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Semelhante a EPA Summer 2013_Portable Pharmacokinetic Parameter Prediction Tool
Semelhante a EPA Summer 2013_Portable Pharmacokinetic Parameter Prediction Tool (20)
EPA Summer 2013_Portable Pharmacokinetic Parameter Prediction Tool
- 1. Emerald Feng Mentors: Chris Grulke, Rocky Goldsmith, Daniel Chang, Cecilia Tan, Mike Tornero
- 2. PBPK Modeling Chemical health risk assessments Used to quantify absorption, distribution, metabolism, and excretion (ADME) Compartments are specific Intrinsic and Extrinsic factors In relation to chemical exposure In silico vs in vitro Definite use in pharmacokinetics
- 3. PBPK Modeling Models vary based on complexity Compartment = theoretical value for a chemical Connections indicate how each parameter calculates another 2 compartment 6 compartment Several Compartments
- 4. Parameters Used to influence organ flows and partitioning into compartments: “factors” related to uptake/circulation and elimination (or ADME) Contains descriptors, such as molecular weight or surface area( MW or TPSA) Derived from different chemical properties Physiological, Chemical, Tissue specific Important!!!!!!!!!! in PBPK modeling Examples: Absorption rate
- 5. Molecular Descriptors to derive ADME-specific parameters Chemical descriptors used to predict ADME models based on known value of a (ADME) response variable Chemical structure and biological activity Calculate descriptors for chemicals in a database Using Molecular Operating Environment (MOE)
- 6. QSAR Modeling Quantitative Structure-Activity Relationship Relationship between chemical structure and biological activity Similar structure indicates similar activity
- 8. Icons
- 9. Background/Our Goal Most experimentation is done on real life organisms In silico models are not favored PBPK modeling doesn’t use real organisms Saves lives and money Create a mobile app that is easily assessable Prevents loss of organisms’ lives
- 10. Methods Prepare Spreadsheet Initial Preparation http://dogwood.rtpnc.ep a.gov/ Computer Version Goal: transfer to mobile app range
- 12. Methods Datasets were first identified in the computer toxicology book, curated, then modeled in MOE Datasets used: Clearance_Oral, Human Clearance, Hepatic Clearance, Human Intestinal Absorption, Human Oral Intestinal Absorption Descriptors: Hba hydrogen bond acceptor count (a_acc), Hbd hydrogen bond donor count (a_don), molecular weight (MW), octanol water partition coefficient (logP), topological polar surface area (TPSA), fraction of rotatable bonds (b_rotR), Number of atoms (a_count)
- 13. Calculations
- 14. Clearance_Oral Dataset Index Compound Parent_SMILES Observed CL(PO,man) MLR (Quadratic) AC-PLS (Quadratic) MC-PLS (Tertiary) Simple Allometry Mahmood Method Ref_Num Reference 1 Meloxicam s1c(cnc1NC(=O)C=1N(S(=O)(= O)c2c(cccc2)C=1O)C)C 0.15000001 0.21 0.275 0.15 0.112 0.044 46 http://onlinelibrary.wiley. com/doi/10.1002/jps.10510 /pdf 2 Ethosuximide O=C1NC(=O)CC1(CC)C 0.152 0.55 0.903 0.58 0.183 0.183 46 http://onlinelibrary.wiley. com/doi/10.1002/jps.10510 /pdf 3 Zonisamide S(=O)(=O)(N)Cc1noc2c1cccc2 0.33000001 0.45 0.473 0.23 0.307 0.204 46 http://onlinelibrary.wiley. com/doi/10.1002/jps.10510 /pdf 4 Flunoxaprofen Fc1ccc(cc1)- c1oc2c(n1)cc(cc2)C(C(O)=O)C 