Computational modelling of drug disposition active transport
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This document discusses computational modeling of active transport mechanisms that influence drug disposition. It summarizes modeling efforts for several major drug transporters, including P-glycoprotein (P-gp), Breast Cancer Resistance Protein (BCRP), nucleoside transporters, peptide transporter 1 (hPEPT1), Apical Sodium-dependent Bile Acid Transporter (ASBT), Organic Cation Transporters (OCTs), Organic Anion Transporting Polypeptides (OATPs), and the Blood Brain Barrier choline transporter. While transporter modeling has advanced, fully incorporating active transport into predictive models remains an ongoing challenge.
Computational modelling of drug disposition active transport
- 1. COMPUTATIONAL MODELLING OF DRUG DISPOSITION ACTIVE TRANSPORT Presented By Sujitha Mary M Pharm St Joseph College Of Pharmacy
- 2. CONTENTS INTRODUCTION ACTIVE TRANSPORT 1. P-GP 2. BCRP 3. NUCLEOSIDE TRANSPORTERS 4. hPEPT1 5. ASBT 6. OCT 7. OATP 8. BBB-CHOLINE TRANSPORTER
- 3. INTRODUCTION Historically , drug discovery has focused almost exclusively on efficacy and selectivity against the biological target. Drug candidates fail at phase II & III clinical trial because of undesirable drug PK propertiesincluding ADME & toxicity. To reduce the attrition rate at more expensive later stage , in-vitro evaluation of ADME properties in the early phase of drug discovery has widely adopted.
- 4. Many high throughput in-vitro ADMET property screening assay have developed & applied successfully . Fueled by ever increasing computational power & significant advance of in silico modeling algorithms , numerous computational program that aim at modeling ADMET properties have emerged
- 5. ACTIVE TRANSPORT 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
- 6. 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. For Eg: P-gp is known to limit the intestinal absorption of the anticancer drug paclitaxel and restricts the CNS penetration of human immunodeficiency virus (HIV) protease inhibitors .
- 7. 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.
- 8. Ekins and colleagues generated five computational pharmacophore models to predict the inhibition of P-gp from in vitro data on a diverse set of inhibitors with several cell systems, including inhibition of digoxin transport and verapamil binding in Caco-2 cells; vinblastine and calcein accumulation in P- gp - expressing LLC-PK1 (L-MDR1) cells; and vinblastine binding in vesicles derived from CEM/VLB100 cells
- 9. 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 aswell as their geometric arrangement were identified to be the substrate requirements for P-gp. Identified transport requirements not only to help screen compounds with potential efflux related bioavailability problems, but also to assist the identification of novel P-gp inhibitors, which when co-administered with target drugs would optimize their pharmacokinetic profileby increasing bioavailability
- 10. BCRP Breast cancer resistance protein (BCRP) is another ATPdependent 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
- 11. Recently, Zhang and colleagues generated a BCRP 3D-QSAR model by analyzing structure and activity of 25 flavonoid analogs. 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. Because the model is only based on a set of closely related structures instead of a diverse set, itshould be applied with caution.
- 12. NUCLEOSIDE TRANSPORTERS Nucleoside transporters transport both naturally occurring nucleosides and synthetic nucleoside analogs that are used as anticancer drugs (e.g., cladribine) and antiviral drugs (e.g., zalcitabine). There are different types of nucleoside transporters, including concentrative nucleoside transporters (CNT1, CNT2, CNT3) and equilibrative nucleoside transporters (ENT1, ENT2), each having different substrate specificities.
- 13. The broad-affinity, low-selective ENTs are ubiquitously located, whereas the high-affinity, selective CNTs are mainly located in epithelia of intestine, kidney, liver, and brain , indicating their involvement indrug absorption, distribution, and excretion The first 3D-QSAR model for nucleoside transporters was generated back in 1990 A more comprehensive study generated distinctive models for CNT1, CNT2, and ENT1 with both pharmacophore and 3DQSAR modeling techniques. All models show the common features required for nucleoside transporter- mediated transport: two hydrophobic features and one hydrogen bond acceptor on the pentose ring.
