human testing To setup meaningful in-vitro release specifications Decreased regulatory burdens To minimize the product cost & time required in additional BA studies

5. Parameters in IVIVC level Malinowski and Marroum, Encyclopedia of Contr. Drug Deliv. Level In-vitro In-vivo A Dissolution curve Input (absorption) curves B MDT MRT, MAT, MDT C Disintegration time, Time to have 10,50,90% dissolution, Dissolution rate, Dissolution efficiency C max , T max , K a , Time to have 10, 50, 90% absorption, AUC (total or cumulative)

6. Methods of IVIVC Malinowski and Marroum, Encyclopedia of Contr. Drug Deliv. Convolution Deconvolution Joins together three signals: input and output , as well as the signal characterizing the system (subject of our studies) Determine an unknown input signal Dissolution data (C) may be evaluated using criteria for in-vivo BA/BE assessment, based on C max  and AUC parameters A numerical method used to estimate the time course of drug input using a mathematical model based on the convolution importance NONMEM can be fitted to the data, model linking the in-vitro and in-vivo components Drug absorbed is estimated using Wagner-Nelson method or Loo Riegelman The relationship between in-vitro release and Cp is modeled directly in a single stage rather than via an indirect two stage approach.  Difficult to calculate in-vivo dissolution data from a blood profile and often requires mathematical and computing expertise.

7. Levels of IVIVC Malinowski and Marroum, Encyclopedia of Contr. Drug Deliv. Level A – It is Point to point relationship First de-convolution to get in-vivo % drug absorbed, then compare with %dissolved The in-vitro dissolution and in-vivo input curves may be directly super-imposable

8. Levels of IVIVC Level B – Statistical moments analysis MRT or MDT in-vivo vs. MDT in-vitro MRT=AUMC/AUC C*t Malinowski and Marroum, Encyclopedia of Contr. Drug Deliv. t AUMC

9. Levels of IVIVC Level C – Single point PK parameter vs. % dissolved Weakest level of correlation Malinowski and Marroum, Encyclopedia of Contr. Drug Deliv.

10. A B C Flow chart of IVIVC Wang et al (2009) Diss Tech , 8, 6-12 IVIVC API – Physicochemical Properties BCS Class PK Data C max AUC, T max Dosage Form Properties Biorelevent Dissolution Profile (MDT) NONMEM Computer Modelling (Using Convolution including, PK Models, and PK Parameters, API properties or Drug Release Data) IVIVR

11. Generation of In-Vitro Release Profile Dissolution apparatus 1 (Basket, 100rpm) or 2 (Paddle, 50 &75rpm) Aqueous medium: 900 ml pH: 1to 7.4 Temperature 37±0.2 C Simulated gastric fluid (class 1) and simulated intestinal fluid (class 3) In-vitro food effect: Effects of oils, enzymes and pH Rotating dialysis cell method

12. Generation of In-Vitro Release Profile In-vitro  dissolution time profile: formulations data were fitted to where F diss , vitro -fraction dissolved at time t,  F inf  is the fraction dissolved at time infinity—fixed to 1, MDT is the M ean D issolution T ime (hours), and  b  is the slope factor. Weibull functions Hill functions

13. Generation of In-Vitro Release Profile The similarity factor: the similarity in the % dissolution between the two curves.   Wt – optimal weighing factor, Rt & Tt – Reference & test dissolution value, n- No. of dissolution time points Note: f2 values ˃50 (50-100) mean equivalence of the two curves.

14. Dissolution Specifications Without IVIVC ± 10% of the label claim from mean dissolution profile of the bio or clinical batch Can be >10% but range not >25% in certain cases With IVIVC All batches should have dissolution profiles with upper and lower predicted bioequivalence Proper or Biorelevant Dissolution conditions Consider medium, volume, duration, apparatus pH 1 – 7.4 Predictive of bioavailability Similar conditions, similar dissolution and similar bioavailability

15. Generation of In-Vivo Release Profile Compartmental Models Wagner-Nelson ( Ke , Ka) Loo-Riegelman Linear Systems Models Deconvolution Convolution Note : mathematically they all yield the same result

16. Other Methods of Generating In-Vivo Release Profiles Non-linear relationships between fraction dissolved and fraction absorbed was also observed using following equation: where, α is the ratio of  a first order permeation rate constant to the first order dissolution rate constant, F inf , is the fraction of the dose absorbed at time infinity, and D, is a fraction of the total amount of drug absorbed at time t. For high values of α, dissolution is rate limiting step in absorption process and a linear level A IVIVC Small values of α give rise to a sort of parabolic relationship, non-linear (1.92) show in vitro rapid initial dissolution rate as compared to that of in vivo.

