Session 1 part 2

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  • What kind of cns drugs? What kind of marketed drugs? Describe more about the categories of compounds included. Examples of each.
  • Set this up a little better.
  • Set this up a little better.
  • Session 1 part 2

    1. 1. Identifying CNS drugs requires unique considerations beyond efficacy <ul><li>BIOAVAILABILITY – drug available in the body to act at target </li></ul><ul><ul><li>Inability to reach target in sufficient amounts during appropriate time window LIMITS opportunity for efficacy – BBB, metabolism, efflux </li></ul></ul><ul><ul><li>Caveat: Bioavailability DOES NOT guarantee drug efficacy </li></ul></ul><ul><ul><li>STARTING POINT: How does an oral drug get into the CNS? </li></ul></ul>Quantification LogBB = comparison of brain, plasma concentrations Relative bioavailability %F = [AUC po ] / [AUC iv ] Molecular properties influence how drugs are absorbed, how they are distributed, how they interact with transporters and metabolizing enzymes Absorption Metabolism Tissue Distribution Time [Drug]
    2. 2. Case study: Antihistamine CNS bioavailability changes impact adverse events <ul><li>First-generation antihistamines characterized by sedative side effects </li></ul><ul><ul><li>Undesirable feature!! </li></ul></ul><ul><li>Second-generation antihistamines lack drowsiness properties </li></ul><ul><ul><li>Better safety index </li></ul></ul>DIPHENHYDRAMINE FEXOFENADINE Brain penetrant Avoids penetrating CNS Antihistamines lacking sedative properties tend to possess limited CNS bioavailability compared to antihistamines with drowsiness Obradovic T et al. (2007) Pharm Res, 24 , 318-327. Avoids P-glycoprotein efflux P-glycoprotein substrate
    3. 3. Case study: CYP2D6 metabolism alters bioavailability, impacts safety/efficacy <ul><li>CYP2D6 - major isoform involved in CNS drug metabolism! </li></ul><ul><li>Genetic polymorphisms affect CYP2D6 expression, function </li></ul>CYP2D6 phenotype correlates with disease progression in breast cancer Morphine toxicity risk with UM phenotype; Poor efficacy with PM phenotype CODEINE MORPHINE CYP2D6 “ Ultra-rapid” metabolizer phenotype “ Poor” metabolizer phenotype Increased CYP2D6 function Decreased CYP2D6 function TAMOXIFEN 4-HYDROXY TAMOXIFEN CYP2D6 “ Ultra-rapid” metabolizer phenotype “ Poor” metabolizer phenotype Increased CYP2D6 function Decreased CYP2D6 function
    4. 4. Bioavailability…it’s a big deal! So, what can you do to find compounds that are bioavailable? Hint: you don’t need to do in vivo testing just yet..
    5. 5. Molecular Properties 101: Physical properties influence how drugs interact with the body <ul><li>Solubility, lipophilicity, size impact ADME outcomes </li></ul>Absorption : Will the drug penetrate across the GI tract to the circulatory system? Distribution : Will the drug remain soluble in the blood? Will it remain bound to plasma proteins? Metabolism : Will the drug be chemically modified by CYPs? How much will be available to get to the target? Excretion : How will the body eliminate the drug? *Modifying one property has consequences on others Figure modified from van de Waterbeemd H. (2009) Chem Biodiv, 6 , 1760-1766. SOLUBILITY Charge Ionization Dissolution LIPOPHILICITY SIZE H-Bonding Shape Amphiphilicity Charge Distribution LogP MW PSA
