Computational modelling of drug disposition

computational modelling of drug disposition is the integral part of computer aided drug design. different kinds of tools being used in the prediction of drug disposition in human body. This topic in the CADD explains the details about the drug disposition, active transporters and tools.

1. COMPUTATIONAL MODELLING OF DRUG DISPOSITION Presented by: - Joshi Lalita Sandeep First year m. pharm Guide: - Dr. Sudhir Pandya Dr. D. Y. Patil college of pharmacy, Akurdi, pune-411044

2. Table of contents Introduction Modelling techniques Drug absorption Drug distribution Drug excretion Active transporters P- gp BCRP Nucleoside transporters hPEPT1 ASBT OCT OACP BBB-choline transporters Current challenges and future directions

3. INTRODUCTION Drug discovery has focused almost exclusively on efficacy and selectivity against the biological target. As a result, nearly half of drug candidates fail at phase II and phase III clinical trials because of undesirable drug pharmacokinetics properties. The pressure to control the escalating cost of new drug development has changed the paradigm since the mid-1990s. To reduce the attrition rate at more expensive later stages, in vitro evaluation of ADMET properties in the early phase of drug discovery has been widely adopted. Many high-throughput in vitro ADMET property screening assays have been developed and applied successfully. For example, Caco-2 and MDCK cell monolayers are widely used to simulate membrane permeability as an in vitro estimation of in vivo absorption.

4. 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. 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 .

5. MODELLING TECHNIQUES There are two main approaches of the drug modelling techniques. Quantitative approach Qualitative approach

6. Modelling technique: - Quantitative approach 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.

7. Three widely used automated pharmacophore perception tools, DISCO (DIStance COmparisons) GASP (Genetic Algorithm Similarity Program) Catalyst/HIPHOP. The application of different flexible docking algorithms in drug discovery has recently been reviewed. The essential interactions derived from either study can be used as a screen in evaluating drug ADMET properties.

8. Modelling techniques: - 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. it is important to select the molecular descriptors that represent the type of interactions contributing to the targeted biological property.

9. It is essential to select the right mathematical tool for most effective ADMET modelling .sometimes it is possible to apply multiple statistical methods and compare the result to identify the best approach. 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 nonlinear regression-type algorithms such as artificial neural networks (ANN) and support vector machine (SVM).

10. 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 modeling drug oral absorption, which mainly occurs in the human intestine. Drug bioavailability and absorption is the result of the interplay between drug solubility and intestinal permeability.

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

12. Even though the calculation process ignores the underlying physiological processes, the molecular descriptor selection and model interpretation still requires understanding of the dissolution process. Selection of field descriptors that adequately describe the physiological process and the appropriate multivariate analysis is essential to successful modeling. The target property for most models is the logarithm of solubility (logS), and many models are trained and verified with the AQUASOL and PhysProp databases.

13. 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 diffusion and active transport. It is a complex process that is difficult to predict solely based on molecular mechanism. 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.

14. C) Other considerations : - The ionization state will affect both solubility and permeability and, as a result, influence the absorption profile of a compound. The charge of a molecule can be determined using the compound’s ionization constant value (pKa), which indicates the strength of an acid or a base. Several commercially and publicly available programs provide pKa estimation based on the input structure, including SCSpKa (ChemSilico, Tewksbury, MA), Pallas/pKalc (CompuDrug, Sedona, AZ), ACD/pKa (ACD, Toronto, ON, Canada), and SPARC online calculator.

15. To correctly predict overall oral absorption, drug metabolism in intestinal epithelial cells by cytochrome P450 enzymes should also be considered. Other than the different approaches mentioned above, commercial packages such as GastroPlus (Simulations Plus, Lancaster, CA) and iDEA (LionBioscience, Inc. Cambridge, MA) are available to predict oral absorption and other pharmacokinetic properties. They are both based on the advanced compartmental absorption and transit (CAT) model, which incorporates the effects of drug moving through the gastrointestinal tract and its absorption into each compartment at the same time.

16. DRUG DISTRIBUTION Distribution is an important aspect of a drug’s pharmacokinetic profile. The structural and physiochemical properties of a drug determine the extent of its distribution, which is mainly reflected by three parameters: Volume of distribution (Vd) Plasm protein binding (PPB) Blood brain barrier (BBB)

17. VD is a measure of relative partitioning of drug between plasma and tissue, an important proportional constant that, when combined with drug clearance, could be used to predict drug half-life. The half-life of a drug is a major determinant of how often the drug should be administered. However, because of the scarcity of in vivo data and the complexity of the underlying processes, computational models that are capable of predicting VD based solely on computed descriptors are still under development. A) Volume of distribution

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

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

20. 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 dosage regime. 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 the presence of active transporters.

21. ACTIVE TRANSPORTER Transporters should be an integral part of any ADMET modeling program because of their ubiquitous presence on barrier membranes and the substantial overlap between their substrates and many drugs. Unfortunately, because of our limited understanding of transporters, most prediction programs do not have a mechanism to incorporate the effect of active transport. However, interest in these transporters has resulted in a relatively large amount of in vitro data, which in turn have enabled the generation of pharmacophore and QSAR models for many of them. These models have assisted in the understanding of the complex effects of transporters on drug disposition, including absorption, distribution, and excretion. Their incorporation into current modelling, programs would also result in more accurate prediction of drug disposition behavior.

