Computational modeling of drug disposition

cadd

1. COMPUTATIONAL MODELING OF DRUG DISPOSITION PRESENTED BY: Supriya Hiremath M. Pharm 2 semester Dept. of Pharmaceutics HSKCOP, B agalkot FACILITATED TO: Dr. L axman Vijapur Professor Dept. of Pharmaceutics HSKCOP, B agalkot 1

2. Contents: Introduction. Modeling techniques. Absorption Distribution Excretion 2

3. Introduction Drug discovery has focused almost exclusively on efficacy and selectivity against the biological target. Half of drug candidates fail at phase II and phase III clinical trials because of undesirable drug pharmacokinetics properties, including absorption, distribution, metabolism, excretion, and toxicity (ADMET). Caco-2 and MDCK cell monolayers are widely used to simulate membrane permeability as an in vitro estimation of in vivo absorption. These in vitro results have enabled the training of in silico models , which could be applied to predict the ADMET properties of compounds even before they are synthesized. 3

4. Fueled by the ever-increasing computational power and significant advances of in silico modeling algorithms, numerous computational programs that aim at modeling drug ADMET properties have emerged. A comprehensive list of available commercial ADMET modeling software has been provided previously by van de Waterbeemd and Gifford. 4

5. DRUG DISPOSITION Any alternation in the drug’s bioavailability is reflected in its pharmacological effects. Others processes that play a role in the therapeutic activity of a drug are distribution and elimination. Together, they are known as drug disposition. 5

6. 6

7. Modeling techniques Two types of Modeling Approaches are : Quantitative approaches. Qualitative approaches. 7

8. Quantitative approaches It is represented by pharmacophore modeling and flexible docking studies investigate the structural requirements for the interaction between drugs and the targets that are involved in ADMET processes. These are especially useful when there is an accumulation of knowledge against a certain target. For example: a set of drugs known to be transported by a transporter would enable a pharmacophore study to elucidate the minimum required structural features for transport. The availability of a protein’s 3-D structure, from either X-ray crystallisation or homology modeling , would assist flexible docking of the active ligand to derive important interactions between the protein and the ligand. 8

9. Three widely used automated pharmacophore perception tools : DISCO ( DIStance Comparison) GASP(Genetic Algorithm Similarity Program) Catalyst/HIPHOP All three programs attempt to determine common features based on the superposition of active compounds with different algorithms. 9

10. 10

11. Docking Types Autodock DOCK GOLD SwissDock DockingServer 1-ClickDocking iGemdock 11

12. 12 iGemdock

13. Qualitative approaches It is represented by quantitative structure activity relationship (QSAR) and quantitative structure property relationship(QSPR) studies utilize multivariate analysis to correlate molecular descriptors with ADMET- related properties. A diverse range of molecular descriptors can be calculated based on the drug structure . Some of these descriptors are closely related to a physical property and are easy to comprehend (e.g. Molecular weight) whereas the majority of the descriptors are of quantum mechanical concepts or interaction energies at dispersed space points that are beyond simple physicochemical parameters. 13

14. When calculating correlations, it is important to select the molecular descriptors that represents the type of interactions contributing to the targeted biological property . A set of descriptors that specifically target ADME related properties has been proposed by Cruciani and colleagues. The majority of published ADMET models are generated based on 2D descriptors. 14

15. Even though the alignment dependent 3D descriptors that are relevant to the targeted biological activity tend to generate the most predictive models . The difficulties inherent in structure alignment thwart attempts to apply this type of modeling in a high throughput manner. A wide selection of statistical algorithms is available to researchers for correlating field descriptors with ADMET properties including simple multiple linear regression(MLR), multivariate partial least squares (PLS) and the non linear regression type algorithms such as artificial neural network (ANN) and support vector machine(SVM). 15

16. 16

17. 17

18. 18

19. 19

20. 1) Drug absorption Because of its convenience and good patient compliance, oral administration is the most preferred drug delivery form. As a result, much of the attention of in silico approaches is focused on modelling drug oral absorption, which mainly occurs in the human intestine. In general, drug bioavailability and absorption is the result of the interplay between drug solubility and intestinal permeability. 20