    6. 6. Improving the odds: Using properties guidelines can increase bioavailability odds <ul><li>“ Rule of 5” - Christopher Lipinski </li></ul><ul><ul><li>Poor absorption/permeation MORE LIKELY if: </li></ul></ul><ul><ul><ul><li>>5 Hydrogen bond donor atoms (HBD) </li></ul></ul></ul><ul><ul><ul><li>MW > 500 </li></ul></ul></ul><ul><ul><ul><li>LogP > 5 </li></ul></ul></ul><ul><ul><ul><li>N + O > 10 </li></ul></ul></ul><ul><ul><li>1990s: analyses used to identify ways to improve attrition due to poor bioavailability </li></ul></ul><ul><li>Today = Smarter screening platforms </li></ul>CAVEAT: The Ro5 is NOT CNS specific! Gleevec (imatinib) LogP 2.89 MW 493.6 PSA 86.28 HBD = 8 N + O = 8 Norvir (ritonavir) LogP 2.33 MW 720.6 PSA 202.26 HBD = 11 N + O = 11
    7. 7. CNS drug discovery properties analysis <ul><li>What molecular properties are most relevant to CNS? </li></ul><ul><ul><li>LogP – lipophilicity, solubility in octanol/H 2 O </li></ul></ul><ul><ul><li>MW – size </li></ul></ul><ul><ul><li>PSA – polar surface area (N’s, O’s) </li></ul></ul><ul><li>How do I calculate these? </li></ul><ul><ul><li>Experimental </li></ul></ul><ul><ul><ul><li>pION* www.pion.com </li></ul></ul></ul><ul><ul><ul><li>CEREP www.cerep.fr </li></ul></ul></ul><ul><ul><ul><li>Protocols – “ home grown ” </li></ul></ul></ul><ul><ul><li>In silico – calculate estimated values derived from real structures </li></ul></ul><ul><ul><ul><li>ACD/Labs* </li></ul></ul></ul><ul><ul><ul><li>Schroedinger </li></ul></ul></ul><ul><ul><ul><li>ChemAxon* </li></ul></ul></ul><ul><li>*Discounts available for academics </li></ul>DISCOVERY TIP: Prior to purchasing or screening libraries – look at the property landscape. How much is CNS relevant?
    8. 8. CNS drug discovery properties analysis – what are “ good ” values? <ul><li>CNS drugs occupy a more restricted molecular properties space </li></ul><ul><li>Properties guidelines also depend on development status (hit versus lead versus drug) </li></ul>Rees et al. (2004) Nat Rev Drug Discov, 3, 660-672. Lipinski CA et al. (2001) Adv Drug Deliv Rev, 46, 3-26. CNS Drugs LogP < 4 MW < 400 PSA < 80 Chico et al. (2009) Nature Rev Drug Discov , 8, 892-909. Fragments LogP < 3 MW < 300 PSA < 90 Oral Drugs LogP < 5 MW < 500 PSA < 140
    9. 9. Case Study: CNS properties analysis identifies guidelines Properties were computed using ACD Labs (v.11). Data shown are mean±SEM. Student’s t-test used to compare mean values with CNS means. *, p <0.05; ***, p <0.001. Chico et al. (2009) Nature Rev Drug Discov , 8, 892-909. PSA discriminates CNS+ better than LogP Pgp+ compounds possess higher LogP, MW than Pgp- compounds
    10. 10. Case study: Properties guidelines help prioritize CNS drug discovery efforts Simple properties filters helped prioritize the top 6% of candidates! <100 compounds were synthesized from start  lead  clinical candidate. Wing et al. (2006) Curr Alz Res, 3, 205-214. Chico et al. (2009) Drug Metab Dispos, 37, 2204-11. Chico et al. (2009) Nature Rev Drug Discov, 8 , 892-909. 5 amines + 18 alkyl/aromatic groups = 1700+ possibilities PSA <80Å 2 MW <400 LogP < 4 (80%) (80%) (80%)
    11. 11. Case study: Overlapping properties analyses focuses discovery efforts <ul><li>Most property analyses focus on one outcome or endpoint… </li></ul><ul><li>… but CNS bioavailability involves multiple outcomes (penetration, metabolism for example). </li></ul><ul><ul><li>CNS+/CYP2D6- = good! </li></ul></ul><ul><ul><li>CNS+/CYP2D6+ = bad! </li></ul></ul><ul><li>Future direction of the field – perform properties analysis on multiple outcomes and “overlap” results </li></ul><ul><li>Query: where are we most likely to find compounds that are both CNS+ AND CYP2D6-? </li></ul><ul><ul><li>Approach: Superimpose properties to find “hotspots” associated with CNS+/CYP2D6- candidates </li></ul></ul>Chico et al. (2009) Drug Metab Dispos, 37 , 2204-11. Chico et al. (2009) Nature Rev Drug Discov, 8 , 892-909.