0.37900001 0.62 0.709 0.56 1.52 0.894 46 http://onlinelibrary.wiley. com/doi/10.1002/jps.10510 /pdf 5 Fluconazole Fc1cc(F)ccc1C(O)(Cn1ncnc1)C n1ncnc1 0.40000001 0.5 0.64 0.44 0.41 0.16 46 http://onlinelibrary.wiley. com/doi/10.1002/jps.10510 /pdf Calculated Normalized a_acc a_count a_don b_rotN logP(o/w) TPSA Weight b_rotR a_acc a_count a_don b_rotR logP(o/w) TPSA Weight b_rotR 5 36 2 3 0.94 99.6 351.407 0.12 0.384615 0.290323 0.25 0.166667 0.144794 0.4552 0.427007 0.252 2 21 1 1 0.25999999 46.17 141.17 0.1 0.153846 0.169355 0.125 0.055556 0.040049 0.213735 0.171541 0.21 3 22 1 2 0.19 86.19 212.229 0.1333 0.230769 0.177419 0.125 0.111111 0.029267 0.394597 0.257887 0.28 3 33 2 3 3.6700001 63.33 285.274 0.1304 0.230769 0.266129 0.25 0.166667 0.565311 0.291286 0.346647 0.273913 5 34 1 5 -1.124 81.65 306.276 0.2083 0.384615 0.274194 0.125 0.277778 -0.17314 0.374079 0.372167 0.4375 Sample group of Chemicals: Different Descriptor Values:
- 15. Clearance_Oral Dataset Cont’d Index Rank Compound Parent_SMILES a_acc a_count a_don b_rotN logP(o/w) TPSA Weight b_rotR Distance 1 59 Meloxicam s1c(cnc1NC(=O)C=1N( S(=O)(=O)c2c(cccc2)C =1O)C)C -0.23077 -0.27419 0 -0.05556 0.163278 - 0.44108 -0.42458 3.948 4.014935 2 57 Ethosuximide O=C1NC(=O)CC1(CC) C 0 -0.15323 0.125 0.055556 0.268022 - 0.19962 -0.16911 3.99 4.012801 3 45 Zonisamide S(=O)(=O)(N)Cc1noc2 c1cccc2 -0.07692 -0.16129 0.125 0 0.278805 - 0.38048 -0.25546 3.92 3.962538 4 47 Flunoxaprofen Fc1ccc(cc1)- c1oc2c(n1)cc(cc2)C(C( O)=O)C -0.07692 -0.25 0 -0.05556 -0.25724 -0.27717 -0.34422 3.926087 3.968267 5 29 Fluconazole Fc1cc(F)ccc1C(O)(Cn1n cnc1)Cn1ncnc1 -0.23077 -0.25806 0.125 -0.16667 0.481208 - 0.35996 -0.36974 3.7625 3.84935 A_acc = a A_count = b A_don = c B_rotN = d logP(o/w) = e TPSA = f Weight = g b_rotR = h The equation: Descriptor Coefficients
- 16. Clearance_Oral Dataset Cont’d descriptor test molecule value 1 a_acc 2 2 a_count 6 3 a_don 3 4 b_rotN 4.00 5 logP(o/w) 0.39 6 TPSA 300.00 7 Weight 3.00 8 b_rotR 0.41 Fu model (0=>90,1:(gt30,lt90),2:(lt30)) 3-class 0 molecule similar Fu 1 Ranitidine 10.40 2 Nizatidine 12.80 3 Recainam 10.70 4 Felbramate 0.70 5 Tamsulosin 0.52 top 3 mean/sd 11.30 1.31 top 5 mean/sd 7.02 5.93
- 17. Histograms
- 18. Decision Tree Classifier Process
- 19. Decision Trees Hand drawn process from the computerized version Right: yes; Left: no Total indicates misclassification rate Example:
- 20. Final Project Dataset includes 671 chemicals
- 21. Distance Calculation Entry ID rank SMILES Formula Name Weight logP(o/w) TPSA a_count a_acc a_don b_rotN Distance 1 307 OC[C@H]1C[C@@H](n2c 3nc(nc(NC4CC4)c3nc2)N )C=C1 C14H18N6O Abacavir -0.05897 0.058892 -0.04989 -0.08444 -0.10714 -0.13636 -0.01639 0.216586 2 372 O(C)c1cc2c([nH+]c(N3CC c4cc(OC)c(OC)cc4C3)cc2 N)cc1OC C22H25N3O4 Abanoquil -0.11956 -0.08726 -0.01955 -0.15556 -0.10714 0 -0.03279 0.242987 3 655 O1[C@H](C)[C@@H]([N H2+][C@H]2C=C(CO)[C @@H](O)[C@H](O)[C@ H]2O)[C@H](O)[C@@H] (O)[C@H]1O[C@H]1[C@ H](O)[C@@H](O)[C@H] (O[C@@H]1CO)O[C@H]( [C@H](O)CO)[C@H](O)[ C@@H](O)C=O C25H43NO18 Acarbose -0.25718 0.439646 -0.37601 -0.30222 -0.60714 - 0.59091 -0.16393 1.112124 4 412 O(C[C@@H](O)C[NH2+] C(C)C)c1ccc(NC(=O)CCC )cc1C(=O)C C18H28N2O4 Acebutolol -0.08708 -0.00778 -0.03633 -0.14667 -0.10714 - 0.09091 -0.13115 0.259651 5 227 O=C(NCC[NH+](CC)CC) c1ccc(NC(=O)C)cc1 C15H23N3O2 Acecainide (N- acetylprocainami de) -0.05459 0.027862 0.005334 -0.10667 -0.03571 - 0.09091 -0.09836 0.185411
