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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.
  • Transcript

    • 1. Identifying CNS drugs requires unique considerations beyond efficacy
      • BIOAVAILABILITY – drug available in the body to act at target
        • Inability to reach target in sufficient amounts during appropriate time window LIMITS opportunity for efficacy – BBB, metabolism, efflux
        • Caveat: Bioavailability DOES NOT guarantee drug efficacy
        • STARTING POINT: How does an oral drug get into the CNS?
      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. Case study: Antihistamine CNS bioavailability changes impact adverse events
      • First-generation antihistamines characterized by sedative side effects
        • Undesirable feature!!
      • Second-generation antihistamines lack drowsiness properties
        • Better safety index
      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. Case study: CYP2D6 metabolism alters bioavailability, impacts safety/efficacy
      • CYP2D6 - major isoform involved in CNS drug metabolism!
      • Genetic polymorphisms affect CYP2D6 expression, function
      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. 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. Molecular Properties 101: Physical properties influence how drugs interact with the body
      • Solubility, lipophilicity, size impact ADME outcomes
      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. Improving the odds: Using properties guidelines can increase bioavailability odds
      • “ Rule of 5” - Christopher Lipinski
        • Poor absorption/permeation MORE LIKELY if:
          • >5 Hydrogen bond donor atoms (HBD)
          • MW > 500
          • LogP > 5
          • N + O > 10
        • 1990s: analyses used to identify ways to improve attrition due to poor bioavailability
      • Today = Smarter screening platforms
      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. CNS drug discovery properties analysis
      • What molecular properties are most relevant to CNS?
        • LogP – lipophilicity, solubility in octanol/H 2 O
        • MW – size
        • PSA – polar surface area (N’s, O’s)
      • How do I calculate these?
        • Experimental
          • pION* www.pion.com
          • CEREP www.cerep.fr
          • Protocols – “ home grown ”
        • In silico – calculate estimated values derived from real structures
          • ACD/Labs*
          • Schroedinger
          • ChemAxon*
      • *Discounts available for academics
      DISCOVERY TIP: Prior to purchasing or screening libraries – look at the property landscape. How much is CNS relevant?
    • 8. CNS drug discovery properties analysis – what are “ good ” values?
      • CNS drugs occupy a more restricted molecular properties space
      • Properties guidelines also depend on development status (hit versus lead versus drug)
      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. 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. 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. Case study: Overlapping properties analyses focuses discovery efforts
      • Most property analyses focus on one outcome or endpoint…
      • … but CNS bioavailability involves multiple outcomes (penetration, metabolism for example).
        • CNS+/CYP2D6- = good!
        • CNS+/CYP2D6+ = bad!
      • Future direction of the field – perform properties analysis on multiple outcomes and “overlap” results
      • Query: where are we most likely to find compounds that are both CNS+ AND CYP2D6-?
        • Approach: Superimpose properties to find “hotspots” associated with CNS+/CYP2D6- candidates
      Chico et al. (2009) Drug Metab Dispos, 37 , 2204-11. Chico et al. (2009) Nature Rev Drug Discov, 8 , 892-909.
    • 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. 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. Takeaways – how can I use properties guidelines in my discovery efforts?
      • Library screening/selection
        • Properties can help you focus screening on most “CNS”-relevant members.
        • Some libraries are more CNS friendly than others.
      • Hit-to-lead refinement
        • It is easier to add than subtract later!
        • Start low – expect to increase as you proceed
          • Applying guidelines allows chemists to budget their selections
      • Guidelines are guidelines – NOT rules
        • Don’t get tripped up by numbers. Rationale trumps rules!!
      • Resources
      • Experimental
        • pION www.pion.com
        • CEREP www.cerep.fr
      • In silico
        • ACD/Labs
        • Schroedinger
        • ChemAxon
      CNS LogP < 4 MW < 400 PSA < 80 Fragments LogP < 3 MW < 300 PSA < 90 Oral Drugs LogP < 5 MW < 500 PSA < 140
    • 15. Thank you for your time
    • 16. Synthetic Chemistry Essentials for Biologists February 2012 Heather Behanna, PhD Biotechnology Research Associate [email_address] (312) 768-1795
    • 17.
    • 18. http://www.sciencecartoonsplus.com/pages/contact.php
    • 19. An overview of the drug discovery process Nature Review Drug Discovery,8, 892 2009.
    • 20. The Drug Discovery Chemist Synthetic chemistry- How to make things Medicinal chemistry- What makes a drug Pattern recognition and recall
    • 21. Pattern recognition and recall TNT Salinsporamide – clinical trials for cancer Point of covalent attachment to proteins Azo-blue
    • 22. Chemical space versus drug-like space Lipinski, C and Hopkins A, Nature , 2004 , 432(16) 855. Nature Biotechnology 24, 805 - 815 (2006)
    • 23. Scaffolds for drug design
      • Core structures (scaffolds) tend to be heterocycles
        • Rings (that can be involved in  stacking and hydrophobic interactions
        • Heteroatoms (non-carbon atoms) for potential hydrogen bonding interactions
      • Heterocylces can interact with proteins through both hydrogen bonds and hydrophobic factors
      • Scaffolds must have synthetic “handles”
        • Accessible chemistry
    • 24. Properties of scaffolds
      • Some scaffold changes or substitutions will drastically affect activity
        • Privileged scaffolds
      Viagra Levitra No serotonergic and dopaminergic activity Strong M1 receptor ligand
      • The scaffolds of some drugs can be modified without changing the mechanism of action
        • Might show changes of ADME properties
    • 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. How to get a hit?
      • High throughput screening
        • Screen a library for activity against a target or phenotype
          • Traditional assays
      • Adaption of patented compounds or natural products
        • Test for some activity and against others
      • Fragment screening
        • Screen for binding to a target (may not have activity)
          • Biophysical methods
    • 27. High throughput screening (HTS)
      • Advantages:
        • Ability to screen hundreds of thousands of compounds in weeks
        • Automated systems
        • Novel in-house libraries
      • Disadvantages
        • Limited to chemical space in the library
        • Lead to discovery of “red flag” compounds
        • Generally larger than “optimal” leads
    • 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. Adaption of natural products
      • Genistein – natural product shown to have promise for:
        • Cancer (topoisomerase inhibitor)
        • Cystic Fibrosis (CFTR corrector)
        • Anthelmintic (inhibits glycolysis)
        • Tumor metastisis (MEK4)
      US 2010/0137425 A1
    • 30. Fragment based approach
      • Fragments consist of
        • Low MW
        • Low LogP
        • High ligand efficiency (binding energy per atom)
        • Combination of hydrophobic and H-bonding properties
      • Fragments are screened for binding to a target
        • Expanded to gain efficacy
        • Structure assisted
      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. How can we do that?
    • 32. Hit criteria
      • Regardless of how a hit is generated, it must pass certain criteria
        • Show potency in cell assays
          • Precursor to a drug, not just a ligand!
        • Show potential chemical handles for structure modification
        • Possess certain ADME properties
      • Quality of the library will strongly influence the chance of finding drug-like suitable hits
          • Fragment libraries tend to have better properties as hits than HTS libraries
          • Library properties should be considered
          • Interdisciplinary teams are best for hit evaluation
            • Not all active compounds are worth pursuing as a drug
          • Certain compounds come with “red flags”
    • 33. An overview of the drug discovery process “ Hit to Lead” Nature Review Drug Discovery,8, 892 2009.