17. IVIVC Bench Issues Reliable and bio-relevant dissolution method and apparatus suitability Qualification and calibration of equipment, sink conditions Ability to discriminate non-BE lots Apparatus and media for continuous IVIVC (minimum 3 lots) and tuning with GI conditions Accurate deconvolution of the plasma concentration-time profile %absorbed in-vivo may be reflective than release; absorption rate limitation is common for CR products Dissolution Specifications Based on biological findings rather than pharmacopeia

18. IVIVC Modeling Issues Intra- and Inter-subject variation : High variations can alter the mean data and in rotate the deconvolution Enterohepatic recycling or second peak Reproducibility of reference profiles Modeling Smoothness of input and response functions Stability of numerical methods Jumps in input rate e.g., delayed release or gastric emptying Statistical properties of the models (Cmax, AUC)

19. In-vitro - in silico- in-vivo Correlation In silico  is an expression performed on computer or via computer simulation. Miramontes used the term “ in silico ” to characterize biological experiments carried out entirely on a computer. in silico  studies predict how drugs interact with the body and with pathogens. For example: software emulations to predict how certain drugs already in the market could treat multiple-drug-resistant and extensively drug-resistant strains of tuberculosis.

20. In-vitro - in silico- in-vivo Correlation This approach is used in drug discovery and early preclinical phases where PK data is not available. simulation of structural properties of a molecule To generate experimental data There are two in silico approaches for prediction of in vivo oral absorption: Statistical models Mechanism-based models

21. In-vitro - in silico- in-vivo Correlation There is a variety of  in silico  techniques are discuss: Bacterial sequencing techniques  – As an alternative to  in vitro  methods for identifying bacteria, In this the sequence of bacterial DNA and RNA have been developed. P olymerase C hain R eaction ( PCR ) . PCR takes a single or few copies of a piece of DNA and generating millions or more copies of a particular DNA sequence. It allow to detect bacteria associated with a variety of conditions with increasingly high sensitivity.

22. In-vitro - in silico- in-vivo Correlation Molecular modeling  –  in silico  work, demonstrating how drugs and other substances interact with the nuclear receptors of cells. The computer-based emulations show that 25-D, one of the vitamin D metabolites, and Capnine, a substance produced by bacteria, turn off the Vitamin D Receptor These results have since been validated by clinical observations. Whole cell simulations  –built a computer model of the crowded interior of a bacterial cell Find its response to sugar in its environment accurately reproduce the behavior of living cells.

23. In-vitro - in silico- in-vivo Correlation Mechanism based model used GastroPlus TM . Inputs to software include: Oral dose Physiochemical properties (pH-solubility profile, permeability etc.) Physiological properties (species, GI transit, GI pH, food status etc.) Formulation properties (release profile, particle size etc.) PK parameters (optional) The output includes: Fraction of oral dose absorbed Plasma Concentration time profiles (if PK parameters are given)

24. In-vitro - in silico- in-vivo Correlation CASE STUDY by GastroPlus TM whether or not the mean particle size requirement of Compound I (aqueous solubility>100 mg/mL) may be relaxed from 35µm to approximately 100µm without affecting its oral bioavailability. A simulation suggested that the extent of absorption is not sensitive to changes in particle size in the range of 35–250 µm. This helps in decision making with respect to dosage form design.

25. Failure of Level A IVIVC For Level A analysis, Fa is plotted against Fd (requires linear regression) IR products is less successful as they do not show dissolution limited absorption. A reason for this lack of success and acceptance Controlled release products , rather than IR products, are the focuses in the IVIVC Indicate that dissolution from such products as an alternate for bioavailability.

26. Acceptance criteria IVIVC According to FDA guidance 1) ≤15% for absolute prediction error (%P.E.) of each formulation. 2) ≤ 10% for mean absolute prediction error (%P.E.). Prediction error For C max For AUC: (5) (6)

27. Applications of IVIVC Biowaivers This is main role of establishing IVIVC and dissolution test as a surrogate for human studies Establishment of dissolution specifications Dissolution specifications may be used to minimize the possibility of difference between in-vitro & in-vivo performance.

28. Applications of IVIVC Product development of new formulations , pre-formulation studies, laboratory scale trials. Optimization of the formulation/process predicted from the IVIVC validated. Scale-up and post-approval changes (SUPAC): the dissolution data are used to judge the impact of process changes Design and analysis of clinical studies possibly needed for generating the IVIVC. Optimization of in-vitro dissolution system to be a predictor of in-vivo performance. Development and validation of Level A & C, including linear and nonlinear models.

29. IVIVC Software WinNonlin- IVIVC Toolkit GastroPlus v. 6.1 IVIVCPlus PDx-IVIVC DDDPlus v. 3.0 ivivc for R

30. Assignment Problem Distinguish the convolution and deconvolution model? What do you understand by in-vitro in silico in-vivo method and write Case study on it? Discuss in detail the IVIVC?

31. For further reading https://mpkb.org/home/patients/assessing_literature/in_vitro_studies J Emami, In vitro - In vivo Correlation: From Theory to Applications. JPharm Pharmaceut Sci (www.cspscanada.org) 9(2):169-189, 2006 Biopharmaceutics & pharmacokinetics by D.M.Brahmankar & Sunil B. Jaiswal. Biopharmaceutics & pharmacokinetics by P.L.Madan. Applied Biopharmaceutics and Pharmacokinetics, 7 th edition by Leon Shargel

32. Thanks for Participation