    12. 12. Find the “sweet spot” of CNS+/CYP2D6- using overlapping analyses CNS+/CYP2D6+ Avoid this region CNS+/CYP2D6- Minimized risk of CYP2D6 involvement, but still have CNS+ CNS+ PSA ≤ 80Å 2 LogP ≤ 4 MW ≤ 400 Database summary statistics:
    13. 13. Multidimensional properties analyses helps refine “CNS” space Wager et al. (2010) ACS Chem Neurosci, 1 , 420-434. Wager et al. (2010) ACS Chem Neurosci, 1, 435-449 Analyzing properties associated with multiple ADME features helps identify more restrictive guidelines, increases probability of finding CNS+ compounds.
    14. 14. Takeaways – how can I use properties guidelines in my discovery efforts? <ul><li>Library screening/selection </li></ul><ul><ul><li>Properties can help you focus screening on most “CNS”-relevant members. </li></ul></ul><ul><ul><li>Some libraries are more CNS friendly than others. </li></ul></ul><ul><li>Hit-to-lead refinement </li></ul><ul><ul><li>It is easier to add than subtract later! </li></ul></ul><ul><ul><li>Start low – expect to increase as you proceed </li></ul></ul><ul><ul><ul><li>Applying guidelines allows chemists to budget their selections </li></ul></ul></ul><ul><li>Guidelines are guidelines – NOT rules </li></ul><ul><ul><li>Don’t get tripped up by numbers. Rationale trumps rules!! </li></ul></ul><ul><li>Resources </li></ul><ul><li>Experimental </li></ul><ul><ul><li>pION www.pion.com </li></ul></ul><ul><ul><li>CEREP www.cerep.fr </li></ul></ul><ul><li>In silico </li></ul><ul><ul><li>ACD/Labs </li></ul></ul><ul><ul><li>Schroedinger </li></ul></ul><ul><ul><li>ChemAxon </li></ul></ul>CNS LogP < 4 MW < 400 PSA < 80 Fragments LogP < 3 MW < 300 PSA < 90 Oral Drugs LogP < 5 MW < 500 PSA < 140
    15. 15. Thank you for your time
    16. 16. Synthetic Chemistry Essentials for Biologists February 2012 Heather Behanna, PhD Biotechnology Research Associate [email_address] (312) 768-1795
    17. 17.
    18. 18. http://www.sciencecartoonsplus.com/pages/contact.php
    19. 19. An overview of the drug discovery process Nature Review Drug Discovery,8, 892 2009.
    20. 20. The Drug Discovery Chemist Synthetic chemistry- How to make things Medicinal chemistry- What makes a drug Pattern recognition and recall
    21. 21. Pattern recognition and recall TNT Salinsporamide – clinical trials for cancer Point of covalent attachment to proteins Azo-blue
    22. 22. Chemical space versus drug-like space Lipinski, C and Hopkins A, Nature , 2004 , 432(16) 855. Nature Biotechnology 24, 805 - 815 (2006)
    23. 23. Scaffolds for drug design <ul><li>Core structures (scaffolds) tend to be heterocycles </li></ul><ul><ul><li>Rings (that can be involved in  stacking and hydrophobic interactions </li></ul></ul><ul><ul><li>Heteroatoms (non-carbon atoms) for potential hydrogen bonding interactions </li></ul></ul><ul><li>Heterocylces can interact with proteins through both hydrogen bonds and hydrophobic factors </li></ul><ul><li>Scaffolds must have synthetic “handles” </li></ul><ul><ul><li>Accessible chemistry </li></ul></ul>
    24. 24. Properties of scaffolds <ul><li>Some scaffold changes or substitutions will drastically affect activity </li></ul><ul><ul><li>Privileged scaffolds </li></ul></ul>Viagra Levitra No serotonergic and dopaminergic activity Strong M1 receptor ligand <ul><li>The scaffolds of some drugs can be modified without changing the mechanism of action </li></ul><ul><ul><li>Might show changes of ADME properties </li></ul></ul>