21. Solubility A drug generally must dissolve before it can be absorbed from the intestinal lumen . By measuring a drug’s log P ( log of partition co efficient of compound between water and n-octanol) and its melting point, one could indirectly estimate solubility using ‘ general solubility equation’. To predict the solubility of compound even before synthesizing it, in silico modeling can be implemented. There are mainly two approaches to model solubility: 1) Based on the underlying physiological processes 2) Other is an empirical approach The dissolution process involves the breaking up of solute from its crystal lattice and the association of the solute with solvent molecules. Empirical approaches represented by QSPR utilize multivariate analysis to identify correlations between molecular descriptors and solubility. 21

22. B. Intestinal permeation Intestinal permeation describes the ability of drugs to cross the intestinal mucosa separating the gut lumen from the portal circulation. It is an essential process for drugs to pass the intestinal membrane before entering the systemic circulation to reach their target site of action. The process involves both passive and active transport. It is a complex process that is difficult to predict solely based on molecular mechanism. As a result, most current models aim to simulate in vitro membrane permeation of caco-2, MDCK or PAMPA, which have been a useful indicator of in vivo drug absorption. 22

23. C. Other consideration The ionization state will affect both solubility and permeability which results in the influence of the absorption profile of a compound. Given the environment pH, the charge of a molecule can be determined using the compounds ionization constant value ( pKa ), which indicates the strength of an acid or a base. Several commercially and publicly available program providepKa estimation based on the input structure, including SCSpKa ( ChemSilico , Tewksbury, MA), Pallas/ pKalc ( CompuDrug , Sedona,AZ ), etc Both influx and efflux transporters are located in intestinal epithelial cells and can either increase or decrease oral absorption. Influx transporters are actively transport drugs had mimic their native substrates across the epithelial cell such as human peptide transporter1, apical sodium bile acid transporter and nucleoside transporters. Efflux transporters actively pump absorbed drugs back into the intestinal lumen such as P- glycoprotein, multidrug resistance associated protein (MRP) and breast cancer resistance protein (BCRP ). 23

24. 2) Drug distribution Distribution is an important aspect of drug’s pharmacokinetic profile. The structural and physiochemical properties of a drug determine the extent of distribution, which is mainly reflected by three parameters: 1.volume of distribution (Vd), 2.plasma-protein binding (PPB) and 3.blood-brain barrier (BBB) permeability. 24

25. A. Volume of distribution Vd is a measure of relative partitioning of drug between plasma and tissue, an important proportional constant that, when combined a drug is a major determinant of how often the drug should be administered. However , because of the scarcity of in vivo data and complexity of the underlying processes, computational models that are capable of prediction Vd based solely on computed descriptors are still under development. 25

26. B. Plasma protein binding Drugs binding to a variety of plasma proteins such as serum albumin, as unbound drug primarily contributes to pharmacological efficacy. The effect of PPB is an important consideration when evaluating the effective (unbound) drug plasma concentration. The models proposed to predict PPB should not rely on the binding data of only one protein when predicting plasma protein binding because it is a composite parameter reflecting interactions with multiple protein. 26

27. C. Blood B rain Barrier The BBB maintains the restricted extracellular environment in the central nerve system. The evaluation of drug penetration through the BBB is an integral part of drug discovery and development process. Again , because of the few experimental data derived from inconsistent protocols, most BBB permeation prediction models are of limited practical use despite intensive efforts. Most approaches model log blood/brain ( logBB ), which is a measurement of the drug partitioning between blood and brain tissue. The measurement is an indirect implication of BBB permeability, which does not discriminate between free and plasma protein-bound solute. 27

28. 3) Drug excretion The excretion or clearance of a drug is quantified by plasma clearance, which is defined as plasma volume that has been cleared completely free of drug per unit of time. Together with Vd, it can assist in the calculation of drug half-life, thus determining the dosage regimen . Hepatic and renal clearances are the two main components of plasma clearance. No model has been reported that is capable of predicting plasma clearance solely from computed drug structures. Current modeling efforts are mainly focused on estimating in vivo clearance from in vitro data. Just like other pharmacokinetic aspects, the hepatic and renal clearance process is also complicated by presence of active transporters. 28

29. 29

30. 30

31. REFERENCE Computer Applications in Pharmaceutical Research and Development, Sean Ekins,2006, John Wiley and Sons. https :// hemonc.mhmedical.com/content.aspx?bookid=1810&sectionid=124489864 ( 9 th Mar, 2019 ). 31