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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.
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Session 1 part 2 Presentation 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.