Sandeep Modi Phildelphia nov10 Drug safety


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Sandeep Modi Phildelphia nov10 Drug safety

  1. 1. Use of Informatics for Risk Assessments in 21st Century Sandeep Modi November 2010
  2. 2. SEAC Discovery Process : The challenges Only 1-2 chemicals reach market out of hundreds of leads, which might come from thousands of chemicals synthesized. Currently the whole process may take about 14-15 years and ~ $800. million.
  3. 3. SEAC in-vivo Decides in-vitro Guides in-silico Designs Discovery / Role of Informatics in 20th Century
  4. 4. SEAC Why use informatics tools? HTS (“Fail fast, fail cheap”–new mantra for R&D) • need of decisions, more quickly e.g. Library Design (can be done on virtual compds) Need to do more than just screen molecules • need of understanding SAR relationships e.g. how to “alter” undesirable properties
  5. 5. Commit to product type Commit to target Tractable hit Candidate selection FTIM PoC Target to lead Gene- function- target association FTIM to PoC Pre-clinical / Safety Lead to candidate Target family selection Disease selection Decision points Where in the discovery process informatics methods could be used?
  6. 6. SEAC Identify Problem/disease Isolate protein Find comp Which binds target Animal testing/ Clinical Trials How informatics can help in different steps for candidate selections Genomics / Proteomics / Bioinformatics Assay development, HTS screening, Analysis, Combinatorial chemistry / Libraries, Virtual screening Structural Biology Xray structures, molecular modelling In-vitro and in-silico ADMET models PBPK modelling (Exposure / Population differences)
  7. 7. SEAC Genomics / Bioinformatics ● Genomics involves determining the entire DNA sequence of organisms and fine-scale genetic mapping efforts. ● Bioinformatics entails the creation and advancement of databases, algorithms, computational and statistical techniques. – Sequence analysis – Genome annotation – Computational biology – Analysis of gene expression / regulation – Comparative genomics – Prediction of protein structures
  8. 8. SEAC Known Structures (similar sequece to target) : MTIKEMPQPKTFGELKNLPL…… Unknown Structure (target) : MGLEALVPLAVIVAIFLLLV…….. Copy Conserved Region : Add loops and calculate structure of non- conserved parts Structural Biology (e.g. Homology Modelling)
  9. 9. SEAC Structural Biology (in 21st Century) 21st Century: ● We now have access to more structures. ● And also computational methods are becoming better and more intelligent
  10. 10. SEAC High throughput screening ● Assumptions – If we screen large no. of compounds, we will find right chemicals – In-vitro data is good measure of reality • We understand biology enough that hitting a given target will have desired effect on the disease. ● 20th Century – Far too many hits – False +ve rate due to expt errors / purity of sample – Bad ADMET profile (safety needs to be considered) ● 21st Century – Include safety in selection / screening. – Understanding of ADR. – Need to have smart screening instead of blind screening • Use of informatics and QSAR models – Use of diverse library of compounds (diverse set)
  11. 11. SEAC Combinatorial Chemistry : Chemical reactions in plate (Use of informatics approaches) R3 R1 R2 R3 O R1 R2 ● Better design using ADMET / Safety considerations (coming later)
  12. 12. SEAC Beside activity, it needs to be able to reach target, maintains its conc., doesn’t reaches toxicity levels & have no side effects Balance of activity with safety (ADMET) Good Potency towards desired TARGET ABSORPTION (Gut-Blood) DISTRIBUTION (Blood-Tissues) METABOLISM (Enzymes) EXCRETION (Urine, Bile, Faeces) TOXICITY (Complex) These issues are important for all industries
  13. 13. SEAC Plasma Drug Concentrations Following Oral Dosing 1 10 100 1000 10000 0 4 8 12 16 20 24 Time (hours) Toxicity Activity Need to be Safe, and also effective concentrations needs to be maintained in circulations Depending on target/needs (e.g. in food or personal care Industries we may not like to have any plasma levels.