- 14. HPEPT1 The human peptide transporter (hPEPT1) is a low-affinity highcapacity oligopeptide transport system that transports a diverse range of substrates including β-lactam antibiotics and angiotensin-converting enzyme (ACE) inhibitors . It is mainly expressed in intestine and kidney, affecting drug absorption and excretion. Apharmacophore model based onthree highaffinity 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 .
- 15. The antidiabetic repaglinide and HMG-CoAreductase inhibitor fluvastatin were suggested by the model and later verified to inhibit hPEPT1 with submillimolar potency .
- 16. ASBT The human apical sodium-dependent bile acid transporter (ASBT) is a highefficacy, 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. Baringhaus and colleagues developed a pharmacophore model based on a training set of 17 chemically diverse inhibitors of ASBT. The model revealed ASBT transport requirements as one hydrogen bond donor, one hydrogen bond acceptor, one negative charge, and three hydrophobic center
- 17. OCT The organic cation transporters (OCTs) facilitate the uptake of many cationic drugs across different barrier membranes from kidney,liver,and intestine epithelia. Abroad range of drugs or their metabolites fall into the chemical class of organic cation (carrying a net positive charge at physiological pH) including antiarrhythmics, β-adrenoreceptor blocking agents, antihistamines, antiviral agents, and skeletal muscle-relaxing agents .
- 18. Three OCTs have been cloned from different species, OCT1, OCT2, and OCT3. A human OCT1 pharmacophore model was developed by analyzing the extent of inhibition of TEAuptake in HeLa cells of 22 diverse molecules. The model suggests the transport requirements of human OCT1 as three hydrophobic features and one positive ionizable feature
- 19. OATP Organic anion transporting polypeptides (OATPs) 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 onlyorganic anionic drugs, asoriginally 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 metapharmacophore approach recently. The metapharmacophore model identified three hydrophobic features flanked by two hydrogen bond acceptor features to be the essential requirement for OATP1B1 transport
- 20. BBB-CHOLINE TRANSPORTER The BBB-choline transporter is a native nutrient transporter that transports choline, a charged cation, across the BBB into the CNS. Its active transport assists the BBB penetration of cholinelike compounds, and understanding its structural requirements should afford a more accurate predictionof BBB permeation. Even though the BBB-choline transporter has not been cloned, Geldenhuys and colleagues applied a combination of empirical and theoretical methodologies to study its binding requirements . Three hydrophobic interactions and one hydrogen bonding interaction surrounding the positively charged ammonium moiety were identified to be important for BBB-choline transporter recognition.
- 21. CURRENT CHALLENGESAND FUTURE DIRECTION The major recent advancement in ADMET modeling isin elucidating the roleand successful modeling of various transporters. Incorporation of the influence of these transporters in the current models is an ongoing task in ADMET modeling. Some commercial programs have already implemented the capability of modeling active transport, such as recent version of GastroPlus (Simulation Plus, Lancaster,CA), PK-Slim (Bayer Technology Services, Germany) and ADME/Tox WEB (Pharma Algorithms, Toronto, Canada). In the latter software, compounds are first screened against pharmacophore models of different active transporters. The compound that fits these models is removed for further predictions, which is based solely on physiochemical properties.
- 22. Not all pharmaceutical companies can afford the resources to generate their own in-house modeling programs, so the commercially available in silico modeling suites have become an attractiveoption. Some modeling programs such as Algorithm Builder (Pharma Algorithms, Toronto, Canada) are offering flexibility for costumers to generate their in- house models with their owntraining set and the statistical algorithm of their choice. These trends will accelerate the shift of model building from computational scientists to experimental scientists.
- 23. CONCLUSION Computational Modelling reduces cost and complexity as faced in vivo testing. We believe that data quality is the weakest link, thereby effectively limiting the practical application of ADMET models. Although all these models are simplifications of the reality, some of the models do provide valuable insight of the Drug Disposition phenomena
- 24. REFERENCES: Ekins S, “Computer Applications in Pharmaceutical Research and Development”, (2006) John Wiley and Sons Inc., chapter 20,pp495-508 www.slideshare.net www.google.com