- 22. Input descriptor test molecule value 1 Weight 179 2 logP(o/w) 2 3 TPSA 66 4 a_count 20.00 5 a_acc 1.00 6 a_don 0.00 7 b_rotN 3.00 Fu model (0=>90,1:(gt30,lt90),2:(lt30)) 3-class 0 molrank similar Fu 1 Acetylsalicylic Acid 0.68 2 Pyridostigmine 1.00 3 Gabapentin 0.97 4 Mexiletine 0.36 5 Tranexamic acid 0.00 top 3 mean/sd 0.88 0.18 top 5 mean/sd 0.60 0.42 EXPT VDss (L/kg) EXPT CL (mL/min/ kg) EXPT fu EXPT MRT (h) EXPT t1/2 (h) QPlogS CIQPlogS QPlogHE RG QPPCaco QPlogBB QPPMDC K QPlogKp #metab QPlogKhs a HumanO ralAbsorp tion PercentH umanOra lAbsorpti on 0.22 12.00 0.68 0.30 0.26 -1.67 -1.58 -1.23 124.94 -0.57 66.44 -3.33 0.00 -0.77 3.00 71.37 1.10 9.60 1.00 1.80 1.50 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.71 1.70 0.97 7.00 5.30 -0.82 -0.37 -1.60 31.96 -0.33 16.84 -5.71 3.00 -0.66 2.00 47.43 5.90 8.30 0.36 12.00 9.90 -1.31 -1.31 -4.49 916.00 0.35 497.79 -3.59 5.00 -0.08 3.00 92.54 0.38 2.40 0.00 2.60 2.30 -0.82 -0.14 -1.68 22.39 -0.41 11.46 -6.11 3.00 -0.69 2.00 43.63
- 23. Conversion to a Mobile Device SpreadsheetConverter Hid specific sheets Simplified the spreadsheet to fit into the smaller area Converted spreadsheets to URL compatible Created a tiny.url for the newly made webpage QR code then calculated for the specific URL End-user of package is now able to view
- 24. URL and QR Code http://goo.gl/UDR4U http://goo.gl/3X0pX ADME by Analog App Physiology App
- 25. Snapshots from the Mobile App:
- 26. Snapshots from the Mobile App
- 27. Works Cited "Assessment of chemicals - Organisation for Economic Co-operation and Development." Organisation for Economic Co- operation and Development. OECD, n.d. Web. 12 July 2013. <http://www.oecd.org/env/ehs/risk-assessment/intro ductiontoquantitativestructureactivityrelationships.htm>. MacDonald, Alex J., and Neil Parrott. "MODELLING AND SIMULATION OF PHARMACOKINETIC AND PHARMACODYNAMIC SYSTEMS - APPROACHES IN DRUG DISCOVERY." Beilstein-Institut. Beilstein-Institut Workshop, 22 July 2005. Web. 16 July 2013. <www.beilstein- institut.de/bozen2004/proceedings/MacDonald/MacDonald.htm>. U.S. Environmental Protection Agency, Office of Research and Development. (2008). Uncertainty and variability in physiologically based pharmacokinetic models: Key issues and case studies (EPA/600/R-08/090). Washington, DC: National Center for Environment Assessment. Zhao, P. Food and Drug Administration, Center for Drug Evaluation and Research. (2011). Applications of physiologically based pharmacokinetic (pbpk) modeling and simulation during regulatory review (21191381). Retrieved from Office of Clinical Pharmacology website: http://www.ncbi.nlm.nih.gov/pubmed/21191381
Notas do Editor
- ADME-absorption, distribution, metabolism, excretion; all interlinked processes compartments-separated into categories like organs, organ systems, absorption paths, etc Intrinsic: inside and natural; Extrinsic: outside and not apart of the nature of a person examples: intrinsic-age; extrinsic-drug to drug interactions In silico: computer experimentation; in vitro: actual experimentation on an organism