  14. 14. SEAC Therefore lots of efforts are going into in-silico modelling in ADMET area Reasons for termination of development of New Chemical Entities by 7 UK based companies 0 5 10 15 20 25 30 35 40 C linicalSafetyToxicology PK/Bioavailability EfficacyForm ulationC om m ercial C ostofG oods O ther 1991 2001 The continuing High Safety Failure, about 30% (Clinical Safety & Toxicology) 40% PK/Efficacy failure?
  15. 15. SEAC QSAR Experimental Data (E) Description of Molecules (P1,P2...) Statistical method Model e.g E=f(P1,P2...) Validated Released for use Refined based on new data
  16. 16. SEAC O OH N H N2H O O OH N H N H OH OH OH propranololsalbutamol atenolol LogP 0.11 -0.11 2.75 PSA 80 93 43 logD@pH7.4 -1.79 -2.21 0.59 Common Molecular Features Property Salbutamol Atenolol Propranolol Clearance route renal/hepatic renal hepatic Vd (lkg-1) 3.4 0.7 3 Protein binding ~10% ~5% ~90% CNS penetration low low high Different Properties Descriptors: Relate Structure to Properties which can reflect expt data QSAR
  17. 17. SEAC Different Methods for Predictive Chemistry SAR / alerts ● Simplest approach ● Only works on +ves QSAR ● Work equally on +ve & -ves ● Can be a black box Read across / kNN Prediction based on analogues from same chemical class with experimental data ● Can work on +ve & -ves ● How to define “SIMILARITY”
  18. 18. SEAC What is available currently ? Enzyme Inhibition 1A2, 2D6, 2C9, 2C19, 3A4 Metabolic (P450 Mediated) Biliary Systemic Exposure Bioavailability First Pass Met AbsorptionDistribution Clearance PPB Vol Tissue (e.g CNS) Renal Hepatic Gut Stability Solubility Permeation Drug-drug interactions Enzyme Induction Pgp (Transporters) PXR (induction) hERG (Tox) Genetic Tox hepatoTox
  19. 19. SEAC It has now been possible to suggest changes for desired ADMET property •Predicted as: • Pgp non-substrate • high brain penetration N O F N H F compA New Suggestion •Pgp non-substrate •Low brain penetration BB ratio of < 0.05:1 BB ratio of 1.8:1
  20. 20. SEAC QSAR / Read-across in 21st Century ● Data availability and integration ● Role of integrated approaches ● Validation sets / models applicability domain ● Move away from black box methods ● Building on gaps in Models
  21. 21. SEAC QSARs Data and Text Mining Structure Alerts Bioinformatics Tools Safety Risk Assessments Metabolites In-vitro Assays ADMET Profile Physchem Properties Hazard Identification Hazard Characterisation QSAR / Read-across in 21st Century Tox Pathways Exposure PKPD Modelling
  22. 22. SEAC 0 10 20 30 40 50 60 70 80 90 100 3Q01 4Q01 1Q02 2Q02 3Q02 % cpds with poor AUC median AUC/20 0 10 20 30 40 50 60 70 80 90 2Q01 3Q01 4Q01 1Q02 2Q02 3Q02 4Q02 1Q03 2Q03 Time %tested low IC50 medium IC50 high IC50 Project1 (oral PK) Time Time Project2 (CYP2C9) Project3 (AUC) 0 5 10 15 20 25 30 35 40 45 Mar01–Jul01 Aug01–Nov01 Dec01-Jan02 Feb02–Mar02 Date Average AUC (rat po) % of Cmpds. with AUC=0 0 0.2 0.4 0.6 0.8 1 1-2Q03 2-3Q03 3-4Q03 4Q03-1Q04 1Q04-2Q04 L M H Project4 (CNS) Time Time H L M Application of informatics in 20th Century: 1D approach
  23. 23. SEAC Could also highlight the potential problems at very early stage Multi-optimisation (21st Century) Solubility Absorption Metabolic stability Potency SafetyX Lead XDrug Property 1 Property2 Skin penetration Reactivity Peptide Depletion SafetyDesired Effect A possible scenario in case of consumer products
  24. 24. SEAC Absorption Solubility Metabolic stability Potency Safety X X Lead Drug Property 1 Property2 Assessing the path of Lead Optimisation