    25. 25. An overview of the drug discovery process Looking for a starting point – either binding or weak activity that can then be optimized Obtainment of a Hit
    26. 26. How to get a hit? <ul><li>High throughput screening </li></ul><ul><ul><li>Screen a library for activity against a target or phenotype </li></ul></ul><ul><ul><ul><li>Traditional assays </li></ul></ul></ul><ul><li>Adaption of patented compounds or natural products </li></ul><ul><ul><li>Test for some activity and against others </li></ul></ul><ul><li>Fragment screening </li></ul><ul><ul><li>Screen for binding to a target (may not have activity) </li></ul></ul><ul><ul><ul><li>Biophysical methods </li></ul></ul></ul>
    27. 27. High throughput screening (HTS) <ul><li>Advantages: </li></ul><ul><ul><li>Ability to screen hundreds of thousands of compounds in weeks </li></ul></ul><ul><ul><li>Automated systems </li></ul></ul><ul><ul><li>Novel in-house libraries </li></ul></ul><ul><li>Disadvantages </li></ul><ul><ul><li>Limited to chemical space in the library </li></ul></ul><ul><ul><li>Lead to discovery of “red flag” compounds </li></ul></ul><ul><ul><li>Generally larger than “optimal” leads </li></ul></ul>
    28. 28. HTS pitfalls - Bad Hits and Frequent Hitters J Chem Inf Model. 2007 Jul-Aug;47(4):1319-27. Pattern recognition and recall Compounds that are potent in HTS are not necessarily Hits!
    29. 29. Adaption of natural products <ul><li>Genistein – natural product shown to have promise for: </li></ul><ul><ul><li>Cancer (topoisomerase inhibitor) </li></ul></ul><ul><ul><li>Cystic Fibrosis (CFTR corrector) </li></ul></ul><ul><ul><li>Anthelmintic (inhibits glycolysis) </li></ul></ul><ul><ul><li>Tumor metastisis (MEK4) </li></ul></ul>US 2010/0137425 A1
    30. 30. Fragment based approach <ul><li>Fragments consist of </li></ul><ul><ul><li>Low MW </li></ul></ul><ul><ul><li>Low LogP </li></ul></ul><ul><ul><li>High ligand efficiency (binding energy per atom) </li></ul></ul><ul><ul><li>Combination of hydrophobic and H-bonding properties </li></ul></ul><ul><li>Fragments are screened for binding to a target </li></ul><ul><ul><li>Expanded to gain efficacy </li></ul></ul><ul><ul><li>Structure assisted </li></ul></ul>Nature Reviews Drug Discovery 3, 660-672 (2004) Curr Top Med Chem 7, 1600-1629 (2007); Current Topics in Medicinal Chemistry, 5, 751-762 (2005)
    31. 31. How can we do that?
    32. 32. Hit criteria <ul><li>Regardless of how a hit is generated, it must pass certain criteria </li></ul><ul><ul><li>Show potency in cell assays </li></ul></ul><ul><ul><ul><li>Precursor to a drug, not just a ligand! </li></ul></ul></ul><ul><ul><li>Show potential chemical handles for structure modification </li></ul></ul><ul><ul><li>Possess certain ADME properties </li></ul></ul><ul><li>Quality of the library will strongly influence the chance of finding drug-like suitable hits </li></ul><ul><ul><ul><li>Fragment libraries tend to have better properties as hits than HTS libraries </li></ul></ul></ul><ul><ul><ul><li>Library properties should be considered </li></ul></ul></ul><ul><ul><ul><li>Interdisciplinary teams are best for hit evaluation </li></ul></ul></ul><ul><ul><ul><ul><li>Not all active compounds are worth pursuing as a drug </li></ul></ul></ul></ul><ul><ul><ul><li>Certain compounds come with “red flags” </li></ul></ul></ul>
    33. 33. An overview of the drug discovery process “ Hit to Lead” Nature Review Drug Discovery,8, 892 2009.

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