- We created a needed assessment survey for a variety of needs, such as different types of organisms, organs, and absorption rates. For example, the first bar graph indicates that the most observed organism is the rat. By discovering which organism was most wanted by the modelers, we now knew which parameters should be most important. Additionally, most of the PBPK modelers surveyed did not want in silico models for modeling parameters, an important point for my project.
- These are different icons that I created. The general types of icons that were created were different organs, organisms, and absorption rates.
- This tab from the spreadsheet is on Weight Estimate This is taking the chemical and its parameters and estimating the stage of life the human is in and its current weight. My job was to shrink this table down into one that would fit on a mobile device. For this, I kept on the human’s data and calculated only human values.
- I followed the same process to calculate the different descriptors.
- For these normalized values for the parameters in each chemical, I took the maximum and minimum value of each descriptor. Then, for each descriptor value, I plugged it in an equation, along with the max/min value to calculate the normalized values
- This table is used to find the distance between two points with normalized attributes. To do this step, we utilized an equation that is similar to the Pythagorean theorem.
- On this slide, we wanted to compare all of the chemicals in this specific dataset to a “test molecule” represented at the left. This method allowed us to find the top five chemicals that are similar to another chemical in dimensional space.
- These histograms graphed all of the descriptor values and the observed values for the chemicals. This helped me calculate the decision tree classifier by demonstrating how all of the data is distributed
- This process is how to calculate the decision tree classifier. After calculating descriptors, I created a class named “EF_CLASS”. Using the data found in the histogram, I sorted all of the chemicals into three different bins: 0,1, and 2. After sorting the chemicals into bins, I compute a classification model under the class “EF_CLASS”. I selected all of the descriptors I calculated and created the model. Indicated here, this is the misclassifcation rate. This number indicates that the rate is 16.2% inaccurate, which is also 83.8 percent accurate.
- After computing each classifier, I was able to hand draw each classification tree for the different datasets. At this top tree for Clearance_Oral, the separation is based on if the chemical in question followed each equation. If it did, then it branched off to the right, and if it didn’t, it branched off to the left. At the end, the “total” is the misclassification rate. Clearance_Oral’s dataset had a misclassification rate of 18.4%, which also indicates that it is 81.6% accurate. This then indicates that the calculator would be at least 80% accurate each time it computed a value for a desired chemical.
- This dataset is known as the Obach dataset and it contains 671 chemicals.
- The bottom row represents all the other parameters that need to be calculated for this specific dataset. Each parameter is also calculated for the chemical listed above.