  25. 25. SEAC AUC T1/2 CYP Plasma Binding Potency Profile plot shows that the compounds with the highest scores have good properties for multiple endpoints Ranking using Multi-optimisation
  26. 26. SEAC Ability to visualize multiple databases
  27. 27. SEAC Need for all steps to come together (21st Century) Identify Problem/disease Isolate protein Find comp Which binds target Safety Risk Assessments Genomics / Proteomics / Bioinformatics Assay development, HTS screening, Analysis, Combinatorial chemistry / Libraries, Virtual screening Structural Biology Xray structures, molecular modelling In-vitro and in-silico ADMET models PBPK modelling (Exposure / Population differences)
  28. 28. SEAC Linking biology with chemistry
  29. 29. SEAC Exposure Internal Real Dose Biologically Effective Dose Early Biological Effects Metabolites (Altered Structures) Clinical Disease Route / Bioavailability PPB / Transporters Exposure-Dose Response Paradigm
  30. 30. SEAC Use of PBPK models 78k 77k 83c79k 76k 83c 82m 82m 81m 81m 80r 75k 75k 83c RESPONSEAPPLIED DOSE BBDR MODELPBPK MODEL Chemical Disposition (bodies effect on the chemical) Information to Develop the PBPK Model • Target site (s) (organ, tissue, cell). • Chemical specific ADME rates. • Species specific parameter values (tissue volumes, blood flow rates. • Which internal dose metric to use (based on mode of action). 0.1 1 Biological Response (chemical’s effect on the body) Information to Develop BBDR Model • Target site. • Adverse effect (what constitutes a significant deviation from normal). • Mode of Action (i.e., key events leading to an effect). • Best measure of effect (s). INTERNAL DOSE AT TARGET (e.g., TISSUE, ORGAN) 0.1 1 Slide adopted from Kenyon et al, EPA
  31. 31. SEAC Me S O S N O Important structural features Chemistry Structural Biology Linking biology/chemistry with other data •Auruus •WOMBAT •GVKBio •DrugEBIlity (soon to be public) Tox end Pt QSAR Target specific QSAR X-ray/NMR Homology Information Biology Assays Activity, e.g, pos/neg Text/Data Mining Exposure
  32. 32. SEAC • Good/bad Chemical Features • Mechanism / mode of action • QSAR predictions How Chemical is bound to Tox target Pathway Analysis Chemical / Biological similarity Linking biology/chemistry with other data
  33. 33. SEAC GENET GENOM PROTEOM BIOINFORMAT MEDINFORMAT CHEMOGENOM CHEMOINFORMAT PROTEOCHEMOMETR- “-ics” – an old Latin suffix that means “way too much / organised knowledge” -ICS One of the challenges in 21st Century is how we convert this information rich –ICS technologies, to knowledge Question Answer Process Information Methods In-slico In-vitro Expert Opinon Knowledge Information Rich “-ICS” approaches
  34. 34. SEAC Need for Intelligent Information Harvesting Integrated Information
  35. 35. SEAC Safety Integrated approach
  36. 36. SEAC in-vivo Decides in-vitro Guides in-silico Designs ADMET in 21st Century (where would like to be) 20th Century Decides 21st Century
  37. 37. SEAC ● “Welcome in-silicoids to the ‘real world, real time zone’; get this right and do it now, and we’ll make you the President.” And Finally - our challenge (Dennis Smith (Pfizer), DDT, 7, 2002, 1080-1081) ● “Hello…. I am from Insilico, take me to your President”
  38. 38. SEAC Acknowledgements “Part of Unilever’s ongoing effort to develop novel ways of delivering consumer safety” ● Andy White ● Andrew Garrow ● Michael Hughes ● Yeyejide Adeleye ● Matt Dent ● Paul Carmichael ● Jin Li ● Carl Westmoreland ● And other members of Unilever, SEAC