22. 1) P- gp P-glycoprotein (P-gp) is an ATP-dependent efflux transporter that transports a broad range of substrates out of the cell. It affects drug disposition by reducing absorption and enhancing renal and hepatic excretion . It is also responsible for multidrug resistance in cancer chemotherapy. Because of its significance in drug disposition and effective cancer treatment, P- gp attracted numerous efforts and has become the most extensively studied transporter, with abundant experimental data. By comparing and merging all P-gp pharmacophore models, common areas of identical chemical features such as hydrophobes, hydrogen bond acceptors, and ring aromatic features as well as their geometric arrangement were identified to be the substrate requirements for P-gp.

23. 2) BCRP(Breast cancer resistance protein) Breast cancer resistance protein (BCRP) is another ATP-dependent efflux transporter that confers resistance to a variety of anticancer agents, including anthracyclines and mitoxantrone . In addition to a high level of expression in hematological malignancies and solid tumors, BCRP is also expressed in intestine, liver, and brain, thus implicating its intricate role in drug disposition behavior. The model emphasizes very specific structural feature requirements for BCRP such as the presence of a 2,3-double bond in ring C and hydroxylation at position 5. Satisfying the transport model would render a compound susceptible to BCRP, but not fitting into the model does not necessarily exclude the candidate from BCRP transport.

24. 3) Nucleoside transporter Nucleoside transporters transport both naturally occurring nucleosides and synthetic nucleoside analogues that are used as anticancer drugs (e.g., cladribine) and antiviral drugs. There are different types of nucleoside transporters, including 1. concentrative nucleoside transporters (CNT1, CNT2, CNT3) 2. equilibrative nucleoside transporters (ENT1, ENT2) each having different substrate specificities. The broad-affinity, low-selective ENTs are ubiquitously located, The high-affinity, selective CNTs are mainly located in epithelia of intestine, kidney, liver, and brain, indicating their involvement in drug absorption, distribution, and excretion. A more comprehensive study generated distinctive models for CNT1, CNT2, and ENT1 with both pharmacophore and 3DQSAR modeling techniques .

25. 4) hPEPT1 (human peptide transporter ) Low-affinity high-capacity oligopeptide transport system that transports a diverse range of substrates including β-lactam antibiotics and angiotensin-converting enzyme (ACE) inhibitors. Mainly expressed in intestine and kidney, affecting drug absorption and excretion. A pharmacophore model based on three high-affinity substrates (Gly -Sar, bestatin, and enalapril) recognized two hydrophobic features, one hydrogen bond donor, one hydrogen bond acceptor, and one negative ionizable feature to be hPEPT1 transport requirements.

26. 5) ASBT(Apical sodium-dependent bile acid transporter ) A high-efficacy, high-capacity transporter expressed on the apical membrane of intestinal epithelial cells and cholangiocytes. It assists absorption of bile acids and their analogs , thus providing an additional intestinal target for improving drug absorption. The model revealed ASBT transport requirements as one hydrogen bond donor, one hydrogen bond acceptor, one negative charge, and three hydrophobic centers

27. 6) OCT(organic cation transporters) facilitate the uptake of many cationic drugs across different barrier membranes from kidney, liver, and intestine epithelia. antiarrhythmics, β- adrenoreceptor blocking agents, antihistamines, antiviral agents, and skeletal muscle-relaxing agents. Three OCTs have been cloned from different species , OCT1, OCT2, and OCT3 The model suggests the transport requirements of human OCT1 as three hydrophobic features and one positive ionizable feature.

28. 7) OATP(Organic anion transporting polypeptides) Influence the plasma concentration of many drugs by actively transporting them across a diverse range of tissue membranes such as liver, intestine, lung, and brain. Because of their broad substrate specificity, OATPs transport not only organic anionic drugs, as originally thought, but also organic cationic drugs. Currently 11 human OATPs have been identified, and the substrate binding requirements of the best-studied OATP1B1 were successfully modeled with the meta pharmacophore approach recently.

29. 8) BBB-Choline Transporter Native nutrient transporter that transports choline, a charged cation, across the BBB into the CNS. Its active transport assists the BBB penetration of choline like compounds , and understanding its structural requirements should afford a more accurate prediction of BBB permeation. Three hydrophobic interactions and one hydrogen bonding interaction surrounding the positively charged ammonium moiety were identified to be important for BBB-choline transporter recognition

30. CURRENT CHALLENGES AND FUTURE DIRECTIONS The major recent advancements in ADMET modelling is in elucidating a role and successful modelling of various transporters. Incorporation of the influence of these transporters in the current models is an ongoing task in ADMET modelling. Some commercial programs are already implemented the capability of modelling active transport, such as recent version of GastroPlus (simulation plus, Lancaster, CA), PK- slim (Bayer technology services, Germany) and ADMET/Tox web(pharma algorithms, Toronto, Canada).

31. In the latter software, compounds are screen against the pharmacophore models of different active transporters. the compounds that fits these models is removed for further prediction, which is based solely on physicochemical properties. Not all pharmaceutical companies can afford to design their own in- house modelling programs, so the commercially available in silico suits have became an attractive option. Some modelling programs such as algorithm builder(pharma algorithms, Toronto, Canada) are offering flexibility for consumers to generate their in- house models with their own training set and statistical algorithm of their choice. These trends will accelerate the shift of models building for computational scientists to experimental scientists.

32. Reference Ekins Sean, “computer application in pharmaceutical research and development”, Wiley series in drug discovery and development, Binghe wang, series editor, “John Wiley and sons, INC., publication”, page no 495-508

33. Thank you